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Uncertainty-Aware Variational Inference for Target Tracking
In the low Earth orbit, target tracking with ground based assets in the context of situational awareness is particularly difficult. Because of the nonlinear state propagation between the moments of measurement arrivals, the inevitably accumulated errors will make the target state prediction and the measurement likelihood inaccurate and uncertain. In this article, optimizable models with learned parameters are constructed to model the state and measurement prediction uncertainties. A closed-loop variational iterative framework is proposed to jointly achieve parameter inference and state estimation, which comprises an uncertainty-aware variational filter (UnAVF). The theoretical expression of the evidence lower bound and the maximization of the variational lower bound are derived without the need for the true states, which reflect the awareness and reduction of uncertainties. The evidence lower bound can also evaluate the estimation performance of other Gaussian density filters, not only the UnAVF. Moreover, two rules, estimation consistency and lower bound consistency, are proposed to conduct the initialization of hyperparameters. Finally, the superior performance of UnAVF is demonstrated over an orbit state estimation problem.
I. INTRODUCTION
Aerospace technologies have witnessed a significant development in recent years [1], especially for autonomous vehicles in the low Earth orbit. However, this creates a number of challenges, such as in collision avoidance, and in security due to the growing number of resident space objects [2], [3]. Detection and tracking are essential components of such systems. In response to such challenges, this article focuses on low Earth orbit tracking problems with ground-based assets.
A. Related Work
To cope with the nonlinear estimation problem, a popular classical method is the Gaussian density filter (GDF) [4], which comprises the state prediction stage and the measurement update stage within the Bayesian update framework [5], [6]. These two stages have a number of unknown parameters that need to be updated, but cannot be calculated analytically due to intractable nonlinear integrals. By using different numerical methods to approximate the posterior update parameters of the GDF framework, many nonlinear filters are proposed, such as extended Kalman filter (EKF), unscented Kalman filter [7], cubature Kalman Filter [4], Gaussian-Hermite quadrature filter [8], sparse-grid quadrature filter (SGQF) [9], and their improved variants [10]- [13]. However, due to numerical approximations, there is an inevitable error in the state prediction, which propagates further and impacts the estimation results. Moreover, in order to improve the approximation accuracy, the iterated posterior linearization filter (IPLF) [14] is proposed, which performs iterated statistical linear regression with respect to the posterior instead of the prior so that the approximation accuracy can be improved.
Within the Kalman filtering framework [15], between the moments of measurement arrivals, repeated state predictions are performed until the receipt of the measurements. This could lead to error accumulation in the likelihood, which affects the accuracy of the nonlinear state propagation.
Particle filters [16] have also been popular in solving nonlinear non-Gaussian state estimation problems. However, they face challenges with the state initialization and with high-dimensional spaces [17]. Next, Markov chain Monte Carlo (MCMC) methods [18] are proposed to enhance particle filters where the estimates of the initial state conditions are uncertain [19]. However, as the computational cost of MCMC methods [19] increases with time, conventional MCMC can perform well if the needed convergence conditions are met [18].
Compared with MCMC methods, the variational inference (VI) is a faster and powerful tool [20]. There are the following four kinds of VI, i.e., scalable VI, generic VI, accurate VI, amortized VI [21]. The idea of VI is to first posit a family of densities and then to find the member of that family close to the target, which is measured by Kullback-Leibler (KL) divergence. In VI-based methods [22], [23], the introduction of hyperparameters is inevitable, which need to be artificially set in advance. However, there are no well-established adaptation approaches to determine these hyperparameters. If the hyperparameters deviate from their true values, the estimation performance will be influenced greatly.
B. Contributions
This article has the following main contributions.
1) A state prior model (SPM) and a measurement likelihood model (MLM) are proposed to cope with inaccuracies during the state and measurement predictions. Prior parameters and fitting parameters are introduced in the SPM and MLM, respectively, to reflect the uncertainty in predictions. 2) A closed-loop coordinate ascent variational iterative framework is designed to infer states and introduced parameters by maximizing the variational lower bound (VLBO). It includes model optimization and state estimation, which comprises a novel uncertainty-aware variational filter (UnAVF). 3) Theoretical expressions for the evidence lower bound (ELBO) and the maximization of VLBO are derived, by which the higher estimation accuracy of UnAVF compared with GDF can be theoretically explained. 4) Two rules, estimation and lower bound consistencies, are proposed, which can determine the values of hyperparameters reasonably and adaptively in the VI approach. There is no need to artificially and randomly set hyperparameters in advance.
In the UnAVF, the objective function, (VLBO for parameter inference and state estimation) and the ELBO are known and can be calculated without the need for the true states. However, in conventional Kalman filters, the calculation of the root mean square error (RMSE) needs the true states. Hence, the UnAVF can be adaptively aware and reduce the impact of uncertainties in the state and measurement predictions by monitoring the ELBO and maximizing the VLBO. The proposed UnAVF is validated and tested over an orbit state estimation problem.
C. Organization and Notation
This rest of this article is organized as follows. The considered problem is formulated and the motivation of UnAVF is discussed in Section II. In Section III, the theoretical derivation of the modeling and optimization process in the UnAVF are shown. Section IV proposes a quantitative evaluation indicator for different filters and two rules, estimation consistency and lower bound consistency, to initialize hyperparameters. Section V reports simulation results to demonstrate the superior performance of UnAVF. Finally, Section VI concludes this article.
We will use the following notations. The superscripts "−1" and " " represent the matrix inverse and transpose operations, respectively; | · | and Tr(·) denote the matrix determinant and trace, respectively; N (x|μ, P) denotes that variable x obeys Gaussian distribution with mean μ and covariance P; E [ · ] denotes mathematical expectation; p(x|z) represents the conditional density of x on z; I m denotes the unit matrix; the superscripts "∧" and "∼," used as the hat of random variables, represent the estimate and the estimation error, respectively; for example,x denotes the estimate of variablex and its estimation error isx = x −x; C(·) and D(·) denotes two functions with expressions C(x) = xx and D(x,P) = x Px, respectively.
II. PROBLEM FORMULATION
We consider the nonlinear state-space system given by where x τ ∈ R n and z k ∈ R m denote the system state and the measurement, respectively; τ and k represent the state propagation time and the measurement sampling time, respectively. The sampling intervals of state and measurement are t x = t τ − t τ −1 and t z = t k − t k−1 , respectively. The system noise w τ and the measurement noise v k are zeromean Gaussian white noises with covariances Q and R, respectively. Note that the sampling interval t z of measurements is always larger than the sampling interval t x of states. Hence, states have a long nonlinear propagation during t z without any measurement to correct the accumulated error in mean and uncertainty in covariance of state prediction. When the next measurement arrives, the state and measurement predictions have been influenced by the difference in the sampling rates. This will affect the posterior state estimation processes.
A. Gaussian Density Filter
GDF is a powerful framework for solving the nonlinear state estimation problems. In GDF, the joint state and measurement predictive probability density function (PDF) is assumed to be Gaussian where the state prior PDF and the measurement likelihood PDF are given by where Z 1:k−1 =[z 1 , z 2 , . . . , z k−1 ] . The parameters ξ x , xx , ξ z , zz are calculated via nonlinear integrals in (8)- (11). Based on the assumption in (2), the tractable update formula of the state posterior PDF q GDF (x k ) can be derived naturally [24] as However, the basic GDF framework has some disadvantages. During the long measurement sampling interval t z , GDF can only approximately calculate the state prior PDF p(x k |Z 1:k−1 ) under the minimum mean square error (MMSE) criterion in (8) and (9). The error in mean and uncertainty in covariance of the state prior PDF will accumulate and increase with the nonlinear state propagation. Then, the measurement likelihood PDF will also become inaccurate, because its calculation in (10) and (11) also relies on the corrupted state prior PDF. Finally, the joint Gaussian PDF assumption in (2) cannot reflect the true prior information when the next measurement arrives In spite of this, these inaccurate assumption, state prior, and measurement likelihood PDFs in (8)- (11) are still directly used to update the state posterior PDF in the measurement update stage (5)-(7) without any optimization process. As a result, the estimation performance of GDF will be influenced greatly.
B. Main Idea
In order to accommodate the accumulated error in mean and uncertainty in covariance of the state prediction discussed earlier, we propose an optimizable SPM as where η and are prior parameters.
REMARK 1: In (12), the state prediction PDF p(x k |Z 1:k−1 ) is approximated by the parameterized SPM N (x|η, −1 ). The prior parameters are introduced to reflect the error in mean and uncertainty in the covariance in the state prediction caused by the long nonlinear propagation during t z . Its advantage is that the inaccurate state prediction is only the initial value of the prior parameters at the beginning of the variational iteration. In detail, based on the VI theory, the convergency of the coordinate ascent variational iteration can be guaranteed theoretically. When the variational iteration converges, the optimized SPM will only slightly depend on the inaccurate initial state prediction. Hence, through inferring the prior parameters η and , SPM can accommodate the propagation error in mean and uncertainty in covariance of the state prediction.
Moreover, in order to characterize the uncertainty in the measurement likelihood, an optimizable MLM with fitting parameters is constructed as follows: whereR = R −1 ; H k ∈ R m×n , and u k ∈ R m denote the Jacobian matrix and the first-order constant term in the Taylor expansion of the measurement function, respectively, The fitting parameters μ ∈ R m and the scalar λ need to be optimized.
REMARK 2: Indeed, the MLM (13) essentially fits the latent variable x and the measured data z as a linear parametric Gaussian regression process. Due to the Gaussianity in the MLM, the simple and explicit results of parameter inference and state posterior estimation can be obtained. Moreover, through minimizing the KL divergence to infer the fitting parameters μ and λ, the error in mean and uncertainty in covariance caused by the inaccurate state prediction will be decreased. Specially, the fitting parameter μ is introduced to adjust the mean of p(z k |x k , μ, λ). The fitting parameter λ is designed as a ratio to adjust the covariance. If the approximate error of mean is large, λ will also amplify the covariance to reflect the uncertainty of measurements.
The Gaussian-Wishart and Gaussian-Gamma, distributions [25] are commonly adopted to represent parameters in the VI framework.
where η 0 , β 0 , W 0 , ν 0 , μ 0 , M 0 , c 0 , and d 0 are hyperparameters [25], which denote the prior initialization values for depicting the prior distributions of the introduced parameters. Here, the prior distributions of μ and η are typically Gaussian, which is an usual expression in using VI for regression analysis [26]. The prior parameters and λ are used to manage the covariance accuracy in (12) and (13). It is a standard processing to consider the prior PDFs of and λ as the Wishart and Gamma distributions [27], [28]. Abovementioned constructed forms in (14) and (15) guarantee that the posterior distributions of the fitting and prior parameters conjugate with their prior ones for easing the following maximization of the VLBO. Note that the correlated characteristics can be expressed in other forms, as discussed in [21]. REMARK 3: In the iterative EKF (IEKF) and other Kalman filter types, only the mean and covariance of the state posterior PDF are estimated. There is no optimization of the state model and of the measurement model parameters to reduce the approximation error (such as linearization) of measurement likelihood and state PDFs. Hence, even if the nonlinear functions are approximated via the updated posterior state, the approximation error still exists and is not compensated. In the IPLF, even if there are unknown parameters to be optimized in approximation, only the first moment of their unknown parameters is considered, which cannot capture and reflect the uncertainty and inaccuracy of the state and measurement predictions well, caused by the difference in the sampling rates in this article. Then, in both IPLF and IEKF, based on the approximation which does not contain optimizable parameters or only considers the first moment of parameters, their posterior states will become less informative and more conservative, i.e., their covariances of posterior states will become large. Accordingly, their posterior estimation accuracy will also be influenced. (12) and (13), we distribute the unknown parameters η, , μ, λ, which contain both first and second moments information. Hence, we need to iteratively infer the posterior distributions of the introduced parameters η, , μ, λ and update state posterior estimation, instead of directly calculating the values of unknown parameters (first moment) and posterior states. By inferring the posterior distributions of the introduced parameters, the approximation (12) and (13) in UnAVF can capture and aware the uncertainty and inaccuracy of the state and measurement predictions. Then, based on the approximation considering both the first and second moments of the introduced parameters, the posterior state estimation will become more informative and less conservative, i.e., the covariance of posterior states will become small and estimation accuracy will also be guaranteed. This is also the main reason why our proposed method is called UnAVF. The abovementioned analysis is also demonstrated in the simulations in Figs. 9-10
REMARK 4: In UnAVF in the approximation
The core idea of the proposed UnAVF is to construct an interaction between the model optimization and the state estimation via the maximization of the VLBO, as shown in Fig. 1. Based on the optimized SPM and MLM, the accurate state estimation results are obtained in the VI measurement update. Given the state estimation results, prior and fitting parameters are inferred to optimize the SPM and MLM in the VI state and measurement predictions. The prediction and update comprise the coordinate ascent variational iteration in the proposed UnAVF. In the variational iteration, the inaccurate and uncertain state and measurement information only provide initial values for the optimization of the SPM and MLM. As the convergency of the variational iteration, the finial iterative estimation results will not be influenced largely by the uncertain initial information. In (14) and (15) need to be initialized at the beginning of the variational iteration. Hence, is there a rule to rationally conduct the adaptive initialization, instead of setting their values artificially and casually?
III. UNCERTAINTY-AWARE VARIATIONAL FILTER
In Section II, the motivation and core idea of the Un-AVF have been discussed. Through answering the first question, the complete UnAVF algorithm will be proposed in Section III. Based on the VI framework, the VI state and measurement predictions and the VI measurement update will be derived to iteratively maximize the VLBO for jointly optimizing the SPM and MLM and estimating states.
To estimate the state x k and inferring the prior parameters η, and the fitting parameters μ, λ, the joint posterior PDF p(x k , η, , μ, λ|Z 1:k ) needs to be calculated. Because there is no analytical solution to the joint posterior PDF in the nonlinear system (1), the VI approach is, therefore, employed to obtain a suboptimal approximation for the joint posterior PDF. Based on the VI approach, we are going to look for an approximate solution by making the following variational approximation: where = {x k , η, , μ, λ}; q(.) denotes the variational posterior PDF of p(.). The variational posterior PDFs q(x k ), q(η, ), q(μ, λ) can be calculated by minimizing the following KL divergence between the factorized variational posterior PDFs q(x k )q(η, )q(μ, λ) and the true joint posterior PDF p( |Z 1:k ) [21], [25], [26] as p(x) dx is the KL divergence between q(x) and p(x). Based on the VI theory [25], the minimization of the KL divergence (18) is equal to the maximization of the VLBO as follows: The optimal solution to (19) satisfies the equation [25] as where θ is an arbitrary element of and ( =θ) is a subset of with θ ∪ ( =θ) = . The operator E ( =θ) [· · · ] denotes an expectation with respect to the variational posterior PDF q( ( =θ) ). Because the calculations of q(x k )q(η, )q(μ, λ) are coupled with each other, the coordinate ascent variational iteration is needed to solve (21). In detail, t denotes the number of variational iteration. Based on the results of the tth variational iteration q t ( ( =θ) ), the variational posterior PDF q(θ) of an arbitrary element θ is calculated as q t+1 (θ) at the t + 1th variational iteration by solving the expectation in (21).
In the following, we will calculate the variational posterior PDFs q(η, )q(μ, λ)q(x k ) at the t + 1th variational iteration, denoted as q t+1 (η, )q t+1 (μ, λ)q t+1 (x k ) given by Theorems 1-3, respectively. To this end, using the conditional independence properties of the Gaussian Gamma state-space model in (1) and (14) and (15), the joint complete-data likelihood PDF can be factored as The direct probability graph is given in Fig. 2 to illustrate the factorized complete-data likelihood PDF. The state x k is controlled by the prior parameters η, , and the measurement z k is controlled by the fitting parameters μ, λ and the state x k . There is no direct connection between the fitting parameters and the prior parameters so they are independent with each other. Now, given the factorized complete-data likelihood PDF in (22), based on the solution (21), the VLBO can be maximized gradually by our derived coordinate ascent variational iteration process. At the same time, the model optimization and state estimation can also be iteratively achieved in Theorems 1-3.
Theorem 1 (VI state prediction): Let θ = {η, } and accordingly the variational posterior PDF of ( =θ) at the previous tth variational iteration is then, based on (21) and (22), the variational posterior PDF of the prior parameters at the t + 1th iteration are calculated as PROOF: See Appendix A.
Theorem 3 (VI Measurement Update): Let θ = {x k } and accordingly the variational posterior PDF of ( =θ) at the t + 1th variational iteration is then, based on (21) and (22), the variational posterior PDF of states at the t + 1th variational iteration is calculated as PROOF: See Appendix C.
REMARK 5: In Theorems 1 and 2, the optimization of the SPM and MLM is achieved. Then, based on the optimized models, the state is estimated in Theorem 3. Accordingly, the state posterior estimation will also contribute to the optimization in Theorems 1 and 2. The VI state and measurement prediction and the VI measurement update in Theorems 1-3 comprise the variational iteration by maximizing the VLBO in the UnAVF. The uncertain and inaccurate state prior and measurement likelihood PDFs are only the initial value of the variational iteration. As the increase of the VLBO in the variational iteration, the prior and fitting parameters can be inferred so that the impact of uncertainties on the state estimation will be reduced gradually.
REMARK 6: In our proposed UnAVF, the optimization of the MLM, i.e., Theorem 2 (VI measurement prediction), can be considered as relinearization. Specifically, in Theorem 2, the distributions of the learned parameters μ, λ in the MLM (13) is updated. Besides, in each iteration, statesx k is also updated so H k will also be recalculated using the The terminal condition of the variational iteration at each sampling time is to measure the difference between the tth and t + 1th variational iteration results. If the difference is too small, we can assess that the coordinate ascent variational iteration has converged and should be stopped. Hence, in the proposed UnAVF, the KL divergence with respect to the state posterior PDFs in the tth and t + 1th variational iteration is considered as the terminal index. The setting of the threshold value δ will be given in the simulation part.
IV. PERFORMANCE ANALYSIS
As discussed in Section III, the hyperparameters in the UnAVF need to be initialized at beginning of variational iteration. In Section IV, two rules will be proposed to conduct the adaption initialization of hyperparameters. Moreover, we need to derive an index to evaluate the KL divergences of different filters for quantitatively comparing their accuracy performance. To this end, it is necessary to transform the KL divergence evaluation into the corresponding lower bound evaluation.
A. Quantitative Assessment by the ELBO
The VLBO in (20) is the objective function of the UnAVF with respect to states and parameters η, , μ, λ.
Through iteratively maximizing the VLBO, states and parameters can be joint inferred. This means that the increase of the VLBO is due to the mutual effect of states and parameters. Consequently, the VLBO cannot measure the accuracy of other filters, which do not introduce these parameters. Hence, we need to derive a new lower bound only with respect to states to fairly and theoretically evaluate the performance of different filters. To this end, the ELBO is derived from the following equation: where The subscript "E" aims to distinguish with the VLBO in (20) and q(x k ) denotes the variational posterior PDF of states at the tth variational iteration.
REMARK 7: From (44), q(x k ) will approximate the true posterior PDF p(x k |Z 1:k ) if the ELBO in (46) increases. Thus, the ELBO has the ability to quantitatively evaluate the accuracy performance of different filters, i.e., that the higher the filter's ELBO is, the better the estimation performance is.
Calculating the ELBOs of the GDF and UnAVF starts from According to the modeling process in the GDF and Un-AVF, the expression forms of the measurement likelihood PDF, the state prior PDF and the state posterior PDF can be summarized, respectively, as The difference of (48)-(50) in the GDF and UnAVF depends on the expressions of these parameters A k , B k , C k ,x k/k−1 , P k/k−1 ,x k/k ,P k/k . Given (48)-(50), the completely uniform expression of the ELBO in both GDF and UnAVF is where m and n represent the dimensions of measurement and state vectors, respectively.
1) The ELBO of GDF
In GDF, expressions of parameters A k , B k , C k ,x k/k−1 , P k/k−1 ,x k/k ,P k/k are given by Then, we can obtain the following complete expression of the ELBO in GDF: 2
) The ELBO of the UnAVF Based on the modeling of the SPM and MLM in Section II, expressions of parameters
Then, the complete expression of the ELBO in the UnAVF can be calculated as Summarizing the above, computing ELBOs of different filters is achievable and tractable, without nonlinear integrals. Moreover, it provides a quantitative assessment to the accuracy performance of different filters. In (44), the ELBO increase reflects the decrease of the KL divergence so that the variational approximate distribution q(x k ) approximates well the true posterior p(x k |Z k ). If the ELBO of the UnAVF is higher than that of GDF, then the UnAVF can outperform GDF.
B. Initialization of Hyperparameters at the Beginning of the Variational Iteration
The principle of initialization is to ensure that the Un-AVF can at least outperform GDF. To this end, two rules, estimation consistency and lower bound consistency, are proposed to conduct the initialization of hyperparameters. 1) Estimation Consistency: the state estimation of the UnAVF at the beginning of variational iteration t = 0 at least is identical with that of GDF. According to Theorem 3, only if the hyperparameters μ 0 , c 0 , d 0 , η 0 , W 0 , ν 0 are initialized as the state posterior estimation of the UnAVF q t=1 (x k ) will be identical with that of GDF q GDF (x k ) in (5). The similar proof can be seen in [29] in Appendix B. In other words, GDF only provides a prior estimation to the proposed UnAVF. Then, due to the variational iteration, the UnAVF's performance will become better than that of GDF.
2) Lower Bound Consistency: Based on the estimation consistency, the ELBO of the UnAVF at the beginning of the variational iteration should also be identical with that of GDF. The aim of the lower bound consistency is to initialize the hyperparameters and to further theoretically explain why the UnAVF can outperform GDF At the beginning of the variational iteration t = 0, the ELBOs of the UnAVF and GDF are calculated in (57) and (58), respectively. Based on the estimation consistency, the third terms in (57) and (58) are the same. To achieve the lower bound consistency, the key point is to guarantee that the first and second terms in (57) equal to that in (58), which are calculated in (61), (62) and (63), (64), respectively. As a result, two conditions are yielded as Summarizing abovementioned derivation and analysis, we can obtain the following proposition for initializing hyperparameters at the beginning of the variational iteration.
Proposition 1: Based on both estimation consistency and lower bound consistency, the hyperparameters in the UnAVF should be initialized as follows: where ξ x and xx are calculated in the same way as GDF in (8) and (9). Although there are many hyperparameters, based on Proposition 1, only c 0 and ν 0 need to be initialized. Frankly speaking, there may exist other initialization methods, but at least Proposition 1 provides an available initialization scheme with both operability and practicability. REMARK 8: Note that with the condition of the two rules in Proposition 1 to determine the hyperparameters, at the beginning of the variational iteration t = 0, the ELBO of UnAVF is the same as that of GDF. As shown in Fig. 3, then, with the variational iteration proceeding, the iterative optimization of the SPM and MLM can further promote the ELBO of the UnAVF. Finally, the ELBO promotion in the UnAVF implies its accuracy improvement and theoretically Fig. 3. Increase of the ELBO explains why the posterior estimation of the UnAVF is more accurate than that of GDF.
V. SIMULATION
In Section V, the proposed UnAVF is compared with the EKF, SGQF, IEKF, and IPLF. The performance of the UnAVF is demonstrated in the orbit estimation problem.
In order to evaluate the performance of different filters, we run different filters with 1000 Monte Carlo simulations and use the RMSE where N mc = 1000 is the number of Monte Carlo simulations; K is the simulation length in time steps; x n k [i] is the true value of state andx n k [i] is the estimated value of state in nth simulation at time k. Furthermore, we used the average RMSE (ARMSE) with respect to times k 1 s to k 2 s We also considered the mean absolute error (MAE) over time k of the nth simulation run The dynamic model of the low Earth orbit satellite [9] is given byr where r = [x, y, z] is the position of satellite in the inertial coordinate frame (I-J-K); scalar r = x 2 + y 2 + z 2 . Vector ν is the zero-mean Gaussian state noise [9]. Vector a G is the acceleration caused by the J 2 perturbation [30]. Vector a D is the atmospheric drag [31]. The measurement model is described by where the azimuth (az), the elevation (el), and the range ρ = [ρ u , ρ e , ρ n ] can be obtained by the radar site on the ground with respect to the local observer coordinate system. n az , n el , and n ρ are the white Gaussian noise. The transformation relationship between range ρ of measurement and position r of state in the inertial coordinate frame is described by where L = 6378.1363 km is the Earth radius; ε and ϑ are the latitude and local sidereal time of the observer, respectively. Generally speaking, a single radar cannot track a satellite with the entire orbit. In this simulation, the rational tracking time is 300 s. The measurement update interval t z is 5 s. For the accurately describing the state propagation, the dynamic model of the low Earth orbit satellite is discretized by fourth-order Runge-Kutta algorithm with the step size t x = 0.1 s. Hence, the satellite states have long nonlinear propagation without any measurement. Other information about the radar is identical with that in [9]. The trajectory of the low Earth orbit satellite is shown in Fig. 4.
In the simulation of the low Earth orbit satellite, the reasonable measurement noise covariance is assumed to be [9] R k = diag (0.015 • ) 2 (0.015 • ) 2 0.025 2 (km) 2 . (72) The six dimensional state is .841182]km/s In each Monte Carlo simulation, the initial states of different filters are both generated randomly from the Gaussian distribution N (x 0|0 ,P 0|0 ), wherê P p 0|0 = 10 4 , 10 4 , 10 4 km 2 P v 0|0 = 10 −2 , 10 −2 , 10 −2 (km/s) 2 . (77) According to the Proposition 1 in Section IV, only hyperparameters c 0 and ν 0 in the UnAVF need to be initialized as In the following, we will evaluate and compare performance of different filters from estimation error and estimation credibility aspects. Moreover, we will demonstrate the validity of the variational iteration in the UnAVF.
A. Estimation Accuracy
First, the ARMSEs of different filters with respect to different times are shown in Tables I and II. The ARMSE of the UnAVF is always the smallest from 1 to 300 s. From times 1 to 100 s, the ARMSEs of different filters are similar. However, when the results of different filters converge (from 201 to 300 s), the accuracy of UnAVF is increased by 51.52 and 40% in the position and velocity, respectively, compared with the IEKF.
Then, the RMSEs of different filters and the posterior Cramer-Rao lower bound (PCRLB) [32] are shown in Fig. 5. At each sampling time of measurements, the UnAVF is better than other filters in the RMSEs of the position and Tables I and II with different times. Note that we do not show the EKF's RMSE curves, because its RMSE is quite large compare with other filters. Hence, we only report its ARMSE in Tables I and II. As we discussed in Section IV, only two hyperparameters ν 0 , c 0 need to be initialized at the beginning of the variational iteration at each sampling time. Tables I and II and Fig. 5. Hence, for clearly reporting the difference of RMSE curves, we show most of the curves. Moreover, there is a trend that the larger c 0 and ν 0 is, the lower the estimation error of the UnAVF is.
The trend of the results matches the theoretical analysis. According to Proposition 1, we always set d 0 = c 0 and η 0 = ξ x , which means that E (λ) = c 0 (d 0 ) −1 = 1 and E (η) = ξ x [ξ x is the GDF's state prediction mean, which is calculated by (8)]. However, we do not know any information about λ and ξ x is also inaccurate. A direct and efficient method to make the UnAVF understand that E (η) and E (λ) are not reliable is to increase the variances of λ and η. The larger variance means the lower credibility and validity of mean. Hence, the parameters c 0 and ν 0 should be small so that the variances of λ and η will be large where V(·) denotes a variance. This can clearly explain why smaller c 0 and ν 0 are more rational and can yield more accurate estimation results.
B. Estimation Credibility
Besides the estimation error, the estimation credibility is significant as well. For evaluating different filters' estimation credibility, the contour of the state prior and posterior PDFs at times 1 s, 150 s, and 300 s are shown in Figs. 8-10. At time 1 s, the initial state prior PDFs of all filters are almost the same and inaccurate. However, at times 150 s and 300 s, the state prior PDF's mean in the UnAVF is obviously more accurate and its covariance is also smaller. This is because the flexible SPM in the UnAVF can be iteratively optimized to fit the true state situation by inferring prior parameters using Theorem 1, which outperforms the unadjustable state prior PDF in GDF. Moreover, based on a more accurate state prior PDF and the approximation considering both the first and second moments of introduced parameters, the calculation of the state posterior PDF will be more credible and informative, which means a smaller covariance. In Figs. 9 and 10, the posterior PDF's contours of the UnAVF are more tight and the contours' centers of the UnAVF are more close to the true state value as well. These simulation results can further demonstrate that the UnAVF can handle the inaccurate and uncertain state and measurement predictions.
C. Validity of Variational Iteration in the UnAVF
In the proposed UnAVF, the variational iteration consists of the VI state and measurement predictions and the VI measurement update. The state estimation in the VI measurement update has been reported in above simulation results. Hence, to demonstrate the validity of the variational iteration, we will focus on the optimization of the SPM and MLM in the VI state and measurement predictions, respectively, and the maximization of the ELBO. As discussed in Section IV, the ELBO is an evaluation about the accuracy performance of different algorithms. The higher the ELBO is, the better the estimation performance is. In Fig. 11, ELBOs of different filters are shown. The final ELBO of the UnAVF is obvious higher than that of other filters. The higher ELBO of the UnAVF can further explain why the state posterior PDF of the UnAVF is closer to the true one than that of other filters. Moreover, the increase from the initial to final ELBOs can also demonstrate the validity and contribution of the variational iteration at each sampling times.
The KL divergence of the estimated and true measurement likelihood PDFs at each sampling time are shown in Fig. 12. The final KL divergence of the UnAVF is smaller than that of other filters. Thus, the optimized MLM in the UnAVF can fit the true measurement model better. The decrease from the initial to final KL divergence further demonstrates the validity of the variational iteration in the UnAVF.
In addition, for more directly exhibiting the variation of the MLM and SPM in the variational iteration, the changes of the measurement likelihood PDF and the state prior error are shown in Figs. 13 and 14, respectively. As the variational iteration proceeding, the optimized measurement likelihood PDF can gradually fit the true one and the state prior error is decreased step by step. Hence, these changes of the ELBO, the KL divergence and the state prior error can clarify the impact and validity of the variational iteration in the UnAVF.
VI. CONCLUSION
Based on the VI framework, this article develops a novel nonlinear estimation method, which dynamically optimizes the parameterized state prior and MLMs by maximizing the VLBO. In the VI state and measurement predictions, the prior parameters and fitting parameters are inferred so that the SPM and MLM will be self-adaptively adjusted. Correspondingly, based on the optimized SPM and MLM, the state posterior PDF can be calculated more accurately in the VI measurement update. The uncertainties and inaccuracies in the state priori and measurement likelihood PDFs can only have effect at the beginning of the variational iteration. As the maximization of the VLBO by the variational iteration proceeding, the uncertainties' effect will be decreased gradually. Moreover, the estimation and lower bound consistency are proposed, which can rationally guide the initialization of hyperparameters at the beginning of the variational iteration at each sampling time. In the simulation, we have shown that the performance of the UnAVF is better and the validity of the variational iteration is demonstrated. | 8,705.2 | 2023-02-01T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
In Situ Ellipsometric Monitoring of Gold Nanorod Metamaterials Growth
An in situ transmission-based system has been designed to optically monitor the ellipsometry constants of a hyperbolic plasmonic metamaterial during electrochemical growth. The metamaterial, made from an array of vertically aligned gold nanorods, has demonstrated an unprecedented ability to manipulate the polarization of light using subwavelength thickness slabs, making in situ ellipsometric data a powerful tool in the controlled design of such components. In this work, we show practical proof-of-principle of this design method and rationalize the ellipsometric output on the basis of the modal properties of the nanorod metamaterial. The real-time optical monitoring setup provides excellent control and repeatability of nanostructure growth for the design of future ultrathin optical components. The performance of the ellipsometric method was also tested as a refractive index sensor. Monitoring refractive index changes near the metamaterial’s epsilon near zero (ENZ) frequency showed a sensitivity on the order of 500°/RIU in the ellipsometric phase for a metamaterial that shows 250 nm/RIU sensitivity in its extinction.
INTRODUCTION
Optical in situ monitoring during growth and etching processes is a common procedure in the semiconductor industry. It is seen as a noninvasive method for controlling the fabrication and morphology of precision thin films. The most frequently used techniques include reflectance anisotropy spectroscopy (RAS), 1,2 surface photon absorption (SPA), 3,4 and ellipsometry. 5,6 Although in situ optical monitoring of nanostructure fabrication is not unseen, 7−9 their characterization is largely performed post growth and is often done at the expense of destroying the sample. Real-time in situ optical monitoring could open the door to the scalable and repeatable production of functional nanostructured systems, in particular resonant plasmonic nanostructures, whose optical properties are strongly sensitive to geometrical parameters, including refractive index, size, and shape. 10,11 Real-time monitoring can also show the interplay and development of new modes as the growth of complex nanostructures progresses, providing a better understanding of their underlying behavior, but most of all allowing one to design optical functionalities on demand in vivo.
One of the most comprehensively studied plasmonic nanostructured systems, and also one of the most promising for practical implementation based on its ease and scalability to fabricate, are nanorod-based metamaterials. 12 Nanorods of various materials can be grown in solution chemically 13,14 or by electrodeposition into an alumina template. 15−18 Gold nanorods are particularly interesting as they exhibit anisotropic optical properties throughout the visible and near-infrared spectral range, a property related to localized surface plasmon resonances (LSPR) across their long and short axes. 19 When incorporated into an alumina template of suitable geometry, LSPR supported by nanorods when isolated, electromagnetically couple providing the formed metamaterial with unique optical properties that can be tailored to address a range of applications from biosensing 20 to ultrafast optical switching. 21 It has recently been demonstrated that such a uniaxial nanorod metamaterial can be used as an ultrathin optical component to effectively manipulate the polarization of light. 22,23 In particular, it was shown that both the reflection from and transmission through a λ/20 thick slab of the metamaterial can provide a 90°linear polarization rotation, a performance not observed in natural materials. The polarization conversion efficiency comes from the plasmonic nature of the modes supported by the metamaterial, providing a significant difference between the ordinary and extraordinary permittivities. The ability to manipulate the polarization of light is essential in many applications including liquid crystal displays, 24,25 medical diagnostics, 26 and telecommunications. 22 For example, such hyperbolic metamaterials could replace conventional birefringent materials, such as rutile, 27 which require tens of micrometers to achieve similar polarization conversions. However, for nanorod metamaterial polarization converters to be adopted for practical use, precise engineering of the anisotropy in the metamaterial is required.
In this paper, we have devised a simple optical setup whereby the ellipsometric constants and the transmittance of the metamaterial are measured simultaneously during the electrochemical growth process. This in situ measurement technique not only improves upon the control and repeatability with which these metamaterials may be fabricated, but also provides a means for understanding their optical response as their modal structure evolves during growth. Additionally, given the importance of sensing in many applied fields, as in functional label-free biosensing, the sensitivity performance of the ellipsometric data to refractive index changes is further demonstrated.
METHODS
2.1. Nanorod Metamaterial Fabrication. The fabrication technique used to create gold (Au) nanorod metamaterials has been published elsewhere in great detail. 15 The general geometry of the system is presented in Figure 1. Briefly, thin films of tantalum pentoxide (Ta 2 O 5 ), gold, and aluminum are deposited using magnetron sputtering to form the sample template. The aluminum top layer is then anodized in a 0.3 M sulfuric acid bath at 35 V to produce an array of pores with an average center-to-center separation of d = 80 nm. In this instance the thickness of the aluminum layer was set to 400 nm. The aluminum is then chemically etched in a 30 mM NaOH solution to remove the barrier layer at the base of the pores. This step is also used to controllably widen the pores, thus determining the diameter, 2r, of the nanorods electrochemically grown in the subsequent step. For the present study, the electrodeposition of gold was carried out at a constant voltage (−0.45 V) in a gold chloride electrolyte solution to grow nanorods of various lengths L in the pores. This final step completes the fabrication of the metamaterial (Figure 1b).
Metamaterial Structural
Characterization. An experimental setup was designed to simultaneously measure the transmission and ellipsometric phase angles of the gold nanorods based metamaterial in situ during growth ( Figure 2). The sample was mounted in the electrodeposition cell to probe both the sample's extinction and ellipsometric parameters in transmission at an incidence angle of 45°. This configuration is chosen for diagnostics purposes only, to allow probing transmission and ellipsometric parameters simultaneously for the same angle of incidence on different paths. More generally, both
ACS Applied Materials & Interfaces
Research Article measured quantities can be obtained from ellipsometric measurements alone, therefore lifting the constraint on angle of incidence.
The blue outline in Figure 2 highlights the transmission measurement arm of the setup. The incident light, produced by a Tungsten halogen source in the visible and near-infrared regime (400−900 nm), is incident to the sample through a series of optical components controlling collimation and polarization. The latter was set to p-polarized light, where the electric field oscillates with a component both along the length and the diameter of the nanorods, i.e. in the (x, z) plane of Figure 1a and Figure 2, thus simultaneously probing both ordinary (x) and extraordinary (z) axis of the metamaterial. Transmission measurements recorded the spectrum of the light source after transit through the sample via an optical fiber bundle coupled to a spectrometer. The transmission and corresponding extinction were plotted during electrodeposition, giving an indication of the height of the nanorods and in turn providing direct control over their growth. The electrodeposition voltage and corresponding deposition current were also recorded across the working electrode and a platinum wire reference, giving additional information on the nanostructure growth characteristics. 15 2.3. In Situ Spectroscopic Ellipsometry. The red outlined arm in Figure 2 shows a schematic of the in situ ellipsometry measurement setup. For this proof-of-principle experiment, it is placed orthogonal to the transmission arm in order to provide both arms with the same angle of incidence. The ellipsometric measurements provide ellipsometric angles which are a measure of both amplitude ratio and phase shift between the p-and s-components of the light transmitted by the nanorod metamaterial, where the p-and scomponents of the incident electric field are those in the (x, z) plane and along the y-axis, respectively (see Figures 1 and 2). Here we chose characterizing the ellipsometric angles in transmission as they provide for a direct insight into the hyperbolic properties of the metamaterial as will be shown below. When taken in transmission, the ellipsometric angles Ψ and Δ are defined by the following equation
ACS Applied Materials & Interfaces
where t p and t s are the transmission coefficient for p-and s-polarized light, respectively, tan Ψ = |E p |/|E s |, with |E p | and |E s | the norm of the transmitted electric field for p-and s-polarized light, and Δ is the relative phase difference between p-and s-polarized transmitted light. A commercial M-88 rotating analyzer ellipsometer (J. A. Woollam, Inc.) was used to perform the standard ellipsometry measurements. It was equipped with a Xenon arc lamp source for simultaneous measurements in the [450 nm-750 nm] wavelengths range; a full spectrum was completed in 1/20 s and the WVASE software was used to compute the corresponding ellipsometric components Ψ and Δ during nanorod growth. The instrument was mounted on an automated goniometer stage for measurements at variable angles of incidence, for postdeposition characterization.
2.4. Effective Medium Theory Modeling. The unique optical properties of gold nanorod metamaterials are ultimately governed by LSPRs. 19,28 Their optical response can be modeled using effective medium theories (EMTs), simplifying composite layers of nanoscale metallic inclusions with position and frequency-dependent permittivity ε Au (r⃗ ,ω) embedded in a matrix material with position and frequencydependent permittivity ε d (r⃗ ,ω), into a layer with a frequencydependent effective permittivity ε eff (ω) (Figure 1c, d). 19,29−31 This effective permittivity can be found from the metamaterial's components volume fractions and polarizabilities within a Maxwell-Garnett approximation as shown before by R. Atkinson et al. 19 In this formulation, the metamaterial layer is then considered a uniaxial anisotropic film (Figure 1d) with an effective permittivity expressed in the Cartesian referential system of Figure 1 , where z, taken along the nanorod length, corresponds to the extraordinary axis of the metamaterial, while the ordinary axis lies in the (x, y) plane. Gold nanorod-based metamaterials are attracting a lot of attention due to their anisotropic optical response in the vicinity and beyond the so-called epsilon-nearzero (ENZ) frequency which marks the transition between elliptic and hyperbolic dispersion regimes. 22,32,33 Hyperbolic materials are interesting in many aspects, as they show strong enhancement of spontaneous emission, 34 negative refraction 35,36 and enhanced superlensing effects. 37,38 A material is said to show hyperbolic dispersion when the electric permittivity or magnetic permeability effective tensors has components of different signs. The nanorod metamaterial considered here typically offers two dispersion regimes. An elliptic dispersion regime where both ε x,y > 0 and ε z > 0, and a hyperbolic dispersion, where ε x,y > 0 and ε z < 0, with the transition between the two regimes being characterized by the ENZ frequency for which |ε z | → 0. 39 Although the hyperbolic dispersion offers important optical properties, 20,40 operating in the vicinity of the ENZ regime |ε z | ≈ 0 also provides highly desirable linear and nonlinear optical properties as additional waves can be excited in low-loss metamaterials, i.e., when Im (ε z ) → 0. This has been shown to provide enhanced nonlinear optical properties, for example, 21,41 but has also been the basis for spectral refractive index characterization. 42 These effects all rely on the resonant response of the metamaterial as it transitions from the elliptic and hyperbolic dispersion regimes. One particularly appealing property of the nanorod metamaterial is the extensive flexibility it provides in tuning the ENZ frequency by geometrical means. For example, by using nanorods made of Au the ENZ frequency was shown to be continuously tunable in the 520−1700 nm range by controlling selected geometrical properties, such as the nanorod length, diameter, spacing, or the refractive index of the embedding medium. 43
RESULTS AND DISCUSSION
The in situ apparatus was used to characterize the optical properties of a gold nanorod metamaterial during growth. The nanorods were grown to a final height of approximately 350 nm in 180 s, giving an average growth rate of ∼2 nm/s, and have average dimensional parameters of 38 nm in diameter and 80 nm in center-to-center spacing, as set by the AAO template. Figure 3a, b shows the extinction −log 10 (Transmission) of the metamaterial as measured as a function of wavelength and metamaterial thickness, along with the corresponding ellipsometric parameters Ψ (Figure 3c, d) and Δ (Figure 3e, f) obtained simultaneously during growth. The corresponding EMT calculation results are shown in Figure 3g−l providing excellent agreement with the experimental observations, although it must be noted that the assumed metamaterial geometry during growth, including the average growth rate, can be at the origin of small discrepancies between experimental and EMT observations in Figure 3. In particular, the growth dynamics is a complex phenomenon and its assumed linear time behavior, especially at early stages, is an approximation that affects the comparison between experiments and calculations. Here we are making use of the EMT results to provide a general understanding of the experimental data and rationalize the ellipsometric response to allow their use in the design of ultrathin phase-shaping metamaterials with chosen and controlled polarization properties. As a result, approximations on electrochemical growth dynamics and metamaterial geometry during the growth do not affect the generality of our analysis below.
The overall extinction of the metamaterial increases steadily during growth as Au is deposited into the alumina pores to form the rods (Figure 3a, b). After about 100 s of growth time, or for a metamaterial thickness of about 200 nm, two distinct resonances can be observed, one at a wavelength of around 550 nm and another at around 625 nm. The short-wavelength peak, referred to as the T-mode in the literature, is associated with a transverse LSPR originating from free electrons oscillating across the diameter of the nanorods. 19,42 The second peak, observed in the extinction at around 625 nm, corresponds to the resonant excitation of the free electron density along the long axis of the rods and is referred to as the L-mode in the literature. 19,42 The field distribution for these two modes is presented in Figure S1. In contrast to the T-mode, the L-mode is delocalized over several nanorods and arises from Re (ε z ) → 0, signaling the transition of the nanorod metamaterial from an elliptic to a hyperbolic dispersion. 40,44 Further metamaterial growth leads to the observation of an increased spectral splitting of the two modes with a blue-shift of the T-mode to a wavelength of about 525 nm and a red-shift of the L-mode to about 650 nm. The spectral behavior of these resonances and their underlying nature as a function of metamaterial thickness has been thoroughly discussed elsewhere. 19,39,42,43,45,46 The analysis of Ψ in Figure 3c, d is consistent with the measured extinction of Figure 3a, b. Indeed, for |E p | > |E s |, we expect tan Ψ > 1 i.e., 45°< Ψ ≤ 90°, whereas for |E p | < |E s | we expect tan Ψ < 1, i.e., 0 ≤ Ψ < 45°with the case |E p | = |E s | corresponding to tan Ψ = 1 and Ψ = 45°. Figure 3c, d shows that the T-mode maximum corresponds to Ψ ≈ 45°, which is expected for an angle of incidence of 45°, whereas Ψ → 0°at the L-mode maximum with most of the transmitted field transmitted by the metamaterial in this spectral range being s-polarized. Again, this general behavior is also retrieved from the EMT calculations of (Figures 3i and 4c) results both qualitatively and quantitatively very well, providing a direct link between Ψ and the resonant response of the metamaterial.
Research Article
Although the Ψ signal shows an absorptive shape, the phase signal Δ of Figure 3e, f shows a differential shape for metamaterial thicknesses exceeding ∼150 nm. This thickness corresponds to the transition from the assembly of nanorods acting as an ensemble of weakly interacting resonators to a metamaterial made of strongly interacting nanorods. In fact, the differential shape is a signature of the dispersive behavior of the metamaterial transitioning between the elliptic and hyperbolic dispersion regimes. In the elliptic regime, the s-polarized field lags the p-polarized field (blue region in Figure 3e), while the opposite situation arises in the hyperbolic regime (red region in Figure 3e). Again, this observation is confirmed through the EMT calculations of Figure where λ 0 is the free-space wavelength of the transmitted light, n p and n s are the respective refractive indices experienced by pand s-polarized fields, and L p and L s are the geometrical pathlengths for p-and s-polarized fields, respectively. Expressions for the refractive index are chiefly those governing the propagation of light in uniaxial materials such as , where θ is the angle of incidence in air as measured from the z-axis. To a first approximation the pathlengths can be expressed as Figure 3k and Figure 4d. The change in sign of the dephasing between p and s waves follows that of the term (n p L p − n s L s ). In fact, for incident angles exceeding normal incidence Snell's law predicts opposite trends for n p − n s and L p − L s , with the geometric path length decreasing for increasing refractive index. As a result, n p − n s and L p − L s have a compensating effect on Δ but for the geometry considered here, the EMT calculations show that the behavior of Δ follows that of n p − n s with max (|n p − n s |) > 1, whereas max (|L p − L s |) ∼ 50 nm (see Figures S2−S4). For smaller metamaterial thicknesses, the length of the nanorods is close to their diameter leading to n p ≈ n s , and subsequently L p ≈ L s and Δ ≈ 0 as measured in Figure 3c, d. As the length of the nanorods increases, the spectral behavior of Δ reflects that of n p − n s with the s-polarized field leading the ppolarized field by more than 90°in the spectral range of elliptic dispersion to the s-polarized field lagging the p-polarized field by almost 60°in the spectral range of hyperbolic dispersion. The cross over where Δ = 0 occurs at the L-mode resonance, slightly red-shifted from the ENZ frequency.
We finish our study by assessing the performance of the ellipsometric response of the nanorod metamaterial as a sensing platform. This is done in situ by monitoring the ellipsometric parameters in transmission while etching the alumina matrix surrounding the gold nanorod in the metamaterial. The change in the local refractive index resulting from this etch is a simple way of testing the refractive index sensitivity of ellipsometric measurements. 42 These will be evaluated against the more commonly used resonant changes resulting from the same refractive index change measured simultaneously via the extinction of the metamaterial. 42 For the experiment, the metamaterial is placed in a cell and immersed in 0.03 M sodium hydroxide solution with both ellipsometric parameters and extinction measured simultaneously as the alumina is gradually replaced by the etching solution changing the refractive index of the medium embedding the nanorods from n ≈ 1.6 (alumina) to n ≈ 1.33 (etching solution). With alumina as the embedding medium (n ≈ 1.6), the metamaterial transitions from the elliptic to the hyperbolic regime at a wavelength of around 650 nm. This spectral range is the most sensitive to changes in refractive index when monitored in transmission as it corresponds to a resonance for incident TM-polarized light. 42 The ellipsometric spectra recorded for changing refractive index for both Ψ and Δ are shown in Figure 5. The spectral map for Ψ ( Figure 5a) and Δ (Figure 5b) show the total shift in the transition wavelength of 70 nm from about 650 nm to about 580 nm. This corresponds to the full removal of the alumina template and replacement by the etching solution as the embedding refractive index is lowered from n ≈ 1.6 to n ≈ 1.33. This is confirmed by the extinction measurements (not shown) and gives a bulk sensitivity of approximately 250 nm/RIU when monitoring the L-mode resonance. This refractive index sensitivity is typical for nanorod-based metamaterials when monitored in transmission, although improvements by an order of magnitude can be achieved via both thermal annealing and by shifting the transition wavelength to lower frequencies, the latter increasing both sensitivity and dynamic range. 47 Both Ψ and Δ data in Figure 5a, b demonstrate a behavior similar to our observations in Figure 3. The etching process starts at a time of around 30s (Figure 5a, b), following a nonlinear behavior until completion. This nonlinear behavior reflects both the density profile of the alumina matrix and is indicative of the etching mechanism. 42 In fact, the alumina matrix density increases gradually away from the pores containing the nanorods, corresponding also to a gradual increase in its refractive index. As a result, when subjected to the etching solution, the alumina is uniformly removed in the matrix along the length of the nanorods first, where the matrix
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Research Article has the lowest density, creating a shell around the nanorods whose thickness grows outward toward neighboring nanorods as the etching process proceeds. 42,48 A saturation in the resonance shift is reached after a time of around 120s as a result of the spectral dispersion of the metamaterial in this spectral range, which makes the 250 nm/RIU sensitivity obtained from the extinction measurements a lower estimate value. Figure 5c, d shows selected cross-sections of Ψ and Δ, respectively, for various wavelengths in the 70 nm bandwidth of the resonance shift. Although the change in the amplitude parameter Ψ is limited to about 20°by the change in T T / p s , as explained earlier the dephasing angle Δ varies by more than 150°during etching over the same spectral range as a result of the transition between elliptic and hyperbolic dispersions. This translates into a sensing sensitivity for Δ on the order of 500°/RIU, far exceeding the 250 nm/RIU sensitivity simultaneously obtained from the extinction of the same metamaterial. Importantly, it must be noted that unlike extinction, the amplitude in the variations of Δ, and Ψ to a lesser extent, are robust and relatively independent of bandwidth as long as some shift in the resonance occurs as a result of the refractive index change to trigger the transition between the elliptic and hyperbolic dispersion regimes. By replacing the sample cell with a flow-cell one could create a fully functional label-free biosensor, which benefits from dual optical outputs (phase and extinction) and an extremely sharp phase transition.
■ CONCLUSION
We studied the in situ ellipsometric response of a nanorodbased plasmonic metamaterial during growth. The ellipsometric angle data, measured in transmission, were rationalized with respect of the optical properties of the metamaterial on the basis of the extinction measurements, measured simultaneously. The ellipsometric angles show significant variations in the vicinity of the ENZ frequency, where the metamaterial transitions from the elliptic to the hyperbolic dispersion regimes. In particular, the amplitude ratio shows an absorptive behavior with a maximum absolute change on the order of 50°, whereas the relative phase shows a dispersive behavior about the ENZ frequency with an amplitude change exceeding 150°. These variations have been associated with both the relative amplitude and relative phase of p-and s-waves transmitted through the metamaterial. The in situ ellipsometric data provide for a reliable method to grow these subwavelengththick metamaterials to provide adjustable optical properties for applications as ultrathin polarization convertors. Additionally, we assessed the sensitivity of the ellipsometric parameters to changes in the refractive index within the metamaterial. Here changes in the relative phase exceed 500°/RIU for a metamaterial that shows a 250 nm/RIU sensitivity in its extinction.
Author Contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
Notes
The authors declare no competing financial interest.
■ ACKNOWLEDGMENTS
The Centre for Nanostructured Media acknowledges the Department of Education and Learning (DEL) and the Engineering and Physical Sciences Research Council (EPSRC -UK) for financial support (Grants EP/H000917, EP/ I014004). G.W. acknowledges support from the EC FP7 project 304179 (Marie Curie Actions).
ACS Applied Materials & Interfaces
Research Article | 5,636.4 | 2017-05-05T00:00:00.000 | [
"Materials Science",
"Physics"
] |
Photocatalytic Degradation Pathways of the Valsartan Drug by TiO2 and g-C3N4 Catalysts
The photocatalytic degradation of the valsartan (VLS) pharmaceutical using TiO2 and g-C3N4 catalysts under simulated solar light is studied in this paper by high-resolution Orbitrap mass spectrometry. •OH radicals were the major oxidant species for the degradation of valsartan using TiO2, while positive holes (h+), followed by a much lesser amount of •OH radicals, were the major species in the case of g-C3N4. Valsartan degradation followed first order kinetics by both catalysts with TiO2 being the catalyst with the better photocatalytic efficiency. The transformation products (TPs) and their evolution profiles are identified and monitored, respectively, by means of LC-HRMS. Based on TPs identification, the degradation mechanisms are discussed. The major degradation pathways for g-C3N4 include decarboxylation and subsequent oxidation, hydroxylation, and cleavage of C–N bond, while for TiO2 cyclization, TPs are abundant and the hydroxylation occurs in the first stage products. The study highlights the complex nature of TPs formed during such processes, the different mechanisms involved and the necessity for the identification of TPs for the assessment of the treatment and the tracking of such TPs in different environmental compartments.
Introduction
During the last few years, the studies on the fate and transformation of pharmaceutical compounds are gaining scientific attention because of their continuous occurrence and increasing concentrations in different types of water, such as wastewater, surface water, ground water and even drinking water [1][2][3][4][5]. Valsartan (VAL) is an Angiotensin II receptor antagonist belonging to the commonest antihypertensive drugs [6,7]. As a result, high concentration levels of VAL in different types of water have been reported in several previous studies around the world. As example, the median and maximum concentration of VAL in surface waters from 33 European countries was 1507 and 7479 ng/L, respectively [8]. In addition, according to the studies of Castro et al. [9] and Peña Guzman et al. [10], maximum concentrations of VAL observed in wastewaters for Spain and 11 countries of Latin America were 9986 ng/L and 1900 ng/L, respectively. Finally, the presence of Valsartan in raw, secondary treated wastewaters and surface waters has been previously reported in our previous studies [11,12].
Because of this widespread occurrence of pharmaceutical residues, such as VLS, in different systems and the possible adverse effects that can exert in different organisms, advanced treatment technologies should be applied for environmental protection. Advanced oxidation processes (AOPs) have earned the attention because they can degrade/mineralize biorecalcitrant pollutants in short times. In addition, they can be applied to simulate oxidation processes in environmental media. Semiconductor photocatalysis is one of the most studied processes among AOPs with TiO 2 being by far the most studied catalyst because of its chemical and photochemical stability, low cost and high photocatalytic performance. catalyst because of its chemical and photochemical stability, low cost and high photocatalytic performance. However, the photocatalytic activity of TiO2 using solar light is limited, because of its band gap (3.0-3.2 eV) [13,14]. g-C3N4, an alternative and promising polymeric semiconductor photocatalyst with visible light response (band gap 2.7 eV), has gained attention in recent years. This metal-free catalyst with a two-dimensional (2D) nanostructure demonstrates high thermal (up to 600 °C, in air) and chemical stability in acidic and basic media due to its s-triazinic structure, while it is insoluble in common solvents (ethanol, DMF, water) [15,16].
To date, very few studies performed lately deals with valsartan degradation in aqueous matrices by AOPs, i.e., by persulfate activation via sulfate radicals [17], photo-electro-Fenton [18], sonochemical degradation [19] and ozonation [20]. Some of these studies focused on the process parameters, while few data on the identification of degradation products are available. As a result, there is a lack of data concerning the heterogeneous semiconductor photocatalytic degradation of VLS in aquatic media.
Based on the above, the photocatalytic degradation of VAL by TiO2 (P25) and graphitic carbon nitride (g-C3N4) catalysts in aqueous suspensions is studied in the present study. The particular aims of the present work are: (a) the application of heterogeneous photocatalysis using TiO2 and g-C3N4 under simulated solar light for the removal of valsartan residues from water matrices, a topic that has not been investigated to date; (b) The identification of transformation products via the powerful instrumentation of ultraperformance liquid chromatography combined with high resolution and accurate mass linear ion trap-orbitrap mass spectrometer (UPLC-LIT-Orbitrap-MS); (c) the elucidation of transformation pathways by two different oxidant species, • OH radicals and positive holes (h + ).
Preliminary Experiments
Valsartan photocatalytic degradation kinetics using TiO2 and g-C3N4 catalysts are presented in Figure 1, while the linear transformation plot (natural logarithm of normalized concentration against irradiation time) is shown in the figure inset. Pseudo-first order kinetics were observed in both TiO2 and g-C3N4 cases (R 2 > 0.979 and R 2 > 0.9927) with 0.205 min −1 and 0.028 min −1 being the corresponding reaction rate constants. The degradation of valsartan in the dark followed very slow kinetics (k = 0.0003 min −1 ), while photolysis (k = 0.0059 min −1 ) under the same irradiation conditions was also a much slower process than photocatalysis. The concentrations of • OH radicals generated by TiO 2 and g-C 3 N 4 catalysts, as determined by the hydroxy-terephthalic acid fluorescence method (Figure 2), were 22.4 µM and 0.32 µM, respectively. The above results are consistent with the catalysts conduction band energy levels, showing that TiO 2 photocatalytic degradation proceeded mainly through the generation of hydroxyl radicals, while g-C 3 N 4 photocatalytic degradation proceed mainly through the positive holes and only a small amount of • OH radicals was formed, probably through the oxygen reduction pathway with the sequential formation of superoxide radical anion, hydrogen peroxide and finally • OH radicals. E VB and E CB of g-C 3 N 4 were calculated at 1.58 eV and −1.24 eV, respectively while the OH -/ • OH and O 2 /O 2 •− potentials are 2.4 and −0.33 eV, respectively.
The concentrations of • OH radicals generated by TiO2 and g-C3N4 catalysts, as determined by the hydroxy-terephthalic acid fluorescence method (Figure 2), were 22.4 μΜ and 0.32 μΜ, respectively. The above results are consistent with the catalysts conduction band energy levels, showing that TiO2 photocatalytic degradation proceeded mainly through the generation of hydroxyl radicals, while g-C3N4 photocatalytic degradation proceed mainly through the positive holes and only a small amount of • OH radicals was formed, probably through the oxygen reduction pathway with the sequential formation of superoxide radical anion, hydrogen peroxide and finally • OH radicals. EVB and ECB of g-C3N4 were calculated at 1.58 eV and −1.24 eV, respectively while the OH -/ • OH and O2/O2 •− potentials are 2.4 and −0.33 eV, respectively.
Photocatalytic Transformation Products and Pathways of Valsartan by UV-Vis/g-C3N4 Process
LC-HRMS identification data of VLS TPs by g-C3N4 photocatalysis are summarized in Table 1. Firstly, the parent compound (VLS) and sodium adduct [M + Na] + were observed at m/z 436.2336 (C24H30O3N5 + ) and 458.2145 (C24H29O3N5Na + ), respectively, while MS 2 and MS 3 fragments are closely identical to those reported elsewhere [18]. In addition, VLS showed also a pseudo-molecular ion [M-H] -at m/z 434.2194 (C24H28O3N5) in the negative ionization mode due to the presence of the carboxylic acid functional group. The screening of TPs performed under negative ESI ionization can be used as an indicative tool for the presence of the carboxylic acid group in the structure of TPs. Table 1. UPLC-ESI-HR-MS data (pseudo-molecular ions (M + H) + or (M − H) − */(M + Na) + ; chemical formula; mass error Δ(ppm); and ring-double\bond equivalents, RDB) for VLS and transformation products by g-C3N4 photocatalytic oxidation.
Photocatalytic Transformation Products and Pathways of Valsartan by UV-Vis/g-C 3 N 4 Process
LC-HRMS identification data of VLS TPs by g-C 3 N 4 photocatalysis are summarized in Table 1. Firstly, the parent compound (VLS) and sodium adduct [M + Na] + were observed at m/z 436.2336 (C 24 H 30 O 3 N 5 + ) and 458.2145 (C 24 H 29 O 3 N 5 Na + ), respectively, while MS 2 and MS 3 fragments are closely identical to those reported elsewhere [18]. In addition, VLS showed also a pseudo-molecular ion [M-H] − at m/z 434.2194 (C 24 H 28 O 3 N 5 ) in the negative ionization mode due to the presence of the carboxylic acid functional group. The screening of TPs performed under negative ESI ionization can be used as an indicative tool for the presence of the carboxylic acid group in the structure of TPs.
Positive holes (h + ) react principally with the electron-rich moieties of organic substances via electron transfer [21]. In the case of valsartan, such electron-rich functional group sites are the phenyl and tetrazole moieties as well as the carboxylic acid and tertiary amine groups. TP1, with [M + H] + /[M + Na] + at m/z 392.22437/414.2253, respectively, differs 44 amu less than the parent drug and the matching chemical formula (C 23 H 30 ON 5 + ) indicates clearly the loss of CO 2 group. The formation of TP1 can be rationalized through a photo-Kolbe decarboxylation according to the following equation: was assigned to N-((2 -(1H-tetrazol-5-yl)biphenyl-4-yl)methyl) pentanamide. The alkyl radical formed after decarboxylation can react rapidly with molecular oxygen, leading to the formation of peroxy radical. Peroxy radicals are disproportionate to alcohols and ketones via the Russell and/or Bennet-Summers mechanisms [22], but also form alkoxy radicals that lead to aldehyde, as well as fragmentation to form new alkyl radicals [23]. The formation of TP2, TP5 and TP6 can be rationalized by the above-mentioned mechanisms. Based on the TPs profile (Figure 3), the peak concentrations of the above products were observed in the same time framework; thus, it can be considered that took place concurrently. Table 1. UPLC-ESI-HR-MS data (pseudo-molecular ions (M + H) + or (M − H) − */(M + Na) + ; chemical formula; mass error ∆(ppm); and ring-double\bond equivalents, RDB) for VLS and transformation products by g-C 3 N 4 photocatalytic oxidation. Alternatively, the attack of holes to phenyl moieties via the electron transfer mechanism may lead to the generation of an unstable carbon-centered cationic radical that is subsequently hydrolyzed to produce the corresponding hydroxy derivative, OH-VLS. In the same way, the attack to the tetrazole moiety can lead also to the formation of a hydroxy analogue. TP3 and TP4 with [M − H] − at m/z 450.2148 and 450.2144, respectively, and a chemical formula C 24 H 28 O 4 N 5 − , differing 16 amu from VLS, were attributed to the hydroxy derivatives of VLS. Taking into account the MS 2 /MS 3 characteristic fragments, the suggested hydroxylation position is on biphenyl or tetrazole ring. In a VAL sonochemical oxidation study, an • OH-based process, Serna-Galvis and co-workers [19] identified four hydroxy-TPs of VLS at phenyl, tetrazole and valeryl functional groups. The similar hydroxylation pathway may be applied for the first stage generated products leading to the formation of secondary TPs, such as TP9 and TP7 as justified also by their evolution profiles (Figure 3 . No significant removal of TOC was observed in the presence of g-C 3 N 4 , which, in addition to the slower degradation rates of TPs, could be probably due to the partial dissolution or presence of g-C 3 N 4 nanoparticles. One-electron oxidation of tertiary amine group yielding a nitrogen centered cation radical [24] can represent another degradation pathway. This intermediate cation radical is subsequently subjected to C-N cleavage to generate transformation products TP10 and TP8 with concurrent hydroxylation. . No significant removal of TOC was observed in the presence of g-C3N4, which, in addition to the slower degradation rates of TPs, could be probably due to the partial dissolution or presence of g-C3N4 nanoparticles.
VLS/TPs (M + H) + /(M + Na
Based on the mass spectra maximum intensities, the formation of TPs followed the sequence TP5 > TP6 > TP9 > TP3 > TP2 > TP1 > TP10 ≥ TP7,TP8,TP4, while the time interval for the formation of maximum concentration followed the trend TP5, TP6, TP1, TP2 (15-30 min) < TP4,TP3 (60 min) < TP7, TP9, TP10 (180-240 min). As a result, decarboxylation and further oxidation can be regarded as the major path, while hydroxylation and C-N cleavage may be considered as secondary paths. Based on the identification of TPs, as described previously, as well as on the TPs evolution kinetics, the tentative transformation pathways are summarized in Figure 4. In conclusion, VLS degradation by the g-C3N4 pho- Based on the mass spectra maximum intensities, the formation of TPs followed the sequence TP5 > TP6 > TP9 > TP3 > TP2 > TP1 > TP10 ≥ TP7,TP8,TP4, while the time interval for the formation of maximum concentration followed the trend TP5, TP6, TP1, TP2 (15-30 min) < TP4,TP3 (60 min) < TP7, TP9, TP10 (180-240 min). As a result, decarboxylation and further oxidation can be regarded as the major path, while hydroxylation and C-N cleavage may be considered as secondary paths. Based on the identification of TPs, as described previously, as well as on the TPs evolution kinetics, the tentative transformation pathways are summarized in Figure 4. In conclusion, VLS degradation by the g-C 3 N 4 photocatalytic oxidation process followed three major pathways (
Photocatalytic Transformation Products and Pathways of Valsartan by UV-Vis/TiO2 Process
LC-HRMS data on the identification of VLS TPs by TiO2 photocatalysis are summarized in Table 2, while the respective chromatograms are depicted in Figure 5. TiO2 photocatalytic oxidation is based on • OH radicals, which react preferably with the electronrich groups of organic substances via addition/elimination pathways, H-abstraction, and electron transfer [21]. The • OH additions to aromatic moieties or unsaturated bonds compete overwhelming to H-abstraction. Electron transfer with • OH is limited to very electron rich systems. In the case of valsartan, such electron-rich functional groups are the phenyl and tetrazole moieties. For tetrazole, • OH radicals react at the -NH position either by electron transfer or H-abstraction [25]. The oxidation of primary, secondary, and tertiary aliphatic carbons is initiated by hydrogen atom abstraction forming a carbon-centered radical. The stabilization of such carbon-centered radicals can lead to the addition of molecular oxygen to form peroxy radicals, which subsequently yield the corresponding oxidation products as mentioned previously. Moreover, for the N-methylated amide function, abstraction by • OH radicals takes place mainly from the N-methyl group [26]. Finally, the reaction of carboxylic acids (i.e., 2-methylbutanoic acid group of VLS) with • OH radicals results in little decarboxylation and preferably proceeds through H-abstraction [27].
Photocatalytic Transformation Products and Pathways of Valsartan by UV-Vis/TiO 2 Process
LC-HRMS data on the identification of VLS TPs by TiO 2 photocatalysis are summarized in Table 2, while the respective chromatograms are depicted in Figure 5. TiO 2 photocatalytic oxidation is based on • OH radicals, which react preferably with the electronrich groups of organic substances via addition/elimination pathways, H-abstraction, and electron transfer [21]. The • OH additions to aromatic moieties or unsaturated bonds compete overwhelming to H-abstraction. Electron transfer with • OH is limited to very electron rich systems. In the case of valsartan, such electron-rich functional groups are the phenyl and tetrazole moieties. For tetrazole, • OH radicals react at the -NH position either by electron transfer or H-abstraction [25]. The oxidation of primary, secondary, and tertiary aliphatic carbons is initiated by hydrogen atom abstraction forming a carbon-centered radical. The stabilization of such carbon-centered radicals can lead to the addition of molecular oxygen to form peroxy radicals, which subsequently yield the corresponding oxidation products as mentioned previously. Moreover, for the N-methylated amide function, abstraction by • OH radicals takes place mainly from the N-methyl group [26]. Finally, the reaction of carboxylic acids (i.e., 2-methylbutanoic acid group of VLS) with • OH radicals results in little decarboxylation and preferably proceeds through H-abstraction [27].
The identified TPs can be rationalized based on the above principal reaction mechanisms of • OH radicals towards the functionalities present in VLS molecule. TPs (T-TP8, T-TP6) is the higher retention times compared to the precursor TPs due to the less polar character. Such cyclization derivatives are reported for VLS during oxidation by persulfate activation to SO 4 •− [17], but they have been identified also for the structurally related drug irbesartan, during photolysis [28] and hypochlorite treatment [29]. (Figure 6). T-TP1, T-TP3-TP5, and T-TP7 maximized at 15 min and can be considered as primary products, while all cyclization derivatives (T-TP2, T-TP6, T-TP8 and T-TP9) peak up at longer treatment times and are considered as secondary TPs. Finally, TP10 peak up at 90 min and can be considered as late stage product. The corresponding TOC removal after 240 min of irradiation under the current experimental conditions was 15%. Table 2. UPLC-ESI(+)-HR-MS data (pseudo-molecular ions (M + H) + /(M + Na) + ; chemical formula; mass error ∆(ppm); and ring-double bond equivalents, RDB) for VLS transformation products by TiO 2 photocatalytic oxidation. Based on the structure assignments and TPs evolution profiles, the tentative transformation pathways are summarized in Figure 7. In conclusion, the VLS TiO2 photocatalytic degradation process followed four major pathways (Figure 7): (a) N-lateral chain oxidation; (b) cyclization; (c) hydroxylation; and (d) cleavage of the amide bond. Two main differences can be noted compared to the g-C3N4 process., i.e., the presence of cyclization TPs and the absence of hydroxy-derivatives of VLS. Based on the structure assignments and TPs evolution profiles, the tentative transformation pathways are summarized in Figure 7. In conclusion, the VLS TiO 2 photocatalytic degradation process followed four major pathways (
Materials and Chemicals
Valsartan (98%) was purchased from Sigma-Aldrich. Methanol (MeOH) and water of LC-MS grade was supplied by Fisher Scientific (Loughborough, UK). Urea (99.5%) was obtained from Acrōs Organics (Geel, Belgium). Oasis HLB (divinylbenzene/Nvinylpyrrolidone copolymer) cartridges (60 mg, 3 mL) from Waters (Mildford, MA, USA) were used for the extraction of TPs. Two different semiconductor materials were used. The first included the aeroxide TiO 2 -P25 supplied by the Evonik-Degussa Corporation (BET specific surface area (SSA) 50 ± 15 m 2 g −1 , 80% anatase, 20% rutile, average primary particle size 21 nm, E g = 3.15 eV). The second, the g-C 3 N 4 catalyst, was synthesized with the use of urea as a precursor compound. The urea was placed in an aluminum crucible and was dried in 90 • C for 24 h. After that, was calcinated in air at 500 • C with heating rate of 10 • C min −1 . The furnace was cooled down naturally and the collected yellow color solid was ground well into a powder in an agate mortar. The main physicochemical properties of g-C 3 N 4 were as follows: specific surface area 35 m 2 g −1 , particle size of 25 nm, E g = 2.82 eV [30].
Photocatalytic Treatment Experiments
A solar simulator (Suntest XLS+, Atlas, Linsengericht, Germany) equipped with a xenon lamp (2.2 kW) was used for performing photocatalytic experiments UV-vis irradiation (simulated solar light, λ > 290 nm) using a 290 nm cut-off glass filter. Before illumination, 0.1 mL of valsartan standard solution in methanol (C = 5000 mgL −1 ) was added to 100 mL bidistilled water in order to achieve an initial concentration (C 0 ) of 5 mgL −1 and then 100 mgL −1 of photocatalyst was added under stirring (600 rpm). The suspension was loaded in Pyrex reactor (250 mL) and kept in the dark for 30 min in order to achieve the adsorption equilibrium. Adsorption experiments took place in dark conditions showed adsorption percentages of 14.3% and 1.8% for TiO 2 and g-C 3 N 4 , respectively. The above experimental conditions were selected in order to obtain not so fast kinetics and to facilitate the identification and the follow-up of the transformation products. A constant radiation intensity (500 W m −2 ) was kept throughout the experiments, while the temperature was kept at 23 ± 1 • C by a water circuit in the double-jacked photoreactor and air circulation. Samples ≈ 2 mL were periodically withdrawn and filtered by 0.22 µm filters for further LC-MS and total organic carbon (TOC) analysis using a TOC-L CSH/CSN (Shimadzu, Kyoto, Japan) analyzer.
Identification of Transformation Products by Liquid Chromatography-High Resolution Mass Spectrometry
The analysis of the photocatalytically treated aqueous samples was performed by accurate mass high resolution liquid chromatography-mass spectrometry (LTQ-Orbitrap XL, Thermo Fisher Scientific, Inc., GmbH, Bremen, Germany). The instrument combines a UHPLC Accela LC system (Accela LC pump, Accela Autosampler) and a linear-iontrap-Orbitrap™ hybrid mass spectrometer. ESI ionization was performed in both positive and negative mode. The analysis was performed according to the following conditions: gradient elution with 0.1% formic acid in LC-MS grade water (mobile phase A) and 0.1% formic acid in LC-MS grade methanol (mobile phase B); Speedcore-Fortis diphenyl (2.1 mm × 50 mm, 2.6 µm particle size) column operated at 35 • C; gradient elution started with 95% A (2 min) and progressed according to 90% A, 70% A and 50% A in 3, 5 and 10 min, respectively, finally returned to 80% A and initial conditions in 15 and 18 min, respectively, with 1 min column re-equilibration time; flow rate 0.25 mL/min; injection volume 20 µL. The mass range was scanned within 90−600 m/z, while mass spectra data were recorded using a resolving power of 60.000 and 15.000 in FT-MS mode for MS and MS 2 /MS 3 scans, respectively. A mass accuracy of ±5 ppm was adopted by performing external calibration of the Orbitrap mass analyzer. The processing of data was performed by Thermo Xcalibur 2.1 software (Thermo Electron, San Jose, CA, USA).
Determination of • OH Radicals by Fluorescence Measurements
The terephthalic acid (TA) (98%, Sigma-Aldrich, St. Louis, MO, USA) method was used for the quantification of the generated • OH radicals. Aqueous solutions (100 mL) of TA (5 × 10 −4 M) and NaOH (2 × 10 −3 M, 99% Riedel de Haẽn, Seelze, Germany) were prepared and then the catalysts were added to the solutions, which were placed to the reactor. The experimental conditions remained the same with the photocatalytic experiments. Samplings (5 mL) were performed at different time intervals and samples were filtered with 0.22 µm filters. The fluorescence peak at 425 nm (excitation wavelength at 310 nm) is ascribed to 2-hyroxyterephthalic acid (TAOH). A calibration curve plotting the fluorescence intensity of standard TAOH took place for measuring the concentrations of • OH.
Conclusions
The photocatalytic degradation pathways of the valsartan pharmaceutical were studied in the presence of g-C 3 N 4 and TiO 2 catalysts using LC-MS-Orbitrap high resolution accurate mass spectrometry. Ten transformation products were identified for each catalyst, but only three of them are in common, suggesting the different degradation pathways followed. For g-C 3 N 4 , the major paths included decarboxylation and subsequent oxidation, hydroxylation, and cleavage of C-N bond. On the other hand, in the presence of TiO 2 cyclization, TPs are abundant and hydroxylation occurs in the first stage products due to the higher production of • OH radicals. Thus, the generation of transformation products is greatly influenced by the catalytic mechanism suggesting that their identification is highly significant in photocatalytic processes or in other oxidoreductive processes in the environment. In the case of TiO 2 , all transformation products were also degraded for more than 60%, but, in the presence of g-C 3 N 4 , some products still increased their concentrations within the time framework of 240 min. Overall, this work demonstrated the importance of identifying the TPs for the assessment of photocatalytic processes since they can be more persistent and/or toxic depending on the catalyst used. | 5,525.8 | 2022-02-03T00:00:00.000 | [
"Chemistry",
"Environmental Science"
] |
A comparison of two chemistry and aerosol schemes on the regional scale and resulting impact on radiative properties and liquid-and ice-phase aerosol-cloud interactions
The complexity of the atmospheric aerosol causes large uncertainties in its parameterization in atmospheric models. In a process-based comparison of two aerosol and chemistry schemes within the regional atmospheric modeling framework COSMO-ART, we identify key sensitivities of aerosol parameterizations. We consider the aerosol module MADE in combination with full gas-phase chemistry and the aerosol module M7 in combination with a constant-oxidant-field-based sulfur cycle. For a Saharan dust outbreak reaching Europe, modeled aerosol populations are more sensitive to structural differences between 5 the schemes, in particular the consideration of aqueous-phase sulfate production, the selection of aerosol species and modes and modal composition, than to parametric choices like modal standard deviation and the parameterization of aerosol dynamics. The same observation applies to aerosol optical depth (AOD) and the concentrations of cloud condensation nuclei (CCN). Differences in the concentrations of ice-nucleating particles (INP) are masked by uncertainties between two ice-nucleation parameterizations and their coupling to the aerosol scheme. Differences in cloud droplet and ice crystal number concentrations 10 are buffered by cloud microphysics as we show in a susceptibility analysis.
Introduction
Atmospheric aerosol poses the most uncertain factor in quantifying the anthropogenic forcing of the climate system (Myhre et al., 2013).This uncertainty is rooted in the complexity of aerosol characteristics and processes: aerosol particles feature many microscopic degrees of freedom, like their chemical composition, mixing state or shape, and interact with several atmospheric components like atmospheric chemistry, the planetary surface as source of primary emissions, radiation by scattering and absorption, and the hydrological cycle via aerosol-cloud interactions (Lohmann et al., 2016).Given their microscopic scale, all these processes and characteristics have to be parameterized to be represented in atmospheric models.
Approaches to represent aerosol particles in atmospheric models employ discrete (binned) or continuous (modal) distributions of particle sizes (Jacobson, 2005).They consider different selections of chemical species like sea salt, dust, sulfate, nitrate and classes of organics, e.g., soot and primary or secondary organic aerosol, that are grouped in internally and/or externally mixed particle classes.The parameterizations of aerosol microphysical processes like gas-to-particle conversion, coagulation, and dry and wet deposition depend on these structural aerosol characteristics (e.g., Vignati et al., 2004;Vogel et al., 2009).
Modeled aerosol particles can be coupled to an atmospheric host model to different degrees: atmospheric chem-Published by Copernicus Publications on behalf of the European Geosciences Union.
F. Glassmeier et al.: A comparison of two chemistry and aerosol schemes istry can be considered from simplified sulfur cycles using climatological oxidant fields (e.g., Zubler et al., 2011) to full chemistry including aqueous-phase reactions (e.g., Knote and Brunner, 2013).Primary aerosol emissions may be prescribed from inventories or modeled online taking into account surface conditions (e.g., Vignati et al., 2004;Vogel et al., 2009).Aerosols can also be coupled to radiation via their absorbing and scattering properties and to cloud formation by their ability to serve as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs) (Lohmann et al., 2016).The latter aerosol-cloud interactions constitute the largest source of uncertainty in anthropogenic aerosol forcing (Myhre et al., 2013).Clearly, the challenge lies in choosing the right degree of complexity for a given task, e.g., air-quality applications or climate projections.An informed choice requires an understanding of key processes and sensitivities of aerosol parameterizations.
While aerosol microphysics take place on the microscale, aerosols can be transported globally (Lohmann et al., 2016).Regional atmospheric models are valuable tools to increase our process understanding because they compromise between process representation that is improved at higher spatial resolutions and larger-scale transport patterns (e.g., Possner et al., 2015;Rieger et al., 2014;Athanasopoulou et al., 2013;Knote and Brunner, 2013;Bangert et al., 2012;Fountoukis et al., 2011;Zubler et al., 2011).Nevertheless, our current understanding of aerosols remains insufficient (Myhre et al., 2013).While for air-quality applications in general and case studies in particular, the uncertainties in aerosol representation can be somewhat controlled by tuning the parameterization to match observations, reducing the uncertainty of climate projections depends on improving our understanding of key sensitivities of aerosol parameterizations (Lee et al., 2016).
Multi-model intercomparisons and sensitivity studies using a single model are complementary approaches to assess uncertainties of aerosol parameterizations: intercomparisons compare different representations of aerosol characterizations, process parameterizations and parameter choices in a statistical fashion.Observed differences are judged in comparison to observational data and can usually not be attributed to specific processes or characteristics and their implementation.The AQMEII (Air Quality Modelling Evaluation International Initiative) is an example of a statistical intercomparison and evaluation of multiple regional aerosol and chemistry transport models and reports large variability between different models that seems related to aerosol deposition but could not be explained at the process level (Solazzo, 2012).On the global scale and with a focus on climate applications, the AeroCom (Aerosol Comparison) multi-model intercomparison initiative likewise reports large model diversity and concludes from observational biases that emissions and gas-to-particle conversion are insufficiently understood (Mann et al., 2014).Differences in model per-formance could not be attributed to specific process parameterizations in most cases, however.
Numerical sensitivity studies test the effect of changing a certain parameter or the description of a specific process or aerosol characteristic on the variables of interest and can help to explain model variability.A sensitivity study of model performance to updated process representations, for example, allows Zhang et al. (2012) to attribute an improvement in modeled aerosol water content in comparison to the Ae-roCom multi-model mean to a κ-Köhler approach to water uptake.Lee et al. (2012) assess the parametric uncertainty regarding simulated CCN concentrations using an emulator technique that reveals the importance of interactions between different parameters and thus highlights the importance of comparing specific sets of parameters and parameterizations rather than varying them one at a time.
This study might be considered a hybrid between the model comparison and sensitivity studies discussed above and naturally takes into account combinations of parameters and parameterization approaches: we will present a detailed comparison of two different modal aerosol schemes, one developed by the climate community and one that emerged from air-quality and weather prediction applications, that are embedded into the same regional atmospheric model.This study intends to highlight key sensitivities to be considered when designing or choosing a modal aerosol scheme.It does not aim to identify the "better" of the two schemes, which will depend on the specific application.Our analysis comprises targeted sensitivity studies that require an adapted setup of the two aerosol schemes as well as a model comparison of both schemes in their default setups.For the latter, we additionally discuss resulting impacts on the radiative aerosol properties and implications for liquid-and ice-phase aerosol-cloud interactions.
The rest of the paper is organized as follows: detailed model descriptions are given in Sect. 2. Section 3 describes the different model setups that our analysis is based on.Section 4 compares an adapted version of both aerosol schemes in a sensitivity study, while Sect. 5 is concerned with the differences between the two schemes in their default setups as well as aerosol optical properties and aerosol-cloud interactions.We summarize and discuss our results in Sect.6.A list of abbreviations and terms is provided in Appendix A. An earlier version of this paper constitutes a chapter of the doctoral thesis of Franziska Glassmeier (Glassmeier, 2016).
Model descriptions: COSMO-ART and COSMO-ART-M7
We employ the atmospheric aerosol and chemistry modeling framework COSMO-ART (Vogel et al., 2009), which is based on the regional atmospheric model COSMO (Consortium for Small-Scale Modelling; www.cosmo-model.org).
The ART (Aersosol and Reactive Trace gases) extension of COSMO features online-coupled gas-phase chemistry and the modal two-moment aerosol scheme MADE (Modal Aerosol Dynamics model for Europe) as well as aerosolradiation and aerosol-cloud interactions.COSMO-ART has a tradition of air-quality modeling and has been extended to investigate the role of interactive aerosol in weather prediction (e.g., Bangert et al., 2012;Rieger et al., 2014).We compare this standard version of COSMO-ART to a new assembled model version called COSMO-ART-M7.This new version integrates the modal two-moment aerosol module M7 (Vignati et al., 2004;Stier et al., 2005) and the computationally efficient sulfur chemistry of Feichter et al. (1996), as an alternative to the full chemistry and MADE, into the COSMO-ART framework.The efficient chemistry is implemented using the code generator KPP (Damian et al., 2002) that is available within COSMO-ART.The implementation of the aqueous-phase chemistry relies on the reaction rate implementation from GEOS-CHEM (map.nasa.gov/GEOS_CHEM_f90toHTML/).Our implementation of the Feichter sulfur cycle is coupled to the updated version of the M7 aerosol microphysics as implemented in the global climate model ECHAM-HAM2.2 (Zhang et al., 2012).Primary emissions and dry and wet deposition as well as aerosolcloud interactions from COSMO-ART are adapted to M7 aerosol modes.The implementation of aerosol-optical properties is M7-specific and is described in Zubler et al. (2011).
The M7 module has been developed for climate applications in global models.COSMO-ART-M7 can be considered an updated version of COSMO-M7 (Zubler et al., 2011): next to the current versions of COSMO and M7, COSMO-ART-M7 profits from the state-of-the-art droplet activation and ice-nucleation parameterizations of COSMO-ART.In contrast to COSMO-M7, COSMO-ART-M7 includes aerosolcloud interactions in cirrus clouds.The remainder of this section provides details on the parameterizations and adaptations.
Aerosol
The aerosol module MADE of COSMO-ART represents atmospheric aerosol by 12 coated and uncoated lognormal modes in the Aitken, accumulation, coarse and giant size ranges.For the composition of aerosol particles, 13 chemical species are considered: dust (DU), sea salt (SS), sulfate (SO 4 ), nitrate (NO 3 ), ammonium (NH 4 ), black carbon/soot (BC), primary organic carbon (POA), four volatility classes for secondary organic aerosols (SOA) representative of different SOA species (Athanasopoulou et al., 2013) Inter-and intra-modal coagulation is considered for anthropogenic Aitken and accumulation modes (modes labeled if, ic, so, jf and jc in Table 1) but omitted for the sea salt (sa, sb, sc) and dust modes (da, db, dc) and the PM 10 mode (ca) as indicated by the dashed line in Fig. 1.Sources of MADE aerosols include primary emissions of SS, DU, POA, BC, PM 2.5 and PM 10 and gas-to-particle conversion of SO 4 , NO 3 , NH 4 and SOA.Emissions of SS (Lundgren, 2012) and DU (Vogel et al., 2006) are calculated online based on wind speed.Primary anthropogenic aerosols are based on emissions inventories.Emitted BC is assigned to the pure soot mode (so) and POA is distributed to the Aitken (if) and accumulation mode (jf) without soot core.The POA partitioning follows the emission preprocessor described in Knote (2012).Emissions are assumed to follow the initial modal size distributions summarized in Table 1.SOA, NO 3 and NH 4 condense onto existing particles (Binkowski and Shankar, 1995).For sulfate, nucleation from the gas phase is additionally considered (Kerminen and Wexler, 1994) and particles are assigned to the soot-free Aitken mode (if).Hygroscopic growth of aerosols is based on ISORROPIA2 (Fountoukis and Nenes, 2007) for inorganic compounds and discussed in Athanasopoulou et al. (2013) for organic aerosol.As aerosol sinks, sedimentation and dry deposition (Riemer, 2002) and impaction scavenging (Rinke, 2008) by rain are considered.The description of impaction scavenging is based on an aerosol-and hydrometeor-size dependent collection efficiency.It considers inertial impaction and impaction from Brownian diffusion and interception but not phoretic effects.The parameterization is applied to the wet aerosol radius such that the hygroscopicity of an aerosol particle may affect its scavenging efficiency by impaction.Nucleation scavenging is not considered.
The M7 aerosol scheme considers four lognormal modes with soluble coating and three insoluble lognormal modes, including a nucleation mode but excluding giant modes.Table 1 compares the physical characteristics of these modes to the modes of MADE.M7 features a mode reorganization routine that transfers the largest particles within a mode to the next larger mode if the modal radius exceeds the boundaries indicated in the table.M7 includes fewer chemical species than MADE.It transports DU, SS, BC, POA and SO 4 .To be consistent with its simplified chemistry scheme, M7 sulfate is interpreted as sulfuric acid.M7 does not account for nitrogen species and secondary organic aerosols.The chemical composition of M7 modes is illustrated and compared to MADE in Fig. 1.Inter-modal coagulation is considered for all modes; intra-modal coagulation is neglected for the coarse modes (cs, ci) and accumulation mode dust (ai).Primary emissions are identical to MADE and follow MADE size distributions.They are assigned to M7 modes based on the mode correspondence shown in Table 1: BC is emitted into the insoluble carbon mode (ki), POA is partitioned to the soluble Aitken (ks) and accumulation mode (as) in the same way as for MADE.Giant dust and sea salt emission is ignored and accumulation and coarse-mode dust emissions are assigned to the pure dust modes (ai, ci) in M7.Sulfate can nucleate into the nucleation mode (ns) (default scheme used in this study: Kazil and Lovejoy, 2007;optional: Vehkamäki et al., 2002) or condense onto the larger soluble modes (ks, as, cs).Hygroscopic growth of the soluble modes (nc, ks, as, cs) is based on κ-Köhler theory (Petters and Kreidenweis, 2007).Aerosol removal by dry deposition and impaction scavenging follows the same parameterizations as for MADE.Table 2 summarizes the process differences of the M7 as compared to the MADE aerosol dynamics.
Sulfur chemistry
As part of the full gas-phase chemistry, COSMO-ART considers the following sulfur oxidation reactions: coagulation coefficients due to Brownian diffusion as harmonic mean of free molecular regime and continuum regime (Pratsinis, 1987) Condensation all in-cloud sulfate is assumed to be in the aerosol phase explicit treatment of condensation of sulfuric acid Water uptake κ-Köhler theory (Petters and Kreidenweis, 2007) (Binkowski and Shankar, 1995); thermodynamic bulk equilibrium of inorganic and organic compounds and water (Fountoukis and Nenes, 2007) Nucleation explicit cluster-based parameterization (Kazil and Lovejoy, 2007) binary nulceation of sulfuric acid and water with empirical formulation for critical concentration of H 2 SO 4 (Kerminen and Wexler, 1994) where the reaction equations are restricted to prognostic species such that non-prognostic species have been omitted.Aqueous-phase chemistry, namely in-droplet oxidation of SO 2 (aq), is not included in the standard setup.DMS emissions are calculated online based on wind speed (Nightingale et al., 2000).Anthropogenic gaseous emissions are based on inventory data.Dry deposition according to Baer and Nester (1992) and gas-to-particle conversion are considered as sinks of gas-phase species.
The efficient M7 chemistry consists of DMS, SO 2 (g), SO 4 (g) and SO 4 (aq) as interactive variables and requires external input for the reactive oxidants HO, O 3 , NO 2 and H 2 O 2 .To prescribe theses species, spatially heterogeneous monthly mean values are typically used.A steady-state value for NO 3 is additionally derived from the NO 2 , O 3 and DMS input fields.The following sulfur oxidation reactions are considered.
-Aqueous-phase chemistry: -Daytime gas-phase chemistry: -Nighttime gas-phase chemistry: Non-prognostic products have been omitted.Day-and nighttime reactions are exclusive and the seasonal variability of day length is taken into account.Aqueous-phase chemistry requires the presence of cloud water but is independent of solar insolation.The dissolution of the gaseous species for the aqueous-phase reactions is based on the effective Henry constants determined by the cloud droplets pH value.Assuming that most cloud droplets have emerged from the activation of accumulation mode aerosol, SO 4 (aq) resulting from the aqueous-phase reaction is in most cases assigned to the mixed accumulation mode (mode as in Fig. 1 and Table 1) and in fewer cases to the mixed coarse mode (mode cs).This is implemented by a number-based partitioning that favors the more numerous accumulation mode.
Aerosol-radiation interactions
The optical properties of MADE and M7 aerosol particles, i.e., extinction coefficient, single-scattering albedo and asymmetry factor are parameterized based on Mie calculations.Optical properties of MADE aerosols are distinguished on a modal basis such that for each mode a representative refractive index is assumed and calculations are performed for modal diameters of emitted particles (Table 1).The parameterization for mixed and anthropogenic modes is discussed by Vogel et al. (2009), for sea salt by Lundgren (2012) and for dust by Stanelle et al. (2010).In contrast to MADE, optical properties of M7 aerosol are species-based: the modal refractive index is the mass-weighted average of the refractive indices of the different species (Zubler et al., 2011).This method requires a look-up table of Mie properties, which also allows us to consider the simulated modal diameters instead of the values at emission applied in MADE.
Aerosol-cloud interactions
The activation of aerosol particles to cloud droplets is described in Bangert et al. (2011Bangert et al. ( , 2012)).The CCN spectrum is based on classical Köhler theory (Köhler, 1936) (Kumar et al., 2011) for non-hygroscopic particles (MADE modes da, db, dc; M7 modes ki, ai, ci).Supersaturation follows the parameterization of Nenes and Seinfeld (2003) and Fountoukis and Nenes (2005), which is based on adiabatic parcel ascent.To take into account the sub-grid-scale updraft variability, the number concentration of activated aerosol particles is determined by numerically averaging over a Gaussian probability density function (PDF) of updraft velocities about the grid mean value rather than using the number concentration of particles that are activated for the grid mean updraft.The standard deviation of the PDF depends on the turbulent kinetic energy (TKE).The activation parameterization takes into account the competition of different particles and solves the supersaturation balance equation based on population splitting into kinetically limited and equilibrating activated aerosol particles.For cloud-base activation, entrainment of below-cloud aerosol is considered (Ghan et al., 1997).For in-cloud activation, the depletion of supersaturation by existing droplets is accounted for by treating these droplets as giant CCN following Barahona et al. (2010).
Ice nucleation is based on the empirical, aerosol-surfacebased INP spectrum of Phillips et al. (2008), which does not distinguish between different freezing modes.As an alternative, Ullrich et al. (2017) have recently derived and implemented nucleation spectra for immersion freezing of dust and deposition nucleation on dust and soot based on the icenucleation-active site approach and measurements from the AIDA cloud chamber.Table 3 summarizes how INP spectra are applied to MADE aerosols in the standard setup of COSMO-ART and to MADE and M7 aerosol for this study.The implementation of ice nucleation (Bangert et al., 2012) is based on Barahona and Nenes (2009a, b).For temperatures higher than the onset temperature of homogeneous freezing, i.e., T > 235 K, grid-scale supersaturation with respect to ice is applied to determine the ice-nucleation rate from the INP spectrum.At lower temperatures, the competition of heterogeneous ice nucleation and homogeneous freezing of solution droplets is taken into account via the ice-supersaturation equation for an ascending parcel.For its updraft, a PDF about the grid mean value is applied.
The activation and ice-nucleation parameterizations are coupled to a two-moment microphysics scheme with five hydrometeor classes (cloud droplets, rain drops, ice crystals, snow flakes and graupel) (Seifert and Beheng, 2006;Noppel et al., 2010).This scheme does not distinguish between warm, mixed-phase and cirrus clouds, but its processes are based on temperature, saturation, and liquid and ice water content in the respective grid box.We will therefore use the term liquid cloud or warm cloud to denote cloudy regions without cloud ice, mixed-phase cloud to denote cloudy regions in which both cloud liquid and cloud ice are present, and ice cloud for regions which contain cloud water in the form of ice but no liquid.The latter may correspond to glaciated clouds or to cirrus clouds.We reserve the expression cirrus for ice clouds at temperatures lower than 235 K, in which homogeneous freezing of solution droplets occurs.
The coupling of the activation and ice-nucleation routines to the cloud microphysics scheme is adapted from the standard setup of COSMO-ART and is identical for both aerosol schemes in this study.As for the standard version of COSMO-ART, neither liquid nor ice-phase nucleation scavenging is considered.The coupling of the parameterized number of activated aerosol particles to microphysics in the standard setup of COSMO-ART is based on the assumption that in-cloud activation is largely inhibited by the depletion of supersaturation on preexisting cloud droplets.CCN depletion is only accounted for by limiting the number of cloud droplets to the total number of soluble Aitken and accumulation mode particles.In this study, CCN depletion is taken into account by subtracting the number of existing cloud droplets from the number of newly activated droplets predicted by the activation parameterization.
In the standard setup, ice nucleation in mixed-phase as well as ice clouds is coupled to the cloud microphysics scheme based on the assumption that ice nucleation converts water vapor into ice.Ice nucleation in mixed-phase clouds is thus assumed to proceed purely by condensation nucleation (Table 3).For mixed-phase clouds in this study, we assume that immersion and contact freezing convert cloud droplets into ice crystals such that cloud droplet number concentration and mixing ratio are reduced by mixed-phase ice nucleation.Ice nucleation in ice clouds follows the previous approach of MADE and converts water vapor into ice.Unmodified from the standard setup, INP depletion is accounted for by a number adjustment that subtracts the existing number of ice crystals and snow flakes from the crystal number predicted by the parameterization.
Setup
Simulations for this study are performed for a Saharan dust outbreak reaching Europe in May 2008.Following Bangert et al. (2012), we choose a dust event to ensure sufficiently high INP concentrations inside our simulation domain in order to compare the implications of aerosol schemes not only on liquid-phase processes but also on ice-nucleation rates in mixed-phase and ice clouds.The domain covers the dust sources in northern Africa and extends to western and central Europe (Fig. 2).The model setup has a horizontal resolution of 25 km at a time step of 30 s.The vertical resolution decreases with height, starting with 20 m in the surface layer and reaching 1000 m at the model top, corresponding to a height of 22 km.We simulate a 90 h period, starting on 22 May, 00:00.To allow for spin-up of aerosol concentrations, we analyze the time average of the hourly output from the last 24 h of the simulation.
Table 3. Coupling of aerosol modes to ice-nucleation parameterizations.The table summarizes which ice-nucleation modes are considered for the pure dust and soot modes and modes with dust and/or soot core, depending on the aerosol scheme and ice-nucleation parameterization.In the standard setup of COSMO-ART, the condensation freezing parameterization, which takes into account MADE aerosol, is combined with a droplet freezing routine from the cloud microphysics scheme, which is not coupled to MADE.Homogeneous freezing of solution droplets follows Barahona and Nenes (2009b) Meteorological initial and boundary conditions are provided by the global model GME (Majewski et al., 2002).For the full ART chemistry, initial and boundary conditions of gases with the exception of DMS, SO 2 and SO 4 are based on the global chemistry model MOZART (Emmons et al., 2010).For DMS, SO 2 , SO 4 and aerosols, no initial and boundary conditions are provided.Anthropogenic emissions follow the TNO/MACC inventory (van der Gon et al., 2010;Kuenen et al., 2011).The inventory does not provide emissions for Africa, so no anthropogenic but only natural emissions are considered in this region.Surface properties for parameterized emissions rely on the GLC2000 dataset (Bartholomé and Belward, 2005) and on Marticorena et al. (1997) for dust.
Table 4 summarizes the six different model settings used for this study."Sim", "simSIG", "Passive" and "Coupled" simulations are performed with both MADE and M7."SimAQ" and "simCL" simulations are specific to and only performed with M7 such that overall 10 simulations have been performed.
Aerosol-radiation interactions are disabled for all simulations; aerosol-cloud interactions are restricted to Cou-pled simulations.All other simulations thus feature passive aerosols such that the simulated meteorology is identical for simulations with MADE and M7.Without aerosol-cloud interactions, the two-moment cloud microphysics is not required.We therefore employ the operational one-moment scheme (Reinhardt and Seifert, 2006) in simulations with passive aerosol.
Sim simulations aim to make the model setup of M7 and MADE as similar as possible: the M7-only aqueous-phase chemistry, the MADE-only giant modes, and SOA, NO 3 , NH 4 and unspeciated PM 2.5 as MADE-only species are disabled; a universal standard deviation of σ universal = 1.7 is used for all MADE and M7 modes instead of the default standard deviations indicated in Table 1; for the oxidant fields required by the M7 chemistry hourly outputs of the respective fields from MADE simulations are used instead of climatological values.Sim simulations aim to investigate the sensitivities of aerosol burden, aerosol size distribution and gas-phase chemistry without taking into account the disabled structural differences.
Passive simulations correspond to default setups of MADE and M7 and allow us to explore additional sensitivities aris-F.Glassmeier et al.: A comparison of two chemistry and aerosol schemes ing from aqueous-phase chemistry, climatological oxidant fields, different modal standard deviations and additional aerosol species.For these simulations, we additionally investigate the optical and cloud-and ice-forming properties of the aerosol distributions by offline diagnostics: routines for optical properties, droplet activation and ice nucleation are called without passing the results on to the cloud microphysics and radiation scheme of the model.The ice-nucleation routine is called in mixed-phase setting when the one-moment cloud microphysics scheme predicts both cloud ice and cloud water and in ice-phase setting when cloud water is absent.The activation routine is applied in its setting for new cloud formation, i.e., without cloud-base entrainment of aerosol and without considering supersaturation depletion by existing droplets.It is called in all grid boxes where cloud water is predicted by the one-moment scheme.For computational reasons, the updraft PDF is replaced by applying an updraft w * = w + 0.8 √ TKE, where w is the grid-scale updraft and TKE denotes the sub-grid-scale turbulent kinetic energy (Bangert, 2012).
SimSIG, simAQ and simCL simulations feature settings intermediate to sim and Passive and are intended to individually investigate the effects of modal standard deviation, aqueous-phase chemistry or climatological oxidant fields, respectively.Coupled simulations with two-moment microphysics and aerosol-cloud coupling are conducted to investigate the relationship between CCN, INPs, cloud droplet and ice crystal numbers.In Coupled simulations, the same updraft parameterization as in Passive simulations (i.e., no PDF) is applied for the online as well as offline calculation of CCN.
Results from the sensitivity experiments
Figure 2 illustrates the dominant transport patterns for aerosols on the analysis day: following the transport from Africa over the Mediterranean to central Europe, the flow turns to a low-pressure system off the Bay of Biscay.The corresponding M7 aerosol burdens of sea salt, dust, BC and POA are illustrated in Fig. 3 (left column): dust is transported from the Saharan source regions over the Mediterranean Sea to the southern parts of Germany and France.Sea-salt-containing maritime air is advected over most of the domain, with the exception of eastern Africa.Strong winds south of Britain explain the strongest sea salt emissions and burdens in this region.For the African part of the domain, no anthropogenic emissions are available.Accordingly, BC and POA are largely restricted to continental Europe, the Mediterranean Sea and the Atlantic part of the domain.The corresponding SO 4 burden is depicted in Fig. 4 (middle row).It is restricted to the northern and western half of the domain because continental Africa neither provides anthropogenic emissions of SO 2 nor natural DMS-derived sulfate.In some parts of the following analysis we distin- guish between different regions based on aerosol composition (Fig. 2): the region denoted as "Atlantic" comprises maritime regions in which surface dust is absent; the expression "Mediterranean", in contrast, characterizes dusty maritime regions."Europe" stands for continental areas with anthropogenic emissions and "Africa" for continental sites without anthropogenic emission.Aerosol burdens for MADE and M7 agree within 20 % (Fig. 3, Table 5), which confirms our strategy for sim simulations in choosing the setup such that MADE and M7 are very similar.Dust burdens are identical for M7 and MADE: the transfer of dust into the soluble M7 modes via condensation is ineffective (coagulation is neglected due to large particle sizes; Sect.2) such that MADE and M7 both describe dust by two identical pure modes.The low coating in sim simulation is a result of a general underestimation of sulfate in this setup (cf.Table 7).The M7 sea salt burden is increased by ∼ 20 % as compared to MADE, while the SO 4 burden is decreased by ∼ 20 %.BC and POA burden are decreased by less than 10 %.The following discussion of sim, simSIG, simAQ and simCL simulations is greatly facilitated by this similarity.
Sensitivities of aerosol size distributions and removal
Primary emissions are identical for simulations with MADE and M7 (Sect.2) such that differences in primary aerosol burdens are attributable to the aerosol sinks, i.e., dry deposition and impaction scavenging.Differences in sulfate burden be- tween MADE and M7 are likewise dominated by differences in removal and not in the sulfate production rate (Fig. 4).
The efficiency of both removal processes depends on particle size and becomes inefficient if particle radii approach the Greenfield gap at 0.1 µm.Whether a shift in the size distribution results in increased or decreased removal depends on its relative position to the Greenfield gap: a shift in an Aitken mode to smaller sizes or of a coarse mode to larger sizes enhances removal.The effect of an accumulation mode shift depends on the details of the Greenfield gap and cannot easily be predicted.Removal is dominated by impaction scavenging in the cloudy northern half of the domain, where the abundance of sulfate, sea salt, BC and POA is largest.Only for dust is dry deposition important, especially in the African source regions (not shown).
Sensitivity of size distribution to modal composition
Figure 5 depicts domain-averaged volume size distributions of different species for MADE and M7 obtained from sim simulations (red).The size distribution of M7 sea salt is shifted to smaller particle sizes as compared to MADE.This is a result of the internal mixture of sea salt in M7 as compared to the externally mixed sea salt modes of MADE (Table 1): sea salt emissions only contribute a fraction of the total number of particles in the M7 mixed modes such that the average sea salt mass per mixed aerosol particle is reduced as compared to the average mass of emitted particles and the corresponding size of MADE sea salt.For dust, not only the burdens but also the size distributions are effectively identical for MADE and M7.
While MADE-sulfate is found in a single broad peak of a large Aitken or small accumulation mode, M7 sulfate mass shows a distinct trimodal structure.The position of the pronounced M7 sulfate coarse mode corresponds to that of coarse-mode sea salt.As dust is hardly coated and BC and POA are not abundant in the coarse-mode size range, the mixed M7 coarse mode corresponds to sulfate-coated sea salt.In contrast, the MADE sea salt coarse mode is not significantly coated.The M7 coarse-mode coating could be more effective because sea salt is more abundant and particles are smaller such that a larger surface for condensation is available.In addition, while MADE sulfate is restricted to condensation as a process for transfer into the coarse mode, M7 sulfate can additionally be transferred from the accumulation to the coarse mode by mode reorganization once the median radius exceeds the maximum value for its mode.
The separated Aitken and accumulation mode peaks in M7 sulfate as compared to the single peak for MADE correspond to the BC and POA size distributions: M7 BC is located at smaller size and M7 POA at larger ones than for MADE.The location of the M7 POA peak corresponds to the M7 accumulation mode sea salt peak and indicates that POAcontaining particles in M7 are enlarged by internal mixture with sea salt.The increase in MADE accumulation mode BC as compared to M7 likely results from different strategies to describe growth by condensation in MADE and M7: for both schemes, BC is emitted into a pure Aitken mode and rapidly coated.In M7, coated BC is assigned to the mixed Aitken mode and can subsequently be transferred to the accumulation mode by mode reorganization (Vignati et al., 2004).The extent of the mode reorganization is not directly coupled to the size of the coated soot particles but to the characteristics of the mixed Aitken mode with sizes being influenced by, e.g., the transfer of small particles from the nucleation mode or the transfer of large particles to the accumulation mode.
The MADE coating routine directly assigns a fraction of the newly coated BC to the accumulation mode (Riemer, 2002).
The differences in aerosol burdens between MADE and M7 can be traced back to the size distributions: the M7 sulfate burden is decreased in comparison to MADE in the northern part of the domain due to increased removal of M7 coarse-mode sulfate.The burden of M7 sea salt is increased due to the smaller size of the mixed coarse mode as compared to the MADE sea salt coarse mode, which results in less efficient impaction scavenging for M7.The BC burden of M7 is smaller than that of MADE because of increased removal due to the smaller sizes of BC-containing particles in M7.The decrease in M7 POA burden can be explained by the position of the M7 mixed accumulation mode being shifted away from the Greenfield gap as compared to the MADE accumulation modes.
Sensitivity of size distribution to modal standard deviation
The effect of the modal standard deviation σ on the size distribution is illustrated in Fig. 5. Plotting the data of sim sim-ulations with the default standard deviations of the aerosol schemes (green; see Table 1 for values of σ default ) instead of the universal standard deviation σ universal = 1.7 used to generate the data illustrates the structural effect of the standard deviation as opposed to the effects arising from the influence of σ on aerosol microphysical processes.The structural effect is most pronounced for dust: with σ da = 1.7 = σ universal , the width of the MADE accumulation mode remains unchanged, while the MADE coarse mode becomes slightly narrower with σ db = 1.6.For M7, the default accumulation mode is narrowed (σ ai = 1.59) and the default coarse mode broadened (σ ci = 2).The effect of σ on aerosol microphysics can be assessed by comparing the differences between the sim simulation plotted with default standard deviations (green) and simSIG simulations where the size distributions were generated using the default standard deviations (black).Effects are strongest for the coarse modes of dust and sea salt.Dust mass in the coarse mode is determined by the efficiency of dry deposition, which is the dominant removal process in the cloudfree African source regions.The sedimentation velocity of a lognormal mode is given by v sedi ∝ r 2 exp(8ln 2 σ ) (Slinn and Slinn, 1980) such that dry deposition is increased for larger σ , which corresponds to an increased number of very large particles.The dust burden of MADE accordingly increases by 4 % when applying the smaller default standard deviation, while the M7 dust burden decreases by about 20 % for the enlarged σ ci .A similar argument for the impaction scavenging of coarse-mode sea salt explains a 40 % decrease in MADE and M7 sea salt burden when using σ default = σ cs = σ sb = 2 instead of σ universal .
Sensitivity of chemical sulfate production to aqueous-phase reactions and oxidant fields
Figure 4 compares the chemical sulfate production as sources of atmospheric sulfate arising from MADE and M7 aerosol dynamics with full and efficient gas-phase sulfate chemistry for sim simulations (recall from Sects.2.2 and 3 that nitrate chemistry is not considered).With domain-averaged differences below 10 % (Table 5), the M7 gas-phase chemistry (Eqs.R7-R11) and the ART chemistry (Eqs.R1-R5) are equally efficient in producing SO 4 .The importance of aqueous-phase chemistry as a source of atmospheric sulfate aerosol is illustrated in Fig. 6.The aqueous-phase reaction rate in the simAQ simulation is about 2 times larger than the gas-phase reaction rate in the sim simulation without aqueous-phase chemistry (compare Figs. 4 and 6).As the occurrence of SO 2 coincides with cloudy conditions in the northern and western part of the domain, the aqueous-phase reaction efficiently consumes SO 2 and leads to an 80 % reduction in its concentration as compared to the sim simulation (Table 6).The gas-phase reaction erent species and standard deviations.Distributions are obtained by weighting the total dry volume distribution by the fraction the respective species contributes to the total mass.The total distribution is the sum of all model modes, which are determined from the vertical sum and horizontal averages of the corresponding dry masses and numbers.Species include sea salt (SS), dust (DU), sulfate (SO 4 ), soot (BC) and primary organic carbon (POA).The figure compares simulationssim , generated with the universal standard deviation ‡ universal = 1 .7 for all modes (red), simulations sim but plotted using default standard deviations ‡ default as given in Table 1 (green) and simulationssimSIG , generated with ‡ default (black).17 Figure 5. Domain-averaged volume distributions for different species and standard deviations.Species include SS, DU, sulfate (SO 4 ), BC and POA.Individual lognormal modes are determined from the vertical sum and horizontal average of the corresponding dry masses and numbers.The full, multimodal distribution emerges as the sum of individual lognormal modes.For mixed modes, lognormal modes of individual species are obtained by weighting the mixed-modal distribution by the fraction that the respective species contributes to the total mass in the mixed mode.The figure compares sim simulations, generated with the universal standard deviation σ universal = 1.7 for all modes (red), sim simulations but plotted using default standard deviations σ default as given in Table 1 (green), and simSIG simulations, generated with σ default (black).
rate in the simAQ simulation is reduced by 100 % in comparison to sim due to the competition with the aqueous-phase reaction for SO 2 .The resulting sulfate burden of simAQ is increased by 140 % as compared to sim.The use of monthly-mean climatological oxidant fields instead of hourly values simulated by the full ART gas-phase chemistry influences the sulfate burden by less than 10 % (Fig. 6, Table 6).The almost identical results in our case are the consequence of compensating effects on the aqueousphase reaction rates, which dominates total sulfate produc-tion: a 40 % reduction in the H 2 O 2 climatological oxidant field as compared to the detailed chemistry is largely compensated for by a 12 % increase in O 3 (Table 6) and thus only results in a 2 % reduction in aqueous-phase production of sulfate (Fig. 6, Table 6).The gas-phase production rate of sulfate exhibits an inconsequential signal, which probably emerges from the interplay of enhancing effects of a locally dampened aqueous-phase reaction rate and dampening effects of decreases in the climatological concentrations of www.atmos-chem-phys.net/17/8651/2017/Atmos.Chem.Phys., 17, 8651-8680, 2017 OH and NO 2 by 60 and 80 %, respectively, as compared to the hourly values (Fig. 6, Table 6).
The effect of the different chemistry setups on the sulfate level is summarized in Table 7, which compares the average surface concentrations of SO 4 over continental Europe.According to, e.g., Fountoukis et al. (2011), concentrations of about 1-2 µgm −3 are expected.These values are not reached with gas-phase sulfate chemistry alone but require the efficient aqueous-phase reaction, which is consistent with the findings of previous studies and especially by Knote et al. (2011).
Results from comparison of default setups
Differences in the sulfate budgets of MADE and M7 in their default configuration (Passive simulations according to Table 4) are dominated by the M7-only aqueous-phase chemistry (Table 5).As discussed in the previous section, Table 6.Horizontal averages of relative differences.= 2(simAQ − x)/(simAQ + x) between different M7 chemistry setups in percent for the simAQ simulation in comparison to simulations x = sim, simCL.Values of SO 2 and SO 4 correspond to Fig. 6, where the productions rates are vertically integrated.Differences in oxidant fields are based on weighted vertical averages as in Fig. 2, with the gas-phase production rate of SO 4 as weight for the gas-phase oxidant OH and gas-phase oxidant precursor NO 2 and weighted with the aqueous-phase reaction rate for the aqueous-phase oxidants H 2 O 2 and O 3 .Where the sign of a difference signal is not uniform throughout the domain, representative quadrants have been chosen.aqueous-phase chemistry is about twice as efficient in oxidizing SO 2 as the gas-phase chemistry.The M7 sulfate burden is increased by about 140 % for aqueous-and gasphase chemistry (simAQ) as compared to gas-phase chemistry alone (sim).In the sim simulation, the different gasphase chemistries for M7 and MADE result in almost identical sulfate burdens.Consequently, when comparing M7 with gas-and aqueous-phase chemistry to MADE in Passive simulations, a 140 % increase for M7 is observed (Table 5).Comparing the size distribution of M7 sulfate mainly produced by aqueous-phase chemistry (Fig. 7) to that produced by the gas-phase reaction (Fig. 5) illustrates that the aqueous-phase chemistry deposits sulfate mainly into the accumulation and to a lesser extent into the coarse mode (the partitioning between these two modes is based on number and thus favors the more numerous accumulation mode), while gas-phase chemistry additionally transfers sulfate to Aitken modes particles via condensation or coagulation with nucleation-mode particles.
As discussed, M7 aqueous chemistry produces much higher sulfate concentrations, while MADE also contains nitrate and ammonium as additional inorganic soluble species.Similar to different secondary organic aerosol (SOA) species, which are often lumped together, we combine sulfate, nitrate and ammonium into a secondary inorganic aerosol (SIA) class, to obtain a quantity that can be compared between MADE and M7.Note that for M7, SIA is identical to sulfate aerosol.From this perspective, the higher contribution of M7 sulfate to the total aerosol burden is compensated for by MADE nitrate and ammonium (Fig. 8).In the Atlantic part of the domain, overcompensation occurs and the SIA burden is reduced by about 40 % for M7 as compared to MADE (Table 5).The SIA burden in the central (Mediterranean) part of the domain is increased by about 30 % for M7 in comparison to MADE.
The sea salt size distributions of MADE and M7 from Passive simulations (not shown) are qualitatively similar to the sim simulation (Fig. 5).The impaction scavenging efficiency of sea salt remains higher for MADE than for M7.This effect is not compensated for by additional sea salt mass in the MADE giant mode, keeping the sea salt burden of M7 enhanced as compared to MADE (Table 5).The importance of MADE giant sea salt is probably limited because the main emission regions of sea salt coincide with rainy regions such that most particles are immediately removed by impaction scavenging.
In contrast to sim simulations, the Passive M7 dust burden is decreased by about 80 % in comparison to MADE due to increased dry deposition of the wider coarse mode and because the M7 dust burden has no contribution from the giant mode.The additional MADE dust leads to a strongly enhanced difference between MADE and M7 in the height of the coarse/giant mode peak in the size distribution (not shown) that otherwise remains qualitatively similar to that from the simSIG simulation (Fig. 5, black).
Differences in BC burden remain similar to the sim simulation (Table 5) as does the BC size distribution (not shown, but see Fig. 5).Similar to SIA, SOA and unspeciated aerosol from MADE are considered as part of an organic aerosol (OA) class.For M7, OA is identical to POA.SOA and unspeciated aerosols enhance the MADE OA burden to a 40 % increased value as compared to M7 (Table 5).The OA size distribution (not shown) is qualitatively similar to that of POA from the sim simulation (Fig. 5).
Radiative properties
Figure 9 compares 550 nm AOD (aerosol optical depth) for the Passive simulations of MADE and M7.Comparing the pattern of AOD to the species burden in Fig. 3 5).In the regions of strongest flow (Fig. 2), differences of up to 200 % occur (Fig. 9).The increased MADE AOD can be attributed to the additional modes and species of MADE, i.e., the giant dust mode, nitrate, ammonium, SOA and unspeciated aerosol.The AOD difference pattern is matched by the difference pattern of the total wet aerosol burden in the accumulation and coarse-mode size ranges (Fig. 9), which dominate the radiative effect because particles sizes correspond to the considered wavelength of 550 nm.The difference pattern between MADE and M7 accumulation and coarse-mode soot does not correspond to the AOD differences pattern (not shown) and confirms that differences are caused by the additional scattering of MADE species and not by the differences in the distributions of absorbing soot in the Aitken and accumulation modes in MADE and M7 (Fig. 5).Differences in aerosol radiative properties between MADE and M7 are dominated by differences in burden arising from the structural differences and not by differences in the parameterization of optical properties (Sect.2.3).An estimate of the latter can be obtained from the sim simulation: for this setup, dust burden and size distribution are identical for MADE and M7, and dust is the only aerosol species over Africa in the lower-left quadrant of the domain (Figs. 3 and 5).An 18 % increase in M7 AOD as compared to MADE in this region is thus caused by differences in the parameterization of aerosol optical properties alone.The decreased M7 AOD for the Passive simulation (Fig. 9) illustrates that this parameterization effect is less important than the structural effect of the additional MADE giant dust mode.
Droplet-activation properties
MADE produces 100 % more CCN than M7 (Fig. 10, Table 5).The number distribution of soluble aerosols depicted in Fig. 11a illustrates that the increase in MADE CCN corresponds to an increased number of MADE aerosol particles in the Aitken mode size range that are large enough for activation as measured by a threshold radius of 35 nm based on the empirical activation parameterization by Lin and Leaitch (1997).MADE, on the one hand, features more particles in This fi sponds to SO 4 in Figure 5 but shows SIA for the passive simulations.SIA (secondary inorga corresponds to SO 4 for M7 and additionally includes NO 3 and NH 4 for MADE.ble 6).As discussed in the previous section, aqueous-phase chemistry is about twi in oxidizing SO 2 as the gas-phase chemistry.The M7 sulfate burden is about 70% aqueous-and gas-phase chemistry ( simAQ ) as compared to gas-phase chemistry In simulation sim , the di erent gas-phase chemistries for M7 and MADE result in alm cal sulfate burdens.Consequently, when comparing M7 with gas-and aqueous-pha to MADE in passive simulations, a 70% increase for M7 is observed (Table 6).
the size distribution of M7 sulfate mainly produced by aqueous-phase chemistry ( that produced by the gas-phase reaction (Figure 5) illustrates that the aqueous-pha deposits sulfate mainly into the accumulation and to a lesser extent into the coarse gas-phase chemistry additionally transfers sulfate to Aitken modes particles via con coagulation with nucleation-mode particles.Note that to consider the additional MAD species in the comparison with M7, we combine sulfate, nitrate and ammonium into inorganic aerosol (SIA) class.For M7, SIA is identical to sulfate aerosol.
The higher M7 sulfate burden is compensated for by MADE nitrate and ammo studying SIA (Figure 8).In the Atlantic part of the domain, overcompensation o the SIA burden is reduced by about 20% for M7 as compared to MADE (Table 6 burden in the central (Mediterranean) part of the domain remains about 15% increa in comparison to MADE.20 the Aitken size range due to additional emissions of unspeciated PM 2.5 particles that are not considered in M7.On the other hand, MADE Aitken mode particles are larger due to additional coating from SOA, nitrate and ammonium.Note that aqueous-phase-formed M7 sulfate cannot compensate for these species because it is predominantly assigned to accumulation mode particles, which are already large enough to be activated.
Figure 10 illustrates the situation for liquid clouds.Relative changes are comparable in mixed-phase clouds (Table 5), while absolute CCN numbers are about 40 % lower than in liquid clouds (not shown) due to a general decrease in aerosol number concentration with height.Also note the CCN predicted in the absence of soluble Aitken and accumulation mode particles in the lower-right quadrant of the domain (Fig. 3): these result from adsorption activation of hydrophilic dust.
Ice-nucleation properties
Dust and soot are considered as ice-nucleation-active species in our simulations (Sect.2.4).Dust has a much higher icenucleation potential so that it dominates ice nucleation when present.This is the case over Africa and the Mediterranean Sea (Fig. 3).Soot determines ice nucleation in the Atlantic part of the domain that is not affected by the dust outbreak.
Dust-dominated ice nucleation
As illustrated by the aerosol-surface distribution of dust in mixed-phase and ice clouds in Fig. 11b, the average surface of MADE dust particles available for ice nucleation is enhanced as compared to M7. Reasons for this increase are the MADE-only giant dust mode and increased dry deposition of dust from the M7 coarse mode due to its larger σ (Sect.4.1.2).At comparable number concentrations, the ice-nucleation potential increases with the average surface of particles (Phillips et al., 2008) such that INP concentrations tend to be increased for MADE as compared to M7 in clouds in dusty regions (Figs. 12, 13, Table 8).
In mixed-phase clouds (Fig. 12), M7 INPs are reduced by about 30 % as compared to MADE for the Phillips and by more than 180 % for the Ullrich parameterization.The strong difference for the Ullrich as compared to the Phillips parameterization is a result of a similarly dramatic difference in ice-nucleation-active dust, occurring because all MADE dust but only coated M7 dust is considered for the Ullrich ice-nucleation parameterization in mixed-phase clouds (the Phillips parameterization is based on total dust in both cases (Table 3); Fig. 11b is thus only relevant for the Phillips parameterization, and the corresponding distribution for the Ullrich parameterization is not shown).As the dominance of aqueous-phase sulfate production in M7 strongly restricts condensation and coating, most M7 dust remains uncoated: in the Mediterranean region about 10 % is coated and over the Atlantic values up to 100 % are reached.The number concentration of INPs is thus strongly constrained by the coating efficiency of dust, which depends on the aerosol model and its specific assumptions.
In dust-dominated ice clouds, we distinguish between two regions: the high dust concentrations in the southern part of the domain prevent supersaturations high enough for homogeneous freezing of solution droplets such that ice nucleation proceeds purely heterogeneously as indicated by the absence of frozen solution droplets in Fig. 13.For ice clouds over the Mediterranean Sea, dust concentrations are not large enough to prevent homogeneous freezing completely such that heterogeneous nucleation and homogeneous freezing compete.Due to inefficient coating, the surface distributions of uncoated dust, which is relevant for the Ullrich parameterization, is practically identical to that of total dust, which is relevant for the Phillips parameterization.Differences in Phillips and Ullrich INPs are thus both caused by practically identical differences in ice-nucleation-active dust between MADE and M7.Surprisingly, Phillips INPs are reduced by 20 % for M7 as compared to MADE, and Ullrich INPs are reduced by less than 2 % (Table 8) in both regions.This on the one hand points toward very different relative sensitivities of the Phillips as compared to the Ullrich parameterization.On the other hand, the difference might be influenced by the total INP concentration that the calculation of percentage changes is based on: the Ullrich parameterization results in absolute INP concentrations that are about 2 orders of magnitude higher than for the Phillips parameterization.Similarly dra- 3).matic differences between the Phillips parameterization and an earlier version of the Ullrich parameterization have been discussed by Niemand et al. (2012) for mixed-phase clouds.As a consequence of the low absolute INP concentrations, the Phillips parameterization results in homogeneous freezing being the dominant ice-nucleation mechanism over the Mediterranean Sea, while a competition between homogeneous and heterogeneous freezing occurs for the Ullrich parameterization.
Soot-dominated ice nucleation
In the Atlantic part of the domain, soot is the dominant source of INPs.The Ullrich parameterization does not consider soot as mixed-phase INPs such that mixed-phase Ullrich INPs are absent over the Atlantic (Fig. 12).For the Phillips parameterization, INPs are 34 % increased for MADE as compared to M7.This MADE increase corresponds to an increase in the average surface of MADE soot in comparison to M7 (Fig. 11d).The differences in surface distribution between MADE and M7 are probably a consequence of the distinction between mixed aerosol (modes if and jf in Fig. 1, Table 1) and coated soot (modes ic, jc) in MADE, while M7 features only a single type of mixed mode (modes ks, as, cs): as discussed for sea salt in Sect.4, M7 BC mass is distributed to the large number of all mixed particles, which is interpreted as every mixed particle having a soot core with a smaller size as compared to soot particles in the insoluble Aitken mode.
Soot-dominated ice nucleation in ice clouds according to the Ullrich parameterization results in a competition between heterogeneous and homogeneous freezing and thus corresponds to the situation of dust over the Mediterranean Sea discussed previously.Due to this competition, differences in the number concentrations of INPs between MADE and M7 control those of frozen solution droplets for the Ullrich pa-rameterization.Ullrich INPs are more than 120 % increased for M7 as compared to MADE.This can be attributed to a reduced efficiency of BC coating for M7 in comparison to MADE as illustrated by the surface distributions of uncoated BC in Fig. 11c.The less efficient coating of M7 BC has two reasons: on the one hand, the SIA burden over the Atlantic is 40 % smaller for M7 than for MADE due to MADE nitrate and ammonium.On the other hand, M7 sulfate is primarily produced via aqueous-phase chemistry, which prevents condensation as compared to the gas-phase production pathway of MADE.The increase in M7 Ullrich INPs as compared to MADE shifts the competition between homogeneous and heterogeneous nucleation towards heterogeneous nucleation and leads to a 180 % decrease in frozen solution droplets.This mechanisms corresponds to a negative Twomey effect (Kärcher and Lohmann, 2003) with M7 corresponding to polluted and MADE to clean conditions.
For the Phillips parameterization, total INP concentrations are reduced as compared to the Ullrich parameterization such that homogeneous nucleation is the dominant freezing process in the Atlantic part of the domain for this parameterization (Fig. 13).Differences between MADE and M7 in terms of INP and frozen solution droplet number concentrations can thus be analyzed separately.A 40 % higher abundance of frozen droplets in MADE as compared to M7 reflects dif- 8).
Comparison of default combinations
The standard version of COSMO-ART applies the Phillips ice-nucleation parameterization, while the Ullrich parameterization is more suitable for M7 to make use of the simulated difference between coated and uncoated dust.The discussion above shows that the differences between icenucleation parameterizations, as drastically illustrated by the absolute number of INPs in ice clouds in dust-dominated regions, and in the coupling of the ice-nucleation parameterization to the aerosol scheme, i.e., the consideration of coated vs. uncoated dust and the selection of modes participating in homogeneous freezing, mask the differences between the aerosol schemes.As a consequence, differences between MADE Phillips and M7 Ullrich are dominated both by differences in the ice-nucleation parameterization and its coupling to the aerosol scheme.
Buffering effect of cloud microphysics
While the optical properties of aerosols directly influence radiation, their cloud-forming properties as quantified by CCN and INPs can affect radiation and precipitation only indirectly via their influence on cloud droplet and ice crystal number concentration.In simulations with aerosol-cloud interactions (Coupled simulations in Table 4), CCN and INPs correspond to the activation and ice-nucleation rate and thus to sources of droplet and crystal number concentration.In addition, droplet and crystal number concentrations are subject to cloud microphysical processes, including number sinks like collision-coalescence, riming and aggregation.These processes modify the effect of aerosol differences on droplet and crystal number concentration as compared to their effect on CCN and INPs.The mediating effect of cloud microphysics on relative changes N/N in a hydrometeor number concentration N that result from a relative change CN / CN in the concentration of a cloud nuclei can be quantified by a relative sensitivity or susceptibility (McComiskey et al., 2009;Glassmeier and Lohmann, 2016): This universal equation quantifies liquid-phase (ice-phase) aerosol-cloud interactions when applying them to the droplet (ice crystal) number concentration N droplet (N crystal ) and CCN (INPs) by substituting N = N droplet (N = N crystal ) and CN = CCN (CN=INP).These susceptibilities can be determined from double logarithmic plots of ln N as a function of ln CN.The susceptibility is a characteristic of the cloud microphysics scheme, and its value will be different for different cloud regimes and states.To get a sampling of these regimes and states that is representative of the studied case, we make use of the horizontal variability in the domain and use spatially resolved data from simulations with twomoment microphysics (Coupled simulations in Table 4), temporally and vertically averaged over cloudy grid points in the same way as the data depicted in the contour plots of Figs. 10 to 13.As the cloud microphysics scheme that mediates the relationship between CN and N is the same for MADE and M7, we combine data points from simulations with both schemes.The resulting fits are shown in Fig. 14, for warm, mixed-phase and cirrus clouds.
Cloud droplet number concentrations are significantly lower than predicted concentrations of CCN (Fig. 14a).On the one hand, this arises because our CCN diagnostic does not take into account the competition of different droplets for water vapor, which is considered in the nucleation rate computed in the Coupled simulations (Sects.2.4, 3).On the other hand, growth by collision-coalescence as a droplet sink plays a role in modifying N (a similar argument is given by McComiskey et al., 2009): colors encode the average mass of cloud droplets and rain drops.Small values correspond to recently formed clouds where droplets are too small for efficient collisions.The corresponding data points thus lie closest to the 1 : 1 line.Large average mass corresponds to raining clouds with efficient collisions and strongly reduced drop number concentrations.The coefficient of determination may be interpreted such that 70 % of the relative variability in warm cloud droplet number can be explained by relative variability in the available CCN. Differences in the microphysical state of the cloud, for which hydrometeor size is a proxy, partly account for the unexplained 30 % of the variance as indicated by the systematic color pattern.
Ice crystal number concentrations are always higher than the number concentration of INPs in mixed-phase clouds (Fig. 14b).This can be attributed to ice crystal sources other than the heterogeneous freezing of cloud droplets.In our model, these are the freezing of rain drops, ice multiplication by rime splintering and the sedimentation of ice crystals from aloft.These INP-independent ice crystal source processes can also explain that crystal numbers are only weakly dependent on INPs, which account for only 20 % of variance.An additional factor is crystal sedimentation, which provides a number sink that has no analog in warm clouds because cloud droplet sedimentation is negligible.For a given INP concentration, crystal concentration instead increases with increasing glaciation as defined by the fraction of frozen to total cloud water.Like hydrometeor size in the warm case, this glaciation fraction might be considered a proxy for the state of the cloud.In cirrus clouds, ice crystal number concentrations tend to be smaller than the sum of INP and frozen solution droplets (Fig. 14c), likely as a result of sedimentation as an ice crystal sink.Sedimentation is most effective for large hydrometeors in the snow category of the microphysics scheme that result from the aggregation of individual ice crystals.Although aggregation is not very efficient at the low temperatures of cirrus clouds, the degree of aggregation seems a possible proxy for the microphysical cloud state: the color-coding based on the number of aggregates in Fig. 14c can explain variance in addition to differences in the number concentration of INPs and frozen droplets, which explain 20 %.Data points above the 1 : 1 line are probably related to the homogeneous freezing of cloud droplets that are advected to regions with temperatures colder than 235 K.This freezing of cloud droplets has to be distinguished from the homogeneous freezing of solution droplets predicted by the ice-nucleation parameterization.Homogeneous freezing of cloud droplets is restricted to cirrus or ice clouds of liquid origin, e.g., outflows from convective clouds or high-reaching tops of nimbostratus clouds.It seems that this cirrus regime contributes to the variability of the relationship between INPs and frozen solution droplets and ice crystal number concentrations in the regime of low crystal concentrations.Our discussion of Fig. 14 illustrates that a detailed understanding of the influence of cloud microphysics on the coupling between parameterized cloud nuclei and the number concentration of cloud droplets and ice crystals would require a more detailed analysis, including a separation of cloud regimes and microphysical cloud states.This is beyond the scope of the current study.By rearranging Eq. ( 1) according to the values of the fitted slopes with s < 1 nevertheless show that cloud microphysics dampens, or buffers, the effect of differences in the aerosol representation, i.e., MADE vs. M7, on N as compared to CN: an aerosol signal in CN will overestimate the signal in N and thus an effect on clouds (Table 9).The buffering effect on the ice phase is stronger than in liquid clouds.Nevertheless, Table 9 shows that the details of the chemistry and aerosol scheme have a nonvanishing effect on all three cloud types investigated.Note that we prefer the susceptibility-based estimate over a direct comparison of N for Coupled simulations with MADE and M7 because Coupled simulations do not have identical meteorologies for the different aerosol schemes.Differences in meteorology, specifically in supersaturation and temperature, influence differences in CN in addition to aerosol differences.Not taking this additional meteorological variability into account would result in an overestimation of CN / CN and consequently of N/N .
Conclusions
We have coupled the M7 aerosol scheme (Vignati et al., 2004) and the computationally efficient sulfur chemistry of Feichter et al. (1996) with the regional aerosol and reactive trace gas model COSMO-ART with interactive meteorology (Vogel et al., 2009).While the M7 aerosol framework was designed for climate applications, the full gas-phase chemistry and the aerosol scheme MADE in COSMO-ART emerged from regional-scale air-quality applications.The availability of the two different descriptions of aerosol and aerosol-related chemistry within the same modeling frame-work allows for a detailed comparison and process-level understanding of their differences.As both aerosol schemes adopt a modal two-moment approach, this comparison reveals especially clearly the uncertainty in aerosol modeling arising from design and parameter choices within this framework.Here, we have compared the aerosol modules in a case study of a Saharan dust outbreak reaching Europe.
For this case, a sensitivity study with identical emissions and identical parameterizations of dry and wet deposition for both schemes shows the following sensitivities of simulated atmospheric aerosol burden, sorted in order of decreasing importance: 1. consideration of sulfate production by aqueous oxidation (140 % difference; Fig. 6, Table 5) 2. coarse-mode composition (120 % difference, affecting the sulfate burden in Fig. 4) 3. modal standard deviation (20-40 % difference in dust/sea salt size distributions in Fig. 5) 4. accumulation and Aitken mode composition (10 % difference in POA and BC burden in Fig. 3) 5. oxidant fields for sulfate production (2 % difference; Table 6).
The strong sensitivity of the aerosol burdens to sources is well recognized in the aerosol modeling community (Mann et al., 2014).Our example especially stresses that uncertainties are not limited to prescribed anthropogenic emissions but extend to parameterized aerosol sources like chemically derived sulfate (1).It needs to be pointed out, however, that the importance of aqueous oxidation displays a strong regional dependence as it depends on cloud cover and droplet pH values.Aerosol sink processes are similarly sensitive and strongly increased for large internally mixed aerosol particles (2).Modal standard deviation is an inevitable but is an important parameter of a two-moment scheme, especially for the dry deposition and impaction scavenging of coarse-mode particles (3).Aitken and accumulation mode aerosol mass is less affected by dry deposition and impaction scavenging such that their composition and standard deviation is less important in determining aerosol burden (4).
In contrast to these sensitivities, we find that climatological oxidant fields perform as well as hourly values in this example (5).In the investigated case, emissions of SO 2 are largely restricted to cloudy regions such that sulfate is predominantly produced by aqueous-phase chemistry.Also, the effect of opposing deviations of the climatological oxidant fields from the hourly values compensate for their effect on the overall aqueous-phase reaction rate.Although our result is not generally applicable, it hints at low sensitivities of the sulfate burden to oxidant fields in at least some cases and confirms the validity of a constant-oxidant-field approach for efficient aerosol-related chemistry.It has to be explored in future research whether extensions of the approach to other secondary aerosols, namely SOA, nitrate and ammonium, can provide a sufficiently accurate and computationally feasible way to account for these species in climate applications.
A comparison of both aerosol schemes in their default setups is strongly influenced by those aerosol species that are only considered by MADE, namely nitrate, ammonium, SOA and unspeciated aerosol, and by the likewise MADE-specific giant modes, especially for dust.We find that the additional sulfate produced by the M7 aqueous chemistry partially compensates for the additional nitrate and ammonium aerosol specific to MADE.
The additional MADE species play a large role for the sensitivities of CCN and optical properties to the aerosol scheme.MADE features 40 % (mixed-phase clouds) to 100 % (liquid clouds) higher CCN concentrations than M7 due to MADE-specific soluble species, i.e., nitrate, ammonium, SOA and unspeciated aerosol.M7-specific aqueousphase-derived sulfate mainly increases particles that are already CCN-sized and hardly affects the M7 CCN concentration.MADE AOD is about 100 % increased as compared to M7 in anthropogenically influenced regions.Over dustdominated African regions, MADE AOD is 20 % larger than M7 AOD, partly due to the MADE-only giant dust mode.Differences in the parameterizations of aerosol optical properties between MADE and M7 are found to be less important than the differences in burden.
The INP potential of an aerosol depends on its surface area.For the Phillips ice-nucleation parameterization, which is independent of the mixing state and coating of an aerosol, we find that differences in dust and soot burden and surface area explain differences in INPs.For the Ullrich parameterization, which depends on the coating state of soot and dust, the abundance of secondary inorganic aerosol available for coating becomes more important in explaining differences in INP numbers than the burden and size of ice-nucleationactive species.In conditions where ice nucleation is dominated by homogeneous freezing of solution droplets, ice crystal concentrations are influenced by the number of soluble aerosol particles available for homogeneous freezing.As the total aerosol number is dominated by freshly nucleated particles, we find the minimum size of particles considered large enough to freeze homogeneously to be a relevant parameter.Large differences between the Phillips and Ullrich ice-nucleation parameterizations show, however, that uncertainties in parameterizing ice nucleation, in terms of the ice-nucleation spectrum as well as concerning the choice of inputs from the aerosol scheme, are more important than uncertainties in modeled aerosol number, amount and composition.
Applying a susceptibility-based approach, we find that cloud microphysics dampens the differences in CCN and INPs arising from differences between MADE and M7 aerosol microphysics along the line of clouds as buffered systems (Stevens and Feingold, 2009).The effect is especially pronounced for the ice phase.Nevertheless, both schemes result in significantly different cloud droplet and ice crystal number concentrations.Uncertainties in representing aerosol and aerosol processes thus carry over not only to the direct optical properties of aerosol but also to the representation of clouds.For a propagation of this signal to precipitation, however, further buffering effects are expected (Glassmeier and Lohmann, 2016).
In summary, differences between the two aerosol microphysics schemes and resulting differences in radiative properties and aerosol-cloud interactions originate mainly in different structural assumptions of the schemes, in particular concerning aerosol species, chemical reactions, modal composition and standard deviation, and inputs for the icenucleation parameterization.Resulting impacts on radiative properties and aerosol-cloud interactions are buffered: on the one hand by compensating for structural differences between additional sulfate from aqueous-phase chemistry for M7 and additional nitrate, SOA and unspeciated aerosol for MADE, on the other hand by sublinear relationships between aerosols and clouds.
We conclude that the new model version COSMO-ART-M7 simulates satisfying aerosol burdens in comparison to the established and observationally validated modeling framework COSMO-ART (Knote et al., 2011).Differences in burdens can be attributed to the choice of uncertain parameters, in particular modal standard deviation, and different structural assumptions in the form of missing species like SOA, nitrate and ammonium, and the choice of modes in terms of solubility and mixing state.This study provides the opportunity to discuss these choices in terms of the air-quality and climate objectives they are designed for.For climate applications, a computationally efficient aerosol scheme, such as M7, is needed that permits as realistic as possible a computation of radiative effects and aerosol-cloud interactions.As discussed earlier, simplified chemistry seems a viable option to save computational cost.In terms of aerosol-cloud interactions, the M7 approach to distinguish soluble from insoluble aerosol but to only consider one mixing state might be biased towards warm clouds.Ice nucleation not only depends on the mixing state of dust but also on an accurate representation of the dust surface.The latter is lost for internally mixed dust and soot.This raises the question of whether the representation of dust surfaces in M7 should be improved by following MADE in excluding dust from the mixed modes and adding a separate, coated dust mode.To keep the original number of modes and the corresponding computational costs the same, the uncoated accumulation and coarse dust modes could be replaced by a coated and an uncoated dust mode of intermediate size.When applying MADE for airquality applications, the chemical speciation as well as the abundance of individual aerosol species, precursor gases and pollutant gases such as tropospheric ozone are of great interest.For this purpose, as simplified a treatment of chemistry as is used in M7 is no longer justified and a far more com-
Figure 1 .
Figure 1.Comparison of chemical composition of aerosol modes for MADE and M7.The dashed line indicates modes that are considered for inter-and intra-modal coagulation in MADE.For M7, all modes participate in coagulation or intra-modal transfer by coagulation.The size and standard deviation of modes can be determined from Table 1 based on the two-letter abbreviations stated at the upper left of each mode (ns: M7 nucleation mode; ks: M7 solute-containing Aitken mode; ki: M7 insoluble Aitken mode; as: M7 solute-containing accumulation mode; ai: M7 insoluble accumulation mode; cs: M7 solute-containing coarse mode; ci: M7 insoluble coarse mode; if: MADE soluble Aitken mode without soot core; ic: MADE soluble Aitken mode with soot core; so: MADE pure soot mode; jf: MADE soluble accumulation mode without soot core; jc: MADE soluble accumulation mode with soot core; ca: MADE unspeciated anthropogenic coarse mode; da: MADE accumulation dust mode; db: MADE coarse dust mode; dc: MADE giant dust mode; sa: MADE accumulation sea salt mode; sb: MADE coarse sea salt mode; sc: MADE giant sea salt mode).
Figure 3 .
Figure3.Aerosol burdens of dust (first row), sea salt (second row), BC (third row) and POA (last row) for M7 (left column; data points exceeding the scale have been clipped to the maximum value) and differences to MADE (right column; to prevent diverging values, percentage differences (f 1 − f 2 )/[0.5(f 1 + f 2 )], f 1 : M7, f 2 : MADE, are only determined for data points with f 1/2 (lat, long) > 0.01 • P 95 (f 1 ), where lat and long denote latitude and longitude of the horizontal position and P 95 the 95th percentile of all data points in the domain) for sim simulations.Unless explicitly mentioned otherwise, aerosol burdens refer to dry aerosol mass.
Figure 4 .
Figure 4. Sulfate budget for sim simulations.Comparison of M7 (left column) to MADE (percentage-difference plots in the right column) in terms of the vertical integral of the gas-phase production rate of SO 4 (first row), sulfate burden (second row), and the sum of dry deposition and vertically integrated impaction scavenging rate of sulfate (last row).See Fig. 3 for plot details.
Figure 5 :
Figure5: Domain-averaged volume distributions for di erent species and standard deviations.Distributions are obtained by weighting the total dry volume distribution by the fraction the respective species contributes to the total mass.The total distribution is the sum of all model modes, which are determined from the vertical sum and horizontal averages of the corresponding dry masses and numbers.Species include sea salt (SS), dust (DU), sulfate (SO 4 ), soot (BC) and primary organic carbon (POA).The figure compares simulationssim , generated with the universal standard deviation ‡ universal = 1 .7 for all modes (red), simulations sim but plotted using default standard deviations ‡ default as given in Table1(green) and simulationssimSIG , generated with ‡ default (black).
Figure 6 .
Figure 6.Sulfate production from aqueous-phase chemistry and using climatological oxidant fields.The figure compares the simAQ simulation (left columns) to sim (percentage-difference plots in the middle column) and simCL simulations (percentage-difference plots in the right column; note the different color scales).The first and fourth rows show the burden of SO 2 and sulfate aerosol.The second and third rows depict vertical integrals of gas-and aqueous-phase production rates of SO 4 .See Fig. 3 for plot details.
Figure 7 :
Figure 7: Domain-averaged volume distributions of SIA for default setups.This fi sponds to SO 4 in Figure5but shows SIA for the passive simulations.SIA (secondary inorga corresponds to SO 4 for M7 and additionally includes NO 3 and NH 4 for MADE.
Figure 7 .
Figure 7. Domain-averaged volume distributions of SIA for default This figure corresponds to SO 4 in Fig. 5 but shows SIA for the Passive simulations.SIA corresponds to SO 4 for M7 and additionally includes NO 3 and NH 4 for MADE.
Figure 8 .
Figure 8. SIA burden for default setups.This figure corresponds to SO 4 in Fig. 3 but shows SIA for the Passive simulations.SIA corresponds to SO 4 for M7 and additionally includes NO 3 and NH 4 for MADE.
Figure 9 .
Figure 9. Aerosol optical properties.Aerosol optical depth at a wavelength of 550 nm (top row) and total wet aerosol burden in accumulation and coarse modes (bottom row) resulting from M7 (left column) and MADE (percentage-difference plots in the right column) aerosol for the Passive simulation.See Fig. 3 for plot details.
Figure 10 .
Figure 10.Liquid-cloud CCN concentrations derived from aerosol compositions predicted by M7 (left) and MADE (percentage-difference plot on the right) for Passive simulations.Contours show vertical averages of concentrations in grid boxes that contain liquid-phase but no ice-phase cloud water.
Figure 11: Size distributions of cloud-active aerosol for simulation passive using MADE and M7.Shown are the CCN-relevant number distribution of soluble aerosol in liquid clouds (a) as well as surface distributions of dust (b) and uncoated (c) and total soot (d) in the cloud regimes where these species serve as INP (Table2).See Figure3for plot details.
24 µFigure 11 .
Figure 11.Size distributions of cloud-active aerosol for the Passive simulation using MADE and M7.Shown are the CCN-relevant number distribution of soluble aerosol in liquid clouds (a) as well as surface distributions of dust (b) and uncoated (c) and total soot (d) in the cloud regimes where these species serve as INPs (Table3).
Figure 12 .
Figure 12.Mixed-phase INP concentrations derived from aerosol compositions predicted by M7 (left column) and MADE (relativedifference plots in the right column) using the Ullrich (first row) and Phillips (second row) parameterizations for Passive simulations.See Fig. 3 for plot details.Contours show vertical averages of concentrations in grid boxes that contain both ice-and liquid-phase cloud water.
Figure 13 .
Figure 13.INP and frozen solution droplet concentrations in ice clouds derived from aerosol compositions predicted by M7 (left column) and MADE (relative-difference plots in the right column) using the Ullrich (first and second row) and the Phillips (third and last row) icenucleation parameterizations.Contours show vertical averages of concentrations in grid boxes that contain ice-but no liquid-phase cloud water.See Fig. 3 for plot details.
Figure 14 .
Figure 14.Relationship between CCN/INP and cloud droplet number concentration N cl and ice crystal number concentration N ci .The figures show double logarithmic scatterplots of the cloud droplet number concentration N cl as a function of CCN concentration in liquid clouds (a) and the ice crystal number concentration N ci as a function of INP concentration in mixed-phase clouds (b) and as a function of the combined concentration of INPs + frozen solution droplets in cirrus clouds (c).Warm and mixed-phase clouds are defined as in Figs. 10 and 12; cirrus clouds are restricted to regions with competition of heterogeneous and homogeneous freezing as indicated by nonvanishing numbers of both INP and solution droplets.Data points represent temporally and vertically averaged values from Coupled simulations with the Phillips ice-nucleation parameterization at every grid point (see text for details).Only every fifth data point used for the fit is displayed.Solid black lines illustrate least-square fits with slope and coefficient of determination r 2 as indicated in plot titles.All fits are significant.Black dashes indicate 1 : 1 lines.Colors denote the mass of individual hydrometeors, averaged over all cloud droplets and rain drops (a), the glaciation fraction, i.e., the ratio of frozen to total cloud water (b), and the number of ice crystal aggregates (c).
Table 1 .
Comparison of modal parameters for MADE and M7 modes.Each mode is identified by a two-letter abbreviation (italic font), which allows us to identify its chemical composition in Fig.1.Modal standard deviation is denoted by σ .Modal median radii of the number distributions, r, refer to initial and emission radii for MADE.In contrast to MADE, M7 features a mode repartitioning ensuring that the radii of M7 modes are restricted to the indicated ranges.Mode reorganization in MADE is limited to ensure that the radii of Aitken modes remain smaller than those of accumulation modes.MADE and M7 modes are grouped to show correspondence.
Table 2 .
Comparison of aerosol dynamical processes for MADE and M7. .
mass transport as represented by the weighted vertical average x = i w i x i / i w i of the horizontal wind field x where weights w are given by the total dry aerosol mass concentration.Wind direction is indicated by arrow heads and its strength encoded in line thickness where the thickest lines correspond to 40 m s −1 .The background colors illustrate the geographic regions Africa (yellow), Mediterranean Sea (light blue), Europe (red) and Atlantic (dark blue).See main text for details of region definitions.
Table 4 .
List of simulations.A "y" shows that a model feature is active if applicable (aqueous-phase chemistry and climatological oxidant fields are only active for M7 simulations and giant modes only apply to MADE simulations).An "n" indicates it is not active.See main text for details.
Table 5 .
Horizontal averages of relative differences.=2(M7− MADE)/(M7 + MADE) between MADE and M7 in percent for sim and Passive simulations.Values correspond toFigs.3,4, 8, 9and 10, where production rates are vertically integrated and concentrations are vertically averaged.With the exception of secondary inorganic aerosol (SIA, see text) and accumulation and coarse-mode burden, aerosol burden and SO 4 production and removal are not illustrated for the Passive simulation.AOD (aerosol optical depth) and CCN are not shown for sim simulation.If not explicitly stated otherwise, aerosol burdens correspond to dry aerosol mass.OA: organic aerosol.
Table 7 .
SO 4 surface concentrations in µg m −3 for simulations according to Table4.Values are horizontal averages over continental Europe.
Glassmeier et al.:A comparison of two chemistry and aerosol schemes ferences in the availability of solution droplets for freezing: MADE features a larger number of solution droplets than M7 (+160 %, Table8) because freshly nucleated particles in the MADE Aitken mode are considered for homogeneous nucleation, while the corresponding particles in the M7 nucleation mode are excluded (Table3).Differences in Phillips INPs and the corresponding soot distributions between MADE and M7 qualitatively follow the mixed-phase case with about 80 % more MADE than M7 INPs (Table www.atmos-chem-phys.net/17/8651/2017/Atmos.Chem.Phys., 17, 8651-8680, 2017 8672 F.
Table 9 .
Relative difference in cloud droplet and ice crystal number N between MADE and M7 as predicted according to Eq. (2) from fitted susceptibilities s and domain-averaged values of relative differences in cloud nuclei CN / CN from the Passive simulation (Table8). | 18,664.8 | 2017-07-17T00:00:00.000 | [
"Environmental Science",
"Physics"
] |
Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.
Connecting the network components with edges to represent the identified interactions.
a. IRF, IRF TP, and corresponding DNA binding sequence were connected with stimulatory edges (black arrows) including all possibilities detailed in the Table S3 (e.g. IRF8 can bind to EICE with ETS, to AICE with AP-1 or ISRE with IRF1), preserving the and/or logic (i.e. IRF8 cannot bind to ISRA without IRF1: transition "and", IRF 1 can connect to ISRA either on its own or hetero-dimerised with IRF8: transition "or"). The Boolean logic gates "and" and "or" have been recreated using two nodes to transition ("and") and two transition to node ("or") (Signal flow through "and" ad "or" gate is presented in Figure S2) f. Genes identified by ChIP-seq analysis of IRF1,4, and 8 were associated with the DNA binding sequence, and with the output biological process. Assumption: controlling IRF homo/heterodimer determines DNA binding sequence. If a gene can be controlled by two transcription factors/two DNA sequence (e.g. IL18 via IRF1 or IRF8/ETS complex) both possibilities were included in the diagram.
Adding entry transitions for input nodes: (transitions: black bars)
a. An entry transition was added before each entry node to allow setting up initial marking of the network and input of the numerical data.
Converting diagram edges into appropriate interactions (stimulatory: black arrows, inhibitory: red open diamonds)
a. Each edge drawn is initially a black stimulatory edge. To convert the interaction to an inhibitory, the arrow was replaces with an open diamond shape end. For clearer visualization the inhibitory edges are colored red. presented in octagons on the right side of the diagram. The diagram is drawn in a Petri net notation, where the interacting elements of GRN (nodes, gene transcripts) are interspaced with transitions (vertical black lines, and black diamonds). The diagram captures the combinatory nature of immune activation, depending on the levels of expression, timing and interactions between the regulatory elements. The flow of the signal through the diagram can be modelled mathematically using experimental or simulated data and activity flow visualised in BioLayout Express3D.
b-e) Effect of signal transmission through "and" and "or" Boolean gates
Petri Net network motifs demonstrating the principles of signal flow through "and" (b,d) and "or" (c,e) gates with input from single (b,c) and multiple (d,e) transitions. Initial network marking = 100, token accumulation after gate are shown in the right column, 100 time blocks, 500 runs, simulation under the conditions of standard distribution. In silico profiles of gene expression in programmes "A" and "B" , measured at the output node when the input nodes are marked as per the gene expression values during LCs stimulation with TNF-α and TSLP, Signalling Petri Nets: BioLayout Express3D, 100 time blocks, 500 runs. b) Expression profiles of individual genes in "Programme A" as measured in the microarray experiment. c) Expression profiles of individual genes in "Programme B" as measured in the microarray experiment.
Figure S4: Ability of LC to cross-present antigens is modified by TNFα and TSLP.
Activation of antigen-specific CD8+ T cells by medium (white), TNFα (grey), TSLP (black) and a combination of TNFα and TSLP (black checkerboard grey) matured LCs, pulsed with a long peptide antigen requiring cross-presentation, IFN-γ production measured in co-culture ELISpot assay, n=2 in triplicate, mean +/-SE. Human epidermal biopsies (a) were exposed to PI3Kγ inhibitor or control media for 48h. Table S5: Genes regulated by expression programme "A" and "B" in the IRF-GRN c d e f Table S1. Search strategy to identify components of the IRF GRN network search term number of publications "Interferon regulatory factor" or IRF and antigen presentation 71 "Interferon regulatory factor" or IRF and dendritic cell and T cell stimulation 22 "Interferon regulatory factor" or IRF1 or IRF4 or IRF8 and *transcripton partner* as per the transcription partner list 510 Interferon regulatory factor or IRF1 or IRF4 or IRF8 and ChIP-seq 15 | 1,198.4 | 2017-04-06T00:00:00.000 | [
"Biology"
] |
Modeling and performance evaluation of in-line Fabry-Perot photothermal gas sensors with hollow-core optical fibers
: We study photothermal phase modulation in gas-filled hollow-core optical fibers with differential structural dimensions and attempt to develop highly sensitive practical gas sensors with an in-line Fabry-Perot interferometer for detection of the phase modulation. Analytical formulations based on a hollow-capillary model are developed to estimate the amplitude of photothermal phase modulation at low modulation frequencies as well as the -3 dB roll-off frequency, which provide a guide for the selection of hollow-core fibers and the pump modulation frequencies to maximize photothermal phase modulation. Numerical simulation with the capillary model and experiments with two types of hollow-core fibers support the analytical formulations. Further experiments with an Fabry-Perot interferometer made of 5.5-cm-long anti-resonant hollow-core fiber demonstrated ultra-sensitive gas detection with a noise-equivalent-absorption coefficient of 2.3 × 10 − 9 cm − 1 , unprecedented dynamic range of 4.3 × 10 6 and < 2.5% instability over a period of 24 hours.
Introduction
Photothermal interferometry (PTI) is a highly sensitive spectroscopic technique for trace-gas detection. PTI typically uses a pump-and-probe configuration: pump absorption of gas molecules generates localized heating, modulating the refractive index (RI) of gas-phase material and hence the phase of a probe beam propagating in the material. The photothermal (PT) phase modulation is proportional to the concentration of absorptive molecules and can be detected by using optical interferometry [1]. Recently, all-fiber gas detection with PTI has been demonstrated with a hollow-core photonic crystal fiber (HC-PCF) as the sensing element [2]. A HC-PCF confines gas-phase material, pump and probe light fields simultaneously in the hollow-core, providing an ideal platform for efficient light-gas interaction over a long distance. The all-fiber system greatly extends the capability of PTI for remoted sensing and applications in harsh environment [3].
To develop practical optical fiber PTI sensors with high sensitivity, various interferometric schemes, including Mach Zehnder interferometer (MZI) [2,4], Sagnac interferometer (SI) [5,6] and Fabry-Perot interferometer (FPI) [7], have been examined for the detection of the PT phase modulation. With a MZI configuration, short-term noise-equivalent gas (acetylene) concentration down to low parts-per-billion (ppb) level has been demonstrated with 1 to 10 meters of HC-PCFs [2,5,6]. However, the operation point of the MZI is not stable over a longer term (e.g., an hour) even with a servo-control loop, making the system difficult for practical field applications. A SI could achieve better stability since the phase difference between the two counter-propagating waves in the same fiber is detected, which is relatively insensitive to low-frequency environmental perturbations. However, the differential phase detection could also mean lower sensitivity to PT phase modulation. To achieve high sensitivity detection of PT phase modulation, which is typically in the frequency range from a few to tens of kHz [5,6], many kilometers of single mode fiber (SMF) is required to form the Sagnac loop, which in turn increases the system noise.
A single fiber low-finesse FPI can be formed by natural reflections at the joints between hollow-core fiber (HCF) and SMFs, as illustrated in Fig. 1. Absorption of the modulated pump by gas molecules produces PT phase modulation, which is detected by the low-finesse FPI operating at the probe wavelength. The FPI detection scheme enables very compact sensing configuration as well as better immunity against environmental perturbation since the SMFs are merely used for light transmission and would not affect the stability of the interferometer. Low frequency external perturbation on the SMF has minimal effect on the PT phase detection since the reflected probe beams pass through the SMF almost at the same time (e.g., the time difference is less than 1 ns for 15 cm long sensing HCF) and hence the two beams basically experience the same perturbation and hence the differential difference is insensitive to the perturbation. In this article, we focus on PT gas sensing with FPI for phase detection and study issues to achieve high sensing performance including modeling and selection of HCFs and pump modulation frequencies to maximize PT phase modulation, demonstration of stable operation via servo-control of FPI operation point, testing of the system performance in terms of noiseequivalent gas concentration, dynamic range, response time and long term stability, and comparing with previously reported PT gas detection systems.
Optimizing PT phase modulation in hollow-core fibers
Different types of HCFs such as HC-PCFs and anti-resonant hollow-core fibers (AR-HCFs) may be used for PT gas detection [2,[4][5][6][7][8][9]. Understanding PT phase modulation dynamics in these fibers with varying structural parameters is important for the design of sensors with optimal performance. In this section, we report a simple analytical model to estimate the amplitude as well as the roll-off frequency of PT phase modulation for a giving HC-PCF. The model is based on a simple gas-filled capillary and the results are compared with the more accurate numerical simulation using COMSOL Multiphysics and verified experimentally with a real HC-PCF and AR-HCF.
Analytical model
The dynamics of PT phase modulation in a gas-filled HCF has a complex dependence on the size and geometry of the HCFs, the mode fields of the pump and the probe beams as well as the absorption and thermal properties of gases in the hollow-core. However, we find that the magnitude as well as the -3 dB roll-off frequency of the PT phase modulation may be estimated analytically with a simple gas-filled capillary model. Several approximations are made to simplify the analysis: (a) the gas absorption is weak and the pump power can be regarded as unchanged along the propagating path, allowing us to analyze the problem with a 2D model shown in Fig. 2; (b) the thermal conduction is the dominant process of heat dissipation [6]; (c) the HCF guides mainly the fundamental mode with a radially symmetric Gaussian intensity profile. Under these conditions, the steady-state temperature distribution, corresponding to PT phase modulation at the low frequency limit, obeying the differential equation [10]: where k is the gas thermal conductivity, Q(r) is volume heat source given by: where α is gas absorption coefficient, C is gas concentration, P pump is the power and w pump is the mode field radius of the pump beam. The PT phase modulation accumulated over a round-trip of the FPI with a HCF of length of L may be expressed as [6]: where n 0 is the gas RI, λ probe is the wavelength and w probe the mode-field-radius of the probe beam. T abs is the ambient temperature. Under the condition that the pump and the probe wavelengths are not far from each other, we may approximate w pump = w probe = w. Given the boundary conditions: T(r = R) = T abs and, dT dr r=R = 0, the steady state phase modulation may be analytically expressed as: where µ Euler = 0.5772 is a Euler constant, From Eq. (4), it clarifies that for HCFs with different radius R, the PT phase change is determined by the ratio of R to w. Though the peak amplitude of the volume heat source is inversely proportional to the w 2 pump as shown in Eq. (2), contrary to the intuition, smaller mode field diameter (MFD) will not necessarily benefit the measurement of the PT phase change, because for HCFs with smaller MFD, generally, its physical radius of the core R decreases as well, and the ratio of R/w may not change much.
At a higher pump modulation frequency, the PT phase modulation would be reduced due to (i) thermal relaxation time of gas molecules that reduces the heating efficiency (Eq. (2)) by a factor (1 + (2πf τ) 2 ) 1/2 , (ii) slow gas thermal conduction that makes temperature modulation being unable to follow the heat source. In general, an analytic expression for ∆φ as a function of modulatin frequency is difficult and the results could only be calculated numerically. However, for gases with small τ (i.e., 2πf τ 1), the -3 dB roll-off frequency of PT phase modulation, corresponding to modulation amplitude dropping to half of its maximum value, may be estimated with high accuracy using a simple expression. Based on the thermal dynamic analysis presented in [10], the -3 dB roll-off frequency of the PT phase modulation may be estimated by: where β = k/ρC P is the gas thermal diffusivity. k is the gas thermal conductivity, ρ the gas density and C P the specific heat capacity.
Numerical computation
For practical applications, multi-component gas mixture is typically involved. The thermal conductivity k m for the mixed gas may be obtained based on the equations in [11]. The gas density and specific heat capacity of the mixed gas may be determined by using ρ m = C i ρ i and C Pm = C i C Pi , where C i , ρ i and C Pi are the concentration, density and specific heat capacity of the ith gas component, respectively. Accordingly, the thermal diffusivity of the mixed gas may be expressed as: β m = k m /ρ m C Pm . However, it could be very difficult to predict the thermal relaxation time of the gas to be measured in a mixed gas environment. More molecular de-excitation paths may be provided by different gas components. The relaxation time of the targeted gas molecules may be calculated by following the analytical model in [12,13]. Based on the model in Fig. 2 and with the COMSOL Multiphysics software, we numerically computed the PT phase modulation for varying pump modulation frequency, capillary core radius and mode field radius. The pump and probe beams are assumed in the fundamental mode that has a Gaussian intensity profile in the hollow core. Hence the heating source is set as Gaussian distribution according to Eq. (2). The cladding material of the HCF is silica and the hollow region is filled with 1 ppm acetylene balanced in nitrogen. The gas pressure is 1 bar, and the environment temperature is set to be 293 K. The absorption coefficient of the acetylene is 1.16 cm −1 . The thermal relaxation time of acetylene is 77 ns. The thermal diffusivity of the mixed gas is 2.1 × 10 −5 m 2 /s. The pump power is sinusoidally modulated at frequency f. Figure 3(a) shows the computed frequency-dependent PT phase modulation for a fixed R/w of 1.25. At low modulation frequencies (e.g., f <1kHz), the magnitudes of PT phase modulation are approximately the same for different capillary core radius, agreeing with the prediction that the steady-state phase modulation depends only on R/w. However, the roll-off frequency reduces from ∼ 0.9 MHz for R = 5.5 µm to ∼10 kHz for R = 50 µm, agreeing with the prediction from Eq. (5). For a fixed mode field radius w = 4.5 µm, the magnitudes of PT phase modulation at low frequencies, determined by COMSOL Multiphysics and by Eq. (4), as function of capillary core radius R and the ratio of R/w are shown in Fig. 3(b). The PT phase modulation calculated using the two methods agree with each other and increases by ∼5 times for R from 5.5 to 40 µm. For the fixed R/w of 1.25, -3 dB roll-off frequency as function of R is shown in Fig. 3(c), the -3 dB roll-off frequencies determined by the two methods are very close, which decreases with increase core radius R.
Experiments
Through a combination of theoretical analysis and numerical computation, it is found that larger PT phase modulation can be obtained using HCFs with a larger ratio of R/w. Here we experimentally test two types of HCFs with different R/w values. The first one is a HC-PCF (HC-1550-06 fibre from NKT Photonics) with cross-section shown in Fig. 4(a). The radius of the hollow core for this fiber is about 5.5 µm and the mode field radius is about 4.5 µm. The second one is an AR-HCF with cross-section shown in Fig. 4(b). This AR-HCF has an outer silica cladding with inner radius of 35 µm and 7 capillary rings with diameter of 17.5 µm and thickness of ∼430 nm, which forms an inscribed air core with radius of ∼17.5 µm. The experiment setup is shown in Fig. 5. The sensing unit is made by fusion splicing one end of the HCF (HC-PCF or AR-HCF) to a SMF, while the other end is butt coupled to a SMF via ceramic sleeve and ferrules, which are fixed together by using ultraviolet (UV) curing glue. Natural reflections of the probe beam at the HCF/SMF joints form a low finesse FPI, which is used to detect PT phase detection. A micro gap is kept at the butt coupling joint for gas filling by diffusion. A section of the HCF is mounted on a piezoelectric transducer (PZT) to stretch the HCF to maintain the FPI operating at quadrature via servo control. The lengths of the FPI made by the HC-PCF and the AR-HCF are 4 cm and 5.5 cm, respectively.
The pump beam is from a 1.53-µm distributed feedback (DFB) laser. Its power is amplified by an erbium-doped fiber amplifier (EDFA) and intensity-modulated by an acoustic-optic modulator (AOM). At the same time, the wavelength of DFB laser is slowly scanned across the P(13) absorption line of acetylene at 1532.830 nm, which has the absorption coefficient is α=1.05/cm . The probe beam is an external cavity diode laser (ECDL) and its wavelength λ probe is fixed at 1551.3 nm. The length of the HCF is tuned by the PZT so that FPI is always operating at quadrature at the probe wavelength. The pump and probe beams are combined through a 1550 nm/1530 nm wavelength-division multiplexer (WDM) and co-propagating in the HCF. The first harmonic (1f ) component of the PT phase modulation is demodulated by the lock-in amplifier. The PT phase modulation is calibrated by a reference phase modulation produced by the PZT, by following a procedure described in Ref. [14].
The amplitude of modulation remains unchanged at low modulation frequencies and drops at higher modulation frequencies. The measured normalized frequency responses of PT phase modulation for the two fibers are presented in Fig. 6 as the red and blue dots, respectively. The -3 dB roll-off frequencies for the HC-PCF and the AR-HCF are ∼750 kHz and ∼34 kHz, respectively. Based on Eq. (5) we may define an effective heating dissipation radius R e and they are 6.3 µm and 32 µm for the HC-PCF and the AR-HCF, respectively. By using the capillary model with radius of R e , the frequency responses of the two fibers are calculated with COMSOL Multiphysics and shown as the solid red and blue lines in Fig. 6. It can be seen that the micro-structured inner silica cladding has significant influence on the measured PT phase modulation. For the HC-1550-06 fiber, the wall of the inner hollow core is connected to the solid outer cladding through a micro-structured silica web. The large air-filling fraction reduces speed of heating conduction into the surrounding silica, causing R e being larger than physical radius of the inner hollow-core. However, the interconnected web maintains good thermal conduction (much better than air) and hence the R e is only slightly larger than R, i.e., R e =1.1 R. For the AR-HCF, the capillary rings are not connected to each other and the contact area between the rings and the outer cladding is also small, making heating dissipation from the center of the core even less inefficient and leading to an even larger R e =1.8R. Moreover, the deviation of the experimental result from that obtained from the simplified capillary model become larger in the frequency range from 1 to 20 kHz, indicating that the thermal conduction of the capillary region is considerably different from that of the solid silica model. The ratios of R e /w for the HC-PCF and the AR-HCF are 1.4 and 2.56, respectively.
Gas detection with high sensitivity and stability
From last section, we understood that the AR-HCF has a larger R e /w ratio, which enables larger PT phase modulation and hence better gas detection sensitivity at low pump modulation frequencies.
In this section, we test the performance of the AR-HCF for gas detection. The experimental setup is shown in Fig. 7. This setup is basically the same as the one in Fig. 5, but instead of intensity modulation, the pump is wavelength modulated to achieve a higher signal-to-noise ratio (SNR), since harmonic detection at higher frequency may achieve lower system noise level. A 5.5 cm-long AR-HCF is used to form the FPI and the pump is modulated at 15 kHz. The FPI cavity length is locked to the probe laser so that it continuously operates at quadrature. The second harmonic (2f = 30 kHz) component of the probe phase modulation is demodulated by the lock-in amplifier. The experiments were firstly conducted with 1000 ppm acetylene balanced with nitrogen. Figure 8(a) shows the 2f lock-in output as the PT signal when the pump is scanned across the P(13) absorption line of acetylene with different pump power levels delivered into the AR-HCF. The system noise is estimated by tuning the pump wavelength off the absorption line and being fixed at 1532.5 nm. The PT signal amplitude increases linearly with pump power, while the standard deviation of the noise remains almost unchanged, as depicted in Fig. 8(b). Figure 9(a) shows the PT signals for 10, 50 and 142 ppm acetylene in nitrogen with ∼125 mW pump power delivered to the AR-HCF. Figure 9(b) shows the peak-to-peak amplitude of the PT signal as functions of acetylene concentration from 2 ppm to 21.3% acetylene in nitrogen. The noise floor in Fig. 9(b) is determined by measuring the standard deviation of the noise with pure nitrogen filled in the gas chamber. For a lock-in time constant of 1 second with a filter slope of 18 dB/Oct, the noise floor is ∼0.22 µV, giving the noise-equivalent-concentration (NEC) of ∼30 ppb for SNR of unity. An approximately linear relationship is obtained for acetylene concentration up to ∼13% and non-linearity starts to appear beyond this value, giving a dynamic range of ∼4.3×10 6 , which is nearly an order of magnitude larger than the state-of-the-art gas detection systems [2]. Allan deviation analysis is conducted to investigate the stability of PT spectroscopy with the FPI made of the AR-HCF. Figure 10 shows the time trace and Allan plot of the baseline noise, which is recorded for 2 ppm acetylene in nitrogen recorded for 6 hours when the pump light is tuned away from the absorption line and fixed at 1532.5 nm. The pump power delivered to the AR-HCF is 125 mW. The optimal averaging time τ is determined to be ∼670s, at which the s.d. of the noise level is 0.015 µV, corresponding NEC for an SNR of unity of 2.3 ppb. The NEC for 10 and 100 s averaging time are ∼14 and 4.5 ppb, respectively The signal stability is evaluated by monitoring the variation in the peak value of the PT signal when the pump wavelength is fixed to the peak of the P(13) absorption line of acetylene at 1532.83 nm. Figure 11 shows the time trace and the Allan plot of the relative fluctuation of the PT signal amplitude detected with 1000 ppm acetylene in nitrogen over 24 hours. The overall relative signal variation over 24 hours is ∼ 2.5%. With ∼800s averaging time, the relative signal variation is ∼4 × 10 −6 . Finally, the response time of the gas sensor is estimated by filling the gas chamber (12cmx3cmx2 cm) with acetylene in nitrogen. At first, the gas chamber is filled with atmospheric air. The pump wavelength is fixed at the center of the P(13) absorption line with ∼60 mW pump power delivered to the AR-HCF. At ∼10 s, pure acetylene mixed with pure nitrogen is filled into gas chamber with flow rate of ∼300 sccm. At ∼125s, the acetylene concentration reaches equilibrium. The mixed acetylene concentration is estimated to be ∼1100 ppm. At ∼135s, pure nitrogen is filled into the gas chamber with flow rate of ∼300 sccm. The measured 2f -signal as a function of time during gas loading and unloading process is shown in Fig. 12. The response time t 90% , which is defined as the time taken to reach 90% of the applied concentration, is ∼52 s. Since the gas chamber volume is much larger than that of the hollow core, it would take much longer time to fill the gas chamber and reach a homogenous distribution of the acetylene. Hence, it is safe to conclude that the response time of the AR-HCF based gas sensor is less than 52s.
Discussion
HCFs with a larger R e /w ratio benefits gas detection for its larger PT phase modulation. The freedom to design HC-PCF with larger R e /w is limited and in general R e /w is only slightly larger than one. However, for AR-HCFs, it is possible to design the radius of the capillary rings to reduce the MFD of the fundamental mode but still keeping a large R e , leading to a larger ratio of R e /w. Further enhancement of PT phase modulation may be realized by optimizing the geometry of the AR-HCF to achieve a much larger ratio of R e /w [15].
The PT phase modulation signal is linearly proportional to the pump power while the noise level remains nearly unchanged. Hence it is possible to further improve the detection limit by simply increasing the pump power level. The overlap between the fundamental optical mode and the silica in an AR-HCF is very small (<0.1%), which leads to a high damage threshold. Detection of sub-ppb-level acetylene with 1 s time constant is theoretically achievable by simply increasing the pump power. The performances of some state-of-the-art gas sensors based on HCFs are summarized in Table 1. We use the noise-equivalent-absorption (NEA) which is independent of gas types and absorption strength to evaluate the detection sensitivity of different systems. The current work achieves best NEA among these HCF-based gas detection systems and with the shortest length of HCF.
Conclusion
Theoretical investigation based on a gas-filled capillary model shows that the PT phase modulation depends on the ratio of the capillary radius over mode field radius, i.e., R/w, with a larger ratio gives larger PT phase modulation at low modulation frequencies. Analytical expressions are derived to predict the amplitude of PT phase modulation at the low frequency limit as well as the -3-dB roll-off frequency. The AR-HCF has more freedom in achieve larger R/w and hence larger PT phase modulation. With a 5.5 cm-long AR-HCF gas cell and FPI for phase detection, ultra-sensitive PT gas sensors with excellent long-term stability is demonstrated. The extension of this technique to other wavelength bands (e.g., visible band and MIR band) is straightforward since AR-HCFs have broadband transmission windows. | 5,322.8 | 2020-02-12T00:00:00.000 | [
"Physics",
"Engineering"
] |
Action Recognition Using Deep 3D CNNs with Sequential Feature Aggregation and Attention
: Action recognition is an active research field that aims to recognize human actions and intentions from a series of observations of human behavior and the environment. Unlike image-based action recognition mainly using a two-dimensional (2D) convolutional neural network (CNN), one of the difficulties in video-based action recognition is that video action behavior should be able to characterize both short-term small movements and long-term temporal appearance information. Previous methods aim at analyzing video action behavior only using a basic framework of 3D CNN. However, these approaches have a limitation on analyzing fast action movements or abruptly appearing objects because of the limited coverage of convolutional filter. In this paper, we propose the aggregation of squeeze-and-excitation (SE) and self-attention (SA) modules with 3D CNN to analyze both short and long-term temporal action behavior efficiently. We successfully implemented SE and SA modules to present a novel approach to video action recognition that builds upon the current state-of-the-art methods and demonstrates better performance with UCF-101 and HMDB51 datasets. For example, we get accuracies of 92.5% (16f-clip) and 95.6% (64f-clip) with the UCF-101 dataset, and 68.1% (16f-clip) and 74.1% (64f-clip) with HMDB51 for the ResNext-101 architecture in a 3D CNN.
Introduction
One of the main objectives of artificial intelligence is to build a model that can accurately learn human actions and intentions [1]. Human action recognition is important because it has been applied to various applications, such as surveillance systems, health care systems, and social robots. Recently, a three-dimensional (3D) convolutional neural network (CNN) for action recognition with spatiotemporal convolutional kernels achieved better performance than 2D CNNs that can only cover the spatial kernel. The representative research in video-based action recognition is based on twostream architectures [2], recurrent neural networks (RNN) [3], or spatiotemporal convolutions [4,5]. Two-stream approaches use two separate CNNs, one using red-green-blue (RGB) data, and the other using optical flow images to deal with movement.
Recently, most of the research has relied on the modeling of motion and temporal structures. Temporal segment network (TSN) [6] uses a sparse segment to model long-range temporal structure. Other 3D CNN methods [4,7,8] tried to solve temporal modeling issues by using an additional dimension of convolution on the temporal axis with the ambition that the models learn hierarchical motion patterns as in the image space. In [9,10], the temporal modeling is further enhanced through tracking feature points and body joints over time. The above-mentioned methods demonstrated the advantages of the 3D CNN over the 2D CNN because of the ability to learn spatiotemporal characteristics. However, all previous methods have the limitation of analyzing action changes in consecutive frames because of limited coverage of convolutional filter. Since action recognition is a fine-grained recognition problem, identifying and differentiating small changes in consecutive frames and connecting all frames logically is important. Another milestone of action recognition is that applying 3D CNN on action recognition causes overfitting problems because of a huge number of parameters. To alleviate the overfitting problem, Carreira and Zisserman launched the Kinetics dataset [11], which is large enough to train a 3D CNN successfully while coping with the challenge of overfitting. Moreover, Hara et al. [12] conducted experiments to train residual architectures on four different action datasets and achieved an outstanding result. However, the main idea of [12] was to check whether the dataset is efficient to tackle a huge number of parameters of 3D CNN in action recognition or not. So, we believe that using more complex sequential-based architectures pretrained with large datasets could achieve better results.
Drawing inspiration from [12], we propose a sequential version of squeeze-and-excitation (SE) and self-attention (SA) modules, prove the aggregation of both modules, and propose a new method for recognizing video action behavior. We assume that the connection of spatial information with a temporal stream logically provides a better understanding of action behavior. Figure 1a demonstrates ResNext-101 and ResNet-18. Due to the simplicity of applying additional modules, we choose the residual architecture mentioned above. It consists of four blocks and each block consists of convolution filter, batch normalization, Rectified Linear Unit (ReLU) activation, and max pooling. Figure 1b is residual architecture with our proposed module, i.e., SE and SA. We located SE and SA modules after the last layer of each block. The SE module for channels is first; after that, the SE module is used for the sequence (number of frames), and then lastly, the SA module is applied. Implementing exactly in that order provides us the best performance. The reason is that first the SE module, explicitly capturing the correlation between channels of convolutional features, learns to selectively differentiate information features and removes less useful features. After that, the SA module computes the response as a weighted sum of characteristics at positions that contain more useful features (since the SE removes less useful features). In this way, using the SE module improves the channel and sequence interdependencies, and the SA module learns each part of the image, which provides useful information to identify and differentiate small changes in consecutive frames. Note that the detailed architecture of the proposed network is given in Appendix A like [13]. To our best knowledge, it is the first time that sequential-based action recognition has used an adaptive feature aggregation scheme with large pretrained weights. In summary, our contributions are as follows:
We propose a sequential version of SE and SA modules and apply them to create a new approach for efficiently analyzing action behavior on 3D CNN. We validate our proposed modules in both quantitative and qualitative ways and attain s tate-of-the-art results with a marginal computation. Figure 1. The overall framework of our network. Squeeze-and-excitation and self-attention follow after each layer. The number of blocks is four.
Squeeze-and-Excitation
SE is a module that is designed to improve the network's representation ability by allowing dynamic channel-specific recalibration. VGG-Nets from the Visual Geometry Group at the University of Oxford [14] and Inception [15] showed that increasing the depth of a network can lead to a considerable increase in the quality of representation. The same idea can be applied to the SE module to enhance the representation ability of feature analysis. Hu et al. [16] came up with a mechanism for explicitly modeling dynamic, non-linear dependencies between channels using global information that can facilitate the learning process and enhance the representation ability of networks. Based on the previous success history of SE experiences, we show how we can make a sequential version of the SE module and how the SE module can be useful for video action recognition.
Self-Attention
SA is a commonly used module in computer vision and other fields of artificial intelligence. For example, the SA module has been widely used in many tasks, such as machine translation, skeleton hand-gesture recognition, and task-independent sentence representation [17][18][19]. Vaswani et al. [17] implemented a self-attention module in machine translation to analyze semantic and temporal relationships among words within a sentence. Chen et al. [18] demonstrated the self-attention mechanism represented by graphs to learn the spatiotemporal information contained in hand skeletons. Wang et al. [8] formalized self-attention as a non-local process to define the spatiotemporal dependencies in a video sequence. Despite such huge progress, the self-attention module has not been implemented for video-based action recognition yet. Therefore, in the next section, we propose basic SE and SA modules and how to modify them for sequential information.
In this section, we will discuss the basic knowledge about SE and SA modules and the method of applying these modules to a 3D CNN. Moreover, we will analyze the importance of active movement in one way of applying both short-term and long-term temporal information.
Proposed SE Module
SE is a module for enhancing channels' interdependencies with a low computation cost. Using an SE module can improve feature representation by explicitly developing channel interconnections so that the network can increase its sensitivity to information. More specifically, we give access to global data, and reset filter responses in two steps, i.e., squeeze and excitation, before transforming them into the next transformation.
We first consider the signal to each channel of output features to address the problem of the exploitation of channel dependencies. Since each convolutional filter operates only within a local field and is not able to provide information outside of this region, a squeeze global spatial operation is implemented to solve this problem. We use global average pooling to squeeze each channel into a single numeric value. The formula of the squeeze operation is: where the transformation output, , is a collection of local descriptors that are expressive for the entire video. H, W, and S represent height, weight, and sequence, respectively. Bias terms are omitted to simplify the notation. To use the information in the squeeze operation, we implement a second excitation operation. The idea of the excitation method completely captures channel-wise dependencies provided by the squeeze operation: where δ represents the ReLU activation function, Fex is the excitation operation, ∈ × , and ∈ × .
In contrast to the original SE for a 2D CNN [16], we consider not only the channel but also the number of frames ( Figure 2). We consider the concatenation of channels and sequences after each layer in our network, which makes our module more effective and allows us to achieve better efficiency. Figure 3 (left) demonstrates the SE module for image-based action recognition, and Figure 3 (middle) represents the SE (channel) and SE (sequence) parts of the proposed method for videobased action recognition, which consider both channel and sequence of frames. Figure 3 (right) is an abstract block diagram of Figure 3 (middle) which consists of two parts. Each part consists of global pooling, two fully connected (FC) layers, ReLU, and sigmoid. In the first part, we applied the SE block for channels. When we implement the SE operation, we set all the values (weight, height, and sequence) to 1 except for the channel value. So, SE operates on the channels (in other words, we need only channels). The output of each SE block for channels (X ) is obtained using rescaling with the activation s: where ( , ) is a channel-wise multiplication of scalar and the feature map ∈ ℝ × .
In the second part, to conduct an experiment on the sequence, we swap channel and sequence places using the transpose function. Then the same process is applied as in the first part, but this time for the sequence. The output of the SE block for the sequence is obtained by: where ( , ) is sequence-wise multiplication of scalar and the feature map ∈ ℝ × . . Squeeze-and-excitation (SE) module. We divide the SE module into two parts: channels and sequences. S represents sequence, and C represents channel. Reduction values: = 16, = 2.
In the end, we again apply the transpose function to make the order of weight, height, sequence, and channels the same as at the beginning. The reason for using the transpose function is to continue further operation; the output and input of the SE block should be the same. Then, we add the output of the transpose function with initial X and provide X, which is the final output. Reduction numbers (d and r) are hyper-parameters that allow us to vary the capacity and computational cost of the SE blocks in the network. To provide a good balance between performance and computational cost, we conduct experiments with the SE block for different r values. Setting r = 16 (for channel) and d = 2 (for sequence) gave us the best trade-off between performance and complexity. Note that by applying squeeze-and-excitation for both channel and sequence, we can consider both long and short-term action correlations adaptively.
Self-Attention (SA) Module
Self-attention is a module that calculates the response as a weighted sum of the features at all positions. The main idea of self-attention is to help convolutions throughout the image domain to capture long-range, full-level interconnections. The network implemented with a self-attention module can help to determine images with small details that are connected with fine details in different areas of the image at each position [20][21][22].
Our task in this experiment is to extend the SA idea to a 3D CNN; more concretely, we implemented the self-attention idea for multiple frames (a sequence). Unlike [23], where the SA module is implemented on a single image, we examine a multi-frame module for video frames. Since we apply the SA block to every single image in a sequence (16 or 64 frames), the benefit we can get from the SA module is more valuable compared to the single-frame case, and the overall performance is higher.
Most videos in UCF-101 and HMDB51 involve human and item interactions. While the previous methods only focused on a single action [23], in not all cases can a sequence exactly present the interaction. We implemented self-attention for sequential actions to better understand the interactions of humans and items, since the environment around humans is also an important part of defining actions. Since we consider a 3D CNN with multiple frames, the SA module helps to capture all images to make a better prediction based on the action aspects in images, and helps us use this knowledge to connect all frames logically. Figure 4 demonstrates our self-attention module for a 3D CNN. Compared to the basic self-attention module, we concatenate these operations sequentially and embed to our SA module for the 3D CNN, as shown in Figure 1.
Dataset and Training Configuration
We adopted UCF-101 [24] and HMDB51 [25] datasets for evaluating the performance of our model. The UCF-101 dataset contains 13,320 images of action from 101 classes of human actions. Both datasets include three training/testing splits (70% and 30%, respectively). UCF-101 consists of unconstrained videos downloaded from YouTube with challenges such as poor lighting, cluttered backgrounds, and severe camera movement. To remove non-action frames, the videos were temporarily cut. The average duration of each video is about seven seconds. The HMDB-51 dataset contains 6766 videos from 51 human action classes. Similar to UCF-101, the videos were cut to an average length of three seconds. The training/testing split was the same as UCF-101. The main difference between the two datasets is the number of classes and instances of actions, dynamic backgrounds, and camera movements.
Details of data preprocessing are as follows. We conducted experiments with the same data augmentation for 16 frames (16f-clip) and 64 frames (64f-clip). In training, we randomly sampled a 112 × 112 crop from a random clip of the given length and applied random horizontal flipping, which includes reversing the horizontal axis in the case of flow input. Since ResNext-101 achieved state-of-the-art performance on both UCF-101 and HMDB51 datasets, we chose it as our main architecture for implementation. We used stochastic gradient descent with a momentum of 0.9 to train a mini-batch dataset. The initial learning rate for the 16f-clip was 0.1 and for the 64f-clip, it was 0.01. Finally, we used the top-1 mean accuracy for evaluating the action recognition dataset.
To assess the suggested techniques, we conducted experiments on the two distinct architectures: ResNext-101 and ResNet-18. Both architectures have four layers. Compared to ResNet-18, ResNext-101 (using the setting of 32 × 4 ) adds an additional (cardinality) block to the base network, which makes it more powerful at extracting convolution feature maps [12]. We expanded the SE module of image-based models into video-based models to consider the number of frames. After the first, second, third, and fourth blocks, the number of frames in a sequence was 8, 4, 2, and 1, respectively (see Figure 1). Similar to the SE approach, we expanded the SA module of the 2D CNN to a 3D CNN to consider the number of frames in Figure 1.
Experimental Results
In this section, we provide experimental results and a comparison of our results with earlier state-of-the-art methods of the 3D CNN. Note that before training, we fine-tuned the networks using the Kinetic400 dataset. In Tables 1 and 2, we see higher performance than in previous work in which SE and SA modules were not applied. The only difference between Tables 1 and 2 is the number of video frames used for training. As SE and SA modules were added, the performance improvements were 0.5% and 0.4%, respectively, with UCF-101 in Table 1. In addition, we can see the same performance increment with HMDB51 in Table 1. However, most importantly, the aggregation of SE and SA achieved an additional 0.8% performance improvement. We can also say that the performance tendency is consistent in 64-frame clips. We also performed experiments on ResNet-18 to prove model generalization. Results with the ResNet-18 architecture are in Table 3. Note that the synergy effect of using both SE and SA is much higher than that in previous experiments (2.8% on UCF-101 and 2.6% on HMDB51). We can conclude that for the shallower model, our approach shows better improvement compared to the more complex ones. All the obtained results indicate that both squeeze-and-excitation and self-attention were successfully implemented and worked well enough in the 3D CNN. These observations are compatible with our assumption that the layers of self-attention are useful in capturing structural data and long-distance dependence. The black values in precision represent how many times each label was predicted during the testing period. In other words, precision demonstrates qualitative results, while recall demonstrates quantitative result. The overall accuracies are 95.06% and 74.06% for the UCF101 and HMDB51 datasets, respectively. The corresponding balanced accuracies are 95.03% and 74.05%. According to the results of Figures 5 and 6, the model has almost the discriminative ability to all classes (low variance), which means that our methods are effective at identifying action changes for all actions. In addition, we provide the normalized confusion matrices for all classes on the UCF101 and HMDB51 datasets. From Figure 7 on UCF101, we can see that our model performs very well on most categories. Categories that contain only objects without interaction with items such as 'Body Weight Squats', 'Handstand Pushups', and 'Jumping Jacks' give the best accuracies. However, our model misclassifies some samples from 'Walking', 'Diving', 'Golf Swing', and 'Soccer Penalty' because of similar action characteristics between actions. As a result, we found that the background context information is very important when analyzing action behaviors. Figure 7. Still, we can see the misclassifications for several pairs of labels. This is due to the difficulty of identifying complex action behaviors such as for instance basketball, catching, jumping, and throwing. It is usually difficult to understand such actions because they are composed of many different sub-actions. The comparison of our method with other state-of-the-art methods is given in Table 4. The accuracies for ResNext-101 (fourth and fifth rows) are our own training results obtained by using this paper's configuration with their default parameter settings. Except for [4,6], all methods were finetuned 3D CNN models on the Kinetics dataset. The results of this experiment are essential in determining whether the squeeze-and-excitation and self-attention methods are useful for 3D CNN action recognition or not. Our method achieved state-of-the-art performance confined to using only RGB sequence frames. For example, in a 64-frame clip, the performance of prior ResNext on UCF-101 was 95.2%, compared to the performance of ResNext with SE and SA at 95.6%. For ResNet-101, we can see a performance improvement of 2.6%. Table 4. Top-1 accuracies on UCF101 and HMDB51 compared with the state-of-the-art methods.
Discussion of SE and SA Modules
From the results we acquired, we can conclude that both SE and SA work for 3D CNN action recognition well enough; however, in all cases, the SE module performs better than the SA module except for the 64f-clip in the ResNext-101 architecture. The reason that the overall SE module works better than the SA module is that we consider both channel and sequence for SE, but only the channel for SA, and because the sequence and channel concatenation did not yield the meaningful results we expected.
Integration Strategy of the SE Module
The objective of Table 5 is to conduct an ablation study of analyzing the influence of the SE module one stage at a time. More specifically, we added the SE module after each specific block and checked which layer has more impact in terms of analyzing action behavior, i.e., block1, block2, block3, and after each layer. As expected, the performance was higher with each subsequent layer. The reason is that as the number of layers increases, the amount of information (for both channels and sequences) increases as well. We noted that SE blocks produce performance advantages when they are implemented in each of these architecture phases. The gains produced by SE blocks at various stages are complementary in the sense that they can be effectively combined to further improve the network performance. The results are given in Table 5.
Computation Inference Time of SE and SA Modules
The reason for showing Table 6 is to check which module (SE or SA) was heavier to train and provides a good balance between inference time and network complexity. As you can see from Table 6, SA makes our network inference time longer (heavier) compared to the SE module. That is why we removed the SA module after the first and second blocks and only trained after the third and fourth block, since the SA module provides more efficiency after the third and fourth layers compared to the first and second. The result shows (ResNext-101 + SE + SA **) that our inference time decreased pretty much; however, the performance did not decrease too much. So, it means that the SA after third and fourth blocks can provide almost the same performance with much less inference time. The purpose of this experiment is that other approaches in future work can apply the SA module after only the third and fourth layers with about the same performance to save time. Table 6. Top-1 accuracy and complexity time for 16f-clips on HMDB51. All the streams are fine-tuned from Kinetics400 pretrained models. * means SA implemented after each layer, ** means SA implemented only after the third and fourth layers.
Qualitative Results.
The attention map is a method to analyze the implicit attention of a CNN. We implemented an activation map [27] to make our findings more comprehensive. The class activation map indicates the discriminative image regions used by the CNN to identify an action. For example, we demonstrate some results for public datasets (see Figure 9) and the other results for randomly chosen images from web crawling (see Figure 10). From the results, we can see that the main focus is on action, and there is comparably less attention to other aspects of the image. Since the idea with SA is to learn the entire picture, the network starts learning every single detail around the action, and the final decision on action recognition is made based on the aggregation of action and the environment. From qualitative results, we can conclude that the network's ability to identify the action region increases. All of these prove that our trained model can focus on important and meaningful actions.
(a) (b) Figure 9. Class activation maps on red-green-blue (RGB) frames. After implementing SE and selfattention (SA), we can see that our activation map's active region increased a bit and also covered non-active areas: (a) results before and (b) after the implementation of our approach. Figure 10. Qualitative analysis for real images. These samples do not belong to our dataset, and they were randomly chosen by web crawling. It can be seen that areas such as the human arm have a significant influence on behavior judgment.
Conclusions
In this paper, we proposed a novel approach to video action recognition, which uses the aggregation of squeeze-and-excitation and self-attention modules. Using these two modules together, we showed that dynamic changes across frames can be captured more accurately and efficiently with almost no additional computation cost. We also presented qualitative results from experiments to make a balanced decision based on both types of data. Extensive experiments demonstrated the effectiveness of our approach, which achieved state-of-the-art performance across different datasets and architectures.
Author Contributions: The work described in this article is the collaborative development of all authors. All authors contributed to the idea of data processing and designed the algorithm. F.A. and D.H.K. made contributions to data measurement and analysis and B.C.S. led us to research direction. All authors participated in the writing of the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by an Inha University research grant.
Conflicts of Interest:
The authors declare they have no conflicts of interest. Table A1 provides the layer-by-layer description of our network, which corresponds to Figure 1b. This table includes every detail of the proposed network. | 5,800.2 | 2020-01-12T00:00:00.000 | [
"Computer Science"
] |
Co-Varying Collexeme Analysis of Chinese Classifiers 棵 kē and 株 zhū
The numeral classifier is a grammatical category in plenty of East Asian languages, with Chinese being one of the most widely reported. In Chinese, there are many classifiers that are near-synonymous, meaning that certain classifiers may be interchangeable in certain contexts. However, these classifiers are used with semantically similar nouns and, as a result, the distinction between the various usages is not always clear. In view of this issue, we propose to study near-synonymous classifiers using the co-varying collexeme method and the Euclidean distance, by exploring the case of the classifiers 棵 kē and 株 zhū. We report results that not only partially confirm but also complement what has been found in previous raw-frequency-based research.
1
Near-Synonymy. What It Is and the State of the Art 1 The linguistic issue of near-synonymy is never an easy one. For decades, there have been different approaches trying to discuss and settle how different words have similar meanings and in what situations they do, based on conceptual semantic discussions, usage dictionaries, or a scrutiny of a body of linguistic samples. Among the numerous types of efforts, recent decades have witnessed the rise of corpus linguistics, which offers a methodological opportunity to approach linguistic phenomena in a way that can be faithful to how a word is actually used in real-world context. Based on the principle that one should "know a word by the company it keeps" (Firth 1957, 11), there have been numerous studies applying such rubric in the study of lexical semantics, generalising the contextual information over a number of usages of a particular word, in order to understand the lexical and grammatical company kept by the word at issue. In corpus linguistics, there are several methods used to study similar and potentially confusing words, with the one most relevant to the present study being collostructional analysis (Stefanowitsch, Gries 2003;Schmid 2010;Schmid, Küchenhoff 2013), which is a family of corpus-based quantitative methods that helps measure mutual attraction between lexemes and constructions. Collostructional methods do not simply rely on numbers of lexical frequencies, but also measure the degree of probability that the patterns of analysed frequencies are due to chance. Such analyses work under the rubrics of construction grammar (Goldberg 1995), which claims that lexical and grammatical constructions are symbolic form-meaning pairings. 2 Collostructional analyses compare the strength of association between the analysed constructions and the chosen lexical elements in the actual use found in linguistic corpora.
In the present study, we employ the collostructional method called co-varying collexeme analysis (Stefanowitsch, Gries 2005; Classifiers are linguistic devices that help humans categorise objects in the world. In language, classifiers are words that encode "salient perceived or imputed characteristic of the entity to which the associated noun refers" (Allan 1977, 285). Tai (1994) takes a similar stance and argues that Chinese classifiers are used to denote a group of perceptually-or functionally-based attributes associated with a given noun. Among all the systems of classifiers, the numeral classifier system is one of the most commonly recognised type (Aikvenhald 2003;Saalbach, Imai 2012). The usage of numeral classifiers is mostly compulsory with counting objects in a classifier language, which is also the case for Chinese. In a classifier language, a typical classifier construction consists of a numeral, a classifier, and a noun (Allan 1977, 288). In Chinese, the grammatical schema of such construction is The choice of a numeral classifier is never random but is based on the perceived properties of the head noun (Tai 1994;Jiang 2017). For the choice of a classifier in a usage like (1), when a speaker of Chinese (or a learner of Chinese as a second language) expresses the quantity of a noun such as 狗 gǒu, the noun needs to take a suitable classifier from the conceptual category of animacy 4 that captures the imputed characteristics associated with dog. As there are multiple classifiers in each linguistic category and as some of them overlap in meaning, by using a classifier, the speaker profiles (Langacker 2008, 66) a perceptual or a functional aspect of the noun. For instance, the classifiers for plant 棵 kē and 株 zhū are near-synonymous and interchangeable in certain contexts, as exemplified by (2a) and (2b) (cited from Dosedlová, Lu 2019, 115). In their study, Dosedlová and Lu argue that 棵 kē and 株 zhū conceptually profile slightly different aspects of plant -by observing the span of nouns the classifiers co-occur with, the authors report that 株 zhū occasionally co-occurs with nouns of plant that invoke small and vulnerable, such as 苗 miáo 'seedling' and 花 huā 'flower', and nouns of micro-organism, such as 霉 méi 'mold', 细菌 xìjùn 'bacterium', 病毒 bìngdú 'virus', and so on, but that pattern is not seen among the nouns that co-occur with 棵 kē as a classifier. However, a methodological insufficiency of that paper is that the observations are based merely on separate raw frequency counts of each of the slots in the classifier construction, while no attention is paid to how the multiple slots in the construction interact. 5 Therefore, to investigate the interaction between different slots within a construction, an alternative must be sought.
From an onomasiological point of view, it will be useful to find out the interaction and the detailed relationship between the classifier and the noun within [QUAN]-[clf]- [N]. Therefore, we would like to focus on how the two slots in that particular construction (and only in that particular construction, not elsewhere in the language/corpus) co-vary. After all, a word with classifier as part of its syntactic function may occur in various grammatical constructions in Chinese, which is the case for 只 (also as an adverb when pronounced as zhǐ or as a noun when pronounced as zhī), 棵 kē (also as a noun), and 株 zhū (also as a noun or a verb), among numerous others, but that is something we would certainly like to exclude in order to achieve a more statistically-precise result. For this purpose, we consider it suitable to conduct the so-called co-varying collexeme analysis. Such an analysis always begins with a construction and studies which lexemes tend to be attracted to that particular construction and which do not. A typical collostructional analysis relies on frequency measures of tokens of different types of lexemes extracted from a corpus. Once obtained from the language sample, the frequencies are used for calculating the p-values of the list of collexemes (lexemes that may be attracted to a particular construction), which show the degree of association between the collexemes and the construction. Each lexeme analysed has its own p-value, which indicates its collocational strength with the construction. The calculation is done via the Fisher-Yates Exact test.
3
Co-Varying Collexeme Analysis and Euclidean Distance In a co-varying collexeme analysis, it is important to identify the association strength between pairs of lexical items appearing in two different slots of the same construction. In our study, the lexical slots to examine are the clf and the N within the [QUAN]-[clf]-[N] construction. To conduct such an analysis, we first need to find out the span of lexemes that may occur in each of the slots investigated. We also need the frequency of the construction (C) investigated (which is the total number of concordance lines included in the sample), the frequency of the first target word (L1) in a particular slot (S1) in C in the sample, and the frequency of the second target word (M1) in the other slot (S2) in C in the sample. A template is shown in table 1 below. in S1 in C frequency of S2(M1) and S1(L1) in C frequency of S2(⌐M1) and S1(L1) in C total frequency of S1(L1) in C other words (L2, L3…) in S1 in C frequency of S2(M1) and S1(⌐L1) in C frequency of S2(⌐M1) and S1(⌐L1) in C total frequency of S1(⌐L1) in C total total frequency of S2(M1) in C total frequency of S2(⌐M1) in C total frequency of C We illustrate such a template with the case study of the distribution of the causing event and the resulting event in the English into causative (Stefanowitsch, Gries 2005), as in we must not fool ourselves into thinking there is no longer any problem. To determine the extent of the correlation between fool (as the causing event) and think (as the resulting event) in fool into thinking, a distribution table for this pair of lexemes is given in table 2. Such a table is submitted to a contingency test and the whole procedure is done for each word pair appearing in the construction in question. The data of the tables is submitted to Fisher-Yates Exact test. The result of this test is a p-value that indicates the association strength between the lexeme and the construction. The strongest mutual association between a lexeme and a construction is the one with the smallest p-value (Desagulier 2014, 157). Co-varying collexemes are those pairs of words that co-occur more frequently than by pure chance (Stefanowitsch, Gries 2003, 2005. The final result can be submitted to further analysis, such as cluster analysis (Divjak 2010;Divjak, Fieller 2014), for a more detailed understanding of the results. Table 3 shows the information needed for studying the correlation between a classifier and the noun in Cluster analysis is a family of statistical methods used for deciding the distance and similarities between entities, which may be applied to the study of language to measure the internal structure of a set of synonymous lexical constructions. Divjak and Gries (2006), for in-stance, study nine Russian verbs that all share the tentative meaning of try. The paper examines 1,585 concordance lines by tagging the individual usages using morphosyntatic cues that may influence the behavioural profile of the nine verbs. The authors find that the nine verbs form three groups and that each group exhibits similar internal behaviours, which means that the members in a group have smaller conceptual semantic distances with each other than with members outside the group. The first step in conducting a cluster analysis is to choose the variables. There are several kinds of variables to choose from, which can be numerical, categorical, or ordinal. 6 We illustrate this with a simplified example below. Let us suppose we have four constructions (C1, C2, C3 and C4) to analyse. We also assume there are four possible variables that may factor in learning about the conceptual semantic distance between the four words, including: frequency in the corpus, co-occurrence with Word x, co-occurrence with Word y, and co-occurrence with an adjective. The hypothetical situation is put forth in table 4. The next step is to decide on a method for calculating the similarities among the words involved. In a cluster analysis, one of the most common methods for calculating distances (similarities) is Euclidean distance. The result of such method is a dissimilarity matrix table, which shows the distances among all the entities within a dataset. The Euclidean distance between two objects is gained by summing the squared differences between the pairs of corresponding values for the two individuals and taking the square root of the sum (Divjak, Fieller 2014, 417). The formula for the calculation of Euclidean distance is as follows: Following the hypothetical situation outlined in table 4, a Euclidean distance analysis can be conducted using the above formula for the set of the target words. For instance, the similarity distance between C1 and C2 can be figured out as follows: The same can be done between each two of the four: the results are summarised in table 5. The lowest number in each column in bold indicates the smallest distance (or the highest degree of similarity) between words. As the table shows, the closest items are C1 and C4, with a distance of 50.23 (underlined, in bold), and the most dissimilar items are C2 and C3, with a distance of 312.5 (underlined only). Having introduced the related statistical algorithms, now we move on to a detailed description of the research issues and the research steps.
Research Issue, Scope, and Steps
In this paper, we address the following issues: first of all, what can we learn about the relationships between a pair of synonymous classifiers using a co-varying collexeme analysis? In what way does the Euclidean distance help? We believe that the relationships between the synonymous classifiers can be made available based on the nouns that collocate with each of these classifiers and that a co-varying collexeme analysis will provide useful data related to the behaviour of the classifiers involved, including the collocational strength and certain association measures. Such results are what we may further submit for a cluster analysis in order to explicate the internal structure of the synonymous set. Secondly, does the co-varying collexeme analysis and an analysis based on the Euclidean distance tell us anything beyond an analysis informed only by a raw frequency count of the lexical items in question? To answer the questions above, we chose to investigate the classifiers 棵 kē and 株 zhū, which had already been examined based on a raw frequency approach in Dosedlová and Lu (2019). In that paper, the authors used data extracted from Sketch Engine 7 and observed the types of nouns that occurred in their language sample, and the token frequencies of each of the nouns, which allowed the authors to come up with the conceptual similarities and differences between the two classifiers. In order to see how a different methodological approach may shed alternative light on the same linguistic phenomenon, we extracted the collocating nouns and analysed the data to calculate their T-score, MI score and logDice. After that, we calculated the Euclidean distance between the nouns in the dataset. The steps are outlined below.
In order to properly sample the usages of each of the classifiers investigated, we built a corpus for each of the classifiers by extracting random concordance lines from a large representative body of authentic linguistic data. To this end, we used the function 'sample' of Sketch Engine, which created a random collection of concordances that involved the two target classifiers. We set the size of each subcorpus five hundred lines, which was more than sufficient to investigate the semantics of a common word. 8 After we input the extracted data to Excel, we went through the data manually to look for the collocating nouns and their frequencies in the sub-corpora. In addition, we looked up the frequencies of each of the collocating nouns in each of the sub-corpora. All the information acquired from the above steps was used to calculate the association measures and collocational strengths in the co-varying collexeme analysis. These association measures included: 1) T-score, which indicates the level of certainty with which one can argue for a clear association between the linguistic units analysed. A T-score higher than 2 is seen as statistically significant, which means that the co-occurrence of the two linguistic units is more than mere chance. 2) logDice, which is a measure of the typicality of the co-occurrence of the classifier and its collocating noun. The maximum logDice value is 14, which means the exclusive collocation between the linguistic units investigated (that all occurrences of X co-occur with Y and vice versa). A negative value means that the XY collocation is not statistically significant. 3) MI score, which stands for the extent to which words co-occur compared to the frequency of their separate appearance. An MI score higher than 3 is an indicator of a statistically significant collocation. The lower the MI score, the more likely the linguistic units co-occur only by chance.
The three association measures may or may not converge, as we will show in the body of the analysis.
Results
In this section, we report the findings based on the data retrieved from Sketch Engine following the steps outlined above.
Nouns in [QUAN]-[kē]-[N]: Their T-Score and logDice
In the sub-corpus of 棵 kē, we found 38 different nouns that co-occurred with the classifier. Below, we discuss the association measures of T-score and logDice.
It is important to bear in mind that each of these measures takes a different approach in measuring the strength of the collocation. If we look at the most frequent noun collocating with 棵 kē, i.e 树 shù 'tree', its T-score and logDice are the highest among all collocating nouns, but its MI score is not. The reason is that the MI score is strongly influenced by the size of the corpus, hence it is usually considered subsidiary if compared to the T-score. As for the T-score, it promotes pairings that are frequently observed but does not concern the total frequencies of each of the linguistic units, hence the size of the corpus is irrelevant. For instance, if we look at the noun 木棉树 mùmiánshù 'cotton tree', the T-score is relatively low because there are only three tokens of its collocation with 棵 kē, but the MI score is quite high, as the MI score takes into account all the other occurrences of both of the words. As for the logDice, it is an important indicator of the typicality of a collocation. Therefore, in this study, T-score and logDice are our main foci. Table 6 lists the first five nouns with the highest T-score and the highest logDice in the sub-corpus of 棵 kē.
Nouns in [QUAN]-[zhū]-[N]: Their T-Score and logDice
The same analysis was done with the nouns that co-occurred with 株 zhū. In the sub-corpus, there are 75 different nouns found to co-occur with 株 zhū. We also calculated the T-score and the logDice for each of the nouns, now listing the top five in terms of the T-score and the logDice in table 7. As we can see in table 7, the top five collocates in terms of each of the association measures still largely overlap, which confirms the status of 树 shù, 苗 miáo, 植树 zhíshù, and 苗木 miáomù as the most statistically significant lexemes that are attracted to [QUAN]-[zhū]- [N]. However, if we compare all the five most significant collocates between the two classifiers in the corpora, we see that 棵 kē generally collocates with nouns that contain 树 shù as part of it, whereas the significant collocates of 株 zhū are more diversified (that is, do not necessarily involve 树 shù as part of the lexeme). In addition, 株 zhū has collocates that invoke small and vulnerable, such as 苗 miáo, 花 huā, and 菌 jùn. We will return to this point when we compare the results from this co-varying collexeme analysis with the results in Dosedlová and Lu (2019).
A comparison of tables 6 and 7 allows us to identify 树 shù as the lexeme that appears in both tables, meaning that it is the lexeme that has the highest T-score and logDice in both [QUAN]-[kē/zhū]-[N], indicating the strongest attraction between 树 shù and the two classifier constructions. Based on this fact, we may say that 树 shù is the prototypical lexical instantiation of plant that collocates with both 棵 kē and 株 zhū (but only within the particular construction of [QUAN]-[clf]-[N] and only when it co-varies with 棵 kē and 株 zhū, rather than in Chinese in general). In addition to 树 shù, 苗 miáo is also a lexeme that has a very high T-score and logDice in [QUAN]-[zhū]- [N], so is another prototypical lexical instantiation of plant in that classifier construction. We will return to this point in our discussion.
Secondly, we calculated the Euclidean distance between the fourteen nouns that co-occurred with 棵 kē and 株 zhū within the construction [QUAN]-[clf]-[N], following the formula introduced in § 3 and using the raw frequency, T-score, MI value and logDice of the fourteen lexemes as the possible variables. A summary of the Euclidean distances is given as table 9. The summary in table 9 allows us to compare the Euclidean distance between all the nouns involved and the prototypical plant within the two particular grammatical constructions. Remember that 树 shù is the lexical prototype in both constructions. In table 9, we can see that among the fourteen lexemes shared by the two classifier constructions, 核桃 hétáo and 樱花 yīnghuā are the two lexemes that have the highest Euclidean distance from 树 shù, with a Euclidean distance value of 14.0385 and 12.8982 (in bold), respectively. This means that the behaviours of these two lexemes are the most different from the prototype in the corpora. On the other hand, the two lexemes that have the smallest Euclidean distance with 树 shù are 树木 shùmù and 植树 zhíshù, having a Euclidean distance value of 2.5257 and 3.3374 (underlined), respectively, meaning that the two lexemes have the most similar behaviour with 树 shù in the corpora. Note that the two lexemes are also conceptually closer to 树 shù than the other lexemes, as they do not refer to any particular type of tree, so are at the same level with 树 shù in terms of taxonomy. Therefore, the similar behaviour between 树 shù, 树木 shùmù and 植树 zhíshù is natural.
Discussion and Concluding Remarks
The statistically informed analysis in the present paper largely confirms the results in Dosedlová and Lu's (2019) study based on raw lexical frequencies, but it also turns up meaningful patterns that were not reported in the previous study.
In particular, based on the T-score and the logDice, we firstly confirm that 树 shù is the lexeme that has the strongest association meas- . This matches the fact that 树 shù is the most frequent noun that co-occurs both with 棵 kē and with 株 zhū (Dosedlová, Lu 2019, 123). Following on from that, we see that the raw frequency, T-score and logDice constitute pieces of converging evidence that jointly support the claim that 树 shù is the prototypical lexical instantiation of plant in [QUAN]-[kē/ zhū]- [N]. Secondly, the statistically informed analysis allows us to confirm that [QUAN]-[zhū]-[N] does attract nouns that invoke small and vulnerable, such as 苗 miáo, 花 huā, and 菌 jùn (Dosedlová, Lu 2019, 122). In the above two respects, the results obtained via a co-varying collexeme approach echo the findings based on raw lexical frequency.
However, a co-varying collexeme analysis can build on the previous analysis and can allow us to see patterns beyond an exclusively raw-frequency-based approach -first of all, it allows us to identify 苗 miáo as another lexeme that is strongly associated with [QUAN]-[zhū]- [N]. According to the list of token frequencies in Dosedlová and Lu (2019, 123), 苗 miáo accounts for 14.3% of the total usages in [QUAN]-[zhū]- [N], but that is only less than one third of the percentage of 树 shù (which is 47.3% in their table). Accordingly, a study merely based on the token frequency may not give the collocation between 苗 miáo and 株 zhū too much weight. But once the T-score and the logDice are included, that brings the lexeme back to our at-tention. Secondly, another linguistic fact that is uncovered through the Euclidean distance is the similarity between each of the fourteen shared lexemes with the prototype 树 shù. For instance, the Euclidean distance analysis indicates 树木 shùmù and 植树 zhíshù to be the lexemes that are most similar to 树 shù in terms of the behavioural profile, which cannot be captured by a simple frequency count -that would only identify 木 mù and 植 zhí being infrequent lexical types in the corpus, about one eighth of 树 shù in [QUAN]-[kē]-[N] (Dosedlová, Lu 2019, 121) and one fourth of 树 shù in [QUAN]-[zhū]-[N] (Dosedlová, Lu 2019, 123). In addition, the cluster analysis has found the behavioural profiles of 核桃 hétáo and 樱花 yīnghuā to be the most distant from the prototype among the fourteen shared lexemes, meaning that the two lexemes behave most differently from 树 shù in [QUAN]-[kē/zhū]- [N], which is an observation that can be made only through a Euclidean distance analysis.
Despite of the advantages of a co-varying collexeme analysis and a cluster analysis mentioned above, we maintain and emphasise that an analysis based on type and token frequencies is still capable of uncovering linguistic facts about near-synonymy that cannot be seen through a collostructional analysis, and that the two approaches should be considered complementary to each other. An interesting part of the conceptual semantic difference between 棵 kē and 株 zhū, for instance, lies in the fact that [QUAN]-[zhū]-[N] has an extended group of usages that covers entities that do not invoke plant, such as mold, bacterium, biological substance and chemical substance (Dosedlová, Lu 2019, 122-3). These usages are peripheral members of the linguistic category (defined by the categorising structure [QUAN]-[zhū]- [N]) and are very low in lexical frequency. Such periphery of a linguistic category is typically difficult to observe given its low frequency, but may contain important conceptual information that helps define the linguistic category. Such information may become available only through an extensive type frequency analysis of the language sample.
Finally, we would like to conclude by proposing a synergy between different quantitative methods for analysing the near-synonymy of classifiers, similar to the advocacy for a methodological synthesis in Janda, Kudrnáčová and Lu (2019). As we have shown in this paper, each research method has its strengths and its limitations, so we consider it always advisable to try to obtain converging and consolidating evidence from different angles, or to try to obtain comprehensive results from complementary methodological approaches. | 5,912.2 | 2020-04-20T00:00:00.000 | [
"Linguistics"
] |
Distributing Web Components in a Display Ecosystem Using Proxywork
In order to carry out this task, Web browsers connected to the Proxywork proxy receive a modified version of Web pages they have requested to the proxy. This modification attaches a menu on each UI component to display, hide, copy, or distribute the UI component to the rest of the browsers that are connected to the Proxywork proxy. Therefore, the Proxywork proxy is also in charge of orchestrating how UI components are displayed on devices running on different platforms.
INTRODUCTION
In a distributed user interface (DUI) scenario, users distribute one or many elements/s of one or many user interface/s to support one or many user/s to carry out one or many task/s on one or many domain/s in one or many context/s of use [9].Web applications do not offer the possibility to distribute UI components from one device to another one.For instance, suppose that you are viewing a Web site using a Smartphone, and you want to read an article in a bigger display such as your desktop computer.
In an ideal situation, you should be able to "select" the article from your Smartphone, and "migrate" it to the Web browser running in the desktop computer.However, in the real life, it is not as simple as it seems, because Web browsers do not support this feature.
In this paper, we present the Proxywork system which offers the ability to transform Web applications designed to run on a single display into Web Applications running on a DUI.This transformation is performed at runtime by the means of a Web proxy which is able to distribute a Web application UI across different platforms.
In order to carry out this task, Web browsers connected to the Proxywork proxy receive a modified version of Web pages they have requested to the proxy.This modification attaches a menu on each UI component to display, hide, copy, or distribute the UI component to the rest of the browsers that are connected to the Proxywork proxy.Therefore, the Proxywork proxy is also in charge of orchestrating how UI components are displayed on devices running on different platforms.This article is organized as follows: Section 2 presents the most relevant related work regarding DUIs.A description of the Proxywork system is presented in Section 3. Section 4 presents a case of study that shows how Proxywork supports the Web site scenario we have mentioned.Later on, the Section 5 shows a quantitaive evaluation of the Proxywork system.Finally, Section 6 presents conclusions and future work.
RELATED WORK
DUIs are relatively recent and are continuously evolving; therefore, there are few frameworks that support the distribution of, elements or the whole, user interface.Moreover, most frameworks do not support full-fledged DUIs.
From the common programming language perspective, UI development toolkits such as Java Swing or Windows Presentation Foundation (WPF) do not support DUI concepts.They just allow developers to assign UI components to a single container/context.However, once these components are assigned to their container, they cannot be reassigned or redistributed to another container or context residing on different runtime platforms.An example of a fixed distribution of UI elements between smartphones and TVs is presented in [8].Note that the UI component 1 distribution is predefined because no reconfiguration is allowed at runtime.Some approaches allow the distribution of UI components at runtime with limitations.For instance, Web browsers allow users to open a new window to display images that are embedded into a Web page.However, the distribution is limited to the same runtime platform.In addition, the distribution is limited to images and a couple of HTML elements, such as links.You cannot distribute a DIV tag defining a panel on a form.
The granularity of UI components to be distributed is fixed, and often limited to coarse-grained UI components such as dialogs or windows.It leads to the fact that is not possible to distribute or replicate widgets.
Han et al. [3] presented a work where a Web page is split into several partial pages which are distributed to all the users.Our approach supports multi-device and multi-user Web browsing where clients are connected to the server that delivers the page.In this work, the proxy splits pages according to device and user constraints.Therefore, each Web page is represented by a XML file containing specific tags to configure how the Web page is split among users and devices.Although, this work is similar to our proposal, the advantage of our approach lays on the lack of the definition of configuration tags at design time to distribute the UI.These tags are replaced by the enrichment of the Web page with distribution primitives at runtime.
A similar work, implemented by Luyten and Coninx in [4], shows how an interactive system can be distributed among several peer devices.Our approach is significantly different from this work because it allows all Web applications be distributed independently of the computing resources and the application design.
Luyten et al. [5] limit the granularity of the UI component distribution to design time.
A toolkit supporting DUIs was proposed in [6].It is based on a widget distributed structure composed by 2 main parts.While the first part (the widget 'proxy') remains stationary within the process that created the widget; the other part (the renderer) is distributed to where the user can interact with it.This solution requires that the user interface to be implemented as an extension of the TCL/TK toolkit.The main difference regarding to our approach is the Web as a platform, we are focusing on migrating parts of Web applications using XHTML, CSS and Javascript.Moreover, contrary to [6], our solution does not impose any authoring technique to deploy applications (the proxy modifies pages automatically to support the distribution).
Another solution regarding partial Web migrations is presented in [2].This approach allows users to select parts of existing interfaces interactively, and migrate them to different target devices.To achieve this goal, this approach uses a native application that allows users to select the parts of the web application UI to migrate.The main difference between this work presented and ours is the lack of a native application to distribute UI components, the interface to distribute UI components is embedded into UI components, instead.
Another way to achieve the distribution of UIs is presented in [1].In this work, Bandelloni el al. claim that a Web UI can be partially or completely migrated.A partial migration of the UI implies the splitting the UI in two or more parts that run on separate devices.To achieve this goal, extra information should be added to the UI definition using a flexible language to describe the UI presentation.Again, this approach requires extra information that should be added to Web applications beforehand in order to distribute the UI.
Melchior et al. [7] presents a catalogue of distribution operations, and a toolkit to build applications based on this catalogue.The catalogue of distribution operations defines the following set of primitives: SET, DISPLAY, UNDISPLAY, COPY, MOVE, REPLACE, MERGE, SEPARATE, SWITCH and DISTRIBUTE.The toolkit provides a native command line interface to allow manual redistribution of UI components at runtime.The approach we are presenting in this paper does not depend on a native application to distribute UI components, and distribution operations are attached to components, instead.It allows users to manipulate components directly from the UI.
PROXYWORK: DISTRIBUTING WEB APPLICATIONS
Proxywork acts as a proxy where a set of devices are registered.These devices send all their Web requests to the proxy in order to get a response enriched with distribution operations.The Figure 1 shows the overview of the Proxywork architecture.
Let suppose that Proxywork proxy is hosted on the x.x.x.x IP address listening on port 80. Devices that are part of the same display ecosystem should set the Web browser proxy IP address to x.x.x.x and the port to 80.
Once the device Web browser is registered, the request follows these steps to display the Web page enriched with distribution operations: First, the request for the page http://www.yyy.comdeparts from device browser, and arrives to the Proxywork proxy.The Proxywork system requests the Web resource to the Web server where the application is hosted (http://www.yyy.com).The Web server returns the Web resource to the Proxywork.Proxywork modifies the resource by inserting HTML, CSS and JavaScript extra code in the page in order to add distribution primitives, and provide a list of devices where users are able to distribute UI components.Proxywork returns the modified Web page supporting distribution primitives to the device Web browser that sent the request.Finally, the Web browser shows the distributable Web page (http://www.yyy.com).The Device Manager module keeps the status of the Web browsers connected to the distribution environment managed by the proxy.
The Link Manager module is responsible for translating Web page links to suit distributed user interface behaviour.
The Granularity Manager module sets which parts of the Web can be distributed, and which cannot.
The Distribution Manager module is responsible for showing or hiding UI components of the UI.It keeps the distribution state for each device.
The granularity
The granularity of a Web page defines which parts of the Web page users are able to distribute (or make sense to be distributed) across devices, and which are not.
These parts are identified in terms of HTML tags.Therefore, Proxywork sets the granularity to the <DIV> HTML tag level by default because this tag represents groups of graphically related tags.However, users are able to set other HTML tags to make the distribution more flexible.
The Proxywork distribution process
This section describes the workflow process that the Proxywork system follows to transform a Web Application into a distributed Web Application.
The process begins when a Web request arrives to the Proxywork.If the device is not registered, the request is forwarded to a registration page to request the device name.Once the device is registered, Proxywork forwards the request to the Web page that was initially requested.If the device is already registered, the proxy checks if any content was distributed to this device.If it is so, the device request is forwarded to the content assigned to this device.
If the device browser does not need to reload its contents (no changes), the proxy checks if the request is a distribution operation.If the URL matches to a distribution operation, the device and operation IDs are extracted from the URL and the proxy updates the devices affected by the distribution operation in the registration table.
If the request is not a distribution operation, the proxy checks whether the request is related to an "internal navigation link".If the request is related to an "internal navigation link", the proxy extracts the link distribution parameters (i.e. the HTML tag ID and the target URL) to update the device Web browser contents accordingly.However, if the request is not an "internal navigation link", the proxy sends the request to the Web server that hosts the requested resource.
When the proxy receives the resource, it checks if it is a HTML page.If is not a HTML page, the resource is returned to the device without any modification.However, if the requested resource is a HTML page, the proxy modifies the page to transform it into a distributable Web page.
To carry out all this process, the Code Manager delegates to the Link Manager the modification of the navigation links of the Web page.The Code Manager also delegates to the Granularity Manager the selection of the HTML tags that are enriched with distribution capabilities.Finally, the Code Manager sends the modified page back to the browser that made the request.
Distribution operations
This section describes the set of distribution operations supported by Proxywork.
The Connect operation associates the IP address of a device Web browser to a device name.The connection occurs when the Web browser request a Web resource for the first time.As result the user sends the device name to the proxy through a form.
Once the device is registered, the name is used to parameterize the distribution operations that require a target device Web browser (i.e.Copy and Distribute).
The Disconnect operation releases a device Web browser from the distribution environment.Once the device is disconnected, the device name is removed from the list of parameters that are set to distribution operations.
The Rename operation allows users to change the registered name of a device Web browser.
The Display/Hide operation allows users to display/hide UI components.
The Copy operation allows users to copy UI components from one device Web browser to another one connected to the same distribution environment.This operation requires the target device Web browser as parameter.
The Distribute operation sends UI components from one device Web browser to another one connected to the same distribution environment.This operation requires the target device Web browser as parameter. To
CASE OF STUDY
This section presents how Proxywork is employed to support DUI for Web applications in a real case of study 1 .
The case of study describes how the navigation bar of a Web application can be distributed from a desktop or laptop computer to a smartphone.The idea is the creation of a "remote control" of the Web application using the smartphone touch screen.
The distribution is carried out on 2 devices: Dell XPS M1530 laptop computer running the Microsoft Windows 8 operating system and Chrome browser; and Nokia Lumia 900 smartphone running the Microsoft Windows Phone 8 operating system and Internet Explorer 9 browser.
The proxy of both Web browsers points to the IP address and port where Proxywork is running.We register the laptop as the "DellXPS" device and the smartphone as the "Lumia" device.Then, we set the URL of the laptop web browser to http://www.uclm.es.As soon as the page loads, users are able to see the context menus that show distribution operations.
The Figure 2 (a) shows the laptop display when the user cursor is over the navigation menu and select the device to distribute the UI.In this case, the "Lumia" device (see zoom on Figure 2 (a)).
When users click on the device, the navigation bar disappears from the "DellXPS" display (see Figure 2 (b)) and appears on the "Lumia" device (see Figure 2 (c)).Thus, when users click on an anchor of the navigation bar that was distributed from the "DellXPS" to the "Lumia" device, the target of this content is set to the "DellXPS" (see Figure 2 (d))
USABILITY EVALUATION
To validate the Proxywork proposal presented in this work, we have performed a preliminary quantitative evaluation with users within a possible application scenario.
The set of Proxywork functions to test are: (a) configure a device to use Proxywork, (b) distribute parts of a web page between devices and (c) share parts of a web page between devices.To perform the evaluation, we will focus on the effectiveness, efficiency and satisfaction.
Goal and apparatus
The goal of the quantitative evaluation is to compare Proxywork against a set of systems that users currently employ to transfer parts of web pages among devices.Thus, the main goal is to prove the hypothesis: "Users are more efficient, using Proxywork to perform the task of copying and distributing parts of a web page between two devices, than employing the user's choice system".
In addition, we measured users' satisfaction after using Proxywork.
The hardware employed in the test was the same used in the case study plus the user's device.
Participants
The profile of the participants potentially interested in the application's functionality is broad.We selected a user population of 6 users.All of them have an advanced technological knowledge in the specific technique that they have selected to realize the tasks' test, and have previously transferred information among computers using alternative methods.In all cases, this was the first time that they have used Proxywork.
Definition of tasks
All participants were asked to perform the same 3 tasks in the same conditions.These tasks are defined as follows: Task 1: The user must configure the devices that he/she wants to use so that they can be used on the raised scenario.In the case of Proxywork, the browsers must be configured with the IP and port of Proxywork, in addition the devices must be registered.
Task 2: On her device, the user has a web page with information relevant to the meeting.He/she has to show some of that information in the projector of the room.
Task 3: On her device, the user has a web page with an image that must copy to the organizer of the meeting.
Procedure
The testing period was a week from the beginning to the end of the experiment.Before the test, users were introduced with an explanation of the tasks to be performed.Later, they were asked to choose the system they wanted to use to transfer the information as the "competitor" system.
In addition, the laboratory was setup with users' selected system and the Proxywork.Users performed the three tasks with the system they have chosen, and later, the same three tasks were performed using the Proxywork (within-subjects test).After each testing session, users fulfilled the System Usability Scale (SUS) [2] questionnaire.
Dependent variables
The efficiency evaluation is measured by the task completion time metric.The satisfaction evaluation is based on the analysis of the SUS questionnaire where each question was valued in a scale from the range of 1 to 5 (1 strongly disagree, 5 strongly agree).
Results and discussion
As result of the test session we collected the information shown in Table 1.The Table 1 shows users' time to complete each task.Note that all users completed all tasks with both systems.The collected data reveals that using the Proxywork system, both task 2 and task 3, were performed on an average time that is less than a quarter the time employed to perform the same task with the Email technique and less half the time employed to perform the same task with the Pendrive technique.Figure 3 shows that users are more efficient when they use the Proxywork instead of the system they have chosen.
On the other hand, the SUS score is 86.8 regarding a maximum of 100.This score indicates that users have reached an acceptable level of satisfaction with Proxywork.
A detected usability problem is that Proxywork configuration (Task 1) is too complicated for novice users (see Figure 3).
CONCLUSION AND FUTURE WORK
This paper presents Proxywork, a novel system to support multi-platform distributed user interfaces.It was implemented as a proxy that transforms traditional Web pages into DUI Web pages dynamically on runtime.It was developed using Web standards such as XHTML, CSS and JavaScript in order to be supported by most platforms and not to depend on native applications.
This proposal implements 7 distribution operations or primitives: Connect, Disconnect, Rename, Display, Hide, Copy and Distribute.However, it is not difficult to add new operations due to the flexibility of the implementation.
To show the capabilities of Proxywork, a case of study was presented, which shows how to deal with the distribution of Web application navigation bars where users distribute the navigation bar from a laptop or desktop computer to a smartphone in order to use the smartphone as a "remote control" of the computer display.
The results collected in the Evaluation section have validated the hypothesis: "Users are more efficient using Proxywork to perform the task of copying and distributing parts of a web page between two devices, than employing user's choice system".
As future work, we are working on a new version of the environment that implements new distribution operations such as Clone, Replace, Merge, Switch and so on.
Figure 1 :
Figure 1: Overview of the Proxywork architecture To carry out the transformation of a simple Web page into a distributed Web page, Proxywork defines 5 modules to process Web pages.The Code Manager module is responsible for inserting the code containing the distribution operations into the Web pages requested by Web browser devices.
Figure 2 :
Figure 2: Distributing the navigation menu.The user decides to move the main menu (a) and is removed (b).The main menu appears in the Lumia device (c) and change the content in the first device (d).
Figure 3 :
Figure 3: The confidence intervals around the average time for each task in sec.
Table 1 :
Completion time for each task (in sec.) | 4,669 | 2013-09-09T00:00:00.000 | [
"Computer Science"
] |
Bounded Motions of the Dynamical Systems Described by Differential Inclusions
and Applied Analysis 3 It is obvious that δ∗ μ∗, ·, ·, · ∈ Δμ∗ δ∗ · . Let us choose an arbitrary h · ∈ Δμ∗ δ∗ · . For given t0, x0 ∈ 0, θ × R, U∗, δ∗ · ∈ Upos × Δ 0, 1 , h · ∈ Δμ∗ δ∗ · , we define the function x · : t0, θ → R in the following way. The function x∗ · on the closed interval t0, t0 h t0, x0, U∗ t0, x0 ∩ t0, θ is defined as a solution of the differential inclusion ẋ∗ t ∈ F t, x∗ t , U∗ t0, x0 , x∗ t0 x0 see, e.g., 13 . If t0 h t0, x0, U∗ t0, x0 < θ, then setting t1 t0 h t0, x0, U∗ t0, x0 , x∗ t1 x1, the function x∗ · on the closed interval t1, t1 h t1, x1, U∗ t1, x1 ∩ t1, θ is defined as a solution of the differential inclusion ẋ∗ t ∈ F t, x∗ t , U∗ t1, x1 , x∗ t1 x1 and so on. Continuing this process we obtain an increasing sequence {tk}k 1 and function x∗ · : t0, t∗ → R, where t∗ sup tk. If t∗ θ, then it can be considered that the definition of the function x∗ · is completed. If t∗ < θ, then to define the function x∗ · on the interval t0, θ , the transfinite induction method should be used see, e.g., 14 . Let ν be an arbitrary ordinal number and {tλ}λ<ν are defined for every λ < ν, where tλ ∈ t0, θ and tλ1 < tλ2 if λ1 < λ2. If t∗ supλ<νtλ θ, then it can be considered that the definition of the function x∗ · on the interval t0, θ is completed. Let t∗ < θ. If ν follows after an ordinal number σ, then setting x∗ tσ xσ,we define the function x∗ · on the closed interval tσ , tν ∩ tσ , θ , where tν tσ h tσ , xσ,U∗ tσ , xσ , as a solution of the differential inclusion ẋ∗ t ∈ F t, x∗ t , U∗ tσ , xσ , x∗ tσ xσ. If ν has no predecessor, then there exists a sequence {tλi}i 1 such that tλi1 < tλi2 < · · · and tλi → tν − 0 as i → ∞. Then we set x∗ tν limi→∞x∗ tλi .Note that it is not difficult to prove that via conditions a – c , this limit exists. Since the intervals tλ, tλ 1 are not empty and pairwise disjoint then tν θ for some ordinal number νwhich does not exceed first uncountable ordinal number see, e.g., 15, 16 . So, the function x∗ · is defined on the interval t0, θ . From the construction of the function x∗ · it follows that for given t0, x0 ∈ 0, θ × R, U∗, δ∗ · ∈ Upos×Δ 0, 1 , μ∗ ∈ 0, 1 , h · ∈ Δμ∗ δ∗ · such a function is not unique. The set of such functions is denoted by Yμ∗ t0, x0, U∗, h · . Further, we set Zμ∗ t0, x0, U∗, δ∗ · ⋃ h · ∈Δμ∗ δ∗ · Yμ∗ t0, x0, U∗, h · . 2.2 The set Zμ∗ t0, x0, U∗, δ∗ · is called the pencil of step-by-step motions and each function x · ∈ Zμ∗ t0, x0, U∗, δ∗ · is called step-by-step motion of the system 1.1 , generated by the strategy U∗, δ∗ · from the initial position t0, x0 . It is obvious that for each step-by-step motion x · ∈ Zμ∗ t0, x0, U∗, δ∗ · there exists an h∗ · ∈ Δμ∗ δ∗ · such that x · ∈ Yμ∗ t0, x0, U∗, h∗ · . By X t0, x0, U∗, δ∗ · we denote the set of all functions x · : t0, θ → R such that x · limk→∞xk · , where xk · ∈ Zμk t0, x0, U∗, δ∗ · , μk → 0 as k → ∞. X t0, x0, U∗, δ∗ · is said to be the pencil of motions and each function x · ∈ X t0, x0, U∗, δ∗ · is said to be the motion of the system 1.1 , generated by the strategy U∗, δ∗ · from initial position t0, x0 . For every initial position θ, x0 we set X θ, x0, U, δ · {x0} for all U, δ · ∈ Upos × Δ 0, 1 . Using the constructions developed in 3, 4 it is possible to prove the validity of the following proposition. Proposition 2.1. For each t0, x0 ∈ 0, θ × R, U∗, δ∗ · ∈ Upos × Δ 0, 1 the set X t0, x0, U∗, δ∗ · is nonempty compact subset of the space C t0, θ ;R and each motion x · ∈ X t0, x0, U∗, δ∗ · is an absolutely continuous function. 4 Abstract and Applied Analysis Here C t0, θ ;R is the space of continuous functions x · : t0, θ → R with norm |x · | max‖x t ‖ as t ∈ t0, θ . 3. Positionally Weakly Invariant Set Let W ⊂ T × R be a closed set. We set W t {x ∈ R : t, x ∈ W}. 3.1 Let us give the definition of positionally weak invariance of the set W ⊂ T × R with respect to dynamical system 1.1 . Definition 3.1. A closed setW ⊂ T ×Rn is said to be positionally weakly invariant with respect to a dynamical system 1.1 if for each position t0, x0 ∈ W it is possible to define a strategy U∗, δ∗ · ∈ Upos ×Δ 0, 1 such that for all x · ∈ X t0, x0, U∗, δ∗ · the inclusion x t ∈ W t holds for every t ∈ t0, θ . We will consider positionally weak invariance of the set W ⊂ T × R, described by the relation W { t, x ∈ T × R : c t, x ≤ 0}, 3.2 where c · : T × R → R1. For t, x ∈ 0, θ × R, f ∈ R we denote ∂ c t, x ∂ ( 1, f ) lim sup δ→ 0 ,‖y‖→ 0 [ c ( t δ, x δf δy ) − c t, x δ−1. 3.3 Let us formulate the theorem which characterizes positionally weak invariance of the set W given by relation 3.2 with respect to dynamical system 1.1 . Theorem 3.2 17 . Let ε∗ > 0, and let the set W ⊂ T × R be defined by relation 3.2 where c · : T × R → R1 is a continuous function. Assume that for each t, x ∈ 0, θ × R such that 0 < c t, x < ε∗, it is possible to define u∗ ∈ P such that the inequality sup f∈F t,x,u∗ ∂ c t, x ∂ ( 1, f ) ≤ 0 3.4 holds. Then the set W described by relation 3.2 is positionally weakly invariant with respect to the dynamical system 1.1 . Abstract and Applied Analysis 5 Theorem 3.3. Let ε∗ > 0, and let the setW ⊂ T×Rn be defined by relation 3.2 where c · : T×Rn → R1 is a continuous function. Assume that for each t, x ∈ 0, θ × R such that 0 < c t, x < ε∗, the inequalityand Applied Analysis 5 Theorem 3.3. Let ε∗ > 0, and let the setW ⊂ T×Rn be defined by relation 3.2 where c · : T×Rn → R1 is a continuous function. Assume that for each t, x ∈ 0, θ × R such that 0 < c t, x < ε∗, the inequality inf u∈P sup f∈F t,x,u ∂ c t, x ∂ ( 1, f ) ≤ 0 3.5 is verified. Then for each fixed t0, x0 ∈ W and ε ∈ 0, ε∗ it is possible to define a strategy Uε, δε · ∈ Upos ×Δ 0, 1 such that for all x · ∈ X t0, x0, Uε, δε · the inequality c t, x t ≤ ε holds for every t ∈ t0, θ . For t, x, s ∈ T × R × R we denote ξ t, x, s inf u∈P sup f∈F t,x,u 〈 s, f 〉 . 3.6 Here 〈·, ·〉 denotes the inner product in R. The function ξ · : T × R × R → R is said to be the Hamiltonian of the system 1.1 . We obtain from Theorem 3.3 the validity of the following theorem. Theorem 3.4. Let ε∗ > 0, and let the setW ⊂ T×Rn be defined by relation 3.2 where c · : T×Rn → R1 is a differentiable function. Assume that for each t, x ∈ 0, θ ×Rn such that 0 < c t, x < ε∗, the inequality ∂c t, x ∂t ξ ( t, x, ∂c t, x ∂x ) ≤ 0 3.7 holds. Then for each fixed t0, x0 ∈ W and ε ∈ 0, ε∗ it is possible to define a strategy Uε, δε · ∈ Upos ×Δ 0, 1 such that for all x · ∈ X t0, x0, Uε, δε · the inequality c t, x t ≤ ε holds for every t ∈ t0, θ . 4. Boundedness of the Motion of the System Consider positionally weak invariance of the setW ⊂ T × R given by relation 3.2 where c t, x 〈E t x − a t , x − a t 〉 − 1, 4.1 E · is a differentiable n×n matrix function, a · : T → R is a differentiable function. Then the set W is given by relation W { t, x ∈ T × R : 〈E t x − a t , x − a t 〉 − 1 ≤ 0}. 4.2 If the matrix E t is symmetrical and positive definite for every t ∈ T, then it is obvious that for every t ∈ T the set W t ⊂ R is ellipsoid. 6 Abstract and Applied Analysis Theorem 4.1. Let ε∗ > 0, and let the set W ⊂ T × R be defined by relation 4.2 where E · is a differentiable n×n matrix function, a · : T → R is a differentiable function. Assume that for each t, x ∈ 0, θ × R such that 0 < 〈E t x − a t , x − a t 〉 − 1 < ε∗ the inequality 〈[ dE t dt x − a t − ( E t E t )da t dt ] , x − a t 〉 ξ ( t, x, [ E t E t ] x − a t ) ≤ 0 4.3 holds. Then for each fixed t0, x0 ∈ W and ε ∈ 0, ε∗ it is possible to define a strategy Uε, δε · ∈ Upos ×Δ 0, 1 such that for all x · ∈ X t0, x0, Uε, δε · the inequality 〈E t x t − a t , x t − a t 〉 − 1 < ε 4.4 holds for every t ∈ t0, θ . Here E t means the transpose of the matrix E t . Proof. Since the function c · given by relation 4.1 is differentiable and ∂c t, x ∂x [ E t E t ] x − a t , ∂c t, x ∂t 〈[ dE t dt x − a t − E t da t dt − E t da t dt ] , x − a t 〉 4.5 then the validity of the theorem follows from Theorem 3.4. We obtain from Theorem 4.1 the following corollary. Corollary 4.2. Let ε∗ > 0, and let the set W ⊂ T × R be defined by relation 4.2 where E · is a differentiable n × n matrix function, a · : T → R is a differentiable function and E t is a symmetrical positive definite matrix for every t ∈ T. Assume that for each t, x ∈ 0, θ × R for which 0 < 〈E t x − a t , x − a t 〉 − 1 < ε∗, 4.6
Introduction
Consider the dynamical system, the behavior of which is described by the differential inclusion ẋ ∈ F t, x, u , 1.1 where x ∈ R n is the phase state vector, u ∈ P is the control vector, P ⊂ R p is a compact set, and t ∈ 0, θ T is the time.It will be assumed that the right-hand side of system 1.1 satisfies the following conditions: a F t, x, u ⊂ R n is a nonempty, convex and compact set for every t, x, u ∈ T × R n × P ; b the set valued map t, x → F t, x, u , t, x ∈ T × R n , is upper semicontinuous for every fixed u ∈ P ; c max{ f : f ∈ F t, x, u , u ∈ P } ≤ c 1 x for every t, x ∈ T × R n where c const, and • denotes Euclidean norm.
given in the form 1.1 see, e.g., 1-3 and references therein .The investigation of a conflict control system the dynamic of which is given by an ordinary differential equation, can also be reduced to a study of system in form 1.1 see, e.g., 3-5 and references therein .The tracking control problem and its applications for uncertain dynamical systems, the behavior of which is described by differential inclusion with control vector, have been studied in 6 .In Section 2 the feedback principle is chosen as control method of the system 1.1 .The motion of the system generated by strategy U * , δ * • from initial position t 0 , x 0 is defined.Here U * is a positional strategy and it specifies the control effort to the system for realized position t * , x * .The function δ * • defines the time interval; along the length of which the control effort, U * t * , x * will have an effect on.It is proved that the pencil of motions is a compact set in the space of continuous functions and every motion from the pencil of motions is an absolutely continuous function Proposition 2.1 .
In Section 3 the notion of a positionally weakly invariant set with respect to the dynamical system 1.1 is introduced.The positionally weak invariance of the closed set W ⊂ T × R n means that for each t 0 , x 0 ∈ W there exists a strategy U * , δ * • such that the graph of all motions of system 1.1 generated by strategy U * , δ * • from initial position t 0 , x 0 is in the set W right up to instant of time θ.Note that this notion is a generalization of the notions of weakly and strongly invariant sets with respect to a differential inclusion see, e.g., 5, 7-11 and close to the positional absorbing sets notion in the theory of differential games see, e.g., 3-5 .In terms of upper directional derivatives, the sufficient conditions for posititionally weak invariance of the sets W { t, x ∈ T × R n : c t, x ≤ 0} with respect to system 1.1 are formulated where c • : T × R n → R is a continuous function Theorems 3.2 and 3.3 .In Section 4, the boundedness of the motions of the system is investigated.Using the Hamiltonian of the system 1.1 , the sufficient condition for boundedness of the motions is given Theorem 4.3 and Corollary 4.4 .
Motion of the System
Now let us give a method of control for the system 1.1 and define the motion of the system 1.1 .
A pair U, δ • ∈ U pos × Δ 0, 1 is said to be a strategy.Note that such a definition of a strategy is closely related to concept of ε-strategy for player E given in 12 .
Now let us give a definition of motion of the system 1.1 generated by the strategy At first we give a definition of step-by-step motion of the system 1.1 generated by the strategy U * , δ * • ∈ U pos × Δ 0, 1 from initial position t 0 , x 0 ∈ 0, θ × R n .Note that step-by-step procedure of control via strategy U * , δ * • uses the constructions developed in 3, 4, 12 .
For δ * • ∈ Δ 0, 1 and fixed μ * ∈ 0, 1 , we set The function x * • on the closed interval t 0 , t 0 h t 0 , x 0 , U * t 0 , x 0 ∩ t 0 , θ is defined as a solution of the differential inclusion ẋ * t ∈ F t, x * t , U * t 0 , x 0 , x * t 0 x 0 see, e.g., 13 .If t 0 h t 0 , x 0 , U * t 0 , x 0 < θ, then setting x 1 and so on.Continuing this process we obtain an increasing sequence {t k } ∞ k 1 and function x * • : t 0 , t * → R n , where t * sup t k .If t * θ, then it can be considered that the definition of the function x * • is completed.If t * < θ, then to define the function x * • on the interval t 0 , θ , the transfinite induction method should be used see, e.g., 14 .
Let ν be an arbitrary ordinal number and {t λ } λ<ν are defined for every λ < ν, where Note that it is not difficult to prove that via conditions a -c , this limit exists.
Since the intervals t λ , t λ 1 are not empty and pairwise disjoint then t ν θ for some ordinal number ν which does not exceed first uncountable ordinal number see, e.g., 15, 16 .So, the function x * • is defined on the interval t 0 , θ .
From the construction of the function x * • it follows that for given The set Z μ * t 0 , x 0 , U * , δ * • is called the pencil of step-by-step motions and each function x • ∈ Z μ * t 0 , x 0 , U * , δ * • is called step-by-step motion of the system 1.1 , generated by the strategy U * , δ * • from the initial position t 0 , x 0 .
It is obvious that for each step-by-step motion is said to be the pencil of motions and each function x • ∈ X t 0 , x 0 , U * , δ * • is said to be the motion of the system 1.1 , generated by the strategy U * , δ * • from initial position t 0 , x 0 .
For every initial position θ, x 0 we set X θ, x 0 , U, δ Using the constructions developed in 3, 4 it is possible to prove the validity of the following proposition.Proposition 2.1.For each t 0 , x 0 ∈ 0, θ × R n , U * , δ * • ∈ U pos × Δ 0, 1 the set X t 0 , x 0 , U * , δ * • is nonempty compact subset of the space C t 0 , θ ; R n and each motion x • ∈ X t 0 , x 0 , U * , δ * • is an absolutely continuous function.
Here C t 0 , θ ; R n is the space of continuous functions x • : t 0 , θ → R n with norm |x • | max x t as t ∈ t 0 , θ .
Positionally Weakly Invariant Set
Let W ⊂ T × R n be a closed set.We set Let us give the definition of positionally weak invariance of the set W ⊂ T × R n with respect to dynamical system 1.1 .Definition 3.1.A closed set W ⊂ T × R n is said to be positionally weakly invariant with respect to a dynamical system 1.1 if for each position t 0 , x 0 ∈ W it is possible to define a strategy U * , δ * • ∈ U pos × Δ 0, 1 such that for all x • ∈ X t 0 , x 0 , U * , δ * • the inclusion x t ∈ W t holds for every t ∈ t 0 , θ .
We will consider positionally weak invariance of the set W ⊂ T × R n , described by the relation
3.3
Let us formulate the theorem which characterizes positionally weak invariance of the set W given by relation 3.2 with respect to dynamical system 1.1 .
Then the set W described by relation 3.2 is positionally weakly invariant with respect to the dynamical system 1.1 .Then for each fixed t 0 , x 0 ∈ W and ε ∈ 0, ε * it is possible to define a strategy U ε , δ ε • ∈ U pos × Δ 0, 1 such that for all x • ∈ X t 0 , x 0 , U ε , δ ε • the inequality c t, x t ≤ ε holds for every t ∈ t 0 , θ .
Boundedness of the Motion of the System
Consider positionally weak invariance of the set W ⊂ T × R n given by relation 3.2 where Then the set W is given by relation If the matrix E t is symmetrical and positive definite for every t ∈ T, then it is obvious that for every t ∈ T the set W t ⊂ R n is ellipsoid.
Here E T t means the transpose of the matrix E t .
Proof.Since the function c • given by relation 4. Then for each fixed t 0 , x 0 ∈ W and ε ∈ 0, ε * it is possible to define a strategy U ε , δ ε • ∈ U pos × Δ 0, 1 such that for all x • ∈ X t 0 , x 0 , U ε , δ ε • the inequality holds for every t ∈ t 0 , θ .Now let us give the theorem which characterizes boundedness of the motion of the system 1.1 .
Using the results obtained above, we illustrate in the following example that the given system has bounded motions.
Theorem 3 .2 17 .
Let ε * > 0, and let the set W ⊂ T × R n be defined by relation 3.2 where c
Theorem 3 . 3 .
Let ε * > 0, and let the set W ⊂ T ×R n be defined by relation 3.2 where c • :
Theorem 4 . 1 .
Let ε * > 0, and let the set W ⊂ T × R n be defined by relation 4.2 where E holds.
t * sup λ<ν t λ θ, then it can be considered that the definition of the function x * • on the interval t 0 , θ is completed.Let t * < θ.If ν follows after an ordinal number σ, then setting x * t σ x σ , we define the function x * • on the closed interval t σ , t ν ∩ t σ , θ , where t ν t σ h t σ , x σ , U * t σ , x σ , as a solution of the differential inclusion ẋ * t ∈ F t, x * t , U * t σ , x σ , x * t σ x σ .If ν has no predecessor, then there exists a sequence {t λ Let ε * > 0, and let the set W ⊂ T × R n be defined by relation 4.2 where E • is a differentiable n × n matrix function, a • : T → R n is a differentiable function and E t is a symmetrical positive definite matrix for every t ∈ T. Assume that for each t, x ∈ 0, θ × R n for which R n → R is defined by 4.11 .It is obvious that t, x ∈ W if and only if t ∈ T and x ∈ B a, r . | 5,908 | 2009-06-18T00:00:00.000 | [
"Mathematics"
] |
Construction and Analysis of Intelligent Transportation Based on Computer Network Technology
With the continuous development of urbanization in China, urban traffic problems are gradually highlighted. In the future, intelligent transportation system will be the most important method to solve the demand of urban traffic. The realization of intelligent transportation system requires real-time perception and monitoring of road traffic conditions. Fortunately, with the continuous development of mobile communication, satellite positioning, Internet of Things, big data and other technologies, GNSS, RFID, microwave, geomagnetic, video and other acquisition methods have been widely used in information perception in the field of urban traffic. Based on the concept of intelligent transportation, this paper constructs an intelligent transportation system based on Internet network technology, which has a certain guiding significance for the construction of intelligent transportation under computer network technology.
Introduction
With the rapid development of China's economy, China's various fields of technology has also made a relatively big breakthrough, especially the computer network technology, these technologies make our life more convenient and intelligent, for the development of our society has also made a great contribution [1]. Intelligence traffic is use a new generation of information technology in the field of transportation, travel, transportation to the public, traffic management and traffic network construction whole process control and service support, make the traffic system at the regional, urban and even greater range of time and space have connected, perception, analysis, control and prediction ability, such as use of cloud computing, artificial intelligence, Internet, mobile Internet and other related technology to complete the transportation process of industrial transformation and upgrading.
Intelligent transportation concept
The current intelligent transportation system is completely based on the combination of multiple information fields such as the Internet of Things, the Internet and the Internet of Vehicles. Meanwhile, as time goes by, the relationship among the three is constantly optimized. Intelligent transportation technology has strong technical characteristics, and can effectively improve the application of mobile Internet and Internet of Things technology [2]. At the same time, with the comprehensive advancement of intelligent transportation application, it provides a more perfect model for the development of 5G network technology. At the same time, in the specific process of 5G network application work, it can provide effective, high-definition and combined with 3D image technology and other services with large traffic demand, which is a good experimental basis for the development of 5G network technology. At the same time, 5G network technology also provides technical support for the operation of intelligent transportation and guarantees its normal application, which is a good process of complementing each other and jointly promoting development.
Significance of intelligent transportation construction based on internet technology
The analysis of the specific connotation of intelligent transportation refers to the effective combination of computing network system, Internet of Things, Internet of Vehicles and other technologies in the transportation industry, so as to truly construct its perfect transportation system [3]. In the collection of these technologies to optimize the current traffic network new communication, the final implementation of the overall management and control of the traffic network system; In this way, it can effectively analyze, manage and supervise the current traffic system to a certain extent. And based on the good operation of the information system, in order to effectively build the value points of transportation, to provide people with a more perfect transportation environment.
In the intelligent transportation system, it should have a certain expansibility application, and can restore its basic maintenance function effectively and quickly. The current traffic information technology is being continuously improved, and a complete operation mechanism has emerged. Especially with the advent of 5G network era, information between people and vehicles can be crossed and specific conditions of the current vehicle can be fed back, which makes the traffic field developing in a new direction. Due to the limited early traffic information control technology, from the past development history of intelligent transportation, the traditional traffic mode still has traces to follow, and has not been completely changed. When there are certain limitations and problems in information [4]. After the collaborative analysis of roads, vehicles and drivers, we can find that these traffic is traceable and can effectively realize the convenience and convenience of information.
In the current 5 g the advent of the era of network, great change have taken place in the intelligent transportation information integration, for example, for the current traffic running state of intensification of information collection and adjustment, can real time to the current operation of vehicles and pedestrians, co-ordinated management and supervision, based on mobile information make full use of the information, can achieve the intelligent information management [5]. Based on the current 5 g information network, based on the vehicle information data collection, it is to belong to a reform and innovation of intelligent traffic management, which had great progress for the current platform, finally realizes the artificial intelligence and 5 g network information together effectively, and can the new model have a strong push value innovation traffic, it can be seen as shown in figure 2.
Figure 2. Intelligent information management diagram under traffic operation state
As far as the current theoretical information is concerned, it is also very challenging to realize the artificial intelligence judgment by changing the way of anchor points. Therefore, the current situation and problems of information jam in transportation can be solved by combining value-added technologies [6]. Therefore, the use of 5G network computing in intelligent transportation information is a very important link. When intelligent transportation organically integrates things, resources, things and other information into a new mechanism, information can be rapidly diffused and become the current top-level mode. At the same time, it can also well meet the current people's demand for information and be applied to all scenes. When using 5G information network, it can effectively avoid the limitation of time and space, and it can be based on more flexible information support, which has become an important part of the current intelligent transportation.
Construction of intelligent transportation
Construction of traffic neural network The terminal of the nerve is mainly AL camera. It has the functions of all-weather intensive image capture, accurate and fast AL model recognition, and makes image flow into recognition data stream [7]. The current AL camera has a very strong shooting ability, which can capture 300+ faces/frame for high-density portraits. The video parsing ability is also very strong. The algorithm training time for tens of millions of pictures has been reduced from days to minutes in the past. In addition, there are holographic sensing nodes, vehicle-mounted T-box and other nerve endings.
Figure 3. AL camera in intelligent transportation
The backbone of neural network is to build a communication network. Using the new generation of optical transmission backbone network, MSTP metropolitan transmission network technology and 5G network technology, the data generated by the nerve endings can be quickly and large-capacity transmitted and gathered into the cloud fusion data lake. After these identification data are fused, invocation services can be provided through private cloud, public cloud and other ways [8].
Building an intelligent traffic brain is actually building intelligent applications based on usage scenarios. With a complete neural network, resources, data, algorithms and tasks can be coordinated and consistent, and the cloud edge coordination ability can be built, so that the management, maintenance and use of traffic facilities can be further extended based on the same neural network. Based on this, application scenarios such as traffic fusion command, road network situation awareness, operating vehicle management and control, and intelligent toll inspection can be built.
Build the neural network and brain of traffic, make them work together, and the whole traffic agent is built [9]. Through a comprehensive, accurate, real-time, microscopic traffic data, support from the monitor to the intelligent early warning, from coordination command to information release, humanized service to dynamic emergency disposal of the whole process of intelligent control, build up "holographic awareness and precisely integrated monitoring, early warning, dispatching command, full service" the wisdom of the traffic control system, the traditional transportation industry is undergoing a "Internet +", intelligence traffic upgrades and digital transformation is just around the corner.
Conclusion
With the rapid development of Internet technology, artificial intelligence technology has been applied in various industries. Especially in the fields of transportation, it solves of the problem of our country highway construction and management of related difficulties, but it is because of the involved area is relatively more, mutual penetration and cross between disciplines, in the actual operation not only have the transportation expert, expert, also have artificial intelligence technology for intelligent traffic system is based on 5 g network technology in the new system, and traffic development is of vital significance for the future [10][11]. In combination with the characteristics of the current network information technology, the intelligent transportation system must establish its emergency mechanism and safety protection measures, in order to build its perfect service system, so as to scientifically and | 2,083.2 | 2021-11-01T00:00:00.000 | [
"Computer Science",
"Business"
] |
Structure-activity study of furyl aryloxazole fluorescent probes for the detection of singlet oxygen
In this study, we report the synthesis and the photochemical behavior of a series of new "click-on" fluorescent probes designed to detect singlet oxygen. They include a highly fluorescent chemical structure, an aryloxazole ring, linked to a furan moiety operating as singlet oxygen trap. Their activity depends on both the structure of the aryloxazole fluorophore and the electron-donating and electron-accepting properties of the substituents attached to the C-5 of the furan ring. All probes are selectively oxidized by singlet oxygen to give a single fluorescent product in methanol and produce negligible amounts of singlet oxygen themselves by self-sensitization. The most promising dyad, (E)-2-(2-(5-methylfuran-2-yl)vinyl)naphtho[1,2-d]oxazole, FN-6, shows outstanding reactivity and sensitivity: it traps singlet oxygen with a rate constant (5,8 ± 0.1) x 107 M-1 s-1 and its fluorescence increases by a factor of 500 upon reaction. Analysis of the dyads reactivity in terms of linear free energy relationships using the modified Swain and Lupton parameter F and the Fukui condensed function for the electrophilic attack, suggests that cycloaddition of singlet oxygen to the furan ring is partially concerted and possibly involves an exciplex with a "more open" structure than could be expected for a concerted cycloaddition.
To understand in depth the mechanisms associated with cellular stimuli originated by the presence of 1 O 2 and/or to appropriately control the photooxidation process in PDT treatments, the detection and quantification of 1 O 2 is undoubtedly one the most relevant and a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 critical factors. Common 1 O 2 detection methods include electron paramagnetic resonance [16][17][18], chemiluminescence [19][20][21] and fluorescence spectroscopy [22][23][24][25][26][27][28][29]. These methods are based on the observation of a signal produced by a probe attached to a 1 O 2 chemical acceptor that responds to the oxidation stage of the acceptor. Typical chemical acceptors are electron-rich dienes, naphthalenes, anthracenes and furans.
Furan derivatives such as 2,5-diphenyl-3,4-isobenzofuran (DPBF) or 2,5-dimethylfuran, have been widely used as a 1 O 2 scavengers since the 70s [30,31]. They are advantageous chemical traps of 1 O 2 because they can be quantified by using routine analytical techniques such as spectrophotometry or gas chromatography, they react with 1 O 2 mainly via a chemical channel to form endoperoxides with a minimal or null contribution of physical quenching [32], and the overall quenching rate constant, k q , shows a very modest solvent dependence. In addition, furans react specifically with 1 O 2 and thus are ideally suited to develop selective 1 O 2 probes [33,34].
Our group has long been interested in the detection and quantification of 1 O 2 using furan derivatives [27,35,36]. We recently started a program for the design, synthesis and study of new "click-on" furan-based fluorescent probes for 1 O 2 sensing [27]. The novel molecular entities are dyads comprising an aryloxazole fluorescent moiety linked to a furan trap. In their native state, the inherently strong fluorescence of the aryloxazole moiety is quenched by the electron-rich furan. Upon reaction with 1 O 2 , the furan is oxidized and the quenching process ceases to operate, restoring the intrinsically strong fluorescence of the aryloxazole ring. Moreover, the absorption and fluorescence spectra of the native and oxidized forms of the probe are different, which allows the selective photoexcitation of either form, thereby further enhancing the fluorescence contrast to an unmatched level [22,27].
Having established the proof of concept for this new family of probes, we have now set out to explore the effect of structural modifications on the performance of aryloxazole-furan probes and we report herein the results of our studies. We aimed at rationalising the fluorescent response of the probes and their reactivity towards 1 O 2 to understand the factors affecting their performance. Thus, we have been able to design an optimum candidate by modifying the structure of the aryloxazole moiety ( Fig 1A) and by including different substituents at the C-5 position of the furan ring ( Fig 1B).
solutions was set below 0.1 at the excitation wavelength and the fluorescence emission spectra were corrected using rhodamine G as reference. Sample quantum yields were evaluated using Eq (1): where Grad X and Grad Act are the slope of integrated fluorescence vs. absorbance plots for the sample and the actinometer, respectively, and η x and η Act are the refractive index of sample and actinometer solutions, respectively. All measurements were carried out in nitrogenpurged solutions at (20.0 ± 0.5)˚C. Fluorescence decays were recorded with a time-correlated single photon counting system (Fluotime 200; PicoQuant GmbH, Berlin, Germany) equipped with a red-sensitive photomultiplier. Excitation was achieved by means of a 375 nm picosecond diode laser working at 10 MHz repetition rate. The counting frequency was maintained always below 1%. Fluorescence lifetimes were analyzed using the PicoQuant FluoFit 4.0 software. Light irradiation of NMB at 660 ± 5 nm in steady-state experiments, was performed in 1 cm spectrophotometer cuvettes using a ThorLabs M660L4 LED light source (15 mW cm -2 ). The same setup was employed to determine the chemical reaction rate constant, k r , in methanol. The distance between the light source and the cell was set for each experiment so that the initial substrate concentration would diminish about 50% in 15 min. Dyad consumption was evaluated by observing the decrease of absorbance over time and k r values were derived from the slopes of the linear pseudo first-order photoconsumption plots using 9,10-dimethylanthracene (k r = 6,3 x 10 7 M -1 s -1 [40]) as reference. Photooxidation products for HPLC-MS analysis were prepared by long-term irradiation of the samples (> 90% conversion).
Singlet oxygen measurements
The phosphorescence of 1 O 2 was detected by means of a customized PicoQuant Fluotime 200 system. A Diode-pumped Q-switched laser (Pulselas-A-660-50, AlphaLas) working at 2 kHz repetition rate was used for excitation of NMB at 660 nm. The luminescence exiting from the side of the sample was filtered by a 1100 nm cut-off filter (Edmund Optics) and a narrow bandpass filter at 1270 nm (NB-1270-010, Spectrogon) to remove any scattered laser radiation. A plane-convex lens (23 mm diam. X 75 mm) was used to focus the light emitted onto the photomultiplier window. A near-IR sensitive photomultiplier tube assembly (H1033A-45; Hamamatsu Photonics) was used as detector. Photon counting was achieved with a multichannel scaler (PicoQuant's Nanoharp 250). Time-resolved emission signals S t were analyzed using the PicoQuant FluoFit 4.0 data analysis software to extract lifetime (τ t and τ Δ ) and amplitude (S 0 ) values. Quantum yields for 1 O 2 production (F Δ ) were calculated from the amplitudes using the following Eqs (2)-(4): Perinaphthenone was used as reference for which F Δ = 1 was taken [41]. The rate constant for 1 O 2 quenching by the dyads (k q ) was determined by measuring the 1 O 2 lifetime as a function of the dyad concentration. 1 O 2 was generated by photoexcitation of a 50 μM NMB solution at 665 nm and the concentration of the dyads was varied in the range (0.1-1 mM). A plot of the reciprocal lifetime, 1/τ Δ , vs. the concentration of the dyad afforded k q as the slope of the linear fit, Eq (5), where τ 0 Δ is the 1 O 2 lifetime in the neat solvent.
Synthesis of furyl aryloxazoles
Furyl derivatives of aryloxazoles were synthesized using the method of El'chaninov et al [42]. Typically, 1.1 mmol of amino arylalcohol and 1 mmol of furoyl chloride in 4 mL of dry 1-methyl-2-pyrrolidone were stirred under nitrogen by 1 h. Addition of 6 mL of cold water gives a precipitate that was filtered and washed with 20 mL of cold acetonitrile. Recrystallization of the solid in acetonitrile affords the product with high purity.
Synthesis of furyl vinyl aryloxazoles
Furyl vinyl aryloxazoles were obtained using the method of Zajac et al. [43], employing dimethylsulfoxide as solvent and KOH as the base. Typically 1.1 mmol of 2-methylaryloxazole and 1 mmol of furfural were dissolved in 2 mL of dimethylsulfoxide. Then, were added 0.124 mL of an aqueous solution of KOH 50% and the mixture was stirred by 1 h at room temperature. Addition of 10 mL of water afforded a yellow precipitate, which was filtered, washed with cold water and cold methanol and recrystallized from acetonitrile giving the product in high purity with yields between 50-60%.
Photophysical characterization of unsubstituted furyl aryloxazoles
Compounds FN-1 to FN-5 are dyads composed by an unsubstituted furan ring linked to an aryloxazole moiety through either a single C-C bond or a vinyl bridge. The aim of studying this series of compounds was to determine the most promising aryloxazole and link structures to detect and quantify 1 As can be observed in Fig 2 and from the data in Table 1, the position of lowest-energy band is insensitive to the solvent polarity, a behavior previously observed for related compounds [27,44]. Furthermore, the large value of the experimental molar absorption coefficients and molecular-orbital analysis of the minimum energy structures obtained from DFT calculations (6311g+dp orbital base) with Gaussian 04W indicate that the lowest energy bands correspond to π-π Ã transitions. Also, comparison of wavelength maxima of FN-2 (naphthalenelike), FN-4 (anthracene-like) and FN-5 (phenanthrene-like), shows that the greater aromaticity of the dyad the smaller the transition energy [45][46][47]. On the other hand, these results indicate a strong electronic coupling between the oxazole moiety and the furan ring as visualized from the shape of the HOMO orbitals for FN-1, FN-4 and FN-5 (S1 Fig). The spectra of 2-(2-(furan-2-yl)ethyl)naphtho [1,2-d]oxazole, compound that has a saturated bridge between the aryloxazole group and the furan ring, shows a 40 nm blue-shifted absorption maximum [27].
The furyl derivatives clearly behave in a different way. Furyl vinyl aryloxazoles, FN-2, FN-4 and FN-5, show a very low fluorescence quantum yield that, in addition, is essentially independent of the solvent polarity, i.e. when the furyl group is linked to the aryloxazole moiety through a vinyl bridge, the intrinsic fluorescence of aryloxazole is quenched by the furyl substituent [27]. This is a very important piece of information because the biological cell contains microenvironments of very different polarity. On the contrary, when the furyl substituent is linked directly to the oxazole ring, e.g., in FN-1 or FN-3, the intramolecular quenching process does not operate and the fluorescence quantum yields are in the range of 0.2 to 0.7.
Reactivity of unsubstituted furyl aryloxazoles towards singlet oxygen
To evaluate the reactivity of the probes with 1 O 2 we employed steady-state experiments to observe the evolution of absorption and emission spectra, and time resolved methods to measure changes in the decay kinetics of 1 O 2 luminescence. All probes showed a strong absorbance decrease of the lowest energy band in a light-dose dependent fashion, a result that indicates that reaction occurs between 1 O 2 and the dyad. A careful examination of the spectra corresponding to FN-2, FN-4 and FN-5, also reveal clear isosbestic points at 305, 289 and 298 nm, respectively, suggesting that a mayor photoproduct is formed when a furyl vinyl aryloxazole is the substrate. HPLC experiments confirm that only one product is formed in the photooxidation reaction (S2 Fig). The modification of the absorption spectrum upon oxidation is a distinctive feature of this family of probes as compared to the most popular ones. The main benefit is that it allows to selectively exciting the fluorescence of the oxidized probe, which leads to very high fluorescent enhancement. The fluorescence changes upon reaction with 1 O 2 are shown in s -1 in methanol, respectively, were determined. Notice that the k q values in methanol, are 25-and 36-fold lower, respectively, than that of 2-methylfuran in the same solvent (k q = 9,9 x 10 7 M -1 s -1 ) [34]. This important decrease in the furan reactivity is consistent with a substantial electronic delocalization of the furan π-electrons through the vinyl bridge.
Data reported in this section indicate that the most promising dyads for 1 O 2 -detection are FN-2 and FN-5, in which the furan and aryloxazole rings are linked by a vinyl bridge. Although FN-5 is slightly less reactive than FN-2, it shows a larger fluorescence enhancement. We therefore selected FN-5 as a basis for further development of fluorogenic 1 O 2 probes and proceeded to evaluate the effect of substituents in C-5 of the furan ring.
Photophysical characterization of substituted furyl vinyl naphthoxazoles
Compounds FN-6 to FN-9 (Fig 1B) are dyads related to FN-5 that include electron-withdrawing and electron-donating groups in position C-5 of the furyl moiety. Their absorption spectra are shown in Fig 5. As for compounds belonging to the first series, they are nearly independent on the solvent polarity, the absorption coefficients are in the range 25,000-50,000 M -1 cm -1 , and molecular calculation analysis showed that the lowest energy transition is also π,π Ã . It is worth mentioning that the λ max value is red-shifted by 30 nm for FN-9.
Likewise, their emission spectra (Fig 5) are also independent of the solvent polarity. Emission quantum yields are very low, particularly in polar solvents such as methanol, consistent with intramolecular charge transfer quenching the fluorescence emission [27]. Fluorescence lifetimes, Table 3, are around of 6 ns, independent of solvent and similar to those measured for other naphthoxazole derivatives [44].
Reactivity of substituted furyl vinyl naphthoxazoles towards singlet oxygen
The ability of probes FN-6 -FN-9 to react with 1 O 2 was evaluated using steady-state and timeresolved methods. 1 O 2 was photogenerated by irradiation of the sensitizer NMB in methanol. Changes in the absorption spectra of the probes upon reaction with 1 O 2 are shown in Fig 6. All dyads showed a notable loss of absorbance upon reaction with 1 O 2 and the relative rates of photobleaching were FN-6 % FN-9 > FN-5 >FN-7 % FN-8. Clear isosbestic points were observed, indicating a clean reaction with a single major product, which was confirmed by HPLC.
The reaction of FN-6 -FN-9 with 1 O 2 produces likewise a notable increase on the fluorescence intensity, which is accompanied by a hypsochromic shift of the emission maxima ( Fig 7). The rate of fluorescence growth is largest for FN-6 and the fluorescence intensity of its oxidized form is >500-fold larger than that of the native form at the optimum excitation wavelength (330 nm).
The rate constants for overall 1 O 2 quenching by the dyads (k q ) were determined by timeresolved luminescence spectroscopy (S3 Fig). Likewise, the reactive rate constants k r were obtained by comparing the rate of consumption of the dyads to that of the reference compound dimethylanthracene. Both are collected in Table 4. Values of k q in methanol included in Table 4 follow the order FN-6 > FN-9 >> FN-7 > FN-5 > FN-8. Thus, FN-6 and to a lesser extent FN-9, are excellent 1 O 2 quenchers, with efficiency comparable to that of dimethylfuran (k q = 2.4 x 10 7 M -1 s -1 ) and methylfuran (k q = 10.1 x 10 7 M -1 s -1 ) in methanol [31]. Regarding the k r values, some observations are worth highlighting: (i) Their reactivity with singlet oxygen follows the same trend as their overall quenching efficiency, FN-6 and FN-9 being the most reactive compounds; (ii) the trend is not affected by solvent polarity, as expected for a cycloaddition [4 + 2] reaction [50]; (iii) compared to the unsubstituted homologous FN-5, the methyl-and phenyl-substituted compounds FN-6 and FN-9 are 26-and 8-fold, respectively, more reactive; (iv) the least reactive compound is the Brsubstituted FN-8 as could possibly be expected by the electron-accepting character of the substituent, which decreases the electron density on the C-5 position of furan ring; (v) although it is not the compound with the highest singlet oxygen trapping efficiency (k r / k q = 56%), FN-6 shows nevertheless the highest reactivity and therefore appears as the best candidate for being use as a singlet oxygen fluorescent probe.
Structure-reactivity relationships
Substituents effects on the reactive rate constant were analyzed using the Hammett free energy relationship. Thus, the measured rate constants were correlated with σ m and σ p Hammett Table 4. Rate constants for overall (k q ) and reactive (k r ) quenching of singlet oxygen by furyl vinyl naphthoxazoles, singlet oxygen trapping efficiency (k r /k q ), and quantum yields for singlet oxygen photosensitization. Structure-activity study of furyl aryloxazole fluorescent probes parameters [51] and with F and R, the Swain and Lupton modified parameters [52], that redefine the substituent σ-parameter in terms of field effects, F (inductive and pure field) and the resonance effects, R. Correlations of k q with σ m , σ p and R did not show clear trends. Better correlations in both methanol and ACN were obtained with the field parameter F. Fig 8A shows that there is a good correlation between the inductive donor effect of the substituent and the dyads reactivity. Additional insight on the substituent effect upon the reaction rate and the electron density on the C-5 of the furan was obtained from correlations of the reaction rate with local Fukui coefficients. Condensed Fukui functions, f k +/-, account for physicochemical properties of atoms or functional groups in a molecule, such as the nucleophilicity and electrophilicity of different sites in molecule [53][54][55]. Fig 8B shows These results suggest that the [4 + 2]-cycloaddition of 1 O 2 to the furan ring does not occur through a concerted mechanism, as for other furan derivatives [56], but possibly the attack proceeds in a partially concerted manner with a primary interaction of 1 O 2 with the center of greater electronic density, on the C-5 of the furan ring, as has been suggested by Lemp et al. in the cycloaddition of 1 O 2 to mono-substituted anthracenes [57], forming an exciplex of a "more open" structure with a large charge separation (Fig 9).
Moreover, mass spectra of main products obtained in the photooxidation of FN-5 and FN-6 (S4 Fig) shows the same fragmentation pattern, suggesting a common reaction mechanism. Both mass spectra are compatible with a classical photooxidation mechanism that involves a [4 + 2]-cycloaddition of 1 O 2 to the furan ring followed of methanolysis to give the final product (S5 Fig).
Self-sensitization of 1 O 2 by the dyads and reactivity towards other ROS
A drawback of common 1 O 2 fluorescent probes is the evolution of fluorescence due to self-sensitization of 1 O 2 [58]. Other probes suffer from poor selectivity towards 1 O 2 and are capable of reacting also with other ROS. We investigated whether the naphthoxazole dyads are affected by the same problems. All dyads sensitized the production of 1 O 2 in methanol, however for FN-6 and FN-9 quantum yields (Ф Δ , Table 4) were 3-fold smaller than for the most popular probe SOSG [58]. Reactivity towards other ROS was also tested for the series FN-5 -FN-9. We found negative results for all probes against superoxide (KO 2 ) and H 2 O 2 (S6 and S7 Figs). Structure-activity study of furyl aryloxazole fluorescent probes After 50 min. the extent of reaction is lower than 1,5% indicating a high degree of specificity for 1 O 2 .
The results described in previous sections suggest that dyads which include a polycyclic aromatic naphthoxazole [1,2-d] system with a vinyl bridge at the 2-position of the heterocycle linked to a substituted furan ring with an alkyl group at carbon-5 are the ones with the highest fluorescent response. Accordingly, this structure could be employed to engineer a "click-on" probe to detect and quantify singlet oxygen in biological interest media, a potential solution if a quick response is needed in a test system. However, various characteristics of the biological systems must be considered to planning a successful probe: i) aromatic fluorescent molecules, such as naphthoxazole-furan dyads, could form complexes with proteins [59,60], altering the expected fluorescent increases after the 1 O 2 addition to the scavenger moiety and restricting substantively probe internalization in cells. Different approaches have been proposed to solve this problem, such as probe binding to nanoparticles, which maintains its reactivity towards 1 O 2 , reducing the interaction with proteins [61][62][63]; ii) in biological media, a complex redox system maintaining cell viability is always present, which may affect the fluorescent response of the dyad if oxidant and/or reducing biomolecules react with the products yielded by the reaction between the dyad and 1 O 2 . Nevertheless, various "off-on" fluorescent dyads based on polyaromatic systems have been employed to detect 1 O 2 in a diversity of cellular environments by the use of bioimaging techniques [59,[64][65][66][67][68][69][70]. Independent of the advantages or drawbacks of each probe, all of them afford highly repetitive results showing that, even in systems under oxidative stress, primary products of reaction with 1 O 2 (typically an endoperoxide), are stable Structure-activity study of furyl aryloxazole fluorescent probes under oxidizing or reducing conditions in cellular systems, a behavior also expected for furyl vinylnaphthoxazole endoperoxides; iii) the solubilization locus of the dyad can also modify the dyad fluorescent response due to the diversity of microenvironments in cells. However, we previously show that furan derivatives are very appropriate probes to monitor singlet oxygen dynamics in systems mimicking biological interest organizations [35,36]. In these systems, furan reactivity is nearly independent of the solubilization site although different sensitivities of the dyad towards 1 O 2 are expected, since the fluorescence quantum yield slightly increases in non-polar solvents; iv) at first, lasers employed to excite the dyad in cells after the reaction with 1 O 2 could involve high light intensities and/or focusing and it is reasonable to assume that high heat release could occur under these experimental conditions. These thermal effects open a new reaction pathway whereby thermally decomposition of endoperoxide could produce 1 O 2 and/or 3 O 2 . Nevertheless, high molar absorptivity's of dyads described herein can help to avoid severe thermal loads.
Conclusions
Summing up, "click-on" dyads constructed by linking a furan ring to an aryloxazole at 2-position of the heterocycle via a vinyl bridge possesses appropriate properties to monitor and quantify singlet oxygen in solution. They show very low fluorescence quantum yields, are chemically stable, produce negligible amounts 1 O 2 by self-sensitization, and react selectively with 1 O 2 at high rate. Photooxidation yields a major product in which the structure of the fluorescent fragment is maintained but the non-radiative deactivation channel is cancelled upon oxidation, which leads to increasing the relative fluorescence up to an unprecedented 500-fold value. Reaction proceeds via a partially concerted [4 + 2] cycloaddition involving the formation of a loose or "open" structure exciplex, in which the oxygen is weakly bound to the furan ring. Dyads described herein are promising platforms to develop probes for monitoring singlet oxygen behavior in biological systems. | 5,335.4 | 2018-07-02T00:00:00.000 | [
"Chemistry"
] |
A Monocular Vision Relative Displacement Measurement Method Based on Bundle Adjustment Optimization and Quadratic Function Correction
In order to meet the requirements of high-precision target displacement measurement, this paper proposed a measurement method based on monocular vision measurement system by which the advantages of simple structure, convenient debugging, easy to install and calibrate is recognized. This method first uses the bundle adjustment algorithm with conditional constraints to optimize the solution results of the orthogonal iterative algorithm, then calculates the relative poses of the optimization results, finally establishes an error correction model to perform secondary optimization on the relative poses. The experimental results show that the method proposed in this paper can reduce more than 54% average error of displacement measurement results, and the error between the final displacement measurement results and the actual displacement values is controlled at 0.4mm level, which proves that the method has good practical value.
Introduction
In recent years, accurately measuring the pose parameters of the target is significance in many research fields [1]. With the advantages of non-contact and high accuracy, visual measurement has been widely used. By extracting the pixel coordinates and corresponding actual coordinates of the cooperative target on the measured target, the pose parameters between the target coordinate system and the camera coordinate system can be solved.
How to measure the pose parameters more accurately has been the focus of scholars. The classic direct linear transformation (DLT) algorithm proposed by Abdel-Aziz and Karara [2] regards the nine variables of the projection matrix as independent variables that establish linear constraints. However, because the orthogonal constraints of the rotation matrix are ignored, the DLT algorithm requires a large number of control points to obtain an accurate solution. Přibyl [3] proposed the DLT-Combined-Lines algorithm based on the DLT algorithm, which uses Perspective-n-Line (PnL) linear formula to solve a large number of lines, but it is not accurate enough to use in engineering. Lepetit [4] proposed a high-efficiency and high-precision linear algorithm called EPnP, which uses 4 virtual control points to represent points in object coordinates. However, the algorithm may get bad results when the control points are coplanar and the depth changing dramatically. Chen [5] proposed a measurement method using three marker points, but the three-point method does not have enough points to optimize, resulting in large error and susceptibility to interference. In order to avoid the influence of interference, Oberkampf [6], [7], [8] proposed a POSIT algorithm, which iteratively estimates the perspective projection model by using the initial value obtained from the scaled model. Placing the camera and adjusting the field of view, using Zhang's [12] calibration method to get the camera's internal parameters. As shown in figure 2, an object coordinate system is established on the cooperative target. The actual coordinates of each point can be calculated from the actual distance between the points.
Establishing the projection matrix (1) between the object coordinates and the corresponding pixel coordinates. The model (2) for minimizing collinear errors of objects in object space based on the collinear projection relationship between object space points and corresponding image points in object space. Formula (2) can be regarded as a quadratic function about the translation matrix T. When the optimal rotation matrix R can be given, the optimal solution of T with R can be obtained by using partial derivatives.
( ) ( ) After the new rotation matrix R and the translation matrix T are obtained, the new projection point It can be seen from the above formulas that the problem becomes the absolute orientation problem. The SVD singular value decomposition method can be used to obtain the new rotation matrix R. Until the error is less than the set value or the number of iteration reaches the set value, the obtained 1 n R + and 1 n T + are considered as the optimal rotation and translation matrix.
The Bundle Adjustment Algorithm with Conditional Constraints
In order to obtain more accurate pose parameters, this paper uses the bundle adjustment algorithm with constraints for the first optimization.
The external parameters of the camera and the object coordinates of the points are both regarded as observation values. And establishing a formula with the constraints such as the distance between the 4 control points and the object coordinates of all points should be in the same plane. The constraint formula and the bundle adjustment formula are combined to obtain formula (5).
The Establishment of Bundle Adjustment Error Formula
According to the imaging rule of the camera, the calculation formula (6) of the pixel coordinates involves the distortion correction formula and the collinear formula. Combining these formulas , using Taylor expansion and take the first order term.
The obtained formula is shown in formula (7). Formula (8) can be obtained from observation value + observation correction value = approximate value + approximate correction value. ISAIC Combining formulas (5) (6) (7) (8), the matrix form of each parameter in error formula is obtained: In the above formulas, i = 1, 2, , n refers to the number of object points, and j = 1, 2, , m refers to the number of photos.
The Establishment of Constraint Formula
Calculating the distance between two points by formula (13).
And all points should be in the same Z plane, so the plane constraint formula (14) is established.
ij ww In formulas (13) (14), i and j refer to the serial numbers of two points, and S is the actual distance between the two points. Combining (5) (13) and (14), the matrix form of the constraint formulas (15) is established:
Correcting Systematic Error of Displacement Measurements
Formula (16) represents the transformation relationship between the camera coordinate system, the starting object coordinate system S P , and the target position object coordinate system E P .
Both S Q and E Q belong to the same camera coordinate system during the measurement process, so the camera coordinate system can be used as a reference to establish the relationship between the target position object coordinate system and the starting position object coordinate system.
( ) Because of the limitations of a single camera, there will be a systematic error in measuring relative displacement. The error is caused by the magnification of the uncorrected distortion due to the large measurement field of view, and the insensitivity of the single camera to depth direction information.
As shown in figure 3, when the target is moving in non-depth of field, the errors in the X and Y directions are linear, while the errors in the Z direction is quadratic. In order to facilitate the establishment of the modified model, a quadratic function model is used.
As shown in figure 4, selecting some positions which the relative distances is known, as reference positions. Establishing an optimization model based on the pose parameters, the calculated relative displacements and the real values of these reference positions. Then inputting the pose parameters of other positions in the model, the optimized relative displacement can be obtained. c c The calculated value is modified by the model to make it closer to the true relative displacement.
Experiment and Analysis
In this paper, the experiment used Nikon D7200 SLR camera photos, the lens model of the camera is AF-S DX NIKKOR 18-140mm f/3.5-5.6G EDVR, and the focal length f is fixed at 35mm when taking photos. After placing and calibrating the camera, orthogonal iteration algorithm (OI), orthogonal iteration + bundle adjustment algorithm with constraints (OI+BA), orthogonal iteration + bundle adjustment algorithm with constraints + systematic error correction algorithm (OI+BA+EC) are used to solve, and analyzing the results. Three experiments were conducted, in each experiment, the moving distance along the X-axis of the model was 1000mm.
As shown in figure 5, the reference positions are taken every 200mm to establish the model. The model is placed on a high-precision captive ballistic system (CTS) to ensure the accuracy of each movement. As shown in figure 6 and table 1, compared with the original algorithm, after using the bundle adjustment optimization, the standard deviation of the X direction error is reduced from 0.349 to 0.324, while the standard deviation of the Y direction error is reduced from 0.312 to 0.287, and the standard deviation of the Z direction error is reduced from 0.852 to 0.507. After adding the error correction method, the standard deviation of the X direction error is further reduced to 0.162, the standard deviation of the Y direction error is further reduced to 0.0398, and the standard deviation of the Z direction error is further reduced to 0.0705. | 1,990.8 | 2021-02-01T00:00:00.000 | [
"Computer Science"
] |
The Global Attractor of the Allen-Cahn Equation on the Sphere
In this paper we study the attractor of a parabolic semiflow generated by a singularly perturbed PDE with a non-linear term given by a bistable potential, in an oval surface; the Allen-Cahn equation being a prototypical example. An additional constraint motivated by a variational principle for closed geodesics originally proposed by Poincaré arising from geometric considerations is introduced. The existence of a global attractor is established by modifying standard techniques in order to handle the constraint. Based on previous work on the elliptic case, it is known that when the perturbation parameter tends to zero, minimal energy solutions exhibit a sharp interface concentrated on a closed geodesic. We provide numerical simulations using Galerkin's method. Based on the analytical and numerical results we conjecture that, when the perturbation parameter tends to zero and for large times, the transition layers of the solutions of this PDE consists of closed geodesics or a union of arcs of such geodesics, thus characterizing the structure of the attractor.
INTRODUCTION
The qualitative study of dynamical systems in infinite dimensions has been of fundamental importance. In the case of dynamical systems associated with partial differential equations of evolution having variational structure, many of the ideas and methodologies of gradient-like systems can be extended to infinite dimensions. In particular, the study and characterization of attractors is of special interest.
In this paper, we prove the existence of the global attractor of the parabolic equation associated to: on an oval surface M 1 (see Figure 1) where u : M → R, 0 < ǫ ≪ 1, represents the Laplace-Beltrami operator on M and W(u) is a non-linear term, which in particular includes the Allen-Cahn non-linearity. The flow will be considered in a space of functions satisfying a geometric constraint to be explained later. Equation (1) arises in many contexts among which we may mention materials science, superconductivity, population dynamics, and pattern formation.
An important case for W(u) is given by W(u) = (1 − u 2 ) 2 , which has been widely studied both analytically and numerically for example in Hutchinson and Tonewaga [1] and Padilla and Tonewaga [2] and references therein. In a bounded domain ⊂ R n , n ≥ 2, with suitable initial and boundary conditions, in Bronsard and Kohn [3], it is shown that, when ǫ → 0, the solution u of (1) separates in two regions where u ≈ 1 and u ≈ −1, respectively, and the transition layer, moves with normal velocity equal to its principal curvatures. A similar behavior occurs on an oval surface for non-trivial solutions of (1). Using results in Hutchinson and Tonewaga [1] and Padilla and Tonewaga [2], in Garza-Hume and Padilla [4] it is established that, when ǫ → 0, non-trivial minima of the corresponding energy function (with a suitable restriction) have a transition layer located at the shortest closed geodesic.
This fact is obtained using the variational structure of the problem, because (1) is the Euler Lagrange equation of the functional: in a suitable functional space. For ǫ → 0, functions u with uniformly bounded energy E ǫ (u) < E 0 , can be proved to be close to ±1 in most of the domain, except for a transition curve. The proof follows from a classical result in differential geometry due to Birkhoff that guarantees the existence of a closed geodesic on a surface diffeomorphic to the sphere (see Poincaré [5] where the corresponding variational principle was first conjectured, later demostrated by Berger and Bombieri [6]): Proposition 1. Suppose that γ is a closed curve on M that under the Gauss map, g, divides the unit sphere in two parts of equal measure. Assume further that among all the curves satisfying the above conditions, γ has minimal length. Then γ is a closed geodesic.
This fact suggests a natural constraint for the problem under consideration. The function u belongs to the space of functions that satisfies: where g is the Gauss map.
On the other hand, solutions of (1) correspond to stationary points of the associated gradient flow: The main goal of this paper is show the existence of the attractor of the associated parabolic equation to (1) (i.e., Equation 4), and conjecture its structure in terms of functions that possess transition layers determined by closed geodesics or arcs of geodesics. In other words, given any initial condition, the corresponding parabolic semiflow determined by (4) approaches a function with transitions in geodesics. This will be done by considering the special case in which M = S 2 and W(u) = (1 − u 2 ) 2 . This will simplify both the analysis and the numerics. From now on we consider solutions of (4) satisfying the constraint (3). Under the above restrictions, it becomes: which will be incorporated into the equation later on as a Lagrange multiplier. As a first step, we will proof the existence of an attractor for (4) under the constraint (5). We will recall some standard facts in dynamical systems theory, Sobolev spaces on Riemannian manifolds as well as Gronwall's inequality, which are presented in the following section. This is done for the sake of completeness and to introduce notation and may be skipped by readers familiar with dynamical systems and analysis on manifolds.
Having shown the existence of the attractor, some numerical experiments are performed using the Galerkin method. A few words are in order regarding the limitations of our numerical approach. Even when in principle the method should be applicable for any initial condition, we only considered some that already exhibit a relatively well-defined interface. The aim of the numerical simulations is to make plausible our conjecture on the structure of the global attractor and a more detailed study of the method is not carried out. As for the analytical approach, we remark that the problem of establishing the existence of a global attractor for other surfaces or manifolds in similar situations seems to be a reasonable extension of the methods and ideas here presented. In particular for the case of surfaces with nonzero Euler characteristic as is done in Del Río et al. [14] for the elliptic case.
Semigroups of Operators
The notation and terminology used in this section is adapted or quoted from Temam [7], although arguments and results in Sell and You [8] and Robinson [9] are also used. Since these are standard results and references, no explicit references are made.
We will consider dynamical systems whose state is described by an element u(t) of a metric space H. In most cases, and in particular for dynamical systems associated with partial or ordinary differential equations, the parameter t (the time or the timelike variable) varies continuously in R or in some interval of R. Usually the space H will be a Hilbert or Banach space.
The evolution of the dynamical system is described by a family of operators S(t), t ≥ 0, that map H into itself and enjoy the usual semigroup properties: If φ is the state of the dynamical system at time s, then S(t)φ is the state of the system at time t + s, and The semigroup S(t) will be determined in our case by the solution of a PDE. The basic properties of the operators S(t) which are needed will be established in the next subsection but, for the time being, we assume that: These operators may or may not be one-to-one; the injectivity property is equivalent to the backward uniqueness property for the dynamical system. When S(t), t > 0, is one-to-one we denote by S(−t) its inverse which maps S(t)H onto H; we then obtain a family of operators S(t), t ∈ R, which have the property (6) on their domains of definition, ∀s, t ∈ R. It is clear that for t < 0, the operators S(t), are not usually defined everywhere in H.
Definition 1.
For u 0 ∈ H the orbit or trajectory starting in u 0 is the set t≥0 S(t)u 0 .
Definition 2.
When it exists, an orbit or trajectory ending at u 0 is the set t≥0 S(−t) −1 u 0 .
where closures are taken in H.
Definition 4.
When it exists, the α-limit set of
Proposition 2. φ ∈ ω(A)
if and only if there exists a sequence of elements of φ n ∈ A and a sequence t n → ∞ such that Remark 1. Analogously, φ ∈ α(A) if and only if there exists a sequence ψ n converging to ψ in H and a sequence t n → ∞, such that φ n = S(t n )ψ n ∈ A, ∀n.
Invariant Sets
We say that a set X ⊂ H is positively invariant for the semigroup When the set is both positively and negatively invariant, we call it an invariant set or a functional invariant set.
The simplest examples of invariant sets are equilibrium points, heteroclinic orbits and limit cycles. Lemma 1. Assume that for some subset A ∈ H, A = ∅, and for some t 0 > 0, the set t≥0 S(t)A is relatively compact in H. Then ω(A) is non-empty, compact, and invariant.
Absorbing Sets and Attractors
Definition 7. An attractor is a set A ∈ H that enjoys the following properties: A possesses an open neighborhood U such that, for every u 0 ∈ U, S(t)u 0 converges to A as t → ∞. This means that: The distance in (2) is understood to be the distance of a point to a set: d(x, y) denoting the distance of x to y in H.
Frontiers in Applied Mathematics and Statistics | www.frontiersin.org Definition 8. If A is an attractor, the largest open set U that satisfies (2) is called the basin of attraction of A. Alternatively, we say that A attracts the points of U.
Definition 9. It is said that
The convergence in the above definition is equivalent to the following: for every ǫ > 0, there exists t ǫ such that for t ≥ t ǫ , S(t)B is included in U ǫ , the ǫ-neighborhood of A. When no confusion can occur we simply say that A attracts B.
Definition 10. We say that A ∈ H is a global (or universal) attractor for the semigroup {S(t)} t≥0 if A is a compact attractor that attracts the bounded sets of H (and its basin of attraction is then all of H).
It is easy to see that such a set is necessarily unique. Also such a set is maximal for the inclusion relation among the bounded attractors and among the bounded functional invariant sets. For this reason it is also called the maximal attractor.
In order to establish the existence of attractors, a useful concept is the related concept of absorbing sets. Definition 11. Let B be a subset of H and U an open set containing B. We say that B is absorbing in U if the orbit of any bounded set of U enters B after a certain time (which may depend on the set): We say also that B absorbs the bounded sets of U.
The existence of global attractor A for a semigroup {S(t)} t≥0 implies that of an absorbing set. Indeed, for ǫ > 0, let V ǫ denote the ǫ-neighborhood of A (i.e., the union of open balls of radius ǫ centered on A). Then, for any bounded Conversely, it is a standard result that a semigroup that possesses an absorbing set and enjoys some other properties possesses an attractor.
In order to prove existence of an attractor when the existence of an absorbing set is known, we need further assumptions on the semigroup {S(t)} t≥0 , and we will make one of the two following: • The operators S(t) are uniformly compact for t large. By this we mean that for every bounded set B there exists t 0 which may depend on B such that is relatively compact in H.
Alternatively, if H is a Banach space, we may assume that S(t) is the perturbation of an operator satisfying (11) by a (nonnecessarily linear) operator which converges to 0 as t → ∞. More precisely: • If H is a Banach space and for every t, S(t) = S 1 (t) + S 2 (t) where the operators S 1 (·) are uniformly compact for t large and S 2 (t) is a continuous mapping from H into itself such that the following holds: For every bounded set C ⊂ H, as t → ∞. Of course, if H is a Banach space, any family of operator satisfying (11) also satisfies (12) with S 2 = 0.
Theorem 1.
Assume that H is a metric space and that the operators S(t) are given and satisfying (6), (9) and either (11) or (12). We also assume that there exists an open set U and a bounded set B of U such that B is absorbing in U.
Then the ω-limit set of B, A = ω(B), is a compact attractor which attracts the bounded sets of U. It is the maximal bounded attractor in U (for the inclusion relation).
Furthermore, if H is a Banach space, if U is convex, and the mapping t → S(t)u 0 is continuous from R + into H, for every u 0 in H; then A is connected too.
The proof of this theorem is carried out through several steps, which can be found in Temam [7].
Sobolev Spaces in Riemannian Manifolds
The notation and terminology used in this section can be found in Hebey [11] and Aubin [12].
Let (M, g) be a smooth Riemannian manifold. Given k an integer, and p ≥ 1 real, set 13. Given (E, ||·|| E ) and (F, ||·|| F ) two normed vector spaces with the property that E is a subspace of F, we say that the embedding of E in F is compact if bounded subsets of (E, || · || E ) are relatively compact in (F, ||·|| F ). This fact is written as E ⊂⊂ F. This means that bounded sequences in (E, || · || E ) possess corvergent subsequences in (F, || · || F ). Clearly, if the embedding of E in F is compact, it is also continuous, i.e., if there exists C > 0 such that for any x ∈ E, ||x|| F ≤ C||x|| E .
The following theorem is needed in order to prove the existence of the attractor of the equation in consideration.
Differential Inequalities
The following inequality is derived from Gronwall's lemma and will be used later on.
EXISTENCE AND STRUCTURE OF ATTRACTOR
The main result is the following in which the existence of a global attractor is shown for equation (4) subject to constraint (5).
Proof: The existence of a solution proposed equation is equivalent to finding the minimum of: for all u ∈ H 2 1 (M), subject to the constraint: where f (y) is the Jacobian determinant of the transformation of S 2 into M. This determinant can be considered to be positive, and this factor is the Gaussian curvature in y.
For fixed ǫ > 0, the existence of this minimum is a consequence of this functional satisfies the Palais-Smale condition (see Struwe [13]), is bounded below and the constraint defines a closed lineal subspace.
On other hand it should be noted that: This last statement ensures the existence of a global solution for t > 0. This is sufficient to define the associated semiflow to given equation. Another way to verify the above statement, is to first prove the existence and uniqueness of a solution of (4)-(5) subject to a suitable initial condition; then the backward uniqueness in order to show existence for all t ∈ R. Finally apply the theorem 4 for the characterization of global attractor.
In the usual way, we shall see the existence of an absorbent set in L 2 (S 2 ) and subsequently, the compactness of the mentioned semigroup, according to theorem 1.
The Euler-Lagrange equation associated to (2) with the constraint (3) (for each ǫ i ), contain a Lagrange multiplier λ i as follows: In Del Río et al. [14], it is shown that these multipliers are bounded. This fact will be used later.
In order to prove the existence of an absorbing set in L 2 (S 2 ), we multiply (2) by u and integrate over S 2 . Using Green's formula we obtain: where || · || L 2 denotes the norm L 2 (S 2 ). By a standard corollary (see for instance 1) H 2 1 (S 2 ) ⊂⊂ L 2 (S 2 ), therefore there exists a constant c 0 such that ||u|| L 2 ≤ c 0 ||u|| H 2 1 , and there exists a c 1 such that: An estimate of the third integral in (15) is required, for which the following inequality is used: and by Hölder's inequality, for a C > 0: and for certain A, B > 0: Thanks to (15) and the previous relationship, we conclude that there exists a c ′ 1 > 0 such that: Thus: this meaning that: According to (16) concluded from (17), there exists a c ′ 2 = 2(4πc ′ 1 ) such that: By using the classical Gronwall lemma, we obtain that: There exists an absorbing set B 0 in L 2 (S 2 ), namely, any ball of L 2 (S 2 ) centered at 0 of radius R > ρ 0 , as if B is a bounded set of L 2 (S 2 ), included in a ball B(0, R) of L 2 (S 2 ), then In order to prove the uniform compactness of operators, we proceed using by an argument proposed by B. Nicolaenko (see Temam [7]) and making use of the absorbent set in L 2 (S 2 ) whose existence was established in the previous paragraph. By Holder inequality: Analogously to (15), we conclude that: where y = ||u|| 2 L 2 , γ = 1 π , δ = 8πc ′ 1 . Lemma 2 shows that: Let ρ 2 be a real number greater than (γ /δ) 1/2 and The above relations show that for any set B of L 2 (S 2 ), bounded or not, S(t)B is included in the ball B 2 centered at 0 of radius ρ 2 , if t ≥ T 0 , thus demostrating the existence of an absorbent set in H 2 1 (S 2 ). The uniform compactness of operators S(t) follows from the fact that a bounded set B is included in a ball B(0, R) for all t ≥ t 0 , that which is bounded in H 2 1 (S 2 ) and relatively compact in L 2 (S 2 ) (corollary 1). The existence of the global attractor follows from theorem 1.
Having shown the existence of a global attractor, the question of characterizing its structure arises. This question can be answered provided there is a suitable Lyapunov functional.
Definition 14.
A Liapunov functional for {S(t)} t≥0 on a set F ⊂ H is a continuous function F : F → R such that: 1. For each u o ∈ F , the function t → F(S(t)u 0 ) is non-increasing. 2. If F(S(τ )u 1 ) = F(u 1 ) for some τ > 0, then u 1 is a fixed point of {S(t)} t≥0 , i.e., S(t)u 1 = u 1 , ∀t > 0. The following standard theorem establishes the structure of the attractor.
Theorem 4.
Let there be a given semigroup {S(t)} t≥0 which enjoys the properties (6), (7). We assume that there exists a Lyapunov functional as in the definition 14, and a global attractor A ⊂ F . Let E denote the set of fixed points of the semigroup. Then Furthermore, if E is discrete, A is the union of E and of the heteroclinic curves joining points of E and Remember that, M + (X) is the set (maybe empty) of points u * , which belongs to an orbit {u(t), t ∈ R} such that d(u(t), X) → 0 as t → ∞.
The details of this proof can be found in Temam [7]
THE EQUATION IN S 2
Once the existence of an attractor is proved, in this section we provide a numerical method for its characterization. In this implementation the Galerkin method is used.
Then, the Laplacian in these coordinates is given by: Using r = 1 in the above expression, the Laplace-Beltrami operator in S 2 is obtained: Then (4) becomes: (18) By implementing Galerkin's method, we can approximate the attractor. This is done by projecting Equation (18), with a suitable initial condition on a finite dimensional subspace, thus reducing it to a system of ordinary differential equations. The details are provided in the next section.
GALERKIN METHOD
The idea is to obtain a finite dimensional reduction of (18). One way to do this is using Galerkin method, which will be described below (for more details see Kythe et al. [15] and Evans [10]).
We consider the problem: Assume that the funtions w k = w k (θ , φ), (k = 1, . . .) are smooth, {w k } ∞ k=1 is an orthogonal basis of H 2 1 (S 2 ) and an orthonormal basis of L 2 (S 2 ). For instance, we could take {w k } ∞ k=1 to be the complete set of eigenfunctions of in S 2 .
Fix now a positive integer m. We will look for an approximation u m of the form where we will select the coefficients d k m (t), (0 ≤ t ≤ T, k = 1, . . . , m) so that: and Here (·, ·) denotes the inner product in L 2 (S 2 ), ′ = d dt , B[u m , w k ; t] is the bilinear form: and Thus, we look for a function u m of the form (21) that satisfies the projection (23) of problem (19)-(20) onto the finite dimensional subspace spanned by {w k } m k=1 . By the standard theorem on existence and uniqueness of systems of ordinary differential equations, we have the following result: Functions w k , will be selected via the method of separation of variables, applied to the equation u = 0 on S 2 , i.e., we assume that u = (θ ) (φ), where we have: The corresponding solutions for are of the form sine and cosine, while those corresponding to are solutions to the Legendre equation, in which the substitution x = sin φ has been made. Thus, we use the associated Legendre polynomial denoted by P(k, l, x), which is defined by: As mentioned in the previous section the legendre equation is involved, we can also choose the Legendre polinomial as follows. If the following functions are now chosen, u m = m k=1 a k (t) sin(kθ )P(k, sin(φ)) + b k (t) cos(kθ )P(k sin(φ)) , (31) where P(k, sin(φ)) is the Legendre polynomial of k degree, with m = 2 and ǫ = 0.01 the corresponding projection is, 1110.33a 1 (t) 3 + −1973.92 + 1572.97a 2 (t) 2 + 1110.33b 1 (t) 2 +1592.97b 2 (t) 2 a 1 (t) + 4.9348 we obtain the following expressions for u 2 for the values t = 0, t = 0.0055, and t = 0.02. Figure 5 shows the graph and level curves of u 2 for the values mentioned above. u 2 (0) = −0.877583 sin(θ )P(1, sin(φ)) − 0.479426 cos(2θ )P(2, sin(φ)), (34) u 2 (0.0055) = −1.2858 sin(θ )P(1, sin(φ)) − 0.1449 cos(2θ )P(2, sin(φ)), u 2 (0.02) = −1.3333 sin(θ )P(1, sin(φ)). (36)
CONCLUSIONS
All the numerical simulations show that the graph of the solution on S 2 approaches values close to 1 and −1 when t increases, as can be seen in Figures 3A,C,E-5A,C,E found in grayscale color, while in the Figures 3B,D, 4B,D, the transition layer (show in red color) takes place along the level set θ = π which is a closed geodesic (great circle). It can also be noted that in Figure 5B the transition layer at the value t = 0 is not a straight line, but as t increases, this curve becomes a straight line, θ = π, as mentioned above. This suggests that, for ǫ sufficiently small, the attractor will consist of functions concentrating in −1 or +1 with transitions along great circles.
DATA AVAILABILITY STATEMENT
All datasets generated for this study are included in the article/supplementary material. | 5,780.6 | 2020-06-17T00:00:00.000 | [
"Mathematics",
"Physics"
] |
Hydrogen production by photocatalysis using new composites based on SiO 2 coated by TiO 2
In this study new TiO 2 photocatalysts core@shell type were synthesized using SiO 2 as structural support. The coating was con fi rmed by scanning electron microscopy and infrared spectroscopy. Adsorption isotherms revealed that the surface area of such composites is about 26% higher than pure oxide (W50). X-ray diffractograms combined with Raman spectroscopy revealed that the synthesized TiO 2 presents a structure based on the coexistence of anatase and brookite. The composite W50S50 presented the best photocatalytic performance of H 2 production, with 13.5 mmol in 5 h, corresponding to a speci fi c rate of 32.5 mmol h − 1 g − 1 . In the reuse assays, this composite presented a good stability in the production of H 2 . However, its performance presented a reduction of 23% over the reuse cycles. Considering the H 2 production in a solar simulator, W50S50 produced about 25.0 m mols, which is equivalent to 48.0 m mols h − 1 g − 1 , suggesting the good performance of this material for photocatalytic hydrogen production.
INTRODUCTION
The rapid increase in global demand for energy has compromised the integrity of the environment with unprecedented speed. This is largely due to the current composition of the global energy matrix, predominantly composed of non-renewable sources such as oil, coal and natural gas (International Energy Agency, 2018). In view of this, new challenges in relation to energy generation and consumption have imposed to the society, influencing the research on energy generation, whose bias has changed towards the development of new renewable and non-polluting energy matrices. In this scenario, hydrogen (H 2 ) stands out as an alternative to the issues related to the energy matrix, since this gas can contribute to the production of a clean, safe and sustainable energy. However, the economically viable H 2 production still takes place through the exploitation of non-renewable sources. Currently, about 48% IEA of the global demand for this gas is met by steam reform of natural gas, 28% by oil reform, 20% by coal gasification, and 4% by other processes, such as electrolysis, biological and photocatalysis (Levin & Chahine, 2010). In the future, the H 2 surface, aiming to potentiate the photocatalytic production of H 2 and its reuse in this noble application.
Preparation of the photocatalysts
The standard photocatalyst, called W50, was obtained by the sol-gel method following a description done in a previous study (Machado & Machado, 2020). This process consists in the solubilization of titanium tetraisopropoxide in isopropanol at 3 C under ultrasonic stirring for 20 min, followed by its hydrolysis by the addition, by dripping, of a water/acetone 50% v/v mixture, and precipitation under ultrasonic stirring. The resulting amorphous solid was washed with distilled water, centrifuged, being later sintered in conventional furnace at 400 C for 5 h.
The composite SiO 2 @TiO 2 was synthesized by the sol-gel method, coating SiO 2 nanoparticles with TiO 2 . Prior to this synthesis, SiO 2 was prepared using the Stober method (Stöber, Fink & Bohn, 1968). In this method a mixture of 15 mL of deionized water, 4 mL of ammonium hydroxide, 100 mL of ethanol and 3 mL of tetraethyl orthosilicate are reacted at room temperature, under constant magnetic stirring for 1 h. Due the alkalinity of the resulting solution, it was subsequently neutralized with a solution 5.0 mol L −1 of HCl. The resulting solid, SiO 2 , was washed using deionized water, centrifuged and dried.
The coating of SiO 2 by TiO 2 involved the previous dispersion of silica, in the estimated amount by stoichiometric calculation, in 150 mL of 2-propanol under magnetic stirring for 1 h, followed by the rapid addition of 10 mL of titanium isopropoxide to the suspension. This mixture was maintained under vigorous magnetic stirring for 19 h. Subsequently, in the hydrolysis of the titanium precursor, a water/acetone (50% v/v) mixture was added drop by drop to the mixture, which was kept under magnetic stirring for 1 h. Finally, the resulting colloidal suspension was centrifuged, being the precipitate separated and submitted to the same heat treatment provided to the photocatalyst used as reference (Machado & Machado, 2020).
Characterization of photocatalysts
The composites were characterized by different techniques: By infrared spectroscopy (FTIR), using a Perkin Elmer MIR Frontier Single spectrometer. The analysis of the samples was performed in the solid state, in the region between 4,000 and 220 cm −1 , and resolution of 4 cm −1 , using an Attenuated Total Reflectance (ATR) accessory.
The scanning electron microscopy (SEM) images and the EDS spectra were obtained using a Tescan Vega 3 electronic microscope equipped with a secondary electron detector, with an acceleration voltage of 5.0 kV. From the images obtained by SEM and with the help of the ImageJ software, it was possible to calculate the particle size by randomly selecting approximately 100 particles per image. From there, the histograms were built for the synthesized oxides, which illustrate the average particle size distribution.
The surface area, porosity and pore volume measurements were performed from the analysis of adsorption and desorption isotherms of N 2 , using Quantachrome equipment, model NOVA touch LX1. In these assays, the samples were pretreated under flow of gaseous N 2 for 12 h, at 120 C, in order to remove adsorbed gases and water. The measures were done at 77 K using liquid N 2 to maintain the temperature during the analyses. The surface areas were estimated using the method proposed by Brunauer, Emmett & Teller (1938) (BET) to analyze the adsorption data, while the method proposed by Barrett, Joyner & Halenda (1951) (BJH) was used to calculate the pore volume.
The X-ray diffractograms were obtained using a Shimadzu XRD-6000 diffractometer (Shimadzu, Kyoto, Japan), equipped with a CuKa (λ = 1.54148 nm) monochromatic font, in the 10 ≤ 2θ ≤ 80 angular range. The counting step was 0.02 and scanning speed of 0.5 /min. Finally, the spectra were refined by Rietveld's method using the software FullProf (Roisnel & Rodriguez-Carvajal, 2001). As a criterion of reliability and quality of refinement, the obtained S factor was less than 1.37 for all photocatalysts.
The Raman spectra were obtained using a Horiba LabRAM HR Evolution spectrometer (Horiba, Kyoto, Japan) with 600 lines/mm grid, equipped with an excitation laser at 633 nm, with power of 5 mW. The spectra obtained were the result of the accumulation of eight scans in the range between 100 and 1,000 cm −1 .
The optical absorption spectra in the diffuse reflectance mode were obtained using a Shimadzu UV-1650 spectrophotometer coupled to an integrating sphere, using barium sulfate as standard. These spectra were obtained at room temperature in the spectral range between 200 and 800 nm, being converted in terms of Kubelka-Munk's function (F(R)), thus being possible to directly estimate the band gap (Eg) of the studied materials (Liu & Li, 2012).
Photocatalytic production of H 2
This was evaluated by three different approaches. In the first, the most active composite was identified among the synthesized materials, which was done on bench scale experiments, monitoring the production of H 2 achieved in the same time interval by each photocatalyst. In the second, also on bench scale, the most efficient composite was submitted to reuse assays, in a process involving four consecutive photocatalytic cycles. Finally, in a solar simulator the production mediated by the most efficient composite and pure oxide (W50), was evaluated.
The photocatalytic system used in bench scale (Machado & Machado, 2020) is based in a reactor built in borosilicate glass with total volume of 1.5 L, possessing a cooling jacket also made in borosilicate glass. It is connected to a thermostatic bath, which keeps the reaction medium at 20 C. The reactor was positioned on a magnetic agitator, used to promote homogenization of the aqueous suspension containing the catalyst and reactive species. A 400 W high pressure (HPL-N) mercury lamp without its protective bulb was used as radiation font. The photonic flow of this lamp was estimated to be 3.3 × 10 −6 Einstein/s (Machado et al., 2008), and irradiance equal to 100 W/m 2 in the UVA. The lamp was positioned laterally at 15 cm from the reactor.
Before the assays of H 2 production the photocatalysts were loaded by photoreduction with 0.1% m/m of Pt, obtained from a solution of hexachloroplatinic acid. The photocatalyst loaded with Pt was suspended in 750 mL of a water/methanol mixture containing 20% v/v of methanol, used as a sacrificial reagent. The pH of the reaction medium was adjusted to 6.2, isoelectric point of TiO 2 , pH at which its photocatalytic activity is favored according to studies by Hoffmann et al. (1995). For this, 0.1 mol L −1 solutions of HCl and NaOH were used for adjustment. Before each experiment, the dissolved gases, especially oxygen, inside the reactor were purged with N 2 for 5 min. Finally, with the lamp on, the photocatalytic assays were started. During the reaction, aliquots of the gases produced were collected every hour, in a total period of 5 h. The gases were analyzed by gas phase chromatography using a PerkinElmer Clarus 580 chromatograph (PerkinElmer, Watham, MA, USA), equipped with two packed columns (porapak N 2 mm and molecular sieve) and a thermal conductivity detector (TCD). All experiments were carried out at least in triplicate.
In the assays of H 2 production using a solar simulator a smaller volume reactor was used. All experimental parameters such as reaction time, initial pH, photocatalyst concentration, sacrifice reagent and cocatalyst were maintained proportionally equal to those employed on bench scale, for comparative purposes. The solar simulator, described by Nunes, Patrocinio & Bahnemann (2019), is constituted by a reactor, also made of borosilicate glass, with an internal volume of 80 mL, a 300 W xenon lamp, used as radiation font, and an AM1.5 filter, which simulates solar conditions after radiation passes through 1.5 times the atmospheric mass. This is equivalent to the direct incidence of solar radiation on earth's surface, with a deviation of 48.2 from the angle of zenith (Honsber & Bowden, 2019). The reactor cooling system was connected to a thermostatic bath to keep the reaction medium at 20 C. During the reactions, the content inside the reactor was maintained under stirring. The reactor was positioned at 15 cm from the radiation source, being exposed to an irradiance of 20 W/m 2 in the UVA.
For comparative purposes, since different photocatalytic systems were used, in addition to the amount, in mols, of produced H 2 , the results were expressed in terms of specific rate of H 2 production (SRHP) (Machado, Alves & Machado, 2019;Lin & Shih, 2016), where n is the number of mols of H 2 , obtained by integration in the interval between 4 and 5 h; t is the time of reaction; m is the mass of catalyst (g).
RESULTS AND DISCUSSION
Characterizations Figure 1 presents the FTIR spectra of pure TiO 2 , SiO 2 , and of the TiO 2 /SiO 2 composites (W50S25, W50S50 and W50S75). It is possible to distinguish three typical vibrations related to pure SiO 2 : bands at 438 and 803 cm −1 and a band centered at 1,050 cm −1 related, respectively, to bending and symmetrical and asymmetric stretching of Si-O-Si, and a secondary vibration at 960 cm −1 related to silanol groups (Si-OH) (Panwar, Jassal & Agrawal, 2016;Kermadi et al., 2015). For pure TiO 2 , three characteristic bands are observed: a wide and intense at 403 cm −1 and two more subtle, at 530 and 730 cm −1 , both related to Ti-O-Ti stretching (Mohamed, Osman & Khairou, 2015). The composites present the two most intense bands of the respective oxides, with small displacements: a band centered at 1,102 cm −1 , associated to silica, and a band at 403 cm −1 , related to TiO 2 .
The low signal intensity referring to the silanol groups in the composites proves the silica coating by TiO 2 . These groups, present on the silica surface, assists in the stability of metal charges through the Si-O-M bonds, favoring the dispersion of TiO 2 over the support (Almeida et al., 2004;Chen et al., 2018). It is also observed that composites with higher concentrations of SiO 2 have a more intense band centered at 960 cm −1 due to silica not covered by TiO 2 .
The Si-O-Si vibration, in general at 1,050 cm −1 , is slightly shifted to higher frequencies (approximately 1,102 cm −1 ) due to calcination of these materials at high temperature, suggesting the strengthening of the Ti-O bond (Kermadi et al., 2015). In general, in all In Figs. S1-S5, the images obtained by SEM are shown. They are accompanied by their EDS spectra and histograms, that illustrate the average particle size distribution of photocatalysts (Table 1). From the analysis of the figures, it is observed that the particles of pure TiO 2 have a dense aspect, with irregular spherical shape and average particle sizes ranging from 0.2 to 1.0 mm. Pure silica presents particles of regular spherical shape, with uniform average size. However, for them there is a tendency to aggregation, giving rise to bulky clusters of SiO 2 . For the studied composites, when compared to the W50, the reduction of the average particle size between them is evident, and this occurs as consequence of the coating of SiO 2 by TiO 2 (Li et al., 2013). For composite W50S75 it is verified through the analysis of the histogram, the existence of excess of SiO 2 , with the presence of particles with average diameter of 0.2 mm, in addition to a considerable increase in the aggregation state.
The isotherms, Fig. S6, suggest that the photocatalysts under study are type IV, characterized by being mesoporous, with average pore diameter between 2 and 50 nm, corroborating with the average pore diameter values, as displayed in Table 1. The profile of hysteresis are similar to type H 2 , which correspond to complex mesoporous structures, in which the distribution of pore size and shape is not well defined. Silica, in turn, presented type I isotherm and hysteresis characteristics of microporous materials, composed by agglomerates of spheroidal particles with close size distribution (IUPAC, 1985). Due to this, it is observed that the silica hysteresis loop does not close a typical behavior of materials with very narrow pores or bottle-shaped pores. This evidences the low average pore size, that prevents the diffusion of adsorbed N 2 (Tang et al., 2017). The observed porosity also corroborates with the formation of the composite since the immobilization of TiO 2 on the silica surface leads to the formation of new pores, resulting in increased porosity and consequent increase in the surface area (Salgado & Valentini, 2015). The composite W50S50 presented the highest porosity among the synthesized composites, which ensures greater adsorption of reagents on its surface, consequently increasing its photocatalytic action.
By analyzing the diffractograms shown in Fig. 2, and confronting with information reported in literature (Machado & Machado, 2020;Neto et al., 2017) and with the JCPDS Table 1 Morphological parameters related to SiO 2 , TiO 2 and composites.
Photocatalyst
Average particle size (µm) Surface area (m 2 /g) Porosity (%) Mean pore diameter (nm) On the other hand, it is observed that the intensity of the peaks decreases as the concentration of silica increases, indicating a decrease in the crystallinity of the composites, consequence of the non-crystallinity of SiO 2 (Machado, Alves & Machado, 2019). From the diffractograms, it was possible to obtain, through Rietveld refinement, the proportion of crystalline phases, average size and the average deformation of the crystallite for synthesized species, Table 2. The diffractograms, accompanied by their respective calculated diffraction profiles, experimentally obtained profile, residual curves and Bragg diffraction adjusted by the Rietveld method, can be viewed in Fig. S7. The reliability factors of refinement are shown in Table 1.
The presence of brookite phase in the composites was verified in the oxide designated as W50, synthesized in a previous study (Machado & Machado, 2020). This involved the use of water/acetone mixtures to control the hydrolysis of titanium tetraisopropoxide. The presence of acetone during the synthesis affects the organization of the critical nuclei formed from the oligomeric network generated from titanates, which tends to favor the formation of the brookite phase (Machado & Machado, 2020). On the other hand, the addition of silica during the synthesis of the materials evaluated in the present study apparently did not influence the formation of any crystalline phase. In the present study, the composite W50S50 presented the highest percentage of brookite among the synthesized materials-an increase of 25% compared to pure oxide. As shown in Table 2, the other composites showed similar proportions between the crystalline phases. The average crystalline size of the composites for both crystalline phases decreased with increasing silica concentration in the structure. A more expressive reduction was observed for composite W50S75, which presented contraction of the anatase phase greater than five times in comparison with pure oxide. This suggests that the presence of silica should inhibit the growth and surface diffusion processes of TiO 2 nanoparticles due the curvature of the silica surface and the formation of interfacial bonds between oxides (Machado, Alves & Machado, 2019;Li et al., 2013). It was also found that the behavior of the mean maximum deformation was inversely proportional to the average crystallite size, since the formation of interfacial bonds tends to compromise the integrity of the anatase and brookite crystals. According to Staykov (Staykov et al., 2017), the strong Si-O-Ti bonds at the composite interface tensions the crystalline network of TiO 2 and can change the coordination sphere of Ti 4+ from six to five coordinated O 2− . Thus, the increase in silica concentration in the TiO 2 structure leads to an increase in tension in the crystalline network, which tends to increase the average deformation of the material (Staykov et al., 2017).
As well as the diffractograms obtained by X-ray diffraction, the Raman spectra shown in Fig. 3 also evidence the biphasic composition of these photocatalysts. The active modes corresponding to the anatase phase are located at 144 cm −1 (E g ), 197 cm −1 (E g ), 399 cm −1 (B 1g ), 513 cm −1 (A 1g ), 519 cm −1 (B 1g ) and 639 cm −1 (E g ) (Staykov et al., 2017;Sekiya et al., 2001). For the analyzed samples, five of these main bands are observed in the following regions: 144 cm −1 (E g ), 198 cm −1 (E g ), 399 cm −1 (B 1g ), 519 cm −1 (B 1g ) and 640 cm −1 (E g ). The mode A 1g at 513 cm −1 was probably not visualized due to its low intensity in combination with the overlay of the mode B 1g , more intense and close to 519 cm −1 (Iliev, Hadjiev & Litvinchuck, 2013;Fang et al., 2015). In the insert in Fig. 3, between 200 and 500 cm −1 four subtle bands attributed to the brookite phase are observed in 245 cm −1 (A 1g ), 321 cm −1 (B 1g ), 365 cm −1 (B 2g ) and 452 cm −1 (B 3g ). The band of higher intensity for the phase brookite is close to 153 cm −1 (A 1g ). It may be overlapthed with the anatase band, much more intense, at 144 cm −1 (E g ), which should influence the wider width of this Raman mode (Yin et al., 2007;Hellawell et al., 2015). In Fig. S8, the Raman spectrum of pure silica is shown, in the spectrum well-defined Raman bands between 200 and 600 cm −1 are not observed, but it has a single wide band between 1,000 and 2,250 cm −1 , characteristic of tetrahedrons SiO 4 jitters typical of amorphous SiO 2 as shown by X-ray diffraction (Sahoo, Arora & Sridharan, 2009).
The estimated values for Eg from the diffuse reflectance spectra, Fig. 4, are 3.25 eV for W50, 3.32 eV for W50S25 and W50S50, and 3.35 eV for W50S75. These results agree with the reported in the literature, which suggests an Eg of approximately 3.2 eV for pure TiO 2 (Neto et al., 2017). As observed above, the estimated value for E g for the synthetized composites is slightly higher, probably due the formation of interfacial bonds (Si-O-Ti), which tends to change the electronic frontier states. In addition, it is known that the electronic properties of particles tend to change significantly with the reduction of their size due the occurrence of quantum confinement, which tends to increase the Eg value (Kumar & Devi, 2011). As the results obtained by TEM and XRD suggest, composites that have higher silica content in their composition have smaller particulate and crystalline sizes and consequently higher Eg values. It is worth noting that amorphous silica possesses Eg higher than 8.0 eV (Machado, Alves & Machado, 2019;Nekrashevich & Gritsenko, 2014).
Photocatalytic production of H 2 in a bench-scale
Being known the morphological and optical properties of the materials presented in this work, the photocatalytic activity regarding the photocatalytic production of hydrogen was evaluated through bench-scale tests under UV-vis irradiation. After defining the most efficient photocatalyst, its potential for reuse was evaluated. Following, the performance of these same composites in the production of H 2 , compared to that of pure oxide, was evaluated using a solar simulator.
It is observed, from Fig. 5, that the synthesized composites obtained better performance in the production of H 2 than the pure oxide (W50), with the exception of W50S75, responsible for the lowest productivity of the set, about 20% less than that achieved using the W50. As observed in SEM images along with histograms and EDS spectra, the poor performance of the W50S75 should be attributed to excess of free silica, photocatalytically inert, in its composition. The most efficient composite, W50S50, produced approximately 13.5 mmols of H 2 in 5 h of reaction, performance 40% higher than that obtained using the W50, which produced about 9.6 mmols in the same time interval. Using the W50S25, a production of 11.0 mmols was achieved, a value approximately 14% higher than that obtained using pure oxide. It is noteworthy that H 2 production tests were also performed using pure silica associated with platinum and platinum powder. However, as expected, no H 2 production was obtained in both situations, in 5 h of reaction.
The more efficient production of H 2 by W50S50 was favored by the higher anatase/brookite heterophasic crystalline composition, possibly due the more negative cathode potential of the conduction band of the brookite phase. This tends to favor the reduction of protons during the production of H 2 (Patrocinio et al., 2015;Machado & Machado, 2020;Tay et al., 2013). Morphological aspects, such as the high surface area, high porosity and lower particle size, should also contribute to potentiate the photocatalytic activity of this oxide. In a previous study published by us (Machado, Alves & Machado, 2019) 5.5 mmols of H 2 were produced in 5 h of reaction, which is equivalent to a SRHP of 13.6 mmol g −1 h −1 , using a TiO 2 /SiO 2 composite based on TiO 2 100% anatase, and approximate composition of 80% of TiO 2 and 20% of SiO 2 . The increase in photocatalytic performance in terms of H 2 production using the biphasic composite W50S50 is indisputable since, using these same experimental conditions, it was achieved, in the present study, a SRHP approximately two and a half times larger. Regarding a comparison of the photocatalytic action of W50S50 and the other catalysts evaluated in the present study with other catalysts reported in the literature, developed for the same purpose, it is worth mentioning the work of Lin and coworkers (Lin, Yang & Wang, 2011) who evaluated the efficiency of Nb 2 O 5 combined with different metals as cocatalyst. They used a halogen lamp of 400 W as radiation font and aqueous solutions containing 20% of methanol. Under the best conditions, using platinum as cocatalyst, a SRHP of 4.6 mmol h −1 g −1 was reached. In the present study, using W50S50 as catalyst, a SRHP seven times higher was achieved. In a study involving the photodeposition of CuO on the surface of ZnO, Liu and coworkers obtained, under the best conditions, a SRHP of 1.7 mmol h −1 g −1 (Liu et al., 2011), using the catalyst suspended in aqueous solutions containing 10% v/v of methanol and irradiated by a 400 W mercury vapor lamp. Also, using the composite W50S50 as catalyst, we obtained a much higher SRHP.
Potential for reuse of W50S50 for H 2 production in a bench-scale
The evaluation of the potential of reuse of the photocatalyst consisted of measuring the reproducibility of catalytic action of the W50S50 by repetitive tests, called cycles, using the same initial conditions applied to the system, only with the pH of the medium being adjusted at the beginning of each additional cycle. The reuse assays were performed in sequence of four photocatalytic cycles, each involving 5 h of reaction. The first cycle was equivalent to the H 2 production test carried out in a single cycle of 5 h. Figure 6 shows the very good stability in H 2 production in each cycle, which maintains, individually per cycle, a regular upward pattern, within 20 h of the tests. However, there is a subtle decrease in production in each cycle when compared to the previous cycle. Table 3, that contains the compilation of Fig. 6 data, shows the number of mols of H 2 produced in each cycle of reuse together with the respective SRHP. There was a reduction of approximately 18% in the production of H 2 between the first and last cycle. The SRHP of 32.0 mmol h −1 g −1 reached in the first cycle was reduced to 31.0 mmol h −1 g −1 in the second cycle, an almost equivalent value, whereas in the last cycle this value was reduced to 26.0 mmol h −1 g −1 . This can be attributed to the exhaustion of the sacrificial reagent stock. Methanol, when oxidized, tends to become formaldehyde and formic acid (Mcmurry, 2011). The pH at the end of each cycle was monitored in order to qualitatively verify the formation of formic acid during the reaction. In fact, it was observed that the pH was reduced during the reuse cycles. From the third cycle, a significant reduction in the pH as well as of SRHP is evident. Figure 7 shows the amount of H produced by each photocatalyst using the solar simulator, accompanied by their respective standard deviations. It is observed that the compound W50S50 maintains a high performance in comparison with pure oxide. Similar to that observed in bench-scale production, the production using this composite is also approximately 40% higher. Despite the electronic absorption by this material is subtly shifted to the region of higher energies (Fig. 4), its production in 5 h of reaction was 25 mmols against 18 mmols using the pure oxide, which is equivalent to, respectively, SRHPs of 66.6 and 48.0 mmol g −1 h −1 . Thus, it is clear that the synergism between SiO 2 and TiO 2 tends to favor the photocatalytic activity in the production of H 2 . Even with the subtle hypsochromic displacement observed, in the mediation of the photocatalytic process by the solar simulator using the composite, the result was superior than that achieved using pure oxide.
Due the amount of H 2 produced using the solar simulator and the detection limit of the gas chromatograph, in this experiment it was not possible to monitor the temporal evolution of H 2 . In this case, only aliquots collected with 5 h of reaction were analyzed, and each experiment was replicated at least three times.
CONCLUSIONS
Infrared and dispersive energy spectra in addition to scanning electron microscopy images confirmed the coating of SiO 2 nanoparticles by TiO 2 . The images obtained by scanning electronic microscopy also revealed that the spherical shape of the composite nanoparticles present high regularity most likely due the immobilization of TiO 2 on Stober' sílica surface. It is also worth mentioning that the average size of the composite nanoparticles was uniform with a slight reduction compared to the size of the pure oxides that constitute them. The N 2 adsorption and desorption isotherms demonstrated that the synthesized composites are mesoporous materials with mean pore sizes between 3 and 4 nm with approximately 20% of porosity, but without defined distribution and form. The surface area of these composites, calculated by employing the BET method, is approximately 26% higher in relation to pure oxide (W50).
The difratograms, together with the Raman spectra, revealed crystalline materials with the coexistence of anatase, as the main phase, and brookite. In addition, the diffractograms refined by the Rietveld method demonstrated that the composites maintained a proportion of about 75% and 25%, respectively of anatase and brookite. The average size of the crystallites has changed due the synthesis. In both crystalline phases there was a reduction in the average size with the increase of silica concentration in the structure. This suggests that the presence of silica inhibits the growth and surface diffusion processes of TiO 2 nanoparticles, probably due the curvature of the silica surface and formation of interfacial bonds between oxides.
The band gap energies estimated for the composites were slightly higher than that shown by standard oxide (3.2 eV). This should possibly be related to the mixture of electronic states of both materials (SiO 2 @TiO 2 ), since Stober's amorphous silica presents an Eg higher than 8.0 eV.
In the photocatalytic assays of H 2 production on bench scale, the composites, in general, showed excellent photocatalytic performance, probably due the lower mobility of TiO 2 in view of its fixation to silica surface. The composite W50S50 proved to be the most efficient, since with 5 h of reaction it was achieved the production of approximately 13.5 mmol of H 2 , a value 40% higher than that achieved using pure TiO 2 . The good performance of this composite should be related to its morphological parameters, such as its high surface area, crystallite size, particle size smaller than other composites, and mainly to its higher heterophasic crystalline composition, since the cathode potential of brookite phase conduction band is more negative, favoring the reduction of protons in the production of H 2 . Silica, which does not present photocatalytic activity, did not produce H 2 .
In the reuse assays of W50S50, it showed excellent stability during the production of H 2 . However, there was a decrease of 23% between the first and the last cycle, attributed to the exhaustion of the stock of sacrificial reagent.
In the photocatalytic production of H 2 using a solar simulator, the performance of W50S50 remained 40% superior to that obtained using the pure oxide. In 5 h of reaction, this composite produced 25 mmols against 18 mmols using the pure oxide, which equates to a SRHP of, respectively, 66.6 and 48.0 mmol g −1 h −1 . | 7,292.6 | 2022-11-02T00:00:00.000 | [
"Materials Science",
"Chemistry",
"Environmental Science"
] |
Tax Rate of Management Control: The Mexican Income Tax Rates System for Resident and Non-Residents
: The aim of this study is to show the tax rate of management control of the legislation according to the tax residence of the people who obtain income from wages. The questions considered here are: Is the income tax rate applied to national resident workers and to residents abroad proportionally? Under the same circumstances, in both cases do they pay similar amounts? The empirical analysis was based on the evaluation of the income tax and tax rate of management control in Mexico based on the Suits progressivity index. It was found that, under similar conditions, the amount of the tax to be paid by a resident abroad is less than that paid by a national resident.
Introduction
A recurring topic in the tax literature is the principles that rule the establishment of a tax. The main objective of the changes that take place in fiscal law is to increase government revenue. Occasionally, this is carried out whilst disregarding mandatory precepts. According to doctrine, the Congress has the faculty to impose the necessary taxes to cover the budget. This is the case of Mexico, but it may be different in other jurisdictions. Due to its taxation nature, this process originated in the lower chamber whose members represent the majority of people. In the last ten years, tax income has grown. In Mexico, from 2010 to 2020, collection increased by 206.3%, while tax collection did so by 278.1%. The increase in income tax (Impuesto Sobre la Renta, ISR) rose by 295.7%, becoming the tax with the highest levy when compared to others [1]. This increase is largely due to tax withholding of 34,918,517 employed taxpayers who represent 76% of the federal registry of taxpayers. This registry includes 11,826 taxpayers who do not permanently live in Mexico: natural persons who receive salaries abroad but pay taxes because the origin of their wealth is located in Mexico. These taxpayers are the subjects of our research.
According to the Organization for Economic Cooperation and Development (OECD), in Mexico, the "government income mostly depended on oil" [2] (p. 67). However, this criterion changed according to data from the tax management control of the Ministry of Finance and Public Credit. In 2020, the budget revenue estimated to collect 3 billion 505.8 million Mexican pesos in income tax. Tax management control reported that it collected 3 billion 338.9 million Mexican pesos, which includes an income tax of 1 billion 762.9 million Mexican pesos, including big companies (40.07%) and natural persons (2.53%), withholding wages (56.96%) and including foreign income [3] (p. 6). Tax management control collected 1 billion 4.1 million Mexican pesos by withholding wages (Impuesto Sobre la Renta, ISR) [3] (p. 10) from taxpayers who provide a subordinate personal service and natural persons in a labor relationship (employer-employee). The latter were the taxpayer group that contributed the most to government revenue in that year and made sustainability possible in public finances.
As another author has mentioned [4], tax management control may differ in progressivity and the effects between employees and business owners, but the key point is to understand the following questions: Are taxpayers of this tax regime the ones who should contribute the most to public expenditure? Additionally, is the contribution to public expenditure provided by taxpayers in this tax regime proportional to their economic capacity without harming their quality of life?
The establishment of a tax is regulated by a tax system that, naturally, involves compliance with constitutional principles, among which is the principle of proportionality (progressivity) and equity, described in Article 31, Section 4, of the Federal Political Constitution [5]. Previous studies provide evidence on the implementation of such constitutional principles [6], where the obligation to contribute to expenditure is included, according to the principle of proportionality, making a difference between tax rates from fiscal residence, whether in Mexico or abroad. Bosses or employers are liable to tax payments; therefore, their obligation is to withhold their employees' taxes and report the moment that salaries are given to them. To comply with such an obligation, tax withholding involves applying a monthly (annual) rate whose fiscal technique, by doctrine, should comply with the constitutional principle of proportionality, bound by the principle of equality; both are principles of a higher order on which the Mexican tax system is based.
Tax withholding of a natural person living in Mexico involves applying the progressive rate. The fixed rate is added to the lower limit of the income received. For residents who live abroad, the tax rate is proportional. Firstly, an exemption is applied to the income received according to the established limit. Two tax rates are applied to the remaining amount, depending on the income established by law. Both cases include the implementation of the principle of proportionality as established by the income tax law (Ley del Impuesto Sobre la Renta, LISR) [7]. These are the legal principles that motivate our research questions: Is the tax withholding determined with the progressive rate for a person living in Mexico the same as the one determined with a tax rate for a resident living abroad? Does the monthly (annual) rate of a person living in Mexico have the same fiscal meaning as the tax rates for a resident who lives abroad? Do the constitutional progressivity and proportionality [5] limits effectively withhold the tax without harming the human right to the vital minimum?
This study aims to show the behavior of the monthly (annual) rate in tax withholding of a natural person living in Mexico who receives income from salaries granted for a subordinate personal service, as established by Article 96 of the income tax law LISR. We compare that rate against the tax rates of tax withholding applied to a natural person living abroad who receives income from a salary granted for a subordinate service as stated in Article 154 of the income tax law [7], applying the constitutional principle of proportionality (progressivity), according to the salary tax system, and considering the fiscal residence of the subjects studied. We also consider the effect that the implementation of this principle (proportionality-progressivity) has on the taxpayer's quality of life [5]. The hypothesis is that income tax withholding of natural persons who obtain income for providing a subordinate personal service and live abroad is lower because the rates applied to them follow the principle of proportionality regarding their income. On the contrary, the monthly (annual) rate applied to the tax withholding of a natural person living in Mexico who receives income for providing a subordinate service follows the principle of progressivity. According to doctrine, the second one violates the human right to the vital minimum.
Review Literature
We review the information as required by the doctrine. We compile economic-theoretical-legal information in laws, codes, regulations, and other dispositions using the legislative and newspaper research techniques [8]. We study the literature on the figure of tax determination considering income from salary, according to human rights and theoretically legally based on the human right to the vital minimum within the constitutional limits of fiscal matters. We know the tax rates established by legislation to identify the percentages to be applied when determining the tax to withhold from employees, according to their fiscal residence. We review the Official Journals of the Federation to find the origin of the constitutional limits of proportionality and the implementation of the principle [5], Ley del Impuesto Sobre la Renta [7], across the tax history of Mexico.
In Mexico, the need to pay taxes is established by the fiscal residence. Natural persons who receive salaries are forced to pay income tax on all their income, regardless of the location of the source of wealth. In the case of residents living abroad, the obligation remains for those taxpayers who receive an income from a permanent establishment or one whose source of wealth is located in Mexico, when they are not permanently settled in the country, or when they live in Mexico, but their income is not originated in the country (Article 1) [7]. According to these ideas, residents living abroad who receive income from any source of wealth located in the country or have a permanent establishment in the country must pay taxes in Mexico. Natural persons who are Mexican citizens are considered to be residents in Mexico unless proven otherwise.
One of the concerns of the first marketers was understanding the ways to increase wealth; one of them is through international trade [9]. The school of marketers thought the State must intervene in international trade to increase collection by promoting exportations "through the creation of duties" [10] (p. 90). Physiocrats shared the same interest, although they directed it towards agriculture. Both held that wealth affects economic growth and aimed for these variables to affect taxation. The classics also studied wealth in economic growth, as did Adam Smith, who added the variable of "accumulation of factors of production". The theoretician stated that the origin of wealth "is found both in labor and available resources" [10] (p. 91) and made a relevant contribution to the declaration of principles that should guide the creation of the tax structuring that have since been applied. Smith considered that the obligation to contribute had always been bound to "the acknowledgement of a set of basic principles that guided taxation". This criterion was supported by Adolph Wagner, Harold M. Sommers, and Neumark Frits [11]. Adam Smith explained his theory of justice defining taxation as an "amount or percentage of private wealth that citizens must deliver under equality criteria" [12] (p. 143). Marin-Barnuevo also acknowledges it when stating before the Spanish Court the following axiom in the 2 April ruling 76/1990, in which the doctrine of previous years is summarized: "the principle of equality demands that equal legal consequences are applied to equal budgets" [13] (p. 46). The principle of justice or proportionality presented by Smith considers that this principle is inherent to the individual's economic capacity. Tax proportionality is directly proportional to tax capacity. This is an economic reason to pay a reasonable proportion of the tax and a constitutional limit to establish the tax. Arnold, Martinez and Zuñiga [14] (p. 68) state that "proportionality became a constitutional principle that protects fundamental rights". If the income capacity is higher, then there is a higher tax. If more wealth is obtained, then the tax rate will be higher; then, there will be a higher tax payment.
Based in an economic-political and legal system, it establishes a link between the State and the individual; this principle has been considered to have the highest regulatory status. The establishment of a tax carries the constitutional principles (obligation, generality, relation with public expenditure, proportionality, equality, and legality) [5] to be applied. These principles are often protection mechanisms when establishing a tax and, even though they are integrally relevant, we will only consider the principle of proportionality [15]. This is why there are authors who argue that income taxes have less effect, and lead to lower income inequality [16].
Burgoa [17] asserts that proportionality contains taxation elements that demonstrate wealth and that neutralize tax capacity. Then, the economic capacity of the individual determines the amount of tax. In this sense, the tax is inherent to the income. Sanchez underlines the principle of proportionality as the "most important methodological [tool] of constitutionalism" [18] (p. 471), which becomes the constitutional tax limit in a body of control under the faculty of the State.
When the tax principle is applied, the Supreme Court of Justice states that the principle of proportionality is fulfilled "through progressive rates since with them a higher amount of tax is covered by the taxpayers with the highest resources while a smaller tax is paid by taxpayers with lower incomes. In addition, a consistent difference between the levels of income is established" [19].
On the other hand, Article 31, the Constitution of the Mexican United States [5], includes the basis of the right to the vital minimum. This is the guideline for tax legislators "after which they should refrain from imposing taxes to certain concepts or incomes whenever that implies leaving the person without any medium to subsist". The right to the vital minimum as an expression of the principle of tax proportionality includes all those incomes destined to satisfy the individual's essential needs and that are not part of the tax capacity. The vital minimum is a principle derived as a projection of the principle of tax proportionality, which identifies the taxpayer's tax capacity and, at the same time, respects the resources necessary to subsist [20]. It is the result of the principles of the social state of law, human dignity, and solidarity linked to the fundamental rights to life, personal integrity, and equality. The aim of the vital minimum is to "prevent a person from having his or her intrinsic value as a human reduced due to the lack of material conditions that allow for having a decent existence" [21]. According to Tenorio, this right seeks to guarantee that the person "does not become an instrument to other ends [. . .] aiming to protect the person from any form of degradation that compromises his or her intrinsic value, namely regarding basic and essential material conditions to ensure a decent and autonomous survival".
The Supreme Court of Justice of the Nation has recognized that the right to the vital minimum transcends tax, respecting human dignity stated in Article 25 of the Constitution, becoming a limit for the legislator to impose a tax which "constitutes a guarantee based on human dignity" [22]. It explains that tax capacity must be appreciated based on the person's real context given that the principle individualizes the persons' situations and "the State, in terms of disposition, cannot overrule the necessary material resources to have a decent life", especially when these people have the right to subsist [23].
The pro homine principle is enshrined in Article 29 of the American Convention on Human Rights [24] and considers "recurring to the widest norm, or the most extensive interpretation when dealing with the recognition of protected rights" [25] (p. 92), a principle that is consistent with the first article in the Political Constitution of the Mexican United States regarding norms on human rights interpreted with the Constitution and international treaties so that persons receive the widest protection. Understanding the criterion of this fundamental principle, along with the inherent pro homine principle, Orozco considers that "the norm that represents the greatest protection for the person or that involves the least restriction must prevail". In this sense, human rights in the constitutional text are materialized, along with those established in the international treaties ratified by the State.
Aguilera and López consider the State as a "legitimized medium to guarantee the fundamental rights of the citizens and is politically illegitimate if it does not" [26] (p. 55). This is the main objective of the protective model when it provides effectiveness and seeks to guarantee fundamental rights. From the expression of L. Ferrajoli, the protective model is conceptualized in three manners: a model of rule of law, law theory, and a basis of the State that recognizes rights [27]. Then, the State is responsible for the protection and guaranteeing of (vital) natural rights of the citizens. The author states that "what is natural, previous and primary, are individuals and their rights, needs, and interests". In this sense, the State "is only legitimized as long as it aims to protect those rights and individual goods".
In summary, in its substantial dimension, it is a "condition of validity ensured by the observance of fundamental rights", social rights that must satisfy or preserve the rights of freedom. The Political Constitution of the Mexican United States forces all authorities, within their capacities, to comply with the obligation to promote, respect, protect, and guarantee human rights. For this reason, the Supreme Court of Justice has explained the principle of progressivity in human rights "that are derived from economic, social, educational, scientific, and cultural norms", asserting that the principle "demands that as the level of development of a State improves, so should the compromise to guarantee economic, social, and cultural rights" of the citizens [22].
Materials and Methods
The aim of this work is to analyze the constitutional principle of proportionality applied to the monthly rate to determine the income tax of a person living in Mexico and compare it against the rates established for a person living abroad when determining income tax. This research is based on our professional experience and aims to explain how a natural person living in Mexico who receives income from salary is subject to tax withholding. This is based on the principle of progressivity, whereas a taxpayer living abroad who receives income from a source whose wealth is located in Mexico is subject to tax withholding based on the principle of proportionality [5,7]. We show the difference between the constitutional principles in tax rates to evidence the economic benefit a resident abroad may have in terms of interpretative, critical, and value use of the protective model.
Unit of Analysis
We analyzed the monthly-annual rate (Articles 96 and 152) and tax rates (Article 154) considering the fiscal residence of the natural persons who receive income from salaries in a national and foreign subordinate relation [7].
The unit of analysis is constituted by the tax regime of the natural persons living in Mexico with income from salaries granted for a subordinate service included in a comparative study that also contains the salaries of natural persons living abroad who receive income whose source of wealth is located in Mexico. The objective is to determine the difference between the implementation of the rate in Article 96 (national resident) and the rates in income from salary for a resident abroad (Article 154) and compare the application of tax rates for both types of residents to show how the human right to the vital minimum is violated [19][20][21].
The data used in the analysis considered the tax rate tables for national residents and those living abroad (Table 1). We used the lower and upper limits to define the accumulative income of the rate in Article 154 of the income tax and tax exemption as well as the monthly (annual) rate in Article 96 of the income tax. The doctrine describes tax withholding determination for salaries and national residence by applying the monthly or annual rate of the tax, so we will not refer to the effective rates described by Beltrán but to "statutory marginal tax rates; that is, those that are reflected on the corresponding laws" [28] (p. 182). We deal with the analysis starting with a national resident who does not pay income tax for exempt income (listed in Article 93) and has the right to exert authorized deductions to determine annual tax (Article 152) [7]. In this study, we did not consider exempt income nor authorized deductions because they are not taxed. Therefore, we only considered taxable income for income tax. We located the income between the lower and upper limits to locate the excess and apply the progressive tax rate to it in order to determine the marginal tax and add the result to a fixed rate. The result is the tax the employer must withhold from a natural person who obtains income from a subordinate relationship.
The monthly (or annual) tax rate contains eleven tax ranges that are defined by lower and upper limits where the income is located to apply the percentage of tax rate to the excess. The rate goes from 1.92% in the first range to 35% in the last one, which represents the maximum tax for a natural person ( Table 1). The successive increase in the rate shows the apparent progressive behavior of the tax, but which progressivity is it? How is it comparable to the withholding of which residents abroad are subjects as compared against that applied to national residents?
In the case of determination of a tax for a natural person who receives income from salary and lives abroad, an exemption of 125,900 Mexican pesos is firstly applied. Then, a 15% rate is applied to the difference as long as it does not exceed 1,000,000.00 Mexican pesos [7]. A second rate of 30% is added to the next income layer exceeding the same amount of Mexican pesos (Figure 1). In order to compare the implementation of the principle of proportionality for national and foreign residents, we considered the tax hierarchy of the monthly rate and tax rates ( Table 1). The result of the analysis is shown in Table 2. We studied the rate of the income tax to determine the tax withholding and considered the upper level (496.07 Mexican pesos of the first range) as cumulative income. We reduced the lower level whose difference is the excess and added the percentage to the latter, from which we obtained the marginal tax (9.52). Then, we added the fixed rate which is zero in the first range. By applying the legal order, we considered the upper limit again as cumulative income to determine the tax and obtained a marginal tax of 237.72 Mexican pesos, to which the fixed rate was added (9.52 Mexican pesos). The result is a tax withholding of 247.24 Mexican pesos. We applied the fiscal regulation to the following ranges and identify the fixed rate in all the cases is the tax withheld of the previous range (Table 1). The income tax withheld determined in the previous range is the fixed rate added to the marginal tax determined in the following range. Therefore, the fixed rate is the income tax withheld determined in the previous range. The principle of progressivity is supposed to be present in the continuous growth of the tax rate; however, how progressive is the tax?
Following this notion, we analyzed the increase in the percentage (rate) applied to the excess of the lower limit per range. Range eight, showing lower and upper income limits between 32,736.84 and 62,500.00 Mexican pesos, is the one with the highest increase (6.48%), followed by the one in range four (5.12%), whose income is between 7399.43 and 8601.50 Mexican pesos. Then come ranges two and three with 4.48%. In contrast, ranges nine and ten show lower increases (2%), while range eleven exhibits the lowest increase (1%), the range with income above 250,000.00 Mexican pesos ( Table 2).
The analysis shows that the tax rate contains the tax rate of the lower range and that the increase in the rate from one range to the next integrates the highest rate. In this sense, we determined the percentage that represents each of the ranges on the totality of the tax rate, considering that 35% is 100%, the maximum rate.
From this approach, range eight is evidently the one with the highest percentage of tax rate (18.51%), while range eleven has the lowest proportion of maximum tax rate (2.86%).
Even though residents abroad do not have the same fiscal technique, tax structure nor the monthly (annual) rate as national residents do, we calculated withholding, applying the tax rate established for that case, according to Table 1. The income established by law was added to these tax rates, along with an exemption, while a 15% rate was applied to a difference of lower than a million Mexican pesos. The next income over a million pesos received a 30% tax rate. The sum of these two withholdings is the total tax to be withheld. The results are shown in Table 3.
Measuring Progressivity of the Tax
The Suits index or progressive index is one of the best-known indices for calculating the measure of tax escalation more fully [29,30]. Suits' index is a statistical quantitative method and is inspired and related to Gini's well-known index [31][32][33]. Let X be a pre-tax income variable and Y be a general variable that in some cases will represent tax or benefit T and in others, the post-tax income, defined as Y = X − T. We suppose that X and Y are non-negative random variables. The Suits index, which is interpreted as an average measure of tax progressivity, is based on the relative concentration curve (RCC) of the Y-variable, which plots its cumulative percentage against the cumulative percentage of pre-tax income X when both variables have been ordered in ascending order of X [34].
The concentration curve is the bivariate analogue of the Lorenz curve. The relative concentration curve allows one to visualize and measure in summary the degree of nonproportionality between any two distributions by analogous distance and area measures.
The RCC of the Y-variable plots the cumulative percentage of tax liability (ordered by pre-tax income) against the cumulative percentages of pre-tax income. We denote the RCC of Y as L Y (q), where q, with 0 ≤ q ≤ 1, is the value of the Lorenz curve associated with the population rank p ∈ (0, 1). The Suits index, is defined as twice the area between OB proportionality line in Figure 2 and the region below RCC [35]. Thus, (1) Geometrically, the Suits index is built by considering the ratio between the region area above the OB proportionality line in Figure 2 and the region below the relative concentration curve (RCC) [35]. Let L between the RCC curve L Y (q) and the x-axis and K be the area above the curve in a situation of proportionality (OB). It facilitates exposition to represent the accumulated percent income, measured on the horizontal axis, as a variable y that ranges from 0 to 100. Areas K and L are illustrated in Figure 3. The Suits index is defined with the following formula By linking the area concept to the integral, we can write the Suits index with the following formula: where y and T(y) are the percentages of total accumulated income and their corresponding accrued tax rates. Suits [35] provided a tool for this progressive index using numerical approximations for the area integral, i.e., (T(y i ) + T(y i−1 ))(y i − y i−1 ).
As can be seen, simple calculations are required to estimate the value of the progressive index when avoiding the use of the integral. Like the Gini index, the Suits index ranges from −1 to 1. The key difference between the Suits index and the Gini coefficient is that curves can go above the line of proportionality. Figure 4 is a graphic representation of this correlation based on the deviation of the Lorenz curve from its diagonal. For a proportional tax, L approaches K, so the Suits index S approaches zero. Since the Lorenz curve corresponding to a progressive tax falls below the line of proportionality, area L is smaller than K. As a result, the index S is positive for a progressive tax. In the limiting case where the highest income bears the entire tax burden, the Lorenz curve lies along sides OA and AB, so L equals zero and hence S = 1. With a regressive tax, the Lorenz curve arches above the line of proportionality, making the area L larger than K, so S is negative. An index of minus one indicates that a tax system is completely regressive. An index value of zero identifies a tax system as proportional [29]. In summary, Suits' rate is positive for progressive taxes, negative for regressive taxes, and zero for proportional taxes. Namely, as a tax becomes more regressive, Suits' index will approach its minimum value of −1 and the more progressive the Suits index will approach 1. It is important to notice that the Suits index is a measure of the average progressivity of a tax system over the full income range. Some tax systems may be progressive over one range of income and regressive over another range [29].
The Suits index also provides a tool for determining the progressiveness index for a system of two or more taxes in terms of weighted averages of the S x , S z progressive indexes for x and z rates with average tax rates r x and r z , respectively [35]. The formula for calculating the progressiveness rate of this tax system with two tax rates is given by Consequently, if the value of S xz is positive, the tax system will be progressive. If S xz is negative, the system then will be regressive and when it is a proportional system S xz will be zero.
Results
In this application of tax rates, we will present it as a line of proportionality. For this purpose, the distribution of income will be divided into deciles. For a progressive tax, the percentage of the tax burden supported by the first decile is less than 20% and is increasing as shown in the data in Table 4. Figure 5a shows the tax on domestic residents; in Figure 5b, the tax on foreign residents is shown. Figure 5a shows that for national residents the Lorenz curve is "arched" above the diagonal, suggesting a negative progressiveness rate. This was confirmed by calculating the Suits index (Equation (3)). For national residents, the information in columns 2 and 4 of Table 4 was considered, and the Suits index for domestic residents was obtained as Thus, this means that this is a highly regressive tax, confirming the information provided by Figure 5a.
In the case of taxing of foreign residents, we have the Lorenz curve in Figure 5b, as we can see the graph shows that before the fifth decile the curve arches below the proportionality curve and after that decile is above. Using Equation (3), the Suits index for foreign residents must be S F = −0.02.
We can also observe a large increase in the progressivity for national residents (Figure 5a) compared to foreign residents (Figure 5b). Note that the Suits index for foreign residents is very close to zero. So, we have a slightly regressive tax. Being a numerical approximation and according to the information in Figure 5b, we can conclude that the tax tends to be proportional. Now, we present the global progressivity index and our results seem to confirm that the total tax system is regressive. This is confirmed by the both taxes (see Figure 6). In fact, the value of the escalation index S FN for the total tax system is the weighted average of the values for domestic and foreign residents (Equation (4)) with r F = 15 and r N = 3.18. Using the data in Table 4, we have S FN = −0.15.
As a result, the tax system is slightly regressive.
Discussion and Final Considerations
This paper demonstrated that the principle of proportionality applies differently in the case of domestic residents or residents living abroad, which is adversely associated with the right to the living minimum of the national resident, a principle immanent to the obligation to contribute [20][21][22]; these principles are universal because they are principles of economic equality [12,13] and applicable in other tax management controls in many countries that illustrated in the in the Mexican doctrine [11][12][13][14].
It is important to emphasize that a national resident accrues the entire income to a single range of the monthly fee. A foreign resident disaggregates income by first differentiating an exemption; then, a tax rate is applied and after a certain income limit a higher rate applies. The principle of proportionality applies here, as the theorists claim [14][15][16][17]. As noted above, applying the corresponding tax technique results in a monthly income of 250,000 Mexican pesos, and a domestic resident would pay 78,403.66 Mexican pesos [7], while an overseas resident would have to pay only 18,615 Mexican pesos. It is evidence of the inconsistency between income, payment of tax, and residence, Figure 5a, and not as stated by count [22]. This work was verified by Suits' progressiveness index, analyzing the application of the two tax rates [35]. The tax rate that applies to the different income levels of a national resident together turns out to be highly regressive. By contrast, the tax rate of a foreign resident is sparsely regressive and prone to proportionality [6,19,22,23]. Nadirov and Dehning have talked about the positive and negative effects of progressivity [4]. This is the contribution of our study, which the Suits index distinguishes, in the first place, the principle of proportionality and the progressivity of the income tax evidenced to be not equal (Figure 1 ([22])). Second, it measures the degree of regressivity of tax rates according to the taxpayer's residence ( Figure 6). The Suits index (mathematical-economic indicator) shows the effect that the principle of progressivity has on the income of the worker residing in Mexico, testing our hypothesis. Therefore, this result shows the economic impact of the tax rate on his income, thus affecting his vital minimum, that is, his quality of life. The Suits index is a method that is appropriately related to the doctrine and demonstrates mathematically what statistics could not do.
It is desirable that the government seek to increase revenue from other approaches, as suggested by mercantilists ( [9]), and to control the income tax rate, to reduce this effect [16][17][18][19]. Agreeing with the author, tax equity could be achieved as a legal principle of tax rate management control, considered as the first principle [36] (p. 12) of application of the "modern tax system".
Rethinking the tax mechanics in accordance with the constitutional limits will probably mean reducing tax evasion and increasing the effect on tax collection. It would mean that the justice system would be complying with the provisions of Article 29 of the American Convention (ratified by the Mexican government in 2012), which mentions that human rights cannot be violated by the State, and it offers a more protective position. This omission would violate the pro person principle.
Our study has as a limitation in that it is focused on the tax regime of wages. However, it is this that contributes the most to federal public spending; therefore, the national resident wage regime will continue the regime of greater sustainability of public finances [3]. In addition, it is a regime, which in addition to containing a fixed rate [16], becomes progressive as income increases.
As a conclusion, we think that the State, as a guarantor of human rights, should promote that any reform of its tax laws does not invade the economy of citizens in such a way as to decrease the possibility of a dignified life [20][21][22][23]. Otherwise, the State must define with certainty what it refers to with the right to the minimum of life, which meets the minimum conditions of a dignified life, because then this would be the basis for determining the tax rate and acting as a true guarantor of people [23,28]. This is likely to involve legislators establishing a different causation technique such as for residents abroad.
In future research, we intend to study progressivity in other tax systems, trying to understand changes and tax rates as management control. | 8,153.6 | 2021-08-17T00:00:00.000 | [
"Economics"
] |
CDM Based Servo State Feedback Controller with Feedback Linearization for Magnetic Levitation Ball System
This paper explains the design of Servo State Feedback Controller and Feedback Linearization for Magnetic Levitation Ball System (MLBS). The system uses feedback linearization to change the nonlinear model of magnetic levitation ball system to the linear system. Servo state feedback controller controls the position of the ball. An integrator eliminates the steady state error in servo state feedback controller. The parameter of integral gain and state feedback gains is achieved from the concept of Coefficient Diagram Method (CDM). The CDM requires the controllable canonical form, because of that Matrix Transformation is needed. Hence, feedback linearization is applied first to the MLBS then converted to a controllable form by a transformation matrix. The simulation shows the system can follow the desired position and robust from the position disturbance. The uncertainty parameter of mass, inductance, and resistance of MLBS also being investigated in the simulation. Comparing CDM with another method such as Linear Quadratic Regulator (LQR) and Pole Placement, CDM can give better response, that is no overshoot but a quite fast response. The main advantage of CDM is it has a standard parameter to obtain controller’s parameter hence it can avoid trial and error. Keywords—magnetic levitation ball system; nonlinear; feedback linearization; servo state feedback; coefficient diagram method.
I. INTRODUCTION
Magnetic Levitation Ball System (MLBS) consists of a mass object that levitates by the force of an electromagnet [1]. The controller is attached to a drive to the electromagnet. The position sensor is also used to measure the distance of the ball from the electromagnet. Fig. 1 represents the model of the MLBS. MLBS is a modern technology that has high efficiency and frictionless characteristics [2]. This technique is applied to various systems, such as Suspension [3], Wind Turbine [4], Microbots [5], Bearing [6], Medical [7] and Vehicles [8]. The latest and famous application of MLBS is a Magnetic Levitation (Maglev) train [9].
The other characteristics of MLBS are highly nonlinear, unstable, and difficult to control [10]. Electromagnet coil of MLBS causes the system to be nonlinear. MLBS gives unstable response due to this nonlinearity. Because of that, we need the suitable controller.
Some authors have proposed nonlinear controllers such as Feedback Linearization [11], Sliding Mode Control (SMC) [12], Backstepping [13], High-Gain Observer [14], and Passivity-Based Control [15]. Nonlinear control has advantages in controlling high nonlinear systems such as this MLBS, but it also has disadvantages. SMC has the chattering effect; backstepping is not robust from disturbance, and high-gain observer shows the time response still overshoot. Feedback linearization is sufficient to change nonlinear system to linear system but the parameter gain of linear control that applied still trial and error.
The linear controller can be used to control nonlinear systems. However, the system must be linearized first which has an advantage in its simplicity of the design and implementation rather than a nonlinear controller. The linear controller has been proposed such as PID controller [16]. PID controller is applicable in controlling MLBS, but the system has to be linearized first, and the result of the linearization must not contain an integrator. Recent author has proposed a PID controller optimized by genetic algorithm as in [17], leading to a better response than conventional PID controller.
Another controller is the fuzzy logic controller (FLC) [20]. FLC also can be applied for controlling MLBS, but it will need more time to design. FLC also needs data from another controller that will be compared to be data for FLC. FLC needs to know the numerical value of the real parameter system model.
Earlier proposed the optimal control in [20], Linear Quadratic Regulator (LQR) is applied by the author to find the state feedback gains parameter correctly. However, the parameter setting of weighting matrix in LQR method still using trial and error.
In this research, the nonlinearity of MLBS will be controlled by feedback linearization which is sufficient to control the nonlinearity. After getting the linear system, servo state feedback controller is chosen to control the position of MLBS. State feedback controller is the most straightforward controller in modern control. The control signal of state feedback is determined by an instantaneous state gain [21]. The main problem of servo state feedback controller is how to choose the effective parameter of integral gain and state feedback gain. These gain change poles of the system that affects the stability and performances of the system. The system will be stable if it has poles on the left side of the imaginary axis. This problem will be solved using Coefficient Diagram Method (CDM).
In CDM, the performance specification is rewritten in a few parameters (stability index and equivalent time constant ) which specify the closed loop transfer function and are related to the controller parameters algebraically in the explicit form [22]. So, trial and error can be avoided by tuning the state feedback gain parameter based on CDM.
As told earlier, the MLBS should change to be linear system and converted into a controllable canonical form to implement servo state feedback controller based on CDM. The controllable form is achieved only by using a transformation matrix.
A. The Concept of CDM
The CDM is an algebraic control design approach with the polynomials, and polynomial matrices are used for system representation and also is a contemporary design [23]. CDM model is based on its stability index and equivalent time constant [22]. The solution process for CDM design will be as follows. The first step is defining the polynomial characteristic of the closed loop transfer function as Then, it is needed to analyze the performance specifications and design specifications in CDM. Two necessary CDM parameters are the equivalent time constant and the stability index. From the polynomial characteristic ) given in (1), the stability index and equivalent time constant are respectively described in general term as follows = . (3) From the CDM design point of view, only the settling time ! " is meaningful because it gives an upper bound of the equivalent time constant as Thus, the standard form of the equivalent time constant can be described as ( Besides the equivalent time constant, we need to analyze the stability index. According to [22], the recommended standard form for CDM is = . .. = * = ( = 2, = 2.5.
Another parameter is a stability limit + * which defined as follows However, recording of its relation to the stability limit, the condition of stability index can be relaxed as follows The polynomial characteristic is known as the desired characteristic polynomial which is expressed by the coefficient diagram , the equivalent time constant , and stability index . Then should be expressed as (10) The next step is assuming controller in the purest possible form and express it in the left polynomial form. Finally, the unknown variables can be solved to get the controller parameters. The adjustment may be needed to satisfy the performance specification.
B. Model of Magnetic Levitation System
The mathematical model of MLBS will be achieved by implementing Lagrangian analysis. The Lagrangian (@) is a difference between the kinetic and potential energy of the system. The equations of motion for a mechanical system with generalized coordinates A ∈ ℝ 1 and Lagrangian (@) are given by where Υ is the external force acting on the &L generalized coordinate [24]. The kinetic energy and the potential energy of MLBS can be written respectively as following Where @ N) is a representation of the coil inductance, P is the mass of the ball, N is the position of the ball, and O is the current.
The correlation between coil inductance with the ball position is written as Where @ is the electromagnet coil inductance constant, @ is the additional inductance, and N is the reference position. Hence, Lagrangian @ of MLBS is given as From the mechanical point of view, the external force of MLBS is air friction force (U) and later assumed that U = 0 and can be written as From the electrical point of view, the external force of MLBS can be described as W − OX where W is the applied voltage to MLBS and X is the resistance of the electromagnet coil. Assuming that O is the derivation of , we can get the equation as By substituting (15) to (16), we can get the equation as follows By substituting (14) to (15) and (15) to (17), we can get the equation as follows State variables that represented MLBS are the position of the ball (N), the velocity of the ball (NI ), and the current (O). The input signal of MLBS is the applied voltage (W), so that = W. The output of MLBS is the position of the ball (N). Then we can represent the MLBS in state space representation as follows
C. Feedback Linearization
The central idea of feedback linearization is to transform a nonlinear system dynamics into (entirely or partly) linear ones so that linear control techniques can be applied. This differs from conventional linearization (i.e., Jacobian linearization) because feedback linearization is achieved by exact state transformation and feedback, rather than by linear approximations of the dynamics. The basic idea of simplifying the form of a system by choosing a different state representation, the choice of coordinate systems. Feedback linearization techniques can be viewed as ways of transforming original system models into equivalent models of a more straightforward form [25].
The nonlinear system has a form of state representation as The derivative + is given by The system is feedback linearizable if and only if a function ℎ satisfies the partial differential equations and subject to the condition [26] as In other words, the derivation of + must be repeated until it has the dependent variable of the control signal as As a consequence, the derivation of + can be repeated until its q &L derivation where q is called the relative degree of the nonlinear system The output y derivation of MLBS is shown as (36) In feedback linearization, there is a change of variables which is notated by i that transform the nonlinear system into an equivalent linear system iI as Remembering the nonlinear system representation before, the component of transformation matrix M N) is described as And The control signal u is written as follows where v is the new input designed for the linear system.
Then, by substituting equation (47) to (46), the nonlinearity can be canceled. Then, we can get the linear model of MLBS that can be written as
D. Servo State Feedback Controller
The augmented system (MLBS) is using an integrator to eliminate steady-state error. Diagram block of servo state feedback controller with Feedback Linearization (FL) is shown in Fig. 2.
To apply state feedback control, the system must fulfill controllability condition as lw v w v ( w v * w m,
Moreover, state controllability condition as
The control signal ~ which given to the system can be written as where are state feedback gains and ˆY is an integral gain. Noting that • !) is a step input, we have • ∞) = • !) = • (constant) for ! > 0. So that the closed loop system (50) with control law (55) is w ‚ + + + CDM will design the integral gain and state feedback gains of the control law.
E. Controller Design
The open loop polynomial of the linear MLBS is stated as The equation (50) is not in state variable controllable form, because of that matrix transformation M is needed that can be written as M = • Ž (59) where, We can get the system in the controllable canonical form as where According to CDM concept, we need to make the desired characteristic polynomial based on stability index and the equivalent time constant. Therefore, we have the coefficients of the desired polynomials to in (9). Finally, from (9), (58), (65) and (59), the gain matrix for the augmented system can be found as where = l |ˆYm.
III. RESULTS AND DISCUSSION
The nonlinear model of MLBS uses feedback linearization to change the system to the linear model. Then using the linear control servo state feedback to control the position of the ball. Finally, servo state feedback is tuned by CDM Concept.
The system is simulated, and the model is generated in SIMULINK MATLAB. Parameters of the simulation are shown in Table 1. CDM gives a good performance and also provides excellent stability of the system, noticed from the values of to . Coefficient diagram which provides stability can be seen in Fig. 3. The most bend curve shows the most stable system. The most bend and stable is case 5 but the standard parameter CDM in case 3 is good enough to make the system stable as shown in Fig. 4. Simulations use unit steps • = 1 as the desired position, and the system must follow it. The CDM standard parameters can give a good performance of the position value to reference, without needed to modify the CDM parameters. Comparing with the most bend curve, the standard parameters of CDM have better response on rising time as shown in Fig. 4. It proves that the CDM standard parameter has given good enough performance and stability.
The simulation to investigate the response of the system from disturbance shows in Fig. 5. The simulation uses a standard parameter of CDM (No. 3) as a parameter for servo state feedback gains. The disturbance is given at second, fourth, sixth and eighth seconds. Response from system shows that the controller can make the system stable and follow the desired position. The simulation about the uncertainty of mass, inductance, and resistance is done to find out how the system performs due to change of the parameter. The simulation is shown in Fig. 6, 7, and 8.
The uncertainty parameter makes the system unstable and affects the position of the steel ball. Object mass uncertainty is given as ±50% and ±90% of the original mass as shown in Fig. 6. The simulation result shows that the uncertainty of mass does not affect the steel ball position too much. The applied controller still can handle the given change of the mass. The uncertainty of inductance is provided between ±25% and ±50% of the original inductance value as shown in Fig. 7. The simulation result indicates that the inductance uncertainty affects the ball position. It also shows that the applied controller still can stabilize the position of its reference point. This is not happening in the real system because the change value of the position exceeds the limit of the position. parameter varies to a slight value. A small value of the parameter uncertainty can give an extreme change of ball position. However, the controller still able to stabilize the system and made the system follows the given reference point as shown in the simulation result. Same as the change of the inductance, the result becomes unreal because it exceeds the position limit. The simulations that show the comparison of the CDM, LQR, and pole placement method implementation is shown in Fig 9. The comparison is made by showing the system' performance which is affected by the implementation of the method. Based on that figure, the simulation shows that CDM implementation makes the system performs without giving any overshoot and with quite a fast rise time. By applying pole placement method, the system performs the slowest among all even though there is no overshoot in the response. LQR implementation makes the system performs with an overshoot in response.
The completed response also can be seen in Table III. Based on that table, CDM give the best settling time to stabilize the system and give the smallest overshoot. While LQR gives the fastest rise time but the rise time of CDM is good enough. The difference between CDM, LQR and pole placement method is CDM has a standard parameter and able to minimalize the effort to obtain controller parameter. By using LQR, we still need to determine weighting matrix and also it does not have standard weighting matrix. It is same with pole placement method that needs to determine the pole location. If the pole is too far from the y-axis, then it will have significant control signal even though the system performs well and stable. However, if it is too close to the yaxis, then it will give bad system's response. has been done. Feedback linearization can transform the nonlinear model of MLBS to the equivalent linear system so that the linear controller can be applied. The linear servo state feedback controller can be used to control object position. CDM also can be used to get servo state feedback gain or parameter tuning. The implementation of CDM standard parameter can make the object gets the reference point with a good value of rising time and settling time, and also no overshoot performance is obtained. This parameter also gives excellent stability of the system to respond to the disturbances. Comparing CDM with another method such as LQR and pole placement shows that CDM gives fast rise time and no overshoot in system performance. While LQR gives overshoot in the system response and Pole Placement gives a slow response.
The change of plant parameter is an issue to design MLBS controller. Feedback linearization cannot handle the problem of MLBS parameter change. Adaptive control or nonlinear control can be implemented to this matter.
The choice of parameter is a matter of research. CDM concept can be applied in the tuning of the controller parameter. CDM theory can avoid trial and error process, so parameter tuning becomes more efficient. | 4,128.6 | 2018-06-26T00:00:00.000 | [
"Mathematics"
] |
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
: This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with different training sample sizes. We found that: (1) SVM and NB were superior to CART and KNN, and both could achieve high classification accuracy (>90%); (2) the setting of tuning parameters greatly affected classification accuracy, particularly for the most commonly-used SVM classifier; the optimal values of tuning parameters might vary slightly with the size of training samples; (3) the size of training sample also greatly affected the classification accuracy, when the size of training sample was less than 125. Increasing the size of training samples generally led to the increase of classification accuracies for all four classifiers. In addition, NB and KNN were more sensitive to the sample sizes. This research provides insights into the selection of classifiers and the size of training samples. It also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing these parameters.
Introduction
Urban landscapes are extremely complex and heterogeneous. To adequately quantify the heterogeneity of urban land cover, high spatial resolution images are needed. A considerable amount of research has shown that object-based approaches are superior to traditional pixel-based methods in the classification of high spatial resolution data [1][2][3][4]. Consequently, object-based approaches have been increasingly used for urban land cover classification [5][6][7].
With object-based classification approaches, objects generated from image segmentation can be typically classified using a rule-based procedure (a set of rules) [8] or using machine learning algorithms (MLA) based on training samples [1]. While rule-based procedures, which use expert knowledge, have been increasingly used for classification, the majority of the studies have used supervised classifications [5,9,10]. Many different kinds of MLA have been applied for supervised classifications. These algorithms are commonly categorized as parametric and non-parametric classifiers. The two widely-used types of parametric algorithms are the maximum likelihood classifier (MLC) and Bayes classifiers, and the frequently-used non-parametric classifiers include K nearest neighbor (KNN), decision tree (DT) and support vector machine (SVM).
Previous studies have shown that the use of different classifiers may lead to different classification results. Therefore, many studies have been conducted to investigate the effectiveness and efficiency of different classifiers [11][12][13][14]. However, these studies have been mostly conducted using pixel-based approaches. With the wide use of object-based approaches, there has been an increasing interest in comparing different machine learning classifiers using object-based methods [5,9,[15][16][17]. When using these machine learning classifiers, we should consider at least four key factors that can dramatically affect the classification accuracy and efficiency. Specifically, these are image segmentation, training sample selection, feature selection and tuning parameter setting [1,5]. While the first three factors have been investigated in many previous studies [16,18,19], few studies have investigated the effects of the setting of tuning parameters [5]. However, setting tuning parameters is the very first step, as well as one of the most important steps to appropriately use these machine learning classifiers. In addition, previous comparison studies of machine learning classifiers have been mostly focused on non-urban areas, such as grasslands, farmlands and coal mine area [5,9,20].
The overall objective of this study is to evaluate the four most frequently used MLAs for urban land cover classification, with an object-based approach, using very high spatial resolution imagery. In particular, we aim to investigate how tuning parameters affect the classification results, especially with different training sample sizes. The four classifiers are: (1) normal Bayes (NB), a parametric algorithm; (2) SVM, a statistical learning algorithm; (3) KNN, an instance-based learning algorithm; and (4) the classification and regression tree (CART) classifier, a commonly-used DT algorithm. The results from this study can provide insights into classifier selection and parameter setting for high resolution urban land cover classification.
Study Site
The study site is an urban area located in the Haidian District of Beijing, China, between latitudes 39°58′30″ and 40°0′47″ and longitudes 116°17′55″ and 116°20′12″. The study area is a complex urban area with many land use types, including parks, universities, construction sites and residential areas. Land cover types are mainly impervious surface, vegetation cover, bare soil and water, which are typical in urban areas. The dominant land cover in parks is vegetation and water, while in the universities and the residential areas, the primary land cover is impervious surfaces, mixed with dispersed small patches of greenspace. Bare soil is the dominant land cover type in construction sites ( Figure 1).
Data
We used WorldView-2 satellite imagery, acquired on 14 September 2012, for land cover classification. WorldView-2, launched in October 2009, is the first high resolution 8-band multispectral commercial satellite (Table 1). The dynamic range is 11 bits. To take advantage of both the high spatial resolution and multispectral features of WorldView-2, a principal component merging algorithm was used to merge the multispectral bands and panchromatic band into a new multispectral image with 0.5-m spatial resolution using ERDAS TM 10. Four land cover types were identified for the study area: (1) impervious surfaces; (2) vegetation; (3) water; and (4) bare soil. Impervious surfaces were mainly roads and building roofs. Vegetation included trees and grass. Water mostly occurred in parks and bare soil in construction sites. Shadows from buildings and trees are common in very high resolution images of urban areas. Therefore, we included the shadow class and separated shadows from unshaded land cover types [21].
Methods
The object-based classification procedure includes image segmentation, training sample selection, classification feature selection, tuning parameter setting and, finally, algorithm execution. We first segmented the image into land cover segments and then chose a certain amount of segments of different land cover types as training samples. After comparing the training sample characteristics of different land cover types, we selected certain object features for classification. Finally, we adjusted the tuning parameters of different classifiers to generate high classification accuracy. For all four classifiers, we used the same procedure for image segmentation and the selection of training samples and classification features. The optimal setting of tuning parameters, however, were determined separately for each classifier. Following the classifications, object-based accuracy assessment was applied to evaluate different classifiers.
Image Segmentation
Many approaches to image segmentation have been applied to land cover classification [8]. Here, we used the multi-resolution segmentation approach embedded in Trimble eCognition. The multi-resolution segmentation algorithm is a bottom-up approach that consecutively merges pixels or existing image objects into larger ones, based on the criteria of relative homogeneity. Scale, shape and compactness parameters can be customized to define the size and shape of segmented objects. The scale parameter defines the maximum standard deviation of the homogeneity criteria in regard to the weighted image layers for generating image objects [22]. In general, the greater the scale value, the larger the size of objects and the higher the heterogeneity. In this study, we selected the scale parameters using an iterative "trial and error" approach [8]. Two object levels were created with the scale value setting at 30 and 100 (afterwards referred to as Level 1 and Level 2), respectively. The relatively small value of 30 was set to create homogeneous segments and, thus, to avoid the influence of mixed land cover objects. The coarser value of 100 was set to generate larger segments that depict a larger land cover of interest ( Figure 2). Through the trial-and-error approach and experience from previous studies [1,10], we assigned both object levels with the color weight of 0.9 and the shape weight as 0.1 to generate meaningful objects. The two parameters for compactness and smoothness were set equally as 0.5, based on visual inspection of the segmentation results. Equal weight was set for each of the 8 original image layers for segmentation. The number of segmented objects for Level 1 and Level 2 were 409,024 and 55,502, respectively.
Selection of Training, Testing Samples and Classification Features
There are some basic principles for the selection of training samples for pixel-based classification [18,19]. The number of object-based training samples, however, is usually determined based on the researcher's experience. Using Google Earth, we randomly chose 1500 object samples, 300 for each class, for the classifications and accuracy assessment. These samples were chosen at Level 1 to ensure "pure" objects that contained only one land cover type. We then randomly divided the 1500 object samples into two sets: 1000 as training samples and 500 as testing samples. To investigate the sensitivities of classifiers to the size of training samples, 8 training sample subsets were generated by randomly sampling from the total training sample set. The sizes of the training samples of those subsets were 125, 250, 375, 500, 625, 750, 875 and 1000, respectively. Within a training subset, the numbers of samples for each of the five classes were equal, and thus, the sample numbers of each class within the 8 training sample subsets were 25, 50, 75, 100, 125, 150, 175 and 200, respectively. Likewise, in the testing sample set, there were 100 samples per class.
Following the selection of the training and testing samples, spectral and spatial features of the training samples were selected for land cover classifications. There are more than one hundred object features that could be potentially incorporated into classifications [5,9]. Therefore, the selection of optimal object features was determined based on an approach that integrates expert knowledge and quantitative analysis. First, we chose a large number of object features that were frequently used in previous studies [1,5]. We then used the feature space optimization tools available in Trimble eCognition, combined with comparisons of the histograms of each feature among five land cover types to determine the selection of optimal object features. Consequently, we selected out 36 object features. These 36 features included 32 features calculated based on the 8 multispectral bands, that is mean value, standard deviation, mean difference to the super-object and standard deviation difference to the super-object of the 8 multispectral bands. In addition, we chose Brightness, Max. diff. (max intensity difference), NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) for classifications ( Table 2). Mean difference to super-object a The difference between the mean input layer value of an image object and the mean input layer value of its super-object. Distance of 1.
SD difference to super-object a The difference of the SD input layer value of an image object and the SD input layer value of its super-object. Distance of 1.
Brightness
Mean value of the 8 multispectral bands Max. diff.
Max intensity difference of the 8 multispectral bands a Object features were calculated for each of the 8 multispectral bands.
Classifiers and Primary Tuning Parameters
When using CART, KNN and SVM, one of the key steps is to set the tuning parameters, which are different for different classifiers. For each classifier, we tested a series of values for its tuning parameters to determine the optimal parameters, that is by which the classifier generates the highest overall classification accuracy. When comparing different classifiers, we used the classification results under the optimal parameters. In addition, the sensitivity of each classifier was examined using the 8 training sample subsets. The algorithms of the 4 classifiers were based on OpenCV [23].
DT, first developed by Breiman et al. (1984) [24], is a typical non-parametric model used in data mining. We used the classification and regression tree (CART) algorithm, one of the most commonly-used DT in this study. With CART, a tree can be developed in a binary recursive partitioning procedure by splitting the training sample set into subsets based on an attribute value test and then repeating this process on each derived subset. The tree-growing process stops when no further splits are possible for subsets. The maximum depth of the tree is the key tuning parameter in CART, which determines the complexity of the model. In general, a larger depth can build a relatively more complex tree with potentially higher overall classification accuracy. However, too many nodes may also lead to over-fitting of the model. In this study, we tested the value of "maximum depth" from 1 to 20 for all 8 training sample subsets, setting other parameters at the default value (e.g., cross-validation folds and min sample count both set to the default value of 10).
SVM is also a non-parametric algorithm that was first proposed by Vapnik and Chervonenkis (1971) [25]. With the SVM algorithm, a hyperplane is first built based on the maximum gap of the given training sample sets, and then, it classifies the segmented objects into one of the identified land cover classes (in this study, four classes). To map non-linear decision boundaries into linear ones in a higher dimension, the four most frequently used types of kernel functions in SVM algorithms are linear, polynomial, radial basis function (RBF) and sigmoid kernels [26]. In this study, we chose the most frequently used RBF kernel, which has been proven superior to other kernels in previous studies [5,14]. The RBF kernel has two important tuning parameters-"cost" (C) and gamma-which can affect the overall classification accuracy [27]. A large C value may create an over-fitted model, while adjusting the gamma will influence the shape of the separating hyperplane. The optimal value of parameters C and gamma are often estimated with the exhaustive search method [28], which uses a large range of values to identify the optimal value. To examine how these two key parameters affect the performance of SVM within the object-based approach, we systematically tested 10 values for both C and gamma. Specifically, we tested the 10 values of C-10 −1 , 10 0 ,10 1 , 10 2 , 10 3 ,10 4 , 10 5 , 10 6 , 10 7 and 10 8 -and 10 values of gamma-10 −5 , 10 −4 , 10 −3 , 10 −2 , 10 −1 , 10 0 , 10 1 , 10 2 , 10 3 and 10 4 . Consequently, we ran 100 experiments with different combinations of C and gamma for each of the 8 training sample subsets.
The non-parameter algorithm KNN uses an instance-based learning approach, or "lazy learning". With this algorithm, an object is classified based on the class attributes of its K nearest neighbors. Therefore, K is the key tuning parameter in this classifier, which largely determines the performance of the KNN classifier. In this study, we examined K values from 1 to 20 to identify the optimal K value for all training sample sets.
Normal Bayes (NB) is a probabilistic classifier based on Bayes' theorem (from Bayesian statistics). The NB classifier assumes that feature vectors from each land cover type are normally distributed, but not necessarily independently distributed, different from the other commonly-used classification model, naive Bayes [23,29]. With the NB classifier, the data distribution function is assumed to be a Gaussian mixture, one component per class [23]. Using the training samples, the algorithm first estimates the mean vectors and covariance matrices of the selected features for each class and then uses them for classification. Compared with the other three classifiers, one of the advantages of the NB classifier is that there is no need to set any tuning parameter (s), which could be subjective and time-consuming.
Accuracy Assessment
Object-based accuracy assessment was used to evaluate the land cover classifications [30]. We conducted accuracy assessment for overall accuracies, resulting from different classifiers, those from the same classifier, but with a different size of training samples, and those from the same classifier and same size of training samples, but different tuning parameters. Consequently, there were 1128 classification maps in total, with 800 generated from SVM, 160 from DT, 160 from KNN and 8 from NB, respectively. For each of the 4 classifiers, we chose one thematic map with the highest overall classification accuracy for each of the 8 training sample subsets and then compared these 32 thematic maps based on overall accuracy, the kappa coefficient and the user's and producer's accuracy. In addition, we repeated the random selection of training and testing samples within the 1500 samples 10 times and then compared average overall accuracy for all classifiers with the optimal parameter values.
Based on an error matrix of those thematic maps, we further calculated the Z-statistics to evaluate whether the classification results were significantly different between two classifiers [13]. Two results are significantly different at the 95% confidence level when the absolute value of the Z-statistics is greater than 1.96. Z-statistics were used to compare both the classifications from different classifiers, and those from the same classifier, but using different numbers of training samples.
The Comparisons of the Four Classifiers on Land Cover Classifications
The results showed that SVM generally had the best performance among the four classifiers (Table 3).
With the optimal parameter setting, the minimum overall accuracy and kappa coefficient of SVM was 92.6% and 0.9075, which was higher than the maximum overall accuracy and kappa coefficient of DT (88.4% and 0.855) and KNN (86.8% and 0.835). Figure 3 shows the classification results with the highest overall accuracy for each classifier. Using the Z-statistics, we found that the overall accuracies of SVM were significantly greater than those of DT and KNN, regardless of the size of training samples. In addition, SVM had significantly higher overall accuracy than NB, when the size of training samples was relatively small. However, when training samples were greater than or equal to 100 per class, the classification accuracies of NB and SVM were similar and significantly higher than the accuracies of the other two classifiers. In general, when the size of samples per class was less than 125, the accuracies of the four classifiers increased with increasing size of the training samples, and NB and KNN were more sensitive to sample sizes than SVM and DT. When the size of training samples increased from 25 to 125 per class, the classification accuracies of NB and KNN increased by 17% and 8.2%, while SVM and DT increased by 3.6% and 4.6%, respectively (Table 3; Figure 4). NB was the most sensitive to sample size. This may be because this parametric classifier used training samples to estimate parameter values for the data distribution, and thus, more training samples can lead to more accurate parameter estimation. In contrast, SVM is the least sensitive to sample sizes, because SVM only uses the support vectors instead of all training samples to build the separating hyperplane. Thus, adding more training samples may not significantly affect the classification accuracy. However, when the size of the samples is more than 125 per class, all four classifiers become insensitive to the increase of sample sizes. The classification accuracies of NB, KNN, SVM and DT fluctuated from 95% to 96.4%, 85.2% to 86.8%, 96.2% to 97.6% and 87.2% to 87.6%, respectively, with the sample size increasing from 125 to 200 per class. This result indicated that the training sample size of 125 per class might be a turning point, beyond which the increase of sample size does not necessarily lead to a significant increase in classification accuracies. These results have important implications for determining the appropriate sample size. These findings also have important implications for the selection of appropriate classifiers. In general, SVM is the best candidate classifier for urban land classifications. Using SVM could achieve the best overall classification accuracy, even with a relatively small amount of training samples. However, SVM is sensitive to its tuning parameter setting, which could be subjective and time-consuming. NB can be a practical choice when the training samples are sufficient. NB could achieve similarly high accuracy to that of SVM, but with no need to set any tuning parameter. One of the advantages of using DT is the generation of the "decision tree", which includes the information of features that are used in classification and the classification rules. This information can be helpful for better understanding the classification process. In addition, in case we want to build a ruleset to conduct a classification, "decision tree" can be a good reference.
The Effects of Tuning Parameters on Classification Accuracies
The tuning parameters of the classifier have a great impact on the classification accuracy. We found that SVM was the most sensitive to the setting of tuning parameters, followed by DT. However, KNN was relatively insensitive to the tuning parameter, that is the K. Using the largest training sample set, the classification accuracies of SVM, DT and KNN varied from 21% to 96.4%, 39.4% to 87.2% and 79.4% to 86.8%, respectively, when the settings of tuning parameters were different. For SVM, the optimal settings of tuning parameters varied with the sample size. With the training sample sizes of 25, 125 and 150 per class, the optimal values of C and gamma were 10,000 and 0.001, respectively. With the other sample sizes, the optimal values of C and gamma were 1,000,000 and 0.00001, respectively ( Table 3). The matrixes of the classification accuracy ( Figure 5) further provide some insights into how different values of C and gamma, or their combination, affect the classification accuracies of the SVM classifier: (1) when the value of gamma was greater than 0.1, the classification accuracies were relatively low, ranging from 20% to 74%, no matter what value the parameter C was; this result indicated that the value of gamma should not be greater than 0.1; (2) there were ranges of values of C and gamma, at which relatively high accuracies (up to or greater than 90%) could be achieved; in general, these values of C were between 1,000,000 and 100,000,000, and gamma between 0.00001 and 0.001; (3) the sizes of training samples seem not to influence the optimal setting of C and gamma. Very similar patterns were found when using different sizes of training samples. This clearly shows that the effects of tuning parameters on classification accuracy were much greater than that of the size of the training samples.
Similarly, for DT, the optimal maximum depth varied greatly with different sizes of training samples ( Figure 6). Generally, the optimal maximum depth mostly fell between five and eight, and the overall classification accuracy became relatively stable when the maximum depth was greater than or equal to five. For KNN, with the increasing of the parameter K, the classification accuracy showed a decreasing fluctuation, regardless of the training sample size (Figure 7). The highest accuracy was often achieved when parameter K was either one or three, suggesting that the performance of KNN is very similar to the nearest neighbor classifier. Figure 8 shows the average overall accuracies and the variations of the four classifiers with optimal parameter settings, that is K was three for KNN, the maximum depth was five for DT and C and gamma were 1,000,000 and 0.00001, respectively, for SVM. sample sizes from the 10-times random sampling were similar to those using the one-time samples (Figure 4), suggesting that the classifiers were relatively insensitive to the selection of samples. This is particularly true for SVM when the training sample size was greater than 125 per class.
Conclusions
In this study, we evaluated and compared the performance of four machine learning classifiers, namely SVM, NB, CART and KNN, in classifying very high resolution images, using an object-based classification procedure. In particular, we investigated how the tuning parameters of each of the classifiers (except for NB) affected the classification accuracy, when using different sizes of training samples. The results showed that SVM and NB were superior to CART and KNN in urban land classification. Both SVM and NB could achieve very high classification accuracy, with appropriate setting of the tuning parameters and/or enough training samples. However, each of the two classifiers has its advantages and disadvantages, and thus, the choice of the appropriate one may be case dependent. SVM could achieve relatively high accuracy with a relatively small amount of training samples, but the setting of tuning parameters could be subjective and time-consuming. In contrast, NB does not need the setting of any tuning parameter, but generally requires a large amount of training samples to achieve relatively high accuracy.
Both the size of training samples and the setting of tuning parameters have great impacts on the performance of classifiers. When the size of training samples is less than 125 per class, increasing the size of training samples generally leads to the increase of classification accuracies for all four classifiers, but NB and KNN were more sensitive to the sample size. Increasing the size of training samples does not seem to significantly improve the classification once the size of training samples reaches 125 per class. The tuning parameters of the classifier had a great impact on the classification accuracy. SVM was the most sensitive to the setting of tuning parameters, followed by DT, but KNN was relatively insensitive to the tuning parameter. While the optimal settings of tuning parameters varied with the size of training samples, some general patterns occurred. For SVM, setting C between 1,000,000 and 100,000,000, and gamma between 0.00001 and 0.001 usually achieved the best overall accuracy. With DT, the best classification accuracy was generally achieved when the max depth of classification tree was between five and eight. For KNN, the optimal K value was either one or three. These findings provide insights into the selection of classifiers and the size of training samples when implementing an object-based approach for urban land classification using high resolution images. This research also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing those parameters. | 5,990.6 | 2014-12-24T00:00:00.000 | [
"Computer Science",
"Environmental Science"
] |
An Efficient Analytical Approach for the Solution of Certain Fractional-Order Dynamical Systems
Ya Qin 1,2, Adnan Khan 3, Izaz Ali 3, Maysaa Al Qurashi 4 , Hassan Khan 3,* , Rasool Shah 3 and Dumitru Baleanu 5,6,7 1 Data Recovery Lab of Sichuan Province, Neijiang Normal University, Neijiang 641112, China<EMAIL_ADDRESS>2 School of Mathematics and Information Science, Neijiang Normal University, Neijiang 641112, China 3 Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan<EMAIL_ADDRESS>(A.K<EMAIL_ADDRESS>(I.A<EMAIL_ADDRESS>(R.S.) 4 Department of Mathematics, King Saud University, Riyadh 11495, Saudi Arabia<EMAIL_ADDRESS>5 Department of Mathematics, Faculty of Arts and Sciences, Cankaya University, 06530 Ankara, Turkey<EMAIL_ADDRESS>6 Institute of Space Sciences, 077125 Magurele-Bucharest, Romania 7 Department of Medical Research, China Medical University, Taichung 40402, Taiwan * Correspondence<EMAIL_ADDRESS>
Introduction
Coupled schemes of fractional-order partial differential equations (PDEs) are commonly applied in phenomena that occur in biomechanics and engineering. Various implementations of coupled PDE schemes arise in the modeling of electrical movement of the heart in biomechanics (see, for instance, [1][2][3]). They similarly occur when modeling other problems in biochemical and physical engineering, such as a device that includes a continuous stirred boiler container and a series plug or container [4,5]. The coupled FPDEs can be used for the combination of different-deformable objects with a fractional-order continuum of standard lightly surfaces [6,7]. Coupled PDE schemes also occur in modeling several significant gravitational and electromagnetic problems (see, for instance, [8][9][10][11][12][13]).
In 1965, Harry Bateman introduced a differential equation [14], which was later renamed as the Burger equation [15]. In science and engineering, the Burger equation has several implementations, particularly in problems that have the structure of non-linear problems. The Burger equation has interesting and important applications and defines various types of physical processes such as dynamic modeling, turbulence, acoustic waves heat transfer, and several others [16][17][18]. In many other cases, this type of non-linear PDE should be addressed utilizing special techniques because it does not support analytical approaches. In modern years, several scholars and mathematicians have developed an analytical technique for the solution of fractional-order problems such as the high order spectral volume formulation of Kannan et al. [19][20][21][22][23], homotopy perturbation (HPM), differential transformation, homotopy analysis, variational iteration and Adomian decomposition methods [24][25][26][27][28].
Recently, researchers have shown a greater interest in the study of fractional-calculus and Fractional differential equations (FDEs). Several important implementations have been explored in a number of different fields [29][30][31][32][33]. Researchers have also shown that several engineering and practical phenomena can be described well by FDEs systems as compared to classical differential equation systems and that equivalent FDEs and fractional integral equations give better precise and practical insights into the systems under discussion [34][35][36][37][38]. Many of these engineering challenging problems are addressed by using deterministic mathematical models that are represented by either partial differential equations of integer order or fractional-order. These mathematical models can further be classified into a scheme of ordinary differential equations, integro differential equations, and partial differential equations [39,40]. The existence of fractional differential equations is also discussed in [41]. In 1998, He [42,43] introduced HPM. In this technique, the solution is assumed to be in series form with a large number of terms that converge quickly towards the actual derived solution. The technique has the capability to solve nonlinear PDEs adequately. The HPTM results were compared with the actual solution to the problems and confirmed a higher degree of accuracy. This technique has also been used to solve address non-linear wave equations [44], bifurcation of nonlinear problems [45], and boundary value problems [46].
In the present research work, an efficient analytical technique is utilized to solve fractional-order Burger equations. The current is found to be very effective for the systems of FDEs. The present methodology is very attractive and has less computational cost. The present technique has shown a sufficient degree of accuracy.
Preliminaries
In this section, we present fractional calculus definitions along with properties of Laplace and Shehu transform theory. Definition 1. The Rieman-Liouville fractional integral is defined by [47][48][49] showing that the integral on the right side converges.
(2) Definition 3. Shehu transformation is new and similar to other integral transformation which is defined for functions of exponential order. We take a function in the set A define by [50][51][52][53] The Shehu transformation which is defined by S(.) for a function ν(τ) is expressed as The Shehu transformation of a function ν(τ) is V(s, µ): then ν(τ) is called the inverse of V(s, µ) which is defined as Definition 4. Shehu transform for nth derivatives. The Shehu transformation for nth derivatives is defined as [50][51][52][53] S ν (n) (τ) = s n u n V(s, u) − Definition 5 (Shehu transform for fractional order derivatives [50][51][52][53]). The Shehu transformation for the fractional order derivatives is expressed as
Using Shehu transform, we can write Equation (8) as [50][51][52][53] Now, by taking inverse Shehu transform, we get [50][51][52][53] where Now, perturbation technique having parameter in the form of power series is given as where is perturbation parameter and ∈ [0, 1]. The nonlinear term can be expressed as where H n are He's polynomials in term of ξ 0 , ξ 1 , ξ 2 , ..., ξ n , and can be determined as where D k = ∂ k ∂ k . Putting Equations (13) and (14) in Equation (10) and introducing the Homotopy, we get the couple of HPSTM as On comparing coefficient of on both sides, we obtain The component ξ k (ν, τ) can be calculated easily, which leads us to the convergent series rapidly. By taking → 1, we obtain Similarly, the procedure of the Laplace transform as special case for u = 1 of Shehu transform is used to derived similar results as Shehu transformation.
Applications
In this section, the solutions of numerical examples are presented to confirm the validity of the suggested methods. Example 1. Consider the following system of fractional-order Burger's equations [54][55][56] Taking the Shehu Transform of Equation (18), we have Taking Inverse Shehu Transform, we obtain By applying homotopy perturbation method as in Equation (16), we get On comparing coefficient of on both sides, we obtain . . .
Thus, by taking → 1 we get convergent series form solution as Particularly, putting γ = 1, we get the exact solution The homotopy perturbation Laplace transform method which is the special case for u = 1 of the homotopy perturbation Shehu transform method is used to obtain the same results of Example 1.
In Figure 1, the graphs a and b represent the exact and HPSTM solutions of Example 1. It is observed that the exact and HPSTM solutions are in closed contact and justify the validity of the proposed method. In Figure 2, the sub-graphs a and b have shown the plot of HPSTM solutions at various fractional-order of the derivatives in two and one dimensions of Example 1 respectively. The convergence phenomena of the fractional-order solutions towards integer-order solution is observed by using sub-graphs a and b. In Table 1, the solutions of Example 1 at fractional-orders γ = 0.8, 1 have been investigated. For this purpose, the homotopy perturbation method (HPM) with two different transformations is implemented to obtain the solutions. The results of HPM, homotopy perturbation Laplace transform method (HPLTM) and homotopy perturbation Shehu transform method (HPSTM) are compared in Table 1 for the variable ξ and ζ. The comparison has confirmed the best contact among the solutions of the suggested methods. The comparisons have been done in terms of absolute error. It is analyzed from the table that the proposed techniques have the desire degree of accuracy towards the exact solution of the problems. Example 2. Consider the following system of fractional PDEs [47] ξ γ τ + ζ ν η µ − ζ µ η ν = −ξ ζ γ τ + η ν ξ µ − ξ ν η µ = ζ η γ τ + ξ ν ζ µ − ξ µ ζ ν = η, (27) with initial conditions ξ(ν, µ, 0) = exp ν+µ ζ(ν, µ, 0) = exp ν−µ η(ν, µ, 0) = exp µ−ν (28) Taking Shehu Transform of Equation (27), we have Taking Inverse Shehu Transform, we get ξ(ν, µ, τ) = exp ν+µ +S −1 u γ s γ S −ζ ν η µ + ζ µ η ν − ξ .
In Figures 3-5 the sub-graphs a and b are respectively the graphs of the exact and HPSTM solutions at γ = 1 of example 2 for variables ξ, ζ and η. The graphical representation has confirmed the closed contact of the exact solution with HPSTM solution. In Figure 6, the sub-graphs a and b have shown the plot of HPSTM solutions at various fractional-order of the derivatives in two dimensions of Example 2 for variables ξ and ζ respectively. In Figure 7, the sub-graphs a and b have shown the plot of HPSTM solutions at various fractional-order of the derivatives in two and one dimensions of Example 2 for variable η respectively. The convergence phenomena of the fractional-order solutions towards integer-order solution is observed by using sub-graphs a and b.
Conclusions
In this paper, some systems of FPDEs are solved by the homotopy perturbation method along with Laplace and Shehu transformations. The derivatives with fractional-order are expressed in term of the Caputo operator. The suggested technique is implemented to find the solution of certain numerical examples. The solutions of these illustrative examples are determined for derivatives at different fractional-orders. The significant extent between the actual and approximate solutions is observed. Furthermore, fractional solutions are found to be convergent to integer-order solution for every targeted problem. It is observed that the proposed methods are simple, straightforward, have low computational cost, and can be modified for the solutions of FPDEs in science and engineering. In future, the proposed method can be extended to find the analytical solutions of nonlinear higher dimension fractional partial differential equations and systems of fractional partial differential equations. | 2,311.4 | 2020-05-28T00:00:00.000 | [
"Mathematics"
] |
Phosphatase inhibition by LB-100 enhances BMN-111 stimulation of bone growth
Activating mutations in fibroblast growth factor receptor 3 (FGFR3) and inactivating mutations in the natriuretic peptide receptor 2 (NPR2) guanylyl cyclase both result in decreased production of cyclic GMP in chondrocytes and severe short stature, causing achondroplasia (ACH) and acromesomelic dysplasia, type Maroteaux, respectively. Previously, we showed that an NPR2 agonist BMN-111 (vosoritide) increases bone growth in mice mimicking ACH (Fgfr3Y367C/+). Here, because FGFR3 signaling decreases NPR2 activity by dephosphorylating the NPR2 protein, we tested whether a phosphatase inhibitor (LB-100) could enhance BMN-111–stimulated bone growth in ACH. Measurements of cGMP production in chondrocytes of living tibias, and of NPR2 phosphorylation in primary chondrocytes, showed that LB-100 counteracted FGF-induced dephosphorylation and inactivation of NPR2. In ex vivo experiments with Fgfr3Y367C/+ mice, the combination of BMN-111 and LB-100 increased bone length and cartilage area, restored chondrocyte terminal differentiation, and increased the proliferative growth plate area, more than BMN-111 alone. The combination treatment also reduced the abnormal elevation of MAP kinase activity in the growth plate of Fgfr3Y367C/+ mice and improved the skull base anomalies. Our results provide a proof of concept that a phosphatase inhibitor could be used together with an NPR2 agonist to enhance cGMP production as a therapy for ACH.
Introduction
Achondroplasia (ACH), the most common form of dwarfism, is due to a gain-of-function mutation in the fibroblast growth factor receptor type 3 (FGFR3) gene (1,2). FGFR3 is expressed in growth plate cartilage and bone, which explains the bone anomalies observed in patients with ACH. The characteristic features of these patients are short arms and legs, macrocephaly, hypoplasia of the midface, lordosis, foramen magnum stenosis, and spinal compression (3). The generation of Fgfr3-specific mouse models has highlighted the role of FGFR3 during bone growth. In the absence of Fgfr3, the most prominent phenotype of the mice is overgrowth, thus indicating that FGFR3 is a negative regulator of bone growth (4,5). Conversely, mice expressing a Fgfr3-activating mutation develop dwarfism and have reduced linear growth and impaired endochondral ossification, with reduced chondrocyte proliferation and reduced hypertrophic differentiation (6)(7)(8)(9)(10). A complex intracellular network of signals, including FGFR3, mediates this skeletal phenotype. Activating mutations in FGFR3 lead to upregulated FGFR3 protein (11) and to increased activity of several downstream intracellular signaling pathways, including MAPK, PI3K/AKT, PLCγ, and STATs (12).
During development, the rate of longitudinal bone growth is determined by chondrocyte proliferation and differentiation and is regulated by several secreted growth factors and endocrine factors, including parathyroid hormone-like peptide, Indian Hedgehog, bone morphometric proteins, transforming growth factor β, insulin like growth factor, and C-type natriuretic peptide (CNP, ref. 13). CNP and its receptor, the guanylyl cyclase natriuretic peptide receptor 2 (NPR2, also known as guanylyl cyclase B), are expressed in chondrocytes as well as in osteoblasts and are recognized as important regulators of longitudinal bone growth and bone homeostasis. NPR2 possesses guanylyl cyclase activity that leads to synthesis of cyclic guanosine monophosphate (cGMP), and dysregulation of this pathway is responsible for skeletal disorders. In clinical studies, inactivating mutations of NPR2 were found to cause a rare form of extreme short stature, called acromesomelic dysplasia, Activating mutations in fibroblast growth factor receptor 3 (FGFR3) and inactivating mutations in the natriuretic peptide receptor 2 (NPR2) guanylyl cyclase both result in decreased production of cyclic GMP in chondrocytes and severe short stature, causing achondroplasia (ACH) and acromesomelic dysplasia, type Maroteaux, respectively. Previously, we showed that an NPR2 agonist BMN-111 (vosoritide) increases bone growth in mice mimicking ACH (Fgfr3 Y367C/+ ). Here, because FGFR3 signaling decreases NPR2 activity by dephosphorylating the NPR2 protein, we tested whether a phosphatase inhibitor (LB-100) could enhance BMN-111-stimulated bone growth in ACH. Measurements of cGMP production in chondrocytes of living tibias, and of NPR2 phosphorylation in primary chondrocytes, showed that LB-100 counteracted FGF-induced dephosphorylation and inactivation of NPR2. In ex vivo experiments with Fgfr3 Y367C/+ mice, the combination of BMN-111 and LB-100 increased bone length and cartilage area, restored chondrocyte terminal differentiation, and increased the proliferative growth plate area, more than BMN-111 alone. The combination treatment also reduced the abnormal elevation of MAP kinase activity in the growth plate of Fgfr3 Y367C/+ mice and improved the skull base anomalies. Our results provide a proof of concept that a phosphatase inhibitor could be used together with an NPR2 agonist to enhance cGMP production as a therapy for ACH.
Previous studies have indicated that, among its diverse signaling effects, activation of FGFR3 results in reduced phosphorylation and activity of NPR2 in the growth plate (26,27). Because CNP activation of NPR2 requires that the receptor is phosphorylated on multiple serines and threonines (28,29), FGF-induced NPR2 dephosphorylation lowers cGMP and opposes bone growth. The significance of this aspect of FGF signaling for ACH was definitively established by the recent finding that, in a mouse model of ACH, bone growth is restored by replacing the NPR2 protein with a dephosphorylation resistant form of NPR2 (NPR2 7E/7E , also known as GC-B 7E/7E ) with a modified version of the protein that cannot be dephosphorylated (30). Treatment with CNP or a protease-resistant CNP analog, known as BMN-111 or vosoritide, also increases bone growth in mouse models of ACH (31,32), and BMN-111 is currently in clinical development, with phase 2 and 3 results showing additional height gain in ACH patients (33,34). These accumulating results, together with evidence that a PPP-family phosphatase mediates the FGF-induced dephosphorylation and inactivation of NPR2 (26,27), suggest that a PPP-family phosphatase inhibitor could enhance bone growth in ACH patients if applied together with a CNP analog.
Here, we tested this concept using a semiselective PPP family phosphatase inhibitor, LB-100 (35). In studies of animal cancers, LB-100 has been shown to enhance responses to immunotherapy, CAR T cell therapy, and tyrosine kinase inhibitors (36)(37)(38). Phase 1 clinical trials concluded that the safety, tolerability and preliminary evidence of antitumor activity supported continued testing as a potentially novel treatment for human cancers (39). Here, we find that LB-100 counteracts the FGF-induced dephosphorylation and inactivation of NPR2, complementing the CNP stimulation and promoting bone growth in a mouse model of ACH. Our results provide evidence for the concept that an inhibitor of NPR2 dephosphorylation could be used together with an NPR2 agonist to enhance cGMP production as a therapy for ACH.
Results
LB-100 counteracts the inactivation of Npr2 by FGF in growth plate chondrocytes. NPR2 activity in chondrocytes of intact growth plates was measured as previously described, using mice expressing a FRET sensor for cGMP, cGi500 (27). The mice were WT for Fgfr3. Tibias were isolated from newborn mice, and the overlying tissue was excised to expose the growth plate for confocal imaging ( Figure 1A). When the NPR2 agonist CNP was perfused across the growth plate, the CFP/YFP emission ratio from cGi500 increased, indicating an increase in cGMP, due to stimulation of the guanylyl cyclase activity of NPR2 ( Figure 1B). Similar results were obtained with protease-resistant BMN-111 (Supplemental Figure 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.141426DS1). Perfusion of A-type natriuretic peptide (ANP), which activates the NPR1 guanylyl cyclase, or perfusion of a nitric oxide donor (DEA/NO), which activates soluble guanylyl cyclases, did not increase cGMP (Supplemental Figure 2), showing that -among the several mammalian guanylyl cyclases -only NPR2 is active in the chondrocytes of the mouse growth plate. As previously shown (27), exposure of the growth plate to FGF18 suppressed the cGMP increase in response to CNP perfusion ( Figure 1B), indicating that FGF receptor activation decreases NPR2 activity.
Based on previous evidence that a PPP-family phosphatase inhibitor, cantharidin (100 μM), inhibits the inactivation of NPR2 in growth plate chondrocytes by FGF (27), we tested whether a less toxic cantharidin derivative, LB-100, would increase NPR2 activity and long bone growth. LB-100 was originally reported as a specific inhibitor of the catalytic subunit of PPP2 (PPP2C) but was later shown to also act as a catalytic inhibitor of PPP5C (35). Since cantharidin demonstrates only modest selectivity for PPP2C versus PPP1C (40), we tested the ability of LB-100 to inhibit PPP1C activity using 2 established assays that use different substrates. We determined that LB-100 also inhibits PPP1C with an IC 50 < 2 μM ( Figure 1C and Table 1). Based on its structural similarity with cantharidin, 10 μM LB-100 is not likely to inhibit PPP3C/calcineurin or PPP7C/PPPEF (41).
To investigate if LB-100 counteracts the inactivation of NPR2 by FGF, we preincubated the tibia with or without LB-100 and then with FGF. Following these incubations, the tibia was placed in a perfusion slide for confocal imaging, and cGMP production by NPR2 was monitored by measuring the increase in the CFP/ YFP emission ratio in response to CNP. The 2-hour incubation with 10 μM LB-100 caused no visible change in chondrocyte morphology, as imaged in the live growth plate (compare Figure 1D with the control in Figure 1A). After FGF treatment, the cGMP increase in response to CNP was small ( Figure 1E). However, when the tibia was preincubated with 5 or 10 μM LB-100 before applying FGF, the CNP-induced cGMP increase was enhanced ( Figure 1, E and F). A concentration of 1 μM LB-100 had no effect ( Figure 1F). The CFP/YFP emission ratio attained after CNP perfusion in tibias that had been incubated in 5 or 10 μM LB-100 before the FGF treatment was similar to or greater than the ratio in control tibias without FGF ( Figure 1F). Figure 1F summarizes the CNP-stimulated increases in the CFP/YFP emission ratio from cGi500 under these various conditions and demonstrates that LB-100 counteracts the inactivation of NPR2 by FGF. LB-100 was more effective than cantharidin, with 5 μM LB-100 resulting in a stimulation equivalent to that seen with 10 μM cantharidin ( Figure 1F). Table 1. (D) Confocal image of cGi500 fluorescence in growth plate chondrocytes after pretreatment with 10 μM LB-100 for 2 hours. No difference in morphology was seen compared with control tibias (A) without LB-100. Scale bar: 100 μm. (E and F) Effect of LB-100 (or cantharidin) preincubation on CNP-stimulated cGMP production in FGF-treated tibias. Tibias expressing cGi500 were preincubated with solutions with or without LB-100 for 60 minutes. FGF was then added, and 80 minutes later, tibias were placed in a perfusion slide for cGi500 imaging during CNP perfusion. (E) The CFP/YFP emission ratio as a function of time after CNP perfusion. (F) The CFP/YFP emission ratio at 15 minutes after CNP perfusion. Symbols indicate individual tibias (n = 5-27). For E and F, data are shown as mean ± SEM. Data were analyzed by 1-way ANOVA followed by the Holm-Sidak multiple comparison test. **P < 0.01, ***P < 0.001, ****P < 0.0001.
LB-100 counteracts the FGF-induced dephosphorylation of NPR2 by FGF in primary chondrocyte cultures.
To investigate if LB-100 counteracts the FGF-induced dephosphorylation of NPR2, we used Phos-tag gel electrophoresis (42) to analyze the phosphorylation state of NPR2 in isolated chondrocytes from the ribs of newborn mice. The mice were WT for Fgfr3. To allow specific labeling of the NPR2 protein, the mice were genetically modified to insert a 9-amino acid hemagglutinin (HA) tag on the N-terminus of NPR2 (HA-Npr2; ref. 43) (Supplemental Figure 3). We compared the phosphorylation state of NPR2 in chondrocytes with and without LB-100 preincubation -and with and without subsequent exposure to FGF. Treated and untreated chondrocytes had a similar appearance (Supplemental Figure 4).
Chondrocyte proteins were separated by Phos-tag gel electrophoresis, which slows migration of phosphorylated proteins, and Western blots were probed for NPR2 ( Figure 2A). Without FGF treatment, NPR2 protein from the rib chondrocytes was present in a broad region of the gel. With FGF treatment, the ratio of the signal in the upper versus lower regions decreased ( Figure 2, A and B; compare lanes 1 and 2), indicating NPR2 dephosphorylation in response to FGF and confirming, with primary chondrocytes, a previous study using a rat chondrosarcoma (RCS) cell line (26). However, if the chondrocytes were preincubated with 10 μM LB-100, the dephosphorylation in response to FGF was only partial, indicating that LB-100 counteracts the FGF-induced dephosphorylation of NPR2 (Figure 2, A and B; compare lanes 2 and 4).
To more closely mimic conditions used in experiments to be described below, the NPR2 phosphorylation state was also analyzed using chondrocyte cultures to which we added the protease-resistant BMN-111. The addition of BMN-111 caused some reduction in NPR2 phosphorylation (Figure 2, A and B; compare lanes 1 and 5), independently of treatment with FGF ( Figure 2, A and B; compare lanes 2 and 6). This is consistent with previous evidence that some NPR2 dephosphorylation occurs in response to prolonged agonist (CNP) exposure (28). However, the addition of BMN-111 did not change the conclusions that FGF causes NPR2 dephosphorylation ( In Fgfr3 Y367C/+ femurs, LB-100 enhances the stimulation of bone growth by the protease-resistant NPR2 agonist BMN-111. Previously, we showed that the protease-resistant CNP analog BMN-111 increases bone growth in a mouse model of ACH, in which tyrosine 367 is changed to a cysteine (Fgfr3 Y367C/+ ), resulting in constitutive activation of FGFR3 (32,44). However, BMN-111 only partially rescued the effect of the FGFR3-activating mutation. Our finding that LB-100 opposes the FGF-induced dephosphorylation and inhibition of NPR2 activity in chondrocytes suggested that applying LB-100 together with BMN-111 might enhance the stimulation of growth in bones from Fgfr3 Y367C/+ mice ( Figure 3A).
As previously reported (32), 0.1 μM BMN-111 increased the growth of cultured femurs from E16.5 Fgfr3 Y367C/+ mice (Figure 3, B-D). Over a 6-day culture period, the mean increase in bone length in the BMN-111-stimulated Fgfr3 Y367C/+ femurs was 1.78 times that in vehicle-treated bones ( Figure 3C). LB-100 alone also increased the extent of elongation, showing a growth ratio of 1.30 for LB-100/control ( Figure 3C). However, when Fgfr3 Y367C/+ femurs were cultured with BMN-111 together with 10 μM LB-100, the mean increase in bone length was 2.06 times that in untreated bones ( Figure 3C). Thus, the combination of BMN-111 and LB-100 resulted in elongation during the culture period that was 16% greater than with BMN-111 alone.
We also measured the effect of LB-100 and BMN-111 on the increase of the total bone and cartilage area, defined as the area within the periphery of a photograph of the femur (Supplemental Figure 5). LB-100 and (35). PPP1C, PPP2C, and PPP5C assays were conducted using the same assays, conducted by the same investigators, and employed the same batch of substrate and assay conditions.
JCI Insight 2021;6(9):e141426 https://doi.org/10.1172/jci.insight.141426 BMN-111 each individually increased this area, with a growth ratio of 1.40 for LB-100/control, and a growth ratio of 1.51 for BMN-111/control ( Figure 3D). The combination of LB-100 and BMN-111 was even more effective, with a growth ratio of 1.93. Thus, the combination of BMN-111 and LB-100 enhanced the increase in bone and cartilage area by 27% compared with BMN-111 alone ( Figure 3D). Combined treatment with LB-100 and BMN-111 improves growth plate cartilage homeostasis in Fgfr3 Y367C/+ femurs. Histological analyses of the epiphyseal growth plates of Fgfr3 Y367C/+ femurs showed that combining BMN-111 and LB-100 treatments improved cartilage growth homeostasis (Figure 4). Prehypertrophic and hypertrophic chondrocytes produce an extracellular matrix rich in Collagen type X (COLX); we used COLX immunostaining to label the hypertrophic region and to visualize and measure individual cells. This labeling revealed a highly beneficial effect of the combined treatment on the size of the cells in the hypertrophic area of Fgfr3 Y367C/+ femurs (Figure 4, A and B). The mean cross-sectional area of individual hypertrophic chondrocytes of Fgfr3 Y367C/+ mice was reduced by about half compared with that in the Fgfr3 +/+ growth plate ( Figure 4, A and B; measured as described in Supplemental Figure 6). As previously reported (32), BMN-111 increased the size of the Fgfr3 Y367C/+ hypertrophic chondrocytes, but the cells remained smaller than for the WT (Figure 4, A and B). However, with the combined treatment of BMN-111 and LB-100, the mean area of the Fgfr3 Y367C/+ hypertrophic cells in the proximal growth plate was 32% greater than with BMN-111 alone and was similar to that of Fgfr3 +/+ hypertrophic cells, indicating that the final differentiation of the chondrocytes was restored by the treatment (Figure 4, A and B). Corresponding measurements for the distal growth plate showed a similar trend (Supplemental Figure 7).
We also observed a beneficial effect of the combined treatment on the proliferative region of the growth plate of Fgfr3 Y367C/+ mice. We measured the area of the proliferative region by subtracting the hypertrophic area, identified by COLX labeling, from the total growth plate area. Based on these measurements, the combined treatment increased the total proliferative growth plate area of the femur by an average of 33% over vehicle, compared with 20% for BMN-111 alone ( Figure 4C). Thus, the combined treatment increased the proliferative area by 13% compared with BMN-111 alone ( Figure 4C).
CNP signaling through NPR2 in the growth plate inhibits the MAP kinase pathway and its extracellular signal-regulated kinase 1 and 2 (ERK1/2) (refs. 31, 45, 46; Figure 5A). Therefore, we investigated the impact of treatment with LB-100 and BMN-111 on the phosphorylation of ERK1/2 in growth plates from Fgfr3 Y367C/+ embryos. As expected, immunolabeling showed a high level of phosphorylated ERK1/2 in the proximal and distal parts of the cartilage compared with WT controls (Figure 5, B and C). The combined LB-100 and BMN-111 treatment of Fgfr3 Y367C/+ femurs decreased the activity of the MAP kinase pathway, as demonstrated by the decreased phosphorylation of ERK1/2 in the proximal and distal growth plates of the femurs ( Figure 5, B and C).
The combination of LB-100 and BMN-111 enhances growth and improves chondrocyte differentiation in the ex vivocultured Fgfr3 Y367C/+ skull base. Because compression of the spinal cord at the level of the foramen magnum (part of the skull base) is a critical clinical feature of ACH, contributing significantly to infant morbidity, we tested whether the combination of LB-100 and BMN-111 improves the defective growth of the skull base observed in Fgfr3 Y367C/+ mice. To investigate this question, we developed a model of ex vivo culture of the skull base isolated from mouse embryos (E16.5; Figure 6A). Over a 6-day culture period, we observed that the elongation of the cranial base was altered in explants from Fgfr3 Y367C/+ embryos compared with Fgfr3 +/+ embryos, because of a reduced size of the spheno-occipital and interoccipital synchondroses, localized respectively between the basioccipital bone (BO) and basisphenoid bone (BS), and between the interoccipital bone (IO) and BO ( Figure 6, B-D).
The combination of LB-100 and BMN-111 increased the percent of growth of the 2 synchondroses in explants from Fgfr3 Y367C/+ embryos ( Figure 6, C and D), leading to a rescue of the skull base anomalies, with similar bone elongation comparing Fgfr3 +/+ and treated Fgfr3 Y367C/+ explants ( Figure 6, C and D). Histological analyses of the synchondrosis showed that combined BMN-111 and LB-100 treatments improved cartilage homeostasis in Fgfr3 Y367C/+ explants ( Figure 6E). COLX immunolabeling revealed a highly beneficial effect of the combined treatment on the size of the cells in the hypertrophic area of Fgfr3 Y367C/+ cartilage.
Discussion
Understanding of the mechanisms by which FGF/FGFR3 and CNP/NPR2 regulate longitudinal bone growth has allowed the development of an effective therapeutic strategy using a CNP analog (vosoritide; BMN-111) to treat ACH (33,34). The findings described here identify the PPP-family phosphatase inhibitor LB-100 as a stimulator of bone growth when used in combination with this CNP analog to stimulate production of cGMP by NPR2. Firstly, using isolated WT bones incubated with FGF to mimic an ACH-like condition, we show that pretreatment with LB-100 counteracts the decrease in NPR2 guanylyl cyclase activity by FGFR3. Secondly, our results show that FGFR3 activation leads to NPR2 dephosphorylation in primary cultured WT chondrocytes and that LB-100 suppresses the dephosphorylation. Moreover, application of a combination of BMN-111 and LB-100 to long bones from the ACH mouse model Fgfr3 Y367C/+ results in growth that exceeds that stimulated by BMN-111 alone, and this combination also increases growth of the cranial base. This beneficial impact of the treatment on skull base elongation in Fgfr3 Y367C/+ mice and the correction of their defects are promising because the stenosis of the foramen magnum of ACH patients results from defective cranial base elongation. These results provide a proof of concept that BMN-111 and a PPP-family phosphatase inhibitor could potentially be used in combination for treatment of skeletal dysplasias such as ACH.
Our data also show the benefit of this treatment for growth plate cartilage during bone development in Fgfr3 Y367C/+ mice. During the process of endochondral ossification, chondrocytes actively proliferate in the resting and proliferating chondrocyte zone and then differentiate to hypertrophic chondrocytes, which lose the capacity to proliferate. The terminally differentiated hypertrophic cells are removed by cell death or transdifferentiate into osteoblasts. It is well known that FGFR3 signaling decreases bone growth by inhibiting both proliferation and differentiation of chondrocytes (47), and it has been proposed that FGFR3 acts by way of ERK1/2 to restrict hypertrophic differentiation (48). Here, we showed that treatment with BMN-111 and LB-100 reduced the levels of phosphorylated ERK1/2, thus modifying chondrocyte differentiation and allowing bone growth. In addition, we noted an impressive increase in the size of the hypertrophic cells. We concluded that the treatment restored cartilage homeostasis, and we hypothesize that the elevated cGMP resulting from this treatment could be a key regulator of transdifferentiation of hypertrophic cells into osteoblasts and could control the chondrogenic or osteogenic fate decision.
The increase in NPR2 phosphorylation by LB-100 is correlated with improved chondrocyte proliferation and differentiation in Fgfr3 Y367C/+ femurs, consistent with results with a mouse model (Npr2 7E/7E ) mimicking constitutive phosphorylation of NPR2 (27,30). Because LB-100 inhibits multiple PPP-family phosphatases (35) (Table 1), and because its safety for long-term use in children is unknown, our results provide only a proof of principle for a possible combination treatment. Future studies to determine which phosphatases act to dephosphorylate NPR2 in chondrocytes are clearly warranted, and increased height in children with mutations in particular PPP2 regulatory subunit genes provides a clue (49). Identification of these phosphatases and development of more specific inhibitors targeting them could lead to future therapies.
Recent mouse studies indicate that, in addition to increasing prepubertal bone elongation, phosphorylation of NPR2 increases bone density, due to an increase in the number of active osteoblasts at the bone surface (50). Because low bone density is one of the key clinical features of ACH (51), the combination a CNP analog and a phosphatase inhibitor could also have a beneficial impact on bone density for patients with ACH and related conditions. In addition, such a treatment could have potential for treatment of osteoporosis and, because CNP/NPR2 also plays a key role in regulation of joint homeostasis, could be beneficial for preventing or minimizing cartilage loss and promoting repair of the damaged articular cartilage in skeletal disorders and osteoarthritis (52). More generally, the combination of natriuretic peptides and phosphatase inhibitors could have therapeutic potential for multiple disorders involving NPR2 and the related guanylyl cyclase NPR1 that also requires phosphorylation for activity (53).
In summary, the combined (LB-100 and BMN-111) treatment acts on both chondrocyte proliferation and differentiation, thus promoting better bone growth. In ACH, the homeostasis of the growth plate is disturbed, and proliferation and differentiation are affected by the overactivation of FGFR3. Currently, BMN-111 (vosoritide) is being studied in children with ACH and, as demonstrated in preclinical studies (32), mostly restores the defective differentiation in the growth plate. Recently reported phase 2 and 3 data demonstrate that BMN-111 results in a sustained increase in annualized growth velocity for up to 42 months in children 5-14 years of age with ACH (33,34). The present study provides a proof of concept that a combination of BMN-111 and a phosphatase inhibitor has the potential to increase bone growth rate in ACH patients to a higher level than BMN-111 alone.
Methods
Mice. Three mouse lines were used for this study: cGi500 (54), HA-Npr2 (43), and Fgfr3 Y367C/+ (44). The cGi500 mice were provided by Robert Feil (University of Tübingen, Tübingen, Germany). The strain for all mouse lines was CB7BL/6J; no sex selection was made. All experiments were performed using E16.5 embryos or 1-to 2-day-old newborns, as described for individual procedures.
Measurements of cGMP production in tibia growth plates using cGi500. cGMP production in chondrocytes within intact growth plates was measured using tibias dissected from newborn mice (0-to 1-day-old mice) that globally expressed 1 or 2 copies of the cGi500 FRET sensor, as previously described (27). Tibias were dissected and cultured overnight on Millicell organotypic membranes (PICMORG50; MilliporeSigma) in BGJb medium (Thermo Fisher Scientific, catalog 12591-038) with 0.1% BSA (MP Biomedicals, catalog 103700), 100 units/mL of penicillin, and 100 μg/mL of streptomycin (Thermo Fisher Scientific, 15140-122). In preparation for imaging, each tibia was slit to remove the tissue overlying the growth plate. Where indicated, the tibia was incubated in LB-100, cantharidin, or control medium, followed by addition of FGF18 (0.5 μg/mL + 1 μg/mL heparin) or control medium containing heparin only. The tibia was then placed in a perfusion slide (ibidi USA, catalog 80186, special order with no adhesive), and the growth plate was imaged on the stage of a confocal microscope, as previously described (27).
Determination of the effect of LB-100 on PPP1C phosphatase activity. The coding sequence of human PPP1CA was expressed as a maltose binding protein fusion in a BL-21 strain of E. coli and purified as previously described (55). Phosphohistone phosphatase assays were performed as previously described (55,56). Briefly, LB-100, at the indicated concentrations, or vehicle control (H 2 O) was added to enzyme/buffer JCI Insight 2021;6(9):e141426 https://doi.org/10.1172/jci.insight.141426 aliquots about 10 minutes prior to starting assays by the addition of [ 32 P]-phosphohistone substrate (to a final assay concentration of 300 nM incorporated phosphate). [ 32 P]-phosphohistone was prepared by the phosphorylation of bovine brain histone (MilliporeSigma, type-2AS) with cAMP-dependent protein kinase (PKA) in the presence of cAMP and [ 32 P]-ATP using established methods (56,57). Phosphatase activity was measured by the quantitation of [ 32 P]-labeled orthophosphate liberated from the substrate using established protocols (57). 6,8-Difluoro-4-methylumbelliferyl phosphate-based (DiFMUP-based) inhibition assays were conducted as described (56,57), in a 96-well format using DiFMUP (Invitrogen) (100 μM final assay concentration). IC 50 values were calculated from a 10-point concentration/dose response curve by a 4-parameter logistic fit of the data, using 3-8 replicates per concentration.
Rib chondrocyte cultures. Rib cages were dissected from newborn mice (0-2 days old) and trimmed to remove the skin, spinal cord, and soft tissue around the sternum and ribs. Nonchondrocyte tissue was digested away by incubating the rib cages in 2 mg/mL pronase (Roche, catalog 10165921001) in PBS for 1 hour in a shaking water bath at 37°C and was then incubated in 3 mg/mL collagenase D (Roche, catalog 11088866001) in medium for 1 hour. After washing, the rib cages were transferred to a dish with fresh collagenase D and incubated for 5-6 hours, with trituration at 2 hours, to release the chondrocytes. The isolated cells were passed through a 40 μm nylon cell strainer (Corning, catalog 431750), resuspended in DMEM/F12 medium (Thermo Fisher Scientific, catalog 11320-033) with 10% FBS (Thermo Fisher Scientific, catalog 10082-139), 100 units/mL of penicillin, and 100 μg/mL of streptomycin. The cells were plated in 35 mm tissue culture dishes, at a density corresponding to 1 newborn mouse per dish, and cultured for 3 days, at which point the cells were approximately 75%-90% confluent. They were then washed with PBS and incubated in serum-free medium for 18 hours. The cells were then incubated in LB-100 (10 μM), or control medium, followed by addition of FGF18 (0.5 μg/mL + 1 μg/mL heparin) or control medium containing heparin only.
At end of the incubation period, dishes were washed in PBS, and cells were lysed in 250 μL of 1% SDS containing 10 mM sodium fluoride, 1 μM microcystin-LR (Cayman Chemical, catalog 10007188), and protease inhibitor cocktail (Roche, catalog 04 693 159 001). Protein content was determined by a BCA assay (Pierce, catalog 23225). The protein yield per newborn mouse was approximately 200-300 μg.
Phos-tag gel electrophoresis and Western blotting. Proteins were separated in a Phos-tag-containing gel, as previously described (58), except that chondrocyte lysates (30 μg protein) were used without immunoprecipitation. Phos-tag and protein size markers were obtained from Fujifilm Wako Pure Chemical (catalogs AAL-107 and 230-02461, respectively). For these studies, we used mice with HA-tagged NPR2 (43), and blots were probed with an antibody against the HA tag (Cell Signaling Technology, catalog 2367, 1:1000 dilution). The specificity of this antibody is validated in Supplemental Figure 3. Note that molecular weight markers are only approximate for Phos-tag gels.
Ex vivo culture of fetal femurs and skull base. Femurs from E16.5 embryos were cultured ex vivo, as described previously (32,47). The left femur was cultured in the presence of LB-100 (10 μM), BMN-111 (0.1 μM), or LB-100 (10 μM) + BMN-111 (0.1 μM) and was compared with the vehicle-treated right femur. The bone's length was measured on day 0 (D0) and D6. Images were captured with an Olympus SZX12 stereo microscope and quantified using cellSens software (Olympus). The results were expressed as the increase in femur length or area (D6 -D0) in the presence or absence of LB-100, BMN-111, or LB-100 + BMN-111. Bone length and area were measured as shown in Supplemental Figure 5. To generate the graphs shown in Figure 3, the length or 2-dimensional area on D0 was subtracted from the length or area on D6 to calculate the amount of growth. These measurements of growth in drug-treated bones were divided by the mean values from corresponding measurements of control (vehicle-treated) bones; the graphs show the ratio of treated/control growth.
Embryonic skull base (E16.5) dissections were performed under an Olympus SZX12 stereo microscope and the skull bases (including the spheno-occipital and interoccipital synchondroses) were placed on top of 250 μL of Matrigel (BD Biosciences) in 24-well plates and cultured for 6 days in DMEM with antibiotics and 0.2% BSA (MilliporeSigma) supplemented with vehicle or LB-100 (10 μM) + BMN-111 (0.1 μM). The distances between the BS, BO, and IO bones were measured on D0 and D6 using cellSens software (Olympus). Percentage increases in BS-BO and BO-IO were calculated for each sample by comparing D0 and D6. The mean of the left and right BO-IO measurements were used to calculate the BO-IO increase. Five embryos were used for each group.
Mean areas of individual hypertrophic chondrocytes were measured from COLX-labeled sections, within a 166 μm wide × 76 μm high box positioned 50 μm from mineralization front (Supplemental Figure 6). The measurements were made manually using Fiji software and the freehand selection tool. For analysis of the effect of the drug treatments on the area occupied by proliferative chondrocytes, these cells were identified by their round or columnar shape, as seen with HES staining, and by the absence of COLX labeling. We measured the total area occupied by chondrocytes within the whole growth plate and the area occupied by COLX + chondrocytes. The area for proliferating chondrocytes was calculated by subtracting the COLX + area from the whole growth plate area.
Statistics. Data were analyzed using Prism 6 (GraphPad Software). To compare more than 2 groups, we used 1-way ANOVA followed by 2-tailed t tests with the Holm-Sidak correction for multiple comparisons, or 2-way ANOVA followed by Sidak's multiple comparisons tests. Two groups were compared using either paired or unpaired 2-tailed t tests, as indicated in the figure legends.
Study approval. All experiments were conducted as approved by the animal care committees of the University of Connecticut Health Center and the Imagine Institute, Université de Paris. | 7,380.8 | 2021-05-10T00:00:00.000 | [
"Biology",
"Medicine"
] |
Closed Loop Guidance During Close Range Rendezvous in a Three Body Problem
The paper presents a novel application of the State Dependent Riccati Equation (SDRE) guidance approach with state constraints for a chaser spacecraft in the close proximity of a passive target. The dynamics are described by full 6 degree of freedom rigid-body relative motion. The final trajectory is defined by a passively safe approaching cone, which acts as path constraint and follows the attitude motion of target. A Near Rectilinear Halo Orbit in the Earth-Moon system is the selected rendezvous scenario to fully validate the proposed solution, even though the parameters related to the constraints and weighting functions are kept as general as possible, thus applicable to other similar missions.
Introduction
In the past few years there has been an increased interest in space exploration. In particular, the International Space Exploration Roadmap was proposed in February 2018 [1] indicating, as objectives, a permanent return to the Moon, and unmanned and manned missions to Mars.
Within this context, an international effort is undergoing to plan a mission to the Moon consisting in a target space station located in a near rectilinear halo orbit (NRHO) about the L2 Lagrangian points of the Earth -Moon system, and a lunar lander equipped with a rover collecting lunar samples and bringing them back to the station, to later be returned to Earth [2]. There are many advantages in using a NRHO, such as stability of the orbit with low ΔV for station-keeping, continuous visibility from Earth for communications, low periselene altitude, etc. However rendezvous and berthing dynamics and control require in depth study primarily because of the non Keplerian environment the vehicles will move in, where classical control strategies based on linearized two-body dynamics no longer apply.
The rendezvous mission considered here consists of a series of orbital maneuvers and controlled trajectories, which successively bring the active vehicle (chaser) close and eventually into contact with the passive vehicle (target). The complexity of the rendezvous approach results from the multitude of conditions and constraints that must be fulfilled. The target station may impose safety zones, approach-trajectory corridors and hold points along the way to verify the chaser's trajectory accuracy, and to switch between appropriate sensor suites. Any dynamic state (position and velocities, attitude and angular rates) of the chaser vehicle outside the nominal limits of the approach trajectory could lead to collision with the target, a situation dangerous for crew and vehicle integrity [3].
The problem of control of rendezvous dynamics in Earth's orbit has been studied since the 1960s' [4,5] and performed in the past, starting with the Apollo program, the historical 1975 Apollo -Soyuz mission, and occurring at the present time with the activities related to the international space station. While long range rendezvous and phasing are generally automated, most of the close range rendezvous is still performed manually [6,7]. Traditionally, rendezvous and proximity operations are performed using open-loop maneuver planning techniques, and ad hoc error corrections. Examples of constrained maneuvers include the thrust magnitude constraints, constraints on the approaching spacecraft to maintain its position within a Line-of-Sight (LOS) cone emanating from the docking port on the target platform, and constraints on the terminal translation velocity for soft-docking are proposed for instance in [8][9][10].
From a guidance and control standpoint, several methods can be found in the literature. Some of the studies use terminal sliding mode control, which enables a time-fixed process with the flight prescribed a priori [11]. A fixed-time glideslope guidance algorithm on a quasi-periodic halo orbit can be found in [12]. Another interesting reference on guidance algorithms for low Earth orbit (LEO) is [13], where linear optimal regulator control combined with proportional navigation was proposed. Hartley and coworkers applied model predictive control techniques for a Keplerian rendezvous [14]. An application of H-infinity control can be found in [5], which shows good performance for the case of elliptic orbit, provided linearization bounds are maintained.
A State Dependent Riccati Equation (SDRE) method provides a systematic approach for solving the infinite horizon optimal control of nonlinear systems, avoiding the solution of the associated Hamilton-Jacobi-Bellman partial differential equation, generally unpractical. The technique guarantees local stability and optimality, robustness with respect to non-modeled dynamics and uncertainties, as well as real time implementation. SDRE effectiveness has been proven extensively on a wide variety of applications, see [15] for instance. The method has been also used for relative motion control in a classical two-body scenarios with good results in the control of translation-attitude coupling [16,17]. The paper proposes and verifies a State Dependent Riccati Equation technique as an effective approach to the close range rendezvous in a three-body problem, with particular reference to the future Artemis program. To the authors' knowledge, this is a novel application due to the nature of the underlying dynamics, except perhaps for the work in [18], where the authors proved the efficiency of SDRE for a station-keeping and reorientation for formation flight in a Sun -Earth scenario, with solar pressure perturbations.
The paper is organized as follow: the mathematical model of relative motion of two spacecraft is provided in Sect. 2. Section 3 describes the motion constraints introduced in the State Dependent Riccati Equation general algorithm. The guidance structure for the problem and numerical examples are presented in Sect. 4 and conclusions in Sect. 5.
Equations of Motion
This section summarizes the relative motion dynamics in the restricted three body problem. More details can be found in [19] and [20], among others. Although general in nature, the application in the paper will be based on the proposed Lunar Orbital Platform Gateway (LOP-G) consisting of a a space station in a lunar NRHO orbit, and a Lunar Ascent Element (LAE) returning from the Moon for berthing with the station. They will be also referred to as target and chaser respectively. The chaser spacecraft is the only actively controlled element.
Relative Translation
Let us consider two spacecraft performing a rendezvous maneuver. The two spacecraft are subjected to the gravitational action of the two primary bodies (in this case Earth and Moon).
The relative motion between the two vehicles is described with respect to a widely used reference system L ∶ t ;̂ ,̂ ,̂ , local-vertical local-horizon (LVLH), which is appropriate for control design, with the unit vector defined as follows: where mt is the target position with respect to the Moon-centered rotating frame, with magnitude r mt = || mt || , t∕m = mt × ̇ mt M is the target angular momentum with respect to the Moon, with magnitude h t∕m = || t∕m || . In general, the unit vectors ̂ ,̂ ,̂ are also known as V-bar, H-bar e R-bar (strictly speaking defined only for Keplerian motion). Note that if we introduce the M ∶ m ;̂ m ,̂ m ,̂ m frame centered in the center of mass of Moon, the unit vectors ̂ m −̂ m lie in the moon orbital plane: Also, em is the Moon position with respect to the Earth, r em = || em || , m∕e = em × ̇ em I is the specific angular momentum of the Moon with respect to the Earth, and h m∕e = || m∕e || . The equations describing the dynamics of the relative position between the two spacecraft, in the LVLH frame, are taken from [19] and are shown below: Referring to Fig. 1a we have: In Eq. (3) we have the relative position , and its derivatives with respect to the LVLH frame; the angular velocity l∕i of the LVLH frame with respect to the inertial frame, and the target and chaser positions with respect to the Moon-centered frame mt , mc , respectively.
The proposed target orbit is shown in Fig. 2, with the average period of 7 days, and aposelene and periselene distances of 70,000 km and 6,000 Km, respectively.
The close range rendezvous and berthing are assumed to occur during aposelene passage for safety reasons, since the target's velocity is the lowest, this also allows for the simplification of the equations. In this case in fact, the approximation of the primary bodies revolving in circular orbits, (Circular Restricted Three-Body Problem CR3BP assumption) appears appropriate [21]. With this assumption, the number of time-varying parameters in Eq. (3) reduce. Indeed em is constant, m∕i = n̂ m and m∕i M = 0 . The use of equations derived from CR3BP, while still nonlinear, allows the reduction of variables with respect to the elliptical case. In fact for target information we only need: mt , mt L .
Relative Attitude
In the study of rendezvous operations, during long range approach, the translational motion is considered sufficient to describe the relative distance propagation and even the design of a reference trajectory. As the chaser moves near the target,
Fig. 1
Reference frames attitude and attitude rates dynamics and control become of paramount importance for the safety of the maneuver as well as the precision required during the final phase (be that either berthing or docking). The procedure adopted here consists of a separate computation of chaser and target attitude, since the latter is undergoing passive motion [22].
Chaser Attitude
The chaser spacecraft (LAE) is the part of the lander, and will depart the Moon's surface once the ground operations are complete. It is assumed to be cylindrical, and its side view is depicted in Fig. 3. Its preliminary configuration can be found in [23]. With reference to the body fixed frame C shown in Fig. 1c, which has the origin in the center of mass of the rigid chaser spacecraft and axes parallel to the principal axes of inertia, the attitude dynamics are given by: where is torque vector, is the inertia matrix and is the angular velocity of the rotating frame. Note that is defined with respect to the inertial frame, and it can be computed as: where c∕l is the angular velocity of chaser with respect to L and l∕i is the angular velocity of L with respect to inertial frame. The kinematic motion can be described by standard Euler angles and by means of quaternions as well. In this work the following definition was used: ⊤ is the Euler rotation eigenaxis and is the rotation angle around .
The differential relationship between quaternions and angular velocity is given by: with c∕l the quaternion that describes the relative attitude between C and L , and [⋅] × denotes the operator that transforms a vector into the associated antisymmetric matrix. The set of differential equations given by Eqs. (4) and (5) provide the nonlinear attitude model of chaser [24].
Target Attitude
For the target's attitude we take as reference the international space station (ISS) dynamics, which is attitude controlled using a two sided limit cycle controller, and has a sawtooth profile. This motion can be modelled as an harmonic oscillator [22] and described in Eq. (6) below using quaternion formulation: where t∕l is the quaternion that describes the attitude of target body frame with respect to LVLH, ̇ t∕l is the time derivative of quaternion, t∕l is the angular velocity of target with respect to LVLH frame; ̇ t∕l is the angular acceleration of target with respect to LVLH frame, (⋅) is the matrix that relates the time derivative of the quaternion with the angular velocity, and qt is a diagonal matrix containing the eigen frequency for each axis. Note that the fixed target frame is defined similarly to that of the chaser (Fig. 1c).
Relative Attitude
The relative attitude between two rotating objects is based on the difference of the respective angular velocities expressed in an appropriate frame [22]. In this case the difference is expressed in the C reference: cl ( ∕ ) is the matrix that transforms the components of a vector from frame L to frame C.
Once the relative angular velocity c∕t has been determined, we can compute the derivative of the associated quaternion as: ̇ c∕t = 1 2 ( c∕t ) c∕t .
Control Synthesis
As described in the introduction, the SDRE methodology is used to synthesize of a closed loop guidance in the final berthing phase of the mission. A short review of the methodology is described below for clarity's sake. The reader can refer to [15] and [25], for more details. The next section specializes the general structure to the specific problem addressed by the paper. Consider a nonlinear regulator problem that minimizes the following quadratic cost function: subjected to nonlinear differential constraints affine in the control of the form: where ∈ ℝ n is the system's state vector (see later for the present application), ∈ ℝ m is the control vector, ∶ ℝ n → ℝ n , ≠ 0 ∀ ∈ ℝ n ; ( ) ≥ 0 and ( ) > 0 are the weight matrices of the state vector and the input vector respectively. If the dynamics of the system in Eq. (10) can be written in a pseudo-linear form by the introduction of a State Dependent Coefficient (SDC) as: with state and input matrices functions of the state, then the SDRE control method assumes the form of a LQR-like controller and can be summarized in the following two steps: In order to obtain a valid solution ( ) of the algebraic Riccati equation, the pair ( ); ( ) must be pointwise stabilizable in the linear sense [15].
• Motion Constraints
The rendezvous maneuvers can be performed by imposing constraints on the relative position and relative velocity of the two spacecraft when they are approaching, especially in the final phase of the rendezvous. This can be incorporated in the general SDRE design as well.
Consider the system described by Eq. (10), with initial conditions ( ) = 0 ∈ Ω and a set of allowable states defined by: it is possible to design a controller such that the closed loop system is stable and does not exceed Ω , the boundary of Ω , defined by: The sufficient condition for to remain in Ω is ∇ ( )̇ = 0 . The controller that satisfies these conditions forces the trajectories of the closed loop system to follow the level curves of the Ω set. Incorporating the constraint as a quadratic term, the cost function becomes: The second term on the RHS of the cost function introduces the constraints, and can be represented by the fictitious output , defined as follow: z is a p × p matrix, selected such that its i-th element has a large value when is near the border of the i-th constraint and small elsewhere. This means that in the cost function the component expressed by J Ω ( , ) is predominant with respect to J 0 ( , ) when the state does not respect the constraint, and becomes negligible when the constraint is satisfied [16].
In cases when ∇ ( ) is orthogonal to ( ) , the term ( ) = 0 . An alternative way of choosing z ( ) is then to penalize the state, that is the i-th element of the weight assumes a large value when we are in a region of the state space to be penalized and zero otherwise [25].
With the introduction of state constraints, the control law becomes: where For the purpose of the present work, the assumption of full state availability was made, when deriving the control law in Eq. (18), that is relative position and rate vectors, attitude quaternions, and angular rate.
If full state is not available to the controller, a discrete state estimate can be considered referring to the work in [26], and improved in [27], in order to avoid possible loss of observability due to the size of selected time intervals. Recalling [27], we consider a stochastic nonlinear system of the form: where is a Gaussian zero-mean white noise associated with the process, is a Gaussian zero-mean measurement noise. By means of Euler's discretization, with step s , we have k = n + s A(x k ) , and k = (x k ).Where (.) is one possible SDC parametrization of a continuous system and (.) is a possible SDC parametrization for the output of the system in Eq. (20). The nonlinear discrete time system can be viewed as a frozen-in-time linear equation. Traditionally, there are two formulations of the discrete SDRE estimator based on Kalman filter. Here we use the two-step recursive update (see [27] for details).
SDRE Guidance Law
The general SDRE controller defined in Eqs. (11), (16) and (18) is now detailed in terms of problem specific state dependent coefficient (SDC) parametrization [15], and the definition of state constraints. Numerical results of the SDRE closed loop guidance applied to the cis-lunar rendezvous will be then presented and discussed in the next section.
SDC Parametrization for Translation
The equations of relative motion described by Eq. (3), can be parametrized since all the conditions of existence are guaranteed. Note that the nonlinearities of the system are due to gravitational terms. The term that takes into account the gravitational attraction due to the Moon can be rewritten as follows:
SDC Parametrization for Attitude
Similarly, it is possible to derive a SDC parameterization for the dynamics and kinematics of the chaser's attitude (see Eqs. (4), and (5)). The parameterization was taken from [28].
where = −0.0001 is a small constant added to the spacecraft quaternion kinematics for numerical reasons in the solution of algebraic Riccati equation. Notice that the addition of correction is only an artifact since the quaternions parameters represent only three independent parameters The coefficients a ij are:
Constraints and Keep-out-Zone
In the final phase and proximity operations of the rendezvous, the chaser should approach the target from a direction bound by a safety zone for collision avoidance mitigation. Typical constraints for close range are selected as spheres of a given radius (for instance in the Heracles-ESA mission the rendezvous sphere has a 10 Km radius, the approach sphere 2 Km, and the keep-out-zone 0.2 Km, respectively). In this work a simpler cone-like final approach corridor is considered as in [9]. Figure 4 shows a qualitative constraint illustration, in which is the unit vector of the path direction, and is the maximum cone angle of the corridor and the main design parameter. The constraint geometry is rewritten using Eq. (14) formalism and becomes: is the system's state vector. The constraint is represented as a fictitious output as in Eq. (17) expressed in a target T frame so, as we can see from Eq. (25), the direction of cone axis depends on target's attitude. Note that in our case ∇l( )⊥ ( ) , so the weight function z was selected to penalize the state when it is far from the imposed constraint. In other words, we chose the weight function that depends on the 3D distance between the chaser's a 11 , a 12 , a 13 center of mass and the line described by the unit vector . In this way, z has a large value when the chaser's center of mass is far from the cone axis and small value when it is close to, as mentioned earlier.
Results
In this section, numerical simulations are presented to validate the proposed method. The target moves on a NRHO and we simulate rendezvous maneuvers both at the aposelene, as well as in the worst case condition, i.e. close to periselene. Table 1 summarizes the initial conditions. We assume that the attitude motion of the target has a maximum amplitude of 5 • and the eigen-frequency of Eq. (6) is equal to k qt = 0.1571 rad s −1 [3]. The chaser is is modeled as a cylinder with inertia matrix = diag(0.0011, 0.0006, 0.0006) kg ⋅ km 2 [20]. The direction vector of the approaching cone is T = [−1, 0, 0] ⊤ and the maximum cone angle is set to = 25 • .
The main equations used for motion propagation are those relative to the circular restricted three body problem. For better numerical stiffness the equations are normalized as in [29]; the distances are normalized to the Moon's orbit semi-major axis, the time in units of the inverse mean motion of the NRHO orbit, and the masses are expressed such as M e + M m = 1 . The terminal conditions selected for the tests are ≤ 1 m for relative position, and ̇ ≤ 0.03 m/s for relative velocity. Note that for ESA's ATV mission concept [30] the constraints were 20 m in relative position, and 0.01 m/s for relative velocity [3]. Simulations were run using Simulink TM , the guidance and navigation algorithm runs at 1 Hz, and the Dorman-Price integration algorithm was used.
Aposelene Approach
The aposelene is considered the most feasible area for the docking/berthing. Current literature indicates that as the most likely location, and it has been shown that CR3P equations are sufficiently accurate for the dynamic description of the relative motion [6,21]. In this case, the target has the slowest orbital velocity. The docking/berthing zone is indicated in red in Fig. 2. The SDRE controller was tested by means of a limited Montecarlo simulation for six different relative distances = 5, 8, 11, 14, 17, 20 Km. For each , 20 random uniformly distributed points were selected.
The weighting matrices coefficients used for the translation are: The constraint matrix depends on distance between cone axis and chaser, and is set as follow: where axis (x, y, z) is the classical formula that defines the distance of one point from line in 3D space. All weights were selected by trial and error in order to maximize the accuracy and minimize the control effort.
The position is assumed to be an available measurement, so ( ) = 3×3 0 3×3 . The measurement error is considered as purely random with Gaussian distribution, zero mean and standard deviation = 1∕3 × 10 −2 m [16]. The process error model is based on [31], and is considered as purely random with Gaussian distribution, zero mean and standard deviation = 1∕3 × 10 −6 Km∕s 2 . The control weighting matrices are the same as in Eq. (27), and the process noise and measurement covariance matrices are given by: The initial condition for the error covariance matrix is given by: The filter initial conditions are: where 0 is the real relative position and velocity of chaser, p and v are 3 × 1 vectors of uniformly distributed random numbers, in the interval (0, 10) [cm] and (0, 1) [cm/s], respectively.
The performance analysis was based on normalized values of position error, error rate and amount of control, over the time of flight period.
, v are the error vectors respectively between real position and estimated position, real velocity and estimated velocity, and equivalent propellant consumption. Figure 5 shows the relative position between chaser and target from the Montecarlo simulation. The time of flight and control effort are shown in Figs. 6 and 7.
As we can see from Figs. 6 and 7, the total control effort v and time of flight t of increase with increasing relative distance, as expected. In addition, the linearity in Fig. 6 confirms the feasibility of aposelene approach in terms of validity of the equations of motion. When the chaser initial conditions are not inside the LOS cone and the relative distance is less than 15 Km, the chaser moves very slowly as it reaches the approach corridor. The corresponding standard deviation could be reduced by increasing the number of tests. The average errors in position and rate, between real and estimated values, are shown in Table 2. To evaluate the attitude behavior at the aposelene, we consider a sample trajectory from those simulated above and shown in Fig. 8. The rendezvous maneuver lasts about 2 hours starting from relative distance of about 11 Km. The results in terms of ΔV expenditure compare favorably with the results in [32], where different thrust allocation algorithms were used, and the results in [33], where continuous thrust was implemented using the adjoint method.
For the selected example, the control forces and torques are shown in Figs. 9 and 10, respectively.
The large initial values depend on the fact that the chaser is controlling both its translation and rotation, the coupling is noticeable especially along the z axis, with the oscillatory behavior of the z force component. The control amount could be tuned further by changing the weights in the optimization. The angular behavior in terms of quaternions and angular rates is shown in the next figures (See Fig. 11). Figure 9 shows the target and chaser quaternions, while the time histories of chaser and target angular velocities are shown in Fig. 12.
The weighting strategy used to satisfy the constraint imposes a severe penalty on velocity, due to the orthogonality of ∇ ( ) ⟂ ( ) . As matter of fact we have initially a high velocity in R-bar and then, when the chaser reaches the axis of the approaching cone, the R-bar component decreases and the V-bar velocity has a plateau, with the chaser moving towards the target.
Periselene Approach
A rendezvous at the periselene of the NRHO orbit is not considered practical for several reasons: first of all the target vehicle is at its maximum speed, thus making the safety requirements very critical and too restrictive, secondly to maintain a desired relative position and velocity, the requirements on Δ V could be too high for the mission. Thirdly the circular restricted three body problem may lose accuracy during propagation. This case is then used only for the purpose of evaluating the behavior of the proposed guidance in a worst case scenario. The approach zone at the periselene is shown in red in Fig. 13. The Montecarlo simulations were performed with the same parameters used for the aposelene approach. The propagation equations are based on the elliptical restricted three body problem. The resulting trajectories, time of flight, and control effort are shown in Figs. 14, 15, and 16, respectively.
The most significant difference between the two cases is the increase of control amount required to perform the rendezvous at periselene, as suspected. Table 3 shows very similar errors in position and a slightly increase in rate error for the periselene approach. An interesting comparison is shown in Fig. 17. On the left a zoomed set of trajectories obtained using the ER3BP are shown (taken from Fig. 14), while on the right the trajectories are computed using the CR3BP equations. This indicates that the CR3BP equations could be sufficient for the relative motion description, see Table 4. Figure 18 shows the influence of the gain in the weighting matrix z described in Eq. (28). The higher the gain and the faster the trajectory moves towards a rectilinear V − bar path (figure to the right). The numerical values for the two cases are shown in Tables 5 and 6, respectively.
Conclusions
The paper presents a State Dependent Riccati Equation approach to closed loop guidance with state constraints. The technique is applied in a rendezvous scenario between two spacecraft around the Moon in a NRHO environment, due to nonlinear nature of the relative dynamics. The constraints are formulated as a conic area that depends on the target's attitude, so the chaser's center of mass must follow the complete target's motion. Although simulations are based on a somewhat limited Montecarlo analysis, the method provides successful control and feasible ΔV requirements at the aposelene, and also a satisfactory behavior at the periselene, with additional control effort. The weighting selection on the constraints allows the designer to modify the trajectory in order to acquire quickly a desired V − bar direction, which appears to be desirable in standard rendezvous maneuvers. The mission scenario used for the synthesis is based on current information on the lunar gateway study, thus the numerical data could be subjected to variations in the future, as well as the computational requirements of SDRE, with respect to mission design.
Acknowledgements The present work was performed with partial support of the European Space Agency under contract No. 4000121575/17/NL/CRS/hh/CCN1. The views expressed in this paper can in no way be taken to reflect the official opinion of the European Space Agency. The contribution of the third author was made while a Ph.D. student in the Department of Information Engineering at the University of Pisa.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission | 6,785.8 | 2022-01-23T00:00:00.000 | [
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Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude
Despite extensive ongoing efforts on improving the long-term prediction of El Niño-Southern Oscillation, the predictability in state-of-the-art operational schemes remains limited by factors such as the spring barrier and the influence of atmospheric winds. Recent research suggests that the 2014/15 El Niño (EN) event was stalled as a result of an unusually strong basin-wide easterly wind burst in June, which led to the discharge of a large fraction of the subsurface ocean heat. Here we use observational records and numerical experiments to explore the sensitivity of EN to the magnitude of the heat buildup occurring in the ocean subsurface 21 months in advance. Our simulations suggest that a large increase in heat content during this phase can lead to basin-wide uniform warm conditions in the equatorial Pacific the winter before the occurrence of a very strong EN event. In our model configuration, the system compensates any initial decrease in heat content and naturally evolves towards a new recharge, resulting in a delay of up to one year in the occurrence of an EN event. Both scenarios substantiate the non-linear dependency between the intensity of the subsurface heat buildup and the magnitude and timing of subsequent EN episodes.
that the onset of EN usually begins during the second half of the year before the event, being identified as a fundamental process independent of the flavor of the episodes. A buildup in heat content along the equator has indeed preceded all the major EN events since 1980, and the magnitude of EN usually scales in proportion to the magnitude of the heat content buildup 2 to 3 seasons in advance 3,29 . Reference 30 used ocean-atmosphere coupled simulations to show that the prescription of a warm heat content anomaly immediately before the spring barrier can lead to the generation of a moderate EN event. These same simulations also showed that prescribed westerly wind bursts alone do not lead to significant EN anomalies, but they can instead greatly amplify heat content anomalies and generate a strong EN event when they are superimposed to an initially recharged ocean state 30 . Nonetheless, results also suggest that the heat content buildup may be a necessary but not sufficient condition for EN to occur 3 . For example, ref. 31 have recently shown that the 2014/15 EN event was stalled as a result of an unusually strong basin-wide easterly wind burst in June 2014, which discharged the basin, suppressed the Bjerknes feedback and impeded the growth of the strong episode that was expected for the end of the year. At that time, basin-wide uniform warm conditions were instead observed in the equatorial Pacific, which were followed by the record-breaking EN episode in boreal winter 2015/16.
To illustrate the importance of the THC at different lead times, Fig. 1a depicts the relationship between the December anomaly of the Niño3.4 (N34) Index and leading WWV anomalies 32 in March of the same and/or the preceding year (here referred to as years 0 and − 1, respectively) for all the EN episodes since 1980 (see ref. 33 for the classification of events). WWV is defined as the volume of water masses above the 20 °C isotherm within 120E-80W and 5S-5N. This figure confirms the widely accepted and well understood 9-month lead association between high WWV anomalies and EN events (blue circles), but importantly, it also shows that the same relationship holds for WWV anomalies one year earlier (i.e. 21 months before EN, green squares). Both relationships are strong, with correlations of 0.75 and 0.63 respectively, which increase to 0.85 (r 2 = 72%) when March WWV anomalies are averaged for both years (red diamonds). Note that this double relationship is in general also valid for all years since 1980 (Fig. 1b), with only one major exception: WWV was largely positive 21 months before the 1998 LN event, given that it was preceded by the very strong 1997 EN episode and its associated 9-month leading recharge phase in spring 1997 (dashed lines in Fig. 1b). In this way, when the 1998 LN event is not taken into account, the correlation between the December N34 Index and 21-month leading WWV anomalies is equal to 0.55 for all the years since 1980 (green squares in Fig. 1b).
Given the strong relationship observed between this very long-lead heat buildup and subsequent EN events here we use a state-of-the-art Earth System Model to explore the response to a decrease or increase in the magnitude of the heat content stored in the ocean subsurface (see Methods). We performed 11 sets of ensemble experiments, with initial conditions corresponding to an early phase of the onset of an EN episode in March of year − 1 (i.e. lead time of 21 months). Previous studies have explored this relationship at shorter lead times of up to one year, through statistical analyses of observational data (e.g. ref. 3) or through numerical experiments (e.g. ref. 30). As we wanted to specifically study the dynamics of very strong EN events, we prescribed anomalous conditions to mimic as close as possible an episode of magnitude similar to the recent 2015/16 event (i.e. N34 = + 2.8 °C). In each of these sets of ensemble experiments, the intensity of the subsurface warm anomaly was decreased (negative sign representing a discharge in heat content) or increased (positive sign or recharge) by ± 20%, ± 40%, ± 60%, . As such, differences among experiments are explained by both the magnitude of the initial subsurface heat content and the strong coupling between the ocean and the atmosphere that characterizes the dynamics of ENSO. We note that unlike in ref. 30 we only prescribe anomalies in the ocean subsurface, and therefore the atmosphere is only indirectly modified when the readjustment of the ocean affects the ocean surface and the interaction between the ocean and the atmosphere (see Supplementary Figures 2-4). Figure 2 shows the longitude-time Hovmöller diagram of equatorial potential temperature and zonal current anomalies at the level of the thermocline for experiments representative of the different types of ocean responses to the prescribed anomalies, Fig. 3 shows the corresponding anomalies of equatorial SST and zonal wind stress, and The REF ensemble is found to correctly reproduce the main features of a canonical ENSO oscillation (Figs 2-4d). It is initially characterized by easterly wind and cold SST anomalies in the central Pacific and the generation of the subsurface heat buildup in the western Pacific, which peaks in spring of year − 1 (i.e. beginning of the simulations). Reference 26 showed that meridional and eastward heat advection due to equatorward subsurface mass convergence and transport along the equatorial undercurrent contribute to this long-leading subsurface warming at 170E-150W, while surface horizontal convergence and downwelling motion have a leading role in subsurface warming in the warm pool. Westerly wind anomalies appear at the beginning of the following year, when the warm waters start to propagate to the eastern Pacific along the equatorial thermocline as downwelling Kelvin waves. Some few months later, in spring of year 0, the warm anomalies reach the eastern Pacific subsurface during the basin-wide recharge phase of the ENSO oscillation, which is immediately followed by the beginning of the warming of the ocean surface. Equatorial SST anomalies exceed the + 1 °C threshold in the central and eastern equatorial Pacific between the summers of this and the following year (i.e. year + 1). The eastward surface current anomalies rapidly become westward just after the peak, favoring the decaying phase of EN. The warm phase of ENSO is associated with the shoaling of the thermocline and the accumulation of subsurface cold waters in the western Pacific, which propagate to the east as upwelling Kelvin waves once the EN event starts to decay and the zonal wind anomalies become easterly in the western Pacific. A LN event develops as soon as the subsurface cold anomalies reach the eastern Pacific, although the magnitude of its peak is found to be approximately one half of the magnitude of the preceding EN episode.
When the initial heat buildup is modified by ± 40%, the growth and propagation of the subsurface warm anomaly, as well as the subsequent onset of the EN event, remain very similar in timing and approximately proportional in magnitude to that shown in the REF ensemble (Figs 2-4c,e). This result is found to be valid both for surface and subsurface temperatures, as well as for surface winds and ocean currents, showing that the same dynamical mechanisms operate during the phases of the oscillation that precede and follow the peak of EN events. In the particular case of the + 40% (− 40%) ensemble, the magnitude of the event is nearly proportionally larger (weaker), with the anomaly of the N34 index reaching + 3.5 °C (+ 2 °C) and representing an increase (decrease) of about 30% relative to the REF ensemble.
Further increasing the initial heat buildup up to 80% of the REF simulation induces some interesting differences (Figs 2-4f). The excess heat in the western Pacific is released and quickly starts to warm the eastern part of the equatorial Pacific subsurface. Nevertheless, the surface warming at the end of year − 1 is found to be weak and uniformly distributed along the equatorial Pacific. This configuration does not favor the activation of the Bjerknes feedback and therefore the Walker circulation remains in a neutral phase (i.e. westerlies in the west but easterlies in the east), resulting in weak EN conditions (N34 < + 1 °C). As a result, the accumulated heat is not discharged towards higher latitudes, and therefore this initial EN-like event only represents a step in the slow but steady warming of the basin, characterized by an initial warm base state of the equatorial Pacific, and enhanced by strong westerly wind anomalies progressing to the east throughout year 0. The subsequent EN event is found to be very strong (N34 = + 4 °C) and followed by a strong LN event one year later.
The picture is however completely different in the ensemble of simulations in which the initial heat buildup is reduced by 80% (Figs 2-4b). The equatorial easterly wind anomalies (or, more precisely, the off-equatorial wind stress curl) observed before and after the beginning of the simulations are associated with the positive change rate in subsurface meridional convergence [10][11] . This tendency towards equatorward mass convergence is associated with upwelling of subsurface cold waters that favors the persistence of cold SST and easterly wind anomalies in the central Pacific, which in turn deepen the thermocline and accumulate subsurface warm waters in the western Pacific 26 . A weak LN event therefore develops at the end of year − 1, which re-activates the generation of the subsurface heat buildup in the western Pacific. From this point onward, the evolution of the accumulated heat mimics that of the REF ensemble in terms of magnitude, timing, propagation and mechanisms, but with a one-year delay, leading to the growth of a strong EN episode that peaks in December of year + 1 (N34 = + 2 °C, i.e. same magnitude as in the − 40% experiment, but one year later).
A similar evolution is found when the accumulated heat in the subsurface is completely suppressed in the − 100% experiment (Figs 2-4a). In this case, however, given that the initial THC is weaker and the heat buildup is completely removed, the renewed recharge of the tropical Pacific results in a delayed EN event of smaller magnitude (N34 = + 1 °C).
The relationship between initial THC and ENSO variability is summarized in Fig. 5. THC is defined here as the average heat content within 120E-80W, 5S-5N and the upper 300 m. Results show that the greater the initial THC, the greater its magnitude up to the spring of year 0, when the peak in THC is observed in the initially-recharged simulations (Fig. 5a). This dependency is strong during this initial period (correlation ≈ 1, pink line in Fig. 5b), but the recharge does not occur at the same pace (regression = 0.61 J/J in January of year 0, red line in Fig. 5b), as this process is faster in the initially-discharged simulations. The relationship between the initial and the time-varying THC becomes negative in autumn of year 0 (red and pink lines equal to zero in Fig. 5b), when all the simulations exhibit similar THC values (Fig. 5a). This includes the few initially-discharged experiments in which the heat content is still increasing at the end of year 0 (i.e. − 100% and − 80%), as well as all the other experiments (i.e. from − 60% to + 100%), in which the heat content is already being discharged at this point in time (Fig. 5a). The relationship then becomes negative, but it is still strong (correlation = − 0.92 and regression = − 1.35 J/J in June of year + 1, Fig. 5b).
Results also show that the greater the initial THC, the greater the magnitude of the subsequent EN event (see Fig. 5c at the end of year 0). The relationship is again strong (correlation = 0.92 and regression = 1.56 °C/10 16 J in December of year 0, blue and cyan lines in Fig. 5b), but not completely linear. On the one hand, in the initially-recharged simulations, negative feedbacks in the central and eastern Pacific limit the growth and magnitude of EN during its mature phase 10,11,[34][35][36] , which explains why the N34 Index in December of year 0 is only + 1 °C warmer in the + 100% ensemble than in REF (Fig. 5c). On the other hand, the spread in the initially-discharged simulations is large, given that the Bjerknes feedback is not activated in some experiments (i.e. the N34 Index is equal to + 2.8 °C in REF and negative in − 100%, Fig. 5c). The relationship becomes negative in early summer of year + 1 (blue and cyan lines equal to zero in Fig. 5b), when the transition between warm and cold conditions is found in most, albeit not all, simulations (Fig. 5c). Indeed, in the initially-discharged simulations, the timing of the peak of EN depends on the prescribed THC, with a clear phase-locking to the seasonal cycle that characterizes the jump between the winters of years 0 and + 1 [37][38][39][40] ; Fig. 5d). Instead, all of the initially-recharged simulations are found to peak in October of year 0, defining a stepwise relationship between the initial THC and the timing of EN maxima (Fig. 5d).
The phase evolution of the system shows the traditional counterclockwise trajectory, in which the change rate of the N34 Index is approximately proportional to the THC, and the radius of the trajectories monotonically increases as a function of the initial THC (Fig. 5e, see also ref. 30 for a similar approach based on ocean energetics). Nonetheless, the trajectories of all the initially-discharged simulations tend to evolve towards the diagram values that correspond to the recharge phase of the REF ensemble (i.e. + 0.5·10 16 J ≤ THC ≤ + 1·10 16 J and − 0.5 °C ≤ N34 ≤ + 0.5 °C), and only then do they diverge to reach weaker N34 values than in REF. This indicates that, in our model configuration, the system compensates for the initially-prescribed reduction in heat content, and evolves towards a new recharge in THC and the generation of EN events through the memory of the system, regardless of the magnitude of the initial THC, and even when it is completely removed.
This general increasing trend of THC in the initial period of all the simulations is explained by the recharge theory, in which the off-equatorial wind stress curl is associated with the positive change rate in subsurface meridional convergence. In the initially-recharged simulations, this tendency towards the deepening of the thermocline in the central Pacific contributes to the transition towards the recharge phase that leads to EN by 2 to 3 seasons 10,11 . Instead, in the simulations in which the initial heat content has been completely or largely suppressed (i.e. − 100% and − 80%), the equatorward mass convergence is associated with the upwelling of subsurface cold waters that favors the persistence of cold SST and easterly wind anomalies in the central Pacific, which in turn deepen the thermocline and generate a new heat buildup in the western Pacific 26,27 . We must however note that, in a more general framework, the initial THC could increase at a different rate or even decrease if wind stress anomalies were also prescribed or a different time frame was chosen as initial conditions for the experiments.
The new recharge process in the − 100% and − 80% experiments can result in a delay in the occurrence of the EN event, which highlights the non-linear dependency between the intensity of the subsurface heat buildup and both the magnitude and timing of subsequent EN episodes. The numerical simulations reported here show that the accumulation of warm waters in the western Pacific determines the timing of the transition between LN and EN conditions, which is here seen to increase by one year when the initial subsurface heat is largely reduced. We found that the stepwise relationship between the initial THC and the timing of EN maxima also affects the magnitude of the events. For example, the EN episode at the end of year 0 in the − 40% ensemble has similar magnitude to the event occurring one year later in the − 80% experiment, because the longer timescale of the recharge process compensates the magnitude of the initial discharge. In this respect, our results provide new insight into the fundamental role of the ocean heat content, in this case at longer lead times than traditionally described, and therefore they have important implications for the understanding of the genesis of EN events, their dynamics and their predictability. CESM was chosen to conduct the experiments reported here because CCSM was found to be one of the three best models in the simulation of the dynamic warm pool edge among 19 coupled ocean-atmosphere general circulation models from the Coupled Model Intercomparison Project Phase 5 47 . This feature corresponds to the easternmost edge of the western Pacific warm pool, the maximum in zonal salinity gradient and the area of surface ocean convergence, downward motion and advection and subsurface divergence 26,[48][49][50] . The correct simulation of these dynamical features is seen to be key for the reproduction of the ENSO oscillation and the transition between events of opposite sign, including the generation of the heat buildup in the western Pacific and the eastward propagation of the accumulated heat 21,47 .
Estimates
We performed 11 sets of ensemble experiments, with initial conditions corresponding to a very early phase of the onset of an EN episode, in March of the year preceding the winter peak of a strong warm event (i.e. a lead time of 21 months with regard to the December maximum), chosen from a reference 100 year spin-up simulation. This stage of the ENSO oscillation is characterized by cold LN-like conditions in the tropical Pacific and the generation of a subsurface heat buildup in the western tropical Pacific (Supplementary Figure 1b,e; 21,26 ). In each of these sets of ensemble experiments, the intensity of the subsurface warm anomaly was decreased (negative sign representing a discharge in heat content) or increased (positive sign or recharge) by ± 20%, ± 40%, ± 60%, ± 80% and ± 100%. The word "anomaly" in the experiments refers to the difference of a monthly value with regard to the long-term mean annual cycle computed from the REF simulation. We note that warm temperature anomalies were fully modified only in the inner three-dimensional box Figure 1c,f) show the initial condition in the − 100% (+ 100%) experiment, in which the subsurface warm anomaly was suppressed (doubled). For the sake of clarity, we use the terminology ''initially discharged'' (''initially recharged'') simulations or ensembles to refer to the experiments with initially reduced (increased) subsurface warming, and we also compare the magnitude of these prescriptions by saying that the − 100% (+ 100%) ensemble is ''more initially discharged'' (''more initially recharged'') than for example the − 20% (+ 20%) experiment. Each set of experiments in turn consists of 10 simulations with slightly perturbed initial conditions, from which only the ensemble average is shown here. | 4,754.4 | 2016-11-03T00:00:00.000 | [
"Environmental Science",
"Physics"
] |
COMPARISON OF ENERGY FLOW STREAM AND ISENTROPIC METHOD FOR STEAM TURBINE ENERGY ANALYSIS
In this paper, a comparison of two different methods for a steam turbine energy analysis is presented. A high-pressure steam turbine from a supercritical thermal power plant (HPT) was analysed at three different turbine loads using the energy flow stream (EFS) method and isentropic (IS) method. The EFS method is based on steam turbine input and output energy flow streams and on the real steam turbine produced power. The method is highly dependable on the steam mass flow rate lost through the turbine gland seals. The IS method is based on a comparison of turbine steam expansion processes. Observed energy analysis methods cannot be directly compared because they are based on different sources of steam turbine energy losses, so, an overall steam turbine energy analysis is presented. Unlike most steam turbines from the literature, the analysed HPT did not have the highest overall energy efficiency at a full load due to exceeding the water/steam critical pressure at the turbine inlet during such operation.
Introduction
The scientific and professional literature offers many different energy and numerical analysis of entire steam power plants as presented by Erdem et al. [1], Mitrović et al. [2], Kumar et al. [3], Noroozian et al. [4], Ahmadi and Toghraie [5] and Uysal et al. [6].The energy and numerical analysis can also be applied to a research of steam power plant components, such as steam turbines [7], [8], steam condensers [9], steam generators [10,11] and air heaters for steam generators [12], feed water heaters [13], gland steam condensers [14] and many others.An investigation of combined power plants [15], CHP (Combined Heat and Power) plants [16], solar power plants [17] or power plants, which use solar assisting [18], can also be performed by various types of energy analysis methods.
Along with the energy, an exergy analysis of various power plants: solid fuel-fired [19], CHP [20], multigeneration [21], steam supercritical [22] and nuclear [23], which takes into account the ambient state (temperature and pressure of the ambient) in which power plant and all of the plant's components operate, is widely used nowadays.
Energy and exergy analyses are also commonly used for the efficiencies and losses research of marine propulsion systems [24] and power plants [25], components of such systems [26,27] or the entire complex energy systems on the cruise ships [28], container ships [29] or chemical tankers [30].A several industrial plants can also be evaluated and optimized by using energy and exergy analyses, such as a sugar factory [31], milk powder production system [32], milk processing factory [33] and industrial-scale yogurt production plant [34].
An energy analysis of some power plant components will result in energy power losses equal to zero and energy efficiency equal to 100%.For such components, a specific enthalpy of the operating medium at component inlet and outlet remains constant (change in the operating medium specific enthalpy can be neglected).For such components, the only relevant analysis is the exergy analysis.Pressure reduction valves (throttle valves) [35,36] and steam turbine labyrinth seals [37] are the best examples of such components.
The most complex analyses of power plants and its components are 3E (Energy, Exergy and Economic/ Environmental) [38,39] and 4E (Energy, Exergy, Economic and Environmental) [40,41] analyses, which provide a complete insight into a power plant operation from various aspects.Such analyses are often used for power plant optimization and research of the pollutants reduction possibilities [42,43].A critical review of 4E analysis for a various power plants can be found in [44].
An essential element of any steam power plant energy analysis is the main steam turbine with all of its cylinders [45].Such analysis usually does not take into account the flow details within the turbine [46] or other steam turbine inner details.
In scientific literature, two methods of the steam turbine energy analysis are presented.The first method is an energy flow stream method, which is based on the turbine input and output energy streams and real turbine developed power.The results of the energy flow stream method can be found in [5,47].In order to calculate the steam turbine energy power loss and energy efficiency by using the energy flow stream method, the authors presented data of steam mass flow rates lost through each turbine gland seal (or cumulative steam mass flow rate lost through both turbine gland seals).
The second method for steam turbine energy analysis is the isentropic method and the results of the steam turbine energy analysis using this method can be found in [48,49].The isentropic energy analysis method is based on a comparison of turbine steam expansion processes -ideal (isentropic) and real (polytropic) [50].In an isentropic energy analysis of a steam turbine, the authors do not present the data of steam mass flow rates lost through turbine gland seals [51] (in some situations, cumulative steam mass flow rate lost through both turbine gland seals is neglected because it is approximately 1% of the steam mass flow rate at the turbine inlet).The usage of the energy flow stream method for the energy analysis of a steam turbine in such situations will result with turbine energy power loss equal to zero, while the energy efficiency of the steam turbine will be equal to 100%.Without data (or neglecting) regarding steam mass flow rates lost through turbine gland seals, the isentropic method is the only relevant method for the energy analysis of any steam turbine.
As the authors of this paper, so far, did not found a comparison of the energy flow stream and isentropic method for the energy analysis of any steam turbine in any literature, in this paper, there is an energy analysis of a high pressure steam turbine (HPT) from a supercritical thermal power plant [52] with both energy analysis methods presented.The HPT is analysed at three different turbine loads in order to obtain a complete insight into results of both energy analysis methods.Obtained results were compared and discussed.The main conclusion obtained from the performed analysis is that the results of the energy flow stream method and isentropic method cannot be directly compared because each method presents a different cause of steam turbine energy losses (and consequently different energy efficiencies).Overall, the HPT energy analysis, which represents a combination of steam turbine energy power losses and energy efficiencies obtained by both of observed methods, is presented.An overall energy analysis of any steam turbine (not only of the researched HPT) completely defines the steam turbine energy power losses and energy efficiencies.
2.
High pressure steam turbine from supercritical thermal power plant
General energy analysis equations
An energy analysis, in general, is defined by the first law of thermodynamics [53].Mass and energy balance equations for a standard volume in a steady state disregarding potential and kinetic energy are defined according to [54]: The energy power of a flow for any fluid stream can be calculated by using the equation [55]: The energy efficiency may take different forms and types, which are dependable on the analysed component (control volume) or system.Usually, energy efficiency can be written, according to [56] as:
High pressure steam turbine (HPT) energy analysis
The scheme and steam flow marks of the analysed high pressure steam turbine (HPT) from a supercritical thermal power plant are presented in Fig. 1.The analysed steam turbine has one steam flow inlet, two steam flow extractions and one steam flow outlet.The second steam extraction (point 3 in Fig. 1) is positioned near a steam turbine outlet, therefore, the steam operating parameters (pressure and temperature) at the second steam extraction are equal to the steam operating parameters on the HPT outlet (steam generator inlet on re-heat, point 4 in Fig. 1).The analysis of steam mass flow rates lost through the front and rear gland seal requires introducing two additional operating points (points x and y in Fig. 1).In the operating point x -the steam has an identical temperature and pressure as the steam at the turbine inlet (point 1 in Fig. 1) and as the steam lost through the front gland seal.The steam mass flow rate in the operating point x expand through the HPT.In the operating point y -the steam has an identical temperature and pressure as the steam at the turbine outlet and as the steam lost through the rear gland seal.The steam mass flow rate in operating point y is the steam mass flow rate at the end of the expansion process (after the second steam extraction).
The steam mass flow rate lost through the front gland seal (1-x) is the difference of the steam mass flow rates at the turbine inlet (point 1 in Fig. 1) and at the beginning of the expansion process (point x in Fig. 1), while the steam mass flow rate lost through the rear gland seal (y-4) is the difference of the steam mass flow rates at the end of the expansion process (after second steam extraction -point y in Fig. 1) and at the turbine outlet (point 4 in Fig. 1).
The required steam specific enthalpies and specific entropies in both the HPT energy analysis methods and in all turbine operating points were calculated from the known pressure and temperature of each flow stream by using Nist REFPROP 9.0 software [57].
Energy flow stream method
The energy flow stream method for the HPT (or any other steam turbine) energy analysis is based on turbine input and output energy flow streams (along with the real steam turbine developed power).This method is highly dependable on a steam mass flow rate lost through the turbine gland seals and that the lost steam mass flow rate (for both front and rear gland seal) is the essential component, which defines the steam turbine energy power loss.
According to Fig. 1, the HPT energy flow stream analysis equations (which define energy power loss and energy efficiency) are as follows: • Steam turbine real (polytropic) developed power: where: • HPT energy power input: • HPT energy power output: Ėen,OUT,EF S = Ėen,2 + Ėen,3 + Ėen,4 where: • Cumulative steam mass flow rate lost through both the HPT gland seals (front and rear): • Cumulative steam mass flow rate lost through both the HPT gland seals can be distributed on the steam mass flow rate lost through the front gland seal: and on a steam mass flow rate lost through the rear gland seal: In equations ( 12) and ( 13), z f ront (%) and z rear (%) represents the percentages of ṁlost,cumulative , which is lost through the front and rear gland seal.The steam, which passes through the front gland seal has the same specific enthalpy as the steam at the turbine inlet (point 1 in Fig. 1), while the steam, which passes through rear gland seal, has the same specific enthalpy as the steam at the turbine outlet (point 4 in Fig. 1), as declared in [58].
• HPT energy power loss: • HPT energy efficiency: During the usage of the energy flow stream method, it is important not to include the steam mass flow rates lost through the turbine front and rear gland seal in the equation for the energy power output (9) and into the equation for the energy efficiency (15).In the literature, it can be found that some authors include this steam mass flow rates (multiplied with steam specific enthalpies) into the equations for the energy power output and energy efficiency.The result is that the energy power input and output becomes the same, which further resulted in a turbine energy power loss equal to zero, the equation ( 14), and turbine energy efficiency is then equal to 100%, equation (15).
Isentropic method
The isentropic steam turbine energy analysis method is based on a comparison of turbine steam expansion processes [59].The real turbine steam expansion process is polytropic and according to this steam expansion, equation (5), the real HPT developed power is defined.An ideal steam turbine expansion is isentropic, because this expansion assumes that the steam specific entropy remains constant throughout the whole HPT process.A comparison of ideal (isentropic) and real (polytropic) steam expansion processes for the analysed HPT are presented in Fig. 2, according to steam flow streams, Fig. 1.The main equations of the HPT energy analysis by using isentropic method are: • Steam entropy on the isentropic expansion line (according to Fig. 2): • HPT ideal (isentropic) developed power: HPT real (polytropic) developed power is calculated according to equation ( 5).
• HPT energy power loss: • HPT energy efficiency: 3. Operating parameters of the analysed HPT at three different loads The HPT operating parameters for all steam flow streams, Fig. 1, were found in [52] and presented in Table 1.Specific enthalpies, isentropic specific enthalpies and specific entropies of all steam flow streams are calculated with Nist REFPROP 9.0 software [57].
In Table 1, specific exergies of each steam flow stream are also presented.As specific exergy is dependable on the conditions of the ambient in which the analysed turbine operates, specific exergies presented in Table 1 are calculated for the ambient pressure of 1 bar (0.1 MPa) and ambient temperature of 25 °C (298 K), as proposed in [60].By using specific exergies, the exergy analysis of the researched HPT can be performed at each load.The specific exergy of each steam flow stream is also calculated with Nist REFPROP 9.0 software [57].
The analysed high pressure steam turbine is an integral part of supercritical thermal power plant process with a maximum power of 660 MW (entire power plant power, not only the HPT) at the highest load.Authors in [52] analysed the complete power plant process, under constant and pure sliding pressure operation at three different loads (loads of 60%, 80% and full load of 100%).
As presented in Table 1, the cumulative steam mass flow rate lost through both the turbine gland seals is known while the steam mass flow rate lost through each HPT gland seal is not known.According to the equations ( 11), ( 12) and ( 13), the steam mass flow rate lost through both HPT gland seals can be distributed on each gland seal in different percentage ratios.Such distribution will surely influence the steam turbine energy analysis (not only researched, but also any other steam turbine energy analysis), regardless of used energy analysis method.
The HPT gland seal distribution from Table 2 is researched.It should be noted that the first and the last combination (No.1 and No.11) are not usual combinations, which can be expected during the HPT operation, because certain steam mass flow rate will surely be lost through both (front and rear) gland seals, but such combinations can be researched numerically.For each combination from Table 2, both steam turbine energy analysis methods (energy flow stream method and isentropic method) at each observed HPT load (load of 60%, 80% and full load of 100%) were performed.
Stream flow (Fig. 1 Steam mass flow rates extracted from the HPT (stream flows 2 and 3 in Fig. 1) remains the same at each turbine load as presented in Table 1 regardless of the lost steam mass flow rate distribution.
Validation
In order to ensure that all operating parameters of each steam flow stream and that for each HPT load they are calculated correctly, the data presented in Table 1 need to be validated.The steam temperature, pressure and mass flow rate of each flow stream were found in [52].The authors in [52] analysed the whole supercritical steam power plant from the energy and exergy aspect and they assumed, for each turbine cylinder (as well as for the HPT) at each load, that the energy power loss is equal to zero and that energy efficiency is equal to 100%.
From the above, it can be concluded that the HPT analysed in this paper cannot be compared with the results from [52] on the basis of the energy power loss or energy efficiency regardless of used energy analysis method.The parameter, on which a comparison of calculated results can be performed is the HPT real developed power at each turbine load.A good match in the HPT real developed power will confirm the proper calculation of steam specific enthalpies as well as other steam parameters.
For a validation purposes, the HPT real developed power is calculated using equation ( 5), with a note that the steam mass flow rate lost through both turbine gland seals is calculated as it would be lost only on the rear gland seal (No.11 in Table 2), which was the calculation procedure in [52].The compared re- [52] and by this analysis.
sults of the calculated HPT real developed power are presented in Table 3.
It can be seen from Table 3 that the difference in the calculated HPT real developed power between [52] and this analysis is in the range of ±0.15% for all observed turbine loads.This fact proves that all of steam operating parameters, in each analysed turbine operating point, Fig. 1, are correctly calculated.
Calculation results of two
presented HPT energy analysis methods with discussion
Calculation results of HPT energy flow stream method
Calculation results of the energy flow stream method for all observed lost steam mass flow rate combinations (Table 2) are presented in Table 4.
A decrease in steam mass flow rate lost through front gland seal resulted with an increase in the steam mass flow rate that expands through the HPT, which finally leads to an increase in the turbine real developed power at each observed load.
The HPT energy power input represents an amount of energy, which is delivered by steam at the turbine inlet.For each turbine load, the energy power input is calculated using an equation (8).Regardless of the lost steam mass flow rate through gland seals distribution, at each turbine load, the steam mass flow rate and steam operating parameters (pressure and temperature) at the turbine inlet are the same, therefore, the energy power input remains constant and is equal to 1083.6 MW for the HPT load of 60%, 1441.4MW for HPT load of 80% and 1858.6 MW for HPT full load (100%).
The energy power output of the analyzed HPT increases during the increase in the steam mass flow rate, which expands through the turbine, which is a valid conclusion for each turbine load.Such occurrence can be easily explained by using equation ( 9) -the decrease in the steam mass flow rate lost through the front gland seal increases the turbine real developed power, which proportionally leads to an increase in the turbine energy power output (at each turbine load, other components of the equation ( 9) remain unchanged).
The energy power loss of the HPT decreases during the decrease in the steam mass flow rate lost through the front gland seal at each turbine load, this can be explained by using the last expression of equation (14).The steam specific enthalpy at the HPT inlet (h 1 ) is much higher than the steam specific enthalpy at the turbine outlet (h 4 ), therefore, any decrease in the steam mass flow rate lost through the front gland seal (and the proportionaly increase in the steam mass flow rate lost through the rear gland seal) will lead to decrease in HPT energy power loss.
In the energy flow stream method, the equation ( 15) defines the change of the HPT energy efficiency for each observed load.An increase in the steam mass flow rate, which expands through the HPT, results in an increase in the turbine real developed power (turbine real developed power is the numerator in equation ( 15)).The denominator of equation ( 15) is a constant for each turbine load, defined by data from Table 1., so the increase in the HPT real developed power results with a simultaneous increase in the turbine energy efficiency.
From the comparison of different turbine loads, a conclusion can be made that the HPT average energy power loss increases with an increase in the turbine load -from 12.14 MW on average at the HPT load of 60%, to 13.85 MW on average at the HPT load of 80% and finally to 16.25 MW on average at the HPT load of 100%.The dominant reason for such HPT energy power loss trend is the increase in the steam mass flow rate lost through both gland seals during the increase in the turbine load, equation (14).
The increase in the HPT load also results in an increase in the average turbine energy efficiency -from 91.45% on average at the HPT load of 60%, to 92.58% on average at the HPT load of 80% and finally to 92.66% on average at the HPT load of 100%.Such occurrence can be explained by using the equation (15) where the HPT real developed power has a higher intensity of increase than the denominator during the increase in the turbine load.The increase in the HPT real developed power is proportional to the increase in the steam mass flow rate at the turbine inlet (and simultaneously with the increase in the steam mass flow rate, which will expand through the turbine) during the load increase.
The main conclusion, which can be derived from the results of the energy flow stream method is that this method is the most dependable on the steam mass flow rates (which expand through the turbine and get partially lost through both gland seals).The results obtained by the energy flow stream method are expected for the analysed HPT -an increase in the turbine load resulted with an increase in the turbine energy power loss and with an increase in the turbine energy efficiency.
Calculation results of HPT isentropic method
The calculation results of the isentropic method for all observed lost steam mass flow rate combinations (Table 2) are presented in A decrease in the steam mass flow rate lost through the front gland seal simultaneously increases the steam mass flow rate, which expands through the HPT, which results in an increase in both real (polytropic) and ideal (isentropic) turbine power at each observed turbine load.For each HPT load and for each combination of steam mass flow rate lost through both gland seals, the real developed turbine power is calculated according to equation (5), while an ideal turbine power is calculated by using an equation (17).The same increase in the steam mass flow rate which expands through the HPT will result in a more intensive increase in the turbine ideal than real developed power, because steam specific enthalpy differences in equation ( 17) are higher than in equation ( 5).
In the isentropic energy analysis method, more intensive increase in the HPT ideal power, in comparison with the real power during the increase in the steam mass flow rate, which expands through the turbine, will result in an increase in the energy power loss, equation (18), which is a valid conclusion for each observed turbine load.However, a change in the steam mass flow rate, which expands through the HPT, has a negligible influence on the turbine energy efficiency while using the isentropic energy analysis method, at any observed turbine load.
It can be seen from Table 5 that the change in the steam mass flow rate, which expands through the analysed HPT, results in a very small change in the turbine energy power loss and in a negligible change in the turbine energy efficiency while using the isentropic method.Therefore, it can be concluded that the change in the steam mass flow rate, which expands through the HPT, at any load is not the dominant element, which defines the turbine energy losses and energy efficiencies in the isentropic energy analysis method.
When comparing different HPT loads by the isentropic energy analysis method, it can be concluded that the turbine average energy power loss increases with an increase in the turbine load -from 3.417 MW on average at the HPT load of 60%, to 5.611 MW on average at the HPT load of 80%, and finally, a significant increase can be noted at the HPT load of 100% (22.943MW on average).
In the isentropic method, an increase in the HPT load resulted with a continuous decrease of the turbine energy efficiency.An increase in the HPT load from 60% to 80% resulted in a small decrease in the turbine energy efficiency -from 97.437% to 96.855%.A further increase in the HPT load from 80% to 100% resulted with a significant decrease in the turbine energy efficiency -from 96.855% to 89.944%.
The results obtained by the isentropic energy analysis method show an unexpected HPT behaviour during increase in turbine load -the HPT average energy power loss significantly increases at a full turbine load, while the energy efficiency continuously decreases.
As it was proved before, the change in steam mass flow rate, which expands through the HPT, does not have a significant influence on the turbine energy power loss and energy efficiency in the isentropic method, at any observed load.The most dominant element in the applied isentropic method for the HPT (or any other steam turbine) energy analysis is a real (polytropic) steam expansion process and its comparison with an ideal (isentropic) steam expansion process.The higher difference of the polytropic steam expansion process from the isentropic steam expansion process will result in a higher energy power loss and, simultaneously, in a lower energy efficiency -this conclusion is valid for an energy analysis of any steam turbine while applying the isentropic method.The difference in steam expansion processes (ideal and real) can also be observed as a difference in steam specific enthalpies between polytropic and isentropic expansion processes (between two same steam pressures) -a higher difference in steam specific enthalpies will result in a higher energy power loss and lower energy efficiency of any steam turbine.
A plot of steam expansion processes for the analyzed HPT at each load, shown in Fig. 3, proves the above made conclusion.The real (polytropic) steam expansion process at the HPT load of 60% is the closest to an ideal (isentropic) steam expansion process -therefore, at the lowest observed load, the HPT will have the lowest energy power loss and the highest energy efficiency.At the HPT load of 80%, the polytropic steam expansion process deviates from the isentropic expansion process a little more than during the HPT load of 60% -therefore, at the HPT load of 80%, the analysed turbine will have a higher energy power loss and lower energy efficiency in comparison with the HPT load of 60%.Fig. 3 clearly presents that the polytropic steam expansion process significantly deviates from the isentropic steam expansion process at the HPT full load (load of 100%).From such polytropic steam expansion process at the HPT load of 100%, a much higher energy power loss and much lower energy efficiency of the analyzed turbine can be expected when compared to lower loads.The reason of such difference between polytropic and isentropic steam expansion processes at the HPT full load is exceeding the critical pressure at the HPT inlet (at the steam generator outlet, Fig. 1), as the analyzed turbine operates in a supercritical thermal power plant.The water/steam critical pressure, as noted in the fluid information of Nist REFPROP 9.0 software [57] is 220.64 bars.It can be seen from Table 1 that the critical pressure at the HPT inlet is exceeded only at the turbine load of 100%.The exceeding of the critical pressure significantly influenced the HPT steam real (polytropic) expansion process (in comparison with polytropic steam expansion processes under the critical water/steam pressure).
Comparison of energy flow stream and isentropic energy analysis methods for researched HPT
In this section a direct comparison of the energy flow stream and isentropic methods for the HPT energy analysis are presented.Energy power losses and energy efficiencies of the analyzed HPT at each observed turbine load and for each observed combination of lost steam mass flow rate through both gland seals were compared.
At the HPT load of 60%, the difference in the turbine energy power loss calculated by the energy flow stream method and isentropic method is between 7.92 MW and 9.54 MW, Fig. 4.An increase in the steam mass flow rate, which expands through the turbine, results in a decrease in the energy power loss difference between observed methods.At the same HPT load (load of 60%), the difference in the turbine energy efficiency calculated by the isentropic method and the energy flow stream method is between 5.43% and 6.54%, Fig. 4, while the increase in the steam mass flow rate, which expands through the turbine, results in a decrease in energy efficiency difference between observed methods (Table 4 and Table 5).
The same trends in differences of energy power loss and energy efficiency between the energy flow stream method and isentropic method are observed at the HPT load of 80%, Fig. 5.The energy power loss difference between the energy flow stream method and isentropic method at the HPT load of 80% is between 7.31 MW and 9.17 MW, while the difference in the turbine energy efficiency between the isentropic method and energy flow stream method is between 3.79% and 4.76%, Fig. 5.An increase in the steam mass flow rate, which expands through the HPT, results in a decrease in both energy power loss difference and energy efficiency difference between the observed methods (Table 4 and Table 5) also at the HPT load of 80%.
The trends in differences of the HPT energy power loss and energy efficiency between energy flow stream method and isentropic method are reversed at the HPT load of 100% when compared to lower turbine loads, Fig. 6.The reason of such occurrence can be found in the fact that at the HPT load of 100%, the energy power loss of the analysed steam turbine calculated by the isentropic method is much higher than the energy power loss calculated by the energy flow stream method, while the turbine energy efficiency calculated by the isentropic method is lower than the energy efficiency calculated by the energy flow stream method, for each steam mass flow rate, which expands through the turbine (Table 4 and Table 5).Therefore, at the HPT load of 100%, the energy power loss difference between the isentropic method and the energy flow stream method is between 5.61 MW and 7.77 MW, while the difference in the turbine energy efficiency between the energy flow stream method and the isentropic method is between 2.28% and 3.16%, Fig. 6.At the HPT load of 100%, an increase in the steam mass flow rate, which expands through the analysed turbine, results in an increase in the energy power loss difference and energy efficiency difference between observed methods.
When the observed HPT loads are compared, it can be concluded that the average differences in the turbine energy power loss and the energy efficiency between the energy flow stream method and isentropic method decreases during the increase in the turbine load.An increase in the HPT load results in a decrease of the average energy power loss difference between the energy analysis methods from 8.73 MW on average at the HPT load of 60%, to 8.24 MW on average at the HPT load of 80% and finally to 6.69 MW on average at the HPT load of 100%.In the same turbine load range, the average energy efficiency difference between the observed energy analysis methods decrease from 5.99% on average (load of 60%) to 4.28% on average (load of 80%) and finally to 2.72% on average (load of 100%).
A direct comparison of the energy flow stream method and the isentropic method for the energy analysis of the HPT showed that these two methods cannot be directly compared.This conclusion is valid not just for the analysed, but also for any other steam turbine.
As presented for the HPT, the energy flow stream method and isentropic method cannot be directly com-pared because they present different steam turbine energy losses (and consequently different energy efficiencies).On the one hand the energy flow stream method presents steam turbine energy efficiencies and energy power losses which arise from the steam mass flow rates (which expand through the turbine and get partially lost through both turbine gland seals).The isentropic method, on the other hand, presents energy power losses and energy efficiencies, which arise from the steam real (polytropic) expansion process through the turbine and its comparison with the ideal (isentropic) steam expansion process.The change of steam mass flow rates (which expand through the turbine and lost one through both turbine gland seals) has almost a negligible influence on the results of the isentropic method, similarly, the comparison of steam expansion processes (ideal and real) has an almost negligible influence on the results of the energy flow stream method -which is proved by the presented analysis of the HPT.
The question which arises from the presented comparison is -which method for the energy analysis of any steam turbine gives more reliable and more precise results and which can be used as the relevant one?The proper answer to this question is that a complete (overall) energy analysis of any steam turbine should be performed by using both of the presented methods.Combination of the steam turbine energy power losses and energy efficiencies obtained by both of these methods are relevant and such combination gives a better insight in the energy analysis of any steam turbine.
The overall HPT energy analysis
An overall energy analysis of the researched HPT (or any other steam turbine) incorporates energy power losses and energy efficiencies of both energy flow stream and isentropic methods.Both of these methods contribute to the overall HPT (or any other steam turbine) energy efficiency and energy power loss analysis.The overall energy power loss of the analyzed HPT is calculated as a sum of energy power losses obtained by the energy flow stream method and isentropic method: Ėen,P L,OV ERALL = Ėen,P L,EF S + Ėen,P L,IS , (20) while the overall energy efficiency of the analyzed HPT is calculated by multiplying the energy efficiencies obtained by the energy flow stream method and the isentropic method: The change in the overall energy power loss of the analyzed HPT for each observed turbine load and for each observed steam mass flow rate, which expand through the turbine, is presented in Fig. 7.
When different HPT loads are compared, the lowest overall energy power loss can be seen at the lowest observed HPT load (load of 60%) due to the lowest energy power losses calculated by both observed energy analysis methods (Table 4 and Table 5) with average value of 15.56 MW.The energy flow stream method and isentropic method give a slightly higher energy power losses at the HPT load of 80% (compared to the HPT load of 60%), therefore, the overall energy power loss of the analyzed turbine at the load of 80% has an average value equal to 19.47 MW (Table 4 and Table 5).A comparison of the highest observed HPT load (load of 100%) with a lower HPT load (load of 80%) shows that the overall turbine energy power loss significantly increase and its average value at the highest observed load is 39.20 MW.At the HPT load of 100%, the energy flow stream method gives a higher (but not significantly) energy power loss when compared to lower turbine loads, while the isentropic method gives a significantly higher energy power loss when compared to lower turbine loads (Table 4 and Table 5).Such increase in the energy power loss of the isentropic method at the highest observed HPT load is caused by exceeding the critical water/steam pressure at the HPT inlet (polytropic steam expansion significantly deviates from the isentropic steam expansion (Fig. 4.)).
At each observed HPT load, increase in the steam mass flow rate, which expands through the turbine, results in a decrease in the overall turbine energy power loss due to the decrease of the HPT energy power loss calculated by the energy flow stream method (the same increase in the steam mass flow rate, which expands through the turbine, results in a small change in the energy power loss calculated by the isentropic method).The range of the overall HPT energy power loss during the increase in the steam mass flow rate, which expands through the turbine, is between 14.79 MW and 16.33 MW at the HPT load of 60%, between 18.59 MW and 20.34 MW at the HPT load of 80%, and, finally, between 38.33 MW and 40.06 MW at the HPT load of 100%.
The change in the analysed HPT's overall energy efficiency for each observed turbine load and for each observed steam mass flow rate, which expands through the turbine, is presented in Fig. 8.
A comparison of different HPT loads shows that the lowest overall energy efficiency is obtained at the highest observed HPT load (load of 100%), which is caused by exceeding the critical water/steam pressure at the HPT inlet and a high deviation of the polytropic steam expansion process from the isentropic steam expansion process -the result is that the isentropic method gives a much lower HPT energy efficiency when compared to the lower turbine loads (regardless of the highest energy efficiency obtained with the energy flow stream method at the same HPT load).The average overall energy efficiency of the HPT at a load of 100% is 83.34%.The analysed HPT, at a load of 60%, has a much higher overall energy efficiency when compared to the highest observed turbine load -at a load of 60%, the HPT has an average overall energy efficiency equal to 89.11%, Fig. 8, due to the highest energy efficiency obtained by the isentropic method (when compared to other HPT loads).
The highest HPT average overall energy efficiency is obtained at the HPT load of 80% and is equal to 89.67%.The reason of such occurrence can be found in comparison of the analysed turbine energy efficiency obtained by both observed energy analysis methods at a load of 60% and at a load of 80%.The energy flow stream method gives higher HPT energy efficiencies at a turbine load of 80% in comparison with turbine load of 60%, while the isentropic method resulted in a reversed conclusion (the energy efficiency of the HPT obtained by the isentropic method is higher at a load of 60%, in comparison with a load of 80%).The difference in the HPT's energy efficiency between the load of 60% and the load of 80% is higher for the energy flow stream method, therefore, the equation ( 21) results in higher values of the overall energy efficiency for the load of 80% than for the load of 60%.
The increase in the steam mass flow rate, which expands through the analyzed turbine, results in an increase in the turbine's overall energy efficiency, which is a valid conclusion for each observed HPT load, Fig. 8.The reason of such occurrence is found in the following fact -the increase in the steam mass flow rate, which expands through the turbine, increases the HPT energy efficiency calculated by the energy flow stream method, while the same increase in the steam mass flow rate, which expands through the turbine, results in a negligible change in the turbine energy efficiency calculated by the isentropic method.The range of the overall HPT energy efficiency during the increase in the steam mass flow rate, which expands through the turbine, is between 82.95% and 83.74% at the HPT load of 100%, between 88.57% and 89.65% at the HPT load of 60% and, finally, between 89.20% and 90.13% at the HPT load of 80%.
Conclusion
In this paper, a comparison of two methods for a steam turbine energy analysis is presented.A high pressure steam turbine from a supercritical thermal power plant was analysed with both presented methods -the energy flow stream method and isentropic method, at three different turbine loads.
The energy flow stream method and its results are mostly influenced with two steam mass flow ratesthe first one is the steam mass flow rate, which expands through the HPT, and the second one is the cumulative steam mass flow rate lost through both HPT gland seals (and its distribution on the front and rear gland seal).The steam real (polytropic) expansion process throughout the turbine and its deviation from the ideal (isentropic) expansion process has a negligible influence on the energy flow stream method.For the analysed HPT, only the cumulative steam mass flow rate lost through both gland seals is known.As the steam mass flow rates lost through each (front and rear) gland seal are unknown, various combinations of the lost steam mass flow rate distribution are performed.
The isentropic energy analysis method and its results are mostly influenced by the real (polytropic) steam expansion process and its deviation from the ideal (isentropic) steam expansion process.For the analyzed HPT, this deviation is the highest at the highest observed turbine load (load of 100%) due to exceeding a critical water/steam pressure.A steam mass flow rate, which expands through the analysed turbine, and the cumulative steam mass flow rate lost through both HPT gland seals has a very low influence on the HPT energy power loss and a negligible influence on the HPT energy efficiency calculated by using the isentropic energy analysis method.
The energy flow stream method and the isentropic energy analysis method of any steam turbine (not only the researched HPT) are not directly comparable because they are based on different sources of steam turbine energy losses (and consequently different energy efficiencies).
At the end of this paper, an overall steam turbine energy analysis, which involves energy efficiencies and energy losses of both methods for the steam turbine energy analysis, is presented.The change in energy power loss and energy efficiency of the HPT obtained by the overall energy analysis is: • an increase of the HPT load (load of 60%, 80% and 100%) results in an increase of the average overall HPT energy power loss (15.56 MW, 19.47 MW and 39.20 MW), • an increase of the steam mass flow rate, which expands throughout the HPT, at any load results in a decrease of the overall HPT energy power loss, • the lowest HPT average overall energy efficiency (83.34%) is obtained at a turbine load of 100% (full load).Such low average overall HPT energy efficiency at a full load is caused mostly because of exceeding a critical water/steam pressure, • the highest HPT average overall energy efficiency (89.67%) is obtained at a turbine load of 80%, while at a turbine load of 60%, the average overall energy efficiency is 89.11%.
Figure 1 .
Figure 1.Scheme of the analysed high pressure steam turbine (HPT) along with steam flow stream marks.
Figure 2 .
Figure 2. Comparison of isentropic and polytropic steam expansion processes for the analysed HPT.
Figure 4 .
Figure 4. Comparison of energy flow stream method and isentropic method -difference in energy power loss and energy efficiency (HPT load of 60%).
Figure 5 .
Figure 5.Comparison of energy flow stream method and isentropic method -difference in energy power loss and energy efficiency (HPT load of 80%).
Figure 6 .
Figure 6.Comparison of energy flow stream method and isentropic method -difference in energy power loss and energy efficiency (HPT load of 100%).
Figure 7 .
Figure 7. Overall energy power loss change of the analyzed HPT.
Figure 8 .
Figure 8. Overall energy efficiency change of the analyzed HPT.
LatinSymbols: Ė energy stream flow power [kJ/s] h specific enthalpy [kJ/kg] ṁ mass flow rate [kg/s] P power [kJ/s] Q heat transfer [kJ/s] s specific entropy [kJ/(kg K)] Greek Symbols: η efficiency Subscripts en energy EF S energy flow stream (method) IN inlet (input) IS isentropic OU T outlet (output) P L power loss RE real Abbreviations: HP T high pressure turbine
Share of cumulative steam mass flow rate lost through both gland seals in the steam mass flow rate at the HPT inlet (%) -load 60%; 80%; 100% 1.19; 1.02; 0.93
Table 2 .
Distribution of HPT cumulative steam mass flow rate lost through both gland seals -researched combinations.
Table 3 .
Comparison of calculated HPT real developed power from
Table 4 .
Calculation results of energy flow stream method.
Table 5 .
Calculation results of isentropic method. | 10,235.4 | 2019-04-30T00:00:00.000 | [
"Physics",
"Engineering",
"Environmental Science"
] |
Asymmetric dependence of intraday frequency components in the Brazilian stock market
The multivariate dependence plays an important role in financial instrument management. Due to the inherent characteristics in the financial market, such as heavy tails in the returns unconditional distribution and asymmetry between gain and loss, we obtained the asymmetric dependence structure in different short-term variation scales based on the wavelet technique MODWT. The study sought to capture the relations between financial returns represented by its frequency components. Intraday returns series was used in the 15-min sampling interval from stocks and applied the D-Vine pair-copula to decompose in trade frequencies of 15 min, 1 h, 1 day, and 1 week with margin adjustments of ARIMA-APARCH class and BB7 copula function, responsible for measuring the dependence on tails. The results indicated the prevalence of a high dependence during market upturns, rising over the analyzed frequencies. Being an important tool in financial management and allowing short-term strategies of diversification.
Introduction
The behavior of the multivariate dependence structure of financial markets configures a relevant point on funding instruments management. Since Modern Portfolio Theory (Markowitz 1952) a general discussion of this topic, including other aspects as risk and return, expected return, measures of risk and volatility, and diversification, has been in the current literature. Several studies such as Ergen (2014), Jondeau (2016) and Caldeira et al. (2017) have shown that the measuring of dependence existing among the returns of a portfolio is essential to investment strategy development, mainly in the diversification context, which consists of the efficient allocation of distinct assets to minimize risks.
Specifically, in risk management, portfolio selecting, and asset pricing, there are important aspects such as non-linearity, asymmetrical dependence, and also heavy tails of the marginal and joint probability distribution (Wang and Xie 2016). To deal with these questions, inferences based on tail multivariate probabilities are necessary. Tail dependence refers to asset returns that exhibit greater dependence during market downturns or during market upturns and has long been an issue of interest to academics, fund managers, and traders, as it has important implications for portfolio allocation and asset pricing. Patton (2004), Malevergne and Sornette (2006) and Hatherley and Alcock (2007) demonstrated that incorporate the effects of asymmetric asymmetrical dependence in asset allocation proved to be better for protect portfolios and minimize risk. Besides that, Cherubini et al. (2004) and Chollete et al. (2011) showed that most economic policies of systemic risk involve tail dependence.
The evidences raised above have been widely reported over the years, principally the marginal distribution skewness and dependence structure asymmetry. According to Peng and Ng (2012) and Patton (2001), an inappropriate dependence model can lead to inefficient portfolios and imprecise evaluations of risks expositions. To deal with these problems, the application of the copulas approach is proposed. Copulas are functions that connect multivariate distribution functions to their marginal distributions (Cherubini et al. 2004). They contain all the relevant information about the dependence structure among the variables, for both symmetric and asymmetric correlation structures. In financial data, the copulas form an ideal tool for analyzing extreme dependence movements without the restrictions imposed by the classic multivariate models, reflecting the dependence between assets (Embrechts et al. 2003).
There are different copulas approach applications for the optimization of returns of assets seen on Righi and Ceretta (2013), Kakouris and Rustem (2014), Bartels and Ziegelmann (2016) and Abbara and Zevallos (2017). One of these methods is the multivariate pair-copula models of Joe (1997), extended by Bedford and Cooke (2001) and Bedford and Cooke (2002) with a hierarchic graphic construction of bivariate copulas called regular vines copulas. According to Joe et al. (2010), the modeling of dependence with multivariate copulas, such as the Vine approach, enables to development of appropriate parametric families for multivariate financial data with different dependence structures. Joe and Kurowicka (2011) provides an extensive review of Vine copulas, including applications of this methodology in financial.
The purpose of the present paper is to provide the behavior of asymmetrical relation between financial returns in the domain time-frequency. Frequency is a relevant factor in assets analysis, relating to the changes in the investment horizons of the players of the market, ranging from short-run to long-run. The significance of this analysis lies in considering the impact of the time horizons of investment rules on the portfolio analysis, measuring asymmetric dependence in different timescales. The time horizons of economic decisions are related to the stock price changes, then different temporal frequencies (scales) of a returns series are useful to capture subjacent financial information from these data, as seen by Jammazi and Reboredo (2016), Shah et al. (2018), Biage (2019) and Berger and Gençay (2019). This multiscale financial behavior can be captured applying the wavelet decomposition, which enables to identify the trend in different periods of time and to locate the relevant oscillation moments (Crowley 2007;Gallegati 2014).
The portfolio analyzed in this study was composed of stocks traded in the Brazilian financial market (B3). We consider the 15-min sampling interval as the regularly spaced time for the 7 h of continuous negotiation in the B3 from February 17th to May 8th of 2020 of six relevant stocks: PETR4 (Petrobras), AZUL4 (Azul), USIM5 (Usiminas), BBDC4 (Bradesco), WEG3 (Weg) e MGLU3 (Magazine Luiza). The period analyzed reflects the negative effects of the COVID-19 pandemic on the financial markets, reinforcing the importance of modeling this event, providing tools for decision making. The stock choice was based on different economic segments to generate a diverse portfolio, and Chang et al. (2008) showed that high-frequency horizons are important to investigate the effects of short market trade activities, which reflect changes in an asset trajectory at many different scale levels (Crowley 2007).
The decomposed series for each intraday stock returns sample was obtained, applying the wavelet technique by Percival and Walden (2000) using the Daubechies wavelet filter of length 2 (two null moments) by Daubechies (1992). The short-term trade frequencies are the variations scale series regarding 15 min, 1 h, 1 day and 1 week. To carry out the analysis of asymmetric multivariate dependence analysis, we applied the D-Vine pair-copula constructions according to Joe (1997) and Bedford and Cooke (2002) in the decomposed series. The marginal distributions were specified as the process from ARIMA and ARIMA-APARCH classes by Ding et al. (1993), to capture important characteristics evidenced in these series. The pair-copula analysis proceeds with the standardized residuals. The BB7 copula function is estimated for its property of capturing asymmetrical dependence in financial data as shown by Nikoloulopoulos et al. (2012).
Wavelet analysis
The maximal overlap discrete wavelet transformation (MODWT) is a modification of discrete wavelet transformation (DWT), proposed by Percival and Walden (2000). Both the DWT and the MODWT draw on multiresolution analysis to decompose a signal into different levels of resolution. At each level, weighted moving average values (smooths) and the information to reconstruct the signal (details) from the averages are obtained describing the original signal at coarser and coarser levels of resolution.
In contrast to the DWT, the MODWT not is characterized by a data reduction (to the half) by each decomposition, keeping the data length constant. Actually, the MODWT presents essential proprieties in the time series decomposition: the translation is non-orthogonal and invariant, conserving the original series variation. This enables the impact of any event to be analyzed over specific timescales, so this method will be used in this paper. Percival and Walden (2000) presented an extensive revision of the MODWT characteristics in time series.
The MODWT follows the same pyramid algorithm (Mallat 1989) as the DWT (see Percival and Walden 2000). Letting j = 1, … , J be the scale numbers and the initial series entrance s 0,t = X N−1 t=0 . The decomposition process occurs with the successive filtering of a time series X t with low-pass filters {g j,l } and high-pass {h j,l } given by and where L j = (2 j − 1)(L − 1) + 1 correspond to filter size associated to each scale j and modN is the modulus operator. In Eqs. (1) and (2) the MODWT filters h j,l = h j,l ∕2 j and g j,l = g j,l ∕2 j , respectively, are expressed in terms of DWT rescaled filters g j,l and h j,l that satisfies useful proprieties in the decomposition of a sign: (1) , similarly for the g j,l . Several wavelet filters do exist and the choice of the adequate filter heavily depends on the purpose of its application. Thus, considering that intraday series present non-stationary and drastic fluctuations the Daubechies wavelet filter by Daubechies (1992) was employed in this paper. With the compact support advantage and orthogonality the general form of Daubechies filters is given by h l,j = (−1) l−L j g L j −1−l . Applications with Daubechies filters in multiscale analyzes as intraday financial series can be seen in Sun et al. (2011), Xue et al. (2014, and Xu (2018).
At each scale, the MODWT coefficients s j,t and d j,t constitute a time series describing X t in non-aggregated over time way, such that X t = ∑ J j=1 �d j,t +s J,t � . At the levels j = 1, … , J and in the time t, the scale coefficients s j,t represent the smooth coefficients that capture the trend of X t , while the detail coefficients d j,t capture the short oscillations, as structural changes, representing the detailing of X t . Taking into account these brief description of properties of the MODWT, we estimated the asymmetric dependence of portfolios using the detail series obtained from the decomposition.
Copulas and pair-copulas
The concept of copula is introduced in the statistical literature by Sklar (1959). Let the random variables X 1 , … , X d with joint distribution function H, such as , represent the details series obtained from the MODWT decomposition in the intraday log-returns. The dependence between X 1 … , X d can be completely described by a d-dimensional copula function C, such as H( Then, according to Sklar (1959), a C is defined as a function of joint distribution in [0, 1] d with Uniform marginals. Assuming C is absolutely continuous, and by taking the partial derivatives, one obtains: where c represents the copula density.
For the multivariate case modeling, Aas et al. (2009) explained that a pair-copula decomposition is a flexible alternative and easily implemented. The pair-copulas is a hierarchical construction, based on bivariate copulas chosen between any parametric family. The variables are sequentially incorporated into the conditioning sets as one moves from the first modeling level d until the last level d − 1 . The pair-copula factorization, according to Joe (1997), is obtained from the following decomposition of h: where for d variables at T time points, assumed that the observations of each variable are independent over time.
Based on the joint density in Eq.
(3), all conditional densities in Eq. (4) can be expressed from only univariate marginal distributions and bivariate copulas by means: where c xv j | −j (.) corresponds to the density of a bivariate copula, and −j denotes the vector excluding the jth component.
For the representation of Eq. (5) there is different pair-copulas construction (PCC). Then Bedford and Cooke (2001) and Bedford and Cooke (2002) introduced the systematic model called regular vines that involves the construction of hierarchic graphic models. Each of these models provides a specific way of decomposing the d-dimensional h density. The main types are the hierarchical canonical vines (C-vines) and the drawable vines (D-Vines).
In this paper, the h density was estimated from the D-Vine PCC, which is written as where index j identifies the trees, while i runs over the edges in each tree. In a D-vine, no node in any tree T j is connected to more than two edges. There are d(d − 1)∕2 bivariate copulas density in the d − 1 trees. The tree T j of the D-vine has d − j bivariate copulas, j = 1,…, (d − 1). Those in tree 1 are unconditional, and all others are conditional (Aas et al. 2009).
In the inference process of the D-Vine PCC, it is necessary to obtain the respective functions of conditional distribution F(x| ) in a sequential way, this is where is the vector parameters of the C x,v j | −j specified in the j tree.
The bivariate copulas involved can belong to different families in a way of reflecting various ways of dependence, including tail dependence (see Joe 1997). The concept of tail dependence refers to the amount of dependence on the right higher quadrant tail or on the left lower quadrant tail of a bivariate distribution (Embrechts et al. 2003).
This feature enables construct h estimating different margins independently. In the presence of temporal dependence, univariate time series models for the conditional mean and the conditional variance can be fitted to the margins and the analysis could henceforth proceed with the residuals standardized. The standardized residual vectors are converted to uniform variables using the empirical distribution functions before further modeling (Nikoloulopoulos et al. 2012).
The data and context
The high-frequency data used were the log-return stocks of B3 and covers the 55 working days from February 17th of 2020 to May 8th of 2020, presented in Fig. 1. In the analyzed period, it is needed to emphasize the expressive influence of COVID-2019 in the worldwide financial markets. According to Laurini and Chaim (2020), the COVID-19 pandemic drop in prices in March 2020 has spurred volatility increases with levels faster. Along with the phenomenon, the Brazilian stock market has been suffering an impact on internal political instability.
The sampling interval regarded was of Δ = 15 min as the spaced time for 7 h of continuous trading. The number of sampled observations per trading session is m = 28 interval/day with a total of N = 1497 observations. The data filtering process was made according to Morettin (2017), keeping the circuit-breakers in the final sample. Plots in Fig. 1a, b indicate the intraday prices and log-returns behavior in the period.
General information about the data classification with the base on the sector and action segment is presented in Table 1.
The MODWT constructed by the Daubechies wavelet filter with length 2 (two moments null) D2 was applied to the intraday series using the methodology Fig. 1 Series behavior in the analyzed period for the series: a quotes and b log-returns submitted in Sect. 2.1, obtaining J = 10 decomposition levels. Once the variance of the original returns series is preserved, we can measure the dependence using the series from the decomposition. Thus, the D-Vine PCC was obtained four details series for each original series in four dyadic scales of variation: 15 min, 1 h, 1 day and 1 week. The frequencies are measured according to Table 2, conform to the 7 h of B3 trading, and Fig. 2 illustrate the series generated by the MODWT decomposition in levels j = 1, 3, 6, 8.
Dependence estimation
Since we are mainly interested in the dependence structure between wavelet series obtained, the estimation process of copulas was made through the methods of maximum likelihood in two steps according to the inference function for margins approach by Joe and Xu (1996), they are (1) univariate adjustment of margins and (2) adjustment of the copula with the standardized residues of margins under pseudo-observations.
As stated in Sect. 2.2, the observations of each variable must be independent over time. Hence, in the first stage, the margin distributions were estimated by models of the conditional mean and variance. The ARIMA(p, d, q)-APARCH(1, 1) process by Ding et al. (1993) was used. That is, for details series j in time t = 1, … , N , we have the following model: where the standardized residual z j,t ∼ t − skewed( j , j ) to consider the conditional heteroscedastic heavy-tailed behavior of the financial assets. For the mean equation, represents the p autoregressive components and the q moving average components. In the variance equation, corresponds to the unconditional variance, allows to estimate of other powers to the standard conditional deviation, through a Box-Cox transformation, 1 captures the leverage effects, 1 and 1 together depict the volatility persistence. We use the modified Q-statistic (Ljung and Box 1979) to validate the modeling. For the second adjustment stage, initially defined the PCC order estimation. The originals series were ordered by the non-linear dependence, measured through Kendall's tau. After, adjustment the D-Vine PCC with the BB7 copula function performed on the standardized residuals of margins ( z j,t ). The standardized residual vectors are converted to uniform variables u 1 and u 2 using the empirical distribution functions before the adjust. The BB7 bivariate copula captures the tail dependence and has representation given by Joe (1997): with = 1∕ log 2 2 − U and = −1∕ log 2 L the parameters related to dependence coefficients of the higher and lower tails, respectively U , L ∈ (0, 1). These measures were used to quantify the asymmetric dependence, i.e, determine if the relationship between the intraday log-returns, in the different timescales, has intensified in periods of market downward ( L ) or during the market upward ( U ).
The results were obtained with the software (Team 2019). The data were provided by alphavantager package by Dancho and Vaughan (2019). The analysis was performed with the packages wtmsa by Constantine and Percival (2017)
Modelling of marginal distributions
The univariate margin models were defined with ARIMA(p, 0, q)-APARCH(1, 1) in the 15-min, 1-h and 1-day scales, which presented stochastic noise and short variations. As the 1-week frequency case reflects the trend of the short term, just the conditional mean was adjusted appealed to ARIMA(p, 1, q) class models. The specification of margins is according to the results of Table 3.
The estimate results of the coefficients are found in Tables 4, 5, 6 and 7. In general, the results corroborating statistical characteristics commonly present in financial time series. In the equation, it became evident that stock returns price movements to become more persistent (Schulmeister 2009) and the presence of intraday seasonality (Morettin 2017). And, the results reflect volatility persistence, heavytails, and asymmetry (Patton 2004). In some cases were evidenced significant leverage effects ( 1 > 0 ), the phenomenon that arises when periods of falling prices are followed by significant volatility (Ding et al. 1993).
Copula modelling
Subsequent to this marginal specification, we obtained matrix of dependence through Kendall's tau to select the order in PCC estimation. The criterion adopted was the absolute sum of dependence between each index with all others. The D-vine
BBDC4
(1,0,1)(1,1) (0,0,7)(1,1) (4,0,2)(1,1) (9,1,2)*(0,0) PCC order result was USIM5, PETR4, MGLU3, BBDC4, AZUL4, and WEGE3. The results are verified in Table 8. It is observed a moderate positive association between all stocks analyzed (34% in mean). The result shows that some stocks pairs can move together, emphasizing the diversification question. The greatest magnitude of dependence was related to the pair PETR4 and USIM5, reaching 41%. Moreover, we noted that the stocks with a higher dependency are associated with the sectors that are sensitive to the actual world economic situation due to COVID-19 impacts, for example, the commodity sector. With order among log-returns established, the D-Vine PCC was adjusted with standardized residuals of the marginal distributions. The dependence parameters of BB7 copula were converted in the measures of the lower tail ( L ) and upper tail ( U ) presented in Table 9.
As demonstrated in the literature, the asymmetry pattern is captured in the majority of relationships between the stock's returns, in the different time frequencies analyzed. The general pattern of association between these stocks is more intense during the market upward ( U > L ) in all scales. It means that a rising in B3 prices tends to occur simultaneously in the period (de Melo Mendes and Accioly 2012). This may also suggest an asymmetry to the right in the multivariate distribution as indicated (Silva Filho et al. 2014). Some left asymmetry ( L > U ) between pairs of stocks has been observed in variations intraday (15 min and 1 h) in the first trees. Note that in all scales, in the trees with USIM5,AZUL4|PETR4,MGLU3,BBDC4 and PETR4,WEGE3|MGLU3,BBDC4,AZUL4 have concerned L = 0 , indicating independence of the lower tail. This condition indicates that, in general, the adjusted BB7 D-vine has multivariate dependence of the higher tail (Joe et al. 2010).
The results of the magnitude of dependence in the trees demonstrate that the estimates of U and, mainly, L have presented decreasing behavior due to the nature of the hierarchical construction, as indicated by Joe et al. (2010). These results highlight the importance of asset diversification in the way that (Markowitz 1952) had intended. The decrease in the joint probability obtained in tails indicates that it possibilities to minimize portfolio risk based on asset allocation in these stocks, especially in times of negative innovations, such as the scenario of the COVID-19 pandemic. Among the scales, increments at the magnitude of dependence measures were noticed in the majority of trees in lower frequencies, which can reflect the effects of continuous changes in the movements of the prices of the assets in time horizons of minutes and hour (Billio et al. 2012;Xu 2018).
Final remarks
In the relevance's face of multivariate analysis in the financial area, in this paper, we explored the asymmetric dependence from intraday frequency components of The evidence about financial markets like non-linearity, kurtosis excess, asymmetry dependence structures, and high-frequency was considered. We quantified the higher and lower tail dependence through the D-Vine PCC by Bedford and Cooke (2002) at intraday, daily, and weekly scales. The D-Vine PCC method reflects the dependence on extremes with the construction of a multivariate distribution, estimating different marginal without normality presupposition. For this purpose, the estimation process was based on the MODWT details series which reflects the financial market variations, capturing the effects of the trade activity in the different time horizons. The frequencies analyzed were related to short-term trade: 15 min, 1 h, 1 day and 1 week.
The univariate marginal distributions were specified as ARIMA(p, 0, q)-APARCH(1, 1) and ARIMA(p, 1, q) models by Eq. (8). We can see that all the scales the information passed of series affect the conditional mean and conditional variance of returns, reflecting the dynamic of stock price movements and seasonality intraday. In addition, asymmetric and heavy tails were evidenced for scales related to minutes, hour, and day. The asymmetric dependence was captured based on BB7 copula parameters, present in Eq. (9), that quantified the dependence on extremes with the tail dependence coefficients. The upper tail dependence exceeded the absolute and lower tail ones in many cases, which indicates the presence of asymmetry in many relationships and a market upward pattern. It was observed also a decreasing magnitude of the dependence in all cases, due to the nature of the D-Vine PCC. These results reflecting important practical aspects, regarding financial management. First, the importance of skewness and asymmetric dependence in stock returns for asset allocation. We conclude that a portfolio constructed based on the distribution model that allowed for asymmetric dependence can lead to significantly better asset allocation decisions in time horizons analyzed. Based on the traditional mean-variance analysis by Markowitz (1952), studies such as Patton (2004), Hatherley and Alcock (2007), Jondeau (2016) and Wang and Xie (2016), and others have indicated that these benefits are results of more flexibility specifying the dependence structure on the portfolio. The measurement of asymmetric dependence allows diversifying the allocation of resources in portfolios, providing a balance between risks and returns.
A second point is the performance with changes in the investment time horizon. The attention to the horizon to be employed in investment analysis is evidenced by papers as Gunthorpe and Levy (1994). Considering the frequency dynamics enabled us to study the different degrees of behaviors of stock returns of the B3 market and its relations stemming from heterogeneous shocks. We show that the different investment planning horizons can change the portfolio strategies as Ibragimov et al. (2011) and Chakrabarty et al. (2015). A view that focuses on short fluctuations, with the use of dealing strategies in short-term scales, can also result in a portfolio with considerable profits as Zhang et al. (2016), Baralis et al. (2017) and Berger and Gençay (2019) suggests.
When incorporating the effects of asymmetric correlations in asset allocation, in different time frequencies, this study contributes to emphasizes the importance of statistics applications about financial analysis, principally in the short-term. A multiscale multivariate financial analysis through wavelet techniques allows obtaining specific information of certain periods, which jointly with the flexibility of copulas methods for measuring the asymmetrical dependence of non-aggregated way over time has the potential of assisting as in the strategies process of selection/diversification of investment portfolios, as in the control and management of risks. | 5,819.8 | 2021-05-26T00:00:00.000 | [
"Computer Science"
] |
Biopolymer Composites: Synthesis, Properties, and Applications
Petroleum-based plastics can be found everywhere in our habitual life in diverse applications such as automobiles, aerospace, and medical science [...].
Petroleum-based plastics can be found everywhere in our habitual life in diverse applications such as automobiles, aerospace, and medical science. However, apart from the economic field, most petroleum-driven materials induce an unsustainable environment. In this regard, environmentally friendly, biodegradable, and nontoxic materials, especially from renewable resources, have attained great levels of attention, and a strong effort has been focused on research on biodegradable and biocompatible polymers to replace petroleum-based commodity plastics [1,2]. Biopolymers are polymers synthesized from natural sources: either chemically synthesized from a biological material or completely biosynthesized by living organisms. However, biopolymers frequently display poor mechanical properties and restricted processing capability and end-use application. In order to overcome these drawbacks and develop advanced materials for a broad range of applications, biopolymers can be reinforced with fillers or nanofillers [3]. Composites based on biopolymers are named as "green composites". They can be degraded by the act of environmental factors such as air, light, heat, or microorganisms. In a biopolymer composite, the biopolymer matrix mainly rules structure, environmental tolerance, and durability, while reinforcement conditions the stiffness and strength of the composite [4,5]. Value-added new applications of biopolymer composites guarantee possible developments in international marketplaces. The efforts to develop environmentally friendly composite products with better performance have attained some foremost worldwide applications and are still ongoing. This Special Issue, with a collection of eight research articles, offers representative examples of the most recent advances in the synthesis, properties and applications of biodegradable biopolymer composites. Attention has been focused on production, processing, and application of biodegradable composites prepared from polymers such as polylactic acid (PLA) [6], chitosan [7], alginate [8], natural rubber [9], and so forth, which are amongst the most promising matrices for green composites in the future.
One of the most widely used biopolymers is PLA, a fully biodegradable and biocompatible polymer widely used in packaging, agricultural, personal care, cosmetic, biomedical, and tissue engineering sectors [10]. Many researchers have explored bactericidal films based on PLA with the addition of different reinforcements such as plant tree oils [11,12]. An innovative reinforcement that can impart antibacterial properties is birch tar (BT), a typical product of the distillation of wood and birch bark. The chemical components of BT are mostly phenol derivatives (guaiacol, creosote, and pyrocatechin), organic acids, and resin substances. Thus, biopolymer composites including PLA, poly(ethylene glycol) (PEG) as plastizer (5 wt%), and BT (1, 5 and 10 wt%) have been developed and characterized [6]. PLA film with 10 wt% BT displayed the best antibacterial effect against plant pathogens, i.e., X. campestris, A. tumefaciens, P. corrugata, and P. syringae. The addition of BT results in a material with biocidal effects and advantageous physicochemical and structural properties for agricultural and horticultural applications.
Another alternative to develop antimicrobial nanomaterials for agricultural applications is based on natural polysaccharides such as arabinogalactan (AG), arabinogalactan sulfate (AGS), and κ-carrageenan (κ-CG) [13]. In this regard, the effect of a trace element, Mn, on the physicochemical properties of the composites was evaluated. The results obtained demonstrate that these nanocomposites can be used as safe and biodegradable carriers of mineral trace elements possessing biologically active properties for plant protection, for instance, against Gram-positive bacterium such as R. erythropolis.
Other important polysaccharide-based matrices include alginate, which is widely used to developed hydrogels used for cellular applications and drugs. Cross-linked alginate hydrogels are used in many fields, including waste removal agents, drug carriers, wound dressing materials, food products, and tissue engineering and so forth. In addition, alginate is one of the biofilm substances produced by bacteria, and alginate hydrogels are used as a model biofilm in laboratory research [14]. In this regard, alginate hydrogels containing rhamnolipids, a group of biosurfactants produced by many microorganisms such as pathogen Pseudomonas aeruginosa, have been used to investigate the existence of a relationship between rhamnolipids and bacterial biofilm [8]. The presence of rhamnolipids changes the mechanical properties of the alginate: at concentrations below the CMC, the addition of this biosurfactant decreases compression loads, while it increases at concentrations above CMC.
Another polysaccharide widely used in biomaterials as it may be isolated from food industry byproducts is chitosan [15]. It is safe and nontoxic; thereby, it may have contact with human tissues [16]. The foremost drawback of chitosan is its low stability; hence, cross-linkers are required to improve its properties [4]. Polyphenols such as tannic acid, gallic acid and ferulic acid are able to form strong hydrogen bonds with polymers; thereby, they are considered as effective cross-linkers for polysaccharides [7]. Polyphenols provided effective antimicrobial activity; hence, chitosan/phenolic acid-based materials have potential applications in food technology, as encapsulating agents, biomaterials, bioadsorbents, or coatings.
Packaging materials must meet requirements, such as non-toxicity, water vapor and oxygen impermeability, transparency, and good mechanical properties. Previously, synthetic antioxidants, such as butylated hydroxyanisole (BHA) or butylated hydroxytoluene (BHT), were widely applied in food packaging materials to avoid lipid oxidation. Nonetheless, currently, they are substituted by compounds such as polyphenols, plant extracts, or essential oils [17,18]. A recent study has assessed the antioxidant capacity and stabilization efficiency of cannabidiol (CBD) extracts from cannabis plants in two biopolymers, PLA and ethylene-norbornene copolymer (Topas), that are widely used in packaging materials [19]. The addition of CBD as a natural additive results in an increase in oxidativeinduction time/temperature. In addition, this additive can also be considered as an aging color indicator, which allows controlling changes that occur during the lifetime of the polymeric products.
A group of polymers with very versatile properties is polyurethanes, which are obtained by the formation of urethane bonds due to the reaction of polyols and polyisocyanates [20]. The use of properly selected polyols and polyisocyanates allows obtaining polyurethane materials in various forms, including elastomers, coatings, adhesives, rigid and flexible foams, films, and fibers. As a result, polyurethanes have been widely used in many fields, such as thermal and electrical insulation and coatings, biomedical applications, construction, high-performance adhesives, the footwear market, and packaging or furniture [21]. In a novel study, ground plum stones and salinized ground plum stones were used as natural fillers for polyurethane composite foams. The influences of 1, 2, and 5 wt% of fillers on cellular structure; foaming parameters; and mechanical, thermomechanical, and thermal properties of manufactured foams were evaluated [22]. The results showed that the morphology of the biocomposites is affected by the type and content of filler. In addition, the modified polyurethane foams showed enhanced mechanical properties, such as higher compressive and flexural strength, improved thermal stability, and hydrophobic character.
Cellulose nanofibers (CNF) isolated from plant biomass have also attracted considerable benefits in polymer engineering. The restrictions associated with CNF-based nanocom-posites are related to time-consuming preparation methods and the absence of desired surface functionalities [23]. On the other hand, ZnO nanoparticles display outstanding properties including good antibacterial properties, mechanical strength, low coefficient of thermal expansion, and high thermal conductivity and are great potential candidates for biopolymers for applications in medicine and food industries [24][25][26]. In a novel investigation, the feasibility of preparing multifunctional CNF-ZnO nanocomposites with dual antibacterial and reinforcing properties via a facile and efficient ultrasound route has been reported [27]. The nanocomposites showed improved thermal stability compared to raw CNF and inhibitory activities against Gram-positive S. aureus and Gram-negative S. typhi bacteria. A CNF-ZnO-reinforced natural rubber nanocomposite film, manufactured through latex mixing and casting methods, showed 42% improvement in tensile strength compared with the neat rubber. These novel nanocomposites are perfect candidates for use in the biomedical field and in the development of high strength rubber composites.
On the other hand, different herbs such as peppermint, German chamomile, and yarrow have been used as natural fibers to create natural rubber-based biocomposites [9]. Rubber is one of the most important elastomers in terms of versatility and application level due to its superior elasticity, resilience, and abrasion resistance. Compared to manmade fibers such as glass or carbon, natural fibers have so many advantages such as natural abundance, low cost, low density, competitive specific mechanical properties, reduced energy consumption, carbon dioxide (CO 2 ) sequestration, biodegradability, etc. The developed rubber/fiber composites showed improved barrier and mechanical properties. Moreover, an increase in the cross-linking density of the materials before and after the simulated aging processes, compared to the reference sample, was observed. Overall, these composites are expected to play very important roles in our industries and in our day-to-day life requirements.
Conflicts of Interest:
The author declares no conflict of interest. | 1,985.2 | 2022-02-01T00:00:00.000 | [
"Materials Science"
] |
The Λb→Λ(→pπ-)μ+μ-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{\Lambda _b\rightarrow \Lambda (\rightarrow p\pi ^-)\mu ^+\mu ^-}$$\end{document} decay in the aligned two-Higgs-doublet model
The rare baryonic decay Λb→Λ(→pπ-)μ+μ-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Lambda _b\rightarrow \Lambda (\rightarrow p\pi ^-)\mu ^+\mu ^-$$\end{document} provides valuable complementary information compared to the corresponding mesonic b→sμ+μ-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\rightarrow s\mu ^+\mu ^-$$\end{document} transition. In this paper, using the latest high-precision lattice QCD calculation of the Λb→Λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Lambda _b\rightarrow \Lambda $$\end{document} transition form factors, we study this interesting decay within the aligned two-Higgs-doublet model, paying particularly attention to the effects of the chirality-flipped operators generated by the charged scalars. In order to extract the full set of angular coefficients in this decay, we consider the following ten angular observables, which can be derived from the analysis of the subsequent parity-violating Λ→pπ-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Lambda \rightarrow p\pi ^-$$\end{document} decay: the differential branching fraction dB/dq2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{d}\mathcal{B}/\mathrm{d}q^2$$\end{document}, the longitudinal polarization fraction FL\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_\mathrm{L}$$\end{document}, the lepton-, hadron- and combined lepton–hadron-side forward–backward asymmetries AFBℓ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_\mathrm{FB}^\ell $$\end{document}, AFBΛ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_\mathrm{FB}^\Lambda $$\end{document} and AFBℓΛ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_\mathrm{FB}^{\ell \Lambda }$$\end{document}, as well as the other five asymmetry observables Yi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y_i$$\end{document} (i=2,3s,3sc,4s,4sc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i=\mathrm{2,\,3s,\,3sc,\,4s,\,4sc}$$\end{document}). Detailed numerical comparisons are made between the SM and NP values for these angular observables. It is found that, under the constraints from the inclusive B→Xsγ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B\rightarrow X_s\gamma $$\end{document} branching fraction and the latest global fit results of b→sℓℓ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\rightarrow s\ell \ell $$\end{document} data, the contributions of right-handed semileptonic operators O9,10′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O^{\prime }_{9,10}$$\end{document}, besides reconciling the P5′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_5^{\prime }$$\end{document} anomaly observed in B0→K∗0μ+μ-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B^0\rightarrow K^{*0}\mu ^+\mu ^-$$\end{document} decay, could also enhance the values of dB/dq2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{d}\mathcal{B}/\mathrm{d}q^2$$\end{document} and AFBℓ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_\mathrm{FB}^\ell $$\end{document} in the bin [15,20]GeV2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[15,20]~\mathrm{GeV}^2$$\end{document}, leading to results consistent with the current LHCb measurements.
Introduction
The rare semileptonic b-hadron decays induced by the flavour-changing neutral current (FCNC) transition b → s + − do not arise at tree level and, due to the Glashowa e-mail<EMAIL_ADDRESS>b e-mail<EMAIL_ADDRESS>c e-mail<EMAIL_ADDRESS>Iliopoulos-Maiani (GIM) mechanism [1], are also highly suppressed at higher orders within the Standard Model (SM). In many extensions of the SM, on the other hand, new TeVscale particles can participate in the SM loop diagrams and lead to measurable effects in these rare decays. As a consequence, they play an important role in testing the SM and probing New Physics (NP) beyond it [2,3].
While no solid evidence of NP at all has been found in direct searches at high-energy colliders, it is interesting to note that several persistent deviations from the SM predictions have been observed in rare B-meson decays [3]. Specific to the b → s + − mesonic decays, these include the angular observable P 5 in the kinematical distribution of B 0 → K * 0 μ + μ − [4][5][6][7][8], the lepton-flavour-universalityviolation ratio R K of the decay widths for B → K μ + μ − and B → K e + e − [9][10][11], as well as the differential decay rates for B → K ( * ) μ + μ − [12][13][14] and B s → φμ + μ − [15][16][17]. Motivated by these anomalies and using the other available data on such rare mesonic decays, several global analyses have been made [18][19][20][21][22][23][24][25][26][27], finding that a negative shift in the Wilson coefficient C 9 improves the agreement with the data. However, due to the large hadronic uncertainties involved in exclusive modes, it remains quite unclear whether these anomalies indicate the smoking gun of NP, or are caused merely by underestimated hadronic power corrections [27][28][29][30][31][32][33][34] or even just by statistical fluctuations. In order to further understand the origin of the observed anomalies, it is very necessary to study other processes mediated by the same quark-level b → s + − transition.
In this respect, the rare baryonic b → μ + μ − decay is of particular interest for the following two reasons. Firstly, due to the spin-half nature of b and baryons, there is the potential to improve the currently limited understanding of the helicity structure of the underlying effective weak Hamiltonian [35][36][37]. Secondly, exploiting the full angular distribution of the four-body b → (→ pπ − )μ + μ − decay, one can obtain information on the underlying shortdistance Wilson coefficients of effective four-fermion operators, which is complementary to that obtained from the corresponding mesonic decays [38][39][40]. Experimentally, this decay was observed firstly by the CDF collaboration with 24 signal events and a statistical significance of 5.8 Gaussian standard deviations [41]. Later, the LHCb collaboration published the first measurements of the differential branching fractions as well as three angular observables of this decay [42]. As the b baryons account for around 20% of the b-hadrons produced at the LHC [43], refined measurements of this decay will be available in the near future. On the theoretical side, this decay is challenged by the hadronic uncertainties due to the b → transition form factors and the non-factorizable spectator dynamics [38,[44][45][46]. As the theory of QCD factorization at low q 2 [47,48] is not yet fully developed for the baryonic decay, we neglect all the non-factorizable spectator-scattering effects. For the factorizable nonlocal hadronic matrix elements of the operators O 1 -O 6 , O 8 , we absorb them into the effective Wilson coefficients C eff 7 (q 2 ) and C eff 9 (q 2 ) [47][48][49][50][51][52]. For previous studies of this decay, the reader is referred to Refs. .
Interestingly, it has been observed by Meinel and Dyk [81] that the b → (→ pπ − )μ + μ − decay prefers a positive shift to the Wilson coefficient C 9 , which is opposite in sign compared to that found in the latest global fits of only mesonic decays [22,26,27]. This suggests that a simple shift in C 9 alone could not explain all the current data and needs more thorough analyses. In our previous paper [82], we have studied the B 0 → K * 0 μ + μ − decay in the aligned two-Higgs-doublet model (A2HDM) [83], and found that the angular observable P 5 could be increased significantly to be consistent with the experimental data in the case when the charged-scalar contributions to C H ± 7 and C H ± 9,10 are sizeable, but C H ± 9,10 0. In order to further understand the anomalies observed in the b → s + − mesonic decays, in this paper, we shall study the b → (→ pπ − )μ + μ − decay in the A2HDM. As the b polarization in the LHCb setup has been measured to be small and compatible with zero [84], and the polarization effect will be averaged out for the symmetric ATLAS and CMS detectors, we consider only the case of unpolarized b decay. In order to reduce as much as possible the uncertainties arising from input parameters and transition form factors, we shall calculate all of the angular observables in some appropriate combinations [38][39][40]. For the b → transition form factors, we use the latest highprecision lattice QCD calculation [85], which is extrapolated to the whole q 2 region using the Bourrely-Caprini-Lellouch parametrization [86]. These results are also consistent with those of the recent QCD light-cone sum rule calculation [46], but with much smaller uncertainties in most of the kinematic range.
Our paper is organized as follows. In Sect. 2, we give a brief overview of the A2HDM. In Sect. 3, we present the theoretical framework for b → (→ pπ − )μ + μ − decay, including the effective weak Hamiltonian, the b → transition form factors, and the observables of this decay. In Sect. 4, we give our numerical results and discussions. Our conclusions are made in Sect. 5. Some relevant formulae for the Wilson coefficients are collected in the appendix.
The aligned two-Higgs-doublet model
We consider the minimal version of 2HDM, which is invariant under the SM gauge group and includes, besides the SM matter and gauge fields, two complex scalar SU (2) L doublets, with hypercharge Y = 1/2 [83,87]. In the Higgs basis, the two doublets can be parametrized as GeV is the nonzero vacuum expectation value, and G ± , G 0 are the massless Goldstone fields. The remaining five physical degrees of freedom are given by the two charged fields H ± (x) and the three neutral ones ϕ 0 with the orthogonal transformation R fixed by the scalar potential [83,87,88].
The most general Yukawa Lagrangian of the 2HDM is given by [83] a (x) are the charge-conjugated scalar doublets with hypercharge Y = − 1 2 ;Q L andL L are the lefthanded quark and lepton doublets, and u R , d R and R the corresponding right-handed singlets, in the weak-interaction basis. All fermionic fields are written as 3-dimensional vectors and the Yukawa couplings M f and Y f ( f = u, d, ) are therefore 3 × 3 matrices in flavour space. Generally, the couplings M f and Y f cannot be diagonalized simultaneously and the non-diagonal elements will give rise to unwanted tree-level FCNC interactions. In the fermion mass-eigenstate basis, with diagonal mass matrices M f , the tree-level FCNCs can be eliminated by requiring the alignment in flavour space of the Yukawa matrices [83]: where ς f ( f = u, d, ) are arbitrary complex parameters and could introduce new sources of CP violation beyond the SM.
Effective weak Hamiltonian
The effective weak Hamiltonian for b → s + − transition is given by [52] where G F is the Fermi coupling constant, and we have neglected the doubly Cabibbo-suppressed contributions to the decay amplitude. The operators O i≤6 are identical to P i given in Ref. [91], and the remaining ones read where e (g s ) is the electromagnetic (strong) coupling constant, T a the generator of SU (3) C in the fundamental representation, andm b denotes the b-quark running mass in the MS scheme. Within the SM, O 7,9,10 play the leading role in b → s + − transition, while the factorizable contributions from O 1−6,8 can be absorbed into the effective Wilson coefficients C eff 7 (q 2 ) and C eff 9 (q 2 ) [25]: 1,c (q 2 ) + C 8 F where the basic fermion loop function is given by [47] h(m q , and the functions F 2) of Ref. [47], while F (7,9) 1,c (q 2 ) and F (7,9) 2,c (q 2 ) are provided in Ref. [92] for low q 2 and in Ref. [93] for high q 2 . 1 The quark masses appearing in these functions are defined in the pole scheme. The contribution from O 7 is suppressed bym s /m b and those from O 9,10 are zero within the SM.
In the A2HDM, the charged-scalar exchanges lead to additional contributions to C 7,9,10 and make the contributions of chirality-flipped operators O 7,9,10 to be significant, through the Z 0 -and photon-penguin diagrams shown in Fig. 1. Since we have neglected the light lepton mass, there is no contribution from the SM W -box diagrams with the W ± bosons replaced by the charged scalars H ± . The new contributions to the Wilson coefficients read [82] 1 Here we incorporate only the leading contributions from an operator product expansion (OPE) of the nonlocal product of O 1−6,8 with the quark electromagnetic current, because the first and second-order corrections in /m b from the OPE are already well suppressed in the high-q 2 region [49,50]. Although non-factorizable spectator-scattering effects (i.e., corrections that are not described using hadronic form factors) are expected to play a sizeable role in the low-q 2 region [47,48], we shall neglect their contributions because there is presently no systematic framework in which they can be calculated for the baryonic decay [46]. As a consequence, our predictions in the low-q 2 region are affected by a hitherto unquantified systematic uncertainty.
.3) Fig. 1 Z -and photon-penguin diagrams involving the charged-scalar exchanges in the A2HDM with the functions C ( ) i,XY (i = 7, 9, 10; X, Y = u, d) given by Eqs. (A.1)-(A.10). Assuming ς u,d to be real, one has C H ± 7 =m s m b C H ± 7 , and we shall therefore neglect C H ± 7 in the following discussion.
Transition form factors
In order to obtain compact forms of the helicity amplitudes [38], we adopt the helicity-based definitions of the b → transition form factors, which are given by [38,44] for the vector and axial-vector currents, respectively, and for the tensor and pseudo-tensor currents, respectively. Here q = p − p and s ± = (m b ± m ) 2 − q 2 . The helicity form factors satisfy the endpoint relations f All these ten form factors have been recently calculated using (2 + 1)-flavour lattice QCD [85].
• The other five asymmetry observables which, along with the previous observables, determine all the ten angular coefficients K nλ . Here Y 2 also has a zero-crossing point, which lies in the low q 2 region.
In order to compare with the experimental data [97], we also consider the binned differential branching fraction defined by (3.22) and the binned normalized angular coefficients defined by where the numerator and denominator should be binned separately. As the theoretical calculations are thought to break down close to the narrow charmonium resonances, we make no predictions for these observables in this region. Finally, it should be noted that, unlike the strong decay K * → K π in the mesonic counterpart B → K * + − , the subsequent weak decay → pπ − is parity violating, with the asymmetry parameter α being known from experiment [98]. This fact distinguishes the signal with an intermediate baryon from the direct b → pπ − μ + μ − decay, and facilitates the full angular analysis of b → (→ pπ − )μ + μ − decay [38,39].
Input parameters
Firstly we collect in Table 1 the theoretical input parameters entering our numerical analysis throughout this paper. These include the SM parameters such as the electromagnetic and strong coupling constants, gauge boson, quark and hadron masses, 2 as well as the CKM matrix elements. The Weinberg mixing angle θ W is given by sin 2 θ W = 1 − M 2 W /M 2 Z . For the b → transition form factors, we use the latest high-precision lattice QCD calculation with 2 + 1 dynamical flavours [85]. The q 2 dependence of these form factors are parametrized in a simplified z expansion [86], modified to account for pion-mass and lattice-spacing dependences. All relevant formulae and input parameters can be found in Eqs. (38) and (49) and Tables III-V and IX-XII of Ref. [85]. To compute the central value, statistical uncertainty, and total systematic uncertainty of any observable depending on the form factors, such as the differential branching fraction and angular observables given in Eqs. (3.18)-(3.21), as well as the corresponding binned observables and the zerocrossing points, we follow the same procedure as specified in Eqs. (50)-(55) of Ref. [85]. Table 2 The Wilson coefficients at the scale μ b = 4.2 GeV, to NNLL accuracy in the SM
Results within the SM
For the short-distance Wilson coefficients at the low scale μ b = 4.2 GeV, we use the numerical values collected in Table 2, which are obtained at the next-to-next-to-leading logarithmic (NNLL) accuracy within the SM [3,91,100-103]. We show in Fig. 2 the SM predictions for the differential branching fraction and angular observables as a function of the dimuon invariant mass squared q 2 , where the central values are plotted as red solid curves and the theoretical uncertainties, which are caused mainly by the b → transform form factors, are labelled by the red bands. The latest experimental data from LHCb [97], where available, are also included in the figure for comparison. 3 The SM predictions for the corresponding binned observables are presented in Table 3.
As can be seen from Fig. 2 and Table 3, in the bin [15,20] GeV 2 where both the experimental data and the lattice QCD predictions for the b → transition form factors are most precise, the measured differential branching fraction [97], (1.20 ± 0.27) × 10 −7 GeV −2 , exceeds the SM prediction, (0.766 ± 0.069) × 10 −7 GeV −2 , by about 1.6 σ . Although being not yet statistically significant, it is interesting to note that the deviation is in the opposite direction to what has been observed in the B → K ( * ) μ + μ − [12][13][14] and B s → φμ + μ − [15][16][17] decays, where the measured differ- 3 For the differential branching fraction, the error bars are shown both including and excluding the uncertainty from the normalization mode b → J/ψ [98]. ential branching fractions favor, on the other hand, smaller values than their respective SM predictions. Also in this bin, the lepton-side forward-backward asymmetry measured by LHCb [97], −0.05 ± 0.09, is found to be about 3.3 σ higher than the SM value, −0.349 ± 0.013. As detailed in Ref. [81], combining the current data for b → (→ pπ − )μ + μ − decay with that for the branching ratios of B s → μ + μ − and inclusive b → s + − decays, Meinel and Dyk found that their fits prefer a positive shift to the Wilson coefficient C 9 , which is opposite in sign compared to that found in the latest global fits of only mesonic decays [22,26,27]. This means that a simple shift in C 9 alone could not explain all the current data. Especially, a negative shift in C 9 , as found in global fits of only mesonic observables, would further lower the predicted b → μ + μ − differential branching fraction.
Our SM predictions for the zero-crossing points of angular observables A FB , A FB and Y 2 read, respectively, (4.1) The zero-crossing points of the other observables Y i (i = 3s, 3sc, 4s, 4sc), which correspond to the case when the relative angular momentum between the pπ − system and the dimuon system is (l, m) = (1, ±1), are plagued by large theoretical uncertainties. The observables Y 3s and Y 3sc are predicted to be very small within SM and are, therefore, potentially good probes of NP beyond the SM [40]. Fig. 2 The b → (→ pπ − )μ + μ − observables as a function of the dimuon invariant mass squared q 2 , predicted both within the SM (central values: red solid curves, theoretical uncertainties: red bands) and in the A2HDM (case A: blue bands and case B: green bands). The corresponding experimental data from LHCb [97], where available, are represented by the error bars
Results in the A2HDM
In this subsection, we shall investigate the impact of A2HDM on the b → (→ pπ − )μ + μ − observables. For simplicity, the alignment parameters ς u,d are assumed to be real. As in our previous paper [82], we use the inclusive B → X s γ branching fraction [104,105] and the latest global fit results of b → s data [26,81] to restrict the model parameters ς u,d . Under these constraints, numerically, the chargedscalar contributions to the Wilson coefficients can be divided into the following two cases [82]: Case A : C H ± 7,9,10 are sizeable, but C H ± 9,10 0 ; Case B : C H ± 7 and C H ± 9,10 are sizeable, but C H ± 9,10 0.
They are associated to the (large |ς u |, small |ς d |) and (small |ς u |, large |ς d |) regions, respectively; see Ref. [82] for more details. This means that the charged-scalar exchanges contribute mainly to left-and right-handed semileptonic operators in case A and case B, respectively. The influences of these two cases on the b → (→ pπ − )μ + μ − observables are shown in Fig. 2, where the blue (in case A) and red (in case B) bands are obtained by varying randomly the model parameters within the ranges allowed by the global fits [26,81,82], with all the other input parameters taken at their respective central values.
In case A, the impact of A2HDM is found to be negligibly small on the hadron-side forward-backward asymmetry A FB and the observables Y i (i = 3s, 3sc, 4s, 4sc). For the differential branching fraction, on the other hand, visible enhancements are observed relative to the SM prediction, especially in the high q 2 region. For the remaining observables, the A2HDM only affects them in the low q 2 region, but the effect is diluted by the SM uncertainty. In order to see clearly the A2HDM effect in case A, we give in Table 4 the values of the binned observables in the bin [15,20] GeV 2 , including also the SM predictions, the A2HDM effect in case B, as well as the LHCb data (where available) for comparison. Although being improved a little bit, the deviations between the LHCb data and the theoretical values for the differential branching fraction and the lepton-side forward-backward asymmetry are still at 1.3 σ and 3.2 σ , respectively. Including the A2HDM in case A, there are only small changes on the zerocrossing points: In case B, however, the A2HDM has a significant influence on almost all the observables, as shown in Fig. 2. The most prominent observation is that it can enhance both the differential branching fraction and the lepton-side forward-backward asymmetry in the bin [15,20] GeV 2 , being now compatible with the experimental measurements at 0.2 σ and 1.3 σ , respectively (see also Table 4). The magnitude of the hadronside forward-backward asymmetry tends to become smaller in the whole q 2 region in this case, but is still in agreement with the LHCb data, with the large experimental and theoretical uncertainties taken into account. In the high (whole) q 2 region, a large effect is also observed on the asymmetry observable Y 3s (Y 4s ). Adding up the A2HDM effect in case B, the zero-crossing points are now changed to which are all significantly enhanced compared to the SM predictions (see Eq. (4.1)) and the results in case A (see Eq. (4.2)). It should be noticed that our predictions for the zero-crossing points given by Eqs. (4.1)-(4.3) are most severely affected by the hitherto unquantified systematic uncertainty coming from the non-factorizable spectatorscattering contributions at large hadronic recoil, a caveat emphasized already in Sect. 3.1.
Combining the above observations with our previous studies -the angular observable P 5 in B 0 → K * 0 μ + μ − decay could be increased significantly to be consistent with the experimental data in case B [82], we could, therefore, conclude that the A2HDM in case B is a promising alternative to the observed anomalies in b-hadron decays.
Conclusions
In this paper, we have investigated the A2HDM effect on the rare baryonic b → (→ pπ − )μ + μ − decay, which is mediated by the same quark-level b → sμ + μ − transition as in the mesonic B → K ( * ) μ + μ − decays. In order to extract all the ten angular coefficients, we have considered the differential branching fraction dB/dq 2 , the longitudinal polarization fraction F L , the lepton-, hadron-and combined lepton-hadron-side forward-backward asymmetries A FB , A FB and A FB , as well as the other five asym-metry observables Y i (i = 2, 3s, 3sc, 4s, 4sc). For the b → transition form factors, we used the most recent high-precision lattice QCD calculations with 2+1 dynamical flavours.
Taking into account constraints on the model parameters ς u,d from the inclusive B → X s γ branching fraction and the latest global fit results of b → s data, we found numerically that the charged-scalar exchanges contribute either mainly to the left-or to the right-handed semileptonic operators, labelled case A and case B, respectively. The influences of these two cases on the b → (→ pπ − )μ + μ − observables are then investigated in detail. While there are no significant differences between the SM predictions and the results in case A, the A2HDM in case B is much favored by the current data. Especially in the bin [15,20] GeV 2 where both the experimental data and the lattice QCD predictions are most precise, the deviations between the SM predictions and the experimental data for the differential branching fraction and the lepton-side forward-backward asymmetry could be reconciled to a large extend. Also in our previous paper [82], we have found that the angular observable P 5 in B 0 → K * 0 μ + μ − decay could be increased significantly to be consistent with the experimental data in case B. Therefore, we conclude that the A2HDM in case B is a very promising solution to the currently observed anomalies in b-hadron decays.
Finally, it should be pointed out that more precise experimental measurements of the full angular observables, especially with a finer binning, as well as a systematic analysis of non-factorizable spectator-scattering effects in b → (→ pπ − )μ + μ − decay, would be very helpful to further deepen our understanding of the quark-level b → sμ + μ − transition. and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP 3 . | 6,190.8 | 2017-04-01T00:00:00.000 | [
"Physics"
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Finding k-secluded trees faster
We revisit the k -Secluded Tree problem. Given a vertex-weighted undirected graph G , its objective is to find a maximum-weight induced subtree T whose open neighborhood has size at most k . We present a fixed-parameter tractable algorithm that solves the problem in time 2 O ( k log k ) · n O ( 1 ) , improving on a double-exponential running time from earlier work by Golovach, Heggernes, Lima, and Montealegre. Starting from a single vertex, our algorithm grows a k -secluded tree by branching on vertices in the open neighborhood of the current tree T . To bound the branching depth, we prove a structural result that can be used to identify a vertex that belongs to the neighborhood of any k -secluded supertree T (cid:3) ⊇ T once the open neighborhood of T becomes sufficiently large. We extend the algorithm to enumerate compact descriptions of all maximum-weight k -secluded trees, which allows us to count them as well. © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
Introduction
Background We revisit a problem from the field of parameterized complexity: Given a graph G with positive weights on the vertices, find a connected induced acyclic subgraph H of maximum weight such that the open neighborhood of H in G has size at most k.
A parameterized problem is fixed-parameter tractable (FPT) [4,5] if there is an algorithm that given an instance I with parameter k, solves the problem in time f (k) • |I| O (1) for some computable function f .For problems that are FPT, such algorithms allow NP-hard problems to be solved efficiently on instances whose parameter is small.It is therefore desirable for the function f to grow slowly in terms of k, both out of theoretical interest as well as improving the practical relevance of these algorithms.
We say that a vertex set If H is also a tree, we say that H is a k-secluded tree in G.We use N + to denote the positive integers.Formally, the problem we study in this work is defined as follows.
Large Secluded Tree (LST)
Parameter: k Input: An undirected graph G, a non-negative integer k, and a weight function w : V (G) → N + .
Task: Find a k-secluded tree H of G of maximum weight, or report that no such H exists.
Golovach et al. [10] consider the more general Connected Secluded -Subgraph, where the k-secluded induced subgraph of G should belong to some target graph class .They mention that (Large) Secluded Tree is FPT and can be solved in time 2 2 O(k log k) • n O (1) using the recursive understanding technique, the details of which can be found in the arXiv version [9].For the case where is characterized by a finite set of forbidden induced subgraphs F , they show that the problem is FPT with a triple-exponential dependency.They pose the question whether it is possible to avoid these doubleand triple-exponential dependencies on the parameter.They give some examples of for which this is the case, namely for being a clique, a star, a d-regular graph, or an induced path.
Results Our main result is an algorithm for Large Secluded Tree that takes 2 O(k log k) • n 4 time.This answers the question of Golovach et al. [10] affirmatively for the case of trees.We solve a more general version of the problem, where a set of vertices is given that should be part of the k-secluded tree.Our algorithm goes one step further by allowing us to find all maximum-weight solutions.As we will later argue, it is not possible to output all such solutions directly in the promised running time.Instead, the output consists of a bounded number of solution descriptions such that each maximumweight solution can be constructed from one such description.This is similar in spirit to the work of Guo et al. [11], who enumerate all minimal solutions to the Feedback Vertex Set problem in O(c k •m) time.They do so by giving a list of compact representations, a set C of pairwise disjoint vertex subsets such that choosing exactly one vertex from every set results in a minimal feedback vertex set.Our descriptions are non-redundant (no two descriptions describe the same secluded tree), which allows us to count the number of maximum-weight k-secluded trees containing a specified vertex in the same running time.
Techniques Rather than using recursive understanding, our algorithm is based on bounded-depth branching with a nontrivial progress measure.Similarly to existing algorithms to compute spanning trees with many leaves [12], our algorithm iteratively grows the vertex set of a k-secluded tree T .If we select a vertex v in the neighborhood of the current tree T , then for any k-secluded supertree T of T there are two possibilities: either v belongs to the neighborhood of T , or it is contained in T ; the latter case can only happen if v has exactly one neighbor in T .Solutions of the first kind can be found by deleting v from the graph and searching for a (k − 1)-secluded supertree of T .To find solutions of the second kind we can include v in T , but since the parameter does not decrease in this case we have to be careful that the recursion depth stays bounded.Using a reduction rule to deal with degree-1 vertices, we can essentially ensure that v has at least three neighbors (exactly one of which belongs to T ), so that adding v to T strictly increases the open neighborhood size |N(T )|.
Our main insight to obtain an FPT algorithm is a structural lemma showing that, whenever |N(T )| becomes sufficiently large in terms of k, we can identify a vertex u that belongs to the open neighborhood of any k-secluded supertree T ⊇ T .At that point, we can remove u and decrease k to make progress.
Related work Secluded versions of several classic optimization problems have been studied intensively in recent years [1][2][3]7,13], many of which are discussed in Till Fluschnik's PhD thesis [6].Marx [14] considers a related problem Cutting k (connected) vertices, where the aim is to find a (connected) set S of size exactly k with at most neighbors.Without the connectivity requirement, the problem is W [1]-hard by k + .The problem becomes FPT when S is required to be connected, but remains W [1]-hard by k and separately.Fomin et al. [8] consider the variant where |S| ≤ k and show that it is FPT parameterized by .
Organization We introduce our enumeration framework in Section 2. We present our algorithm that enumerates maximumweight k-secluded trees in Section 3 and present its correctness and running time analyses.We give some conclusions in Section 4.
Framework for enumerating secluded trees
We consider simple undirected graphs with vertex set V (G) and edge set E(G).We use standard notation pertaining to graph algorithms, such as presented by Cygan et al. [4].When the graph G is clear from context, we denote |V (G)| and |E(G)| by n and m respectively.The open neighborhood of a vertex set X in a graph G is denoted by N G (X), where the subscript may be omitted if G is clear from context.For a subgraph H of G, we may write a weight function, then for any S ⊆ V (G) let w(S) := v∈S w(s) and for any subgraph H of G we may denote w(V (H)) by w(H).
It is not possible to enumerate all maximum-weight k-secluded trees in FPT time, as the number of such trees can in general not be bounded by f (k) • n O (1) for any function f ; see Fig. 1.However, it is possible to give one short description for such an exponential number of k-secluded trees.Definition 1.For a graph G, a description is a pair (r, X ) consisting of a vertex r ∈ V (G) and a set X of pairwise disjoint subsets of V (G − r) such that for any set S consisting of exactly one vertex from each set X ∈ X , the connected component H of G − S containing r is acyclic and N(H) = S, i.e., H is a |X |-secluded tree in G.The order of a description is equal to |X |.We say that a k-secluded tree H is described by a description (r, X ) if N(H) consists of exactly one vertex of each X ∈ X and r ∈ V (H).
Note that a single k-secluded tree H can be described by multiple descriptions.For example, for a path on v 1 , . . ., v 4 the 1-secluded tree induced by {v 1 , v 2 } is described by (v 1 , {{v 3 , v 4 }), (v 1 , {{v 3 }}), and (v 2 , {{v 3 }}).We define the concept of redundancy in a set of descriptions.Definition 2. For a graph G, a set of descriptions X of maximum order k is called redundant for G if there is a k-secluded tree H in G such that H is described by two distinct descriptions in X.We say X is non-redundant for G otherwise.Definition 3.For a graph G and a set of descriptions X of maximum order k, let T G (X) denote the set of all k-secluded trees in G described by a description in X.
Observation 4. For a graph G and two sets of descriptions X 1 , X 2 we have: Observation 5.For a graph G, a set of descriptions X, and non-empty vertex sets X 1 , X 2 disjoint from (r,X )∈X ({r} For an induced subgraph H of G and a set F ⊆ V (G), we say that H is a supertree of F if H induces a tree and F ⊆ V (H).Let S k G (F ) be the set of all k-secluded supertrees of F in G.For a set X of subgraphs of G let maxset w (X) := {H ∈ X | w(H) ≥ w(H ) for all H ∈ X}.We focus our attention to the following version of the problem, where some parts of the tree are already given.
Enumerate Large Secluded Supertrees (ELSS)
Parameter: k , then the answer is trivially the empty set.In the end we solve the general enumeration problem by solving ELSS with F = T = {v} for each v ∈ V (G) and reporting only those k-secluded trees of maximum weight.Intuitively, our algorithm for ELSS finds k-secluded trees that "grow" out of T .In order to derive some properties of the types of descriptions we compute, we may at certain points demand that certain vertices non-adjacent to T need to end up in the k-secluded tree.For this reason the input additionally has a set F , rather than just T .
Our algorithm solves smaller instances recursively.We use the following abuse of notation: in an instance with graph G and weight function w : V (G) → N + , when solving the problem recursively for an instance with induced subgraph G of G, we keep the weight function w instead of restricting the domain of w to V (G ).
Enumerate large secluded supertrees
Section 3.1 proves the correctness of a few subroutines used by the algorithm.Section 3.2 describes the algorithm to solve ELSS.In Section 3.3 we prove its correctness and in Section 3.4 we analyze its time complexity.In Section 3.5 we show how the algorithm for ELSS can be used to count and enumerate maximum-weight k-secluded trees containing a specified vertex.
Subroutines for the algorithm
Similar to the Feedback Vertex Set algorithm given by Guo et al. [11], we aim to get rid of degree-1 vertices.In our setting there is one edge case however.The reduction rule is formalized as follows.
Reduction Rule 1.For an ELSS instance (G, k, F , T , w) with a degree-1 vertex v in G such that F = {v}, contracting v into its neighbor u yields the ELSS instance (G − v, k, F , T , w ) where the weight of u is increased by w(v) and: The precondition F = {v} ensures that a maximum-weight k-secluded supertree H of F contains v if and only if it contains u.A supertree containing u but not v can be transformed into a heavier one by adding v, which does not increase the size of the open neighborhood.Conversely, if F = {v} then a supertree H of F that includes v also includes at least one other vertex in F , thereby ensuring the unique neighbor u of v is contained in the connected graph H .The reduction rule therefore effectively merges vertices v and w while summing their weights.Note that if F = {v}, a maximum-weight k-secluded supertree may consist simply of {v} and avoid u.
We prove the correctness of the reduction rule, that is, the descriptions of the reduced instance form the desired output for the original instance.Lemma 8. Let I = (G, k, F , T , w) be an ELSS instance.Suppose G contains a degree-1 vertex v such that {v} = F .Let I = (G − v, k, F , T , w ) be the instance obtained by contracting v into its neighbor u.If X is a non-redundant set of descriptions for G − Proof.We first argue that X is a valid set of descriptions for the graph G.For any (r, X ) ∈ X, we have r ∈ V (G − v) and X consists of disjoint subsets of V (G − {v, r}), which trivially implies that r ∈ V (G) and that X consists of disjoint subsets of V (G −r).Consider any set S consisting of exactly one vertex from each X ∈ X .The connected component H of (G − v) − S containing r is acyclic and Clearly H is acyclic as it is obtained from H by possibly adding a degree-1 vertex.We argue that N G (H ) = S.By construction of H we have N G (H ) ⊆ S. For the sake of contradiction suppose that there is some p Observe that any maximum-weight k-secluded supertree H of F in G containing u, contains its neighbor v as well: adding v to an induced tree subgraph containing u does not introduce cycles since v has degree one, does not increase the size of the neighborhood, and strictly increases the weight since w(v) > 0 by definition.Conversely, any (maximum-weight) k-secluded supertree H of F in G that contains v also contains u: tree H contains all vertices of the non-empty set F and F = {v}, so H contains at least one vertex other than v, which implies by connectivity that it contains the unique neighbor u of v. Hence a maximum-weight k-secluded supertree H of F in G contains u if and only if it contains v.
Using this fact, we relate the sets maxset w (S k G (F )) and maxset w (S k G−v (F )).For any H ∈ maxset w (S k G (F )), there is a k-secluded supertree of F in G − v of the same weight under w : Conversely, for any k-secluded supertree H of F in G − v, there is a k-secluded supertree of F in G of the same weight under w: These transformations imply that the maximum w-weight of trees in S k G (F ) is identical to the maximum w -weight of trees in S k G−v (F ).
We say an instance is almost leafless if the reduction rule above cannot be applied.
Hence there is at most one degree-1 vertex.
The following lemma exploits the property of being almost leafless to guarantee that when |N G (T )| becomes sufficiently large, the branching algorithm can make progress in one of two possible ways.
concludes that G does not contain a k-secluded supertree of F (Fig. 2).
Proof.We aim to find a vertex v ∈ V (G) \ F with k + 2 distinct paths P 1 , . . ., P k+2 from N G (T ) to v that intersect only in v and do not contain vertices from T .We first argue that such a vertex v satisfies the first condition, if it exists.
Consider some k-secluded supertree H of F .Since the paths P 1 , . . ., P k+2 are disjoint apart from their common endpoint v while |N G (H)| ≤ k, there are two paths P i , P j with i = j ∈ [k + 2] for which P i \ {v} and P j \ {v} do not intersect N G (H).These paths are contained in H since they contain a neighbor of T ⊆ F ⊆ V (H).
As P i and P j form a cycle together with a path through the connected set T , which cannot be contained in the acyclic graph H , this implies v ∈ N G (H).
Next we argue that if G has a k-secluded supertree H of F ⊇ T , then there exists such a vertex v. Consider an arbitrary such H and root it at a vertex t ∈ T .For each vertex u ∈ N G (T ), we construct a path P u disjoint from T that starts in u and ends in N G (H), as follows.
• If u / ∈ H , then u ∈ N G (H) and we take P u = (u).• If u ∈ H , then let u be an arbitrary leaf in the subtree of H rooted at u; possibly u = u .Since T is connected and H ⊇ T is acyclic and rooted in t ∈ T , the subtree rooted at Because u is a leaf of H this implies that N G ( u ) contains a vertex y other than the parent of u in H , so that y ∈ N G (H).We let P u be the path from u to u through H , followed by the vertex y ∈ N G (H).
The paths we construct are distinct since their startpoints are.Two constructed paths cannot intersect in any vertex other than their endpoints, since they were extracted from different subtrees of H . Since we construct |N G (T )| > k(k + 1) paths, each of which ends in N G (H) which has size at most k, some vertex v ∈ N G (H) is the endpoint of k + 2 of the constructed paths.As shown in the beginning the proof, this establishes that v belongs to the neighborhood of any k-secluded supertree of F .Since F ⊆ V (H) we have v / ∈ F .All that is left to show is that we can find such a vertex v in the promised time bound.After contracting T into a source vertex s, for each v ∈ V (G) \ F , do k + 2 iterations of the Ford-Fulkerson algorithm in order to check if there are k + 2 internally vertex-disjoint sv-paths.If so, then return v.If for none of the choices of v this holds, then output that there is no k-secluded supertree of F in G.In order to see that this satisfies the claimed running time bound, note that there are O(n) choices for v, and k + 2 iterations of Ford-Fulkerson runs can be implemented to run in O(k The last ingredient we need to describe the algorithm is the notion of an extending path; see Fig. 3. ) defined by the following process.
1. Initialize P as the 1-vertex path starting and ending in v = v 1 .
2. Let v i be the last vertex currently on P .
to the path and repeat.(b) Otherwise terminate the procedure: the path P constructed so far is the extending path of v.
Observe that when deg
The general behavior of our algorithm consists of branching on an extending path, to consider three options for how this path P interacts with the desired maximum-weight k-secluded supertrees H of F that have to be enumerated: H can avoid the last vertex v (but must then include (v 1 , . . ., v −1 ) to be of maximum weight), it can avoid a single intermediate vertex on the path (but then has to contain all others and v ), or it can contain the entire path P .
The algorithm will explore these different cases, while also accounting for the setting that some vertices of P belong to F .
The definition of extending path is chosen to ensure that the algorithm can make some type of progress in all cases.For example, if the path P ends in a vertex v such that v has two neighbors in V (P ) ∪ T , then adding the entire path P to T would yield a cycle, and as such this branch does not have to be executed as it does not lead to solutions.Observe that if an ELSS instance is almost leafless, then the extending path of a vertex v cannot end in a vertex of degree 1, and therefore ends at a vertex whose degree is larger than 2, or at a vertex which has two neighbors in V (P ) ∪ T .We capture this in the following observation.
of v satisfies at least one of the following: The case that deg G (v ) ≥ 3 will be useful to ensure some form of progress for the branching algorithm.We will later show (see Lemma 29) that when all of P is added to the tree T , this leads to an increase of the size of the open neighborhood of the tree (or triggers an easy corner case).
The algorithm
In this section we present an algorithm that, given an instance (G, k, F , T , w) of ELSS, produces a valid output in time Otherwise we remove all connected components of G that do not contain a vertex of F .If more than one connected component remains, return ∅.Then, while there is a degree-1 vertex v such that F = {v}, contract v into its neighbor as per Rule 1.While We proceed by considering the neighborhood of T as follows: 1.If any vertex v ∈ N G (T ) has two neighbors in T , then recursively run this algorithm to obtain a set of descriptions X . ., v ) be the unique extending path of v, given by Definition 11. (See Otherwise take X 2 = ∅.(We find the k-secluded trees containing both endpoints of P which have one vertex in P as a neighbor.These can be reconstructed from the (k ] is acyclic, obtain a set of descriptions X 3 by recursively solving (G, k, F ∪ V (P ), T ∪ V (P ), w).Otherwise take X 3 = ∅.(We find the k-secluded trees containing the entire path P .)Let M be the set of minimum weight vertices in P − F − v and define: For each i ∈ [3] let w i be the weight of an arbitrary H ∈ T G (X i ), or 0 if X i = ∅.Return the set X defined as {i∈ [3]|w i =max{w 1 ,w 2 ,w 3 }} X i .
Proof of correctness
In this section we argue that the algorithm described in Section 3.2 solves the ELSS problem.In various steps we identify a vertex v such that the neighborhood of any maximum-weight k-secluded supertree must include v.We argue that for these steps, the descriptions of the current instance can be found by adding {v} to every description of the supertrees of T in G − v if some preconditions are satisfied.
Lemma 13. Let (G, k, F , T , w) be an ELSS instance and let
Then we have: For the sake of contradiction, suppose there is In the other direction, consider some tree J ∈ T G (X ).We show that For the sake of contradiction, suppose that there is The next lemma will be used to argue correctness of Step 3(b) of the algorithm, in which we find k-secluded trees which avoid a single vertex from path P .One should think of P as being an extending path without its last vertex.Lemma 14.Let (G, k, F , T , w) be an ELSS instance and let P be a path in G with deg G (v) = 2 for all v ∈ V (P ) and N G (P ) = {a, b} for some a, b ∈ F .Let X be a set of descriptions for G − V (P ) Then for all p ∈ V (P ) \ F we have: Proof.For any p ∈ V (P ) \ F we define X p = {(r, X ∪{{p}}) | (r, X ) ∈ X}.We show X p is a valid set of descriptions for G.Note that for any set S consisting of exactly one vertex from each set X ∈ X ∪ {{p}} there is an Observe that H − p is acyclic and connected and since p ∈ V (P ) ⊆ V (H ) we have that p ∈ N G (H − p), hence N G (H − p) = S and X p is a valid set of descriptions for G.
G (F ) which has maximum weight (with respect to w) among those satisfying p ∈ N G (H).We show that H ∈ T G (X p ).By Note 7 we have that For the sake of contradiction, suppose there is )), we have that there is a description (r, X ) ∈ X for G − V (P ) that describes H − V (P ).Then (r, X ∪{{p}}) ∈ X p is a description for H in G and we conclude that H ∈ T G (X p ) as required.
Finally we show Suppose for contradiction that there exists such a J for which w( J ) < w( J ). Observe that J − V (P ) and J − V (P ) are both (k − 1)-secluded supertrees of F \ V (P ) in G − V (P ), i.e., they are con- When the algorithm reaches Step 3, it recursively searches for k-secluded supertrees of three different types.The following lemma is used to argue that each relevant tree will be found by exactly one of these recursive calls, so that the combination of their results is non-redundant.
Lemma 15.Let (G, k, F , T , w) be an almost leafless ELSS instance such that G is connected and N G (F ) = ∅.Fix some v ∈ N G (T ) and let P = (v = v 1 , v 2 , . . ., v ) be the extending path of v, given by Definition 11.Then for any maximum-weight k-secluded supertree H of F , exactly one of the following holds: Proof.First note that such a vertex v exists since N G (F ) = ∅ and G is connected, so N G (T ) = ∅.If there is no k-secluded supertree of F , then there is nothing to show.So suppose H is a maximum-weight k-secluded supertree of F .We have v ∈ V (P ) is a neighbor of T ⊆ F ⊆ V (H), so either V (P ) ⊆ V (H) or V (P ) contains a vertex from N(H).In the first case Condition 3 holds, in the second case we have |N(H) ∩ V (P )| ≥ 1.First suppose that |N(H) ∩ V (P )| ≥ 2. Let i ∈ [ ] be the smallest index such that v i ∈ N(H) ∩ V (P ).Similarly let j ∈ [ ] be the largest such index.We show that in this case we can contradict the fact that H is a maximum-weight k-secluded supertree of F .Observe that H = V (H) ∪ {v i , . . ., v j−1 } induces a tree since (v i , . . ., v j−1 ) forms a path of degree-2 vertices (this easily follows from Definition 11) and the neighbor v j of v j−1 is not in H . Furthermore H has a strictly smaller neighborhood than H and it has larger weight as vertices have positive weight.Since F ⊆ V (H ), this contradicts that H is a maximum-weight k-secluded supertree of F .
We conclude that |N(H) ∩ V (P )| = 1.Let i ∈ [ ] be the unique index such that N(H) ∩ V (P ) = {v i }.Clearly v i / ∈ F .In the case that i = , then Condition 1 holds.Otherwise if i < , the first condition of Condition 2 holds.In order to argue that the second condition also holds, suppose that v / ∈ V (H).Then V (H) ∪ {v i , . . ., v −1 } is a k-secluded supertree of F in G and it has larger weight than H as vertices have positive weight.This contradicts the fact that H has maximum weight, hence the second condition of Condition 2 holds as well.
Armed with Lemmas 13 to 15 we are now ready to prove correctness of the algorithm.
Lemma 16.The algorithm for ELSS described in Section 3.2 is correct.
Proof.Let I = (G, k, F , T , w) be an ELSS instance.We prove correctness by induction on |V (G)
Before Step 1
We first prove correctness when the algorithm terminates before Step 1, which includes the base case of the induction.
Note that if G[F ] contains a cycle, then no induced subgraph H of G with F ⊆ V (H) can be acyclic.Therefore the set of maximum-weight k-secluded trees containing F is the empty set, so we correctly return ∅.Otherwise G[F ] is acyclic.
Clearly any connected component of G that has no vertices of F can be removed.If there are two connected components of G containing vertices of F , then no induced subgraph of G containing all of F can be connected, again we correctly return the empty set.In the remainder we have that G is connected.By iteratively applying Lemma 8 we conclude that a solution to the instance obtained after iteratively contracting (most) degree-1 vertices is also a solution to the original instance.Hence we can proceed to solve the new instance, which we know is almost leafless.In addition, observe that the contraction of degree-1 vertices maintains the property that G is connected and G[F ] is acyclic.
After exhaustively adding vertices v ∈ N G (T ) In the case that N G (F ) = ∅, then since G is connected it follows that F = T = V (G) and therefore T is the only maximum-weight ksecluded tree.For any r ∈ V (G), the description (r, ∅) describes this k-secluded tree, so we return {(r, ∅)}.In the remainder we have N G (F ) = ∅.
Since N G (F ) = ∅ and the instance is almost leafless, we argue that there is no 0-secluded supertree of F .Suppose G contains a 0-secluded supertree H of F , so |N G (H)| = 0 and since H ⊇ F is non-empty and G is connected we must have H = G, hence G is a tree with at least two vertices (since F and N G (F ) are both non-empty) so G contains at least two vertices of degree-1, contradicting that the instance is almost leafless.So there is no k-secluded supertree of F in G and the algorithm correctly returns ∅ if k = 0.
Observe that the value |V (G) \ F | cannot have increased since the start of the algorithm since we never add vertices to G and any time we remove a vertex from F it is also removed from G. Hence we can still assume in the remainder of the proof that the algorithm is correct for any input Step 1 Before arguing that the return value in Step 1 is correct, we observe the following.
Claim 18. If H is an induced subtree of G that contains T and v ∈ N G (T ) has at least two neighbors in T , then v ∈ N G (H).
Proof.Suppose v / ∈ N G (H), then since v ∈ N G (T ) and T ⊆ V (H) we have that v ∈ V (H).But then since T is connected, subgraph H contains a cycle.This contradicts that H is a tree and confirms that v ∈ N G (H).
Now consider the case that in
Step 1 we find a vertex v ∈ N G (T ) with two neighbors in T , and let X be the set of descriptions as obtained by the algorithm through recursively solving the instance We argue non-redundancy of the output.Suppose that two descriptions (r, X ∪ {{v}}) and (r , X ∪ {{v}}) describe the same supertree H of F in G.Note that then (r, X ) and (r , X ) describe the same supertree H of F in G − v, which contradicts the induction hypothesis that the output of the recursive call was correct and therefore non-redundant.
Concluding this part of the proof, we showed that if the algorithm terminates during Step 1, then its output is correct.On the other hand, if the algorithm continues after Step 1 we can make use of the following in addition to Property 17.
Property 19. If the algorithm does not terminate before reaching Step 2 then no vertex v ∈ N G (T ) has two neighbors in T .
Step 2 In Step 2 we use Lemma 10 if |N G (T )| > k(k + 1).The preconditions of the lemma are satisfied since k > 0 and the instance is almost leafless by Property 17.If it concludes that G does not contain a k-secluded supertree of F , then the algorithm correctly outputs ∅.Otherwise it finds a vertex v ∈ V (G) \ F such that any k-secluded supertree H of F in G satisfies v ∈ N G (H).We argue that the algorithm's output is correct.Let X be the set of descriptions as obtained through , and therefore v ∈ N G (H).It follows that Lemma 13 applies to X so we can conclude that T G ({(r, X ∪ {{v}}) | (r, X ) ∈ X }) is the set of maximum-weight k-secluded supertrees H of F in G for which v ∈ N G (H).Since we know there are no k-secluded supertrees H of F in G for which v / ∈ N G (H), it follows that T G ({(r, X ∪ {{v}}) | (r, X ) ∈ X}) is the set of maximum-weight k-secluded supertrees of F in G as required.Nonredundancy of the output follows as in Step 1.
To summarize the progress so far, we have shown that if the algorithm terminates before it reaches Step 3, then its output is correct.Alternatively, if we proceed to Step 3 we can make use of the following property, in addition to Properties 17 and 19, which we will use later in the running time analysis.
Step 3
Fix some v ∈ N G (T ), which exists as N G (T ) = ∅ by Property 17.Let P = (v = v 1 , . . ., v ) be the extending path of v given by Definition 11.By Lemma 15 we can partition the set maxset w (S k G (F )) of maximum-weight k-secluded supertrees of F in G into the following three sets: Consider the sets X 1 , X 2 , and X 3 of descriptions as obtained through recursion in Step 3 of the algorithm.By induction we have the following: Let X 1 , X 2 , and X 3 be the sets of descriptions as computed in Step 3 of the algorithm.
Claim 21.The sets X 1 , X 2 , and X 3 consist of valid descriptions for G.
Proof.To argue that X 1 = {(r, X ∪ {{v }}) | (r, X ) ∈ X 1 } consists of valid descriptions for G we show for an arbitrary description (r, X ) ∈ X 1 for G − v that (r, X ∪ {{v }}) is a valid description for G. Clearly r ∈ V (G) and X ∪ {{v }} consists of pairwise disjoint subsets of V (G − r).Consider any set S consisting of exactly one vertex from each set X ∈ X ∪ {{v }}.
Next we argue X 2 consists of valid descriptions for G. Recall that X 2 = {(r, X ∪ {M}) | (r, X ) ∈ X 2 } where M is the set of minimum weight vertices in P − F − v , so it suffices to show for an arbitrary description (r, X ) ∈ X 2 for G − V (P − v ) that (r, X ∪ {M}) ∈ X 2 is a valid description for G. Again it is easy to see that r ∈ V (G) and X ∪ {M} consists of pairwise disjoint subsets of V (G − r).Consider any set S consisting of exactly one vertex from each set X ∈ X ∪ {M}.
r is a supergraph of H and so S ⊆ N G (H ).All that is left to argue is that H is acyclic and m ∈ N G (H ).Let u be the vertex in T that is adjacent to v 1 .Note that this vertex is uniquely defined since no vertex in N G (T ) has two neighbors in T .Since H is a supertree of (F \ V (P )) ∪{v } and u, v ∈ F , it follows that P − v is a path between two vertices in H , of which S contains exactly one vertex chosen from M. Consequently, the component Finally since X 3 is a set of descriptions for G and X 3 = X 3 , the claim holds for X 3 .
Before we proceed to show that the output of the algorithm is correct, we prove two claims about intermediate results obtained by modifying the output of a recursive call.
Proof.We show Lemma 14 applies to the instance (G, k, F ∪ {v }, T , w).Recall that by induction We now show that all maximum-weight k-secluded supertrees of F in G are described by some description in our output.More formally, we show that maxset 3] in Claims 24 to 26.Claim 22).Since H ∈ T 1 was arbitrary we conclude T 1 ⊆ T G (X 1 ) completing the proof.
It suffices to show that when considering a set S consisting of one element from each set of a description (r, X As the two neighbors of V (P − v ) both belong to F ∪ {v }, the subpath of P before u and subpath after u are both reachable from r in G − S. Hence Proof.Recall X 3 is defined to be equal to X 3 , so we show in G − v , and clearly H is described by both (r 1 , X 1 \ {{v }}) and (r 2 , X 2 \ {{v }}), contradicting that X 1 is nonredundant for G − v. • If (r 1 , X 1 ) ∈ X 2 , then without loss of generality we can assume that (r 2 , X 2 ) / ∈ X 1 since otherwise we can swap the roles of (r 1 , X 1 ) and (r 2 , X 2 ) and the previous case would apply.Recall that X 2 = {(r, X ∪ {M}) | (r, X ) ∈ X 2 } where M ⊆ V (P − F − v ), so (r 1 , X 1 \ {M}) ∈ X 2 and (r 2 , X 2 \ {M}) ∈ X 2 .By construction we have that V (P ) ⊇ M ∩ N G (H) = ∅.Suppose that (r 2 , X 2 ) ∈ X 3 = X 3 , then H ∈ T G (X 3 ) and we have by induction that T G (X 3 ) = maxset w (S k G (F ∪ V (P ))) hence v ∈ F ∪ V (P ) ⊆ V (H).This contradicts that M ∩ N G (H) = ∅.This leaves as only option that (r 2 , X 2 ) ∈ X 2 .Since X 2 is a set of valid descriptions for G − V (P − v ) (by induction) we have that X 1 \ {M} and X 2 \ {M} contain only subsets of V (G) Observe that since the path P − v is connected to H := H − V (P − v ) only via its endpoints, and H does not contain m ∈ V (P ) we have that H remains connected so H is a (k − 1)-secluded tree in G − V (P − v ) described by (r 1 , X 1 \ {M}) as well as (r 2 , X 2 \ {M}).However this contradicts that X 2 is a non-redundant set of descriptions for G − V (P − v ) as given by induction.
• If (r 1 , X 1 ) ∈ X 3 = X 3 , then without loss of generality we can assume (r 2 , X 2 ) ∈ X 3 = X 3 since otherwise we can swap the roles of (r 1 , X 1 ) and (r 2 , X 2 ) and one of the previous cases would apply.But then H is a k-secluded tree in G described by two distinct descriptions from X 3 , i.e.X 3 is redundant for G contradicting the induction hypothesis.
This concludes the proof of Lemma 16 and establishes correctness.
Runtime analysis
If all recursive calls in the algorithm would decrease k then, since for k = 0 it does not make any further recursive calls, the maximum recursion depth is k.However in Step 3(c) the recursive call does not decrease k.In order to bound the recursion depth, we show the algorithm cannot make more than k(k + 1) consecutive recursive calls in Step 3(c), that is, the recursion depth cannot increase by more than k(k + 1) since the last time k decreased.We do this by showing in The following lemma states that under certain conditions, the neighborhood of T does not decrease during the execution of a single recursive call.
Lemma 28.Let (G 0 , k 0 , F 0 , T 0 , w 0 ) be an ELSS instance such that all leaves of G 0 are contained in T 0 .If the algorithm does not terminate before Step 3,then Proof.Since the algorithm does not terminate before Step 3 it follows that Steps 1 and 2 are not executed, so consider the part of the algorithm before Step 1.Throughout the proof we use (G, k, F , T , w) to refer to the instance at the time the algorithm evaluates it; initially (G, k, F , T , w) = (G 0 , k 0 , F 0 , T 0 , w 0 ), but actions such as contracting leaves may change the instance during the execution.Suppose that all leaves of G are contained in T .We infer that G[F ] is acyclic, as otherwise the algorithm would return ∅ before reaching Step 3. Removing the connected components of G that do not contain a vertex of F does not alter |N G (T )|.Afterwards we know that G is connected, as otherwise the algorithm would return ∅.Consider a single degree-1 contraction step of a vertex v with F = {v} that results in the instance (G − v, k, F * , T * , w * ).Since we assume that all leaves are contained in T , we have that v ∈ T .Let u be the neighbor of v.By Rule 1 we have Next consider the step where if Again these arguments can be applied iteratively.For the instance (G, k, F , T , w) to which this step can no longer be applied, Next we get that N G (F ) = ∅ as otherwise the algorithm would return {(r, ∅)} for some r ∈ F .We also get k > 0, as otherwise ∅ would have been returned.
Since none of the steps decreased the size of the neighborhood of T , for the instance (G , k , F , T , w ) at the time Step 3 is executed we conclude |N G (T )| ≥ |N G 0 (T 0 )| as required.
In the next lemma we show that as we make the recursive call in Step 3(c), either the size of the neighborhood of T strictly increases, or a vertex u appears in the neighborhood of the augmented tree T ∪ V (P ) having least two neighbors in T ∪ V (P ).Such a vertex u will trigger Step 3(a), which leads to a decrease in k.Definition 11 and Observation 12).The precondition of Step 3(c) gives that G[F ∪ V (P )] is acyclic.Since T ⊆ F , this implies that G[T ∪ V (P )] is acyclic.Due to Observation 12, it follows that V (P )
Lemma 29. If the instance when executing Step 3 is (G, k, F , T , w) and Step 3(c) branches on the instance
Then the second condition holds; u has at least one neighbor in T as u ∈ N G (T ) \ {v} and u is adjacent to v / ∈ T \ {v}.
) is adjacent to at least two vertices in T ∪ V (P ).Since we know that the recursive call on (G , k , F ∪ V (P ), T ∪ V (P ), w ) reaches Step 3 with the instance (G, k, F , T , w), we can rule out that some vertex u ∈ N G (T ∪ V (P )) is adjacent to at least two vertices in T ∪ V (P ) as this would mean the recursive call ends in Step 1.We can conclude instead that |N G (T ∪ Note that since (G , k , F , T , w ) is the instance in Step 3 we have that Properties 17, 19 and 20 apply.In particular, (G , k , F , T , w ) is almost leafless, implying that all leaves in G are contained in T .It follows that all leaves in G are also contained in T ∪ V (P ), so Lemma 28 applies to the input instance (G , k , F ∪ V (P ), T ∪ V (P ), w ) (as recursively solved in Step 3) and the instance (G, k, F , T , w) (as considered in Step 3 of that recursive call).So we ob- Since we know in Step 3 that |N G (T )| ≤ k(k + 1) (by Property 20) we can now claim that there are at most k(k + 1) consecutive recursive calls of Step 3(c), leading to a bound on the recursion depth of O(k 3 ).We argue that each recursive call takes O(kn 3 ) time and since we branch at most three ways, we obtain a running time of 3 However, with a more careful analysis we can give a better bound on the number of nodes in the recursion tree.
Lemma 31.The algorithm described in Section 3.2 can be implemented to run in time 2 O(k log k) • n 3 .
Proof.Consider the recursion tree of the algorithm.We first prove that each recursive call takes O(kn 3 ) time (not including the time further recursive calls require).We then show that the recursion tree contains at most 2 O(k log k) nodes.from T G (X i ) for any i ∈ [3] involves selecting and arbitrary description (r, X ) ∈ X i and then selecting, for each X ∈ X and arbitrary vertex v ∈ X .Now the tree can be found using DFS starting from r exploring an acyclic graph until it reaches the selected vertices from a set X ∈ X .This all takes O(n) time.The weights of the selected secluded trees can be found in O(n) time as well.Finally we take the union of (a selection of) the three sets of descriptions.Since these sets are guaranteed to be disjoint, this can be done in constant time when representing each set as a linked list and updating the pointers between them.
Number of nodes
We now calculate the number of nodes in the recursion tree.To do this, label each edge in the recursion tree with a label from the set {1, 2, 3a, 3b, 3c} indicating where in the algorithm the recursive call took place.Now observe that each node in the recursion tree can be uniquely identified by a sequence of edge-labels corresponding to the path from the root of the tree to the relevant node.We call such a sequence of labels a trace.
Note that for all recursive calls made in 1, 2, 3a, and 3b the parameter (k) decreases, and for the call made in 3c the parameter remains the same.If k ≤ 0 we do not make further recursive calls, so the trace contains at most k occurrences of 1, 2, 3a, and 3b.Next, we argue there are at most k(k + 1) consecutive occurrences of 3c in the trace.Suppose for the sake of contradiction that the trace contains k(k + 1) + 1 consecutive occurrences of 3c.Let (G, k, F , T , w) be the instance considered in Step 3 where the last of these recursive calls is made.By Lemma 30 we have |N G (T )| > k(k + 1).This contradicts Property 20, so we can conclude the trace contains at most k(k + 1) consecutive occurrences of 3c.Hence any valid trace has a total length of at most k • k(k + 1) = O(k 3 ), since no further recursive calls are done once k reaches 0.
In order to count the number of nodes in the recursion tree, it suffices to count the number of different valid traces.
Since a trace contains at most k occurrences that are not 3c we have that the total number of traces of length is k We derive the following bound on the total number of valid traces of length ≥ 1: Using the fact that (k c ) k = (2 log(k c ) ) k = 2 O(k log k) , we can conclude that the total number of nodes in the recursion tree is at most 2 O(k log k) so the overall running time is 2
Counting, enumerating, and finding large secluded trees
With the algorithm of Section 3.2 at hand we argue that we are able to enumerate k-secluded trees, count such trees containing a specified vertex, and solve LST.
Theorem 32.There is an algorithm that, given a graph G, weight function w, and integer k, runs in time 2 O(k log k) • n 4 and outputs a set of descriptions X such that T G (X) is exactly the set of maximum-weight k-secluded trees in G.Each such tree H is described by |V (H)| distinct descriptions in X.
Proof.Given the input (G, k, w), we proceed as follows.For each v ∈ V (G), let X v be the output of the ELSS instance (G, k, F = {v}, T = {v}, w) and let w v be the weight of an arbitrary k-secluded supertree in T G (X v ), or 0 if X v = ∅.Note that all k-secluded trees described by X v have weight exactly w v .Let w * := max v∈V (G) w v .If w * = 0 then there are no k-secluded trees in G and we output X = ∅; otherwise we output X : Clearly T G (X) is the set of all k-secluded trees in G of maximum weight.Since each X v is non-redundant, each maximum-weight k-secluded tree H is described by exactly |V (H)| descriptions in X.
By returning an arbitrary maximum-weight k-secluded tree described by any description in the output of Theorem 32, we have the following consequence.The following theorem captures the consequences for counting.
Theorem 34.There is an algorithm that, given a graph G, vertex v ∈ V (G), weight function w, and integer k, runs in time 2 O(k log k) •n 3 and counts the number of k-secluded trees in G that contain v and have maximum weight out of all k-secluded trees containing v.
Proof.Construct the ELSS instance (G, k, F = {v}, T = {v}, w) and let X be the output obtained by the algorithm described in Section 3.2.Note that this takes 2 O(k log k) n 3 time by Lemma 31.Since the definition of ELSS guarantees that X is nonredundant, each maximum-weight tree containing v is described by exactly one description in X.To solve the counting problem it therefore suffices to count how many distinct k-secluded trees are described by each description in X.
Fig. 2 .
Fig. 2. Setting of Lemma 10 with k = 2. Since the instance is almost leafless, |N G (T )| ≥ 7, and there is a k-secluded supertree H of the encircled set T , there must exist a vertex v which is the endpoint of k + 2 = 4 paths from N G (T ) intersecting only at v. Such a vertex, along with 4 paths, is drawn in the right image.This vertex must belong to the neighborhood of any k-secluded supertree of T .
Fig. 3 .
Fig. 3. Branching done in Step 3 for the extending path P shown at the top.The encircled vertex set represents T = F .In the bottom row, the dashed lines show 2-secluded supertrees of F that are found in the various branches.Left shows the instance solved recursively in Step 3a.The transparent vertex is the removed part.Similarly, middle and right show Steps 3b and 3c respectively.
Property 20 .
If the algorithm does not terminate before reaching Step 3 then |N G (T )| ≤ k(k + 1).
the following three lemmas that |N G (T )| increases as consecutive recursive calls in Step 3(c) are made.Since the algorithm executes Step 2 if |N G (T )| > k(k + 1), this limits the number of consecutive recursive calls in Step 3(c).
and therefore their size is equal.If u / ∈ T , then observe that u cannot be a leaf in G by assumption and thereforeN G (u) \ {v} = ∅.Since T is connected and v is a leaf in T we get (N G (u) \ {v}) ∩ T = ∅.It follows that |N G−v (T * )| ≥ |N G (T )|.These arguments can be applied for each consecutive contraction step to infer |N G (T )| ≥ |N G 0 (T 0 )| for the instance (G, k, F , T , w) after all contractions.
Consider the input instance (G, k, F , T , w) with n = |V (G)| and m = |E(G)|.We can verify that G[F ] is acyclic in O(|F |) time using DFS.Again using DFS, in O(n +m) time identify all connected components of G and determine whether they contain a vertex of F .We can then in linear time remove all connected components that contain no vertex of F and return ∅ if more than one component remains.Finally exhaustively contracting degree-1 vertices into their neighbor is known to take O(n) time.Updating F and T only results in O(1) overhead for each contraction.Exhaustively adding vertices v ∈ N G (T ) ∩ F to T can be done in O(n) time since it corresponds to finding a connected component in G[F ] which is acyclic.For Step 1 we can find a vertex v ∈ N G (T ) with two neighbors in T in O(n 2 ) time by iterating over all neighbors of each vertex in T .Determining the size of the neighborhood in Step 2 can be done in O(n 2 ) time.Applying Lemma 10 takes O(kn 3 ) time.So excluding the recursive call, Step 2 can be completed in O(kn 3 ) time.For Step 3 an arbitrary v ∈ N G (T ) can be selected in O(1) time, and the path P can be found in O(|P |) = O(n) time as described in Definition 11.Finally the results of the three recursive calls in Step 3 are combined.Selecting an arbitrary tree
Corollary 33 .
There is an algorithm that, given a graph G, weight function w, and integer k, runs in time 2 O(k log k) • n 4 and outputs a maximum-weight k-secluded tree in G if one exists.
Since any H ∈ maxset w (S k G (F )) contains u if and only if it contains v, they also show that an induced subgraph H of G containing either both {u, v} or none belongs to maxset w (S k G (F )) if and only if H Finally we combine Lemmas 28 and 29 to show |N G (T )| is an upper bound to the number of consecutive recursive calls in Step 3(c).If the recursion tree generated by the algorithm contains a path of i ≥ 1 consecutive recursive calls in Step 3(c), and (G, k, F , T , w) is the instance considered in Step 3 where the i-th of these recursive calls is made, then |N G (T )| ≥ i.Proof.We use induction of i.First suppose i = 1 and let (G, k, F , T , w) be the instance considered in Step 3 where the first of these recursive calls is made.If |N G (T )| = 0, then G[T ] is a connected component of G.However, since (G, k, F , T , w) is an instance considered inStep 3we know that Properties 17, 19 and 20 apply.In particular N G (F ) = ∅, ruling out that T = F .However if T = F , then there are at least two connected components in G containing a vertex from F , contradicting that G is connected (Property 17).By contradiction we can conclude that |N G (T )| ≥ 1 = i.Suppose i ≥ 2 and let (G, k, F , T , w) be the instance considered in Step 3 where the i-th recursive call is made.Let (G , k , F , T , w ) be the instance considered in Step 3 where the (i − 1)-th recursive call is made.By induction we know |N G (T )| ≥ i − 1.Let P be the extending path as in Step 3 where the (i − 1)-th recursive call is made, then by Lemma 29 we have that |N G | 13,879.6 | 2022-06-20T00:00:00.000 | [
"Mathematics",
"Computer Science"
] |
Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators
: To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.
Introduction
Manipulators are multi-degree-of-freedom robots capable of autonomous operation and task execution. They have been utilized in fields including manufacturing, medical care and aerospace [1]. These manipulators operate autonomously and perform tasks efficiently. In manufacturing, manipulators streamline production, handle materials and ensure consistent quality. In medical care, they enable precise and minimally invasive surgeries, leading to faster recovery and improved outcomes. Aerospace benefits from manipulators for assembling and maintaining components in challenging environments. However, as industrial level and job requirements continue to increase, the performance requirements for manipulators in various industries are becoming increasingly stringent. As a result of these requirements, several experts and scholars have dedicated a lot of time and effort to researching issues such as trajectory planning, path planning [2] and tracking control [3] of manipulators [4].
An important aspect of manipulator design is trajectory planning. It holds the key to minimizing operation time, reducing energy consumption and maximizing productivity. In manufacturing, optimized trajectory can streamline production processes and improve overall efficiency. In medical applications, precise trajectory planning allows for minimally invasive procedures with enhanced patient safety. Similarly, in aerospace, accurate trajectory planning ensures smooth and agile movements in challenging environments. It can be divided into multi-objective trajectory planning and single-objective trajectory planning. The planning of single-objective trajectory is mainly concerned with time, energy [5] and impact [6], while multi-objective trajectory planning combines multiple single-objective goals to meet different working environments [7,8]. Time-optimal trajectory planning is a crucial focus of current research due to its profound impact on manipulator performance. By enabling manipulators to complete tasks in the shortest possible time, this optimization technique significantly improves work efficiency, leading to enhanced productivity and reduced operational costs. With industries seeking streamlined processes and faster task execution, time-optimal trajectory planning plays a pivotal role in maximizing the potential of manipulators, making it a critical area of exploration and innovation in the field.
The paper [9] proposes an adaptive cuckoo algorithm, which has good convergence and convergence ability and combines with a quintic B-spline curve to obtain a smooth time-optimal trajectory. The paper [10] combines the original teaching-learning-based optimization algorithm with the variable neighborhood search (VNS) algorithm to improve escape ability from local optima and combines with a quintic B-spline curve to obtain time-optimal trajectory for the manipulator. The paper [11] proposes a local chaotic particle swarm optimization (PSO) algorithm, which solves the problem of early convergence into local optima in traditional particle swarm algorithm and combines with piecewise polynomial interpolation function to generate time-optimal trajectory. The paper [12] proposes an improved sparrow search algorithm, which uses tent chaotic mapping to optimize the generation of initial population, combines with an adaptive step factor to make the algorithm have good convergence effect and finally obtains a good operating trajectory.
In 2016, Mirjalili proposed a novel intelligent optimization algorithm known as whale optimization algorithm (WOA). Compared with other optimization algorithms such as the PSO, cuckoo search and genetic algorithm, the WOA has the advantages of fast convergence speed, simple algorithm and high convergence accuracy. These features make it an ideal choice for time-optimal trajectory planning in manipulators. The WOA exhibits rapid convergence, allowing the discovery of global optima within a limited number of iterations, thus reducing computation time. Additionally, its high accuracy ensures that planned trajectories closely approximate optimal solutions. In the context of time-optimal trajectory planning, precise trajectories are crucial for efficient manipulator motion. By improving the WOA, we can effectively address challenges in time-optimal trajectory planning, leading to improved motion efficiency and better alignment with industrial application requirements. The paper [13] proposed an improved whale optimization algorithm (IWOA), which designed dynamic inertia weights for two behaviors by improving the contraction-expansion mechanism and the spiral updating mechanism, thus enhancing the search ability of the algorithm. However, it was observed that in later stages, the algorithm tended to get trapped in local optima. In paper [14], a multi-strategy whale optimization algorithm (MSWOA) was proposed, which incorporated adaptive weights, Lévy flight and evolutionary population dynamics to enhance the algorithm's search capability. However, it was found that the algorithm failed to converge to the global optimum in some test functions. The paper [15] proposed a modified whale optimization algorithm (MWOA) that employs probabilistic prey selection and adjusts the initialization of the population and the search strategy during the development phase to reduce the likelihood of getting trapped in local optima, thereby enhancing the algorithm's robustness. Nevertheless, the algorithm exhibits a relatively high time complexity while tackling optimization problems. Although all of these algorithms have achieved good results, they may not perform well in some target optimization problems.
Therefore, this study presents an enhanced version of the whale optimization algorithm (RLVWOA) that combines reinforcement learning and the VNS algorithms. First, an inertia weight is designed for the surrounding prey and bubble-net attack behavior of whales and the control weight value is optimized using the q-learning and SARSA algorithms to enable each generation of populations to obtain suitable inertia weight, thereby enhancing the global search capability of the algorithm. Then, combined with the VNS algorithm, the local search capability of the algorithm is improved through continuous neighborhood search. Compared to the standard WOA, the RLVWOA can adaptively control surrounding prey and bubble-net attack behaviors and with the assistance of VNS algorithm, it can effectively escape from local optima, thereby achieving robust search capabilities. Finally, the RLVWOA is used in conjunction with a quintic non-uniform B-spline (NURBS) curve to perform time-optimal trajectory planning for the manipulator and its feasibility is verified in MATLAB.
The primary contribution of this study lies in the development of the RLVWOA algorithm, which innovatively integrates reinforcement learning algorithm and VNS algorithm. This integration leads to substantial performance improvements and presents an enhanced solution for the time-optimal trajectory planning problem in manipulators. The proposed enhancements significantly accelerate convergence and optimize the algorithm's capabilities, while mitigating the risk of getting trapped in local optima, thereby facilitating the discovery of more efficient trajectory paths. Consequently, this paper introduces a novel method for manipulator trajectory planning, leading to heightened work efficiency and smoother operations and exhibiting promising prospects for widespread application across various industries, encompassing manufacturing, medical care and aerospace.
The subsequent sections of this paper are organized as follows: Section 2 introduces the basic concepts of NURBS interpolation. Section 3 provides an overview of WOA, reinforcement learning and VNS algorithms. In Section 4, the proposed method for improving the WOA is described and a comparison between the RLVWOA and other commonly used single-objective algorithms is conducted on test functions. Section 5 focuses on the modeling of the PUMA560 robotic arm and compares the trajectory planning results obtained using the RLVWOA and traditional single-objective algorithms. The final section highlights the contribution of this study and suggests potential directions for future work.
Basic concepts of NURBS curves
The NURBS interpolation is a widely used curve or surface fitting technique, which is also widely used in the manipulator trajectory planning. Compared with traditional B-spline curves, NURBS curves have greater flexibility and accuracy and can better fit complex curve shapes. Based on the mathematical model of control points and nodes, it can generate smooth and continuous trajectories. By optimizing the weight of control points and the distribution of nodes, the optimal manipulator trajectory planning can be achieved, thereby improving the accuracy and efficiency of the manipulator. Using the NURBS interpolation for trajectory planning can help solve complex manipulator motion problems, while also improving the reliability and stability of the manipulator. A k-th NURBS curve can be expressed as a segmented rational polynomial function [16], as shown in Eq (1).
Where the weight factor of the NURBS curve is denoted by ω, di is the control vertex of the NURBS curve, k is the degree of the NURBS curve, x is the parameter of the NURBS curve and Ni,k(x) is the basis function of the k-th NURBS curve. Here, Ni,k (x) can be obtained by the De Boor-Koch formula from the node vector X = [x0, x1, ⸱⸱⸱, xn+k, xn+k+1], as shown in Eqs (2) and (3) and 0/0 is defined as 0 [17].
The quintic NURBS curve matrix
The equation for calculating the derivative of a NURBS curve of degree k is expressed by Eq (5) [19]: According to Eq (6): It can be derived that when solving for NURBS curves with n + 1 unknowns, four boundary conditions need to be added to ensure a unique solution to the equation system. Therefore, according to the actual motion conditions of the manipulator, the following four boundary conditions are added as shown in Eq (7): Where v and a represent the angular velocity and angular acceleration of the manipulator. Substituting Eq (7) into Eq (1), the matrix equation for solving all control points can be obtained as shown in Eq (8). The joint motion trajectory angle curves of the manipulator can be obtained using Eq (1). By using Eq (5) to solve the derivatives of the curve equation up to the third order, the angular velocity, angular acceleration and angular jerk curves for each joint can be acquired.
Whale optimization algorithm
In 2016, Mirjalili et al. proposed the WOA, which is a recently developed metaheuristic search algorithm. The authors studied and analyzed the optimization ability of WOA from different perspectives such as structure and mathematical models. Experimental results showed that WOA not only has strong search ability and positive feedback, but also can achieve global optimization [20].
The most remarkable feature of a humpback whale is its sociality. Typically, a group of six or so humpback whales search for prey and confirm the target's position. Other groups of whales approach the prey through encircling contraction and spiral contraction and eventually succeed in eating the prey at the appropriate time. The algorithm consists of the following three stages: (1) Surrounding prey It is assumed that the optimal solution corresponds to the position of the target prey in the WOA. Each whale updates its relative position with respect to the target position using Eqs (9) and (10): In these two equations, X * (t) represents the best position, X (t) represents the present position and t represents the present iteration. A and C are adjustment factors, defined as: where, rand1 and rand2 are random values uniformly distributed between 0 and 1 and a is a decreasing factor with a gradual reduction from 2 to 0, represented as: In the equation, tmax represents the maximum number of iterations.
(2) Bubble-net attack In the WOA, the bubble-net attack is categorized into the contraction and encirclement mechanism and the spiral updating mechanism. The contraction and encirclement mechanism is the same as the formula for surrounding the prey, but with the range of A changed from [-a,a] to [-1,1]. The spiral updating mechanism is represented by Eq (14): Here, l is a random number between -1 and 1. The constant b is used to represent the logarithmic spiral shape. Dq represents the distance between the whale and the prey, which is expressed by Eq (15).
( )
Assuming that a whale chooses between the shrink-wrap and spiral update mechanisms with a probability of 50% during the hunting of a target prey, the position update is given by the Eq (16).
(3) Searching for prey The whale decides to use the shrink and encircle mechanism or search for prey mechanism based on the size of parameter A. When A ≥ 1, the whale cannot obtain the optimal position of the prey and therefore needs to randomly search for the target within its range, as expressed in Eqs (17) and (18).
Reinforcement learning algorithm
The reinforcement learning algorithm is proposed by Misky in 1954, which mainly consists of agent, environment, state, action and reward components [21].
Reinforcement learning is a type of machine learning algorithm inspired by biology that aims to learn through experimentation within the possible state-action pairs to find a mapping from states to actions that maximizes the cumulative reward [22]. In reinforcement learning, an agent interacts with its environment by exploring and making decisions based on the present state. The agent first explores and observes the current state St, then makes an action decision actiont based on the perceived current state. The environment changes its state from St to St+1 in response to the agent's action and returns a reward (or punishment) signal rt to the agent. The agent adjusts its action decisions based on the reward feedback from the environment and trains itself to maximize current and future rewards. This process is called a Markov decision process. The basic principle is shown in the Figure 1. Q-learning and SARSA are both value-based reinforcement learning algorithms. Their goal is to find the optimal policy by learning and optimizing the value function. Q-learning algorithm is an offline learning algorithm based on a greedy strategy, which learns the optimal value function by updating the state-action pairs. At each time step, the agent observes the current state and selects the next action based on the current policy function and value function. The agent then observes the next state St+1 and receives the corresponding immediate reward rt. On the other hand, SARSA algorithm is an online learning algorithm, which selects the next action and learns based on the current state and policy function. Therefore, SARSA's learning process is a continuous and constantly updated process, which can dynamically adapt to changes in the environment [23]. Specifically, the value function update formula for Q-learning and SARSA are as shown in Eqs (19) and (20): In these equations, Q(s,a) represents the value function of taking action in state S, α is the learning rate, γ is the discount factor, r is the immediate reward and max ' a is the operation of taking the maximum value among all possible action' in the next state S'.
Variable neighborhood search algorithm
The VNS algorithm is a heuristic optimization algorithm based on neighborhood search that can effectively solve many complex optimization problems. The original proposal of the algorithm can be attributed to Mladenovic and Hansen. It has gained extensive utilization in subsequent research endeavors [24]. The principle of the VNS algorithm is to search on different neighborhood structures and gradually approach the optimal solution by continuously expanding or reducing the neighborhood structure. During the search process, the VNS algorithm jumps out of local optimal solutions and seeks better solutions.
The main steps of the VNS algorithm are as follows: Step 1. Initialization: Randomly generate an initial solution and set the initial neighborhood structure.
Step 2. Neighborhood structure: Generate new solutions by changing the current neighborhood structure. In each neighborhood structure, define a set of operations, such as insertion, deletion, exchange, etc., to generate new solutions.
Step 3. Neighborhood search: Search in the current neighborhood structure to find the best solution. If a better solution is found, go to Step 4. Otherwise, go to Step 5.
Step 4. Neighborhood expansion: Expand the neighborhood structure to better search for possible solutions.
Step 5. Neighborhood contraction: Contract the neighborhood structure to better search for possible solutions.
Step 6. Convergence check: Check if the algorithm has converged. If not, go back to Step 2. Otherwise, output the optimal solution.
The core idea of VNS algorithm is to continuously expand and contract the neighborhood structure to better search for possible solutions. In each neighborhood structure, a set of operations is defined and the best solution is selected based on greedy strategy.
Improved algorithm
The three behaviors of the WOA have a crucial impact on finding the optimal position, while the value of the inertia weight also plays a vital role in the optimization and search capability of the algorithm. The IWOA with dynamic inertia weight proposed in paper [13] introduces an inertia weight value in the surrounding prey and bubble-net attack behaviors, as shown in Eqs (21) and (22). Although this accelerates the convergence speed and improves the convergence capability of the algorithm, the inertia weight value is simply linearly decreased based on the current iteration, which may not be suitable for the current population. Therefore, this paper improves the IWOA algorithm by using reinforcement learning to optimize the control of the inertia weight value, making it more suitable for the current population and enhancing the convergence speed and optimization capability of the algorithm. Additionally, the VNS algorithm is introduced to improve the local search capability of the algorithm and obtain better optimal solutions.
The design of the Q-table
The initial Q-table is a zero matrix of size m × n, where m is the number of states and n is the number of actions. When the environment and actions change, the Q-table is updated according to Eqs (19) and (20), as shown in Eq (23).
According to the results proposed in [25], SARSA algorithm has faster convergence rate, while Q-learning has better overall performance. Moreover, [23] has verified that the combination of SARSA and Q-learning algorithms yields better convergence. The algorithm presented in this study utilizes both Q-learning and SARSA algorithms. However, it employs them at separate stages, as illustrated in Eq (24), where tmax represents the total number of iterations.
The design of the states
To ensure that the WOA obtains better optimization capability and faster convergence speed with appropriate inertia weight values, the state design of the reinforcement learning algorithm needs to be considered. The design of the state should take into account the convergence, diversity and balance of the WOA. Therefore, the following aspects are taken into account in the design of the state: In this equation, t represents the iteration number of the algorithm, f (xi t ) represents the fitness function value of the i-th individual in the t-th iteration and Ct represents the ratio of the sum of fitness values of all individuals in the t-th iteration to that in the initial iteration, which reflects the convergence of the algorithm. Dt represents the ratio of the maximum fitness value of the t-th generation to that of the first generation, which reflects the diversity of the algorithm. Bt represents the ratio of the mean value to the standard deviation of each generation, which reflects the balance of the population in each generation. Equation (28) calculates the state value of each generation by weighted sum. Considering the importance of convergence and diversity of the algorithm, ω1 and ω2 are set to 0.35 and ω3 is set to 0.3.
The design of the actions
Action refers to the agent's response, which is determined by the present state. With each successive population iteration, the agent selects suitable inertial weight values based on the environment. Larger values of ω may cause the algorithm to be trapped in a local optimal solution, while smaller values may affect the algorithm's global search ability. Therefore, ω is defined as 10 actions between (0-1), where the first action, a1, generates a random number from (0.0-0.1) and the second action, a2, generates a random number from (0.1-0.2) and so on. The detailed action values are shown in the Table 1.
The design of the rewards
The agent does not choose actions on its own, but selects the appropriate action based on the Qtable and the current state, in order to obtain more positive feedback. Designing a reward function as shown in Eq (29) can simultaneously take into account the convergence, diversity and balance of the algorithm, making the algorithm more capable of searching. The goal of this paper is to minimize the function value and the smaller state value, the better the performance of the algorithm. Therefore, when St-1 is greater than St, the reward is positive, otherwise it is negative.
Action selection strategy
When the algorithm starts, the values in the Q-table are initialized to zero, which means the agent has no experience to rely on and must explore and learn by experience. By continuously investigating unknown environments, the agent gains more experience, it learns valuable knowledge to inform its actions. The ε-greedy strategy is a method that balances exploration and exploitation, as shown in Eq (30).
Where ε represents the greedy rate and the value of k0-1 is a randomly generated number within the range of 0 to 1. When ε ≥ k, the agent chooses the action that maximizes the Q value, also known as the greedy strategy. When ε < k, exploration is performed and a random action is chosen.
The design of neighborhoods
The objective of this paper is to minimize the optimization problem. Therefore, the design of the VNS aims to expedite the discovery of the global minimum by exploring various neighborhoods. The three neighborhoods are designed as follows: 1) Randomly choose a variable and reduce its value through a certain amount.
2) Randomly choose a variable and multiply it through a generated number within the range of 0 to 1.
3) Randomly select two variables and swap their positions.
The algorithm processes
The combination of reinforcement learning algorithm, the VNS algorithm and the WOA requires considering reward, state, action and action selection strategy. The WOA is treated as the environment and the state S is calculated based on Eq (28) and at each iteration, St is updated to St+1. The learning component comprises the agent and the reward r. The entire procedure can be divided into four sequential steps. To begin with, the agent obtains the environment state St for the t-th iteration, then chooses action based on the Eq (30) and adjusts ω value. The WOA will iterate using the updated ω. After completing one iteration, the environment state will transition from St to St+1. Lastly, the reward r is calculated based on the Eq (29) and the Q-table value is updated by Eq (19) or Eq (20). After t iterations, the agent will select optimal ω based on prior exploration experience for the current state. The algorithm flowchart of the RLVWOA is shown in Figure 2.
Comparative validation
To verify the feasibility of the RLVWOA, twenty standard benchmark functions were selected for testing [26], as shown in Table 2 and compared with the reptile search algorithm (RSA) [27], snake optimization (SO) [28], WOA, IWOA, MSWOA and MWOA. To ensure the fairness of the experiment, using the same computer, the population number of all algorithms N = 30, dimension D = 30, number of iterations tmax = 300 and other parameter settings for each algorithm are shown in Table 3. Each testing function is run 30 times using each algorithm separately. The comparative results are shown in Table 4 and the time it takes for each algorithm is shown in Table 5. The highlighted section denotes the algorithms that achieved the highest performance for each testing function. According to the results from Table 4 and Table 5, although RLVWOA exhibits longer running time and fails to converge to the theoretical optimal values on some test functions such as F5, F6 and F9, it demonstrates relatively better convergence accuracy and attains the best mean ranking. For the sake of brevity, this paper only presents the convergence figures of F1, F4, F9, F12, F17 and F20, which include two unimodal test functions, two multimodal test functions and two fixed-dimension test functions. To make these figures more intuitive, we use the same initial population and set tmax = 50.
As shown in the Figure 3, although RLVWOA requires more running time, it demonstrates better convergence performance, enabling faster convergence compared to other algorithms. Therefore, it fully demonstrates that the RLVWOA, which combines the reinforcement learning algorithm and the VNS algorithm, can solve the unstable optimization performance of the WOA well.
Model establishment
The problem of time-optimal trajectory planning for manipulators can be likened to solving a constrained optimization problem to find the minimum value. It heavily relies on the algorithm's search capability to navigate through the vast solution space and identify the optimal trajectory that minimizes the completion time while satisfying the constraints imposed by the manipulator's dynamics and task requirements. The efficiency of the optimization algorithm plays a pivotal role in achieving time-optimal solutions, ensuring the manipulator's swift and precise execution of tasks in various industrial applications. To facilitate better understanding and avoid the need to learn about different manipulator structures, this paper chooses to use the common PUMA560 manipulator as the model for trajectory planning. Its modified D-H parameters and kinematic constraints are shown in Tables 6 and 7, respectively. The goal of this paper is to find the time-optimal trajectory for the manipulator. Therefore, the fitness function of the algorithm is defined as depicted in Eq (31): In the Eq (31), f denotes the overall execution duration of the manipulator and ti represents the time to reach the i-th path point.
Based on the data in Tables 6 and 7, The selected path points that satisfy the kinematic constraints are shown in Table 8. Based on these path points, by substituting it into Eq (1) and Eq (31), the timeoptimal trajectory planning for the manipulator is conducted.
Trajectory planning
The time-optimal trajectory planning for the manipulator using the RLVWOA is conducted. In order to further validate the performance of the algorithm, the RSA, SO, WOA, IWOA, MSWOA and MWOA algorithms are also utilized for the trajectory planning of the manipulator. Each algorithm utilizes the same number of iterations T = 300 and population size N = 30, while other specific parameters are taken from the data presented in Table 3. The specific results are shown in Table 9, where the results obtained by the RLVWOA are highlighted in bold. The convergence comparison figure is shown in Figure 4. Based on the data in Table 9 and Figure 4, it can be observed that the RLVWOA achieves superior results in terms of obtaining the shortest running trajectory for the manipulator. The RLVWOA, compared to the standard WOA, achieves a reduction of 39.39% and compared to other improved WOAs achieve a minimum reduction of 11.51%. Additionally, the RLVWOA demonstrates faster convergence speed, further validating its superior search capability as proposed in this paper. The trajectory planning plot is depicted in the Figure 5.
According to Figure 5, all curves are uniform, continuous and devoid of any abrupt changes. Furthermore, they adhere to the kinematic constraints outlined in Table 7. Therefore, it can be concluded that the RLVWOA is capable of obtaining a superior time-optimal trajectory.
Conclusions
This paper proposes an improved RLVWOA that combines reinforcement learning to enhance global search capability and introduces VNS algorithm to improve local search capability. A comparison with other algorithms demonstrates the superior performance of RLVWOA. Subsequently, the RLVWOA is employed in conjunction with the quintic NURBS for trajectory planning of the manipulator. The result is a smooth, uniform and continuous trajectory, which outperforms the results obtained by other optimization algorithms in terms of reduced the manipulator operation time.
The main contribution of this paper is the proposal of an improved RLVWOA that exhibits superior search capability compared to other algorithms. However, there are still some issues that need to be addressed in future work. This paper only combines reinforcement learning algorithms. It would be worthwhile to explore the use of deep learning algorithms such as deep q-learning network (DQN) algorithm, deep deterministic policy gradient (DDPG) algorithm and twin delayed deep deterministic policy gradient (TD-3) algorithm as potential alternatives. Additionally, while the introduction of the VNS algorithm has improved the search capability, it has also increased the algorithm's runtime significantly. Future work could involve redesigning more suitable neighborhoods or adding termination thresholds to control the runtime of the algorithm.
Use of AI tools declaration
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article. | 6,617.8 | 2023-08-14T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
Describing small-angle scattering profiles by a limited set of intensities
An indirect Fourier transform method is presented which describes a solution scattering profile from a reduced set of intensities. Equations are derived to fit the experimental profile using least squares and to calculate commonly used size and shape parameters directly from the reduced set of intensities, along with associated uncertainties. An analytical equation is derived enabling regularization of the real-space pair distribution function. Convenient software is provided to perform all described calculations.
S1. Extension of Moore's IFT
Moore uses a trigonometric series to define a function Q(r) = P (r)/r. This definition resulted in a convenient relationship between the real space Q(r) and the reciprocal space U (q) = qI(q), where the two are Fourier mates. This results in equations 17 through 18 defining P (r) and I(q): P (r) = r 2π 2 ∞ n=1 a n sin nπr D (17) I(q) = D π ∞ n=1 a n q sin(qD − nπ) qD − nπ − sin(qD + nπ) qD + nπ (18) where a n are weights for each term in the series, the Moore coefficients, and D is the maximum particle dimension (Note: modest variations compared to Moore's original description of these functions by a factor of 2π are due the use of q = 4π sin(θ)/λ rather than s = 2 sin(θ)/λ, where 2θ is the scattering angle and λ is the wavelength).
Key to Moore's approach (and other IFT methods (Glatter, 1977;Svergun, 1992)) is that the weights a n define both the real space and reciprocal space profiles, using the appropriate basis functions. Least squares can be used to determine the a n 's and the associated standard errors by minimizing the χ 2 formula (equation 19): where I e is the experimental intensity for data point i, I c is the intensity calculated at q i given by equation 18, σ i is the experimental error on the intensities, and N is the total number of data points.
Moore's use of Shannon information theory to define I(q) resulted in a selection of q values, namely q n = nπ/D, termed "Shannon channels" (Feigin & Svergun, 1987;Svergun & Koch, 2003;Rambo & Tainer, 2013). The intensities at q n , i.e. I n = I(q n ), therefore become important values as they determine the a n 's and thus can be used to completely describe the low-resolution size and shape of a particle obtainable by SAS.
It is therefore convenient to derive the mathematical relationship between I n and a n .
Note that here we will further use m to refer to a particular term in the series, and we will use n when referring to the terms in the function defining the entire series. The intensity I m at q m = mπ/D is Since sin((n − m)π) (n − m)π − sin((n + m)π) (n + m)π = 0 : n = m 1 : n = m the sum reduces to a single term when m = n, resulting in and therefore Defining the basis functions B n as I(q) can now be expressed as a sum of the basis functions B n weighted by physical intensity values at q n As in Moore's original approach, the B n functions are determined by the maximum dimension of the particle, D. B n 's for D = 50Å are illustrated in Figure 1. The P (r) function can be determined from the continuous I(q) according to equation 27: P (r) can also be represented using the series of I n values by inserting equation 23 into equation 17, resulting in equation 28: or by defining real space basis functions S n as follows: As intensity values measured precisely at each q m are typically not collected during experiment, least squares minimization of χ 2 can be used to determine optimal values for each I m from the oversampled SAS profile, resulting in greatly increased precision for each I m compared to measured intensities. To determine the set of optimal I m values, let Values for each I m are sought which minimize χ 2 , i.e. where δχ 2 /δI m = 0, yielding for all m. Let and Furthermore the errors on the calculated I c (q) curve can be calculated as: and the errors in P (r) are
S2. Derivation of Size Parameters and Error Estimates
Here the detailed derivation is presented for calculating R g from the I n 's. Derivations for the remaining parameters and errors can be determined similarly. R g can be calculated from the P (r) curve according to the following equation: To determine the equation relating I n coefficients to R g , we substitute 28 into equation
Since
the denominator can be simplified to The numerator becomes Since Porod's law shows that intensity (and thus the I n 's) decays as q −4 for globular particles, and similarly as q −2 for random chains, the infinite sum is guaranteed to converge to a finite value. Since the sum converges, the Fubini and Tonelli theorems (Fubini, 1907;Tonelli, 1909) show that the infinite sum can be exchanged with the finite integral as follows Pulling constants out of the integral results in The integral can be solved by three iterations of integration by parts and evaluated at the limits to obtain which can be combined with equation 40 to ultimately obtain equation 8. Similar steps can be followed for the remaining parameters.
The average vector length in the particle, r, is defined as The Porod volume can then be calculated, using its definition containing the Porod invariant (Porod, 1982), by the following equation The Volume of Correlation (Rambo & Tainer, 2013), V c , is defined as where c is the length of correlation (Porod, 1982). V c can thus be estimated from the Since the matrix C −1 contains all the information on the variances and covariances of the I n 's, the uncertainties in each parameter can be derived using error propagation.
The error in I(0) is thus The error in R g is In equation 8 it can be seen that R g has a non-linear dependence on I n , and thus the error on R g is dependent on R g itself. The error in r is The error in Q is The error in V p is The error in V c is The error in c estimated from equation 49 becomes
S3. Derivation of Analytical Regularization of P (r)
To enable the regularization of the P (r) curve for the derivation described above, S has been chosen to take the commonly used form of equation 58: where P (r) is the second derivative of P (r) with respect to r. The second derivative is often chosen as it is sensitive to large oscillations in the P (r) function, i.e. smoother functions will have fewer oscillations and thus S will be small. This representation allows for an analytical solution to the problem of regularization of the P (r) curve.
To begin, the second derivative of P (r) can be calculated as Equation 58 Since the term in square brackets outside the sum is independent of n, it can be brought inside the sum, and the integration and summation exchanged: The terms in the integrand can now be expanded, yielding four terms in total: Each term can now be integrated and evaluated at the limits to obtain the following: δS δI m = ∞ n=1 I n 0 − (−1) m+n m 2 n 4 π 2 2D 5 (m 2 − n 2 ) + (−1) m+n m 4 n 2 π 2 2D 5 (m 2 − n 2 ) − (−1) m+n (mn) 2 (m 4 + n 4 )π 2 2D 5 (m 2 − n 2 ) 2 .
These terms can be combined and represented by G mn below. Note that when m = n the equation is undefined, so the integration has been repeated after taking the limits as m approaches n for the special case when m = n. Taken together, the derivative of S with respect to I m can be now be represented as equation 65: where G mn = π 2 2D 5 (mn) 2 m 4 + n 4 (m 2 − n 2 ) 2 (−1) m+n : m = n π 2 48D 5 n 4 2n 2 π 2 + 33 : m = n .
Following the same procedure outlined in equations 31 through 34 and now including 65, equation 15 can now be solved by least squares minimization to yield the optimal values for each I m while accounting for the regularizing function S according to the following modified equations: | 2,063.6 | 2021-05-24T00:00:00.000 | [
"Computer Science"
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“Is It Valid or Not?”: Pre-Service Teachers Judge the Validity of Mathematical Statements and Student Arguments
There is a wide recognition that reasoning abstractly, constructing arguments, or critiquing arguments should be an important educational goal in the mathematical experiences of all students in the standards for school mathematics. Seeing these standards as an essential element for developing deep mathematical understanding; however, call for a strong knowledge of proof for teachers. Thus, the purpose of this study is to investigate how pre-service middle school teachers (PSMTs) decide whether a presented mathematical statement is true or false and how they verify student arguments presented for these statements. 50 PSMTs participated in the study. Individual interviews were conducted with 7 PSMTs to further delve into the verification processes of the PSMTs. The results of the study demonstrated that meeting the expectations of the current standards is not an easy feat by documenting that most of the PSMTs struggled with evaluating mathematical tasks and constructing arguments.
INTRODUCTION
Standards for school mathematics have increasingly focused on the importance of student reasoning abstractly, constructing viable arguments, critiquing others' reasoning, and attending to precision across their K-12 experience ( Stylianides, Bieda, & Morselli, 2016). While there has been a strong emphasis in various policy documents for the inclusion of constructing and critiquing mathematical arguments in all grades, these documents are generally thin in describing how to teach proofs in this vision and what this requires for teachers. In reality, the place of proof in mathematics classrooms is far from that vision (Buchbinder & McCrone, 2020). used the phrase classroom-based interventions in the area of proof to describe interventions designed to improve understanding or use of proof as captured by these standards at any grade level. stated, "the number of such studies is small and acutely disproportionate to the number of studies that have documented problems of classroom practice [in the area of proof] for which solutions are sorely needed" (p. 253).
Seeing these standards as an essential element for developing deep mathematical understanding and making it a crucial element of students' mathematical experiences obviously call for a strong mathematical knowledge for teachers (Buchbinder & McCrone, 2020;Lesseig, 2016;Mata-Pereira & da Ponte, 2017). Teachers are expected to decide what conjectures proposed by students or textbooks are worth pursuing, to judge whether students have the requisite background such as key definitions or theorems to produce a valid argument for the conjectures, and to check what implicit or explicit warrants support the argument (Dawkins & Weber, 2017;Mata-Pereira & da Ponte, 2017;Stylianides, 2007).
Yet, research indicates that teachers do not have experience supporting mathematical argument in their classroom and, in fact, are not sure what mathematical proof is, or should look like in a classroom setting (Dawkins & Weber, 2017;Mata-Pereira & da Ponte, 2017;Stylianides, Bieda, & Morselli, 2016). All these suggest that teachers should have more experience with evaluating mathematical grounds on which claims or arguments could be accepted or rejected in classrooms.
The purpose of this study is to investigate how pre-service middle school teachers (PSMTs) decide whether a presented mathematical claim is true or false and then how they verify student arguments presented for these claims. It is hypothesized that using statements that are not always hold true can better illuminate the ability of pre-service teachers' verification of mathematical claims. Furthermore, it is also hypothseized that using arguments that use invalid modes of reasoning can better illuminate the parts of pre-service teachers' conceptions of what constitutes a proof. More specifically, this study is guided by the following two research questions: 1. How do pre-service middle school teachers (PSMTs) verify given statements that are not always true? 2. How do PSMTs judge the validity of given arguments? Ellis et al. (2012) mentioned that the development of new knowledge passes through several stages of which the construction of a mathematical argument is considered as the last stage. Earlier stages of this complex processes of developing new knowledge typically includes exploration of particular cases, generation (or refinement) of conjectures, and then attempts to develop arguments that may translate into a proof (Ellis, Bieda, & Knuth, 2012;Stylianides, 2008;Zazkis, Liljedahl, & Chernoff, 2008). This conceptualization is thought to be useful for comprehending PSMTs' ability to verify mathematical statements and then to evaluate students' arguments. Thus, the definition of these concepts will be addressed in this section so that the definitions could be used to shed light onto the participants' verification and evaluation processes.
Clarification of the Terms
Conjecturing involves reasoning about mathematical relationships to develop statements that are tentatively thought to be true but are not known to be true (Lannin, Ellis, & Elliott, 2011, p. 13). Stylianides (2008) defined conjecture as a reasoned hypothesis about a general mathematical relation based on incomplete evidence (p. 11). Stylianides (2008) described that the term 'reasoned' was used to emphasize the non-arbitrary character while the term 'hypothesis' was used to indicate a level of doubt in the definition. Similarly, Harel and Sowder (2007) defined conjecture as an observation made by a person who has doubts about its truth. These elements of doubt and non-randomness; therefore, are essential components of conjecturing (Lannin, Ellis, & Elliott, 2011). Similarly, Canadas and colleagues highlighted the non-arbitrary character of conjecturing by arguing that conjecturing involves the following seven stages: (1) observing cases, (2) organizing cases, (3) searching for and predicting patterns by imagining that such patterns might apply to the next unknown case, (4) formulating a conjecture about all possible cases based on empirical facts, (5) validating the conjecture for a specific case through some independent method, (6) generalizing the conjecture, and (7) justifying the generalization (2007, p. 63).
Although Canadas and colleagues (2007) conceptualized justifying as the last step of conjecturing, justifications could be constructed separately from conjecturing processes. Justifying, in a general sense, is a coordinated collection of reasons that an individual provides for believing that a mathematical statement is true (Czocher & Weber, 2020, p. 51). Thus, justifying includes any attempts to use mathematics to convince oneself or others, regardless of whether the argument is complete or would be accepted as a proof. Indeed As Czocher and Weber (2020) stated, all proofs are justifications but not all justifications could be counted as proofs. Then the question remains: what properties a justification must possess to qualify as a proof (Cirillo, Kosko, Newton, Staples, & Weber, 2015;Czocher &Weber, 2020).
Although there is not a consensus among mathematics educators and mathematicians as to what a mathematical proof should look like (Czocher & Weber, 2020), Stylianides (2007) proposed a definition of a proof as follows: "Proof is a mathematical argument, a connected sequence of assertions for or against a mathematical claim, with the following characteristics: 1. It uses statements accepted by the classroom community (set of accepted statements) that are true and available without further justification; 2. It employs forms of reasoning (modes of argumentation) that are valid and known to, or within the conceptual reach of, the classroom community; and 3. It is communicated with forms of expression (modes of argument representation) that are appropriate and known to, or within the conceptual reach of, the classroom community." (Stylianides, 2007, p. 291) Thus, justifications should encapsulate several characteristics to be considered as a mathematical proof as follows: the arguments accepted as proofs use true statements, valid forms of reasoning, and appropriate forms of expression, whereby the terms "true", "valid" and "appropriate" should be conceptualized as part of classroom community. This conceptualization of proof is thought to be helpful to evaluate how the PSMTs verify the statements that are not always true as well as student arguments, which will be addressed next.
Participants
Participants of the study were 50 pre-service middle school teachers (PSMTs) who are certified to teach mathematics in grades 5 through 8. The participants were juniors at a public university in Turkey when the data was collected. They completed several mathematics courses and two consecutive mathematics methods courses prior to the study. The PSMTs enrolled in a mathematics education course, which was taught by the author of this study in the spring semester of 2019. However, it should be noted here that the data was not collected as a part of the course. Instead, the data was collected by the end of the course and all participants were informed that the participation to the study was voluntary. 50 PSMTs volunteered to complete a questionnaire by the end of the semester. Among these 50 PSMTs, semistructured individual interviews were conducted with 7 PSMTs. All these 7 PSMTs volunteered to further participate to the study.
Tasks
The tasks used in the study were only true for some cases but not for all. Several researchers have called for increased emphasis on such tasks for instructional purposes (Ball, Hoyles, Jahnke, & Movshovitz-Hadar, 2002;Brown, 2014;Stylianides & Stylianides, 2009). For instance, Harel and Sowder (2007) argued that using example-based reasoning should be cautioned due to its tentative nature; therefore, employing patterns that do not always hold true could be helpful to get students recognize the limitation of using empirical-based arguments. Employing patterns that would not always hold true could also be used to justify the norm that students should only employ inferential techniques that are valid (or explicitly justify why a technique is valid in this situation). Stylianides and Stylianides (2009) and Brown (2014) found some success with employing such patterns to teach students the dangers of empirical induction, non-generalizable deduction, and diagrams. Three tasks that were only hold true for some cases were used to investigate the PSMTs' ability to verify the tasks (see Table 1).
The tasks were in the conceptual reach of the PSMTs, yet they still provided a productive struggle since the tasks only held true for some cases and required a justification. Three student arguments for the tasks were also used to investigate the PSMTs' processes of evaluating mathematical arguments. The presented student arguments were designed as empirical arguments that purported to justify the statements for a subset of the classes covered; therefore, fell short of being accepted as mathematical proofs (see Harel & Sowder, 2007 for details). Studies have shown that students at all levels have difficulty recognizing universally and existentially quantified statements (especially when the quantifier is implicit) and struggle to understand that a universally quantified statement must be proved for all elements in the domain, and fail to recognize the limitation of relying on supportive examples for proving universal statements (Buchbinder & Zaslavsky, 2019). Therefore, using empirical arguments is thought to be useful for investigating how the participants evaluate student arguments.
Table 1. Tasks and arguments used in the study
Task 1: Students have been working on an area and perimeter task. One student-Nermin-proudly proclaims that she discovers a new math conjecture: "whenever the perimeter of a rectangle increases, its area also increases". Do you think her claim is true or false? If so, how would you prove it?
Task 1 a: You think that it is a good opportunity to engage students with proofs and ask them to prove whether Nermin's claim is true or false. Nermin provides an argument as follows: Would you accept her argument as a proof? Why? Why not?
Task 2: Ali claims that "if the vertices of a quadrilateral are on the consecutive sides of a rectangle, then the area of the quadrilateral inside is always half of the area of the rectangle". Do you think Ali's claim is true or false? If so, how would you prove it?
Task 2a: Ali provides an argument as follows: If you take four points on the sides of a rectangle as follows, there are eight congruent triangles formed. Since four of these triangles are inside of the inner quadrilateral, the area of the quadrilateral is half of the area of the rectangle.
Would you accept his argument as a proof? Why? Why not?
Task 3: "At least one of the diagonals cuts the area of a quadrilateral in half" Do you think that this claim is true or false? How would you prove it? (adapted from: Ball, Hoyles, Jahnke & Movshovitz-Hadar, 2002).
Task 3a: Leyla: "Diagonals cut a square in two congruent triangles so that the areas will be the same. If we fold a square along its diagonal like this, we can see that the areas of the triangles are the same. We can do the same for a rectangle, parallelogram, and a rhombus. So, it is true." Would you accept her argument as a proof? Why? Why not?
Data Collection Process
The participants were administered a questionnaire which consisted of eight open ended questions with several sub questions during 75 minutes by the end of the spring semester of 2019. The questionnaire consisted of two parts. The first part contained the tasks that the PSMTs were asked to verify and then to provide a justification while the second the part included hypothetical student arguments. The PSMTs were instructed to complete the first part and then to move to the second part. Three of the tasks were analyzed in this study (see Table 1 for the tasks employed in the study). The PSMTs were informed that their responses would not be graded and would only be used for educational purposes to ensure that they reflected their own thoughts comfortably in their responses. 7 PSMTs volunteered to further participate to the study. The PSMTs were interviewed individually among 30-45 minutes and were asked to elaborate more upon their responses to the three questionnaire questions during the individual interviews. The individual interviews were recorded by a video camera. The video camera was positioned in such a way that the participants' gestures, written responses, and drawings were captured. The individual interviews took place in an office where only the interviewer and the interviewee were present. All the papers that the participants used during the interviews were collected by the interviewer for data analysis.
Data Analysis
The data analysis started with transcribing the individual interviews and reviewing the PSMTs' responses to the three questions. A constant comparative method (Glaser & Strauss, 1967) was employed to construct a coding scheme as follows: (1) the author independently reviewed all the responses and created an initial coding scheme depending upon the related literature and the related definitions mentioned previously; (2) two graduate students, who are familiar with the literature related to reasoning-and-proving, and the author compared the descriptions of the codes in the preliminary coding scheme with the sample of responses to see whether the features of the responses captured by the codes or indicated any mismatches with the codes that could lead to the generation of new codes or adjustment of existing codes. After finalizing the coding scheme as displayed in Table 2 by collaborating with the coders, coding of the data process started and occurred in two steps. In the first step, the PSMTs' responses to the tasks were coded in two categories as Correct-if the PSMTs were able to realize that the tasks were not true for all cases-and Incorrect-if the PSMTs thought that the tasks were true for all cases. Later, how the PSMTs' attempted to support their decisions was coded from a mathematical point of view. If the PSMTs correctly identified that the tasks were not true (Correct Category), then they were expected to provide an example that succeed in refuting the statement, which was coded as Valid Counterexample. If the PSMTs provided an example that failed to refute the statement, then their responses were coded as Invalid Counterexample. No /Unfinished Counterexample category, on the other hand, included all the responses that had no counterexample constructed or unfinished attempts to provide a valid counterexample.
If the PSMTs thought that the tasks were true for all cases (Incorrect Category), they were then expected to provide an argument to support their decisions. Given that the presented tasks included the statements that were only true for some cases but not for all, the PSMTs were expected to construct a justification rather than a mathematical proof. Analyzing their constructed justifications, if the PSMTs provided an invalid general argument that used a sequence of assertions that refer to all cases in the domain of the statement but one or more of these assumptions used in the argument were built upon an incorrect mathematical inference, their responses were coded as Incorrect Inference. Incorrect Inference arguments, therefore, fail to meet the criterion of employing true sets of statements-the first criterion of the definition implemented in the study. The term "inference" was used instead of "conjecture" intentionally for two reasons: (1) The definitions of conjecture mentioned previously highlighted that conjecturing involves reasoning about mathematical relationships by observing, organizing cases, and then formulating the relationship that thought to hold true for all possible cases. Therefore, conjecturing involves non-arbitrary hypothesis. Since conjecturing was not one of the purposes of the study, the PSMTs' processes of observing, organizing cases and then formulating relationships were not evident in the study. Instead, it rather seemed like the PSMTs were formulating an invalid mathematical relationship based on their insights or previous knowledge given that there were no signs of investigating and/or organizing different cases in their responses. (2) The definition of conjecturing included an element of doubt in its nature. However, the PSMTs in this study did not mention any signs of doubt in their arguments. Rather, they seemed very confident in their inferences so that they did not attempt to further justify them. In addition to Incorrect Inference category, if the PSMTs provided an argument that purported to show the truth of the mathematical statement by validating the statement in a proper subset of all possible cases covered by the statement, their responses were coded as Empirical Argument. Thus, Empirical Arguments fail to meet the criterion of modes of argumentation by employing an invalid form of reasoning. No/Unfinished Argument included all the responses that were incomplete or no response at all. All the irrelevant arguments that were constructed to justify the tasks were also coded in No/Unfinished Argument category.
In the second step of the analysis process, the PSMTs' responses to the presented student arguments were coded in two categories as Proof-if the PSMTs thought that the student arguments could be classified as mathematical proofs-and Not Proof-if the PSMTs thought that the student arguments could not be classified as mathematical proofs. In the Proof category, the PSMTs responses were coded in one of the following three categories: Valid/ Mathematical, Appropriate for Student Level, and Other Reasons. The responses that considered the modes of reasoning used in the student arguments as valid and/or mathematical were coded as Valid/Mathematical. If the responses highlighted that the employed modes of reasoning or modes of representation was appropriate for middle grade standards and students, then these responses were coded in Appropriate for Student Levels. All the other responses that did not mention employed modes of reasoning as valid or connected the argument to students' levels of thinking were coded in Other Reasons category. In Not Proof category, the PSMTs responses were either coded as Not General or Invalid/Not Mathematical. Not General category included all the responses that highlighted the fact that the arguments did not guarantee the truth of the assertion for all cases in the domain of the statements. Invalid/Not Mathematical category, on the other hand, included all the responses that mentioned the limitation of the arguments as employing an invalid method for proving.
Given that the PSMTs were asked to evaluate the student arguments presented and to state their reasons -not restricted to provide only one reason-for their evaluations, the PSMTs sometimes provided more than one reason. In that case, the first reason that was stated by the PSMTs was accepted as their primary reason and were coded. Focusing only on the PSMTs' primary reasons while evaluating the presented student arguments as described above consisted of two reasons as follows: (1) reflecting the PSMTs' primary reasons since they thought to be important and should be more elaborated and (2) ensuring that the paper being more concise by displaying important piece of the data instead of all the data collected. Two graduate students coded a random sample of 20% of the PSMTs' responses. The coders reached an agreement on 85% of these codes, and all disagreements were resolved through discussion. In the results section next, the PSMTs' responses that belonged to each category of the coding scheme will be displayed and described.
RESULTS
This section will be organized around the two research questions. First, the results related to the PSMTs' ability to verify given the statements to be true or false will be presented. Later, the results about in what ways PSMTs judge the validity of presented student arguments will be shared.
Verifying Mathematical Tasks and Justifying Decisions
The results of the PSMTs verification of mathematical statements and then providing justifications to support their decisions are displayed cumulatively in Table 3. As can be seen in Table 3 for Task 1, most of the participants (36 PSMTs) failed to recognize that the task was not true for all cases. Only 14 PSMTs were able to recognize that the task would not always hold true and coded in correct category. 13 PSMTs, who recognized that the task was not always true, were also able to provide a valid counterexample that refuted the task while 1 PSMT failed to provide a valid counterexample. 29 PSMTs believed that to increase the perimeter of a rectangle, at least one side should be increased in length while the other side should be kept the same (or increased as well). Thus, they argued that the area of the rectangle should increase as a result. These responses were coded as Incorrect Inference for Task 1. A sample of these responses is displayed in Figure 1. The PSMT argued that increasing the perimeter of a rectangle by a certain amount-k-requires increasing one of the sides of the rectangle by k/2 while keeping the other side the same. Although the numbers of the PSMTs were not as high, 7 PSMTs argued that the task held true and they provided an empirical argument to support their decision for Task 1. A sample of these responses is displayed in Figure 2. The PSMT drew a general conclusion based on a particular case-rectangles with the side lengths of 4 by 6 and 6 by 8.
13 PSMTs were not only able to recognize that the statement was not always true, but they were also able to provide a valid counterexample (see Figure 3). For Task 2, while 30 PSMTs were able to recognize that the task was not true for all cases (Correct Category), 20 PSMTs failed to recognize that the task was only true for a subset of the classes covered by the statement (Incorrect Category). Out of these 30 PSMTs, who correctly evaluated the task, 14 PSMTs were able to provide a valid counterexample while 14 PSMTs provided no counterexample at all or failed to complete their counterexamples. For instance, the PSMT in Figure 4 attempted to construct a general counterexample by selecting four random points on the side lengths of the rectangle and assigning different variables to the side lengths to signify the randomness. Later, he attempted to calculate the areas of the polygons to justify that the statement would not hold true. However, the PSMT failed to calculate the areas of the polygons formed inside of the rectangle correctly due to lengthy calculations. The question mark that he put by the end of his response may demonstrate that he got stuck by the lengthy calculation and failed to complete his counterexample. Only 4 PSMTs provided arguments that were coded as Incorrect Inference for Task 2. For instance, the PSMT in Figure 5 made a logical flaw by arguing that in a right trapezoid, the area of the triangle formed by connecting two vertices with a vertex taken on the right side of the trapezoid is equal to the sum of the areas of the other two triangles. However, this assumption would only be true if the vertex taken on the right side of the trapezoid was the midpoint. Although the PSMT attempted to justify the task for all possible cases covered by the statement, his argument was built upon an incorrect inference and did not provide further justification for why it might be the case. The numbers of empirical arguments constructed for Task 2 is higher than the other tasks. The PSMTs, who constructed empirical arguments for Task 2, only considered choosing the midpoints of the sides of a rectangle as opposed to considering any random points. As can be seen in Figure 6, the PSMT picked the midpoints of a rectangle and calculated the area of the rectangle and the quadrilateral formed by the midpoints to justify that the task was true. 2 PSMTs believed that the statement was true; however, they could not complete their arguments to justify their decisions. In the excerpt below, One PSMT-Mustafa 1 -attempted to construct an argument to justify the statement; however, he failed to complete his argument.
Mustafa: I know that this statement is true. But, I could not prove it. Well, if I picked the midpoints, I could do it easily. Because, I could show that the triangles were the same. But, I did not know how to do it otherwise. Like, if I did not pick the midpoints, I could not do it. Let's choose arbitrary points (Labelling the side lengths and angles in Figure 7). But these do not intersect perpendicularly (Referring to the diagonals of the inner quadrilateral). I am trying to show that the triangles are congruent. There are eight triangles in total and four of them formed the quadrilateral. But I do not know how to do that. I know they are the same but do not know how to show it.
Figure 7. Mustafa's argument that was coded as Unfinished Argument for Task 2
Mustafa believed that the statement was true. Seeing the statement held true for a specific caseconnecting the midpoints of the sides of the rectangle-convinced him that the statement would hold true for all cases. When asked to justify the statement, he indeed attempted to construct an argument for a more general case. However, he failed to complete his argument since he did not know how he could show that the triangles had the same area. Although he could not proceed with how to justify that the triangles were congruent, he still was convinced that the statement held true. Therefore, Mustafa's response was coded as an unfinished argument since his attempt to construct an argument to justify that the statement was true was not completed. The responses in Figure 4 and in Figure 7 could be interpreted similarly since both responses attempted to investigate the case in which arbitrary points were selected as opposed to the mid points of the rectangle. However, the purposes of constructing these examples differed since one was constructed to refute the statement (see Figure 4) while the other one was constructed to justify the statement (see Figure 7).
For Task 3, on the other hand, most of the PSMTs (42 PSMTs) were able to evaluate the task correctly as opposed to the other two tasks. Out of these 42 PSMTs, 39 of them were also able to construct a valid counterexample while 3 PSMTs failed to provide a valid counterexample that refuted the statement. The majority of the PSMTs constructed a trapezoid as a counterexample for Task 3. As can be seen in Figure 8, the PSMT not only refuted the task by providing a valid counterexample, but the PSMT also demonstrated why the example contradicted to the statement by showing that the areas of the triangles formed by the two diagonals were not the same. Thus, it was coded as a valid counterexample for the task.
Figure 8. An argument that was coded as Valid Counterexample for Task 3
Although many of the PSMTs successfully constructed valid counterexamples, 3 PSMTs failed to do so. As can be seen in Figure 9, the PSMT provided four examples, one of which was a kite since she used same notations on the adjacent sides to show that they were congruent. Then, she circled the kite and labelled the areas of the triangles formed by one of the diagonals as A and B to show that they were not equal. Although the PSMT evaluated the task correctly, she provided an invalid counterexample since the example did not contradict the statement. The PSMT only focused on one diagonal and ignored the other one, which indeed cut the area of the kite in equal halves.
Figure 9. An argument that was coded as Invalid Counterexample for Task 3
When looking at the results cumulatively, it was seen that most of the participants struggled to evaluate the presented tasks correctly and were coded in Incorrect Category. Among the PSMTs who failed to evaluate the presented tasks correctly, most of them attempted to justify the statement for all cases covered by the domain of the tasks; however, their arguments built upon a logical flaw-an incorrect inference drawn from particular conditions. Although the number of the arguments that were coded as empirical arguments were not as high, those types of invalid ways of justifications still existed among the participants. Thus, these results could be interpreted that the PSMTs attempted to construct general arguments to justify the statements for the domain of the statements more than they constructed arguments that purported to show the truth of the statement by validating it in a proper subset of all possible cases covered by the statement. Yet, the PSMTs struggled with employing true sets of accepted statements in their arguments. Among the PSMTs who correctly recognize that the statements were partially true for some specific conditions, most of them were also able to construct a valid counterexample. However, some of the participants failed to construct a valid counterexample or complete a counterexample at all. How did the PSMTs evaluate the presented empirical arguments will be discussed next.
Evaluating Student Arguments Provided for the Statements
The results regarding to the PSMTs evaluating student arguments are displayed cumulatively in Table 4. PSMTs believed that the presented argument could not be classified as a Proof and 6 PSMTs argued that the presented argument could be considered as a Proof. Among these PSMTs who believed that the presented student arguments could not be considered as mathematical proofs, most of them argued that the arguments were Not General.
The PSMT-Tugce-, for instance, argued that the presented student work for Task 1 did not implement a valid proving method as the primary reason. Tugce stated: "To prove a statement, she (Referring to the hypothetical student in the task) should either use a direct proving method or should prove by induction. If a statement is wrong, then she should provide a counterexample. What Nermin did is not a proof since providing two examples that show that the statement is true does not fall into any of the proving methods. Her [Nermin's] argument should have shown that the statement was true for all rectangles." Tugce argued that the employed mode of reasoning in the presented argument was not valid since it did not fall into any of the valid proving methods that she mentioned in her response. Therefore, Tugce's response was coded as Invalid/Not Mathematical.
Dilara, on the other hand, argued that the student argument could not be considered as a proof since it only showed that the statement was true for a proper subset of all the cases covered by the statement by stating that the student only tried some numbers. She stated: " ….She only tried some numbers. The fact that these two examples showed that the statement was true does not mean that it would always hold true for all examples. She only could have proved a false statement with this method. Because when she found one wrong example, we could understand that this statement was not true." Dilara mentioned the generic aspect of the argument as questionable and argued that the presented argument failed to provide conclusive evidence to justify the statement for all examples.
It could be argued that both Tugce and Dilara used not being general and not implementing a valid way of proving in their responses. Tugce stated that "her argument (Referring to the hypothetical student in Task 1) should have shown that the statement was true for all rectangles" by the end of her response, which indeed addressed the limitation of the student argument that failed to provide conclusive evidence for the truth of the statement for all cases. Thus, she also questioned the generality of the presented student argument along with implemented proving methods in the argument. Similarly, Dilara stated that "She (Referring to the hypothetical student in Task 1) only could have proved a false statement with this method" to describe the limitation of the employed method of reasoning. However, as described above in the data analysis section, the reasons first stated by the PSMTs were accepted as their primary choices and coded in the case of the PSMTs provided more than just one reason while evaluating the presented student arguments.
CONCLUSION AND DISCUSSIONS
The results of the study will be discussed under the light of current studies in this section. This section is organized around the two research questions that guided the study.
Verifying Presented Statements and Constructing a Justification
There has been a strong emphasis in various policy documents for the inclusion of constructing and critiquing mathematical arguments in all grades (MEB, 2018;NGA/CCSSO, 2010;. However, verifying the truth or falsity of statements accurately is a complex process as individuals should have adequate understandings of mathematical concepts and be able to apply such knowledge flexibly (Buchbinder & McCrone, 2020). Before constructing an argument for a true statement or generating a counterexample for a false one, students and teachers need to be able to accurately decide the truth or falsity of a given proposition. Research investigating undergraduate students' and mathematics teachers' ability to evaluate a given proposition suggest that many of them have difficulty verifying the truth and falsehood of given statements due to their inadequate understanding of the mathematical content (Riley, 2003;Zeybek Simsek, 2020). For instance, Riley (2003) found that roughly 57% of 23 prospective secondary mathematics teachers believed that a false statement in geometry was true. The results of this study documented that the PSMTs struggled with deciding whether the presented three statements held true. The results also demonstrated that the PSMTs struggled with verifying Task 1 the most. When analyzed Task 1 separately, 36 PSMTs believed that the statement held true; while only 14 PSMTs verified the falsehood of the statement correctly. Given that teachers need to critically evaluate and determine what is entailed in student-generated conjectures (Stylianides, 2007), the results of this study demonstrated that it is not an easy feat for preservice teachers.
Zeybek (2017) argued that refuting conjectures and justifying invalid claims is a complex process that goes beyond deductive proof and requires the development of rationality and a specific state of knowledge. Given that counterexamples have power to illustrate why a mathematical statement is false and to refute a mathematical statement only requires a single counterexample (Kinzel & Cavey, 2017), counterexamples play such a significant role in comprehending mathematics and axiomatic system of it. Yet, studies demonstrated that students and teachers struggled to provide a valid counterexample (Zaslavsky & Peled, 1996;Zeybek, 2017) The results of this study also demonstrated that the PSMTs struggled with constructing valid counterexamples (or counterexamples at all) to refute the statements. The possible sources of difficulty in generating such examples were presumed to include the following: incomplete knowledge, inability to process existing knowledge, misconceptions, and insufficient logical knowledge (Zaslavsky & Peled, 1996). The PSMTs, who struggled to construct a counterexample or constructed counterexamples that were coded as invalid, also demonstrated limited knowledge of the contents that underpinned the statements.
For instance, for Task 1, most of the PSMTs believed that there was a relationship between area and perimeter of a rectangle so that they did not even attempt to test the method or to generate examples. Further, 14 PSMTs struggled to provide an example that satisfied the condition for a counterexample for Task 2, which might be resulted from their limited subject matter knowledge. Although the PSMTs struggled with constructing counterexamples for Task 1 and Task 2, for Task 3, on the other hand, it was much easier for them to construct a valid counterexample. Zazkis et al. (2008) argued that the process of constructing counterexamples depends on the extent to which they are in accord with individuals' example spaces. In other words, the process of constructing counterexamples while refuting false claims should be conceptualized with individual's example spaces. Thus, the fact that the PSMTs performed better at constructing counterexamples for Task 3 compared to other two tasks could then be interpreted as a result of the PSMTs' possible example spaces regarding to the underpinning concepts of the statements.
From a mathematical standpoint, the main difference between empirical arguments and proofs lies in the modes of argumentation (Stylianides, 2007, p. 291). Empirical arguments provide inconclusive evidence by verifying its truth only for a proper subset of all the cases covered by the generalization, whereas proofs provide conclusive evidence truth by treating appropriately all cases covered by the generalization. Stylianides and Stylianides (2009) highlighted the importance of realizing this limitation of empirical arguments as methods for validating mathematical generalizations. Yet, the results demonstrated that empirical arguments were pervasive among the participants. Students at all levels have difficulty recognizing universally and existentially quantified statements (especially when the quantifier is implicit) and struggle to understand that a universally quantified statement must be proved for all elements in the domain, and fail to recognize the limitation of relying on supportive examples for proving universal statements (Buchbinder & Zaslavsky, 2019). The PSMTs who constructed empirical arguments in this study indeed failed to recognize the fact that empirical arguments provide inconclusive evidence so that they could not be generalized for all cases covered by the statements.
Although empirical arguments for verifying the statements to be true failed to satisfy the generalization aspect of proofs, the arguments coded as incorrect inference satisfied the generalization aspect of mathematical proof. Yet, they were built upon an incorrect inference, so that they failed to implement true sets of statements. These types of arguments were common among the participants. This finding indeed demonstrated that the PSMTs who participated in this study struggled with employing true sets of statements in their arguments more than employing valid ways of reasoning. This shows that the PSMTs need not only an understanding of what counts as valid argument, but also an adequate knowledge of choosing accepted definitions, axioms, and facts. Various properties and postulates that underlie in an argument made in the proof are usually not spelled out, but rather are assumed to have been already learned and internalized by students (Schleppegrell, 2007;Weiss & Herbst, 2015). Therefore, it might not be surprising to see students have difficulties interpreting or using theorems on their own. Teachers should be able to evaluate the assumptions made during argument construction as well as to check what implicit or explicit warrants support the argument (Dawkins & Weber, 2017). To do so, teachers first should be cognizant about what they used in their arguments themselves. However, most of the PSMTs, in this study, failed to elaborate upon what principles are being used to derive new mathematical inferences and to warrant for the inferences used in their arguments. Given the difficulties that these PSMTs experience, this knowledge of proof entailments seems particularly critical.
Evaluating Presented Student Arguments
Researchers argued that student's poor argument constructions can be misleading indicators of what they think would meet the standard of proof, because they may be well aware of the limitations of their arguments but unable to produce better ones (e.g., Stylianides & Stylianides, 2009;Zeybek Simsek, 2020). Thus, Stylianides and Stylianides (2009) claimed that employing "construction-evaluation" activities together could better illuminate learners' understanding of proofs. Given that students do appear to be better at choosing correct proofs than constructing their own (e.g., Stylianides, Bieda, & Morselli, 2016;Zeybek Simsek, 2020), asking evaluating researcher generated arguments or constructing proofs separately, therefore, might draw different pictures about students' understanding of proofs. It is perhaps because generating a sequence of steps and conceptualizing someone else's proof demand different cognitive skills (Stylianides & Stylianides, 2009) and could not be captured by employing construction or evaluation activities separately. The results of this study showed that the PSMTs were more successful at evaluating arguments than verifying the falsehood of the statements and then justifying their decisions (see Table 4 for details).
Given that the majority of the PSMTs were more successful at evaluating student arguments than verifying the truth of the statements and then constructing their own arguments, it could be argued that it was easier for them to recognize the limitation of the presented arguments. Among the PSMTs who successfully recognized the limitation of the arguments and classified them as not proofs, most of them argued that the presented arguments failed to provide conclusive evidence for the truth of the statement for all cases. Thus, the PSMTs questioned the generality aspect of the presented student arguments. Stylianides and Stylianides (2009) argued that recognizing the difference between a mathematical proof and empirical argument constitutes such an essential goal for mathematics teachers. The high number of the PSMTs, who seemed to recognize the limitation of empirical arguments as methods for validating mathematical statements and then correctly evaluated presented student arguments as not proofs, could, then, be seen as a hopeful picture since they will soon be expected to evaluate students' arguments in their classrooms (Stylianides, 2007).
Researchers argue that employing tasks that do not always hold true during instruction is important for developing an understanding of the role of mathematical proofs and gaining an appreciation for mathematical proofs (Ball, Hoyles, Jahnke, & Movshovitz-Hadar, 2002;Stylianides & Stylianides, 2009). For instance, Harel and Sowder (2007) argued that using example-based reasoning should be cautioned due to its tentative nature; therefore, employing patterns that do not always hold true could be helpful to get students recognize the limitation of using empirical-based arguments. The high number of the PSMTs, who seemed to recognize the limitation of empirical arguments as methods for validating mathematical statements might therefore be a result of employing tasks that do not always hold true.
According to the results of the study, it could be argued that once the PSMTs recognized that the tasks would not always true, it was likely for them to argue that the presented student arguments would not constitute a valid way to prove. However, it should also be noted here that some participants, who believed that the tasks would always hold true, still evaluated the presented student arguments as not a proof. For instance, the majority of the participants failed to recognize that Task 1 would not always hold true. Yet, they still argued that the presented student argument for Task 1 would not constitute a proof (see Table 3 and Table 4 for details). Thus, it would be misleading to conclude that the PSMTs should verify the tasks correctly before evaluating presented student arguments properly. Employing the tasks that do not always hold true could be an essential implication of this study as will be addressed next.
Implications of the Study
There are clearly high pedagogical demands placed on teachers who strive to engage their students in proving at all grade levels as highlighted by current standards. Research show that creating and effectively managing these learning opportunities for students might be challenging and complicated (e.g., Stylianides, 2007). The results of this study demonstrated that the PSMTs struggle evaluating presented mathematical tasks as well as constructing arguments to justify their decisions regarding to the validity of the tasks. All these results suggest nothing but the need for pre-service teachers to gain more experiences with constructing and evaluating mathematical arguments. Possible ways to meet this suggestion of helping pre-service teachers to gain more experience with constructing or evaluating mathematical arguments will be explored next.
Research has demonstrated that various properties and postulates that underlie in an argument made in the proof are not spelled out, but rather are assumed to have been already learned and internalized by students (Schleppegrell, 2007;Weiss & Herbst, 2015). As a result, students have difficulties interpreting, or using theorems on their own (Zeybek Simsek, 2020). The number of the PSMTs who attempted to construct a general argument which failed to employ true sets of statements (Incorrect Inference Category) emphasize the need for spelling out underlying properties and postulates in textbooks or in classrooms. Although the arguments that the PSMTs constructed in this category (Incorrect Inference) captured adequately the generality of the tasks they aimed to justify, the arguments failed to capture the use of mathematical resources (e.g., relevant definitions, properties) that are known or accessible to the PSMTs properly. Thus, the PSMTs' reliance on intuitive reasoning highlights the need for making mathematical resources more accessible to them. Alcock (2004) argued that using examples would be such a useful approach to develop a 'guts feeling' regarding the validity of mathematical conjectures. However, it should be cautioned to use examplebased reasoning (aka 'empirical proof scheme') due to its tentative nature and logical limitations in terms of generalization (Harel & Sowder, 2007). Researchers suggest that employing patterns that do not hold true for an infinite set could be helpful to get students recognize the limitation of using empirical arguments as a valid way of proving (Ball, Hoyles, Jahnke, & Movshovitz-Hadar, 2002;Stylianides & Stylianides, 2009). This study provided a support by showing that the PSMTs who recognized the tasks would not hold true for all cases also evaluated presented arguments as not proofs by highlighting this limitation of empirical arguments. This could be interpreted as that the statements that are not always true could be an essential instructional tool to help learners (i.e., pre-service teachers) begin to recognize the limitations of empirical arguments as methods for validating mathematical generalizations. Furthermore, students are often expected to prove results that seem obvious to them (Dawkins & Weber, 2017;Stylianides & Stylianides, 2009). Consequently, proof is likely to remain meaningless and purposeless in students' eyes. Thus, the element of uncertainty seems important to develop an appreciation of the need to prove. The statements that do not hold true for all cases such as the ones used in this study might therefore be a possible venue for highlighting an intellectual need to learn about more secure validation methods.
Funding: Author received no financial support for the research and/or authorship of this article.
Declaration of interest:
Author declared no competing interest.
Data availability: Data generated or analysed during this study are available from the author on request. | 10,921.4 | 2021-03-17T00:00:00.000 | [
"Mathematics",
"Education"
] |
circ2GO: A Database Linking Circular RNAs to Gene Function
Simple Summary Ribonucleic acids (RNAs) are generally linear chains of nucleotides which function in many cellular processes, best known in protein biosynthesis. In the last decade, circular RNAs have been discovered which are circularized after their synthesis and differ in important features from linear RNAs. These circular RNAs have meanwhile been implicated in important cellular processes in health and disease. Here, we present a comprehensive database, circ2GO, compiling and analyzing circular RNAs found in lung cancer cell lines providing the data in tables as well as visualizing it in transcript maps and in heatmaps. Importantly, we also provide easy-to-use online tools to find circular forms of genes associated with specific molecular functions, biological processes or cellular components or predict their targeted microRNAs. This resource will enable researchers to rapidly identify circular RNAs relevant for their specific research question. Abstract Circular RNAs (circRNAs) play critical roles in a broad spectrum of physiological and pathological processes, including cancer. Here, we provide a comprehensive database—circ2GO—systematically linking circRNAs to the functions and processes of their linear counterparts. circ2GO contains 148,811 circular human RNAs originating from 12,251 genes, which we derived from deep transcriptomics after rRNA depletion in a panel of 60 lung cancer and non-transformed cell lines. The broad circRNA expression dataset is mapped to all isoforms of the respective gene. The data are visualized in transcript maps and in heatmaps, to intuitively display a comprehensive portrait for the abundance of circRNAs across transcripts and cell lines. By integrating gene ontology (GO) information for all genes in our dataset, circ2GO builds a connection between circRNAs and their host genes’ biological functions and molecular mechanisms. Additionally, circ2GO offers target predictions for circRNA—microRNA (miRNA) pairs for 25,166 highly abundant circRNAs from 6578 genes and 897 high-confidence human miRNAs. Visualization, user-friendliness, intuitive and advanced forward and reverse search options, batch processing and download options make circ2GO a comprehensive source for circRNA information to build hypotheses on their function, processes, and miRNA targets.
Introduction
Circular RNAs (circRNAs) constitute a class of single-stranded RNA with a covalent bond of the 3 -end to the 5 -end by back-splicing. The covalently closed continuous loop makes circRNAs resistant against degradation by exonucleases, and hence they have a longer half-life than their linear counterparts [1]. Growing evidence shows that circRNAs are widely expressed in vertebrate cells and show tissue-specific and cell type-specific expression patterns. circRNA biogenesis is believed to be regulated by signals in cis, as well as factors acting in trans to govern the context-dependent efficiency of circularization.
Interestingly, recent studies found that some circRNAs encode functional peptides [9][10][11][12]. However, even though thousands of circRNAs have now been discovered, the underlying mechanisms regulating their biogenesis, function, degradation, and cellular localization remains unclear in most cases.
At the cellular level, circRNAs are important regulators in many cellular processes, such as cell signaling [13], embryonic development [14], cellular senescence [15], and control of the cell cycle [16]. They also play critical roles in the occurrence and development of various types of diseases [17], including cardiovascular diseases (e.g., atherosclerotic vascular disease risk [18,19]), neurological disorders (e.g., Alzheimer's disease [20,21]), osteoarthritis [22], diabetes [23] and, most importantly, in cancer [24,25]. In malignant pathogenesis, circRNAs contribute to distinct human tumor entities including ovarian, prostate, liver, breast and lung cancers [26]. Lung cancer is one of the most fatal malignant diseases in the world. According to the Global Cancer Observatory (GCO) in 2018, 11.6% of total cancer cases (2.1 million) were lung cancer, and 18.4% of total cancer-related deaths (1.8 million) were caused by lung cancers [27]. Lung cancer is divided into small cell lung cancer (SCLC) (15%) and non-small cell lung cancer (NSCLC) (85%), with 40% of NSCLCs being adenocarcinomas [28]. Increasing evidence links circRNAs to many processes in the development of lung cancer [29][30][31]. However, more detailed information about circRNA expression profiles, and pipelines for generating and validating hypotheses about their functions are required to deepen our understanding about the importance and molecular mechanism of circRNAs in cancers.
For the accurate and transcriptome-wide identification of circRNAs, deep RNA sequencing (RNA-seq) approaches which comprehensively cover the circRNA spectrum need to be employed. Since circRNAs are not poly-adenylated (poly-A), they are often strongly depleted from transcriptome sequences based on poly-A enrichment. In contrast, preparing sequencing libraries with rRNA depletion retains circRNAs for RNA-seq in the next step. Hence, we sequenced rRNA-depleted RNAs from 60 lung cell lines (57 lung cancer cell lines and 3 non-transformed lung cell lines) in replicates generating 3.8 billion reads in total, including 2.8 million backsplicing reads quantifying 148,811 circRNAs derived from 12,251 genes [29].
Here, we created the online database circ2GO to provide easy access to this large dataset. The integration of a broad spectrum of important orthogonal data and unique search and prediction options will foster and enhance circRNA research by providing hypotheses for pathways and miRNA targets linked to a circRNA. Mapping circRNAs to the genes in relation to all splice isoforms provides an important overview on how circularization can impact gene function and will also raise awareness for the many different circRNAs that can be derived from the same gene as well as their connection to different linear isoforms.
Data Collection and Database Content
The circRNA dataset was obtained by the sequencing of rRNA-depleted RNAs from 60 lung cell lines (consisting of 50 adenocarcinoma cell lines, 7 other NSCLC cell lines and 3 non-transformed cell lines) with a total of 175 replicates. A total of 148,811 circular RNAs were detected from 12,251 genes. Each entry in the database contains a circRNA name, position, transcript of origin, expression level, gene symbol, GO annotations, and miRNAs with predicted binding sites within the circRNA. The design of the circ2GO website is intuitive and user-friendly. Generally, users can search for, obtain, and visualize information for individual circRNAs, or all circRNAs derived from a specific gene. They can search for all circRNAs derived from genes which are linked to a specified molecular function, biological process, or cellular component (GO). Lastly, they can also search for all circRNAs harboring a binding site for a specified miRNA, or vice versa. These comprehensive search options, visualization features for transcript maps and expression heatmaps, batch analyses, and download options present valuable information to the user ( Figure 1).
Cancers 2020, 12, x FOR PEER REVIEW 3 of 11 function, biological process, or cellular component (GO). Lastly, they can also search for all circRNAs harboring a binding site for a specified miRNA, or vice versa. These comprehensive search options, visualization features for transcript maps and expression heatmaps, batch analyses, and download options present valuable information to the user ( Figure 1).
circRNA Transcript Map Visualization
Uniquely, circ2GO includes the transcript map as a visualization module that depicts the position and abundance of all circRNAs derived from one gene and its relation to all known transcripts. The circRNA transcript map panel allows users to gain more detailed information on the circRNA position in relation to all transcripts of a queried gene, as well as their absolute abundance ( Figure 2a). The map provides a precise alignment of all circRNAs and transcripts at the exon level ( Figure 2b). Vertical green and red lines on the map mark the start and end of the exons, respectively. A bar diagram on the right depicts the circRNA expression profiles, allowing a rapid assessment of the relative abundance of the different circRNAs in this gene locus. Apart from the circRNA visualization, an information card displays the gene ID, gene name, gene aliases, description of the gene and genomic location. Moreover, a list of GO terms for the queried gene is provided, giving an overview of linked molecular functions, biological processes, or cellular components. Additionally, by clicking on a circRNA ID on the circRNA transcript map, a heatmap and an additional scatter plot for the selected circRNA is plotted in the circRNA heatmap panel, illustrating its expression throughout the 60 cell line panel.
circRNA Transcript Map Visualization
Uniquely, circ2GO includes the transcript map as a visualization module that depicts the position and abundance of all circRNAs derived from one gene and its relation to all known transcripts. The circRNA transcript map panel allows users to gain more detailed information on the circRNA position in relation to all transcripts of a queried gene, as well as their absolute abundance ( Figure 2a). The map provides a precise alignment of all circRNAs and transcripts at the exon level ( Figure 2b). Vertical green and red lines on the map mark the start and end of the exons, respectively. A bar diagram on the right depicts the circRNA expression profiles, allowing a rapid assessment of the relative abundance of the different circRNAs in this gene locus. Apart from the circRNA visualization, an information card displays the gene ID, gene name, gene aliases, description of the gene and genomic location. Moreover, a list of GO terms for the queried gene is provided, giving an overview of linked molecular functions, biological processes, or cellular components. Additionally, by clicking on a circRNA ID on the circRNA transcript map, a heatmap and an additional scatter plot for the selected circRNA is plotted in the circRNA heatmap panel, illustrating its expression throughout the 60 cell line panel.
circRNA Heatmap Visualization
The circRNA heatmap depicts the circRNA read counts in each cell line. The expression profile for the gene of interest can be viewed either as a heatmap (multiple circRNAs included) (Figure 3a), or as a classical scatter plot (only one circRNA included) (Figure 3b). Both plots display the same order of cell lines, and the order of circRNAs matches the order in the transcript map. Heatmap representation is generated through clustering with a complete linkage algorithm. The scale bar for the heatmap shows the abundance level of the circRNAs. Hovering over the heatmap gives the read count value (normalized to library size) and the name of the cell line. In addition to displaying the
circRNA Heatmap Visualization
The circRNA heatmap depicts the circRNA read counts in each cell line. The expression profile for the gene of interest can be viewed either as a heatmap (multiple circRNAs included) (Figure 3a), or as a classical scatter plot (only one circRNA included) (Figure 3b). Both plots display the same order of cell lines, and the order of circRNAs matches the order in the transcript map. Heatmap representation is generated through clustering with a complete linkage algorithm. The scale bar for the heatmap shows the abundance level of the circRNAs. Hovering over the heatmap gives the read count value (normalized to library size) and the name of the cell line. In addition to displaying the expression profile in plots, the average circRNA expression for each cell line, as well as the total circRNA counts can be downloaded.
Cancers 2020, 12, x FOR PEER REVIEW 5 of 11 expression profile in plots, the average circRNA expression for each cell line, as well as the total circRNA counts can be downloaded.
Gene Ontology Search
Gene Ontology (GO) is an important bioinformatics project that aims to uniformly define the representation of gene characteristics and gene products in all species. The main uses of GO are retrieving functional profiles of gene sets by performing enrichment analyses, as well as GO term annotation of individual genes in the categories of molecular function, biological process, and cellular component. All GO terms are listed for the gene of interest in the transcript map section.
As a unique feature, circ2GO offers a reverse search, i.e., the option to search for all circRNAs derived from genes involved in a specific molecular function, biological process, or cellular component via the "GO Search" module in circ2GO. Users can find circRNAs by GO terms and download the data with circRNA expression profiles for further functional exploration of circRNAs.
The basic search option can be applied for a GO ID, a complete GO term, or a part of it ( Figure 4a). The advanced search option allows the combination of keywords from GO terms, and then the selection of a specific GO term from the resulting list (GO accession, GO name, GO evidence code, GO domain) (Figure 4b). For both search options, all genes with the same GO term are listed in an interactive table providing the respective gene IDs and the total circRNA expression from this gene. The search results can be downloaded as a csv file. By selecting one gene in the table, a circRNA transcript map and heatmap for all circRNAs of this gene can be obtained.
Gene Ontology Search
Gene Ontology (GO) is an important bioinformatics project that aims to uniformly define the representation of gene characteristics and gene products in all species. The main uses of GO are retrieving functional profiles of gene sets by performing enrichment analyses, as well as GO term annotation of individual genes in the categories of molecular function, biological process, and cellular component. All GO terms are listed for the gene of interest in the transcript map section.
As a unique feature, circ2GO offers a reverse search, i.e., the option to search for all circRNAs derived from genes involved in a specific molecular function, biological process, or cellular component via the "GO Search" module in circ2GO. Users can find circRNAs by GO terms and download the data with circRNA expression profiles for further functional exploration of circRNAs.
The basic search option can be applied for a GO ID, a complete GO term, or a part of it (Figure 4a). The advanced search option allows the combination of keywords from GO terms, and then the selection of a specific GO term from the resulting list (GO accession, GO name, GO evidence code, GO domain) (Figure 4b). For both search options, all genes with the same GO term are listed in an interactive table providing the respective gene IDs and the total circRNA expression from this gene. The search results can be downloaded as a csv file. By selecting one gene in the table, a circRNA transcript map and heatmap for all circRNAs of this gene can be obtained.
circRNA-miRNA Search
MicroRNAs (miRNAs) are small, single-stranded and highly-conserved non-coding RNA molecules, which can bind to target mRNAs and silence their protein expression by mRNA destabilization or translational inhibition [32,33]. Circular RNAs can function as molecular sponges by binding to miRNAs, with the most prominent example being CDR1-AS (CDR1 Antisense RNA) [34]. MiRNA expression levels in tumors may be altered by circRNAs, which implies that miRNA-circRNA networks may be involved in the development of cancer [35][36][37].
Hence, we included a prediction of miRNA binding sites within circRNAs into circ2GO. For this circRNA-miRNA dataset, 25,166 highly abundant circRNAs from 6578 genes were filtered with the threshold of at least 2 reads (read count normalized) in any cell line. 897 high-confidence human miRNAs were downloaded from miRBase (http://www.mirbase.org) [38]. The prediction for circRNA-miRNA binding sites was performed by using TargetScan [39] and miRanda [40].
circRNA-miRNA Search
MicroRNAs (miRNAs) are small, single-stranded and highly-conserved non-coding RNA molecules, which can bind to target mRNAs and silence their protein expression by mRNA destabilization or translational inhibition [32,33]. Circular RNAs can function as molecular sponges by binding to miRNAs, with the most prominent example being CDR1-AS (CDR1 Antisense RNA) [34]. MiRNA expression levels in tumors may be altered by circRNAs, which implies that miRNA-circRNA networks may be involved in the development of cancer [35][36][37].
Hence, we included a prediction of miRNA binding sites within circRNAs into circ2GO. For this circRNA-miRNA dataset, 25,166 highly abundant circRNAs from 6578 genes were filtered with the threshold of at least 2 reads (read count normalized) in any cell line. 897 high-confidence human miRNAs were downloaded from miRBase (http://www.mirbase.org) [38]. The prediction for circRNA-miRNA binding sites was performed by using TargetScan [39] and miRanda [40].
The circRNA-miRNA search tab allows the search either for all circRNAs targeting a specified miRNA, miRNA family or miRNA seed region sequence, or for all miRNAs with binding sites in a specified circRNA, or in all circRNAs of a specified gene ( Figure 5). Approximate string matching is supported for all of the aforementioned search criteria, allowing fuzzy inputs. The resulting dataset provides detailed information for circRNA-miRNA pairs, including circRNA expression and binding site counts, allowing the identification of circRNA-miRNA pairs with high circRNA abundance and multiple binding sites. For a selected gene, a circRNA transcript map and circRNA heatmap can be obtained with one click. The search results can be downloaded as a csv file. The circRNA-miRNA search tab allows the search either for all circRNAs targeting a specified miRNA, miRNA family or miRNA seed region sequence, or for all miRNAs with binding sites in a specified circRNA, or in all circRNAs of a specified gene ( Figure 5). Approximate string matching is supported for all of the aforementioned search criteria, allowing fuzzy inputs. The resulting dataset provides detailed information for circRNA-miRNA pairs, including circRNA expression and binding site counts, allowing the identification of circRNA-miRNA pairs with high circRNA abundance and multiple binding sites. For a selected gene, a circRNA transcript map and circRNA heatmap can be obtained with one click. The search results can be downloaded as a csv file.
Data Download
This batch download option enables users to easily transfer data for further individual analysis. The users can download the circRNA dataset completely or partially, by selecting cell line names, genes, or miRNAs by multi-line text input with cell line, circRNA, miRNA, gene symbol or miRNA sequence. Approximate string matching is supported. The circRNA dataset is formatted into two different levels: (1) the gene level which contains an aggregation of all circRNAs of one gene; (2) the backsplice level which contains all individual circRNAs separately. The circRNA-miRNA database is also available for download.
Methods and Software
The circRNA dataset contained within circ2GO was derived from RNA-seq data. Libraries for RNA sequencing were prepared by depleting ribosomal RNA. Raw reads were mapped using
Data Download
This batch download option enables users to easily transfer data for further individual analysis. The users can download the circRNA dataset completely or partially, by selecting cell line names, genes, or miRNAs by multi-line text input with cell line, circRNA, miRNA, gene symbol or miRNA sequence. Approximate string matching is supported. The circRNA dataset is formatted into two different levels: (1) the gene level which contains an aggregation of all circRNAs of one gene; (2) the backsplice level which contains all individual circRNAs separately. The circRNA-miRNA database is also available for download.
Methods and Software
The circRNA dataset contained within circ2GO was derived from RNA-seq data. Libraries for RNA sequencing were prepared by depleting ribosomal RNA. Raw reads were mapped using Tophat2 [44] with parameters set as -a 6 -m 2 -g 1 -p 16. Unmapped reads were extracted as a new bam file and were then mapped again to the reference genome with the TopHat-Fusion module (included in TopHat2). CIRCexplorer2 [45] was used to process bam files and obtain the list of circRNAs with standard parameters. The circRNA expression level was calculated by the number of reads that were mapped to a backsplice site. All reads were mapped to the Ensembl GRCh38 gene set in the steps above. In total, 148,811 circular RNAs originating from 12,251 genes were detected and quantified.
The GO annotations were downloaded from the Ensembl BioMart [46]. The GO annotation dataset was integrated with our circRNA dataset according to gene ID, with version suffixes removed.
DESeq2 [47] was utilized for circRNA read count normalization across all samples (n = 175). A total of 25,166 highly abundant circRNAs from 6578 genes were filtered with the threshold of at least 2 reads (read count normalized) in any cell line. With the bed file based on the circRNA coordinates and strand, getfasta was used to obtain sequences for the circRNA exons. All exons within the span of the circRNA splice sites were included. Pieces of exonic sequences were concatenated sequentially to generate a complete circRNA sequence. A total of 897 high-confidence human miRNAs were downloaded from miRBase [38]. miRNA-circRNA interactions were predicted by miRanda [48] and TargetScan (Release 7.2) [39], respectively.
circ2GO was implemented using HTML and in R language (v3.6.0) [49] with shiny package. The Shiny application was built with RStudio [50]. Part of the interface component consists of web pages that were designed and implemented in HTML/CSS. The code is available on GitHub at https://github.com/airbox11/circ2GO.
Availability
The circ2GO database is freely and without registration available at https://circ2GO.dkfz.de.
Conclusions
The functions of circRNAs are gaining considerable interest across many areas of life sciences and have become a key focus in cancer research. To date, thousands of circRNAs have been detected in various species and tissues. While several functions have been proposed for these circRNAs, our understanding of their precise biological roles and significance is still limited for the vast majority of circRNAs. With circ2GO, we present a comprehensive database for human circRNAs, including their expression in a broad cell line panel, their associated GO terms regarding molecular functions, biological processes, and cellular components, as well as comprised miRNA binding sites predicted by two independent algorithms. Visualizations in transcript maps and heatmaps, advanced forward and reverse search options, batch search and download options, combined with its intuitive and easy use will make circ2GO a valuable tool for circRNA research.
We imagine that the most widespread applications of circ2GO will be: (1) the comparison of all circRNAs for a given gene or transcript, including their expression levels; (2) the search for cell lines with a particularly high or low expression of a specific circRNA; (3) the search for GO terms associated with a circRNA by virtue of its linear counterpart to form hypotheses about its potential impact on the functions of pathways to be experimentally tested; (4) the search for all circRNAs derived from genes involved in a specific molecular function, biological process, or cellular component of interest; (5) the search for abundant circRNAs harboring binding sites for a particular miRNA of interest, and their prioritization based on their expression and number of binding sites; (6) the search for all binding sites of high confidence within a specific circRNA of interest to form hypotheses about its potential function as ceRNA (competing endogenous RNA) to be experimentally tested.
While the expression data provided in circ2GO are limited to lung-derived cell lines, other functions of circ2GO are not restricted to lung cancer or cancer research in general, but can be applied to other areas of human life sciences. The "GO Search" tool and the "microRNA search" tool can also be applied if the same circRNA has been identified in any other context. While the expression patterns are derived from a broad panel of human lung cell lines, the sequencing depth as well as the rRNA depletion (instead of polyA-enrichment) of our underlying transcriptomic study gives a comprehensive picture of the landscape of human circRNAs. For comparison, circ2GO includes 148,811 distinct circRNAs, while human studies stored in one of the leading circRNA databases, circBase, add up to only 92,375 circRNAs. Hence, circ2GO provides a map of human circRNAs with a large depth. Moreover, the transcript map visualization, the "GO Search" options, and the "microRNA Search" options are fully separate of the underlying expression dataset, and can therefore be universally applied, independent of the user's research area for this large set of circRNAs. | 5,441.8 | 2020-10-01T00:00:00.000 | [
"Biology",
"Computer Science"
] |
An SO(3)$\times$SO(3) invariant solution of $D=11$ supergravity
We construct a new SO(3)$\times$SO(3) invariant non-supersymmetric solution of the bosonic field equations of $D=11$ supergravity from the corresponding stationary point of maximal gauged $N=8$ supergravity by making use of the non-linear uplift formulae for the metric and the 3-form potential. The latter are crucial as this solution appears to be inaccessible to traditional techniques of solving Einstein's field equations, and is arguably the most complicated closed form solution of this type ever found. The solution is also a promising candidate for a stable non-supersymmetric solution of M-theory uplifted from gauged supergravity. The technique that we present here may be applied more generally to uplift other solutions of gauged supergravity.
Introduction
Kaluza-Klein theory plays an important role as an organising framework in supergravity relating higher and lower-dimensional theories to one another as well as providing a tool by which to derive new theories by dimensional reduction. Nevertheless, one is confronted with some challenging issues, such as the question of whether a lower-dimensional theory can be obtained from a reduction of a higher-dimensional one, and if so, whether the reduction is consistent. That is, whether all solutions of the lower-dimensional theory can be mapped onto a subset of the higher-dimensional solutions. How this is done in practice, i.e. how one uplifts solutions to higher dimensions, is yet another level of complication. Indeed, examples of such results are rare and are mainly confined to truncations with relatively simple scalar sectors.
Eleven-dimensional supergravity compactified on a seven-sphere is one example in which progress has been made; the four-dimensional theory associated with this reduction being maximal SO (8) gauged supergravity. Recently, an uplift ansatz has been derived for the seven-dimensional components of the 3-form potential in terms of the (pseudo)scalars of the gauged theory [1,2]. This complements the uplift ansatz for the seven-dimensional components of the metric given in Ref. [3]. Together, these ansätze give a new method for constructing solutions of D = 11 supergravity, and it is the purpose of the present paper to explicitly demonstrate the utility of this new method. Indeed, without the new uplift formula for the internal flux it is basically impossible to construct the solution to be presented in this paper, or to derive any other solutions of this type that are more complicated than those already in the literature (see for example Refs. [4-6, 3, 7]). This is because in all previous examples of solutions corresponding to critical points, the symmetry of the solution reduces the equations of motion to a set of ODEs. In particular, if one obtains the metric via the metric lift ansatz, the equations for the components of the flux field strength are algebraic and usually easy to solve. The analysis becomes even simpler if one has supersymmetry, where the ODEs are first order, as is the case for the G 2 [3] and SU(3)×U(1) [7] solutions.
The ansätze can be applied to obtain a very general class of solutions of D = 11 supergravity. In particular, they facilitate the uplifting of all stationary points to Freund-Rubin compactifications [8] with flux, viz. with the corresponding metric where (x µ , y m ) are coordinates on the four and compact seven-dimensional spacetimes, respectively; The uplift ansätze are derived within the context of the SU(8) invariant reformulation of the D = 11 theory [9], whereby eleven-dimensional fields are decomposed in a 4 + 7 split, such that one can loosely talk of them as having external/internal indices. Note that SU (8) is the local enhanced symmetry obtained in the toroidal reduction of D = 11 supergravity to four dimensions, with associated global group E 7(7) [10]. Importantly, however, no truncation is assumed and the reformulation remains on-shell equivalent to D = 11 supergravity [11]. The SU (8) structures in the reformulation are obtained by an analysis of the D = 11 supersymmetry transformations in such a 4 + 7 split, and by the enlargement of the original SO(7) tangent space symmetry to a full chiral SU (8) symmetry; the R-symmetry of N = 8 supergravity.
The uplift ansätze for the internal metric and flux are derived by comparing the supersymmetry transformations of particular components of the eleven-dimensional fields, namely those with a single "four-dimensional" index: the graviphoton B µ m and A µmn , which contain the internal metric and 3-form potential components, and the supersymmetry transformation of the associated vectors in four dimensions, which are given in terms of the (pseudo)scalar expectation values.
In this paper, we demonstrate the utility of the uplift ansätze by applying them to the only known stable non-supersymmetric solution of the gauged theory [12,13]: the SO(3)×SO(3) invariant stationary point [14]. This yields a new solution of D = 11 supergravity: see equations (2.20, 2.22) and (7.29, 7.30) for the solution in stereographic and ambient coordinates, respectively. This solution, to our knowledge, is the most non-trivial closed form solution of this type ever found (inspection of the explicit formulae in section 5 of this paper will probably immediately convince readers of the correctness of this claim). Indeed, the remarkable efficiency of the uplift formulae is clearly demonstrated by the fact that it is significantly simpler to write down the solution than to verify that it does indeed satisfy the D = 11 equations of motion.
Note that there are many known stable non-supersymmetric compactifications of D = 11 supergravity of the form AdS 4 × M 7 (see e.g. Ref. [15]) or indeed AdS 5 × M 6 [16][17][18], or even purely eleven-dimensional solutions, such as for example, the eleven-dimensional Schwarzschild-Tangherlini solution [19]. However, the solution we construct here is the first such solution, as far as we are aware, uplifted from gauged supergravity. While we cannot comment on the eleven-dimensional stability of the solution, the fact that the compactification is stable [12] in the sense of Breitenlohner-Freedman (BF) [20] is promising. Eleven-dimensional stability would be established by demonstrating that the fluctuations associated with higher Kaluza-Klein states also remain above the BF bound.
The SO(3)×SO(3) invariant stationary point is a distinguished solution of the gauged theory.
Not only is it the only known stable non-supersymmetric solution, but it also has the most negative value of the cosmological constant of all known stable points and several unstable points [13] and is, therefore, likely [21] to be an attractive IR fixed point for many flows in the world-volume theory on M2-branes [22]. One example of an RG flow in which this solution is the IR fixed point is that considered in Ref. [12], where the UV fixed point is given by the maximally symmetric SO (8) invariant stationary point [8,23]. The study of such RG flows is important in so-called top-down holographic applications to condensed matter systems (see e.g. Refs. [24,25]).
The SO(3)×SO(3) invariant solution is an example of a compactification of the form (1.1). Therefore, the uplift ansätze for the metric and internal flux given in Refs. [3,1] suffice. In this case, the eleven-dimensional field equations 1 where R µ ν and R m n denote components of the eleven-dimensional Ricci tensor R M N , • D m denotes a background covariant derivative and • η m 1 ...m 7 is the permutation tensor with respect to the metric • g mn . All seven-dimensional indices in the equations above are raised with g mn , except for • η m 1 ...m 7 , whose indices are raised with • g mn . We parametrise AdS 4 and the seven-sphere such that (1.10) There are three constants in (1.7)-(1.10), namely, m 4 , m 7 and f FR . It is convenient to choose m 7 as the overall scale of the solution, since it is simply related to the coupling constant, g, of the D = 4 theory [26], (1.11) The remaining two constants are determined by the value of the scalar potential, P cr = −P * g 2 , at the stationary point, or, equivalently, the cosmological constant of the solution in four dimensions, (1.12) The value of the f FR parameter can be obtained from the uplift formulae in [26,27] or the uplift ansatz for the internal components of the 6-form dual [28,29]. In particular, it has been conjectured that the following relation should hold for any stationary point [27] However, a general proof of (1.13) beyond explicit examples remains an open problem. It is straightforward to verify that for vanishing scalar fields one recovers the maximally supersymmetric AdS 4 × S 7 Freund-Rubin solution [8] given by (1.10) with and no internal flux. The outline of the paper is as follows: in section 2.1 we provide the necessary background in order to be able to present the solution without dealing with the technical details. Then, in section 2.2, we introduce the objects in terms of which we find the solution, which is presented in section 2.3. For the reader who is simply interested in the solution, and not the technical details of its derivation, section 2 is sufficient.
In section 3, we state identities satisfied by the SO (3)×SO (3) tensors -an outline of the derivation of the identities is given in appendix B. The metric ansatz gives ∆ −1 g mn and some of the identities listed in section 3.2 are used to invert this to find the metric, g mn , in section 5. Furthermore, the identities are also used to find and simplify the expression for the 3-form potential, A mnp , from the flux ansatz in section 5. The majority of the identities are, however, used, in section 6, to verify that the field equations are satisfied.
We present the SO(3)×SO(3) invariant stationary point of D = 4 maximal supergravity [14] in section 4. In particular, we recapitulate the scalar profile of the SO(3)×SO(3) invariant stationary point, which is uplifted by means of the ansätze, in section 5, to give the internal components of the metric and 3-form potential of the eleven-dimensional solution.
In section 6, we verify that the solution found in section 5 satisfies the D = 11 supergravity field equations. Given the general arguments that guarantee that the ansätze obtained from the uplift formulae solve the equations, this is not strictly necessary. However, we do this in order to demonstrate the full complexity of the solution as well as to give the reader further confidence that the uplift formulae do indeed provide bona fide solutions of the D = 11 equations.
Finally, in section 7 we re-express the eleven-dimensional solution in terms of ambient and local coordinates, which are better adapted to the isometry of the solution than the stereographic coordinates on S 7 used in section 5.
In order to set conventions, we review some basic material, largely contained in Ref. [9], in appendix A. For comparison, we list the identities satisfied by the SO(8) and SO(7) tensors for the G 2 and SU(4) − solutions in appendix C. In appendix D, we demonstrate explicitly that the solution can indeed be expressed solely in terms of a single set of (anti-)selfdual SO(8) tensors, as argued in section 2. In the final appendix, E, we give an explicit representation of seven-dimensional Γ-matrices and an embedding of R 4 ⊕ R 4 in R 8 , which is used in section 7.
The uplift formulae and invariant tensors on S 7
The (pseudo)scalars of the maximal gauged supergravity in four dimensions parametrise the noncompact coset E 7(7) /SU (8). In the unitary gauge, the group elements of the coset are given by the scalar 56-bein [30] where φ IJKL ≡ φ * IJKL is a complex, selfdual tensor field: The uplift formulae for the internal metric and 3-form potential [3,1] are then written in terms of the 56-bein, V(x), and the Killing vectors, K IJ m , and 2-forms, K IJ mn , on S 7 as follows: 2 and In writing these and similar formulae we will adopt and apply the following convention consistently throughout this paper: The raising or lowering of indices on any geometric object on S 7 , is always done by means of the round S 7 metricg mn and its inverse. By contrast, to raise or lower indices on the physical fields of D = 11 supergravity (as they appear for instance in (1.5) and (1.6)), we always employ the full metric g mn and its inverse. This means, in particular, that on the right hand side of the above equations we have K mIJ ≡ • g mn K IJ n and so on. The full metric g mn (x, y) is then obtained by inverting and peeling of the determinant factor using For the 3-form field, A mnp (x, y), one must then insert the result for the densitised metric, ∆g qr , on the right hand side of (2.4). Formulae (2.3) and (2.4) are off-shell in the sense that they give the internal metric, g mn , and the 3-form potential, A mnp , for any configuration of the scalar fields of the maximal gauged supergravity embedded in eleven-dimensional supergravity. In particular, note that the full antisymmetry of A mnp in (2.4) is not manifest, but can be established by means of the E 7 (7) properties of the 56-bein V, and is thus independent of whether the equations of motion are satisfied or not [1].
The main task is thus to construct, from a given scalar field configuration φ IJKL (x), the geometric quantities g mn (x, y) and A mnp (x, y). To gain a better perspective on this problem, let us first discuss the construction in a more general context before we specialise to SO(3)×SO(3) symmetric configurations below. For the most general configuration that has no symmetries at all the scalar field configuration would of course involve the full set of 35 scalars and 35 pseudoscalars. However, we are here interested in specific configurations preserving some symmetry, for which we can restrict attention to 3 φ IJKL form a basis of invariant real selfdual and real anti-selfdual 4-forms (when we are dealing with real tensors the position of the indices I, J, ... does not matter). If one is looking for stationary points preserving a given symmetry, the scalar manifold is accordingly parametrised by coordinates λ (r) , µ (s) . Simple examples of invariant 4-forms (for which the labels r and s are not needed) are For the SO(3)×SO(3) solution we are about to construct, there are two invariant selfdual and two invariant anti-selfdual 4-forms, which are given in (2.16) below. In order to rewrite the solution in terms of geometric objects adapted to the (deformed) S 7 geometry, we define a set of invariant tensors via for the scalars, and for the pseudoscalars. By virtue of their definition and the (anti-)selfduality properties of the invariant 4-forms, these tensors satisfy the relations Of course, at the stationary point, we can group all scalars and pseudoscalars into single SO(8) invariant objects with associated SO(7) tensors, defined in an analogous manner to those defined in (2.7) and (2.8). In this case, one is guaranteed that the solution may be written solely in terms of these reduced set of SO(7) tensors. However, the result will not, in general, take a 'nice' form (see appendix D for a demonstration of this for the SO for all r and s. Furthermore, we have the inversion formulae Φ (r) (2.10) which are, again, valid separately for all r and s. Now, for any specific set of invariant 4-forms we will need further identities. First, such identities are needed to perform the exponentiation required for the calculation of u IJ KL , v IJKL and their complex conjugates in (2.1). Second, we need these identities to solve the uplift formulae for g mn and A mnp and to bring the resulting expressions into a manageable form.
The simplest examples, again, are provided by the SO(7) ± and G 2 solutions for which the invariant 4-forms C ± IJKL obey i.e. their contractions either reproduce the same 4-forms or give the identity. The general case is more complicated because any product of 4-forms may produce new invariant tensors that are not 4-forms. The simplest example here is the G 2 solution that depends on both C + IJKL and C − IJKL , as well as the product C + IJM N C − M N KL , which defines a new invariant tensor (which is not a 4-form); this object then completes the list of G 2 invariant tensors. A more complicated example is the tensor F IJ defined in (2.18) and further invariant objects for the SO(3)×SO(3) solution. Consequently we will need to evaluate products such as (2.12) and either reduce them to previously defined expressions or add them as new objects to the list of invariant tensors. The procedure stops when all products or contractions reproduce objects already contained in the list; exploiting all such identities should enable us to compute u IJ KL and v IJKL in a closed form. Furthermore, as we will explain below in much detail for the SO(3)×SO(3) case, the identities satisfied by the above invariants entail a corresponding hierarchy of identities for the geometric tensors introduced in (2.7) and (2.8). The main use of these identities will be in carrying out the inversion required to derive the metric and 3-form from the uplift formulae (2.3) and (2.4) and in bringing the resulting expressions into a manageable form. This last step is necessary for the verification of the D = 11 field equations which would otherwise be unmanageably complicated.
Before proceeding let us comment on another point. In Kaluza-Klein theory one is usually interested in calculating the mass spectrum of a given compactification, and the massless states in particular. This requires a linearised expansion of the metric (2.3) and the 3-form potential (2.4) in the scalar fluctuations around a given vacuum. For the maximally symmetric S 7 compactification we thus have [23,31] g mn (x, y) = • g mn (y) + A IJKL (x) Y IJKL mn (y) + . . . , (2.14) The formulae (2.3) and (2.4) are thus the consistent non-linear extensions of the above formulae (it is straightforward to check that the linearised formulae follow directly from (2.3) and (2.4) by expanding the latter to first order in the scalar and pseudoscalar fields). One can therefore ask whether it is possible to directly 'exponentiate' the formulae (2.13). The above discussion shows that this is indeed possible for restricted configurations if one has enough tensor identities at hand.
Invariant tensors for the SO(3)×SO(3) solution
The SO(3)×SO(3) subgroup of SO (8), which is the symmetry of the stable stationary point in maximal gauged supergravity, is defined by the following branchings of the three fundamental representations: In the conventions that we are using, the eight gravitini, ψ I , and the Killing spinors, η I , on S 7 , transform under 8 v . We choose the two SO(3) groups to act on the subspaces defined by I = 1, 2, 3 and I = 6, 7, 8, respectively. Then the four invariant noncompact generators of E 7(7) are given by the tensors where Y + IJKL and Z + IJKL are selfdual, while Y − IJKL and Z − IJKL are anti-selfdual. In section 5, we show that the simplest and most symmetric form of the solution is obtained in terms of the following invariants defined by these tensors: 4 as well as two additional tensors Note that, as emphasised before, the objects defined in (2.17) and (2.18) belong to S 7 . Hence, their indices are raised and lowered withg mn and its inverse, for instance ξ mn ≡g mpgnq ξ pq .
The solution
We are now in a position to state the main result of this paper, which is an explicit uplift of the solution at the SO(3) × SO(3) stationary point of the scalar potential written in terms of the geometric quantities introduced above. The solution below is presented in its simplest and the most symmetric form. We refer the reader to section 5 for a more general form of the solution which, in particular, includes an additional parameter, α, corresponding to an accidental U(1) symmetry of the potential. The solution below is for α = −π/4. The internal metric of the uplifted solution is The 3-form flux is where the warp factor, ∆, is given by The solution is now complete modulo two constants, which as discussed in the introduction, are determined by the value of the potential, P * , at the stationary point using (1.12) and (1.13). For the SO(3)×SO(3) point, P * = 14. Hence, In particular, the fact that the value of f FR given above, as determined by equation (1.13), is consistent with a solution of the equations of motion is further evidence for the validity of this conjectured relation (1.13) between f FR and the potential. The remaining constant, m 7 , sets the overall scale of the solution.
One should note that the metric and the 3-form potential in (2.20) and (2.22) are obtained by an application of the identities derived in section 3 to simplify the "raw" expressions that follow from the uplift formulae. We refer the reader to section 5 for details of the derivation and to section 7 for another form of the solution in which the geometry of the internal space is perhaps more transparent.
Identities for SO(3)×SO(3) invariants
In this section, we present in a systematic way a set of identities for the geometric objects
Generic identities
The identities in this section follow from the particular dependence of the SO(7) tensors (3.1) defined in (2.17) and (2.18) on the Killing vectors/spinors. They do not require specific knowledge of how the underlying SO(8) tensors are defined. We refer the reader to Refs. [5,34,35,2] for proofs and further details.
Equations (2.17) and (2.18) can be inverted using the completeness property of the Γ-matrices. This yields, cf. (2.10), Similarly, the background covariant derivative of the SO(7) tensors can be computed using the Killing spinor equation (A.8) We stress once more that both (3.2) and (3.3) do not depend on the particular forms of the SO(8) tensors Y ± IJKL , Z ± IJKL and F IJ .
Special identities
The starting point for proving the identities satisfied by the SO(7) tensors and listed in tables 1-7 are various contraction identities for the SO(8) tensors Y ± IJKL , Z ± IJKL and F IJ . The latter follow directly from the definitions of these tensors in Eqs. (2.16) and (2.18), and can be split into several groups depending on the number of factors and the number of contractions. Each group then gives rise to different types of SO (7) identities. The identities given in this section are sufficient for determining the internal components of the metric and 3-form potential from the uplift ansätze and proving that the metric and 3-form potential thus obtained solve the field equations.
A. Double contraction identities between two of the Y ±
IJKL and Z ± IJKL tensors: and Note that each set of (anti-)selfdual tensors, Y ± IJKL and Z ± IJKL , respectively, do not in themselves lead to simple quadratic identities, but are instead related to each other via quadratic relations. This is pertinent to the discussion in section 2.1 and appendix D, where it is argued that one can always make do with a single set of (anti-)selfdual tensors at the price of working to higher order. Here we see that there are no self-contained set of quadratic identities for a single set of (anti-)selfdual tensors. Therefore, the result is that one must work with expressions that are higher-order in tensors-as illustrated explicitly in appendix D. This is to be contrasted with the previously known uplifts where the situation is simpler, see table 8. In the case of the G 2 invariant quantities, there are quadratic relations between the single set of (anti-)selfdual tensors. While in the slightly more complicated SU(4) − example, the single set of (anti-)selfdual 4-form tensors close on a 2-form tensor, rather than another set of 4-form tensors. More generally, for stationary points with even less symmetry the lesson seems to be that one must include enough (anti-)selfdual tensors in order to have quadratic relations between the tensors. Otherwise, the metric and 3-form potential will not be expressible at most quadratically in the SO (7) tensors.
B. Double contraction identities with triple factors:
as well as and analogous identities obtained by replacing Y by Z in the above identities.
C. Identities involving the F IJ tensor: (3.14) Given the identities for the SO(8) tensors, it is clear from the inversion formulae (3.2) that these identities imply identities satisfied by the SO(7) tensors in (3.1). We list these identities in tables 1-4. Note that we do not use the cubic identities (3.11) in deriving the SO(7) tensor identities-they will be used in section 4 to exponentiate the 56-bein in the unitary gauge.
While it is correct that the SO (7) tensor identities in tables 1-4 are a consequence of substituting the inversion formulae into the SO(8) tensor identities (3.4)-(3.7) and (3.12), (3.14), it is rather laborious to obtain these identities by the said method-at least without the aid of a computer program. In appendix B, we sketch a simpler proof for these identities. Furthermore, in the appendix we explain how the identities listed in tables 5-7 are derived from the identities in tables 1-4. Despite the fact that the derivation of these identities is quite an involved task, we have tried to present the identities as systematically as possible. In particular, the order in which the identities are presented is such as to indicate the fact that identities listed prior to a given identity may have been used to derive or simplify that identity. This means that, for instance, we have included an identity that may be obtained by contracting another identity, allowing the reader to check the consistency of the two. In any case, here we limit the explanation of the derivations to the comments in the table captions, sketching a derivation of the identities in appendix B.
Identities derived from (3.4) and (3.7). Table 2 (i) Identities derived from (3.5) and (3.12). Table 3 (i) Identities derived from (3.6). Table 4 (i) Identities derived from (3.13) and (3.14). Table 5 (i) F -tensor identities derived by contractions of the equations in (iii) and (iv) in table 2 with ξ m , ζ m , F m , ξ mq , ζ mq and F mq . (i) Identities derived from the equations in (iv) and (v) in table 3. Table 7 ( Identities derived by contractions of the equations in (ii)-(iii) in table 3 with ξ p , ζ p and F p ; and contractions of (iii) and (iv) in table 4 with ξ m and ζ m respectively.
The SO(3)×SO(3) solution of gauged supergravity
In the unitary gauge defined in equation (2.1), the u and v matrices are of the form where the parameter α may be freely chosen without loss of generality. This is because, while the relevant SO(3)×SO(3) invariant truncation of the theory contains two complex scalars, the potential corresponding to this truncation is invariant under an extra U(1) symmetry that lies outside the gauge group, namely SO(8) [21]. The α parameter corresponds to this U(1) freedom that leaves the potential invariant. In what follows we will choose to keep the value of α general. Interestingly, from an eleven-dimensional perspective we find that α corresponds to a coordinate transformation of the eleven-dimensional solution along the seven compactified directions (see section 5.3).
In exponentiating the scalar expectation value φ IJKL to find the u and v matrices, it is useful to which, using the cubic identities (3.7) and (3.11), satisfies the following properties Therefore, Π is a hermitean projector, and In particular, using identities (3.7), we find that Hence, the u and v matrices may be written as follows The scalar potential for the scalar λ reads This stationary point is the only known stable non-supersymmetric stationary point of D = 4 maximal supergravity [12,13]. In fact, there clearly exists another stationary point corresponding to s → −s, that is s = −2. From the perspective of the D = 11 solution this corresponds to A mnp → −A mnp under which the equations of motion (1.7)-(1.9) are invariant. We will take s = 2 henceforth, while keeping this in mind. 6 In what follows, we make use of the short-hand notation 4) for the internal metric and 3-form potential [3,1]. In this section we present the details of the calculation leading to the solution in its simplified form.
The internal metric
We apply the uplift formula (2.3) to evaluate the metric from the data at the SO (3) can be used to rewrite the scalar part of the metric ansatz (2.3) as follows Substituting in the expressions for u and v, equations (4.8) and (4.9), we find that Contracting the expression above with K m IJ K n KL and using the completeness relation (B.1) to rewrite the expression in terms of SO(7) tensors gives ∆ −1 g mn (x, y) =g mn + s 2 4 1 9g m[n ξ q] ξ q + 2ξ mp ξ n p + SS mpq SS n pq + 1 9g m[n ζ q] ζ q + 2ζ mp ζ n p + T mpq T n pq − √ 2sc (cos αξ mn − sin αζ mn ) . (5.5) Using the SO (7) identities in table 1, the above expression reduces to where The first four lines of equations in tables 1 and 2 and the identities in table 5 can be used to invert the densitised metric (still for arbitrary α) where and f m (α) = √ 2c cos α ζ m + √ 2c sin α ξ m + 3sF m . (5.10) We can calculate the warp factor, ∆, using (2.5), by evaluating the variations with respect to α and λ. After simplifying (5.11) using identities in tables 1, 2 and 5, one can integrate back to obtain ∆, with the overall normalisation fixed by requiring that ∆ = 1 for λ = 0. This gives 7 This completes the derivation of the uplifted metric tensor, g mn , for arbitrary values of λ and α.
The internal flux
As before, we simplify the scalar part of the flux ansatz (2.4) using the Sp (56) property of the u and v matrices For the u and v matrices corresponding to the SO(3)×SO(3) invariant sector (5.14) Contracting the above expression with K IJ mn K q KL and making use of the completeness relation (B.1), the flux ansatz (2.4) gives sc (cos α SS q mn + sin α T q mn ) .
(5.15)
Upon use of the identities in tables 3 and 6, the expression above simplifies significantly: Multiplying the above equation by the metric and substituting the expression (5.8) for ∆g pq , and making full and repeated use of the SO(7) identities in section 3.2, the resulting expression reduces 7 In fact, it is clear from a simple inspection of equation (5.8) that the determinant has to be some power of the with ∆ given in (5.12). Note that while it is clear that the metric obtained from the ansatz (2.3) is manifestly symmetric in its indices, this is not the case for the 3-form potential (2.4). However, as is shown in Ref. [1], the antisymmetry property of the 3-form potential is guaranteed to hold even off-shell for any values of the scalar fields as is the case for the 3-form potential in (5.17).
This concludes the uplift of the SO(3)×SO(3) stationary point to D = 11 supergravity. It is indeed remarkable that such a complicated solution as this one can be so simply derived in the matter of a few calculational steps.
Choice of α
As remarked earlier, from the point of view of gauged supergravity we are free to choose α without loss of generality, because of an accidental U(1) symmetry of the potential that is outside the gauge group. This is a novel feature of the SO(3)×SO(3) invariant truncation and is absent for other truncations for which the higher dimensional uplift is known. There ought to be a way of understanding this redundancy in the choice of α from an eleven-dimensional perspective. Given that in the four-dimensional theory the U(1) transformation does not lead to a different stationary point, it must be the case that for any choice of α the uplifted solutions are equivalent, viz. they are related by coordinate transformations as we demonstrate here. Specifically, we find that a shift in the parameter α corresponds to a diffeomorphism in the seven compactified dimensions, in the sense that δ α (∆g mn (α)) = L V (∆g mn (α)) , 22) and the metric determinant is: In summary, at the stationary point values given by equation (4.12), we find the internal metric and 3-form potential given in equations (2.20) and (2.22). It is only at the stationary point values, given in equation (4.12), that these expressions solve the equations of motion (1.7)-(1.9). Note also that with the choice of α given in this section, the metric is indeed symmetric under the interchange of tensors defined using invariants Y ± IJKL and Z ± IJKL , while the 3-form is antisymmetric. 9 Given the symmetric form of the solution for the choice of α = −π/4, this is the solution that we work with in order to verify that the field equations are satisfied.
Verification of the Einstein and Maxwell equations
In this section, we verify that the SO(3)×SO(3) invariant solution does indeed satisfy the field equations of D = 11 supergravity, equations (1.7)-(1.9). It is a surprising fact that the verification forms by far the most involved part of the work and requires the use of many of the identities listed in section 3.2. In comparison, finding the solution using the non-linear ansätze is fairly straightforward. This is a testimony to the power of the uplift ansätze, which are non-linear. From the perspective of the SU(8) invariant reformulation, it is clear that the ansätze should lead to internal metric and 3-form potential components that satisfy the D = 11 supergravity equations of motion. This is because they have been derived by the use of supersymmetry transformations which are first order equations, rather than second order as in the case of the field equations. Moreover, the highly nonlinear problem of relating the scalars of the D = 4 maximal gauged supergravity to the components of the internal metric and 3-form has been linearised by packaging the components of the D = 11 fields in the generalised vielbeine. The relation between the scalars of the D = 4 theory and the generalised vielbeine is a linear one. Both of the simplifications alluded to above mean that while the derivation of the solution is relatively simple, its verification in the context of the original formulation of D = 11 supergravity [11] becomes non-trivial. 9 Note that under this interchange we also have Fmn → −Fmn.
We refer the reader to the first equation in table 3 for the antisymmetry of the last term in equation (2.22).
In order to verify the Einstein and Maxwell equations (1.7)-(1.9), we make use of the computer algebraic manipulation program FORM [37] to simplify the expressions for the Ricci tensor and the 4-form field strength.
Components of the Ricci tensor
We begin by computing the components of the eleven-deimensional Ricci tensors R µ ν and R m n that appear in the equations of motion, (1.7) and (1.8), and whose indices are raised with the full metric, g M N . Denoting g µν (x, y) = ∆(y)g µν (x), g µν (x, y) = ∆ −1 (y)g µν (x), (6.1) the Christoffel symbols with mixed index components are Moreover, for convenience, we definê The relevant components of the eleven-dimensional Riemann tensor are It is now straightforward to obtain the expressions for the relevant components of the Ricci tensor, In fact, it is more convenient for us to directly calculate ∆ −1 R µ ν = ∆ −1 R µρ g ρν and ∆ −1 R m n = R mp (∆ −1 g pn ). Using the expression for the internal metric given in equation (2.20) and the expression for the determinant (2.23) as well as equations (3.3) and the SO (7) identities in section 3.2, Recall that in our conventions, the index n on the left hand side is raised with the inverse metric g mn , while on the right hand side we use the inverse metric on the round S 7 , • g mn . The coefficient functions in the above equation are as follows: Note that, like the metric, both R µ ν and R m n are symmetric under the interchange of tensors defined using Y ± IJKL and Z ± IJKL , definitions (2.17). 10
4-form field strength
In this section, we calculate the 4-form field strength of the 3-form potential given in equation (2.22). Using the equations for the derivatives of the SO (7) tensors (3.3) where we have simplified some expressions using the SO(7) identities in section 3.2 and Raising the indices on F mnpq using the inverse metric g mn poses the greatest challenge from a computational point of view. Therefore, we choose to calculate it using the following method Substituting the expression for the inverse metric, equation (5.6) and flux, equation (5.16) at the stationary point values and with α = −π/4, and simplifying the resulting expression using equations (3.3) and the SO(7) identities in section 3.2 gives The field strength of A, F mnpq , and F mnpq also share the antisymmetry property of A mnp under the interchange of tensors defined from Y ± IJKL and Z ± . This allows us to derive an expression for where we have used the expressions for F mnpq and F mnpq , equations (6.15) and (6.17), respectively, as well as equation (6.13) and the SO(7) identities in section 3.2. Finally, contracting the indices in the equation above and using the expression for R m n in equation (6.13) as well the SO(7) identities gives
Solution in ambient coordinates
The solution presented in the previous sections is given in terms of quantities defined on the round seven-sphere. In particular, the metric, g mn , in (2.20) is written as a deformation of the metric,
Ambient coordinates
To find the relation between the coordinates on the round seven-sphere and coordinates that we will use in this section, we introduce coordinates x A on R 8 , where A = 1, . . . , 8. Then the seven-sphere is defined by m 2 7 x · x = 1, (7.1) where in this section we use the notation x · x ≡ x A x A . It is straightforward to see that the above relation is solved by which define stereographic coordinates y m on the round seven-sphere of inverse radius m 7 (with |y| 2 ≡ y m y m ). The relations in the previous section can be viewed as being written in precisely such a coordinate system. Hence, in the previous sections the line element on the round S 7 is given by In fact, the induced metric on the seven-sphere can easily be calculated by substituting equations (7.2) into the flat line element on R 8 , whereupon we find that = (u, v), where u, v ∈ R 4 such that SO(4) acts separately on u and v. 11 The SO(3)×SO(3) invariant tensors in the previous section, written in terms of Killing spinors on the round S 7 , can be expressed in ambient coordinates as follows. In terms of Killing spinors, the 1-form duals of Killing vectors on S 7 are [3] (7.6) However, since the Killing vectors, K IJ a , generate SO(8) in 28, they are related by triality to generators of SO (8) in the vector representation. Or, equivalently, in terms of their 1-form duals where Now we can use these relations to determine the SO(3)×SO(3) invariant tensors in ambient coordinates. We start with the scalar invariant ξ defined in (2.17), and substitute for K IJ m using relation (7.7) Note that since the exterior derivative in K AB , definition (7.8), is with respect to stereographic coordinates, we also use relations (7.2) in deriving the above result. Similarly, Naively, there are three scalar invariants that can be formed from u and v. However, note that from equation (7.1) u · u + v · v = 1. (7.12) Therefore, we only have two scalar invariants and without loss of generality we can pick an embedding of the R 4 in R 8 where For an explicit embedding where the above relations hold see appendix E. Note that any other embedding will correspond to a rotation between u and v, which in the present representation, see appendix E, is given by Γ 45 AB , viz.
This freedom is represented by the parameter α in section 5, which is related to the rotation angle between u and v. In the four-dimensional theory, this corresponds to a redundancy in the description of the SO(3)×SO(3) invariant stationary point and not an invariance. As was shown in section 5.3, this is reflected in the fact that the uplift of all these points correspond to the same solution up to coordinate transformations. Given the expressions for ξ and ζ in ambient coordinates, it is now straightforward to find the tensors ξ a and ζ a in ambient coordinates by differentiating expressions (7.13) and using equations in (3.3): The remaining invariant 1-form F a , (2.18), is found using equations (7.7), (7.8) and the third equation We may again differentiate the tensors ξ a and ζ a to obtain expressions for the symmetric tensors ξ ab and ζ ab , respectively, in ambient coordinates. However, we will instead find these expressions by other means, which will be applicable also to the derivation of the tensors SS abc and T abc .
Using equation (7.7), we rewrite Note that the indices on Y + fully antisymmetrise the indices on the Γ-matrices. Hence we can make use of the following identity [30]: which is a consequence of SO(8) triality and is a decomposition of the object on the left hand side into its anti-selfdual (first term) and selfdual part (second term). Moreover, noting that in the expression for ξ ab the combination of Γ-matrices contracts with a selfdual tensor, Y + IJKL , we obtain Finally using (7.8) and the first equation in (E.6), we find that where we have also used u · u . + v · v . = 0, (7.21) which follows from (7.12). Similarly, we also find We determine SS abc and T abc in an analogous way. For example, Hence, we can again use identity (7.18), but in this case the anti-selfdual part of the decomposition given in equation (7.18) survives and we obtain which can be evaluated using the Γ-matrices and the embedding given in appendix E. All in all, we obtain where we have introduced the convenient notation It is clear that there are two more invariant 3-forms, that do not appear in the expression for SS (3) or T (3) . However, these invariant 3-forms as well as the 3-forms in SS (3) and T (3) do appear in the expression for the internal 3-form potential given below.
Local coordinates
We conclude this section with a construction of local coordinates on S 7 using the Euler angles of the SO(3) × SO(3) isometry group and the two scalar invariants, ξ and ζ. To this end let us consider S 7 as a subspace of 2 × 2 complex matrices Then the SO(4) action on C 4 is the same as the action of SU(2) 1 × SU(2) 2 on such matrices given by under which both (7.34) and (7.35) remain invariant. We use the Euler angles for the two SU(2)s defined by By an SU(2) 1 × SU(2) 2 transformation, one can bring Z to a diagonal form, where (ρ, ϕ) parametrise a disk of radius π/2. Using (7.35), we find ξ = 3 sin ρ cos ϕ , ζ = 3 sin ρ sin ϕ (7.39) so that have |ξ|, |ζ| ≤ 3, which is consistent with identities (i) in table 1 [5]. At a generic point, we have Clearly, Z is invariant under ψ i → ψ i + χ, which shows that a typical orbit is isomorphic with the coset where U (1) is the diagonal subgroup. 13 The local coordinate system on S 7 is now comprised of the angles ρ and φ that parametrise a disk and the Euler angles θ 1 , φ 1 , θ 2 , φ 2 and ψ = ψ 1 − ψ 2 on the coset. The range of these angles are Let us also introduce the left invariant forms on SU (2) 1 × SU (2) 2 , satisfying dσ 1 (j) = σ 2 (j) ∧ σ 3 (j) , etc., and define These forms are then pulled-back onto the coset by setting ψ 1 = −ψ 2 = ψ/2, such that yield a local frame, σ a , a = 1, . . . , 5, along the orbits of the SO(4) isometry.
The round metric on S 7 in these coordinates reads the symmetric tensors: 48) 13 Note that at the center of the disk ξ = ζ = 0 and we simply reproduce the explicit construction of T 1,1 in [39]. and the 3-forms: .
(7.49)
We also have that Rotations by the angle ϕ to obtain the actual SO(7) tensors (3.1) result in even larger expressions. As expected, the explicit formulae for the metric (2.20) and the 3-form potential (2.22) in these local coordinates are quite complicated and we will not write them here. One can easily obtain them using the expressions for the SO(7) tensors given above.
Outlook
In this paper, we have constructed a new and highly non-trivial solution of D = 11 supergravity corresponding to an uplifting of the SO(3)×SO(3) invariant stationary point of maximal gauged supergravity. While this solution is of interest in holographic applications and we hope that readers will find good use for it, we have endeavored to present the derivation of the solution in such a manner as to lend itself to a more general explanation of uplifting solutions of this type, i.e. Freund-Rubin compactifications with internal flux. The uplifting of any stationary point of the gauged theory to eleven dimensions will follow the same steps as those presented for the SO(3)×SO(3) invariant stationary point here, except that, clearly, for stationary points with less symmetry, this will be a more cumbersome process with many different invariant forms to consider. Apart from allowing for a direct derivation of uplift formulae, the rewriting of the elevendimensional theory in an SU(8) invariant reformulation [9], highlights features of the four-dimensional theory in eleven dimensions and makes it possible to prove [26,27], for example, the consistency of the S 7 reduction [8,23].
In recent work [28,40], the ideas initiated in Ref. [9] are taken to their full conclusion giving an on-shell equivalent reformulation of the D = 11 theory in which features of the global group E 7 (7) are also made manifest. As well as breaking manifest eleven-dimensional Lorentz invariance and covariance, one is also compelled to introduce eleven-dimensional dual fields in order to bring out the E 7(7) structure.
The reformulation of D = 11 supergravity given in Ref. [28] provides a very direct and efficient way of studying the relation between four-dimensional maximal gauged theories and D = 11 supergravity via a higher-dimensional understanding [28] of the embedding tensor [41][42][43][44]. In particular, it allows for a simple analysis of which four-dimensional theories arise as consistent reductions of the eleven-dimensional theory (see e.g. [45]). For example, it is very simple to deduce [29] that the new deformed SO(8) gauged theories of Ref. [46,47] cannot be obtained from a consistent reduction of the D = 11 theory.
In fact, given the success of the reformulations described above, we argue that, generally, the most appropriate setting in which to address questions to do with reductions and consistency is one in which the higher-dimensional theory is reformulated in such a manner as to fully resemble a duality covariant reformulation of the lower-dimensional theory, including both the global and local duality groups.
Of particular relevance here is that in the case of the S 7 reduction to the original maximal SO(8) gauged theory [36], Ref. [28] completes the metric and flux ansätze and provides full uplift ansätze for any solution of the gauged theory to eleven dimensions, including dynamical solutions with nontrivial x-dependence [29]. The method can, however, be applied more generally. For example, one can in principle setup a reformulation along the lines of [9,28] for type IIB supergravity and thereby study its S 5 truncation-for a recent conjecture on uplift ansätze in this case see Ref. [48].
An interesting application of these full uplift ansätze [28,29] would be to construct the full interpolating solution for a particular RG flow between two stationary points of the potential, such as the flow between the maximally symmetric SO(8) and the SO(3)×SO(3) invariant stationary points considered in Ref. [12].
A Conventions
We define a set of euclidean, antisymmetric and purely imaginary 8 × 8 Γ-matrices (Γ † = Γ). These are generators of the euclidean Clifford algebra in seven dimensions, We choose a Majorana representation and set the charge conjugation matrix that defines spinor conjugates or raises and lowers spinor indices to be the unit matrix. An explicit representation for the Γ-matrices is given in appendix E. The Γ-matrices can be used to define the 8 × 8 matrices for i = 2, . . . 7. Γ a and Γ ab are antisymmetric matrices and Γ abc is symmetric. These 7 + 21 + 35 = 63 matrices together with the unit matrix span the vector space of 8 × 8 matrices. Thus, we find that Furthermore, it is useful to note that each product of Γ-matrices can be written in terms of the unit matrix, Γ a , Γ ab and Γ abc . We choose the eight Killing spinors of the round S 7 to be orthonormal, The curved Γ-matrices on the round seven-sphere are given byΓ m =e m a Γ a . Hence, in our conventions, the Killing spinors satisfy The Killing spinors define a set of Killing vectors, 2-forms and tensors: respectively, whose equivalents are also defined in flat space.
In the derivations below, we make heavy use of the completeness relation as well as the following useful identities [9]: One can verify these using the inversion formulae (3.2).
B.1 Derivation of the identities in table 1
Identities (i) and (ii) Consider the first equation in (3.4) contracted with K IJ t K t KL : Finally, by contracting the first cubic identity (3.7) with K IJ tu K u KL and simplifying as before, except that the completeness relation (B.1) must be used twice, gives 36 + 2ξ 2 − ξ m ξ m − 18ξ mn ξ mn ξ t = 0. (B.9) There are seemingly two cases to consider: first we consider the case in which the expression in the brackets vanishes. Together, with equations (B.7) and (B.8), we obtain the equations for ξ m ξ m , ξ mn ξ mn and T mnp T mnp in terms of ξ 2 , as they appear in equations in (i) and (ii) in table 1. The equations derived from considering the second case, ξ m ≡ 0, are already contained in equations (i) and (ii). However, in our case, ξ m ≡ 0 anyway. Note that we had to use a cubic identity, (3.7), to derive a quadratic identity. This seems strange and one may wonder whether that was necessary or whether the identity could have been derived from quadratic identities. However, a simple counting of the number of quadratic identities available gives two, whereas the number of unknown quantities that we have expressed in terms of ξ 2 is three. Note, however, that (3.7) is not used anymore in deriving the identities in table 1.
Interchanging Y and Z in the discussion above, or equivalently by considering the second identities in (3.4) and (3.7) gives analogous expressions for ζ m ζ m , ζ mn ζ mn and SS mnp SS mnp .
Identities (iii) and (vi)
This case is similar to the example above. We contract equations (3.4) with K IJ mn K pKL . This gives identity (vi). Identity (iii) is obtained upon letting index p = n and noting that the wedge product of an odd-form with itself vanishes, e.g.
B.2 Derivation of the identities in table 2
Identity (i) The third identity in the line is proved by contracting the last equality in (3.12) by δ IJ . Using the appropriate inversion formulae in (3.2) and we immediately find S mnp T mnp = 0. The first two identities are derived by contracting either equation in (3.5) with K IJ m K m KL and K IJ mn K mn KL . Identity (ii) Contract the last equality in (3.12) with K m IJ , whereupon we find We then make use of the inversion formula for Z − IKLM , (3.2) to find Identity (iii) These are obtained by contracting identity (3.5) with K IJ mn K nKL .
Identities (iv) and (v)
The symmetric, in indices m and n, part of these are derived by contracting (3.5) with K m IJ K n KL and K mp IJ K n p KL . The antisymmetric part is derived by contracting (3.12) with K mn IJ , (B.14)
B.7 Derivation of the identities in table 7
Identity (i) These are derived by contracting identities (ii) and (iii) of Identity (iii) These are the most non-trivial identities to prove. We consider the first of the identities, and the other follows from analogous arguments, or simply interchange symmetry. However, before embarking on the proof, we note that contracting (v) in table 3 with F q and using identity (iii) of table 6 leads to an equation for the sum of the two equations in (iii) and not on each separately. Therefore, we need another method. Contract identity (iii) in The required identity can be deduced by substituting the above equation into expression (B.16) and simplifying using the identities listed in the tables.
C Comparison of stationary points
In this appendix, we present table 8, which gives a list of the various tensors used to construct other stationary point uplifts and the associated identities they satisfy.
List of identities satisfied by G 2 and SU(4) − invariant tensors. We use notation where (X IJKL ) − refers to the anti-selfdual part of tensor X. The SO(7) tensors ξ, S and T are defined according to the general definitions (2.7) and (2.8), and 4K a = F IJ K IJ a , 4K ab = F IJ K IJ ab .
In G 2 , the single set of tensors C ± do not close on themselves at the quadratic level, but one can form new tensors from the contraction of C ± C ∓ . However, the new SO(7) tensors that can be defined for these objects are related to ξ and S at the quadratic level, hence there is no simplification in doing this.
D Choice of SO(3)×SO(3) invariants
The metric (5.8) and the 3-form potential (5.17) have been derived using two sets of SO(3) × SO(3)-invariant geometric objects on S 7 , namely, (ξ, ξ m , ξ mn , S mnp ) and (ζ, ζ m , ζ mn , T mnp ), that are associated with two sets of (anti-)selfdual SO(8) tensors Y ± IJKL and Z ± IJKL , respectively. This choice of invariants is crucial for being able to carry out the simplification of the metric and the 3-form potential in sections 5.1 and 5.2 starting with the uplift formulae (2.3) and (2.4), and also for the explicit check of the equations of motion in section 6.
However, as we have already discussed in section 2.1, one might as well choose to work with a single set of the geometric objects associated with the particular noncompact generator of E 7(7) that parametrises a given stationary point. In our case that means setting Φ IJKL = cos α Y + IJKL − sin α Z + IJKL , Ψ IJKL = cos α Y − IJKL + sin α Z − IJKL , (D.1) and expressing the solution in terms of the corresponding set of SO(7) tensors x mn = cos α ξ mn − sin α ζ mn , x m = cos α ξ m − sin α ζ m , x = cos α ξ − sin α ζ , S mnp = cos α S mnp + sin α T mnp .
(D.2)
To do this one may introduce the complementary set of rotated tensors, z mn , z m , z and T mnp , such that ξ mn = cos α x mn + sin α z mn , ζ mn = − sin α x mn + cos α z mn , etc. .
(D.3)
After rewriting the solution in terms of the rotated tensors, one can check using identities in section 3.2 that all terms involving the additional tensors either cancel out or can be rewritten in terms of (D.2). The calculation is long and, as one might expect, results in more complicated and less symmetric formulae for the metric and the 3-form potential. The reason for this is that the geometric objects that are being eliminated, z mn , . . . , T mnp , are replaced by more complex expressions in terms of sums of products of tensors that are kept. To illustrate this point, let us consider the warp factor, ∆, given in (5.12). At the stationary point (4.12), 14 X 2 2 + 2c 2 X 2 Z 2 + Z 2 2 + Y = 20 cos(2α) + where, to eliminate z 2 , in the last step we used the fact that x m x n S mpq S n pq = 9 − ξ 2 − ζ 2 = 9 − x 2 − z 2 , (D. 5) which follows from the identities in tables 1, 2 and 5.
One may also note that the α-dependence in the first line in (D.4) is completely removed by rewriting the right hand side in terms of the rotated tensors using (D.3). Furthermore, the rotated tensors, x mn , . . . , S mnp and z mn , . . . , T mnp , satisfy the same identities as ξ mn , . . . , S mnp and ζ mn , . . . , T mnp , respectively, in tables 1-7. This means that the calculation is precisely the same for all α and thus we may as well set α = 0. The problem then is simply to rewrite the metric (5.8) and the 3-form potential (5.17) for α = 0, solely, in terms of ξ mn , ξ m and S mnp . With this in mind, we now turn to the metric tensor (5.8).
It can be shown that one can write all SO(7) tensors appearing in the metric in terms of a small number of fields constructed from ξ, ξ m , ξ mn and S mnp only: (i) scalars ξ , Ξ ≡ ξ m ξ n S mpq S n pq , (D.6)
E Ambient coordinate embedding
In this appendix, we provide an explicit embedding of the R 4 in R 8 . We use the following representation of seven-dimensional Γ-matrices in terms of Pauli matrices: In terms of seven-dimensional Γ-matrices the SO(8) generators Γ AB are 15 Γ ab = Γ ab ,Γ a8 = −iΓ a . (E.5) In this representation, Therefore, we can easily verify that for the embedding given by so the α rotation rotates the u coordinates into the v coordinates, and vice versa. | 14,154 | 2014-10-19T00:00:00.000 | [
"Physics"
] |
Influence of Processing Type in the Morphology of Membranes Obtained from PA 6 / MMT Nanocomposites
The nanocomposites have an extensive use in the current process of membrane preparation, taking into account their unique features as membranes. Thus, the study of nanocomposite processing to obtain membranes is highly important. In this work, Brazilian clay was used (Brasgel PA) for the preparation of polyamide/clay nanocomposite. The nanocomposites were produced in a high rotation homogenizer and in a twin screw extruder. From the nanocomposites and pure polymers processed in the two equipments, membranes were prepared by the immersion-precipitation method, using formic acid as solvent. By X-ray diffraction (XRD), the formation of exfoliated and/or partially exfoliated structures with changes in the crystalline phases of the polyamide was observed. From scanning electron microscopy images, it was observed that the processing clearly influenced the membrane morphology.
Introduction
Recently, the membrane technology is applied in several industrial processes presenting numerous advantages, such as continuous processing with low energy consumption and easy combination with other separation processes [1].In the early 1970s, besides the development of classical separation techniques, new synthetic membranes, which can be used as a selective barrier, were developed.The synthetic membranes were designed to improve the characteristics of selectivity and permeability of natural membranes.The addition of inorganic nanoparticles (clay) greatly improves the filtration properties of the membrane.Several studies indicate that the addition of inorganic nanoparticles in the polymer solution, used to prepare membrane by phase inversion, can control the formation and growth of macropores, increase the number of small pores, and improve the hydrophilicity, porosity, and permeability and mechanical and antifouling properties [2,3].
The membranes can be considered polymeric or inorganic films that work as a semipermeable barrier to filtration in a molecular scale, separating two phases and restricting, totally or partially, the transport of one or several chemical species (solutes) present in a solution [4,5].
Most membranes used worldwide and so-called second generation are produced from synthetic polymers, such as polyamide, polysulfone, polyacrylonitrile, polycarbonate, and poly(vinylidene fluoride), among others.They show resistance to the action of strong acids and bases (pH from 2 to 12) and support temperatures close to or even superior to 100 ∘ C.These membranes can also be used with nonaqueous solvents and have long lifetime [6].
Most of polymer membranes used commercially are prepared by phase inversion technique, which consists of three main steps: preparation of polymer solution, spreading the solution on a surface forming a film with controlled thickness, and, finally, precipitating nonsolvent for formation of the polymeric structure of the membranes by using a phase separation system [7,8].
Many materials may be used to prepare polymer membranes, among them is the polyamide.This polymer presents high performance and excellent mechanical and thermal properties [9].In addition, the polyamides being used as nanocomposites matrices where have presented attractive properties, for instance, barrier properties to gas permeation.The hybrids organic/inorganic films obtained from clay present a waterproofing due to the lamellas of montmorillonite that act as a barrier with less loads than the conventional composite films [10].
The polymer nanocomposites are hybrid materials where particles with nanometric size are dispersed in a polymeric matrix.They can be considered a new class of polymer composed of inorganic phases with ultrafine dimensions that interact with the polymer, thus offering a better combination of properties such as toughness and resistance, difficult to be achieved with pure polymer.Reinforced polymers with low content of clay (1 to 5% in mass) have raised interest in academic and industrial environments, due to the considerable improvement in the physical and mechanical properties, as well as permitting the processing from conventional techniques such as extrusion and/or injection [11][12][13].
The aim of this work is to analyze the structure and morphology of polymeric membranes using X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively.The membranes were prepared by the phase inversion method from PA6/MMT nanocomposites.
Materials.
A sample of Brasgel PA clay was used, supplied by Bentonit União do Nordeste (BUN), from Campina Grande, Paraiba, Brazil), with cation exchange capacity (CEC) of 90 meq/100 g, and passed in a sieve ABNT 200 mesh.Polyamide 6 from Polyform B300, with average viscosity IV = 140-160 mL/g, in the form of granules in white was used.Formic acid PA from Vetec, with 98% of purity, was used as solvent to dissolve the polymer and nanocomposites to prepare the membranes.
Preparation of Nanocomposites.
For the nanocomposites preparation, 1% was used in mass of clay to the polymer and two processing equipment: a high rotation homogenizer, MH-50H model, and a corotational twin screw extruder from Coperion.In the first, the clay was dispersed directly in the equipment.In the second, a concentrated (50 : 50% in mass) in the homogenizer was obtained and then incorporated in a polymer matrix in the proportion of 1% in mass of clay, using the extruder.Processing conditions were as follows: mixed in the homogenizer for 30 to 40 seconds until PA6 melting.The processing in the extruder is done in a temperature of 260 ∘ C for 7 existing zones and a screw rotation of 250 rpm.For each processing step, all materials with polyamide 6 were dried in a circulating air oven at 80 ∘ C for 2 h and in a vacuum oven at 80 ∘ C for 24 hours.
Preparation of Membranes.
For the membrane preparation, the phase inversion method was used through the immersion-precipitation technique.The polyamide and its nanocomposites were dissolved in formic acid in a proportion of 20% in mass of polymer under constant stirring for 24 hours, for total polymer dissolution.The solution was spread in a glass plate, previously washed and dried.The spreading was done manually with a spacer and the polymer film was quickly immersed in a precipitation bath with distilled water.Then, the membranes were removed, washed with distilled water to remove the residual solvent, and dried at room temperature.This procedure is according to Leite [14].The nomenclature used is the following: Pure PA6 (polyamide without processing), Extruder PA6, Homogenizer PA6, PA6 + 1%MMT, Extruder PA6 + 1%MMT, and Homogenizer PA6 + 1%MMT.
Materials Characterization.
The nanocomposites were characterized by X-ray diffraction (XRD), using a Shimadzu XRD-6000 equipment, with CuK radiation ( = 1.5418Å), 40 kV, 30 mA, and scanning 2 from 1.5 ∘ to 30 ∘ at a scanning rate of 2 ∘ /min.The membranes were characterized by scanning electron microscopy, using a SSX 550 Superscan from Shimadzu, operating at 15 kV.Both top and cross section surfaces of the membranes were evaluated.For the cross section analysis, the samples were fractured in liquid nitrogen to avoid plastic deformation.The surfaces were coated with gold.
X-Ray Diffraction (XRD).
The analysis of X-ray diffraction of the bentonite clay (MMT) without treatment (Figure 1) revealed the presence of characteristic peaks of bentonite and other minerals such as quartz (Q).A peak was observed in the interplanar distance of 12.77 Å, characteristic of sodium montmorillonite with small hydration [15] (Santos, 1989).Through the X-ray results, the presence of two peaks in the range from 20 ∘ to 23 ∘ can be observed, related to the crystalline planes ( 200) and (002) of the phase of polyamide 6.These results are in agreement with the literature [14,[16][17][18].A crystalline plane (001) that corresponds to the phase of the polymer was also identified.This reflection occurred for all samples, with higher incidence for extruded polyamide 6 (close to 21 ∘ ).In this case, it is possible to observe that the predominant crystalline phase of polyamide is .The polyamide 6 is a semicrystalline polymer and the enlargement of the peaks indicates the existence of amorphous regions.As can be seen, the incorporation of clay can change the shape of these peaks, modifying probably the crystallinity for PA6.According to Khanna and Kuhn [19], the polyamide 6 can assume two crystallographic forms ( monoclinic and monoclinic and/or pseudohexagonal).
From Figure 1, the disappearance of the characteristic peak of the clay can be seen, indicating a possible exfoliation and/or partial exfoliation of nanocomposites formed by polyamide 6 + 1% of clay.Similar behavior was observed by Ray and Okamoto and Fornes et al., for the polyamide 6/clay systems [20,21].
However, for the sample PA6 extruded, a small contribution of the crystalline phase and, more pronounced, the formation of phase, which does not occur for the sample processed in the high rotation homogenizer, should be noted.This behavior indicates that the processing has influenced the formation of crystalline arrangement.
From Figure 2(a), it can be verified that Pure PA6 presents pores with uniform distribution.It is possible to see that there are less pores for both Homogenizer PA6 (Figure 2(b)) and Extruder PA6 (Figure 2(c)), when compared to the morphology presented by Pure PA6.Possibly, this change could be due processing of material.
It was observed that the presence of clay (1%) for the nanocomposites prepared in the homogenizer and in the extruder changed considerably the quantity and uniformity of pores in the membrane, compared to the pure PA6 membrane.The membranes prepared with nanocomposites with 1% of clay presented a greater number of pores due to the presence of clay in the polymer matrix.
3.2.2.Cross Section. Figure 3 presents the SEM images with an overview of the cross sections for the membranes: Pure PA6, Homogenizer PA6, Extruder PA6, and their nanocomposites with 1% of clay.
According to the SEM images, it can observed that PA6 (Figure 3(a)) presented a well-defined surface with micropores, allowing for a greater selectivity.The cross section indicates a thickness of approximately 92.5 m, with uniform distribution of pores in the whole membrane.The Homogenizer PA6 membrane (Figure 3(b)) showed a wide selective layer, when compared with Pure PA6 membrane, with a thickness of approximately 183 m and good pore size distribution with presence of "fingers." The Extruder PA6 membrane (Figure 3(c)) presents a very thin selective layer when compared with pure PA6 and Homogenizer PA6, with a thickness of approximately 109 m, with small, uniform, and interconnected pores.This behavior can be explained by the processing performed in the homogenizer and extruder.Membranes with 1% of clay show "finger" shaped pores with no connections, which block fluid flows through the membrane.The Homogenizer PA6 + 1%MMT presented a thickness of approximately 242 m.For the Extruder PA6 + 1%MMT, a thickness of approximately 161 m was observed.
Conclusions
Membranes were prepared from PA6/MMT nanocomposites.From X-ray diffraction results, it was observed that the nanocomposites presented exfoliated and/or partially exfoliated structure.From SEM images, it was observed that the Pure PA6 membrane presented uniform pores.Membrane PA6, prepared with nanocomposites processed in the homogenizer and extruder, presented small amount of pores, showing that the processing has influence in the morphology of the membranes.Membranes prepared from nanocomposites with 1% of clay presented more pores than PA6 membrane, showing that the presence of clay has considerably influenced the morphology.From the images showing the cross section of the membranes, it was observed that the processing influenced the thickness of the selective layer of the membranes.Membranes with clay had not interconnected pores and a thicker selective layer, which probably blocked flow membrane. | 2,543.8 | 2014-04-16T00:00:00.000 | [
"Materials Science",
"Engineering"
] |
Informational Energy and Entropy Applied to Testing Exponentiality
The exponential distribution is widely used in reliability and life testing analysis. In this paper, two tests of fit for the exponential distribution based on Informational Energy and entropy are constructed. Consistency and other properties of the tests are proved. Using a simulation study, critical values of the proposed tests are obtained and then power values of tests are computed and compared with each other against various alternatives. Finally, we apply the tests for time between failures of secondary reactor pumps and waiting times for fatal plane accidents in the USA from 1983 to 1998.
Introduction
Suppose that the random variable X has distribution function F with density function f . The informational energy ε(f ) of the random variable is defined as Onicescu (1966) justified the name informational energy and its connection to Information Theory in the classical mechanics. Rao (1973) obtained distributions describing equilibrium states in statistical mechanics based on the informational energy. The informational energy has been used in many statistical problems, see Theodorescu (1977), Onicescu and Stefanescu (1979), Pardo and Taneja (1991) and references there in. In non-parametric statistics, an estimator of informational energy is useful for researcher. Pardo (2003) introduced an estimator of informational energy as follows. He noted that ε(f ) can be expressed as ) −1 dp .
Then he constructed its estimator by replacing the distribution function F by the empirical distribution function F n , and using a difference operator instead of the differential operator. The derivative of F −1 (p) is then estimated by a function of the order statistics. Assuming that X 1 , . . . , X n is a random sample, the proposed estimator by 221 Pardo (2003) is as where m is positive integer, m ≤ n 2 , and X (1) ≤ X (2) ≤ · · · ≤ X (n) are order statistics of the sample and X (i) = X (1) Consistency of ε mn is also proved by Pardo (2003). Pardo (2003) showed that among all distributions that possess a density function f and have a support (0, 1), the entropy ε(f ) is minimized by the uniform distribution and based on this property he constructed a test of fit for the uniform distribution. Its test statistic is given as Large values of ε mn indicate that the sample is from a non-uniform distribution. . Therefore, constructing a goodness of test for this distribution will be useful in practice. In this article, we apply the informational energy and introduce a powerful goodness of fit test for the exponential distribution. Then the properties of the test are stated and compared with the existing other tests.
In Section 2, we introduce two tests of fit for exponentality based on informational energy and entropy, respectively. Consistency and other properties of the tests are established. In Section 3, we obtain critical values and then compute power of the tests against a wide variety of alternatives and show that the test based on informational energy has a good performance. Finally, we analyze two real data sets to illustrate the tests.
Test construction
In this section, we explain two methods for testing exponentiality.
Testing exponentiality based on informational energy
Suppose X 1 , . . . , X n are a random sample from a continuous probability distribution F with density f over a non-negative support and with mean µ < ∞. We are interested to test the hypothesis against the general alternative where λ = 1 µ is unspecified.
Without loss of any generality, by the probability integral transformation U = F 0 (X), we can reduce the above 222 INFORMATIONAL ENERGY AND ENTROPY problem of goodness-of-fit, to testing the hypothesis of uniformity on the unit interval. Therefore, if U i = F 0 (X i ) , i = 1, 2, ...., n be the transformed sample, the hypothesis becomes Hence, test of exponentiality convert to test of uniformity.
Here, we apply the test introduced by Pardo (2003) for testing uniformity of the transformed sample, i.e.
Consequently, the proposed test statistic can be stated as It is obvious that the test statistic is invariant with respect to the scale transformations.
Remark 1
When the parameter of the distribution is specified as λ = λ 0 , the test statistic is Similar to the argument in Pardo (2003), the following theorems are stated and proved.
Theorem 1
Let X 1 , . . . , X n be a random sample, we have T mn ≥ 1.
Proof
We know that the geometric mean does not exceed the arithmetic mean, therefore In other hand, we have Therefore,
Theorem 2
Let X 1 , . . . , X n be a random sample from the exponential distribution, if m = o(n) and m ̸ = 1, then has a beta distribution with parameters j and n − j + 1 and we can obtain E (T mn ) as where ψ is the digamma function, we have Since for large value of x,
Therefore, T mn
Pr.
Testing exponentiality based on entropy
The entropy H(f ), of a continuous random variable X with a density function f (x) was defined by Shannon (1948) to be Let X 1 , . . . , X n be a random sample of size n, and X (1) ≤ X (2) ≤ ... ≤ X (n) denotes the order statistics of sample.
Many researchers has been
Vasicek (1976) first time introduced an estimator of entropy as: where the window size m is a positive integer smaller than n/2, He proved the consistency of HV mn for the population entropy H(f ). Gokhale (1983) proposed a test statistic for the exponential distribution based on entropy. Then Ebrahimi et al.
(1992) obtained a test statistic using Kullback-Leibler information for the exponential distribution. Also, Alizadeh Noughabi and Arghami (2013) showed that the tests based on entropy and Kullback-Leibler information are equivalent. We explain exponentiality test based on entropy as follows.
It is known that if X is a nonnegative random variable and its mean E(X) = λ −1 is given then and among all nonnegative random variables the exponential distribution Therefore, Gokhale (1983) proposed the following test statistic.
where HV mn is Vasicek entropy estimator andX is the sample mean. We reject the null hypothesis for small values of T V mn .
Simulation study
For small to moderate sample sizes, the critical values of the test based on informational energy with 30,000 replications and samples of size n are obtained. Table 1 presents the critical values of the T mn -statistic various sample sizes at significance level α = 0.05. Quantiles of T V mn are reported in Gokhale (1983) and we dont present them.
To comparisons of the power values of the considered tests, we select the same three alternatives listed in Ebrahimi et al. (1992) and their choices of parameters: (c) the log-normal distribution with density function We also chose the parameters so that E(X) = 1, i.e. λ = Γ(1 + 1 β ) for the Weibull, λ = β for the gamma and v = −σ 2 /2 for the log-normal family of distributions.
We compute the power values of the informational energy based test with the power values of the entropy based test, for samples of size equal to 10 and 20. Under each alternative, we generated 20,000 samples of size 10 and 20 and then computed the test statistics (T mn , T V mn ). By the frequency of the event the test statistic is in the critical region the power value of the corresponding test was obtained. Table 2 presents the estimated powers at significance levels α = 0.01 and α = 0.05. The power values of the entropy test are based on the window sizes reported in Ebrahimi et al. (1992), which give the maximum power for this test. For the proposed test, the maximum power was typically attained by choosing m = 5 for n = 10, and m = 10 for n = 20. Generally, we can say that with increasing n the optimal choice of m increases.
From Table 2, it is seen that the tests are differ in power. It indicates a superiority of the procedure based on informational energy to entropy test. It is observed that for small sample sizes the tests achieve the same power and for large sample sizes the informational energy test has the most power. The difference of power values of the tests T mn and T V mn are substantial. In the following examples, we use the tests for some real datasets. Histograms of the considered data sets are presented in Figure 1. Suprawhardana and Sangadji (1999) The proposed tests for goodness of fit on inter-occurrence times of fatal accidents are used. After some computing the values of the proposed tests, we concluded that the distribution of the data of the inter-occurrence times of fatal accidents on scheduled large planes in the USA (1983C1998) does not differ significantly from the exponential.
Example 1
Therefore, the inter-occurrence times of fatal accidents suffered by scheduled large planes in the USA from (1983C1998) is exponentially distributed.
Conclusion
In this paper, we first proposed two tests for exponentaility based on the estimated informational energy and entropy, respectively. Consistency and other properties of the test statistics are presented. Then, we obtained the critical values of the proposed test and also computed the power vales of the considered tests using Monte Carlo computations for different sample sizes against various alternatives. We observed that the test based on informational energy performs very well compared with the test based on entropy for Weibull, gamma, and lognormal alternatives. Also, it can be seen that the relative superiority of the proposed test over entropy test increases with sample size. | 2,303 | 2020-02-18T00:00:00.000 | [
"Engineering",
"Mathematics"
] |
ENHANCED SECURITY MECHANISM IN CLOUD COMPUTING USING HYBRID ENCRYPTION ALGORITHM AND FRAGMENTATION: A REVIEW
Cloud is a term used as a metaphor for the wide area networks (like internet) or any such large networked environment. It came partly from the cloud-like symbol used to represent the complexities of the networks in the schematic diagrams. It represents all the complexities of the network which may include everything from cables, routers, servers, data centers and all such other devices. Cloud based systems saves data off multiple organizations on shared hardware systems. Data segregation is done by encrypting data of users, but encryption is not complete solution. We can do segregate data by creating virtual partitions of data for saving and allowing user to access data in his partition only. We will be implementing cloud security aspects for data mining by implementing cloud system. After implementing cloud infrastructure for data mining for cloud system we shall be evaluating security measure for data mining in cloud. We will be fixing threats in data mining to Personal/private data in cloud systems.
INTRODUCTION
Cloud Computing is one of the biggest technology advancement in recent times. It has taken computing in initial to the next level. Cloud computing is one of the biggest thing in computing in recent time. Cloud computing is a broad solution that delivers IT as a service. Cloud computing uses the internet and the central remote servers to support different data and applications. It is an internet based technology. It permits the users to approach their personal files at any computer with internet access . The cloud computing flexibility is a function of the allocation of resources on authority's request. Cloud computing provides the act of uniting. Cloud computing is that emerging technology which is used for providing various computing and storage services over the Internet . In the cloud computing, the internet is viewed as a cloud. By the use of cloud computing, the capital and operational costs can be cut. Cloud computing incorporates the infrastructure, platform, and software as services. These service providers rent data center hardware and software to deliver storage and computing services through the Internet. Internet users can receive services from a cloud as if they were employing a super computer which be using cloud computing. To storing data in the cloud instead of on their own devices and it making ubiquitous data access possible. They can run their applications on much more powerful cloud computing platforms with software deployed in the cloud which mitigating the users burden of full software installation and continual upgrade on their local devices.
CLOUD TYPES
Depending on infrastructure ownership, there are four deployment models of cloud computing [6].
1) Public Cloud: -Public cloud [4] allows users to access the cloud publicly. It is access by interfaces using internet browsers. Users pay only for that time duration in which they use the service, i.e., pay-per-use.
2) Private Cloud: -A private clouds [5] operation is with in an organization's internal enterprise data center. The main advantage here is that it is very easier to manage security in public cloud. Example of private cloud in our daily life is intranet.
3) Community Cloud:-When cloud infrastructure construct by many organizations jointly, such cloud model is called as a community cloud. The cloud infrastructure could be hosted by a third-party provider or within one of the organizations in the community.
4)
Hybrid Cloud: -It is a combination of public cloud [7] and private cloud. It provide more secure way to control all data and applications. It allows the party to access information over the internet. It allows the organization to serve its needs in the private cloud and if some occasional need occurs it asks the public cloud for some computing resources.
ISSUES IN CLOUD COMPUTING
• Security and privacy in the Cloud: Security is the biggest concern when it comes to cloud computing. In a cloud based infrastructure, a company gives the private data and information. This information may be sensitive or confidential. The cloud service provider helps to manage, protect and retain the information. Hence the provider's reliability is very critical. Similarly, privacy in the cloud is another huge issue. Companies and users have to trust their cloud service vendors that they will protect their data from unauthorized users. The various stories of data loss and password leakage in the media does not help to reassure some of the most concerned users.
• Dependency and vendor lock-in: One of the major disadvantages of cloud computing is the implicit dependency on the provider. The implicit dependency is known as vendor lock in. If a user wants to switch from one service provider to another then it can be very painful and cumbersome to transfer huge data from the old provider to the new one • Technical Difficulties and Downtime: Certainly the smaller business will enjoy not having to deal with the daily technical issues and will prefer handing those to an established IT company. Hence in the cloud computing you should keep in mind that all systems might face dysfunctions from time to time. Outage and downtime is possible even to the best cloud service providers.
• Limited control and flexibility: In the cloud computing, the applications and services run on remote, third party virtual environments, companies. The users have limited control over the function and execution of the hardware and software. Hence, remote software is being used, it usually lacks the features of an application running locally.
• Increased Vulnerability: The cloud based solutions are exposed on the public internet and are thus a more vulnerable target for malicious users and hackers. On the internet nothing is completely secure. Hence people may suffer from serious attacks and security breaches. It happens due to the interdependency of the system
SECURITY CONCERNS IN CLOUD COMPUTING
In this section we first introduce some major security concern-• Network Availability The value of cloud computing [2] can only be realized when our network connectivity and bandwidth meet our minimum needs: The cloud must be available whenever we need it. If it is not, then the consequences are no different than a denial-of-service situation.
• Cloud Provider Viability Since cloud providers are relatively new to the business, there are questions about provider viability and commitment [9]. This concern deepens when a provider requires tenants to use proprietary interfaces, thus leading to tenant lock-in. M a y 2 9 , 2 0 1 5
• Disaster Recovery and Business Continuity
Tenants and users require confidence that their operations and services will continue if the cloud provider's production environment is subject to a disaster.
• Security Incidents Tenants and users [3] need to be appropriately informed by the provider when an incident occurs. Tenants or users may require provider support to respond to audit or assessment findings. Also, a provider may not offer sufficient support to tenants or users for resolving investigations.
• Transparency When a cloud provider does not expose details of their internal policy or technology implementation, tenants or users must trust the cloud provider's security claims. Even so, tenants and users require some transparency by providers as to provider cloud security, privacy, and how incidents are managed.
• Loss of Physical Control
Since tenants and users lose physical control over their data and applications, these results in a range of concerns: (a) Privacy and Data With public or community clouds, data may not remain in the same system, raising multiple legal concerns.
(b) Control over Data User or organization data may be comingled in various ways with data belonging to others.
(c) A tenant administrator has limited control scope and accountability within a Public infrastructure-as-a-service (IaaS) implementation [8], and even less with a platform-as-a-service (Paas) one. Tenants need confidence that the provider will offer appropriate control, while recognizing that tenants will simply need to adapt their expectations for how much control is reasonable within these models.
(d) New Risks, New Vulnerabilities There is some concern that cloud computing brings new classes of risks and vulnerabilities. Although we can postulate various hypothetical new risks [10], actual exploits will largely be a function of a provider's implementation. Although all software, hardware, and networking equipment are subject to unearthing of new vulnerabilities, by applying layered security and well-conceived operational processes, a cloud may be protected from common types of attack even if some of its components are inherently vulnerable.
RELATED WORK
Tejinder Sharma, et.al (2013) [1]: In this paper author discuss about the cloud computing. As, the computer networks are still in their infancy, but they grow up and become sophisticated. Cloud computing is emerging as a new paradigm of large scale distributed computing. It has moved computing and data away from desktop and portable PCs, into large data centers. It has the capability to harness the power of Internet and wide area network to use resources that are available remotely.
Sonal Guleria, Dr. Sonia Vatta (2013) [2]: Describes that the Cloud computing is emerging field because of its performance, high availability, least cost and many others. In cloud computing, the data will be stored in storage provided by service providers. Cloud computing provides a computer user access to Information Technology (IT) services which contains applications, servers, data storage, without requiring an understanding of the technology. An analogy to an electricity computing grid is to be useful for cloud computing. To enabling convenient and on-demand network access to a shared pool of configurable computing resources are used for as a model of cloud computing.
Pradeep Bhosale et.al(2012) [3]: Discusses that today's world relies on cloud computing to store their public as well as some personal information which is needed by the user itself or some other persons. Cloud service is any service offered to its users by cloud. As cloud computing comes in service there are some drawbacks such as privacy of user's data, security of user data is very important aspects. In this paper author discuss about the enhancement of data security. Not only this makes researchers to make some modifications in the existing cloud structure, invent new model cloud computing and much more but also there are some extensible features of cloud computing that make him a super power. [3] To enhance the data security in cloud computing used the 3 dimensional framework and digital signature with RSA Encryption algorithm. Virtualization is an emerging IT paradigm that separates computing functions and technology implementations from physical hardware. By using virtualization, users can access servers without knowing specific server details.
Cong Wang et.al (2010)[5]:
In this paper, author discusses about the security in cloud computing. Cloud Computing consists the architecture of IT enterprise. The cloud computing has the many advantages in the information technology field: on demand self service, ubiquitous network access, location independent resource pooling, rapid resource elasticity, usage-based pricing and transference of risk. [5] Cloud computing brings the new and challenging security threats towards users outsourced data. For this purpose, cloud service providers are used. These are the separate administrative entities. M a y 2 9 , 2 0 1 5 Ryan K. L. Ko et.al (2011) [6]: In this paper, author describes the various schemes that are used in the security of cloud computing. Cloud computing signifies a paradigm shift from owning computing systems to buying computing services. In this paper, author encourages the adoption of file centric and data centric logging mechanisms. It helps in increasing the accountability. The security in the cloud computing is a big issue. For this purpose, data transparency, access within the cloud and lack of clarity in data ownership were surfaced. Here author purpose a new scheme, which helps in providing security to cloud computing. This scheme finds out the various approaching traditional security and trust problems. Here the data centric approach is use, which helps in increasing trust and security of data in the cloud.
Shuai Han et.al (2011) [7]: In this paper, author uses a third party auditor scheme. Cloud computing technology acts as next generation architecture of IT solution. It enables the users to move their data and application software to the network which is different from traditional solutions. Cloud computing provides the various IT services, due to which it contains many security challenges. The data storage security is the big issue in cloud computing. In this paper, author purpose a new scheme called third party auditor. It helps in providing the trustful authentication to user.
Jen-Sheng Wang et.al (2011) [8]: In this paper, author about the various methods and techniques which helps in managing the security of cloud computing. The information security is critical issue in the age of Internet. The information is valuable and important. The cloud computing has made information security managing a most significant and critical issue. The information security in cloud computing requires many factors. In this paper, the Key Success Factors are used. These factors include many aspects as: external dimension, internal dimension, technology dimension, and execution dimension. These factors are used to purpose a new scheme, which is used to overcome the various problems in cloud computing that are related to the security.
Eman M.Mohamed, Hatem S.Abdelkader (2013) [9]: In this paper, author discusses about the data security issues in cloud computing. Data security model provides a single default gateway as a platform. It used to secure sensitive user data across multiple public and private cloud applications, including salesforce, Chatter without influencing functionality or performance. Default gateway plateform encrypts sensitive data automatically in a real time before sending to the cloud storage without breaking cloud application. It did not effect on user functionality and visibility. If an unauthorized person gets data from cloud storage, he only sees encrypted data. If authorized person accesses successfully in his cloud, the data is decrypted in real time for our use.
Teemu Kanstren, Sami Lehtonen, Reijo Savola(2015)[10]:
In this paper, author discusses about architecture for providing increased confidence in measurements of such cloud-based deployments. The architecture is based on a set of deployed measurement probs and trusted platform modules across both the host infrastructure and guest virtual machines. The TPM are used to verofy the integrity of the probes and measurements they provide. This allows us to ensure that the system is running in the expected environment, the monitoring probes have not been tampered with and the integrity of measurement dat provided is maintained. Overall this gives us a basis for incrresed confidence in the security of running parts of our system in an external cloud-based environment.
MOTIVATION FOR RESEARCH
In present work firstly client sendsdata to server, afterwards server encrypts the data. Here AES encryption algorithm is used to encrypt or decrypt user's data file. After that encrypted data is placed on the storage cloud. At its core, the architecture consists of four components: 1). A server, then process and encrypts data before it is sent to cloud; 2). A cloud 'A' that archive another half of user's files; 3). A cloud b, that archive another half of the same user's file; and 4). A private cloud that holds the Meta data information.
Figure 1. Client and Cloud Interaction
• Currently encrypted data is stored in different clouds.
• The cloud usage cost is very high and also complexity of the system is increased. M a y 2 9 , 2 0 1 5 • This multi-cloud architecture specifies that the application data is partitioned and distributed to distinct clouds.
• The most common forms of data storage are files and database.
Figure 2. Problem Formulation
Cloud based systems saves data off multiple organizations on shared hardware systems. Data segregation is done by encrypting data of users, but encryption is not complete solution. We can do segregate data by creating virtual partitions of data for saving and allowing user to access data in his partition only. Malicious activity monitoring is a tough task in cloud system as logging data might be spread over multiple hosts and data centres. Restricting user to his own virtual partition only will not allow logs to be dispersed allowing access to logs for monitoring easily. User access is another major concern in restricting user access is a major challenge in cloud based storage system. Use of virtual partition and enhanced user access control in cloud system will allow us to improve data security. Enhanced Cloud system will be compared with existing secure cloud systems. We will compare enhanced system against security, performance & ease of use.
By distributing data on different clouds it introduces performance overhead when client needs to access all data frequently, e.g. client needs to perform a global data analysis on all data. The analysis may have to access data from multiple locations, with a degraded performance. By simply using in single cloud provider can having the following main issues: Less Security. Loss of data; No privacy; Cost of maintenance is high.
OBJECTIVES
This section states the proposed data security model in cloud computing by integrating the OTP based authentication with two encryption algorithms like AES and RSA. In the first phase, client will register and login with the cloud provider. After successful login, cloud server will generate the OTP ( One Time Password ).
Figure 3. OTP Based Authentication Model
Client will perform the RSA encryption before sending the data to the cloud.
The file sent by the client is received at the server end and server will further perform the AES encryption on the received data.
After encrypting the data, server will perform the fragmentation on the encrypted file and will send it to the cloud storage area. M a y 2 9 , 2 0 1 5 Cloud provider will receive the file and will store it in the different zones for security purposes.
Cloud provider will also replicate the data on the backup server.
Figure 4. Proposed Cloud Security Model
We will be using the CloudSim as a simulator for implementing the proposed methodology. Cloud service providers charge users depending upon the space or service provided. In R&D, it is not always possible to have the actual cloud infrastructure for performing experiments. For any research scholar, academician or scientist, it is not feasible to hire cloud services every time and then execute their algorithms or implementations. For the purpose of research, development and testing, open source libraries are available, which give the feel of cloud services. Nowadays, in the research market, cloud simulators are widely used by research scholars and practitioners, without the need to pay any amount to a cloud service provider.
CONCLUSION
With the continuous growth and expansion of cloud computing, security has become one of the serious issues. Cloud computing platform need to provide some reliable security technology to prevent security attacks, as well as the destruction of infrastructure and services. There is no doubt that the cloud computing is the development trend in the future. Cloud computing brings us the approximately infinite computing capability, good scalability, service on-demand and so on, also challenges at security, privacy, legal issues and so on. But to solving the existing issues becomes utmost urgency. To protect against the compromise of the compliance integrity and security of their applications and data, firewall, Intrusion detection and prevention, integrity monitoring, log inspection, and malware protection. Proactive enterprises and service providers should apply this protection on their cloud infrastructure, to achieve security so that they could take advantage of cloud computing ahead of thei r competitors. These security solutions should have the intelligence to be selfdefending and have the ability to provide real-time detection and prevention of known and unknown threats. To advance cloud computing, the community must take proactive measures to ensure security. | 4,580.8 | 2015-05-29T00:00:00.000 | [
"Computer Science"
] |
Investigation of Barkhausen Noise Emission in Steel Wires Subjected to Different Surface Treatments
Steel rope wires represent the main bearing components of bridges whose long-term operation depends on loading conditions, corrosion attack, and/or pre-stressing. Corrosion attack especially can remarkably reduce the effective cross-sectional area, which in turn over-stresses the wires and redistributes stress to the neighboring wires. The premature collapse of many bridges is very often caused by wire rupture as a result of their poor corrosion protection. For these reasons, various processes—such as galvanizing, phosphating, etc.—have been applied to steel wires to increase their resistance against corrosion. However, these processes can alter the microstructure, especially in the near-surface regions. The Barkhausen noise technique has been already reported as a suitable technique for investigating corrosion extent and true pre-stress in the steel rope wires. This study reports that non-homogeneity of the surface state of wires undergoing different surface treatment makes it more difficult to assess the true stress state and increase the uncertainty of Barkhausen noise measurement. Barkhausen noise signals are correlated with metallographic and SEM observations as well as microhardness measurements. The non-homogeneity of the surface state of wires is also investigated by the use of chemical mapping and linear chemical analyses.
Introduction
Magnetic Barkhausen noise (MBN) is a function of the stress state [1], its variation [2] as well as microstructure of ferromagnetic bodies [3]. MBN refers to the irreversible and discontinuous jumps of domain walls (DWs) [4] whose sudden displacement, initiated by altering the magnetic field, produces electromagnetic (as well as acoustic) pulses [5]. These pulses can be detected on the free surface using a suitable pick-up coil. Their magnitude depends on the DWs' alignment, the free path of DW motion, the strength of the magnetic field to unpin DWs, and the capability of the sensing system to distinguish among the individual pulses. It is well known that DWs align with the direction of tensile stress and contribute to the increasing magnitude of MBN pulses, thus producing higher MBN emission, whereas compressive stress aligns DWs in the direction perpendicular to the direction of the stresses, which in turn decreases MBN [6]. This behavior explains the sensitivity of MBN against the different magnitudes and regimes of stresses and has been already reported [7]. The MBN technique has been already introduced as a method capable of assessing the real stress state in steel rope wires [8] and the
Experimental Details
The experiments were carried out using new wires with a nominal diameter of 5.4 mm (ultimate strength of 1867 MPa and hardness of 485 ± 12 HV0.2 in the wire center and 500 ± 9 HV0.2 near to the wire surface-valid for the conventional wires only after quenching and high-temperature tempering). The nominal chemical composition of the investigated wires is shown in Table 1. The bulk microstructure of the wires is composed of a ferromagnetic sorbitic-pearlite structure, as Figure 1 illustrates. Experiments were carried out on the four different wires as follows (the details about the exact conditions were kept secret by the manufacturer): the conventional one (quenched and high temperature tempered), -phosphated (Zn phosphated after quenching + high-temperature tempering), -galvanized (galvanized after quenching + high-temperature tempering), -compacted (galvanized and subsequently plastically deformed by drawing in a die).
Microhardness (HV0.2) was measured using an Innova Test 400 TM (Innovatest, Maastricht, The Netherlands) tester by applying a 200 g force for 10 s on the longitudinal cuts. Microhardness values (as well as the standard deviations) were obtained from five repeated measurements. The microhardness was measured in different positions with respect to the cross-section perimeter of wires. However, the most important information for the microhardness and corresponding dislocation density was obtained from the near-surface measurements due to the limited sensing depth of MBN.
To reveal the microstructure of the wires, all wires were cut using a Struers Secotom 50 (Struers Inc., Cleveland, OH, USA) in the longitudinal and perpendicular directions. The cut specimens were hot molded, ground, polished, and etched with 3% Nital for 5 s. The hot-molded specimens were used for microhardness measurements as well. The macro and microstructure of the samples were observed using the light microscopes (LM) Olympus SZx16 and Zeiss AxioCam MRc5 (Boston Microscopes, MA, USA). Scanning electron microscopy (SEM) was carried out using a Tescan Vega LMU (Tescan, Brno, Czech Republic) microscope equipped with a Brucker energy dispersive X-ray analyzer.
MBN was analyzed using a RollScan 350 (Stresstech, Jyväskylä, Finland) and MicroScan 600 software (mag. voltage 5 V, mag. frequency 125 Hz, sensor type S1-18-12-01, frequency range of MBN pulses 30-180 kHz). MBN values were obtained by averaging six MBN bursts (three magnetizing cycles). MBN refers to the rms (effective) value of the signal. The wires were magnetized along their axis when the 200 mm long wires were loaded an Instron 5985 (Instron, Norwood, MA, USA) device. Tensile stresses gradually increased in 100 MPa increments up to 1200 MPa. A brief sketch of the sensor positioning and magnetization can be found in a previous study [9]. The sensor position during the measurement was fixed using a self-made stand. The force in the interface between the sensor and wire was kept constant using a spring (further details can be found in [10]).
Coatings 2020, 10, x FOR PEER REVIEW 3 of 15 dislocation density was obtained from the near-surface measurements due to the limited sensing depth of MBN.
To reveal the microstructure of the wires, all wires were cut using a Struers Secotom 50 (Struers Inc., Cleveland, OH, USA) in the longitudinal and perpendicular directions. The cut specimens were hot molded, ground, polished, and etched with 3% Nital for 5 s. The hot-molded specimens were used for microhardness measurements as well. The macro and microstructure of the samples were observed using the light microscopes (LM) Olympus SZx16 and Zeiss AxioCam MRc5 (Boston Microscopes, MA, USA). Scanning electron microscopy (SEM) was carried out using a Tescan Vega LMU (Tescan, Brno, Czech Republic) microscope equipped with a Brucker energy dispersive X-ray analyzer.
MBN was analyzed using a RollScan 350 (Stresstech, Jyväskylä, Finland) and MicroScan 600 software (mag. voltage 5 V, mag. frequency 125 Hz, sensor type S1-18-12-01, frequency range of MBN pulses 30-180 kHz). MBN values were obtained by averaging six MBN bursts (three magnetizing cycles). MBN refers to the rms (effective) value of the signal. The wires were magnetized along their axis when the 200 mm long wires were loaded an Instron 5985 (Instron, Norwood, MA, USA) device. Tensile stresses gradually increased in 100 MPa increments up to 1200 MPa. A brief sketch of the sensor positioning and magnetization can be found in a previous study [9]. The sensor position during the measurement was fixed using a self-made stand. The force in the interface between the sensor and wire was kept constant using a spring (further details can be found in [10]). Figure 2 shows that the microhardness in the center of the conventional wires is less than that in the near-surface regions. This behavior is due to the more intensive plastic deformation in the nearsurface region during wires sizing in dies (friction). The phosphated wire exhibits slightly higher microhardness in the deeper wire regions and remarkable lower microhardness in the thin nearsurface layer. The galvanized wire exhibits higher microhardness in the deeper regions (compared with the conventional and phosphated wires). However, thermal softening penetrates much deeper. It is considered that this thermal softening is due to thermal effect during wires' thermo-chemical processing. Furthermore, the microhardness profiles for the galvanized and compacted wires are asymmetrical with respect to the wire diameter. Finally, the compacted wire exhibits the highest microhardness due to strain hardening during drawing as the last step of the wire processing. Figure 2 shows that the microhardness in the center of the conventional wires is less than that in the near-surface regions. This behavior is due to the more intensive plastic deformation in the near-surface region during wires sizing in dies (friction). The phosphated wire exhibits slightly higher microhardness in the deeper wire regions and remarkable lower microhardness in the thin near-surface layer. The galvanized wire exhibits higher microhardness in the deeper regions (compared with the conventional and phosphated wires). However, thermal softening penetrates much deeper. It is considered that this thermal softening is due to thermal effect during wires' thermo-chemical processing. Furthermore, the microhardness profiles for the galvanized and compacted wires are asymmetrical with respect to the wire diameter. Finally, the compacted wire exhibits the highest microhardness due to strain hardening during drawing as the last step of the wire processing. The asymmetry of the microhardness in the case of galvanized and compacted wires originated from the surface state non-homogeneity and the corresponding variable thickness of the galvanized layer, as seen in Figure 3c,d (see also Figures 4 and 5). Missing detail information about the wires processing makes it difficult to explain why the galvanized layer is distributed non-homogenously around the wires surface, why the galvanized layer thickness varies from zero (position 1) up to 42 μm (position 4), see also Figure 4. Figures 4 and 5 also demonstrate that the positions in which zero galvanized layer is found exhibit notches, as contrasted to the positions with a deposited galvanized layer (their surface is more or less smooth). These notches can be found on the surface of phosphated wire as well (see Figure 6) and on the SEM images, such as that illustrated in Figure 7. Zn layer in the notched positions occurs in the form of localized regions whereas the thick and continuous Zn layer of variable thickness can be found in the positions 4 (see Figure 7). The depth of notches strongly varies through the wires periphery. It is considered that the remarkable differences in the wires' appearance with respect of their circumference is driven by non-homogenous conditions during the wires' thermo-chemical processing. No remarkable differences can be found on metallographic images for the conventional wire and the surface state in all positions is comparable with that depicted in Figure 6b. Furthermore, Figures 4 and 5 also illustrate that the galvanizing process initiates surface decarburizing, which appears white on LM images. This zone corresponds to the thermally softened region indicated by microhardness measurement. However, plastic deformation of compacted wires makes the thickness of the galvanized layer more homogenous (as Figure 5 depicts) and produces wire with a hexagonal profile. Due to the remarkable non-homogeneity in thickness of the galvanized layer on the wires' surface, as well as the corresponding depth in which decarburizing takes place, MBN measurements (as well as the conventional destructive analysis) were carried in four different positions, as Figure 3 illustrates. In the case of compacted wire (containing six flat regions), only 1, the galvanizing free surface (position 1), was compared with those containing a galvanized layer (positions 2, 3, and 4).
Microhardness, LM, and SEM Observation
Chemical mapping (shown in Figures 8-11) shows a Zn rich coating layer of variable thickness, indicated by green, and an underlying steel matrix. The positions in which zero coating layer was detected (positions 1) exhibit strongly reduced Zn fraction (see The asymmetry of the microhardness in the case of galvanized and compacted wires originated from the surface state non-homogeneity and the corresponding variable thickness of the galvanized layer, as seen in Figure 3c,d (see also Figures 4 and 5). Missing detail information about the wires processing makes it difficult to explain why the galvanized layer is distributed non-homogenously around the wires surface, why the galvanized layer thickness varies from zero (position 1) up to 42 µm (position 4), see also Figure 4. Figures 4 and 5 also demonstrate that the positions in which zero galvanized layer is found exhibit notches, as contrasted to the positions with a deposited galvanized layer (their surface is more or less smooth). These notches can be found on the surface of phosphated wire as well (see Figure 6) and on the SEM images, such as that illustrated in Figure 7. Zn layer in the notched positions occurs in the form of localized regions whereas the thick and continuous Zn layer of variable thickness can be found in the positions 4 (see Figure 7). The depth of notches strongly varies through the wires periphery. It is considered that the remarkable differences in the wires' appearance with respect of their circumference is driven by non-homogenous conditions during the wires' thermo-chemical processing. No remarkable differences can be found on metallographic images for the conventional wire and the surface state in all positions is comparable with that depicted in Figure 6b. Furthermore, Figures 4 and 5 also illustrate that the galvanizing process initiates surface decarburizing, which appears white on LM images. This zone corresponds to the thermally softened region indicated by microhardness measurement. However, plastic deformation of compacted wires makes the thickness of the galvanized layer more homogenous (as Figure 5 depicts) and produces wire with a hexagonal profile. Due to the remarkable non-homogeneity in thickness of the galvanized layer on the wires' surface, as well as the corresponding depth in which decarburizing takes place, MBN measurements (as well as the conventional destructive analysis) were carried in four different positions, as Figure 3 illustrates. In the case of compacted wire (containing six flat regions), only 1, the galvanizing free surface (position 1), was compared with those containing a galvanized layer (positions 2, 3, and 4).
Chemical mapping (shown in Figures 8-11) shows a Zn rich coating layer of variable thickness, indicated by green, and an underlying steel matrix. The positions in which zero coating layer was detected (positions 1) exhibit strongly reduced Zn fraction (see
MBN Measurements
Tensile stresses usually increase MBN as a result of the preferential alignment of DW in the direction of the exerted load [1]. However, Figure 12 illustrates that MBN decreases progressively (or saturation can be found for the galvanized and compacted wires at higher stresses). Figure 13 demonstrates that the preferential orientation of the wire matrix in the direction of their axis is developed and kept in all wires; the conventional one, as well as the phosphated, galvanized, and compacted. In the case of the galvanized and compacted wires only, the preferential matrix texture is shadowed by decarburizing in the near-surface region (white near-surface region). For this reason, for all wires, DWs were already aligned in the direction of their stress and no additional DW alignment can be initiated by tensile stresses (all magnetocrystalline energy was fully consumed during wire manufacture).
MBN Measurements
Tensile stresses usually increase MBN as a result of the preferential alignment of DW in the direction of the exerted load [1]. However, Figure 12 illustrates that MBN decreases progressively (or saturation can be found for the galvanized and compacted wires at higher stresses). Figure 13 demonstrates that the preferential orientation of the wire matrix in the direction of their axis is developed and kept in all wires; the conventional one, as well as the phosphated, galvanized, and compacted. In the case of the galvanized and compacted wires only, the preferential matrix texture is shadowed by decarburizing in the near-surface region (white near-surface region). For this reason, for all wires, DWs were already aligned in the direction of their stress and no additional DW alignment can be initiated by tensile stresses (all magnetocrystalline energy was fully consumed during wire manufacture).
MBN Measurements
Tensile stresses usually increase MBN as a result of the preferential alignment of DW in the direction of the exerted load [1]. However, Figure 12 illustrates that MBN decreases progressively (or saturation can be found for the galvanized and compacted wires at higher stresses). Figure 13 demonstrates that the preferential orientation of the wire matrix in the direction of their axis is developed and kept in all wires; the conventional one, as well as the phosphated, galvanized, and compacted. In the case of the galvanized and compacted wires only, the preferential matrix texture is shadowed by decarburizing in the near-surface region (white near-surface region). For this reason, for all wires, DWs were already aligned in the direction of their stress and no additional DW alignment can be initiated by tensile stresses (all magnetocrystalline energy was fully consumed during wire manufacture). Figures 12 and 14 also illustrate more pronounced homogeneity in MBN in unloaded conditions with respect to the four different positions on the wire surface for the conventional wire compared with the phosphated one. It is thought that this aspect is driven by the non-homogenous conditions during wire phosphating in the different regions of the wire surface resulting in a non-homogenous micro-hardness distribution (see Table 2). Moreover, the variable surface roughness, when the smooth surface in position 4 is contrasted against the notched one in position 2, also takes a certain role [20]. Increasing height of surface irregularities decrease MBN due to higher demagnetizing fields [20]. However, MBN is lower for positions in which the thick and continuous Zn layer and smoother wire surface is observed. Therefore, it can be reported that influence of Zn layer (with respect to decreasing MBN) prevails and contribution of uneven surface is minor. Progressively increasing tensile stresses remarkably decreases the difference among the different positions in the case of the conventional and phosphated wires (see Figure 14). It could be expected that the phosphated wire would exhibit higher MBN due to lower surface microhardness as a result of thermal softening during phosphating. However, this aspect is compensated by the presence of Zn precipitates in the near-surface phosphated layer. These precipitates slow down DWs speed of motion and/or reduce DWs free path of displacement, which in turn reduces the magnitude of MBN pulses and, thus, the entire MBN emission. Finally, physical background associated with the evolution of MBN versus tensile stress should be discussed. Amiri et al. [22] observed that MBN increased at low tensile stresses, attained a maximum, and then decreased as a result of predominating crystal anisotropy at low stresses, whereas predominating stress anisotropy at higher stresses results in decreasing MBN. Therefore, the easy axis is driven by magnetocrystalline anisotropy at lower stresses when DWs turn in the direction of the magnetic easy axis. However, the new easy axis is controlled by stress at higher tensile stresses [22]. Additionally, Jiles [4] reported that the evolution of MBN is driven by competition between the energy of magnetocrystalline anisotropy and magnetoelastic energy. Wire galvanizing produces a surface non-ferromagnetic layer of variable thickness on the wires. This layer decrease MBN as a result of superimposing the contributions of some aspects. The first one is associated with the increasing gap between the sensor and the wire surface, which makes a weaker magnetic field in the wire. For this reason, some DWs remain unpinned and do not contribute to the MBN emission. A weaker magnitude of magnetizing field also decreases the speed in which magnetizing field H alternates in time t, dH/dt, as the force initiating DWs unpinning and affecting their free path of motion [21]. The second aspect is associated with the attenuation of produced MBN pulses during their propagation towards the free surface through the protective layer. The intensity of MBN pulse attenuation grows along with the increasing thickness of the non-ferromagnetic coating on the ferromagnetic matrix [19]. This background explains why the MBN values for galvanized wire are below the MBN for the conventional or phosphated wires (see Figure 12c). Only position 1 exhibits comparable MBN values since this position contains no galvanized layer on the surface (see Figure 4a). MBN for the galvanized wires in the different positions (especially in the unloaded state) is driven by the thickness of the protective layer, as Figure 15a indicates (see also Figure 12c,d and Table 3) since MBN progressively decreases versus layer thickness. Microhardness, in this case, plays no role since the lower microhardness in the positions with a thicker layer should increase MBN but does not (see also Table 2). The concept of galvanized wires results in quite remarkable non-homogeneity of MBN, as Figure 12c demonstrates. This non-homogeneity is driven by the non-homogenous thickness of the galvanized layer on the wire surface (see Figure 14). However, the concept of compacting makes the galvanized layer thickness on the surface more homogenous, which in turn contributes to the lower ∆MBN in the unloaded state. More pronounced differences can be found, especially between the uncoated and coated positions only (see Figure 15b). It has been already explained that weaker MBN in the coated positions is driven by the attenuation of MBN pulses propagating toward the free surface through the coating as well as weaker magnetic field in the ferromagnetic matrix which slows down DWs motion. Finally, physical background associated with the evolution of MBN versus tensile stress should be discussed. Amiri et al. [22] observed that MBN increased at low tensile stresses, attained a maximum, and then decreased as a result of predominating crystal anisotropy at low stresses, whereas predominating stress anisotropy at higher stresses results in decreasing MBN. Therefore, the easy axis is driven by magnetocrystalline anisotropy at lower stresses when DWs turn in the direction of the magnetic easy axis. However, the new easy axis is controlled by stress at higher tensile stresses [22]. Additionally, Jiles [4] reported that the evolution of MBN is driven by competition between the energy of magnetocrystalline anisotropy and magnetoelastic energy. Finally, MBN for the compacted wires is higher compared with the galvanized wires only despite the higher microhardness of the matrix and thicker galvanized layer (see Figure 12c,d). However, the shape factor plays a role in this case when the sensor magnetizing poles of rounded shape are not in contact with the circular profile wire (as in the case of the galvanized wire, see Figure 3c). A hexagonal final shape is obtained for the compacted wire (see Figure 3d). For this reason, the sensor poles are in contact with the flat surface, which produces more favorable conditions for wire magnetization and MBN pulse acquisition.
Finally, physical background associated with the evolution of MBN versus tensile stress should be discussed. Amiri et al. [22] observed that MBN increased at low tensile stresses, attained a maximum, and then decreased as a result of predominating crystal anisotropy at low stresses, whereas predominating stress anisotropy at higher stresses results in decreasing MBN. Therefore, the easy axis is driven by magnetocrystalline anisotropy at lower stresses when DWs turn in the direction of the magnetic easy axis. However, the new easy axis is controlled by stress at higher tensile stresses [22]. Additionally, Jiles [4] reported that the evolution of MBN is driven by competition between the energy of magnetocrystalline anisotropy and magnetoelastic energy.
The energy of magnetocrystalline anisotropy, E a , is a function of magnetocrystalline anisotropy, as Equation (1) indicates for cubic crystals [23].
where α 1 , α 2 , and α 3 are the direction cosines of the magnetisation vector with respect to three cube edges while K 1 and K 2 represent magnetocrystalline anisotropy constants. The magnetoelastic energy, E σ , can be expressed as [23] where λ s is the isotropic magnetostriction and ϕ defines the angle between the direction of magnetization and the direction of exerted stress σ. Increasing MBN with increasing tensile stresses occurs when the magnetocrystalline energy is higher than the magnetoelastic energy. As soon as the magnetoelastic energy exceeds the magnetocrystalline energy, a decrease in MBN with increasing tensile stress can be found. Such behavior was also found in a previous study in which the real pre-stress in a steel rope wire was investigated after long-term operation [8]. Figure 12 demonstrates that MBN gradually decreases with increasing tensile stress only since the magnetocrystalline energy E a is fully consumed and MBN evolution is completely driven by the predominating stress anisotropy produced during drawing when sizing wires in dies (remarkable texture). Moreover, the wires are stressed during annealing to prevent future possible relaxation of pre-stress in wires during long-term operation. For this reason, also remarkable, the stress annealing anisotropy contributes to the aforementioned evolution of MBN versus stresses. The stress annealing anisotropy can be expressed as [24] E Ku = −K uσ sin 2 ϕ where ϕ denotes the angle between the direction of magnetization and the stress axis and K uσ represents the stress-induced anisotropy constant. As Hilzinger and Rodewald [24] reported, the stress-induced anisotropy K uσ increases along with the annealing temperature and is proportional to the applied stress σ during annealing. Stress-induced anisotropy is negative (as in this particular case) when the tensional stresses create the easy magnetic axis along the stress axis. This process produces wire of remarkable microstructure and the corresponding magnetic anisotropy when DWs are preferentially oriented in the longitudinal direction (along the wire axis).
Conclusions
The main role of the MBN technique can be viewed as monitoring true pre-stress. It was demonstrated and explained that low MBN is associated with high pre-stress and vice versa. Figure 12 illustrates the variation of MBN around the wire surface in the unloaded state for the conventional wire and the more pronounced variation for the phosphated one. However, the variation of MBN for both wires decreases with increasing tensile stress. The real pre-stress in the steel rope wires is usually about 1200 MPa and tends to release due to relaxation (when low corrosion extent during long term operation is considered and the corresponding limited over-stressing). Figure 12 also illustrates that the conventional and phosphated wires exhibit good sensitivity of MBN versus tensile stress within the whole range of investigated stresses. On the other hand, this relationship saturates as soon as tensile stress attains 600 MPa in the case of galvanized and compacted wires. For this reason, the concept of the conventional and phosphated wires could be found acceptable when the MBN technique is considered for their pre-stress monitoring. However, the galvanizing increases the variation of MBN at low tensile stresses and makes it impossible to assess tensile stresses above 600 MPa. The non-homogeneity of the surface state and the corresponding variation of MBN values at the different positions increases measurement uncertainty and makes it difficult to assess the true pre-stress in the galvanized wires (since information about the true thickness of the galvanized layer through which MBN measurement is carried is usually missing in real conditions). To increase the reliability of the true pre-stress assessment, more repeat measurements must be carried out. Funding: This study was supported by APVV project No. 14-0772 and VEGA projects Nos. 1/0413/18 and 1/0045/19.
Conflicts of Interest:
The authors declare no conflict of interest. | 6,148.8 | 2020-09-23T00:00:00.000 | [
"Materials Science"
] |
Recombination spot identification Based on gapped k-mers
Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have proposed several computational methods for recombination spot identification. The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics.
Recombination plays an important role in genetic evolution, which describes the exchange of genetic information during the period of each generation in diploid organisms 1 . The original genetic information is generated from homologous chromosomes. Therefore, recombination provides many new combinations of genetic variations and is an important source for biodiversity [2][3][4] , which can accelerate the procedure of biological evolution.
To improve the predictive accuracy, researchers have proposed several computational methods for recombination spot identification, which are based on some well known machine learning techniques, such as support vector machine (SVM) 5,6 , K-nearest neighbor (KNN) 7,8 , Random Forest(RF) 9,10 , ensemble classifiers [11][12][13][14] , ranking 15 , etc. Various features are employed by these methods. The first computational predictor for recombination identification is based on sequence dependent frequencies 16 . Liu et al. 17 have exploited quadratic discriminant analysis to predict hot or cold spots. However, these methods only consider the local sequence composition information, and ignore all the long-range or global sequence-order effects. To overcome this disadvantage, Li et al. 5 propose a novel method based on nucleic acid composition (NAC), n-tier NAC and pseudo nucleic acid composition (PseNAC). Following this study, researchers have proposed various predictors [18][19][20][21] . It has been shown that recombination not only depends on DNA primary sequences, but also is influenced by the chromatin structure. Getun et al. 22 have exploited nucleosome occupancy to identify mouse recombination hotspots. Besides these features, some other sequence features also influence recombination and representative samples, such as the palindrome structure 23,24 , relatively high GC content 25 , dinucleotides bias 26 , repeats, consensus DNA motifs 27 , etc. Therefore, some computational predictors employ these features, and achieve better performance.
All these computational methods could yield quite encouraging results, and each of them did play a role in stimulating the development of recombination spot identification. However, further study is needed due to the following reason. Among the aforementioned features, k-mer 6,28-32 is one of the simplest, and most widely used features in this field. The k-mer is a nucleotide fragment with k neighboring residues. By using this feature, the local sequence composition information can be extracted. Typically, the value of k is set to 6 or 7, and the length of their corresponding feature is 4 6 = 4096 or 4 7 = 16384. Actually, larger k values are preferred, because more sequence composition information can be incorporated. However, large k values (k > 6) will lead to extremely sparse feature vectors, which may cause a severe over-fitting problem. In order to find a tradeoff between the sparse feature space problem and more sequence composition information, the gapped k-mer has been proposed, and successfully applied to enhancer identification 33,34 . Gapped k-mer allows several gaps to exist in k-mers. Therefore, it cannot only significantly reduce the length of the resulting feature vectors, but also takes the evolutionary process into consideration. The evolution involves changes of single residues, insertions and deletions of several residues, gene doubling and gene fusion. With these changes accumulated for a long period, many similarities between initial and resultant DNA sequences are gradually eliminated, but they may still share many common features. GKM is able to consider these changes in the DNA sequences via using the gaps.
In this study, we apply the gapped k-mer to recombination spot identification, and propose a new computational predictor called SVM-GKM via combining GKM with Support Vector Machines. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms the two state-of-the-art methods in the field of recombination spot identification, and some interesting patterns can be discovered by analyzing the discriminative features in SVM-GKM.
Materials and Methods
Benchmark Dataset. Here, we employ a benchmark dataset taken from Liu et al. 17 to evaluate the performance of various predictors for recombination identification. This benchmark dataset contains a recombination hotspot subset and a recombination coldspot subset, which can be defined as where positive subset ∑ + contains recombination hotspots, negative subset ∑ − contains recombination coldspots, and symbol ∪ represents the "union" in the set theory. There are 490 hotspots in ∑ + and 591 coldspots in ∑ − . The codes of the 1081 DNA samples as well as their detailed sequences are given in the Supplementary S1.
Gapped k-mer. With the increase of word length k, the method based on k-mers could cause the sparse problem. This is because many k-mers are not appeared in one DNA sequence, and thus its feature vector may contain a large amount of zero values. To overcome this disadvantage caused by k-mers, Ghandi et al. 33 propose a new feature named gapped k-mer method (GKM), which uses k-mers with gaps. Experimental results show that this feature is able to obviously improve the performance for enhancer identification. Motivated by its success, in this study, we apply the GKM to the field of recombination hotspots identification, and propose a computational predictor called SVM-GKM, which uses a full set of k-mers with gaps as features, instead of comparing the whole sequence pairs. It treats gaps as mismatches. For most of the predictors, it is critical to calculate the similarity between two elements in the feature space. The similarity score of two sequences is calculated by the kernel function. Therefore, in this section, we will describe how to calculate the kernel function of SVM-GKM. Eqs 2-6 were originally reported in ref. 33. For further explanation of these equations, please refer to ref. 33.
First, each training sample is represented as a series of k-mers, where k is the length of subsequence. The key to calculate the GKM kernel matrix is to compute the number of mismatches between each pair of sequences for all pairs of k-mers. Here, we define a variable m to stand for is the length of matches, so the length of gaps is k− m.
Then feature vector f S of a given sequence S can be defined as where y i S is the count of the i− th gapped k-mer in the sequence S, = ⋅ ( ) M k m b m stands for the number of all gapped k-mers, and b is the alphabet size. For DNA sequence, b = 4. Then the kernel function between two sequences S 1 and S 2 can be defined as Since the number of all possible gapped k-mers grows extremely rapidly as m increases, direct calculation of Eq. 3 is almost intractable 33 . Thus, the inner product in Eq. 3 is computed by the following equation: where n(n ≤ k − m) is the number of mismatches between two k-mers x 1 and x 2 . x 1 is from S 1 and x 2 is from S 2 , N n (S 1 , S 2 ) is the number of pairs of k-mers with n mismatches in sequences S 1 and S 2 , h n is the corresponding coefficient. h n is defined as follows: In order to reduce the error caused by corresponding coefficients, the following equation is used to get h n when calculating the mismatch for two sequences where n 1 is the mismatch number that k-mer x 1 contains, n 2 is the mismatch number that k-mer x 2 contains, and t is the mismatches number, which exists at the k − n mismatch positions for both x 1 and x 2 , r = n 1 + n 2 − 2t − n.
Tree structure. In this paper, a tree structure is employed to count mismatches 33 so as to improve the calculation efficiency of GKM.
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www.nature.com/scientificreports/ The tree is generated by training samples and we construct it by adding a path for every k-mer. Assume that s(t i ) stands for the path from the root to node t i with depth d. d means that the corresponding sub-sequence has a length of d. For a tree, its maximum depth is k, i.e. the length of the k-mer. Therefore, for a terminal leaf node of the tree, the leaf node represents a k-mer. A terminal leaf node can also hold the list of training sequence labels, which contains the information of appeared k-mers and the number of these k-mers in each sequence. We use depth-first search (DFS) 35,36 order to search the tree and obtain the mismatch profile. Based on the method in 37 , we store the list of pointers to all nodes t i at depth d and also store the number of mismatches between two paths s(t i ) and s(t j ). Differing from this method, our method only needs to store the values of the terminal leaf nodes and does not need to store the information of all nodes. Thus, at the end of one DFS traversal of the tree, the mismatch profiles for all pairs of sequences are completely determined. Figure 1 gives an example of a mismatch tree with k = 3. The tree is generated by sequences S 1 , S 2 , and S 3 . We can see that for node t 6 , s(t 6 ) = ' AAA' . Sequence S 1 contains two counts of substring s(t 6 ), but sequence S 2 and sequence S 3 do not contain this substring. For our experiments, we used the gkm-SVM software v1.3 33 as the implementation of the gapped k-mer and tree structure, which is available at http://www.beerlab.org/gkmsvm/. Support Vector Machine. The support vector machine (SVM) method is a widely used method for classification problems 34,[38][39][40][41][42] , which is based on the structural risk minimization principle from statistical leaning theory [43][44][45][46] . The basic idea of SVM is to construct a separating hyper-plane so as to maximize the margin between positive and negative datasets. SVM first constructs a hyper-plane based on the training dataset. This step exploits the mapping matrix called kernel function to organize a discriminant equation. Then it uses the test dataset to perform classification and obtain the final results. This example only contains two alphabets, A and T. We use k = 3 and three sequences S 1 = AAAAT, S 2 = ATTTT, and S 3 = AATA to build k-mer tree. Each node t i at depth d represents a sequence of length d, denoted by s(t i ), which is determined by the path from the root of the tree to t i . At depth d = 3, for node t 6 , s(t 6 ) = ' AAA' , S 1 contains two counts of this k-mer, S 2 and S 3 do not contain this k-mer. For node t 7 , s(t 7 ) = ' AAT' , S 1 and S 3 both contain one count, and S 2 does not contain this k-mer. Compared t 6 with t 7 , the paths to these two nodes only contain one mismatch. Cross-Validation. K-fold cross-validation is a widely used method for evaluating the performance of a computational predictor 47,48 . In this article, following previous studies 49 , we use 5-fold cross-validation to evaluate the performance of various predictors. First we segment the dataset into five sections, This dataset contains both recombination hotspots and recombination coldspots. Then we get four segments of both hotspots and clodspots as training dataset, and the remain segment as testing dataset. We repeat this operation till all five segments have been already used as testing dataset. Finally, we calculate the mean of the prediction accuracy as our final results.
Evaluation Method of the Performance. Here, we use four metrics, sensitivity (Sn), specificity (Sp), accuracy (Acc), and Mathew's correlation coefficient (MCC) to test the predictor 48,50-52 . The following equations show us how to calculate them.
Results
Performance of SVM-GKM. The SVM-GKM predictor is constructed by only using the gapped k-mer as a feature. We first evaluate the impact of the parameter word length k (see method section for details) on the performance of SVM-GKM. Figure 2 shows the Acc (accuracy) values obtained by the SVM-GKM using the word length k from 8 to 15 with match length m set as 7. The performance of SVM-GKM increases significantly with the growth of k values, and SVM-GKM achieves the best performance when k = 13. These results are not surprising, because for larger k values, more sequence order information can be incorporated into the predictor, contributing to higher performance for recombination spot identification.
Performance comparison between SVM-GKM and kmer-SVM. The k-mer is a widely used feature
considering the local sequence order information along the DNA sequences. GKM is an improvement of k-mer by introducing the gaps into k-mers. For comparison, a predictor called kmer-SVM is constructed based on k-mers. The kmer-SVM can be viewed as a special case of GKM-SVM without gaps. Therefore, the implementation of kmer-SVM is the same as that of SVM-GKM except that the gap number n is set as 0, and the tree structure is also employed so as to reduce the computational cost. The performance of these two methods on the benchmark dataset with different parameters is shown in Fig. 2.
As shown in Fig. 2, SVM-GKM consistently outperforms kmer-SVM, especially for lager word length values (k > 9). We can also see that parameter k does not have significant impact on the performance of SVM-GKM, and SVM-GKM achieves its highest accuracy (86.57%) when k = 13. In contrast, kmer-SVM achieves its highest accuracy (82.31%) when k = 10 and then its performance decreases significantly. This is because when k is larger than 10, the dimension of the feature vectors is very large and many values are zeros, leading to extremely sparse problem. For example, when k = 13, the dimension of the feature vectors generated by kmer-GKM is 4 13 ≈ 6.7 × 10 7 . In contrast, for the same word length, the length of feature vectors generated by SVM-GKM is only ⋅ ( ) 13 6 4 6 ≈ 7.1 × 10 6 , which is much smaller than that of kmer-SVM, and therefore, GKM can efficiently avoid the sparse problem. Figure 3 presents the comparison of the four performance measures between these two predictors, from which we can see that SVM-GKM outperforms kmer-SVM in terms of all the four performance measures.
Comparison to Other Related Methods.
We also compare SVM-GKM with other two highly related methods, including iRSpot-PseDNC 53 and IDQD 17 . They both use the local or long range sequence order information extracted from DNA sequences for recombination spot identification, and achieve the state-of-the-art performance. The iRSpot-PseDNC exploits a novel feature vector called 'pseudo dinucleotide composition' based on six local DNA structural properties, including three angular parameters and three translational parameters. The IDQD method is based on sequence k-mer frequencies proposed by Liu et al. Table 1 shows five-fold cross-validation results of the various predictors on the benchmark dataset, from which we can see that the SVM-GKM outperforms all the other competing methods. The main reason for its better performance is that the SVM-GKM can efficiently reduce the dimension of the resulting feature vectors, RETRACTED www.nature.com/scientificreports/ and avoid the risk of sparse and overfitting problems. Therefore, we conclude that SVM-GKM would be a useful tool for recombination spot identification.
Feature Analysis.
It is interesting to explore if the gapped k-mers can reflect the characteristics of the recombination spots or not. Therefore, the discriminative power of different gapped k-mers in SVM-GKM are calculated by using the Principal Component Analysis (PCA) [54][55][56] , and the most discriminative gapped k-mer is 'CCG*T**C**CA*' (*represents the gaps) according to variance ratio. Interestingly, this gapped k-mer is able to reflect the sequence characteristics of two important yeast hotspot motifs M26 and 4095 57 as shown in Table 2, indicating that the gapped k-mer feature can indeed capture the sequence patterns of the hotspots, and it can explain the reason why the SVM-GKM outperforms other computational predictors.
Discussion
As a widely used feature in the field of recombination spot identification, k-mer only incorporates the local sequence composition information of DNA sequences. In order to overcome this disadvantage, gapped k-mer (GKM) has been proposed to incorporate the long range sequence order information and reduce the length of the feature vectors. GKM successfully overcomes the sparse problem caused by k-mers via introducing the gaps into the k-mers, and has been successfully applied to enhancer identification. In this study, we apply the concept of GKM to the field of recombination spot identification, and demonstrate that this approach can obviously improve the predictive performance. These results are not surprising, because previous studies 48, [58][59][60] show that the long range or global sequence order effects are critical for constructing accurate predictors. Therefore, it is important to explore new features that can capture the characteristics of these motifs. However, it is by no mean an easy task due to the extremely sparse feature vector problem. The gapped k-mer overcomes this problem and incorporates long range sequence order information, and therefore, the proposed predictor SVM-GKM based on gapped k-mers outperforms other state-of-the-art predictors. By analyzing the most discriminative feature in 53 . c From Liu et al. 17 . d The parameter used: k = 10.
M26
ATGACGTCAT CCG*T**C**CA* 4095 GGTCTRGAC CCG*T**C**CA* Table 2. Comparison of the most discriminative gapped k-mer with two known motifs in hotspot sequences. a These two motifs in hotspots are reported by 57 . The gapped k-mer 'CCG*T**C**CA*' with top discriminative power matches these two motifs. The matching bases are shown in bold.
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www.nature.com/scientificreports/ SVM-GKM, it shows that the gapped k-mers indeed reflect the characteristics of some motifs of recombination spots. Besides k-mer and gapped k-mer, palindrome structure, relatively high GC content, dinucleotides bias, and consensus DNA motifs have been showed useful for recombination spot identification. Our future study will focus on exploring various feature combinations to construct a computational predictor. Performance improvement can be expected by using some neural-like computing strategies, such as spiking neural models 6,11,[61][62][63][64] , because these features are able to capture the characteristics of recombination spots in different aspects. This Article reports an application of methodology originally reported in Reference 33 to recombination spot identification. Reference 33 of this Article introduced a feature set called gapped k-mer for regulatory sequence prediction; this Article applied these gapped k-mer features to recombination spot identification, and a computational predictor was constructed for recombination spot identification.
In the original version of the Article, the Abstract included ambiguous sentences which failed to give due credit to the authors of Reference 33. The authors apologize for these errors.
"The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics". now reads: "k-mer is one of the commonly used features for recombination spot identification. However, when the value of k grows larger, the dimension of the corresponding feature vectors increases rapidly, leading to extremely sparse vectors. In order to overcome this disadvantage, recently a new feature called gapped k-mer was proposed (Ghandi et al., PloS Computational Biology, 2014). That study showed that the gapped k-mer feature can improve the predictive performance of regulatory sequence prediction. Motived by its success, in this study we applied gapped k-mer to the field of recombination spot identification, and a computational predictor was constructed. Experimental results on a widely used benchmark dataset showed that this predictor outperformed other highly related predictors".
In addition, there were errors in the definition of y i S in Equation 2.
"where y i S is the length of the i − th gapped k-mer in the sequence S, " now reads: "where y i S is the count of the i − th gapped k-mer in the sequence S, " There were errors in the definition of r in Equation 6.
The following sentence has been added to the end of the first paragraph in the 'Gapped k-mer' section: "Eqs 2-6 were originally reported in ref. 33. For further explanation of these equations, please refer to ref. 33".
There were errors in Equation 7. | 5,096 | 2016-03-31T00:00:00.000 | [
"Biology",
"Computer Science"
] |
Spontaneous aggregation and global polar ordering in squirmer suspensions
We have developed numerical simulations of three dimensional suspensions of active particles to characterize the capabilities of the hydrodynamic stresses induced by active swimmers to promote global order and emergent structures in active suspensions. We have considered squirmer suspensions embedded in a fluid modeled under a Lattice Boltzmann scheme. We have found that active stresses play a central role to decorrelate the collective motion of squirmers and that contractile squirmers develop significant aggregates.
Introduction
Collective motion can be observed at a variety of scales, ranging from herds of large to bacteria colonies or the active motion of organelles inside cells. Despite the long standing interest of the wide implications of collective motion in biology, engineering and medicine (as for example, the ethological implications of the signals exchanged between moving animals, the evolutionary benefits of moving in groups for individuals and for species, the design of robots which can accomplish a cooperative tasks without central control, the understanding of tumor growth or wound healing to mention a few), only recently there has been a growing interest in characterizing such global behavior from a statistical mechanics perspective [1].
Although a variety ingredients and mechanisms have been reported to describe the signaling and cooperation among individuals which move collectively, it is important to understand the underlying, basic physical principles that can provide simple means of cooperation and can lead to emerging patterns and structures [2]. We want to analyze the capabilities of basic physical ingredients to generate emerging structures in active particles which self propel in an embedding fluid medium. These systems constitute an example of active fluids, systems which generate stresses by the conversion of chemical into mechanical energy. To this end, we will consider model suspensions of swimming particles (building on the squirmer model introduced by Lighthill [3]) and will analyze a hydrodynamicallycontroled route to flocking. We will use a hybrid description of an active suspension, which combines the individual dynamics of spherical swimmers with a kinetic model for *<EMAIL_ADDRESS>* *<EMAIL_ADDRESS>the solvent. We can identify the emergence of global orientational order and correlate it with the formation of spontaneous structures where squirmers aggregate and form flocks of entities that swim along together. This simplified approach allows us to identify the role of active stresses and self-propulsion to lead both to global orientational order and aggregate formation. Even if in real systems other factors can also control the interaction and collective behaviors of active suspensions, the present description shows that hydrodynamics itself is enough to promote cooperation in these systems which are intrinsically out of equilibrium. This work is organized as follows. In section 2.1 we present the theoretical frame of the simulation technique that we have applied, while in section 2.2 we describe the squirmer model that we have used and introduce the relevant parameters which characterize its hydrodynamic behavior and in section 2.3 we give a detailed explanation of the simulation parameters and the systems we have studied. Section 3 is devoted to analyze the global polar order parameter and to study quantitatively the orientation that squirmer suspensions display. In Section 4 flocking is studied via generalized radial distribution functions, moreover to characterize the time evolution of the formed flocks, we calculated the time correlation function of the density fluctuations, the results are shown in this section also. We conclude in Section 5 indicating the main results and their implications.
Lattice Boltzmann Scheme
We consider a model for microswimmer suspensions composed by spherical particles embedded in a fluid. The fluid is modeled using a Lattice Boltzmann approach. Accordingly, the solvent is described in terms of a distribution function f i ( r; t) in each node of the lattice. The distribution function evolves at discrete time steps, ∆t, following the lattice Boltzmann equation (LBE): that can be regarded as the space and time discretized analog of the Boltzmann equation. It includes both the streaming to the neighbouring nodes, which corresponds to the advection of the fluid due to its own velocity, and the relaxation toward a prescribed equilibrium distribution function f eq j . This relaxation is determined by the linear collision operator Ω ij [4,5,6]. It corresponds to linearizing the collision operator of the Boltzmann equation.
If Ω ij has one single eigenvalue, the method corresponds to the kinetic model introduced by Bhatnagar-Gross-Crook (BGK) [7]. The LBE satisfies the Navier-Stokes equations at large scales. In all our simulations we use units such that the mass of the nodes, the lattice spacing and the time step ∆t are unity and the viscosity is 1/2, the lattice geometry that we have used was a cubic lattice with 19 allowed velocities, better known as D 3 Q 19 scheme [5].
The linearity and locality of LBE makes it a useful method to study the dynamic of fluids under complex geometries, as is the case when dealing with particulate suspensions. Using the distribution function as the central dynamic quantity makes it possible to express the fluid/solid boundary conditions as local rules. Hence, stick boundary conditions can be enforced through bounce-back of the distribution, f i ( r; t), on the links joining fluid nodes and lattice nodes inside the shell which defines the solid particles, also known as boundary links [8]. A microswimmer is modeled as a spherical shell larger than the lattice spacing. Following the standard procedure, the microswimmer is represented by the boundary links which define its surface. Accounting for the cumulative bounce back of all boundary links allows to extract the net force and torque acting on the suspended particle [9]. The particle dynamics can then be described individually and particles do not overlap due to a repulsive, short-range interaction among them, given by where ǫ is the energy scale, and σ the characteristic width. The steepness of the potential is set by the exponent ν 0 . In all cases we have used ǫ = 1.0, σ = 0.5 and ν 0 = 2.0.
Squirmer Model.
We follow the model proposed by Lighthill [3], subsequently improved by Blake [10], for ciliated microorganisms. In this approach, appropriate boundary conditions to the Stokes equation on the surface of the spherical particles (of radius R) are imposed to induce a slip velocity between the fluid and the particles. This slip velocity determines how the particle can displace in the embedding solvent in the absence of a net force or torque. For axisymmetric motion of a spherical swimmer, the radial, v r and tangential, v θ components of the slip velocity can be generically expressed as n-th at the squirmer spherical surface, where P n stands for the n-th order Legendre polynomial and V n is define as e 1 describes the intrinsic director, which moves rigidly with the particle and determines the direction along which a single squirmer will displace, while r 1 represents the position vector with respect to the squirmer's center, which is always pointing the particle surface and thus |r 1 | = R.
Since the squirmer is moving in an inertialess media, the velocity u and pressure p of the fluid are given by the Stokes and continuity equations The velocity field generated by a squirmer is the solution of these equations (5) under the boundary conditions specified by the slip velocity in the surface of its body, eq. (3). We will disregard the radial changes of the squirming motion, and will consider A n = 0, to focus on a simple model that captures the relevant hydrodynamic features associated to squirmer swimming. Accordingly, we will also disregard the time dependence of the coefficients B n and will focus on the mean velocity of a squirmer during a beating period [11]. Hence, from the solution of eqs. (5) using the slip velocity as a boundary condition (eq. (3)), we can write the mean fluid flow induced by a minimal squirmer as where we have taken B n = 0, n > 2, keeping only the first two terms in the general expression for the slip velocity, Eq.( 3). The two non-vanishing terms account for the leading dynamics effects associates to the squirmers. While B 1 determines the squirmer velocity, along e 1 , and controls its polarity, B 2 stands for the apolar stresses that are generated by the surface waves [12]. The dimensionless parameter β ≡ B 2 /B 1 quantifies the relative relevance of apolar stresses against squirmer polarity. The sign of β (determined by that of B 2 ) classifies contractile squirmers ( or pullers) with β > 0 and extensile squirmers (or pushers) when β < 0. The limiting case when B 1 = 0 corresponds to completely apolar squirmers (or shakers [13]) which induce fluid motion around them without self-propulsion. The opposite situation, when B 2 = 0 corresponds to completely polar, self-propelling, squirmers which do not generate active stresses around them. We will disregard thermal fluctuations; therefore B 1 and B 2 are the two parameters which completely characterize squirmer motion.
Simulation Details.
All the results that we will discuss correspond to numerical simulations consisting of N identical spherical particles in a cubic box of volume L 3 with periodic boundary conditions. In all cases we have considered N = 2000, R = 2.3 and L = 100 (expressed in terms of the lattice spacing). This corresponds to a volume fraction φ = 4πN R 3 /(3L 3 ) = 1/10, with a kinematic viscosity of ν = 1/2 (in lattice units) [14]. As we will analyze subsequently, active stresses play a significant role in the structures that squirmers develop when swimming collectively. In Fig. 1 we compare characteristic configurations of suspensions for completely polar, contractile and extensile squirmers. Apolar stresses favor fluctuations in the squirmer concentration and for contractile squirmers there is a clear tendency to form transient, but marked, aggregates. The figure also shows that one needs to distinguish between how squirmers align to swim together and how do they distribute spatially. In the following section we will analyze how active stresses interact with self-propulsion to affect both aspects of collective swimming.
Polar Order Parameter.
In order to quantify the degree of ordering associated to collective squirmer motion, we have computed the global polar order parameter (eq. 7) [15], expressed in terms of the squirmer intrinsic orientation e, which determines the direction of swimming for isolated squirmers, In Fig. 2 we show the temporal evolution of P (t) as a function of time for completely polar, contractile and extensile suspensions. The time is normalized by t 0 which is the time that a single squirmer needs to self-propel a distance of one diameter, t 0 ≡ 2R/(2/3 B 1 ) = 3R/B 1 The three suspensions start from a completely aligned initial configuration where squirmers are homogeneously distributed spatially. This figure shows clearly that squirmers relax from the given initial configuration to the appropriate steady state and that active stresses have a profound impact on the ability of squirmers to swim together. The limiting situation of completely polar swimmers, β = 0, keeps almost perfect ordering. This is because the irrotational flow generated by the translational velocity of the particles is strong enough to maintain a symmetrical [16] with the Normal Mode Wizard (NMWiz) plugin [17].
distortion in the fluid. Hence, a value of P (t) close to one indicates high polarity. The other two curves, corresponding to extensile (β = −1/2) and contractile squirmers (β = 1/2) , indicate that active stresses generically decorrelate squirmer motion due to the coupling of the intrinsic direction of squirmer self-propulsion with the local vorticity field induced by the active stresses generated by neighbouring squirmers. However, we do observe a clear difference because extensile squirmers have completely lost their common degree of swimming while contractile ones still conserve a partial degree of global coherence. In order to quantify in more detail the role of active stresses in the global degree of ordering in squirmer suspensions, we have computed the steady-state value of the polar order parameter, P ∞ , as a function of the relative apolar stress strength, β. Fig. 3 displays P ∞ , computed as the mean average of P (t) over the time period after the initial decay from the aligned state [15]. There are two remarkable observations of the results shown in Fig. 3. First of all, the larger |β| the smaller values of P ∞ observed, which indicate less squirmer coherence due to hydrodynamic interactions controlled by the induced active stresses, or |β|. Secondly, for a given magnitude of the apolar stress, |β|, pullers are more ordered than pushers. Hence, there is an asymmetry between pullers and pushers. This asymmetry can be explained in terms of the differences in the near-field interactions between squirmers [15,18]. Squirmer self-propulsion favors head-to-tail collisions [19] and generates an internal structure that competes with the tendency of squirmers to rotate due to local flows. In fact, head-to-head orientation is stable to rotations for pusher suspensions (as can be clearly appreciated in the last snapshot of Fig. 1, where we can see a lot of pushers interacting head-to-head). In this case, the active stresses favor head-tohead configurations, which competes with self-propulsion and decorrelates faster the comoving swimming configurations of squirmers. On the contrary, the stresslet generated by pullers destabilizes head-to-head configurations favoring the motion of squirmers along a common director. It is worth noting that puller suspensions with β > 3 will evolve to isotropic configurations, in agreement with the long-time polar order parameter displayed in Fig. 3. In order to clarify that global ordering is generic for squirmers composed of spherical particles, and hence that orientation instabilities do not require non spherical propelling particles [20], we have analyzed the collective evolution of squirmer suspensions with initial isotropic configurations. It is clear in Fig. 4.a, that both cases of puller suspensions either initially aligned or isotropic, have a similar long-time polar order; hence we can infer that puller suspensions in either an isotropic or aligned state are unstable and that the steady state is independent of the symmetry of the initial configurations.
P(t)
On fig. 4.b one can clearly appreciate that isotropic puller suspensions (red circles) are also unstable, as shown in Fig. 4.a. On the contrary, isotropic pushers suspensions are stable (black circles) for this regime of β. Similarly to the result for puller suspensions showed in Fig. 4.a, one can appreciate in Fig. 4.b that pushers are driven to the same long-time polar order parameter, and therefore that the final alignment is independent of the initial configuration.
4.
Flocking. Fig. 1 shows that puller suspensions, (β > 0), display a cluster of the size of the box. Due to the absence of attractive forces between squirmers, these observed clusters are statistically relevant but have a dynamic character. As a function of time the observed aggregates evolve and displace; the particles they are form with change. We need then a statistical approach to analyze the formation of emergent mesoscale structures and its correlation with orientational ordering. We have computed the temporal correlation function of the density fluctuations dividing the simulation box in 1000 sub-boxes of side box l = L/10 and counted all the particles N i (t) at each i-th sub-box. This provides the particle temporal mean number, N i (t) t , from which we can determine the instantaneous density fluctuations, δN i (t) = N i (t) − N i (t) t , at each box. The average density fluctuation, δN (t), can then be derived as the mean of δN i (t) over all the sub-boxes at time t, and one can use them to study their temporal correlation. The time correlation of the squirmer density fluctuations, depicted in Fig. 5, shows that pullers have an oscillatory response, associated to the displacement of aggregates with a density markedly above average, while pushers are characterized by a more homogeneous spatial distribution. We can gain more detailed insight into the aggregation and ordering of squirmer suspensions by studying the generalized radial distribution functions [6] g n (r) ≡ P n (cos θ ij ) , where θ ij stands for the relative angle between the direction of motion of the particles i and j at a distance between r and r + dr and P n is the n-th degree Legendre polynomial. For n = 0 we recover the radial distribution function, g 0 (r). The average in eq. (8) is taken over all particle pairs and over time, once the system has reached its steady state. Fig. 6 displays g 0 (r) for three kinds of squirmers, β = {0, 1/2, −1/2}. For comparison, we also show the radial distribution function of a randomly distributed configuration, which constitutes a good approximation for the equilibrium radial distribution function for hard spheres at φ = 1/10. Fig. 6 displays also g 0 (r) for β = −1/5. This case corresponds to a pusher suspension with the same polar order value, P ∞ , than the puller suspension at β = 1/2 and will help to analyze the correlation between global polar order and the suspension structure. One can clearly appreciate that activity enhances significantly the value of the radial distribution at contact, g 0 (r = 2R), compared with the corresponding value for an equilibrium suspension. This value is larger for puller suspensions indicating the larger tendency of pullers to remain closer to each other. The radial distribution function for pullers develops a marked second maximum at r = 4.25R indicating the development of stronger short range structures for pullers. Neither pushers nor totally polar squirmers have a visible second maximum even when we compare puller and pusher suspensions with equivalent polar order parameter, P ∞ . The development of the secondary peak for pullers is consistent with their tendency to form large aggregates, or flocks, in agreement with the snapshot depicted in Fig.1. g 0 (r), β= 0.5 g 0 (r), β= -0.5 g 0 (r), β= -0.2 g 0 (r), β= 0.0 g 0 rndm (r;t=0) 7 displays the generalized radial distribution function, g 1 (r), which provides information on the degree of local correlated polar order around a given squirmer. Initially, all squirmers are parallel, and hence g 1 (r, t = 0) = 1.0 (green diamonds in the Figure). The isotropic initial condition (yellow circles), when g 1 (r, t = 0) = 0, is also shown as a reference. Completely polar squirmer suspensions, β = 0, keep g 1 (r) very close to 1 (violet triangles) showing that most of the particles swim along a common direction even if they are far away from each other; this strong correlation is easily appreciated in the first snapshot of Fig.1. We can observe a similar effect for pusher suspensions at β = −1/5 where we can see how g 1 (r) relaxes to a finite plateau for r > 3R. However, unlike completely polar squirmers, now g 1 (r > 3R) ∼ 0.6 (black diamonds) indicating a loss of coherence in the swimming suspension. The relative alignment for puller suspensions is clearly different, because g 1 (r) decays asymptotically to zero (blue squares) for separations analogous to those on which the radial distribution function decays to one. This indicates that the structure we have identified through g 0 (r) in Fig. 6 corresponds to groups of nearby particles that swim along the same direction. This behavior is consistent with the middle snapshot of Fig. 1 which shows a marked flocking formed by a significant number of particles swimming coherently in the same direction. If the apolar strength is increased, increasing the magnitude of β, for pusher suspensions, the partial coherence that we have seen in the case of β = −1/5 vanishes. The curve of g 1 (r) for β = −1/2 (red triangles) does not display any significant feature, indicating a complete decorrelation in the direction of swimmers at all length scales. The corresponding configuration in Fig. 1 shows clearly the absence of any significant correlated orientation between squirmers. g 1 (r), β= 0.5 g 1 (r), β= -0.5 g 1 (r), β= -0.2 g 1 (r), β= 0.0 g 1 rndm (r;t=0). Isotropic g 1 rndm (r;t=0). Alligned
Conclusions
We have analyzed a model system of swimming spherical particles to show the capabilities of the hydrodynamic coupling as a route to pattern formation, polar ordering and flocking in the absence of any additional interaction among the swimmers (except that swimmers cannot overlap due to excluded volume). We have shown how a numerical mesoscopic model for swimmer suspensions can develop instabilities and long-time polar order and that active stresses play a relevant role to promote flocking due to the coupling of the swimming director with the local fluid vorticity induced by the neighboring squirmers. We have identified the sign of such active stress (which distinguishes pullers from pushers) as the main element which controls squirmer flocking and swimming coherence.
We have shown that spherical squirmers, starting from aligned or isotropic state, develop a unique long-time polar order due to hydrodynamic interactions. We have found that aligned pushers suspension are unstable while isotropic suspensions are stable for β < −2/5: isotropic puller suspensions are also stable for β > 3.0.
We have seen that flocking configurations for pullers leads to large elongated structures, reminiscent of the bands observed in the Vicsek model [21]. However, in this later case hydrodynamics is absent and flocking develops at high concentrations, when the aligning interaction is strong enough to overcome decoherence induced by noise. In the systems we have explored the coherence is hydrodynamic and develops at small volume fractions. The observed elongated, spanning aggregates with internal coherent orientation, in the range 0 < β < 1, are robust and independent of the initial configuration. | 5,083.4 | 2013-09-01T00:00:00.000 | [
"Physics"
] |
based observations at Svalbard
Abstract. In this paper we present Naturally Enhanced Ion Acoustic Lines (NEIALs) observed with the EISCAT Svalbard Radar (ESR). For the first time, long sequences of NEIALs are recorded, with more than 50 events within an hour, ranging from 6.4 to 140 s in duration. The events took place from ~08:45 to 10:00 UT, 22 January 2004. We combine ESR data with observations of optical aurora by a meridian scanning photometer at wavelengths 557.7, 630.0, 427.8, and 844.6 nm, as well as records from a magnetometer and an imaging riometer. The large numbers of observed NEIALs together with these additional observations, enable us to characterise the particle precipitation during the NEIAL events. We find that the intensities in all optical lines studied must be above a certain level for the NEIALs to appear. We also find that the soft particle precipitation is associated with the down-shifted shoulder in the incoherent scatter spectrum, and that harder precipitation may play a role in the enhancement of the up-shifted shoulder. The minimum energy flux during NEIAL events found in this study was ~3.5 mW/m2 and minimum characteristic energy around 50 eV.
Introduction
Naturally enhanced ion acoustic lines are intermittently seen in spectra from incoherent scatter radars (ISR) at high latitudes.The characteristic incoherent scatter spectrum of NEIAL events is easily distinguished from the thermal spectra by the enhancement of one, or both, ion acoustic shoulders.The enhancements are typically 1-2 orders of magnitude in power compared to normal ion line values (Rietveld et al., 1991;Collis et al., 1991).The NEIALs have Correspondence to: J. Lunde (june.lunde@phys.uit.no)been observed in the nightside auroral oval as well as in the cusp/cleft region.The first observation of NEIALs was reported by Foster et al. (1988) using the Millstone Hill radar (440 MHz) pointed to the north.Later on, similar observations were made in Tromsø, which is located within the auroral zone, using the EISCAT radars (224 MHz and 931 MHz) by Collis et al. (1991) and Rietveld et al. (1991).Further north, Buchert et al. (1999) observed NEIALs for the first time in the dayside cusp/cleft region using the EISCAT Svalbard Radar (500 MHz).
Since the first observations, many papers have been published in order to explain the physical mechanisms behind the NEIAL phenomena (see Sedgemore-Schulthess and St.Maurice, 2001, and references therein).Although the understanding of the NEIAL phenomenon is not clear, a common theme among the explanations is instability processes where particle precipitation is directely or indirectely involved.It is therefore of interest to study the characteristics of the precipitation during such events.
NEIALs are typically observed in the upper F-region, between ∼300 and 700 km.They are rarely observed below 200 km or above 1000 km, but they do occur intermittently as low as 138 km (Rietveld et al., 1991) and as high as 1600 km (Ogawa et al., 2006).NEIALs are a fine scale phenomena, Rietveld et al. (1996) estimated the transverse size to be a few km, and Grydeland et al. (2003) observed NEIALs with transverse sizes down a few hundred metres or less.The small scale size, together with the enhanced signal, requires that the scattering cross section inside the filament must be 4-5 orders of magnitude above thermal levels (Grydeland et al., 2003(Grydeland et al., , 2004)).
NEIALs have been observationally associated with a wide range of geophysical phenomena which can be summaries as follows: -Severe Geomagnetic Disturbances (e.g.Rietveld et al., 1991) Published by Copernicus Publications on behalf of the European Geosciences Union.
-Soft Precipitation (e.g.Collis et al., 1991;Forme et al., 1995) -High Electron Temperature (e.g.Foster et al., 1988;Wahlund et al., 1993) -Electric Heating in the F-region (e.g.Rietveld et al., 1991) -Density enhancement in E-region (e.g.Rietveld et al., 1991;Forme et al., 1995 ) -Intense Ion Out-Flow (e.g.Wahlund et al., 1992a;Forme and Fontaine 1999) -Red Aurora (e.g.Collis et al., 1991;Sedgemore-Schulthess et al., 1999) -Enhanced Plasma Lines (Strømme et al., 2005) -Active Aurora (e.g.Sedgemore-Schulthess et al., 1999;Blixt et al., 2005) As illustrated by this list, particle precipitation, directly or indirectly, seems to be frequently involved in the formation of NEIAL.Rietveld et al. (1991), using the EISCAT mainland systems, observed enhanced echoes at times of major geomagnetic disturbances (typically 500 nT deflection of the horizontal component at Tromsø).This was during auroral particle precipitation and E-region density enhancement at 120 km, corresponding to precipitation of a few keV.The enhanced echoes were seen both along and slightly off (14.5 • ) the magnetic field direction.The ISR data showed high electron temperatures at 450 km altitude varying between ∼4000-8000 K, and a high ratio between the electron-and ion-temperatures (T e /T i ≥2) for all events.In addition, increased ion outflow above 350 km altitude where seen in most cases, and an all-sky camera showed red aurora in the F-region and green aurora in the E-region.Collis et al. (1991) reported observations from the EISCAT UHF where all cases of enhanced spectra, for which optical data existed, were accompanied by active and intense red auroral forms in the Fregion.The red line intensity was unusually high with a maximum of 270 kR.In contrast, measurements the three previous winters with the same instrument at the same location, showed a maximum of 15 kR.Forme et al. (1995) and Forme and Fontaine (1999), also using the the EISCAT mainland systems, found that strongly asymmetric enhanced ion lines were often associated with elevated electron temperatures, ion outflow with both large ion flux (∼10 14 m −2 s −1 ) and vertical velocities (∼1300 m/s), auroral arcs and precipitating particles of 100 eV to 10 keV.Interestingly, Forme et al. (1995) found that observations with both ion lines enhanced, corresponded mostly to slightly enhanced electron temperatures, no ion outflow, and an apparent lack of precipitating particles less than 1 keV.The association of NEIALs with ion outflow was confirmed by Wahlund et al. (1992a), also using the EISCAT VHF and UHF radars.They observed strong field-aligned bulk ion outflows from the topside ionosphere.The ions were mainly O + obtaining velocities of 1500 m/s above 900 km altitude.Two different types of ion-outflow were identified: Type I is related to periods of strong perpendicular electric fields, enhanced and anisotropic ion temperatures in the upper ionosphere down to the E-region and very low electron densities below 300 km, the last indicating small amounts of auroral precipitation.Type II is related to auroral arcs and enhanced electron temperatures (>6000 K), as well as isotropic ion temperatures and weak to moderately strong perpendicular electric field.During short intervals of auroral precipitation and Type II outflows, they occasionally observed NEIALs.Sedgemore et al. (1999) observed poleward moving auroral transients in the dayside cusp/cleft region by a meridian scanning phototometer around the time when the enhanced radar spectra appeared.The topside electron-to-ion temperature ratio was about 3.They also observed that coronal forms were present in the optical narrow-angle images only when enhanced spectra were seen.Blixt et al. (2005) reported very dynamic rayed auroras in the dayside cusp/cleft region observed with high-resolution narrow field-of-view auroral imagers simultaneously with occurrences of enhanced ionacoustic echoes.Additionally, red auroral emissions were seen in the photometer data during all events.Strømme et al. (2005) related simultaneous enhancements in the ion and plasma lines due to a low energy electron beam (8-80 eV).
The instability processes proposed in the literature to explain the NEIAL phenomenon are: i) Current Driven Instability (Foster et al., 1988;Collis et al., 1991;Rietveld et al., 1991), ii) Ion-Ion Two-Stream Instability (Wahlund et al., 1992b) and iii) Parametric Decay of Langmuir Waves (Forme, 1993 and1999).They can be summarized as follows: i) Current driven instability, also known as ion-electron two-stream instability, is ion acoustic instabilities driven by thermal electron drifts.This requires large fieldaligned current (FAC) densities, on order of 1 mA/m 2 , which could be produced by parallel electric fields caused by precipitating particles or horizontal conductivity.Such currents have recently been measured with the Ørsted Satellite (Neubert and Christiansen, 2003).It is the thermal electrons that carry the current; hence, it might be difficult to explain the occasional observations of simultaneous enhancements in both ion lines.
ii) Ion-ion two-stream instability (ion-ion drift instability or counter streaming ion populations), is driven by large relative drifts between two or more ion species, for exemple O + and a beam of H + .As long as the drift velocity is of the order of the species thermal velocity, the ion acoustic fluctuation level can be enhanced well above the thermal level, leading to an enhancement in one or both ion lines.The high relative drifts require a sufficient acceleration of H + , which might be possible at high altitudes.Accordingly, with this mechanism, NEIALs are unlikely to occur at low altitudes, but could occur in the upper part of and above the F-region.However, if there are processes that accelerate either O + or NO + , an instability process could occur at lower ionospheric altitudes.
iii) Parametric decay of Langmuir waves is the destabilization of an ion acoustic wave through quasi-linear wave coupling with high frequency Langmuir (plasma) waves, which can lead to enhanced ion acoustic fluctuations.A downward electron beam of 10 to 500 eV resulting in a bump in tail in the electron distribution, can lead to excitation of Langmuir waves which drive the wave-wave interaction.It has been shown that for reasonable beam parameters, the parametric decay of beam-generated Langmuir waves can enhance the right, the left or both ion lines simultaneously.This theory does not require large parallel current densities or large differential ion drifts, but requires low energy precipitating electrons as the triggering mechanism.Thus this theory favours type II outflow, which imply enhanced electron temperature together with auroral precipitation, with typical ratios between the electron-and ion temperature of 3 or higher.Pursuant to this theory, NEIALs are not likely to occur during more energetic precipitation (>500 eV) without a soft component.
The works by Strømme et al. (2005) and Ogawa et al. (2006) support the parametric decay of Langmuir waves, while modelling performed by Nöel et al. (2000) support the theory of current driven instability.In the latter high energy precipitation (mean electron energy 1.62 keV) was used as input, and the current continuity equation solved, allowing for timedependency.The model was computed for nighttime conditions.
The coarse division of energetic electrons into "hard" and "soft" is of course arbitary.Here we take the transition to be at 500 eV.Various values of this transition are found in the literature, often depending on the problem under discussion.For example, 1 keV is frequently used when dealing with aurora in order to distinguish between the particles causing dayside and those causing nightside auroras.In papers on NEIALs, soft electrons are often defined as having energies below 100 eV.We have chosen a limit of 500 eV because the precipitation is poleward of the open/closed boundary (OCB).In the dayside, the auroral oval (soft precipitation) will be located polewards of the OCB.On the equatorward side, high energy particles -typically from the central plasma sheet -dominates.Since our observations are performed during typical cusp hours, 12:00 MLT +/−1.5 h, an input of low energy electron precipitation, presumably from the cusp region, is expected (Heikkila and Winningham, 1971;Frank, 1971;Newell et al., 2004).
Primary auroral electron fluxes have a wide range of energy distributions.The spectral energy distribution of primary electrons in the range of 100 eV to some tens of keV is often found to be almost Maxwellian (Rees and Luckey, 1974).However, satellite data collected over the years show that the spectra can often be more sharply peaked than a Maxwellian distribution, closer to a Gaussian energy distribution (Strickland et al., 1989;Hecht et al. 1999).The Maxwellian form is mostly related to diffuse aurora, while the Gaussian form is associated with discrete aurora (Lyons, 1992).Large electron fluxes below 100 eV consist almost entirely of secondary electrons (Rees and Maeda, 1973), and the energy distribution of secondary electrons is better approximated with power law (Frank and Ackerson, 1971;Ogilvie, 1968).To roughly characterise the energy of the precipitating electrons by a single number, the average over the distributions is always an option.Both Gaussian and Maxwellian distributions can, however, be described by a mathematical parameter, a so-called characteristic energy, E 0 .For a Gaussian distribution the characteristic energy is equal to the average energy, E 0 =<E>, (Strickland et al., 1993), while for a Maxwellian distribution, it is equal to one half of the average energy, E 0 =<E>/2, (Vallance-Jones et al., 1987).
In the isotropic case the Maxwellian distribution can be written as: M (E) denoting the differential electron number flux at the electron energy E, Q M the energy flux, and E 0 the characteristic energy which also is the energy for the peak of the distribution.
Correspondingly the Gauss distribution is expressed as W being a scaling parameter.
For a more complete model of the distribution, a highenergy-tail (HET) and a low energy-tail (LET) should be added to the Gaussian distribution (Strickland et al., 1993), and a LET to the Maxwellian distribution (Meier et al., 1989).When tails are included the relation between <E> and E 0 is modified.We get <E>=1.4E0 for the Maxwellian distribution and <E>=0.5E0 for the Gaussian distribution (Meier et al., 1989).
When discussing the role of the particle precipitation in physical mechanisms behind the NEIALs, most papers, observational as well as theoretical ones, focus on the soft particle precipitation.The authors of this paper believe that including more energetic precipitation may improve the understanding of NEIAL.
We have obtained with the ESR what we believe is a unique data set, where NEIALs occur very frequently over a period of an hour.We combine this with observations from a multi channel meridian scanning photometer, a magnetometer and an imaging riometer.The photometer observations are used to derive energy flux and characteristic energy of the soft part of the particle precipitation, while riometer and magnetometer serve to identify the precence of hard electron precipitation.
Radar
The Eiscat Svalbard Radar (ESR) is located in Longyearbyen (78.15 • N, 16.03 • E geographically and 75.27 • N, 111.65 • E CGM).The ESR is a 500 MHz monostatic incoherent scatter radar, transmitting with a peak power of ∼1 MW and 25% duty cycle, and receiving with the same antenna.The radar system consists of two parabolic antennas, 32 m and 42 m in diameter.In this paper we only use data from the 42 m antenna.This is fixed in the magnetic field-aligned position at ∼300 km, equal to 182.1 • in azimuth and 81.6 • in elevation.The radar was run with a 16 bit alternating code experiment Tau0, version 5.1, with 16 µs sampling interval, which imply a range ambiguity function of width 2.4 km.The range coverage was between 78 km and 1250 km.Integration time was 6.4 s.More detailed description of the ESR facility and techniques can be found in Wannberg et al. (1997).
Optical
The Meridian Scanning Photometer (MSP) is situated at the Auroral Station in Adventdalen, ∼7 km from the radar site.This MSP instrument measures auroral emissions at five wavelengths.In this paper four lines are used: i) the green line at 557.7 nm of atomic oxygen (OI), ii) the red line at 630.0 nm of atomic oxygen (OI), iii) the blue line at 427.8 nm of the first negative band of molecular nitrogen ions (N + 2 1NG), and iv) a near infrared (NIR) line at 844.6 nm of atomic oxygen (OI).
A full meridian scan with the background subtracted is obtained for each of the channels every 16 s.The scan is close to the magnetic meridian with an angular resolution of 1 • .It starts at 10 • elevation in the north and ends at the same elevation in the south, 98 • corresponds to local magnetic zenith.This corresponds to a magnetic latitude ranging from 80 • to 70 • at 120 km and 84 • to 65 • at 300 km.Further description of the MSP instrument can be found in Romick (1976).
In principle, the optical data should be corrected for airglow and proton-excited emissions (Eather and Mende, 1972).However, the intensities recorded always were well above this background level, and for high-latitude daytime aurora, the proton energy is expected to be low.We have therefore ignored both corrections.The calibration is roughly correct, at least the 427.8 nm and 630.0 nm should be reliable, even though that the photometer has not been absolute calibrated for some time.An all-sky imager being op-erated at the same time and place as the MSP instrument, indeed support strong emission intensities, as the large fieldof-view image were almost saturated.It may also be noted that the characteristic energy at ∼09:40 UT, is corroborated by data from the DMSP satelellite (not shown) passing over Longyearbyen at that time, thus confirming the photometer calibration is not seriously wrong.The DMSP data might also otherwise have contributed to this paper, we have, however, limited our scope to the ground based instruments.
Magnetometer
The magnetometer is also situated at the Auroral Station.It is a standard 3-axis-fluxgate-magnetometer measuring in both magnitude and direction of the local magnetic field with a time resolution of 10 s.
Riometer
We have used data from an imaging riometer located in Ny-Ålesund (78.92 • N, 11.95 • E geographically and 76.27 • N, 110.72 • E CGM), 117 km north-west of the radar site.The riometer operates at a frequency of 30 MHz within a protected radio-astronomy band to measure ionospheric absorption of cosmic radio noise in the polar cap and auroral region.The sensitivity of the instrument is 0.1 dB.The system consists of 64 single dipole elements, configured into (8×8) narrow beams by a Butler matrix phasing system.This corresponds to a field of view equal to 200 km×200 km on either side of zenith at a height of 90 km.There are 8 beams in the east-west direction (E1-E8 starting from east) and 8 beams in the north-south direction (N1-N8 starting from north), and Longyearbyen is within the N5E3 beam.The spatial resolution is 20 km for the centre and 40 km at the edge and the sample interval is 4 s.Detailed performances of the IRIS system is reported by Yamagishi et al. (1992) and Sato et al. (1992).There is also an imaging riometer installed in Longyerabyen, but data were not available for this particular day.
Geophysical conditions
The observations took place on 22 January 2004, between 08:00-10:00 UT, the last hour being the more interesting due to the large numbers of NEIALs.Magnetic noon for Longyearbyen is at ∼08:51 UT (IGRF-2004), the observations therefore fall within the cusp/cleft region.The polar cusp is the region of open-field-lines just poleward of the boundary between open and closed field lines on the dayside, while the cleft, also known as the Low Latitude Boundary Layer (LLBL), can be found downstream along the flanks of the magnetotail.Satellite measurements show that electrons in the energy range 30-100 eV and 70-200 eV dominate the cusp and LLBL, respectively (Newell and Meng, 1992).
The 22 January 2004 was geophysically an active day.This was probably related to a halo Coronal Mass Ejection (CME) which occurred 2 days ahead, with a gusty outflow (Fazakerley et al., 2005).The local K-index for Longyearbyen was around 5 with a maximum of 7 just after midnight.This fits well with the statistical studies of 5000 h of data, performed by Rietveld et al. (1996), which show that the K-index is greater than or equal to 4 during NEIAL events.The K p -index showed quite similar values and we conclude that the observations took place during a moderate magnetic storm.This is confirmed by the D st index.Aditionally, MIRACLE, a two-dimensional instrument network for mesoscale studies of auroral electrodynamics (http: //space.fmi.fi/MIRACLE/index.html),shows the presence of an ionospheric equivalent current and Joule heating this day, not only just after midnight, but also significantly immediately before the NEIAL events.In the time span of interest, the interplanetary magnetic field (IMF) B z component was positive (northward) while the B y component was strongly negative (dawnward) in the ecliptic plane.
Radar
By visual inspections of the raw data dumps from the 42 m antenna, a total of 68 individual NEIAL events were identified among the 563 integration periods between 08:00-10:00 UT.An overwiew of the returned backscatter power versus time and altitude is shown in Fig. 1; there the NEIAL events can be seen as extended vertical lines in the F-region.In several cases these lines of increased backscatter extend into the E-region as well.These phenomena in the E-region should not automatically be taken as signs of NEIALs, more likely they are enhanced electron density du to bursts of hard particles.Compared to previously reported observations, the number of events is remarkably high.In contrast, only 5 events were found in a case study with a similar time span on 24 January 1998 at ESR (Sedgemore-Schulthess et al., 1999).In several cases, NEIALs occurred in consecutive dumps, up to 13 in a row, while 41 occurred alone.The first event observed took place at ∼08:53 UT, while the final one ended at ∼09:55 UT.Typically, the NEIALs appear on very short time scales, down to less than a second (Grydeland et al., 2004).However, our time resolution is limited to 6.4 s (the integration time), and the individual number of events may therefore be much larger.The longest duration of a consecutive NEIAL observation is about 2.2 min.Particularly strong NEIALs were found in 18 data dumps.Of these, 13 had strongly enhanced left (down-shifted) shoulder -in some cases more than 20 dB -, 4 cases showed strong enhancement in the right (up-shifted) shoulder, and in only one case both shoulders were strongly enhanced.Examples of the strongest cases are shown in Fig. 2. It is worth noticing, that the right shoulder is more enhanced at a lower altitude than the left, and that at higher altitudes, the left shoulder is enhanced more than the right one.This is also the situation when comparing Fig. 2a with 2c and d.Note that the spectra present in Figs. 1 and 2 are in the raw form, not corrected for the effect of finite pulse length; hence the spectra are slightly smoothed and broadened.
Altogether, 41 NEIAL events have been categorised as moderately enhanced.Among these, 9 showed roughly equal enhancement in both shoulders, in 28 cases the left shoulder was considerably stronger than the right, and in merely 4 cases the situation was reversed.This is in good agreement with the result by Rietveld et al. (1996), who found that the left shoulder was more commonly enhanced above 450 km than the right shoulder, and that enhancement in both shoulders can occur above 300 km.Similar to Blixt et al. (2005), we find the NEIALs to be more abundant above 500 km, rather than having a maximum occurrence rate at 500 km (Rietveld et al., 1996).It should be noted that the latter is a statistical results from the UHF radar.With the VHF radar, a peak is typically seen at about 800 km (Forme et al., 1995).
Figure 3 displays the result of EISCAT routine analysis.An increased electron density, up to a factor 10, is observed throughout the F-layer in the NEIAL period, and above 500 km we see an upward directed ion flow above 400 m/s, this being in agreement with previous observations by Wahlund et al. (1992), Forme and Fountaine (1999).The electron temperature (T e ) is very high (>6000 K) and the ratio between the electron and ion temperatures is higher than two.Around the altitude of 600 km where we have maximum occurrence of NEIALs in our observations, T e increases from roughly 3000 K to 6000 K.A less pronounced increase is seen in the ion temperature (T i ), 2400 K during the NEIAL period, in contrast to 1800 K before and after.The ratio T e /T i is thus increased from 1.7 outside the NEIAL period to 2.5 within it.At 400 km, which is below most of the NEIALs, T e increases much less, from 3000 K to 4500 K. Be aware, however, that all physical parameters derived by standard analysis during the NEIAL events must be regarded as corrupted, as the analysis breaks down or gives unrealistic values because of the highly non-thermal spectra.In fact, the failure of the analysis program to fit realistic physical parameters from ISR data is very often a sign of NEIALs being present.This can typically be seen as data gaps in the standard colour plots and/or unrealistically high electron temperatures.
Optical
The MSP data displays an increased intensity in all channels after ∼08:25 UT and especially after ∼08:43 UT and ∼09:10 UT.A plot of all the emissions, which overlap the 42 m antenna in the field-aligned direction, is presented in Fig. 4. The NEIAL occurrences are marked with red at the prompt emission of 427.8 nm.The optical intensity during periods of NEIALs are found to be between 15 kR and 43 kR for the 630.0 nm, while the corresponding values for Fig. 4. Optical emissions corresponding to the field-aligned radar beam direction between 08:00-10:00 UT.The curves correspond to intensities of: 630.0 nm (red), 844.6 nm (black)×4, 557.7 nm (green)×3 and 427.8 nm (blue)×2.NEIALs are marked with red at the 427.8 nm curve.Note the delay of 630.0 nm emission maxima due to the long lifetime.
Magnetometer
The horizontal-component (H) of the magnetometer (not shown) has a pronounced negative bay which starts at ∼08:31 UT and reaches a maximum deflection at ∼08:43 UT, which is just about 10 min prior to the first NEIAL record, ∼08:53 UT.Between 08:00 and 10:00 UT the component varies irregularly over a range of 379 nT.The vertical component indicates that the current is approximately overhead.This is verified by data from the IMAGE chain; the magnetic activity is located above and nearby Longyearbyen.
Riometer
Within the time of interest, an absorption event took place at 08:14 UT.Thereafter, the values reached more than 0.5 dB at 08:20 UT, returning again to a low value at 08:22 UT.After a short quiet period, the absorption started to increase once more just after ∼08:42 UT with several absorption peaks between 0.5 and 0.8 dB at: ∼08:53, ∼09:10, ∼09:18, ∼09:28, ∼09:30, ∼09:41, ∼09:44 and ∼09:51 UT.By comparing the magnetometer data with the riometer data, we find as expected, that a considerable absorption started at the same time as the negative bay in the magnetic H-component reached its very minimum (∼08:43 UT).
Data analysis
The optical intensities and the ratios between them have been used to derive the total energy flux and the characteristic energy (E 0 ), the latter being described briefly in the introduction.The intensities of the MSP meridian scans have been smoothed by using a weighted triangular filtering of +/−1 • .We assume a pure Maxwellian distribution form, without the energy tails.The LET has no essential effect on the brightness of the lines with the exception of the red line, because the intensity arises primarily from the F-region, where quenching is small and where the low energy portion of the precipitating electron spectrum deposit its energy (Strickland et al., 1989;Meier et al., 1989).A pure Maxwellian distribution can lead to noticeable deficiencies when calculating the red line emissions for characteristic energies greater than about 1 keV (Meier et al., 1989).However, mainly soft precipitating particles are expected in the cusp, hence LET are ignored.
We have taken into account that the effective lifetime of the 630.0 nm emission lines may vary.The metastable O( 1 D) state has a radiative lifetime of 107 s, while the effective lifetime of the O( 1 D) state is a function of altitude.The emission height for 630.0 nm varies between 180 and 400 km, the peak emission altitude being a function of the characteristic energy in the electron beam.Our estimates of the characteristic energy indicates that the average peak emission is at 250 km, in accordance with modelling result from Semeter et al. (2001) and Solomon et al. (1988).We are aware that the variation of the 630.0 nm line should be used with care.Due to the long lifetime of the O( 1 D) atom, 630.0 nm emission acts as a "fossil aurora" (Smith and Minow, 2006).That is O( 1 D) excited in narrow intense arcs can spread with winds and diffusion before the emission occurs.This will lead to errors in our estimates of the precipitation characteristics; in narrow structures the transport away of O( 1 D) will give a decrease in the 630.0 nm intensity and also the 630.0/427.8ratio, which then leads to an overestimate of E 0 .In adjacent regions, the effect will be the opposite.
The N + 2 427.8 nm intensity is nearly proportional to the total incident electron energy flux (Hecht et al., 1985).Previous statistical and modelling studies have given a conversion factor from the 427.8 nm intensity to the total electron energy flux, varying between 210-270 R/(mW m −2 ) (Deehr and Egeland, 1972;Rees et al.,1976;Kasting and Hays, 1977;Strickland et al., 1989).In this study we have used a conversion factor of 233 R/(mW m −2 ) (Strickland et al., 1989).
In principle the intensity ratios 844.6/427.8,630.0/427.8 and 557.7/427.8,can all be used to estimate the characteristic energy of the primary precipitating electrons.To this end we have studied 3 slightly different approaches: Hecht et al. (1999) and references therein, Vallance-Jones et al. (1987) and Rees et al. (1988) and references therein.The first model uses the ratios 844.6/427.8 and 630.0/427.8,and from this model it is also possible to deduce a scaling factor of oxygen (f O ), which represent changes in the O/N 2 ratio at altitudes between 110-140 km (Christensen et al., 1997).The second use the ratio 557.7/427.8 and the third model use 630.0/427.8,neither account for variations of the neutral atmospheric composition.Since 630.0 nm requires knowledge of O relative to N 2 in order to deduce E 0 accurately, the neutral atmospheric composition should be included (Strickland et al., 1989).This is more relevant during night time.All models have used the MSIS (Mass Spectrometer Incoherent Scatter) model atmosphere for neutral densities.Typically, during increased geomagnetic activity, the scaling factor of oxygen will decrease, while the one for O 2 increase.It is also a trend that the characteristic energy will decrease when f O decrease (Hecht et al., 1989).In general, it is known that the O/N 2 ratio decreases with increasing latitude, particularly during disturbed conditions.This could influence the brightness ratio, because it is directly proportional to the O scaling above 130 km.This is especially true for polar nights in the absence of UV radiation which creates O by photo dissociation of O 2 during daylight conditions (Strickland et al., 1997).
In the perspective above, the reason to include the 844.6/427.8ratio in addition to the 630.0/427.8, is to correct for the variation in oxygen concentration (Hecht et al., 1999).Additionally, the 844.6/427.8has the advantage of not being affected by temporal changes in the auroral emission, as well as being almost proportional to the N + 2 rotational temperature (Vallance-Jones et al., 1987).The emission of 557.7 nm is complex and it is not well understood theoretically.It appears that the 557.7/427.8ratio is only useful for energies well above about 8 keV (Vallance-Jones et al., 1987).Accordingly, we have only looked at the remaining ratios.It should be noted that the intensity ratios are affected by variations in the atmospheric composition as well as by scattering in the atmosphere.As much as 20% variation in the spectral intensity ratio could be expected (Gattinger et al., 1991).This would affect the level of our estimated parameters; however, the variations in the parameters will still be the same.
To estimate the characteristic energy from the optical data we used the method developed by Strickland et al. (1989) as well as the by Rees and Roble (1986).The first method is used in further modelling by Hecht et al. (1999) while the latter is used as the input for Rees et al. (1988).In our case they agree reasonable well.Both methods make use of the intensity of the 630.0 nm red line (I 6300 ).Using this intensity to estimate the characteristics of the electron precipitation require that the spatial intensity gradients are small enough to ignore transport effects, and that O( 1 D) is at steady state.That is, variations in the precipitation have to be slower than ∼2 times the altitude averaged O( 1 D) lifetime, τ , which is about 30 s (Gustavsson et al., 2001) for a typical daytime aurora.However, assuming that the characteristic energy of the electron precipitation varies slowly, τ will be approximately constant.Then we can simplify the O( 1 D) continuity equation by integrating in altitude and obtain: where q is the column excitation rate.For a constant source we get the steady state solution of I SS =τ •q.When the column O( 1 D) excitation rate varies slowly, the observed I 6300 will not be at steady state.However, solving Eq. (3) for τ •q, gives a corrected estimate of the corresponding steady state intensity: This way we can improve the estimates of E 0 , and we can extend the use to cases where temporal variations are not faster www.ann-geophys.net/25/1323/2007/Ann.Geophys., 25, 1323Geophys., 25, -1336Geophys., 25, , 2007 Fig. 5.The energy flux versus the characteristic energy.The red squares represent NEIALs events.Two populations can be seen.They mostly coincide with the time before and after 09:00 UT.
than about τ / 2. As can be seen in the keograms in Fig. 4, the spatial structures are not sharp enough to make transport effects impact the estimates.Energetic particle precipitating and Joule heating due to dissipation of electric currents will heat the lower thermosphere during aurora and drive vertical and horizontal transport of the atmospheric gases (Fuller-Rowell, 1985).In general, heating and O/N 2 ratio are anti-correlated; the ratio O/N 2 in the lower thermosphere (E-region) is at a minimum during maximum particle and Joule heating (Hecht et al., 1995;Christensen et al., 1997).Thus, both heating and atmospheric composition will influence the optical brightness ratio.A typical criterion of Joule heating is enhancements in both the electron density and ion temperature in the Eregion, while the electron temperature is unaffected.The Joule heating is found by using the deviation of the horizontal component of the magnetic field from the quiet level.The least active day that month, 8 January, was used for this purpose.The height-integrated Joule heating rate, Q J , is related to the magnetic field perturbations, H, at ground level by; Q J =k ( H) 2 , where k is a constant (Cole, 1971).The choice of the scaling factor, which differs between the morning and evening sectors (Brekke and Rino, 1978), is taken from Duboin and Kamide (1984).We have adapted k equal to 0.8E-7 [Wm −2 (nT) −2 ], which represents an average value in the midnight-morning sector.
Discussion
The observation of Collis et al. (1991) established a clear connection between high intensity of the red line and the occurrence of NEIALs.As shown in Fig. 4, our data confirm this relationship.Our intensities ranges from 4 to 43 kR while NEIAL are observed only at levels roughly above 15 kR.This agrees fairly well with Collis et al. (1991), who observed NEIAL at intensities between 10 and 40 kR.For comparison, typical dayside cusp emission rates for the 630.0 nm line at moderately disturbed conditions (K p ∼2) is between 3 and 8 kR (Zhang and Shepard, 2006).The high intensity in the red line emission is a sign of abundant low energy electron precipitation.Such precipitation is thus an important clue to the understanding of NEIALs.In Collis et al. (1991), all the NEIAL events coincided with active and high intensitiy red auroral forms in the optical data, but they do not say anything about whether they also have such strong intense red lines without NEIAL or not.In our data, however, we do have periods of red line emission above the lower limit without observing any NEIAL.
The routine EISCAT analysis of the ISR data (Fig. 3) reveals enhanced electron temperature and T e to T i ratio during the period of NEIALs.This is consistent with previous investigations, e.g.Foster et al. (1988); Rietveld et al. (1991); Wahlund et al. (1993); Forme et al. (1995).Details of the temperature variation with time are hard to obtain due to low signal to noise ratio and the fact that the analysis breaks down during the NEIAL events.However, the enhanced temperatures and electron densities during the NEIAL period is no doubt a result of particle precipitation, directly by soft particles depositing their energy in the F-layer, or indirectly by harder particles producing hot secondary electrons in the E-layer drifting upwards into the F-layer (Maehlum et al., 1984).
Our result shows that a certain minimum intensity in all channels is required before NEIALs appear, not only the red line (Fig. 4).A closer look on other parameters such as energy flux and characteristic energy could thus be significant.In Fig. 5, the electron energy flux is plotted versus the characteristic energy.We observed that the NEIALs appear only when the flux exceeds a lower limit.The lower limit is equal to ∼3.4 mW/m 2 and the flux during NEIAL events is found to be well above this lower limit most of the time.The characteristic energy during NEIAL events was about 115 eV on average.In contrast to the energy flux, there seems to be a lower as well as an upper limit for the characteristic energy, since NEIALs were observed only in the range from 50 eV to 220 eV.However, considering the obviously large uncertainties in E 0 , especially at values below 100 eV, these upper and lower limits should be regarded as rough estimates only.
As pointed out in Sect.3, the observations' geomagnetic latitude and time of day are compatible with the cusp/cleft.The characteristic energies estimated above, indicate that the precipitating particles are magnetosheath particles with enhanced energy.Together with a red to green line intensity ratio much larger than 2, increased electron densities, and large particle fluxes, we take this to indicate cusp precipitation.However, in addition to the ordinary soft cusp precipitation, there are indications of a component of higher energy: rays of 557.7 nm [OI] emissions, very intense 427.8 nm [N + 2 ] emissions, and a high energy tail seen in the DMSP satellite data just before the first NEIAL are observed.This augmented cusp activity is presumably due to the before mentioned CME in the solar corona prior to our observations.The presence of high energy particles in the cusp is supported by a number of recent satellite programs.In fact, it appears that the cusp is a major source of energetic charged particles for the magnetosphere (Fritz and Fung, 2005).
The auroral displays during the NEIAL period exhibited diffuse as well as discrete forms, all four lines observed with the MSP showed considerable intensities.Electron precipitation all over the energy range of auroral particles, 10 eV-10 keV, is thus presumably present for the period.In Fig. 6, the red/green ratio against the energy flux for each integration periode is plotted.The period with NEIALs are marked in red.The NEIALs seem to be limited to values of the red/green ratio between 5 and 15.The lower limit is undisputable.There is a relatively large number of points below ratio 5 and above the lower limit of 3.4 mW/m 2 in flux, none of which contains NEIALs.The upper limit is much less significant.There are a number of points without NEIALs above ratio 15, but when we exclude the points with energy flux less than 3.4 mW/m 2 , only a handful is left.Considering that after all, only a minority of the points with flux higher than 3.4 mW/m 2 and ratio higher than 15 contain NEIALs, this handful may well be a coincidence.
In the majority of our NEIAL events, both shoulders are detectable, albeit with different enhancement.The right shoulder, which represents ion acoustic waves travelling towards the radar, is associated with thermal-and suprathermal electrons as well as ions, while the left shoulder, which represents ion acoustic waves travelling away from the radar, is associated with soft electrons, current and ion-outflow (Rietveld et al., 1996).Furhermore, the right and left shoulder is dominating at different altitudes, typically below and above 250 km in altitude, respectively.The optical lines represent energy deposition of the particle precipitation at different altitudes.From comparing the ESR and MSP data, it is clear that the red line is associated with the left shoulder, while the more energetic lines are significant to the right one.
Much attention has been paid to the soft part of the particle precipitation during NEIAL.However, acknowledging the fact that the mechanisms behind NEIAL are far from well understood, the harder part of the precipitation should not be ignored.In our data we have three indicators of hard precipitation: i) increased electron density in the E-region, ii) excursions in the geomagnetic component or, equivalently, Joule heating, and iii) cosmic noise absorption.NEIALs are quite often accompanied by indication of hard particles by one or more of theses indicators, although there is not a complete correspondence.In pronounced cases, indicator i) and iii) typically occur at the same time, while the indicator ii) comes slightly ahead of them.Weaker events exhibit a more varied and less clear picture.
Energetic particles precipitation results in increased electron density at lower altitudes.This can be seen as enhanced backscatter power from the E-region.The backscatter is close to proportional to the electron density, and under the night time conditions of our observations, electron precipitation is the only significant source of ionisation.To a first approximation the electron density is then proportional to the square root of the electron flux (Brekke et al. 1989).In our data, energetic particle events occur simultaneous to some of the NEIAL events, for instance: 08:53, 09:10, 09:26, 09:38, 09:48, and 09:55-09:55 UT, see Fig. 1.In fact, roughly 40% of all NEIAL events are associated with hard precipitation in addition to a sufficient soft component.
Energetic particles ionising the E-region will increase the electric conductivity.Provided an electric field is present, electric currents arise and a disturbance of the magnetic field is observed on ground.Our magnetometer records do show a disturbed magnetic field in period of NEIAL events, thus confirming the conclusion drawn from the radar data.The horizontal component of the field reaches a maximum excursion of 380 nT 10 min before the commencement of the NEIAL period.This is somewhat different from the result of Buchert et al. (1999) who found a coincidence between maximum magnetic disturbance and the NEIAL occurrence.
In the auroral and polar cap regions, the cosmic radio noise absorption in the ionosphere as observed by the riometer, is mainly caused by precipitation of auroral particles from the magnetosphere.However, increased electron collision frequency due to enhanced electron temperature associated with plasma instabilities generated by large ionospheric electric fields, may also contribute to the absorption (Stauning, 1996).In our case we assume the absorption observed is the ordinary one caused by increased ionisation in the D-and E-regions by energetic particles since such particles are indicated also by the radar and magnetometer observations.During the period of NEIALs the absorption reached its maximum of 0.8 dB at the same time as the magnetometer excursion.According to Akasofu (1968) this indicates particles with characteristic energies of the order of several tens of keV.
As pointed out by Maehlum et al. (1984) the F-region is far from unaffected by hard precipitation.Direct interaction between the F-region plasma and the fast particles may perturb the electron velocity distribution, or the effect may be indirect by secondary electrons originating from the Eregion.The Joule heating caused by the electric currents in the E-region may represent another indirect source of disturbance of the F-region assuming that the perturbation of the E-layer propagates upwards.In our case the joule heating was 1.7 mW/m 2 on average for the period of interest and 10 mW/m 2 at maximum.Thus excursion of in magnetic field, Joule heating and other indication of E-region activity may signal the possibility of NEIAL being present.
Conclusion
By means of the Svalbard IS-radar we have observed an unusually large number of NEIAL-events over a period of one hour, very likely in the cusp region.Simultaneous auroral activity was recorded with a four channel scanning photometer, and a magnetometer and imaging riometer yielded supplementary data.
At all photometer wavelengths the intensity increased considerably at the onset of the NEIAL period and then dropped to a lower level again, immediately after the last NEIAL.Riometer absorption events and excursions of the magnetometer's horizontal component also characterised the NEIAL period.Clearly the NEIALs were accompanied by not only soft (energies less than 500 eV) electron precipitation which is associated with the down-shifted shoulder in the incoherent scatter spectrum, but also more energetic particles.
From the analysis for the optical observations we conclude that four conditions must be fulfilled for the NEIAL to appear: 1.The intensity of the red line as well as the other lines must be above a certain minimum 2. The characteristic energy must exceed 50 eV 3. The particle energy flux must be above 3.4 mW/m 2 4. The 630.0/557.7 intensity ratio must be higher than 5 When all these conditions were fulfilled, NEIALs occured in 90% of the integration periods.Only a few cases where these requirements were satisfied did not correspond to any NEIAL event.
Our observational results do neither verify nor falsify any of the theories for explaining NEIAL.The observed enhancements in both shoulders in the incoherent scatter spectrum support all three theories, provided that there exist two or more fine structures within the radar beam to account for the use of the current instability theory.The fact that soft particle precipitation clearly is present supports the parametric decay of Langmuir waves.Furthermore, since the enhancements predominantly occur in the upper part of the F-region, the ion-ion two-stream instability can not be excluded before a thorough study of the flow of H + ions during NEIAL events is done.Finally, it should be kept in mind that the three theories are not mutually exclusive.Do the hard precipitation (energies >500 eV) play any role in the production of NEIAL?The question has been touched upon in the literature (Forme et al., 1995;Forme and Fontain, 1999;Nöel et al., 2000), but so far not adequately since all these investigations are dealing nightside condition and not cusp/cleft.During our NEIAL events, hard precipitation no doubt is present, as clearly indicated by the increased electron density in the E-layer, magnetic activity and riometer absorption.A direct association between hard particles and NEIALs seems to be manifest in the enhancement of the upshifted shoulder during such precipitation.This association has not previously been addressed, and the role of hard precipitation in the generation of NEIALs may therefore deserve a closer investigation.
Fig. 2 .
Fig. 2. Examples of some of the NEIALs observed between ∼08:52 and ∼09:55 UT, with enhancement in one or both shoulders; (a) the right shoulder is much more enhanced than the left, (b) both shoulders are enhanced to about the same level, (c) the left shoulder is much more enhanced than the right, (d) a more height defined enhancement in the left shoulder and (e) the enhancement in both shoulders are left shifted (strong ion outflow).Start and stop of the integration time are given on the right hand side.
Fig. 3 .
Fig. 3.A standard analysis of the ESR data where the panels show from the top to the bottom: the electron density, the electron temperature, the ion temperature and the ion drift velocity. | 10,922.8 | 2007-06-29T00:00:00.000 | [
"Environmental Science",
"Physics"
] |
Electroplating of Electronic Materials for Applications in Large Area Electronics : A Review
The attributes of electroplating as a low-cost, simple, scalable and manufacturable semiconductor deposition technique for the fabrication of large-area and nanotechnologybased device applications are discussed. These strengths of electrodeposition are buttressed experimentally using techniques such as X-ray diffraction, Ultraviolet-visible spectroscopy, Scanning electron microscopy, Atomic force microscopy, Energy-dispersive X-ray spectroscopy and photoelectrochemical cell studies. Based on the structural, morphological, compositional optical, and electronic properties evaluated results, it is evident that electroplating possesses the capabilities of producing high quality semiconductors usable in producing excellent devices. In this paper we will describe the progress of electroplating technique mainly for the deposition of semiconductor thin film materials, their treatment processes and fabrication of solar cells.
Introduction
Electroplating has been well explored over the years especially for the purification, extraction, protection, coating of semiconductors, metals and metalloids in the industrial sector [1] to achieve inherent properties.The use of electroplating technique in the deposition of semiconductor materials dates back to the 1970s [2][3][4] with the deposition of semiconductors from the II-VI group.The ascendance of the electrodeposition of semiconductor material led to the growth and fabrication of CdS/CdTe-based solar cell device within a decade afterwards [5].The fabrication of thin-film solar cells with photovoltaic conversion efficiency of ~10% was the stimulus for an intense global research in the electrodeposited semiconductor compounds.The research also spanned into the electrodeposition of II-VI semiconductor materials such as ZnTe [6], ZnSe [6], ZnS [7], ZnO [8] etc and spread into semiconductor material compounds in the binary (III-V, IV-VI), ternary (CuInSe2) [9,10], and quaternary (Cu2ZnSnS4, CuInGaSe2) groups [11].The electroplating of elemental semiconductors and other wide bandgap nitrides has also been captured in the literature.This communication critically appraises the strengths, weaknesses, potentials and the state-of-the-art electroplating technique in the fabrication of large-area electronics and other macro-electronic devices such as photovoltaic (PV) solar panels and display devices.
An overview of electrodeposition technique
Electrodeposition is the process of depositing elemental or compound metals or semiconductors on a conducting substrate by passing an electric current through an ionic electrolyte in which metal or semiconductor ions are inherent [12].The passage of current is required due to the inability of the chemical reaction resulting in the deposition of the solid material on the conducting substrate to proceed on its own as a result of positive free energy change ∆G of the reaction.
Electrodeposition can be categorised based on power supply source, working electrode and electrode configuration (as shown in Figure 1) but the basic deposition mechanism and setup remains similar.The basic deposition mechanism entails the flow of electrons from the power supply to the cathode.The positively charged cations are attracted towards the cathode and negatively charged anions to the anode.The cations or anions are neutralised electrically by gaining electrons (through reduction process) or losing electrons (through oxidation process) and being deposited on the working electrode (WE) respectively [12].
The typical electrodeposition (ED) setup of two-electrode (2E) configuration as shown in Figure 2 (a) consist of deposition container (beaker), deposition electrolyte, magnetic stirrer, hotplate, power supply, a working electrode, a counter electrode and an optional reference electrode (RE) in the case of 3-electrode (3E) configuration (see Figure 2 (b)).The use of potentiostatic power source was due to the effect of deposition voltage on the atomic percentage composition of elements in the electrodeposited layer, which is one of the factors determining the conductivity type [13,14].Cathodic deposition is mainly utilized due to its ability to produce stoichiometric thin-films with good adherence to the substrate as compared to anodic deposition [15].Conversely, the galvanostatic electrodeposition is controlled and measured by maintaining constant current density through an electrolytic cell disregarding the changes in the resistance due to the deposited electroplated layer.
The 2E configuration as shown in Figure 2 (a) was utilised due to its industrial applicability, process simplification and also to eliminate possible Ag + and K + ions doping [16,17] which may emerge from the Ag/AgCl or saturated calomel electrode (SCE) reference electrodes (see Figure 2 (b)).Taking the electrodeposition of n-CdS and n-CdTe layers which are respectively utilised as the main window and absorber layers in this work into perspective, Therefore, any leakage of K + and Ag + into the electrolytic bath may result in compensation leading to the growth of highly resistive material which has a detrimental effect on the efficiency of fabricated solar cells.This has been experimentally shown and reported in the literature [16].
The two-electrode electrodeposition configurations are not without challenges, with the main challenge being the fluctuation or drop in the potential measured across the cathode and the anode during deposition.This is due to the alteration in resistivity of the substrate with increasing semiconductor layer thickness and the change in the ionic concentration of the electrolyte.Unlike the 3-electrode configuration, the potential difference is measured across the working and the reference electrodes while the measured current is between the working and the counter electrodes.In general, other factors such as the pH of the electrolyte [18], applied deposition potential [13,14], deposition temperature [19], stirring rate [20], deposition current density, duration of deposition and thickness [21], underlying substrate [22], and concentration of ions in the deposition electrolyte [18] affects the electrodeposition process and the properties of the deposited layers.Recent publications have demonstrated the similarities between electrodeposited semiconductors using 3-electrode and 2-electrode electroplating configuration [23,24].The electrodeposition of both elements and compounds is governed by Faraday's laws of electrodeposition as mathematically depicted in Equation (1).
where T is the thickness (cm), J is current density (Acm -2 ), t is the deposition time (s), M is the molecular mass (gmol -1 ), n is the number of electrons transferred in the chemical reaction for the formation of 1 mole of substance in gcm -3 , F is the Faraday's constant (96485 Cmol -1 ), and ρ is the density (gcm -3 ).It should be noted that the Faraday's law of electrolysis assumes that all the electronic charges pass through the electrolyte contribute to the deposition of deposited material layer without any consideration of the resistance losses in the system and electronic charge contribution to the decomposition of solvent into its constituent ions [25].
Solutes, solvents and the deposition Electrolytes
The effects of the incorporated solute and solvent utilised are of importance in electrodeposition.Taking the electrodeposition of CdS into consideration, sodium (Na) based precursor (Na2S2O3) has been often utilised [26,27].Although sodium (Na) ions are not electrodeposited at low cathodic voltages, the incorporation of Na in CdS films is achievable through adsorption, absorption or chemical reactions as a result of increased Na accumulation in the electrolytic bath.It should be noted that Na is a p-type dopant in CdS [28] resulting into increasing electrical resistivity of subsequent CdS layers due to Na accumulation.
Further to this, the Na-based precursor (Na2S2O3) is also associated with the precipitation of sulphur during the electroplating.Recent understanding has shown that the replacement of the well-established sulphur precursor with thiourea (NH2CSNH2) (which is more associated with chemical bath deposition (CBD) technique) results in the reduction/elimination of sulphur precipitate [29,30].
The choice of solvent to be utilised also possesses as an important factor in electroplating as demonstrated by the deposition of CdTe from ethylene glycol (C2H6O2) electrolyte containing cadmium iodide (CdI2) [31].Using aqueous solution as solute, Cd-I complexes such as CdI + , CdI2, CdI3 -and CdI4 2-are formed in aqueous solution [32,33] debarring the deposition of Cd and the co-deposition of CdTe. it is noteworthy that CdTe from other Cd precursors have been explored and reported in the literature [34,35].Therefore the choice of solute and solvent for electroplating purposes is a factor to reckon with in addition to a number of other factors inherent in the electrodeposition process to achieve superior qualities of electroplated semiconductor materials.This understanding has been accrued for over two decades of exploration, and careful examination of grown semiconductors at Solar Energy Group within Sheffield Hallam University (SHU), in addition to the literature.A large number of semiconductors explored in SHU is summarised in Table 1.Ability to grow both pand n-type material. [54]
Electrolytic bath pH value
The composition of an electrolytic bath naturally determines the pH of the bath.Basically, the acidity (pH<7.00) of an electrolyte can be increased by the introduction of an acid.The hydrogen ions (H + ) from the dissociated acid reacts with water in aqueous solution to form hydronium ions (H3O + ).On the other hand, the alkalinity of a solution increases (pH>7.00)with the reduction in the H3O + concentration.This is caused by the reaction of dissociated hydroxide ions (OH -) from introduced alkaline with H + ions from water dissociation to form water (H2O) rather than hydronium ions.It is well documented that elemental and compound deposition responds to this chemical dynamic mainly in wet deposition techniques such as chemical bath deposition (CBD) [36] and electrodeposition techniques [37].With emphasis on electrodeposition, the effect of pH of the bath and the deposited layers vary from selective deposition/etching of element [38], alteration of the characteristic properties of the deposited layers [39,40], elemental/compound precipitation [41] and increase in the deposition current density [42].Furthermore, the effect of pH on the dissociation of common solvent such as water is also well documented in the literature [43].With the notion that increase in acidity of electroplating bath resulting into the increase in the concentration of dissociated ions in the aqueous solution [43].Due to the increased ionic concentration, the deposition current density increases until it stabilises or continues to increase, depending on the composition of the solution.
Deposition temperature
It is a known fact that an increase in the temperature of a matter increases the motion of the molecules inside it.As such, the electrolytic bath temperature increases solubility of the solvent, catalyses the reactions, energizes the ions and increases the transport number, which results to an increase in the deposition current density and rate of deposition of constituent element or compound.Further to this, the work performed on electrodeposition depicted that an increase in the crystallinity of as-deposited semiconductor material is achievable at higher growth temperature [3,44].For electroplated semiconductor materials from aqueous solution, there is a limitation on the growth temperature due to the boiling temperature of water at 100 ℃ under standard temperature and pressure.While the electroplating from other electrolytic bath can go as high as 160 ℃ [31].Deposition of materials at higher temperature provides energy required for ions/atoms to move around and deposit in a regular crystalline pattern.
Deposition current density
With regards to Faraday's law of electrodeposition, the deposition current density is directly related to the thickness of the deposited layer.Thus, the deposition current density is dependent on factors effecting the energizing of the inherent ions in the electrolyte such as stirring rate, bath temperature, concentration of constituent [45] and electrical conductivity of the substrate amongst other constraints.While a gradual alteration in the deposition current density is expected depending on the electrical conductivity of the electroplated layers.
With respect to semiconductor materials such as CdTe, literature depicts the effect of current density on the morphological, compositional and the structural properties of the deposited layer [46,47].Based on the deposition configuration, it can be inferred that the deposition current density of 3-electrode configuration and 2-electrode deposition configuration vary.
The incorporation of excessive Te can alter the composition of the deposited CdTe, conduction type to p-CdTe due to Te-richness [50], and reduced adhesion on the underlying substrate.Reduction in the adhesion of CdTe may also occur due to the deficiency of Te concentration in the electrolytic bath.In either condition (excess or deficiency of Te concentration in the electrolyte), the crystallinity, morphology and adhesion of the ensued CdTe layer suffer.
Duration of deposition and thickness
Electroplating of materials with main emphasis on semiconductor commences by the nucleation of the most electropositive element on the points on the conducting substrates with the highest electric field.Therefore, it can be categorically stated that the nucleation and nucleation modes of semiconductor material is conductive substrate dependent [1,22,51].
Consequential to the surface roughness of the underlying working electrode such as glass/fluorine doped tin oxide (g/FTO), the highest electric field is experienced at the peaks of the rough surfaces.The nucleation of the electroplated material spreads out through to the lowest valley from the initiation rough surface peaks resulting into columnar nature of the deposited layers [14].The potency of this mechanism is highly influential at the initial stages of deposition due to unevenness of the deposited layer thickness characterised by pin-holes, voids, gaps and high dislocation density within the semiconductor material [21].This characteristic property is detrimental when thin semiconductor layer with thickness of <100 nm is required [21].At the start of an electrolytic bath, electro-purification of the bath is highly essential to reduce and eliminate the impurity level which is mostly incorporated in the precursors amongst other impurity sources.It should be noted that even with high purity precursor with 99.999% purity carry impurity level of 10 part per million (ppm).The purification is essential due to the effect of impurities even in ppm levels [52] on the characteristic properties of electroplated semiconductor materials.It should be noted that electro-purification of a bath must be performed using similar deposition parameters (such as bath temperature, pH, stirring rate etc.) to the semiconductor deposition.The electro-purification potential utilised should be lower than the deposition potential range of the required elements established using cyclic voltammetry.Based on this characteristic property of electroplating technique, the more layers are deposited, the purer the electrolyte and the electroplated semiconductor gets due to the gradual reduction of background impurities and improved material property.This property does not only increase the purity of the electrolyte and the deposited semiconductor but also increase the longevity of the bath as compared to the batch process of chemical bath deposition (CBD) technique.
To further mitigate other sources of impurities, a fraction of researchers choose 2-electrode over the 3-electrode configuration to avoid possible impurities from the reference electrode.
While the usage of Teflon-ware (polypropylene beaker) is necessitated to house the electrolyte due to possible leaching of elemental sodium and other dopant from glass-wares [53] into acidic electrolytes.
Ease of doping -intrinsic and extrinsic
With the effective purification of the electrolyte, intrinsic doping has been demonstrated in the literature for binary [54,55], ternary [56] and quaternary [57,58] semiconductor materials by changing the deposition voltage.Taking an example of a I-III-VI2 semiconductor materials such as CuInGaSe2, the stoichiometric semiconductor layer consists of 25% of the of In resulting in an n-type semiconductor material as in the case of CuInSe2 (see Figure 3 (a)).While at intermediate voltages, the material exhibits insulating or intrinsic properties.
This electrical characteristic property as demonstrated in the literature [56][57][58] signify the ability of growth of p-, i-and n-type materials from the same bath by cathodic voltage variation (see Figure 3).The incorporation of Ga in CuInGaSe2 [59] increases the bandgap and also make the material p-type.This must be due to the formation of acceptor-like defect in the material (see Figure 3 (b)).
The effect of cathodic voltage on the elemental composition of binary semiconductor has also been demonstrated and documented in the literature [14,50].The effect of alteration in the growth voltage on the elemental composition of electroplated materials even for as low as 1 mV step has been documented [29] (see Figure 4).The ease of intrinsic doping and the effect of extrinsic doping of electroplated semiconductor materials have been well established in the literature [52,60].Due to the simplicity of ED, doping at parts-per-million (ppm) level is made possible [52,60,61].
Bandgap engineering capability
The Control or alteration of the bandgap of materials (with emphasis on semiconductor) is easily achievable in electrodeposition technique.Typically, this can be achieved by controlling the atomic composition of the elemental component of the semiconductor material.Intrinsically, electrodeposition have the ability to change the composition of growing material by a simple alteration of the cathodic voltage [54,[56][57][58]].An ensuing alteration in the bandgap of grown semiconductor material due to change in the growth cathodic voltage has been documented in the literature [59].It is well known that an increase in the atomic concentration of Ga in CuInGaSe2 by increasing the cathodic voltage increases the bandgap of CuInGaSe2.While a reduction in the cathodic voltage of the CuInGaSe2 result in the reduction of bandgap due to the richness of Cu [59] (see Figure 5).This ability provides the ease of bandgap engineering of semiconductor material such as CuInGaSe2 between ~(1.00 to 2.20) eV.Extrinsically, this observation has also been documented for electroplated binary semiconductor materials such as CdTe doped with Ga [52] amongst others.With the bandgap of the resulting doped semiconductor directly affected with the incorporated dopant even at parts per million levels [52,60].
Low-cost and simplicity
There are over 14 different and well-established techniques to grow thin-film semiconductor materials [62] which can be broadly categorised under physical or chemical deposition.The physical deposition refers to the technologies in which material is released from a source and deposited on a substrate using thermodynamic, electromechanical or mechanical processes [1,63].While the chemical deposition techniques are accomplished by the utilisation of precursors either in their liquid or gaseous state to produce a chemical reaction on the surface of a substrate, leaving behind chemically deposited thin-film coatings on the substrate.
Electrodeposition falls under the chemical deposition techniques which can be carried out in an uncontrolled environment and without a vacuum system.The setup for electroplating which is mainly constituted of computerized potentiostat and hotplate/magnetic stirrer with a cost implication of £5000 as compared to other techniques.As compared to the wellestablished metallorgonic chemical vapor deposition (MOCVD) or close space sublimation (CSS) system with an high initial cost implication of about £1 million.In addition, these systems have limitations as concerning the materials that can be grown.Furthermore, the relatively low heat energy required during growth and post growth treatment makes electroplating a more energy-economic deposition technique as compared to a large number of other techniques.More importantly grown semiconductor layers using cost-effective electroplating technique is comparable to semiconductor layers grown using highly expensive techniques [29,64] and they all require post-deposition treatments [65,66].
Scalability and manufacturability
The scalability and manufacturability of electroplating has been demonstrated on an industrial scale by BP solar in the 1980s and 1990s [67,68].BP solar manufactured CdTebased solar cells with solar panel area ~1 m 2 with a conversion efficiency of ~10% [67,68].
As compared to the laboratory scale setup as shown in Figure 2, scaling up requires a larger tank to contain the electrolyte and multi-plate cathode attached to multiple conducting substrates.The use of larger tanks and multi-plate cathode increases the throughput of deposited layers and an added advantage of electroplating on intricate shapes and designs.
Weaknesses of electrodeposition
One of the main disadvantages of electrodeposition includes the need for a conducting substrate as the working electrode in the electroplating setup.Due to this requirement, using conventional characterisation technique such as Hall Effect to determine the electrical properties of the deposited layers on FTO for example will not be possible due to the underlying conducting layer.
Instability of current density during deposition
The control of the electrodeposition process due to the alteration of current density with increasing deposition layer thickness is a challenge (under potentiostatic condition).The electroplating of materials with electrical conductivity level lower than the primary substrate results in the reduction of current density with direct relationship with the thickness of the deposited material [69].This observation is common for both 2E and 3E electroplating configurations but the applied voltage can vary slightly in 2E configuration.
Control and regulation of ions within the electrolytic bath
Control and regulation of ions within the electrolytic bath -as a result of depletion in the ionic concentration and the inability to gauge/measure ionic concentration in the electrolyte during layer deposition, replenishing the bath with the appropriate chemical concentration is vague.Thereby reducing reproducibility tendencies.
Formation of solution based complexes
There is a posibility for the formation of complexes within the electrolyte which might be debarring the deposition of element and/or the co-deposition of a compound [32,33].This is the case of the deposition of CdTe from aqueous solution containing CdI2 as the Cd- precursor.Literature shows that due to the formation of Cd-I complexes in aqueous solution, only p-CdTe layers due to Te-richness is possible [32,33].Unnecessary precipitation remove chemicals from the electrolyte, changing the elemental concentration in the bath.The full characterisation process of both the CdS and CdTe are documented in the literature [74,75].The electronic properties of the fabricated photovoltaic cells obtained using currentvoltage (I-V) and capacitance-voltage (C-V) techniques are summarised in Table 2. CdTe-based devices assuming p-CdTe in CdS/CdTe devices has been documented in the literature [27,79].But based on recent observations, the incorporation of Cd-rich CdTe absorber layer produce high efficiencies.These effects have been independently observed and reported [80,81] and mainly attributed to the reduced defects in Cd-rich CdTe (the layers are deposited using physical deposition processes).C-V under dark condition σ×10 -4 (Ω.cm) -1 1.41 2.85 6.03 NA or ND (cm -3 ) 7.74×10 16 3.10×10 14 9.10×10 14 µ (cm 2 V -1 s - Using mainly n-CdTe absorber layers, few devices incorporating all-electrodeposited from the SHU group have been documented in the literature and summarised in Table 3.
Figure 1 :
Figure 1: The main categories of electrodeposition technique.
4
Strengths and Weaknesses of Electrodeposition 4.1 Strengths of electrodeposition 4.1.1Electrolytic bath life longevity and Self-purification
Figure 3 :
Figure 3: PEC signal for (a) CuInSe2 and (b) CuInGaSe2 with increasing cathodic voltages.Note the ability to grow p + ,p, i, n and n + materials from the same electrolyte, simply by varying the deposition voltage [56-58].
Figure 4 :
Figure 4: Atomic compositions ratio of Cd to S in as-deposited and CdCl2 treated CdS thin films at different deposition cathodic voltages.
4. 2 . 4
Extrinsic doping of electrolytic bath by the electrodes Control of purity throughout the electrolytic bath lifespan -as there has been an observation of increased carbon concentration in deposited semiconductor layers.The incorporation of carbon into the electrolytic bath is due to the deterioration of the anode utilised in the electrolytic cell setup.4.2.5 Non-uniformity of electrodeposited semiconductor layersDue to the unevenness of the underlying conducting substrate such as transparent conducting oxide (TCO), the highest electric field is experienced at the peaks of the rough conducting substrate surfaces.Nucleation starts at the peaks and spreads out through to the lowest valley resulting into layers with columnar nature[14].5 All-electroplated photovoltaic devicesElectrodeposited cadmium telluride (CdTe) and copper indium gallium selenide (CIGS) are amongst the commonly used absorber layers in all-electrodeposited photovoltaic applications[45,70].The versatility of the technique in the growth of all-electrodeposited configuration has been well documented[13,[71][72][73].The band diagrams of possible n-p and n-n+ large Schottky barrier junctions are shown in Figure6fabricated using CdS/CdTe configuration.
Preprints
(www.preprints.org)| NOT PEER-REVIEWED | Posted: 26 June 2018 doi:10.20944/preprints201806.0433.v1Peer-reviewed version available at Coatings 2018, 8, 262; doi:10.3390/coatings8080262It is well known that intrinsic CdS is n-type and remains n-type due to the inherent defect as a result of the presence of S vacancies and Cd interstitials in the crystal lattice of the deposited CdS layers[76].The devices are fabricated by incorporating CdTe deposited at the vicinity of the transition voltage (Vi) from p-type to n-type CdTe or vice versa.Electroplated CdTe can either be p-type (when Te rich) or n-type (when Cd rich) material under as-deposited condition.While a retention or transition of electrical conduction type is possible after cadmium chloride treatment.It is noteworthy that the conversion of the electrical conduction type after post-growth treatment may be attributed to the doping effect as a result of the heat treatment temperature, duration of treatment, initial atomic composition of Cd and Te, the concentration of CdCl2 utilised in treatment, defect structure present in the starting CdTe layer and the material's initial conductivity type as documented in the literature[75,77,78].Therefore depending on the final electrical conduction type, the possible device configurations are possible, and the analysis of device results must be performed with extreme care.
Table 1 :
Summary of explored electronic materials to date at authors' research group using electroplating from aqueous solutions.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 26 June 2018 doi:10.20944/preprints201806.0433.v1
Peer-reviewed version available at Coatings 2018, 8, 262; doi:10.3390/coatings8080262 group I element, 25% of the group III elements and 50% of group VI element.Due to the positive reduction potential of Cu (Eo=0.52V), at low deposition voltages, high elemental composition of Cu (group I) is incorporated in the semiconductor resulting into p-type conduction type.But an increase in the cathodic voltage increases the elemental composition
Table 2
summarises the results of CdS/CdTe solar cells made with CdTe layers grown in the vicinity of Vi=1370 mV.Below the Vi, the CdTe layers are p-type and therefore the devices made are p-n junctions (see Figure6 (a)).Above the Vi, the CdTe layers are n-type and hence the device structures are n-n +Schottky barrier (see Figure6 (b)).As shown in Table2, the devices fabricated with n-CdTe performs better than those made with p-CdTe layers.
Table 2 :
Summary of device parameters obtained from I-V (both under illuminated and dark conditions) and C-V (dark condition) for simple CdS/CdTe-based solar cells grown at different growth voltages in the vicinity of Vi =1370 mV.
Table 3 :
Summary of device parameters obtained from I-V (both under illuminated and dark conditions) and C-V (dark condition) for glass/FTO/n-CdS/n-CdTe/Au and glass/FTO/n-ZnS/n-CdS/n-CdTe/Au solar cells. | 5,843.6 | 2018-06-26T00:00:00.000 | [
"Materials Science"
] |
Solvability of fractional differential inclusions with nonlocal initial conditions via resolvent family of operators
In this paper, we consider mild solutions to fractional differential inclusions with nonlocal initial conditions. The main results are proved under conditions that (i) the multivalued term takes convex values with compactness of resolvent family of operators; (ii) the multivalued term takes nonconvex values with compactness of resolvent family of operators and (iii) the multivalued term takes nonconvex values without compactness of resolvent family of operators, respectively.
Introduction
A differential inclusion is a generalization of the notion of an ordinary differential equation, which is often used to deal with differential equations with a discontinuous righthand side or an inaccurately known right-hand side [1,2]. Differential inclusions are also from the control problem, for instance, for a control problem x′ = f(x, u), u ∈ U, where u denotes a control parameter. It is founded that the aforementioned control system has the same trajectories as the differential inclusion x′ ∈f (x, U) ⋃ u∈U f (x, u). If the set of controls is dependent upon the state x, i.e. U = U(x), then the differential inclusion x′ ∈ F(x, U(x)) is also achieved. This equivalence between control systems and differential inclusions plays a key role in proving existence theorems in optimal control theory. Differential inclusion has wide applications to models in economics, sociology and bioecology etc., and thus, it has been considerably investigated in last decades, see, for instance, [2][3][4][5][6][7][8] and references therein.
The concept of nonlocal initial condition has been introduced to extend the study of classical initial-valued problems. As indicated in [9], this notion can be more natural and more precise in describing nature phenomena than the classical notion since some additional information is taken into account. For nonlocal initial conditions of abstract differential inclusions, we can refer to [4,6,10,11] and references therein.
In a recent paper, some new properties on the compactness of resolvent family of operators related to fractional differential equations have been established [35]. This new characterization of compactness of resolvent family of operators provides a new way to consider mild solutions of abstract fractional differential equations.
Preliminaries
Let (X, ∥ ⋅ ∥) be a Banach space. We denote P cl (X) {Y∈ P(X) : Y closed}, P b (X) {Y ∈ P(X) : Y bounded}, P cp (X) {Y ∈ P(X) : Y compact} and P cv (X) {Y ∈ P(X) : Y convex}. We also denote by L(X) the space of bounded linear operators from X into X.
A multivalued map G : The multivalued map G : X → P(X) is called upper semicontinuous (u.s.c.) on X if for each x 0 ∈ X, the set G(x 0 ) is a nonempty, closed subset of X, and if for each open set N of X containing G(x 0 ), there exists an open neighbourhood If the multivalued map G is completely continuous with nonempty compact values, then G is u.s.c. if and only if G has a closed graph, i.e., x n → x * , y n → y * , y n ∈ G(x n ) imply y * ∈ G(x * ).
The u.s.c. multivalued map G is said to be condensing if for any B ∈ P b (X) with ν(B) ≠ 0, we have ν(G(B)) < ν(B), where ν denotes the Kuratowski measure of noncompactness.
for all ‖x‖ ≤ r and for a.e. t ∈ J.
Lemma 2.1. Let X be a Banach space. Let G : J × X→ P cp, cv (X) be an L 1 -Carathéodory multivalued map with and let Γ be a linear continuous mapping from L 1 (J, X) to C(J, X), then the operator for all u, v ∈ A and all measurable subsets N of J, the function uχ N + vχ J−N ∈ A, where χ denotes the characteristic function.
Let F : J × X → P cp (X). Assign to F the multivalued operator The operator F is called the Niemytzki operator associated to F.
Let Y be a separable metric space and let N : Y → P(L 1 (J, X)) be a multivalued operator. We say that N has property (BC) if (1) N is l.s.c.; (2) N has nonempty closed and decomposable values.
Let (X, d) be a metric space induced by the normed space (X, ∥ ⋅ ∥). Let H d : P(X) × P(X) → R + ⋃ {∞} be defined as for each x, y ∈ X; (ii) a contraction if it is γ-Lipschitz with γ < 1.
For more detailed results on multivalued maps and differential inclusions, we refer to [1,2,4,7,8]. We now give some important properties of resolvent family of operators.
Definition 2.6. [35] Let A be a closed and linear operator with domain D(A) defined on a Banach space X and α > 0. We call A the generator of an (α, 1)-resolvent family if there exists ω ≥ 0 and a strongly continuous function S α : R + → L(X) such that {λ α : Reλ > ω} ⊆ ρ(A) and In this case, the family {S α (t)} t≥0 is called an (α, 1)-resolvent family generated by A.
Definition 2.7. [35] Let A be a closed and linear operator with domain D(A) defined on a Banach space X and 1 ≤ α ≤ 2. We say that A is the generator of an (α, α)-resolvent family if there exist ω ≥ 0 and a strongly continuous function R α : R + → L(X) such that {λ α : Reλ > ω} ⊆ ρ(A) and In this case, the family {R α (t)} t≥0 is called an (α, α)-resolvent family generated by A.
Recall that a strongly continuous family {T(t)} t≥0 ⊆ L(X) is said to be of type (M, ω) if there exist constants M > 0 and ω ∈ R such that ||T(t)|| ≤ Me ωt for all t ≥ 0.
Suppose that S α (t) is continuous in the uniform operator topology for all t > 0, then the following assertions are equivalent: , ω and the following assertions are equivalent: Next, we list some well-known fixed point theorems.
Let Ξ be a bounded, convex and closed subsets of a Banach space X and let ϒ : Ξ → Ξ be a condensing map. Then, ϒ has a fixed point in Ξ.
Lemma 2.6. [1] Let Ξ be a bounded and convex set in Banach space X. ϒ : Ξ → P(Ξ) is an u.s.c., condensing multivalued map. If for every x ∈ Ξ, ϒ(x) is a closed and convex set in Ξ, then ϒ has a fixed point in Ξ.
where Fix(G) denotes the fixed point set of G.
Existence results
In this section, we shall investigate some existence results for mild solutions to Eqs. For the problem (1.1)-(1.2), according to [35], we have the following definition.
Definition 3.1. Let A be the generator of an (α, 1)-resolvent family S α (t); the mild solutions of the problem (1.1)-(1.2) are defined as follows: We list the following assumptions: Remark 3.1. (i) Of concern, for useful criteria for the continuity of S α (t) in the uniform operator topology, one can refer to the work [37]. For instance, this property holds true for the class of analytic resolvent.
(ii) According to Lemma 2.3, the condition (A1) implies S α (t) is compact for all t > 0.
Proof. Consider the operator N : C(J, X) → P(C(J, X)) defined by where v ∈ S F,x . Clearly, the fixed points of N are mild solutions to (1.1)-(1.2). We shall show that N satisfies all the hypothesis of Lemma 2.6. The proof will be given in several steps.
Step 1. There exists a positive number r such that N( If it is not true, then for each positive number r, there exists a function x r such that h r ∈ N(x r ) but ‖h r (t)‖ > r for some t ∈ J, where v r ∈ S F, x r . However, on the other hand, we have Dividing both sides by r and taking the lower limit as r → ∞, we obtain the following equation: which contradicts the relation (3.1). Step Let θ ∈ (0, 1). Then for each t ∈ J, we have Due to the fact that F has compact values, we may pass to a subsequence if necessary to get that v n converges to v in L 1 (J, X) and hence v ∈ S F,x . Then for each t ∈ J, Step 4. N is u.s.c. and condensing. Now, we decompose N as N 1 + N 2 as We only need to prove that N 1 is a contraction and N 2 is completely continuous.
To show that N 1 is a contraction, for arbitrary x 1 , x 2 ∈ B r and each t ∈ J, we have from (A3) From the relation (3.1), we conclude that N 1 is a contraction.
Next, we show that N 2 is u.s.c. and condensing.
Then there exists a selection v ∈ S F,x such that Then, For the term I 1 , as t 1 → t 2 , we have Next for the term I 2 , we have
Now take into account that
and S α (t 1 − s) − S α (t 2 − s) → 0 in L(X), as t 1 → t 2 (see (A1)). By the Lebesgue's dominated convergence theorem, For t = 0, the conclusion obviously holds. Let 0 < t ≤ b and ε be a real number satisfying 0 < ε < t. For x ∈ B r and v ∈ S F,x such that In view of (A1) and Lemma 2.3, we have S α (t) which is compact for t > 0. Therefore, the set Therefore, let ε → 0, we see that there are relatively compact sets arbitrarily close to the set V(t) = {m(t):m(t) ∈ N 2 (B r )}. Hence, the set V(t) = {m(t):m(t) ∈ N 2 (B r )} is relatively compact in X.
As a consequence of the above steps and the Arzela-Ascoli theorem, we can deduce that N 2 is completely continuous. (iv) N 2 has a closed graph. Let x n → x * , m n ∈ N 2 (x n ) and m n → m * . We shall show that m * ∈ N 2 (x * ). Now m n ∈ N 2 (x n ) implies that there exists v n ∈ S F, x n such that We must prove that there exists v * ∈ S F, x * such that Consider the linear continuous operator defined by From Lemma 2.1, it follows that Γ∘S F is a closed graph operator. Moreover, we have m n (t) ∈ Γ(S F, x n ).
Since x n → x * and m n → m * , it follows again from Lemma 2.1 that m * (t) ∈ Γ(S F, x * ). That is, there must exists v * ∈ S F, x * such that Therefore, N 2 is u.s.c. On the other hand, N 1 is a contraction, hence N = N 1 + N 2 is u.s.c. and condensing. By the fixed point theorem Lemma 2.6, there exists a fixed point x(⋅) for N on B r . Thus, the problem (1.1)-(1.2) admits a mild solution.
▫ Replace the condition (A2)(b) by (b′). There exists a constant τ ∈ (0, 1) and a function ϕ ∈ L 1 (J, R + ) such that From the above proof of Theorem 3.1, we can obtain the following result.
where 1 < α < 2, v ∈ L 1 (J, X). By Laplace transform, we have that is Thus, we have Now, we can give the following definition. 4) can be given as follows: Let us list the following basic assumptions: (A4) Let 1 < α < 2 and A generates an (α, 1)-resolvent family {S α (t)} t≥0 of type (M, ω). (λ α − A) −1 is compact for all λ > ω. (A5) q : C(J, X) → C(J, X) is continuous and there exists L q > 0 such that where v ∈ S F,x . Clearly, the fixed points of N are mild solutions to (1.1)-(1.2). We shall show that N satisfies all the hypothesis of Lemma 2.6. The proof will be given in several steps.
Step 1. There exists a positive number r such that N(B r ) ⊆ B r , where B r {x ∈ C(J, X) : ‖x‖ ∞ ≤ r}. If it is not true, then for each positive number r, there exists a function x r such that h r ∈ N(x r ) but ‖h r (t)‖ > r for some t ∈ J, where v r ∈ S F, x r . However, on the other hand, we have Dividing both sides by r and taking the lower limit as r → ∞, we obtain 1 ≤M L p + bL q + ϕ L 1 , which contradicts the relation (3.3).
Step 2. N(x) is convex for each x ∈ C(J, X).
Let δ ∈ (0, 1). Then for each t ∈ J, we have Because S F,x is convex (since F has convex values), Step 3. N(x) is closed for each x ∈ C(J, X). Let{h n } n≥0 ∈ N(x) such that h n → h in C(J, X). Then h ∈ C(J, X) and there exist {v n } ∈ S F,x such that for Due to the fact that F has compact values, we may pass to a subsequence if necessary to get that v n converges to v in L 1 (J, X) and hence v ∈ S F,x . Then for each t ∈ J, Step 4. N is u.s.c. and condensing. Now, we decompose N as N 1 + N 2 as We only need to prove that N 1 is a contraction and N 2 is completely continuous.
To show that N 1 is a contraction, for arbitrary x 1 , x 2 ∈ B r and each t ∈ J, we have from (A3) and (A5) From the relation (3.3), we conclude that N 1 is a contraction.
Next, we show that N 2 is u.s.c. and condensing. (i) N 2 (B r ) is obviously bounded. (ii) N 2 (B r ) is equicontinuous.
Indeed, let x ∈ B r , m ∈ N 2 (x) and take t 1 , t 2 ∈ J with t 2 < t 1 . Then, there exists a selection v ∈ S F,x such that Then For the term I 1 , as t 1 → t 2 , we have Next for the term I 2 , we have
Now take into account that
, as t 1 → t 2 (see (A4)). By the Lebesgue's dominated convergence theorem, For t = 0, the conclusion obviously holds. Let 0 < t ≤ b and ε be a real number satisfying 0 < ε < t. For x ∈ B r and v ∈ S F,x such that In view of (A4) and Lemma 2.4, we have R α (t) which is compact for t > 0. Therefore, the set K {R α (t − s)v(s), 0 ≤ s ≤ t − ε} is relatively compact. Then, conv K is compact. Considering m ε (t) ∈ tconv K for all t ∈ J, the set V ε (t) {m ε (t) : m ε (t) ∈ N 2 (B r )} is relatively compact in X for every ε, 0 < ε < t. Moreover, for m ∈ N(B r ), Therefore, let ε → 0, we see that there are relatively compact sets arbitrarily close to the set V(t) = {m(t):m(t) ∈ N 2 (B r )}. Hence, the set V(t) = {m(t):m(t) ∈ N 2 (B r )} is relatively compact in X.
As a consequence of the above steps and the Arzela-Ascoli theorem, we can deduce that N 2 is completely continuous. (iv) N 2 has a closed graph. Let x n → x * , m n ∈ N 2 (x n ) and m n → m * . We shall show that m * ∈ N 2 (x * ). Now m n ∈ N 2 (x n ) implies that there exists v n ∈ S F, xn such that We must prove that there exists v * ∈ S F, x * such that Consider the linear continuous operator defined by From Lemma 2.1, it follows that Γ∘S F is a closed graph operator. Moreover, we have m n (t) ∈ Γ(S F, x n ).
Since x n → x * and m n → m * , it follows again from Lemma 2.1 that m * (t) ∈ Γ(S F, x * ). That is, there must exist v * ∈ S F, x * such that Therefore, N 2 is u.s.c. On the other hand, N 1 is a contraction, hence N = N 1 + N 2 is u.s.c. and condensing. By the fixed point theorem Lemma 2.6, there exists a fixed point x(⋅) for N on B r . Thus, the problem (1.1)-(1.2) admits a mild solution.
▫ According to the above proof of Theorem 3.2, we can also have the following result. Let X be a separable Banach space X. We list the following condition: Proof. Hypotheses (A2)(b) and (C1) imply that F is of l.s.c. type. In view of Lemma 2.2, there exists a continuous function f : C(J, X) → L 1 (J, X) such that f (x) ∈ F (x) for all x ∈ C(J, X). Now consider the following equation:
6)
Notice that if x ∈ C(J, X) is a solution of the problem (3.5)-(3.6), then x is also a solution of the problem (1.1)-(1.2). Next, we transform the problem (3.5)-(3.6) into a fixed point problem by defining N : C(J, X)→ C(J, X) as We shall show that N satisfies all the hypothesis of Lemma 2.5. The proof will be given in several steps.
Step 1. There exists a positive number r such that This can be conducted similarly as Step 1. in the proof of Theorem 3.1.
We decompose N as N 1 + N 2 as Step 2. N 2 is continuous on B r . Let {x n } be a sequence such that x n → x in B r . Then Note that ϕ ∈ L 1 (J, R + ), ∫ t 0 ||f (x n )(s) − f (x)(s)|| ds → 0, n → ∞ by the Lebesgue's dominated convergence theorem. Hence, N 2 is continuous.
Step 3. N is condensing. Similarly conducted as the proof of Theorem 3.1, we can prove that N 1 is a contraction and N 2 is completely continuous.
From the above three steps, we can complete the proof via Lemma 2.5.
Similarly conducted as the proof of Theorems 3.2 and 3.3, we can prove that N 1 is a contraction and N 2 is completely continuous. Thus, Lemma 2.5 can be applied to complete the proof.
Proof. Transform the problem (1.1)-(1.2) into a fixed point problem. Let the multivalued operator N : C(J, X)→ P(C(J, X)) be defined as in Theorem 3.1. We shall prove that N admits at leas one fixed point. We divide the proof into two steps.
This can be proved just as Step 3 in the proof of Theorem 3.1.
Step 2. For each x,x ∈ C(J, X), there exists a constant 0 < γ < 1 such that H d (N(x), N(x)) ≤ γ x −x ∞ . Let x,x ∈ C(J, X) and h ∈ N(x). Then there exists v ∈ S F,x such that for each t ∈ J Consider U : J → P(X) defined as Because U(t) W(t)⋂ F(t,x) is measurable (see [38,Proposition III.4]), there exists a functionṽ(t), which is a measurable selection for U. Hence,ṽ(t) ∈ F(t,x(t)) and For each t ∈ J, we now definẽ Then for each t ∈ J, we have By an analogous relation, obtained by interchanging the roles ofx and x, we can obtain Owing to relation (3.7), we conclude that N is a contraction. Thus, by Lemma 2.7, N admits a fixed point, which just is one mild solution to the problem (1.1)-(1.2). ▫ Theorem 3.6. Let 1 < α < 2 and A generates an (α, 1)resolvent family {S α (t)} t≥0 of type (M, ω). Suppose that conditions (A3), (A5) and (A6) are satisfied, then the problem (1.3)-(1.4) has at least one mild solution on J provided thatM Proof. Transform the problem (1.3)-(1.4) into a fixed point problem. Let the multivalued operator N : C(J, X)→ P(C(J, X)) be defined as in Theorem 3.2. We shall prove that N admits at least one fixed point. We divide the proof into two steps.
Step 1. For each x ∈ C(J, X), N(x) ∈ P cl (C(J, X)). This can be proved just as Step 3 in the proof of Theorem 3.2.
Step 2. N is a contraction.
By an analogous relation, obtained by interchanging the roles ofx and x, we can obtain H d (N(x), N(x)) ≤M L p + bL q + ||l|| L 1 x −x ∞ .
Owing to relation (3.8), we conclude that N is a contraction. Thus, by Lemma 2.7, N admits a fixed point, wh1ich just is one mild solution to the problem (1.3)-(1.4). ▫ Example 3.1. As a simple application, we consider the following equations: It is well known that A generates a compact and analytic (and hence norm continuous for all t > 0) C 0 -semigroup {T(t)} t≥0 on X such that ‖T(t)‖ ≤ 1. Now, we can extract an (α, α)-resolvent family {R α (t)} t≥0 of type (1, 1) (see [39]). Meanwhile, the compactness of T(t) implies that (λ α − A) −1 is compact.
According to Theorem 3.2, the problem (3.9)-(3.11) has at least one mild solution on J.
Conclusions
In this paper, we establish some sufficient conditions to guarantee the existence of mild solutions to abstract fractional differential inclusions with nonlocal initial conditions under conditions that (i) the multivalued term takes convex values with compactness of resolvent family of operators; (ii) the multivalued term takes nonconvex values with compactness of resolvent family of operators and (iii) the multivalued term takes nonconvex values without compactness of resolvent family of operators.
The main results are based upon theories of resolvent family of operators, multivalued analysis and fixed point approach. It is noted that several partial differential equations arising in physics and applied sciences can be described by fractional differential equations of degenerate type (cf. [40,41]); we propose to investigate the existence of solutions to fractional stochastic equations of degenerate type via the resolvent family in future works. | 5,390 | 2020-09-18T00:00:00.000 | [
"Mathematics"
] |
Rendering Immersive Haptic Force Feedback via Neuromuscular Electrical Stimulation
Haptic feedback is the sensory modality to enhance the so-called “immersion”, meant as the extent to which senses are engaged by the mediated environment during virtual reality applications. However, it can be challenging to meet this requirement using conventional robotic design approaches that rely on rigid mechanical systems with limited workspace and bandwidth. An alternative solution can be seen in the adoption of lightweight wearable systems equipped with Neuromuscular Electrical Stimulation (NMES): in fact, NMES offers a wide range of different forces and qualities of haptic feedback. In this study, we present an experimental setup able to enrich the virtual reality experience by employing NMES to create in the antagonists’ muscles the haptic sensation of being loaded. We developed a subject-specific biomechanical model that estimated elbow torque during object lifting to deliver suitable electrical muscle stimulations. We experimentally tested our system by exploring the differences between the implemented NMES-based haptic feedback (NMES condition), a physical lifted object (Physical condition), and a condition without haptic feedback (Visual condition) in terms of kinematic response, metabolic effort, and participants’ perception of fatigue. Our results showed that both in terms of metabolic consumption and user fatigue perception, the condition with electrical stimulation and the condition with the real weight differed significantly from the condition without any load: the implemented feedback was able to faithfully reproduce interactions with objects, suggesting its possible application in different areas such as gaming, work risk assessment simulation, and education.
Introduction
Dealing with "haptics" means providing cutaneous (tactile) and kinesthetic (force) feedback, two different but complementary aspects of a single and complex afferent message to our nervous system [1]. Haptic illusion is the most common approach adopted to merge virtual and augmented realities [2]: it can be achieved through vibrotactile [3] or ultrasonic [4] stimulations or with robotic force fields [5]. Depending on the desired feedback to provide the user during a virtual experience, it can be possible to adopt different technologies. Vibrotactile devices can deliver additional tactile feedback and improve, for example, human motor learning [6] or immersive virtual environments [7]. Commonly, such tools are composed of wearable vibration units or motors that can be placed on different body locations and controlled independently to generate the desired feedback [8]. Another approach to producing tactile feedback is using ultrasonic stimulation: with such methodology, it is possible to obtain acoustic radiation force, producing small skin deformations and thus elicit the sensation of touch [9]. In both cases, the limitation of tactile feedback alone during a virtual experience is of course the lack of information regarding the inertia of the object being manipulated in the scenario. the center while holding in the right hand a small cube. In front of the user was shown a white phantom whose arm posture the user had to match (Figure 1a). The phantom was seen by the user during the entire experimental duration (Figure 1a).
The experimenter helped the subjects wear the suit by ensuring a proper electrode positioning: this procedure was required to be started at least 20 min before the task to obtain the right fitting between the suit electrodes and the skin.
During this time frame, users were set with the metabolic consumption system. Figure 1. (a) Experimental setup: the subject while wearing the NMES-based suit (Teslasuit), the 3D visor (Oculus Rift s), and the metabolic consumption device (k5-Cosmed). On the right is shown the scenario rendered on the 3D visor during the task (user view). Underneath represents the complete view of the implemented virtual scenario in which the user can see its posture (black avatar) and the one to match (white avatar) while handling the virtual cube (cube). (b) Real-time control scheme of the NMES-based haptic feedback: the biomechanical model implemented within the NMES stimulation module, received as input of the elbow angle read by the suit sensors and, depending on the phase of the elbow movement (flexion/extension, red arrows), delivered electrical stimulation to the respective muscle antagonistic to the one activated during the detected phase (triceps/biceps, red areas).
Before the measurement, this device was warmed up for 30 min and calibrated through a high-quality calibration gas. Lastly, users placed the visor over their eyes to clearly see the virtual scenario ( Figure 1a).
The task consisted of tracking, with the right arm, the phantom's arm movement ( Figure 1). The movement involved both elbow extensions (fully arm extension) and flexions (90 deg elbow angle) with a constant speed of 45 deg/s. The experimental session comprised three main conditions randomly proposed among participants: (1) Visual and Physical weight handled (0.5 kg) (Physical): the user received visual feedback from the virtual scenario combined with the haptic feedback of the handled physical weight; Figure 1. (a) Experimental setup: the subject while wearing the NMES-based suit (Teslasuit), the 3D visor (Oculus Rift s), and the metabolic consumption device (k5-Cosmed). On the right is shown the scenario rendered on the 3D visor during the task (user view). Underneath represents the complete view of the implemented virtual scenario in which the user can see its posture (black avatar) and the one to match (white avatar) while handling the virtual cube (cube). (b) Real-time control scheme of the NMES-based haptic feedback: the biomechanical model implemented within the NMES stimulation module, received as input of the elbow angle read by the suit sensors and, depending on the phase of the elbow movement (flexion/extension, red arrows), delivered electrical stimulation to the respective muscle antagonistic to the one activated during the detected phase (triceps/biceps, red areas).
The experimenter helped the subjects wear the suit by ensuring a proper electrode positioning: this procedure was required to be started at least 20 min before the task to obtain the right fitting between the suit electrodes and the skin.
During this time frame, users were set with the metabolic consumption system. Before the measurement, this device was warmed up for 30 min and calibrated through a high-quality calibration gas. Lastly, users placed the visor over their eyes to clearly see the virtual scenario ( Figure 1a).
The task consisted of tracking, with the right arm, the phantom's arm movement ( Figure 1). The movement involved both elbow extensions (fully arm extension) and flexions (90 deg elbow angle) with a constant speed of 45 deg/s. The experimental session comprised three main conditions randomly proposed among participants: Each condition lasted 4 min, in which a total of 32 movements (flexion and extension) were proposed. Between conditions, participants rested for 15 min in order to avoid fatigue effects. The overall session was completed in about 1 h 30 min.
Subjects
A group of twelve healthy, young, and right-handed participants (10 females, 2 males, 27.4 ± 3.8 years old, mean ± std, weight 62.25 ± 7.9 kg, height 165.2 ± 6.2 cm) took part in the model validations and tests. All participants provided their informed consent before the experiment, and the experimental protocol was approved by Heidelberg University Institutional Review Board (S-287/2020): the study was conducted following the ethical standards of the 2013 Declaration of Helsinki. Experiments were carried out at the Aries Lab (Assistive Robotics and Interactive Exosuits) of Heidelberg University. Subjects did not have any evidence or known history of neurological diseases and exhibited a normal joint range of motion and muscle strength.
NMES Calibration and Biomechanical Model
We designed a model-based real-time controller to provide NMES haptic feedback during object interaction. It consisted of the NMES stimulation module, developed in Unity engine ® , which combined, in real-time, the arm kinematics to compute the respective NMES power to be delivered to the biceps or triceps muscle depending on the movement phase (i.e., extension or flexion, respectively), Figure 1b.
Our application aimed to make the virtual reality experience as immersive as possible, allowing the user to feel the weight and resistance of the visualized object during its holding and lifting. Since the heavier the actual object is, the stronger the counterforce produced on a human system, the administered artificial NMES haptic feedback has been fashioned to ensure such a sensation when a virtual object is manipulated. A prerequisite for implementing the physicality of the desired handled item was the parameterization of the same item by defining its shape (cubic), mass (m cube ), and size (l cube ). Then, it was possible to implement a biomechanical model that can modulate over time and, according to the arm's position, the NMES acting on the user's antagonist muscle (triceps or biceps depending on the lifting phase).
When the arm lifts an object, the most part of the work is performed by the major elbow flexion muscle (i.e., the long head of the biceps), which provides haptic feedback to the human body through the muscle spindle receptors. To achieve the same sensation in a virtual environment, the system had to stimulate its major antagonist muscle (i.e., the long head of the triceps) in order to provide the torque at the elbow level corresponding to a similar lifting task. Following the aforementioned rationale, a complementary situation occurs when the arm brings the object to the starting position; the gravity effort generates an extension torque to the elbow, which is stabilized by the triceps: to perceive it, the biceps muscle has to be stimulated (Figure 1b). The expected result is to reproduce a realistic haptic experience in the virtual world.
Before starting our experiment, we characterized the muscular response of both the biceps and triceps to different NMES stimulations in terms of the resulting measured forces. This procedure was not subject-specific: we enrolled a single sample subject to tune the parameters. We built a single-degree-of-freedom elbow platform to calibrate the NMES feedback, as shown in Figure 2. During the controlled NMES muscle contraction, the force sensor measured the respective end-effector force (F stim ) generated by the biceps/triceps stimulations ( Figure 2).
The calibration setup consisted of horizontal arm support at the subject's sho height, resulting in an elbow angle q equal to 45°, and a customized force sensing sy holder positioned to match the subject's wrist anatomical landmark (PL) where the output was measured. A force sensor (Futek, FSH04416, Irvine, CA, USA) has mounted in the force-sensing system to record and transmit data to a dedicated acq tion board (Quanser QPIDe, Markham, ON, Canada) at 1 kHz.
During the calibration, we administered to the subject muscle (biceps/triceps) te creasing NMES stimulations with a duration of 2 s each, followed by a 5 min rest ph Figure 2. Calibration setup: top-view of the single-degree-of-freedom elbow platform to cal the NMES system. The whole arm was lying on the support; the wrist was positioned in con tance with the force sensor holder, against which the subject applied the force generated aft NMES stimulation. On the left panel, the NMES stimulation targeted the biceps muscle (pin ored oval), the resultant force generated (F stim ), and the torque acting on the elbow ( ⃗ ). O right panel, an equal representation of when the NMES stimulation targeted the triceps m (pink-colored oval).
Two distinct acquisitions were performed to the right triceps and the biceps mu We modulated the NMES parameter Pulse Width, PW (half-wave width rang tween 1-60 μs, normalized in percentage with an interval of 10% between each stim during each stimulation and saved the respective force output read by the load cell stimulation frequency was fixed at 60 Hz, while the maximum current per channe equal to 150 mA and the maximum possible voltage was 60 V. We obtained the de relationship between the administered pulse width PW and the corresponding ou force recorded through the force sensor, , generated against the flat and rigid f sensing system ( Figure 3), with an accuracy equal to R 2 = 0.9834: where a, b, c are constants, that, in our subject-specific case, assumed values equ 0.0028, 0.1123, and 0.5816, respectively. This force acted on the elbow joint by follo the relationship: where is the force's moment arm. The calibration setup consisted of horizontal arm support at the subject's shoulder height, resulting in an elbow angle q equal to 45 • , and a customized force sensing system holder positioned to match the subject's wrist anatomical landmark (PL) where the force output was measured. A force sensor (Futek, FSH04416, Irvine, CA, USA) has been mounted in the force-sensing system to record and transmit data to a dedicated acquisition board (Quanser QPIDe, Markham, ON, Canada) at 1 kHz.
During the calibration, we administered to the subject muscle (biceps/triceps) ten increasing NMES stimulations with a duration of 2 s each, followed by a 5 min rest phase.
Two distinct acquisitions were performed to the right triceps and the biceps muscles. We modulated the NMES parameter Pulse Width, PW (half-wave width range between 1-60 µs, normalized in percentage with an interval of 10% between each stimulus) during each stimulation and saved the respective force output read by the load cell. The stimulation frequency was fixed at 60 Hz, while the maximum current per channel was equal to 150 mA and the maximum possible voltage was 60 V. We obtained the desired relationship between the administered pulse width PW and the corresponding output force recorded through the force sensor, F stim , generated against the flat and rigid force-sensing system ( Figure 3), with an accuracy equal to R 2 = 0.9834: where a, b, c are constants, that, in our subject-specific case, assumed values equal to 0.0028, 0.1123, and 0.5816, respectively. This force acted on the elbow joint by following the relationship: where r m is the force's moment arm. On the x-axis, the PW values given to the subject via the NMES system are represented. On the y-axis, the muscle response with respect to the force measured by the forc sensor is depicted. PW range is between 1 and 60 μs, normalized in percentage with an interval o 10% between each delivered stimulus.
In order to provide haptic feedback during the experiment, we modulated the ne torque at the elbow level using muscle stimulations. During free motions, the joint torqu can be modelled as: where ⃗ is the biomechanical torque of the forearm acting on the joint during move ments, while ⃗ is the contribution of the simulated virtual interaction. Assuming th arm is parallel to the chest (i.e., shoulder angles= [0 0 0]), we can model ⃗ as: where q is the elbow angle acquired from the NEMS system IMUs [29], and are, respectively, the moment of Inertia and the mass of the object (of which it is desired to simulate the holding during the task), and is the distance between the object's bar ycenter and the elbow joint fulcrum.
To provide participants with the tuned haptic feedback (PW) according to the elbow kinematics (q) and the object, the following system has to be solved: where ⃗ is the torque provided by the musculoskeletal system. By solving the abov system, the Pulse Width modulation was tuned in order to generate a resistive action on the elbow, considering the inertial properties of the object as: and is equal to the subject's forearm length. As the second step of the calibration, we performed a brief and ad hoc subject safet procedure before starting the experiment to set the NMES intensity's minimum and max imum values. Since the skin impedance is vastly different among subjects, this step wa mandatory before the suit utilization and was crucial to avoid uncomfortable events.
Outcome Measures
To assess the human performance, we quantitatively highlighted the onset of fatigu by measuring the metabolic expenditure with a wearable system (K5, Cosmed), known On the x-axis, the PW values given to the subject via the NMES system are represented. On the y-axis, the muscle response with respect to the force measured by the force sensor is depicted. PW range is between 1 and 60 µs, normalized in percentage with an interval of 10% between each delivered stimulus.
In order to provide haptic feedback during the experiment, we modulated the net torque at the elbow level using muscle stimulations. During free motions, the joint torque can be modelled as: where → τ arm is the biomechanical torque of the forearm acting on the joint during movements, while → τ object is the contribution of the simulated virtual interaction. Assuming the arm is parallel to the chest (i.e., shoulder angles = [0 0 0]), we can model → τ object as: where q is the elbow angle acquired from the NEMS system IMUs [29], I object and m object are, respectively, the moment of Inertia and the mass of the object (of which it is desired to simulate the holding during the task), and r d is the distance between the object's barycenter and the elbow joint fulcrum.
To provide participants with the tuned haptic feedback (PW) according to the elbow kinematics (q) and the object, the following system has to be solved: where → τ arm is the torque provided by the musculoskeletal system. By solving the above system, the Pulse Width modulation was tuned in order to generate a resistive action on the elbow, considering the inertial properties of the object as: where r m = L arm ·sin (q), and L arm is equal to the subject's forearm length.
As the second step of the calibration, we performed a brief and ad hoc subject safety procedure before starting the experiment to set the NMES intensity's minimum and maximum values. Since the skin impedance is vastly different among subjects, this step was mandatory before the suit utilization and was crucial to avoid uncomfortable events.
Outcome Measures
To assess the human performance, we quantitatively highlighted the onset of fatigue by measuring the metabolic expenditure with a wearable system (K5, Cosmed), known for being reliable during several exercise modalities [30][31][32][33].
To evaluate the metabolic consumption variations occurring in the three experimental conditions, we evaluated the Respiratory Exchange Ratio (RER) [34,35], from the ergospirometry variables provided by the COSMED K5, which was operating in mixing chamber mode. Specifically, the volume of oxygen consumption (VO2) and carbon dioxide production (VCO2) were assessed for computing the RER as follows: RER values are typically comprised between 0.7 and 1.2. During non-steady-state and high-intensity exercises, the volume of the carbon dioxide produced by the human body increases due to hyperventilation with a consequent rise of the RER. From the NMES system IMUs, we recorded elbow angle trajectories at 100 Hz and offline filtered using a 6th order low-pass Butterworth filter with a 10 Hz cutoff frequency. We extrapolated the indicators for characterizing subjects' kinematic performance as the primary output. The The Root Mean Squared Error (RMSE) measures the participant's elbow angle trajectory deviation from the ideal phantom trajectory. It is defined as: where q user is the user elbow angle trajectory, q phantom is the phantom elbow angle trajectory, both evaluated at sample i, and N is the total number of samples considered on the entire trial. We evaluated the fitting between the ideal trajectory of q phantom and the user trajectory q user using the correlation coefficient r 2 .
Moreover, we considered the Normalized Smoothness, following the approach of Balasubramanian et al. [36], which is a slightly modified version of the original Spectral Arc Length (SAL) definition: where V(ω) is the Fourier magnitude spectrum v(t),V(ω) is the normalized magnitude spectrum, normalized with respect to the DC magnitude V(0), and ω c is fixed to be 40π (corresponding to 20 Hz). In this modified version, we adopted the SPARC for SPectral ARC length by setting: We evaluated, for NMES and Physical conditions, the torque at the elbow generated by virtual and real weight, respectively.
Finally, participants answered on a 7-point Likert scale (from −3 = completely disagree, to +3 = fully agree) to evaluate the Pleasantness and Naturalness of the three different experimental conditions [37]. This test was essential to understand the ecological validity of the immersive environment.
Statistical Analysis
We used a repeated-measures analysis of variance (rANOVA) on the dependent variables, and we considered as the within-subjects factor ("Feedback") the kind of provided haptic feedback (Physical, NMES, Visual). Data normality was evaluated using the Shapiro-Wilk Test, and the sphericity condition was assessed using the Mauchly test. Statistical significance was considered for p-values lower than 0.05. Post hoc analysis on significant main effects was performed using Bonferroni corrected paired t-tests (p < 0.0025).
For the Likert scale outcomes, Pleasantness and Naturalness, non-parametric paired tests were employed. The Kruskal-Wallis test was used for comparisons among the three trials (p < 0.05), while the Wilcoxon signed-rank test was used for the paired comparisons (p < 0.0025). Outliers were removed before any further analysis using a Thompson Tau test. Figure 4a depicts the torque comparison between the torque obtained with the NMES condition ( → τ elbow ) and the one obtained during the Physical condition ( → τ object ) for a representative subject. From this comparison, we found high r 2 values for all subjects (mean ± SE: 0.993± 0.002) and low differences by means of RMSE values (mean ± SE: 0.116 ± 0.020 (Nm)), Figure 4b. This result validates our calibration, and it evidences the appropriateness of our approach for all participants.
OR PEER REVIEW 8 of 14
Shapiro-Wilk Test, and the sphericity condition was assessed using the Mauchly test. Statistical significance was considered for p-values lower than 0.05. Post hoc analysis on significant main effects was performed using Bonferroni corrected paired t-tests (p < 0.0025). For the Likert scale outcomes, Pleasantness and Naturalness, non-parametric paired tests were employed. The Kruskal-Wallis test was used for comparisons among the three trials (p < 0.05), while the Wilcoxon signed-rank test was used for the paired comparisons (p < 0.0025). Outliers were removed before any further analysis using a Thompson Tau test. We encountered similar performances among the three proposed conditions, highlighting that the NMES-based haptic feedback (NMES condition) does not interfere with the physiological range of motion. The statistical analysis confirmed such a result: for the AER.O.M. (Figure 5a), we found no significant effect between the three conditions ('Feedback' effect: F = 0.035, p = 0.966). We also reported the RMSE (Figure 5b) and r 2 (Figure 5c) with
Metabolic Consumption during the NMES Condition Is Comparable with the Physi
We evaluated the metabolic consumption via the Respiratory Exchange Rat parameter to understand if the exercise intensity changed during the three expe conditions. The results are illustrated in Figure 6, which shows, as expected, that t intensity of the exercise was obtained during the Visual condition. From the s analysis with rANOVA, we highlighted an effect of the condition ('Feedback' ef 18.226, p < 0.001). From further post hoc analysis, we found a significant differ tween the conditions Visual and Physical (post hoc: p = 0.001) and between the co Visual and NMES (post hoc: p < 0.001). A noteworthy result is the non-signifi obtained between the Physical and NMES conditions, which highlights the sim fatigue between the physical object handled and the NMES-based artificial stimu (Figure 5a), we found no significant effect between the three conditions ('Feedback' effect: F = 0.035, p = 0.966). We also reported the RMSE (Figure 5b) and r 2 (Figure 5c) with analogous findings for both the parameters ('Feedback' effect: F = 0.151, p = 0.861 and F = 0.300, p = 0.744, respectively). Moreover, we analyzed the Normalized Smoothness of participants' movements compared to the reference trajectory. As expected, we found that the proposed NMES-based haptic feedback, due to the delivered muscle stimulation, partially affects the smoothness of the natural movement. This downside of our feedback was confirmed by the statistical analysis. The rANOVA evidenced a significant effect of the feedback ('Feedback' effect: F = 5.523, p = 0.013). The subsequent post hoc analysis showed a significant difference between the Physical and NMES conditions (p = 0.0082). The other two comparisons denoted no significant differences (Visual-Physical p = 0.2727, Visual-NMES: p = 0.05).
Metabolic Consumption during the NMES Condition Is Comparable with the Physical One
We evaluated the metabolic consumption via the Respiratory Exchange Ratio (RER) parameter to understand if the exercise intensity changed during the three experimental conditions. The results are illustrated in Figure 6, which shows, as expected, that the lower intensity of the exercise was obtained during the Visual condition. From the statistical analysis with rANOVA, we highlighted an effect of the condition ('Feedback' effect: F = 18.226, p < 0.001). From further post hoc analysis, we found a significant difference between the conditions Visual and Physical (post hoc: p = 0.001) and between the conditions Visual and NMES (post hoc: p < 0.001). A noteworthy result is the non-significant one obtained between the Physical and NMES conditions, which highlights the similarity in fatigue between the physical object handled and the NMES-based artificial stimulus. analysis with rANOVA, we highlighted an effect of the condition ('Feedback' effect: F 18.226, p < 0.001). From further post hoc analysis, we found a significant difference be tween the conditions Visual and Physical (post hoc: p = 0.001) and between the condition Visual and NMES (post hoc: p < 0.001). A noteworthy result is the non-significant on obtained between the Physical and NMES conditions, which highlights the similarity i fatigue between the physical object handled and the NMES-based artificial stimulus.
Naturalness and Pleasantness
The Naturalness of the experiment was significantly higher in the conditions NMES and Physical than in the Visual condition, as is shown in Figure 7. The statistical analysis with Kruskal-Wallis tests confirmed this result, highlighting a significant effect depending on the feedback ('Feedback' effect: χ 2 (2) = 12.193, p = 0.002). The following Wilcoxon signedrank test showed that the sensation with the NMSE condition was perceived to be more natural than the one with the Visual feedback (Z = −2.264, p = 0.024). On the contrary, no significant differences were detected between the task during the NMES condition and the one during the Physical condition (Z = −1.633, p = 0.102), highlighting the faithfulness of the proposed feedback with stimulation compared to the natural sensation. As expected, we found significant differences between the Physical and the Visual condition (Z = −2.262, p = 0.023). Regarding the Pleasantness, users perceived the NMES-based haptic feedback (NMES condition) to be slightly uncomfortable, as shown in Figure 7. However, no significative feedback effect was detected ('Feedback' effect: χ 2 (2) = 0.892, p = 0.640).
Naturalness and Pleasantness
The Naturalness of the experiment was significantly higher in the conditions NMES and Physical than in the Visual condition, as is shown in Figure 7. The statistical analysis with Kruskal-Wallis tests confirmed this result, highlighting a significant effect depending on the feedback ('Feedback' effect: χ 2 (2) = 12.193, p = 0.002). The following Wilcoxon signed-rank test showed that the sensation with the NMSE condition was perceived to be more natural than the one with the Visual feedback (Z = −2.264, p = 0.024). On the contrary, no significant differences were detected between the task during the NMES condition and the one during the Physical condition (Z = −1.633, p = 0.102), highlighting the faithfulness of the proposed feedback with stimulation compared to the natural sensation. As expected, we found significant differences between the Physical and the Visual condition (Z = −2.262, p = 0.023). Regarding the Pleasantness, users perceived the NMES-based haptic feedback (NMES condition) to be slightly uncomfortable, as shown in Figure 7. However, no significative feedback effect was detected ('Feedback' effect: χ 2 (2) = 0.892, p = 0.640).
Discussion
Virtual reality (VR) and augmented reality (AR) are two forms of modern technological advancements that have revolutionized the standard concept of visual communication over the years. However, despite their broad expansion, there is still a wide gap in their practical applications (e.g., emergency simulations, teaching, surgical training) due
Discussion
Virtual reality (VR) and augmented reality (AR) are two forms of modern technological advancements that have revolutionized the standard concept of visual communication over the years. However, despite their broad expansion, there is still a wide gap in their practical applications (e.g., emergency simulations, teaching, surgical training) due to the lack of immersive interactions that can be assimilated into tangible experiences. The missing piece is to interact with virtual objects that can be perceived as authentic by the human body.
NMES Feedback Reliability and Its Quantitative Assessment
The proposed study revealed the feasibility of a multimodal technological system combining Neuromuscular Electrical Stimulation (NMES) provided using a wearable suit with VR in order to increase the immersive sensation of a weightlifting task within a virtual environment. Based on the concept that the feeling of lifting an object could be obtained by providing electrical stimulation to the antagonist's muscles to those exerting the movement, we developed a biomechanical model able to give a sensory response based on the real-time user's elbow movements. The results from 12 volunteers provided experimental evidence that the NMES-based haptic feedback robustly simulates the physical exertion of a real object. Such a finding was possible thanks to a priori calibration which allowed a robust biomechanical model suitable for all the participants to be obtained. As highlighted by an early study with NMES for haptic feedback [18], the calibration phase is crucial to properly stimulate the muscle, detect noticeable pose changes, and enhance user comfort. In their study, Kruijff et al. [18] showed the importance of a proper calibration to perceive the right amount of current without generating user discomfort. For this reason, we performed an isometric calibration process before the experiments. This preliminary procedure is one of the most delicate steps that for traditional systems with electrodes requires the accurate positioning of them, a factor that was greatly simplified by the use of our wearable device; in fact, the latter allowed us to obtain a biomechanical model suitable for different subjects with slightly different anthropometric characteristics.
The study's central finding is related to the kinematic reliability of the simulated weight and a comparable metabolic consumption between Physical and NMES conditions. These results are consistent with studies found in the literature, highlighting that NMES is a well-suited technology for providing more realistic haptic feedback during interaction with objects in a virtual environment [16]. Lopes et al. [24,38] explored how to integrate haptics to walls and heavy objects in VR through NMES: they showed how adding haptic feedback through electrodes on the user's arms could increase the sense of presence in the virtual interactive application. However, no quantitative analysis of system performance was carried out. In the current study instead, two of the main subjects' physiological metrics have been analyzed: kinematic performance and metabolic consumption.
First, the recorded kinematic measurements related to the accuracy of the movement (AE R.O.M. , RMSE, and r 2 ) showed that haptic feedback via the NMES condition did not affect the final kinematics, rendering the movement as accurate as in conditions without haptic feedback (Visual) or with the real weight (Physical).
On the other side, the metabolic consumption outcome (RER) revealed that NMESbased haptic feedback (NMES) was assimilable to the Physical condition, and in both cases, as hypothesized, the metabolic consumption was higher compared to the condition without haptic feedback (Visual). This result is consistent with previous works, which showed that the RER increase with the exercise intensity [34,35]. The sensation of muscle activation generated by the NMSE condition was comparable to that required during the Physical condition yielding similar metabolic demands. Finally, we recorded users' opinions from the questionnaire (7-point Likert scale), which revealed that the Naturalness was significantly higher during the NMES and Physical conditions compared to the condition without haptic feedback (Visual).
Integration of NMES-Based-Haptic Feedback in Virtual Scenarios
The previous findings highlight the potential of the implemented NMES-based haptic feedback in multiple application areas. Interaction with virtual objects of different nature, capable of returning not only visual feedback but also haptic sensations, would increase the chances of learning more complex tasks [39][40][41]. In fact, to perceive the external environment, our brain uses multiple sources of sensory information derived from different modalities, and vision is only one of the several systems involved in the sensory process. A stimulation capable of being assimilated with an actual physical condition and integrating the various perceptive information is an essential step in granting cognitive benefits, such as an increased embodiment and involvement in the virtual scenario [37,42,43]. Our interface represents the first step in developing a virtual environment fully parameterizable and modellable according to the main characteristics of the objects to be manipulated and usable in the field of simulation, such as industrial safety and surgical training.
Limitations
Our system is still embryonic: firstly, more muscles would be necessary to appreciate the NMES haptic feedback entirely. Even if participants appreciated the feedback and considered it as natural as a real weight, they complained about the lack of stimulation from other muscle channels (e.g., shoulder deltoid muscles and forearm muscles). This step would require a more complex biomechanical model, for which it will be necessary, in the future, to include a preliminary electromyographic study, or a simulative environment, depending on the desired movement.
Secondly, more degrees of freedom should be included in the virtual scenario: since the adopted suit is able to provide full-body stimulation, it would be interesting to study more complex movements involving a more significant number of degrees of freedom. All these improvements would benefit even the so-called "engagement," an aspect widely considered in the field of pure AR/VR research, which will certainly be included in our future studies.
Another aspect that affected the Pleasantness of the task was the lack of receiving feedback on the hand palm during the NMES condition, where, in the virtual scenario, the object was displayed. To validate our model, we decided to place the virtual object directly on the palm so as not to introduce collisions, which would have required additional computation. However, this is something we will improve in the future by including a vibrotactile surface in order to provide tactile sensation (e.g., vibrotactile gloves).
In addition, our NMES haptic feedback affected the movement smoothness with respect to movement with the physical weight. This physiological effect generated by electrical stimulation on afferent pathways can be reduced by implementing an improved stimulation paradigm. Only the comparison with the Visual condition highlighted this effect in our data, thus making this aspect of no concern.
Moreover, the developed haptic feedback was tested only on a few healthy subjects to probe the system's feasibility. The availability of a single suit size precluded the inclusion of a wide range of participants in terms of anthropometric measures. This aspect has also affected the results that emerged from the statistical analysis. In the future, further subjects should be added to increase the sample size and the reliability of the results.
In addition, the evaluation of the metabolic cost significantly contributed to the feedback assessment, bringing with it quantitative evidence that the Physical and NMES conditions were comparable. However, this evaluation system affected both the duration and the task ergonomics: in the future, this measurement will be evaluated at the discretion of the users who will have to use the interface.
Conclusions
The current study presents a novel paradigm to provide haptic feedback via neuromuscular electrical stimulation that can increase the immersion and the quality of the experience during the execution of a task in a virtual reality environment. | 8,241.8 | 2022-07-01T00:00:00.000 | [
"Engineering"
] |
Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics
Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observations. This is especially to be expected for sequential imaging data taken from time series. In this study, we evaluate the use of statistical measures to test the independence of sequential ultrasound image data taken from the same case. A total of 1180 B-mode liver ultrasound images with 5903 regions of interests were analyzed. The ultrasound images were taken from two liver disease groups, fibrosis and steatosis, as well as normal cases. Computer-extracted texture features were then used to train a machine learning (ML) model for computer-aided diagnosis. The experiment resulted in high two-category diagnosis using logistic regression, with AUC of 0.928 and high performance of multicategory classification, using random forest ML, with AUC of 0.917. To evaluate the image region independence for machine learning, Jenson–Shannon (JS) divergence was used. JS distributions showed that images of normal liver were independent from each other, while the images from the two disease pathologies were not independent. To guarantee the generalizability of machine learning models, and to prevent data leakage, multiple frames of image data acquired of the same object should be tested for independence before machine learning. Such tests can be applied to real-world medical image problems to determine if images from the same subject can be used for training.
Introduction
Image data availability is vital for the implementation of machine learning (ML) methods in clinical settings [1,2]. Large datasets with high-quality images are essential for training, validation, and testing of algorithms for clinical applications. Typically, ML algorithm performance for predictive tasks increases with increased training data volume [3][4][5]. If numerous parameters are to be studied, there is a need to train ML models commensurately on abundant data to obtain a generalizable model. However, there is limited access to medical images, and the preparation of image data is a costly and time-intensive process [6]. Most health care systems are not adequately equipped to share large numbers of medical images [3]. Medical data are often stored in silos and are not available in enterprise-wide core clinical systems. These data silos prevent relevant data from being shared even between departments within the same institution due to the interoperability issues of the current institutional enterprise systems [7]. Image data therefore cannot be accessed easily by AI algorithm development for widespread clinical practice.
In addition to the data availability issues, in ML a common assumption is that the given data points are realizations of independent random variables [8]. However, this assumption is often violated when the data points are highly interdependent (e.g., when the data exhibit temporal or spatial correlations) [9]. Similar scenarios are typical situations in visual recognition and computational biology [10]. Dependent data arise whenever there are inherent correlations in between observations. This is to be expected for time series of imaging data, where we would intuitively expect that instances with similar time stamps have stronger dependencies than ones that are far away in time.
A common approach to bypass the problem of limited data is to use multiple images from the same subject as separate training instances for ML [11][12][13][14]. However, this approach raises the question of whether the data are independent. Depending on the independence assumptions of the learning algorithms, the performance of the resulting models trained and tested on the same patient(s) and same body region(s) might be inflated, and the models might not be generalizable to future images. In this situation, the predictive model developed using conventional ML algorithms could be biased, inaccurate, and tend to produce unsatisfactory classifiers. A common example where ML algorithms are well known to exhibit variations in prediction accuracy is when ML is provided with imbalanced training sets due to the imbalanced ratio of pathological and normal cases typically seen in Sultan et al. Page 2 medical imaging [15]. Previous studies reported that a close-to-balanced training is required for best model performance, while data imbalance can have negative influence on the model performance.
In this study, we propose an approach to test the data independence for building a reliable diagnostic ML model using a liver disease data images set. For this purpose, we examined the independence of sequential ultrasound image frames acquired from the same cases of liver disease. We algorithmically extracted numerical liver texture features from the ultrasound images for machine learning. All these computer-generated features were used to train models. The independence between image region grayscale distributions were quantified by Jensen-Shannon (JS) divergence, a bounded symmetrization of the unbounded Kullback-Leibler (KL) divergence [16][17][18][19]. JS divergence was measured for B-mode ultrasound images acquired from images of three pathologies: normal cases, and then two groups of liver disease, namely steatosis (fatty liver) and fibrosis.
Image Acquisition and Computerized Analysis
1180 B-mode ultrasound images acquired in vivo from rat livers were used for analysis. The images were taken from 3 different rat groups as follows: 450 images from fibrosis cases (rats n = 6), 450 images from steatosis cases (rats n = 4), and 280 from normal (rats n = 4).
Four video clips of B-mode images were acquired from each rat in standard transverse and sagittal imaging planes of the right and left lobes of the liver. Each clip consisted of average of 25-35 images. Imaging presets (gain = 18 dB, high sensitivity, 100% power, transmit frequency 21 MHz, and high line density) and time compensation gain were optimized and standardized.
Five to six identical rectangular regions of interest (ROI) were placed manually on each image to ensure comprehensive inclusion of multiple representative parts of the liver parenchyma and exclusion of imaging artifacts such as acoustic shadowing, enhancement, or reverberation. A total of 5903 regions of interest were placed on the images.
A number of texture features were extracted from the ROIs, which include:
1.
First order histogram features: including echo intensity, heterogeneity (regional variance between ROIs, internal heterogeneity (local variance within ROIs) [20]. Echo intensity and heterogeneity represent the mean and standard deviation of intensity within an ROI. Heterogeneity is the standard deviation of the echo intensity between the ROIs in all the planes measured throughout the liver.
2.
Run length features include gray-level nonuniformity (GLNU) and run length nonuniformity (RLNU). These features represent the length of the run, usually the number of pixels for the horizontal or vertical scan direction, or the number of pixels multiplied by a diagonal direction [21].
3.
Entropy: a gray level connectivity texture feature was also studied [21]. Sultan et al. Page 3 All image analysis was performed using a custom application written in the IDL (Interactive Data Language) programming language (version 8.5; Harris Geospatial, Broomfield, CO, USA) [21].
Feature Statistics and Machine Learning Diagnostic Models
The mean and standard error for the ultrasound texture features of the three different groups were compared by two-tailed paired Student's t-tests. p < 0.05 was considered significant. One-way analysis of variance (ANOVA) was used to compare the difference between the three study arms. Statistical analysis was performed using MedCalc (version 19.0.5, MedCalc Software Ltd., Ostend, Belgium).
Two classifiers were used for machine learning analysis. Random Forest [22] was used for multicategory classification, while logistic regression is used for the two groups' separation.
Leave-one-out cross-validation approach (round-robin) was used for training and testing the data with both classifiers. Training and testing of data were performed using Weka software (version 3.8.5, University of Waikato, Hamilton, New Zealand) [23].
Intra-and Inter-Case Divergence Analysis
Jenson-Shannon (JS) divergence [16][17][18][19] was used to quantify the difference in grayscale distribution between two regions, for both intracase and intercase sampling. JS divergence offers an information-theoretic set-similarity measure that works naturally for pair-wise comparisons. To evaluate intra-and inter-divergence, we compared intracase to intercase pairs, calculating JS divergence for every pair. Intracase pairs were sampled for every possible time shift, and their divergence distributions were then tested against the divergence distribution of the intercase region pairs. The goal was to find the minimum time difference between image regions of the same case such that their divergences were distributed similarly to regions sample from completely different subjects. For each test at each time shift, the null hypothesis was that the distributions were different, so we performed a t-test for significant similarity or equivalence (not the more common Student's t-test for significant difference) [24].
For equivalence, to demonstrate a "lack of difference": The t-test for equivalence, where δ depends on how much nonequivalence is acceptable in the research study. In our example, we were unwilling to accept more than 5% reduction in intra-divergence compared to inter-divergence, so we set δ equal to 0.05 M 1 (where the 1 subscript indicates inter, and 2 subscript indicates intra): The denominator is simply the standard error; df is degrees of freedom or n 1 + n 2 − 2; and the M is mean. This was a one-sided test in this study, because we wanted to prove that intrasampled cases do not have significantly lower divergence than intercases. It is a Sultan et al. Page 4 noninferiority test because we wanted to prove that choosing our samples from intracases performed no worse than choosing from our intercases.
Histopathologic Validation
Liver disease was confirmed by histopathological examination. The liver lobes were assessed in a blind fashion by a vet pathologist for fibrosis and lipidosis on gross pathology. Portions of liver were preserved in 10% phosphate-buffered formalin and transferred to 50% ethanol after 48 to 72 h and then embedded in paraffin and processed for histological examination with hematoxylin and eosin (H&E) and trichrome staining. Each histologic section was graded according to the METAVIR scoring system for fibrosis. Lipidosis was investigated in addition to presence of balloon cells, which is critical to finding fatty liver changes.
The Classification Performance of Ultrasound Features
All features showed statistically significant differences between the three groups. Table 1 shows the difference in mean values of liver texture ultrasound features between the three groups Logistic regression two-class analysis showed high performance. First, the model was able to detect the disease from normal cases with AUC 0.917 ( Figure 1). Then, differentiation of the two liver disease groups, namely steatosis and fibrosis, showed a very high diagnostic performance with AUC of 0.928.
Random forest learning for multicategory classification also showed high performance in differentiation of the three groups ( Figure 2). The model showed that the features can differentiate all three groups from each other with high diagnostic performance ranging from 0.854 to 0.917 with sensitivity up to 83.8 and specificity reaching 83.3.
Divergence Testing for Image Independence
Of the three tested liver pathologies, only normal cases demonstrated that intracase region divergence is statistically close to the intersampled case divergence (Table 2, Figure 3). In Figure 3C, we can observe that the mean divergence for intrasampling was almost the same as for intersampling.
On the other hand, steatosis and fibrosis cases failed the similarity test. Inter-divergence was significantly higher than intra-divergence (
Discussion
For successful application of ML methods in medical imaging research and deployment of high-performance generalizable models, there is a need for a sufficient number of training samples from large images databases. We used the common training practice of adding samples by training on a large number of sequential image frames taken from the same case. The results of ML models showed high classification performance for the three studied disease groups. However, when we tested the independence of the sequential images that were taken from the same case using JS divergence, of the three tested liver pathologies, only normal cases demonstrated statistically that intracase region divergence is close to the intersampled case divergence. This means that the normal cases diverged similarly between patients and within a patient, but for fibrosis and steatosis, samples within a patient were more similar to each other than samples from different patients. Two regions sampled from the same normal case were just as different as two regions sampled from completely different cases according to the t-test for equivalence, within 5%. Therefore, for normal cases we can reject the null hypothesis that there is a significant difference between interand intrasampling. In general, intra-divergence for all the three groups was small, but different. Fibrosis showed the highest JS divergence in comparison to other groups. This finding is expected as fibrosis is often associated with heterogenous tissue changes between different regions of the liver in comparison to more uniform changes seen in steatosis [25,26]. Yet, inter-and intracase JS fibrosis divergence are not statistically close to each other; therefore, we cannot claim that the intracase image frames are independent enough to train on as separate cases.
The intuition that divergence should increase with time-shift between samples proved to be incorrect, because any gradual divergence trend was overpowered by the cyclical effects of breathing motion. Looking at the graphs of divergence with increasing time change, it is evident that divergence between pairs of sampled regions is periodic, with a period of 35 frames or 4.5 s. This is believed to be due to breathing. A region sampled from a later frame will be most similar (less divergent) to a region sampled earlier at the same relative point in the respiratory cycle and least similar to a region sample out of phase in the cycle. For all three tested pathologies, but especially steatosis and fibrosis, divergence change within the breathing cycle was much stronger than divergence drift over the entire time-course of the study. In theory, if this divergence oscillated perfectly with constant-period and constantamplitude cycles, to maximize sampling differences, maximally out-of-phase time points could be chosen within or even crossing multiple periods. For instance, if the reference interdivergence was 1, and the respiratory period was 1 s (1 Hz), then the peak-to-valley maximum divergence between frames within a cycle would be at half a cycle, or 0.5 s. If the divergence difference over this half-cycle was at least equal to our reference threshold of 1, then exactly two samples that were this half-period apart, or some integer multiple of this half period, could be taken for the *entire* time of the study to ensure that they were as different from each other as two samples from entirely different cases. All of our data show that, over the time of the study, the amplitude of the divergence oscillation within a cycle increases over time: in a later breathing cycle after an earlier reference breathing cycle, a sampled region will diverge more from the earlier reference sample even when both are still in phase; and if the two samples are taken out of phase, they will also diverge much more over time. The strongest guarantee of sufficient divergence would be if the lower bound connecting the out-of-phase valleys on the increasing-oscillating curve increased by at least the reference intersampling divergence, but none of these studies showed such an effect, possibly because they were not long enough.
In this paper, we proposed the use of a statistical preliminary analysis to assess the quality of the imaging data before constructing an ML model. To our knowledge, the JS divergence test has not been evaluated to test the independence of image data. However, similar preliminary exploratory data analyses have been reported in literature for assessing the quality of data before going to ML models. One example of the exploratory analysis reported includes the use of Dynamic Time Warping (DTW) [27,28]. DTE is a measure of how similar two temporal sequences are in a time series analysis. DTW looks for the optimal alignment between the two series as opposed to looking at the Euclidean distance between two points at each time series. DTW was evaluated as a distance metric of fMRI time series with repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies.
One limitation of this study is that we did not test each of our measured radiomics features in this way but presented a method to perform it and tested it on a fundamental image property. Since most features depend somewhat on grayscale distribution, if ROIs between frames were not independent enough in those distributions, we did not look further. Grayscale histograms and first-order statistics are perhaps the simplest ways of characterizing image regions, so if those distributions are significantly more similar within a case than they are between cases, and more similar at certain regular time intervals within the clip, then many derived features may behave similarly. An image, though, is a spatial distribution of grayscale values, and it is very true that engineered features may only partly depend on the grayscale *value* distribution or may not depend on it at all. The methodology we presented, though, with enough samples to compare distributions (i.e., a radiomics image that maps the feature value to each pixel location) could test any quantitative feature to see if it was independent enough between frames to allow those frames to count as sufficiently independent images for that measurement. A more in-depth analysis of sampling divergence behavior over time is, however, beyond the scope of this research and would not be generalizable to other organs or modalities. The important result is that different pathologies might have different dependencies when sampling frames from video, since in this research one of three pathologies passed the independence test: sample regions between video frames of a normal case are as different from each other as samples from completely different cases. Future studies will also evaluate the effects of inter-and intrauser variability in ROI selection on the model performance. The study results are specific to ultrasound imaging, and findings could have been impacted by factors related to the choice of animal, tissue, ROI, and analysis. Future studies on a large scale are required for the proposed approach to be generalized to other imaging modalities and to be applied in human studies.
Conclusions
Whenever image frames taken from same case are used for training machine learning models, if that model assumes independence between images, an independence test should be performed. Not only might there be within-case image dependence, but that dependence also could be related to time separation of samples in a video study, so that enforcing a time interval between samples could meet the independence assumptions of the model. For our liver images, however, no such simple time interval could be found, because the periodic breathing motion was too strong an effect. However, for one of the three pathologies-the "normal" cases-image frames were as independent within a case as between cases. Such a result could be important for machine learning. In a clinical setting, it is not uncommon to acquire more disease images than normal controls, possibly creating an unbalanced imaging database. The result of this research on liver diseases suggests that it is acceptable to take many frames of the same normal case as independent training cases, demonstrating one possible application for thoroughly testing image frames for independence.
Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the evolving nature of the project. The diagnostic performance of quantitative liver texture ultrasound features using logistic regression (LR) machine learning for two-step pathology differentiation. (A) shows the diagnostic performance of ultrasound texture features in differentiating normal from liver disease including both fibrosis and steatosis cases, while (B) demonstrates the diagnostic performance of ultrasound features in differentiating steatosis from fibrosis cases. AUC refers to area under the curve, Sn: sensitivity, and Sp: specificity.
Panels (A,B)
show examples of B-mode ultrasound liver images taken from two normal cases. Panel (A) shows three sequential from the same case with region of interests (ROIs) for quantitative analysis. Panel (B) shows three sequential images from a second normal case. Panel (C) shows the intra-and intercase JS divergence for cases in general. Intra-and intercase divergence for normal cases are close to each other, indicating that intrasampled cases may be just as independent inter sampling. Five to six regions of interests are placed (red rectangular boxes) on each image for quantitative analysis.
Panels (A,B)
show examples of B-mode ultrasound liver images from two steatosis cases. Panel (A) shows three sequential images from the same case with region of interests (ROIs) for quantitative analysis. Panel (B) shows three sequential images from a second steatosis case. Panel (C) displays the intra-and intercase JS divergence for steatosis cases in general. Intra-and intercase divergence for steatosis cases is far apart, indicating that we cannot claim independence of intrasampling in these cases. Five to six regions of interests are placed (red rectangular boxes) on each image for quantitative analysis. Panel (A) shows three sequential images from the same case with region of interests (ROIs) for quantitative analysis. Panel (B) shows three sequential images from another steatosis case. Panel (C) shows the intra-and intercase JS divergence for fibrosis cases in general. Intra-and intercase divergence for fibrosis cases is far apart, indicating that we cannot claim independence of intrasampling in these cases. Five to six regions of interests are placed (red rectangular boxes) on each image for quantitative analysis. Sultan et al. Page 14 | 4,982.6 | 2022-09-01T00:00:00.000 | [
"Medicine",
"Computer Science"
] |
Lasing of self-organized helical cholesteric liquid crystal micro-droplets based on emulsification
Lasing of self-organized helical cholesteric liquid crystal (CLC) micro-droplets was achieved based on emulsification of CLC/Polymer/Water mixture. It was found that the concentrations of CLC and polyvinyl alcohol play an obvious role on the improvement of lasing performance as the ratio of their concentrations is in the range of 1:10~1:9. In addition, the size of CLC micro-droplet is dependent on aforementioned concentrations, and shows to be proportional to lasing energy threshold. ©2016 Optical Society of America OCIS codes: (230.3720) Liquid-crystal devices; (160.3710) Materials; (140.0140) Lasers and laser optics. References and links 1. I. P. Ilchishin, E. A. Tikhonov, V. G. Tishchenko, and M. T. Shpak, “Generation of a tunable radiation by impurity cholesteric liquid crystals,” JETP Lett. 32, 24–27 (1980). 2. V. I. Kopp, B. Fan, H. K. M. Vithana, and A. Z. Genack, “Low-threshold lasing at the edge of a photonic stop band in cholesteric liquid crystals,” Opt. Lett. 23(21), 1707–1709 (1998). 3. J. P. Dowling, M. Scalora, M. J. Bloemer, and C. M. Bowden, “The photonic band edge laser: A new approach to gain enhancement,” J. Appl. Phys. 75(4), 1896–1899 (1994). 4. J. Schmidtke and W. Stille, “Fluorescence of a dye-doped cholesteric liquid crystal film in the region of the stop band: theory and experiment,” Eur. Phys. J. B 31(2), 179–194 (2003). 5. Y. Huang, Y. Zhou, C. Doyle, and S.-T. Wu, “Tuning the photonic band gap in cholesteric liquid crystals by temperature-dependent dopant solubility,” Opt. Express 14(3), 1236–1242 (2006). 6. K. Funamoto, M. Ozaki, and K. Yoshino, “Discontimuous shift of lasing wavelength with temperature in cholesteric liquid crystal,” Jpn. J. Appl. Phys. 42(2), L1523–L1525 (2003). 7. J.-H. Lin, P.-Y. Chen, and J.-J. Wu, “Mode competition of two bandedge lasing from dye doped cholesteric liquid crystal laser,” Opt. Express 22(8), 9932–9941 (2014). 8. A. Chanishvili, G. Chilaya, G. Petriashvili, R. Barberi, R. Bartolino, G. Cipparrone, A. Mazzulla, and L. Oriol, “Phototunable lasing in dye-doped cholesteric liquid crystals,” Appl. Phys. Lett. 83(26), 5353–5355 (2003). 9. T.-H. Lin, Y.-J. Chen, C.-H. Wu, A. Y.-G. Fuh, J.-H. Liu, and P.-C. Yang, “Cholesteric liquid crystal laser with wide tuning capability,” Appl. Phys. Lett. 86(16), 161120 (2005). 10. A. Chanishvili, G. Chilaya, G. Petriashvili, R. Barberi, R. Bartolino, G. Cipparrone, A. Mazzulla, and L. Oriol, “Lasing in dye-doped cholesteric liquid crystals: two new tuning strategies,” Adv. Mater. 16(910), 791–795 (2004). 11. S. Furumi, S. Yokoyama, A. Otomo, and S. Mashiko, “Phototunable photonic bandgap in a chiral liquid crystal laser device,” Appl. Phys. Lett. 84(14), 2491–2493 (2004). 12. G. Chilaya, A. Chanishvili, G. Petriashvili, R. Barberi, R. Bartolino, G. Cipparrone, A. Mazzulla, and P. V. Shibaev, “Reversible tuning of lasing in cholesteric liquid crystals controlled by light-emitting diodes,” Adv. Mater. 19(4), 565–568 (2007). 13. L.-J. Chen, J.-D. Lin, and C.-R. Lee, “An optically stable and tunable quantum dot nanocrystal-embedded cholesteric liquid crystal composite laser,” J. Mater. Chem. C Mater. Opt. Electron. Devices 2(22), 4388–4394 (2014). 14. H. Yu, B. Tang, J. Li, and L. Li, “Electrically tunable lasers made from electro-optically active photonics band gap materials,” Opt. Express 13(18), 7243–7249 (2005). 15. J. Schmidtke, G. Junnemann, S. K. Baumann, and H. Kitzerow, “Electrical fine tuning of liquid crystal lasers,” #260004 Received 25 Feb 2016; revised 16 Mar 2016; accepted 16 Mar 2016; published 18 Mar 2016 © 2016 OSA 1 Apr 2016 | Vol. 6, No. 4 | DOI:10.1364/OME.6.001256 | OPTICAL MATERIALS EXPRESS 1256 Appl. Phys. Lett. 15(12), 974–977 (2013). 16. H. Finkelmann, S. T. Kim, A. Munoz, P. Palffy-Muhoray, and B. Taheri, “Tunable mirrorless lasing in cholesteric liquid crystalline elastomers,” Adv. Mater. 13(14), 1069–1072 (2001). 17. B.-W. Liu, Z.-G. Zheng, X.-C. Chen, and D. Shen, “Low-voltage-modulated laser based on dye-doped polymer stabilized cholesteric liquid crystal,” Opt. Mater. Express 3(4), 519–526 (2013). 18. L. Saadaoui, G. Petriashvili, M. P. De Santo, R. Hamdi, T. Othman, and R. Barberi, “Electrically controllable multicolor cholesteric laser,” Opt. Express 23(17), 22922–22927 (2015). 19. C.-T. Wang and T.-H. Lin, “Polarization-tunable chiral nematic liquid crystal lasing,” J. Appl. Phys. 107(12), 123102 (2010). 20. M. Humar and I. Muševič, “3D microlasers from self-assembled cholesteric liquid-crystal microdroplets,” Opt. Express 18(26), 26995–27003 (2010). 21. D. J. Gardiner, S. M. Morris, P. J. W. Hands, C. Mowatt, R. Rutledge, T. D. Wilkinson, and H. J. Coles, “Paintable band-edge liquid crystal lasers,” Opt. Express 19(3), 2432–2439 (2011). 22. P. J. W. Hands, D. J. Gardiner, S. M. Morris, C. Mowatt, T. D. Wilkinson, and H. J. Coles, “Band-edge and random lasing in paintable liquid crystal emulsions,” Appl. Phys. Lett. 98(14), 141102 (2011). 23. D. J. Gardiner, W.-K. Hsiao, S. M. Morris, P. J. W. Hands, T. D. Wilkinson, I. M. Hutchings, and H. J. Coles, “Printed photonic arrays from self-organized chiral nematic liquid crystals,” Soft Matter 8(39), 9977–9980 (2012). 24. Z.-G. Zheng, B.-W. Liu, L. Zhou, W. Wang, W. Hu, and D. Shen, “Wide tunable lasing in photoresponsive chiral liquid crystal emulsion,” J. Mater. Chem. C 3(11), 2462–2470 (2015).
Introduction
Cholesteric liquid crystal (CLC) is a typical self-organized photonic superstructure which possesses many unique and interesting properties with potential applications.By virtue of its one-dimensional periodic structure, and embedded some amount of laser dyes, the CLC laser was achieved with an external pumping [1].Such kind of lasing system has attracted tremendous attention due to the competitive advantages including simple fabrication, lower threshold and wide tuning range over the conventional lasers.The mechanism of CLC lasing can be explained by the photonic crystal model, i.e., the photon density of state (DOS) is suppressed in a certain energy gap called photonic band gap (PBG) but enhanced at the band edges where the group velocity of the photons approaches zero [2,3].Lasing generally occurs at the band edges owing to the maximum DOS.Schmidtke et al. experimentally verified that the lasing usually generates at the long-wavelength edge of the CLC band gap [4].Accordingly, the approach of utilizing external stimuli such as temperature [5][6][7], light [8][9][10][11][12][13], electric field [14,15], and mechanical stress [16] to change the periodic structure (i.e., helical pitch) of CLC and consequently tune the lasing wavelength has been extensively adopted in series of previous works.In addition, both of the emission intensity and polarization can be electrically controlled [17][18][19].An omnidirectional lasing of CLC droplets based on the mixture of CLC and glycerol was reported [20].
Recently, a novel CLC micro-droplet laser based on emulsion consisting of aqueous solution of the polymer, polyvinyl alcohol (PVA), and the CLC doped with a small amount of laser dye was demonstrated [21][22][23].Distinct from the conventional CLC laser which should be confined in a planar aligned liquid crystal (LC) cell, such CLC micro-droplet laser can be formed just by covering the emulsion on a single substrate without any surface alignment, thereby significantly simplifying the preparation process and enabling the realization of film laser by coating the material on a flexible substrate.The drying of wet emulsion under room temperature leads to a shrinkage of the material on thickness, thereby forcing the random distributed helical axes of CLC droplets to transform into a uniform alignment perpendicular to substrate.Excited with a certain pumping light, the laser is emitted along the normal direction of substrate.In the very recent, a broad wavelength-tuning of such micro-droplet laser stimulated by light was realized through the doping of photosensitive azobenzene based chiral molecular switch [24].
Generally, the performances of such CLC micro-droplet laser are closely related with the arrangement of LC molecules in the droplet; while the arrangement is mainly influenced by the size of CLC droplets and the density of polymer network if the anchoring and pumping conditions are invariable.In other words, there must exist an optimized condition for the content of the components-PVA and CLC, which plays an essential role in the characteristic of the dried laser film and further in the performance of laser emission.However, the relevant works aiming at such aspect are rarely and lack of systematic study at present.In this paper, the concentrations of PVA and CLC were varied, in order to explore their influences on the characteristic of laser film as well as the performance of laser emission.Furthermore, the size and the distribution of CLC micro-droplets, and their lasing energy thresholds (LETs) were investigated.
Materials and experiments
CLC was composed by a commercial nematic liquid crystal SLC1717 (from SliChem, China) and a certain amount of chiral dopant, R811 (from Merck).A small amount of laser dye (~0.5 wt%), 4-dicyanomethylene-2-methyl-(6-4-dimethylaminostryl)-4H-pyan (DCM, from Aldrich), was doped into CLC as the gain medium.The weight ratio of R811 was 26.3% for the consideration of the matching between the PBG of CLC and the emission band of DCM.Such laser dye-doped CLC was mixed with the aqueous solution of polyvinyl alcohol (PVA, provided by ACROS ORGANICS, molecular weight: 16000) in further to form the CLC emulsion.The emulsion was uniformly coated on a glass substrate using the doctor blade; the thickness was controlled by two 80-μm-thick Kapton strips as illustrated in Fig. 1(a).The samples were reserved in a dark ambient at room temperature until the water was evaporated.Consequently, the thickness of dried film shrank, forming many dispersed oblate LC droplets.
A linearly polarized second-harmonic switched neodymium-doped yttrium aluminium garnet (Nd:YAG) pulsed laser (λ = 532 nm; pulse width: 8 ns; Beamtech Co. Ltd.Canada) was used as the pumping source to excite the laser dye in the samples; and a quarter wave plate was set to convert the pump laser to the left-handed circular polarization, i.e., opposite to the handedness of CLC.The emitted laser was detected and tested by a fiber connected USB spectrometer (Avaspec-2048 from Avantes, resolution: 1.60 nm).Several samples with different concentrations of CLC and PVA were prepared.The concentration of the CLC-x-varied from 1wt% to 5wt%, specifically 1wt%, 2wt%, 2.3wt%, 2.5wt%, 3wt%, 4wt% and 5wt%; likewise, concentration of PVA-y-varied from 10wt% to 24wt% with the interval of 2wt% (the mixture would go beyond saturation if the ratio of PVA exceeds 24wt%).The remaining part was water.To evaluate the characteristic of laser emission of the samples, a parameter-F-was defined, representing the number of effective CLC micro-droplets (herein, effective droplet was defined as a droplet possesses the well-defined helical structure and can be excited to emit the typical laser).The larger of F, the better lasing performance of the sample.For facilitating numerical statistic of such microdroplets, herein, as depicted in Fig. 1(b), the entire sample was uniformly divided into twentyone regions.The number of effective CLC micro-droplets in every region were counted and summed up to obtain the value of F. Similar experiment was carried out for three times and the average value of F was calculated.Consequently, the optimized weight ratios of the components in the mixture were determined.
Results and discussions
The typical texture of an effective CLC micro-droplet (inset of Fig. 2(a)) and its corresponding lasing spectrum are shown in Fig. 2(a).A uniform yellow-and-green reflection color was observed due to a well-defined helical structure of CLC with the helical axis perpendicular to the substrate; the defect rings were caused by the molecular anchoring from the curve surface of CLC micro-droplet.The spectrum clearly shows a single-mode laser emission with a peak at 607 nm which corresponds to the long-wavelength edge of the PBG of CLC micro-droplet.The band-width is about 1.5-1.7 nm, however the accurate band-width may be smaller than the tested value due to the resolution limitation of the spectrometer.The relationship between the pump energy and emission energy shown in Fig. 2(b) indicates an abrupt linear rising of emission energy as the pump energy increases to a turning point which is generally defined as the LET.Characteristic and performances of lasing connect closely to the contents of CLC and PVA as reflected in Table 1.It is obvious that the number of effective CLC micro-droplets-F-decreases almost monotonically with the increasing of the weight ratio of CLC when the concentration of PVA-y-is constant; while there exists a maximum of this number as the content of CLC is invariable.In addition, it is noteworthy that the effective CLC microdroplets are significantly less in the cases of higher CLC-content (x≥2.5 wt%) and lower PVA-content (y≤16.0wt%).Because of the phase separation between CLC and PVA and the emulsification, the above two cases usually lead to a large size of CLC droplets as shown in Figs.3(a) and 3(b), as well as the aggregation of these droplets, which disturbs the welldefined helical arrangement of LC molecules in the droplets and generates lots of defects as presented in Fig. 3(c), consequently influencing the laser emission.However, it is not implied that a better lasing performance can be achieved at the situations of much lower concentration of CLC and higher concentration of PVA.Table 1 shows that the number of effective CLC micro-droplets is only 10 as the concentrations of CLC and PVA are 1.0 wt% and 24.0 wt%, respectively.This reason may lie in the size-decreasing of CLC micro-droplets and stronger anchoring of the polymer-PVA, which cause the light scattering and the distortion of the helical structure of CLC micro-droplets, resulting in the disappearance of laser emission, but arrangement defects, resulting in the increasing of the density of LET which leads to the abrupt rising of LET value starting from 4393 μm 2 as shown in Fig. 4, however the linear relationship is still presented.Such linear relationships in both cases of large droplet size and small droplet size confirm the independence of the density of LET on the droplet size.The slope of line is proportional to the density of LET.It is noteworthy herein that the pumping light is focused to a smaller diameter which is comparable to the diameter of micro-droplet, and therefore the tested value of LET is significantly smaller than the previous reported one.Besides, the well helical arrangement of CLC and the thicker film (i.e., long gain-length) are another two possible reasons for the lower LET.
Conclusions
In conclusion, lasing of self-organized helical CLC micro-droplets was achieved based on the emulsification of CLC/Polymer/Water mixture.Such material is independent on the confinement of LC cell, and very easy to be achieved just by coating the material on one substrate without any alignment treatment, so it is promising in the future micro-photonic and display applications.The characteristic of the material and lasing performance are closely dependent on the component of materials.Herein, the influences of the concentrations of CLC and PVA on the performance of laser emission were investigated.The results indicate that the concentrations of CLC and PVA play an important role on the size of CLC micro-droplets, the distribution and number of effective CLC droplets, and the lasing energy threshold of the sample.Either lower or higher concentrations of CLC and PVA are not favorable to a better laser emission, therefore, a proper optimization of their concentrations is necessary for improving the lasing performance.Furthermore, the experiment shows a linear relationship between LET and the square of the diameter of CLC micro-droplet.This work might have profound significance on practical application of such laser material and facilitate mass production of the devices based on the material.
Fig. 1 .
Fig. 1.Schematics of (a) the preparation of CLC micro-droplet laser (:emulsion :Kapton strip :doctor blade :glass substrate); (b) numerical statistic of effective CLC micro-droplets (the whole sample was divided into twenty-one square regions as depicted by blue solid crosslines; number of effective droplets in every square region as enclosed by red dashed circle was counted one by one and summed up).
Fig. 2 .
Fig. 2. (a) Emission spectrum of a CLC micro-droplet and the corresponding optical texture (the inset) and (b) the pump energy dependent lasing emission energy of such micro-droplet.Concentrations of CLC and PVA are 2.0 wt% and 18.0 wt%, respectively.Diameter of the focused pumping light is manipulated to approximately equal to the diameter of the microdroplet, which is ~120 μm, and the reputation rate of pumping light is 1 Hz.
Fig. 4 .
Fig. 4. Relationship between LET and the square of diameter of CLC micro-droplet.Hollow triangles indicate that the probability of the lasing of smaller droplet is lower than that of larger ones. | 3,694.6 | 2016-04-01T00:00:00.000 | [
"Physics"
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Behind the screen: drug discovery using the big data of phenotypic analysis
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.
Introduction
Constant innovation in drug discovery makes it difficult for undergraduate courses to access up-to-date technology for teaching current methods in pharmaceutical research.Exposing students to large data sets collected or modeled by data generated in real laboratories can help increase engagement (Freeman et al., 2014;Kontra et al., 2015) and allows universities to provide students with hands-on activities without having to budget for expensive lab equipment.This type of pedagogical tool would be especially helpful for teaching current methods in pharmaceutical science as lab equipment for these methods are expensive, hard to maintain, and are sometimes not very accessible due to privacy within industry and academia.
With high throughput screening and big data analysis becoming more vital in scientific fields, it is important for students to be trained in these methods to make them more prepared for future careers in STEM (Miller, 2014;Stephens et al., 2015;Barone et al., 2017;Howe et al., 2017;Williams and Teal, 2017).A High-throughput Discovery Science and Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network has been assembled to address how faculty can introduce advancements in STEM fields.The HITS network was motivated by the slow progress undergraduate programs had made toward updating curricula to more modern quantitative standards (Robertson et al., 2021).The goal of HITS was to develop innovative curriculum materials in the form of casebased studies that involve hands-on activities with large high throughput datasets.The HITS initiative has built an interactive network that has successfully circulated high throughput case-based datasets across the country while also generating tools to help instructors develop their own case-based lesson plans (Bixler et al., 2021;Robertson et al., 2021).High throughput cases developed by the HITS network directly address common barriers to incorporating big data into curricula by using publicly available datasets, well detailed teaching notes, and highly adaptable cases (Williams et al., 2019).Case studies are a great way for students to learn high throughput methodology in tandem with high throughput quantitative skills (Samsa et al., 2021).Problem-based learning tools, such as case studies, urge students to solve real world problems which improves student motivation to learn and understand key topics (Gallagher et al., 1995;Lombardi and Oblinger, 2007).Case studies are interactive and faculty that have implemented case studies in their curriculum have observed an increase in student critical thinking and understanding of scientific concepts (Yadav et al., 2007).
High throughput screening is necessary for drug development with screens developed and optimized for a large variety of target pathways.Providing students with case studies and real-world datasets can teach students how to analyze high throughput screening data in addition to interactive teaching of current methods in drug discovery.In drug discovery there are two types of screens: target-based and phenotypic-based (Swinney, 2013).Target-based screens are used when a cellular target is known to be involved in disease progression and are based on change in activity of a specific protein with a known role in the cellular pathway of interest (Croston, 2017).Target-based screens are ideal for diseases with known cellular targets but are not applicable for drug discovery for diseases with no known cellular targets.Phenotypic screening is based on a cellular biomarker and is often target agnostic.Phenotypic screens are useful for discovering new therapeutic targets but are harder to optimize for high throughput use and may need more customized statistical metrics compared to target-based screens (Moffat et al., 2017).Phenotypic screens have been successful in drug discovery campaigns for a variety of diseases such as bacterial and parasitic infection (Battah et al., 2019;Saccoccia et al., 2020)."Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis" describes the development and use of a high throughput screen for detecting compounds that interfere with a cancer-specific pathway from the perspective of a graduate student.Students learn the difference between target-based and phenotypic-based screens (Swinney, 2013) and how experimental design and statistical analysis differs depending on assay readout (Markossian et al., 2004;Zhang, 2011).Target-based and phenotypic screening methods are very different, especially in the type of samples used in screening and assay readout (Strovel et al., 2016) (see supplemental teaching notes).These differences lead to variation in how datasets from each type of screen are analyzed.Customizing statistical analysis to best match the scientific protocol is very important and must be done without compromising a researcher's ethical responsibility in data reporting.It has been reported that a large percentage of published research articles do not report statistical analysis properly or responsibly (Chiu et al., 2017;Diong et al., 2018).Incorrect data analysis and interpretation can have drastic effects on the development of future studies in all fields by inaccurately informing researchers.It is important for authors to understand how to properly choose statistical methods and report their results responsibly.In high throughput screening campaigns, methods of statistical analysis should be chosen based on experimental design and parameters of the data rather than which method gives desired results (Markossian et al., 2004;Zhang, 2011;Lindner et al., 2018).This case study introduces students to drug discovery screening techniques while also prompting them to think critically about the ethics of statistical analysis and data reporting.This case is customizable, making it applicable to a wide variety of curriculums.The case discusses cancer biology (Griffith et al., 1999;Henson et al., 2009;Cesare and Reddel, 2010), high throughput screening, statistics, and ethics in science (see supplemental teaching notes).Any combination of these topics can be emphasized for a particular course.To demonstrate the adaptability of the case, we implemented it in 4 undergraduate courses (BIOL 459: Molecular Biology, SCI 458: Scientific Research and Analysis, BIO 4610: Animal Physiology, and PHRS 500: Innovations and Transformations in Pharmacy and Pharmaceutical Sciences) and a graduate level course (PHRS 802: Introduction to Drug Development).Each implementation was catered toward course curriculum while also meeting the case learning objectives.We found that our implementations in undergraduate and graduate courses met the learning objectives and improved student comfort in discussing the case material.
Pedagogical framework(s)
It is hard to give students hands-on experience with experimental methods in high throughput screening as the equipment needed to run experiments is costly, hard to maintain, and often hard to access.Case studies can be a valuable interactive tool for teaching topics involving high throughput screening and statistical analysis of large datasets (Mahdi et al., 2020).Here, we describe the implementations of "Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis" in 4 undergraduate courses and 1 graduate course.The case study was taught to over 70 students across 3 universities in the United States and students were surveyed to assess the case's ability to meet the learning objectives.
The lesson plan included a pre-class reading assignment and question set, an in-class lecture and data analysis activity, and students were sent home with a post-class homework assignment involving a data analysis activity and a question set.Students who consented to being evaluated were given paper surveys at the beginning and end of the in-class session to measure improvement of student understanding after the in-class portion of the lesson.The pre-class reading, teaching notes, in-class activity dataset, step-by-step instructions for data analysis, and homework dataset and questions are provided (Supplementary material).
Methods: learning environment; learning objectives; pedagogical format
Learning environment PHRS 802 graduate level drug development and professional skills development-PHRS 802 was an introductory course for first year graduate students in the Pharmaceutical Sciences PhD program at the University of North Carolina at Chapel Hill.All 19 students in this course had at least a bachelor's degree and the student age range was 22-35.The class met once a week for a 60 min in-person class session.One of the main purposes of this course was to expose students to methods commonly used in each stage of drug development (Sun et al., 2022).Since "Behind the Screen" describes high throughput screen development in the context of drug discovery, this case fit well into the course curriculum.For PHRS 802, we emphasized the case themes in high throughput drug discovery methods and customization of statistical analysis for phenotypic screening.The implementation of "Behind the Screen" was done in one 60-min class session of PHRS 802.
The session included a 30-min lecture and a 30-min in-class activity (supplemental in class dataset).The in-class activity was done together as a whole class.
PHRS 500 innovations and transformations in pharmacy and pharmaceutical sciences-PHRS 500 was a summer course held at the University of North Carolina at Chapel Hill for undergraduate students interested in pursuing careers in pharmaceutical science.A majority of the 15 students in this course were visiting from out of the country and the age range of the class was 18-25.The goal of this course was to expose students to methods commonly used in each stage of drug development as well as give students an idea of what a graduate career in pharmaceutical sciences looks like."Behind the Screen" fit very nicely into the PHRS 500 curriculum as it describes high throughput screen development in the context of drug discovery.For PHRS 500, we highlighted high throughput drug discovery methods and customization of statistical analysis for phenotypic screening.This case was especially applicable to PHRS 500 because it is written from the perspective of a graduate student.Since the participants in this course were interested in attending graduate school, this narrative gave them some insight into what graduate education might look like.The implementation of "Behind the Screen" was done in one 90-min class session of PHRS 500.The lecture took 30 min, leaving 60 min for the in-class activity.The in-class activity was done together as a whole class.
SCI 458 scientific research and analysis
Scientific Research and Analysis SCI 458 is an upper-level class for undergraduates at Crown College.There were 6 students in the class and the prerequisites were Applied Statistics and at least one science course.This case study was relevant to this course since it exposed students to data analysis methods and decision-making.While the biology content was less relevant, students from a range of majors can understand the drug discovery process more broadly and appreciate the value.The pre-work (supplemental case study for students) was given and then the slides presented in class as the instructor walked through the case study with the students over about 3 50-min class periods.This implementation was the pilot run of the case study, and some changes were made which were then used in the remaining classes.Since changes were made to the lesson materials after this implementation, the data collected from this course was not included in the data analysis for this study.
Bio 4610 animal physiology
Animal Physiology BIO 4610 is an upper-level class for undergraduate students at the University of North Carolina at Pembroke.The course functions as an advanced physiology course and is required for biomedical majors.There were 24 students in the class and the prerequisites were Anatomy and Physiology I and II.The class meets three times a week for 50 min for lecture and once a week for 90 min for lab.We used the case in our unit on data analysis and did not emphasize the cancer biology aspect.This case was especially relevant for the course, as most of the students were seniors applying to graduate school or medical school.The implementation of "Behind the Screen" was done with pre-work before class and one 90-min lab block of in-class time.The class block included a 30-min lecture and 60 min of in-class activity.The in-class activity was done in pairs.
BIOL 459 molecular biology
Molecular Biology BIOL 459 is an upper-level course for undergraduate students at Hastings College.The course comprised of 6-students and was an elective for Biology and Biochemistry majors, with Introduction to Genetics and Cell Biology as prerequisites.It met three times a week for 80 min and twice a week for 130 min.Unlike most courses at the college, this course focuses on lab work and reading literature with a small amount of lecture material and class activities.This case was relevant to the course in understanding aspects of experimental design and data analysis.Students were introduced to the case study and worked through most of the pre-work in one 80-min period and then worked on the in-class portion the next week in an 80-min period.Students worked together in class and finished the remaining homework on their own.
Pedagogical format
This pedagogical tool has 3 components: pre-class reading and questions (supplemental case study for students), in class lecture and activity (supplemental teaching notes and in class dataset), and post-class homework activity and questions (supplemental homework dataset and homework).The pre-class reading is a story-like description of a graduate student, Merry, joining a lab and being introduced to her first project.The narrative includes dialog between the graduate student and a senior member of the lab which explains the key topics of the case.The pre-class reading has questions embedded throughout the narrative as well as some at the end to help gage if the student is understanding the key takeaways of the reading assignment.
The in-class portion of the case study includes a lecture and in-class activity.The lecture is very customizable so lecturers could focus on the elements of the case that are most suited toward the course curriculum (supplemental teaching notes).For the implementations, lecture times typically ranged from approximately 15-30 min.Lectures were focused on what the instructor found most important for students to understand from the pre-class reading.The lecture portion included a power point that reviewed the main topics of the case (drug screening methods, cancer biology, statistical analysis methods, how quantitative polymerase chain reaction (qPCR) works, etc.) as well as time for students to ask questions about the pre-class assignment and the lecture content.The in-class activity followed the lecture and lasted anywhere between 30 min to about 2 h (over multiple class sessions).The in-class activity involved the class following the instructor through analyzing a data set using a target-based statistical metric and a metric more conducive to phenotypic screening (supplemental in class dataset).The homework assignment for the students included a second data set (supplemental homework dataset) which students were expected to analyze with both metrics to confirm the phenotypic screening metric was most appropriate.The homework assignment also included a few questions for the students to complete, to make sure they understood what their data meant in a biological context.Students were also given written step-by-step instructions on how to do the data analysis to help if they got stuck doing the homework (supplemental case study for students-last section).The homework assignment is intended to take 45-60 min to complete.
Learning objectives
The course learning outcomes relevant to the case study state that on successful completion of the course students should be able to Table 1:
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Define phenotypic cell-based screening and identify appropriate screening controls.
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Apply statistical modeling to a phenotypic screen to identify biologically meaningful results.
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Interpret the biological significance of a Z'-value and a Z*-value.
Data collection
Consent forms were handed out to students in the class session before the implementation to ensure they had ample time to read over the form.Study participants handed signed consent forms in to the instructor before the implementation session started.
Students were expected to have completed the pre-class reading and questions prior to the lecture to give students a foundational understanding of screening types and why they differ in the way they are statistically analyzed.Pre class questions from the reading were expected to be completed as homework before class and were turned in except for in BIOL 459 and SCI 458.Consenting participants were asked to fill out a paper survey before the lecture.The survey included questions about participant demographic and asked students to rank their level of agreement with a list of statements.The statements were focused on how comfortable the student was in describing themselves as a scientist as well as how familiar they were with the case study topics.
After the in-class activity was completed, consenting students were given a second paper survey to fill out that asked the same questions as the pre-class survey.Student preand post-class responses were compiled and analyzed by Wilcoxon test to determine if student understanding of the key topics improved after the lecture and in-class activity.
A small multiple choice question set is provided to help further assess students pre-and post-implementation (supplemental class quizzes).
In all courses the homework dataset and questions were turned in for a grade.The homework questions were focused on assessing the students' ability to understand the biological significance of their statistical analysis.
Results (to date) Study demographics
Overall, we collected data from 21 undergraduate students and 12 graduate students.The undergraduate participants were aged 18-35 with a majority (57%) of students falling in the 18-21 age range (Figure 1).Of the students surveyed, 52% had no previous lab experience and 40% were first generation college students.The majority (71%) of undergraduate participants were female, with less than 5% of students preferring not to report their gender.
The graduate level participants were either in the 22-25 age range or 30-35, with most of the students (83%) aged 22-25 (Figure 2).At least 58% of the graduate students were female, with 8% preferring not to disclose their gender.Most of the graduate students (75%) were not first-generation college students.Unsurprisingly, 100% of the graduate students had previous lab experience.
Analysis of in-class data analysis activity
Most of the undergraduate students were interested in biology and enjoyed the course they were participating in as most students agreed or strongly agreed with the statements "biology excites me" (81%), "I am engaged in this class" (71%), and "I like to participate in this class" (67%) in the pre-class survey (Figure 3).Before the in-class lecture and statistics activity, a majority of the undergraduate students felt neutral, disagreed, or strongly disagreed with the statements "I know what a phenotypic screen is"(71.4%),"I can define Z' and Z*"(81%), I feel comfortable performing statistical analysis"(81%), and "I can determine when a statistical method is appropriate"(66.6%).After the in-class section of the lesson plan, student responses for the statements "I know what a phenotypic screen is" and "I can define Z' and Z*" skewed significantly more toward agree and strongly agree (p < 0.0001).Students also had an increased comfortability in performing statistical analysis (p < 0.01) as well as determining which statistical method to choose for a screening project (p < 0.05).We also saw a significant increase in student confidence in sharing ideas in a group setting (p < 0.05) as well as explaining quantitative topics to peers (p < 0.05).
The graduate students were very comfortable with biology and most identified as scientists with most of the participants agreeing or strongly agreeing with the statements "I am a scientist" (92%), "I am a researcher" (100%), and "biology excites me" (91%) in the preclass survey (Figure 4).The graduate students displayed a slight increase in their confidence in determining what a phenotypic screen is (p < 0.05) and defining Z' and Z* (p < 0.05).It should be noted that the graduate student participant group was at a higher education level than the undergraduate participants.The graduate students likely had more experience in statistical analysis and quantitative topics compared to the undergraduate student population.
Student feedback
In the post-class survey, students were asked two open-ended questions.The first question was: compared to a traditional lecture, how did the format affect your experience?The second question asked for feedback on what worked well in the in-class activity and what aspects needed to be adjusted to improve student experience.Based on student responses to the first question, it seemed that some students found the content a little hard to understand at first, but overall felt more comfortable with their quantitative skills after the activity.Many students found the lesson format more engaging and preferred the hands-on activity to traditional lectures.A selection of undergraduate and graduate student responses to question one that represent the main points are shown below: • "I really enjoyed the flipped classroom style.I felt like I came into class with all the pieces of the puzzle but the lecturer and activity put the pieces together into a picture." • "It allows for more engagement with the material and gave hands-on experience in analyzing data." • "This was hard to understand, but it did help with computational skills.I feel more comfortable with Excel now." • "I enjoyed the interactive nature of examining the data.It made it more hands-on and tangible." Student responses to the second question mostly mentioned the length of the pre-class reading assignment and the amount of time spent on the lecture.A few students found the pre-class reading to be a little long and took a long time to read.One graduate student felt that the dialog aspect of the pre-class reading was distracting and did not contribute to their understanding.Students also suggested a shorter lecture would allow for more time to be spent on the hands-on data activity, which they felt they got more benefit from compared to the lecture.A selection of student responses that represent the main improvement suggestions have been listed below: • "The class can be a little more interactive.Let the students do [the analysis] themselves first and then give the answer." • "I like this module so much, but maybe the pre-class part can be written in an easier way to [understand] because it's a little bit difficult to understand for students [new to this topic]." • "Maybe a bit more time build into [class] for excel because some
•
[parts] move too fast." • "More time on hands-on example and shorter lecture."
Discussion
The implementations of this case demonstrate that it can be used in a wide variety of undergraduate and graduate courses to teach students topics in drug discovery research.
Student survey data showed that the case was effective in improving student confidence in ability to discuss the key topics in undergraduate and graduate level courses.We had a relatively small population of participants (21 undergraduate and 12 graduate students) and we would likely get a better idea of the effectiveness of the course with a larger survey group.The undergraduate classes were small (less than 25 students), so while we were able to reach a wide range of courses, the survey data was limited.We implemented a single graduate level course, which resulted in very limited survey data for students at this level.Implementing in other graduate courses would certainly provide a better view of how the case improves graduate student understanding of drug discovery methods.We did not compare the graduate student responses to the undergraduate student responses as our sample size was too small.Understanding if there is a difference in efficacy between these two student populations would be great to assess with additional implementation data.It is also worth noting that the graduate student assessment was identical to the undergraduate student assessment, and it is possible that the case may need to be adjusted for graduate courses to improve the efficacy of the case study.
There was also some variation in how the case was implemented in each course.Most courses allotted one 50-60-min class period for the lesson, however, one implementation was in a 90-min session and one was implemented over multiple 50 min class sessions.There was also some variety in data software used for the in-class activity.Some students preferred to use google sheets, while others used Microsoft Excel.These slight variations between implementations also factor into the limitations of this study, and more survey data may be informative for the best way to teach the case in the future.
Our implementation data shows that this in-class activity is an engaging and accessible way to teach students about drug discovery research methods.We observed throughout our implementations that many students preferred to use Google Sheets (a free resource) as they were most familiar with this software.The use of the free-ware, Google Sheets, allows students to get hands-on experience with real-world datasets at no cost to the university.The case study also includes dialog between a new graduate student and their mentors, which may be of interest to undergraduate students who are considering pursuing a graduate degree."Behind the Screen" discusses a variety of scientific topics, making it easy for instructors to customize the case for their course.The main themes in this case are high throughput drug discovery methods, cancer biology, statistical analysis of large datasets, and ethics of data analysis and reporting.
We hope that "Behind the Screen" will be customized and implemented in multiple undergraduate courses.Each instructor that participated in this study was able to successfully tailor the case to fit into the curriculum of their course.For example, in the pharmaceutical science course implementations (PHRS 802 and PHRS 500), the lecture and in-class discussions were focused mostly on how target-based and phenotypic-based screens differ in drug discovery and when to use each type.The lecture was also dedicated to discussing the two types of statistical analyses described in the case and where and when each method would be applied.In addition to emphasizing these scientific points, the undergraduate pharmaceutical course implementation also had some discussion about graduate school, as these students were all interested in pursuing graduate careers.These classes focused less on the biology of the assay.This class also did not discuss the ethical implications that may occur when choosing statistical analysis methods.
For implementation in biology courses, it may be beneficial to focus more on the biological significance of the screen (supplemental teaching notes).This aspect will highlight the importance of understanding disease-specific cellular pathways in the design and implementation of high throughput screens.The instructor could then discuss the benefits and drawbacks of implementing phenotypic or target-based screens.Once the high throughput screening process has been introduced, methods for statistical analysis of screening data can be discussed.If ethics is part of the course curriculum, this is a great place to emphasize proper statistical procedures and discuss responsible data reporting.Following these discussions, the data from the phenotypic screen can be introduced.The instructor can explain that the assay uses qPCR to detect changes in a DNA biomarker, and "hits" (drugs that detectably change biomarker levels) are samples that fall above or below the statistical cutoff described in the case study [3 times the median of absolute deviation (MAD)] (Zhang, 2011).Lastly, we recommend making sure all students in the class understand the basics of how qPCR analysis works.In-depth methodology knowledge is not necessary, but since the in-class dataset involves working with cycle threshold (C T ) values, it is important to ensure that students grasp the origin of these data values to gain a better understanding of statistical significance in data analysis.After the lecture, the instructor can answer any student questions and proceed to the in-class activity.
Conclusion
High throughput screening commonly used in drug discovery campaigns, and while it is essential that students are taught how to analyze large datasets, it is difficult for undergraduate institutions to provide hands-on experience with these methods."Behind the Screen" is an interactive and highly versatile case study that provides students the opportunity to work with large datasets modeled from a real-world first-in-class screen.The case aims to increase students' confidence in their ability to define phenotypic screening and proper controls, apply statical modeling to a phenotypic screen, and interpret the biological significance of a Z'-and Z*-value.Implementations of this case proved it to be successful in significantly improving undergraduate and graduate confidence in ability to confidently discuss the learning objectives.While the case was effective in these student populations, our sample sizes were small.Further implementation will allow us to evaluate if the case performs differently between undergraduate and graduate students.Undergraduate student demographics.Demographic breakdown of undergraduate participants in all implementations.Data was collected by survey questions given to consenting students and included multiple choice questions about the individual's race, ethnicity, gender, and age.Students were also asked if they had previous experience in a lab setting and if they were first generation (gen) college students.Results were compiled and depicted as pie charts.Pre-and Post-class survey questions demonstrate improvement in undergraduate student understanding of key topics.Distribution of student responses to survey questions before (pre) and after (post) the in-class portion of implementation.Students were asked to select which response (strongly agree, agree, neutral, disagree, or strongly disagree) best described their sentiment toward the statements listed.Pre-and post-class responses were analyzed via Wilcoxon test to determine if there was a significant increase in "agree" or "strongly agree" responses to any of the statements.Results suggested the case improved student comfortability sharing ideas in large groups, explaining quantitative topics, and determining appropriate statistical methods (*p < 0.05).There also was significant improvement in student comfortability in statistical analysis (**p < 0.01) as well as understanding of phenotypic screens, Z' analysis, and Z* analysis (****p < 0.0001).Pre-and Post-class survey questions demonstrate moderate improvement in graduate student understanding of key topics.Distribution of student responses to survey questions before (pre) and after (post) the in-class portion of implementation.Students were asked to select which response (strongly agree, agree, neutral, disagree, or strongly disagree) best described their sentiment toward the statements listed.Pre-and post-class responses were analyzed via Wilcoxon test to determine if there was a significant increase in "agree" or "strongly agree" responses to any of the statements.Results indicate the case moderately improved student understanding of phenotypic screens, Z' analysis, and Z* analysis (*p < 0.05).
The in-class activity demonstrates what happens when you try to use a target-based metric to analyze a phenotypic screening dataset.Students used what they learned in the pre-class reading and lecture to explain which analysis made the most sense in these experiments and why.Some course instructors (PHRS 802 and 500) took time at the end of the in-class activity to discuss the biological significance of the data the class analyzed and helped students understand what the next steps would be in a drug screening campaign.
FIGURE 2 .
FIGURE 2.Graduate student demographics.Demographic breakdown of graduate participants from the implementation done in PHRS 802.Data was collected by survey questions given to consenting students and included multiple choice questions about the individual's race, ethnicity, gender, and age.Students were also asked if they had previous experience in a lab setting and if they were first generation (gen) college students.Results were compiled and depicted as pie charts. | 7,310.8 | 2024-02-14T00:00:00.000 | [
"Medicine",
"Computer Science"
] |
Generalized Poincar é Beams in Tight Focus
: We study the tight focus of generalized (hybrid) Poincar é beams. A conventional Poincar é beam is a coaxial superposition of two optical vortices, one with left circular polarization and a topological charge (TC) of m , while the other has a right circular polarization and a TC of − m . The generalized Poincar é beams are also composed of two optical vortices, but their TCs are different, for instance, p and q . Here, we theoretically investigate the generalized Poincar é beams with the TCs p = m + 1 and q = − m in tight focus. In this case, both transverse components of the strength vector of the initial electric field have a topological charge of 1/2, and the beam itself is a cylindrical vector beam of fractional order m + 1/2. Analytical expressions are derived for the components of the strength vectors of the electric and magnetic field at the focus as well as for the intensity distribution, the longitudinal component of the spin angular momentum (SAM), and for the components of the Poynting vector (energy flow density). We show that the intensity at the focus has 2 m − 1 local maxima residing evenly in a certain circle radius with the center on the optical axis. We also demonstrate that the radial spin and orbital Hall effects occur at the focus, i.e., the longitudinal SAM component has different signs in the circles of different radii, and the azimuthal component of the transverse Poynting vector also has different signs.
Introduction
Poincaré beams, whose polarization state is related to the polarization Poincaré sphere [1][2][3], are actively studied in optics, starting with works [3][4][5][6].In a general case, a Poincaré beam is a superposition of two optical vortices with different topological charges (TC) p and q and with orthogonal polarizations.For the optical vortices, the conventional Laguerre-Gaussian beams of different indices [7][8][9] can be chosen, or diffraction-free Bessel beams, or the Bessel-Gaussian beams generated by axicons [10][11][12].The Poincaré beams can be generated similarly to all the other vector beams, by using liquid-crystal light modulators, half-wave and quarter-wave plates [13][14][15], or by using lasers and q-plates [16], and metasurfaces [12].In Ref. [17], the polarization singularity index (Poincaré-Hopf index) of the Poincaré beams was studied.In Ref. [18], the optical Hall effect was theoretically discovered in the tight focus of the Poincaré beams.The optical (or photonic) Hall effect is divided into spin [19,20] and orbital [21,22].Usually, the Hall effect in optics is observed when a light field is reflected from an interface between media [21,22], or when it passes through multilayered media [23], crystals [24,25], or through a metasurface [26].There are known works investigating the Hall effect in the tight focus of a laser radiance [27,28] or the vicinity of the focal plane [29].We note that the abovementioned works do not contain theoretically obtained key characteristics of the generalized Poincaré beams in tight focus using the Richards-Wolf formalism [30]: amplitudes of the electric and magnetic vectors, intensity distribution, distributions of components of the Poynting vector, and the spin angular momentum (SAM) vector.In this work, adopting the Richards-Wolf approach, we obtain analytical expressions, describing key characteristics of the generalized Poincaré beams in a case when the topological charges (TC) of the two optical vortices with left and right circular polarization are equal with respect to p = m + 1 and q = −m.We demonstrate that at the focus of such beams, radial spin and orbital Hall effects take place.We note that in [31], we demonstrated the spin Hall effect for fractional-order cylindrical vector beams at the focus plane.In the current work, at p = m + 1 and q = −m, there is also a cylindrical vector beam with a fractional order of m + 1/2.Therefore, we can expect that there is also the spin Hall effect at the focus of such a generalized Poincaré beam.The work of [31] does not contain analytical expressions for electric field components at the focus of fractional-order cylindrical vector beams.In the current work, we derive such analytical expressions.
In our previous work [32], we have shown that the orbital Hall effect occurs before and after the focus of the conventional vectorial cylindrical beams, which are a special case of Poincaré beams when the optical vortices have the TCs m and −m, and that local areas in the beam cross section, where the transverse energy flow is rotating clockwise or counterclockwise, reside in pairs on a certain circle radius with the center on the optical axis.In this work, energy flows, rotating clockwise or counterclockwise, reside on circles with different radii.Therefore, this orbital Hall effect is called radial.
We note that the Richards-Wolf formalism [30] adequately describes the light field only near the focus.The work of [33] investigates the behavior of light at the focus by using an exact solution of the Helmholtz equation in the spherical coordinates, which is correct in the whole space.However, generating such a light field at the focus requires generating in the initial plane all three components of the electric vector.This is a challenging problem.In our case, only the transverse components of the electric field should be generated in the initial plane, which is easy to implement in practice.
Vector Field in the Initial Plane
We consider here the following Jones vector of the initial light field: with (r, ϕ) being the polar coordinates in the initial plane, and a, and b being complex constants.If p = −n and q = n, the beam from Equation (1) reduces to a conventional Poincaré beam [4][5][6].If a = b = 1/ √ 2, then the field (1) becomes maximally inseparable [18]: When p = q, the field (1) reduces to a linearly polarized optical vortex with the topological charge (TC) q.When p = −q, the field (1) is a cylindrical vector beam of the order q [34].When p = −m and q = m + 1, the field (2) is given by The field (3) is interesting because it is a cylindrical vector beam of a half-integer order.In Ref. [35], the beam (3) is not quite correctly called a vector vortex beam with a fractional topological charge.As was already shown in [31], in the tight focus of the fractional-order cylindrical vector beams, subwavelength areas are generated with elliptic polarization of different handedness, that is, the polarization vector in these areas is rotating clockwise or counterclockwise.We note that the initial light field (3) is linearly polarized at each point of its cross section.Therefore, similarly to [31], it should be expected that the focused field (3) should also contain the areas with elliptic polarization of different handedness.It is also seen from Equation (3) that the initial field is a coaxial superposition of two optical vortices with left and right circular polarization and with different topological charges of m + 1 and -m.Since these topological charges do not compensate for each other, it is reasonable to expect circular energy flow at the focus.This means that at the focus, a nonzero distribution of the axial component of the angular momentum vector should be present.Below, we show that this is indeed so.
Components of the Strength Vector of the Electric Field at the Focus
The Richards-Wolf formalism [30] allows access to all components of the strength vector of the electric field at the tight focus of the initial field (3): where where k = 2π/λ is the wavenumber of light with the wavelength λ, f is the focal length of an aplanatic system (ideal spherical lens), ν = 0, 1, and 2, J µ (ξ) is the µth-order Bessel function of the first kind, ξ = krsinθ, θ is the polar angle that defines the tilt of the optical axis of rays converging into the focus, θ 0 is the maximal angle that defines the numerical aperture of the aplanatic system (NA = sinθ 0 ), (r, ϕ, z) is the cylindrical coordinate system with the origin at the focus (z = 0 is the focus plane), and A(θ) is the amplitude of the initial circularly symmetric field (real-valued function).
Intensity Distribution of the Electric Field at the Focus
From the components of the electric vector (4), we can derive the intensity distribution of the light field at the focus plane (z = 0): where the first two terms in the round brackets describe the transverse intensity I ⊥ = I x + I y , whereas the third term in the round brackets describes the longitudinal intensity I z .As seen from Equation ( 6), the intensity is a nonnegative function (I ≥ 0) since each term in the round brackets in Equation ( 6) is nonnegative, for the sum of two squared numbers is equal to or greater than their doubled product.Equation ( 6) also follows that the intensity distribution contains 2m − 1 local maxima and 2m − 1 local minima (or intensity nulls) that reside on a certain circle radius with the center on the optical axis.Thus, the number of these intensity maxima and nulls is always odd (2m − 1).
Longitudinal Component of the Spin Angular Momentum Vector at the Focus
Using the components of the electric field vector at the focus (4), we can derive the longitudinal component of the spin angular momentum (SAM) vector of the field (3), since the longitudinal SAM component S z is equal to the third Stokes parameters S 3 , whose magnitude indicates the presence of elliptic or circular polarization in the beam cross section.The SAM vector is defined by the following expression [28]: Im(E * × E), (7) with ω being the angular frequency of light.Below, we omit the constant factor 1/(16πω) for brevity.It can be seen from Equation ( 7) that the longitudinal SAM component (without the constant) coincides with the nonnormalized third component of the Stokes vector: Substitution of expressions (4) for the electric field components into Equation ( 8) yields A comparison of Equations ( 6) and ( 9) reveals that if the transverse intensity is a sum of two positive terms A and B, then and the longitudinal SAM component is a difference between these terms: According to Equation ( 9), similarly to the intensity distribution in Equation ( 6), the SAM distribution also has 2m − 1 local maxima and 2m − 1 local minima.As seen from Equation (11), if B > A, then S z > 0 (polarization vector is rotating counterclockwise), and vice versa, if B < A, then S z < 0 (polarization vector is rotating clockwise).In the areas where B = A (S z = 0), the polarization is linear.The points in the beam cross section at the focus, where S z = 0, are called [36] topological spin defects.Thus, it follows from Equations ( 9) and ( 11) that there are areas with different spins at the focus: positive (S z > 0) and negative (S z < 0).The spatial separation of areas with left circular and right circular polarization is called the spin Hall effect [27][28][29].In the Simulation section below, these conclusions are confirmed by concrete examples.
Energy Flow Density at the Focus
Here we derive the Poynting vector (energy flow density) at the focus of the field (3).
To do this, we should first obtain the components of the strength vector of the magnetic field at the focus.In the same way, as we obtained the components of the electric vector (4) by using the Richards-Wolf theory [30], we can also obtain the magnetic vector: The Poynting vector is defined by the well-known formula [30]: where c is the vacuum speed of light, Re is the real part of a complex number, E × H is the cross product, and * is the complex conjugation.Below, we omit the constant c/(8π) for brevity.Substituting components (4) and ( 12) into Equation ( 13), we obtain the components of the Poynting vector at the focus of the field (3): Passing to the polar components P r and P ϕ of the transverse Poynting vector, we obtain: As seen from Equation ( 14), the longitudinal component of the Poynting vector at the focus has a circularly symmetric distribution and does not depend on the azimuthal angle ϕ.It is also seen from Equation ( 14) that if m = 1 or m = 2 then there is a reverse energy flow on the optical axis since for m = 1 or m = 2, we obtain the following on the optical axis: (15) indicates that the transverse energy flow at the focus is rotating along a closed trajectory with the center on the optical axis, clockwise if Q(r) > 0, and counterclockwise if Q(r) < 0. Since the function Q(r) is of different signs on different radii r, it can be stated that the radial orbital Hall effect occurs at the focus of the light field (3).This also follows on from the expression for the longitudinal component of the angular momentum vector J of field (3) when it is written by definition using the azimuthal component of the energy flow [28]: The energy flow at the focus is rotating along a spiral around the optical axis since the topological charges of the two optical vortices, which are present in the superposition in the initial field (3), do not compensate for each other as they have different magnitudes: m + 1 and −m.
Simulation
Using the Richards-Wolf formalism [30], we computed the distributions of intensity and the longitudinal component of the SAM vector (spin density) at the tight focus of the light field using the initial distribution given by Equation (3).We supposed that the field amplitude in the initial plane was constant, i.e., A(θ) = 1, wavelength λ = 532 nm, focal length f = 10 µm, and the numerical aperture NA = 0.95. Figure 1 shows the distributions of the longitudinal component of the spin angular momentum S z (Figure 1a-d As seen in Figure 1 (2nd row), the number of local maxima in the intensity distribution at the focus is consistent with the theory [Equation (6)] and is equal to 2m − 1: 1 (Figure 1e), 3 (Figure 1f), 5 (Figure 1g), and 9 (Figure 1h).It is also seen in Figure 1 (1st row), that, according to Equation ( 9), the SAM distribution also contains 2m − 1 local maxima (red color in Figure 1a-d), where S z > 0, which reside on a certain circle with the center on the optical axis.On a circle with a larger radius, (blue color in Figure 1a-d), S z < 0. The black color in Figure 1a-d denotes the areas with zero spin, i.e., where polarization is linear.Since the brightness of the blue color in Figure 1a-d is 2-3 times lower than that of the red color, elliptic polarization in the areas of positive spin is closer to circular polarization, whereas the polarization ellipses in the areas of negative spin are more elongated and closer to linear polarization.Nevertheless, the spatial separation of the areas with positive and negative spin at the focus demonstrates the spin Hall effect.length f = 10 μm, and the numerical aperture NA = 0.95. Figure 1 shows the distributions of the longitudinal component of the spin angular momentum Sz (Figure 1a-d As seen in Figure 1 (2nd row), the number of local maxima in the intensity distribution at the focus is consistent with the theory [Equation ( 6)] and is equal to 2m − 1: 1 (Figure 1e), 3 (Figure 1f), 5 (Figure 1g), and 9 (Figure 1h).It is also seen in Figure 1 (1st row), that, according to Equation ( 9), the SAM distribution also contains 2m − 1 local maxima (red color in Figure 1a-d), where Sz > 0, which reside on a certain circle with the center on the optical axis.On a circle with a larger radius, (blue color in Figure 1a-d), Sz < 0. The black color in Figure 1a-d denotes the areas with zero spin, i.e., where polarization is linear.Since the brightness of the blue color in Figure 1a-d is 2-3 times lower than that of the red color, elliptic polarization in the areas of positive spin is closer to circular polarization, whereas the polarization ellipses in the areas of negative spin are more elongated and closer to linear polarization.Nevertheless, the spatial separation of the areas with positive and negative spin at the focus demonstrates the spin Hall effect.
Figure 1i-l (3rd row) confirms theoretical predictions [Equation ( 15)] and demonstrates that the transverse energy flow at the focus plane rotates.On a circle closer to the 15)] and demonstrates that the transverse energy flow at the focus plane rotates.On a circle closer to the optical axis (blue color in Figure 1i-l), P ϕ < 0, i.e., the transverse energy flow is rotating clockwise.On a larger circle radius (red ring in Figure 1i-l), P ϕ > 0, and the energy flow is rotating counterclockwise.The spatial separation of the orbital energy flux in opposite directions is a manifestation of the radial orbital Hall effect at the focus.
Discussion of the Results
Here, we compare the transverse components of the electric field in the initial plane (3) and the focus plane (4).Although in the initial plane, the components E x and E y of the field (3) have the same phase, and thus the field has inhomogeneous linear polarization, at the focus, the transverse components of field (4) acquire a relative phase delay of π/2 or 3π/2.This leads to the areas with elliptic polarization at the focus.On the other hand, the longitudinal SAM component (7) in the initial plane is equal to zero S z = 0, while the energy flow (13) has only one longitudinal component, which is equal to the unit: P z = 1.At the focus plane, the SAM density is given by Equation ( 9), but if the function S z is integrated over the whole focus plane, then it is equal to zero.Thus, the full longitudinal SAM component is conserved and equal to zero.The field (3) in the initial plane has a nonzero density of the longitudinal component of the orbital angular momentum (OAM) vector [37]: If the OAM density ( 17) is integrated over the angle ϕ from 0 to 2π, then the full OAM in the initial plane is nonzero and equal to half of the initial beam power W/2.
At the focus plane, the longitudinal OAM component can also be obtained: According to Equation (18), the OAM density depends on the angle as sin(2m − 1)ϕ.This means that on a certain circle radius with the center on the optical axis, the OAM has (2m − 1) local maxima and minima, similar to the SAM distribution (9).It can be shown that if the OAM density is integrated over the whole focus plane, then this also yields half of the initial beam power W/2.Thus, in this case, the full SAM and OAM are conserved separately.Therefore, it can be concluded that the spin Hall effect at the focus of the beam (3) arises due to the conservation of the full longitudinal SAM of the beam.Since the longitudinal SAM of the whole beam is zero, the areas with the spin of a different sign should arise in pairs.In the same way, the radial orbital Hall effect at the focus occurs due to the conservation of the full longitudinal OAM of the beam.
Summing the SAM (9) and OAM ( 18) densities, we obtain: The comparison of the density of the longitudinal component of the angular momentum (AM) vector in Equation ( 16) with the sum of the longitudinal SAM and OAM components in Equation ( 19) reveals that they are not equal to each other: J z = L z + S z .We considered the reason for this inequality earlier in [37].
In concluding this section, we consider the difference between the Hall effect near the tight focus [18,31,32,37] and the Hall effect which occurs when light is reflected off the interface between two media [22,38].As was shown in [22], when an optical vortex is reflected from a plane glass surface, the annular intensity distribution becomes inhomogeneous.For the optical vortices with the topological charges m and −m, the intensity maxima on the ring appear in different places, i.e., shifted relative to each other (orbital Hall effect).In Ref. [38], it was shown that when a p-polarized Gaussian beam (polarization vector is in the incidence plane) is reflected from the glass surface under an angle close to the Brewster angle, the spin Hall effect occurs when the reflected light is split into two beams with opposite spins in the direction orthogonal to the incidence plane.In the tight focus [18,31,32,37], spin and orbital Hall effects occur due to the conservation of the full angular momentum.The light with opposite spins and/or with opposite energy rotation at the focus plane is concentrated in different places.A different manifestation of the Hall effect at the focus, as investigated in the different works [18,31,32,37], is explainable since the different types of the initial vector fields were considered.In Ref. [18], the focusing of the conventional Poincaré beams was considered, while [31] dealt with the focusing of fractional-order cylindrical vector beams.In Ref. [32], the Hall effect of the cylindrical vector beams of an integer order arises before and beyond the focus, whereas it is absent at the focus itself.In Ref. [37], the Hall effect was studied at the focus of a circularly polarized optical vortex.In contrast to these works, we here studied the tight focusing of a generalized Poincaré beam, whose topological charge is equal to 1/2 and whose order of inhomogeneous linear polarization is equal to m + 1/2.It is impossible to predict in advance, based on the initial light field, whether or not the Hall effect will arise at the tight focus.Thus, each new type of initial vector beam should be considered separately.
Conclusions
Based on the Debye integrals [30], we have investigated both theoretically and numerically generalized (hybrid) Poincaré beams at a tight focus.A generalized Poincaré beam is a coaxial superposition of two optical vortices with left and right circular polarization and with the TC of p and q.For certainty, we studied the case when p = m + 1 and q = −m [Equation ( 3)].Simple analytical expressions have been obtained for the components of the electric and magnetic strength vectors at the focus [Equations (4) and ( 12)], for the intensity distribution [Equation ( 6)], for the longitudinal component of the spin angular momentum [Equation ( 9)], and for the components of the Poynting vector [Equation (14)].It has been shown that the intensity at the focus has 2m − 1 local maxima, residing evenly on a certain circle radius with the center on the optical axis.In addition, radial spin and orbital Hall effects have been demonstrated.This means that the longitudinal SAM component has different signs on circles with different radii in the focal plane, and the azimuthal component of the transverse Poynting vector also has different signs (Figure 1).Such beams can be used for the simultaneous trapping of several micro-or nanoparticles (Figure 1h) into the local intensity maxima that should simultaneously rotate around their centers of mass (Figure 1d) and move along the ring (Figure 1l).In addition, when moving along the ring, the particles will need to overcome the 'breaks' in the intensity distribution (Figure 1h).
Photonics 2023,10, 218 ) (red and blue colors denote positive and negative values), the intensity I (Figure 1e-h) (black and yellow colors denote zero and maximal values), and the angular component of the Poynting vector P ϕ (Figure 1i-l) (red and blue colors denote positive and negative values) of a light beam with polarization (3) of different order m at the tight focus.The beam orders in Figure 1 are m = 1 (Figure 1a,e,i), m = 2 (Figure 1b,f,j), m = 3 (Figure 1c,g,k), and m = 5 (Figure 1d,h,l).The arrows in Figure 1i-l illustrate the directions of the angular energy flow.The scale mark in each figure denotes 1 µm.
) (red and blue colors denote positive and negative values), the intensity I (Figure 1e-h) (black and yellow colors denote zero and maximal values), and the angular component of the Poynting vector Pφ (Figure 1i-l) (red and blue colors denote positive and negative values) of a light beam with polarization (3) of different order m at the tight focus.The beam orders in Figure 1 are m = 1 (Figure 1a,e,i), m = 2 (Figure 1b,f,j), m = 3 (Figure 1c,g,k), and m = 5 (Figure 1d,h,l).The arrows in Figure 1i-l illustrate the directions of the angular energy flow.The scale mark in each figure denotes 1 μm.
Figure 1 .
Figure 1.Distributions of the longitudinal component of the spin angular momentum (a-d) (red and blue color denote, respectively, positive and negative values), intensity (e-h) (black means zero and yellow means maximum), and the angular component of the Poynting vector (i-l) (red means positive and blue means negative values) of a light beam with polarization (3) and with a different order at the tight focus.Arrows (i-l) denote the directions of the angular energy flow.The scale mark in each figure denotes 1 μm.
Figure 1 .
Figure 1.Distributions of the longitudinal component of the spin angular momentum (a-d) (red and blue color denote, respectively, positive and negative values), intensity (e-h) (black means zero and yellow means maximum), and the angular component of the Poynting vector (i-l) (red means positive and blue means negative values) of a light beam with polarization (3) and with a different order at the tight focus.Arrows (i-l) denote the directions of the angular energy flow.The scale mark in each figure denotes 1 µm.
Figure
Figure 1i-l (3rd row) confirms theoretical predictions [Equation(15)] and demonstrates that the transverse energy flow at the focus plane rotates.On a circle closer to the optical axis (blue color in Figure1i-l), P ϕ < 0, i.e., the transverse energy flow is rotating clockwise.On a larger circle radius (red ring in Figure1i-l), P ϕ > 0, and the energy flow is rotating counterclockwise.The spatial separation of the orbital energy flux in opposite directions is a manifestation of the radial orbital Hall effect at the focus. | 6,098.4 | 2023-02-16T00:00:00.000 | [
"Physics"
] |
The GMD-biplot and its application to microbiome data
Exploratory analysis of human microbiome data is often based on dimension-reduced graphical displays derived from similarities based on non-Euclidean distances, such as UniFrac or Bray-Curtis. However, a display of this type, often referred to as the principal coordinate analysis (PCoA) plot, does not reveal which taxa are related to the observed clustering because the configuration of samples is not based on a coordinate system in which both the samples and variables can be represented. The reason is that the PCoA plot is based on the eigen-decomposition of a similarity matrix and not the singular value decomposition (SVD) of the sample-by-abundance matrix. We propose a novel biplot that is based on an extension of the SVD, called the generalized matrix decomposition (GMD), which involves an arbitrary matrix of similarities and the original matrix of variable measures, such as taxon abundances. As in a traditional biplot, points represent the samples and arrows represent the variables. The proposed GMD-biplot is illustrated by analyzing multiple real and simulated data sets which demonstrate that the GMD-biplot provides improved clustering capability and a more meaningful relationship between the arrows and the points.
Introduction
A biplot simultaneously displays, in two dimensions, rows (samples) of a data matrix as points, and columns (variables) as arrows. Based on a matrix decomposition of the data matrix, the biplot is a useful graphical tool for visualizing the structure of large data matrices. It displays a dimension-reduced configuration of samples, as in a PCoA plot, and the variables with respect to the same set of coordinates. If meaningful sample groupings are observed, this allows for visualizing which variables contribute most to the separation. The traditional biplot, as first introduced in [1], displays the first two left and right singular vectors of the singular value decomposition (SVD) of the data matrix as points and arrows, respectively. This biplot, which we hereafter refer to as the SVD-biplot, uses the SVD to find the optimal least-square representation of the data matrix in a low-dimensional space. The SVD-biplot can show Euclidean distances between samples and also display approximate variances and correlations of the variables. It also has the appealing property that the singular values obtained from the SVD are non-increasing, indicating that the decomposition of the total variance of the data matrix into each dimension is non-increasing.
In many scenarios, the Euclidean distance may not be optimal for characterizing dissimilarities between samples. An important example arises in the analysis of microbiome data, in which marker gene sequences (e.g., 16s rRNA) are often grouped into taxonomic categories using bioinformatics pipelines such as QIIME [2] or Mothur [3]. These taxon counts can be summarized into a data matrix with rows and columns representing samples and taxon abundances, respectively. A variety of non-Euclidean distance measures, including nonlinear measures, are then used to quantify the similarity between samples. One common measure of dissimilarity is the UniFrac distance (weighted or unweighted), which is a function of the phylogenetic dissimilarity of a pair of samples [4; 5].
Other non-phylogenetic, non-Euclidean dissimilarities include Jaccard or Bray-Curtis distances (see, e.g., [6] and the references therein). Plotting the samples in the space of the first few principal components (PCs) of the similarity matrix obtained from such non-Euclidean distance matricesoften referred to as principal co-ordinates analysis (PCoA)-may reveal an informative separation between samples. However, the configuration of samples yielded by PCoA only keeps pairwise distances between samples and lacks a coordinate system that relates to the taxa which constitute each sample. Hence, it does not shed any light on which taxa may play a role in this separation.
One approach for addressing this problem is to simply plot an arrow for each taxon based on its correlation with the first two PCs of the non-Euclidean similarity matrix [7]. However, in such a "joint plot" [8], the direction and length of an arrow does not represent the taxon's true contribution to the dissimilarity between samples. In addition, due to the lack of a coordinate system, one cannot add sample points for future observations into this "joint plot".
Three main approaches have been recently proposed to extend the SVD-biplot to more general distances defined on the samples. The R package "ade4" [9] provides a biplot that can handle weighted Euclidean distances but cannot apply to non-Euclidean distances. The second approach, 3 proposed by [10], aims to approximate the non-Euclidean distance by a weighted Euclidean distance.
Weights are estimated for variables and the biplot can subsequently be constructed using weighted least-square approximation of the matrix. This approach has a straightforward interpretation.
However, the estimated weighted Euclidean distance may not capture all the information from the original non-Euclidean distance. A recent proposal in [11] appears to be the first to address the lack of mathematical duality between the samples' locations (points) and the variables' contribution (arrows) to those locations. This approach seeks an approximate SVD-like decomposition of the data matrix, which directly takes the non-Euclidean distance into consideration. This SVD-like decomposition has the following two advantages. First, the left singular vectors are the eigenvectors of the similarity measure derived from the non-Euclidean distance, which preserve the role of the non-Euclidean distance in classifying the samples. Second, an approximate matrix duality (AMD) between the left and right singular vectors is restored, which simply means that each set of vectors can be immediately obtained from the other. To emphasize this connection, we hereafter refer to this decomposition as the AMD. Unfortunately, the AMD also suffers from two drawbacks. First, the AMD is only an approximate decomposition of the data matrix, and hence may not capture all the variation of the original data. In particular, the configuration of samples displayed in an AMDbiplot is independent of the data matrix, since the left singular vectors of the AMD only depend on the non-Euclidean distances. Ignoring the data matrix for classifying samples seems non-intuitive since the data matrix is typically assumed to contain some information on the sample similarities.
Second, the AMD may result in non-decreasing "singular values". While these seem like minor technical issues, the second drawback can have important practical implications: which of the left and right singular vectors should be displayed in the resulting biplot? The authors of [11] suggest constructing the AMD-biplot based on the two left and right singular vectors that correspond to the 4 two largest singular values. This AMD-biplot assures that the arrows for variables are as meaningful as possible, but may fail to reveal meaningful sample clusters if the information of sample clusters is only associated with the first several left singular vectors. An alternative approach may be to simply display the first and second left and right singular vectors of the AMD (as done for the SVD). Unfortunately, this strategy does not solve the problem either: although we may observe meaningful sample clusters, the arrows may not be meaningful due to the small singular values.
There is thus a lack of clarity regarding which singular vectors should be used to construct the AMD-biplot.
The drawbacks of the AMD motivate our proposal which is based on the generalized matrix decomposition (GMD) [12]. The GMD is a direct generalization of the SVD that accounts for structural dependencies among the samples and/or variables. This approach has several advantages.
First, as with the AMD, it directly handles any non-Euclidean distance matrix. Specifically, the full information from that distance matrix is used. Second, unlike the AMD, which provides an approximate decomposition of the data matrix, the GMD provides an exact decomposition of the original data matrix without losing any information. Third, the GMD restores the matrix duality in a mathematically rigorous manner, unlike the approximate matrix duality obtained with the AMD; it naturally extends the duality inherent in the SVD and allows one to plot both the configuration of samples and the contribution of individual variables with respect to a new coordinate system. Fourth, the GMD gives non-increasing GMD values and so the resulting GMD-biplot can be directly constructed based on the first two left and right GMD vectors. Lastly, unlike the AMD-biplot whose sample clusters only depend on the distance, the GMD-biplot uses both the non-Euclidean distance and the data matrix for classifying samples, which more directly connects the contribution of the individual variables to the configuration of samples. Additionally, besides accounting for the non-Euclidean distances between samples, the GMD can also account for auxiliary information on (dis)similarities between the variables.
In the following, we first summarize the GMD-biplot framework and then compare the GMD, AMD and SVD biplots in three numerical studies. We then discuss advantages of the proposed GMD-biplot and further extensions.
Materials and Methods
We denote the data matrix by X ∈ R n×p , where n is the number of samples and p is the number of variables (taxa). We assume that the columns of X are centered to have mean 0 and rank(X) = K ≤ min(n, p). For any matrix M, we denote its i th row (sample) and its (i, j) entry by m i and m ij , respectively. We denote the transpose of M by M T .
Biplot, distance measure and the AMD
A biplot is a graphical method to simultaneously represent, in two dimensions, both the rows (as points) and columns (as arrows) of the matrix X on the same coordinate axes. Given a decomposition of X as X = AB T , a biplot displays two selected columns of A and B. The SVD-biplot is based on the SVD of X, i.e. X = USV T , where U T U = I K , V T V = I K and S = diag(σ 1 , . . . , σ K ) with σ 1 , . . . , σ K being a sequence of non-increasing and positive singular values. Here I K is a rank The SVD-biplot displays the first two columns of US and V, which can explain (σ 2 1 +σ 2 2 )/tr(XX T ) of the total variance of X. The SVD of X is closely related to the eigen-decomposition of the sim-ilarity kernel XX T , as we can write XX T = US 2 U T . Thus, the eigen-decomposition of XX T provides a way to calculate U and S. Once U and S are calculated, one can calculate V from the duality between U and V; that is, VS = X T U. The similarity kernel XX T characterizes the Euclidean distance between samples. To see this, we define the Euclidean squared distance between the i th and j th sample as 1 n is an n × 1 vector of ones. It can then be seen that The AMD addresses this problem by fixing U H and then seeking a matrix V H with orthonormal columns and a diagonal matrix D H with non-negative elements that minimize the objective function
GMD and the GMD-biplot
The concept of the generalized matrix decomposition (GMD) was introduced by Escoufier [13] and further developed in [12]. It is a generalization of the SVD with additional structural dependencies taken into consideration. We briefly review the key ideas behind the GMD. Let H ∈ R n×n and R ∈ R p×p be two positive semi-definite matrices, which, respectively, characterize the similarities between samples and between variables. The H, R-norm of X is defined as X H,R = tr(XRX T H).
For any q ≤ K, the GMD solution ( U, V, S) finds the best rank-q approximation to X with respect to the H, R-norm, that is, subject to U T HU = I q , V T RV = I q and diag(S) ≥ 0. Here, U and V are the left and right GMD vectors, respectively, and S is a diagonal matrix containing the GMD values. Note that U and V are orthogonal with respect to H and R respectively, but they may not be orthogonal with respect to the Euclidean norm unless H = I n and R = I p . The following property of the GMD provides a way to calculate the GMD components; the proof can be found in [13].
Proposition 1: The GMD solutions ( U, V, S) satisfy: Proposition 1(a) suggests that the diagonal elements of S and corresponding columns of U are eigenvalues and corresponding eigenvectors of XRX T H respectively. Proposition 1(b) establishes the duality between U and V, meaning that V can be immediately obtained given U and S.
Alternatively, an efficient algorithm for finding the solution to Eq. (1) was proposed in [12], which 8 is less computationally intensive compared to finding the eigenvalues and eigenvectors of XRX T H.
The algorithm also ensures that the diagonal elements of S are non-increasing.
Note that the GMD can handle the non-Euclidean similarity kernel H just by taking R = I p .
Based on the GMD of X with respect to H, the GMD-biplot can be constructed with respect to the coordinate system provided by the first two columns of V. More specifically, letting v j be the j-th column of V, the i-th sample point can be configured by the coordinates of x i , given To plot the arrow for the j-th variable, we consider the vector e j ∈ R p , which has a 1 in the j-th element and 0's elsewhere. Then, the arrow for the j-th variable can be configured by the coordinates of e j , given by (e T j v 1 , e T j v 2 ). This coordinate system also allows the configuration of future samples. Letting x * ∈ R p be a future sample, we can add x * into the GMD-biplot as a point located at ( . Similar to the SVD-biplot, the GMD-biplot can explain (σ 2 1 +σ 2 2 )/tr(XX T H) of the total variance of X with respect to the H, I p norm, whereσ k is the k-th diagonal element of S for k = 1, 2.
Since the GMD values are non-increasing, for the purpose of constructing the GMD-biplot, we can choose q = 2 in the GMD problem (Eq. (1)), which may save considerable computational time.
In contrast, since the AMD may produce non-decreasing "singular values", we have to find the full decomposition of X by the AMD before deciding which singular vectors to plot in the AMD-biplot; this may become computationally intensive for large n and p.
Results
In the results below, we compare the GMD, AMD and SVD biplots on three data sets in the manner that each has been proposed recently for microbiome data. In particular, in [11], the AMD-biplot is advocated specifically for relative abundance data, while in [14] the SVD-biplot is advocated for data that have been scaled by the centered log-ratio (CLR) transformation. The GMD-biplot is constructed using the CLR-transformed data. We first examine the performance of all biplots using the smokeless tobacco data set explored in [11]. In the second study, we compare their performances using the human gut microbiome data from [15]. In the third analysis, we simulate a data set based on the smokeless tobacco data to illustrate a dilemma that the AMD-biplot may face.
Analysis of the smokeless tobacco data
Since H is not positive semi-definite, we enforced it to be positive semi-definite by removing its negative eigenvalues and corresponding eigenvectors. The resulting similarity kernel, denoted H * , has rank 27.
For the GMD-biplot, we consider the CLR transformation of X. Specifically, denoting the geometric mean of a vector z by g(z) = ( p k=1 z k ) 1/p , the CLR transformation of x i ; i = 1, . . . , 45 is given by We denote the resulting data matrix by X = ( x 1 , . . . , x 45 ) T . For the AMD-biplot, we converted each row of X into the empirical frequencies, and further centered the rows and columns to have mean 0, as done in [11]. We denote the resulting data matrix byX.
We constructed the GMD-biplot and the AMD-biplot based on H * using X andX, respectively. Fig. 1(d) displays the proportion of variance captured by each GMD component. It can be seen that the first two GMD components capture more than 80% of the total variance of X, which assures that the resulting GMD-biplot ( Fig. 1(a)) visualizes the data well. As shown in Fig. 1(a), the GMD-biplot is perfectly successful at separating the different tobacco products (dry, moist and toombak). Furthermore, the replicates corresponding to the same product are tightly clustered.
By examining the arrows for taxa in Fig. 1(a), we see that moist samples may be characterized by elevated levels of alloiococcus and halophilus, while aerococcaceae appears elevated in toomback samples. Fig. 1(e), which is the same as the right bottom panel of Fig. 1 in [11], shows that the AMD singular values are not necessarily decreasing. It should be noted that Fig. 1(b) is slightly different from Fig. 3 in [11]; this difference may be due to the use of H * here as opposed to H in [11]. This is because we wanted the the AMD-biplot to be directly comparable to the GMD-biplot since the GMD requires both H and R to be positive semi-definite. From Fig. 1(b), it can be seen that the AMD successfully separates toombak samples (purple points) from dry (blue) and moist (orange) snuffs, although the separation between dry and moist snuffs in the AMD-biplot is not as definitive as that in the GMD-biplot ( Fig. 1(a)).
Additionally, we included the SVD-biplot and its corresponding scree plot in Fig.1 (c) and (f) respectively. As the SVD-biplot assumes the Euclidean distance between samples, it is more appropriate to construct the SVD-biplot using the CLR transformed data X than the relative abundance dataX [14]. It can be seen from Fig. 1(c) and toombak), and obtained p−values representing the strength of association between each taxon and the tobacco groups. We then sorted these p−values in a non-decreasing order, and obtained the rank of each taxon based on the sorted p−values. Hence, it is desirable that the taxa with the lowest ranks can be identified by the biplots. Table S1 summarizes the ranks of the top 10 taxa identified by each biplot. It can be seen that the top 10 taxa identified by the GMD-biplot have lower ranks on average than those identified by the AMD and SVD biplots, indicating that the GMD-biplot may identify more meaningful taxa with respect to the separation of the samples than the AMD and SVD biplots.
Analysis of human gut microbiome data
We consider the human gut microbiome data collected in a study of healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas [15]. The original data set X consists of counts for 149 taxa for 100 samples. The squared unweighted UniFrac distance matrix ∆ ∈ R 100×100 , computed using the R package phyloseq [16], was used to measure the distance between samples. Here, the distance between two samples is based entirely on the number of branches they share on a phylogenetic tree. The distance hence accounts only for the presence/absence of each taxon (not its abundance). The corresponding similarity kernel H was then derived as H = − 1 2 J ∆J , which is a positive semi-definite matrix with rank 99. Let X anď X, respectively, denote the CLR transformed data and the relative abundance data. Similar to the first study, the GMD-biplot and the AMD-biplot were constructed based on the similarity kernel H using X andX respectively, and the SVD-biplot was constructed based on the SVD of X.
As concluded in [15], shared features of the functional maturation of the gut microbiome are identified during the first three years of life. We thus define a binary outcome h i based on the age of each sample as: for i = 1, . . . , 100. Approximately 70% of the samples are assigned to group 0 and the remaining 30% are assigned to group 1.
In all biplots, the i th sample is colored by age i and symbolized by h i . Fig. 2(d) indicates that more than 80% of the total variance is explained by the GMD-biplot in Fig. 2(a), which provides a good visualization of sample clusters across age. By examining the relationship between the arrows and the color of the sample points in Fig. 2(a), we see that prevotella may be elevated in adults, while parabacteroides appears to be elevated in infants. In contrast, Fig. 2(e) shows that less than 15% of the total variance is explained by the AMD-biplot in Fig. 2(b) and the AMD values are not decreasing. As shown in Fig. 2(b), the AMD-biplot also displays potential clusters across age, but the sample points are not as tightly clustered as those in Fig. 2(a). Odoribacter appears to be elevated in adults in Fig. 2(b), while lactobacillus appears associated with infants. As a reference, Fig. 2(c) shows the SVD-biplot of X, which looks very similar to Fig. 2(a).
To further quantify the classification accuracy, for each biplot we predicted the probability that each sample belongs to group 1 based on leave-one-out cross validation using the binary logistic regression of the group index h i on the two selected components. We then plotted an ROC curve for each biplot based on the predicted probabilities (Fig. S1) and calculated the area under the ROC curve (AUC): the GMD, AMD and SVD biplots, respectively, yield an AUC of 0.989, 0.976 and 0.990. The AUC results indicate that the GMD-biplot provides a better separation of age groups than the AMD-biplot, but there is not a clear difference between the GMD-biplot and the SVDbiplot. This may be because, for the CLR-transformed data X, the unweighted UniFrac distance is not as informative with respect to age as the weighted UniFrac distance was in the tobacco data with respect to product groups.
We emphasize that both the GMD-biplot and the SVD-biplot identify prevotella and parabacteroides as top taxa, while the AMD-biplot identifies completely different ones. As [15] confirms that the trade-off between prevotella and bacteroids (including parabacteroides) considerably drives the variation of microbiome abundance in adults and children between 0.6 and 1 year of age in all studied populations, the GMD and SVD biplots may thus identify more biologically meaningful taxa than the AMD-biplot. It should, however, be noted that these bacterial are "identified" based on circumstantial, not statistical, evidence, and more work is needed to examine statistical associations in this context.
Incorporating a kernel for variables into the GMD-biplot
The GMD problem defined in Eq. (1) allows not only the similarity kernel for samples, but also a kernel for the variables. Including both kernels may further improve the accuracy of sample classification as well as the identification of important variables. We illustrate this advantage by incorporating a kernel for variables in the analysis of the human gut microbiome data. More specifically, we first calculate a matrix ∆ R ∈ R 149×149 of squared patristic distances between the tips of the phylogenetic tree for each pair of taxa and then derive a similarity matrix R as Fig. 3(a) shows the GMD-biplot with the additional kernel R incorporated. The ROC analysis based on the leave-one-out cross validation for Fig. 3(a) yields an AUC of 0.984, which is higher than that of the AMD-biplot (Fig. 2(b)) but slightly lower than Fig. 2(a) and Fig. 2(c). This may be because both H and R highly depend on the phylogenetic tree. Thus, incorporating R may be redundant and may reduce the accuracy of the sample clustering in this case. The top 3 taxa identified in Fig. 3(a) include prevotella but not parabacteroides, which may explain the lower clustering accuracy.
Including an additional kernel for variables in the GMD-biplot is related to method of double principal coordinate analysis (DPCoA) [17]. DPCoA, as shown in [18], is equivalent to a generalized PCA which essentially incorporates an additional similarity kernel for variables into the analysis, as described in Proposition 1, but for H = I n . As suggested in [19], DPCoA can provide a biplot representation of both samples and meaningful taxonomic categories. Hence, the GMD-biplot can also be viewed as an extension of DPCoA biplots because the GMD allows kernels for both samples and variables, while DPCoA only allows a kernel for variables.
Simulation
In this section, we conduct a simulation study based on the smokeless tobacco data to illustrate a scenario in which the AMD-biplot may fail to separate the samples, whereas the GMD-biplot performs well. Let H * and X be the similarity kernel and data matrix from the smokeless tobacco data, respectively. We consider the eigen-decomposition of H * as H * = BΛB T : B is a 45 × 27 matrix whose columns are eigenvectors of H * and Λ = diag(λ 1 , . . . , λ 27 ) is a diagonal matrix whose elements are the eigenvalue of H * . Then, the AMD-biplot is based on the following approximated orthogonal decomposition of X: where D = diag(d 1 , . . . , d 27 ) and V is a 271 × 27 matrix with orthonormal columns. As shown in Fig. 2(d), d 1 , . . . , d 27 may not be decreasing. For j = 1, . . . , 27, we define and construct the simulated data set X S as X S = BD S V T , where D S = diag(d 1,S , . . . , d 27,S ). For i = 1, . . . , 45, we define a binary outcome w i that indicates the group index of the i th sample as: The GMD-biplot and the AMD-biplot of X S with similarity measure H * are presented in Fig. 4(a) and 4(b), respectively. It can be seen that the two groups are completely mixed up in the AMDbiplot because the first column of B is not selected for visualization. In contrast, the GMD-biplot successfully visualizes the sample groups by displaying the first and second GMD components.
To see why this occurs, we summarize the first three diagonal elements of Λ, D S and D 2 S Λ in Table 1 and notice that d 1,S < d 2,S < d 3,S . Consequently, the AMD-biplot displays the second and third columns of BD S , and hence it completely fails to classify the samples because the group index w i only depends on the first column of B. In contrast, Proposition 1(a) shows that the GMD-biplot is based on the two largest eigenvalues and the corresponding eigenvectors of X S X T S H * . It can be further seen that Eq.
(3) implies that the diagonal elements of D 2 S Λ are the eigenvalues of X S X T S H * and columns of B are the corresponding eigenvectors. Hence, it can be seen from Table 1 that d 2 1,S λ 1 > d 2 2,S λ 2 > d 2 3,S λ 3 , even though d 1,S < d 2,S < d 3,S . Therefore, the GMD-biplot displays the first and second column of BD S Λ 1/2 as sample points, which successfully captures sample classifications.
Discussion
Biplots have gained popularity in the exploratory analysis of high-dimensional microbiome data.
The traditional SVD-biplot is based on Euclidean distances between samples and cannot be directly applied when more general dissimilarities are used. Since Euclidean distances may not lead to an optimal low-dimensional representation of the samples, we have extended the concept of the SVD-biplot to allow for more general similarity kernels. The phylogenetically informed UniFrac distance, used in our examples, defines one such kernel. In settings where a general (possibly nonlinear) distance matrix is appropriate, our approach provides a mathematically rigorous and computationally efficient method, based on the GMD, that allows for plotting both the samples and variables with respect to the same coordinate system.
Our first data example with the smokeless tobacco data set from [11] demonstrates the merits of the proposed GMD-biplot. We found that the GMD-biplot successfully displays different types of products, while the AMD-biplot is not able to completely separate dry and moist snuffs and the SVD-biplot fails to separate moist and toombak samples. As shown in Table S1, the GMD-biplot is also able to identify biologically more meaningful taxa that are related to the different types of products, compared to the AMD-biplot and the SVD-biplot.
In In practice, we typically do not know what the true configuration of samples look like, so it is impossible to determine whether H or XX T contains more information about sample clusters. Also, it is sensible to assume that XX T and H are "co-informative" in the sense that they exhibit a shared eigenstructure; for instance, both may be informative for clustering samples. The co-informativeness can be quantified precisely using the Hilbert-Schmidt information criteria (HSIC) [20]. For any two kernels K 1 and K 2 , the empirical HSIC is proportional to tr(K 1 K 2 ). Hence, by definition, the GMD problem (1) is equivalent to minimizing the HSIC between X − USV T X − USV T T and H over U, S and V. In other words, if we consider X − USV T as the residual matrix of X, then the GMD solutions can be interpreted as the best approximation to X in the sense that the HSIC between H and the Euclidean kernel of the residual matrix is minimized. Thus, the GMD-biplot considers the co-informativeness of XX T and H. Therefore, in many cases it would be a more robust way to display the sample points compared to the AMD-biplot or the SVD-biplot. Another advantage of the GMD-biplot over the AMD-biplot is illustrated in our simulation study. Since the AMD may not give decreasing singular values, the AMD-biplot may not be able to display the most informative eigenvectors of H, and may thus fail to cluster the samples. In contrast, the GMD assures that the resulting singular values are non-increasing.
Our discussion in this paper has focused on the form biplot, which aims to visualize the relationship between variables and the sample clustering. In other scenarios, where the variation of the data matrix explained by each variable is of particular interest, the covariance biplot may be more appropriate. This biplot considers the GMD of X with respect to H; i.e. X = USV T , where U T HU = I q and V T V = I q . Note that
Supplemental Material
Table S1 Ranks of the top 10 taxa identified by the GMD, AMD and SVD biplot in the analysis of smoke tobacco data. both biplots display the top 6 taxa with the longest arrows. The samples points are colored by the group index (1 = "red"; 0 = "black"). | 7,231 | 2019-10-22T00:00:00.000 | [
"Biology",
"Computer Science"
] |
A key driver to promote HCC: Cellular crosstalk in tumor microenvironment
Liver cancer is the third greatest cause of cancer-related mortality, which of the major pathological type is hepatocellular carcinoma (HCC) accounting for more than 90%. HCC is characterized by high mortality and is predisposed to metastasis and relapse, leading to a low five-year survival rate and poor clinical prognosis. Numerous crosstalk among tumor parenchymal cells, anti-tumor cells, stroma cells, and immunosuppressive cells contributes to the immunosuppressive tumor microenvironment (TME), in which the function and frequency of anti-tumor cells are reduced with that of associated pro-tumor cells increasing, accordingly resulting in tumor malignant progression. Indeed, sorting out and understanding the signaling pathways and molecular mechanisms of cellular crosstalk in TME is crucial to discover more key targets and specific biomarkers, so that develop more efficient methods for early diagnosis and individualized treatment of liver cancer. This piece of writing offers insight into the recent advances in HCC-TME and reviews various mechanisms that promote HCC malignant progression from the perspective of mutual crosstalk among different types of cells in TME, aiming to assist in identifying the possible research directions and methods in the future for discovering new targets that could prevent HCC malignant progression.
Introduction
Liver cancer is the third greatest cause of cancer-related mortality (1). The most common pathological type of primary liver cancer is HCC, which accounts for more than 90% (2). HCC is characterized by high morbidity and mortality and has been a heavy burden for the public health system worldwide (3). Moreover, due to the fact that HCC is predisposed to metastasize, reappear and occur resistant to treatment, the benefits from conventional therapies such as surgical resection, radiofrequency ablation as well as transarterial chemoembolization are limited (4). It is involved in multiple mechanisms ranging from gene mutation and epigenetic alterations to complex cellular crosstalk and signaling pathways which cause abnormal accumulation and function of certain molecules and cells in tumor tissue and ultimately result in HCC malignant progression. Several clinical trials have verified that many kinds of tyrosine kinase inhibitors (TKIs) such as sorafenib and cabozantinib provide survival benefits in HCC patients (5)(6)(7) and that several immune checkpoint inhibitors (ICIs) such as nivolumab and pembrolizumab have potential for advanced HCC therapy (8,9). Moreover, the immunotherapy combining ICIs with other treatments such as kinase inhibitors, anti-angiogenic drugs shows great prospects in the treatment of HCC (10). Nevertheless, the proportion of HCC patients responding to them is very low because of the high genetic, epigenetic heterogeneities and the formation of immunosuppressive TME (11,12). Hence, HCC is still a highly fatal tumor as it is very predisposed to frequent recurrence and distant metastasis after surgery (13). TME is the microsystem that supports tumor cells survival and tumor progression and is always regulated by cellular metabolism, genetic and epigenetic factors. Besides tumor cells, the HCC-TME consists of adaptive and innate immune cells, stromal cells, and liver sinusoidal endothelial cells (LSECs) (Figure 1) as well as noncellular components such as cytokines and signaling proteins secreted by the cells above. Immunosuppressive innate immune cells include tumor-associated macrophages (TAMs), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs). Stromal cells are mainly hepatic stellate cells (HSCs), the primary source of cancer-associated fibroblasts (CAFs). Fibrotic microenvironment in liver is prone to developing into HCC (14). More than 80% of HCC results from extensive liver fibrosis caused by mass CAFs which has been widely reported to be closely related to HCC malignant progression (15). HSCs secrete collagen fibers as well as various components of extracellular matrix (ECM) after being stimulated, strongly contributing to liver fibrosis (16). CAFs-derived soluble factors and exosomes affect cancer cells directly and CAFs can also remodel TME or ECM to regulate HCC progression indirectly (17). TME is an important window to help us acquaint the mechanism of tumor development and people pay more attention to the studies concerning that the changes of TME promote HCC malignant progression, aiming to discover the most effective therapeutic method to prevent it. Many studies have demonstrated that metabolic alterations about modifying the TME are mainly responsible for the development of resistance to ICIs (18). Intricate cellular crosstalk caused by cellular and non-cellular components in TME contributes to the formation of immunosuppressive TME, promotes tumor cells epithelial-mesenchymal transition (EMT), and increases their resistance to TKIs and ICIs. So cellular crosstalk is a key driver that promotes HCC malignant progression and ultimately leads to poorer clinical prognosis and lower survival rate. Clearly outlining the network of cellular crosstalk in TME will assist in identifying the possible research directions and methods in the future for developing targeted agents with higher efficacy and fewer side effects and designing reasonable schemes of multi-target combination treatments. Based on the above, we reviewed the recent studies about specific mechanisms of HCC malignant progression from the perspective of mutual crosstalk among different types of cells in TME.
The accumulation and function of dendritic cells
One of the most important causes of tumor immune evasion is attenuated antigen-presenting ability of antigen-presenting cells (APCs). Dendritic cells (DCs) are the most functional and professional APCs in human body, which can present tumorassociated antigens (TAAs) and activate initial T lymphocytes and then activate the specific antitumor immune responses of the effector T cells (19). Depending on the developmental lineage and differentiation, DC populations exhibit significant variation (20). Conventional DCs (cDCs) play an important role in anti-tumor immunity due to their capacity to present TAAs and release cytokines that modulate T cells survival and effector function. The two types of cDCs-previously known as myeloid DCs-are CD141 + /CD14type 1 cDCs (cDC1s) and CD1c + /CD14type 2 cDCs (cDC2s). The cDC1s are essential for the cross-presentation and activation of CD8 + T cells (21). Intratumoral cDC1s recruit T cells, activate and grow tumor-specific CD8 + T cells, and enhance T cells effector activity by secreting interleukin (IL)-12 (22)(23)(24). The cDC2s are the most common DC type in the human liver, which work by priming T helper (Th) cells to polarize toward Th2 or Th17 and promoting humoral immunity (21,25). However, the reduction of accumulation and antigen-presenting ability of DCs resulting from crosstalk among cells in HCC TME cannot effectively activate antitumor immune responses, which is one of the important mechanisms causing HCC malignant progression ( Figure 2). required for early intratumoral cDC1s accumulation and antitumor immunity, however, tumor-derived prostaglandin E2 (PGE2) can disrupt the NK-DC axis (26). In the mouse tumor model, it was found that PGE2 not only inhibited NK cells from secreting chemokines but also induced downregulated expression of chemokine receptors on cDC1, which limited the accumulation of cDC1 in tumor tissues and failed to activate sufficient anti-tumor immune responses, ultimately leading to tumor immune escape (26). However, whether the same mechanism exists in human HCC remains to be further verified, and the specific molecular mechanisms by which tumor-derived PGE2 interacts with NK cells or cDC1s also need to be further explored. In addition, hypoxia induces the production of hypoxia-inducible factor (HIF)-1, a protein that contributes to the heterogeneity of the TME and is linked to the evolution of malignancy in HCC (27). And the expression of the innate immune checkpoint CD47 molecule is regulated by HIF-1a (28). Commonly overexpressed in cancer cells, CD47 is known as a protein that transmits "do not eat me" signals, preventing phagocytosis by DCs and macrophages through the interaction with the signal regulatory protein (SIRP) (29). In addition, Shuai Wang et al. found that CD47 upregulation coincided with reduced CD103 + DC and NK cell counts and was linked to a poor prognosis (30). Consistently, the blockage of CD47 increased NK cell activation and recruitment in an orthotopic liver tumor model because of the secretion of chemokine (C-X-C motif) ligand (CXCL) 9 and IL-12 by CD103 + DCs and this effect was reversed by CD103 + DC depletion (Batf3-/-mice) and IL-12 blocking in vivo (30). CD47 may partly explain HCC immune evasion and is a promising therapeutic target.
Moreover, tumor cells-derived IL-10 and IL-6, transforming growth factor (TGF)-b as well as vascular endothelial growth factor (VEGF) prevent DCs maturation, showing a tolerant phenotype with downregulated expression of costimulatory molecules (31). VEGF, TGF-b, and alpha-fetoprotein (AFP) were discovered in the culture supernatant of Hepa1-6-1 expressing higher adhesion molecules, and the culture supernatant significantly suppressed the expression of CD86, CD80, and CD40 on DCs, especially CD86 (32). The crosspresenting capacities and immunomodulatory functions of these tolerogenic DCs with downregulated costimulatory molecules are impaired, failing to effectively activate effector T cells and thus resulting in tumor immune escape. Although the specific molecular mechanisms and signaling pathways that tumor-derived cytokines and growth factors induce downregulated expression of costimulatory molecules on DCs are currently unknown, it is undeniable that reversing tolerance DCs to functional DCs is indeed a potential immunotherapy approach.
Tregs-induced DCs inhibition
Tregs can inhibit immune responses and are always as targets for the treatment of infectious diseases, autoimmune diseases, and cancers (33). Human leukocyte antigen-DR isotype (HLA-DR) expressed on the surface of cDC2 is a key antigen-presenting molecule for activating antitumor effector T cells. However, the level of HLA-DR on cDC2 significantly decreases in hypoxic HCC-TME, which impairs the antigen-presenting capacity of cDC2. Tumor tissue-derived cytokines such as CXCL5 and CCL2 make Tregs and cDC2s enrichment in hypoxic tumor tissue, and it is reported that direct interaction between Treg and cDC2 mediates the loss of HLA-DR on cDC2 (34). Notably, HLA-DR + Tregs increased significantly along with the downregulation of HLA-DR on cDC2 surface and the levels of HLA-DR gene expression in both Treg and cDC2 were unchanged, which suggests that Tregs physically extract and ingest HLA-DR from cDC2s under hypoxia. Moreover, it has been demonstrated that these HLA-DR + Tregs exhibit stronger immunosuppressive activity than HLA-DR -Tregs in cervical carcinoma (35). Tregs-mediated downregulation of HLA-DR on cDC2 is a potential immunotherapeutic target for hypoxic HCC, and the antitumor effects of combination with other immunotherapies such as ICIs are expected.
According to studies, Tregs can directly downregulate costimulatory molecules CD80 and CD86 expression or prevent the upregulation of CD80 and CD86 on DC during DC maturation (36, 37), thus weakening the antigen-presenting ability of the DCs. The tumor cells or Tregs interacting with DCs limit the accumulation of DCs, prevent DCs maturation and attenuate their function, which hinders the initiation of effectively anti-tumor immunity.
The co-suppressive molecule cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), a transmembrane receptor on T cells, negatively regulates immune responses (38). The effect of CTLA-4 is achieved in part by competing for CD80 and CD86 mainly expressed on APCs with costimulatory molecules CD28 expressed on effector T cells to suppress antitumor immunity. It was previously found that Tregs could downregulate CD80 and CD86 by trans-endocytosis to downregulate their expression on DCs (39). Recent studies have shown that Tregs lacking CTLA-4's extracellular fraction can also inhibit DCs expressing CD80 and CD86 and that extracellular CTLA-4 function is not crucial for downregulating CD86 and CD80 expression but essential for upregulating the expression of co-inhibitory receptor programmed cell death-ligand 2 (PD-L2) on DCs (40). This novel mechanism of Tregs-mediated DCs inhibition facilitates the discovery of new therapeutic methods to enhance antitumor immunity in HCC.
As intratumoral cDCs are also essential for T cell-based therapies, the low frequency and function of intratumoral cDCs may be partly responsible for the low response rate to ICIs in cancer patients. Hence, increasing the frequency and function of intratumoral DCs is the first step to trigger effective anti-tumor immunity and is probably feasible to combine with other ICIs for HCC treatment. Moreover, it is reported that DCs infiltration might predict the response to camrelizumab and apatinib and tumor recurrence in patients with resectable HCC (41).
The infiltration and antitumor effects of T cells CD8 + T cells are major lymphocyte subtypes infiltrated in TME and extremely important effector T cells in antitumor immunity. A growing body of research suggested that the downregulation of CD8 + T cell activity related to the development of HCC and that patients with HCC may benefit from robust CD8 + T cell responses (42,43). Activated CD8 + T cells destroy tumor cells by releasing massive granzyme, perforin as well as tumor necrosis factor (TNF). Whereas the crosstalk among tumor cells, immunosuppressive cells, and T cells inhibit the infiltration and antitumor immune effects of effector T cells in tumor tissue, which contributes to tumor cells immune evasion and eventually leads to malignant progression of HCC ( Figure 3).
Cancer cells-induced T cells inhibition
Tumor cells occurring specific genetic mutations can reduce infiltration of CD8 + T cells in TME through certain signaling pathways. For example, phosphatase and tensin homolog on chromosome ten (PTEN) inhibits the activation of PI3K signaling, and downregulation or deletion of PTEN leads to increasing PI3K-AKT pathway activity in multiple cancers, including HCC, thus accelerating tumor malignant progression (44,45). Studies have found that PTEN loss in cancer cells suppressed the antitumor effect of CD8 + T cells and reduced T cell transport to tumors in preclinical models of melanoma and was associated with reduced T cell infiltration at tumor tissue in patients (46). Reportedly, PTEN downregulation in the HCC mouse model reduced CD8 + T cells infiltration in tumor tissue, along with increasing immunosuppressive Foxp3 + CD4 + CD25 + Tregs and upregulating PD-L1 expression on tumor cells. Hence, recovering PTEN expression level in cancer cells can increase the infiltration degree and anti-tumor immune responses of CD8 + T cells and reverse immunosuppressive TME.
Tumor-derived exosomes(TDEs) with PD-L1 on them inhibit CD8 + T cells from proliferating and activating (47,48). Lymphocyte function-related antigen-1 (LFA-1) is one crucial integrin on T cells, FIGURE 3 Crosstalk effects of other cells on effector T cells directly or indirectly suppress their infiltration, activation, proliferation and differentiation, which impairs anti-tumor immune effect of T cells and contributes to HCC malignant progression. whose major ligand is intercellular adhesion molecule-1 (ICAM-1) (49). LFA-1 plays a crucial function in effector T cells destroying tumor cells by binding to related ligands expressed on tumor cells (50). ICAM-1 is present on tumor cell-derived exosomes as well, which can bind to leukocytes, thus preventing them from adhering to activated endothelial cells (51). Interferon (IFN)-g upregulates ICAM-1 expression on tumor cells and ICAM-1 on TDEs mediates T cell inhibition principally by interacting with activated LFA-1 on CD8 + T cells (52). A previous study suggested that PD-L1 is of importance for TDEs-mediated CD8 + T cells suppression (53). Nevertheless, there is a significantly reduced interaction between T cells and TDEs via PD-L1/PD-1 with the absence of ICAM-1, which indicates that the adhesion between tumor-derived extracellular vesicles (TEVs) and T cells mediated by ICAM-1/LFA-1 is a precondition for PD-1/PD-L1-mediated immunosuppression (52). Therefore, targeting TEV-derived ICAM-1 can improve the immune system of cancer patients and has the potential to greatly improve the efficacy of antitumor treatment. This mechanism exists in both melanoma and colon cancer models, while it requires further validation whether a similar mechanism exists in human HCC. Moreover, further analysis of TDEs is essential for understanding their protumor mechanisms and may contribute to developing TDEs-based therapeutic strategies.
In addition, tumor-repopulating cells (TRCs) are reported to promote programmed cell death-1 (PD-1) expression on CD8 + T cells via transcellular kynurenine (Kyn)-aryl hydrocarbon receptor (AhR) signaling (54). Mechanically, TRCs are stimulated by interferon (INF)-g to produce and secret more Kyn, the latter gets into neighboring CD8 + T cells and activates AhR, consequently resulting in the upregulation of PD-1. Blockading Kyn-AhR pathway improves the antitumor effectiveness of adoptive T cell therapies.
MDSCs-mediated T cells inhibition
MDSCs, a group of immature cells with high heterogeneity, originate from bone marrow and synthesize and secrete large amounts of immunosuppressive factors, playing crucial roles in suppressing antitumor immunity (55). Tumor-derived granulocytecolony stimulating factor (G-CSF), IL-6, VEGF, and CCL2 cause MDSCs migration to HCC-TME (56). Two enzymes inducible nitric oxide synthase (iNOS) and arginase 1 (ARG1) are highly expressed in MDSCs and they cause the depletion of L-arginine, a conditionally essential amino acid related to T cells proliferation and differentiation (57,58). Mechanically, L-arginine deficiency decreases the levels of CD3 z-chain indispensable for the assemble and stabilization of the TCR-CD3 complex on T cells, which weakens the antigen-recognition capability of T cells, as well as TAA-specific immune responses (59). In addition, arginine starvation impairs the formation of immune synapses between T cells and APCs through hindering the dephosphorylation of actinbinding protein cofilin (60). In general, arginine deprivation is one of the main mechanisms for MDSCs promoting HCC malignant progression.
A disintegrin and metalloprotease 17 (ADAM17), a membrane molecule expressed on MDSCs, prevents T cells from homing and being activated via interacting with L-selectin (CD62L) on T cells (61). Moreover, MDSCs express galectin(GAL)-9 which can interact with T cell immunoglobulin and mucin domain 3 (TIM-3) and consequently induce T cells apoptosis (62). MDSCs hamper the antitumor immunity of effector T cells through various methods, and controlling the expression of these MDSC-derived factors may greatly improve the antitumor immune responses and effects in HCC patients.
CD8 + T cell repression by other cells
Innate lymphoid cells (ILCs) are a newly discovered family of immune cells that have similar cytokine-secreting profiles as T helper cell subsets and that are critical for host defense against infections and tissue homeostasis. It has been demonstrated that ILC3 lacking the natural cytotoxicity-triggering receptor (NCR -ILC3) promoted the development of HCC in response to IL-23 highly expressed in HCC patients and associated with poor clinical outcomes. Furthermore, NCR -ILC3 directly induced CD8 + T cell apoptosis and limited their proliferation by secreting IL-17 upon IL-23 stimulation (63).
A recent study has revealed that CD11b + F4/80 + macrophages in the liver metastatic TME are key drivers for inducing CD8 + T cells apoptosis through Fas-FasL pathway (64). Whether similar mechanisms exist in primary liver cancer such as human HCC needs to be further explored. Reportedly, depleting FasL + CD11b + F4/80 + macrophages by using anti-CSF-1R is prospective but its clinical efficacy for immunomodulatory systemic therapies has not been demonstrated (65). The M2polarization of macrophages induced by the CCL2 can suppress the proliferation of antitumor CD8 + T cells by secreting various cytokines, such as G-CSF, IL-6 and macrophage inflammatory proteins-2 (MIP-2) (66). Prospective studies are desperately needed to identify more reasonable strategies for combinatorial treatment to bypass hepatic resistance and improve the efficacy of systemic immunotherapy.
The research about the cell transplantation model established in immunocompetent mice suggests that HSCs prevent T cell infiltration in tumors (67). Furthermore, activated HSCs reduce responsiveness and cytotoxicity of T cells and increase apoptosis of them in vivo (68). Tregs have immunosuppressive activity and play key roles not only in maintaining body immune homeostasis but also in exhaustion of T cells and immune escape of HCC cells. As a kind of anti-inflammatory cells, Tregs can inhibit the response of T cells by producing IL-6, IL-17 and are connected with the poor prognosis of patients with HCC (69). Moreover, increased regulatory DCs induced by CAFs impaired T cell proliferation and promoted Treg expansion via indoleamine 2,3-dioxygenase (IDO) (70).
Previous study shows that circulating antigens are captured and cross-presented by LSECs, which contributes to CD8 + T cell tolerance rather than immunity (71). Subsequently, it is found that circulating carcinoembryonic antigen (CEA) was preferentially taken up in a mannose receptor-dependent manner and cross-presented by LSECs, but not DCs, to CD8 + T cells, which promoted the tolerization of CEA-specific CD8 + T cells in the endogenous T cell repertoire through the coinhibitory molecule B7-H1 (72). Moreover, a recent research demonstrates that overexpression of PD-L1 on LSECs inhibits the activation of CD8 + T cells and leads to immune evasion of HCC and poor prognosis (73).
In TME, complex cellular crosstalk leads to depletion and exhaustion of effector T cells, which weakens the effect of T cellbased therapies, such as ICI, chimeric antigen receptor T-cell immunotherapy. Hence, blocking various factors leading to T cell depletion and exhaustion is a prerequisite for effective treatment of HCC with other therapeutic methods.
Crosstalk with HCC cells
HSCs are the main source of CAFs, which are crucial for HCC tumor development, metastasis, and treatment resistance (74,75). It has been demonstrated that HSCs could be induced to transform to CAFs by HCC cells-derived exosomal miRNA-21 activating PDK1/ AKT signaling that directly targets PTEN in HSCs (76). Furthermore, activated CAFs in turn promoted HCC malignant progression by secreting VEGF, matrix metalloproteinases (MMP) 2, MMP9, TGF-b and basic fibroblast growth factor (bFGF) (76). Similarly, exosomal miR-1247-3p derived from high-metastatic HCC cells (HMHs) in the lung metastatic niche reportedly triggered and stimulated b1-integrin/NF-kB signaling pathway in fibroblasts by directly targeting beta 1,4-galactosyltransferase, polypeptide 3 (B4GALT3) and activated CAFs in turn accelerated the development of HCC via producing IL-6 and IL-8 (77). A recent study reported that the palmitoylation of hexokinase 1 (HK1) is induced in HSCs after stimulated by TGF-b, thus more HK1 is secreted by forming large extracellular vesicles, which can be absorbed by HCC cells, causing enhanced glycolysis and HCC development (78).
It is found that the upregulation of connective tissue growth factor (CTGF), a matricellular protein secreted by hepatoma cells could activate nearby LX-2 cells (HSC line) and that the activated LX-2 cells promoted HCC cells proliferation by secreting IL-6 that activates STAT3 signaling in HCC cells (79). Similarly, it has been demonstrated that hepatoma cells induced LX-2 cells secreting more growth differentiation factor 15 (GDF15) in an autophagydependent manner to enhance hepatoma cells proliferation (80). Blocking the pro-tumor crosstalk between cancer cells and HSCs presents an opportunity for therapeutic intervention against HCC. It is well-known that forkhead box (FOX) proteins play critical roles in amplifying HCC malignancy. CAFs are found to induce FOXQ1 expression and FOXQ1/N-myc downstream-regulated gene 1 (NDRG1) axis is activated in tumor cells, which contributes to HCC initiation (81). Furthermore, the activation of FOXQ1/ NDRG1 axis can recruit more HSCs to the TME as a supplement for CAFs via inducing pSTAT6/CCL26 signaling (81). The formation of positive feedback loop between CAFs and HCC cells unquestionably accelerates HCC initiation and development.
Nicotinamide N-methyltransferase (NNMT) modulates the metabolism of hepatoma cells and can be induced by activated HSCs. Reportedly, activated HSCs facilitate HCC invasion and migration through upregulating the expression of NNMT that alters the histone H3 methylation on 27 methylation pattern and transcriptionally activating CD44 in tumor cells (82). Although the molecular mechanism of HSCs inducing tumor cells to upregulate NNMT is still unclear, NNMT is a promising prognostic biomarker and therapeutic target for HCC. In addition, tissue inhibitors of metalloproteinases-1 (TIMP-1) secreted by HSCs is upregulated after stimulated by TGF-b, which triggers focal adhesion kinase (FAK) signaling by interacting with CD63 and contributes to proliferation and migration of HCC cells (83).
CD147, a transmembrane protein expressed highly in HCC is a key driver in the metastasis and development of tumor (84). A previous study revealed that CD147 highly expressed on HCC cells mediated the crosstalk between HCC cells and HSCs via activating HSCs characterized by high expression of a-smooth muscle actin (a-SMA), collagen I and TIMP-1 as well as increased secretion of MMP2, which in turn accelerated HCC malignant progression (85). STMN1 known as an oncogene is upregulated in breast cancer, nonsmall cell lung cancer, and gastric cancer, which can induce cell differentiation, proliferation as well as invasion and migration in solid tumors (86,87). Consistently, Rui Zhang et al. also found that STMN1 overexpression in HCC cells could promote cell proliferation, migration, drug resistance, and cell stemness in vitro as well as tumor growth in vivo (88). They also revealed that STMN1 is a bridge mediating complex crosstalk between HCC cells and HSCs by enhancing hepatocyte growth factor (HGF)/MET signal pathway and that STMN1-induced PDGF secreted by HCC cells may be responsible for activating HSC to acquire CAF features and secrete more HGF (88). Thus, the positive feedback loop for mutual crosstalk between HCC cells and HSCs accelerates HCC malignant progression.
As a pro-inflammatory factor, Follistatin-like 1 (FSTL1) has been reported to promote various cancers malignant progression (89-91). Recent research found that FSTL1 mainly derived from CAFs in human HCC could promote tumor growth, metastasis, and therapy resistance by activating AKT/mTOR/4EBP1/c-myc pathway via binding to toll-like receptors 4 (TLR4) on HCC cells (92). It has been reported that c-myc plays a crucial role in hepatocarcinogenesis (93-95) and mTORC1 is vital for the progression of c-myc-driven HCC (96). FSTL1 expression is regulated by TGF-b1 in mouse pulmonary fibroblasts at both transcriptional and translational levels via Smad3/c-Jun pathway during fibrogenesis (97). The crosstalk between CAFs and HCC cells via TGF-b1 and FSTL1 signaling enhances HCC cells malignancy. CXCL11 highly expressed by CAFs promoted HCC cells migration, whereas CXCL11 silencing decreased it (98). Concretely, CXCL11 stimulation upregulated circUBAP2 expression in tumor cells, and the later counteracted miR-4756-mediated inhibition on interferon-induced protein with tetratricopeptide repeats (IFIT)1/3 by sponging miR-4756, resulting in upregulation of IFIT1/3 expression that contributed to IL-17 and IL-1b expression, and elevated the migration capability of HCC cells. In addition, CAF-derived cardiotrophin-like cytokine factor 1 (CLCF1) improved HCC cells self-renewal ability through interacting with ciliary neurotrophic factor receptor (CNTFR) enhancing SOX2 signaling and increased CXCL6 and TGF-b expression in HCC cells via increasingly activating AKT-ERK1/2-STAT3 pathway (99). Moreover, CXCL6 and TGF-b induced CAFs to produce more CLCF1 via activating ERK1/2 signaling, thus forming a positive feedback loop to accelerate HCC malignant evolution (99).
Zhikui Liu et al. demonstrated that stiffness induced HSCs activation via CD36-AKT-E2F3 signaling pathway, driving activated HSCs to produce FGF2. Moreover, HSCs-derived FGF2 promoted HCC cells proliferation and metastasis through binding to FGFR1 on HCC cells to stimulate PI3K/AKT and MEK/ERK signaling pathways (100). Reportedly, Sox9/INHBB axis is upregulated in HCC and contributes to HCC development by driving the secretion of activin B to activate the peri-tumoral HSCs through activin B/Smad signaling (101). In addition, Keratin (KRT) 19 is positively associated with the aggressive phenotype of HCC and is upregulated by HSCs-derived HGF through activating c-MET and the MEK-ERK1/2 pathway in HCC cells (102).
The cross-talk between HCC cells and activated HSCs is considered to be important for modulating the biological behavior of tumor cells. We summarized the mediums or means and the corresponding results of the crosstalk between HCC cells and HSCs in Table 1. It has been demonstrated that coculturing HCC cells with HSCs under hypoxic conditions enhanced their proliferation, migration, and resistance to bile acid-induced apoptosis compared to coculturing under normoxic conditions (103). How to block the crosstalk between HSCs and tumor and Inhibit cells and inhibit HSC activation deserve more attention in the future.
Crosstalk with other immunosuppressive cells in TME
HSCs are important for MDSC-induced immunosuppression. A recent study found that activated HSCs could induce monocyteintrinsic p38 MAPK signaling to enhance reprogramming for the development and immunosuppression of monocytic MDSCs (M- (107). Whereas it was found that blocking HSCs-induced intrinsic p38 MAPK signaling in monocytes inhibited the formation of MDSCs and their enrichment in fibrotic liver, which effectively inhibited HCC growth (104). Also, blocking CXCL10/TLR4/MMP14 signaling to inhibit MDSCs mobilization and tumor cells invasion and metastasis will present a great potential for developing novel treatment strategies against HCC malignant progression and recurrence. HSCs also play critical roles in regulating MDSCs migration in HCC. Another research about MDSC migration suggested that HSCs promoted MDSCs migration to HCC TME through SDF-1/CXCR4 axis (108). Hence, targeting activated HSCs in HCC is a potentially beneficial approach for modulating patients' immune systems. Endosialin, a transmembrane glycoprotein is demonstrated to mainly express in CAFs in HCC and allows CAFs to recruit macrophages though interacting with CD68 and induce M2 polarization of macrophages via regulating expression of GAS6 in CAFs (109). In addition, CAFs could induce macrophages polarize to M2-phenotype TAM (TAM2) and upregulate the expression of plasminogen activator inhibitor-1 (PAI-1) in them by secreting CXCL12, which augmented the malignant characteristic of HCC cells (110).
A lot of previous researches centered on linking various cells in TME with immunotherapy effectiveness. The latest study indicated that TME subtypes of HCC are associated to the immunotherapy efficacy by combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq) and multiplexed immunofluorescence of anti-PD-1-treated HCC patients (111). It suggested that the tumor immune barrier (TIB) structure consisting of SPP1 + macrophages and CAFs near the tumor boundary affected the therapeutic efficacy of ICIs. In addition, it further revealed that the crosstalk between SPP1 + macrophages and CAFs contributed to ECM remodeling and TIB formation, which led to reducing immune infiltration in the tumor tissue. Moreover, in vivo experiments have verified that SPP1 inhibition in mice with liver cancer resulted in better immunotherapy efficacy with anti-PD-1 (111). Although the molecular mechanism of SPP1-mediated crosstalk between SPP1 + macrophages and CAFs is not completely clear and the role of the crosstalk between SPP1 + macrophages and CAFs has not been verified in clinical trials, SPP1-blockading is promising for improving the efficacy of HCC treatment with ICIs.
As stroma cells in TME, activated HSCs and CAFs are the core factors that promote the formation of immunosuppressive microenvironment. They directly or indirectly promote the malignant progression of HCC and improve the resistance of tumor cells to immunotherapy. As the roles of HSCs and CAFs in HCC are extensive and complex, continue researches targeting them should not be slack in the future.
Interactions between tumor cells and TAMs
TAM is a major component of TME playing crucial functions in inflammation-related HCC progression (112)(113)(114). TAMs secrete numerous bioactive molecules such as cytokines, growth factors, and MMPs into TME to promote immunosuppression and angiogenesis as well as tumor cells proliferation and metastasis (115,116). Previously, it is reported that increasing frequency of TAMs correlate with early tumor recurrence in patients with HCC (117, 118) and that macrophage-mediated phagocytosis of tumor cells is inhibited via PD-1/PD-L1 (119). A previous clinical trial showed that the combination of tumor-secreted osteopontin (OPN) and peritumoral macrophages is potential to predict tumor recurrence and survival outcomes in HCC patients (120). Recently, Ying Zhu et al. revealed that tumor cell-intrinsic OPN not only facilitated macrophages migrate to TME and polarize to TAMs but also upregulated PD-L1 expression in HCC through activating the colony-stimulating factor-1 (CSF1)-CSF1 receptor (CSF1R) signaling in macrophages (121). Targeting OPN/CSF1/ CSF1R axis may be an adjuvant for HCC treatment with ICIs. In addition, It is reported that TAM-derived PGE2 contributes to overexpression of UHRF1, an oncogenic epigenetic regulator, in HCC by repressing UHRF1 mRNA-targeting miR-520d (122). Most notably, UHRF1 upregulates CSF1 expression via increasing DNA hypomethylation of the CSF1 promoter, which leads to more TAM accumulation to accelerate HCC malignant progression. Blocking the vicious circle may be an effective approach to the treatment of HCC.
NcRNAs play crucial roles in HCC progression and targeting them may be promising for HCC treatment (123). There are many recent researches about cellular crosstalk between HCC cells and macrophages via ncRNAs-dependent manners. For example, TGFb secreted by M2 macrophages regulates the expression of CD82 in HCC cells via upregulating miR-362-3p mediated by binding Smad2/3 to miR-362-3p promoter, which contributes to EMT state of HCC cells (124). In addition, it has been found that TAMs induce the expression of lncRNA H19 and the later increases HCC aggressiveness by stimulating the miR-193b/ MAPK1 axis (125). As a kind of crucial medium of cellular signal transmission, the crosstalk between tumor cells and macrophages in exosomes-dependent manners has caused a great upsurge among researchers and here are some of the most recent and meaningful research about them. Exosomal miR-23a-3p released by endoplasmic reticulum-stressed HCC cells increased the level of phosphorylated AKT and the expression of PD-L1 by inhibiting PTEN expression in macrophages (126). Moreover, macrophages stimulated by exosomal miR-23a-3p inhibited T cells function and increased their apoptosis when co-cultured with T cells. The loss-of-function and gain-of-function examinations carried by Xue Li et al. demonstrated that HCC-derived exosomal lncRNA TUC339 is an important component in controlling macrophage activation and M2 polarization (127). In addition, exosomal hsa_circ_0074854 derived from HCC cells can be transferred into macrophages and may contribute to M2 polarization (128). However, the downstream pathways for lncRNA TUC339 hsa_circ_0074854 to function in macrophages and the molecules targeting the pathways should be further explored.
Hypoxia exposure gave rise to high-mobility group box1 (HMGB1) produced by hepatoma cells, which induced TAMs enrichment in TME and upregulation of IL-6, consequently enhancing HCC cells invasiveness and metastasis (129). With persistent hypoxia, HCC cells-derived necrotic debris was reported to induce TAMs to secrete potent IL-1b through the TLR4/TIR domain-containing adapter-inducing interferon-b (TRIF)/NF-kB pathway, which promoted HCC cells EMT and immune evasion (130). Besides, HCC-derived TGF-b increased the expression of TIM-3 on TAMs, which enhanced tumor immune tolerance and stimulated tumor growth via NF-kB/IL-6 pathway (131).
Due to a large demand for iron during uncontrolled growth of tumors, iron metabolism is frequently dysregulated in various human malignant solid tumors. HCC cells overexpressed transferrin receptor (TFRC) so that they competed for iron with macrophages and thus limited their iron uptake via transferrin (TF)-TFRC axis. Macrophages with low iron tend to polarize to M2-like TAMs by increasing HIF-1a expression (132). Besides, it has been demonstrated that Wnt ligands produced by HCC cells stimulate macrophages polarize to the M2 phenotype by increasingly activating Wnt/b-catenin pathway, which promotes tumor growth, migration and immunosuppression in HCC. Blocking Wnt ligands secretion by tumor cells and(or) Wnt/bcatenin signaling in TAMs contributed to reversing HCC malignant progression (133).
Lulu Liu et al. reported that SPP1 was identified to predict poor survival outcomes in HCC patients by multiomics analysis and that SPP1 was shown to mediate the crosstalk between HCC cells and macrophages based on SPP1-CD44 and SPP1-PTGER4 association by receptor-ligand pair analysis in scRNA-seq (134). Moreover, SPP1 has been demonstrated to promote the polarization of macrophage to TAM2 in vitro. Nevertheless, the molecular mechanism that SPP1 mediates the crosstalk between HCC cells and macrophages needs to be further verified in vivo experiments and clinical trials. Table 2. Inhibiting macrophage polarization to the TAM2 is essential to reverse immunosuppressive TME and attenuate HCC malignant progression. How to control the M2 polarization of TAMs and how to block the cytokines and exosomes derived from TAMs are the two central points for future experimental research.
Crosstalk among HCC cells and other cells
In human HCC samples from patients with metabolic syndrome, after being stimulated by glucose, insulin, VEGFA or hypoxia, the expression fatty acid binding protein 4 (FABP4), a cytoplasmic fatty acid chaperone protein is upregulated in peritumoral endothelial cells, which promotes hepatoma cells proliferation and migration by upregulating cell cycle-associated pathways and angiogenesis gene expression (135). In addition, there is a research examining the intercellular crosstalk between HepG2 and endothelial progenitor cells (EPCs) in a co-culture system, which revealed that the expression of ephrin-B2, and Delta-like 4 ligand (DLL4) are upregulated in co-cultured EPCs and are associated with increased migration of HCC cells (136). Nevertheless, the molecular mechanisms that HCC cells induce upregulated expression of ephrin-B2 and DLL4 in EPCs and the signaling pathways that ephrin-B2 and DLL4 promote HCC cells migration need further research and exploration. Mesenchymal stem cells (MSCs) have been demonstrated to play critical roles in affecting the aggressive phenotype of several cancers (137-139). A recent study revealed that MSCs could induce upregulation of DNM3OS in HCC cells and accelerate HCC cells proliferation and metastasis through the DNM3OS/KDM6B/TIAM1 axis (140). In addition, liver MSCs-derived S100 calcium-binding protein A4 (S100A4) enhanced HCC cells invasion ability via the miR155-SOCS1-MMP9 axis (141).
It is reported that Piwi Like RNA-Mediated Gene Silencing 1 (PIWIL1) was upregulated in HCC and contributed to the proliferation of HCC cells (142). This study not only revealed that HCC cells with upregulated PIWIL1 induced MDSCs transport to the TME and but also demonstrated that HCC cellsderived complement C3 induced by PIWIL1 increased the expression of immunosuppressive cytokine IL-10 in MDSCs by activating p38 MAPK signaling, ultimately leading to HCC malignant progression. The high-frequency of tumor-associated neutrophils (TANs) is correlated with poor prognosis in HCC (143,144). Neutrophils extracellular traps (NETs) formed by TANs are DNA meshes with associated extracellular cytotoxic enzymes, which partly mediate the crosstalk between cancer cells and TANs (145,146). A recent study revealed that HCC cells induced NETs formation by secreting cytokine IL-8 and NETsassociated cathepsin G (cG) in turn accelerated HCC metastasis (147). Targeting NETs may be promising to block the crosstalk between tumor cells and TANs and thus prevent HCC malignant development at a certain extent. Cellular crosstalk among highly malignant HCC cells, low malignant HCC cells and normal hepatocytes also plays important roles in cancer malignancy progression. Oncoproteins are abundant in the exosomes produced by metastatic HCC cells, such as MET protooncogene, S100 family members and the caveolins, which activate PI3K/AKT and MAPK signaling pathways in normal hepatocytes after absorbing them, resulting in upregulation of MMP2 and MMP9 and such enhancing tumor cells migration and invasion (148). Moreover, exosomes produced by HMHs greatly accelerated the invasion and metastasis of lowmetastatic HCC cells (LMHs). Reportedly, S100A4 in exosomes produced by HMHs could enhance LMHs' capability for metastasis via activating STAT3 signaling and upregulating OPN expression (149). Alpha-enolase (ENO1) takes part in the Warburg effect by promoting tumor cells absorb glucose and produce lactic acid and is involved in tumor malignant progression and chemotherapeutic resistance (150)(151)(152). It has been demonstrated that ENO1 mediates crosstalk between ENO1 high and ENO1 low HCC cells in an exosome-dependent manner and promotes the proliferation and metastasis of ENO1 low HCC cells by upregulating integrin a6b4 expression and activating the FAK/Src-p38MAPK pathway, similarly in ENO1 high HCC cells (153). The effects of highly malignant HCC cells on normal hepatocytes and low malignant HCC cells cannot be ignored in the development of HCC.
Conclusion and expectation
TME is constantly remodeled due to mutual crosstalk among the cells in HCC TME, which is conducive to the maintenance of immunosuppressive microenvironment, and ultimately leads to HCC malignant progression. Although ICIs such as nivolumab, ipilimumab, and atezolizumab as well as TKIs such as sorafenib and lenvatinib have shown impressive efficacy in HCC treatment, especially combination with TKIs and ICIs, only a small proportion of patients responded to them. This may mostly result from tumor heterogeneity and complex cellular crosstalk in TME which leads to constantly remodeling TME and thus developing resistance to therapies.
So for a large number of signal pathways and molecular mechanisms related to HCC malignant progression have been found. With the maturity of scientific theories and the development of biotechnologies, more key targets against to tumor immune evasion and drug resistance will be discovered in the future. At present, most of the researches focus on the mechanical exploration about signal pathways and effective targets of tumor promotion or suppression with single stimulus and single research objective in a single environment background. However, there is an extremely huge and complex network of cellular crosstalk in TME. No matter in vitro or in vivo experiments, the effects of cell interactions should be taken into account as much as possible.
Hence, researches should attach great importance to co-culture of multiple cells derived from TME and co-stimulation of related multiple factors to better simulate TME and increase the consistency of in vivo and in vitro experiments. Basic experiments should be closely combined with clinical trials to better serve clinical medicine. Besides individual differences, different carcinogenic inducements and tumor stages may lead to different TME
PD-1/PD-L1 Limiting macrophage-mediated phagocytosis of tumor cells (119)
Tumor cell-intrinsic OPN Facilitating macrophages migrate to TME and polarize to TAMs and upregulating PD-L1 expression in HCC through activating CSF1-CSF1R signaling in macrophages (121) TAM-derived PGE2 contributes to overexpression of UHRF1 in HCC by repressing UHRF1 mRNA-targeting miR-520d Upregulating CSF1 expression and leading to more TAM accumulation to accelerate HCC malignant progression (122) TGF-b secreted by M2 macrophages regulates the expression of CD82 in HCC cells Contributing to EMT state of HCC cells TAMs induce the expression of lncRNA H19 Increasing HCC aggressiveness by stimulating the miR-193b/MAPK1 axis (125) Exosomal miR-23a-3p released by endoplasmic reticulum-stressed HCC cells Increasing the level of phosphorylated AKT and the expression of PD-L1 by inhibiting PTEN expression in macrophages and the stimulated macrophages inhibiting T cells function and increasing their apoptosis (126) Exosomal lncRNA TUC339 Decreasing macrophage activation and inducing M2 polarization (127) Exosomal hsa_circ_0074854 derived from HCC cells Contributing to M2 polarization after absorbed by macrophages (128) Hypoxia-induced HMGB1 produced by hepatoma cells induced TAMs enrichment in TME and upregulation of IL-6 Enhancing HCC cells invasiveness and metastasis (129) HCC cells-derived necrotic debris induce TAMs to secrete potent IL-1b through the TRIF/NF-kB pathway Promoting HCC cells EMT and immune evasion (130) HCC-derived TGF-b increased the expression of TIM-3 on TAMs Enhancing tumor immune tolerance and stimulating tumor growth via NF-kB/IL-6 pathway (131) HCC cells compete for iron with macrophages via TF-TFRC axis.
Macrophages with low iron tend to polarize to M2-like TAMs by increasing HIF-1a expression (132) Tumor cells-derived Wnt ligands stimulate M2-like polarization of TAMs via Wnt/b-catenin signaling, Contributing to tumor growth, migration and immunosuppression in HCC (133) structures with different cell subsets and contents. For example, it has been revealed that a metabolic network-driven approach can stratify the HCC tumors into three distinct tumor subtypes (154).
The latest study has revealed that TME subtypes of HCC are associated with the immunotherapy efficacy (111). Therefore, researching the structures of TME subtypes in categories and exploring the molecular mechanisms of cellular crosstalk based on that are expected in the future for the development of precision medicine.
Author contributions
PL and LK were responsible for study concept and design. PL, LK, and YL were responsible for study selection and material collection. XL oversaw the project and revised the important intellectual content of this manuscript. PL, GL, and JX drafted the manuscript and contributed to drawing the mechanism diagrams. All authors participated in the interpretation of the results and preparation of the manuscript and agreed to its published version.
All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | 9,888.2 | 2023-03-15T00:00:00.000 | [
"Biology",
"Medicine"
] |
OBSERVATION OF A COHERENT QUASIPARTICLE BAND IN THE PERIODIC HEAVY FERMION SYSTEM CECU6
Abstract The question addressed here is whether there is structure in the narrow renormalized quasiparticle bands due to coherence in a periodic heavy fermion lattice. In CeCu6 the specific heat indicates that the Fermi temperature, TF, is about 3 K. We find that for a crystal of CeCu6, well below TF, the Hall constant shows changes in behavior at about 400 mK and again at 25 mK. This result is evidence that there is structure in the band on a scale smaller than TF due to the periodicity of the Kondo lattice system.
On cooling through a characteristic temperature, TF, heavy fermion systems develop a narrow renormalized quasiparticle band. This band is a Fermi liquid characterized by a Fermi temperature, T v. Evidence of the narrow band is seen in the specific heat data of Amato et al. [1] for CeCu 6 which shows a dramatic increase in C/T below 10 K to a value of 1600 mJ/m-K 2. In a Kondo impurity model [2], this value corresponds to a Kondo temperature of about 3 K. For our concentrated Kondo system, 3 K characterizes the Fermi temperature of the renormalized band. The neutron quasielastic line width of 4 K found by Aeppli et al. [3] and by Walter et al. [4] is another measure of T v, consistent with the specific heat determination.
Martin [5] suggested that in a periodic Kondo lattice there would be structure in the Kondo resonance due to periodicity. Bredl et al. [6] looking for evidence of this structure, found a maximum in C/T at about 0.5 K for both CeA13 and CeCu2Si 2. In substituted alloys, without perfect periodicity, the enormous values of C/T (of order 1 J/m-K 2) remained but the maximum disappeared. They concluded that there was structure in the density of states for the pure periodic systems and that the Fermi level lay in a small minimum. In the case of CeCu6, a maximum is not seen [1] in C/T, so that if there is structure, the Fermi level is not in a minimum of the density of states.
Steglich et al. [7,8] found for both CeA13 and CeCu 2 Si 2 that the thermopower changed sign from negative to positive at approximately the same temperature as the C/T maximum [6]. They at-tributed this change to the same structure in the Kondo resonance that they had invoked for the C/T maximum. Positive thermopower peaks at 100 mK for CeA13 and at 200 mK for CeCuzSi 2 imply structure on this scale as well. Flouquet et al. [9,10] reported peaks in the thermopower and thermal conductivity of CeA13 and concluded that there was a second temperature scale smaller than T K produced by coherence.
In the temperature range above T e, there is a strong Kondo like resistivity ( fig. 1) due to incoherent scattering of individual Ce ions, similar to that observed by Sumiyama et al. [11][12][13], for Ce in LaCu 6. In the region of T v, where C/T rises rapidly, the resistivity falls to a very small fraction of its incoherent value as the Ce ions begin to scatter coherently. For temperatures T << T F, the resistivity follows p = AT 2 as expected for electron-electron scattering in a Fermi liquid [14][15][16][17]. Furthermore, the coefficient A is approximately proportional to (1/TF) 2 [18]. The T 2 behavior is only seen in the extreme degenerate limit for T_< 100 mK. From these results alone, one might conclude that the low temperature behavior is given by just one temperature scale, T v, but that it is necessary to have T << T v to observe the limiting Fermi liquid behavior.
We have reported previously the results of Hall and resistivity measurements on two single crystals of CeCu 6 for temperatures down to 30 mK [19][20][21]. In the incoherent scattering regime, above 30 K, the Hall effect is dominated by skew scattering and follows XOm, the product of the magnetic susceptibility of the Ce ions and the resistivity due 0304-8853/88/$03.50 © Elsevier Science Publishers B.V. T (K) Fig. 1. Resistivity of a single crystalline bar of CeCu 6 with current in the orthorhombic b direction. The solid line is the magnetic scattering remaining after subtraction of the resistivity of the reference compound, LaCu~ [11]. The small resistance at low temperature is indicative of coherent scattering while the large resistance at the peak is from incoherent scattering. Figure from ref. [21].
to the magnetic scattering from the Ce ions. In the region of the resistivity maximum, as the resistivity breaks away from the incoherent impurity behavior so does the Hall constant break away from XPm" In fact the Hall constant goes strongly negative as shown in fig. 2. The incoherent region and the transition to the coherent region have been discussed and relevant work cited in our previous publications [19][20][21]. Subsequently, Hall studies on Ce,.La~ ,Cu 6 by Onuki and Komatsubara [11] have shown that with increasing La substitution, the Hall constant at 1 K changes from strongly negative, as we observe, to being strongly positive as expected for incoherent scattering. Recent theoretical calculations by Levy and Fert [22] show another contribution to the Hall effect in addition to the skew scattering. It is a side jump mechanism which they call an anomalous velocity effect. It may be as strong as the skew scattering for temperatures in the neighborhood of T v. It is still a single site incoherent scattering effect. At present there is no theory for the Hall effect in the coherent regime.
The important issue addressed in this work is the possibility of fine structure in the quasiparticle band, due to coherence which is observable only below T~. Our studies show that the Hall effect is particularly sensitive to such structure. Indeed, we see ( fig. 2) that there is an abrupt change in behavior near 400 mK. In another sample [20] with different orientation, the change takes place at about 200 mK. Winzer [23] finds a negative extremum like ours at 500 mK for a polycrystalline sample of CeCu 6. In some other heavy fermion systems, such as CeAI> CeRu2Si 2, and UAI 2 and UPt> Lapierre et al. [24] find that the Hall constant changes from being strongly temperature dependent at high temperature to being very weakly temperature dependent at low ternperature. For CeAI> which has a similar T~ to CeCu6, this change occurs at about 300 mK.
The thermopower of Amato et al. ]1] is linear above 500 mK, but the slope changes in the region of 300 mK to a much steeper slope at lower temperatures. These Hall and thermopower results indicate that there is structure in the quasiparticle band on the scale of 300 mK. In order to observe the true asymptotic behavior of the Fermi liquid, we measured the Hall effect down to 12 mK. Great care was taken to insure good thermal equilibrium. The sample was a bar taken from the larger bar of figs. 1 and 2.
The new data are shown in figs. 3 and 4. The extremum at 400 mK is seen as before. Below that, R H is approximately linear in T with a negative slope and negative intercept ( fig. 3). The data may be fit just as well to a T 1-25 law and a fit to T 15 is only slightly worse. The remarkable new result is the change in behavior below 25 mK. This deviation from the linear T law observed above 25 mK is seen best in the log T plot, fig. 4. In this plot the solid line is the linear T fit from fig. 3. The dashed line is the extrapolation of the linear T fit. The range from 12 to 25 mK is too small to determine the power law. The slope, however, has clearly changed sign. The T= 0 intercept is still negative. This result reveals that there is structure in the quasiparticle band even on this fine scale.
The recent work of Sato et al. [25] on single crystal CeCu6 gives support to the view that there are changes in behavior at temperatures much smaller than T F. They have measured the resistivity and fit it to P = 00 + A(T) T2 and the thermoelectric ratio G = S/LT, where S is the thermopower and L is the Lorenz number. They find that between 1 K and 30 mK, both A and G change by large amounts. Only below 30 mK are they temperature independent. Coleridge [26] has measured the magnetoresistance up to 10 T at various temperatures of some of the high quality single crystals that were used for the de Haas-van Alphen measurements of Reinders et al. [27]. Coleridge finds both positive and negative contributions to the magnetoresistance for the current along the b axis and the magnetic field along c. He associates the negative part which is dominant down to about 500 mK to incoherent Kondo scattering. Below 30 mK he finds only a positive linear magnetoresistance which he associates with the coherent state. The transition to this state is smooth. Another way to view the magnetoresistance is to measure O vs. T 2 for various magnetic fields as done by Amato et al. [1]. They find that the slope A decreases monotonically with H, in fact A ( H)1/2 scales roughly with 3'(H) and x(H) showing their common origin in T v. The intercept 00 first increases (1.5 T) and then decreases (4.6 T) with field. The region of T 2 behavior increases from about 100 mK at 0 T, to about 150 mK at 1.5 T. This result confirms the necessity for T<< T~.. to observe the T 2 behavior. An interesting conclusion from this field scaling is that T v and m* are strongly field dependent [1]. This is in apparent contrast to the de Haas van Alphen results of Reinders et al. [27] who found a field independent m*. A possible solution to this contradiction is that it is the heaviest masses which dominate the specific heat and fields of order 5-7 suppress the increase in C/T below 2 K [1]. Since the measurement [27] of the field independent mass was for a mass of m*/m = 6, in fields between 6 and 13 T, the apparent contradiction can be understood.
A possible objection to our claim that we have seen the effect of coherence, is that our findings could be the result of some unseen underlying uninteresting magnetic transitions unrelated to the heavy fermion renormalized band. Mignot et al. [28] have studied the low temperature metamagnetic transition in CeRu 2Si2 which occurs at 8 T. Regnault et al. [29] have seen this transition in neutron diffraction as an increase in ferromagnetic fluctuations and a decrease in antiferromagnetic fluctuations. They also see a similar transition in CeCu 6 at 2 T. Reinders et al. [27] see structure in their de Haas-van Alphen data at 2 T. Since the Hall data is taken at 1 T and below, this metamagnetic transition probably has no bearing on our result. Furthermore, the metamagnetic transition field in CeRu2Si 2 has been shown by Mignot et al. to scale under pressure with both X and the A coefficient of the T 2 resistivity. The metamagnetism, therefore, has its origin in the renormalized band. If it does have a bearing on the Hall effect, it is traceable back to the renormalized band.
Recent reviews of heavy fermion systems have been given by Lee et al. [30], Fulde et al.
[31] and Fisk et al. [32]. In the introduction of Fulde et al., there is a very clear description of the different energy scales. There is one scale which we have called T v and is the scale of the quasiparticle bandwidth. In addition, one expects a number of peaks in the density of states due to the structure in the band. The origin of this structure is the periodicity, just as in a normal 3d transition metal. The widths of the peaks, Tp, vary but will be smaller than T v. This point of view, which is the same as Martin's, seems to fit the experiments well, since we find changes in behavior at several hundreds of mK and again at several tens of mK.
There is some confusion in the use of the word coherence because it is used to describe two different but related effects. Firstly, there is the formation of the narrow band of quasiparticles in the temperature region T F. This is due to coherence between wave functions on different sites [30 35]. In a periodic system, as the coherence builds and C/T increases, the resistivity decreases strongly. The formation of the narrow band, however, does not depend greatly on periodicity as demonstrated by the fact that many substituted systems have enormous electronic specific heats but do not show the low resistivity associated with coherence. In these disordered systems, Bloch waves are strongly scattered but there still exists a non-periodic coherence. The second use of the term coherence is to describe the origin of the structure in the quasiparticle band. In a periodic system this structure should be much sharper than in a disordered system, just as in the case of common unrenormalized bands.
Coherence has been discussed by a number of theorists in addition to Martin [5]. Structure in the quasiparticle bands can be seen in the renormalized band structure calculations of Fulde et al.
[31] for CeCu2Si 2 and by Newns and Read [34] for CeSn 3. Brandow [36] has argued that there is but one temperature scale, T v, and no separate coherence temperature. However, he notes that for T << T~. the physical properties will be dominated by the particular quasiparticle band structure. This point of view is similar to that initially proposed by Martin, and discussed by Fulde et al. Coleman [37] finds one temperature scale for the Kondo lattice. This one scale is sufficient for the specific heat and the resistivity. As observed experimentally, the calculated resistivity drops rapidly for T~ T v and for T<< T v it follows AT 2 with A --1/T 2. This point of view is similar to Brandow's. In another study, Koyama and Tachiki [38] have found for the Anderson lattice, a sharp peak in the quasiparticle density of states at E v, but no partial gap.
In other studies, coherence pseudogaps have been explicitly found. Grewe [39] has calculated densities of states for Kondo impurities and the Kondo lattice. He finds in both cases that as the temperature drops below TK, a resonance occurs in the neighborhood of the Fermi energy, E v. However, in the lattice case, there is a partial gap just above E F due to coherence which is about TK/3 wide for his parameters.
Kaga et al. [40] have found that for a Kondo impurity there is a peak in the density of states of width T K at E v. For the Kondo lattice they find a peak T K wide, located T K below E v and a pseudogap at E v. Furthermore, the coherent structure forms before the single site Kondo resonance is fully formed . They define a coherence temperature T 0-0.1T K as the temperature at which the coherent lattice density of states is fully formed. Here T K is the Kondo temperature for the impur-it3,. The transition to the coherent state is smooth.
Lacroix [41] has found that the Kondo resonance of width T K forms without structure for T--~ T K. Then for T < T~ the coherence temperature, a partial gap occurs in the resonance. The coherence temperature is T~ = TK(TK/D) 1/2, where D is the bandwidth of the conduction band. A similar point of view was taken by Moschalkov [42] at this conference. He suggested that the transition could be either continuous or abrupt.
A rather different direction has been taken by Doniach [43] who first considered the Kondo necklace and the competition between the Kondo quenching of local moments and magnetic order. He has shown that the peak in C/T below T v observed in CeA13 and CeRu2Si 2 may be due to the proximity of a spin density wave instability. Associated with this SDW phase is a second temperature scale.
All heavy fermion theories have a quasiparticle band at E v to explain the specific heat data. Most theories also find that for the lattice case the band has more structure than for the impurity case. In most cases it appears to be somewhat a matter of semantics whether this added structure is associated with a new temperature scale. There is a distinction between the formation of a quasiparticle band with structure and a phase transition of incoherent Kondo states into a coherent Kondo lattice.
Conclusion
The extremely narrow renormalized quasiparticle bandwidth of a heavy fermion system has a characteristic temperature, TF, which is measured by the electronic specific heat and is about 3 K for CeCu 6. This bandwidth is not particularly sensitive to periodicity or disorder. In purely periodic systems, the resistivity is very large above T v, but becomes very small with a T 2 dependence for T << T v. No abrupt changes in the resistivity are observed. Our Hall results, however, show sharp features near 400 mK and near 25 mK, which we believe are caused by structure in the quasiparticle band due to the periodicity of the lattice. Thermopower results of others also show changes in behavior on these scales. We cannot determine whether any of these features indicate a phase transition. These observations strongly support the notion that coherence in a Kondo lattice system produces effects on a scale much smaller than T v. | 4,158 | 1988-12-02T00:00:00.000 | [
"Physics"
] |
A Green Synthesis of Ru Modified g-C3N4 Nanosheets for Enhanced Photocatalytic Ammonia Synthesis
Centre for Technology in Water and Wastewater (CTWW), School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Sydney, NSW 2007, Australia State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China Laboratory of Advanced Catalysis for Sustainability, School of Chemistry, The University of Sydney, Sydney 2006, Australia Centre for Advanced Materials & Industrial Chemistry (CAMIC), School of Science, RMIT University, Melbourne, VIC 3000, Australia Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney 2006, Australia Beijing Engineering Research Center of Sustainable Urban Sewage System Construction and Risk Control, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
Introduction
Nitrate (NO 3 -) is a necessary nutrient for plants, whereas high-level of NO 3 enters the food chain via water sources can be toxic and hazardous to the ecosystem [1][2][3]. The main culprits of NO 3 pollution include agricultural runoff, fertilizer abuse, septic systems, industrial plants, and irrigation systems [4]. Currently, NO 3 contamination has been a serious environmental concern as it can affect the quality of groundwater and surface water, causing health problems to humans [5,6]. In an oxygen-deficient environment, such as in the digestive tract, NO 3 can be reduced to more toxic nitrite [7,8]. Nitrite can oxidize low ferritin into methemo-globin in the human body, causing the loss of oxygen transportation. It can also react with secondary amine compounds to produce carcinogenic nitrosamines [9]. Therefore, it is urgent to develop a novel strategy to remove NO 3 pollution with high efficiency and selectivity. Ammonia (NH 3 ) is an important industrial ingredient widely used in fertilizers, pharmaceutical industries, and other areas [10][11][12]. More importantly, it is also an essential energy source. The decomposition of ammonia is a low-cost and facile process; therefore, using ammonia as a hydrogen carrier is able to solve the hydrogen storage problem to a certain extent [13,14]. With the increase of the global population, the demand for NH 3 keeps increasing. Currently, NH 3 is mainly produced via the Haber process, in which gaseous nitrogen (N 2 ) and water gas are converted to NH 3 under high temperature and high pressure with the assist of catalysts [15]. Every year, the synthesis of NH 3 consumes about 2% of global energy, leading to serious carbon dioxide emission [16][17][18]. Thus, it is urgent to develop a green synthesis of NH 3 under ambient conditions.
In recent years, photocatalytic synthesis of NH 3 has become a hot research frontier, where researchers are mainly focusing on the reduction of gaseous nitrogen [19][20][21][22]. However, the research is still limited in the laboratory, because the strong bond energy of N ≡ N bonds and the poor solubility of N 2 make the catalytic NH3 yield efficiency low [16]. Compared with N 2 , the required energy for the cleavage of N=O is only 21.68% as that of N ≡ N bonds. In addition, the solubility of NO 3 is 40000 times higher than that of N 2 [23]. Therefore, if the waste NO 3 can be converted to NH 3 , the conversion of environmental hazards to valuable energy resources will be more sustainable for the planet.
Graphitic carbon nitride (g-C 3 N 4 ) is a metal-free photocatalyst, which is a rising star in photocatalysis because it has the advantages of facile synthesis, wide sources, and good photocatalytic activity for redox reactions [24][25][26]. In recent years, several kinds of g-C 3 N 4 -based photocatalysts have been prepared and applied in NO 3 wastewater treatment, such as TiO 2 /g-C 3 N 4 [27], Mn 2 O 3 /g-C 3 N 4 [28], and g-C 3 N 4 @AgPd heterojunction [29]. However, the main aim is to convert NO 3 to N 2 to reduce environmental hazards. Compared with the generation of N 2 , the reduction of NO 3 to NH 3 is more challenging in terms of kinetics and thermodynamics, as it is an eight-electron process with multiple steps [30]. On the other hand, many ruthenium-containing compounds exhibit good catalytic properties because of their unique electronic structure [31][32][33][34]. Meanwhile, ruthenium (Ru) can absorb the light in the visible spectrum, and it is being actively researched for solar energy technologies [35,36]. Nevertheless, toxic reducing agents like ethylene glycol are usually required to reduce Ru 3+ to Ru 0 , making the synthesis complex, dangerous, and unecofriendly [37]. Until now, Ru modified g-C 3 N 4 photocatalysts for highly efficient NO 3 reduction to NH 3 have never been reported, and the reaction mechanism is not clear.
In this work, we report a green synthesis of Ru modified g-C 3 N 4 nanosheets with significantly enhanced photocatalytic activity on the reduction of NO 3 to NH 3 . Herein, waste green tea bags were used to reduce the Ru 3+ , and the photocatalytic activity of the optimized sample was 2.93-fold as that of bulk g-C 3 N 4 under simulated sunlight irradiation. The material preparation process was ecofriendly without using strong reducing reagent. Based on experimental and theoretical studies, the introduction of Ru to g-C 3 N 4 can not only boost the light absorption, the adsorption of NO 3 -, but also accelerate the separation of electron-hole pairs. The thermodynamic energy barrier for the rate determining step in NO 3 reduction to the NH 3 process is calculated to be less than 0.75 eV, which is much lower than the competing H 2 generation (0.98 eV) and N 2 formation (1.36 eV), leading to the preference of generating NH 3 . This work provides a novel approach to synthesize metallic particle-based photocatalysts for highly efficient photocatalytic NO 3 reduction to synthesize NH 3 .
2.2. Synthesis of g-C 3 N 4 Nanosheets. g-C 3 N 4 was prepared via the thermal polymerization of dicyandiamide. Firstly, 5 g of dicyandiamide was put in a 50 mL corundum crucible without the lid. Then, the crucible was transferred into the oven and heated from 25 to 540°C with the heating rate of 4°C/min. After heating at 540°C for 4 h in air, the bulk g-C 3 N 4 was obtained. The g-C 3 N 4 nanosheets were synthesized via the ultrasonic peeling of bulk g-C 3 N 4 in water.
In details, 500 mg of bulk g-C 3 N 4 was added in 800 mL water and put under ultrasonic bath for 24 h. Finally, the ultrathin g-C 3 N 4 nanosheets were successfully obtained and named as 2D-CN.
2.3. Synthesis of Ru/g-C 3 N 4 . Herein, we used waste green tea as the reduction regent to prepare Ru modified g-C 3 N 4 (Scheme 1). To get simulated waste tea bags, 5 tea bags were put in boiled water for 20 min. Then, the 5 used tea bags were put in 500 mL and boiled for 30 min. After that, the tea water was gone through a filter for further use. A certain amount of RuCl 3 and 0.5 g of g-C 3 N 4 nanosheets was put into 150 mL prepared tea water and under magnetic stir for 24 h. Finally, the powder was washed by water and ethanol for 5 times and then dried at 60°C for 4 h. The amounts of RuCl 3 used were 0.005, 0.01 and 0.038 g, and the obtained samples were named as CN-Ru-0.5, CN-Ru-1, and CN-Ru-3, respectively.
2.4.
Characterizations. The X-ray diffraction (XRD) patterns of the samples were tested with a Bruker D8 Discover XRD with intense Cu Kα radiation (40 kV and 40 mA, λ = 1:5406 Å) at room temperature. The morphology observation on the materials was carried out using a Zeiss Supra 55VP scanning electron microscope (SEM), with an operating voltage of 5-15 kV. The images of SEM-energydispersive X-ray spectroscopy (EDS) for elemental mapping were obtained with the Oxford Ultim Max. High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and EDS mapping were performed on a double-corrected FEI Themis-Z 60-300 transmission electron microscopy (TEM) equipped with ChemiSTEM EDS detector system for ultra-high count rates. The X-ray photoelectron spectra (XPS) of the samples were tested with a Thermo Fisher Scientific K-Alpha+X-ray photoelectron spectroscopy. The UV-vis diffuse reflectance spectra (DRS) of prepared samples were obtained from a Perkin Elmber Lambda 950 UV/VIS/NIR spectrometer, using high-purity 2 Energy Material Advances barium sulfate (BaSO 4 ) as the blank reference. Photoluminescence (PL) spectra were obtained from a Shimadzu RF-6000 fluorescence spectrometer excited at 325 nm. The photocurrent, electrochemical impedance spectroscopy (EIS) and Mott-Schottky curves of the samples were obtained from an electrochemical working station (CHI-760E) in 0.1 M Na 2 SO 4 solution (details shown in supplementary materials).
2.5. Photocatalytic Activity Testing. The photocatalytic nitrate reduction performance was carried out with a selfmade quartz reactor under simulated sunlight irradiation. During the reaction process, the light intensity was 600 mW/cm 2 (HSX-F300), and it was measured by a radiometer. The distance between the light and the reactor was 10 cm. Firstly, 20 mg of the samples was weighed and put into the reactor. Then, 95 mL of NaNO 3 (10 mg/L) solution and 5 mL of formic acid (200 μL/L) were added. Before the reaction, argon was purged into the solution to remove air. After 2 h strong stir to make the powder catalysts uniformly dispersed in the solution, the light was turned on and the light irradiation lasted 4 h. In each hour, 3 mL of liquid was sampled from the reactor. The concentration of ammonium was measured by UV-vis spectrometer using Nessler's reagent method and was further verified by 1H-Nuclear magnetic resonance spectroscopy (NMR) analysis. The concentration of NO 3 and NO 2 was measured by a high-pressure integrated capillary anion chromatography (IC). The exact details of Nessler's reagent method, NMR, and IC are shown in the supplementary materials ( Figure S1-S4).
2.6. Density Functional Calculation. The calculations of geometry structures and energies were carried out by density functional theory (DFT) with Vienna ab initio simulation package (VASP) [38][39][40][41]. The exact details are presented in supplementary materials.
Results and Discussion
3.1. Structure and Morphology. In the XRD patterns of g-C 3 N 4 ( Figure S5 in supporting information, SI), two distinct peaks at 13.1 and 27.1°can be assigned to the (100) and (002) crystal plane diffraction (JCPDS 87-1526) [42]. After peeled off by ultrasonic powder, the peak intensity decreased, indicating the reduced crystallinity.
Since the loaded amount of Ru is not high, it cannot be detected by XRD. Then, HRTEM and SEM were used to explore the morphology of the prepared samples. As shown in Figure 1(a), the g-C 3 N 4 nanosheets showed an ultrathin film layer structure. The CN-Ru-1 sample had the same morphology as 2D-CN with Ru particles loaded on the g-C 3 N 4 nanosheets (dark spots in Figure 1(b)). The size of the Ru particles was less than 5 nm. To further investigate the lattice, the Ru particles were zoomed in and displayed in Figure 1(c). The lattice distance of 0.234 nm
Energy Material Advances
can be indexed to the (100) of metallic Ru. Additionally, the high-angle annular dark-field (HAADF) image and energy dispersive spectroscopy (EDS) for Ru element mapping were obtained to further confirm the component of the CN-Ru sample. As exhibited in Figures 1(d) and 1(e) and Figure S6, Ru particles are uniformly distributed on g-C 3 N 4 nanosheets. SEM image was also employed to study the morphology of CN-Ru-1 (Figure 1(f)). In the SEM image, we can only see the nanosheet structure of g-C 3 N 4 , since the size of Ru particles was too small to be observed by SEM.
Then, XPS was used to study the components of the prepared samples. Figures 2(a) and 2(b) are the narrow C 1 s spectra of 2D-CN and CN-Ru-1. The three peaks of 2D-CN at 293.6, 287.9, and 284.6 eV can be assigned to the conjugated π electrons and sp 2 -hybridized C and C-C bonds [10]. Compared with 2D-CN, two new peaks appeared in the narrow XPS spectra of C of CN-Ru-1. The peak at 286.1 eV was attributed to C-O which came from the green tea (Figure 2(b)) [43]. The other new peak at 280.2 eV is associated with Ru 0 [44]. Both 2D-CN and CN-Ru-1 have 3 peaks in the narrow N 1 s spectra. In Figure 2(c), the three peaks at 404.4, 400.0, and 398.4 eV of 2D-CN are attributed to the charging effect, tertiary nitrogen, and sp 2 -hybridized nitrogen. Notably, the three peaks of N of CN-Ru-1 shifted to 404.9, 400.1, and 398.6 eV, which was caused by the interaction between 2D-CN and Ru [45].
Based on the XRD, SEM, TEM, and XPS results, it can be concluded that metallic Ru particle modified g-C 3 N 4 nanosheets were successfully fabricated.
Enhanced Photocatalytic NO 3
-Reduction to NH 3 . Herein, we used photocatalytic NO 3 reduction to NH 3 to evaluate the catalytic activity of samples. After 4-hour simulate sunlight irradiation, all the samples exhibited apparent photocatalytic activity on the reduction of NO 3 to NH 3 (Figure 3(a)). The NH 3 yield rate of 2D-CN was 1.126 mg/h/g cat , and it was 1.26-fold as that of bulk g-C 3 N 4 . When metallic Ru was used to modify the 2D-CN, the photocatalytic activity increased remarkably. Among all the samples, CN-Ru-1 was the most active one (2.627 mg/h/g cat ), and its activity is 2.93 times higher than bulk g-C 3 N 4 . Cycle stability is crucial for the study and application of photocatalysts. After the fourth cycle, the NH 3 yield of CN-Ru-1 was still 2.32 mg/h/g cat , which was 88.16% as the fresh catalyst, indicating good cycle stability for reusage. The use of formic acid did not influence of the detection by the accuracy of Nessler's reagent method ( Figure S7).
Energy Material Advances
To confirm the source of NH 3 generated, we carried on the 15 N isotope experiment with Na 15 NO 3 . A series of 15 NH 4 Cl solutions with different concentration were firstly used to generate a standard line, and then the concentration of photocatalytic reactions can be analyzed. As shown in Figure 3(c), the standard 1H-NMR spectrum of 15 NH 4 + had two peaks with a coupling constant of 73.14 Hz. In the solution after photocatalytic reaction of CN-Ru-1 with 15 NO 3 -, two characteristic 15 NH 3 peaks were also found, and the calculated concentration of NH 3 was 2.05 mg/L, which well matched the results of UV-vis spectrophotometry. The NMR results can confirm that the formation of NH 3 is attributed to the photocatalytic reduction of NO 3 -. Besides NH 3 , there might be some other products of the NO 3 reduction. Herein, we used ion chromatography to measure the concentration of NO 3 and NO 2 -. After 4 h light irradiation, bulk CN reduced 48.85% of the NO 3 -(10 mg/L), 50.77% of which was converted to NH 3 . Under the same condition, CN-Ru-1 eliminated 92.85% of NO 3 -, and the NH 3 selectivity was 77.9%. The UV-vis spectrophotometry, 1H-NMR, and ion chromatography results confirmed that both the NO 3 conversion rate and NH 3 selectivity were significantly increased by CN-Ru-1, compared with bulk CN.
In Table S1, we summarized several typical g-C 3 N 4 , TiO 2, and other materials for photocatalytic NO 3 reduction in literature for comparison. Many materials can achieve highly efficient NO 3 removal, whereas the ammonia selectivity is quite low. Pd/TiO 2 can get a high NH 3 selectivity of 76.9%, but its catalytic activity is really poor [46]. Nevertheless, the CN-Ru-1 reported in this work can simultaneously realize both high NO 3 removal and high NH 3 selectivity.
Mechanism of the Enhanced Performance.
Light absorption is an essential step for photocatalytic reaction. 5 Energy Material Advances significantly enhanced. The increased light absorption enabled to make the catalysts absorb more photons, boosting the solar energy utilization ratio. The density of states of g-C 3 N 4 and CN-Ru is calculated by DFT (Figure 4(b)). Compared with g-C 3 N 4 , the introduction of metallic Ru can bring some metallicity to the composite catalysts, which is beneficial to the transfer and separation of photocatalytic generated electron-hole pairs [47].
In order to explore more direct evidence about the transfer and separation of photogenerated electron-hole pairs, the photocurrent response, EIS, and PL spectra of bulk CN, 2D-CN, and CN-Ru samples were tested. Figure 4(c) is the photocurrent response image. All the tested samples had apparent and rapid photocurrent response under light irradiation. Among all the samples, the photocurrent intensity of CN-Ru-1 was the strongest, which followed the same trend as the photocatalytic activity, indicating that CN-Ru-1 had the fastest separation and transfer of electrons and holes. The photocurrent slightly decreased in 200 s, which was caused by the decreased interaction between the powder catalysts and the ITO glass [48]. It does not mean the photocatalysts have poor stability. In the EIS Nyquist plot, the bulk CN had the largest arc radius because of its poor conductivity. The 2D-CN got a smaller arc radius as it had better conductivity than bulk CN. When the 2D-CN got modified by metallic Ru, the arc radius of the EIS Nyquist plot significantly decreased, because Ru had strong conductivity. The more metallic Ru loaded, the better conductivity the sample got. The PL spectra of bulk CN, 2D-CN, and CN-Ru samples were displayed in Figures 4(e) and 4(f). Bulk CN showed a broad and strong emission spectrum with profiles slightly deviating from a perfect Guanine curve centered at about Energy Material Advances 460 nm. Compared with bulk CN, the PL intensity of the other samples decreased significantly, which mean the recombination of photoinduced charge carriers was obstructed, and it is helpful for the enhanced photocatalytic activity [49]. Experimental studies on NO 3 reduction have shown that Ru modified g-C 3 N 4 plays an essential role in improving NH 3 production compared with pure g-C 3 N 4 . To further understand the reaction mechanism and the origin of high activity and selectivity of CN-Ru, the density functional theory calculation is performed. NO 3 adsorption, the first step of NO 3 reduction, was first calculated, and the optimized adsorption structures, as well as the adsorption energies, are listed in Figures 5(a) and 5(b). The adsorption energy of NO 3 on g-C 3 N 4 was -1.85 eV, and it was -3.75 eV on CN-Ru. It is clearly seen that Ru modification increased the NO 3 stability by 2.03 times compared with Energy Material Advances pristine g-C 3 N 4 , indicating the following NO 3 reduction step proceeds more easily, consistent with the experimental observations. Furthermore, metallic Ru particles involve a high spin density on Ru cluster and positive Bader charge, as well as a zero gap, which can enhance the electron transfer ability (Figures 5(c) and 5(d)). Since Ru cluster got a positive Bader charge, the photocatalytic generated electrons for g-C 3 N 4 can be accumulated on Ru, making it as the active sites. At the same time, the CN-Ru system got some magnetic properties, and the spin density is concentrated on Ru. All these electronic structure results are responsible for the improved photocatalytic activity of CN-Ru.
To further understand the role of metallic Ru particles in the NO 3 reduction process, we calculated the free energy diagrams of NO 3 reduction on g-C 3 N 4 and CN-Ru. Compared with the generation of NH 3 , there are two main com-peting reactional products for the photocatalytic reduction of NO 3 -including H 2 and N 2 . For g-C 3 N 4 , it is very easy to take part in hydrogen generation reaction, while the formation of N 2 is very difficult because a higher thermodynamic energy barrier of 2.33 eV should be overcome (Figure 6(a)). As for CN-Ru, the thermodynamic energy barriers of the formation of H 2 and N 2 are 0.98 and 1.36 eV (Figure 6(b)). All free energy curves are downhill from NO 3 to HNO 2 , and although the reaction pathway is different, the thermodynamic energy barrier for the rate determining step in this process is all calculated to be less than 0.75 eV (Figure 6(c)), which is much lower than those of 2H ⟶ H 2 (0.98 eV) and 2 N ⟶ N 2 (1.36 eV), suggesting that induced Ru cluster played a key role in the enhancement of the photocatalytic activity and selectivity for NO 3 reduction to form NH 3 , in good agreement with the experimental observations. Figure 6: Calculated free energy differences between the formation of H 2 and N 2 on g-C 3 N 4 (a) and CN-Ru (b). Calculated free energy changes of NO 3 reduction to NH 3 on g-C 3 N 4 and CN-Ru (c).
Energy Material Advances
Based on the experimental and calculated results, the mechanism for the enhanced photocatalytic NO 3 reduction to NH 3 activity can be summarized as follows (Scheme 2). Firstly, the introduction of metallic Ru caused a redshift of the absorption edge as well as broad absorption in visible light range, which boosted the absorption of photons and the utilization of solar light. Secondly, CN-Ru samples have much better conductivity and higher separation efficiency of photogenerated electron-hole pairs than g-C 3 N 4 . Thirdly, the high spin density of Ru cluster and its positive Bader charge can accumulate electrons, making the adsorption and cleavage of NO 3 easier. More importantly, the thermodynamic energy barrier for the rate determining step in this process is all calculated to be less than 0.75 eV (Figure 6(c)), which is much lower than the competing H 2 generation (0.98 eV) and N 2 formation (1.36 eV), leading to the preference of generating NH 3 and higher activity.
Using green tea as reducing reagent to prepare nanomaterials is promising because it is cost-effective. More importantly, the operation and storage are much safer than using strong reducing reagent like sodium borohydride. The tea polyphenols of green tea are regarded as active ingredients to achieve reduction reactions [50,51]. Meanwhile, the average reduction potential of green tea is about 0.219 V, indicating that the approach reported in this work can be expanded to the synthesis of other metal-based materials [52]. In Figure S8, we summarized the elements that can be reduced to zero valent theoretically, including Tc, Ru, and Cu, which would potentially use the proposed approach [53].
Conclusion
In summary, we used a facile and green approach to synthesize novel metallic Ru modified g-C 3 N 4 nanosheets as photocatalysts for enhanced NO 3 reduction to NH 3 . The optimal sample (CN-Ru-1) had the highest NH 3 yield rate of 2.627 mg/h/g cat , and it was 2.93 times as that of bulk CN. After 4-hour light irradiation, CN-Ru-1 eliminated 92.85% of NO 3 -, and the NH 3 selectivity was 77.9%. After four cycles, the sample still had good photocatalytic performance (88.16% as the fresh catalyst). NMR and 15 N isotope labeling provided solid evidence that the N of NH 3 was from the reduction of NO 3 -. The g-C 3 N 4 nanosheets modified by metallic Ru particles have stronger light absorption, better conductivity, and more rapid separation of electron-hole pairs. With the enhanced adsorption energy of NO 3 and the low thermodynamic energy barriers, the photocatalytic activity and selectivity were increased significantly. The results and findings of this work may provide a new platform for the facile and green synthesis of metal particle modified photocatalysts for reducing NO 3 to NH 3 under ambient conditions. | 5,552.4 | 2021-09-06T00:00:00.000 | [
"Environmental Science",
"Chemistry",
"Materials Science"
] |
Forecasting the Price Distribution of Continuous Intraday Electricity Trading
: The forecasting literature on intraday electricity markets is scarce and restricted to the analysis of volume-weighted average prices. These only admit a highly aggregated representation of the market. Instead, we propose to forecast the entire volume-weighted price distribution. We approximate this distribution in a non-parametric way using a dense grid of quantiles. We conduct a forecasting study on data from the German intraday market and aim to forecast the quantiles for the last three hours before delivery. We compare the performance of several linear regression models and an ensemble of neural networks to several well designed naive benchmarks. The forecasts only improve marginally over the naive benchmarks for the central quantiles of the distribution which is in line with the latest empirical results in the literature. However, we are able to significantly outperform all benchmarks for the tails of the price distribution. Naive5 in terms of MAE for the central quantiles. However, the accuracy is significantly better for the tails of the distribution. Considering the RMSE, the improvement over the benchmarks is significant for all quantiles. These results suggest that it is possible to forecast the short-term volatility of the intraday market which is reflected in the tails of the volume-weighted price distribution. At the same time, we can not report a definite improvement over the naive models for the central quantiles considering the inconsistent results for MAE and RMSE.
Introduction
Continuous intraday electricity trading offers market participants the possibility to balance short-term deviations from their planned generation and load schedules. This is especially valuable for agents with a high share of generation from non-dispatchable renewable energy sources like wind and solar. Conventionally, deviations from the day-ahead schedules are compensated through balancing energy which is contracted and centrally dispatched by the transmission system operator. The possibility to trade electricity on short notice can partly explain the counter intuitive situation that the demand for balancing energy in Germany in the last years substantially declined, while at the same time the share of generation from renewable sources increased [1,2]. Thus, intraday markets can be an effective tool to support the transition to a flexible and renewable energy system and have seen steadily growing volumes in recent years [3].
In this work we will focus on the German continuous intraday market. On this market it is possible to trade hourly and quarter-hourly contracts for the delivery of electricity till 30 min before delivery. Inside of the four control zones it is possible to trade until five minutes before the delivery starts. Contrary to the day-ahead auction, the intraday market is operated as a continuous pay-as-bid market, i.e., market participants can submit bids and asks for price-volume combinations which are immediately executed if two offers in the order book can be matched. This results in a potentially large set of prices for the same product. Therefore, price indexes that reflect the volume-weighted average price are a main indicator of market outcomes. The most important one is the ID3 price index which is the volume-weighted average price of all trades in the time interval from three hours before delivery till 30 min before delivery [4]. For a detailed description of the German power markets see [5].
In contrast to the well researched day-ahead markets [6], the literature regarding intraday electricity price forecasting is scarce. Andrade et al. [7] and Monteiro et al. [8] conducted a forecasting study for the Iberian intraday electricity market. However, this market does not resemble the design of the German market. Most importantly, the Iberian intraday market is operated as six separate intraday auctions under a uniform pricing regime. More recently, Maciejowska et al. [9] presented a model that is able to predict the price spread between the German day-ahead auction prices and the corresponding volume-weighted average intraday prices. Finally, Uniejewski et al. [10] and Narajewski & Ziel [11] are to our best knowledge the only two papers that aim to directly forecast ID3 prices. The authors of [11] present evidence that the information available to the market at forecasting time, i.e., three hours before delivery, is already efficiently incorporated by the market participants and therefore the best forecast for the ID3 price is the volume-weighted average price of the most recent 15 min of trading.
Since the intraday market is operated under a pay-as-bid regime, it is possible for market participants to sell or buy contracts at prices that substantially differ from the ID3 price. Furthermore, very different sets of trades can result in the same weighted average price. Therefore, we aim to forecast the entire volume-weighted price distribution instead of only volume-weighted average prices. Note that this distribution is not equivalent to a predictive distribution of the ID3 price, but will often be much wider. Such a forecast is important to enable bidding strategies that use prices away from the ID3 price, e.g., to benefit from especially high or low price offers. To approach our task, we construct empirical cumulative distributions for each trading product in discrete time intervals and describe these distributions using a dense grid of quantiles. This results in a set of multivariate time series of quantile values which non-parametrically approximate the targeted price distributions.
We conduct a forecasting study for the German intraday market in which we aim to forecast the quantiles of the price distribution in the time from three hours to 30 min before delivery. We test several linear regression models as well as a neural network model that accounts for the unique structure of the data. We compare the forecasts from these models to several carefully designed naive benchmark models. Our empirical findings support the evidence in [11], as we are only able to outperform the naive benchmarks by a small margin for the central quantiles. However, the performance of our forecasts for the tails of the distributions improves significantly over the benchmark models.
The remainder of the paper is structured as follows. In Section 2 we describe the data set and preliminary data transformation we apply to obtain the quantiles of the price distributions. We present the linear regression and neural network models as well as a set of naive benchmark models in Section 3 and describe our forecasting strategy along with the employed error measures in Section 4. The empirical findings are discussed in Section 5. We conclude in Section 6.
Data Set & Data Transformation
The German intraday market has undergone two relevant regulatory changes in the last several years. First, since July 2017 it is possible to trade until 5 min before delivery inside of the control zones. Second, in October 2018 the Austrian control zone was split from the former German-Austrian market. For our analysis we consider data on the intraday transactions from 1 July 2017 to 31 March 2019 for the German-Austrian market and German market respectively, i.e., we start our analysis after the introduction of the 5 min delivery horizon while the market split occurred during the time frame of our analysis. We assume that the control zone split did not have a substantial impact on prices and liquidity since the German control zones are large compared to the Austrian control zone and cross border trading is still possible. The continuous intraday trading data we use is commercially available from Epex Spot [12]. We will only consider hourly products in our analysis as their traded volume is more than five times larger than the volume of the quarter hour products. We additionally consider corresponding exogenous hourly data regarding day-ahead auction prices as well as forecasted renewable generation and load which is available from ENTSO-E [13].
Constructing Price Distributions from Intraday Trading Data
The trading data contains all executed trades for hour products to be delivered between 1 July 2017 and 31 March 2019. For this period we define a corresponding set of dates D. Each entry in the raw data set corresponds to a single trade i and is comprised of the identifier for the hour h i ∈ {0, ..., 23} and the day d i ∈ D of delivery, a timestamp of the trading time that indicates the time left until delivery t i ∈ R + , the trade volume v i ∈ {0.1, 0.2, 0.3, ...} in MWh, and the price p i ∈ {−9999.90, −9999.89, ..., 9999.90} in EUR/MWh [14].
Forecasting the data on the level of single trades is likely to be difficult and not necessarily needed for decision making. Hence, one has to aggregate the trading data into a more suitable form that is still able to represent the market's development in sufficient detail. To this end, the literature has so far focused on analyzing and forecasting volume-weighted average prices [9][10][11] which only provide a highly aggregated representation of the market.
We instead propose to work with the entire volume-weighted price distribution of the trades. Therefore, we compute the volume-weighted empirical cumulative distribution function (VWECDF) over the price p, (1) (p) denotes the VWECDF for the hour product h on day d between the time t 1 and t 2 before delivery with t 2 < t 1 . The total volume traded for the product in the given interval is used to normalize the sum in the VWECDF. The VWECDF in Equation (1) for a given product is computationally represented by an ordered set {(q j , r j )} J j=1 of the J trades observed in the time interval t 1 → t 2 , where r j is the empirical quantile and q j is the corresponding quantile value which is given by the price of trade j. This allows us to estimate quantile values for a specific quantile τ using linear interpolation if necessary Using a dense grid of quantiles τ ∈ {0, 0.01, ..., 0.99, 1} we then obtain a vector of quantile values d,h,1 correspond to the cheapest and most expensive trades observed. Figure 1a shows the result of the applied transformation in a fan chart for a single product in 15 min time intervals from 5 h before delivery till the time of delivery. We can observe that the variance increases with the time of delivery approaching. This is characteristic for the intraday market. Figure 1b shows the inverse of the VWECDF and quantile values for selected quantiles for the entire time horizon of 5 h.
Let us note again that this is not a distribution which describes the uncertainty over the volume-weighted average price but a distribution that describes how the traded volume is distributed over the possible prices. To illustrate this point consider the following hypothetical situation. Suppose we would observe the trades for a certain product before issuing a forecast for the observed time frame. Then, we could compute the volume-weighted average price and issue a perfect probabilistic forecast for this average price, a distribution where the entire probability mass is centered at the true, known value. However, this forecast would not inform a trader about the variety of prices that are traded for this product. In contrast, our approach would still forecast a non-trivial distribution that would inform an agent about the dispersion of the traded prices, e.g., we could exactly forecast the marginal value of the cheapest and most expensive 10% of the trades. This is a much richer representation of the market behavior and reflects that different market participants might value electrical energy very differently. Considering a price taker perspective, an agent could then take advantage of the estimated dispersion of prices.
Exogenous Data
Along with the trading data we also include exogenous fundamental data. For each day and hour we consider the load forecast Load d,h , the forecasted in-feed from wind and solar power RES d,h , and the day ahead auction price DA d,h which is already known before the continuous trading starts. We combine all exogenous variables in the vector Additionally we consider the 24-dimensional one-hot encoded column vector s d,h which contains a dummy variable for each hour of the day.
Predicting the Quantiles of the Price Distribution
The most important price index for the intraday market is the ID3 price. It is the volume-weighted average price of the trades in the time interval from three hours before delivery till 30 min before delivery for a given product [4]. We therefore also focus on this time horizon and aim to forecast q 3→0.5 As explanatory variables we use the time series of the observed quantile values from four to three hours before delivery in 15 min time intervals denoted by Q 4→3 where N τ is the number of quantiles. We also consider the corresponding time series from the two neighboring products For ease of notation we will write (d, h + k) to denote the product that has to be delivered k hours before/after the product (d, h) instead of using the correct notation (d + h+k 24 , (h + k)mod 24). Finally, we also use the exogenous variables for all three considered products x d,h−1 , x d,h , x d,h+1 and the vector of hour dummy variables s d,h .
Linear Regression Models
In this section we present a set of linear regression models which use different subsets of the available regressors. This allows us to stepwise infer the contribution of each factor to the forecasting performance. To obtain a forecast for the vector of quantile valuesq 3→0.5
d,h
we have to fit a separate model for each quantile τ. At test time we concatenate the predictions from the N τ individual models and sort the resulting vector to ensure monotonically increasing quantile values.
The first model, which we call AR1, uses only the time series information of the same product for the same quantile Q 4→3 d,h,τ and the vector of dummy variables s d,h . It is given bŷ where w i are row vectors of model parameters. The model ARX1 given bŷ additionally uses the exogenous variables for the same product. The model AR2 given bŷ also utilizes the time series information from the neighboring products for the same quantile but ignores the exogenous variables. The model ARX2 given bŷ additionally includes the exogenous regressors for all three products. Finally the ARXfull model utilizes all available inputs. Hence, this model has 1245 parameters. This will likely result in overfitting for the used training set size of 6 months. Furthermore, many regressors might not carry useful information for the quantile value to forecast. We therefore apply Lasso regularization to automatically select an optimal subset of regressors [15], i.e., the model parameters are estimated using an extended loss function that penalizes the L1 norm of the model weights. Let z d,h,τ be a vector of standardized regressors, w the model weights, and q 3→0.5 d,h,τ the true quantile values, then the Lasso estimator for the optimal weight vector w * is given by where λ τ is the hyperparameter that controls the degree of regularization. Setting λ τ = 0 leads to standard ordinary least squares estimation.
Neural Network Model
The modeling approaches described above result in one model per quantile and can only model linear relationships. Therefore, we also test a multi output neural network model (NN) which uses an architecture that accounts for the structure of the inputs and limits the number of parameters in the hidden layers, see Figure 2 for a visualization. The model outputs a prediction for the vector of quantile values as a function of all available regressorŝ The proposed neural network has two hidden layers. The first hidden layer is a locally connected layer and operates only on the time series data Q 3→2 d,h−1 , Q 4→3 d,h , Q 5→4 d,h+1 . In this locally connected layer a distinct vector of weights w 1,τ , w 2,τ , w 3,τ ] is learned for each quantile. Each local model outputs a scalar value where Q is the ELU activation function [16]. The layer outputs a vector h (1) 0.01 , ..., h (1) is then concatenated with the vectors x d,h−1 , x d,h , x d,h+1 , s d,h and is passed through a fully connected layer with N τ neurons, i.e., the weight matrix W (2) has dimension N τ × (N τ + 9 + 24) and w (2) 0 is a vector of constants with dimension N τ × 1. The last layer outputs the model's prediction using the N τ × N τ weight matrix W (3) and the N τ × 1 vector of constants w 0 . We train the model by minimizing the L2 norm of the difference between the predicted and true vector of quantile values given by where D is the number of days in the training set. We train the model for 50 epochs with a batch size of 32 using the Adam optimizer [17] at standard settings in Keras 2.2.4 [18]. At test time we sort the predictions of the model to ensure monotonically increasing quantile values. For both the linear regression models as well as the neural network model we chose to use one model for all hours of the day. Fitting a separate model for each hour would result in much smaller training sets. However, if the market behaves fundamentally different for different hour products, it might be insufficient to account for these differences by simply introducing dummy variables. We also did not transform the data to stabilize the variance e.g., by applying the asinh-transformation which has been shown to work well for electricity price forecasting tasks [19]. Studying the effectiveness of different modeling strategies, variance stabilizing transformations, or robust loss functions like the absolute loss or the Huber loss [20] for intraday forecasting is outside the scope of this paper but is an interesting avenue for further research.
Naive Benchmark Models
Narajewski & Ziel [11] showed empirically that a strong benchmark for short-term forecasts of the ID3 price is the volume-weighted average price of the last 15 min before forecasting. Based on their findings we test five naive benchmark models of similar type. Let us note the authors of [11] use information up to 3.25 h before delivery while we use information up to 3.0 h before delivery for both the naive and statistical models.
The Naive1 model uses the quantile values of the full trading period till 3 h before delivery and is given byq The Naive2 model uses the quantile values of the last 15 min before forecasting, i.e., This type of naive model performed best in [11] which suggests that the latest market results already reflect the information available at forecasting time. Hence, we expect that this model's forecasts will perform best at least for the central quantiles that are closely related to the ID3.
As the dispersion of the traded prices till three hours before delivery is usually significantly lower than in the last three hours, we consider three more models that scale the variance of the distribution but are centered at the value for the 0.5 quantile q 3.25→3 d,h,0.5 . This is motivated by the expectation that the distribution right before we issue the forecast is a good estimator for the median but not for the variance of the target distribution.
The Naive3 model shifts the distribution of the last finished product by centering it at q 3.25→3 d,h,0.5 The Naive4 model shifts the distribution of the same hour from the day before in similar way and is given byq Finally, the Naive5 model shifts the average distribution of the hour product in the entire training set and is defined asq whereq 3→0.5 h denotes the vector of average quantile values for the hour h in the training set.
Forecasting Strategy
For the empirical forecasting study we consider the entire data set from 1 July 2017 till 31 March 2019 with the initial training, validation, and test split shown in Figure 3. We use the first six months of data from 1 July 2017 till 31 December 2017 as initial training set to forecast the quantiles for all hours of the following day. We then shift the training set by one day, refit all models, and again forecast the following day. We use the first three months of 2018 as a validation set to fix the values for λ τ considering values on an exponential grid given by {λ i = 2 i |i ∈ {−15, −14, ..., 0}}. The value of λ τ for each τ is determined by the lowest mean absolute error. The 12 months between April 2018 and March 2019 form the test set. In cases where there was no trading between two time steps for a product, we reuse the quantile values from the preceding 15 min time interval. If there was no trading in any preceding periods, we set all quantile values to the day-ahead auction price. To account for the numerical instability of the neural network's predictions resulting from the random weight initialization and the non-convex loss function, we train an ensemble of 5 models and average their predictions.
Evaluation
We use two measures to evaluate the accuracy of the predictions for the entire distribution, the Wasserstein distance (WD) and integrated quadratic distance (QD). These distances provide an intuitive way to measure the difference between two empirical distributions in a non-parametric way. For two univariate distributions P and S with cumulative density functions (CDF) F and G the WD is defined as WD(P, S) = +∞ −∞ |F(x) − G(x)| dx and the QD is defined as QD(P, S) = +∞ −∞ (F(x) − G(x)) 2 dx. Hence, we compute the errors by and and mean integrated quadratic distance (MQD) where D is the number of days in the test set. To investigate the difference in forecasting accuracy for different quantiles we compute the mean absolute error (MAE) and root mean squared error (RMSE) values for each quantile separately Since the values of the error measures alone do not allow for a statistically sound conclusion on the outperformance of forecast A by forecast B, we employ the Diebold-Mariano (DM) test [21] in the modified version proposed by Harvey et al. [22] as implemented in the R forecast package [23]. The DM test examines the statistical significance of the difference of the residual time series of two models. We compute the multivariate version of the test as proposed in [24], i.e., we obtain one error for each day by computing a norm for the vector of residuals for the day d.
We consider two variants of the test as we expect a difference in forecasting ability for different quantiles. In the first variant we compute the L1 norm of the WDs and the L2 norm of the QDs over one day Then the loss differential for two forecasts A and B is given by ∆
Results
We present the MWD and MQD values in Table 1 and the corresponding DM test p-values in Figure 4a,b. All statistical models except AR1 show lower MWD and MQD values than the best benchmark model Naive5. The differences in accuracy between the forecasts of AR1, ARX1, and Naive5 are not significant. The forecasts of the ARXfull model give the best results in terms of both measures. The improvements of the ARXfull forecasts in terms of MWD and MQD compared to the best benchmark model Naive5 are only about 2% and 3.5%. In terms of MWD, the improvement in accuracy of the ARXfull forecasts is significant compared to all models. Considering MQD, the improvement in accuracy of the ARXfull forecasts is significant compared to all models except the NN. There is no statistical significant difference in the accuracy of the forecasts from the AR2, ARX2, and NN models. Furthermore, we can not report a significant difference in accuracy between the forecasts from AR1 and ARX1 as well as between the forecasts from AR2 and ARX2. These observations lead to several conclusions. Incorporating exogenous variables does not improve the forecasting performance while considering time series information from the neighboring products leads to a significant improvement. Furthermore, the inclusion of information from other quantiles in combination with automated variable selection using Lasso also improves the forecasting performance significantly. Tables 2 and 3 present the values for MAE τ and RMSE τ for selected quantiles. This allows a more detailed analysis regarding the forecasting accuracy for different regions of the distribution. The forecasts from the ARXfull model show the lowest MAE τ values for all quantiles except Q90 and Q100. For these quantiles the NN forecasts are more accurate. The NN forecasts show larger errors for the central quantiles than the predictions of the much simpler AR2 and ARX2 models. However, the accuracy of the NN forecasts is better for the more extreme quantiles. This could be explained by non-linear effects that can not be modeled by the linear regression approach. The findings are similar for the RMSE. The ARXfull model shows the best performance for the central quantiles while the NN model shows slightly better performance for the tails. In general, errors are larger for the tails of the distribution across all models, especially for the minimum and maximum values. Figure 5a,b show the relative improvement in MAE τ and RMSE τ for the statistical models compared to the forecasts of the best performing benchmark model Naive5. In terms of MAE τ only the ARXfull forecasts show a small relative improvement for the central quantiles of roughly 0.4%. The relative gains in accuracy are larger for the tails for all models, e.g., the ARXfull forecasts show a relative improvement of respectively 3% and 2.4% for Q10 and Q90. Figure 6a,b show the p-values of the DM-test for the loss differential per quantile for the ARXfull forecasts against all other models' forecasts for the L1 and L2 norm, respectively. As can be seen from Figure 6a, we can not conclude a significantly improved forecasting accuracy in comparison to the forecasts of Naive2 to Naive5 in terms of MAE for the central quantiles. However, the accuracy is significantly better for the tails of the distribution. Considering the RMSE, the improvement over the benchmarks is significant for all quantiles. These results suggest that it is possible to forecast the short-term volatility of the intraday market which is reflected in the tails of the volume-weighted price distribution. At the same time, we can not report a definite improvement over the naive models for the central quantiles considering the inconsistent results for MAE and RMSE. Figure 6. The figure shows the p-values for the multivariate DM tests per quantile for (a) the daily L1 norm and (b) the daily L2 norm for the forecasts of the ARXfull model compared against the forecasts of all other models. Dark green cells indicate that the forecasts of the ARXfull model are significantly more accurate than the forecasts from the model on the y-axis for the quantile given on the x-axis.
Conclusions
We analyzed the German continuous intraday electricity market and focused on hour products and the last three hours before delivery. We proposed to non-parametrically approximate the empirical volume-weighted price distribution by using a dense grid of discrete quantiles. This admits a much richer representation of the market behavior than only analyzing volume-weighted average prices. In order to forecast the quantile values of this distribution we constructed a set of simple linear regression models that use different subsets of the available inputs. Furthermore, we used two more advanced models that utilize all available regressors, a Lasso regularized linear regression model and an ensemble of multi-output neural networks. We found that including exogenous variables did not improve the accuracy while considering time series information from neighboring products and quantiles did. We compared the forecasts of the proposed models with several simple but well designed benchmarks. The best performing model turned out to be the Lasso regularized linear regression model. We also studied the forecasting accuracy for different quantiles of the price distribution. Compared to the naive benchmarks, the gains in forecasting performance were small and not significant for the central quantiles of the target distribution. However, the gains in accuracy for the tails of the distributions were larger and significant. Hence, we gather evidence that the German intraday market works efficiently while also showing that it is possible to forecast the variance of short-term intraday prices.
There are several avenues for future work. It would be interesting to see if we would obtain similar findings for quarter hour products which we excluded in this work. It is also worth investigating if information from quarter-hour products could help to improve the forecast accuracy for the hour products and vice versa. We chose to model the price distribution in a non-parametric way which allows a larger degree of flexibility. However, modeling the price distribution in a parametric way is straightforward and worth exploring. Furthermore, we solely focused on prices while an estimate of the expected traded volume as a measure of short-term market liquidity would also be of interest in practice. Finally, future work should explore how to exploit forecasts for the distribution of prices and volumes for short-term trading and risk management.
Conflicts of Interest:
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript: | 6,826.4 | 2019-11-08T00:00:00.000 | [
"Economics",
"Computer Science"
] |
Diesel Consumption of Agriculture in China
As agricultural mechanization accelerates the development of agriculture in China, to control the growth of the resulting energy consumption of mechanized agriculture without negatively affecting economic development has become a major challenge. A systematic analysis of the factors (total power, unit diesel consumption, etc.) influencing diesel consumption using the SECA model, combined with simulations on agricultural diesel flows in China between 1996 and 2010 is performed in this work. Seven agricultural subsectors, fifteen categories of agricultural machinery and five farm operations are considered. The results show that farming and transportation are the two largest diesel consumers, accounting for 86.23% of the total diesel consumption in agriculture in 2010. Technological progress has led to a decrease in the unit diesel consumption and an increase in the unit productivity of all machinery, and there is still much potential for future progress. Additionally, the annual average working hours have decreased rapidly for most agricultural machinery, thereby influencing the development of mechanized agriculture.
Introduction
The limited supply of traditional fossil fuels and the associated consumption limitations needed for dealing with global climate change have considerably restricted economic development in recent years.
OPEN ACCESS
This has raised the concern of energy analysts and policy makers regarding the adverse effects of energy overuse.A series of policy measures aimed at reducing energy consumption have been implemented in order to meet the compulsory targets stated in the China government's Eleventh Five-Year Plan (2006)(2007)(2008)(2009)(2010).Agriculture development in China is not an exception.Calculated at constant prices, the average elasticity of energy consumption in agriculture declined from 2.33 (2001-2005) to 0.29 (2006)(2007)(2008)(2009)(2010).The rapid growth of energy consumption in agriculture was thus restrained without negatively affecting economic development.
With a gradually increasing level of mechanization, agricultural energy consumption in China has increased from 36.88 million tons of coal equivalent (Mtce) in 1996 to 64.77 Mtce in 2010, which translates to an annual increase of 4.10%.Energy plays a critical role in the development of agriculture as it does in the manufacturing, construction and service industry.This has motivated many researchers to focus on agricultural energy issues, and analysis of energy and exergy efficiency in the agricultural sector has become a research hotspot.Two energy resources, namely diesel for tractors and electricity for pumps are usually the research topics in this area.Such analysis has been applied in Saudi Arabia [1], Turkey [2], Jordan [3], Iran [4] and Malaysia [5].The relationship between energy inputs and agricultural production outputs is another research hotspot.These energy inputs usually include direct and indirect energy, i.e., human and animal labor, machinery, electricity, diesel oil, fertilizers, seeds, etc. Rijal and Bansal [6] examined the total energy input and output of subsistence agriculture in the rural areas of Nepal.Ozkan and Akcaoz [7] estimated the input-output ratio in the Turkish agricultural sector for the period of 1975-2000, where their output is composed of 36 agricultural commodities.On the other hand, agricultural output in Hatirli and Ozkan [8] comprises 104 agricultural commodities.Alam and Alam [9] evaluated the impact of energy input on agricultural production output in Bangladesh from 1980 to 2000.There are also researches that provided meaningful econometrics methods.Uri [10] quantified the relationship between the energy price and the use of conservation tillage via Granger causality over the period of 1963-1997.A regression analysis of the relationship between energy use and agricultural productivity was done by Karkacier and Gokalp Goktolga [11].Using co-integration and error correction analysis, Türkekul and Unakıtan [12] estimated the long-and short-run relationship among energy consumption, agricultural GDP, and energy prices from 1970 to 2008 in Turkey's agriculture.
Based on a bottom-up modeling approach, the model named Save Production simulated the development of energy use in the Dutch industry and agriculture [13].Baruah and Bora [14] assessed the energy demands in the state of Assam, India.In that study, they considered four strategic scenarios of mechanization that incorporated some proven technologies.Nevertheless, only a few simulation and forecast models were established to study the energy demand of agriculture and little attention has been paid to the relationship between energy consumption and end-use machinery in agriculture, especially in China.In the Twelfth Five-Year Plan, the Chinese government has planned to reduce the energy consumption per unit GDP by 16% during this five year horizon.A special model, Simulation and Analysis of Energy Consumption for Agriculture (SECA), is designed to answer all kinds of questions on how energy was consumed in different agricultural sectors to achieve the new goal.Furthermore it also serves as the foundation for the agricultural energy demand forecasting model.In this study, we identify the factors (total power, unit diesel consumption, etc.) influencing diesel consumption in China and simulate the diesel flows of agriculture of China in detail.Based on availability, our dataset spans the period of 1996-2010.
Overall Structure
This paper uses a generalized definition of the word agriculture to include farming (i.e., agriculture in narrow definition), fishery, forestry, animal husbandry and services supporting agriculture.Fifteen kinds of the agricultural machinery from seven agricultural subsectors are considered.Figure 1
Operation Module
In SECA, all the agricultural machinery are categorized into five farming related operations (o1~o5, Table 1).These operations include mechanized tillage, mechanized sowing, mechanized harvesting, mechanized irrigation and other operations.They are assumed as follows: Mechanized tillage (o1) and mechanized sowing (o2) are performed by tractors and associated towing farm machinery.Both the large and medium-sized tractors and the small ones are included. Mechanized harvesting (o3) is performed by combine harvesters, swathers and other harvesters. Mechanized irrigation (o4) is performed by irrigation machinery. Other operations (o5) are performed by rest agricultural machinery, the workload statistics of which are not available.Note: T is a generalized symbol which can be replaced by P, L, l i , C, etc.
Main Module
Two kinds of the energy carriers, diesel and electricity, are considered in SECA.The energy required to perform the selected farming operation is estimated using the equation below: wherein AW is the actual workloads of the farming operation; up indicates the weighted average of unit productivity; WS refers to the workload statistics; lf is the load factor, which is used for describing the actual work intensity and is assumed to be proportional to the machinery power in unit area; oec is the other effect coefficient obtained from the Correction Module.Because workload statistics are not available for "other operations" ( 5 o ), in this study we assume that its average annual working hours of unit machinery 5 o t is constant.That constant is determined from the following equation: wherein , s y C indicates the diesel consumption statistics in year y .
Distribution Module
The mechanization of agriculture is a process of replacing human and animal with agricultural machinery powered by either diesel or electricity.The rated power is an important factor affecting the device performance of agricultural machinery.The rated power of the agricultural machinery is negatively correlated with its unit consumption (or motor efficiency) and is positively correlated with its unit productivity.
Smaller agricultural machinery are more widely used than larger equipment in China due to reasons such as the high cost of larger machinery and the small amount of arable land per capita.It is assumed that the change in the number , , In the Distribution Module, the most common power range , , beginning m y end m y p p of machinery is selected on the basis of experience and divided into n intervals.The midpoint of interval i as its representative power , , i m y p can be obtained using the following relationship: , , , , ( ) The total number of machinery , , m y c L and the total power , , m y c P can be obtained from statistics, so another two equations can be established: Equations ( 4)-( 7) should be solved simultaneously to estimate parameters , Tractors are the most common agricultural machinery and play an important role in mechanized agriculture in China.The number and total power of tractors increased from 9.86 million and 1.08 × 10 8 kW in 1996 to 21.78 million and 2.84 × 10 8 kW in 2010, with annual increases of 5.82% and 7.16%, respectively.The rated power of the main tractor models in Chinese market is between 8 and 12 kW, while in China's Department of Statistics, the rated power for tractors starts from 2.2 kW, so in SECA, it is assumed that the number of large and medium-sized tractors, referring to tractors with power ratings greater than or equal to 14.7 kW, still follows an exponential distribution.On the other hand, the number of small tractors, referring to tractors less than 14.7 kW and greater than 2.2 kW, follows a parabolic distribution [Figure 2 wherein , m y c is the undetermined parameter.Another equation is established as follow in order to ensure the continuity of Equations ( 8) and ( 9): Equations ( 5)-( 10) should be solved simultaneously to estimate parameter , c .Then the number of tractors with the representative power in the interval i can be determined.
Unit Consumption Module and Productivity Module
Unit consumption (or motor efficiency) and productivity of agricultural machinery are both functions of the machinery's rated power.The simulation results from the Distribution Module are processed by the Operation Module and then output into the Unit Consumption Module and the Productivity Module which generate the weighted average of unit consumption and that of productivity.The data describing the relationship between the input and output of the functions are collected from the relevant national standards of China and product manuals of those agricultural machinery [15,16].They are fitted by the least square method (Figures 3 and 4).
The fitting equation of the unit consumption is as follows: The unit productivity for each operation is estimated using the following equations: As the output in the Unit Consumption Module and the Productivity, the weighted averages of unit consumption and productivity are obtained according to the following relationships: , , ) ) wherein uc tpc and up tpc are the technological progress coefficients obtained from the Correction Module.
Correction Module
Technological progress and other effects are considered in the Correction Module to narrow the gap between the statistics and the model calculations in the Main Module.The correction coefficient is the correction on the basic assumptions in the other modules of SECA.These assumptions which are either explicit or implicit include: In the Unit Consumption Module, the weighted average of unit consumption changes without being affected by the technological progress.The correction coefficient coc can be calculated with the following relationship: The correction coefficient coc can be divided into two parts: technological progress coefficient tpc and the other effect coefficient oec .The relationship between coc , tpc and oec is assumed to following equation: Technological progress is typically accompanied with the reduction of the unit consumption and the increase of the unit productivity.The relationship between tpc , uc tpc and up tpc is assumed according to Equation (17): The other effect coefficient oec mainly works on the load factor lf mentioned in the Main Module.The other effect coefficient oec means that the load factor is not proportional to machinery power in unit area any longer.In short, we can conclude that technological progress leads to the reduction of average unit consumption and changes in working hours result from the changes in unit productivity and load factor.
Data Sources
The data related to the agricultural land, machinery and energy consumption in this paper are mainly obtained from the China Statistical Yearbook The data related to land and water resources in this paper are given by the China Land and Resources Bulletin [21] and the China Water Resources Bulletin [22].The former is published by the Ministry of Land and Resources of China, and its data, especially the farmland area data, are more reliable than the other data sources [23].The latter is published by the Ministry of Water Resources of China.
Table A1 in the Appendix presents the workload statistics of four operations: i.e., mechanized tillage, mechanized sowing, mechanized harvesting and mechanized irrigation.Considering that parts of the land are repeatedly cultivated in a year, a re-seeding coefficient (the ratio of sowing area to tillage area) is introduced to correct the workload statistic of the mechanized tillage when the data are input into the model.Table A2 presents the number and the total power of the agricultural machinery mentioned in this study.
Changes of Correction Coefficient
Figure 5 presents the changes of the correction coefficient.The correction coefficient has declined from 1.37 in 1996 to 0.84 in 2010, with an average annual decline of 3.46%.The decline in the correction coefficient proves that technological progress has been affecting the unit diesel consumption and the unit productivity of the agricultural machinery positively.One also can find that the load factor has not been growing as expected.The curve of the correction coefficient shows a significant linear downward trend.However, there are step changes in the correction coefficient corresponding to the step changes in the diesel consumption from 2004 to 2007.The step changes of the energy consumption statistics are widespread in most sectors during the Eleventh Five-Year period including the diesel consumption in agriculture.According to careful analysis, it is believed that the step changes cannot reflect the real energy consumption, and they probably result from either changes in statistical methodology or artificially adjusted energy consumption numbers.The latter is more likely the main reason due to the existence of the compulsory target for reducing energy consumption stated in the Eleventh Five-Year Plan of China government, so the correction coefficients from 2004 to 2007 are corrected using linear interpolation.
Changes of Unit Consumption
Table 2 provides the unit diesel consumption of agricultural machinery.The unit diesel consumption generally maintained a steady downward trend from 1996 to 2010. For tractors, the unit diesel consumption declined from 407.96 g/kWh in 1996 to 356.93 g/kWh in 2010, an average annual decline of 0.95%. For harvesters, it declined from 423.67 g/kWh to 352.70 g/kWh, an annual decline of 1.30%. For irrigation machinery, it declined from 450.99 g/kWh to 401.25 g/kWh, an annual decline of 0.83%. For primary processing machinery, it declined from 411.91 g/kWh to 364.70 g/kWh, an annual decline of 0.87%. For animal husbandry machinery, it declined from 426.16 g/kWh to 375.13 g/kWh, an annual decline of 0.91%. For fishery machinery, it declined from 383.17 g/kWh to 328.06 g/kWh, an annual decline of 1.10%. For forestry machinery, it declined from 414.97 g/kWh to 370.71 g/kWh, an annual decline of 0.80%. For transportation machinery, it declined from 408.68 g/kWh to 352.89 g/kWh, an annual decline of 1.04%. For farmland construction machinery, it declined from 338.56 g/kWh to 297.35 g/kWh, an annual decline of 0.92%.Figure 6 shows the changes in the unit diesel consumption compared with that in the previous year.The red part shows the changes in the unit diesel consumption caused by technological progress.The blue part displays the changes in the unit diesel consumption caused by changes in machinery quantity at different intervals (structural influence).It is obvious that the effect of the technological progress plays a major role in almost all agricultural machinery.It can be predicted that the trend will not change in the foreseeable future.However, the effect of technological progress is no longer significant for some machinery with low unit diesel consumption, such as farmland construction machinery.
Another way to reduce unit diesel consumption is to adjust the structure of the distribution of agricultural machinery.Results show that most of the agricultural machinery, such as tractors, harvesters, animal husbandry machinery, fishery machinery, transportation machinery and farmland construction machinery are becoming larger in size and lower in unit diesel consumption with the development of the agricultural economy.This is not the case for irrigation machinery, primary processing machinery and forestry machinery.
Farmland Construction Machinery
Structural Influence Technological Progress
Changes of Unit Productivity
Table 3 shows the unit productivity of the four mechanized operations.The unit productivity generally maintained a steady upward trend from 1996 to 2010: The unit productivity of the mechanized tillage increased from 0.08 ha/h in 1996 to 0.10 ha/h in 2010, an average annual increase of 1.86%. The unit productivity of the mechanized sowing increased from 0.25 ha/h to 0.30 ha/h, an average annual increase of 1.27%. The unit productivity of the mechanized harvesting increased from 0.06 ha/h to 0.19 ha/h, an average annual increase of 8.13%, which is the largest growth rate in these four operations. The unit productivity of the mechanized irrigation increased from 74.41 m 3 /h to 79.87 m 3 /h, an average annual increase of 0.51%.Figure 7 shows the changes in unit productivity compared with that in the previous year.The red part indicates changes in unit productivity caused by technological progress.The blue part indicates changes in unit productivity caused by changes in machinery quantity at different intervals (structural influence).
Basically, technological progress has a positive influence on changes in the unit productivity of four operations.Improvement of unit productivity in mechanized harvesting is less than that in the other operations.
Structural adjustments to the machinery number caused an increase in the unit productivity of mechanized tillage, mechanized sowing and mechanized harvesting.Moreover, it also enables the unit productivity of the mechanized irrigation to decrease gradually with average rated power.
Changes of Working Hours
Table 4 shows the average annual working hours of agricultural machinery.Almost all average annual working hours of the agricultural machinery show a decreasing trend from 1996 to 2010, except for harvesters.
The average annual working hours of the tractors declined from 56.33 h in 1996 to 41.00 h in 2010, an average annual decline of 2.24%. The average annual working hours of the harvesters increased from 87.19 h to 105.65 h, an average annual increase of 1.38%. The average annual working hours of the irrigation machinery declined from 217.24 h to 109.06 h, an average annual decline of 4.80%. The average annual working hours of the other machinery declined from 165.70 h to 114.43 h, an average annual decline of 2.74%.
Figure 8 shows the changes in annual average working hours compared with that in the previous year.The changes in average annual working hours could be caused by changes in actual workloads and changes in the unit productivity.In Figure 8, they are indicated by the red part and the blue part, respectively.
There is no doubt that an increase in unit productivity leads to a reduction in working hours.This is the case for all kinds of agricultural machinery.However, its effect is negligible compared with the effect caused by changes of actual workloads.
Workloads Effect Productivity Effect
Due to the effect of actual workloads, working hours of the tractors, irrigation machinery and other machinery continue to decline from 1996 to 2010.The harvesting machinery is the only one whose working hours have increased.This means that the total power of the agricultural machinery (excluding harvesters) grew faster than the actual workloads.Subsidies policy for purchasing agricultural machines upon 2004 results in the massive growth of the agricultural machinery at the expense of the waste of partial production capacity.Furthermore, economic life of most agricultural machinery does not exceed 15 years in China and a large number of scrapped agricultural machinery need to be recycle every year.Chinese energy policy maker should pay attention to this problem.Some materials such as aluminum and steel, are easily recyclable and thus their post-consumer recycling takes much less energy than production of finished materials from virgin feedstocks [24,25].
In addition, the average annual working hours of agricultural machinery powered by electricity is about 500-600 h in China.It is significantly higher than the working hours of machinery powered by diesel.It is believed that instability of the diesel supply and rising prices are the reasons for the low utilization rate of diesel machinery.
Diesel Flows in Agriculture
Table 5 shows diesel consumption in different sectors of agriculture in China from 1996 to 2010.
Diesel consumption in four farming operations increased from 652.31 × 10 4 ton in 1996 to 884.01×10 4 ton in 2010, with an annual increase of 2.19%.In these four operations, the mechanized harvesting has the largest annual growth rate (i.e., 19.39%) of the diesel consumption.The annual growth rates of mechanized tillage and mechanized sowing are 3.68% and 4.28%, respectively, which are slightly higher than the average level among all farming operations.Slight negative growth in the diesel consumption of mechanized irrigation was witnessed in the past thirteen years. Diesel consumption by primary processing increased from 107.88 × 10 Figure 9 shows the diesel flows of the agriculture in China.It is obvious that farming and transportation are the two largest diesel consumers, while other sectors account for a negligible share.From 1996 to 2010, diesel consumption by farming grew smoothly while diesel consumption by transportation grew sharply.Transportation consumed nearly half of the total diesel in 2010 and its share can be expected to continue to grow in the future.6 shows diesel consumption intensity in different operations from 1996 to 2010.Little change occurred to the diesel consumption intensity of mechanized tillage and its value remained at around 33 kg/ha.The diesel consumption intensity of mechanized sowing declined slightly from 10.46 kg/ha in 1996 to 8.80 kg/ha.By an annual growth rate of 9.99%, the diesel consumption intensity of mechanized harvesting increased rapidly from 7.49 kg/ha in 1996 to 27.27 kg/ha in 2010.Because of the popularity of irrigation machinery powered by electricity, the diesel consumption intensity of mechanized irrigation which is far more than that of other operations declined from 598.99 kg/ha in 1996 to 400.19 kg/ha in 2010, with an annual decrease of 2.75%.
Conclusions
This study is a fundamental research for establishing the agricultural energy demand forecasting model.The simulation results demonstrate that the methodology used in this study is proper and accurate.The conclusions and the relevant policy recommendations are summarized as follows: For agriculture in China, farming and transportation are the two largest diesel consumers, accounting for 86.23% of the total diesel consumption in agriculture in 2010, while the other sectors account for a negligible share.Differing from the farming in this respect, more attention should be paid to the fast growth of the diesel consumption in the transportation in the forecasting model. Technological progress positively affected unit diesel consumption and the unit productivity of all machinery from 1996 to 2010.However, there is great potential in reducing unit diesel consumption and increasing unit productivity.The Chinese government should continue to promote technological progress and to improve in the field of mechanized agriculture. With the development of the agricultural economy, most of the agricultural machinery becomes larger and larger in size, more diesel fuel efficient and productive.However, irrigation machinery has proved to be an exception.Diesel consumption in mechanized agriculture can be reduced by preventing the miniaturization trend of irrigation machinery and raising the proportion of the medium-sized and large-scale agricultural machinery. The annual average working hours of the agricultural machinery (except harvesters) continue to decline from 1996 to 2010.Subsidies policy for purchasing agricultural machines upon 2004 leads to the massive growth of the agricultural machinery at the expense of the waste of the partial production capacity.This means that machinery sits idle in the yard for most of the time.Although this may not directly affect diesel fuel consumption, it directs resources to the manufacturing of agricultural machinery and increases the cost of the agricultural production. The annual average working hours of the agricultural machinery powered by diesel are about 40-120 h which is much fewer than that of agricultural machinery powered by electricity (i.e., 500-600 h).With an adequate power supply and feasible techniques, it is effective to save energy and improve utilization by replacing diesel machinery with electricity machinery.
shows the overall structure of SECA.In the Distribution Module, it is assumed that the change in the number of agricultural machinery with different rated powers follows a certain curved distribution.Simulation results of the curved distribution are processed by the Operation Module and transferred into the Unit Consumption Module and the Productivity Module which generate the weighted average of unit consumption and that of unit productivity, respectively.Then the two weighted averages are input into the Main Module along with workload statistics, machinery capacity and load factors, which are obtained from the database in the Operation Module.Finally, technological progress and other effects are considered in the Correction Module to narrow the gap between the empirical statistics and the model calculations.The following subsections describe each module in detail.
Mechanized sowing m1: Large and medium-sized tractors m2: Small tractors (and associated towing machinery) i follows an exponential distribution as shown in Figure2(a).The equation is expressed below:
Figure 2 .
Figure 2. Distribution curves of the agricultural machinery with rated power.
machinery (excluding tractors) with the representative power , , i m y p in the interval i can be determined.
Figure 3 .
Figure 3. Fitting curves of the unit consumption.
Figure 4 .
Figure 4. Fitting curves of the unit productivity.
In the Productivity Module, the weighted average of unit productivity changes without being affected by the technological progress. In the Main Module, the load factor is proportional to the machinery power in unit area. In the Main Module, the average annual working hours of the other operation is constant the value of which remains unchanged over the year.
[17], China Energy Statistical Yearbook [18], China Rural Statistical Yearbook [19] and China Agriculture Statistical Report [20].The first three data resources are published by the National Bureau of Statistics of China and the last one is published by the Ministry of Agriculture of China.
Figure 5 .
Figure 5. Correction coefficient and diesel consumption statistics.
Figure 6 .
Figure 6.Changes in unit diesel consumption compared with that in the previous year.
Figure 7 .
Figure 7. Changes in the unit productivity compared with that in the previous year.
4 ton in 1996 to 111.78 × 10 4 ton in 2010, with an annual increase of 0.25%. Diesel consumption by animal husbandry increased from 8.07 × 10 4 ton in 1996 to 17.87 × 10 4 ton in 2010, with an annual increase of 5.84%. Diesel consumption by fishery increased from 54.86 × 10 4 ton in 1996 to 68.35 × 10 4 ton in 2010, with an annual increase of 1.58%. Diesel consumption by forestry increased from 0.31 × 10 4 ton in 1996 to 3.95 × 10 4 ton in 2010, with an annual increase of 20.07%. Diesel consumption by transportation increased from 238.74 × 10 4 ton in 1996 to 860.53 × 10 4 ton in 2010, with an annual increase of 9.59%. Diesel consumption by farmland construction increased from 13.94 × 10 4 ton in 1996 to 76.61 × 10 4 ton in 2010, with an annual increase of 12.94%.
Figure 9 .
Figure 9. Diesel flows of the agriculture in China. )
Table 2 .
Unit diesel consumption (g/kWh) of the agricultural machinery.
Table 3 .
Unit productivity of the four operations.
Table 4 .
Average annual working hours (h) of agricultural machinery.Changes in the annual average working hours compared with that in the previous year.
Table 5 .
Diesel consumption (10 4 ton) in different sectors of agriculture in China.
Table 6 .
Diesel consumption intensity (kg/ha) of the operations.
Table A1 .
Workload statistics of the four operations.
Table A2 .
Number and total power of agricultural machinery. | 6,308.4 | 2012-12-06T00:00:00.000 | [
"Agricultural and Food Sciences",
"Economics",
"Environmental Science"
] |
Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter
In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists’ emotions when visiting a city’s tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists’ emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors.
Identification of initial attraction visit dataset
Our entire Chicago visit dataset contains 8,034,025 geo-located tweets originating from 225,805 users collected between May 16, 2014 and April 27, 2015 (missing four days). First, we applied boundarybased identification by finding tweets located within an attraction's boundaries that contain at least one keyword related to that attraction within their texts. In this way, we identified 67,737 attraction visit tweets from 30,574 visitors. Second, we gathered tweets that are shared in six hours of an attraction visit while the visitor is still within the attraction's boundary. In this way, we identified an additional 8,630 tweets from 3530 visitors. Finally, we applied distance-level identification by selecting tweets containing the attraction's full name within the tweet text and located within a one kilometer distance of the attraction's boundary. With this step, we gathered another 5,579 attraction visit tweets from 4248 people. Visit data is shown in Table 1. We eliminated two attractions due to low numbers of tweets (< 50). In total we gathered 81,908 attraction visit tweets from 32,559 unique visitors.
Cleaning the outliers in the initial attraction visit dataset
In order to make sure that we only gathered visit related tweets, we aimed to eliminate outliers from the initial visit tweets based on time of tweeting. Fig 1 illustrates how Chicago attraction visits are distributed over the course of the day. According to the figure, a majority of attraction visits occur between 9AM and 11 PM while the peak attraction visit timeframe occurs between 1PM and 4PM. This result intuitively reflects real-world attraction visit patterns where many attractions are open within these times. Attraction visits tweets also follow a quite different pattern than both general Chicago or USA tweets, in that many of the tweets are shared after 5PM until midnight. This is a positive indication that tweets from attraction visits are distinct from general tweets.
To ensure that the temporality of attraction visits aligns with attractions' operating hours, we use the opening and closing hours of each attraction gathered while compiling the attraction dataset. Excluding attractions that are open 24 hours a day, we filtered the tweets shared outside of business hours of each attraction. We assume visitors can arrive an hour earlier than the opening hour and can
Identification of visitor origin
As mentioned in the main text, we used location information provided in Twitter profiles of visitors contained in the attraction visit list. Table 2 shows the top 30 most commonly reported location information terms used within their profiles. Almost 21% of visitors provided no location followed by a relatively large Chicago/Illinois-related location information. The remainder of the location information mostly refers to major US cities and states. In total, we found 10,615 case-insensitive unique location information from 31,924 visitors.
We apply the two-step visitor origin identification approach to match location information with one of the three visitor origin categories. In the first step, we identify local and out of state visitors providing structured location information. We constructed queries provided in Table 3 to mark these visitors. For the remaining 8,721 visitors, we used Google Maps API to identify their corresponding visitor origins. The US Independence Day celebration related positive tweets from the Navy Pier, Lake Michigan, and Millennium Park-July 04, 2014. We modified the word cloud generation algorithm to account for Twitter jargon (e.g., hashtags) and increase the dictionary for stop-words. We set word clouds output to a maximum of 50 words to maintain readability. Figure 4: The distribution of average daily sentiment values split into four seasons and three visitor types. All visitor types seem to follow the same seasonal sentiment trends explained in the main text. The primary difference is on the magnitude of these scores where internationals have lower median scores than the other two. Figure 5: The distribution of the average daily sentiment values across three visitor types. Local visitors and out of state visitors have very similar score distributions that are relatively higher than international visitors. Looking at the high-level statistics, we noticed that international visitors tend to express neutral sentiment most of the time making their scores lower. | 1,192.8 | 2018-06-14T00:00:00.000 | [
"Computer Science",
"Sociology"
] |
Dose-controlled irradiation of cancer cells with laser-accelerated proton pulses
Proton beams are a promising tool for the improvement of radiotherapy of cancer, and compact laser-driven proton radiation (LDPR) is discussed as an alternative to established large-scale technology facilitating wider clinical use. Yet, clinical use of LDPR requires substantial development in reliable beam generation and transport, but also in dosimetric protocols as well as validation in radiobiological studies. Here, we present the first dose-controlled direct comparison of the radiobiological effectiveness of intense proton pulses from a laser-driven accelerator with conventionally generated continuous proton beams, demonstrating a first milestone in translational research. Controlled dose delivery, precisely online and offline monitored for each out of ∼4,000 pulses, resulted in an unprecedented relative dose uncertainty of below 10 %, using approaches scalable to the next translational step toward radiotherapy application.
Introduction
Cancer represents the second highest cause of death in industrial societies. Today, at a steadily increasing rate, already more than 50 % of all cancer patients are treated with photon or electron radiotherapy during the course of their disease. Radiotherapy by protons or heavier ion beams, due to their inverse depth dose profile (Bragg peak), can achieve better physical dose distributions than the most modern photon therapy approaches. In the case of ions heavier than protons, the higher relative biological effectiveness (RBE) [1,2] might be of additional therapeutic benefit. It is estimated that at least 10-20 % of all radiotherapy patients may benefit from proton or light ion therapy [3,4] and indications are currently evaluated in clinical trials worldwide. Yet, making widespread use of this potential calls for very high levels of clinical expertise and quality control as well as for enormous economical investment and running costs associated with large-scale accelerator facilities. The former point is presently being addressed in clinical research with, e.g., advanced real-time motion compensation techniques, while the latter requires more compact and cost-effective, yet, equally reliable particle accelerators.
As a promising alternative to conventional proton sources, compact laser plasma based accelerators have been suggested [5][6][7][8][9][10][11]. Practically, LDPR originates from hydrogenated contaminants on almost any solid target surface when irradiated with sufficiently intense ultrashort-pulse laser light [12]. Electrons are heated to megaelectronvolt temperatures during the interaction, and driven out of the target volume. In the corresponding electric field, yielding unsurpassed gradients in the megavolt per micrometer range, protons at the surface efficiently gain initial energy [13]. Over the last decade, intense proton pulses with energies exceeding several 10 MeV have been reached with large single-shot laser facilities. Yet, only with the recent generation of table-top 100 TW Ti:Sapphire lasers, operating at pulse repetition rates of up to 10 Hz, energies exceeding 10 MeV [14][15][16][17][18] became accessible for applications where also the average dose rate is of interest, e.g., for providing sufficiently short treatment durations of a few minutes. For the anticipated future application in radiation therapy, a further increase in the proton energy of up to 200-250 MeV is required, which is currently addressed by the investigation of novel acceleration schemes [19][20][21] as well as by ongoing laser development.
Equally indispensable for the development of devices suitable for radiobiological studies and clinical applications is the competitiveness of the laser plasma accelerator with conventional sources in terms of precision, reliability and reproducibility. Research in this field can adequately be performed with available technology and, in particular, presently accessible particle energies as introduced in Ref. [22]. The challenge is the development of a laserbased treatment facility taking into account the specific properties of LDPR, in particular, the comparatively broad energy spectrum and the distribution of the therapeutically required dose in a finite number of very intense pulses, where the level of the peak dose rate in one pulse can exceed the average by up to nine orders of magnitude. This task has to be addressed by translational research, meaning the transfer of the results of the complex and interdisciplinary basic research into clinical practice [23], starting from in vitro cell irradiation, over experiments with animals, to clinical studies. Vice versa the realization of each translational step represents a benchmark of the development status of the laser-driven dose-delivery system to a clinically applicable beam, being the main objective of our work.
In this sense, in this article, we directly compare the RBE of pulsed LDPR and conventionally accelerated continuous proton beams in vitro demonstrating scalable controlled dose-delivery and clinical precision standards for both sources. This work is building on previous work from our group [22] and the radiobiological results are consistent with first experiments performed by Yogo et al. [24,25] and a recent single-pulse study of the RBE by Doria et al. [26] with retrospective dose evaluation.
2 Setup of the laser-driven proton dose-delivery system The experiment was carried out with the ultra-short pulse Ti:Sapphire laser system Draco at HZDR [14], here providing an energy of 1.8 J on target contained in a pulse of 30-fs duration. When tightly focused onto a 2-lm-thick titanium foil target (Fig. 1), a peak intensity of 5 Â 10 20 W=cm 2 can be achieved. All major parameters of the fully computer-controlled high-power laser system are monitored to provide for maximum stability. The proton radiation generated by the target-normal-sheath-acceleration mechanism [12] exhibits an exponential energy Target DNA DSB IC volume irradiation Fig. 1 Left Picture depicting the experimental setup for the irradiation of cell samples with LDPR at the instant of a laser shot. The laser pulse is focused by an off-axis parabolic mirror onto a thin target foil. Protons accelerated in the target normal sheath acceleration regime propagate through a magnetic filter and are transported to the irradiation site inside the air-filled integrated dosimetry and cell irradiation system (IDOCIS). The bottom insert shows a micrograph illustrating immunostained DNA double-strand breaks (DSB) in single-cell nuclei used to quantify radiation induced biological damage. Right The filtered proton energy spectrum at the position of the cell sample is shown. For representative energies of 7, 8.5, and 12 MeV, the normalized energy deposition in water is given as a function of the depth below. As illustrated, the cell monolayer is irradiated in the energy insensitive plateau region of the corresponding Bragg curves, well separated from the range of the energy dependent Bragg peaks, where volumetric sample irradiation would be performed spectrum with a characteristic cutoff energy of up to 15 MeV. The remote-controlled target-alignment procedure ensures a high shot-to-shot reproducibility. A special target foil exchange device allows for about 1,000 shots without breaking the vacuum, sufficient to homogeneously irradiate about 40 cell samples with a dose of 2 Gy.
Directly behind the target the energetic protons pass through a magnetic dipole filter [27] applied to clean the pulse of all protons with energies below 8 MeV. Intrinsically, the direct line-of-sight between the interaction point and the irradiation site is blocked and thus secondary radiation generated in the laser plasma is suppressed. Downstream of the magnetic filter the integrated dosimetry and cell irradiation system (IDOCIS) is located [22,28]. Its interior components for dosimetry and cell irradiation are separated from the vacuum of the target chamber by a thin plastic window. The IDOCIS module integrates a thin transmission ionization chamber for real-time control of dose delivery and a cell holder inset. The latter can be replaced by several reference dosimeters such as a Faraday cup (FC, design adopted from Ref. [29]), radiochromic films (RCF), or CR39 solid state nuclear track detectors to determine the applied 2D dose and spectral energy distribution in the plane of the cell monolayer. For that purpose, an absolute calibration for RCF and FC detectors was carried out before performing the irradiation experiments with laser-accelerated protons for proton energies of 5-60 MeV at the eye tumor therapy centre of the Helmholtz Zentrum Berlin (HZB), Germany [28]. The ionization chamber optimized for lowest ion energies, thus consisting of three metalized kapton foils (each only 7.5-lm thick), is permanently placed in front of the different insets and is used to establish the relationship between FC and RCF and to the real-time control of the dose delivery. It is therefore cross-calibrated to FC and RCF before and after each cell irradiation taking saturation effects at high dose rates into consideration (similar to Ref. [30]).
During the irradiation dose homogenization on a 2 9 6 mm 2 spot size is ensured by multiple rotations of the cell sample. The optimization and control of the homogenous 2D dose distribution in the plane of the cell monolayer and the estimation of the contribution of the inhomogeneity (below 5 %) to the dose error was performed with RCF and CR39 nuclear track detectors.
For the control of the dose deposited into the thin cell monolayer, the proton energy spectrum has to be known. Figure 1 (right) shows a typical normalized spectrum at the cell location deduced from Thomson parabola measurements recorded directly before the cell irradiation campaign and including the transmission of the energy selective beam delivery [22]. During the irradiation experiments, stacks of RCF and CR39, providing a coarse energy resolution due to the energy-range relationship of the stopping power, were used to cross-check the applied energy spectrum in the plane of the cell monolayer and complemented by the online observation of the stability of the spectral filtering with a plastic scintillator positioned between the dipole filter and the IDOCIS entrance pinhole.
In the presented experimental campaign, the use of sufficiently high proton energies at the cell layer position ([6.5 MeV) ensured a constant linear energy transfer (LET). Therefore, significantly less uncertainty in the energy-dependent energy loss was achieved than if the Bragg peak was be positioned at the depth of the cell monolayer. This method yields the most reliable exposure range for systematic studies as illustrated by the normalized energy deposition for the representative energies of 7, 8.5, and 12 MeV as a function of the depth in water depicted below the spectrum in Fig. 1.
Dose effect curves and dose uncertainty
For the irradiation experiment, the radiosensitive human squamous cell carcinoma cell line SKX was used [31]. Cells were seeded 1 day before irradiation on a thin biofilm as bottom of a chamber slide. The plating efficiency was in the range of 15-20 %. Before irradiation, 1 ml of cell culture medium was added, the well was closed with sterile parafilm and the sample was positioned in the horizontal LDPR beam. Further details on derivation, cultivation and handling of the cell line as well as applied sample geometries are provided in Refs. [22,32,33]. The cells were irradiated with a mean dose of 81 mGy per shot that corresponds to a peak dose rate for each proton bunch of 4 9 10 7 Gy/s. The dose was applied in the range between 0.5 and 4.3 Gy (0.43 Gy/min averaged over 1 min) and controlled by means of the ionization chamber in front of the cells.
The biological endpoint of the yield of residual DNA double-strand breaks (DSB) remaining 24 h after irradiation was analysed. It has been shown previously for this cell line that residual DNA DSB correlate closely with cell survival [34]. The DNA DSB were detected by means of fluorescent-labeled antibodies against the active forms of histone c-H2AX [35] and protein 53BP1 [36]. Both molecules were activated and related to the position of radiation-induced DNA DSB [35,36]. The average number of radiation-induced DNA DSB per cell nucleus was counted for each irradiated cell sample and evaluated as a function of the applied dose.
An in-house tandem Van-de-Graaf accelerator served as reference radiation source providing 7.2 MeV protons delivered as a continuous beam with a dose rate of 1.1 Gy/min in a homogeneous beam spot of 35 mm 2 . The equipment and the dosimetry methods, e.g., including IDOCIS module and detectors, horizontal beam application, etc., were identical for both radiation sources (more details in Ref. [28]). As the irradiation setup was initially developed for the polychromatic beam of the laser plasma accelerator, no additional filtering was applied for the case of the monoenergetic tandem beam. For the dosimetry, the spectrum has no further implications, because the cell sample is positioned ahead of the Bragg peak (Fig. 1). Moreover, the location of both radiation sources and a cell laboratory on one site guarantees the direct comparability of radiobiological outcome for laser-driven and conventionally accelerated proton beams. To connect the successive experimental campaigns (LDPR and conventionally accelerated protons), and to identify possible deviations in the biological response arising from biological diversity, reference irradiations with standard 200-kV X-rays (filtered with 7 mm Be and 0.5 mm Cu) were performed in parallel to each proton experiment.
The dose effect curves of the laser-driven proton pulses (red dots) and the conventionally accelerated continuous proton beam (blue squares) are compared in Fig. 2. This direct comparison reveals no significant difference in the radiobiological effectiveness as indicated by the substantially overlapping confidence intervals (2r) of the almost identical linearly fitted curves. Furthermore, a similar level of the relative dose error DD=D could be reached experimentally for both techniques and for each irradiated cell sample. As a major result, this level remains below 10 % as depicted in the inset of Fig. 2 and reaches the order of the clinical precision standard of 3-5 %.
The key to this with respect to LDPR unprecedented level of precision is the synergetic combination of first, the reduction of the uncertainty in the dose delivery caused by beam fluctuations and detector responses using two independent absolute dose formalisms, and second, the reliable operation of the laser-driven proton source based on wellcontrolled laser conditions on target.
The measurement of the precise dose applied to the cell monolayer is based on the implementation of radiochromic films and a Faraday cup into the irradiation site as two distinct, dose rate independent, and absolutely calibrated dosimetry systems. Using these systems, the absolute dose value and the relative dose uncertainty were determined for each irradiated cell sample individually by repeated crosscalibration of the real-time monitor signal of the transmission ionization chamber to RCF and FC directly before and after each irradiation. Performing a weighted average of the RCF and FC signals in combination with the use of sufficiently high proton energies at the cell monolayer position ([6.5 MeV) allowed for this significant reduction of the measurement uncertainty.
A sufficiently high shot-to-shot reproducibility of the proton pulses for the irradiation of single-cell samples could already be demonstrated at Draco in Ref. [22]. Further automation of the laser start-up protocol, monitoring, and the implementation of the target-alignment procedure extended this stability over a total operation period of 3 weeks comprising several thousands of shots. Long-term reliability was investigated by monitoring dedicated proton test pulses on 28 days out of 5 months. Dose and spectrum of these test pulses were measured with an RCF stack positioned 35 mm behind the target foil recording the complete unfiltered spatial proton energy distribution for single reference shots (Fig. 3). The pulse dose measured on the fifth film layer, corresponding to a reference depth of about 1 mm in water, and the characteristic cutoff value of the exponential proton energy spectrum (E max ), were used to characterize the proton beam. The overall average pulse dose of 5 ± 0.8 Gy and the overall average maximum proton energy 13.3 ± 0.6 MeV confirm reproducible system performance at the level required for radiobiological experiments over a period of 5 months.
This in principle allows for extending the experiments presented here to several tumor and normal tissue cell lines as well as to different biological endpoints as it is required to conclude on the RBE of pulsed LDPR for therapeutical applications. As an example, the clonogenic survival assay, commonly referred to as gold standard in radiotherapyrelated research, was independently applied to few homogeneously irradiated probes. A comparison of the survival fraction of cells irradiated with LDPR and the continuous reference beam, using the same protocol as for the previous reference protons laser protons Fig. 2 The averaged number of DNA DSB plotted and linearly fitted as function of the applied dose for each cell sample irradiated with LDPR (red) in comparison with a continuous proton reference beam (blue). The inset shows the relative dose uncertainty for each sample irradiated with LDPR. The error bars on the biological measurements include all systematic errors caused by the used equipment, such as the scale uncertainty of pipettes and the automatic cell counting as well as statistical errors. The background of 0.96 ± 0.06 c-H2AX foci per cell was determined using non-irradiated control samples and is already subtracted for each data point study, is presented in Fig. 4. Again, no difference in the response to the beam properties is observed within the exemplarily investigated dose range.
Conclusions and outlook
Summarizing, laser-driven dose-delivery systems dedicated for the in vitro cell irradiation with proton pulses can be operated at a performance level that is sufficient for radiobiological studies on short as well as on long time-scales and with a precise delivery of prescribed doses. Methods and components of the presented approach such as real-time transmission dose monitoring can be directly scaled to higher proton energies, later required for proton cancer therapy. This performance level has been validated by the independent measurement of two dose effect curves based on different endpoints.
Both radiobiological results are in good agreement with a complementary experiment performed at the Munich Tandem Van-de-Graaf accelerator [37,38] and a recent study of the RBE of intense single pulses of LDPR, where the dose applied to the cells was varying across the probe and analyzed retrospectively for individual irradiated areas [26]. Making use of different pulse modes of the Tandem accelerator, the first study focused on the dependence of the RBE on the peak dose rate by comparing the effect of short-pulses (few nanoseconds) and continuous beams of 20 MeV protons, while the latter directly made use of the intrinsically high peak dose rates of LDPR of up to few Gy per pulse. It thus seems that all existing studies performed for different cell-lines and making use of different sources confirm that in the therapeutically relevant dose range of a few Gy, even if applied in a single pulse of only few nanoseconds duration, non-linear radiobiological effects due to simultaneous multiple damages in cells and, thus, below any timescale of repair mechanisms are unlikely to arise. The next step in translational research will be the extension of the experiments to volume irradiation in animal experiments. In comparison to the studies on biological effects in two-dimensional cell monolayers, these experiments are more complex and require not only higher but also tunable proton energies.
Thus, to be able to proceed independently from the development of laser accelerators, an experimental setup is proposed in Fig. 5. The scheme relies on a refined combination of the techniques described above with an active energy selection filter and dedicated small tumors growing in the ear of mice close to the surface with a volume of about 1 mm 3 . In previous measurements with the Draco laser system, the use of ultra-thin gold disks as targets, similar to the targets presented in Ref. [20], led to an increase in the measured dose per pulse by a factor of at least six in the proton energy range of interest (lower left Fig. 5) with respect to the titanium foil targets used for the dose effect curves in Fig. 2. Simultaneously, these targets enable high-precision alignment at high repetition rates due to lithographic technology-based fabrication.
In addition, the cell irradiation setup presented in Fig. 1 is extended by a pulsed solenoid providing a high capture and transport efficiency of up to 20-25 % measured in [39]. By tuning the delay between laser pulse and solenoid trigger in a multi-shot approach, the energy-dependent beam collimation allows for the active shaping of the reference protons laser protons Fig. 4 The tumor cell survival (for cell line SKX) is depicted after irradiation with LDPR (red) and the reference proton beam (blue) determined using the clonogenic survival assay, as the gold standard in radiobiology. Here, the killing efficiency of the used radiation is measured by plating a certain number of cells after irradiation and counting the number of surviving colonies containing at least 50 cells after 13 days of incubation Dose-controlled irradiation of cancer cells with laser-accelerated proton pulses 441 spectral intensity of the proton energy spectrum given for the cell location in Fig. 1. Thus, a homogeneous proton depth dose distribution (spread-out Bragg peak) can be applied to the tumor without the need to shape the energy distribution in the plasma acceleration process. The demonstrated advances in the performance of compact laser-driven proton sources and dedicated dosimetric techniques not only represent an important milestone on the way to the realization of a clinical laser-based proton treatment facility but also open the door to further applications where ultra-fast and reliable sources are required. . By the use of gold disk targets (around 100 lm in diameter and submicron thickness) the dose per pulse relative to standard planar foils is significantly increased as presented in the depth dose curves measured with RCF stacks 35 mm behind the target (average over 15 consecutive shots each). In addition, the presented cell-irradiation setup is extended by a pulsed solenoid to increase the transport efficiency and to ensure a homogeneous proton depth dose distribution in the tumor (c.f. Fig. 1). The generated polyenergetic divergent proton beam drifts through a pulsed magnetic solenoid lens [39]. By tuning the temporal delay of the laser pulse arrival relative to the current pulse driving the solenoid the proton energy spectrum can be actively shaped on a shot-to-shot basis as illustrated in the right box. The transmission of a certain proton energy ensemble (E 1 \ E 2 \ E 3 ) through the dipole chicane into the IDOCIS module is optimized according to the on-axis magnetic field (B 2 or B 1 ) at the moment the proton pulse passes the coil | 5,110 | 2012-11-25T00:00:00.000 | [
"Physics",
"Medicine"
] |
“Kingdom of God” and Potentia Dei. An Interpretation of Divine Omnipotence in Hobbes’s Thought
The relationships between politics and religion have always been the focus of Hobbesian literature, which generally privileges the theme of the Christian State, i.e. the union of temporal and spiritual power in a sovereign-representative person. This essay presents other perspectives of interpretation, which analyze the relationships between politics and religion in Hobbes’ works by using specifically metaphysical and theological categories – liberty/necessity, causality, kingdom of God, divine prescience, potentia Dei etc. – which allow us to reconsider the nature of political power (and the relevance of modern technology for the contemporary politics). The core of Hobbes’ argumentation concerns the theoretical status of determinism (i.e. the relationships between liberty and necessity) with regard to the reduction of «potentia» to «potestas» not only in political philosophy, but also in metaphysics and theology. In many passages of Hobbes’ works, then, it is possible to understand the role of God’s idea in the natural and political philosophy: God’s idea as first cause or as omnipotence is only a reassuring word useful to describe the necessary, mechanical and eternal movement of the bodies and useful to justify the materialistic determinism in anthropology and politics. Body and movement are the necessary fundaments of the universe which find in itself without reference to the category of «possibility» in politics and in physics the motives and the reasons of his own structure and function (from causes to effects).
Leviathan. Over the last few decades, however, other perspectives of interpretation have become established, which analyze the relationship between politics and religion in Hobbes by using specifically theological categories.1 This essay takes the latter direction, and aims at offering elements to think over the above-mentioned relationship by using two key categories of Hobbesian theology -the kingdom of God and potentia Deiwhich allow us to reconsider the limits of political sovereignty.2
I. Determinism and Divine Prescience: Hobbes against the Theory of Liberty
The theme of potentia Dei is found in the long and articulated debate between Hobbes and Bramhall on the relationship between liberty and necessity.3 The Hobbesian argument is entirely played upon the level of incommensurability: nothing in God -not His wisdom, will, goodness, might or justice -can be judged by the standard of human reality.4 For instance, human justice always implies a law and therefore a contract but this is certainly not the case with divine justice, which is not subjected to any contract or law. God's justice cannot be defined as observance of a law established by a superior power, because such a power does not exist: thus God cannot sin against the law -because He is not subjected to it -and, as a consequence, He cannot be unjust. God's actions are always just simply because He is the one who accomplishes them, the sole bearer of an absolutely irresistible might, the one and supreme holder of all the wills of men: only a person who holds such might is above any law. In Hobbes, the divine attribute of might is thus justified not only at a logical and ontological level, but also at a moral level: The power of God alone without other help is sufficient justification of any action he does. That which men make amongst themselves here by pacts and covenants and call by the name of justice, and according whereunto men are counted and termed rightly just or unjust, is not that by which God Almighty's actions are to be measured or called just, no more than his counsels are to be measured by human wisdom. That which he does is made just by his doing it: just, I say, in him, not always just in us […]. Power irresistible justifies all actions, really and properly, in whomsoever it be found; less power does not, and because such power is in God only, he must needs be just in all his actions.5 God cannot sin. First of all, His actions are just because they derive from His irresistible might. Secondly, only the one who is subjected to a superior law can sin, and this is not the case with God. If so defined -sheltered from any accusation of moral irrationality -divine omnipotence involves, however, two limitations that force Hobbesian argument to stop. First of all, not even God can do everything, because this is something impossible in itself, i.e. something which is self-contradictory. Secondly, there is another kind of things, which are not impossible in themselves, but are rather a consequence of divine decree; all this is incompatible with God's decree itself. The difference between these two kinds of impossible things -the logical impossible and the ontological impossible -is also marked by the role of God's will which, in the first case, is inoperative (and could not be otherwise) whereas, in the second case, it acts precisely to define the present order of the world, the only real one because God wants it. Not only is God's might simply not absolute, but it even seems to be subjected to His own will; yet to formulate an explicit opinion on the nature of that will is not deemed useful by Hobbes, simply because the Divine is unknowable. In spite of this, the Hobbesian argument on universal determinism suggests that divine will is meant to be necessary. In an anti-anthropomorphic vision of God, divine decree must be considered coeternal with Him and coessential to His nature. Divine will is thus eternal, immutable and necessary. It is no coincidence that Hobbes deems it pointless to infer the theological fundaments of universal determinism because the theory of divine prescience is more suitable to justifying the doctrine of necessity. In Hobbesian thought, the relationship between determinism and divine prescience is much tighter than the one between determinism and potentia Dei. It is God's prescience, and not His might, to define His ultimate perfection:6 this latter idea would then be limited if there were agents freed from necessity, i.e. falling outside the range of His prescience. Hobbes sets the issue of potentia Dei at a logical-philosophical and not ethical-political level: determinism and prescience build up the theoretical framework for the actual ordinate action of God. The absolute action of God is only a theoretical model of divine action and relates to the "time preceding" God's decree on the choice of the present order of the world, which, once settled, is eternal and immutable, mechanical and necessary, even when miracles are operated. Hobbes's discourse on potentia Dei is understood as dealing more with liberty and necessity than with omnipotence and miracles; all that happens falls within the range of potentia Dei, as it has been foreseen by Him ab aeterno. Thus there are not two different ways (ordinate and absolute) for God to act in the world, because universal determinism, through the design of divine prescience, is precisely the clearest expression of potentia Dei. Hobbes calls it "God's decree": all things proceed necessarily from His eternal will. God's decree and prescience are one and the same thing with the theory of determinism; they are not two distinct powers in God (necessity and will), but rather the same reality -divine action -that unfolds in the world through the concatenation of the causal series, i.e. through secondary causes. Each event is produced by the convergence of many causal chains and the divine decree is precisely their causal 6 Cf. Q, 18 s., 105, 175 s., 209 ss., 234 s., 246, 423-424, 433 connection, as causal chains are always determined by the immutable will of God.7 The unconditioned beginning of all causal series is to be found in God: so potentia Dei is identified with His being the cause of the mechanical motion of bodies, which has been decided from time immemorial and is therefore immutable. For this reason, Hobbesian materialistic and mechanistic determinism is more a consequence of God's prescience than of His might.
II. "Potentia Dei" in the Kingdom of God by Nature
At the level of philosophical-political argument, many passages of De cive8 and Leviathan9 show the theme of potentia Dei in connection with the image of the "Kingdom of God", which Hobbes uses to point out a precise series of distinctions among a "Kingdom of God by Nature", a "prophetic Kingdom of God" (which is in turn subdivided into Kingdom under the Old Covenant and by the New Covenant) and the "future Kingdom of God". Hobbes immediately identifies the underlying theological-political problem, i.e. the relationship between might and right in the Kingdom of God by Nature: "In the Natural Kingdom God's right to Reign and to punish those who break his laws is from irresistible power alone".10 At this level, there is not any obligation to obey God resulting from a covenant, an alliance, an agreement or a contract, but the divine right to rule arises clearly from nature. Omnipotence gives God the right to rule and man is accordingly obliged to obey him because of his weakness, which generates fear and hopelessness. In the "Kingdom of God by Nature", the relationship between God and men corresponds to the concept characterizing the mutual relationship among men in the state of nature, where the range of natural right extends to the whole range of legitimacy for actions considered useful to the purpose of self-preservation. Hobbes does not ascribe the features of rationality to divine will, because it is a self-determined might: "When we ascribe to God a Will, it is not to be understood, as that of Man, for a Rationall Appetite, but as the Power, by which he effecteth every thing".11 According to Hobbes, if man abides by divine power on the basis of pure might and not of gratitude, this of course must not be considered dishonourable for God; however, it must be noted that such a power has a natural basis and that, at a political level, it corresponds to the one deriving from the claim ex generatione or ex delictu, and not from ex consensu.12 If God is a sovereign whose irresistibility, omnipotence and inscrutability justify obligation and obedience, then He is a sovereign by acquisition, not by Institution; so He is a sovereign ruling over men in a condition similar to the one of the state of nature, in which men live before the foundation of a common power. However, Hobbes soon points out that the expression "Kingdom of God by Nature" contains an anthropomorphic metaphor it would be useful to consider as such, in order to underline the supremacy of might as the divine right to rule the world. This right, however, is not enough to "rule", as it lacks "word", i.e. verbal expression: God is king of the whole earth; and he is not shaken from his throne if a few men deny his Existence or his Providence. God so rules all men through power that no man can do anything which He does not want done; yet this is not Reigning in the precise and proper sense. A ruler is said to reign if he rules through speech rather than action, i.e. if he rules by precepts and threats. In the kingdom of God therefore we do not count inanimate bodies or things without reason as subjects (though they are subject to divine power), because they do not understand God's precepts and threats. Nor do we count Atheists, because they do not believe that God exists, nor those who believe in God's existence but not in his governance here below; for though they too are ruled by the power of God, they do not accept his precepts or fear his threats. The only persons to be numbered in the kingdom of God are those who accept that he is ruler of all things, that he has given precepts to men and set penalties for transgressors. All the rest we should call not subjects but enemies of God.13 The "Kingdom of God by Nature" is built on a juridical foundation that is such only in a metaphorical sense, because His "rule", based on His omnipotence, must never be confused with the "rule" of an earthly sovereign, which is founded on a covenant, an agreement and a decree publicly known, because explicitly pronounced. It is therefore clear that, although defining a virtually all-encompassing range of action over the whole present order of the world, God's might is not a claim to "rule". God is not submitted to the standards of juridical argument, not only because His nature and His actions comply with the extra-juridical language of omnipotence not only because, strictly speaking, He is not a subject (the only category suitable to the specific nature of juridical language), but also because the juridical dimension belongs radically to the human world of becoming and of institutions. In God, ruling and governing are not the same thing. As a matter of fact, although God governs all the creation as the primary cause of every motion, He "rules", in an analogically and metaphorically anthropomorphic form, only over those who believe He is the cause of the world, i.e. over those who recognize Him, through an aware deliberation, as the supreme entity acting directly in the lower world. To simplify: in the Kingdom of God by Nature, the political body does not coincide with the whole of mankind, but only with the community of believers who recognize God as king of the lower world. Therefore, divine omnipotence is not a legitimate right to rule, unless on condition of the voluntary recognition of man. According to Hobbes, ruling through prescriptions means legislating, i.e. to openly proclaim regulations that must be respected by the governed on the basis of the original covenant. In order to respect this very tight link between juridical and linguistic-communicative dimension, divine laws also appear in three manners:14 the rational word (expressed in the dictates of just reason, which are condensed in natural law), the sensible word (expressed in the immediate revelation through the senses) and the word of prophecy (expressed in the display of divine will through a trustworthy intermediary). Among these three manners in which God's words appear, the least relevant in Hobbes's analysis is the one connected with the sensible word (because it only refers to particular people who had direct access to an epiphany of the Divine), whereas the distinction between rational word and word of prophecy is crucial, as it corresponds to the difference between the "Kingdom by Nature" and the "prophetic Kingdom of God":15 A twofold Kingdom is attributed to God; which corresponds to the difference between his Rational and his Prophetic Word. There is the Natural Kingdom in which he rules through the dictates of right Reason. It is a universal kingdom over all who acknowledge the divine power because of the rational nature which is common to all. And there is the Prophetic Kingdom, where too he rules, but by his Prophetic Word. It is a particular kingdom, because he has not given positive laws to all men, but only to a particular people, and to certain specific men whom he himself chose.16 The "Kingdom of God by Nature" is not obviously limited to natural laws, because political societies of an acquisitive and institutive kind can also be established there. The "naturalness" of this divine kingdom depends on how God addresses man (in this case, through the dictates of natural reason), and not certainly on being a state of nature tout court. Thus it is not the mutual relationship among men, but the one between God and men that is in a natural condition, to the extent that the precepts regarding worship and honour of God must be deduced only from natural reason, not from revelation (which is not given at the level of the "Kingdom of God by Nature"). However, the aim of natural laws on divine worship ("to render honour to God") can be achieved more through the establishment of common public policies than through the persistence of private and personal habits, which stand necessarily in mutual contrast. In the "Kingdom of God by Nature" (in which God rules -if recognized -by means of His omnipotence), it is up to the State to judge which attributes and rites render honour to God; a sign of honour to God is all that is decided by the State, the sole legitimate interpreter of natural laws, which is therefore able to establish -being the sole and legitimate holder of the juridical responsibility for worship in front of God -a uniform worship that single individuals must compulsorily abide with.17
III. "Potentia Dei" in the Prophetic Kingdom of God
After the "long night" mankind spent in the darkness of atheism, superstition and idolatry, God led Abraham to the true religion by revealing Himself supernaturally to him, and making a covenant with him through the establishment of the sign of circumcision. In this way Hobbes opens chapter XVI of De cive,18 which is devoted to the Kingdom of God under the Old Covenant. Hobbes asks himself why God accepts to undergo a covenant to obtain obedience, which was already due to Him by nature and recognized to Him as creator of the world?
The words "that I be your God and the God of your descendants after you" do not mean that Abraham satisfies this agreement merely by acknowledging the power and Dominion that God has over men by nature, i.e. by the general recognition of God, which is a matter of natural reason; but by the specific recognition of him […]. It is certain that Abraham believed that the voice was the voice of God and a true revelation, and that he wanted his followers to worship the one who had so addressed him as God, creator of the universe; and his faith lay not in believing that God exists or that his promises are true (which all 17 Cf. E, I.XI.12; C, XV. [16][17][18]XVI.7;L,[570][571][572] Cf. also L, XXXV, XXXVIII, XL. The worship that Abraham owed to God was not the worship of reason but the worship of religion and faith revealed supernaturally. It is no longer the universal God of mankind, who speaks by means of natural reason, but rather the particular God of Abraham, who speaks by means of a special revelation, through which Abraham becomes the only and legitimate interpreter of the law and word of God. Abraham's descendents, the people of Israel, will renew the covenant with the God of Abraham20 by confirming their obligation to Him and, along with that, the establishment of His Kingdom under the Old Covenant. This latter situation is restored and renewed by the revelation of God to Moses on Mount Sinai through which, for the first time in the proper sense, the Scriptures also start to speak of institutive, and of not acquisitive, "kingdom": For although God was their [the people of Israel] king both by nature and by the Agreement with Abraham, they nevertheless owed him not only natural obedience and natural worship, as his subjects, but the religious worship which Abraham had instituted they owed him as subjects of Abraham, Isaac or Jacob, their natural Princes. For the only Word of God that they had received was the natural word of right reason, and there was no agreement between God and themselves except in so far as their wills were included in the will of Abraham, as their Prince. But now, by the agreement made at Mount [Sinai, a kingdom of God by design (institutivum) comes into being over them, as each individual gave his consent.21 In this case, "kingdom" has no anthropomorphic derivation (as was the case, on the contrary, with the expression "Kingdom of God by Nature") because, in His prophetic kingdom, God is a monarch in the proper sense, He is endowed with sovereign power over his subjects who, by making the covenant, accept God as a king, and give their consent to the foundation of the prophetic Kingdom of God, i.e. of a civil kingdom in which God is sovereign under the Old Covenant: "By the Kingdome of God is properly meant a Commonwealth, instituted (by the consent of those which were to be subject thereto) for their Civill Government".22 While analyzing the features of the prophetic Kingdom of God, the Hobbesian argument refers to the theme of potentia Dei, but it does not 19 C,[189][190]L,XXXV. 21 C,[191][192]640. assign a primary role to it. As a matter of fact, in the chapters of De cive and Leviathan devoted to such analysis, discussion hinges on the relationship between civil and ecclesiastical power, and Hobbes tries constantly to carry out a reductio ad unum of the two forms of supremacy in a single sovereignrepresentative person, in an attempt to avoid divisions and conflicts between the two instances of authority, as well as theoretically and practically implement the two philosophical-political theses on unity and indivisibility of sovereignty.23 While tracing back the various periods in the history of the Jews, as summarized in the Old Testament, Hobbes ascertains that civil and ecclesiastical powers have always been an exclusive privilege exercised by a one and single person, holder of the supreme authority to interpret the law and the word of God, who is allowed to decide on the truth of prophecies and authenticity of the prophets, as well as to establish regulations for the conduct of civil life everybody must obey. All the members of the community owe a simple obedience to such a person as regards both religious and civil matters. For this reason, the form of government by right of the Jews can be defined as a "priestly kingdom" or a "royal priesthood", according to whether the king or the priest had the primacy in different historical periods. However, what did not change during all the time leading from Abraham to Jesus Christ was the unity and indivisibility of the two (civil and ecclesiastical) powers in the same function: "In God's Kingdom obedience had to be given to the princes Abraham, Isaac, Jacob, Moses, and the Priest and the King, each in his time […]. If the King or Priest who held sovereign power ordered any other thing that was contrary to the laws, that was an offence by the older of sovereign power, not by the subject; whose duty is to carry out the orders of his superiors, not to dispute them".24 The legitimate authority to legislate and constrain lies only in the function of the secular prince (whether priest or king): so, even in the prophetic Kingdom of God under the Old Covenant, it is clear that awarding God the title of king is not directly relevant, except in an anthropomorphical sense. While ending the section by dealing with the "prophetic Kingdom under the Old Covenant", Hobbes aims to emphasize the two serious forms of degeneration -sometimes mutually interdependent -that undermine the unity of theological-political sovereignty: on the one hand, the attempts to separate civil and ecclesiastical power, thus generating conflicts between the two instances of power that may lead to proper civil wars; on the other 23 Cf. E, II.VII; C, XVI.11-17, XVII.14-28; L, XXXIII, XXXVI-XXXVII, XXXIX-XLII. hand, the claims of single individuals for independence of judgment and interpretation in civil and religious matters. According to Hobbes, the Old Testament supplies arguments that allow avoiding both dangers.
The "prophetic Kingdom of God" refers not only to the revelation of the Old Testament, but also and obviously to the revelation of the New Testament. In this way Hobbes passes from the "Kingdom of God under the Old Covenant" to the "Kingdom of God by the New Covenant".25 Jesus Christ was sent to mankind to restore the Kingdom of God with a new alliance whose sign is baptism, i.e. a new covenant by which man agrees to obey the God of Abraham and believe Jesus is the Christ (whereas God agrees to forgive sins and open the doors of the Kingdom of Heaven). However, the Kingdom of God will not begin until the second coming of Christ on Judgment Day: "hence Christ is not yet seated in the seat of his Majesty".26 The Kingdom of God by new Covenant is far from coming, as the time Christ spent among men does not hint at a kingdom in the proper sense, but rather at a regeneration or a renewal, i.e. a pastoral function. Until the return of Christ, the Kingdom of God is not "of this world" but "of another world", because Christ was not given the authority to rule or legislate by God, but only to teach and advise the way and means of salvation, which cannot be known through natural reason: The Regime under which Christ rules his faithful in this life is not properly a Kingdom, or government (imperium), but a Pastoral office or right to teach, i.e. God the Father has not given him authority to give judgements about mine and thine as he has to the Kings of the Earth, nor to compel by penalties or make laws, but he has given him authority to reveal to the world and to teach the way and the knowledge of salvation, i.e. the authority to preach and to explain to men what they should do to enter into the kingdom of heaven […]. For Christ was sent to strike an Agreement between God and men; no one is obliged to give obedience until after he has entered into an agreement; hence no one would be obliged to accept his verdict, if he had given judgement on questions of right. But in fact, trials of legal matters, whether between believers or unbelievers, have not been entrusted to Christ in this world. This is apparent from the fact that that right belongs without question to Princes, so long as God does not restrict their authority; and he does not do so this side of the day of judgement.27 The office of Christ is composed of three parts, which correspond to three different periods: Redeemer, Shepherd and King. The first part (Redeemer) 25 Cf. C, XVII; L, XLI. 26 C,207. 27 C,[208][209] already came true with Incarnation, in which Christ shows Himself as the Saviour; the second part (Shepherd) is the period in which the return of Christ is awaited and Christ carries out His role of master; the third part (King) coincides with Judgment Day and Salvation. So the kingdom of Christ, which corresponds to the third part of His office, is not of this world and, for this reason, the time of His predication "is not properly a Kingdome, and thereby a warrant to deny obedience to the Magistrates",28 as it -the world to come -will not begin until the final resurrection. The future Kingdom of God will be achieved only in that moment; and it will be achieved on Earth: "Salvation shall be on Earth, then, when God shall reign (at the coming again of Christ) in Jerusalem",29 i.e. not in a coelum empyreum or in the form of a "kingdom of darkness" (which implies the distinction between spiritual and temporal power).30 Discussion on divine omnipotence can be developed at three levels: the ontological and metaphysical dimension; the "Kingdom of God by Nature"; and the "prophetic Kingdom of God". However, it never seems to play a founding role in Hobbes's philosophical system. It is thus clear that Hobbes neutralizes God's might at the level of both natural philosophy and civil philosophy. As to the role of divine omnipotence in the Kingdom of God by Nature, the irresistible power characterizing God in that kingdom has not been of this world ever since the first covenant was made with the Jews, God "rules" over it only in a metaphorical and figurative sense, also because He needs to be recognized to "rule". As to the actual relevance of potentia Dei in the prophetic Kingdom of God, divine intervention in the world is excluded not only by the form of the covenant, but even by the content itself, which postpones the coming of the Kingdom of God to Judgment Day. Moreover, Hobbes denies the presence of divine laws in the Kingdom of God after earthly life.31 So we do not find any trace of divine omnipotence in the actual order of the world. Not by chance Hobbes can afford to say: "For God truly reigns where the laws are obeyed for fear of God, not of men. And if men were as they should be, that would be the best form of commonwealth. But to rule men as they are, there must be power (potentia) (which comprises both right and strength) to compel".32 This power to 28 constrain, the only actual one, is ascribed to the sovereign-representative person, not to God.
IV. The Reduction of "Potentia" to "Potestas" in Political Philosophy
In Hobbes's political philosophy, the personalistic element is a trait which is highly typical of sovereignty.33 However, it is not the only one. In fact, the element of political mechanism is also widely recognised.34 It is no coincidence, then, that Carl Schmitt speaks of the Hobbesian oscillation between decisionism and positivism. Therefore a contrast emerges between two different concepts of the exercise of sovereign power, one absolute and the other ordinate (the theoretical root of which can be traced back to the Medieval distinction between potentia absoluta Dei and potentia ordinata Dei elaborated principally by Ockham and Duns Scotus). On the one hand, we have a concept of a sovereign who exercises ordinate power, in other words "mechanically", his own power adhering to the criteria of positive law which he himself has created. On the other hand, we have a concept of a sovereign who exercises his own absolute power, suspending, in accordance with his own wishes, the validity of positive law, which he himself has created. In this second instance, the civil person, that is the sovereignrepresentative person, is absolute in the sense that he is able to go beyond the totality of positive laws, but, at the same time, the positive laws are the instrument through which the potestas of the State reaches, ordinate, the peak of its own potentia. This parallelism between potentia Dei and the power of the civil person in no way implies an acceptance of the theory of secularisation: the Hobbesian State is the result of human intellect and of human creative ability, and it has it origin only in the pact (pactum unionis), which was conceived in an individualistic manner. Such an individualistic or atomistic foundation does not imply the ideal of a sovereign-representative persona, but, rather, the image of the Leviathan as a machina.35 What matters in the Hobbesian State is not representation through a person, but the factual and real provision of effective protection ("protection in exchange for obedience", Carl Schmitt notes in Der Begriff des "Politischen"), L, 16-18, 324-328, 344, 396, 458, 496-498, 510-512, 518, 540. which can be ensured only by an effectively working command mechanism. This technical character, the concept of machina machinarum, is therefore the specific characteristic of the Modern State, which derives from Hobbes. Thus, personalism is at the service of mechanism and not the other way round. Within this technical-artificial dimension of the State, the legal system performs a central role, above all as the instrument through which laws are transformed into positive commands.36 The State functions as a coercive mechanism which, through the public proclamation of laws, activates justifications of a psychological order, through which the citizen's will can be more directly disposed to obeying whoever equally holds power and might: In Hobbes's sovereign-representative person, power and might are confused, without a meaningful conceptual distinction between them, so much so that on more than one occasion Hobbes uses potentia, potestas and imperium ' equivalent of imperium, then potestas designates the right to command by whoever has the most potentia. Clearly, Hobbes's political mechanism of the State is directly related to the new scientific paradigm asserted by Galileo Galilei. The Hobbesian State must function with mathematical exactness and geometrical precision. However, exactness in the political field means something completely different when compared to exactness in the field of mathematics and geometry. Indeed, mathematics and geometry -unlike politics -are untouched by passions and opinions. In this situation, also, the Leviathan State finds itself radically reduced to technology. The task of the sovereignrepresentative person is to develop techniques that are useful for an effective conservation and running of the State. It is clear that all this presupposes a prior restriction of the political arena, in other words, the restriction of the fundamental questions concerning the purpose of the State to a single condition: the State's role as a guarantor of peace and security. When faced with this compact mechanism which creates and regulates the political order, it is legitimate to ask: does it really make sense to talk of the potentia (or omnipotence) of the State? Are potentia and potestas really the same thing for Hobbes?
With regard to the might of the State, it is right to speak of omnipotence in a wider sense, even though it is nonetheless relative. The State is omnipotent in that it is the foremost power before which nothing -neither "from below" (the citizens), nor from "on high" (God) -can offer any effective resistance. However, no State is omnipotent, neither at a theoretical nor at a practical level. First and foremost it is necessary to consider, at least in theory, the possibility that exonerates the citizen from political obedience: however small, his right to resist is legitimate.40 Secondly, the sovereignrepresentative person has to respect -at the level of his own juridical legitimacy, and even in the absence of effective sanctions -the dictates of natural law.41 Finally, within an international context, each State is confronted by other States, whose sovereignty is equally legitimate.42 Thus, to take it to its absurd conclusion, the Hobbesian State is hypothetically omnipotent only when all rights of resistance and every reference to natural law have been eliminated, and, lastly, once the effective conditions for a worldwide State have been achieved. Clearly, then, the achievement of the conditions required by Hobbesian theory is entirely improbable, as well as theoretically unjustifiable. In speaking of the omnipotence of the Hobbesian State, we are talking about a relative omnipotence, as opposed to one which is absolute.43 However, this does not imply a denial that the English philosopher indicates the way forward for a progressive linguistic and conceptual 'coincidence' of potentia and potestas. In Hobbesian political philosophy this ambiguity is intrinsic, or structural, and it is not restricted only to the relationship between conceptual analysis and terminology (in other words, the Hobbesian 'confusion' between potentia and potestas, might and power). The creation of the Hobbesian State can only be completed when potentia and potestas coincide. The State is the highest and noblest realisation of man's potentia, in the form of potestas. The State is man's greatest work of art, the most important creation of human genius: it is the only historicalconcrete form through which human potential can be realised. The State, therefore, presents itself as an act of human might, as the single legitimate and effective achievement of human ability. As such, the State is -at one and the same time -the pre-eminent legitimate power that can be realised, the strongest coercive force and the highest legal authority (potestas, imperium, auctoritas), because it is the most extraordinary realisation of human might. For this reason, in Hobbesian theory, potentia and potestas are both intelligible in opposition to the difficulties which arise with the actus:44 "It is unthinkable that a man or assembly which has direct and immediate power [in potentia proxima & immediata] of action should hold power [imperium] in such a way that it cannot actually give any commands; for power [imperium] is simply the right to give commands whenever it is possible by nature".45 The act (that is, the State) is that which univocally and legitimately comes into existence as a result of the coincidence of power and might; without the presence of either of these two elements, the act -the State -fails to come about. For the sovereign-representative person, the possibility of exercising acts of command is essential: power and might, together, express the conditions for the possibility of exercising such acts, independently of whether these can effectively be carried out. Moreover, we do not know the content of such acts and, in any case, this does not affect their potential to come about. Misery and nobility, advantages and disadvantages in the action of the State are excluded from the concept of potentia understood as 43 Cf. C,XIV.1,XVI.15;L,X. 44 Cf. C,X.16. 45 C,99. potestas: "For government [imperium] is a capacity [potentia], administration of government is an act [actus]; power [potentia] is equal in every kind of commonwealth; what differs are acts [actus], i.e. the motions and actions of the commonwealth".46 Potentia is that which the sovereign-representative person is able to do in that he has the capacity to do it; whereas potestas is that which the sovereign-representative person is able to do in that he has the legitimacy to do it. The content of the actus is irrelevant to Hobbes's theory of sovereignty. How, then, can men protest against that which is the highest realisation of their own ability and potential? How can a man deny the right of the greatest product of his own intelligence and creativity? Man must be prepared to hand over his own potentia to allow it to be developed by the State as potestas and, therefore, as actus, without which the conditions for the fulfilment of his true abilities cannot exist. Man can fulfil himself completely (and his own well-being) only when the conditions for peace and security exist. Without potestas, there can be no potentia and, therefore, no actus. Without potentia, there can be no potestas and, therefore, no actus. Thus, there is only one way for potentia to become actus, which is by way of potestas created by man himself.
The nature of the relationship between potentia and potestas in Hobbes indicates the absence of a concept of potentia understood as possibility and faculty, as well as readiness to hand and openness. The English philosopher shows the legitimate way to justify the realisation of human potentia in terms of potestas created by man and for man's benefit. Hobbes, however, does not discuss the ends to which the potentia-potestas should be directed. Man possesses a high level of potentia which, in the state of nature, remains completely ineffective. In this situation, it is necessary to provide a theoretical mechanism with which man can autonomously construct a potestas which is able to provide the conditions for the realisation of his own potentia. The resultant institution (the State) is essential for this process of creating potentia-potestas, rather than its end, understood as substantial content. In Hobbes, the actus is a product that has a "formal" value, because it creates the conditions for the effective realisation of human potentia through the mechanism of potestas. However, in Hobbesian theory the ends to which the realisation of potentia must be channelled are totally irrelevant. Therefore, it is indifference with regard to the substantial content of the actus -rather than questions of the amount of power, in other words omnipotence -which influences the Hobbesian interpretation of 46 C, 125. potentia in terms of potestas. It is not by accident, then, that the question of the might of the sovereign-representative person is not expressed by Hobbes in terms of omnipotence. The neutrality of potentia-potestas cannot enter into discussion of the goals of actus. On the one hand, then, the discussion of the actus is superseded by the theory of potentia: what counts is the possession of the capacity to act, independently of the goals to which it is directed. On the other hand, the discussion of the actus is substituted by the theory of potestas: what counts is the legitimacy of the capacity to act, independently of the forms of its concrete use. The goal of Hobbesian political philosophy (the search for peace and security) necessitates the reduction of potentia to potestas through the elimination of the question of the actus.
V. Determinism and Power: "Potentia" as Cause in Natural Philosophy
Hobbes was no fan of the idea of potentia. Nothing in his work, if correctly understood in the light of his complete philosophical system, aims at creating a justification for omnipotence. This is true with respect to both the ethical-political and the metaphysical-theological levels. Even the fact that the Hobbesian God is practically, if not nominally, "impotent" is proof of this: His power is ineffectual at a political level, given that it plays no substantial role in the Kingdom of God by its own nature, nor in the "prophetic kingdom of God".47 Moreover, God's might is even ineffectual at the level of physics, given that this is restricted to His prescience: God's intervention in the world is strictly tied to the eternal laws of causal determinism inherent in its true reality, i.e. the bodies in motion.48 The faults in the mechanism of justification of potentia-potestas can be traced even at the level of Hobbes's political philosophy. Similar considerations also apply to the arguments that Hobbes uses at an anthropological level. Indeed, the fundamentally obscure and miserable condition of man in the state of nature is immediately apparent, for although it is one in which everyone nominally enjoys freedom and absolute power, this proves to be totally inadequate for selfpreservation. On the other hand, human power is dangerous at the level of practical consequences (the war of all against all): where everyone has power, no one has power. Moreover, all traces of man's ineffectual power 47 Cf. C, XV-XVII; L, XXXI, XXXIV-XXXVIII. 48 Cf. E, I.XI; LN,[19][20][21][40][41][42]Cor.,VIII.20,IX.7,XXVI.1. are lost in the civil State; it is, therefore, useful and "productive" (that is effective) only when it is transferred. Lastly, the justification of the most important instrument which is able to develop the power of man, i.e. science, is shown to be both problematic and ambiguous. Despite being the most effective instrument for strengthening human power, science also risks re-involving man in mutual conflict, given that it can produce passions connected with the recognition of the desire for superiority, which has the potential to destroy peace. It is therefore clear that a thinker who tends to 'empty' the idea of power of its contents cannot be labelled as the standard bearer of omnipotence tout court.
However, leaving aside the issue that Hobbes may have wanted to avoid 'filling' the idea of power with more specific contents, it remains undeniable that Hobbes himself was one of the first theorists to initiate -from a formal and argumentative point of view -the modern reduction of potentia to potestas, above all in linguistic terms. This linguistic slippage is clear especially with regard to the determinism of his theory of physics. Hobbes's wish to free himself of the weight of the Aristotelian-Scholastic legacy, especially where the idea of might -one of the key concepts in the teleological tradition of physics -is concerned, is thoroughly understandable. Hobbes's physical mechanism brings to completion the parallelism between cause and effect on the one hand, and might and act on the other. This contributes to the exclusion of the idea of potentia from the semantic field, which is related to the concepts of possibility, faculty and potentiality, and reduces it to an idea of potestas which is mechanically determined in the relationship between cause and effect. Potentia correspond to cause and actus correspond to effect.
When any agent has all the accidents necessarily required by the agent itself to produce an effect on any patient, then we say that agent can produce that effect, if it is applied to the patient. Yet we showed that the accidents make up the efficient cause: so, the very same accidents make up both the efficient cause and the agent's potentia. Therefore the agent's potentia and the efficient cause are actually the same, but they are considered different as to a point: we say cause with reference to the effect which has been already produced and we say potentia with reference to the same effect which still has to be produced, so that the cause relates to the past and potentia relates to the future […]. However they are considered in a different way, since we look to the past for the cause and to the future for potentia. Therefore both the agent's and the patient's potentia, which can also be called entire potentia, is the same as the entire cause […]. Finally, if the produced accident is called effect with reference to the cause, then it will be called actus with reference to potentia. As an effect is produced at the very moment in which the cause is entire, potentia produces likewise the actus it can produce at the very moment in which it is entire. As no effect can arise otherwise than being produced by a sufficient and necessary cause, similarly no actus is produced otherwise than being produced by a sufficient potentia, that could not but produce it.49 It is the deterministic theory of events -and not merely an absolutist option regarding the theory of State or a re-elaboration in modern terms of the medieval concept of potentia Dei -which draws the interpretative background against which Hobbes works out the idea of potentia-potestas. Only a temporal difference (a cause corresponds to an effect in the past, while might corresponds to an act in the future), available exclusively at the linguistic level, distinguishes cause from might. This difference, however, can only be perceived at the human level, given that in itself -on an ontological level -it is absolutely irrelevant. Cause and might are the same because might -where it exists-is not a possibility but a necessity: potentia thus loses its root posse to come together with the idea of necessity. The possible is what is necessary, otherwise it does not exist. It is no accident that future events are contingent only for man, due to his lack of knowledge, but in themselves, they are necessary for the very reason that they have necessary causes, in exactly the same way as past events. Therefore, in reality, might does not exist conditionally; it cannot not exist, otherwise it simply would not be: "The actus, which exists, is a necessary actus; whatever actus there will be, it will be by matter of necessity".50 There is no middle position between not-being and being: might and act are no longer ways of being, but only belong to the realm of representation. For this reason, it becomes substantially impossible to distinguish between active might and the efficient cause, between passive might and material cause, between a plenary might and entire cause, or between act and effect. At this point it is not difficult to see the slippage of the idea of might towards the semantic field delimited by the idea of power. Might does not indicate that which has the possibility of becoming, but is that which has the power of becoming. A power which, indeed, implies the necessity of becoming. No act can exist unless it is produced by a plenary might, in other words an entire cause; and, if the cause is entire, that is, if the might is plenary, it cannot fail to produce an act.51 Potentia is, necessarily, potestas, otherwise it does not exist. 49 Cor., X.1-2. 50 Cor., X.5. 51 Cf. Cor., IX.3-5. | 11,050.2 | 2013-01-01T00:00:00.000 | [
"Philosophy"
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Latest Development on Pulsed Laser Deposited Thin Films for Advanced Luminescence Applications
Currently, pulsed laser deposition (PLD) is a widely used technique to grow thin films for academic research and for industrial applications. The PLD has superior advantages including versatility, control over the growth rate, stoichiometric transfer and unlimited degree of freedom in the ablation geometry compared to other deposition techniques. The primary objective of this review is to revisit the basic operation mechanisms of the PLD and discuss recent modifications of the technique aimed at enhancing the quality of thin films. We also discussed recent progress made in the deposition parameters varied during preparation of luminescent inorganic oxide thin films grown using the PLD technique, which include, among others, the substrate temperature. The advanced technological applications and different methods for film characterization are also discussed. In particular, we pay attention to luminescence properties, thickness of the films and how different deposition parameters affect these properties. The advantages and shortcomings of the technique are outlined.
Introduction
Of late, different physical vapor deposition (PVD) techniques used for coating or thin film growth on solid substrates, such as molecular beam epitaxy (MBE), electron beam physical vapor deposition (EBPVD), pulsed laser deposition (PLD), arc discharge, reactive sputtering and ion beam sputtering, have been used extensively in laboratory research and industrial applications. Among these techniques, PLD have some distinguished capabilities such as the ability to transfer stoichiometry of the material from target to the substrate, high deposition rate, flexibility in wavelength and power density and the ability to deposit multiple layers. The PLD is based on the capability of laser radiation, usually in the ultraviolet (UV) region, to interact efficiently with a solid-state or liquid target resulting in ablation of materials from the target surface and subsequent deposition on a substrate. Depending on the properties of the laser and the target material, the interaction between the laser and the target material is capable of creating a diverse microscopic process, which leads to the local heating and subsequently vaporization of the material. Although the PLD technique was first reported in 1965 [1], the major breakthrough was reported in 1987 when Dijkkamp et al. [2] used the technique to grow YBa 2 Cu 3 O 7 thin films. Over the past 15 years, the PLD has evolved from an academic curiosity into a broadly applicable technique for thin film deposition. Today, different thin film materials including phosphors, complex oxides, polymer-metal composites, nitrides, borides, carbides and even biological materials have been deposited successfully using the PLD technique [3][4][5][6]. Our main interest in PLD is the fact that it can be used for the fabrication of efficient phosphor thin films [7,8]. In addition, the PLD has been lately used to fabricate devices such as photovoltaic cells, electrostimulators, photodetectors, light emitting diodes (LEDs) and superconductors. Considering its utility, only a few synthesis techniques have gained such a rapid popularity in both academic research and industrial applications. Hence, it is worth reviewing the fundamentals of and recent progress made in academic research using this unique research technique.
Herewith a brief background on the principle of operation and some advantages and disadvantages related to the PLD technique are presented. A comprehensive review of luminescence properties, thickness of the films deposited using the PLD and how different deposition parameters such as time, atmosphere (environment), substrate temperature, background gas pressure, the distance between substrate and the target is presented. We also discuss recent modifications of the PLD, and how these modifications improved the quality of the deposited films. In addition, we also discuss some recent applications of the PLD.
Background and Theory
Conceptually, PLD is a very simple technique, which involves the collection of material removed from a target under a pulsed laser irradiation on a nearby substrate. Among other thin film deposition techniques, the PLD has many advantages, which include the controllable rate of film deposition by adjusting the laser parameters, deposition time, target to substrate distance, deposition time and changing the background gas. The key features of the PLD include congruent transfer of the target material to the substrate (i.e., transfer of the stoichiometry of the target material on the substrate). The ability to deposit multi and novel layers by irradiation of consecutive target of individual materials. Deposition at extremely high temperature and high rate.
Contrary to the simplicity of the system set-up, the principle of PLD involves four complex physical phenomena, which include laser beam-target interaction, ablation dynamics and plume formation, ablated materials evaporation onto the substrate and nucleation and growth of thin films.
Laser Beam-Target Interaction
To minimize the amount of energy lost due to carrier or thermal diffusion during absorption, short laser pulses with a wavelength strongly absorbed by the material are recommended. To avoid overlapping between the laser beam and the plume, the beam was irradiated onto the target at an incident angle of 45 • as shown in Figure 1. The ability of lasers to operate in the Q-switch mode (laser ability to produce a pulsed output beam) enabled the femtosecond, excimer (XeCl 308 nm, KrF 248 nm and ArF 193 nm), ruby (694 nm) and Nd:YAG (1064 mm, 532 nm, 355 nm and 266 nm) to deposit an efficient amount of energy into a thin layer of the target. When an incident laser beam hits the surface of the target, it is absorbed and energy is transferred to the electrons in a material. The absorbed energy causes oscillations of the electrons in the material. The energy per unit area absorbed by the target material is a function of the fluence (time integral of laser intensity over the pulse duration) of the laser. The average beam fluence during the film deposition is similar to those needed to heat the target material above its melting temperature and trigger evaporation. The underlying principle behind evaporation of the target material upon laser beam absorption includes collision cascade among the atoms in the material, oscillation of the electrons, electron excitation and electron-lattice (ions) energy transfer. Once the energy transferred by the laser beam to a single atom exceeds its binding energy, the atom is ejected from the surface of the material [9]. The ejection of the atom is followed by a snapping sound and bright-colored plasma (plume) of the emitted particles. The intensity of the laser beam hitting the surface of the target is defined by Lambert-Beer's law [10] given by Equation (1): where I(x) is the reduced intensity at an ablated layer of thickness x below the surface of the target, I(x 0 ) is the intensity before the laser beam hits the target and α is the absorption coefficient of the target material. The optical penetration depth (δ) (attenuation length) is given by Equation (2) [11]: The absorption coefficient of a material is related to the refractive index (n) by Equation (3) [12]: where λ = 2πc⁄ω is the laser wavelength [9], c is the speed of light, ω is the angular frequency, κ α = nκ 0 is the absorption index and κ 0 is the attenuation index. Equations (2) and (3) show that δ is directly proportional to λ (i.e., inversely proportional to energy of the laser light) and inversely proportional to the refractive index (n) of the material.
The Dynamics of Ablation of Materials and Plume Formation
Laser ablation has been analyzed using different models, namely mechanical, photochemical, thermal, photophysical and the defect model. In these models, ablation was treated as the dominant mechanism. The ablation process starts with a single or multi-phonon excitation in the target material. The optical properties of the material are changed with an increasing temperature by the instantaneous conversion of the excitation energy into heat. Thermal ablation occurs when the temperature at the surface of the target material is increased, without the surface melting [13]. The ablation threshold ranges between 0.1 and 1 J/cm 2 depending on the target material and the laser wavelength [9]. The ionization of the target material induces plasma (plume) formation immediately when the ablation threshold of the material is reached. The plume with stoichiometry similar to that of the target material is collected on a substrate placed at a relatively short distance from the target ( Figure 2).
The Evaporation of the Ablated Materials onto the Substrate
The quality of the thin film, to a large extent, depends on how the ablated species is evaporated onto the substrate. This could be affected by the different laser parameters, such as the laser energy, pulse repetition rate and number of pulses. These laser properties can influence the energy of the ejected species arriving on the substrate [14]. A high plume density can be formed when the average intensity of the laser beam exceeds the ionization threshold of the material [15]. It should be noted that damages could be induced on the substrate at a very high plume density. The interaction mechanisms between the incident plume flux and the substrate involve three steps. These include (i) the sputtering of atoms by energetic incident plume from the surface of the substrate and (ii) the creation of collision region (thermalized region) between the incident plume and the sputtered atoms. The collision region serves as particle condensation source that initiates the final step, i.e., (iii) film growth.
The Nucleation and Growth of a Thin Film on the Surface of the Substrate
During the nucleation and growth stage of the film, there is a transition from the plasma (plume) phase to a crystalline (solid) phase on the substrate surface. Crystalline thin film nucleation and growth depends on several factors, namely the laser energy, pulse repetition rate, density and degree of ionization of the ablated material, substrate temperature, physicochemical properties of the substrate and background pressure. However, the two major thermodynamic parameters involved in the growth mechanism are the substrate temperature (Ts) and the supersaturation (S) that takes place between the plasma and solid phase of the material during crystallization. These two parameters are related by Equation (4): where k B is the Boltzmann constant, R is the rate of deposition and R e is the equilibrium deposition value at temperature T [16]. Equation (4) shows that supersaturation varies directly with the substrate temperature. A small value of supersaturation is characterized by large nuclei, which lead to creation of dispersed patches (islands) of the films on the surface of the substrate. At this low value of the supersaturation, the interstep distance between the islands increases and in turn the growing surface becomes smooth [17]. As the number of the clusters impinging on the surface of the substrate increases, the island density increases [18] and the nucleus of the islands shrinks to the atomic level as the supersaturation increases [16]. For a further increase in the supersaturation value (at high substrate temperature) the islands emerges via coalescence phenomenon (which is liquid-like for some cases) [18]. High supersaturation rate may be required to initiate nucleation. However, a low supersaturation rate is needed at a later stage to facilitate a single crystal film growth [19]. The mean thickness t at which the growing and discontinuous thin film reaches continuity is given by Equation (5): where T s is the substrate temperature, R is the deposition rate and A is a constant related to the properties of the material [20]. Generally, there are three modes of thin film growth, namely island or the Volmer-Weber mode, layer-by-layer or the Frank-van der Merwe mode and layer plus island or the Stranski-Krastanov mode.
Island or the Volmer-Weber mode: an island growth occurs when the cohesion between the atoms of the target material is greater than the adhesion between the target atoms and the substrate. As a result, the adatoms (atoms deposited on the surface of the substrate) are more bound to each other than to the substrate, hence forming clusters [18]. This mode of growth is characterized by three-dimensional (3D) islands ( Figure 3a). Layer-by-layer or the Frank-van der Merwe mode: layer-by-layer growth occurs when the adhesion between the adatoms and the substrate is greater than the cohesion between the adatoms. This mode of growth generally results in 2D growth with the adatoms forming smooth monolayers on the surface of the substrate [21] (Figure 3b).
Layer plus island or the Stranski-Krastanov mode: layer plus the island growth mode occurs when islands are formed after the formation of one or two monolayers on the surface of the substrate (Figure 3c) [18].
Advantages and Disadvantages of PLD
Compared to other PVD techniques such as MBE, EBPVD, arc discharge, ion beam sputtering and reactive sputtering and chemical vapor deposition (CVD) techniques, the PLD technique has shown some outstanding advantages. In CVD, precursors are used as the starting material, while in PLD solid targets are deposited on a substrate using laser ablation. In PLD, the rate of the film growth can be controlled by changing background gas, adjusting the laser parameters, deposition time and substrate to target distance [22]. Some of the major advantages of PLD over other PVD techniques are the transfer of the stoichiometry of the target material deposited on the substrate, deposition at high temperature and high deposition rate [14,23]. Another advantage of PLD is that a very short time frame is required for deposition.
A major disadvantage of PLD is the non-uniformity of the particulate size across the surface of the film. This non-uniform distribution of particulate size is due to the presence of molten material (up to 10 µM) in the ablated material. Lately, PLD has been modified in different ways to improve the quality of deposited films.
Recently Modified PLD Arrangement
One of the major drawbacks encountered during deposition of thin films by laser ablation is splashing. Splashing refers to the formation of small melt droplets during ablation, which subsequently land on the substrate. These microsized droplets form islands on the surface of the film. In the last decades, different geometrical configurations have been used in the PLD technique, usually by varying the orientation of the substrate, target and the laser beam side entry with respect to one another [24][25][26][27][28], either to solve the problem of splashing or to generally improve the quality of the deposited film. In a quest to achieve high quality thin films, researchers have continued to modify the geometrical configuration of the PLD set-up as follows:
Scanning Multi-Component Pulsed Laser Deposition
Fischer et al. [29] proposed a "scanning multi-component pulsed laser deposition method" and the set-up they used in their experiment is shown in Figure 4. The multi-component coatings allowed them to deposit single or multilayers onto a substrate through laser induced ablation by horizontal line-scanning of a laser (femto-second laser) beam across a segmented targets. This type of arrangement enabled the deposition of a desired composition of films from the various segments of the target (which contain different materials) by moving the scan line with respect to the target geometry. The advantages of this set-up over the conventional PLD set-up are a large area and uniform deposition of multicomponent coatings.
Combined PLD and Magnetron Sputtering
Benetti et al. [30] demonstrated a thin film deposition system, which combined PLD and magnetron sputtering (MS). The schematic arrangement of the PLD/MS hybrid system is shown in Figure 5. The PLD/MS hybrid system is designed such that the plume generated by pulsed laser on interacting with the target triggers and maintain the magnetron discharge at pressure lower than the standard MS system. The film structure and thickness are influenced by the ionic and neutral species, which are deposited on the substrate after passing through the magnetic field. The major advantage of the combined PLD/MS system over the conventional PLD is the increased deposition rate, which is achieved by the increased plasma sputtering rate and direct pulsed laser deposition of clusters and neutral atomic species.
Matrix Assisted Pulsed Laser Evaporation (MAPLE)
The matrix assisted pulsed laser evaporation (MAPLE) technique has been widely reported for the gentle deposition of inorganic, polymers and biomaterials [31][32][33][34][35][36][37][38]. Yang et al. [39] have published a comprehensive review paper on MAPLE. A schematic diagram illustrating the experimental set-up for MAPLE is shown in Figure 6. Generally, a frozen target is used in MAPLE. The target consists of a polymeric compound (inorganic or biomaterial as the case may be) usually dissolved in a volatile solvent. Upon laser ablation, an ample amount of the laser energy is absorbed by the molecules of the solvent. With this, the molecule of the material is protected from being damaged by the high energy laser beam. The energy absorbed by the solvent molecule heats up the target molecules while the solvent vaporizes [31]. The target molecules are converted to the vapor phase when they have absorbed a substantial amount of energy via collisions with the solvent molecules. The evaporated target molecule is easily deposited on the substrate placed in the opposite direction. The solvent molecules, however, are pumped out by means of the vacuum pump since they have lower adhesion coefficient [32].
Multi-Beam PLD
Multi-beam PLD involves a simultaneous deposition of a thin film from a multitarget of dissimilar material by mixing of more than one plume. This is usually achieved by the ablation of different targets with a different laser beam [40,41]. A schematic showing a typical multitarget PLD experimental set-up is presented in Figure 7. It is comprised of three rotatable targets, which can be ablated using three different lasers [42]. The targets also have a programmable tilt capability and adjustable distance from the substrate. The plumes generated by the three lasers can be time-delayed or synchronous, which gives room to control the respective contribution of each target by either adjusting the laser repetition rate or by blocking the laser beam from reaching a given target using a computer automated shutter.
Off-Axis PLD
Off-axis PLD arrangement involves placement of the substrate perpendicular to the target (Figure 8), in contrast to the on-axis arrangement (used in the conventional PLD) where the substrate is placed in the opposite direction to the target [44]. Thin films deposited using the off-axis technique have shown superior qualities such as about seven times thinner, 15 nm compared to 100 nm obtained from the film deposited using on-axis setup [45] and uniform films with a larger film area, up to 2 inches (~5 cm) in diameter [46].
Overview of Devices Fabricated Using PLD
Recently, PLD has been used for the fabrication of different technological devices photovoltaic cells and light emitting diodes (LEDs). Elhmaidi et al. [47] used the PLD to fabricate photovoltaic cell devices based on p-type Cu 2 ZnSnS 4 (CZTS) layers deposited on n-type silicon nanowires (n-SiNWs). To optimize the photoconversion efficiency of the cells, thin layers of the p-CZTS/n-SiNWs heterojunction devices with CZTS thicknesses ranging from 0.3-10 µM and SiNWs lengths ranging from 1-6 µM were fabricated. They achieved a record breaking power conversion efficiency of 5.5% from the p-CZTS/n-SiNWs heterojunction device with a CZTS thickness of 540 nm deposited on length SiNWs of 2. [53] reported a PLD grown n-ZnO/(InGaN/GaN) multi-quantum-wells/p-GaN green emitting LEDs. They grew the film on a semi-insulating AIN/sapphire. The LEDs showed a characteristic turn-on voltage of 2.5 V and the EL spectrum measured at room temperature showed a maximum around 510 nm. Cheng et al. [54] published n-Zn 1-x Cd x O/p-GaN heterojunction LED with tunable color. The films were grown on c-sapphire and p-GaN substrates. By increasing the concentration of Cd, the color emitted by the LEDs changes from blue to green. Su et al. [55] reported white light emitting n-ZnO/p-GaN heterojunction LEDs. The characteristic turn-on voltage of the LED device was reported to be 3.5 V. Huang et al. [56] published a tunable color heterojunction LEDs based on n-ZnO/CsPbBr 3 /p-GaN layers. The EL spectra of the LEDs changed from violet to greenish-yellow as the thickness of the CsPbBr 3 layer was varied. The introduction of the CsPbBr 3 layer increased the turn-on voltage of the heterojunction LED from 3.7 to 5.0 V. The EL showed a narrow violet emission around 420 nm originating from the p-GaN layer and a broad deep emission band from ZnO. Zhang et al. [57] reported LEDs based on n-ZnO:Ga/i-ZnO/p-GaN:Mg heterojunction fabricated on a sapphire substrate. The turn-on voltage of the LEDs increased from 2.5 to 5.0 V after the incorporation of the i-ZnO layer. The EL spectrum of the n-ZnO:Ga/p-GaN heterojunction exhibited a bluish-violet emission with a maximum around 425 nm, while the heterojunction incorporated with i-ZnO (n-ZnO:Ga/i-ZnO/p-GaN) exhibited EL spectrum with bands centering at 386 and 424 nm. The bands at 386 and 424 (425) nm originated from ZnO and p-GaN layers, respectively.
Experimental Procedure
An example of the experimental procedure for PLD thin film preparation will be discussed in this section. A simplified conventional PLD set-up has been shown earlier in Figure 2, it comprises of a vacuum chamber, a laser beam, sample and the target stage. As previously stated, the target is usually irradiated at an angle between 45 and 60 • . Common substrate materials usually used in PLD are yttrium stabilized zirconium oxide (YSZ), strontium titanate (SrTiO 3 ) [58], magnesium oxide (MgO) and sapphire (Al 2 O 3 ) [59,60]. Some semiconductors such as silicon (Si), gallium oxide (β-Ga 2 O 3 ) [61], fused silica (SiO 2 ) [62], zinc oxide (ZnO) and gallium arsenide (GaAs) [60] have also been used as substrates. For a high temperature deposition, substrate material that can match such temperature should be chosen. The substrate is usually positioned parallel and opposite to the target in a conventional PLD set-up with a variable substrate to target distance. The deposition chamber is commonly pumped down to around 10 −6 Torr. Background gases such as argon (Ar), oxygen (O 2 ), nitrogen oxide (N 2 O), nitrogen (N 2 ) or other gases may be pumped through the chamber to slow down the plume during deposition. In some cases, thin film can be deposited in the vacuum by introducing any gas into the chamber. The laser parameters can be varied. Depending on the wavelength of the laser used, the laser energy can be as low as 40 mJ [63,64] or as high as 200-300 mJ [59]. With these laser energies, a repetition rate of 10-50 Hz, deposition time of 15-55 min and film of thickness between 150 and 500 nm can be grown [63,64].
Structure, Morphologies and Luminescence of Thin Films Deposited Using PLD
The field emission scanning electron microscope (FESEM) cross-section image of the bSi fabricated on a Si substrate reported by Sarkar et al. [51] is shown in Figure 9a. The FESEM micrograph revealed that the bSi formed vertical cone-shaped nanostructures of the diameter between 500 and 700 nm, height between 1 and 3 µM and tips of a few nanometers in diameter. The transmission electron microscope (TEM; Figure 9b) of the cross-section of the Si nanostructure revealed the nanocones shape. The porous structure of the Si nanocones at the surface of the cone resulting from the chemical etching is shown in the inset of Figure 9b. The selective area electron diffraction (SAED) pattern (Figure 9c) confirmed that the core of the nanocones were crystalline despite the pore (2-5 nm) observed at the outer shell. The cross-section of the FESEM image of the CdS thin film laser ablated on a bSi nanocones is shown in Figure 9d. The FESEM micrograph confirmed a uniform coverage of CdS thin film with thickness between 50 and 80 nm on the surface of the bSi nanocones. The thickness of some thin films recently deposited using PLD is summarized in Table 1. The uniformity of the CdS layer on the surface of the nanocones is further confirmed by the FESEM image shown in Figure 9e. As shown in Figure 9f, the SAED pattern of the CdS/bSi heterojunction shows the diffraction rings confirming the polycrystalline nature of the CdS thin film. The SAED pattern also revealed the (002), (110) and (112) planes of wurtzite CdS [51]. λ = wavelength (nm), RR = repetition rate (Hz), LF = laser fluence (J cm −2 ), PW = pulse width (ns), LE = laser energy (mJ). Figure 10 shows the FESEM images of Ga 2 O 3 thin films we reported in our recent work [63]. The films were deposited at different substrate temperatures (room temperature, 100, 200, 300, 400, 500 and 600 • C) and the images are shown in Figure 10a-g respectively. We observed that the morphology of the particles was temperature dependent, with the films deposited at lower temperatures comprising of an agglomeration of spherically shaped nanoparticles while the films deposited at a higher substrate temperature were composed mainly of cubic shaped nanoparticles. The formation of nanocubes is due to preferential crystal plane orientation over others during the growth of the films [76,77]. These variations in the particle morphologies resulted in a significant variation of the shapes of the photoluminescence (PL) spectra depicted in the 13th figure. Figure 11a shows the normalized PL spectra of the bSi, a control CdS thin film and the CdS/bSi heterojunction excited at the wavelength of 325 nm [51]. The PL spectrum of the bSi exhibited a broad band with a maxima around 644 nm assigned to the quantum confinement of the charge carriers present in Si nanocrystals formed on the porous surface area of the bSi nanocones [78][79][80][81]. The control CdS thin film showed a sharp peak around 512 nm assigned to the near band edge (NBE) emission from CdS [82,83] and the broad band with maxima at 714 nm, which is assigned to the transition of trapped electrons from Cd vacancies to the CdS valence band [73,84,85]. The emission spectra from the CdS/bSi nanocones heterojunction comprises of bands from both Si nanocones and CdS thin film. The deconvoluted PL spectrum of the CdS/bSi heterojunction shows three peaks with maxima at 512, 644 and 714 nm as shown in Figure 11b, which are in agreement with the peak positions of the individual peaks obtained from the PL spectra of Si nanocones and CdS films depicted in Figure 11a. The EL emission spectra of the n-CdS/p ± bSi heterojunction at different forward bias voltage (5-30 V) are depicted in Figure 12a [51]. The EL spectra exhibit broad bands covering almost the entire visible wavelengths and some part of the near-infrared (NIR) region. The inset of Figure 12a is the digital image of the EL emission of the device when a forward bias voltage of 15 V was applied. The deconvoluted EL emission spectra of the n-CdS/p ± bSi heterojunction when applying a 10 V bias (Figure 12b) exhibited three bands with a maxima at 530, 652 and 735 nm, which were in good agreement with the deconvoluted PL emission spectrum shown in Figure 11b. The integrated EL intensity of the fabricated n-CdS/p ± bSi heterojunction (Figure 12c) showed almost a linear behavior with increasing bias voltage from 5 to 15 V and later showed a saturation tendency. The Commission Internationale de I'Eclairage (CIE) chromaticity coordinate of the n-CdS/p ± bSi heterojunction calculated from the EL data obtained when 15 V bias was applied is shown in Figure 12d. The chromaticity coordinates of x = 0.35 and y = 0.41 suggests a yellowish white emission. [51] with permission. Figure 13 shows the PL emission spectra associated with the Ga 2 O 3 films whose FESEM images are presented in Figure 10. The spectra were recorded when the films where excited using a 325 nm He-Cd laser. The PL emission spectra in Figure 13a-g are of the Ga 2 O 3 thin films deposited at different substrate temperatures, namely room temperature, 100, 200, 300, 400, 500 and 600 • C, respectively, as reported by Ogugua et al. [63]. The PL emission spectra showed temperature dependent behavior (like the FESEM micrographs (see Figure 10)) with peaks in the UV, blue, green and red region of the spectrum. The positions of the fitted PL peaks observed at the different substrate temperatures are shown in Table 2. These emission peaks were ascribed to the self-trapped excitons [86], defect donor recombination levels created by oxygen vacancies and the hole in the acceptor level formed by gallium vacancies or gallium-oxygen vacancy pair [87,88] and the presence of impurities such as Li, Zr, Be, Ge, Si and Sn [87], respectively. Figure 13h shows the behavior of the PL emission intensities of the films as a function of substrate temperature. [52] reported the PL and EL spectra of PLD deposited ZnO. Their PL of the film excited using a 325 nm He-Cd laser with a maximum emission at 375 nm. The EL recorded using a bias of 7 V showed emission peak with a maximum at 385 nm. Both the PL and the EL emissions were assigned to the near-band-edge emission of ZnO [52]. Tetsuyama et al. [89] deposited ZnO on the p-GaN substrate and the PL spectrum recorded using a 325 nm He-Cd laser showed the near-band-edge emission at 380 nm and a broad visible emission with a maximum around 525 nm attributed to oxygen vacancies [89]. Novoa-De León et al. [90] reported the PL of nitrogen-doped graphene quantum dots with an excitation wavelength assigned to the π→π* transition at 410 nm and emission wavelength with a maximum at 486 nm assigned to the n→π* transition. Caballero-Briones et al. [91] prepared carbon thin films and measured the PL using a 325 nm He-Cd laser.
The PL emission of the carbon film showed a broad spectrum with peaks at 482, 525 and 634 nm and maximum at 577 nm assigned to the radiative recombination in band-tail states created by sp 2 clusters. Yamada et al. [92] reported the PL of Si nanocrystallite deposited on a Si substrate covered with In 2 O 3 thin film, while Kim et al. [67] reported the PL of tin indium oxide (TIO) and aluminum doped zinc oxide (AZO) deposited on a flexible substrate, and Johnson et al. [93] reported the PL of poly [2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) deposited into ITO/MEH-PPV/Al. Table 3 presents some other thin film materials recently prepared using PLD and their PL characteristics.
Other Potential Applications of PLD
Biomaterials are widely used for replacing irreversibly damaged tissues in the human body, which demand good functioning and long-term durability of the implants, namely biocompatibility, good corrosion and fatigue resistance, wear resistance and biomechanical compatibility [107]. In vivo studies of polarized hydroxyapatite ceramics have shown polarized samples to induce improvements in bone ingrowths. Many piezoelectric ceramics proposed for implants are based on perovskite oxide ferroelectric barium titanate BaTiO 3 (BTO) Jelinek et al. [108] has successfully deposited BTiO layers by PLD on TiNb, Pt/TiNb, Si (100) and fused silica substrates using various deposition conditions. Si wafers are extremely utilized substrates to build electronic devices, such as complementary metal-oxide-semiconductor chips and BTO is an important perovskite ferroelectric oxide due to its high-dielectric constant and large piezoelectric coefficient. BTO thin films have been studied for many applications such as piezoelectric detectors, thin film capacitors and magnetoelectric devices. The deposition of BTO thin films with PLD on the Si substrates, paved the way to integrate BTO into microelectronic devices [109].
PLD films have also been applied as protective barriers in corrosive environments. Amorphous Ta-Ni films produced by PLD offer extremely high corrosion resistance in both acid and alkaline solutions. An Y 2 O 3 thin film (160 nm) deposited on the Zn-22Al-2Cu alloy produced an improvement of up to 75% in corrosion resistance in aerated water of pH 4.5, in comparison with the alloy without the film. In addition, the PLD technique can be used to form alumina-stabilized zirconia films on fused silica substrates for both corrosion resistance and thermal barrier purposes [110].
A femtosecond PLD has been used by Murry et al. [111] to fabricate solid state nanoparticulate Si thin films on a fused silica substrate. Fabrication parameters have been studied in order to form high quality thin films with a continuous film profile and a smooth surface, ideal for optical and optoelectronic applications.
Conclusions and Challenges
Luminescence materials otherwise known as phosphors have found applications in electronic information display, advertising, solid-state lighting, solar cells, theft prevention, medicine, data storage, quality control, optical amplifiers, optical laser, scintillation and temperature measurement in both industrial and biological systems. Although phosphors comes in a powder form, most of their applications is based on thin film forms. In most commercial applications, for example in optical industries, microelectronics and other modern technologies, films of uniform thickness on large-area substrates are of interest. PLD is widely used in the research laboratories for the growth of thin luminescent films because of the simplicity of the technique. Furthermore, it is quite promising for diverse commercial applications, particularly, in the growth of large-area thin films. This review was a brief overview of application of the PLD technique in the preparation of luminescence materials. Compared to other popular techniques such as vapor deposition and chemical vapor deposition (CVD), PLD offers flexibility in terms of deposition parameters such as background gas, laser parameters, deposition time and substrate to target distance, which can be varied to enhance the quality and optimize the fundamental properties of the films. Major advantages of the PLD are its ability to transfer the stoichiometry of the target material to the substrate, high temperature deposition and high rate of deposition. Since there are needs to make most devices smaller, it is crucial to develop a technique that can deposit a nanoscale layer thin films. PLD has been used to deposit thin films of thickness as low as 5 nm. Several devices such as photovoltaic cells, electrostimulators, photodetectors and LEDs have been fabricated using the PLD technique. One of the major drawbacks of the PLD is splashing. The splashing phenomenon can be reduced or eliminated by smoothening the surface of the target after each deposition. Smooth surface help in reducing splashing since it minimizes the presence of rough irregularities, which cause defoliation by the laser beam. Another way of reducing splashing is by making the target as dense as possible. A dense target reduces or avoids the exfoliation of the material from the target surface during laser impact. Splashing can also be reduced by using low laser power density. The energy density of the laser should be slightly above the evaporation threshold of the material. Furthermore, different deposition arrangements that have been developed recently to minimize or eliminates splashing during film deposition were discussed in Section 3. | 7,998.2 | 2020-11-09T00:00:00.000 | [
"Materials Science",
"Physics"
] |
Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks
Primary management for head and neck cancers, including squamous cell carcinoma (SCC), involves surgical resection with negative cancer margins. Pathologists guide surgeons during these operations by detecting cancer in histology slides made from the excised tissue. In this study, 381 digitized, histological whole-slide images (WSI) from 156 patients with head and neck cancer were used to train, validate, and test an inception-v4 convolutional neural network. The proposed method is able to detect and localize primary head and neck SCC on WSI with an AUC of 0.916 for patients in the SCC testing group and 0.954 for patients in the thyroid carcinoma testing group. Moreover, the proposed method is able to diagnose WSI with cancer versus normal slides with an AUC of 0.944 and 0.995 for the SCC and thyroid carcinoma testing groups, respectively. For comparison, we tested the proposed, diagnostic method on an open-source dataset of WSI from sentinel lymph nodes with breast cancer metastases, CAMELYON 2016, to obtain patch-based cancer localization and slide-level cancer diagnoses. The experimental design yields a robust method with potential to help create a tool to increase efficiency and accuracy of pathologists detecting head and neck cancers in histological images.
support vector machines with hand-crafted features, such as color and texture 8,9 . Additionally, convolutional neural networks (CNNs), which are a family of machine learning algorithms that learn to extract features from training images, have also been applied to classifying epithelium and stromal tissues from colorectal and breast cancers 10 . Non-small cell lung cancers, including metastatic SCC to the lungs, have been classified in histological images using CNNs that are trained to work regions of the image, called image-patches 11 . Another method for detecting lung cancers in histological images of needle core biopsies used morphological and color features for classification with an ensemble of artificial neural networks 12 . Head and neck SCC was investigated once before, but only in cell lines xenografted into mice, and a CNN was implemented with histological images to predict hypoxia of tumor-invaded microvessels 7,13 . Additionally, computerized methods have been developed for thyroid carcinomas to detect and classify malignant versus benign nuclei from thyroid nodules and carcinomas, including follicular and papillary thyroid carcinomas, in histological images on a cellular level with promising results [14][15][16] . However, most of the work involving thyroid carcinoma has been implemented on a cellular or nuclear level using hand-crafted features, such as texture or shape, and support-vector-machines are employed for nuclei classification, with many algorithms using an ensemble of classifiers [15][16][17][18][19] .
In the field of digital pathology, whole slide imaging (WSI) refers to the acquisition of high-resolution images of stained tissue slides, which retains the ability to magnify and navigate these digital slides just as standard microscopy 20 . After reviewing nearly 2,000 patient cases, it has been concluded that WSI is non-inferior to microscopy for primary diagnosis in surgical pathology across multiple staining types, specimen types, and organ systems 21 . Computer-assisted detection algorithms have recently been implemented using CNNs for diagnosis in WSI with considerable success for identifying metastasis in lymph nodes 22,23 . Several state-of-the-art methods using CNNs have been applied during a grand challenge hosted at the IEEE International Symposium for Biomedical Imaging in 2016 and 2017 to detect breast cancer metastasis in WSI of sentinel lymph nodes (CAMELYON) with AUCs up to 0.99, comparable to expert pathologists performing with an AUC of 0.81 to 0.97, with and without a time constraint 22,24,25 .
This study aims to investigate the ability of CNNs for detecting head and neck SCC and thyroid carcinomas in a novel dataset of digitized whole-slide histological images from surgical pathology. A recent literature review shows that this is the first work to investigate SCC and thyroid carcinoma detection on a WSI level in primary head and neck cancers 7 , and we implement state-of-the-art classification methods in an extensive dataset collected from our institution. The major contribution of this paper focuses on the first application of deep learning for the histological detection of H&N SCC and thyroid cancers in a sufficiently large head and neck cancer dataset that is best suited for a patch-based CNN approach. The anatomical variation of the head and neck is astonishingly complex. The inclusion of multiple, most common locations of SCC yields a successful and substantial generalization for this application. Additionally, three of the major forms of thyroid carcinoma are studied, and despite extensive morphological differences, the method allows successful performance. Altogether, the dataset and applied methodology of this work demonstrate the current potential to create a tool to increase the efficiency and accuracy of surgical pathologists performing real-time SCC cancer detection on WSI for intraoperative guidance during primary head and neck cancer resection operations.
Materials and Methods
In this section, the materials for this study, including the cancer histological datasets, are described. Additionally, the methods of image processing, convolutional neural networks, and performance evaluation are detailed.
Head and neck cancer dataset. Informed, written consent was obtained from all patients consented for our study. All experimental methods were approved by the Institutional Review Board (IRB) of Emory University under the Head and Neck Satellite Tissue Bank (HNSB, IRB00003208) protocol. In collaboration with the Otolaryngology Department and the Department of Pathology and Laboratory Medicine at Emory University Hospital Midtown, freshly excised, ex-vivo head and neck cancer tissue samples were obtained from previously consented patients undergoing surgical cancer resection 26,27 . Tissue specimens collected from patients were de-identified and coded by a clinical research coordinator before being released to our laboratory for research purposes only. Three tissue samples were collected from each patient: a sample of the tumor, a normal tissue sample, and a sample at the tumor-normal interface.
For this study, we present the first application of the histological component of this dataset of 381 WSI from 156 patients, which is detailed by dataset in Table 1. In the upper aerodigestive tract SCC group, there were 228 tissue samples collected from 97 patients. The number of patients and tissue specimens is enumerated per anatomical origin of the SCC in Table 2. The only tissues that were excluded in this study were from three patients that had SCC of Waldeyer's ring. These tissues were excluded because they were comprised of entirely lymphoid tissue, and the samples from only 3 patients of this diverse tissue type was not sufficient for inclusion in this study. The normal specimens collected were non-dysplastic and non-cancerous, which may have inflammation, atypia, or reactive epithelium. The thyroid carcinoma group was comprised of primary papillary, medullary, and follicular thyroid carcinomas. There were 153 tissue specimens collected from 59 patients, which included 47 patients with papillary thyroid carcinoma, 5 patients medullary thyroid carcinoma, and 7 patients with follicular carcinoma. Each dataset was subdivided into separate groups for training, validation, and testing of the proposed computer-assisted cancer detection algorithm.
Fresh ex-vivo tissues were collected from the surgical pathology department and fixed, paraffin embedded, sectioned, stained with haemotoxylin and eosin (H&E), and digitized using whole-slide scanning at an equivalent magnification to 40x objective using a NanoZoomer (Hamamatsu Photonics), which produces a final digital slide with pixel-level resolution of 0.23 μm × 0.23 μm. A board-certified pathologist with expertise in H&N pathology outlined the cancer margins on the digital slides using Aperio ImageScope (Leica Biosystems Inc, Buffalo Grove, IL, USA).
Breast cancer lymph node metastases dataset. For external validation, we implemented the proposed cancer detection algorithm on the open-source CAMELYON 2016 dataset 23,28 , in order to compare the results of our proprietary head and neck cancer dataset since currently no similar independent dataset exists. The CAMELYON 2016 dataset consists of 399 whole-slide digital images from sentinel lymph nodes (SLN) obtained from 399 patients, one SLN from each patient that underwent breast cancer surgical resection. The dataset is collected at two institutions: Radboud University Medical Center (RUMC) Netherlands and University Medical Center Utrecht (UMCU) Netherlands 23,28 . One slide was constructed from one SLN from each patient. Table 1 shows the numbers of patients and slides in each group.
The whole-slide images were digitized at each institution separately, so the different hospitals each use a different scanner. The slides that were digitally scanned at RUMC were produced at 20x objective magnification using a Pannoramic 250 Flash II digital slide scanner (3DHISTECH), which corresponds to the pixel-level resolution of 0.24 μm × 0.24 μm. The slides that were digitized at UMCU were acquired with a NanoZoomer-XR digital slide scanner at 40x objective magnification (Hamamatsu Photonics) with a pixel-level resolution of specimens of 0.23 μm × 0.23 μm 23,28 .
Histological image processing. The histological dataset presented consists of primary tumor specimens acquired from surgical resections. Our SCC and thyroid cancer datasets do not have fine cellular-level annotations. Instead, regions were broadly marked as cancer if there were any cancer cells present, even if surrounded by normal structures, to establish which areas would require surgical removal. For this task, cell-by-cell annotations are not necessary. Clinicians require accurate regional diagnosis of cancer invaded tissues with an estimate of border clearance distance to the edge of the resected tissue. Therefore, the nature of the ground-truth for this work necessitates a patch-based deep learning approach. Moreover, a fully-convolutional network (FCN), as is widely used in the literature, would be problematic for this approach. Firstly, the tissue specimens of primary cancers collected tend to have large regions of each class. Therefore, the large majority of patches tend to be one class (all normal or all tumor), with few border patches that contain both classes. This would create problems with loss calculation and gradient optimization for training an FCN. Lastly, as stated the ground truth is coarse, so if a FCN could be adequately trained to produce fine-level segmentations, not only are they not needed for this task, but the ground truth would call potentially correct areas as misclassifications.
A ground-truth binary mask of the cancer area is produced from each outlined histology slide. The WSIs and corresponding ground-truths were down-sampled by a factor of four using nearest neighbor interpolation. The proposed method classifies the WSI in a patch-based method using a window that slides over the entire image. Due to the unique challenges of working with digital pathology images, which can create datasets of hundreds of www.nature.com/scientificreports www.nature.com/scientificreports/ images that are each tens of gigabytes, it is the current state-of-the-art to perform both down-sampling and patch-based image reconstruction approaches to computationally handle this type of data 22,[29][30][31][32][33][34][35] . Image patches (I) are produced from each down-sampled H&E slide using 101 × 101 pixels and are labeled corresponding to the center pixel, where I Representative patches from H&N SCC are shown in Fig. 1 showing the histological variation of normal anatomical structures and various appearances of SCC of various identifiable difficulty. The SCC and thyroid carcinoma training groups were comprised of patches only from the tumor and normal tissue WSI, and the validation and testing groups were comprised of patches from all slides. Since the lymph node dataset contained more WSI but with smaller cancer areas, the training dataset was constructed by taking up to 5000 image patches from the cancerous area of each of the 101 cancer WSI in the training dataset, and using up to 1000 image patches from each slide of the 149 normal WSI. The training group was approximately balanced between cancer and normal patches for better performance.
Histology slides have no canonical orientation, meaning the tissue will have the same diagnosis from all vantage points. Therefore, the number of image patches were augmented by 8x by applying 90-degree rotations and reflections to develop a more robust diagnostic method. Additionally, to establish a level of color-feature invariance and tolerance to differences in H&E staining between slides, the hue, saturation, brightness, and contrast of each patch were randomly manipulated to make a more rigorous training paradigm.
Convolutional neural network implementation. The three distinct cancer datasets in this study were employed to separately train, validate, and test a 2D-CNN classifier based on the Inception V4 architecture, implemented in TensorFlow on 8 Titan-XP NVIDIA GPUs [36][37][38][39] . The Inception V4 CNN architecture was modified slightly in the early layers, which is detailed in Table 3, in order to accommodate the patch-size selected for this study. The CNN architecture consisted of 3 convolutional layers and 1 max-pooling layer to accommodate the patch-size used, and in total the CNN contained 141 convolutional layers and 18 pooling layers 37,39 . Gradient optimization was performed using the Adadelta optimizer with an initial learning rate of 1.0 that was exponentially decayed by 0.95 every 3 epochs of training data 40 . The softmax cross entropy was used as the loss function. If the k th training patch is denoted as where K is the number of training patches in a batch, the CNN training process is to find a function F: that minimize the following cost function : and g k N and g k P are the ground truth labels for cancer-negative and cancer-positive tissue classes, respectively, corresponding to the k th patch.
The validation groups were used to determine the optimal number of training epochs used for each of the three datasets. Each CNN was trained with a batch size of 128 image patches, and batches were converted from RGB to HSV before being passed into the CNN. Both RGB and HSV were tested in early validation experiments, www.nature.com/scientificreports www.nature.com/scientificreports/ and HSV without any other modification out-performed RGB results. One reason could be the separation of the image intensity from the color information in HSV color model. Additionally, one major challenge of H&E stained images is inconsistency of the stain quality. To demonstrate that color feature augmentation can solve this problem, working in HSV directly, the hue, saturation, and brightness were perturbed randomly in each channel independently. The SCC CNN was trained for 30 epochs of training data, equivalent to 295,000 steps using a batch-size of 128 patches. The random color augmentation was using the native color feature variance in the training group: hue 4%, saturation 15%, brightness 8%, and contrast 2%. The thyroid carcinoma CNN was trained for 70 epochs of training data (equivalent to 433,000 steps). HSV and contrast perturbation was 5%, 5%, 8%, and 5% respectively. The breast cancer SLN metastasis CNN was trained for 20 epochs (equivalent to 203,400 steps). HSV and contrast were each randomly perturbed in range of −10% to 10%.
Image reconstruction and post processing. Each of the N testing slides ( is the average cancer prediction of the patch. Additionally, the results of overlapping image patches were averaged in the overlapping area, as follows. ) of the heat-map. The benefit of this post-processing method was to increase the resolution of the heat-map from 101-pixel image patches to 50-pixel image patches. Moreover, the image patches that constituted the free edge of the tissue were averaged less than four times because they did not have the complete number of neighboring patches. This image reconstruction and post-processing method was determined to increase accuracy by about 2% in early validation experiments.
To investigate the ability of the CNN to detect cancer on histological images, we implemented the gradient class-activated map (grad-CAM) method to visualize gradients activated by each class for the example input image patches 41 . We traced the gradients from the last convolutional layer before the inception modules to the logits layer to separately visualize cancer and normal components. This technique produces a weighted combination of the convolutional filters and gradients as the CNN is activated by a specific input image for each class.
Performance evaluation. The reference standard cancer margin was annotated by hand for all digital slides employed in this study. For the head and neck cancer database, a board-certified pathologist with expertise in H&N pathology outlined the cancer margins on the digital slides. For the breast cancer metastasis database, an experienced lab technician and a clinical Ph.D. student outlined the cancer margins, which were then confirmed by one of two board-certified pathologists with expertise in breast cancer 28 .
Layer
Kernel size/Remarks Input Size www.nature.com/scientificreports www.nature.com/scientificreports/ During training, the performance of the validation group was calculated and monitored. The optimal operating threshold was calculated from the validation group for generalizable results, and it was used for generating performance evaluation metrics for the testing group. To reduce bias in the experiment, the fully-independent testing group was only classified a single time at the end of the experiment, after all the network optimizing had been determined using the validation set. To test the ability to diagnose and localize cancer on WSI, we used AUC, F1 score, accuracy, sensitivity, and specificity to evaluate cancer detection on a patch-based level. Confidence intervals were calculated using a boot-strapping algorithm. Additionally, the ability of the proposed algorithm to diagnose slides with cancer from normal slides was investigated. This slide-level AUC was calculated by assigning the value of the image patch with the maximum cancer probability to the entire WSI.
Informed consent. Informed written consent was obtained from all patients prior to participation in this study. All methods were carried out in accordance with the approved Institutional Review Board protocols and the relevant guidelines and regulations of Emory University.
Results
Head and neck primary SCC was detected on digitized WSI with an AUC of 0.916 and 85% accuracy for patients in the testing group. The ideal threshold for distinguishing SCC from normal tissue was SCC probability of greater than 62%. Thyroid carcinoma was detected on digitized WSI with an AUC of 0.954 and 89% accuracy for patients in the testing group. The ideal threshold for distinguishing thyroid carcinoma from normal thyroid tissue was cancer probability of greater than 50%. Breast cancer lymph node metastasis was detected on digitized WSI with an AUC of 0.967 and 93% accuracy for patients in the testing group. The ideal threshold for identifying metastasis in SLNs was cancer probability of greater than 28%. Reported in Table 4 are the AUC for the validation groups and the AUC, accuracy, sensitivity, and specificity of the testing groups.
Receiver operator characteristic (ROC) curves for slide-level and patch-level cancer detection in the testing groups from all three datasets are shown in Fig. 2. Patch-level ROC curves are generated using all histological images' patch-level data for cancer localization, and slide-level ROC curves demonstrate WSI diagnosis. Additionally, two representative WSI from each of the three testing groups and their corresponding predicted heat-maps are shown in Fig. 3. Several regions of interest (ROI) are detailed in Fig. 4 to identify the strengths and weaknesses of the proposed method in the detection of SCC. The ideal threshold for whole-slide level detection of SCC was above 95% probability, so the regions detailed as true negatives in Fig. 4 fall below this threshold. Additionally, the grad-CAM technique was used to visualize the contributing normal and cancerous components of a few example input images that were corrected classified with high probability (Fig. 5). This approach reveals that a contribution of the cancer prediction is made by nuclear features.
The ability of the proposed method to diagnose the entire WSI that contain any cancer was also investigated. WSIs with SCC were diagnosed with an AUC of 0.944. Thyroid carcinoma WSIs were diagnosed with an AUC of 0.995. WSI of SLN with breast cancer metastases were diagnosed with an AUC of 0.901. Table 4. Cancer detection results, obtained from ROC curves using all histological images' patch-level statistics.
Reported are the AUC for the validation group and the AUC, F1 score, accuracy, sensitivity, and specificity of the testing group with 95% confidence intervals for all values. Also shown in the right-most column is the ability of the proposed method to distinguish slides that contain cancer from slides that are all normal.
Discussion
In this work, we present a new and extensive histological dataset of primary head and neck cancer and implement a state of the art Inception V4 CNN architecture for cancer detection and WSI diagnosis. The results are generalizable because of the division of patients across training, validation, and testing. To the best of our knowledge, this is the first work to investigate SCC detection in digitized whole-slide histological images from primary head and neck cancers.
The digitized, whole-slide histological images were saved as TIF files with resolution equivalent to 40x microscopic objective. After 4x down-sampling, the image patches correspond to 10x objective equivalence. Different down-sampling factors and patch-sizes were explored, but this method yielded the best validation group results, so it was used for testing. Similarly, pathologists detecting SCC in histology slides use a variety of objectives, not exclusively 40x, which may be too zoomed-in to determine if the region is cancerous or benign. We see this issue in our dataset as well. It is not only possible, but likely that in some slides labeled as 'tumor only' , there may be some areas inside the tissue, or in between tumor nests, that is entirely normal. Therefore, it is understandable that classification using 4x down-sampled images obtains the highest accuracy. Other CNN architectures were explored in early experiments using the validation set only, and various patch-sizes were experimented with, but ultimately the Inception V4 CNN architecture with a patch-size of 101 × 101 × 3 in HSV color space, yielded the most promising validation results.
Additionally, the regions of interest that are presented show true negative, false positive, and true positive regions that vary from 1 to 3 mm in size. These results demonstrate the proposed method is able to distinguish normal anatomical structures like epithelium and salivary gland from SCC with high probability. Also, the most common false positive observed in the classified result is tissue areas that contain dense inflammation. This result is most likely a by-product of the training paradigm. As SCC develops, there is an accompanying immune response that leads to massive inflammatory infiltration into the tissue 42 . Therefore, the proposed algorithm learned the association between SCC and inflammation.
To our knowledge, there are no other studies that attempt to detect or diagnose H&N SCC or thyroid carcinoma on WSI, and we used a proprietary dataset collected from patients at our institution. Therefore, we wanted to test the proposed, diagnostic algorithm on a similar, open-source dataset for comparison. Our slide-level results would have placed 3 rd in the original CAMELYON 2016 23,28 .
The grad-CAM technique was used to visualize what components of the input image are determined as useful features with a significant contribution to the cancer prediction from the CNN, as shown in Fig. 5. This reveals that the decision is made by looking at the nuclei, just like a pathologist detects cancer. The proposed method does www.nature.com/scientificreports www.nature.com/scientificreports/ not segment all cancerous nuclei in the image patch, but it identifies a few cancerous nuclei with a high probability of being cancer and uses this information for making the decision. We did not train the proposed algorithm specifically with this in mind. Rather, this phenomenon was learned naturally by the training paradigm. The trained CNN model also has a level of stain invariance.
Limitations
One limitation of the presented approach results from the application of the down-sampled resolution and the patch-based Inception V4 CNN implementation. After down-sampling, each pixel represents approximately 0.91 microns, which produces a patch size that spans about 92 microns in each x-y dimension for the patch size of 101 × 101 that was implemented in this approach. The typical diameter of an SCC single-cell nucleus in our dataset was about 12 microns, which agrees with values reported in the literature of about 13 +/−2 microns 43 . Therefore, the theoretical limit of the smallest carcinoma that could be detected would be a nest of SCC cells with an approximate diameter of 92 microns. This value corresponds with an SCC nest on the order of tens of cells of SCC, depending on the cytoplasmic overlap in the arrangement of the SCC nest.
Another limitation of this approach was that the algorithm suffered from whole-slide scanning artifacts, such as out-of-focus regions and including errors from slide processing, such as tissue folding and tearing. This was discovered after the completion of the experiment, and the effect was substantial, accounting for the reason in misclassification of the lowest performing WSI in the SCC testing dataset, which is shown in Fig. 6. As can been in Fig. 6, the left side of the WSI is classified correctly as a true positive SCC region, but the out-of-focus regions result in misclassification a similar ROI (shown in the cut-out boxes on the right) to be classified as false negative incorrect result. These misclassifications were retained in the testing dataset to not manipulate or bias the results, but in future work, slide scanning artifact detection should be additionally performed to determine which slides cannot be classified because of limited quality.
Digital pathology with WSI allows pathologists to view high-resolution histological images, just as standard microscopy, and it was concluded that digital pathology is non-inferior to microscopy for primary diagnosis in surgical pathology cases across multiple institutions, staining types, and organ systems 20,21 . Therefore, we believe the robust experimental procedure of the proposed method, designed to eliminate bias, has demonstrated potential benefit in a modern, digitized clinical setting. However, primary diagnosis of surgical specimens for intraoperative guidance is performed on frozen-sections rather than formalin-fixed, paraffin embedded tissues, as were investigated in this study. Additionally, frozen-sections are typically lower quality than those created from fixed, embedded specimens because they suffer from many different artifacts and depend heavily on the skill of the operator. Therefore, we believe the presented work demonstrates potential for clinical benefit, but more investigation needs to be performed. Moreover, the generalization of the results beyond head and neck cancers to breast cancer metastasis in sentinel lymph nodes suggests this method is not limited to any organ system and could be adapted to serve multiple purposes if implemented in a more clinical setting.
conclusion
In summary, this work focuses on the first application of deep learning for the histological detection of H&N SCC and thyroid cancers. The proposed method is able to detect and localize primary head and neck SCC on whole-slide, digitized histological images with an AUC of 0.916 for patients in the SCC testing group and 0.954 for patients in the thyroid carcinoma testing group. Moreover, the proposed method is able to discriminate WSI www.nature.com/scientificreports www.nature.com/scientificreports/ with cancer versus normal slides with an AUC of 0.944 and 0.995 for the SCC and thyroid carcinoma testing groups, respectively. The SCC detection method is performed across all anatomical locations, which indicates the algorithm is not limited to one location of the head and neck anatomy. For thyroid cancers, three major thyroid carcinoma are studied together which additionally demonstrates the generalizability of the method. For external validation, we tested the proposed method on an open-source dataset, CAMELYON 2016, and obtained good results. The agreement between validation and testing demonstrate that the technique is generalizable due to the robustness of the training paradigm and the careful experimental design to reduce bias. Together, the novel application to our dataset and promising results of this work demonstrate potential that such methods as the one proposed could help create a tool to increase efficiency and accuracy of pathologists performing head and neck cancer detection on histological slides for intraoperative guidance during head and neck cancer resection operations. | 6,389.6 | 2019-10-01T00:00:00.000 | [
"Computer Science",
"Medicine"
] |
Investigating the Effect of Geocell Changes on Slope Stability in Unsaturated Soil
: The purpose of this research is to investigate the performance and efficiency of reinforced slope in the stability of geocell layers in unsaturated soil conditions. Slope reinforced with geocell acts like a beam in the soil due to the geocell having a height (three-dimensional). Due to its flexural properties, it has moment of inertia as well as bending strength, which reduces the displacement and increases the safety factor of the slope. Taking into consideration unsaturated conditions of soil contributes a lot to making results close to reality. One of the well-known models among elastoplastic models for modeling unsaturated soils is Barcelona Basic Model, which has been added to the FLAC2D software by codification. Changes in thickness, length and number of geocell layers are remarkably effective on slope stability. The results show that the geocell's reinforcing efficiency depends on the number of layers and depth of its placement. As the depth of the geocell's first layer increases, the lateral and vertical side elevation of the upper part of the slope increases with respect to the elevation. Load capacity increases with increasing geocell length. By increasing the length of the geocell layer, the joint strength, the mobilized tensile strength, and the bending moment are increased. At u/H = 0.2, an increase in the bending momentum of about 20% occurs with increasing geocell thickness. In u/H = 1, the increase in bending momentum is 10.4%. In addition, by increasing the thickness of the geocell, the Value of moment of the inertia increases and, as a result, the amount of geocell reinforcement bending moment increases.
INTRODUCTION
Different studies have been conducted on reinforced soil slope. The effect of length and distance of reinforcements on the behavior of reinforced soil slope has been widely examined. The obtained results revealed that as the distances between reinforcements increase, the available load in reinforcement layers and consequently wall deformation increase as well. To investigate the failure mechanism of geosynthetic-reinforced soil slope and evaluate the design hypothesis and design methods for such walls, numerical and experimental studies have been carried out showing that the failure surface is different from the propagation of failure region; rather, its location is dependent on geometry, strength, and stiffness of reinforcement elements [1][2][3][4][5][6]. Employing geocell to reinforce soils has broad applications as an effective and rapid method in civil projects. Geocellreinforced soil is mainly used to resist static and cyclic loads. In fact, this reinforcement is used to increase the load-bearing capacity of soft soil and decrease settlement and displacements of slopes. Geocell functions as a layer confining soil and prevents the soil from moving outward the loading region. Furthermore, soil swelling is reduced, which leads to some variations in the factor of safety of slope. Geocell increases the bending, tensile and shear strengths of soil and, due to its height, functions as a beam providing moment of inertia and consequently bending strength. Although bending stiffness is low with respect to thickness, it can diminish deformations of layers and cause reduction in the settlement of soil-structure system [7].
Fakher and Jones [8] investigated the effect of bending stiffness of geogrid reinforcement using Flac software. Their results show that although bending stiffness is low with respect to the small thickness of geogrid layer, it can diminish the deformation of geogrid layer and consequently decrease the system settlement [8].
Zhang et al. [9,10] simulated the performance of geocell reinforcement considering the resistance of contact surface between soil and geocell and assumed the geocell reinforcement as a beam on an elastic bed.
Dash et al. [11] observed through an experimental effort that the geocell layer functioned as a beam with bending behavior. Their results illustrated that as the height of geocell layer increases, the behavior of deep beam becomes dominant in geocell layer. Yang et al. [12] indicated that geocell benefits form a relatively high bending strength where it is necessary to incorporate bending stiffness in modeling geocell layer.
The present study uses beam element in FLAC2D software to incorporate the properties of geocell layer in the simulation of geocell reinforcement.
Construction projects carried out using more advanced technologies are increasingly developing. One of the restrictions on such projects is the inappropriateness of project implementation site as the structure foundation. Recognition of the land appropriateness to construct the foundation requires the knowledge and expertise of engineers and researchers about the soil behavior in different conditions and states. In other words, researchers should be aware of the variation in soil behavior under different circumstances so as to provide a qualitative and quantitative evaluation of the soil behavior in different conditions. However, the principles of classic soil mechanics founded by Carl Tarzaghi are mostly associated with saturated soils [13,14].
Unsaturated soil is not a specific type of soil but rather a state of soil that can occur for all types of soil based on the filling fluid. Saturation or unsaturation in any region is affected by environmental factors, namely rainfall, evaporation, and rise of groundwater level. In other words, all soils are subjected to either wetting or drying. Therefore, change in the state of pore-water pressure and occurrence of unsaturated conditions are probable for all soils [15].
Full drying conditions of soil, particularly for granular soil, might experience a reduction in the factor of safety by wetting and moisture absorption at the end of construction stages. Moreover, since the shear strength of soil is drastically affected by the degree of soil saturation, it is important to consider correct conditions of saturation or unsaturation of soil once investigating the soil behavior. In fact, although the design is more simplified in geotechnical engineering by not considering unsaturated soil conditions, it increases most of the construction costs [16].
Morgenstern in 1978 [17] proposed a relation to express the shear strength of unsaturated soils where the shear strength was properly separated due to effective stress from the shear strength induced by net stress. In recent years, the effective stress method has been of great interest to many researchers to determine the shear strength of unsaturated soils [18][19][20][21].
In 1998, a relation was proposed based on effective stress, cohesion, and internal friction angle of soil to express the shear strength of unsaturated clay [22]. On the other hand, the effective stress of unsaturated soils is in direct proportion to the extent of matric suction within the soil. In this regard, Alonso et al. were among pioneers and their study attracted a great deal of attention such that one can find a large number of basic models in the respective scientific references. [15] This model, as the most known model proposed in the analysis of unsaturated soil, functions on the basis of three major concepts including state surfaces, soil critical state, and empirical tests. This model can be considered as the development of critical state in unsaturated state considering the effect of suction phenomenon [23].
The result of most studies is summarized in the following three parts: A-Fundamentals of stress states and principal variables employed to create numerous models B-Precise analysis of basic models and investigation of their strengths and weaknesses C-Progress in the modelling unsaturated soil [14].
THEORY 2.1 Barcelona Basic Model
The present study has used the Barcelona Basic Model that works elastoplastically and is applied to express the stress-strain of unsaturated soils based on stiffening plasticity. This model was first proposed by Alonso in 1990 at Polytechnic University of Catalonia. It is founded on the basis of Cam-Clay Model and capable of expressing many principle facets of the behavior of unsaturated soils, namely silty soils, clayey sands, sandy clay, and clay with low plasticity. It is worth noting that this model has been proposed with the purpose of expressing the behavior of partially saturated soil with low or medium inflation capability. This model is one of the most known proposed models to analyze unsaturated soils which is based on three major principles including state surfaces, soil critical state, and empirical tests. This model can be considered as the development of critical state in unsaturated state considering the effect of suction phenomenon. The Barcelona Basic Model has two independent stress variables in the form of net stress and soil suction. [10] ij ij ij ij Where ij σ stands for net stress tensor, σ ij denotes total stress tensor, ∂ ij is Kronecker delta, S is soil suction, u a stands for pore-air pressure, u ij , u w is pore-water pressure. The relations of Barcelona Basic Model are written based on four variables including net mean stress P, deviatoric stress q, nest suction S, and specific volume v.
Where σ 1 , σ 2 , σ 3 are the principal stresses of soil. If the soil is isotopically loaded at constant suction until the net mean stress across the normal consolidation line (NCL), the specific volume is obtained by the following relation.
Here λ(s) is the stiffness parameter along the normal consolidation line at constant suction S, and P c stands for the reference pressure in v = N(s). If unloading and reloading occur at constant suction, then the soil behavior is assumed as elastic. A constant suction is considered for all surfaces in the Barcelona Basic Model. The stiffness parameter on the normal consolidation line is defined at a constant suction as follows: r is a parameter defining the maximum soil stiffness and β controls the soil stiffness increase rate induced by suction. Similar to the actions due to the applied net stress, suction also yields elastic and plastic strains. Once the soil reaches the already-experienced maximum suction, the irrecoverable strain is initiated [23].
In the Barcelona Basic Model, partial volumetric strain dεv depends on the variations of net mean stress, given as the following relation.
The partial strain induced by net mean and deviatoric stresses are divided into two components, namely elastic strain dε e and plastic strain dε p . On the other hand, the partial volumetric strain, due to the suction decrease by wetting or the suction increase by drying, is found to be purely elastic.
This model consists of a suction decrease yield curve showing that the effect of suction change on the soil state to reach the yield point is as important as the effect of variation in the net mean stress. The volumetric elastic strain is generated by the net mean stress in the elastic region.
When the net mean stress meets the pre-consolidation stress p 0 at the constant suction S, the soil is still in the normal consolidation state and the total volumetric strain is obtained by Eq. (11).
Therefore, the plastic volumetric strain is defined by the subtraction of the elastic volumetric strain from the total volumetric strain.
Similarly, elastic, plastic, and total volumetric strains dependent on suction variations are given by relations (13), (14), and (15) Thus, once the yield state occurs, the increment of preconsolidation pressure and yield suction can be presented using stiffening rules, given by the following relations.
Here k s is the stiffness parameter for suction change in the elastic region. In the states of total stress, deviatoric stress q defines the effect of shear stress. The Barcelona Basic Model suggests that the shear strength increases by suction. It is a general attribute of partially saturated soils which is obtained by adding apparent cohesion p s . s p ks = (18) Here k defines the cohesion increase by suction increase. The critical state line at each constant suction (s) is horizontal in saturation conditions (Fig. 1).
Figure 1
Failure surface in space (s, p, q) [23] The respective equation for the critical state line is as follows: Where, M is the slope of critical state line. The nonassociated flow rule is applied to accurately estimate the correct value of k 0 .
Here α is the parameter of non-associated flow rule relation (20). The strain caused by changing deviatoric stress is obtained by relation (21).
FISH is employed to codify the Barcelona Basic Model in FALC2D. The codification method of Barcelona Basic Model is very identical to the modified Cam-Clay Model.
Water-Soil Characteristic Curve
Numerous functions have been proposed so far to describe a water-soil characteristic curve. The present study has benefited from the model proposed by Van Genuchten. This model is defined by relation (22).
Here α, m, and n are fitting parameters. ψ stands for soil suction and θ s , θ r are residual water content and saturated water content, respectively. The slope of curve is affected by m at higher values of suction. m and n are correlated according to relation (23).
Replacing relation (23) in relation (22), the general relation for the function of water-soil characteristic curve is obtained. Regarding this relation, a certain amount of suction is reached for any specific degree of soil saturation [24,25].
Values for relation (24) are represented in Tab. 1. They are based on SWCC curve with the regression R 2 = 0.942 (Fig. 2).
Geocell
Due to the three-dimensional structure, the geocell results in side enclosure of the particles of soil inside the cells. Also, geocell reinforcement causes the vertical enclosure of the soil within the geocells in two ways. Firstly, through the friction between the soil-cellular materials formed by the walls of the cell. Secondly, the geocell reinforcement acts like a soil enclosure layer that prevents soil movement outside of the loading zone [26]. The decorative effect of the geocell layer is also enhanced by the force of tensile strength in the geocell's reinforcement due to resistance to vertical loads (Fig. 3).
Figure 3
Soil Enclosure Characteristic by Geocell [27] Contact resistances due to interactions between the geocell and the soil of the two sides of the geocell layer increase the lateral enclosure and reduce lateral strain. As a result, the modulus of elasticity of the geocell-soil system increases. (Fig. 4). Geocell reinforcement has tensile and shear force in the interface of soil and geocell. Furthermore, due to having thickness and elasticity modulus, it offers moment of inertia and consequently bending moment. As it can be observed in (Fig. 5). T, M, and Q are tensile force, bending moment, and shear force of geocell, respectively. q(y) is applied to the upper part of geocell layer while p(y) induced by the bed reaction is acted in the lower part of geocell layer. h is the thickness of geocell reinforcement and T(x) is the strength of the soil-geocell interface.
Normal and shear forces causing the response of adjoining elements are calculated using the following equations at t + ∆t [30].
Where, ( ) t t n F +∆ is the normal force at t + ∆t, ( ) t t si F +∆ is the shear force at t + ∆t, u n stands for the absolute penetration of the adjoining element node perpendicular to the targeted surface, is relative shear displacement, σ n is normal stress, K n and K s are normal and shear stiffness, respectively, A is the specified area allocated to each node, and σ si is the extra shear stress due to the stress generated in the adjoining element. The values of normal and shear stiffness are calculated using the following relation [27].
Here k is bulk modulus and G is soil shear modulus. Δz min is the width of the smallest adjoining zone in the normal direction. [31]
NUMERICAL MODEL
The Barcelona Basic Model is a soil behavioral model used to investigate the reinforced slope. This model is added to the finite difference Flac software through codification. In FISH code written based on the triaxial test for validation of the Barcelona Basic Model, the generation algorithms of p, q, v, ε, S (suction) have been predicted. Soil properties are given in Tab.1. The conditions of numerical modeling are summarized in four main steps, namely generation of model geometry and reinforced slope, setting boundary conditions and respective stresses, running the program to approach initial equilibrium, and finally investigation of the factor of safety and deformation of reinforced slope and bending variations of geocell in the unsaturated state of soil. The concerned slope has a width of 50 m and a height of 30 m. The sensitivity analysis and modeling have been conducted to select the optimum limit such that any further increase in the limit yields no change in results and merely increases the computational time. Due to the symmetry, only half of the slope has been modeled. The symmetry line is positioned on the right side of the model. To analyze the model more precisely as to determine the factor of safety (FOS) and deformation of the reinforced soil slope, a finer mesh is applied. Moreover, the mesh size becomes larger once moving away from the slope so as to reduce the computational time. The lower boundary of the model has been fixed against any movement and displacement in all directions while the vertical boundary in solely constrained in the horizontal direction (Fig. 6). The investigated parameters to address the effect of geocell reinforcement on the factor of safety and failure surface are as follows: (u) depth of the first geocell layer measured from the slope top level, (N) number of geocell layers, (h) height of geocell layer, and (L) length of geocell layer. To simplify the obtained results, the dimensionless form of all available parameters have been expressed with respect to the slope height (e.g. u/H or L/H).
The secant modulus of geocell (M) has been set to 150 (kN/m) at a strain of 2.5%. Furthermore, the tensile strength and thickness of geocell has been considered to be 60 (kN/m) and 0.5 m, 0.1 m respectively. The Modulus of elasticity has been 50 MPa. The investigated non-reinforced clay slope has a factor of safety of 1.13 and a displacement of 15.6 cm in dry soil. The foundation soil of slope is saturated but the soil of embankment is unsaturated. All of the models are used at the suction of 10 kPa and moisture of 25%, according to SWCC (Turning point).
Validation
The Barcelona Basic Model has been applied to Flac software by making the following assumptions. 1-Net mean stress is equal to total mean stress, which is a practical assumption. 2-Soil suction is a variable affecting both soil strength and stiffness. A single element with axisymmetric conditions is considered for the simulation to model triaxial tests on the soil of the reference model. Boundary conditions of the single element have been taken into account. In practice, the single element exhibits one quarter of the triaxial sample being tested, which has been fixed in two other directions. According to Fig. 7, the curve obtained in the study conducted by Alonso is negligibly different from the curve obtained from the results of Flac software where the error is less than 5%. The validation results suggest that the proposed model has an acceptable capability of explaining the behavior of unsaturated soil.
EFFECT OF NUMBER OF GEOCELL LAYERS ON THE STABILITY OF REINFORCED SLOPE
As shown in Fig. 8, an increase in the number of reinforcement layers enhances the factor of safety. Such a behavior can be attributed to the extension of adjoining zone and higher frictional resistance at the soil-geocell interface. Therefore, higher horizontal shear stress occurs in the soil behind the failure surface. In these conditions, bending stiffness and shear strength of reinforcements are also enhanced, thus avoiding horizontal displacements of soil. Improvement rate of the factor of safety based on the number of layers mainly depends on the depth of the first geocell reinforcement layer. This can be addressed as the ability of the first reinforcement layer to avoid the propagation of sliding surface which can consequently affect the overall slope stability. The performance of other geocell layers is largely associated with the improvement of lateral deformation of slope. Geocell length is 22 m and its thickness 0.5 m. In u/H=0.6 by increasing the number of layers, FOS increases up to 13.8%. (Fig. 8).
Figure 9 Displacement variations against the number of geocell layers
As it can be observed in Fig. 9, an increase in u/H from 0.2 to 0.6 causes a reduction in slope displacement by 22.6%. Therefore, the results reveal that the first geocell layer functions as a wide slap and yields the redistribution of load in a broader surface and reduction in the stress intensity. The first geocell layer dramatically transfers the force to the lower parts and consequently leads to force transfer to other geocell layers along with the enhancement of the stability performance. Based on Fig. 10, as the number of geocell layers increases, the axial force of geocell layer is noticeably reduced. In fact, receiving the main portion of forces in the first geocell layer, the moment of inertia is largely delivered to the first geocell layer and it is consequently diminished in other layers (Fig. 11). Fig. 12 shows the variations of the improvement factor of slope affected by the length of reinforcement layer. The obtained results demonstrate that as the reinforcement layer increases in length, the factor of safety is enhanced as well. This is attributed to the increase in restraining, interface, tensile, and bending strengths by increasing the length of geocell layer. u/H = 0.2 and thickness of geocell is considered to be 0.5 m. As indicated in Fig. 13, the displacement of slope is reduced by lengthening the geocell layer. Increasing L/H ratio from 0.6 to 2.6 in the geocell layer, the displacement is reduced by 7.32%. Furthermore, the displacement is declined by 13.81% in three geocell layers. At L/H = 1.8, as the number of layers increases from 1 to 3, the displacement is reduced by 15.2%.
EFFECT OF LENGTH OF GEOCELL LAYERS ON THE STABILITY OF REINFORCED SOIL
Increasing L/H from 0.6 to 2.6 in one geocell layer, the axial force is reduced in geocell by 7%. Moreover, it is reduced by 14.44% in other three layers of geocell. At L/H = 2.6, increasing the number of layers from 1 to 3, the axial force is declined by 5.18%. The effect of length on three layers of geocell is more tangible than on one layer of geocell (Fig. 14). According to the investigation of bending moment, it is declined approximately by 7% and 11.4% in one and three geocell layers, respectively. At L/H=2.6, the bending moment is reduced by 20% by increasing the number of geocell layers (Fig. 15).
INVESTIGATION OF CHANGES IN THE THICKNESS OF THE GEOCELL LAYER ON STABILITY OF REINFORCED SLOPE
As expected, the coefficient reliability increases (FOS) with increasing elevation of the geocell layer (Fig. 16).
Maximum slope velocity and also shear strain occur at the level of the slopes located above the geocell, and in the shear height below the geocell layer, resistance to the lateral movement of the soil increases. In u/H = 0.6, the maximum effect of the thickness of the geocell layer on coefficient reliability is about 8%. The investigations are carried out under conditions of a geocell layer of 18 meters in length.
Figure 16
The factor of safety slope affected by the thickness change of the geocell layer By increasing the height of the geocell layer, the moment of inertia increases and, consequently, the bending moment of geocell reinforcement increases as well. In this condition, the behavior of geocell layer is identical to a deep beam, reducing the reinforcement deformation and consequently declining the lateral deformation of slope. On the other hand, the reinforcement efficiency is dramatically dropped by decreasing the height of geocell layer.
Figure 17
The displacement of the slope affected by the thickness change of the geocell layer According to Fig. 17, the highest displacement rate occurs in the ratio u/H = 1. In this case, most displacements occur in the geocell sublayer and the geocell layer does not have any effect on controlling the forces due to the weight of the soil. On the other hand, if the geocell layer in this case has a very deep depth of surface (u/H = 1) the amount of lateral displacement in the upper part of the slope increases and all displacement occurs in the upper part of the slope. This reduces the shear coefficient reliability, and in this case it also behaves as a not reinforced slope. In u/H = 0.2, the increase in thickness reduces the displacement of 10%, which has the least effect on the increase compared to the rest of the ratios. By reducing the height of the geocell's surface, the reinforcement efficiency decreases in the redistribution of the load at a wider and deeper level and the three-dimensional array of geocell, such as plate reinforcements, is shown (Fig. 17). By increasing the thickness of the geocell, the amount of moment inertia increases and as a result, the amount of bending moment reinforcing geocell increases. In this case, the behavior of the geocell layer is like a deep beam, which reduces the reinforcing shift and, as a result, reduces the lateral shift of the slope. On the other hand, by decreasing the height of the geocell layer, the amount of reinforcing output extremely decreases. The maximum increase in force at u/H = 0.6 is about 13% and the least effect on u/H = 0.8 is about 8% (Fig. 18). The beam element, due to its height and modulus of elasticity, can create an inertia momentum and cause a bending moment to resist a change in shape. It seems that in this case, the geocell layer of the beam simulated can act like a wide slab and cause a redistribution of load and load transfer at a larger and deeper level of the soil. The depth of placement of the first layer of geocell has a great role in increasing the shear coefficient reliability and reducing the lateral deformations of the slope. The results show that by reducing the thickness of the geocell reinforcement, the created moment of inertia decreases and, as a result, the bending moment of reinforcing decreases (Fig. 19). At u/H = 0.2, an increase in the bending momentum of about 20% occurred with increasing geocell thickness. In u/H = 1, the increase in bending momentum is 10.4%.
Figure 19
Variations of the bending moment in geocell layer affected by the suction change
CONCLUSION
The results show that the effective depth of the geocell layer is in the mid-sectional heights of the slope, and the increase in the number of geocell layers has a greater effect on the stability of the slope rather than the increase in geocell length. By locating the first layer of the geocell in an effective area, the development of the defect plates decreases and leads them to a greater depth. In this regard, other geocell reinforcers behave like a slab that transmits vertical pressures from the highest layer into the deeper depth of the soil. In fact, the first layer causes the relation between geocell layers to transfer strain. If the length of the geocell is very small relative to the sliding surface, the flexural moment of the geocell layer is negated. The reason for this is due to the very small amount of moment formed by the tensile force of the geocell layer.
Increasing L/H from 0.6 to 2.6 in one geocell layer, the axial force is reduced in geocell by 7%. Moreover, it is reduced by 14.44% in other three layers of geocell. At L/H = 2.6, increasing the number of layers from 1 to 3, the axial force is declined by 5.18%. The effect of length on three layers of geocell is more tangible than on one layer of geocell.
The effective length of the reinforcing layer is equal to the length of the geocell, which is located inside the slip surface, and in this area a great deal of tensile, shear and flexural force is mobilized in the geocell. On the other hand, the length of the reinforcing layer should be somewhat higher than the length of the slip surface, in order to prevent the development of possible slip surfaces, and also provide an appropriate length to counteract the pulling out of the reinforcing layer against the forces involved. The improvement in the coefficient of reliability based on the number of geocell layers depends largely on the depth of the first reinforcing layer. The reason for this is the ability of the first layer of reinforcing to prevent the spread of the slip surface, which can affect the stability of the entire slope. The performance of other layers of geocell can be greatly correlated with improvement of lateral displacement of the slope. The results show that by increasing the geocell reinforcing moment, the created inertia of moment decreases, and results in a decrease in the reinforcing flexural moment. In this case, the behavior of the reinforcing geocell approaches to the reinforcing plate and its efficiency decreases. | 6,731.2 | 2020-03-20T00:00:00.000 | [
"Geology"
] |
Inhibition of miR-128-3p by Tongxinluo Protects Human Cardiomyocytes from Ischemia/reperfusion Injury via Upregulation of p70s6k1/p-p70s6k1
Background and Aims: Tongxinluo (TXL) is a multifunctional traditional Chinese medicine that has been widely used to treat cardiovascular and cerebrovascular diseases. However, no studies have explored whether TXL can protect human cardiomyocytes (HCMs) from ischemia/reperfusion (I/R) injury. Reperfusion Injury Salvage Kinase (RISK) pathway activation was previously demonstrated to protect the hearts against I/R injury and it is generally activated via Akt or (and) Erk 1/2, and their common downstream protein, ribosomal protein S6 kinase (p70s6k). In addition, prior studies proved that TXL treatment of cells promoted secretion of VEGF, which could be stimulated by the increased phosphorylation of one p70s6k subtype, p70s6k1. Consequently, we hypothesized TXL could protect HCMs from I/R injury by activating p70s6k1 and investigated the underlying mechanism. Methods and Results: HCMs were exposed to hypoxia (18 h) and reoxygenation (2 h) (H/R), with or without TXL pretreatment. H/R reduced mitochondrial membrane potential, increased bax/bcl-2 ratios and cytochrome C levels and induced HCM apoptosis. TXL preconditioning reversed these H/R-induced changes in a dose-dependent manner and was most effective at 400 μg/mL. The anti-apoptotic effect of TXL was abrogated by rapamycin, an inhibitor of p70s6k. However, inhibitors of Erk1/2 (U0126) or Akt (LY294002) failed to inhibit the protective effect of TXL. TXL increased p70s6k1 expression and, thus, enhanced its phosphorylation. Furthermore, transfection of cardiomyocytes with siRNA to p70s6k1 abolished the protective effects of TXL. Among the micro-RNAs (miR-145-5p, miR-128-3p and miR-497-5p) previously reported to target p70s6k1, TXL downregulated miR-128-3p in HCMs during H/R, but had no effects on miR-145-5p and miR-497-5p. An in vivo study confirmed the role of the p70s6k1 pathway in the infarct-sparing effect of TXL, demonstrating that TXL decreased miR-128-3p levels in the rat myocardium during I/R. Transfection of HCMs with a hsa-miR-128-3p mimic eliminated the protective effects of TXL. Conclusions: The miR-128-3p/p70s6k1 signaling pathway is involved in protection by TXL against HCM apoptosis during H/R. Overexpression of p70s6k1 is, therefore, a potential new strategy for alleviating myocardial reperfusion injury.
INTRODUCTION
Coronary heart disease is the leading cause of death worldwide. For patients undergoing an acute myocardial infarction, timely and successful myocardial reperfusion, with the implementation of thrombolytic therapy or primary percutaneous coronary intervention (PCI), is the most effective strategy for salvaging endangered cardiomyocytes and, thus, improving clinical prognosis (Anderson and Morrow, 2017). However, the process of restoring blood flow to the ischemic myocardium can, paradoxically, induce injury, through a process known as myocardial ischemia/reperfusion injury (MIRI). It was estimated that reperfusion can reduce myocardial infarct size by 40%, while a proportion of the remaining 30% infarct volume results from MIRI and, theoretically, is avoidable (Yellon and Hausenloy, 2007). Activation of the Reperfusion Injury Salvage Kinase (RISK) Pathway by pharmacological (Gao et al., 2002;Kis et al., 2003a;Gross et al., 2004;Tissier et al., 2007;Penna et al., 2012;Zhou et al., 2015) or non-pharmacological (Juhaszova et al., 2004;Tsang et al., 2004;Hausenloy et al., 2005;Zhu M. et al., 2006;Jin et al., 2008) stimulation was shown in dozens of preclinical studies to reduce the size of myocardial infarcts resulting from reperfusion injury. Therefore, pharmacological agents that activate the RISK associated kinases might have powerful cardioprotective properties. These potential target kinases include phosphatidylinositol-4,5-bisphosphate 3-kinase/protein kinase B (PI3K/Akt), extracellular signal-regulated kinase (Erk) and their downstream targets, ribosomal protein S6 kinase (p70s6k) and glycogen synthase kinase 3β (GSK 3β) (Heusch, 2015).
Preparation of TXL Solution
A solution of TXL ultrafine powder (Lot Number: 071201; Shijiazhuang Yiling Pharmaceutical Co., Shijiazhuang, China) was prepared as previously described, with minor modifications (Liang et al., 2011). Briefly, after the powder was dissolved in serum-free Dulbecco's modified Eagle's medium (DMEM; Life Technologies, Grand Island, NY, USA), the suspension was sonicated for 30 min and then centrifuged it at 2,500 rpm for 15 min. Sterile TXL solution was obtained by filtering the supernatant through a 0.22-µm filter. The precipitate was then dried, enabling precise weighing of the dissolved TXL powder. The solution was adjusted to a final concentration of 2,000 µg/mL by adding DMEM and was then stored at 4 • or −20 • C until use.
Cell Viability Assay
To assess cell viability, 4 × 10 3 HCMs were seeded per well in a 96-well plate. To determine the toxicity of TXL in HCMs, various groups of cells were pretreated with TXL at different concentrations (0, 100, 200, 400, 800, and 1,200 µg/mL) for 24 h, under normal culture conditions, before assessing cell viability. To assay protective effects of TXL on HCMs, cell viability assays were performed after H/R. Each group contained triplicate wells in every independent experiment. HCM viability was determined using WST tetrazolium salt (CCK-8, Dojindo) according to the manufacturer's instructions. Briefly, CCK-8 reagent (10 µl) was added to each well and the plates were incubated at 37 • C for 3 h. Absorbances at 450 nm were then determined with a microplate reader.
Animals
Male Sprague Dawley rats (220-250 g) were used in this study. Animal experiments were performed in accordance with the "Guide for the Care and Use of Laboratory Animals" issued by the US National Institutes of Health (Bethesda, MD, USA, NIH Publication No. 85-23, revised 1996) and the "Regulation to the Care and Use of Experimental Animals" of the Beijing Council on Animal Care (1996). The study protocol was approved by the Care of Experimental Animals Committee of Fuwai Hospital.
Cell Culture and Treatments
HCMs isolated from the ventricles of the adult heart were from PromoCell (Heidelberg, Germany). Cells were grown in a monolayer to 80% confluence and then subcultured using Readyto-Use Myocyte Growth Medium (PromoCell). Experiments with HCMs were performed at passages three to seven. Cells were washed with phosphate buffered saline (PBS) and exposed to the various treatments in serum-free DMEM for 30 min prior to hypoxia. HCMs were then incubated in an airtight and hypoxic GENbox jar fitted with a catalyst (BioMérieux, Marcy l'Etoile, France) to scavenge free oxygen, inducing 18 h hypoxia, as previously described and were then moved to normal conditions for 2 h reoxygenation. An anaerobic indicator dye (BioMérieux) was used to assess the oxygen tension of the medium, which enabled confirmation of successful establishment of the in vitro H/R model. U0126 was used as a Mek/Erk inhibitor, LY294002 as an Akt inhibitor and rapamycin as a p70s6k inhibitor, with treatments were performed as previously described. U0126 was administered at 10 µM (Vicencio et al., 2015), LY294002 at 10 µM (Vicencio et al., 2015), and rapamycin at 10 nM (Qiu et al., 2004) in experiments to further investigate the mechanisms underlying the protective effects of TXL on HCMs.
Assessment of Morphological Changes
Cell nuclear condensation and fragmentation were assessed in cells stained with the chromatin dye Hoechst 33,342 (Beyotime, China), as previously described (Zhu W. et al., 2006). Briefly, cells were fixed with 4% paraformaldehyde for 30 min and then exposed to 5 mg/mL Hoechst 33,342 for 30 min, then washed twice with PBS. Finally, stained cells were washed twice with PBS at room temperature and observed under a fluorescence microscope (Leica, Germany). Cells with fragmented and condensed apoptotic nuclei were considered to have undergone apoptosis.
Measurement of Mitochondrial Membrane Potential
Mitochondrial membrane potential (MMP) was assessed using the 5,5 ′ ,6,6 ′ -Tetrachloro-1,1 ′ ,3,3 ′ -tetraethyl-imidacarbocyanine iodide (JC-1) assay (Beyotime) following the manufacturer's instructions. In brief, HCMs cultured in 6-well plates were harvested after indicated treatments, suspended in the mixture (0.5 mL complete medium and 0.5 mL JC-1 staining solution), and then incubated at 37 • C in for 20 min. Eventually, the cells were resuspended in 300 µl staining buffer and analyzed by flow cytometry (FACSAria 2, Becton-Dickinson) after being rinsed twice with ice-cold JC-1 staining buffer. The MMP of each sample were expressed as the ratio of red fluorescence intensity over green fluorescence intensity. And mitochondrial depolarization is indicated by a decrease in the red/green fluorescence intensity ratio.
EdU Assay
The effects of TXL on the proliferation of HCMs were determined by the EdU incorporation assay using the EdU assay kit (Ribobio, China) according to the manufacturer's instructions. Briefly, HCMs were cultured in 6-well plates and were incubated with TXL at different concentrations and 20 µM of EdU during H/R. Then cells were collected, washed with PBS for one time, fixed with 4% paraformaldehyde for 15 min at room temperature and treated with 0.5% Triton X-100 for 20 min at room temperature for permeabilization. After being rinsed twice with PBS, HCMs were incubated with 1 × Apollo R 488 reaction cocktail (300 µl/well) for 10 min. Then HCMs were resuspended in 300 µl PBS and analyzed by flow cytometry (FACSAria 2, Becton-Dickinson) after being washed twice with 0.5% Triton X-100. HCMs stained with Apollo R 488 dye were EdU-positive and considered to be newborn cells.
Determination of Apoptosis by flow Cytometry
Cell apoptosis was assessed using the Annexin V-FITC/PI Kit (Becton, Dickinson and Company, USA), following the manufacturer's instructions. Briefly, HCMs, after experimental treatments, were collected and resuspended in 100 µl 1 × binding buffer. The cell suspension was incubated for 15 min at room temperature in the absence of light after addition of annexin V (5 µl) and propidium iodide (PI) (5 µl) solutions. Next, 400 µl 1× binding buffer was added and HCMs were harvested and analyzed using the FACS Calibur System (Becton-Dickinson). Viable HCMs were defined as annexin V − /propidium iodide (PI) − , early apoptotic HCMs as annexin V + /PI − and late apoptotic HCMs and necrotic HCMs as annexin V + /PI + . The proportion of apoptotic HCMs was calculated after adding together early and late apoptotic cells.
Establishment of the in Vivo Myocardial I/R Injury Model
Male Sprague Dawley rats were anesthetized with sodium pentobarbital (50 mg/kg, intraperitoneally) before endotracheal intubation. I/R was induced by ligating the left anterior descending artery (LAD) for 45 min, followed by loosening the ligature for 180 min, as described previously (Kang et al., 2014). Rats were randomized to four groups: Sham group, in which LAD was encircled by a suture but not occluded; I/R group, in which rats were administered saline by gavage 1 h prior to I/R; I/R + TXL group, in which rats were administered TXL dissolved in saline (0.4 g/kg, an equivalent dose to that used clinically in humans) by gavage 1 h prior to I/R; and I/R + TXL + Rapamycin group, in which the rats were administered rapamycin (0.25 mg/kg) intravenously 30 min prior to I/R, as described previously (Wagner et al., 2010) in addition to receiving the same TXL treatment as the TXL group.
Tunel Assay
Cardiac tissues were fixed in neutral 10% formalin for 24 h, embedded in paraffin, and then cut into 5 µm thick slices. After deparaffinization and rehydration, apoptotic cardiomyocytes were detected in the myocardium using a terminal dUTP nick end-labeling (TUNEL) assay, as previously described (Chen et al., 2017). TUNEL staining was conducted with the in situ Cell Death Detection kit (Roche, Indianapolis, IN, USA). Cardiac slices were also stained for nuclei with 4 ′ , 6-diamidino-2-phenylindole (DAPI) (Invitrogen, CA, USA). Finally, images were acquired with a Leica (SP8) confocal microscopy system at 400 × magnification and the average of the ratios of TUNEL-positive to total nuclei from five representative microscopic fields, obtained from the midventricular section of each heart, was calculated.
Infarct Size Measurement
Infarct sizes were measured in myocardial tissue as previously described, with a minor modification (Ge et al., 2015). The suture encircling the LAD was re-tied after reperfusion, and 1 mL 2% Evans Blue dye was injected into the thoracic aorta to distinguish the ischemic area (area-at-risk, AAR) from the non-ischemic area (Evans blue perfused region). The heart was immediately harvested, once the dye was uniformly distributed, and then stored frozen at −80 • C overnight. Frozen ventricles were sliced into 5 or 6 sections, which were incubated in 1% 2, 3, 5-triphenyltetrazolium chloride (TTC, Amresco) for 15 min at 37 • C to stain ischemic but viable tissue (red) and to visualize the infarcted area (pale white, IA). The IA, AAR, and total crosssectional heart area (TA) were measured using ImageJ software (National Institutes of Health). The infarct size and ischemic area were expressed as a percentage of AAR (IA/AAR) and TA (AAR/TA), respectively.
Statistical Analysis
Quantitative data are presented as means ± standard error of the mean (SEM). One-way analysis of variance (ANOVA) was followed by post-hoc analysis by Tukey's test for multiple comparisons, using GraphPad Prism 5.01. Significance was established at the P < 0.05 level.
TXL Inhibited H/R-Induced Death of HCMs in a Dose-Dependent Manner
WST-8 is a reagent dissolved in the working solution of CCK-8 kit. It can be reduced to soluble formazan by dehydrogenase in mitochondria and has little toxicity to cells. In other words, the more the soluble formazan is generated within the fixed time, the more the viable cells exist in a 96-well plate. Therefore, WST-8 was used to count the number of viable cells in our experiments. Results (Figure 1A) of the CCK-8 assay showed that, compared with the 0 µg/mL group, the 800 and 1,200 µg/mL TXL groups had decreased cell viability, to 75.49 ± 3.00% (P < 0.05) and 52.63 ± 1.80% (P < 0.05), respectively, of values under normal conditions. However, 100, 200, and 400 µg/mL TXL showed no detectable toxicity. Thus, we chose 100, 200, and 400 µg/mL for subsequent experiments.
As shown by the CCK-8 assay results, cell viability was decreased to 52.93 ± 0.86% (p < 0.05) after exposure to H/R, indicating that we had established a cell-based model of I/R ( Figure 1B). This loss of viability was prevented by TXL pretreatment, in a dose-dependent manner, with the greatest effectiveness at 400 µg/mL (89.91 ± 1.03% vs. 52.93 ± 0.86% in the H/R group, P < 0.05).
TXL Pretreatment Protected the Mitochondria of HCMs from H/R-induced Injury
It is well acknowledged that the mPTP not only is central in mitochondrial damage and cell death during I/R, but is also a converging target of cardioprotective signaling (Heusch, 2015). As mPTP opening would lead to depolarization of the inner MMP, we detected pore opening using the JC-1 assay to investigate whether TXL would inhibit mPTP opening. JC-1 existed as an aggregated form (red fluorescence) in the matrix of mitochondria with the normal MMP, and it was converted to the monomeric form (green fluorescence) with the loss of MMP. Therefore, decrease in the red/green fluorescence intensity ratio could indicate the reduction of MMP. Compared with the normal cells, HCMs exposed to H/R exhibited low MMP (5.64 ± 0.21 vs. 0.69 ± 0.07 in the H/R group, P < 0.05). However, pretreatment of HCMs with TXL (100, 200, or 400 µg/mL) increased the red/green ratio after H/R in a dose-dependent manner ( Figure 1C). This was consistent with the trend shown by the CCK-8 assay.
As mPTP opening was reported to result in release of the pro-apoptotic protein cytochrome C, we used western blotting to determine whether TXL pretreatment would affect this process. Groups treated with TXL had lower cytochrome C levels than the H/R group, especially at 400 µg/mL TXL (0.35 ± 0.04 vs. 1.00 ± 0.11 in the H/R group, P < 0.05) ( Figure 1D).
TXL Decreased H/R-Induced HCM Apoptosis in a Dose-Dependent Manner
We previously showed that TXL decreased apoptosis in mesenchymal stem cells under hypoxia and serum deprivation conditions . Therefore, we hypothesized that TXL would alleviate H/R-induced injury in HCMs by inhibiting their apoptosis. Consequently, HCMs without TXL or with TXL at various concentrations (100, 200, or 400 µg/mL) were analyzed, by morphology and flow cytometry, to detect the anti-apoptotic effects of TXL in HCMs under H/R conditions. Hoechst 33342 is a kind of blue fluorescent dye. As it is cell-permeable and can bind to DNA in live or fixed cells, Hoechst 33342 is generally used to assess nuclear morphological changes of cells after stimulation. As shown in Figure 2A, compared with cells in the normal group, cells exposed to H/R had shrunken and condensed nuclei. In contrast, TXL, in a dose-dependent manner, prevented the changes in nuclei induced by H/R, with 400 µg/mL being the most effective concentration.
The apoptotic cells were then quantified by flow cytometry, after staining with annexin V and PI. Annexin V-FITC detects cells in early apoptosis by staining phosphatidylserine translocated to the external surface of the plasma membrane, whereas PI mainly detects late apoptotic and necrotic cells (Vermes et al., 2000;Fimognari et al., 2001). As shown in Figure 2B, flow cytometry results indicated that H/R significantly increased the rate of apoptotic HCMs, compared with in the normal group (43.70 ± 1.28% vs. 8.25 ± 0.77%, respectively, P < 0.05). On the contrary, TXL treatment decreased H/R-induced HCM apoptosis, in a concentration-dependent manner. TXL was anti-apoptotic at 100 µg/mL (37.23 ± 1.01% vs. 43.70 ± 1.28% in the H/R group, P < 0.05) and reached its peak at 400 µg/mL (19.50 ± 1.08% vs. 43.70 ± 1.28% in the H/R group, P < 0.05). Consequently, 400 µg/mL TXL was used for subsequent experiments.
The balance of the pro-apoptotic protein Bax and antiapoptotic protein Bcl-2 is significant for regulating mitochondrial integrity and cell survival (Cory et al., 2003;Gustafsson and Gottlieb, 2008). To explore whether the anti-apoptotic effects of TXL were associated with Bcl-2 family proteins, expression of Bax and Bcl-2 in HCMs was determined by western blotting (Figure 2C). Compared with the H/R group, the group treated with 400 µg/mL TXL had significantly lower Bax expression (0.21 ± 0.04 vs. 1.00 ± 0.10 in the H/R group, P < 0.05) and also had clearly increased Bcl-2 expression (8.90 ± 1.60 vs. 1.00 ± 0.38 in the H/R group, P < 0.05).
TXL Activated the Risk Pathway by Upregulating p70s6k1 and Increasing p-p70s6k1 in Vitro
After demonstrating the anti-apoptotic effects of TXL in HCMs under H/R conditions, we investigated the underlying mechanisms. RISK pathway activation was reported to protect the heart against reperfusion injury Heusch, 2015) and it is generally activated via either Akt or Erk 1/2. Thus, flow cytometry and western blotting were used to determine the roles of Akt and Erk 1/2 in the anti-apoptotic effects of TXL. Previous studies demonstrated that TXL treatment of cells promoted secretion of vascular endothelial growth factor (VEGF) Hu et al., 2011). This effect was confirmed in our protein antibody arrays. Compared with the H/R group, TXL treatment groups had significantly higher levels of VEGF release by cardiac microvascular endothelial cells under H/R condition (Cui et al., 2016). Because phosphorylation of p70s6k1, a common downstream protein of Akt and Erk1/2 in the RISK pathway, was reported to stimulate VEGF expression (Skinner et al., 2004;Fang et al., 2005;Zhou et al., 2007), we also explored whether p70s6k1 mediated the protective effects of TXL. Compared with the H/R group, TXL treated groups had increased p70s6k1 expression (2.15 ± 0.04 vs. 1.00 ± 0.03 in H/R group, P < 0.05). Thus, TXL promoted phosphorylation of p70s6k1 at Thr389 (1.65 ± 0.04 vs. 1.00 ± 0.09 in the H/R group, P < 0.05), but did not affect the upstream proteins (p-Akt/Akt and p-Erk1/2/Erk1/2) of p70s6k1 in the RISK pathway ( Figure 3B).
Although, the results of CCK-8 assay and flow cytometry consistently demonstrated that TXL could increase the proportion of viable cells after H/R in a dose-dependent manner, we still could not exclude the possibility that it was the proliferation of HCMs that accounted for the increased proportion of viable HCMs, for activation of p70s6k1 was previously reported to facilitate cellular proliferation (Fenton and Gout, 2011;Xu et al., 2012). However, it was unlikely that TXL could promote the proliferation of HCMs in the serum/glucose-free medium during H/R. In other words, almost no nutrients were available for HCMs to utilize and then undertake proliferation in our experiments simulating I/R. To confirm this speculation, the EdU incorporation assay was used to assess the effects of TXL on the proliferation of HCMs during H/R. EdU is a nucleoside analog of thymidine and can be incorporated into DNA during active DNA synthesis (Salic and Mitchison, 2008), which means that the EdU + cells can be regarded as the newborn cells. Just as predicted, the proportions of replicating cells were lower (∼4%) in all groups treated with H/R, compared with that in the normal group (10.43 ± 0.14%). In addition, no between-group difference was observed in the proportion of newborn cells in our experiments simulating I/R ( Figure 4C). However, we found that HCMs cultured in complete medium in normal conditions replicated much more quickly if they were treated with 400 µg/mL TXL (data not shown). Taken together, we were inclined to make a conclusion that TXL could protect the HCMs from H/R-induced injury by upregulating p70s6k1 and increasing p-p70s6k1.
TXL Activated the Risk Pathway by Upregulating p70s6k1 and Increasing p-p70s6k1 Levels in vivo
TTC is a redox indicator commonly used to indicate cellular respiration. It can be enzymatically reduced to red TPF (1, 3, 5triphenylformazan) in living tissues by various dehydrogenases (enzymes important in oxidation of organic compounds and thus cellular metabolism), while it remains in its unreacted state in necrotic areas since these enzymes have either denatured or degraded. After being incubated in TTC solution, viable heart muscle will be stained deep red, while infarcted areas will be dyed pale white. Therefore, TTC staining was utilized in our experiments to assess myocardial infarct size after different kinds of treatments. Consistent with the in vitro results, our in vivo experiment showed that the myocardial infarct size in the TXL group was significantly smaller than that in the I/R group (47.92 ± 3.36% vs. 70.35 ± 3.00%, respectively, P < 0.05; Figure 5A). In addition, TUNEL assay was used to detect apoptotic DNA fragmentation, identifying and quantifying apoptotic cells. This assay relied on the use of terminal deoxynucleotidyl transferase(TdT), an enzyme that catalyzed attachment of deoxynucleotides, tagged with fluorescein, to 3 ′hydroxyl termini of DNA double strand breaks. As was expected, the number of positive TUNEL stained cardiomyocytes in the I/R group was greater than that in the Sham group, whereas TXL significantly decreased the number of apoptotic cells, compared with in the I/R group (20.22 ± 0.80% vs. 35.45 ± 2.53%, respectively, P < 0.05; Figure 5B). These protective effects were partly abrogated by rapamycin (Figures 5A,B).
TXL Downregulated miR-128-3p, a Microrna Targeting p70s6k1
To examine the mechanism of TXL upregulation of p70s6k1 expression, we first assessed the levels of p70s6k1 mRNA in HCMs using real-time PCR. As shown in Figure 6A, TXL did not change p70s6k1 mRNA levels, indicating that it might regulate p70s6k1 at the protein level by promoting translation of p70s6k1 mRNA or inhibiting degradation of p70s6k1 protein.
MicroRNAs are a type of noncoding RNA that regulates gene expression and there are microRNAs that recognize over 60% of human protein-coding genes (Friedman et al., 2009). One mechanism by which microRNAs downregulate gene expression is translational repression, that is, decreasing levels of specific proteins without changing those of their corresponding mRNAs (Bartel, 2004). Because TXL was reported to decrease expression of microRNAs and increase levels of their corresponding proteins under certain conditions (Wang J .Y. et al., 2014;Zhang et al., 2014Zhang et al., , 2017, we used quantitative PCR to examine levels of several microRNAs (Shi et al., 2012;Xu et al., 2012Xu et al., , 2015 The effects of TXL on the protein levels of total p70s6k1 and phosphorylated p70s6k1 in myocardium (n = 4 in each group). *P < 0.05 vs. Sham; # P < 0.05 vs. I/R; $ P < 0.05 vs. I/R+TXL; Ra, rapamycin; TA, total cross-sectional heart area; AAR, area at risk; IA, infract area.
(miR-497-5p, miR-145-5p, and miR-128-3p) known to target the mRNA of p70s6k1 in HCMs (Figure 6B). Compared with the H/R group, the level of miR-128-3p in HCMs was significantly lower in the TXL treated group (0.38 ± 0.06 vs. 1.00 ± 0.00 in H/R group, P < 0.05). There were no significant differences in levels of the other two microRNAs, miR-497-5p and miR-145-5p, in these two groups. Furthermore, our in vivo findings confirmed that, compared with that in the I/R group, TXL significantly decreased miR-128-3p levels (0.38 ± 0.09 vs. 1.00 ± 0.00 in the I/R group, P < 0.05; Figure 6C) in the rat myocardium after I/R.
Mir-128-3p was Involved in Protection by TXL against HCM Apoptosis
To explore whether miR-128-3p was involved in the beneficial effects of TXL against H/R-induced apoptosis, mimics were utilized to upregulate levels of miR-128-3p in HCMs. HCMs were transfected with miR-128-3p mimics, preconditioned with 400 µg/mL TXL and then subjected to H/R. By western blotting analysis, the miR-128-3p mimics downregulated expression and inhibited phosphorylation of p70s6k1 ( Figure 7B). Because of the decrease in p-p70s6k1/p70s6k1 levels in HCMs during H/R, TXL pretreatment no longer inhibited cell death in HCMs (Figure 7A), suggesting that upregulation of miR-128-3p abrogated the protective effects of TXL on HCMs.
DISCUSSION
Our study demonstrated, for the first time, that TXL directly protected cardiomyocytes from H/R injury and, thus, alleviated MIRI. This protective effect was dependent on activation of the RISK pathway, mediated by increased expression and phosphorylation of p70s6k1, rather than by affecting its upstream proteins (Akt and Erk). Furthermore, p70s6k1 upregulation by TXL was attributable to downregulation of miR-128-3p in cardiomyocytes during I/R. We believe that ours is the first study demonstrating that p70s6k1 overexpression in cardiomyocytes was sufficient to reduce MIRI, elucidating potential new strategies to decrease MIRI.
TXL is a multifunctional traditional Chinese medicine reported to exert pleiotropic effects such as anti-fibrosis Bai et al., 2013;, antiinflammation , anti-atherogenesis (Wu et al., 2015), anti-apoptosis Wei et al., 2016) and improved microvascular barrier function Li et al., 2015;Qi et al., 2015;Zheng et al., 2015). This multitude of activities can be explained by the variety of active ingredients in TXL (Cheng et al., 2009). Although our prior studies proved that TXL had infarct-sparing effect during I/R in animals, it remained unclear whether TXL could protect human cardiomyocytes from reperfusion injury. As a consequence, for our in vitro experiments, we used human cardiomyocytes, which are widely employed in cardiovascular research (Albrecht-Schgoer et al., 2012;Boon et al., 2013;Baker et al., 2015;Kuo et al., 2015;Nehra et al., 2015;Sharma et al., 2015), rather than cardiomyocytes isolated from animals.
P70s6k is one kinase belonging to the AGC family (Pearce et al., 2010;Prêtre and Wicki, 2017) and can be regulated by the mammalian target of rapamycin (mTOR) pathway (Pearce et al., 2010;Fenton and Gout, 2011;Prêtre and Wicki, 2017). P70s6k was reported to play important roles in diverse cellular processes, including protein synthesis, mRNA processing, glucose homeostasis, cell growth and survival (Fenton and Gout, 2011). Prior studies demonstrated that p70s6k was a protein in the RISK pathway and could be activated by RISK associated kinases like Erk and Akt Heusch, 2015). There are two p70S6K subtypes, type 1 (p70S6K1) and type 2 (p70S6K2) (Shima et al., 1998;Tseng et al., 2005), with type 2 barely detectable in the adult heart (Tseng et al., 2005). Given that TXL was shown by several investigators to stimulate cells to secrete VEGF Hu et al., 2011;Cui et al., 2016) and that increased p70s6k1 phosphorylation stimulated VEGF expression (Skinner et al., 2004;Fang et al., 2005;Zhou et al., 2007), we examined whether p70s6k1 mediated the protective effects of TXL. Indeed, TXL treatment increased p70s6k1 phosphorylation and, thus, protected cardiomyocytes from H/R-induced injury, by promoting p70s6k1 expression. Pharmacological blockade of p70s6k1 activation with its inhibitor (rapamycin) or siRNA abrogated the beneficial effects of TXL on cardiomyocytes, indicating that TXL protected cardiomyocytes by a pathway involving p70s6k1. This finding, to some extent, was consistent with our previous observations that TXL decreased myocardial infarct size induced by I/R through the protein kinase A (PKA)/eNOS pathway (Cheng et al., 2009;Li et al., 2010;Li X. D. et al., 2013). It was demonstrated that p70s6k activation by neuropeptide Y or cysteine-rich, angiogenic inducer 61, promoted eNOS phosphorylation in endothelial cells and led to blood vessel relaxation (Cheng et al., 2012;Hwang et al., 2015). Moreover, two other studies showed that increased p70s6k1 phosphorylation facilitated PKA expression and enhanced its activity in tissues (Soulard et al., 2010;Jiang et al., 2016). Taken together, it is very likely that TXL can reduce MIRI via the p70s6k1/PKA/eNOS pathway, but further studies will be needed to confirm this speculation. To our knowledge, no practical method of stimulating p70s6k1 phosphorylation, by facilitating p70s6k1 expression, has been developed and translated to the clinic to reduce MIRI. Our study showed, for the first time, that TXL can activate p70s6k1 and alleviate MIRI in this manner.
MicroRNAs, a family of small non-coding single-stranded RNAs, are emerging as robust players regulating genes at the post-transcriptional level. MicroRNAs modulate gene expression by two mechanisms. One is by clearing away mRNA that has sufficient complementarity to the microRNA. The other, when the mRNA does not have sufficient complementarity to be degraded but does have a site complementary to the microRNA, is repressing productive translation (Bartel, 2004). The effects of microRNAs on MIRI depend on their types, because some are protective while others are detrimental (Fan and Yang, 2015). Regulation of microRNAs (inhibition of detrimental microRNAs or overexpression of the protective ones) in I/R has been considered as one novel potential strategy for alleviating MIRI (Hausenloy et al., 2017). Our study demonstrated that TXL promoted p70s6k1 expression without changing p70s6k1 mRNA levels, leading us to investigate the role of microRNAs in the anti-apoptotic effects of TXL. Among the three microRNAs (Shi et al., 2012;Xu et al., 2012Xu et al., , 2015 previously reported to target p70s6k1 mRNA and inhibit its translation, only miR-128-3p was downregulated by TXL during I/R. MiR-128-3p was implicated as important in multiple physiological and pathophysiological processes, such as angiogenesis, neuronal plasticity, cholesterol metabolism and differentiation Adlakha and Saini, 2014). Moreover, miR-128-3p inhibition in cells enhanced their resistance to detrimental stimuli like chemotherapeutic agents and H/R (Zhu et al., 2011;Chen et al., 2016;Zeng et al., 2016). The protective effects of TXL on HCMs during H/R were largely abolished by transfection with miR-128-3p, indicating that miR-128-3p mediated the beneficial effects of TXL on HCMs during H/R.
The importance of the RISK pathway in cardioprotection was demonstrated by many studies and activation of this pathway is generally associated with increased phosphorylation of Akt or Erk, as well as of their common downstream kinases such as p70s6k1 Bouhidel et al., 2008;Heusch, 2015). However, in our study we found that, with inhibition of miR-128-3p, TXL enhanced p70s6k1 phosphorylation and, thus, activated the RISK pathway by facilitating p70s6k1 expression, rather than by activating Akt or Erk. In contrast, we previously showed that TXL upregulated Erk phosphorylation and, subsequently, decreased apoptosis in human cardiac microvascular endothelial cells during H/R . Discrepancies between these findings may be attributable to differences in responses of endothelial cells and cardiomyocytes to the same stimulus. For example, inhibition of autophagy was reported to protect both primary cardiomyocytes and cardiomyoblasts (Valentim et al., 2006;Zhang et al., 2012) from H/R induced-injury, while it decreased viability of endothelial cells during H/R in our previous study . In addition, endothelial cells were more vulnerable to I/R, during which apoptosis of endothelial cells preceded that of cardiomyocytes (Scarabelli et al., 2001). Regarding the effects of TXL on Akt during I/R, Yu et al. previously reported that oral administration of TXL (three times a day for 3 days) alleviated cerebral ischemia and reperfusion injury in rats, through Akt activation . The inconsistencies between these findings and ours probably related to use of different modes of TXL administration. In other studies, TXL could have promoted Akt phosphorylation by upregulating VEGF , the secretion of which can be facilitated by p70s6k1 phosphorylation (Skinner et al., 2004;Fang et al., 2005;Zhou et al., 2007). However, in our study, the single dose given shortly before H/R or IR may not have been sufficient to affect Akt through the p70s6k1/VEGF pathway.
Interestingly, we observed that I/R (or H/R) itself could promote the phosphorylation of p70s6k1, which was consistent with results from other research groups (Chen H. T. et al., 2008;Musiolik et al., 2010;Vilahur et al., 2013). However, it seemed that such upregulation of p70s6k1 could not confer protection on the reperfused-hearts, for rapamycin alone, with the effective inhibition of p70s6k1, did not aggravate MIRI in the current study and others' (Kis et al., 2003b;Pagel et al., 2007;Raphael et al., 2008;Wagner et al., 2010). Moreover, two other research groups demonstrated that rapamycin alone could even alleviate MIRI. For instance, Yang et al. Liu et al., 2011) reported that rapamycin could dose-dependently reduce infarct size in the isolated rat hearts if the hearts were perfused with rapamycin for 10 min before I/R. In addition, Kukreja et al. (Khan et al., 2006;Das et al., 2012) proved that whether the mouse hearts were subjected to in-vivo I/R or global I/R in Langendorff mode, administration of rapamycin had infarct-sparing effects. The inconsistencies between the findings of these two groups and ours may relate to the I/R modes (isolated hearts or in vivo hearts) and species differences.
To our knowledge, there may be two reasons explaining the phenomenon that the I/R-induced increase of p-p70s6k1 was not cardioprotective in our study. To begin with, the activation of p70s6k1 by I/R may be too late to reduce MIRI. As previous studies have shown that H 2 O 2 pretreatment could increase the phosphorylation of p70s6k1 in cells (Tu et al., 2002;Gutierrez-Uzquiza et al., 2012;Huang et al., 2015), the upregulation of p-p70s6k1 after I/R may be attributable to the excessive reactive oxygen species induced by I/R. In this case, the oxidative stress in cardiomyocytes or cardiac issues preceded the increase of p-p70s6k1. As reactive oxygen species had extremely short half-life (Dickinson and Chang, 2011;Das and Roychoudhury, 2014) and damaged cells and issues very rapidly, the activation of p70s6k1 would fail to mitigate the irreversible injuries that had already been induced by the reactive oxygen species. Another explanation is that the amount of p-p70s6k1 increased in I/R was not enough to induce cardioprotection during I/R. Consequently and theoretically, timely and sufficient increase of p-p70s6k1 may have the potential to reduce MIRI. Such a speculation has been preliminarily confirmed by a previous study, in which Zeng et al. demonstrated the cardioprotective effect of sevoflurane postconditioning on MIRI was related to the further activation of p70s6k (Chen H. T. et al., 2008). And our present study directly proved that further enhancement of the phosphorylation of p70s6k1 with TXL could protect the hearts from MIRI.
TXL is a traditional Chinese medicine that was originally approved as an anti-angina drug by the CFDA in 1996. Later, the beneficial effects of its chronic use in other diseases such as hypertension , cardiac ventricle remodeling , diabetes Zhang et al., 2010) and stroke (Wu et al., 2007) were validated in numerous clinical trials, where severe adverse effects were seldom reported. In other words, TXL is available as a CFDA-approved drug with an acceptable safety profile. Therefore, it is very possible that the acute use of TXL to reduce MIRI will not bring about serious safety problems in patients. As for the efficacy of TXL in attenuating MIRI, the series of studies (Cheng et al., 2009;Li et al., 2010;Li X. D. et al., 2013) from our lab consistently proved that TXL had infarct-sparing effects in small and large animals. Furthermore, the present study demonstrated that TXL exerted a protective effect on human cardiomyocytes during H/R as well, which, to some extent, indicated its efficacy when applied to clinical practice. Taken together, all these findings suggest that TXL has a great potential to emerge as an anti-reperfusion injury therapeutic strategy. However, as the current dosage form of TXL can only be administered orally and then absorbed through the intestinal tract, it takes a relatively long time for TXL to reach its effective blood concentration after oral administration. In order to fully exploit the therapeutic potential of TXL in alleviating MIRI, further studies will be needed to explore how to change the dosage form of TXL and make sure that it can be administrated intravenously.
A major limitation of our study was that we did not identify which ingredients in TXL, alone or in combination, were responsible for activation of the miR-128-3p/p70s6k1 pathway in cardiomyocytes during I/R. Another limitation is that we did not investigate how TXL downregulated miR-128-3p. Further research will be needed to elucidate whether TXL decreases miR-128-3p levels by directly inhibiting the transcription of its gene or, instead, acts in an indirect manner, such as by facilitating expression of endogenous RNAs (e.g., lnc-LAMC2-1:1 Gong et al., 2016) that compete with miR-128-3p.
In conclusion, we reveal for the first time that TXL can directly inhibit cardiomyocyte apoptosis and thus alleviate myocardial reperfusion injury through the miR-128-3p/p70s6k1 pathway. And overexpression of p70s6k1 might represent a new strategy for alleviating myocardial reperfusion injury.
AUTHOR CONTRIBUTIONS
Designed the experiments: GC, XL, LC, and YY; performed the experiments: GC, CX, JZ, QL, RT, J-yX, XT, PH, and JX; analyzed the data: GC, HC, and CJ; wrote the manuscript: GC and YY; revised the manuscript: GC and YY. | 8,633.8 | 2017-10-30T00:00:00.000 | [
"Biology"
] |
Optical Second Harmonic Generation in Semiconductor Nanostructures
Optical second harmonic generation (SHG) studies of semiconductor nanostructures are reviewed. The second-order response data both predicted and observed on pure and oxidised silicon surfaces, planar Si(001)/SiO2 heterostructures, and the results related to the direct-current-and strain-induced effects in SHG from the silicon surfaces as well are discussed. Remarkable progress in understanding the unique capabilities of nonlinear optical second harmonic generation spectroscopy as an advanced tool for nanostructures diagnostics is demonstrated.
Introduction
In recent decades, optical techniques have been extensively used to study the size dependence of linear-, second-, and third-order optical nonlinearities of nanocrystals in relationship to the quantum confinement [1][2][3][4].A remarkable progress has been achieved in studies of the effects of surfaces and interfaces in semiconductor nanostructures, nanowires, and nanoparticles (see [5] and references therein).
If a high-intensity beam of light strikes a specimen, the latter will respond in a nonlinear manner and higher optical harmonics will be generated in addition to the linear optical response.The strongest measurable nonlinear optical response is the second harmonic generation (SHG).The generated higher harmonics are functions of the specimen atomic structure.Most materials possess odd-order nonlinear optical susceptibility components; however, the evenorder nonlinear susceptibility terms exist only in the systems having noncentrosymmetric atomic geometry [6][7][8].Consequently, if the initial inversion symmetry of a reflecting system (like, e.g., bulk Si, Ge, C crystals, and/or centrosymmetric molecules, ideally shaped nanoparticles,) is for some reason broken, for example, due to crystal truncation, defects, external fields, and so forth, it results in even-order harmonics generation.Note that in central symmetric systems the SHG-active (and other even-order harmonics) areas are restricted by the symmetry reduction regions.In noncentral symmetric systems the symmetry reduced results in an appearance of initially forbidden optical susceptibility tensor components that can be experimentally detected.This extremely high sensitivity of the even-order nonlinear optical response (such as SHG) to the local symmetry makes it a very efficient tool for probing atomic and molecular processes on surfaces and interfaces.In nanostructures where the contribution of the surface-related effects to the total optical response is increasingly important with the size reduction, the unique symmetry sensitivity allows developing an efficient SHG-based nonlinear optical diagnostics probe, as demonstrated in this paper.
The development of the laser provided intense source of monochromatic and coherent light that stimulated extensive research and applications in the field of nonlinear optics [9].Nonlinear optical microscopy has been used to study inorganic, organic, and biological materials [10] for decades.The advantages of the nonlinear microscopes include improved spatial and temporal resolution without the use of pinholes or slits for spatial filtering.In particular, for the applications in biology and medicine it has been demonstrated that 2 Physics Research International multiphoton excitation microscopy has the capacity to image deeper within highly scattering tissues such as in vivo human skin [10].
In this work we summarize some recent experimental and theoretical works on the SHG mostly related to the silicon nanostructures.The work is organized as follows.Section 2 describes the theoretical description of nonlinear optical response.Section 3 describes recent experimental studies of direct-current-(DC-) and strain-induced effects in SHG from crystalline silicon surface.Section 4 presents the experimental and theoretical results related to semiconductor superlattices.
Optical Response
Following the description of the electromagnetic (EM) field in materials, within its penetration depth beneath the surface, the incident light field at the frequency ω(E(ω)) induces different order optical susceptibilities χ (n) [5,11].The linear polarization is given by [3]. (1) i j E j (ω). ( The second-order polarization is given by [3] Equation ( 1) represents linear optics.Nonlinear optical response, originating from anharmonic bond polarizabilities, governs Second Harmonic Generation and is determined through several contributions given by (2) [12], all of them being caused by noncentrosymmetric distortion of crystalline lattice.The first term in (2) describes the secondorder optical excitation process and vanishes in centrosymmetric systems (like bulk Si) in the electric dipole approximation.The second term, which is proportional to the field gradient (∇E), is the electric quadrupole component (in magnetic materials it contains also a magnetic dipole component).External electric field (if present), E DC , breaks the central symmetry thus inducing the SHG and higher evenorder nonlinear response.For example, in bulk Si (and other cubic solids having inversion symmetry) only the two last terms in (2) contribute to the SHG resulting in a very weak signal [3].
The nonlocal (local-field effects) and many body (exciton) contributions can cause substantial corrections to the SHG spectra [11].Moreover, it has been demonstrated theoretically that additional subjections like direct current (DC) or mechanical strain can further decrease the symmetry of silicon thus giving rise to additional, current-induced, and strain-induced components of nonlinear polarization, that is discussed in Section 3.
Direct-Current-and Strain-Induced Second Harmonic Generation in Silicon
This section is focused on an SHG generation in centrosymmetric systems as the result of the symmetry reduction due to the external electrical and mechanical fields.
3.1.Direct-Current-Induced SHG in Si(001).The effect of field-induced nonlinearity in centrosymmetric medium like silicon has been studied both experimentally and theoretically.Electric-and magnetic-field-induced effects in SHG are being extensively explored for the diagnostics of surfaces, interfaces, and nanostructures [13,14].Less studied was a phenomenon of the inversion symmetry break as the result of an external electrical direct current (DC).This effect can play an important role in SHG from the silicon surface.In this case, the current-induced distortion of the electron equilibrium distribution function in a quasi-pulse domain causes the dipole-like second-order nonlinearity that exists in the region affected by the DC.
The first theoretical description of the current-induced SHG (CSHG) was presented in [15], where the CSHG contribution to the nonlinear polarization for a direct band semiconductor was discussed.The CSHG contribution to P 2ω can be introduced in a similar manner to the electric field induced SHG and given by where j is the current density and χ (3)DC is the dipole currentinduced optical susceptibility.In case of direct semiconductors an asymmetry of the electron quasi-pulse distribution leads to the appearance of a current-induced term in secondorder susceptibility with a sharp resonance in the vicinity of the local Fermi level, E F , for majority carriers in the conduction band.It follows from symmetry considerations that χ (3)DC ( j) is an odd function of current density, χ (3)DC ( j) = − χ (3)DC (− j).This shows a possibility for an experimental observation of DC-induced effects in SHG, which was first studied in [16].Figure 1(a) shows a schematic view of the structure used for CSHG studies.Nickel electrodes were thermally evaporated on top of p-Si(001) (ρ ∼ 10 −3 Ωcm) that was used as a substrate.The 200 ± 20 μm wide gap between Ni electrodes was oriented along y crystallographic axis as it is shown in the figure; ohmic resistance of Ni/Si contact was ≈0.02 Ω.It has been proved that the temperature of the sample during the CSHG measurements was less than 40 • C.
CSHG experiments were carried out using the output of a tunable Ti: sapphire laser (wavelength range 710 ÷ 850 nm, pulse duration of 80 fs, average power of 130 mW, repetition rate of 86 MHz) as the fundamental radiation.The laser beam was focused onto the Si(001) surface between the Nielectrodes with a spot size of 40 μm in diameter.SHG radiation was filtered out by appropriate set of filters and detected by a PMT and gated electronics.
Special care has been taken to avoid the influence of the DC-induced heating and strain-induced effects on the CSHG measurements.To that end, only the s-polarization of both the pump and the SHG beams was used, because it is well known that only bulk quadrupole susceptibility contributes to the second harmonic signal in that case [18].Eightfold symmetric anisotropic SHG dependence was observed for this polarization-related geometry configuration.The azimuthal Si(001) orientation resulted in zero SHG intensity that was chosen as a reference for the measurements, because all the SHG contributions from Si vanish for the symmetry reasons, except those caused by the CSHG [16].
In order to reveal the CSHG effect, an external SHG homodyne source (30 nm thick ITO film) was used and the odd character of the dependence χ (3)DC on ( j) was exploited.It is important to note that, under the chosen experimental geometry only transversal DC, where j (OY ), gives rise to a nonzero SHG signal while it vanishes for the longitudinal geometry, where j (OX).Thus the total detected SHG intensity from the sample and the reference depends on the current density and direction and can be described by the expression [16] where and current-independent SH fields from the sample and the reference, respectively; E samp 2ω ( j), E ref 2ω , φ samp , and φ ref are real amplitudes and phases of SH fields, respectively.Interference of the two components of the SH field in (4) results in the appearance of a homodyne cross-term in the SHG intensity that changes its sign under current reversal and leads to odd in j changes in the SHG intensity.
The DC-induced SHG can be characterized by the CSHG contrast according to [16] where Δφ is determined by the dispersion of air at ω and 2ω, the distance between the sample and the reference and the phases φ ref and φ samp .Figure 1(b) shows the CSHG contrast dependence on j measured for a fixed position of the reference SHG sample.The measured linear character of ρ 2ω ( j) dependence is in agreement with (5) and proves that the odd DC-induced SHG is observed.Moreover, the obtained results also prove that the heating has a negligible effect, as otherwise it should bring the even in j contribution to the SHG.
The only SHG source which can disguise the CSHG effect under the chosen experimental conditions was the electricfield-induced SHG (EFISH).It cannot be avoided by a certain choice of the experimental geometry because the symmetries of CSHG and EFISH are the same.Nevertheless there are at least two pieces of evidence that prove that ρ 2ω ( j) dependence reflected the DC influence on nonlinear-optical signal from Si [16].
First, the CSHG intensity spectrum was compared with the EFISH that was measured on p-Si(001) [17] (Figure 2).One can see that EFISH spectrum shows a maximum at 2Dω ≈ 3.34 eV that is close to the E 1 resonance in silicon.At the same time, the CSHG contrast does not have resonant features in this spectral region that proves that observed CSHG effect was neither induced by the quadrupole nonlinearity nor by EFISH.In contrast, the increase Physics Research International of the CSHG data at 2Dω ≈ 3.5 eV is in a qualitative agreement with the theoretical predictions [15].
Theoretical study of the EFISH DC-induced effect in SHG from Si(001) under external electric fields gave a value of the CSHG contrast at least two orders of magnitude lower than observed experimentally.The comparison of the CSHG intensity to that measured in Si(001) and to the SHG intensity observed in crystalline quartz with well-known values of the second-order susceptibility allowed the estimation of the maximum value of DC-induced susceptibility in silicon to be χ (2)DC ( j max ) ≈ 3 • 10 −15 m/V.
Strain-Induced SHG in Si(001).
Recently the straininduced SHG in silicon surface has been observed by several groups [19][20][21][22].The physical mechanism of strain-induced nonlinearity is determined by the breaking of the intrinsic inversion symmetry of Si under mechanical deformation and consequently by a modification of the electronic spectrum.
Most of the efforts were concentrated on the studies of internal strain that may exist at Si/SiO 2 interfaces; however the modification of the SHG intensity as a function of the external biaxial strain was reported in [22].The measurements were performed using the experimental laser setup described in the previous section.As a target, the n-doped (4.5 Ωcm) 0.5 mm thick Si(100) plate was used.The strain was applied by pressing the back side of the Si plate by a metallic sphere fixed at the end of a micrometric stage while a movable base supplies the formation of strain in the Si wafer as is shown; in Figure 3(a).The optically probed depth of Si subsurface layer was about 30 to 50 nm and the deformation was considered to be uniform.This means that the crystalline symmetry of Si(001) can be considered to be undistorted; therefore the strain-induced SHG modification caused by the symmetry changes can be neglected.
The biaxial strain of the subsurface layer corresponds to the spherical deformation geometry as shown in Figure 3(b) [22].Figure 3(c) shows modification of SHG spectrum from silicon under biaxial strain observed at the conditions close to the mechanical break threshold (open circles) compared to the free plate SH spectrum (solid circles) [22].The deformation of the Si wafer was about 100 μm that corresponded to the mechanical stress of 350 MPa.P-polarizations of the fundamental and SHG waves were chosen in such a way that this combination of polarizations corresponded to the maximal strain-induced effect in SHG.It can be seen that both spectra have maxima with the energy locations corresponding to the SHG photon energy of 2Dω ≈ 3.3 eV that is close to the energy of the direct electron transitions near E 1 and E 0 critical points of the Si band structure.Slight variations of the SHG line shape as well as of the SHG intensity were also observed [22].
To characterise qualitatively the strain-induced modulation of the SHG intensity the strain-induced SHG (SSHG) contrast can be described as where I 2ω (0) and I 2ω (σ) are the SHG intensities measured for zero and nonzero value of the strain, σ. Figure 4 shows agreement with the possible theoretical description of straininduced SHG [22]: where E 2ω (σ) ∝ p i jklm σ lm , p i jklm is the piezo-optical tensor.
It can be seen that SSHG curves change their slope sign as the fundamental wavelength is tuned through the E 1 /E 0 Si critical point of combined density of states at 3.33 eV.The results demonstrate a strong strain-induced effect in SHG from the silicon surface.The possible mechanisms of the observed effects can be the strain-induced shifts of the conduction and valence bands, similarly to the linear case [24], or by modification of the charge distribution in the crystal and in dioxide charge traps.The latter mechanism corresponds to the EFISH at the Si/SiO 2 interface; the corresponding static electric field is perpendicular to the silicon surface that can produce an isotropic SHG.At the same time it was proved that the application of a uniaxial strain along the orthogonal in-plane direction OX and OY resulted in different modifications of the reflected SHG.This supports the conclusion of the possibility to detect the strain-induced nonlinearities induced by mechanical distortions of the silicon structure that really was observed experimentally [22].
Second Harmonic Generation from Semiconductor Surfaces and Nanostructures
Increasing amount of high-quality research on nonlinear optical properties of nanocrystals within the past decade is caused by extensive developments of nanotechnology, nanophotonics, and nanoelectronics [5] presenting with unique possibilities for SHG optical metrology and analysis of the nanocrystals.Numerous results obtained within past decades clearly demonstrate how important are the contributions of surface optical excitations to the overall nonlinear response of nanostructures.This section is focused on the SHG from surfaces and interfaces in nanostructures.
As stated before SHG is extremely sensitive to the surfaces in cubic materials [25][26][27].One of the reasons for that is the evidence that in centrosymmetric crystals the SHG response is forbidden by symmetry in bulk but allowed on the surface where the local symmetry is lower [25].
One can clearly see in Figure 5 that plotted charge redistribution causing induced bond asymmetries (thus allowing SHG by reduction of inversion symmetry) involves only few atomic monolayers near the surface.This demonstrates extreme locality of the SHG response that makes it very promising for the diagnostics of nanostructures.
Physics Research International
Demonstrated strong localization of nonlinear optical response is very important in view of the progress in the developments of the nanoparticles combined from crystalline materials (core) and organic molecules.Several studies of such systems indicated dominant contributions of optical excitations in the interfaces regions [5].
A recent surge in the number of Si-SiO 2 system studies within the past few years has been caused by extensive developments of nanostructured electronics which requires a fundamental understanding of the processes at the atomic monolayer scale.Understanding of the oxygen-related processes on Si-SiO 2 interface is very important for both fundamental and applied physics as well as for the high-tech electronics.
For contactless optical characterization of the interface at nanometer scale the nontraditional methods of optical spectroscopy are used: linear optical reflectance differential spectroscopy (RDS), see [28][29][30][31] and references therein, and/or nonlinear optical spectroscopy of second harmonic generation.Development of the next generation of optical metrology will require the detailed interpretation of optical spectra based on microscopic modelling and simulations.The first principle calculations of SHG [3,32] of silicon-based interfaces allowed substantial progress in our understanding of the physics and chemistry of the systems.However such works are still rare (in particular those related to the SHG) because they normally require extensive large scale computations [3,32].
Atomic structure of the intermediate Si and SiO 2 layer is extensively debating in the literature [33,34].Monte Carlo simulations [33] and first principle modelling based on density functional theory (DFT) and total energy minimization method [34] confirmed ordered Si-O-Si bridge structure as a basic unit of the intermediate SiO 2 layer.On the other hand the Rutherford ion scattering data measured on the Si-SiO 2 interface and interpreted using the ab initio DFT study with modified interatomic potential [35] suggested substantial contribution of disordered interface structure.It should be noted that optical spectra are very sensitive to the structural disorder, which smears out well-pronounced atomic orderrelated features.The SHG spectra measured on the systems containing single [36] or multiple Si(001)-SiO 2 interfaces (multiple Si-SiO 2 quantum wells, [37]) showed appearance of new SHG feature in the spectral region near 2.7 eV [37] and 3.8 eV [36,37] that could not be interpreted in terms of the perturbations of the Si bulk-like direct electron gaps.
Realistic modelling of optical functions of solid surfaces and interfaces still remains challenging for the first principle theories [3,30,31].Description of excited states which could be in good agreement with experiment normally requires inclusion of local field, many-body (excitonic) effects, and probably other nonlocal contributions (in particular for SHG) [38,39].This makes theory much more complicated than frequently used independent particles approach (or Random Phase Approximation, RPA).The nonlinear SHG response from the surfaces and interfaces is more challenging for the ab initio theory even within the RPA method because of the more complicated unit cell.Consequently first principles studies of the SHG from solid surfaces are still rare (see [3,32,39] and references therein).
In [26] the first principle computational analysis of nonlinear SHG optical spectra of Si-SiO 2 interface has been performed.Results of the work demonstrated the possibility of unambiguously identifying well-pronounced spectral features in SHG optical spectra of the Si-SiO 2 interface with local atomic oxygen-related configurations.Equilibrium atomic structures of Si(001)-SiO 2 interface are obtained from the total energy minimization method within DFT using ab initio norm-conserving [40,41] and ultrasoft (VASP, [42]) pseudopotentials (PPs).
Calculated self-consistent eigenenergies and eigenfunctions are used as inputs for optical calculations.Within its penetration depth beneath the surface, the incident light field at frequency ω(E(ω)) induces different order optical susceptibilities χ (n) that determine linear-( 1) and secondorder (2) polarization.
The (2 × 2) unit cell has been used to model the clean Si(001) surface.It has been demonstrated before that this model realistically reproduces most significant features of electronic structure and RDS spectra of bare Si(001) surface [3,31,44].Initial oxidation stage of the Si(001) is characterized mainly by two local atomic configurations including oxygen atom: oxygen dimer bridge and oxidized backbonds configurations (see 44,45] and references therein).Fully relaxed atomic structure of hydrogenated Si(001)(2 × 2) surface with one local oxygen dimer bridge configuration per cell is shown in Figure 7(a).
Calculated value of the dimer length of 3.13 Å on hydrogenated surface is slightly bigger then the value of 3.06 Å obtained by [31] on Si(001)(2 × 2) surface without hydrogen.Equilibrium geometry of bridge oxygen located in a broken backbond of monohydride Si(001) surface is shown in Figure 7(b).
The length of the oxidized backbond on hydrogenated surface, 2.93 Å, is higher than the value of 2.61 Å obtained on a bare surface.The last data agree well with those reported earlier in the literature: 2.53 Å [31], 2.60 Å [45].
The calculated tridymite atomic configuration of SiO 2 is used as a basic structural model for initial oxide layer in Si(001) surface.The fully relaxed atomic geometry of the Si(001)-SiO 2 interface is shown in Figure 8. Atomic structure presented in Figure 8 corresponds to the reported earlier atomic configuration for the tridymite [34,46].
It has been demonstrated [30,43,[47][48][49] that SHG is a unique method to study centrosymmetric solid surfaces: the SHG response is forbidden in bulk, and the SHG signal is generated within only a few surface monolayers.Because of that one can expect stronger contribution of oxygen-related process in Si(001)-SiO 2 interface to the SHG spectra than in linear optics.
The calculated SHG efficiency spectra are now compared with available experimental data.The SHG spectra measured on Si(001) surface with native oxide [48] and on Si(001)-SiO 2 with potassium-and NaCl-covered oxides (in order to study electric field effect in the space charge region) [34] indicate strong dependence of the signal on the surface electric field and appearance of a new optical structure in the region 3.6 to 3.8 eV.Electric-field-induced SHG (EFISH) offers incredible possibilities for new generation of optical metrology of the interfaces because of the high sensitivity of the SHG signal to the surface electric field [35,46].From a theoretical prospective, however, the microscopic theory of EFISH is nonlocal, and it is still challenging to model for the first principle theory [30,44].EFISH was beyond the scope of the study by [26].
Here we are focused on the effect of the chemical nature of electronic bond contributions on Si(001)-SiO 2 interface to the SHG. Figure 9 presents the calculated SHG spectra of Si(001)-SiO 2 interface shown in Figure 8.
As expected the nonzero SHG response is predicted only in the spectral region corresponding to optical twophoton excitations of the near-surface located disturbed Si electron orbitals.Due to the high value of Si-O bond energy corresponding contributions are located in far ultraviolet region.The SHG response from the Si-SiO 2 interface located mostly in visible and near-UV regions is attributed to the distorted host atomic bonds [25,30,43].From the data presented in Figure 9 one can immediately extract features related to the oxygen.Removal of the bridge oxygen results in dramatic reductions of the SHG features near 2.7, 3.3, 3.8 to 4.0, and 5.1 eV.Comparison between relaxed and unrelaxed structure calculations of the SHG efficiency (upper and lower panel in Figure 9, resp.)shows a more pronounced effect of mechanical stress in SHG than in RDS spectra (see Figure 9).The SHG features near 3.3 and 5.1 eV are close to the bulk electron transitions E 1 and E 1 .
Note that the theory with the QP correction predicted critical point energies in Si slab at E 1 = 3.4 eV, E 2 = 4.15 eV, Fully relaxed (a) (unrelaxed (b)) atomic structure of the Si(001)-SiO 2 interface is used for optical calculations (adapted from [26]).
and E 1 = 5.0 eV.The peaks near 3.3 and 5.1 eV apparently relate to the bulk-like electron excitations.According to [26] the SHG response near 5.1 eV exhibits strongest sensitivity to oxygen.This spectral region however is still unavailable for experimental study.
The SHG peak near 1.8 eV is of the surface nature related to the Si dimer bond.This peak is strongly affected by oxygen and local stress (see Figure 9).One can expect that it will also be sensitive to the size of nanoparticles since the smaller the particle, the higher the curvature of the interface and the more stress that is introduced at the Si/SiO 2 interface [50,51].Contribution of oxygen in this region is clearly shown in RDS spectra in this spectral region however effect Figure 10: The calculated second harmonic generation efficiency spectra of the relaxed Si(001)-SiO 2 geometry given in Figure 8 are shown in comparison to experimental data.In (a) the calcualted spectrum (dashed) is compared to data (symbols) measured by Avramenko et al. in [37].(b) compares the theoretical spectrum (dashed) to experimental data (symbols) measured by Rumpel et al. in [36] (adapted from [26]).
of the stress relaxation is much less important in RDS than in SHG.The SHG peaks located near 2.7 eV and 3.8 eV are new and they are not predicted on the Si surfaces without oxygen.According to the analysis of PDOS spectra these peaks are directly related to the Si backbonds of the dimer atoms hybridized to the bridge oxygen 2p-electrons.Peak near 4.0 eV is close to the E 2 transition and it is strongly affected by the new feature near 3.8 eV.Comparison between SHG efficiency predicted for relaxed and unrelaxed tridymite structure after removal of bridge oxygen (upper and lower panels in Figure 8, resp.)demonstrates that effects of local stresses and bond rehybridization are equally important in SHG which is in contrast to the linear optical RDS response [28].
The predicted SHG efficiency spectrum of oxidized Si(001) surface is compared next with available experimental data [35,36,48].
In Figure 10 a comparative analysis of the SHG spectra measured on two different systems is presented: the Si(001) surface oxidized [36] or with natural oxide [49] and Si-SiO 2 multiple quantum well structure [37].In order to compare the shape of the predicted and measured SHG spectra the amplitudes of experimental data were scaled to meet the theoretical values at 4.2 eV (lower panel) and at 2.7 eV (upper panel).
The experimental SHG spectrum shown in the upper panel in Figure 9 was measured by [36] on Si(001) sample with 10 nm oxide layer grown by thermal oxidation at 1000 • C. The dominant SHG peak at 4.3 eV was attributed to the E 2 bulk transitions [36].Note that the theoretical bulk Physics Research International 9 value of E 2 = 4.15 eV underestimates experimental one by about 0.1 eV.The results shown in Figure 10 indicate the appearance of a new SHG response in the region of 3.6 to 4.0 eV and a strong enhancement of the E 2 peak.Comparison with the SHG data presented in Figure 9 indicates that this is caused by the combined effect of the oxygen-related rehybridization of Si backbonds and structural reconfiguration.The strong enhancement of both measured and predicted SHG efficiencies caused by boron doping of the Si(001) surface (which was obtained on the relaxed system) was reported earlier [46].In both cases by neglect of the electric field effect, the physical nature of the predicted strong enhancement of the SHG efficiency is impurity related to the rehybridization and structural reconfiguration of the Si backbonds.
New predicted SHG response near 3.6 to 4.0 eV is also accompanied by dramatic increase of the calculated SHG efficiency at 2.7 eV due to the dimer bridge oxygen (see Figures 9 and 10).This part of the predicted SHG spectra agrees well with another type of experimental results by [37] where a strong SHG signal in the region near 2.7 eV on Si(001)-SiO 2 multiple quantum wells (MQWs) was measured.The four MQW systems studied by [37] were fabricated from Si-layers of the thicknesses equal to 1.0, 0.75, 0.5, and 0.25 nm separated by SiO 2 films of fixed 1.1 nm thicknesses.The number of Si-SiO 2 MQW bilayers varied from 30 to 70 in order to provide nearly the same thickness of the whole film stuck [37].
In MQW system the quantum size effect is an additional factor affecting the SHG.However in the presence of multiple Si(001)-SiO 2 boundaries the effect of the interface oxygen should be substantially enhanced.In the absence of microscopic theory the 2.7 eV SHG signal measured in Si(001)-SiO 2 MQW was interpreted by [37] as a result of electron transitions from the bound quantum electron states in QW.In addition to the quantum size effect, the chemical nature of the last SHG feature was also suggested by [37] as an alternative interpretation of their data.The results of the present work suggest that the origin of the feature measured by [37] and the predicted SHG responses near 2.7 eV is a dominant contribution of the rehybridized and reconstructed Si backbonds due to creation of the dimer bridge oxygen structure on Si(001)-SiO 2 interface.Additional argument towards the current interpretation of the 2.7 eV signal is that this response should be accompanied by the SHG features near 3.8 eV as discussed above (see Figure 9).Experimental data of [37] confirm this rule: in addition to the 2.7 eV SHG peak, the authors reported strong SHG response around 3.8 to 4.0 eV which is discussed above.
Analysis of the atom-and orbital-resolved projected electron density of states (PDOSs) calculated in [26] clearly indicate a strong effect of the bridge oxygen which results in additional contribution (hybridization) of oxygen p-orbitals and silicon backbond orbitals.The 2p-orbitals of bridge oxygen contribute to the top of the valence band.Modifications of the c-band are less pronounced.The rehybridization of the host valence electron orbitals caused by both 2porbitals of oxygen and by structural distortions seems to be responsible for the measured and predicted optical anisotropy of this system.The dominating effect in optical anisotropy of Si(001) due to the distorted backbonds and additional hybridization to oxygen has been shown by [30].
Conclusions
This paper reviews a progress in understanding the second harmonic generation spectroscopy for diagnostics of semiconductor surfaces and nanostructures.The unique capabilities of the SHG metrology is caused by its extreme sensitivity to the symmetry of the optically excited systems.Another property of the SHG is related to the substantial contributions of the nonlocal processes caused by electric, mechanical and magnetic fields.
It should be noted that effects of external fields (like electric field, direct current induced SHG, Electric-Field-Induced SHG) and eventually many-body (excitonic) and local field effects [5,11] should be incorporated by interpretation of the SHG in order to achieve more detailed quantitative description of the signal shape which should be considered as a future activity road map in the field.For future experimental and theoretical studies it is important to explore the unique sensitivity of SHG to symmetry and external fields that should make the SHG metrology an advanced diagnostics tool for nanostructures.
Figure 1 :
Figure 1: (a) Schematic view of the experimental scheme for current-induced s-in, s-out SHG.(b) Dependence of the CSHG contrast on the DC value for the fundamental wavelength of 780 nm (adapted from[16]).
Figure 7 :
Figure 7: Atomic structure of Si(001)(2 × 2) surface with one local dimer oxygen configuration per unit cell (a) and atomic structure of Si(001)(2 × 1) surface with an oxygen atom located on a broken back bond.(b) Red and white colored balls correspond to oxygen and hydrogen atoms, respectively (adapted from [26]). | 7,179.2 | 2012-05-17T00:00:00.000 | [
"Physics",
"Engineering"
] |
Inferring gene correlation networks from transcription factor binding sites
Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks , networks constructed by connecting coexpressed genes, through predicting co-expression level from genes promoter’s sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.
INTRODUCTION
Gene expression is at the basis of molecular biology. Through this procedure, cells use the information stored in DNA, the genes, to perform and control the vast number of functions required for them to survive in various conditions (Davidson et al., 2003;Lodish et al., 2007). Gene expression consists of a series of regulated processes, to be precise, transcription, RNA modifications (eukaryotic genes), and translation. By regulating these processes and thereby controlling the amount of produced proteins, cells can respond to any stimulus (Brazhnik et al., 2002;Latchman, 1998;B. Lewin, 2004).
Recently, there have been many efforts for discovering how genes regulate expression of themselves (Hecker et al., 2009;Huynh-Thu et al., 2010;Kabir et al., 2010;Meyer et al., 2007;Yip et al., 2010). Transcription, the first step in the gene expression which converts DNA to RNA via a complex machinery is an extremely regulated process (Jacob and Monod, 1961;Lodish et al., 2007). This step is mainly dependent upon the successful binding of Transcription Factors (TFs) proteins to explicit positions in genes upstream or promoters, also known as TF Binding Sites (TFBSs) (Chua et al., 2004;B. Lewin, 2004;Mahdevar et al., 2012). In general, 4% of all genes produce TFs and usually multiple TFs act together to regulate the expression of a given gene (Bar-Joseph et al., 2003;Schlitt and Brazma, 2007;Yu et al., 2003). Unfortunately, finding these TFs and their corresponding TFBSs is costly and laborious; therefore, computational discovery of TFs and TFBSs has attracted considerable attention (Bailey and Elkan, 1994;GuhaThakurta, 2006;Hertz and Stormo, 1999;Mahdevar et al., 2012;Thijs et al., 2002;Tompa et al., 2005).
Information flow from DNA to proteins begins with transcription process; besides, it is the origin of response to numerous stimuli (Latchman, 1998;Lodish et al., 2007). As a result, regulation of transcription machinery is more crucial to cells than other aforementioned regulatory steps (Davidson, 2001).
TFs that involve in transcription process are also proteins, i.e., products of some genes can influence the expression of other genes. This means that genes are in a network and interact with each other in order to react to stimuli or to perform vital processes of cells. Of course, there are other biological networks in cells, e.g., protein -protein -interaction networks or metabolic pathways. These networks are usually depicted as nodes connected by edges. Nodes represent genes, proteins, or metabolites that are cell response to stimuli. Edges often represent relations, such as the binding of a TF to its target TFBS or direct molecular interactions.
Understanding how genes regulation occurs and identifying the interactions between genes in a living system, namely, network inference, are important topics in today's biology. In this paper we have focused on the later topic.
In general, there are two strategies for modeling gene networks: physical modeling and influence modeling (Gardner and Faith, 2005). Following two paragraphs describe these strategies in brief.
Physical approach tries to find true molecular interaction between TFs and TFBs that control transcript synthesis. Specifically, in physical approach the goal is to identify the TFs and the corresponding TFBSs that regulate transcription process. Restricting regulators to TFs made this approach easy to apply but also weak to infer the network and to describe regulatory mechanism.
The goal in the influence approach is to discoverindirect -regulatory influence of each transcript on the synthesis of all transcripts; that is, these models do not utilize or consider proteins, TFBSs, or TFs data explicitly, but, implicitly.
After choosing the network model, network parameters, specifically, scores of edges between all pairs of genes or nodes in the network must be inferred through available data. As aforesaid, this process is known as network inference or reverse engineering.
High-throughput technologies such as Microarray have led to an exceptional growth in data related to sequence features and gene expression. Essentially, four kinds of data are available to employ in gene networks inference process, namely, 1 -amount of existing transcripts, 2proteins, or 3 -metabolites and 4 -genome data.
Measuring the concentrations of RNA transcripts, produced proteins, or metabolites give an insight into the gene expression and are the fundamental resources for several studies. Comprehensive overviews of these methods studies can be found in (Barabasi and Oltvai, 2004;DeJong, 2002;Filkov, 2005;Hecker et al., 2009;Sîrbu et al., 2010).
Genomics data, e.g., genes and TFBSs, are supportive to the networks inference: relations between TFBSs and gene expression can be revealed by this kind of data. Methods that use genome sequence data fall into two categories: those that rely on prior knowledge of TFBSs, and those that discover TFBSs by computational effort and do not take prior information about TFs and TFBSs into consideration. The research work done by Beer and Tavazoie (2004) is a good example for the first category; they employed both known instances of TFBSs and novel TFBSs extracted from promoter sequences to predict the expression level of Saccharomyces cerevisiae (yeast) genes by training a Bayesian Network on microarray data of environmental stress and cell cycle (Beer and Tavazoie, 2004). Accuracy of their method on yeast genome was around 0.70. In a more recent work that belongs to the latter category, Pavesi and Valentini (2009) applied a machine learning approach to classify sets of co-expressed genes using information extracted from their regulatory regions.
In this paper, we propose a new method that infers genes correlation network from their promoter sequences by using a neural network without exploiting any a priori knowledge about input promoters, their potential TFBSs, or need to clustered input. A collection of genes promoter sequences is the only input of the proposed method and the result is correlation network of genes corresponding to these promoters. In brief, the method begins by extraction of potential TFBSs from all input sequences through computation; then, it tries to find expression similarity between all pairs of inputs with respect to the discovered TFBSs by a specific type of neural networks; at last, it uses calculated similarities to find groups of inputs and to build correlation network of given genes. Performance and limitations of the proposed method on inferring correlation networks form both simulated and biological data are presented in the result section.
METHOD
In this section, we explain the proposed method in detail. Simply, the input of the method is a collection of promoter sequences, P = {p 1 , p 2 ,…, p n }, with n sequences of arbitrary lengths; and the output is correlation network of input, inferred from properties of computationally discovered TFBSs that exist among input promoter sequences. The predicted network is shown by graph G = (V, E) with n nodes.
In order to assign weight to edge set E from discovered collection of TFBSs, a neural network and a neighbor joining method are utilized, as follows: TFBSs occurring in P are used as inputs to the neural network, its outcome will be the amount of correlation between expression patterns of two P members. At last, an application of neighbor joining method to these correlations yields elements of E.
The general steps of our method, including Extracting Promoters Information, Finding Expression Correlation, and Building Correlation Network are described in the following subsections.
Extracting promoters information
Aim of this step is to extract information of all input sequences. TFBSs, short subsequences in promoter sequences with high frequency in comparison to random sequences (Das and Dai, 2007;GuhaThakurta, 2006), are very important to transcription machinery (Jacob and Monod, 1961). Because of their higher frequency, it is possible to discover known and novel TFBSs by searching for overrepresented subsequences. Certainly, many computationally TFBSs discovery methods are available; for an overview of these methods see (MacIsaac and Fraenkel, 2006;Tompa et al., 2005). Among them, AmotiF tool (Mahdevar et al., 2012) has good sensitivity and specificity, runs fast, and is planned to find overrepresented pattern with respect to the biological facts about TFBSs (See Mahdevar et al. (2012) for more details about this method). Therefore, we have chosen AmotiF tool to find TFBSs. Experiments with other TFBSs discovery methods confirm the validity of this choice: AmotiF achieved the best accuracy in the discovery of random TFBSs planted in a set of sequences. Precisely, the accuracy of AlignACE (Roth et al., 1998), AmotiF (Mahdevar et al., 2012), BioProspector (Liu et al., 2001), and MEME (Bailey and Elkan, 1995) in finding 10 TFBSs planted in 100 random sequences of 500 nucleotides was 39%, 62%, 56%, and 53%. Where, TFBSs range from 6 to 20 base pairs in length and have 5 to 10 instances with 60% to 90% identity. Furthermore, AmotiF gained the highest score in the famous assessment provided by Tompa et al. (2005), as stated by Mahdevar et al. (2012). By investigating TFBSs of S. cerevisiae Promoter Database (SCPD) (Zhu and Zhang, 1999) and TFBSs of Escherichia coli Database (RegulonDB) (Salgado et al., 2004), we have found that TFBSs positions are far from being random, to be exact, TFBSs positions have little standard deviation: 28% of maximum value, which is consistent with the results of Beer and Tavazoie (2004). Moreover, we have found that in these two species 70% of all TFBSs are placed in the first 250 nucleotides of the promoter sequence. We have employed these properties to adjust scores of TFBSs returned by AmotiF; simply, by reducing score of TFBSs that violates them.
Existing TFBSs discovery methods may split a TFBS into two or more TFBSs. For instance, they may report first few nucleotides of a TFBS as a single TFBS and the rest of it as another one. Thus, the proposed method merges TFBSs whose occurrences are very similar in all input sequences into one TFBS. Furthermore, the proposed method removes TFBSs that exist in almost all inputs, since these types of TFBSs have no discriminative information.
At last, score of every TFBS in each input promoter will where m is the number of remaining TFBSs and t i,j is the score of jth TFBS in the ith sequence, normalized by dividing it to the square root of number of nonzero t k,j , where, 1 ≤ k ≤ n. Explicitly, , for all 1 ≤ i ≤ n and 1 ≤ j ≤ m. This normalization reduces scores of weak TFBSs interspersed throughout the input promoters.
Finding expression correlation
Here, the goal is to achieve expression correlation between all two members p i and p i of P from matrix T and store them in We have developed a Self -Organizing Map (SOM), a special type of neural networks, for this purpose.
We choose SOM because it is an unsupervised clustering method with no needs to specify the exact number of clusters or even estimating this number by fine-tuning a parameter. SOM is relatively stable, e.g., its outcome is not dependent to the initial weights, in contrast to methods such as K-means clustering (Martin et al., 2007). SOMs are not prone to over-fitting or overlearning (Meissner et al., 2009) and their training procedure is just a simple iteration (Kohonen, 1989). Furthermore, replacing SOM by other clustering methods (e.g. K-means or hierarchical clustering) has resulted in worse performance.
Fundamentally, neural networks are particular type of machine learning algorithms which consist of a set of neurons and applicable for pattern classification, identifying correlations in the data, and many more problems. There exist two kinds of learning for a neural network: supervised and unsupervised.
In supervised learning, training patterns with known classification repeatedly are presented to the network and network parameters or weights are adjusted such that the number of misclassified inputs decreases. These two procedures, i.e. presenting patterns to the network and adjusting the weights of the network, are iterated until some convergence criterion is satisfied.
Unsupervised learning is appropriate when there is no prior information about classification of inputs. In unsupervised learning, the task is to classify or to cluster inputs such that close members, or members who belong to single class or cluster, share common properties or show higher similarity according to a predefined measure.
SOMs are unsupervised neural networks suitable to both classifying and clustering data according to their similarities, consisted of two layers of neurons (Kohonen, 1989): input layer and output layer, as illustrated in Fig. 1.
Input of the developed SOM is information collected from every input promoter sequence, stored as rows of T; therefore, the network has m neurons in its input layer.
Each input neuron connects to every output neurons via variable connection weights. Weights are random at start and adjusted during training process, such that close nodes become more sensitive to similar rows. In order to get to stable weights, training or presenting whole input to the network and adjusting its weights, should be iterated multiple times. By examination, we have found that the number of training (λ) should at least be proportional to n and 50 × n is an appropriate value. Networks weights would eventually become fixed, because the training procedure has a decreasing variable called the learning rate; in almost all cases that we have tested, 50 × n is enough to reach this point and further iterations are unnecessary.
Output neurons are in connection with their adjacent or neighboring neurons and compete to have minimum distance to an input pattern. There are ways to define neighborhood relationship and topology of the output layer, e.g., 2D, 3D, honeycomb, or grid neighboring. Output layer of SOM in this paper has grid structure as depicted in Fig. 1. In order to capture all distinct patterns exist in inputs, number of the output neurons that are not very close to each other should be more than the number of possible input patterns. Again, by examination we have found that 121 × min(n, 2 m ), which depends on both m and n, is appropriate for the number of neurons in output layer, σ. Since we have n sequences, and each of which is composed of m two valued variables, number of distinct patterns is less than or equal to the minimum of n and 2 m . Thus, to have distance of 10 neurons between winning neuron of each pattern on the output layer, number of output neurons should be (10 + 1) 2 × min(n, 2 m ) or more. (Calculating network specifications from the number of input sequences and number of discovered TFBSs has made our method more practical on inputs with various sizes.) After training procedure, Pearson correlation coefficient is calculated between activities of output neurons when ith or jth rows of input are presented to input neurons and results reported as correlation between ith and jth input promoters. Algorithm 1 shows procedure of finding expression correlation through the scores of TFBSs found in the input promoters with more computational details.
Algorithm 1: Training SOM Initialization: let the weight between input neuron 1 ≤ i ≤ m and output neuron 1 ≤ j ≤ σ , w i,j , be a random value in range [-1, +1]; Training: for p ← 1 to λ for r ← 1 to n and function δ(.), neighborhood function, is defined as (4) where functions row(.) and col(.) respectively return row and column of input index in the output layer. μ is time decreasing variable that affects neighboring magnitude .
( 5 ) end for end for Compute Correlations: Let c i,j be the Pearson correlation coefficient of the output layer activity when information of p i or p j are presented to the input layer; specifically, let (6) for all 1 ≤ i, j ≤ n.
Building correlation network In this step, the goal is to construct graph G = (V, E) from matrix C. Nevertheless, in the biological networks genes are in groups with many interactions (Barabasi and Oltvai, 2004), these groups connect to other groups via special member genes with large number of connections. We are interested to find these groups. Graph G has n nodes correspond to n genes, that is, V = {v 1 , v 2 , …, v n }. The edge between gene i and j, e i,j , has initial weight of c i,j . In the beginning, genes are placed randomly on a n by n 2d plain; then, each one slightly moves toward genes with higher mutual correlation by a small amount. To be exact, position of gene 1 ≤ i ≤ n at time t + 1, , is computed from by the following formula: (7) where function α(t) is time -decreasing temperature function, calculated according to the rule , and , 1 ≤ i ≤ n, are randomly scattered on a n by n 2d plain.
This procedure repeats until genes positions become fixed or temperature reaches specific value of α(t) < 0.001. Now, genes with similar expressions are closer and it is easy to identify gene groups. We have used a method called neighbor joining to find these groups as follows.
Initially, each group consists of only one unique gene; then, two groups with minimum distance, i.e., neighbors, join to form a new group. Distance of newly formed group to others is average of distances of two joined neighbors to them. Groups join if the distance between them is smaller than a certain threshold value. Experiments revealed that this threshold value should increase to some extent by growth of n, and ln(n) works well. (The threshold value for joining groups should depend to some extent on the number of genes (n), because in the beginning genes are randomly scattered on a n by n plain and initial distances between them depends on n. We have examined several functions of n, among them ln(n) is good for this purpose.) After finding groups, edges weights will be adjusted: weight of edges whose both vertices are in the same group increases by 25%; and, finally, edges with low weights, less than 0.25, will be removed.
RESULTS
This section presents the results of running our method on both simulated and biological datasets. Nonetheless, we need some measures to evaluate them; thus, we discuss employed measures at the outset.
Measures used to evaluate the results
In order to judge the accuracy of proposed method on inferring correlation networks, a number of well -known standard statistical measurements, shown in Table 1, are employed. Measures introduced in Table 1 rely on four statistics: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP is the number of edges in real network that predicted to be edges by the method; TN is the number of edges that does not exist in both real and constructed networks; FN is the number of edges ignored by the method; and, FP is the number of edges that the method draws incorrectly.
Simply, sensitivity (SN) and specificity (SP) quantify prediction accuracy, respectively, via true positive and true negative values. Correlation coefficient (CC) calculates same value by considering all four statistics. By dividing the number of true prediction by all predictions, accuracy (ACC), probability of obtaining true predictions, can be calculated.
Simulated datasets
We have used rMotifGen tool (Rouchka and Hardin, 2007) to generate simulated promoters datasets with respect to properties of biological promoters. Each dataset has 5 to 50 random promoters, each of which is a sequence of length 200 to 600 base pairs. Promoters are in separate groups and members of each group have common TFBSs. A TFBS is 7 to 19 base pairs long, has zero to at most one-third of its length random mutations, and standard deviation of its position in promoters is 28% compared to random positioning. Figure 2 presents the average of SN, SP, CC, and ACC over 100 datasets: all four measures are very high when sequences have at least one TFBS. Moreover, Fig. 2 reveals that accuracy of predictions can be improved by increasing the number of common TFBSs; also it shows that if sequences have no TFBSs at all, then these four measures will dramatically reduce to about 0.20. This means that sequences are clustered according to their TFBSs and networks inferred by our method are more similar to real networks in comparison to those generated v t i + ( ) at random. Figure 3 shows the network generated for a dataset with three sets of sequences; members of each set share a single common TFBS with one mutation, so, the result must be a tripartite graph. There are only five wrong edges in displayed graph and SN, SP, CC, and ACC respectively are 1.00, 0.94, 0.90, and 0.95.
Comparison with gene ontology resource The Gene
Ontology™ (GO) project provides another resource to look for functional, locational, or procedural similarities within a list of genes. In brief, GO describes genes characteristics by an organized vocabulary of three categories or ontologies: molecular function, cellular component, and biological process. Ontologies are hierarchies of defined terms and each gene might be labeled with one or more terms of ontologies.
Several studies have provided us with evidences that genes that are biologically related would maintain this relation both in their expression levels as well as in their GO data (Sevilla et al., 2005). For instance, Sevilla et al. (2005) have concluded that correlation coefficient between gene expression and GO data is about 0.70 when Resnik measure of ontology similarity were exploited to estimate genes relationship (Sevilla et al., 2005). Consequently, it is reasonable to compare networks generated by GO data with networks generated by promoter data which our method provides. To perform this comparison, we built 100 datasets, each with 20 to 40 promoter sequences that were selected randomly from yeast genome (promoter sequences were downloaded from Saccharomyces Genome Database Project (Cherry et al., 1998)). We run our method on these datasets and compare resulted network with networks generated by GO data. SN, SP and ACC of this comparison are 0.60, 0.64 and 0.61, respectively.
In another experiment, we build several datasets from yeast genes that contribute in disparate biological processes, e.g. translation process and cytokinesis process. Since genes of a dataset belong to very distinct processes, their expression should be very dissimilar (Wang et al., 2004); and, networks of these datasets should tend to be multi -partite. Running our method on these datasets resulted in networks that are very similar to multi -partite graphs, as expected; SN, SP, and ACC all are about 0.60.
Biological dataset Our biological dataset contains 500 base pairs of upstream sequences of S. cerevisiae (yeast) genes downloaded from YEASTRACT (Teixeira et al., 2006). Among all yeast promoters, we have chosen 254 genes which known to be regulated by 29 TFs.
Here our goal is to calculate the accuracy of the proposed method in predicting expression correlation from downloaded upstream sequences; thus, we have compared the output of our method for each pair of genes in the dataset with correlation reported by microarray technology (Lee et al., 2007). Mean square deviation of this comparison was 0.27. Comparison derived a SN of 0.51, SP of 0.56, and CC of 0.41.
It is common to evaluate the accuracy of methods that use microarray data to infer regulatory interactions by their ability to reproduce the experimental data or by their ability to identify verified interactions. For example, Sîrbu et al. (2010) have compared evolutionary algorithms presented for regulatory network inference; they concluded that five studied methods, namely DE+AIC (Noman and Iba, 2006), GA+ANN (Keedwell and Narayanan, 2005), GLSDC (Kimura et al., 2003), PEACE1 (Kikuchi et al., 2003), and GA+ES (Spieth et al., 2004) have overall accuracy of 0.44 in predicting identified interactions, which is comparable with accuracy of our method: correlation coefficient value of our method is 0.41. Also, they have found that average of mean square deviation of five studied methods over five sample genes is about 0.21; as noted above, mean square deviation of our method is 0.27, which is slightly higher than this value. These results suggest that capability of our method for approximating the microarray data of yeast is 3. A sample network generate by our method. Each five genes, starting from gene number 1, have their unique TFBS; therefore the perfect result is a tripartite graph. Nodes with higher correlation are closer and edge thickness indicates correlation magnitude between its two heads. Inferring gene correlation networks from TFBSs as good as other five methods. Figure 4 shows two networks that are generated from microarray data (left) and from the data that are provided by our method (right) for eight yeast genes: HSC82, HSP26, SIS1, SSA1, and SSA4 that are regulated by the heat shock factor protein (HSF), and DAL2, DAL4, and DAL7, which have a TFBS known as the upstream induction sequence (UIS). Furthermore, according to the GO™ database the first five genes are involved in the protein folding process and the rest contribute to the heterocycle metabolic process (Ashburner et al., 2000); further exploration using the Amigo ® browser ("AmiGO: official online tool-set for searching GO at http:// amigo.geneontology.org,") has revealed that these two processes are separated just in the second level from the root of the GO biological processes hierarchy. Thus, expression pattern of genes that have protein folding process label should be different from those that share heterocycle metabolic process label (Wang et al., 2004). Genes DAL2, DAL4, and DAL7 have strong expression correlation with each other and correctly are positioned far from other genes in both networks, whereas according to microarray data there is no similarity between expression patterns of DAL4 and HSP26 (Lee et al., 2007) and our method incorrectly identified slightly strong correlation between them (Fig. 4, right). Also predicted similarities between DAL2 and SIS1 or SSA4 are incorrect. Sensitivity, specificity, correlation coefficient, and accuracy of this example are 0.89, 0.94, 0.53, and 0.75, respectively.
CONCLUSION
This paper presented a new method for inferring gene correlation networks. The input of this method is a set of genes upstream sequences or promoters, each with arbitrary length, and the output is correlation network of given genes.
Briefly, the method begins by extracting TFBSs of input promoters, altering their scores according to the standard deviation of positions where they occurred; and, merging similar ones into single one. Finding expression correlation between each pair of genes from these scores by a self -organizing neural network is the next step. In the final step, it generates the network graph by moving genes with similar expression patterns towards each other and joining them into genes groups.
This method has been tested on simulated and biological data. Results show that the accuracy and efficiency of this method in inferring correlation networks, merely, from promoters is almost equivalent to the accuracy of methods that use microarray data as input, assessments of some of which have been presented by Sîrbu et al. (2010) and Allen et al. (2012); and to the accuracy of methods that start with clustered promoters, as presented by Beer and Tavazoie (2004).
Accuracy of the proposed method is a function of the quality of its input, which is the result of a TFBSs discovery method; therefore, improving those methods will improve the proposed method as well.
Collecting the results of two or more TFBSs discovery methods on the given upstream sequences and presenting those TFBSs that are reported by many of them to the network, seems to be another promising way to improve accuracy. Also studies have found associations between histone acetylation patterns and gene activity (Kurdistani et al., 2004), which in turn could prove helpful to our method.
When biological data are used instead of simulated data, the accuracy of proposed method drops by 0.30 in terms of CC; the reason for this significant drop is that there are more determining factors in regulation of genes Fig. 4. Networks generated by two data sources: left by microarray data, right by data that our method provides. Expression patterns of genes DAL2, DAL4, and DAL7 are very different from others, so they are positioned at distant relative to others in both networks. Genes with high expression correlation are close to each other and weak correlation coefficients (< 50%) are depicted by dotted lines. than presence or absence of TFBSs; factors like those stated in the above paragraph or epigenetic regulation (van Driel et al., 2003).
Combining data provided by this method with other existing data, e.g. GO data and microarray data and testing above mentioned possibilities is our planning for future works. | 6,746.6 | 2013-10-01T00:00:00.000 | [
"Biology"
] |
Integrating philosophy, policy and practice to create a just and fair health service
To practise ‘fairly and justly’ a clinician must balance the needs of both the many and the few: the individual patient in front of them, and the many unseen patients in the waiting room, and in the county. They must consider the immediate clinical needs of those in the present, and how their actions will impact on future patients. The good medical practice guidance ‘Make the care of your patient your first concern’ provides no guidance on how doctors should act when they care for multiple patients with conflicting needs. Moreover, conflicting needs extend far past simply those between different patients. At an organisational level, financial obligations must be balanced with clinical ones; the system must support those who work within it in a variety of roles; and, finally, in order for a healthcare service to be sustainable, the demands of current and future generations must be balanced. The central problem, we propose, is that there is no shared philosophical framework on which the provision of care or the development of health policy is based, nor is there a practical, fair and transparent process to ensure that the service is equipped to deal justly with new challenges as they emerge. Many philosophers have grappled with constructing a set of principles which would lead to a ‘good’ society which is just to different users; prominent among them is Rawls. Four important principles can be derived using a Rawlsian approach: equity of access, distributive justice, sustainability and openness. However, Rawls’ approach is sometimes considered too abstract to be applied readily to policymaking; it does not provide clear guidance for how individuals working within existing institutions can enact the principles of justice. We therefore combine the principles derived from Rawls with Scanlonian contractualism: by demanding that decisions are made in a way which cannot be ‘reasonably rejected’ by different stakeholders (including ‘trustees’ for those who cannot represent themselves), we ensure that conflicting needs are considered robustly. We demonstrate how embedding this framework would ensure just policies and fair practice. We illustrate this by using examples of how it would help prevent injustice among different socioeconomic groups, prevent intergenerational injustice and prevent injustice in a crisis, for example, as we respond to new challenges such as COVID-19. Attempts to help individual doctors practise fairly and justly throughout their professional lives are best focused at an institutional or systemic level. We propose a practical framework: combining Scanlonian contractualism with a Rawlsian approach. Adopting this framework would equip the workforce and population to contribute to fair policymaking, and would ultimately result in a healthcare system whose practice and policies—at their core—were just.
INTRODUCTION
Healthcare systems face 'competing, and sometimes conflicting, demands' 1 ; resolving conflicts fairly between groups with differing priorities presents a significant challenge. The good medical practice guidance 'Make the care of your patient your first concern' 2 provides no guidance on how doctors should act when they care for multiple patients with conflicting needs. 3 Conflicts are not always a matter of resource allocation-issues surrounding how services are structured, how staff are treated and how information is shared can also be sources of tension.
To address these conflicts, ethical guidance has been drawn up to offer at best help and, at worst, post hoc justification for policymakers. Often ethical goals (eg, treating people fairly) are elided with executive virtues (eg, being flexible). The central problem, we propose, is that there is no shared philosophical framework on which the provision of care or the development of health policy is based, nor is there a practical, fair and transparent process to ensure that the service is equipped to deal justly with new challenges as they emerge.
Perhaps because of this, there are many examples where current practice is unjust at local, regional, national and intergenerational levels. Take the recent response to COVID-19, which saw prioritisation of those being treated and working in acute care over those in primary care and care homes. Look at discrepancies in accessing care (and information about care) between those in different socioeconomic groups, even within the same region. Consider the depleted workforce which future patients will face because of poor investment in maintaining our nurses and doctors.
We believe that to achieve justice and fairness in practice and policy, a philosophical framework must be made explicit, in particular to help guide the development of principles which treat groups with different demands fairly. We believe that if we develop and embed such a framework for policy decisions, then the individual decisions which clinicians make on a day-to-day basis will also become fairer and cause less moral discomfort. If the process for considering and weighing conflicting demands is fair, then the resulting outcomes will be just.
Many philosophers have grappled with constructing a generalisable set of principles which would lead to a 'good' society which is just to different users, notably John Rawls. [4][5][6] While his theory has previously been rejected as being useful in resolving individual issues in healthcare, 7 we have argued that it can provide a useful framework for making policy decisions, guiding how a just health service should be constructed and sustained. 8
Original research
Daniels has also previously drawn on Rawls: he argued, in 'Just Health', that resource allocation decisions can be made fair by ensuring that they are public, relevant, amenable to appeals and that the process is regulated. 9 Badano argued that the 'relevant' condition rendered this process too unfair for individuals, whose interests are sacrificed for the sake of groups. 10 We note it also fails to recognise the importance of intergenerational justice. Like Daniels, we argue that Rawls can be used as an example of procedural justice to make healthcare decision-making more just; we extend Daniels' argument (which centres on how resources can be allocated) to other aspects of healthcare policy, to fairly balance the needs of a range of users in this, and future, generations.
There is a significant challenge in creating a framework which is philosophically robust, yet easily accessible to politicians and clinicians, and which can be fairly applied to create just healthcare policies. In this essay, we will demonstrate how this can be done by combining Scanlonian contractualism with explicit principles derived from a Rawlsian framework.
First, for those unfamiliar with it, we will summarise Rawls' Theory of Justice and the principles which emerge from it, and discuss how it could be used as a philosophical framework to guide the development of a just healthcare system. We will then consider practical difficulties with implementing this Rawlsian approach, and examine the benefits (and problems) of using Scanlonian contractualism as an alternative. We will explore how combining a Rawlsian framework with elements of Scanlonian contractualism could helpfully guide decision-making within the National Health Service (NHS) as it stands. Finally, we will illustrate, with examples, how this might be used to approach conflicting demands facing the health service.
A RAWLSIAN APPROACH FOR THE HEALTH SERVICE
John Rawls' 'Theory of Justice' aims to determine a set of principles which, if followed, will give rise to a just basic structure for society. 4 Rawls imagines people in what he terms the 'original position', where they are behind a veil of ignorance. Behind the 'veil', people do not know what their position in society will be-they are ignorant as to their race, gender, wealth, natural endowments and religious/political convictions. 4 Rawls asks what principles 'free and rational persons concerned to further their own interests' would agree to from this position. People in this conception are 'capable of reasonableness', and will act on whatever principles are agreed to. The terms that such agents would agree to from behind the veil are, by virtue of their agreement alone, just. Rawls uses the phrase 'justice as fairness' to reflect this principle: it 'conveys the idea that the principles of justice are agreed to in an initial situation that is fair', 4 https:// www. gov. uk/ government/ groups/ moral-and-ethical-advisory-group# meeting-summaries Rawls argues that, by using this procedure, two main principles of justice would emerge: 1. All persons should have equal basic liberties. -Primary goods should be distributed equally, unless an unequal distribution would be to everyone's-and in particular the worst-off 's-advantage (known as the 'difference principle'). Rawls addresses intergenerational justice by making people in the original position ignorant to the position that their own generation holds in the timeline of generations. Someone behind this version of the veil would not agree to a system in which early generations are able to use resources at an unsustainable rate, to protect themselves from the possibility of being in a much later generation with a paucity of resources. Yet they would also not agree to a system where early generations are forced to be so frugal with resources that they cannot make use of them at all. Rawls terms this balance the 'just savings principle', which he describes as 'an understanding between generations to carry their fair share of the burden of realizing and preserving a just society'. 4 The difference principle determines distributive justice within one generation, while the just savings principle determines resource distribution between generations. The just savings principle thus constrains the difference principle, and emphasises that people have a duty to create a system which permits the realisation and maintenance of a just basic structure over time. 6 We previously applied this approach to healthcare and asked what a health service would look like if those making policies did not know if they were a patient, healthcare worker or manager-in this generation or the next. 8 We concluded that its basic structure would bear similarities to the NHS as it currently exists (it would provide comprehensive services which are free at the point of need) but it would not be identical. Two important general principles emerge in addition to equity of access; we believe that articulating and emphasising these in policymaking would lead to a more just health service.
First, in considering examples where the immediate needs of individuals conflict with the future needs, it is clear that there is a strong requirement for a healthcare service which is sustainable (in its training and treatment of staff, and in encouraging research). Second, increased openness is required: since power and opportunities are primary goods, individuals-both healthcare professionals and the public-should have the opportunity to exert some power over the system which provides their healthcare. The system must thus be open in terms of its transparency of decision-making to patients and staff, and accountable in terms of the individuals and organisations which make decisions about the running of the health service.
The process of applying a Rawlsian analysis to the health service could provide a normative basis for the principles that would best guide the development of a just health service. As demonstrated in figure 1, four important principles can be derived: equity of access, the difference principle, the just savings principle and openness.
It is important to consider to whom these principles apply, something which Rawls termed the 'subject' of justice. 4 For Rawls, the primary subject of justice is the basic structure of society-the major political, social and economic institutions, which act as public regulatory structures. Rawls focuses on the justness or unjustness of institutions or social systems, rather than the actions of individuals. Indeed, Rawls 'does not consider justice as a personal virtue, but the first virtue of social institutions' 11 ; the principles of justice should not be applied to personal conduct, but to the rules which constitute the basic institutions. We thus argue that the principles to guide a just healthcare system (as outlined in figure 1) do not apply to the individual actions of those working within or using the NHS, but to the structures which make up the health service itself. As such, the principles do not necessarily provide specific guidance on how individual issues should be managed; they provide broader guidance on how a just health service should be constructed and maintained.
The focus of Rawls' Theory of Justice on institutions, as opposed to individuals, can make it challenging to implement in practice. As Shevory has noted, Rawls' work 'is highly abstract and leaves to others the task of matching real-world circumstances to ethical principles consistent with the overall theory'. 7 Rawls relies on an abstracted veil of ignorance to develop principles which should be applied to the institutions in order to produce a just system in ideal circumstances. It is not always clear how actual agents, who exist in non-ideal conditions, should use his principles to guide the regulation of existing institutions.
Perhaps the most pressing question for us, then, is how a Rawlsian approach can be applied in practice to guide policy and resolve conflicts within the existing NHS. Individuals who make healthcare policy decisions need the above principles, and guidance on how to practically apply them to existing institutions to make the system more just. To do this, we combine Rawls with elements of Scanlonian contractualism.
SCANLONIAN CONTRACTUALISM
Scanlon argued that just principles could be derived from debate between individuals who are motivated by a desire for reasonable agreement, acting in a way which others could not 'reasonably reject' 12 (in contrast with Rawls' ideal of finding principles which everyone would agree to). The agents in Scanlon's model are aware of their self-interests and position in society. They wish to justify themselves to others (who have their own interests to pursue); if an action can be reasonably rejected by those affected by it, by Scanlon's definition, it is wrong. This theory thus appeals to the normative notion of reasonableness to produce a theory of interpersonal obligations, focusing on what we owe to each other.
Why, then, should we not simply rely on Scanlonian contractualism as a way of developing just principles for a healthcare service? We argue that there are several key advantages to applying a Rawlsian approach to consensus building and reasonableness.
One issue with Scanlon's approach is that any individual group can only represent their current interests-it does not lend itself as well to intergenerational conflicts, as only the views of current persons are considered. Some have argued that Scanlonian contractualism can be extended to consider the rights of future persons, as 'the fact that future people do not co-exist with us… does not prevent us from hypothetically contracting with them by considering them among those to whom we have reason to justify principles'. 13 We could thus ask whether future people could reasonably reject the principle when considering the morality of an action. Overall, however, Rawlsian justice has been applied to intergenerational conflict more widely, and more compellingly, than Scanlonian contractualism has. 14 15 Problems where the rights of current users must be balanced with future users of a healthcare system are more convincingly addressed using a Rawlsian approach, which explicitly considers the issue of sustainability through discussions of how just institutions should be maintained across time. Applying Rawls' just savings principle would, for example, provide justification for research and innovation in the NHS to improve the service provided to future generations, even if there is some cost to current generations.
A further problem is that Scanlonian contractualism aims to produce a general moral theory to determine what we owe to each other, but does not defend any substantive principles of distributive justice. Scanlon aims to develop 'principles of right and wrong for individual actions in such a way that the interests of each affected person are taken fairly into account', 16 while Rawls focuses on the basic institutions and aims to develop principles which, if followed, would result in a just society. In this sense, Scanlonian contractualism might be helpful in guiding reasoning about specific individual healthcare decisions (and has already been suggested to guide decision-making around; eg, broad-spectrum vs narrow-spectrum antibiotic use 17 ), but is less useful than a Rawlsian approach in guiding the overall development of a just healthcare system. An example of this can be seen in the application of Rawls' work to justify publicly funded
Original research
universal access to healthcare, most notably by Daniels. 9 Rawls' Theory of Justice applies to the institutions which make up the basic structure of society; in this way, it can be of help in justifying universal access to healthcare in a way that Scanlon's work, focused as it is on interactions between individuals, cannot.
A PRACTICAL SOLUTION: COMBINING SCANLONIAN CONTRACTUALISM WITH A RAWLSIAN APPROACH
We have shown that relying purely on the work of either Rawls or Scanlon alone would not provide a practically useful framework to guide policy. Rawls' Theory of Justice relies significantly on the hypothetical veil of ignorance; the principles derived from it could be applied to the institutions making up a healthcare system, but can be difficult for policymakers working within existing institutions to enact. Scanlon's concept of self-interested contemporaneous agents debating principles is more practically applicable as it does not demand the veil of ignorance, but it is overly focused on what individuals owe each other. Consequently, it does not provide as compelling an account of how to create a healthcare system which is just to current and future generations.
We thus propose a method of implementing an adapted Rawlsian approach for the NHS by combining it with some elements of Scanlonian contractualism, as demonstrated in figure 2. Scanlonian contractualism could initially be used: a committee of healthcare workers, patients, relatives and managers could come together as themselves and represent their own interests, contemporaneously discussing how different policies might impact on them (with a reminder to consider from present and future positions). To ensure that those less able to represent themselves (or at all) were represented, additional independent advocates would be appointed to participate in the process. Scanlon refers to this as the 'trustee model', in which objections to certain principles could be raised by 'trustees' representing those who themselves lack the capacity to assess reasons and express objections (eg, infants or the cognitively impaired). 12 In this way, Scanlonian contractualism would elucidate the impact of actions on different stakeholders, as assessing 'the comparative strength of individuals' objections to various proposed principles will centrally involve comparing the immediate and long-term gains and losses to their well-being'. 18 The second step entails the committee of stakeholders explicitly considering if policies being considered are in keeping with the general principles derived from the Rawlsian approach. The principles themselves come from applying the 'veil of ignorance' to the healthcare service's institutions as a whole (see figure 1); here they act as a checklist to be considered by the stakeholders when making decisions or drawing up policies. This step ensures that each policy decision is examined to see if it meets the principles of distributive justice, openness and sustainability.
We recognise, of course, that there are already many examples of engaging with patients and of 'stakeholder involvement'. Several of these have gone some way towards making healthcare practice and policy more just: patient and public involvement in research planning and execution; lay membership on boards; including multiple stakeholder perspectives in decision-making around healthcare rationing 19 and ongoing efforts to ensure that individuals with a range of ethnicities and genders are included in many committees and groups. Citizens' assemblies, such as those held in Ireland, have been convened to consider important policy decisions, but this has not frequently happened in healthcare.
While these efforts to widen representation in the decisionmaking body are laudable, it is not the same as explicitly requesting that different groups they have their interests considered in a Scanlonian way. Efforts to include different stakeholders in decision-making do not generally encompass the 'reasonable rejection' criteria, which we have proposed in the first step of our model. By demanding that decisions are made in a way which cannot be reasonably rejected by stakeholders, we ensure that their needs are considered far more robustly.
How then might this approach be embedded to help ensure just policies and fair practice, and how might it be applied to help the NHS deal flexibly with new challenges such as COVID-19?
APPLICATIONS OF THE FRAMEWORK Preventing injustice among different socioeconomic groups
There are many recognised injustices in access to and delivery of healthcare across socioeconomic groups. We recognise that this inequity is acknowledged and periodically investigated, and that it is difficult to meaningfully measure whether interventions to change socioeconomic disparities have direct effects on health outcomes. 20 However, our proposal will provide a stop check to consider whether new policies (unintentionally) worsen these inequities. The process of explicitly ensuring advocacy for those who may be less able to articulate or argue their own case will help make policies fairer for them (see figure 2); the check to ensure equity of access will help keep this at the forefront as policies are made. We recognise that, to some extent, this is already done: efforts are made to seek representation when new health services are being developed to ensure that they are accessible to those who need them most. But there is a distinction between consulting with a representative and ensuring that their view cannot be reasonably rejected. We argue that there is a value in explicitly applying the 'difference principle' so that if a change (eg, the location of a new health provider, or a new screening programme) disproportionately benefits one group in society, it benefits that group which is currently most disadvantaged.
Preventing intergenerational injustice
Currently policies are often made without explicit consideration of the impact on future generations. National policies are driven by a 'quick return' in reaction to the most pressing problem, and to ensure that voters will see the results of the actions taken and re-elect those in power. There is no obligation or accountability to consider the impact of policy decisions on future generations, and little incentive to do so. The strength of explicitly acknowledging Rawls' 'just saving principle' as part of our proposed framework (see figure 2), means that sustainability is considered more robustly. The 2015 policy of removing student grants for nurses, for example, led to a 30% drop in applications: in 2019 the nursing shortage in England had increased to 40 000. 21 This policy, which led to immediate savings but could be confidently predicted to result in a depleted nursing workforce, is clearly not in keeping with the just savings principle.
In addition to preventing short-sighted policies, our framework would give voice and power to policies which involved proactive planning for a fairer future: recruiting staff to specialities or regions which were underserved, or investing in preventative medicine and research to prepare for future health crises. The model we propose here represents the inclusion of a step to ensure that policies are designed to establish and preserve just healthcare institutions over time.
Preventing injustice in a crisis
While our proposed framework can be applied to deal with challenges which we are already aware of, it becomes even more useful when an unexpected challenge arises. Instead of having to start from first principles, an established method of reasoning and a set of accepted principles can be drawn on. This would have been invaluable in approaching the COVID-19 pandemic.
Radical changes had to be made to the health service in light of COVID-19, and many of the decisions necessitated balancing conflicting needs from different users. We suggest that a fundamental problem in the response to the crisis was the lack of clear philosophical framework or just decision-making process. Of note, the UK government's Moral and Ethical Advice Group met, but only very brief notes were published: in them, there was no reference to a process by which decisions were considered, nor of the use of philosophical principles. 22 This lack of coherent ethical framework guiding policy decisions was evident in the-at times-narrowly focused and frantic attempts to restructure services.
At the beginning of the pandemic in the UK there was a fear-based on experiences in Italy-that there would be an insufficient number of ventilators. Significant attention was thus bestowed on the demands of the most critically ill: the nightingale hospitals were built at pace, and multiple ventilator projects were spawned. Those in care homes and in primary care were, certainly in the early stages of the response, overlooked. Vulnerable patients were discharged back to nursing homes where they may have spread disease, while primary care and care home staff were left with insufficient personal protective equipment.
Had our framework been used, nursing home residents and staff would have reasonably rejected these policies. The needs of patients without COVID-19 might also have been recognised earlier had their voice (or that of their 'trustee') been included in debates. Suspending cancer screening services or cancelling urgent surgeries might have been reasonably rejected by those who needed this care. The requirement for sustainability would also have encouraged policymakers to think about developing alternative streams for these patients, away from COVID-19 admission units, rather than putting off the problem for a future date. Finally, those working within the health service were often given little opportunity to challenge policies or contribute to decision-making: the principle of openness was not adhered to, and the resulting decisions were weaker as a result.
A full exploration of exactly what policies might have been adopted using our suggested framework is beyond the scope of this essay; we would instead like to highlight that the way some decisions were made did not afford adequate consideration to how they would impact a range of health service users and workers-both present and future-including those without COVID-19 and those in primary care or care homes. Had the proposed framework been in place, then the impact of policies on all stakeholders would have been fairly considered, including minority groups and the most vulnerable in society. An explicit consideration of the principles of distributive justice, openness and sustainability could have provided an ethical basis for guiding policy made in response to the challenges of COVID-19.
CONCLUSION
We propose that attempts to help individual doctors practise fairly and justly throughout their professional lives are best focused at an institutional or systemic level. At the moment, individual clinicians face moral discomfort as they make decisions within a healthcare system which is at times unfair: they refer a patient to their local hospital knowing it is less well resourced than the one 100 miles away; they work with colleagues whose training has been compromised and whose pay is inadequate; they discharge patients who are potentially transmitting infectious diseases back to care homes to make way for more acutely sick patients.
Establishing guidance that will help doctors negotiate specific cases of injustice is challenging: every situation is different. Moreover, while individual policies to address each injustice could be constructed, this would not solve the problem: more unexpected injustices would appear, as we have unfortunately just witnessed.
We recognise that implementing a philosophical framework to guide just healthcare policy would not suddenly remove all unfairness in the system, and doctors would still sometimes find themselves in situations where their actions might seem unfair to current or future users of the health service. Yet by introducing a just process for policymaking, clinicians would less frequently find themselves in such situations. If clinicians were confident that the policies being made were based on a consistent and robust ethical frameworkand one which was open to engagement and challenge-then their individual decisions would become less morally challenging.
At the moment, there is a striking absence of an underlying philosophical framework on which healthcare policy decisions within the NHS are made. This has resulted in a system which is not always just in its resolution of conflicting demands from different users, current or future. We have proposed a practical framework-a transparent Scanlonian process and a set of principles derived from Rawls (as depicted in figures 1 and 2)-on which the development of health policy could be based, and which could be drawn on as new challenges arise. Having a clear process for the development of healthcare policy, based on philosophical principles and designed to be used in practice, could help make the NHS fairer to all of its users. Adopting this framework would equip the workforce and population to contribute to fair policymaking, and would ultimately result in a healthcare system whose practice and policies-at their core-were just. | 6,601 | 2020-10-07T00:00:00.000 | [
"Philosophy",
"Medicine"
] |
Magnetoconductivity in quasiperiodic graphene superlattices
The magnetoconductivity in Fibonacci graphene superlattices is investigated in a perpendicular magnetic field B. It was shown that the B-dependence of the diffusive conductivity exhibits a complicated oscillatory behavior whose characteristics cannot be associated with Weiss oscillations, but rather with Shubnikov-de Haas ones. The absense of Weiss oscillations is attributed to the existence of two incommensurate periods in Fibonacci superlattices. It was also found that the quasiperiodicity of the structure leads to a renormalization of the Fermi velocity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v_{F}$$\end{document}vF of graphene. Our calculations revealed that, for weak B, the dc Hall conductivity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{yx}$$\end{document}σyx exhibits well defined and robust plateaux, where it takes the unexpected values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 4e^{2}/\hslash \left( 2N+1\right) $$\end{document}±4e2/ℏ2N+1, indicating that the half-integer quantum Hall effect does not occur in the considered structure. It was finally shown that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{yx}$$\end{document}σyx displays self-similarity for magnetic fields related by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau ^{2}$$\end{document}τ2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau ^{4}$$\end{document}τ4, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document}τ is the golden mean.
Scientific Reports
| (2020) 10:21284 | https://doi.org/10.1038/s41598-020-78479-9 www.nature.com/scientificreports/ theoretically that the electronic spectra of GSLs exhibit a multifractal structure, which manifests in the properties of different transport quantities in the absence 17 and presence 18 of external fields. A further motivation to use Fibonacci GSLs is associated with the possibility of studying experimentally their magnetoconductivity characteristics, as occur for GaAs/AlGaAs Fibonacci lateral SLs 19 , where complicated commensurability oscillations of the magnetoresistence were observed. The paper is organized as follows. In Sect. 2, we present the theoretical approach. Section 3 is devoted to the results and the corresponding discussions. Finally, the conclusions are presented in Sect. 4.
Theory
As mentioned above, in this work we are concerned with the dc conductivity in quasiperiodic Fibonacci GSLs in a perpendicular magnetic field − → B = B z . Specifically, we will focus our attention on the diffusive (band) σ yy and Hall σ yx conductivities, ignoring the collisional one, which is determined by transport through localized states. We use the Landau gauge − → A = (0, Bx, 0) for the vector potential and take the Fibonacci SL potential V(x) along the x-axis.
To perform the calculations we will use the Kubo formula for σ yy and σ yx , which, for non-interacting electrons in the LL representation, can be written down as 20,21 where S = L x L y is the SL area, β = 1/k B T , f(E) the Fermi distribution function, J α = −ev F σ α the α-component of the current operator − → J , σ α the Pauli matrix, Ŵ the LL broadening parameter, and τ the scattering time, which we assumed constant for all states. For low energy excitations around the K point of the Brillouin zone, E ν and |ν) , with ν = i, j in Eqs. (1) and (2), are the eigenvalues and eigenfunctions of the massless Dirac-like equation. We can then write |ν) in coordinate representation in the form 22 : where n = 0, ±1, ±2, . . . is the energy quantum number, k y is the conserved wavevector along the y-axis, and the spinor � nk y (x) satisfies the system of differential equations 22 : the raising and lowering operators, k x = −i∂/∂x and x 0 = k y l 2 B the conserved center position. It is clear that to calculate σ yy and σ yx , for given values of Ŵ and the chemical potential µ , it is necessary to solve first the eigenvalue problem for the energy operator h , which is only possible if the Fibonacci SL potential V(x) is known. Here we suppose that V(x) is formed by rectangular barriers (layers a) and wells (layers b) arranged according to the Fibonacci sequence 23 : where W 1 = a and W 2 = b . We assume that V (x) = V 0 /2 in the barriers and −V 0 /2 in the wells. Now, when using the potential V(x) associated with W n to solving Eq. (4), the origin of coordinates is taken at the inversion center of W n , which is obtained from W n by removing its two extreme layers 24 .
It should be pointed out that the transformations (ab → a, abb → b) and (b → a, ab → b) transfer the element W n into W n−2 and the reverse of W n−1 , respectively, indicating that the Fibonacci SLs exibit a fractal or self-similar structure. Consequently, to obtain self-similarity in the length scale 23 , it is necessary that where τ is the golden mean, d a the barrier width and d b the well width. A simple inspection of Eq. (6) indicates that the spatial separation between adjacent interfaces is d a or d b = τ d a , with d a and d b arranged according to a Fibonacci sequence. This means that d a ( d b ) corresponds to a periodic spacing between interfaces with period d a ( d b ). Since d a and d b are relatively irrational, a Fibonacci SL represents a quasiperiodic structure with two incommensurate periods.
Results and discussion
At this point, it should be noted that, according to the above discussion, a quasiperiodic Fibonacci SL is an infinite self-similar structure generated according to W n for n → ∞ . In contrast to infinite periodic SLs, W ∞ has not translational symmetry and an exact and systematic procedure to study its electronic properties does not www.nature.com/scientificreports/ exist. It is clear however that a good description of these properties can be achieved if W ∞ is approximated by W n provided the order n is large enough. The latter requires that the spatial length L(W n ) = d a F n−2 + d b F n−1 of W n must be sufficiently large in comparison with the corresponding magnetic length l B , where the sequence F n = F n−2 + F n−1 , with F 1 = F 2 = 1 , determines the Fibonacci numbers. It is easy to show, for instance, that the patterns W 15 , W 17 , and W 19 , with the geometrical and physical parameters defined below, satisfies these conditions. Thus, any one of them can be used in the study of the corresponding electronic properties. Numerical calculations are performed for the W 17 Fibonacci superlattice with d b /d a = τ and weak magnetic fields satisfying the inequality l B >> d , where d = d a + τ d b is the average periodicity of the Fibonacci potential V(x). This allows us to use the quasi-classical approach to understand some of the results that will be presented below. We take d = 20 nm and V 0 /E F = 2π , where E F = ℏv F /d = 33 meV is an energy scale parameter 7 . With that value of d, it is easy to show that d a = d/(1 + τ 2 ) ≈ 5.5 nm and d b = d a τ ≈ 9 nm. Diffusive conductivity. Using the numerical solutions of Eqs. (4) and (3), we calculated the magnetic field dependence of the diffusive conductivity σ yy for different temperatures and chemical potentials µ . Such a dependence is shown in Fig. 1 for weak magnetic fields, low temperatures and µ = 0 . As can be seen, σ yy displays oscillations as a function of B whose characteristics depend on the range of magnetic fields considered. For the low-field range (0.12 B 0.36) , σ yy exhibits a complicated oscillatory behavior, characterized by relatively rapid oscillations, whereas for the higher one ( 0.36 B 1.4 ), these oscillations are much simpler and wellbehaved.
Before presenting the analysis of these results, it is interesting to note that, as mentioned in the Introduction, similar complicated oscillations of the magnetoresistence were experimentally observed in GaAs/AlGaAs Fibonacci lateral SLs 19 . It was shown in that work, using a Fourier analysis, that these oscillations can be described by the superposition of a small number of incommensurate periodic (sinusoidal) modulations, with periods successively scaled by the golden mean τ . The latter is a direct consequence of the self-similarity properties exhibited by Fibonacci SLs. Although this approach gives useful information about such oscillations, it does not relate them to the corresponding magnetic subbands, which can give further insight into the physical origin of these oscillations. Here we adopt the LL-energy approach to describe and understand the results depicted in Fig. 1.
To carry out the study, it is necessary to take into account that 25 the Fibonacci SL broadens the Landau levels of graphene into magnetic subbands and shifts all them rigidly downwards. The B-dependence of this shifting is given approximately by www.nature.com/scientificreports/ if a magnetic subband is completely full or empty, its contribution to the diffusive conductivity vanishes exactly. This implies that, for sufficiently low temperature, the minimum values of σ yy occur at those magnetic fields for which the chemical potential (µ = 0) is in between two magnetic subbands, whereas the maxima appear (approximately) when the center of the subbands crosses the chemical potential (µ = 0) , as shown in Fig. 2. The distribution of maxima and minima in the high-field range (0.36 B 1.4) are well defined because the corresponding magnetic subbands are energetically well separated (see Fig. 2). In contrast, the complicated oscillatory behavior observed in the low-field range is due to the overlap between the subbands. For a further characterization of the σ yy oscillations, the bandwidths of the n ≤ 5 magnetic subbands are depicted in Fig. 3. This choice is due to the fact that these are the subbands determining the oscillatory structure of σ yy in Fig. 1. Now, it is seen that the widths of the n = 0, 1, 2 subbands increase with B and do not exhibit a clear oscillatory behavior, whereas those of the remaining ones increase non-monotonically with B exhibiting a slowly oscillating behavior. These bandwidth characteristics are in stark contrast to those of 1D periodic SLs, where the B-dependent width of the n-subband is proportional to the sum of two successive Laguerre polynomials 10,11,26 and, therefore, exhibits oscillations with vanishing amplitude (minima) at certain values of the field. The number of such oscillations increases with n, especially in the range of weak magnetic fields, giving rise to the Weiss oscillations in the magnetoresistance, which originate from the commensurability between the SL period and the cyclotron diameter in the 1D periodic SL. This suggests that the oscillatory structure of σ yy shown in Fig. 1 cannot be associated with Weiss oscillations, but rather with Shubnikov-de Haas ones. This result is a direct consequence of the existence of two in-commensurate periods in Fibonacci SLs 27 .
Furthermore, since l B >> d we can carry out a quasiclassical analysis of the results presented in Fig. 1. In this approach, the band structure of the W 17 Fibonacci GSL in the absence of B, which can be calculated considering a periodic SL whose unit cell is based on the W 17 potential 28 , is not altered by the magnetic field, which only quantizes it and the charge-carrier motion is along equal energy curves in k-space. Under these conditions, we can assume that the cyclotron orbits in monolayer graphene are not substantially modified by the Fibonacci potential. Taking then into account that the band structure of graphene is given by E( − → k ) = ±ℏv F k = ±ℏv F k 2 x + k 2 y , the area enclosed by the cyclotron orbit (circumference in − → r -space) associated with the Landau level E n = sgn(n)ℏω C √ |n| is given by www.nature.com/scientificreports/ where R n is the cyclotron radius. As assumed above, Eq. (7) also applies to the case where the Fibonacci potential is considered, but replacing E n and the Fermi velocity v F by E ′ n and v ′ F , respectively, where E ′ n /ℏω C represents the center of the shifted n magnetic subband corresponding to the LL E n /ℏω C = sgn(n) √ |n| of monolayer graphene, with n = 0, ±1, ±2, . . . being the Landau index. The velocity v ′ F was introduced to include a possible small modification of the cyclotron orbits.
To compare the results obtained from Eqs. (7) and (8), it is necessary to take into account that E n /ℏω C is measured from the n = 0 level. This implies that E ′ n /ℏω C in Eq. (8) must be measured from the center of the shifted n = 0 magnetic subband (see Fig. 2), i. e., ε n = E ′ n /ℏω C − β(B) . Consequently, when the center of the n subbands crosses the µ = 0 chemical potential ε n = 0 and thefore E ′ n /ℏω C = β(B) . Considering now those magnetic fields B for which σ yy exhibits maxima and requiring that A ′ n ≈ A n , one obtains The numerical evaluation of this ratio is straightforward if the shifting β(B) = E ′ n /ℏω C = −(1.17, 1.43, 1.67, 1.85) of the magnetic subbands, shown in Fig. 2, and the corresponding values |n| = (2, 3, 4, 5) of the Landau index are used in Eq. (9). This leads to the conclusion that such a ratio is independent of n and takes the value v ′ F /v F ≈ 0.83 ≈ τ/2 , indicating that one of the effects of the Fibonacci potential is to renormalize the Fermi velocity v F . Finally, one observes in Fig. 1 that when the temperature T increases, the peak structure of σ yy tends to broaden and to be suppressed, except the peak associated with the higher magnetic field ( B = 1.18 T), which is robust against temperature in the T-range considered. The latter behavior is due to the fact that the magnetic www.nature.com/scientificreports/ subband giving rise to that peak ( n = +2 subband in Fig. 2a) is well separated from the nearest-neighbor ones, and the thermal excitations only slightly affect the corresponding peak structure, in contrast to the remaining peaks.
Hall conductivity. Let us now pay close attention to the dc Hall conductivity σ yx , especially to its properties when the Fibonacci GSLs is subject to magnetic fields related by integer powers of the golden mean τ . The latter choice guarantees similarity in the magnetic-field length scale of the structure 23 . That quantity computed from Eq. (2) is shown in Fig. 4 as a function of the chemical potential µ for B = 0.18 T, B ′ = Bτ 2 = 0.46 T, B ′′ = Bτ 4 = 1.2 T, V 0 /E F = 2π and sufficiently low temperatures. We see that, for all these fields, σ yx exhibits well defined plateaux directly associated with the energy separation between the n = 0 , ±1, ±2 magnetic subbands. Notice that the center of steps when µ goes from holelike to electronlike carriers is situated at β(B) = −1.14 , −1.85 and −3.1 in panels (a), (b) and (c), respectively, as required by the center positions of the corresponding n = 0 magnetic sudbands. It is interesting to note that σ yx takes the unexpected value ±4e 2 /ℏ(2N + 1) on the visible plateaux, i. e., on the first (N = 0) , second (N = 1) and third (N = 3) ones. This is an unexpected result because it does not follow the well-known sequence 4e 2 /ℏ N , for conventional semiconductors, neither ±4e 2 /ℏ(N + 1/2) for pristine graphene and periodic graphene SLs with only a Dirac cone in a weak field 9 . Thus, the half-integer quantum Hall effect does not occur in Fibonacci GSLs. It should be added that the sequence ±4e 2 /ℏ(2N + 1) was reported in Ref. 7 to describe the Hall conductivity steps in periodic graphene SLs with additional Dirac cones. Since such cones do not exist in the energy spectra of Fibonacci graphene SLs, these arguments can not be invoked here to justify such Hall conductivity values at the mentioned plateaux. It is clear, however, that the occurrence of these plateaux are a direct consequence of the fractal properties of Fibonacci SLs, in particular, of the existence of two incommensurate periods in them 27,29 . Further works are needed for a better understanding of the Hall conductivity values at these plateaux. Finally, a comparison between the results shown in Fig. 4a-c shows that the curves associated with the chosen fields exhibit a very similar behavior as functions of the scaled chemical potential µ/ ω C , where ω C ∼ √ B is the cyclotron frequency. Indeed, it is apparent that if these figures are superimposed, such curves essentially coincide in that range of µ/ ω C where the principal plateaux are located, whereas a slight difference is observed outside it. The latter is due to the overlap of the magnetic subbands. Thus, the Hall conductivity exhibits a self-similar structure for the considered magnetic fields, which are related by τ 2 and τ 4 . www.nature.com/scientificreports/ Conductivity in periodic GSLs. A brief comparison. To compare our results with the corresponding ones for periodic GSL, it is necessary to take into account the properties of its energy spectra for the same parameters as those used above for the Fibonacci GSL, i. e., for barrier height V 0 /E F = 2 π and magnetic field B such that l B ≫ d , where d is the SL period. It is well known that 9,30 , for these parameters, the magnetic subbands of the periodic SL are essentially flat, whereas its band structure in the absence of B only exhibits one Dirac point (cone). Now, since �i|J y |i� = �n, k y |J y |n, k y � ∼ ∂E n (k y )/∂k y in Eq. (1) and the magnetic subbands are nearly flat, the contribution of the diffusive conductivity to the periodic-SL conductivity can be neglected, in contrast to the contribution shown in Fig. 1 for the Fibonacci GSL.
Further, due to the flat character of the magnetic subbands, the Hall conductivity exhibits well-defined plateaux around the mentioned Dirac point, with values given by ±(N + 1/2) 4 e 2 /h at these plateaux, as shown in Ref. 9 . These plateaux have the same origin as those shown in our Fig. 4, but the Hall conductivity values contrast to those of the Fibonacci GSL.
Summary and conclusions
We have studied the magnetoconductivity properties of Fibonacci GSLs in the presence of a perpendicular magnetic field B. It was shown that the B-dependence of the diffusive conductivity exhibits a complicated oscillatory behavior whose charcateristics depend on the range of B considered and cannot be associated with Weiss oscillations, but rather with Shubnikov-de Haas ones. The absense of Weiss oscillations is attributed to the existence of two incommensurate periods in Fibonacci SLs. It was also found that the quasiperiodicity of the structure leads to a renormalization of the Fermi velocity v F of graphene. Our calculations revealed that, for weak B, the dc Hall conductivity σ yx exhibits well defined and robust plateaux as a function of the chemical potential, where it takes unexpected values. It was finally shown that σ yx displays self-similarity for magnetic fields related by τ 2 and τ 4 .
Data availability
All the files with tables, figures, and codes are available. The corresponding author will provide all the files in case they are requested. | 4,702.6 | 2020-12-01T00:00:00.000 | [
"Materials Science"
] |
The Application of Projection Word Embeddings on Medical Records Scoring System
Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician’s score.
Introduction
With the increasing advancement of technology, the data amount generated by humans is growing explosively [1]. Effectively taking advantage of these growing data may bring valuable information, which many successful cases from different industries [2] have already proved. However, the majority of these data are not structured [3], which cannot be directly used by traditional analytical methods. At the same time, it is expected to employ new algorithms to use these data to allow for stronger decision-making capacity [4,5]. In recent years, with the breakthrough developments of the deep neural network in diverse fields, we are already capable of directly analyzing data in the forms of videos, texts, and voices. Hence, the focus of researches is now to develop applications to solve practical problems.
The medical system is an important field that is very suitable to develop the abovementioned applications. Medical knowledge is accumulating quickly, making it more and more possible for doctors to have knowledge gaps [6], which may cause misdiagnoses and, thus, urgently need to be solved [7]. Computer-aided diagnosis systems have been greatly developed in recent years, aiming to solve this problem, yet unsuccessfully so far [8]. This is probably because the majority of medical data are non-structural data [9]; take cancer, for example, where about 96% of cancer diagnoses are made from pathological section reports, the data of which, however, are recorded in text descriptions and videos [10]. Thus, it is difficult for traditional models to link these original non-structural data with diagnosis information directly. With the advancement of artificial intelligence (AI) technology, the new generation of computer-aided diagnosis systems is expected to make great contributions to the intellectualization of medical systems. It can further eliminate human errors to increase the quality of medical care [11]. In 2012, AlexNet was the ILSVRC champion, leading the 3rd AI revolution [12]. Since then, more powerful deep learning models have been developed, such as VGGNet [13], Inception Net [14], ResNet [15], DenseNet [16], etc. This revolution led by deep learning has made enormous progress in image recognition tasks, driving breakthroughs in related research. Computer-aided diagnosis tools built based on deep learning technology have led to an increase in medical care quality [11]. Examples include lymph node metastasis detection [17], diabetic retinopathy detection [18], skin cancer classification [19], pneumonia detection [20], bleeding identification [21], etc. There have been over 300 studies (mostly in the last 2 years) using such technologies in medical image analysis [22]. It is worth mentioning that the most impressive capacity of deep learning technology is automatic feature extraction. With the precondition of a large database for annotation, it has been proven to reach, or even surpass, the level of human experts [15,23,24].
The current method to use a large amount of information from medical records is to code through recognition by experts and according to ICD (The International Statistical Classification of Diseases and Related Health Problems). This work is not only necessary for our national health insurance declaration system but may also be used in disease monitoring, hospital management, clinical studies, and policy planning. However, artificial classification is not only expensive but is also time-inefficient, which is the most important. For example, in disease monitoring, since the outbreak of infectious disease will cause large casualties [25], many countries have developed their disease monitoring systems specifically aiming at contagious diseases, such as the Real-time Outbreak and Disease Surveillance (RODS) system [26]. To ensure time efficiency, this system stipulates emergency physicians to input data within required time limits when identifying notifiable diseases, making it hard to be promoted to other diseases. With the advancement of data science, it has been universally expected that an automatic disease interpretation model can be developed to solve the high-cost and time-inefficient problems of artificial interpretation.
Due to the popularization of medical records electronization, a great number of studies have attempted to use this information for text mining and ICD code classification. The current technology primarily uses a bag-of-words model to standardize text medical records, then uses a support vector machine (SVM), random forest tree, and other classifiers for diagnosis classification [27][28][29][30][31]. However, previous studies have found that these methods were incapable of accurate diagnosis classification because of the particularity and diversity of clinical terms, where synonyms need to be properly processed before data preprocessing [10]. A complete medical dictionary integrates the currently recommended forms of clinical terms; yet, it is almost impossible due to the complexity of clinical terms. Therefore, traditional automatic classification programs can hardly make significant progress. In addition, the bag-of-words model treats different characters as different features and counts the number of features in one article. Although this makes it possible to use a dictionary to handle the synonym problem, similar characters would be considered two different features. Thus, the number of features integrated by the bag-of-words model will be strikingly huge, causing a curse of dimensionality when classified by subsequent classifiers, leading to inefficiency and slow progress of traditional algorithms.
Other than classification efficiency, the greatest challenge for traditional algorithms is new diseases. For instance, there was an H1N1 outbreak in 2009, with related cases that had never been recorded before 2008. Traditional classification algorithms are completely unable to perform proper classification of newly emerged words [27][28][29][30][31]. This disadvantage makes it absolutely impossible for traditional methods to reach full automation. Regarding this issue, we proposed word embedding as a technical breakthrough in disease classification. Since the 20th century, word embedding has been an important technology to allow computers to understand the semantic meaning further. Its core logic is hoping to characterize every single word into a vector in high-dimensional space and expecting similar vectors for similar characters/words to express semantic meaning [32,33]. The word2vec published by the Google team in 2013 is considered the most important breakthrough in recent word embedding studies. It has been verified to allow similar characters to have very high cosine similarity and very close Euclidean distance in vector space [34]. However, this technology has a disadvantage that, once applied, it converts an article into an unequal matrix, making it inapplicable for traditional classifiers, such as SVM and random forest trees. A general solution is to average or weighted average the word vector of all characters in an article as semanteme [35]. However, from the MultiGenre NLI (MultiNLI) Corpus competition release by the natural language research team of Stanford (https://nlp.stanford.edu/projects/snli/), we can still see that combining modern AI technology gives better efficiency to models. Language processing conducts analysis mostly based on Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). Its core principle is to use convolutional layer (does not have memory but can gradually integrate surrounding single-character information in higher-order features, requires more layers) or Long Short-Term Memory Unit (has short-and long-term memory, thus needing fewer layers) for feature extraction and is able to process information in matrix form [36]. CNN has become the primary method in all computer vision competitions. Its reason for success is a fuzzy matching technique of convolutional layer, allowing for integrating similar image features. We will be able to change the convolutional layer from recognizing similar image features to recognizing similar vocabularies through certain designs. Hence, CNN has been applied in text mining, such as semantic classification [37], short sentence searching [38], and chapter analysis [39], and has shown considerably good efficiency. In the most recent study, Bidirectional Encoder Representations from Transformers (BERT), developed by Google, has swept all kinds of natural language process competitions [40]. Yet, its core is still good work/sentence/paragraph embedding. Generally speaking, combining good embedding technology with modern deep learning neural networks is undoubtedly the best option for current natural language processing tasks.
Our team has already applied it in disease classification of discharge record summaries and proved that it compared with traditional models. AI model with combined word embedding model and CNN reduces 30% error rate in disease classification tasks, makes modeling easier by avoiding troublesome text integration preprocessing, and learns external language resources through unmonitored learning to integrate similarity among clinical clauses [41]. However, although the combination of word embedding and CNN is better in disease classification tasks than traditional methods, its accuracy still cannot be compared with humans. One of the reasons is the error in understanding the semantic meaning. Therefore, improving the word embedding model's understanding of the meaning of medical terms might increase its subsequent analytical efficiency [42]. There are two studies that have evaluated the application of word embedding models trained by different resources on biomedical NLP and found EHR-trained word embedding could better capture semantic property [43,44]. On the other hand, external data resources have a neglected advantage in that the vocabulary diversity of external internet data resources is far more than that of internal task database. This advantage will greatly affect real disease coding tasks. Hence, an embedded training process needs to be developed to maintain the vocabulary diversity of internet resources and medical terms' understanding of the internal task database. A recent word embedding comparison study showed that EHR-trained work embedding could usually better capture medical semantic meaning [43]. Even the research team of abroad Mayo Clinic uses an EHR with a large amount of data. The total number of words is only about 100,000, the vocabulary diversity of which is still far less than the external database [43,44]. This is due to the lack of some rare diseases and periodic diseases, such as the 2003 SARS outbreak and the 2009 H1N1 outbreak. Therefore, EHR-trained word embedding models are unable to include enough vocabulary. For this reason, our team developed a projection word embedding model that has the vocabulary diversity of Wikipedia/PubMed, as well as an understanding of medical terms in EHR [45].
A medical record is a historical record and also the foundation of a patient's medical care. It records the patient's conditions, reasons, results of examinations/tests, treatment methods, and results during care processes. It integrates and analyzes patients' related information, presents the executive ground of medical decisions, and even affects national health policy. The basic purpose of medical records is to remind oneself or other medical care colleagues of a patient's daily conditions and attending physician's current thoughts. When medical treatment is being performed, the medical record serves as the communication tool among physicians and means for continuous treatment. In other words, the medical record is the only text material that records a patient's conditions and focuses on all medical care personnel. A medical record is an index of medical care quality reflecting a physician's clinical thinking and diagnostic basis. It serves as the reference for learning, research, and education. Meanwhile, it also serves as the evidence for medical disputes to clarify the attribution of liabilities. The medical record is the foundation of patient care as it records the contents of patient care provided by medical personnel. Thus, all results obtained from observation or examination can be found on the medical record. Therefore, any change in the patient's condition can be found from the medical record so that the patient's current condition can be evaluated for suitable treatments. Moreover, communication with a patient should also be included in the medical record so that medical personnel can learn the patient's expectations on the treatment, resulting in a closer doctor-patient relationship. For other professionals, a detailed medical record saves a lot of communication time and avoids misunderstanding or missing the patient's previous conditions that may lead to mistreatment.
The content of medical records also has legal effects. It is the basis of insurance benefits and even affects national health policy. For example, public health studies usually need to include case information under national health insurance, and, through studying a large number of medical records, such studies can help public health researchers and medical officials to establish more suitable public health decisions and administrative rules that protect the rights and interests of both doctors and patients. Clinical decision-making guides formulated by many specialized medical associations also used information from medical records. The implicit demographic information from these medical records is also collected at the national level and published as national health demographic information to compare with other countries so as to serve as a way to communicate and learn from each other for mutual benefits.
In this study, as shown in the graphical abstract, a scoring database was established by experts performing scoring on medical records. An AI model was trained to learn experts' scoring logics so as to screen high-quality medical record summaries. In contrast, the database made up of which will have the chance to promote the establishment of other subsequent AI models, improve model accuracy, and serve as a teaching example to improve medical education efficiency.
Data Source
In this study, inpatient medical records from Tri-Service General Hospital from 1 January 2016 to 31 December 2019 were used as the basic database, which was ethically approved by institutional review board (IRB NO. A202005104). Physicians of different levels from different departments were invited for medical records summary scoring. Scoring dimensions include different indexes, based on clinical writing standards, it contains 12 scoring items from each detailed structure of the QNOTE scale's inpatient record, including chief of complaint, history of the present illness, problem list, past medical history, medications, adverse drug reactions and allergies, social and family history, review of systems, physical findings, assessment, plan of care, and follow-up information. The completeness of each item's record, as well as the 5 structures (completeness, correctness, concordance, plausibility, and currency) of electronic medical records' examination information, are evaluated in 5 levels of the Likert scale: strongly disagree, disagree, no comment (not agree nor disagree), agree, and strongly agree. Specialists from different departments were required to review 227,689 medical records and preliminarily score them on a 10-point Likert scale based on the average of above 5 structures. These scores were then used as the training target of the AI model to represent medical record writing quality. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, and then they were evaluated by different departments. Data of the testing set was compared with the actual scores for analysis, and MAE from the Likert scale was used as the evaluation index for model performance. In the end, the aforementioned model was applied in Tri-Service General Hospital. A medical record auto-scoring system was established in the hospital so as to screen high-quality medical records for future teaching and research studies.
AI Algorithm
The collected medical records and various writing quality indicators can be used for artificial intelligence model training. The model architecture uses the word embedding and LSTM model developed by our team. The word embedding also uses the projection word embedding comparison table to perform single-character conversion mathematical vectors and uses the entire input article as the input matrix. We used projection word embedding to construct a deep convolutional network model to enable the network to integrate the transformed semantic vectors and extract written medical records based on different word combinations. First, we used the word embedding comparison table trained by Wikipedia and PubMed library, and then we used EHR to perform projection word embedding training. Next, we connected the converted text matrix in parallel so that the network can refer to two different word embedding sources simultaneously. In addition, we used different word embeddings separately as conversion sources to compare their effects on prediction performance.
Long Short-Term Memory (LSTM)
In RNN, the output can be given back to the network as input, thereby creating a loop structure. RNNs are trained through backpropagation. In the process of backpropagation, RNN will encounter the problem of vanishing gradient. We use the gradient to update the weight of the neural network. The problem of vanishing gradient is when the gradient shrinks as it propagates backwards in time. Therefore, the layers that obtain small gradients will not learn but will, instead, cause the network to have short-term memory.
The LSTM architecture was introduced by Hochreiter and Schmidhuber [46] to alleviate the problem of vanishing gradients. LSTMs can use a mechanism called gates to learn long-term dependencies. These gates can learn which information in the sequence is important to keep or discard. LSTMs have three gates: input, forget, and output. This is the core of the LSTM model, where pointwise addition and multiplication are performed to add or delete information from the memory. These operations are performed using the input and forget gate of the LSTM block, which also contains the output "tanh" activation function. In addition to using the original architecture and model parameters, the other settings are Epochs = 20, Batch size = 300, and Learning rate = 0.001.
Bidirectional Encoder Representation from Transformers (BERT)
Other than the original word embedding and LSTM architecture, BERT architecture was also used for feature extraction. BERT is a recent attention-based model with a bidirectional Transformer network that was pre-trained on a large corpus. This pre-trained model is then effectively used to solve various language tasks with fine-tuning [40,47]. In brief terms, the task-specific BERT architecture represents input text as sequential tokens. The input representation is generated with the sum of the token embeddings, the segmentation embeddings and the position embeddings [40]. For a classification task, the first word in the sequence is a unique token which is denoted with [CLS]. An encoder layer is followed with a fully-connected layer at the [CLS] position. Finally, a softmax layer is used as the aggregator for classification purposes [47]. If the NLP task has pair of sentences as in question-answer case, the sentence pairs may be separated with another special token [SEP]. BERT multilingual base model (cased) is used as transfer feature learning, and other parameters are set to Epochs = 30, Batch size = 32, and Learning rate = 0.00001.
Through these two methods, we can enable the network to learn the semantic meanings of different individual characters. We can also let the network learn from different texts, such as from Wikipedia and PubMed. Then, through EHR for Fine-tune retraining, the BERT architecture that has finished learning only needs to change from predicting its context output to predicting the categories of multiple medical record quality dimensions; then, it can be trained with medical record information.
Linear Mixed Model Function for Medical Records Scoring Prediction
Suppose data are collected from m independent groups of observations (called clusters or subjects in longitudinal data).
Here, Y m is an n × 1 vector of the dependent variable for patient m, and X i is an n × q matrix of all the independent variables for patient m. B m is a q × 1 unknown vector of regression coefficients, and e m is an n × 1 vector of residuals. This results in a multi-level mixed model with random effects for all samples, which is expressed as where Z is a matrix of known constants included in the information of the independent variables with random effects, and u is a matrix of random effects for all patients. The best linear unbiased prediction (BLUP) is important for predicting the medical record score in each patient, and it can be calculated by following the steps in [48].
Y m is an n × 1 vector of the dependent variable for patient m, and X i is an n × q matrix of all independent variables for patient m. Moreover, Z m is an n × p matrix of independent variables with random effects for patient m. These matrices contain the observed data and are defined as After building the prediction tool, we have the G matrix, B vector and σ 2 . G is a variance co-variance matrix of the random effects (p × p), and B is the fixed effect coefficients vector (q × 1). σ 2 is the variance of the residuals. We can calculate a matrix R (n × n) using If the independence assumption holds (i.e., Finally, the BLUP of the random effect in patient m can be estimated using We can estimate the regression coefficients (B m ) in patient m based on the above result, and B m can be used to predict the disease progression. B m can be calculated using Note that this calculation cannot make direct forecasts without the co-variable values. Thus, the co-variables information at the time of interest must be generated. We propose two methods for generating this information: (1) assume consistency between the last time and the time of interest and (2) predict the linear expectations. We will assess these methods in our analysis. Unquestionably, clinicians can use the most reasonable values based on their judgment to predict the co-variables at the time of interest. In summary, we can combine this method with population information to predict the medical record score.
Evaluation Criteria
We evaluated the generalization performance of each model in the training and testing samples. Mean absolute error (MAE) were used to compare the performance of the models, as follows:
Results
The research scheme is shown in Figure 1, where a total of 227,689 medical records were scored by experts. In AI model training, the medical records were divided into the training set and testing set based on year, where 74,959 records were used to establish BERT and LSTM models, and 152,730 records were used to test record scoring. LMM was then employed to modify BERT and LSTM to establish another two models. In the end, MAE was used to compare the four models' efficiencies in predicting medical record scores.
were scored by experts. In AI model training, the medical records were divided into the training set and testing set based on year, where 74,959 records were used to establish BERT and LSTM models, and 152,730 records were used to test record scoring. LMM was then employed to modify BERT and LSTM to establish another two models. In the end, MAE was used to compare the four models' efficiencies in predicting medical record scores.
Figure 1.
Training and testing sets generation. Schematic of the data set creation and analysis strategy, which was devised to assure a robust and reliable data set for training and testing of the network. Once a medical records data were placed in one of the data sets, that individual's data were used only in that set, avoiding 'cross-contamination' among the training and testing sets. The details of the flow chart and how each of the data sets was used are described in the Methods. Table 1 shows the distribution of medical records in different departments. It can be seen that 74,959 records were included for modeling, and then 152,730 records were used for prediction. The average score from experts was 7.24 ± 1.02 for the training set and 7.67 ± 0.84 for the testing set; after BERT and LSTM modeling of medical record scoring, the average score of BERT prediction in the testing set was 7.47 ± 0.89, and 7.15 ± 1.05 for LSTM. After training through the BERT and LSTM models, the artificial intelligence model had already scored the medical records. Figure 1. Training and testing sets generation. Schematic of the data set creation and analysis strategy, which was devised to assure a robust and reliable data set for training and testing of the network. Once a medical records data were placed in one of the data sets, that individual's data were used only in that set, avoiding 'cross-contamination' among the training and testing sets. The details of the flow chart and how each of the data sets was used are described in the Methods. Table 1 shows the distribution of medical records in different departments. It can be seen that 74,959 records were included for modeling, and then 152,730 records were used for prediction. The average score from experts was 7.24 ± 1.02 for the training set and 7.67 ± 0.84 for the testing set; after BERT and LSTM modeling of medical record scoring, the average score of BERT prediction in the testing set was 7.47 ± 0.89, and 7.15 ± 1.05 for LSTM. After training through the BERT and LSTM models, the artificial intelligence model had already scored the medical records. Our team's projection word embedding model allowed the model to have both the vocabulary diversity of Wikipedia/PubMed and an understanding of medical terms in EHR. The concept of projection word embedding used the results of our previous studies, a concept in linear algebra that projects through matrix multiplication to allow all coordinates to convert into a new coordinate system. Such conversion changes the correlation of certain points while at the same time maintaining all current coordinates. In addition to the original projection word embedding and LSTM architecture, we attempted to use BERT architecture for feature extraction. BERT stands for Bidirectional Encoder Representations from Transformers, the elementary unit of BERT architecture is the encoder's Multi-Head Self-Attention Layer in the transformer. In contrast, the overall architecture of BERT is stacked by a bidirectional Transformer Encoder Layer. As shown in Table 2, in general, on the ground of experts' scoring, the trained scoring model BERT had a prediction score of 7.49 ± 0.28. In contrast, LSTM had 7.17 ± 0.31; after modification by the linear mixed model (LMM), BERT's and LSTM's prediction scores were 7.36 ± 0.56 and 7.33 ± 0.65, respectively. After layering different departments, such as internal medicine, surgery, obstetrics, and pediatrics, it can be learned that BERT all had higher prediction scores than LSTM, while, after LMM modification, all LSTM prediction scores increased. Through further looking into different departments, it was found that most departments' BERT prediction scores were higher than that of LSTM, and the latter increased after LMM modification. Table 2. BERT and LSTM original prediction scores and LMM-modified scores. Table 2. Cont.
LMM-Modified LSTM Prediction Scores
It can be learned from Table 3 that, when reviewer physicians' scores and AI scores were calculated using mean absolute error (MAE), both BERT and LSTM AI scores were 0.6~1.3 points lower than reviewer physicians' scores; thus, the linear mixed model (LMM) was introduced for modification, thereby reducing the score difference to 0.3~1 points, showing a significant reduction (p < 0.001) in score difference. The reason for the modification using LMM is that an ordinary linear regression contains only two influencing factors: fixed effect and noise. The latter is a random factor not considered in our model, while the former are those predictable factors that can also be completely divided. The AI scoring of medical records after modification by LMM is also more realistic. After department layering, it was found that, in some departments, LMM-modified MAE was not significantly reduced comparing with the original MAE. Hence, experts' scores were made into a heat map (Figure 2), where it was found that some groups of scoring physicians and scored physicians had closer scores, and were separately analyzed. In Table 4, medical record prediction scores and MAE are analyzed from Block A to H, respectively, and, except for block F, most blocks had similar record scores with previous results, and the MAE of LSTM prediction scores significantly reduced (p < 0.05) after LMM modification. scoring of medical records after modification by LMM is also more realistic. After department layering, it was found that, in some departments, LMM-modified MAE was not significantly reduced comparing with the original MAE. Hence, experts' scores were made into a heat map (Figure 2), where it was found that some groups of scoring physicians and scored physicians had closer scores, and were separately analyzed. In Table 4, medical record prediction scores and MAE are analyzed from Block A to H, respectively, and, except for block F, most blocks had similar record scores with previous results, and the MAE of LSTM prediction scores significantly reduced (p < 0.05) after LMM modification.
Figure 2.
Heat map of medical record scores from scoring and scored physicians. X-axis: physicians who wrote the medical records; Y-axis: scoring physicians and their departments. A redder grid means record scoring physicians give a higher score to record writing physicians. There are clusters in some areas; thus, we put out some blocks and observe the block (A to H) characteristics in Table 4. . Heat map of medical record scores from scoring and scored physicians. X-axis: physicians who wrote the medical records; Y-axis: scoring physicians and their departments. A redder grid means record scoring physicians give a higher score to record writing physicians. There are clusters in some areas; thus, we put out some blocks and observe the block (A to H) characteristics in Table 4.
In spite of this, we were still unable to identify the reason why the MAE of certain departments had no significant reduction after LMM modification. Thus, heat map analysis was performed on LMM-modified LSTM prediction scores. Figure 3 shows that some reviewers' LMM-modified LSTM prediction scores had relatively greater MAE. After grouping using LMM modified MAE (Grade-LMM modified LSTM), experts' scores were close among groups, but BERT and LSTM prediction scores were lower than the original experts' scores. In Figure 4, We further using MAE to evaluate model efficiency, and then comparing MAE (|Grade-LMM modified BERT|, |Grade-LMM modified BERT|) of LMM-modified BERT or LSTM with the MAE (|Grade-BERT|, |Grade-LSTM|) of the original BERT or LSTM, it was found MAE was effectively reduced through LMM modification in Q1~Q3, but not in Q4. Thus, it is suspected that some scoring physicians in Q4 may have scored incorrectly.
Healthcare 2021, 9, x 15 of 20 In spite of this, we were still unable to identify the reason why the MAE of certain departments had no significant reduction after LMM modification. Thus, heat map analysis was performed on LMM-modified LSTM prediction scores. Figure 3 shows that some reviewers' LMM-modified LSTM prediction scores had relatively greater MAE. After grouping using LMM modified MAE (Grade-LMM modified LSTM), experts' scores were close among groups, but BERT and LSTM prediction scores were lower than the original experts' scores. In Figure 4, We further using MAE to evaluate model efficiency, and then comparing MAE (|Grade-LMM modified BERT|, |Grade-LMM modified BERT|) of LMM-modified BERT or LSTM with the MAE (|Grade-BERT|, |Grade-LSTM|) of the original BERT or LSTM, it was found MAE was effectively reduced through LMM modification in Q1~Q3, but not in Q4. Thus, it is suspected that some scoring physicians in Q4 may have scored incorrectly. Figure 3. MAE heat map of LMM-modified LSTM prediction scores from scoring and scored physicians. X-axis: physicians who wrote the medical records; Y-axis: scoring physicians and their departments. By subtracting the MAE of the original score from the LMM modified LSTM prediction score, and using the MAE and coring physicians to conduct a heat map analysis, it can be found that some reviewer scores are on the high side. Figure 3. MAE heat map of LMM-modified LSTM prediction scores from scoring and scored physicians. X-axis: physicians who wrote the medical records; Y-axis: scoring physicians and their departments. By subtracting the MAE of the original score from the LMM modified LSTM prediction score, and using the MAE and coring physicians to conduct a heat map analysis, it can be found that some reviewer scores are on the high side.
Discussion
In this study, the projection word embedding model was used to develop an AI system to evaluate the writing quality of inpatient medical records. The AI system is already capable of accurate classification to level 3 ICD-10 coding, combined with results from previous studies. Since level 3 coding is already at the disease level, subsequent coding will all just be remarks (such as location), and reaching such a level will allow for the possibility of full automation of common disease classification tasks, as well as extraction of disease features from other medical descriptions, through this algorithm. In addition to the original word embedding and LSTM architecture, BERT architecture was also employed to extract disease features for medical record scoring. LMM was further used for modification to get AI scores closer to actual reviewer physicians' scores. Moreover, it was also identified that some physicians over-scored medical records. If these scoring standards can be improved in the future, a better medical writing quality could be expected.
In addition, why is the quality of medical record writing so important? Because the medical record is the historical record of the patient's health care; it is also the basis of care, and its content records the patient's condition during the care process, the reason and result of the inspection, and the treatment method and result. In recent studies, it is feasible to use electronic health records (EHR) to predict disease risk, such as atrial fibrillation (AF) [49], coronary heart disease in patients with hypertension [50], fall risk [51], multiple sclerosis disease [52], and cervical cancer [53]. Over the past two decades, the investigation of genetic variation underlying disease susceptibility has increased considerably. Most notably, genome-wide association studies (GWAS) have investigated tens of millions of single-nucleotide polymorphisms (SNPs) for associations with complex diseases. However, results from numerous GWAS have revealed that the majority of statistically significantly associated genetic variants have small effects [54] and may not be predictive of disease risks [55], and many diseases are associated with tens of thousands of genetic variants [56]. These findings have led to the resurgence of the polygenic risk score (PRS), an aggregate measure of many genetic variants weighted by their individual effects on a given phenotype. However, epidemiologic studies are expensive and complex to run, which raises the question of whether a PRS could be developed and applied in a clinical setting using genetic data that are more readily available. Recently, some scholars proposed new ideas for developing and implementing PRS predictions using biobank-linked EHR data [57].
For the medical records scoring system, this not only saves doctors the time for scoring medical records but also can get feedback immediately after the writing is completed to improve the quality of medical record writing. In the past research, clinicians spent 3.7 h per day, or 37% of their work day, on EHR [58]. There was a marked reduction in EHR time with both clinician and resident seniority. Despite this improvement, the total time spent on EHR remained exceedingly high amongst even the most experienced physicians [58]. The significance of an increasing shift towards EHR is a growing paradigm that cannot be understated, particularly in the current era of healthcare, and there is increasing scrutiny on documentation [59,60]. These increased demands can lead to EHR fatigue and physician burnout. In a survey of a general internal medicine group, 38% reported feeling burnt out, with 60% citing high documentation pressure and 50% describing too much EHR time at home [61]. Burnout has been linked to an increased risk of resident's wellbeing [62].
There are still some limitations for electronic medical records. First, this scoring system can only be used in our hospital because the medical record system of different hospitals do not talk to each other. Second, entering data into an EHR requires a doctor to spend a lot of time doing so, leading to most physicians experiencing burnout symptoms due to EMRrelated workloads. Third, cyber-attacks are a perennial concern for EHRs. It is, therefore, imperative that cybersecurity is continually enhanced. Fourth, timing discrepancies occur in EHRs, and they can lead to serious clinical consequences.
In summary, combining projection word embedding and LSTM with LMM can give better prediction scores. This system can be used to assist medical record scoring so that young physicians can get immediate writing feedback, so as to improve the quality of medical record writing in my country and let the public, Medical units, and insurance units can all get better help. In the future, it may be possible to actively introduce such technologies into hospitals to achieve personalized precision medicine. | 8,761.2 | 2021-09-29T00:00:00.000 | [
"Medicine",
"Computer Science"
] |
Population Balance Model of Heat Transfer in Gas-Solid Processing Systems
In modeling heat transfer in gas-solid processing systems, five interphase thermal processes are to be considered: gas-particle, gas-wall, particle-particle, particle-wall and wall-environment. In systems with intensive motion of particles, the particle-particle and particle-wall heat transfers occur through inter-particle and particle-wall collisions so that both experimental and modeling study of these collision processes is of primary interest. For modeling and simulation of collisional heat transfer processes in gas-solid systems, an Eulerian-Lagrangian approach, with Lagrangian tracking for the particle phase (Boulet et al., 2000, Mansoori et al., 2002, 2005, Chagras et al., 2005), population balance models (Mihálykó et al., 2004, Lakatos et al., 2006, 2008), and CFD simulation in the framework of EulerianEulerian approach (Chang and Yang, 2010) have been applied. The population balance equation is a widely used tool in modeling the disperse systems of process engineering (Ramkrishna, 2000) describing a number of fluid-particle and particleparticle interactions. This equation was extended by Lakatos et al. (2006) with terms describing also the direct exchange processes of extensive quantities, such as mass and heat between the disperse elements as well as between the disperse elements and solid surfaces by collisional interactions (Lakatos et al., 2008). The population balance model for describing the collisional particle-particle and particlesurface heat transfers was developed on the basis of a spatially homogeneous perfectly mixed system (Lakatos et al., 2008). In order to take into consideration also the spatial inhomogeneities of particles in a processing system a compartment/population balance model was introduced (Süle et al., 2006) which has proved applicable to model turbulent fluidization and the gas-solid fluidized bed heat exchangers (Süle et al., 2008). However, the spatial transport of gas and particles in turbulent fluidized beds usually is modeled by continuous dispersion models (Bi et al., 2000) thus, in order to achieve easier correlations of the constitutive variables, it has appeared reasonable to formulate the population balance combining it with the axial dispersion model (Süle et al., 2009 2010). Particle-particle and particle-wall heat transfers may result from three mechanisms: heat transfers by radiation, heat conduction through the contact surface between the collided bodies, and heat transfers through the gas lens at the interfaces between the particles, as well as between the wall and particles collided with that. Heat conduction through the contact surface was modeled by Schlünder (1984), Martin (1984) and Sun and Chen (1988) developing analytical expressions for particle-particle and particle-wall contacts. Often, however, the conductive heat exchange can hardly be isolated from the mechanism
Introduction
In modeling heat transfer in gas-solid processing systems, five interphase thermal processes are to be considered: gas-particle, gas-wall, particle-particle, particle-wall and wall-environment. In systems with intensive motion of particles, the particle-particle and particle-wall heat transfers occur through inter-particle and particle-wall collisions so that both experimental and modeling study of these collision processes is of primary interest. For modeling and simulation of collisional heat transfer processes in gas-solid systems, an Eulerian-Lagrangian approach, with Lagrangian tracking for the particle phase (Boulet et al., 2000, Mansoori et al., 2002, Chagras et al., 2005, population balance models (Mihálykó et al., 2004, and CFD simulation in the framework of Eulerian-Eulerian approach (Chang and Yang, 2010) have been applied. The population balance equation is a widely used tool in modeling the disperse systems of process engineering (Ramkrishna, 2000) describing a number of fluid-particle and particleparticle interactions. This equation was extended by Lakatos et al. (2006) with terms describing also the direct exchange processes of extensive quantities, such as mass and heat between the disperse elements as well as between the disperse elements and solid surfaces by collisional interactions . The population balance model for describing the collisional particle-particle and particlesurface heat transfers was developed on the basis of a spatially homogeneous perfectly mixed system . In order to take into consideration also the spatial inhomogeneities of particles in a processing system a compartment/population balance model was introduced (Süle et al., 2006) which has proved applicable to model turbulent fluidization and the gas-solid fluidized bed heat exchangers . However, the spatial transport of gas and particles in turbulent fluidized beds usually is modeled by continuous dispersion models (Bi et al., 2000) thus, in order to achieve easier correlations of the constitutive variables, it has appeared reasonable to formulate the population balance combining it with the axial dispersion model (Süle et al., 2009(Süle et al., 2010. Particle-particle and particle-wall heat transfers may result from three mechanisms: heat transfers by radiation, heat conduction through the contact surface between the collided bodies, and heat transfers through the gas lens at the interfaces between the particles, as well as between the wall and particles collided with that. Heat conduction through the contact surface was modeled by Schlünder (1984), Martin (1984) and Sun and Chen (1988) developing analytical expressions for particle-particle and particle-wall contacts. Often, however, the conductive heat exchange can hardly be isolated from the mechanism www.intechopen.com Heat Transfer -Mathematical Modelling, Numerical Methods and Information Technology 410 occurring through the gas lens at the interfaces of the colliding bodies. Based on this mechanism, Vanderschuren and Delvosalle (1980) and Delvosalle and Vanderschuren (1985) developed a deterministic model for describing particle-particle heat transfer, while a model for heat transfer through the gas lens between a surface and particles was derived by Molerus (1997). Mihálykó et al. (2004) and Lakatos et al. (2008), based on the assumption that most factors characterizing the simultaneous heat transfer through the contact point and the gas lens are stochastic quantities described the collisional interparticle heat transfer by means of an aggregative random parameter. In this chapter, a generalized population balance model is formulated to analyze heat transfer processes in gas-solid processing systems with inter-particle and particle-wall interactions by collisions, taking into consideration the thermal effects of collisions and the gassolid, gas-wall and wall-environment heat transfers. The population balance equation is developed for describing the spatial variation of temperature distribution of the particle population, and that of the gas and wall. The heat effects of energy change by collisions are included as a heat source in the particles. An infinite hierarchy of moment equations is derived, and a second order moment equation model is applied to analyze the thermal properties and behavior of bubbling fluidization by simulation.
The population balance approach
Consider a large population of solid particles being in intensive, turbulent motion in the physical space of a process vessel under the influence of some gas carrier. If the particulate phase is dense then particle collisions show significant effects on the behavior of the system therefore particle-particle and particle-surface heat transfer by collisions also may play important role in forming the thermal properties of system. Let us assume that follow. 1) The two phase system is operated under developed hydrodynamic conditions. 2) Particles are mono-disperse and their size does not change during the course of the process.
3) Only thermal processes occur in the system without any mass transfer effects. 4) The temperature inside a particle can be taken homogeneous. 5) Heat transfer between the gas and particles, the wall and gas, as well as the wall and environment of the process vessel are continuous processes modeled by linear forces. 6) Heat transfer by radiation is negligible.
Under such conditions the state of a particle at time t is given by the vector ( ) ,, ppp T xu where p x denote the space coordinates, p u are the velocities along the space coordinates, and T p stands for the temperature of particles. The space coordinates, according to the nomenclature of population balance approach are external properties of particles, while the particle velocities and temperature are internal ones. Since a dense gas-solid system consists of a sufficient number of particles in the vicinity of each space coordinate x therefore discontinuities can be smoothed out by introducing the population distribution function ( .. n, , t, depending on the practical reasons, are interpreted as the states of particle populations.
Integral forms with transition measures
According to the assumptions of former section particles are in intensive, stochastic motion in some domain X⊆R 7 of the metric space R 7 of external and internal properties therefore the time variation of particle state is described by the set of stochastic differential equations subject to the appropriate initial conditions. In Eqs (3)-(5), ∑ f are deterministic forces while () t W is a Wiener process, inducing motion of particles in the physical space, and are deterministic functions characterizing the continuous motion in the physical and temperature subspace, while the integrals in Eqs (4) and (5) denote, respectively, the velocity and temperature jumps induced by those. The set of stochastic differential equations (3)-(5) describes the behavior of the population of particles entirely by tracking the time evolution of the state of each particle individually. However, numerical solution of Eqs (3)-(5) is a crucial problem although it would provide www.intechopen.com Heat Transfer -Mathematical Modelling, Numerical Methods and Information Technology 412 detailed information about the life of each particle. Monte Carlo simulation means a good method for solving this system but to generate realistic results rather a large sample of particle population is required leading to long computer times. However, for engineering purposes we usually are interested only in the behavior of the particle population as a whole. Naturally, this information would be generated also by solving the system of stochastic differential equations (3)-(5) but we can obtain it directly applying the population balance approach. Namely, taking into consideration that the system of stochastic equations (3)-(5) induces a Markov process having continuous sample paths with finite jumps (Gardiner,1983, Sobczyk,1991, the particulate system exhibits all the properties of interactive disperse systems for which we can derive a conditional transition measure ˆc P by means of which the variation of the state of population of particles is described by the transformation is interpreted as the probability that there exists a solid particle in the state domain ( ) ", ", ", " ", " ", " " Td d T d T ++ + xu x xu u possibly interacting with a particle of state ( ) ', ', ' T xu and the result of this interaction event is expressed by the conditional transition measure ˆc P . The properties of the conditional transition measure ˆc P are determined by the physicalchemical processes of the system that induce motion and/or formation of particles under given operational conditions. When the action of these processes can be described by means of some vector of random parameters θ with distribution function F θ (.), and all particles are moved and/or formed under the same conditions then a lot of different realisations, described by the distribution function of the random parameters θ, can act on the particles. As a result, the final population is formed as expectation for the vector of parameters θ so that Eq.(6) can be rewritten using randomization: denotes the probability that a particle, residing at space coordinate x and having temperature T possesses velocity u, introducing (11) into Eq.(9) and integrating both sides of Eq.(9) over variable u we obtain Here, expression (13) denotes the transition measure by means of which transformation (12) provides the amount of particles of all velocities which are converted into the state to time t of particles of all velocities being in X at time s under the conditions of interactions with particles of the same state domain of any velocities. As a consequence, transformation (12) describes motion of particles with possible heat exchange interactions in the 3+1 dimensional subspace of physical and temperature coordinates determined over a given, fully developed velocity field. Eq.(12) is a reduced form of the multidimensional population balance equation (9), given also in integral form.
Integral-differential equation form
Taking into account the conditions assumed and following the steps of derivation of the population balance equation of interactive disperse systems as it was presented by Lakatos et al.(2006), the population balance equation describing the behavior of population of the particles in the system is obtained from Eq. (9) subject to the appropriate boundary and initial conditions. Here, ( ) ,t x N denotes the total number of particles in the vicinity of position x of the physical space at the moment of time t. The second and third terms on the left hand side of Eq.(14), next to the accumulation term describe variation of the population density function n due to continuous thermal and velocity interactions of particles with the carrier gas environment, respectively, while the first and second terms on the right hand side describe motion of particles in the physical space. Here, the rate expressions T G , u G and x G are interpreted as convective terms of motion along the temperature, velocity and physical coordinates, and x D denotes the dispersion tensor of motion of particles in the physical space. The last two terms on the right hand side of Eq.(14) describe the momentum and heat exchange between particles interacting with each other by collisions during their motion in the physical space. Namely, the third term describe the rate of decrease of number of particles of temperature T and velocity u collided with particles of temperature T' and velocity u', whereas the last term provides the rate of increase of number of particles having temperature T and velocity u resulted in collision interactions of particles of temperature T' and T" as well as of velocity u' and u". The collision interactions are described by means of a product of two operators: the activity and conversion functions ˆp p S and ˆp p b , respectively. In the present model, the activity function ˆp p S characterizes the intensity of collision interactions between the particles inducing temperature change, being, in turn, a product of the frequency of collisions ˆ( , ', ') col ST T uu and the efficiency term ˆ( , ST T uu expresses the ratio of all events which are to be successful in inducing also heat and momentum exchange events. Naturally, since heat diffusion, deciding at this level on homogenization of temperature is a spontaneous process, while momentum exchange by collision of two solid bodies also occurs the efficiency term is equal to unity identically, i.e. ˆ( , , d + uu u as a result of interactions of all particles of temperature T' and T" and velocity u' and u" in a unit time. The (3+4) dimensional population balance equation (14) is a so called cognitive model that describes the behavior of particle population in detail taking into account also the velocity distribution of particles. However, a simplified population balance equation can be obtained from Eq.(12) for the population density function ( ) ,, nT t x not including the explicit velocity dependence into the model. Indeed, again following the steps of derivation of the population balance equation of interactive disperse systems we obtain subject to the appropriate boundary and initial conditions. Here, the constitutive expressions in Eq. (15) have similar meanings as it was described regarding Eq.(14) excluding the explicit velocity dependence. Note that in this simplified form the effects of collisions on motion of particles in the physical space can be taken into consideration by applying empirical models for the convective and dispersion terms x G and x D , respectively. In the context of the population balance equation (15), the particle-solid surface boundary condition is relevant that can be formulated as the heat exchange rate between the particle population and solid surface at a space coordinate x of surface S where T w (x,t) denotes the temperature of surface S at position x at time t. Eq.(15) with the boundary condition is an applicative or, in other words, a purpose-oriented partially distributed population balance model aimed for describing thermal processes in fluid-solid particulate systems. Introducing the constitutive expressions and making use of appropriate symmetric conditions it can be applied for determining spatially low dimensional population balance models. Here, the one-dimensional axial dispersion/population balance and the zero-dimensional well stirred vessel/population balance model will be presented to describe thermal processes in gas-solid particulate systems.
4. Constitutive equations for particle-particle heat transfer by collisions 4.1 Heat effects of particle-particle collisions To close the population balance equation (15) only binary collisions as it is shown schematically in Figs 1 and 2. Here we assumed that particles in the particulate system are of the same volume and mass but their shape usually are irregular. As a consequence, in this case the collision events are of random nature therefore practically all processes induced by collisions, among others the change of kinetic energy and exchange of extensive quantities between the colliding particles should be treated as random.
Let us now consider the encounter of two solid particles of mass m j and m k as it is shown in Fig.1. If the velocity vectors of these bodies are given by u j and u k , and their difference is denoted by u jk =u j -u k then the translational kinetic energy change during collision E is where e is assumed to be a random coefficient of restitution. If the two particles are of equal mass, i.e. m j =m k =m p then and accounting for the probability that a particle is colliding with an another particle in the time interval (t,t+Δt) the energy change is expressed as and taking into consideration that the coefficient of restitution is a random variable with the probability density function f e , independent from the velocities, the mean value of energy change can be expressed as Making use of evaluation of the definite integral (Gidaspow, 1994) ( ) As a consequence, assuming that this heat arise in particles as it was generated by an internal heat source we can write ( ) that results in the following rate of temperature change for a particle ( )
Heat transfer by collisions of two particles
Let us consider the heat transfer process induced by collision of two solid bodies, shown schematically in Fig. 1, as described by the set of simple first order differential equations with linear driving forces ( ) (28) are of such forms as the temperature of particles would be uniform. However it is not the case so that these equations have to be interpreted as they were written for the mean values. In this case the variables are, in essence, mean values, i.e.
Since the temperature of a particle is not uniform the actual temperature values in the driving force in Eq.(28) are uncertain so that this should be treated also as random. Therefore the left hand sides of Eq.(28) are random and we allocate this randomness into the transfer coefficient.
The solutions of the first order differential equations (28) Here θ denotes the contact time which is, in principle, also a random quantity.
Interpretation of Eq.(12) is that in heat transfer process characterized with parameter ω pp a particle with temperature T p ' has to collide with particle of temperature T p " to achieve final temperature T p . Taking now into consideration the definitions of conversion functions, the conversion distribution function of particle-particle heat transfer takes the form so that the corresponding density function is
Heat transfer through the gas lens and the contact surface
Heat transfer between two particles may be considered as a combination of transfers through the gas lens between the particles and through the contact surface as it is shown in Fig. 1. These processes depend strongly on the shapes and velocities of the colliding bodies that is why the parameters and θ are treated as random variables. However, under symmetrical conditions for particles of regular spherical forms explicit expressions have been determined.
The heat flow rate through the gas lens between two particles with diameter d p in the angle ( ) max max , αα −+ was developed by Delvosalle and Vanderschuren (1985) In the case of intensive motion of particles, the parameters h, s and max in Eq.(38) as well as the contact time θ are, in principle, random quantities therefore is also a random function of these arguments. The heat flow rate through the contact surface was studied by Sun and Chen (1988) where a c denotes the maximum contact area. Again, in Eq.(42) the maximum contact area and contact time depend on the velocities of collided particles so that these quantities are to be treated also as random variables.
Comparing the equations (31), (39) and (42) all these are expressed by linear driving forces so that the overall heat transfer flow can be given as superposition of the heat flows through the gas lens and the contact surface.
Heat effects of particle-wall collisions
Let us consider again the encounter of two solid bodies of mass m j and m k as it is shown in Fig. 2 that results in the following rate of temperature change for a particle and the wall ( )
Particle-wall heat transfer by collisions
When a particle and the wall are two colliding bodies, as it shown in Fig. 2 Eq.(51) expresses the fact that in heat transfer process between a particle and the wall, characterized with parameter ω pw , the temperature T p ' of the particle becomes T p if the temperature of the wall was T w '. As a consequence, the conversion distribution function of the particle-wall heat transfer for particles takes the form At this moment all collisional constitutive expressions have been derived and the heat balance model of the process, applying the population balance equation (15) can be determined.
Moment equation model
Substituting the constitutive equations into the (3+1)D population balance equation (15) where the second term describes the change of population density function due to continuous thermal interactions of particles with gas and energy dissipation generated by particle-particle collisions while the boundary condition for surfaces playing the role of input and output of the system takes the form where ( ) ,, Tt x n is a given function stimulating the system or specifying the surface conditions. This is a boundary value problem of the (3+1)D integral-differential equation (57). This is still a cognitive model and it appears to be too complex for numerical solution. However, accounting for the constitutive expressions of the collision heat transfer terms a useful closed moment equation hierarchy can be determined introducing the moments with respect to the temperature variable xxx denote, respectively, the total number and the mean temperature of particles in the vicinity of position x at time t. In this case the first two moments have physical meaning while by means of the higher order ones the temperature distribution can be specified more exactly. The infinite set of moment equations becomes In Eqs (62) pp coll H Δ denotes the internal heat source generated by particle-particle collisions, in correspondence with Eq. (27) denotes the k th order moment of a given function stimulating the system across the inlet surface and that providing the outlet surface conditions. Specifying the appropriate symmetry conditions for the spatial terms in Eq.(62) results in 3D, 2D and 1D applicative, i.e. purpose-oriented moment equation models for different gasparticle processing systems. When applying the moment equation model, the computational results are assessed by means of the first three of the normalized moments www.intechopen.com Here, beside the zero order and the normalized first order moments, i.e. the total number and mean temperature of particles, the variance defined as 21 22 21 00 ,, ,, , ,, is used for characterizing the temperature distribution of particles.
Heat transfer in bubbling fluidized beds
Gas-solid fluidized beds may operate in several different flow regimes: bubbling, slug flow, turbulent and fast fluidization. With increasing superficial velocity there is a transition between the lower velocity bubbling and higher velocity turbulent fluidization states (Thompson et al., 1999, Bi et al., 2000. In the bubbling regime, as the gas flow rate increases the total volume of bubbles also increases and accounting for their coalescence and breakage, the bubble phase can be described by an axial dispersion model containing no particles. The bubbling regime, however, is characterized by intensive pressure fluctuations so that the dense emulsion phase, containing the particle population and being in intensive motion can be modeled as a perfectly stirred part of the bed as it is shown schematically in Fig. 3. At higher velocities, because of disappearance of bubbles the amplitudes of pressure fluctuations decrease significantly, and the distribution of particles along the height of the vessel becomes smoother. In this case the spatial distribution of particles is described by an axial dispersion model too, as it was analyzed by Süle et al. (2010).
-heat flux n in (T p ,τ), q p T g (ξ,τ), ε g V T g,in (τ), q g n(T p , τ), ε e V ε g q g ε e q g T g (ξ=1,τ), q g n(T p , τ), q p ξ As regards the model of bubbling fluidization we start with the following assumptions.
• The bubble phase is described by an axial dispersion model, and is connected thermally with the emulsion phase through the gas and with the wall.
•
The emulsion phase is a perfectly stirred cell containing the homogeneously distributed particle population. Inside the emulsion phase heat transfer occurs between the gas and particles, and there exists thermal connection of the gas and particles of the emulsion phase and the wall.
•
Homogeneous temperature distribution is assumed in the wall through which the bed is connected thermally with the jacket filled with liquid.
•
Liquid through the jacket flows with constant flow rate and is assumed to be thermally homogeneous.
•
The bubbling fluidized bed is operated continuously. Introducing the dimensionless variables and parameters the axial dispersion model for the bubble phase is written as while the corresponding boundary conditions take the forms , , is the overall heat effects generated by the particle-particle and particle-wall collisions, in correspondence with Eqs. (27) and (47). Naturally, when the particles are homogeneously distributed in the emulsion phase then (,) (,,) nT t n T t ≡ x .
In this case the moment equations (62), taking into account Eq. (72) and written using the dimensionless variables and parameters are as follows. Zero order moment: where M 0,in =10 8 /m 3 is the total number of particles in a unit volume, and φ=0.6 is the ratio of particles having different temperatures. Fig. 4 shows that there may remain rather significant inhomogeneities in the temperature distribution of particles in the bed. These inhomogeneities are decreasing with increasing interparticle collision frequency therefore the particle-particle interaction play important role in homogenization of temperature inside the population of particles. In this case the input temperature of gas was 180 ºC and the initial temperature of gas in the emulsion and bubble phases were of the same values. It is seen how the temperature profiles vary in interconnections of the different parts of the bed. In steady state, under the given heat transfer resistance conditions of the particle population, the wall and liquid is heated practically only by the gas in the emulsion phase while the bubbles flow through the bed without losing heat assuring only the well stirred state of the emulsion phase. Eq. (76) shows clearly that when the number of particles is constant, i.e. under steady state hydrodynamic conditions interparticle heat transfer does not influence the mean value of temperature of the particle population but, as it is demonstrated by Fig. 4, it affects the variance. However, as Fig. 7 gives an evidence of that the mean value of temperature of the particle population depends on the particle-wall collision frequency. This figure indicates also that because of the heat transfer interrelations of different parts of the bed oscillations may arise in the transient processes which becomes smoothed as the particle-wall collision frequency decreases. At the same time, in this case the particle-wall collision frequency affects also the variance of temperature distribution of the particle population as it is shown in Fig. 8. It is seen that with increasing particle-wall collision frequency the variance de-creases. i.e. increasing particle-wall heat transfer intensity gives rise to smaller inhomogeneites in the temperature distribution of particles.
Conclusion
The spatially distributed population balance model presented in this chapter provides a tool of modeling heat transfer processes in gas-solid processing systems with interparticle and particle-wall interactions by collisions. Beside the gas-solid, gas-wall and wall-environment heat transfers the thermal effects of collisions have also been included into the model. The basic element of the model is the population density function of particle population the motion of which in the space of position and temperature variables is governed by the population balance equation. The population density function provides an important and useful characterization of the temperature distribution of particles by means of which temperature inhomogeneities and developing of possible hot spots can be predicted in particulate processes. In generalized form the model can serve for cognitive purposes but by specifying appropriate symmetry conditions useful applicative, i.e. purpose-oriented models can be obtained. The second order moment equation model, obtained from the infinite hierarchy of moment equations generated by the population balance equation, as an applicative model can be applied successfully for analyzing the thermal properties of gas-solid processing systems by simulation. The first two moments are required to formulate the heat balances of the particulate system while the higher order moments are of use for characterizing the process in more detail. Applicability of the second order moment equation model was demonstrated by modeling and studying the behavior of bubbling fluidization by numerical experiments. It has proved that collision particle-particle and particle-wall heat transfers contribute to homogenization of the temperature of particle population to a large extent. The particle-particle heat transfer no affects the mean temperature of particle population and, in fact, no influences any of temperatures of the system whilst the particle-wall heat transfer collisions exhibits significant influence not only on the steady state temperatures but on the transient processes of the system as well. It has been demonstrated that the second order moment equation model can be effectively used to analyze both the dynamical and steady state processes of bubbling fluidization
Acknowledgement
This work was supported by the Hungarian Scientific Research Fund under Grant K 77955.
Symbols
maximal value min -minimal value p -particle pg -particle-gas pp -particle-particle pw -particle-wall w -wall wb -wall-gas Over the past few decades there has been a prolific increase in research and development in area of heat transfer, heat exchangers and their associated technologies. This book is a collection of current research in the above mentioned areas and describes modelling, numerical methods, simulation and information technology with modern ideas and methods to analyse and enhance heat transfer for single and multiphase systems. The topics considered include various basic concepts of heat transfer, the fundamental modes of heat transfer (namely conduction, convection and radiation), thermophysical properties, computational methodologies, control, stabilization and optimization problems, condensation, boiling and freezing, with many real-world problems and important modern applications. The book is divided in four sections : "Inverse, Stabilization and Optimization Problems", "Numerical Methods and Calculations", "Heat Transfer in Mini/Micro Systems", "Energy Transfer and Solid Materials", and each section discusses various issues, methods and applications in accordance with the subjects. The combination of fundamental approach with many important practical applications of current interest will make this book of interest to researchers, scientists, engineers and graduate students in many disciplines, who make use of mathematical modelling, inverse problems, implementation of recently developed numerical methods in this multidisciplinary field as well as to experimental and theoretical researchers in the field of heat and mass transfer. | 7,382.2 | 2011-02-14T00:00:00.000 | [
"Engineering",
"Physics",
"Materials Science"
] |
Non-fungible token integration in neurosurgery: a technical review
In the rapidly evolving world of digital technology, few concepts have garnered as much attention and intrigue as non-fungible tokens (NFTs). Originating from the domain of art and collectibles, NFTs have transcended their initial use cases to impact various fields, from entertainment to real estate, and now, healthcare [1]. While fields like ophthalmology, dermatology, and plastic surgery have begun exploring the potential applications of NFTs, neurosurgery stands at the threshold of a new era, where digital ownership and blockchain can revolutionize patient care, data management, and surgical innovations [2–5]. Neurosurgery, with its intricate procedures and reliance on detailed imaging and data, offers a fertile ground for NFT applications [6]. Whether it’s ensuring the authenticity of patient scans, safeguarding the intellectual property of novel surgical techniques, or even creating a transparent ledger for pharmaceutical transactions, NFTs hold promise to bring about enhanced security, autonomy, and innovation to the realm of neurosurgery. As technological advancements intertwine with medical progress, the importance of integrating secure and transparent digital systems becomes paramount. In this context, this review delves into the nuances of NFTs, elucidating their potential roles and impacts within the multifaceted domain of neurosurgery. For a clearer understanding of the specialized terminology used in this paper, readers are encouraged to refer to the glossary of key terms presented in Table 1. Methodology
Introduction
In the rapidly evolving world of digital technology, few concepts have garnered as much attention and intrigue as non-fungible tokens (NFTs).Originating from the domain of art and collectibles, NFTs have transcended their initial use cases to impact various fields, from entertainment to real estate, and now, healthcare [1].While fields like ophthalmology, dermatology, and plastic surgery have begun exploring the potential applications of NFTs, neurosurgery stands at the threshold of a new era, where digital ownership and blockchain can revolutionize patient care, data management, and surgical innovations [2][3][4][5].
Neurosurgery, with its intricate procedures and reliance on detailed imaging and data, offers a fertile ground for NFT applications [6].Whether it's ensuring the authenticity of patient scans, safeguarding the intellectual property of novel surgical techniques, or even creating a transparent ledger for pharmaceutical transactions, NFTs hold promise to bring about enhanced security, autonomy, and innovation to the realm of neurosurgery.As technological advancements intertwine with medical progress, the importance of integrating secure and transparent digital systems becomes paramount.In this context, this review delves into the nuances of NFTs, elucidating their potential roles and impacts within the multifaceted domain of neurosurgery.
For a clearer understanding of the specialized terminology used in this paper, readers are encouraged to refer to the glossary of key terms presented in Table 1.
Methodology
The author (ALM) conducted a comprehensive scoping review to explore the potential applications of NFTs in neurosurgery.A systematic search was carried out using key electronic databases: PubMed, Embase, IEEE Xplore, and Google Scholar, without date restrictions.The search was executed in August 2023.To ensure a broad capture of relevant literature, the search terms utilized were "("nonfungible token*" OR NFT OR NFTs OR blockchain OR "block chain") AND (neurosurg* OR "neurological surgery")."Given the novelty of the topic, grey literature sources, including preprint repositories and conference proceedings, were also scrutinized to capture emerging research and concepts in the field.Articles not written in English, those that were merely abstracts, and case reports were excluded to maintain the specificity of the review.Articles that specifically discussed the applications, challenges, or innovations of NFTs within neurosurgical practices or related patient data management were included.Further pertinent articles were identified and sourced from the reference lists of the initial set of articles.Additionally, relevant texts from non-neurosurgical medical domains and ancillary fields were examined to gain a comprehensive understanding and to identify potential cross-disciplinary applications and insights.Data from these articles were extracted and synthesized to inform the main content of the review.
Blockchain and NFTs: a primer
At the foundation of NFT technology is the blockchain -a decentralized ledger system that supports cryptocurrencies and decentralized applications.But what exactly is a blockchain?In essence, a blockchain is a chain of blocks, where each block contains data, and each subsequent block carries a unique cryptographic signature, ensuring the integrity and immutability of the data stored within [7][8][9].
NFTs are unique tokens minted on blockchains.Unlike cryptocurrencies like Bitcoin or Ethereum, which are 207 Page 2 of 8 fungible and interchangeable, NFTs are distinct and cannot be exchanged on a one-to-one basis [10].This uniqueness is what grants NFTs their name -non-fungible.When an NFT is minted, it's associated with a specific piece of digital content, be it an artwork, music, or even medical data.The metadata of this content, akin to a certificate of authenticity, is recorded on the blockchain.This ensures proof of ownership and provides a transparent trail of any transactions or changes made to the associated content.While the most popular platform for NFTs is currently the Ethereum blockchain, various other blockchains also support NFTs, each bringing its own set of features and benefits [11].
For a detailed, sequential overview of how blockchain technology and NFT function, especially in the context of medical data management, refer to Table 2.
Potential applications of NFTs in neurosurgery
NFTs, with their unique ability to authenticate and provide undisputed digital ownership, can offer transformative solutions to longstanding issues in data management, procedure verification, and patient engagement [12,13].This section delves into specific applications where NFTs might play a pivotal role in reshaping neurosurgical practices, enhancing both patient care and operational efficiency.
Patient data ownership and control
In neurosurgery, the precision and clarity of diagnostic imaging are paramount [14].Procedures often rely on detailed MRI and The process of creating a new NFT Non-fungible token (NFT) A type of digital asset representing ownership of a unique item or piece of content, usually stored on a blockchain Proof-of-stake An alternative to proof-of-work, where participants prove ownership of a certain amount of cryptocurrency Proof-of-work A consensus algorithm used in blockchain where participants solve complex mathematical problems Public/private key A cryptographic system that uses pairs of keys: public keys (known to everyone) and private keys (kept secret) Smart contract Self-executing contracts with the terms of the agreement directly written into code Tokenize The process of converting rights to an asset into a unique digital representation on a blockchain This data is represented digitally, ready to be linked to a unique token 3. Blockchain initialization A digital ledger, known as a blockchain, is set up to record transactions.This can be on existing platforms like Ethereum or newer healthcare-specific chains 4. NFT minting A unique non-fungible token (NFT) is created ("minted") to represent the specific piece of data.This process gives the data a unique identifier on the blockchain 5. NFT ownership Once minted, the NFT is assigned to an owner, usually the patient or the institution that created the data 6.Data verification Anytime the data is accessed or transferred, the blockchain verifies the authenticity and ownership using the NFT 7. Data sharing/selling The owner can choose to share or sell the data.If sold, the NFT's ownership can be transferred to the new owner 8. Royalties and resale If the NFT is set up with royalties, any future sales or transfers ensure the original owner receives a percentage of the transaction 9. Data access control The owner can grant or revoke access permissions to the data linked to the NFT 10.NFT destruction If needed, NFTs can be "burned" or destroyed, removing their representation from the blockchain Page 3 of 8 207 CT scans.NFTs can provide a framework for patients to have indisputable ownership of their diagnostic images.This ownership isn't just about possession; it's about control.Patients can choose to share their scans with specific medical professionals, research entities, or even for broader educational purposes.This controlled sharing ensures patient privacy while still allowing for the potential aggregation of data for larger studies or machine learning applications.
Genomic data in neurological disorders
Neurological conditions often have underlying genetic components.As genomic sequencing becomes more commonplace, there's an increasing amount of data about individual genetic predispositions to certain neurological conditions or even the genetic constitution of individual tumors [15].NFTs can serve as a tool for individuals to control and share their genomic data [16].While the primary purpose might be for personalized treatment plans, there's also the potential for individuals to contribute their data to broader research initiatives, aiding in the development of treatments or understanding disease progression.
Enhancing clinical trial record security
Clinical trials are the cornerstone of medical advancements, and the integrity of their records is paramount [17].Neurosurgical trials, given their complexity, generate a vast array of data points, ranging from patient demographics to intricate procedural outcomes [18].
Ensuring the authenticity, tamper-proof nature, and traceability of this data is essential [19].NFTs can be employed to tokenize individual trial records, granting them a unique digital identity on the blockchain [20,21].This ensures that each trial entry is original and hasn't been altered post-registration.Moreover, the transparent nature of the blockchain ensures a verifiable trail of any data changes or access, instilling confidence in both participants and regulatory bodies.By leveraging NFTs, neurosurgical clinical trials can achieve enhanced data security, minimize fraudulent activities, and uphold the highest standards of scientific research.
Verification of neurosurgical procedures and achievements
The field of neurosurgery is constantly evolving, with new techniques and procedures being developed regularly [22].
NFTs can be employed to validate the introduction and successful implementation of these novel methods.Surgeons could use NFTs as a form of credentialing for specific procedures, ensuring that they've undergone the necessary training and have achieved a certain level of proficiency.Furthermore, institutions can issue NFT-based verifications for completed training programs, workshops, or milestones achieved by neurosurgeons [23].
Pharmaceutical implications
Neurosurgical treatments often involve the use of specialized medications, for example, 5-aminolevulinic acid hydrochloride (Gliolan) for tumor resections, selumetinib (Koselugo) for neurofibromatosis type 1-related inoperable plexiform neurofibroma, or bevacizumab (Avastin) for neurofibromatosis type 2-related vestibular schwannomas [24][25][26].Ensuring the authenticity of these medications is crucial.NFTs can serve as a verification tool, ensuring that the drugs used are genuine and have passed all necessary quality controls [27].By integrating NFTs into the pharmaceutical supply chain, it's possible to track the journey of a drug from the manufacturer to the patient, ensuring transparency and reducing the risk of counterfeit medications entering the system [28].
Tokenized assurance for refurbished medical devices
Refurbished medical devices present economic advantages, yet their reintroduction to the healthcare system often sparks concerns about quality and potential counterfeit risks [29,30].
An NFT-based mechanism can address these challenges by providing a transparent and verifiable record of each device's refurbishment journey.By employing NFTs, each stage of refurbishment can be documented, thereby bolstering confidence in the device's authenticity and safety [31].Such a system may not only deter fraudulent activities but can also enhance the trustworthiness of these vital medical tools within neurosurgery.
Virtual reality and augmented reality in neurosurgery
Virtual reality (VR) and augmented reality (AR) are becoming integral in surgical planning and patient education.Surgeons can use VR to simulate complex procedures, while patients can use AR to better understand their conditions and treatments [32,33].NFTs can be utilized to authenticate these VR and AR models, ensuring they are based on accurate data and have been developed by certified professionals [34].Such authentication may be vital in maintaining the trust and reliability of these digital tools.
Monetization models for NFTs in neurosurgery
NFTs, celebrated for their unique value proposition around digital ownership and authenticity, introduce a myriad of monetization possibilities in neurosurgery [35].It's crucial to clarify that the following exploration of potential monetization models is presented for consideration and discussion, and not as an endorsement.While patient care and ethical considerations remain paramount, it's beneficial to be aware of the diverse ways NFTs might be integrated into the economic fabric of neurosurgery.
Patient-driven sales of medical data
In the current era of data-driven medical research, there's an increasing interest in acquiring expansive datasets [36].
With NFTs solidifying patients' unequivocal ownership of their medical data, individuals have the unique opportunity to monetize their anonymized records for research endeavors [37,38].Such a model necessitates a comprehensive and transparent consent process, ensuring patients are fully aware of the ramifications and potential benefits of their choices [16,39].
Licensing of surgical techniques and innovations
Innovative surgical techniques or proprietary surgical tools could be tokenized and licensed to other professionals or institutions.Instead of a one-time sale, neurosurgeons or institutions could charge a recurring fee for the continued use of their innovations.
Educational content, scientific manuscripts, and virtual training modules
Neurosurgeons could create and tokenize detailed training modules, virtual surgeries, case studies, scientific manuscripts, and their supporting datasets.By doing so, they not only ensure the authenticity and originality of their work but also pave the way for a new era of digital dissemination and access.Medical students, institutions, researchers, or other professionals could purchase access to these educational and research-based NFTs, ensuring genuine content while simultaneously providing a potential revenue stream for the creators.
Premium patient services
In a more patient-centric approach, neurosurgical clinics could offer tokenized premium services.Patients holding a particular NFT could receive benefits such as priority scheduling, access to detailed digital reports, or virtual consultations.
As an example, NFTs could be harnessed to facilitate access to specialized medical services, such as outpatient rehabilitation.Consider a patient who requires a tailored rehabilitation program following a complex neurosurgical procedure.Instead of traditional referral systems or insurance approvals, healthcare providers or insurers could issue NFTs that represent a package of specific rehabilitation services.Upon acquisition, patients could redeem these NFTs at partnered outpatient centers, ensuring they receive the exact suite of services tailored to their recovery needs.Not only could this streamline the patient's access to care, but it also offers a transparent and verifiable method of ensuring service quality and authenticity.Furthermore, the decentralization aspect of blockchain, upon which NFTs are built, can empower patients by giving them direct control over their rehabilitation journey.They can choose when and where to redeem their NFT and even transfer or sell it should they decide on an alternative rehabilitation pathway.
Collaborative research grants and funding
Tokenized research projects could attract funding from institutions, pharmaceutical companies, or even public grants.By purchasing an NFT associated with a research project, funders could receive periodic updates, early access to findings, or even acknowledgment in publications.
Royalties from resales
One of the intrinsic features of NFTs is the ability to embed royalties [40].Every time an NFT is resold, the original creator can receive a percentage of the sale.This ensures that neurosurgeons or institutions continue to benefit from the increasing value of their tokenized assets.
Challenges and limitations
While the integration of NFTs into neurosurgery offers promising avenues, it's essential to approach this frontier with a balanced perspective.The implementation of any new technology, especially one as disruptive as NFTs, brings with it inherent challenges and potential limitations [7,41].From technical hurdles to ethical dilemmas, these challenges warrant careful consideration to ensure that the adoption of NFTs aligns with the overarching goals of patient safety, data security, and ethical medical practice [42].
Energy and environmental concerns
One of the primary criticisms of blockchain technology, and by extension NFTs, is the energy consumption associated with maintaining and validating the blockchain, especially for proof-of-work systems [43].This energyintensive process has raised environmental concerns, especially given the carbon footprint of large-scale mining operations.While strides are being made in transitioning to more energy-efficient consensus mechanisms, such as proof-of-stake, the environmental impact remains a significant point of contention [44].
Data security and privacy implications
Though blockchain is praised for its security and immutability, it's essential to differentiate between the security of the transaction history and the data to which an NFT might link [45].If an NFT points to data stored on a centralized server, that data remains vulnerable to breaches or hacks.Furthermore, while the blockchain can verify the authenticity of an NFT, it cannot guarantee the accuracy of the associated data [46].For neurosurgical applications, where accuracy is paramount, this is a significant consideration.
Ethical considerations
The ability to tokenize medical data, procedures, and even genomic information introduces a host of ethical questions [16].If patients can monetize their medical data, it could lead to potential exploitation, especially in populations that might be financially motivated to share sensitive information [47].Additionally, the idea of tokenizing surgical techniques or procedures could limit the free exchange of knowledge in the medical community, potentially hindering collaborative advancements.
Technical barriers and usability
While the concept of NFTs and blockchain might be familiar to tech-savvy individuals, it remains a complex topic for many.For widespread adoption in the neurosurgical community, there's a need for user-friendly interfaces and platforms that abstract away the technical complexities while retaining the benefits of the technology.Training and education will also play a pivotal role in bridging this gap.
Regulatory and legal challenges
The integration of NFTs into the medical field will undoubtedly face regulatory scrutiny [48].Governments and regulatory bodies will need to establish frameworks for the use, exchange, and sale of medical data or tokenized procedures [49].These legal challenges could slow adoption and introduce additional complexities for practitioners wishing to leverage the technology.
Future prospects and research directions
The path ahead requires targeted research to validate applications, refine methodologies, and ensure optimal integration.The upcoming segments detail specific areas of interest and potential research directions that can shape the future role of NFTs in neurosurgery.
Optimizing blockchain technology for healthcare
The traditional proof-of-work blockchain model, with its high energy consumption, might not be sustainable for widespread healthcare applications.However, emerging consensus mechanisms, such as proof-of-stake and federated blockchains, offer more energy-efficient alternatives.Research into making these models compliant with healthcare requirements can open doors for more sustainable NFT use in neurosurgery.
Developing secure platforms for medical data exchange
As the digitization of medical records and imaging continues to grow, there's a clear need for secure platforms that allow for easy exchange of this data [50].Integrating NFTs into these platforms can provide added layers of authentication and ownership.Research can focus on creating user-friendly platforms that harness the power of NFTs without overwhelming users with technical details.
Ethical and patient-centric approaches to data monetization
While monetizing medical data has its pitfalls, it also offers opportunities.Future research can focus on developing models that prioritize patient welfare, ensuring that patients understand the implications of sharing their data and are fairly compensated for it.Ethical guidelines and best practices can be formulated to guide the process.
Collaborations between tech developers and neurosurgeons
For NFTs to be effectively integrated into neurosurgery, collaborations between blockchain experts, software developers, and neurosurgeons are crucial.Such collaborations can lead to tools and platforms specifically tailored for neurosurgical applications, ensuring that the technology meets the unique needs of the field.
Regulatory frameworks and standardization
With the growing interest in NFTs in healthcare, there's a clear need for standardized protocols and regulatory frameworks [13].Research can be directed towards understanding the implications of NFTs in healthcare settings and developing guidelines that ensure patient safety, data privacy, and compliance with existing medical laws.
Exploring NFTs in medical education and training
Beyond patient data and procedures, there's potential for NFTs to play a role in medical education.Tokenized modules, virtual surgeries, or even patient cases can be used for training purposes, ensuring that students and trainees are accessing authentic and approved educational material.
Conclusion
The integration of NFTs into neurosurgery presents a blend of exciting opportunities and notable challenges.As with any technological advancement in healthcare, the primary focus remains the betterment of patient care, ensuring safety, authenticity, and precision in treatments and interventions.
NFTs have the potential to reshape the way neurosurgeons interact with patient data, authenticate procedures, and even contribute to research and education.Their capacity to offer undisputed ownership and control over digital assets can redefine data sharing, research collaborations, and even the monetization of specific medical assets.However, it's imperative to navigate the ethical, environmental, and technical challenges associated with this technology.
While the current state of NFTs in neurosurgery is nascent, the trajectory suggests a growing interest and potential for wider adoption in the coming years [42].Collaborative efforts between technologists, neurosurgeons, and regulatory bodies will be crucial in ensuring that NFTs find a place in neurosurgery that is both innovative and ethically sound.As the field continues to evolve, it remains crucial for the neurosurgical community to stay informed, engaged, and proactive in shaping the direction of NFT integration, ensuring that it aligns with the core values and goals of the profession.
Table 1
Glossary of key terms in blockchain and NFTs
Table 2
Blockchain and non-fungible tokens (NFTs) -a sequential overview.This table provides a concise step-by-step breakdown of how blockchain and NFT function, especially in the context of medical dataStep Description 1. Data creation Medical data, be it imaging, genomic information, or patient records, is generated 2. Digital tokenization | 4,795 | 2023-08-22T00:00:00.000 | [
"Medicine",
"Computer Science",
"Engineering"
] |
A bipartite signaling mechanism involved in DnaJ-mediated activation of the Escherichia coli DnaK protein.
The DnaK and DnaJ heat shock proteins function as the primary Hsp70 and Hsp40 homologues, respectively, of Escherichia coli. Intensive studies of various Hsp70 and DnaJ-like proteins over the past decade have led to the suggestion that interactions between specific pairs of these two types of proteins permit them to serve as molecular chaperones in a diverse array of protein metabolic events, including protein folding, protein trafficking, and assembly and disassembly of multisubunit protein complexes. To further our understanding of the nature of Hsp70-DnaJ interactions, we have sought to define the minimal sequence elements of DnaJ required for stimulation of the intrinsic ATPase activity of DnaK. As judged by proteolysis sensitivity, DnaJ is composed of three separate regions, a 9-kDa NH2-terminal domain, a 30-kDa COOH-terminal domain, and a protease-sensitive glycine- and phenylalanine-rich (G/F-rich) segment of 30 amino acids that serves as a flexible linker between the two domains. The stable 9-kDa proteolytic fragment was identified as the highly conserved J-region found in all DnaJ homologues. Using this structural information as a guide, we constructed, expressed, purified, and characterized several mutant DnaJ proteins that contained either NH2-terminal or COOH-terminal deletions. At variance with current models of DnaJ action, DnaJ1-75, a polypeptide containing an intact J-region, was found to be incapable of stimulating ATP hydrolysis by DnaK protein. We found, instead, that two sequence elements of DnaJ, the J-region and the G/F-rich linker segment, are each required for activation of DnaK-mediated ATP hydrolysis and for minimal DnaJ function in the initiation of bacteriophage lambda DNA replication. Further analysis indicated that maximal activation of ATP hydrolysis by DnaK requires two independent but simultaneous protein-protein interactions: (i) interaction of DnaK with the J-region of DnaJ and (ii) binding of a peptide or polypeptide to the polypeptide-binding site associated with the COOH-terminal domain of DnaK. This dual signaling process required for activation of DnaK function has mechanistic implications for those protein metabolic events, such as polypeptide translocation into the endoplasmic reticulum in eukaryotic cells, that are dependent on interactions between Hsp70-like and DnaJ-like proteins.
The DnaJ, DnaK, and GrpE proteins of Escherichia coli were first identified via genetic studies of E. coli mutants that are incapable of supporting the replication of bacteriophage DNA (1)(2)(3). Later, it was found that DnaJ, DnaK, and GrpE are all prominent bacterial heat shock proteins, which comprise a set of about 30 proteins whose expression is transiently induced when cells are grown at elevated temperatures (reviewed in Refs. 4 and 5). In recent years it has become apparent that eukaryotic cells contain families of proteins that are homologous to each DnaJ and DnaK (6,7). Intensive investigations in numerous laboratories have demonstrated that these universally conserved proteins participate in a wide variety of protein metabolic events in both normal and stressed cells, including protein folding (8,9), protein trafficking across intracellular membranes (10,11), proteolysis, protein assembly, as well as disassembly of protein aggregates and multiprotein structures (reviewed in Refs. 12 and 13). Because several of these DnaJ and DnaK family members have the capacity to modulate polypeptide folding and unfolding, they have been classified as molecular chaperones (14).
The available evidence indicates that DnaJ, DnaK, and GrpE of E. coli often cooperate as a chaperone team to carry out their physiological roles. Each of these three proteins functions in (i) regulation of the bacterial heat shock response (15,16); (ii) general intracellular proteolysis (17); (iii) folding of nascent polypeptide chains, maintaining proteins destined for secretion in a translocation-competent state, and disassembly and refolding of aggregated proteins (reviewed in Ref. 18); (iv) flagellum synthesis (19); (v) replication of coliphages and P1 and replication of the F episome (20). DnaK, the primary Hsp70 homologue of E. coli (21), is believed to play a central role in these processes. Like other members of the Hsp70 family, DnaK possesses a weak ATPase activity (22). The DnaK ATPase activity is stimulated by the DnaJ and GrpE heat shock proteins (23,24), as well as by many small peptides that are at least 6 amino acids in length (24,25). Peptide interactions with DnaK may be representative of the binding of Hsp70 proteins to unfolded or partially folded polypeptides.
In contrast to the situation for DnaK and Hsp70 proteins, relatively few investigations have focused on DnaJ or other members of the Hsp40 family. It is known from in vitro studies of DNA replication that DnaJ participates along with DnaK in the assembly and disassembly of nucleoprotein structures that form at the viral replication origin (26 -30). DnaJ may play a dual role in this and other Hsp-mediated processes. In addition to activating ATP hydrolysis by DnaK, DnaJ, by binding first to multiprotein assemblies or nascent polypeptides, may also assist DnaK by facilitating its interaction with polypeptide substrates (26, 30 -34).
Comparisons of the amino acid sequences of DnaJ family members has led to the identification of three conserved sequence domains in E. coli DnaJ (35). These sequence domains, proceeding from the amino terminus, are: 1) a highly conserved 70-amino acid region, termed the J-region, that is found in all DnaJ homologues; 2) a 30-amino acid sequence that is unusually rich in glycine and phenylalanine residues; and 3) a cysteine-rich region that contains four copies of the sequence Cys-X-X-Cys-X-Gly-X-Gly, where X generally represents a charged or polar amino acid residue. The COOH-terminal portion of E. coli DnaJ, comprising residues 210 -376, is not well conserved.
To investigate the functional roles of the conserved sequence domains of DnaJ, we constructed a series of recombinant plasmids that express truncated DnaJ proteins, each of which carries a deletion of one or more of the conserved sequence elements. Following purification, each deletion mutant protein was examined for its capacity to activate ATP hydrolysis by DnaK. Our results indicate that the J-region and the Gly/Pherich segment of DnaJ must both be present in cis in the DnaJ deletion mutant protein to achieve stimulation of DnaK-mediated ATP hydrolysis. We have, however, discovered that the J-region alone is capable of activating ATP hydrolysis by DnaK if it is supplemented in trans with small peptides that have high affinity for the polypeptide-binding site on DnaK. We discuss the relevance of these findings for protein metabolic events mediated in part by cooperative action of Hsp70 homologues and DnaJ-like proteins.
Bacteriophage and E. coli Replication Proteins-Bacteriophage and E. coli replication proteins except DnaJ and DnaK were prepared as described elsewhere (36). The purification schemes for DnaJ protein and DnaJ deletion mutant proteins are described in this article. The purification of DnaK protein has been described previously (24). DnaK protein elutes in three distinct peaks following chromatography on a Mono-Q resin. All ATPase reactions described in this report were carried out with peak I material, i.e. DnaK protein in the earliest eluting peak.
Determination of Protein Concentration-The protein concentrations of samples containing partially purified proteins were determined by the method of Bradford (38), using bovine ␥-globulin as a standard. The concentrations of purified DnaJ and DnaJ deletion mutant proteins were determined in denaturation buffer, using their individual molar extinction coefficients (⑀ M ) as determined by the method of Gill and von Hippel (39). The concentration of GrpE was determined by a modification of the method of Lowry et al. (40) using bovine serum albumin as a standard. The concentration of DnaK was determined using the calculated molar extinction coefficient of the native protein, 15,800 M Ϫ1 cm Ϫ1 (24).
Strains and Plasmids-Two E. coli strains were used for the expression of DnaJ and DnaJ deletion mutant proteins; RLM569 (C600, recA, hsdR, tonA, lac Ϫ , pro Ϫ , leu Ϫ , thr ϩ , dnaJ ϩ ) and PK102 (⌬dnaJ15), which carries a deletion of the primary portion of the dnaJ coding sequence (41). Plasmid pRLM76 was used as the expression vector for DnaJ and DnaJ deletion mutant proteins. Plasmid pRLM76 is a derivative of plasmid pHE6 (42) that is deleted for the DNA sequence that encodes the amino-terminal portion of the N gene. Plasmid pRLM76 contains a polycloning linker downstream from a phage p L promoter. Thermosensitive cI857 repressor protein, which represses transcription from p L at 30°C, is constitutively expressed from a mutant cI gene present in pRLM76. Expression of genes cloned into the polycloning linker of pRLM76 can be greatly induced by shifting the growth temperature of cells harboring the plasmid to 42°C. Incubation at this temperature results in a rapid inactivation of cI857 repressor protein and leads to an enormous increase in transcription from the strong p L promoter. Plasmid pRLM76 was constructed as follows: plasmid pHE6 DNA was digested to completion with HincII and the 375-bp fragment carrying both the p L promoter and a portion of the N gene was isolated. This fragment was further digested with HaeIII to produce two fragments of 148 and 227 bp. The 148-bp fragment carrying the p L promoter was isolated and ligated to a 3573-bp fragment isolated from pHE6 DNA that had been digested with SmaI and partially digested with HincII. This ligation mixture was transformed into RLM569 and ampicillinresistant clones that carried a 3.7-kilobase plasmid were identified. A plasmid having the p L promoter in the desired orientation (i.e. directing transcription across the polylinker sequence) was identified and named pRLM76 (3721 bp).
Plasmids carrying the wild type dnaJ gene or a dnaJ deletion mutant gene were constructed by cloning a DNA fragment produced by polymerase chain reaction (PCR)-mediated amplification of E. coli genomic DNA with the aid of synthetic oligonucleotide primers. The sequences of the forward primers used were: oligonucleotide A (5Ј-CCACCGGATCCAGGAGGTAAAAATTAATGGCTAAGCAAGATTATT-AC-3Ј), oligonucleotide B (5Ј-CCACCGGATCCAGGAGGTAAAAATTA-ATGGCTGCGTTTGAGCAAGGT-3Ј), and oligonucleotide C (5Ј-CCACCGGATCCAGGAGGTAAAAATTAATGCGTGGTCGTCAACGT-GCG-3Ј). Each forward primer contained a BamHI recognition site, a consensus ribosome binding site, and an ATG initiation codon juxtaposed to dnaJ coding sequence (underlined in the primer sequences listed above). These coding sequences correspond to dnaJ nucleotides 1-21 for primer A; dnaJ nucleotides 217-234 for oligonucleotide B; and dnaJ nucleotides 318 -333 for primer C. The sequences of the reverse primers were: oligonucleotide D (5Ј-CCACCTCTAGACTGCAGGTCGA-CATCTTAGCGGGTCAGGTCGTC-3Ј), oligonucleotide E (5Ј-CCAC-CTCTAGACTGCAGGTCGACATCTTACTCAAACGCAGCATG-3Ј), and oligonucleotide F (5Ј-CCACCTCTAGACTGCAGGTCGACATCTTAA-CGTCCGCCGCCAAA-3Ј). Each reverse primer contains a PstI recognition site, the complement of two tandem translation stop codons, and the complement of dnaJ coding sequence (underlined). The sequences complementary to dnaJ coding sequence were: oligonucleotide D, complement of dnaJ nucleotides 1131-1114; primer E, complement of dnaJ nucleotides 225-211; and oligonucleotide F, complement of dnaJ nucleotides 318 -304. PCR amplification was performed in a reaction mixture (100 l) containing 120 ng of high molecular weight E. coli DNA, 100 pmol each of one forward and one reverse primer, 50 M of each of the four dNTPs, 10 mM Tris-HCl, pH 8.3, 50 mM KCl, 1.5 mM MgCl 2 , 0.01% gelatin, and 2.5 units of Amplitaq DNA polymerase.
Plasmid pRLM232 was constructed by PCR amplification of the entire E. coli dnaJ coding sequence, using primers A and D. Following amplification, the PCR fragment was digested with BamHI and PstI, and ligated to pRLM76 DNA which had been similarly digested at the unique BamHI and PstI sites present in the polylinker carried by this vector. The DNA in the ligation mixture was transformed into E. coli strains RLM569 and PK102. Ampicillin-resistant clones were selected at 30°C and screened for their capacity to overproduce a polypeptide of the size of full-length DnaJ protein (i.e. 41 kDa) when grown at 42°C. Plasmid pRLM233 (a pRLM76 derivative that expresses DnaJ1-75) was constructed and identified as above, except that the primers A and E were used for the initial PCR amplification and that the ampicillin- 1 The abbreviations used are: DTT, dithiothreitol; bp, base pair(s); J1-75, DnaJ1-75; J1-106, DnaJ1-106; J73-376, DnaJ73-376; J106 -376, DnaJ106 -376; MES, 2-(N-morpholino)ethanesulfonic acid; PCR, polymerase chain reaction; PAGE, polyacrylamide gel electrophoresis; MALDI, matrix-assisted laser desorption/ionization; ER, endoplasmic reticulum. resistant transformants were screened for their capacity to thermally induce overproduction of a protein of 9 kDa. Plasmids pRLM234, pRLM238, and pRLM239, i.e. pRLM76 derivatives for expression of DnaJ1-106, DnaJ73-376, and DnaJ106 -376, respectively, were constructed and identified by similar procedures. Several plasmids of each type were selected for DNA sequence analysis.
DNA Sequencing-The dnaJ gene region in plasmid clones containing the dnaJ gene or dnaJ deletion mutants was sequenced on both strands using the dideoxynucleotide chain termination method with modified T7 DNA polymerase (Sequenase) as described by the manufacturer (U. S. Biochemical). Plasmids free of nucleotide substitutions were selected for further analysis.
Expression and Purification of DnaJ, DnaJ73-376, and DnaJ106 -376 -E. coli RLM569 cells carrying a pRLM76 derivative that expresses DnaJ or a DnaJ deletion mutant protein were grown aerobically in a Fernbach flask at 30°C in 700 ml of Terrific Broth to an optical density of 3.0 at 600 nm. The cultures were induced by the addition, with rapid mixing, of 300 ml of Terrific Broth that had been prewarmed to 70°C. The cultures were aerated at 42°C for 2-3 h and subsequently the cells were collected by centrifugation. The cell pellets were resuspended in 25 ml of lysis buffer, quick-frozen in liquid nitrogen, and stored at Ϫ80°C.
Frozen cell suspensions (58 ml, equivalent to about 8 g of cell paste) were thawed and cell lysis was induced by three cycles of quick-freezing in liquid nitrogen and thawing in water at 4°C. Egg white lysozyme was added to a final concentration of 0.1 mg/ml to the cell lysate, which was further incubated at 4°C for an additional 30 min. The lysate was supplemented with 20 ml of lysis buffer and particulate material was removed by centrifugation for 60 min at 40,000 rpm in a Beckman 45Ti rotor (120,000 ϫ g). All subsequent purification steps for DnaJ and each of the DnaJ deletion mutant proteins were carried out at 0 -4°C. To the supernatant (Fraction I, 70 ml), ammonium sulfate was slowly added to 40% saturation (0.226 g of ammonium sulfate/ml of supernatant), and the suspension was stirred for 30 min at 4°C. The precipitate that formed was removed by centrifugation at 30,000 ϫ g for 1 h. Solid ammonium sulfate was added to the supernatant to 55% saturation (0.089 g of ammonium sulfate/ml of supernatant), and, after stirring for 30 min, the precipitate was collected by centrifugation at 30,000 ϫ g for 1 h. The pelleted precipitate was resuspended in 50 ml of buffer B and dialyzed for 16 h against 4 liters of buffer B. The dialyzed protein (Fraction II, 130 mg, 60 ml) was applied to a Bio-Rex 70 column (5 ϫ 10 cm) that had been equilibrated with buffer B. The column was washed with 600 ml of buffer B and bound proteins were subsequently eluted with a 800-ml linear gradient of 0.15-1.0 M NaCl in buffer B at a flow rate of 3 column volumes per h. The peak DnaJ-containing fractions were pooled (150 ml; ϳ0.42 M NaCl) and concentrated to 40 ml using an Amicon stirred cell concentrator fitted with a PM-10 membrane. The concentrated protein sample (Fraction III, 50 mg, 40 ml) was dialyzed for 16 h against 4 liters of buffer C and applied to a P-11 phosphocellulose column (2.4 ϫ 11 cm), that had been equilibrated with buffer C. The column was subsequently washed with 150 ml of buffer C and bound proteins were eluted with a 250-ml linear gradient of 0.15-1.0 M NaCl in buffer C at a flow rate of 1.4 column volumes per h. DnaJ eluted at approximately 0.35 M NaCl. The primary DnaJ-containing fractions were pooled (80 ml) and concentrated to 40 ml with an Amicon concentrator as described above. This sample was dialyzed against 2 liters of buffer C to produce Fraction IV (35 mg, 40 ml). Fraction IV protein was applied to a hydroxyapatite column (2.4 ϫ 11 cm) equilibrated with buffer C. The column was washed with 150 ml of buffer C and bound protein was eluted with a 250-ml linear gradient of 120 -500 mM potassium phosphate in buffer C at a flow rate of 1.4 column volumes/h. DnaJ eluted at approximately 0.4 M potassium phosphate. Fractions containing the predominant portion of DnaJ protein were pooled (40 ml), concentrated to 10 ml in an Amicon apparatus, and dialyzed extensively against buffer A. The dialyzed sample (Fraction V, 20 mg, 10 ml) was diluted with an equal volume of buffer E, containing 2 M ammonium sulfate, to produce a conductivity equivalent to that of buffer D. This sample was applied to a Pharmacia-Biotech Butyl-Sepharose 4B column (2.4 ϫ 11 cm) equilibrated with buffer D. DnaJ was eluted with a 200-ml linear gradient of 100% buffer D to 100% buffer E, followed by 50 ml of buffer E, at a flow rate of 1 column volume/h. DnaJ eluted at approximately 95-100% buffer E. Fractions containing DnaJ at greater than 90% purity, as analyzed by SDS-PAGE, were pooled (30 ml) and concentrated to 10 ml using an Amicon apparatus (Fraction VI, 10 mg, 10 ml). This protocol routinely produces approximately 1.2 mg of DnaJ at greater than 90% purity per gram of cell paste.
The physical properties of DnaJ73-376 and DnaJ106 -376 are similar to those of wild type DnaJ. Consequently, these DnaJ deletion mutant proteins could be purified by using a slightly modified version of the purification protocol used for DnaJ. Frozen cells were resuspended in lysis buffer and lysed as described above, except that the lysis buffer also contained 0.1% octyl--D-glucopyranoside and 1 mM phenylmethylsulfonyl fluoride and that the cell lysate was mixed gently for 12 h on a shaker at 4°C. The lysate was centrifuged at 120,000 ϫ g for 1 h and the supernatant was supplemented with ammonium sulfate to 40% saturation (0.226 g/ml of supernatant). The precipitated protein was collected by centrifugation at 30,000 ϫ g for 1 h. The protein pellet was dissolved in 50 ml of buffer A and dialyzed extensively against buffer B. All of the remaining purification steps were identical to those used for purifying wild type DnaJ. The final preparations of DnaJ73-376 and DnaJ106 -376 deletion mutant proteins were estimated to be greater than 90% pure. They were quick-frozen in liquid nitrogen and were stored frozen at Ϫ80°C.
Expression and Purification of DnaJ1-75-RLM1340 (RLM569/ pRLM233) cells were grown, thermally induced, harvested, and lysed as described above for wild type DnaJ. A cell lysate from 8 g of cells was centrifuged at 120,000 ϫ g for 1 h. The supernatant (Fraction I, 70 ml) was supplemented with ammonium sulfate to 75% saturation (0.476 g of ammonium sulfate per ml of supernatant), stirred at 4°C for 30 min, and centrifuged at 30,000 ϫ g for 1 h. The supernatant was concentrated to 25 ml, using an Amicon stirred cell concentrator fitted with a YM3 membrane, and dialyzed against 2 liters of buffer F (Fraction II, 80 mg, 30 ml). Fraction II was applied to a Bio-Rex 70 column (3.4 ϫ 11 cm) equilibrated in buffer F and the column was subsequently washed with 300 ml of buffer F. Bound proteins were eluted with an 400-ml linear gradient of 0.025-0.7 M NaCl in buffer F at a flow rate of 3 column volumes per hour. DnaJ1-75 eluted at approximately 0.12 M NaCl. The fractions containing the primary portion of DnaJ1-75 polypeptide were pooled (80 ml) and concentrated to 30 ml in an Amicon apparatus (Fraction III, 30 mg). Fraction III protein was applied to a Bio-Rad hydroxyapatite column (2.4 ϫ 11 cm) that had been equilibrated with buffer C. The column was washed with 150 ml of buffer C and eluted with a 250-ml linear gradient of 0.05-0.5 M potassium phosphate at a flow rate of 1.4 column volumes per h. DnaJ1-75 eluted at approximately 0.09 M potassium phosphate. Fractions containing DnaJ1-75 at greater than 95% purity were pooled (25 ml) and concentrated to 15 ml in an Amicon apparatus. This sample (Fraction IV, 20 mg, 15 ml) was quick-frozen in liquid nitrogen and stored at Ϫ80°C.
Expression and Purification of DnaJ1-106 -RLM1341 (RLM569/ pRLM234) cells were grown, thermally induced, harvested, and lysed as described for wild type DnaJ. A cell lysate from 8 g of cells was centrifuged at 120,000 ϫ g for 1 h. The supernatant (Fraction I, 70 ml) was supplemented with ammonium sulfate to 55% saturation (0.236 g of ammonium sulfate/ml of supernatant), stirred for 30 min, and centrifuged at 30,000 ϫ g for 1 h. The supernatant, which contained the vast majority of the J1-106 polypeptide, was brought to 70% saturation with ammonium sulfate (0.093 g of ammonium sulfate/ml of supernatant) and was stirred for 30 min. The protein precipitate was collected by centrifugation at 30,000 ϫ g for 1 h. The pellet was resuspended in 50 ml of buffer F and dialyzed against 2 liters of buffer F (Fraction II, 75 mg, 60 ml). Fraction II protein was applied to a Bio-Rex 70 column (3.4 ϫ 11 cm) equilibrated with buffer E. Subsequently, the column was washed with 300 ml of buffer E and bound proteins were eluted with a 400-ml linear gradient of 0.025-1.0 M NaCl in buffer E at a flow rate of 3 column volumes/h. DnaJ1-106 eluted at approximately 0.15 M NaCl. The fractions containing the highest concentration of DnaJ1-106 were pooled (90 ml) and concentrated to 40 ml in an Amicon stirred cell concentrator fitted with a YM-3 membrane (Fraction III, 35 mg, 40 ml). Fraction III was dialyzed against 4 liters of buffer C and applied to a hydroxyapatite column (2.4 ϫ 11 cm) equilibrated with buffer C. The column subsequently was washed with 150 ml of buffer C and bound proteins were eluted with a 250-ml linear gradient of 50 -500 mM potassium phosphate in buffer C at 1.4 column volumes/h. DnaJ1-106 eluted at approximately 0.12 M potassium phosphate. Fractions containing DnaJ1-106 at greater than 95% purity were pooled (40 ml) and concentrated to 10 ml in an Amicon apparatus (Fraction IV, 25 mg, 10 ml). The preparation of DnaJ1-106 was quick-frozen and stored at Ϫ80°C.
Single Turnover ATPase Assay-ATPase reaction mixtures ( 29,659. Prior to the addition of ATP as the final component, all reaction mixtures were preincubated at 25°C for 2 min. The reaction was initiated by the addition of ATP and incubated at 25°C. At each time point 15-l portions were removed to tubes containing 2 l of 1 N HCl. This treatment lowered the pH to between 3 and 4 and quenched the ATPase reaction (control experiments indicated that little or no additional hydrolysis of ATP occurred subsequent to the addition of HCl). Portions (4 l) from each quenched reaction mixture were applied to polyethyleneimine-cellulose thin layer chromatography plates that had been prespotted with 1 l of a mixture containing ATP and ADP (each at 20 mM). The plates were developed in 1 M formic acid and 0.5 M LiCl. The migration positions of ATP and ADP were visualized by short wave UV irradiation, and the level of each in the reaction mixture was determined by scintillation counting. The kinetic data obtained from the single turnover ATPase reactions were fit to a first-order rate equation using the nonlinear regression program, "Enzfitter" (Biosoft, Cambridge, UK). K A values, for activation under single turnover conditions of the ATP hydrolysis step in the DnaK ATPase reaction cycle by DnaJ and DnaJ deletion mutant proteins, were obtained from a replot of k hyd values versus concentration of DnaJ or DnaJ deletion mutant protein. For each activator protein, at least five different activator concentrations (over a 100-fold range of concentration) were examined to generate the kinetic data used for the determination of the individual K A values.
DNA Replication Assay-The in vitro assays for DNA replication were performed essentially as described (36 DnaJ deletion mutant proteins were added to the replication assay as indicated. Following assembly of the replication reaction mixture, it was incubated for 40 min at 30°C. The amount of DNA synthesis was determined by measuring the level of [ 3 H]dTMP that had been incorporated into acid insoluble material, which was collected on a glass fiber filter (Whatman AH) and counted in a liquid scintillation counter.
Papain Digestion-Papain (50 g/ml) was activated by incubation for 15 min at 37°C in a buffer containing 50 mM MES, pH 6.5, 1 mM DTT, 5 mM cysteine-HCl, and 0.1 mM -mercaptoethanol. DnaJ protein and DnaJ deletion mutant proteins were treated with activated papain, at 1% (w/w) papain:DnaJ protein, at 30°C for varying times as indicated. Proteolytic digestion was stopped by the addition of an excess of E64, a papain inhibitor (43). Samples (30 l) were mixed with 70 l of SDS-PAGE sample buffer, boiled, and analyzed by electrophoresis in a SDSpolyacrylamide gel as described below. Undigested proteins and proteolytic polypeptide fragments were visualized by staining the gel with Coomassie Brilliant Blue R-250.
Gel Electrophoresis and Amino Acid Sequence Analysis-Protein samples were mixed with an equal volume of SDS-PAGE sample buffer, boiled for 5 min, and subjected to electrophoresis in a 10 -20% gradient SDS-polyacrylamide gel as described by Laemmli (44). Acid/Triton X-100/urea gel electrophoresis (45) was performed in 12% polyacrylamide gels. For determination of amino acid sequences, proteins were blotted from SDS-polyacrylamide gels onto Immobilon-P transfer membrane filters (polyvinylidene difluroide membrane filters, Millipore). The protein bands were visualized by staining the filter with Coomassie Brilliant Blue R-250. The protein bands of interest were excised and each polypeptide was subjected, while still bound to the filter, to NH 2terminal amino acid sequence analysis using a modified Edman protocol. Amino acid sequence analysis was performed by the Johns Hopkins University Peptide Synthesis Facility.
Matrix-assisted Laser Desorption/Ionization Mass Spectral Analysis (MALDI-MS) of Proteins and Polypeptides-MALDI-MS analysis of DnaJ was performed by the Middle Atlantic Mass Spectroscopy Facility (Johns Hopkins University School of Medicine) with a Kratos Kampact
III linear time-of-flight mass spectrometer equipped with a nitrogen laser (337 nm). Protein samples (20 g), consisting of DnaJ or papainresistant fragments of DnaJ, were prepared for mass spectral analysis using Bond-Elute disposable, solid phase extraction, C8 columns, according to the manufacturer's specifications (Varian Inc.). Briefly, 100 l (20 g) of protein sample was loaded onto a 0.5-ml Bond Elute C8 column equilibrated in buffer I (0.1% (v/v) trifluoroacetic acid in deionized H 2 O). The column was washed with 2.0 ml of buffer I and the protein sample was eluted with 500 ml of buffer I containing 95% aqueous acetonitrile. The eluted protein sample was dried in a Speed-Vac centrifugal concentrator and redissolved in 20 l of buffer I containing 20% aqueous acetonitrile. The sample, or analyte (0.3 l), was deposited on a sample site of a 20-site stainless steel slide that contained 0.3 l of a saturated solution of the matrix (3,5-dimethoxy-4hydroxycinnamic acid; 207.8 Da) in 50:50 (v/v) ethanol:water. The analyte-matrix solution was air-dried and the slide was subsequently inserted into the mass spectrometer. The spectra acquired represent the accumulation of data collected from 45 laser shots.
RESULTS
Partial Proteolysis of DnaJ-Analysis of the amino acid sequences of the DnaJ heat shock protein family indicates that there are two large regions that are conserved in multiple members of this family. The most highly conserved region is the 70 amino acid "J-region," which is found in all members of the DnaJ family. The second region, which is present in several, but not all, DnaJ homologues, contains multiple Cys-rich motifs. We wished to determine if these conserved regions represent stable structural domains of DnaJ. A nonspecific protease, such as papain, can be useful for delimiting structural domains in proteins, since its enzymatic activity is generally ineffectual on stable secondary and tertiary structures in substrate proteins. DnaJ was digested with papain and the resulting polypeptide products were subjected to analysis by SDS-PAGE. Proteolysis of DnaJ with papain produced two stable fragments of approximately M r ϭ 9,000 and 30,000 (Fig. 1, J1-376). Edman analysis of the NH 2 -terminal amino acid sequences of these fragments yielded the amino acid sequences AKQDYY for the 9-kDa fragment and GGRGRQ for the 30-kDa fragment, which correspond, respectively, to amino acids 2-7 and 104 -109 of DnaJ. This demonstrates that the 9-kDa polypeptide encompasses the highly conserved "J-region," whereas the 30-kDa fragment includes the cysteine-rich motifs of full-length DnaJ, but does not contain most of the Gly/Pherich segment of the native molecular chaperone.
To obtain a more refined estimate of the positions of the COOH termini in each papain-resistant fragment of DnaJ, we digested DnaJ with papain again, purified each polypeptide by reverse-phase chromatography, and subjected each fragment to MALDI. This analysis revealed that the small NH 2 -terminal J-region fragment was actually a series of fragments ranging in mass from approximately 8770 to about 9900 Da. Comparison to the known sequence of DnaJ indicates that the smaller protease-resistant fragments consist of polypeptides containing DnaJ amino acid residues 2-75 through 2-89. More prolonged digestion of a related polypeptide (DnaJ2-106, see below) with papain yielded polypeptides whose masses were approximately equivalent to DnaJ2-75 and DnaJ2-78. Thus, extensive papain treatment of DnaJ results in nearly complete digestion of the Gly/Phe-rich segment (amino acids 77-107). We conclude that the papain-resistant structural domain encoded by the J-region corresponds to DnaJ2-75 (Fig. 2). The removal of the NH 2 -terminal methionine of DnaJ, however, is not the result of papain action, but rather seems to be due to post-translational processing in vivo, since our sequence analysis indicated that alanine 2 is the NH 2 -terminal amino acid of purified DnaJ.
The larger COOH-terminal polypeptide produced by the repeat papain digestion of DnaJ was found by MALDI analysis to have a mass centered about 30,115 Da (data not shown). If it is assumed that the COOH terminus of DnaJ is resistant to proteolytic cleavage by papain, then this preparation of the COOH-terminal papain fragment seemingly includes DnaJ residues 99 -376 (M r ϭ 30,130). More prolonged digestion of DnaJ with papain results in a COOH-terminal polypeptide of approximately 29 kDa that has DnaJ residue 112 at its amino terminus, as revealed by NH 2 -terminal sequence analysis. We conclude that the initial papain cleavages of DnaJ occur between amino acid residues 80 and 100 and that the remainder of the Gly/Phe-rich segment of this heat shock protein is excised following more extensive papain treatment (Fig. 2).
Expression and Purification of DnaJ Deletion Mutant Proteins-Based on the identification of stable protease-resistant domains in DnaJ and on the location of sequence motifs that are conserved in multiple DnaJ homologues, we designed a series of DnaJ deletion mutant proteins to be used in structurefunction studies. These mutant proteins, depicted schematically in Fig. 3, consist of the NH 2 -terminal J-region (DnaJ1-75) and the COOH-terminal, 30-kDa papain-resistant domain (DnaJ106 -376) as well as derivatives of each that also contain the Gly/Phe-rich segment (DnaJ1-106 and DnaJ73-376, respectively).
DnaJ1-75 was designed for investigations of the functional significance of the highly conserved J-region. PCR was used to amplify the sequence for dnaJ codons 1-75. In this amplification, as well as in other amplifications of segments of the dnaJ gene by PCR, one of the primers included a "hang-off" sequence encoding a consensus E. coli ribosome binding site and an ATG initiator codon. Similarly, the second primer used in the PCR amplification included a hang-off sequence encoding the complement of two tandem stop codons. These "3Ј" primers were designed such that tandem stop codons were juxtaposed, in the proper reading frame, to the 3Ј terminus of dnaJ coding sequences in the amplified DNA. The PCR products were inserted into the polylinker site on pRLM76, an expression vector that provides thermoinducible expression of genes cloned downstream from a p L promoter present on the plasmid. The resulting plasmid, pRLM233, was transformed into strain PK102, a dnaJ deletion mutant of E. coli (41). Induction of expression of the cloned gene fragment by aeration at 42°C of cells harboring pRLM233 resulted in production of DnaJ1-75 (J1-75) to amounts greater than 10% of the total cellular protein (data not shown).
The DnaJ1-106 (J1-106) deletion mutant protein was designed for investigations of the functional significance of the Gly/Phe-rich sequence distal to the J-region. As for J1-75, a pRLM76-derivative that expresses J1-106 was constructed (pRLM234). Overexpressed J1-106 protein, like J1-75, was highly soluble and constituted approximately 10% of the total cellular protein following induction. Both J1-75 and J1-106 were purified to greater than 95% homogeneity as described under "Experimental Procedures" (Fig. 1). Although J1-75 and J1-106 have the same relative electrophoretic mobility in a 10 -20% gradient SDS-polyacrylamide gel (Fig. 1), these two polypeptides can be readily resolved by electrophoresis in a
FIG. 2. Schematic representation of the partial proteolysis of
DnaJ protein with papain. The linear map of DnaJ protein is depicted at the top. The positions of the major conserved sequence elements are indicated, including the J-region, the glycine-and phenylalanine-rich segment (G/F), and the cysteine-rich motifs (Cys-rich). Papain treatment of wild type DnaJ produces 9-and 30-kDa stable proteolytic fragments that retain the sequence elements depicted. The NH 2 -terminal amino acid sequences, in the standard one-letter code, of each papain-resistant DnaJ fragment are shown. The sizes of the proteolytic fragments were determined by laser desorption mass spectrometry (see "Experimental Procedures" and the text for details). N, amino terminus; C, carboxyl terminus. 12% acid-urea polyacrylamide gel (Fig. 4).
Two additional DnaJ deletion mutant proteins, J73-376 and J106 -376, were designed for investigations of the functional role the COOH-terminal end of DnaJ. Both polypeptides contain the cysteine-rich motifs and the COOH-terminal end of DnaJ. Although neither mutant protein contains the J-region, J73-376 does contain the Gly/Phe-rich segment that links the two papain-resistant structural domains found in wild type DnaJ (Fig. 3). DNA sequences that encode J73-376 and J106 -376, as well as appropriate translation signals, were inserted into the expression site on pRLM76 to produce plasmids pRLM238 (J73-376) and pRLM239 (J106 -376). E. coli transformants harboring these plasmids were thermally induced and the overexpressed J73-376 and J106 -376 proteins were purified to greater than 90% homogeneity as described under "Experimental Procedures." We found that both of these mutant proteins had to be purified from a dnaJ deletion mutant (PK102), since purification of J73-376 and J106 -376 from a dnaJ ϩ strain (RLM569) resulted in the copurification of a protein that we deduce is wild type DnaJ protein, based on its electrophoretic mobility in SDS-PAGE and its reactivity with polyclonal antibodies elicited against purified DnaJ protein (data not shown).
It is possible that one or more of the deletion mutant proteins fails to fold into a stable tertiary structure. Therefore, we probed the structural integrity of each DnaJ deletion mutant protein by examining its sensitivity to partial proteolysis with papain. J1-75 is apparently a compact, well-folded protein. It was not noticeably affected by papain digestion (Fig. 4). In contrast, the J1-106 polypeptide was converted by papain to a fragment that comigrates with J1-75 during electrophoresis in a 12% acid-urea polyacrylamide gel (Fig. 4). Mass spectrometry revealed that papain-mediated proteolysis of the J1-106 deletion mutant protein produced two primary fragments, one with a molecular mass of 8960 Da and the other of mass 8713 Da. These probably correspond to J-region fragments containing amino acid residues 2-78 (M r ϭ 8962) and 2-75 (M r ϭ 8720), respectively.
We obtained evidence that the J73-376 and J106 -376 deletion mutant proteins had also folded properly. Partial proteolysis of these mutant proteins with papain resulted in the production of polypeptide fragments that comigrate with the 30-kDa proteolytic fragment of wild type DnaJ during electrophoresis under denaturing conditions (Fig. 1). Further-more, both J73-376 and J106 -376, like wild type DnaJ, bind Zn 2ϩ and display extensive secondary structure as revealed by atomic absorption and circular dichroism spectroscopy, respectively. 2 Capacity of DnaJ Deletion Mutant Proteins to Stimulate the ATPase Activity of DnaK-We wished to determine if any of the DnaJ deletion mutant proteins retained any of the functional activities characteristic of wild type DnaJ, for example, its capacity to stimulate the weak intrinsic ATPase activity of DnaK (23,24). Because DnaJ stimulates the DnaK ATPase specifically at the hydrolytic step in the ATPase reaction cycle, 3 the DnaK ATPase activity is especially sensitive to the presence of DnaJ when the ATPase assay is performed using single turnover conditions (i.e. when the concentration of DnaK greatly exceeds that of ATP). Under these conditions, DnaJ strongly stimulates ATP hydrolysis by DnaK (Fig. 5). The rate constant for ATP hydrolysis by DnaK is increased at least 200-fold at saturating levels of DnaJ, from 0.04 min Ϫ1 to more than 8.5 min Ϫ1 . These data yielded an apparent K A of 0.2-0.3 M DnaJ for activation of the DnaK ATPase (Fig. 5 and Table I).
It has been suggested (48) that the highly conserved J-region, which we have shown is essentially equivalent to the NH 2 -terminal structural domain of DnaJ, interacts with DnaK. We used the single turnover ATPase assay to determine if the J-region is both necessary and sufficient for stimulation of DnaK's ATPase activity. Purified J1-75 failed, even at very high concentrations, to produce any detectable activation of the DnaK ATPase activity (Fig. 5). We next examined whether J1-106, which contains both the J-region and the Gly/Phe-rich segment, had any capacity to stimulate ATP hydrolysis by DnaK. In striking contrast to J1-75, J1-106 is capable of stimulating DnaK's intrinsic ATPase activity (Fig. 5). However, the interaction of DnaJ1-106 with DnaK is clearly deficient in ATPase assays were performed as described under "Experimental Procedures." These data were used to determine the first-order single turnover rate constant for ATP hydrolysis by DnaK at each concentration of DnaJ, DnaJ1-75, or DnaJ1-106. The single turnover rate constant for DnaK alone was determined to be approximately 0.04 min Ϫ1 . some respects. At saturation, J1-106 yielded a significantly slower ATPase rate constant than did wild type DnaJ. Moreover, the apparent K A for J1-106 was determined to be approximately 4 M (Table I). This concentration is about 20-fold higher than the concentration of wild type DnaJ required for half-maximal stimulation of DnaK's ATPase activity.
These results suggested to us that two conserved DnaJ motifs, i.e. the J-region and the Gly/Phe-rich segment, participate jointly in the activation of the ATPase activity of DnaK. However, it was still possible that the Gly/Phe-rich segment alone is responsible for DnaJ's capacity to activate the DnaK ATPase. It is known in this regard that peptide C binds to DnaK in an extended conformation (49) and that many short peptides of 7 to 9 amino acids or longer are capable of stimulating the intrinsic ATPase activity of DnaK as well as the intrinsic ATPase activities of its eukaryotic counterparts in the Hsp70 family (24,25,46,47). For DnaK, the level of stimulation depends on the peptide sequence and can be as much as 30-fold (24). 4 To examine the potential role of the Gly/Phe-rich segment in the DnaJ-mediated activation of the DnaK ATPase, we synthesized a set of five overlapping peptides, each 15 amino acids in length, that span the entirety of the Gly/Phe-rich segment and tested each for its capacity to serve as an effector of the DnaK ATPase. Under single turnover conditions, all of these peptides failed to produce a significant activation of the DnaK ATPase; no more than a 2-or 3-fold stimulation of the ATPase rate was observed, even at millimolar concentrations of peptide (data not shown). These results lend additional support to the hypothesis that the J-region and the Gly/Phe-rich segment collectively contribute to the DnaJ-mediated activation of DnaK.
Two additional DnaJ deletion mutant proteins, J73-376 and J106 -376 (Fig. 3), were studied to examine whether the cysteine-rich motifs and the COOH-terminal portion of DnaJ play any independent role in activation of DnaK's ATPase activity. Neither mutant protein contains the J-region, but J73-376 does contain the flexible Gly/Phe-rich segment in addition to the COOH-terminal structural domain of DnaJ. Our results indicate that neither J73-376 nor J106 -376 (in the range between 0.1 and 20 M) was capable of providing detectable stimulation of the intrinsic ATPase of DnaK (Table I and data not shown). The inability of J73-376 to activate the DnaK ATPase provides additional evidence that the Gly/Phe-rich segment and the J-region must be simultaneously present in the DnaJ polypeptide in order to achieve significant stimulation of ATP hydrolysis by the DnaK heat shock protein.
Activation of the DnaK ATPase by a Combination of DnaJ1-75 and Peptide-Polypeptides, such as full-length DnaJ and J1-106, that have a covalent linkage between the J-region and the Gly/Phe-rich segment, have the capacity to stimulate ATP hydrolysis by DnaK. The apparently unstructured nature of the Gly/Phe-rich segment, as judged by its sensitivity to proteolysis by papain, suggested the possibility that it interacts with the peptide-binding site on DnaK during DnaJ-mediated activation of the DnaK ATPase activity. We therefore sought to determine if a free peptide could replace the Gly/Phe-rich segment and complement the J-region for activation of DnaK. Incubation of DnaK with both J1-75 and any of the five synthetic peptides derived from the Gly/Phe-rich region produced no detectable stimulation of DnaK's ATPase activity under single turnover conditions (data not shown). However, when we used peptides, such as peptide C (24) or peptide NR (47), that are capable of stimulating DnaK's ATPase activity, a significant further increase in the rate constant for ATP hydrolysis was observed in the presence of both J1-75 and peptide (Fig. 6). At saturating levels of J1-75 and peptide, the rate of ATP hydrolysis by DnaK was roughly equivalent to the enhanced DnaK ATPase rate elicited by the presence of wild type DnaJ. The maximal rate constant obtained in the presence of peptide and J1-75 was more than 200-fold greater than that for DnaK alone under similar conditions and more than 15-fold higher than that obtained when DnaK was supplemented with just peptide C or peptide NR. The concentration of J1-75 which produced half-maximal stimulation of DnaK's ATPase activity (i.e. apparent K A ) in the presence of added peptide was determined to be ϳ 1.3 M (Table I). Under similar conditions, those DnaJ deletion mutant proteins that lack the 70-amino acid J-region, J73-376 and J106 -376, were unable to activate hy- P]ATP) as described under "Experimental Procedures." DnaJ and each DnaJ deletion mutant protein was included in the assay mixture at a range of concentrations and the single-turnover rate constant for ATP hydrolysis by DnaK was determined at each concentration. Where indicated, peptide was present in the reaction mixture at a concentration of 500 M. The apparent K A for activation of ATP hydrolysis by DnaJ and by each DnaJ deletion mutant protein was determined as described under "Experimental Procedures." A listing of "None" indicates that the specified DnaJ deletion mutant protein failed to activate ATP hydrolysis by DnaK at the highest protein concentrations tested (Ն14 M).
b The relative molar specific activities of DnaJ protein or DnaJ deletion mutant proteins in the in vitro DNA replication system are listed.
DNA replication assays were performed as described under "Experimental Procedures," except that, where indicated, a DnaJ deletion mutant protein was substituted for DnaJ. 100% activity represents 24 pmol of deoxyribonucleotide incorporation per min per pmol of DnaJ in the standard DNA replication assay. c ND, not done. Free peptide, at the level (500 M) required for maximal stimulation of ATP hydrolysis by DnaK, acts as a potent inhibitor of DNA replication in vitro (25) . FIG. 6. DnaJ1-75 and peptide cooperate to activate ATP hydrolysis by DnaK. ATPase assays were conducted under single-turnover conditions as described under "Experimental Procedures" and the legend to Fig. 5, except that each reaction mixture also contained peptide C (500 M) and either DnaJ1-75 or DnaJ73-376 as indicated. The reaction progress curves at each concentration of DnaJ1-75 or DnaJ73-376 were used to determine the first-order single turnover rate constants. The single turnover rate constant for ATP hydrolysis by DnaK in the presence of 500 M peptide C alone was determined to be approximately 0.5 min Ϫ1 . Neither DnaJ1-75 nor DnaJ73-376 alone was capable of stimulating ATP hydrolysis by DnaK under single turnover conditions ( Table I). drolysis of ATP by DnaK beyond that yielded by peptide alone (Fig. 6 and data not shown). Based on these data, we conclude that the DnaJ-mediated stimulation of ATP hydrolysis by DnaK occurs as the result of two separate interactions between these two molecular chaperones. ATP hydrolysis by DnaK apparently is maximally activated when it simultaneously interacts both with the amino-terminal domain of DnaJ and with a flexible peptide that can adopt an extended conformation. Wild type DnaJ protein provides both signals in the form of the J-region and the Gly/Phe-rich sequence, respectively.
Replicative Potential of DnaJ Deletion Mutant Proteins-We have previously demonstrated that the DnaJ and DnaK molecular chaperones are absolutely required for the initiation of phage DNA replication in a system that is reconstituted with 10 highly purified and E. coli proteins (26,36). We examined various DnaJ deletion mutant proteins for their capacity to support DNA replication in the reconstituted multiprotein system. We wished to determine if there was a direct correlation between the capacity of a particular mutant protein to stimulate ATP hydrolysis by DnaK and its ability to support the initiation of bacteriophage DNA replication. Only a few nanograms of wild type DnaJ is sufficient to support maximal DNA replication in vitro (Fig. 7). In contrast, the DnaJ1-75 deletion mutant protein was inactive in this replication assay, even at very high protein concentrations (Fig. 7). DnaJ1-106, which contains both the J-region and the Gly/Phe-rich segment, supported limited DNA synthesis in the replication assay (Fig. 7). This response was extremely weak, however, requiring on a molar basis ϳ1000-fold more J1-106 than fulllength DnaJ to attain a similar level of replication. In related studies, we found that both J73-376 and J106 -376 were inactive in the replication assay (Fig. 8). These data indicate that linkage of the J-region to the Gly/Phe-rich segment produces the minimal combination of DnaJ sequence elements that is capable of both activating ATP hydrolysis by DnaK as well as supporting initiation of DNA replication in vitro.
DISCUSSION
Our investigation of the capacities of various DnaJ deletion mutant proteins to stimulate ATP hydrolysis by the E. coli DnaK molecular chaperone has identified two regions of DnaJ that mediate this activation. The first region consists of the highly conserved J-region, located at the amino terminus of DnaJ. This 70-amino acid region, which is the signature sequence of each member of the ubiquitous DnaJ (Hsp40) family of molecular chaperones, forms a stable structural domain in DnaJ (50,51). The Gly/Phe-rich region of DnaJ, a polypeptide segment that links the NH 2 -and COOH-terminal domains of DnaJ, also plays a central role in the activation of ATP hydrolysis by DnaK. The hypersensitivity of this segment to proteolysis, as well as the preponderance of glycine residues in this region, suggest that this polypeptide linker segment is both relatively unstructured and highly flexible. This conclusion is consistent with the results of a recent NMR structure determination of an amino-terminal DnaJ fragment (DnaJ2-108) which found that the Gly/Phe-rich region was flexibly disordered in solution (50). Mutant DnaJ proteins that contain either the J-region or the linker region alone are incapable of stimulating ATP hydrolysis by DnaK (Table I). Furthermore, in preliminary experiments, we have not observed any capacity of these two regions of DnaJ to complement one another and stimulate DnaK under conditions where the required regions are located in trans on separate polypeptides (e.g. when DnaJ1-75 and DnaJ73-376 (Fig. 3) are mixed together with DnaK). A truncated DnaJ polypeptide produced detectable activation of DnaK's ATPase activity only when both the J-region and the Gly/Phe-rich linker region were present in cis on the same DnaJ deletion mutant polypeptide, as with, for example, DnaJ1-106.
We sought to localize more definitively the amino acid sequence or sequences present in the Gly/Phe-rich linker region of DnaJ that contribute to the activation of ATP hydrolysis by DnaK. However, none of a series of overlapping 15-amino acid synthetic peptides corresponding to subsections of this linker region were found to provide significant activation of DnaK, whether or not the J-region domain (i.e. DnaJ1-75) was also present in the incubation mixture. This result was interesting, especially in view of the fact that previous studies have established that random peptides with as few as 6 -9 amino acid residues are capable of stimulating the intrinsic ATPase activity of DnaK (24,46,47). We have not determined if the synthetic DnaJ linker peptides simply fail to bind stably to DnaK or if, on the other hand, they bind to DnaK but fail to provoke a necessary response, e.g. a conformational change in DnaK needed to potentiate ATP hydrolysis.
Further exploration of the factors that influence ATP hydrolysis by DnaK led us to the conclusion that DnaK must simultaneously undergo two separate interactions to acquire optimal activation. One such interaction is with the J-region of its partner chaperone, DnaJ. But, as discussed above, the presence of the J domain alone has no discernible impact on ATP hydrolysis by DnaK. Thus, our results are not in agreement with a previous study that concluded that the J-region of DnaJ is both necessary and sufficient to stimulate ATP hydrolysis by DnaK (52). We have shown that a second stimulatory interaction is required, one that involves binding of a peptide or protein substrate to the polypeptide-binding site on DnaK. Proteins, such as wild type E. coli DnaJ or DnaJ deletion mutant DnaJ1-106, that carry both the J-region and the Gly/ Phe-rich linker segment can individually furnish in cis both interactions needed for activation of the DnaK ATPase (52). We have demonstrated, however, that the requisite stimulatory interactions with DnaK can also occur in trans. A combination of the J domain (DnaJ1-75) and any short peptide that has high affinity for the polypeptide binding site of DnaK produced an activation of the DnaK ATPase comparable to wild type DnaJ alone (e.g. compare the data in Figs. 5 and 6). While peptide C alone can stimulate ATP hydrolysis by DnaK as much as 20-fold (24), addition of the J-domain to a reaction mixture containing peptide C and DnaK resulted in a 15-20fold further enhancement of the rate constant for ATP hydrolysis.
Considerable genetic and biochemical evidence has been accumulated in support of the idea that proteins of the DnaJ family functionally cooperate with specific Hsp70 proteins in all organisms to mediate protein folding, protein assembly, and disassembly events, and translocation of polypeptides across intracellular membranes. Although direct physical evidence for a stable protein-protein interaction between these two ubiquitous chaperone types has been observed only in a thermophilic bacterium thus far (53), recently it was demonstrated that the primary E. coli Hsp70 protein, DnaK, does bind to DnaJ when ATP is present (54).
Genetic suppression studies in the yeast Saccharomyces cerevisiae (55), as well as subsequent biochemical and cell biological analysis (56,57), provide additional support for the occurrence of both functional and direct physical interactions in the endoplasmic reticulum (ER) between chaperone-like proteins of the Hsp70 and Hsp40 families, i.e. between Kar2p, a DnaK and BiP homologue, and Sec63p, a member of the DnaJ family, respectively. These two proteins are thought to play a central role in the translocation of polypeptides from the yeast cytosol into the ER (10,11,58,59). While the precise molecular role of ER-localized Hsp70 proteins in protein translocation remains to be defined, it is reasonable to assume that the translocation process takes advantage of the capacity of such molecular chaperones to couple ATP hydrolysis and binding to polypeptide binding and release. Although Sec63p is an integral membrane protein associated with the polypeptide translocation complex in the ER, it does contain a 70-amino acid J-domain that faces the ER lumen (60). Interestingly, Sec63p does not contain a segment that is homologous to the Gly/Phe-rich linker polypeptide of DnaJ. Thus, it is highly probable that Sec63p itself, like the J-domain (DnaJ1-75), is capable of contributing only one of the two signals required for activation of ATP hydrolysis by the Kar2 Hsp70 protein. In reaching this conclusion, we make the presumption that the dual signal requirement for maximal ATP hydrolysis we identified for DnaK has been conserved during evolution in all primary Hsp70 family members.
If Sec63p indeed only contributes a J-domain to the activation process for Kar2p, then what is the source of the second signal? Since the missing signal involves interaction of a peptide or polypeptide with the polypeptide-binding site on the COOH-terminal domain of Kar2p (BiP), we suggest that it is the translocating polypeptide itself that supplies the other required signal for activation of the Kar2p ATPase. This proposal is consistent with the polypeptide binding specificity of the Hsp70 COOH-terminal domain as well as the presumed structure of translocating polypeptide chains. The available evidence indicates that Hsp70 proteins prefer to bind to extended polypeptide chains containing substantial hydrophobic character (25,47,49,(61)(62)(63). Accordingly, translocating polypeptides associated with Sec63p and the ER translocation apparatus would be expected to be in an unfolded or partially folded state as they emerge from the lipid bilayer of the ER. In our proposal, simultaneous interaction of a molecule of the ATP-bound form of BiP (Kar2p) with both the translocating polypeptide and Sec63p would activate ATP hydrolysis by BiP. Recent findings suggest that such ATP hydrolysis by BiP would effectively lock the polypeptide substrate onto a BiP⅐ADP enzyme complex (34,64). Moreover, this stable Hsp70-polypeptide interaction, mediated in part by the J-domain of Sec63p, may render polypeptide translocation into the ER irreversible, a role that has also been suggested for Hsp70-polypeptide interactions that occur during protein translocation into mitochondria (65,66). The translocating polypeptide, presumably still in an unfolded or partially folded conformation, would be anticipated to remain firmly bound to BiP until the ADP present on the enzyme is exchanged for ATP (64). 4 Since no GrpE homologue in the ER lumen has yet been identified, this nucleotide exchange step may be slow (23,64).
A number of instances have been described where it is DnaJ, rather than DnaK, that first binds to a protein substrate of this chaperone system. This situation was initially found for binding of DnaJ to a nucleoprotein preinitiation complex formed at the bacteriophage replication origin (26,27,30) and for binding of DnaJ to the P1 phage-encoded RepA replication initiator protein (67). DnaJ also has high affinity for the E. coli 32 heat shock transcription factor (31,68) and there is experimental support for the idea that DnaJ may bind to nascent polypeptides as an early step in protein folding in vivo (33,69,70). In each of these cases, it seems likely that DnaJ may play roles both in recruiting one or more molecules of DnaK to the locale of the protein substrate and in subsequently facilitating the action of DnaK on the substrate.
While our data and that of others (52,59,71) provide clear biochemical evidence that the J-domain is critical to the process of Hsp70 recruitment and activation, the potential involvement of the Gly/Phe-rich segment of DnaJ in Hsp70 recruitment, suggested by the findings in this report, draws attention to a possible mechanistic problem. For example, we have concluded here that the Gly/Phe-rich segment of DnaJ provides one of the signals for DnaK activation by binding to the polypeptide binding site of DnaK. Thus, DnaK recruited to close spatial proximity of a protein substrate via interactions with DnaJ apparently would first have to release the Gly/Pherich segment of DnaJ before it could bind to its protein substrate. Our biochemical studies are consistent with this pathway for DnaK action. The inability of any of the synthetic peptides derived from the DnaJ Gly/Phe-rich region to stimulate ATP hydrolysis by DnaK to a significant extent suggests that the interaction of the DnaJ Gly/Phe-rich region with DnaK must be both weak and transient. Perhaps the interaction between DnaK and the J-domain present in the DnaJ-substrate complex is sufficiently strong to keep DnaK from disso-ciating completely from DnaJ until DnaK has had the opportunity to bind to the protein substrate. Moreover, a prediction of this model is that binding of DnaK to the protein substrate would be expedited by the high effective concentration of the substrate which would arise because both DnaK and the substrate are tethered to the same molecule of DnaJ. If it is presumed that DnaK is bound to the NH 2 -terminal J-domain and that the protein substrate interacts with the COOH-terminal domain of DnaJ, it is possible that the conformational flexibility of the Gly/Phe-rich segment linking these two structural domains of DnaJ is an important factor contributing to the optimization of DnaK-substrate interactions. Wall et al. (68) have recently suggested a similar model, as well as other possible scenarios, to explain the properties of a DnaJ deletion mutant protein that is missing the Gly/Phe-rich linker region.
We have provided evidence that a fragment of DnaJ consisting of the amino-terminal 105 amino acid residues is capable of activating ATP hydrolysis by DnaK. Nevertheless, our results indicate that the COOH-terminal domain of wild type DnaJ must play some role in the activation process. For example, the maximal rate constant of ATP hydrolysis by DnaK elicited by DnaJ1-106 saturates at a level more than 5-fold lower than that produced by wild type DnaJ (Fig. 5). Furthermore, the K A for DnaJ1-106 in this process (4 M) is approximately 20-fold higher than the K A for wild type DnaJ (Table I and Fig. 5). This suggests that DnaK interacts more effectively with the fulllength DnaJ polypeptide than with DnaJ1-106. However, The beneficial effect of the COOH-terminal domain of DnaJ on the interaction with DnaK may be indirect. It is conceivable that the COOH-terminal domain of DnaJ simply serves to lock or position the two required elements, i.e. the J-domain and the Gly/Phe-rich region, in a configuration that is optimal for interaction with DnaK. On the other hand, we have not rigorously excluded the possibility that the COOH-terminal domain of DnaJ directly enhances interactions with DnaK by providing other sequence elements that bind to the polypeptide binding site on DnaK. Our findings suggest, however, that if such elements exist, they must have relatively low affinity for the DnaK polypeptide-binding site. This conclusion is based on our finding that the DnaJ106 -376 and DnaJ73-376 deletion mutant proteins, which each contain an intact COOH-terminal domain, fail to complement DnaJ1-75 for activation of ATP hydrolysis by DnaK.
It is interesting that the DnaJ1-106 mutant protein can support initiation of DNA replication in vitro, albeit at a much reduced level (52) (Fig. 7). The apparent specific activity of DnaJ1-106 in this process is approximately 1000-fold lower than that of wild type DnaJ (Table I). None of the other deletion mutant proteins described here could support detectable levels of DNA replication at the highest concentrations tested (Table I). Thus, the minimal sequence elements of DnaJ required for initiation of DNA replication include both the J-domain and the Gly/Phe-rich linker segment, which apparently must both be present in cis. Since these are the same two DnaJ sequence elements required for activation of ATP hydrolysis by DnaK, it is conceivable that DnaJ1-106 aids DNA replication simply by converting DnaK into a more active ATPase. Activated DnaK, presumably composed of a complex of DnaJ1-106 and DnaK, may have an improved capacity, because of its heightened ATPase activity, to bind directly to nucleoprotein preinitiation structures formed at ori. This nonspecific route would, in effect, by-pass the normal initiation pathway whereby DnaK is apparently recruited to bind at specific sites, i.e. at those locations where wild type DnaJ is already bound to the preinitiation complex assembled at the replication origin (26,27,30). The greatly lowered specific activity of DnaJ1-106 in initiation of DNA replication may well reflect the inability of this truncated DnaJ mutant to bind specifically to preinitiation nucleoprotein structures; as a consequence, DnaJ1-106, unlike wild type DnaJ, is not capable of directing DnaK to act at precise sites on specific substrate molecules. A recent characterization of the properties of a similarly truncated DnaJ polypeptide lends additional support to this interpretation (71). An amino-terminal fragment of DnaJ, DnaJ12 (equivalent to DnaJ2-108), was found to be capable of activating DnaK to bind to one of its physiological substrates, the 32 heat shock transcription factor. Furthermore, in contrast to the behavior of wild type DnaJ, the DnaJ12 mutant protein was reported to be capable of activating DnaK to bind the 32 polypeptide in the absence of any prior interaction of the DnaJ12 protein itself with this heat shock factor.
There are two discrepancies between the findings reported here and previously published data that merit further discussion. First, in contradiction to this report, it was previously concluded that the DnaJ J-domain alone was both necessary and sufficient to activate ATP hydrolysis by DnaK (52). This inference was based on the properties of a DnaJ deletion mutant protein, DnaJ12, composed of the first 108 amino acids of DnaJ. The properties of the DnaJ12 mutant protein appear to be nearly identical to those of the DnaJ1-106 protein described here. It is evident that the mutant DnaJ protein used to reach the earlier conclusion in fact contained not only the J-domain, but also the essential Gly/Phe-rich region as well. Second, Wall et al. (68) have recently described a DnaJ deletion mutant protein, DnaJ⌬77-107, that is missing 31 amino acids covering the entire Gly/Phe-rich region. These authors demonstrated that this mutant protein, nevertheless, is still capable of activating the ATPase activity of DnaK. One possible explanation for the difference between our findings is that, as alluded to earlier, DnaJ may contain multiple sequence elements capable of interacting with the polypeptide-binding site on DnaK. In addition to the element reported here in the Gly/Phe-rich segment, other potential DnaK interaction sites could reside in the COOH-terminal structural domain of DnaJ. A second possibility is that the random six amino acid linker, HMGSHM, that replaced the Gly/Phe-rich segment as a consequence of the construction of the DnaJ⌬77-107 deletion mutant protein (52), can itself serve as a polypeptide binding element for DnaK. Perhaps almost any unstructured and flexible polypeptide chain of sufficient length (i.e. greater than 5 amino acids (24)) covalently linked to the J-domain will support productive interactions between DnaJ and DnaK. | 14,464.8 | 1996-05-10T00:00:00.000 | [
"Biology",
"Chemistry"
] |
Novel Mechanism for FcϵRI-mediated Signal Transducer and Activator of Transcription 5 (STAT5) Tyrosine Phosphorylation and the Selective Influence of STAT5B over Mast Cell Cytokine Production*
Background: STAT5 is a transcription factor that is vital for mast cell function. Results: Loss of Fyn kinase prevents, whereas loss of Lyn, Gab2, or SHP-1 enhances, FcϵRI-mediated STAT5 tyrosine phosphorylation. Conclusion: IgE-mediated STAT5 activation in mast cells requires Fyn kinase. Significance: Elucidating the mechanisms of mast cell activity is essential to understanding and treating allergic pathologies. Previous studies indicate that STAT5 expression is required for mast cell development, survival, and IgE-mediated function. STAT5 tyrosine phosphorylation is swiftly and transiently induced by activation of the high affinity IgE receptor, FcϵRI. However, the mechanism for this mode of activation remains unknown. In this study we observed that STAT5 co-localizes with FcϵRI in antigen-stimulated mast cells. This localization was supported by cholesterol depletion of membranes, which ablated STAT5 tyrosine phosphorylation. Through the use of various pharmacological inhibitors and murine knock-out models, we found that IgE-mediated STAT5 activation is dependent upon Fyn kinase, independent of Syk, PI3K, Akt, Bruton's tyrosine kinase, and JAK2, and enhanced in the context of Lyn kinase deficiency. STAT5 immunoprecipitation revealed that unphosphorylated protein preassociates with Fyn and that this association diminishes significantly during mast cell activation. SHP-1 tyrosine phosphatase deficiency modestly enhanced STAT5 phosphorylation. This effect was more apparent in the absence of Gab2, a scaffolding protein that docks with multiple negative regulators, including SHP-1, SHP-2, and Lyn. Targeting of STAT5A or B with specific siRNA pools revealed that IgE-mediated mast cell cytokine production is selectively dependent upon the STAT5B isoform. Altogether, these data implicate Fyn as the major positive mediator of STAT5 after FcϵRI engagement and demonstrate importantly distinct roles for STAT5A and STAT5B in mast cell function.
tory progression. For example, STAT6 is required for T H 2 responses mediated by IL-4 and IL-13 (for review, see Refs. 2,4). In mast cells, the stem cell factor (SCF) receptor, c-Kit/CD117, activates STATs1, 3, 5, and 6 (2, 5-7), partly through the tyrosine kinase JAK2. Among these, our laboratory previously discovered that STAT5 is requisite for mast cell development and survival (8). We subsequently found that like c-Kit, Fc⑀RI rapidly induces STAT5 tyrosine phosphorylation and that STAT5 deficiency greatly reduces early and late mast cell responses (9). The critical role for STAT5 in IgE-mediated inflammation, the most common cause of allergic disease, justifies mechanistic studies. Understanding STAT5 activation could offer novel insight into patient care.
Fc⑀RI signaling has been studied in detail and is generally accepted as proceeding via two major pathways. Rapid activation of the Lyn tyrosine kinase allows binding of the kinase Syk, which subsequently activates adapter molecules such as linker of activated T cells (LAT) and SLP-76. A more recently described and somewhat distinct pathway descends from Fyn kinase through the adapters LAT2 and Grb-2 associated binder 2 (Gab2), with robust activation of PI3K. Although distinctions exist (10,11), these pathways are interrelated. Lyn kinase, through its recruitment of negative regulators, suppresses Fyn function (12,13). Herein, we explored the hypothesis that STAT5 associates in close proximity with Fc⑀RI and is rapidly activated by one of the two major pathways stemming from Fc⑀RI. We scrutinized STAT5 tyrosine phosphorylation by perturbing factors such as Fyn and Lyn kinases via biochemical and gene/RNA ablation techniques. Finally, total STAT5 actually accounts for two very similar, but separable, proteins: STAT5A and STAT5B (14). Our prior work demonstrated that the introduction of STAT5A into total STAT5A/B-deficient (KO) mast cells rescues survival, but the importance of STAT5B remained as yet unknown (8,9). Here, we tested the idea that the two STAT5 proteins play distinct roles in mast cell activation.
In sum, we have found that STAT5 is indeed in close association with Fc⑀RI. Moreover, IgE-mediated STAT5 activation proceeds selectively through the Fyn pathway, with negative feedback from Lyn kinase. Gab2 appears to suppress STAT5 tyrosine phosphorylation, which may be due to SH2 domaincontaining phosphatase-1 (SHP-1) recruitment. Finally, studies utilizing STAT5A-or B-specific RNA interference demonstrated that STAT5B is the central and selective mediator of cytokine induction, independent of STAT5A. These studies offer a novel mechanistic view of a central pathway controlling allergic disease.
Mice-All procedures involving mice were reviewed and approved by the local institutional animal care and use committee. Six-to 8-week-old wild-type C57BL/6 mice were obtained from the Jackson Laboratory (Bar Harbor, ME). Bruton's tyrosine kinase (BTK) KO and Gab2 KO (kind gifts of Toshio Hirano, Osaka University) (15) were bred on the C57BL/6 background (16).
Cell Isolation and Culture-Bone marrow-derived mast cells (BMMCs) were differentiated from tibia and femur bone marrow of respective mice. The marrow cultures were maintained in RPMI 1640 medium supplemented with IL-3-containing supernatant from WEHI-3 cells and SCF-containing supernatant from BHK-MKL cells. The final concentration of IL-3 and SCF was adjusted to 1 ng/ml and 10 ng/ml, respectively, as measured by ELISA. Medium also contained 100 units/ml penicillin, 100 g/ml streptomycin, 2 mM L-glutamine, 1 mM HEPES, 10% (v/v) FBS, and 1 mM sodium pyruvate. Cultures contained purified BMMCs by 4 weeks, and experiments using KO strains were compared with age-matched wild-type controls. Cultures were maintained and sensitized to DNP-specific IgE (0.5 g/ml for 18 h) in this growth medium. For immunoblotting experiments involving Tyr(P)-STAT5, BMMCs were rinsed free of growth medium and starved for 4 h in complete RPMI 1640 medium (medium without supplementary cytokines). If a chemical treatment (inhibitors or MCD) was involved, the noted agent was added to the cultures after 3 h of cytokine starvation. After the total 4 h, cultures were stimulated with 100 ng/ml DNP-HSA for 15 min and then immediately lysed.
Preparation of Membrane Sheets and Transmission Electron Microscopy-Detailed methods for preparation and immunogold labeling of membrane sheets have been described (17)(18)(19). In brief, BMMCs were primed overnight with DNP-specific IgE, rinsed by centrifugation, and allowed to settle on glass coverslips for 15 min prior to 5-min incubation at 37°C Ϯ DNPcolloidal gold. For the resting condition, cells were primed with DNP-specific IgE conjugated to quantum dots at a 1:1 ratio as in Ref. 19. Reactions were stopped by addition of 0.5% paraformaldehyde. After 10 min, samples were rinsed with PBS, lowered onto poly-L-lysine coated electron microscope grids, and ripped. Membrane sheets on grids were then fixed with 2% paraformaldehyde, rinsed, and sequentially stained with primary and secondary antibodies as in Ref. 18. At least two experiments were performed for each condition, for which at least 10 images were acquired on a Hitachi H-7500 transmission electron microscope.
Immunoblotting-Cell cultures were lysed at 1 ϫ 10 5 cells/l in lysis buffer (Cell Signaling Technology) supplemented with 1.5ϫ ProteaseArrest (G-Biosciences, Maryland Heights, MO). Protein concentrations were determined with the Pierce BCA protein assay kit (Thermo Scientific). Proteins were resolved by SDS-PAGE using 50 g of total protein per sample on 8 -16% Tris-glycine gels (Novex system; Invitrogen). Transfer was made onto nitrocellulose membranes subsequently blocked for 1 h at room temperature in Tris-buffered saline containing 0.05% (v/v) Tween 20 (TBST) and 5% (w/v) nonfat dried milk. Membranes were rinsed in TBST six times for 5 min and then incubated overnight at 4°C in TBST containing 5% (w/v) BSA and primary antibody diluted 1:1000. Membranes were rinsed again (same protocol) then incubated at room temperature for 1 h in TBST and 5% milk containing peroxidase-conjugated secondary antibody diluted 1:2500, either goat anti-rabbit or mouse IgG (Jackson ImmunoResearch, West Grove, PA). Membranes went through a final rinsing protocol before exposure to Amersham Biosciences ECL Reagents (GE Healthcare) and film development.
Immunoprecipitation-Cell lysates were collected using radioimmuneprecipitation assay buffer (Cell Signaling Technology). Protein A/G beads (Invitrogen) were washed three times with PBS and centrifugation, and then lysates were precleared for 2 h at 4°C with a 50% slurry of beads added one part for every two parts lysate. The beads were removed by centrifugation, and lysates were incubated for 18 h at 4°C with monoclonal rabbit anti-STAT5, or anti-Fyn (Santa Cruz Biotechnology). Freshly rinsed beads were added to the lysate antibody mixture and incubated for 2 h at 4°C, then centrifuged and rinsed three more times with PBS. The final bead pellets were suspended in Tris-glycine sample buffer (Invitrogen) and boiled for 10 min to dissociate the precipitate from the beads. Samples were centrifuged and supernatants immunoblotted.
ELISA-BMMCs were sensitized to DNP-specific IgE (0.5 g/ml, at least 18 h) then antigen-stimulated with DNP-HSA (50 ng/ml) for 18 h. Murine IL-13, MIP-1␣, and TNF-␣ ELISA kits were purchased from PeproTech and performed using culture supernatants according to the manufacturer's protocols. ELISAs were developed using BD OptEIA reagents from BD Biosciences.
Nucleofection of siRNAs-Wild-type C57BL/6 BMMCs were transfected with a Dharmacon ON-TARGET SMARTpool of STAT5A, STAT5B, Lyn, Fyn, or TRPC1 siRNAs or nontargeting negative controls (Thermo Scientific). The Amaxa Nucleofector II and Cell Line Nucleofector kit V (Lonza Cologne GmbH, Cologne, Germany) were utilized according to the manufacturer's protocol for human monocytes. For the initial nucleofection step, a concentration of 2 M siRNA and three million cells were used. The cultures were allowed to rest for 72 h in growth medium added to dilute the siRNAs to a final concentration of 57 nM. Efficiency of knockdown was determined through immunoblot analyses.
Image and Statistical Analyses-Immunoblot films were digitized and analyzed using ImageJ (National Institutes of Health). Optical density (OD) of the immunoreactivity of inter-est, e.g. Tyr(P)-STAT5, STAT5A/B, was normalized to total STAT5 or -actin OD. Normalized values were compared among at least three independent experiments and are presented graphically as mean values Ϯ S.D. Differences between pairs were deemed significant for p Ͻ 0.05 with Student's t tests. For electron micrographs, gold particle and quantum dot (QD) distributions were grabbed using a plug-in to ImageJ; the Ripley's bivariate K function was then applied as a statistical test for co-clustering (20,21).
RESULTS
STAT5 Co-localization with Fc⑀RI-Although our past studies established STAT5 as an indispensible component of high affinity IgE receptor signaling, there was additional curiosity with regard to the proximity of the transcription factor with the receptor. To investigate this, membrane sheets were prepared from resting and antigen-stimulated BMMCs followed by immunogold labeling for STAT5 and imaging by transmission electron microscopy. To visualize Fc⑀RI under resting conditions, BMMCs were primed with QD-conjugated IgE (QD-IgE) as in Ref. 19. The electron-dense QDs appear as small rods (some of which are circled) in the transmission electron microscopy image in Fig. 1A, where their distribution is clearly segregated from the 5-nm gold label for STAT5. The lack of co-localization for Fc⑀RI and STAT5 in resting cells was confirmed by the Ripley's bivariate K because the data line in the plot falls between the two dashed lines, indicating the tolerance interval. To track activated Fc⑀RI, BMMCs were primed with DNP-specific IgE and then stimulated with colloidal gold particles coated with antigen (DNP-gold; Fig. 1B). In this case, STAT5 (marked with 5-nm immunogold) was shown to be recruited to signaling patches that form after Fc⑀RI aggregation. The co-clustering of STAT5 and Fc⑀RI in antigen-stimulated BMMCs is statistically significant, as confirmed by Ripley's bivariate K analysis, i.e. the data line is above the confidence interval.
Disruption of Lipid Rafts Diminishes IgE-mediated STAT5 Activity-Having observed that STAT5 and Fc⑀RI co-localize in activated mast cells (Fig. 1, A and B), we explored the dependence of STAT5 with respect to membrane subdomain stability. Field et al. established that Fc⑀RI aggregates into lipid rafts upon ligation with antigen-IgE complexes (22). The cholesterol-depleting, chelating agent MCD has been utilized for the disruption of lipid rafts (23,24). BMMCs were sensitized to IgE (0.5 g/ml) for 18 h in their normal growth medium. After sensitization, the cultures were washed twice and starved for 4 h in complete RPMI 1640 medium to extricate the effects of IL-3 because this cytokine potently stimulates STAT5 phosphorylation independent of Fc⑀RI ligation (25). MCD was added to the cultures (final concentration of 10 mM) 1 h prior to antigen stimulation, which was performed for 15 min at 37°C with DNP-HSA (100 ng/ml). Immunoblotting for Tyr(P) STAT5 and total STAT5 protein was performed to assess the effect of raft depletion on signaling. Detection of Tyr(P)-STAT5 is expected to increase in antigen-stimulated (cross-linked) samples, indicative of an intact Fc⑀RI signaling apparatus. Indeed, this is the case for control-stimulated BMMCs (Fig. 1C, left two lanes). However, MCD treatment reduced Tyr(P)-STAT5 lev- els to approximately those of unstimulated cells (Fig. 1, C and D). Although MCD is a broad acting cholesterol depletion agent, these experiments support the EM observations that STAT5 associates with Fc⑀RI.
Src Kinase Family Members Are Likely Intermediaries of Fc⑀RI-induced Tyr(P)-STAT5-Next, we dissected the machinery responsible for IgE-mediated STAT5 activation by employing chemical inhibitors or gene-deficient BMMC cultures. Following 3 h starvation from WEHI/BHK-containing medium, BMMC cultures were pretreated with each of the listed inhibitors for 1 h at 37°C before cross-linking. BTK KO BMMCs or treatment of WT with the substrate-competitive BTK inhibitor LFM-A13 (at 100 M) showed no significant decrease in Tyr(P)-STAT5 detection ( Fig. 2A; KO not shown). Targeting of Syk with ATP-competitive Syk inhibitor II (SykiII, 2 M) also had no effect on STAT5 activation (Fig. 2B). Additionally, targeting of Akt either upstream at PI3K with LY294002 (20 M) or directly with Akt inhibitor VIII (1 M) did not prevent STAT5 tyrosine phosphorylation following receptor cross-linking (Fig. 2, C and D). Similarly, inhibiting the common STAT5-activating kinase JAK2, through use of AG490 (50 M), had no effect on IgE-mediated STAT5 activation (Fig. 2E). In contrast, the ATP-competitive inhibitor of the Src family of protein kinases, PP2 (10 M), ablated Tyr(P)-STAT5 to an undetectable level in our immunoblots (Fig. 2F). Immunodetection of the respective phosphorylated targets of each inhibitor was performed to determine inhibitor efficacy, showing either lack of target activation or complete ablation of the signal. A statistical summary of inhibitor effects showed that of those examined here, only inhibition of Src family kinases significantly reduced Tyr(P)-STAT5 (Fig. 2G).
Fyn Is Required for Fc⑀RI-mediated STAT5 Activity-Having narrowed our search for the link between STAT5 and Fc⑀RI to Src kinases, we assessed the importance of Lyn and Fyn because these Src family members are essential for the homeostasis of Fc⑀RI signals. First, we addressed Fyn by growing Fyn KO BMMC. Fyn KO BMMCs cross-linked for 5, 15, and 30 min revealed a profound and sustained abrogation of STAT5 tyrosine phosphorylation, indicating Fyn as the essential STAT5 regulator (Fig. 3A). It should be noted that total STAT5 resolves at a lower molecular mass in Fyn KO samples versus respective WT, an interesting phenotype, possibly due to impaired phosphorylation and other post-translational modifications, that requires further investigation.
Lyn deletion has been shown to enhance IgE signaling, partly due to Fyn hyperactivity. These effects have been most overt on the 129/Sv genetic background, which appears to have increased Fyn expression (12). To address the importance of Lyn and Fyn under controlled conditions, we depleted each kinase through siRNA transfection, using C57BL/6 BMMCs. We obtained nearly complete depletion of each kinase. This approach confirmed the importance of Fyn, as Fyn depletion reduced STAT5 tyrosine phosphorylation. Interestingly, Lyn depletion enhanced IgE-induced Tyr(P)-STAT5 nearly 3-fold of control levels (Fig. 3B), fitting the description of Lyn as a negative regulator of Fyn function.
Because active Fyn is likely recruited to Fc⑀RI complexes (26), and considering our previous observations that STAT5 and Fc⑀RI aggregate during antigen stimulation (Fig. 1, A and B), we assessed STAT5 association with Fyn. Wild-type BMMCs were untreated or IgE-sensitized and then exposed to antigen for 5 or 15 min, after which total STAT5 was immunoprecipitated. Immunoblotting for Fyn demonstrated that the two proteins co-precipitated and that this association appeared to decrease following receptor ligation. When normalized to STAT5 levels, Fyn association significantly decreased in activated versus unstimulated cells according to STAT5-specific immunoprecipitates (Fig. 3, C and D). The reverse co-immunoprecipitation was also attempted wherein Fyn was the pulldown target. There was a striking reduction in the ability to immunoprecipitate Fyn in cross-linked cultures regardless of time (Fig. 3E). However, total Fyn is readily detected in whole cell lysates using a harsher, standard immunoblotting lysis buffer, e.g. Fig. 3B. We posit that the lack of Fyn pulldown in activated cells is due to epitope masking through protein associations that are intentionally preserved by the lysis conditions of immunoprecipitation.
Lack of Transient Receptor Potential Channel 1 (TRPC1) Does Not Prevent Early IgE-mediated Tyr(P)-STAT5-Recently, Suzuki et al.
have demonstrated that the nonselective cation channel TRPC1 is involved in Ca 2ϩ influx, F-actin depolymerization, and subsequent degranulation in activated mast cells (27). Considering that STAT5 tyrosine phosphorylation is an early response to Fc⑀RI engagement, it was important to determine whether TRPC1 has any input in this activation. BMMCs were transfected with pools of siRNA against TRPC1, and efficiency was assessed 3 days after treatment (Fig. 4A). These cells were sensitized to DNP-specific IgE for 18 h and then assayed for Tyr(P)-STAT5 after 15 min of receptor crosslinking. No significant difference in Tyr(P)-STAT5 level was observed between cells with TRPC1 knockdown and those that received nontargeting siRNA (Fig. 4B). Furthermore, siTRPC1 BMMCs were subjected to a longer term (18 h) exposure of DNP (50 ng/ml), and no difference in secreted cytokines was seen (Fig. 4C).
SHP-1 and Gab2 Deficiencies Enhance Tyr(P)-STAT5
Detection-Fyn is linked to PI3K signaling through an intermediate, Gab2 (26). To map STAT5 regulation downstream of Fyn, we examined Fc⑀RI-mediated STAT5 phosphorylation in Gab2 KO BMMCs. Unlike Fyn deletion, loss of Gab2 increased STAT5 activation 2.2-fold (5Ј cross-linked) and 4.4-fold (15Ј cross-linked) respective of WT controls (Fig. 5, A and B). In addition to its link to PI3K, Gab2 is known to associate with negative regulatory proteins, including Lyn and SHPs. Having already observed enhanced Tyr(P)-STAT5 in Lyn-depleted BMMCs, we addressed the role of SHP-1. SHP-1-deficient BMMCs cultured from Motheaten mice (me) (28) demonstrated modestly enhanced STAT5 tyrosine phosphorylation upon activation compared with their respective controls. This increased over time during Fc⑀RI cross-linking, peaking at 5 min and still significantly greater at 15 min after ligation (Fig. 5, C and D). Together, these data indicate that IgE-mediated STAT5 activity is under exquisite control of not just Fyn kinase but also negative regulators such as SHPs and other proteins associated with Gab2.
Distinct Roles for STAT5A and STAT5B-Our earlier work found that STAT5 deficiency had some inhibitory impact on IgE-mediated degranulation, but the most significant effects were in cytokine secretion (9). Because we and others have found that STAT5A is critical for mast cell survival (8,29), we sought to determine whether STAT5A and B have distinct roles in IgE-mediated cytokine induction. We assessed this through the use of siRNA pools targeting STAT5A or STAT5B. After IgE sensitization and antigen exposure, conditioned media were assayed by ELISA for MIP-1␣ and IL-13 production. There were no remarkable differences between nontargeting siRNA-treated BMMCs and siSTAT5A with respect to MIP-1␣ and IL-13 production (Fig. 6A, middle and right panels). This is in the context of an average of 66% STAT5A knockdown and 22% decrease of STAT5B. Nucleofection of siSTAT5B pools into wild-type BMMCs ablated STAT5B protein (Fig. 6B, left panel, immunoblots). STAT5A expression was also slightly decreased, but when adjusted for protein loading in separate experiments not nearly to the extent of STAT5B silencing, demonstrating higher selectivity for STAT5B targeting. BMMC treated with siSTAT5B prior to IgE stimulation displayed significantly less MIP-1␣ and IL-13 production than cells treated with control siRNA (Fig. 6B, right panels). Because siSTAT5A-treated BMMCs produced cytokines normally, these data collectively pointed to STAT5B as the regulator of IgE-mediated cytokine secretion.
DISCUSSION
It is estimated that allergic disease affects nearly one quarter of the developed human population (30). Chronic asthma, just one of these disorders, was most recently predicted by the Global Initiative for Asthma to affect ϳ300 million people (31). This estimate is conservative, considering the age of the report and factors such as cryptic, or missed diagnoses in older populations, which account for Ͼtwo-thirds of asthma-related mortality (32). At the crux of allergic pathologies, and best studied in asthma, is the activated mast cell. Better understanding the intricacies of mast cell biology is critical to discovering new avenues for targeted treatment of mast cell-mediated pathologies. To this end we have established that STAT5 is essential for mast cell development, vitality, and function.
STAT5 is one of the more promiscuous members of the STAT family, expressed ubiquitously and with pleiomorphic effects (2,33). However, over the past decade it has come to the front that tissue niche and/or cell lineage play a large role in determining STAT5 effects (4,33). This is especially true in the context of myeloid cells, including mast cells. There, dogma indicates that STAT5 activity can be largely dependent upon JAK2 activation (4). Indeed, JAK2 is the major intermediary between SCF/c-Kit and activated STAT5 in mast cells (2,33).
The present data indicate STAT5 apposition with the high affinity IgE receptor, Fc⑀RI. This is novel in light of several lines of thought regarding how STAT5 is typically activated. First, under circumstances of more steady-state STAT5 activation, or perhaps "JAK-skewed" pSTAT5, there is potent cross-talk with signaling cascades such as the PI3K-Akt pathway. Here, we report that inhibition of JAK2 or the PI3K-Akt pathways, as well as other signaling factors such as BTK and Syk, has no effect on IgE-mediated STAT5 tyrosine phosphorylation. Second is the potential for regular cycling of STAT5 between the cytosol and nucleus as a result of chronic activation by means other than Fc⑀RI engagement, e.g. due to c-Kit (33). Our analyses indicate that IgE-induced STAT5 tyrosine phosphorylation is dependent upon Fyn kinase and furthermore, that unphosphorylated STAT5 associates with Fyn. As Fyn is suspected of associating with Fc⑀RI, this supports the suggestion of a STAT5 store sequestered outside the nucleus in a juxta-Fc⑀RI compartment. Interestingly, pSTAT5 cytoplasmic accumulation has been reported in a cancerous setting (34), raising the question of whether or not a similar situation could arise in noncancerous persistently STAT5-active conditions, e.g. IgE-induced inflammation. Our inability to co-immunoprecipitate Fyn and STAT5 after stimulation could likely be due to additional protein modifications and interactions that require further detailed studies.
It is important to contrast these findings to recent results from another group. By using RNA interference in cell lines and BMMCs, Barbu et al. reported that the contributions of Fyn and Gab2 with respect to Fc⑀RI signaling are not as relevant as that from Syk (35). Although our results show that Syk is not required for early Fc⑀RI-related STAT5 activity, these data certainly do not preclude Syk as one of the major mediators of Fc⑀RI signaling. Our findings do, however, support an important role for Fyn. This complements other work demonstrating Fyn dependence for chemokine, cytokine, and eicosanoid production (36). Parravicini et al. originally presented the Fc⑀RI-Fyn link and showed that Fyn, not Lyn, is required for mast cell degranulation (26). Suzuki et al. examined the role of Fyn in degranulation further and discovered that loss of TRPC1, a nonspecific cation channel, contributes to the degranulationimpaired phenotype of Fyn-deficient BMMCs (27). In the present study, silencing of TRPC1 had no effect on IgE-mediated Tyr(P)-STAT5 detection. This demonstrates separability between the physiological events behind degranulation and IgE-mediated STAT5 signaling discussed here. Interestingly, we observed that Gab2 deficiency enhanced STAT5 phosphorylation in response to Fc⑀RI. This was a surprise considering the various positive regulators of signaling that associate with this scaffold protein. Barbu et al. assert that Gab2 is most important for late phase mast cell responses, rather than processes of degranulation (35). Indeed, in our experience total STAT5-KO BMMCs show only limited, yet significant, reduction in histamine and leukotriene B 4 release (9). It is notable, however, that the protein phosphatases SHP-1 and SHP-2 also associate with Gab2 (37), and that in another system, osteoclast development, Lyn complexes with Gab2 and SHP-1 (38). We hypothesize that the assemblage of these negative regulators with Gab2 contributes to the increase in Tyr(P)-STAT5 observed under the setting of a Gab2, SHP-1, or Lyn deficiency. Lyn depletion likely enhances Fyn activity, as the inhibitory C-terminal Src kinase (also known as CSK) should be recruited poorly without Lyn (12). We also documented a modest enhancement of Tyr(P)-STAT5 in me mast cells, possibly due to the distal position of SHP-1 in this transduction pathway, whereas Lyn and Gab2 occupy more apical positions. Other possible negative regulators remain unidentified, and our current summary of this transduction pathway is illustrated in Fig. 7.
STAT5 expression is required for the development of the various hematopoietic lineages (2,4,33). Mice totally deficient in STAT5 are most often unviable by the perinatal period, and those few that survive present with severe combined immunodeficiency characterized by defective B cell, T cell, and lymphoid development (39). However, STAT5 is duplicated in many metazoans as STAT5 A/B (14). Work in our laboratories has demonstrated the importance of STAT5 in mast cell survival, development, and cytokine production, the hallmark of the late phase mast cell response. Specific use of a dominant negative FIGURE 7. Summary of IgE-mediated STAT5 signal transduction. Multiple IgE molecules engaged to independent Fc⑀RI interact with antigen, known as cross-linking. This induces an early signal of STAT5 tyrosine phosphorylation that requires the presence of Fyn kinase. The scaffolding protein Gab2 acts as a brake on Tyr(P)-STAT5, likely due to negative regulators recruited to it, such as the phosphatase SHP-1, or Lyn kinase. Tyrosine phosphorylation of STAT5 allows dimerization and shuttling to the nucleus where it assists in the transcription of cytokines (STAT5B) and survival signals (STAT5A).
STAT5A reduced viability, whereas STAT5A transfection conferred survival to STAT5 A/B KO BMMCs (8,9). Ikeda et al. confirmed this and additionally showed that STAT5A KO mast cells have enhanced apoptosis with reduced Bcl-xl expression (29). Li et al. further dissected the role of STAT5A in survival, determining that the N-domain is required for bcl-2 induction (40). Together, these data suggest that STAT5A could be the key to mast cell survival. Using siRNA pools to STAT5A or STAT5B we found that cytokine production in the context of STAT5B removal was significantly impaired. Therefore, considering accumulating data regarding the separable STAT5 proteins, STAT5A appears to be most critical for mast cell development and survival, whereas the B isoform influences cytokine regulation.
This separation of duties between STAT5A and B warrants further study. Tools are now becoming available to examine STAT5 proteins, particularly with regard to their various phosphorylation states. In general, the importance of STAT5 serine phosphorylation is becoming more recognized and explored with time, as in a recent report by Friedbichler et al. demonstrating that Ser(P)-STAT5A is required for hematopoietic transformation into leukemic and myeloproliferative phenotypes (41). We posit that divergence in STAT5A and B function is worth investigating, as advocated by our siSTAT5B effects on the late phase of mast cell function. These data suggest STAT5 as a plausible target in allergic disease. Several indirect inhibitors are available including Lestaurtinib (CEP701), which has been shown to inhibit STAT5 signaling while not affecting erythroid proliferation in cancer-free individuals (42). This drug presents an attractive opportunity because its clinical safety has already been evaluated, but other candidates originally derived for oncologic practice should not be forgotten (43). Therapeutics that more directly target STAT5 will be valuable for study, especially given JAK-STAT pleiotropy and our data demonstrating JAK-independent Fc⑀RI-mediated STAT5 regulation. | 6,340.6 | 2011-11-30T00:00:00.000 | [
"Biology",
"Medicine"
] |
The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm
: The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.
Introduction
Retaining walls are structures widely used in engineering for supporting soil laterally. The design of these walls is a problem of interaction between the soil and the structure to retain a material safely and economically. When the height of a cantilever wall becomes important, the volume of concrete required begins to be considerable. From a height of 8-10 m, buttressed walls economize its design. The design of these structures is mainly carried out following rules very much linked to the experience of structural engineers [1]. If the initial design dimensions or material qualities are inadequate, the structure is redefined. With this procedure of trial and error, the different designs obtained do not go beyond a few tests. This process leads to a safe, but not necessarily economic, design [2]. Structural optimization methods have clear advantages over experience-based design.
Presently, the optimum design of reinforced concrete (RC) structures constitutes a relevant line of research. In practical structural optimization problems, the variables used must be discrete, so they are combinatorial optimization problems. However, combinatorial problems are found in a large number
•
A hybrid Particle Swarm Optimization (PSO) based on a db-scan clustering technique is proposed. The db-scan is very effective in binary combinatorial problems [6,10]. PSO is often used to solve continuous optimization problems and its tuning is very simple.
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The contribution of the db-scan in the discretization process was studied through a random operator. In addition, the discretization performed by db-scan was compared with methods using k-means [5,26].
•
The proposed algorithm is applied to obtain low-carbon, low-cost counterfort wall designs. This hybrid algorithm is compared with an efficient algorithm adapted from the harmony search (HS) proposed in [18].
The rest of this paper is structured as follows: in Section 2 we develop a state-of-the-art of hybridizing metaheuristics with machine learning; in Section 3 we define the optimization problem, the variables involved, and the restrictions; then in Section 4 we detail the discrete db-scan algorithm; we move on with the experiments and results obtained in Section 5; and conclude with Section 6 in which we summarize the conclusions and new lines of research.
Hybridizing Metaheuristics with Machine Learning
Metaheuristics is a broad collection of incomplete optimization techniques inspired by some real-world phenomenon in nature or in the behavior of living beings [27,28]. The objective is to solve problems of high computational complexity in a reasonable execution time so that its optimization mechanism is not significantly affected when the problem to be solved is altered. Then again, the set of techniques capable of learning from a database are the so-called machine learning algorithms [29]. Depending on the learning mode, these techniques are divided into learning by reinforcement, supervised learning, and unsupervised learning. It is common for these algorithms to be used in a wide range of problems such as regression, dimensionality reduction, transformation, classification, time series or anomaly detection and computer vision problems.
The integration of the machine learning techniques with the metaheuristic algorithms can be done basically with two approaches [30]. Either machine learning techniques are used to increase the quality of the solutions and the convergence rates obtained by metaheuristics, or metaheuristic are used to enhance the performance of machine learning techniques [30]. However, metaheuristics often improves the efficiency of an optimization problem concerning machine learning. Based on the work of [30], we propose an extension of the scheme of techniques in which metaheuristics and machine learning are combined ( Figure 1). Machine learning can be used as a metamodel to determine from a set of metaheuristics the best one for each instance. In addition, specific machine learning operators can also be embedded into a metaheuristic, resulting in three different groups of techniques: hyper-heuristics, algorithm selection, and cooperative strategies [30].
If we automate the design and tuning of metaheuristics to solve a large number of problems, we obtain the so-called hyper-heuristics. The aim of the cooperation strategies is to obtain methods that are more robust by combining the algorithms in a parallel or sequential way. The cooperation mechanism can share the whole solution, or only a part of it. In [31], a multi-objective optimization of an aerogel glazing system through a surrogate model driven by the cross-entropy function was developed with the implementation of the supervised machine-learning method. In [32] the multilevel thresholding image segmentation-based hyperheuristic method was addressed. Finally, in [33] an agent-based distributed framework was proposed to solve the problem of permutation flow stores, and in which each agent is implementing a different metaheuristic.
On the other hand, there are operators that allow enhancing the performance of a metaheuristic integrating machine learning operators. Initialization, population management, solution disruption, binaryization, local search operators and parameter setting and are examples of such operators [30]. Binary operators using unsupervised learning techniques can be integrated into metaheuristics that operate in continuous spaces to perform the binarization process [6]. In [34], a percentile transition-ranking algorithm was proposed as a mechanism to binarize continuous metaheuristics. In [9], the application of Big Data techniques was applied to improve a cuckoo search binary algorithm. The tuning of the parameters of metaheuristics is another line of research of interest. In [35], a tuning method was applied on different sized problem sets for the real-world integration and test order problem. In [36] a semi-automatic approach designs the fuzzy logic rule base to obtain instance-specific parameter values using decision trees. The use of machine learning techniques improves the initiation of solutions, without the need to start it randomly. A cluster-based population initialization framework was proposed in [37] and applied to differential evolution algorithms. In [38] a case-based reasoning technique was applied to initiate a genetic algorithm in a weighted circle design problem. To solve an economic dispatch problem, Hopfield neural networks were used to start solutions of a genetic algorithm [39].
Metaheuristics improve the machine learning algorithms in problems such as feature selection, grouping, classification, feature extraction, among others. Image analysis to identify breast cancer can be enhanced by a genetic algorithm [40] that improves the performance of the Support Vector Machine (SVM). The medical diagnoses and prognoses were tackled in [41] combining swarm intelligence metaheuristics with the probabilistic search models of estimation of distribution algorithms. In [42], the authors used swarm intelligence metaheuristics for the convolution neural network hyper-parameters tuning. In [32], a multiverse optimizer algorithm was used for text documents clustering. An improved normalized mutual information variable selection algorithm for soft sensors in [43] was used to perform the variable selection and validate error information of artificial neural networks. A dropout regularization method for convolutional neural networks was tackled in [44] through the use of metaheuristic algorithms. In [45], a firefly algorithm was combined with the least-squares support vector machine technique to address geotechnical engineering problems. Metaheuristics contributed to the problems of regression, as is the case with the prediction of the strength of high strength concretes in [46]. Another example is the integration of artificial neural networks and metaheuristics for improved stock price prediction [47]. In [48], proposes a sliding-window metaheuristic optimization for predicting the share price of construction companies in Taiwan. In [49] , the least squares support vector machine hybridizing a fruit fly algorithms is applied to simulate the nonlinear system of a MEL time series. Metaheuristics also apply to unsupervised learning techniques, such as clustering techniques. For example, in [50] a metaheuristic optimization was used for a clustering system for dynamic data streams. Metaheuristics have also been integrated into clustering techniques in the search for the centroids that best group the data under a certain metric. A bee colony metaheuristic was used for energy efficient clustering in wireless sensor networks [51]. In [52], a clustering search metaheuristic was applied for the capacitated helicopter routing problem. In [53], a hybrid-encoding scheme was used to find the optimal number of hidden neurons and connection weights in neutral networks. Four metaheuristic-driven techniques were used in [44] to determine the dropout probability in convolutional neural networks. In [54] the bat algorithm and cuckoo search were used to adjust the weights of neural networks. An algorithm was proposed using simulated annealing, differential evolution, and harmony search to optimize convolutional neural networks was proposed in [55]. In [56] long-term short memory trained with metaheuristics were applied in healthcare analysis.
In this paper, the study proposes a hybrid algorithm in which the unsupervised db-scan learning technique to obtain binary versions of the PSO optimization algorithm. This hybrid algorithm was used to obtain a sustainable design buttressed walls. Recently, the db-scan binarization algorithm obtained versions of continuous metaheuristics that have been used to solve the set covering problem [6] and the multidimensional knapsack problem [10] which are NP-hard problems.
Problem Definition
This Section describes the optimization problem. First, the equations to be optimized are defined. The variables that define the structure and the parameters applied to solve the problem are described below. In addition, finally, the restrictions and the calculation method applied to verify the structure are summarized.
Optimization Problem
The goal is to minimize the objective functions F i for a width of 1 m of a buttress wall. The economic cost in euros will be valued for F 1 , and for F 2 the CO 2 equivalent emissions in kg produced in the execution of all parts of the structure. The evaluation of both functions is carried out with precision, and depends on the r construction units used, such as formwork, concrete, steel, excavation and fillings. The values of the units applied to this problem were obtained from [2,18], and were reflected in Table 1. The prices were provided by local Valencian road construction contractors and CO 2 emissions from the BEDEC PR / PCT ITeC (Institute of Construction Technology of Catalonia) database [57]. The objective functions are represented in the following Equation (1). The formula represented values both the cost and the emissions produced during the construction of the wall. It is calculated as the sum of the cost or emissions of each construction unit, being for each unit the product of the unit cost or the unit's emission by its quantity. Being a ij the unit value taken from Table 1, for i = 1 in cost and i = 2 in emissions, corresponding to the measurement of the construction units x j . The evaluation of Equation (1), in addition to the group of variables, depends on a set of parameters that remain constant throughout the optimization, and as long as the restrictions of the ultimate and service limit states (ULS and SLS) are met.
Problem Design Variables
Design variables allow defining the structure. These variables are discrete and modified by the solution search algorithm during the optimization process. There are 3 groups of variables: geometric, material qualities and reinforcing steels. In total there are 32 variables. The definition, arrangement and characteristics of the variables are defined in [58].
Of the set of variables, 24 are shown in Figures 2-4. Figures 2 and 3 represent the configuration of the reinforcement (A1-12), with the diameter and number of steel bars. Three flexural reinforcing bars defined as A1, A2 and A3 contribute to the main bending of the stem. A4 represents the vertical reinforcement of the base at the rear of the stem. The secondary longitudinal reinforcement is provided by A5 for shrinkage and thermal effects on the stem. A6 represents the longitudinal reinforcement of the buttress. The area of the reinforcing bracket from the bottom of the buttress is provided by A7 and A8. A9 and A11 define the upper and lower heel reinforcements and A12, the shear reinforcement in the footing. Finally, the longitudinal effects on the toe are defined by A10. Figure 4 represents most of the geometric variables.These variables are the thickness of the stem (b), the thickness of the footing (cz), the thickness of the buttresses (ec), the length of the heel (lt), the length of the toe (lp), the distance between the buttresses (d), two classes of steel B500S and B400S, and six classes of concrete between C25/30 and C50/60 by discrete intervals of 5 MPa. Table 2
Problem Design Parameters
All the cases analyzed in this study are completely defined by constant contour values called problem parameters. These parameters are described in [58] and are represented in Table 3.
Problem Constrains
The viability of the structure is verified as described in [58], in accordance with the Spanish technical standards defined in [59] and the detailed recommendations for foundations in road works [60]. The bending and shear limit states, and the cracking limit state are verified. The structure is checked according to the stem stress distribution [61] for non-cohesive granular materials. The rectangular distribution of soil stresses in the foundations is considered according to the criteria in [62]. The structural hyperstatic model assumes that the top of the stem functions as a cantilever, while the bottom of the stem is embedded between the footing and the lower part of the buttress.
The bending stress verified in the horizontal T-shaped cross section is performed taking into account the effective width, in accordance with [63]. The checking of the mechanical shear and the flexural capacity is carried out considering the equations expressed in [62] and with the verifications in [59]. To assess the controls against overturning and sliding, and the limit of soil stresses, the effect of buttresses is included [58]. It is taken into account that the favorable overturning moments are sufficiently greater than the unfavorable overturning moments with a safety coefficient for frequent events. A slip safety coefficient and a coefficient of base-friction foundation against sliding are also considered.
The db-Scan Discrete Algorithm
As a first step, the algorithm generates a set of valid solutions. These solutions are randomly generated. In this procedure, first, it is validated if the solution variables are started. In the case that they are not all started, the variables are started randomly. Once all the variables are generated, the next step is to verify if the solution obtained is valid. In the event that it is not valid, all variables are removed and regenerated. The detail of the initiation procedure is shown in Figure 5. Subsequently, PSO is used to produce a new solution in the continuous space. The PSO algorithm will be described in Section 4.1. Subsequently, the db-scan operator is applied to the continuous solution in order to transform continuous movements into groups associates to transition probabilities. The db-scan operator will be detailed in Section 4.2. After the db-scan operator generates the groups, the discretization operator applies the corresponding transition probability to each group, generating a new discrete solution. The discretization operator is detailed in Section 4.3. Finally, the new solution is validated and, in case it meets the restrictions, it is compared with the best solution obtained. If the new value is higher, it replaces the current one. The detailed flow chart of the hybrid algorithm is shown in Figure 6.
Are all variables started?
Begin End no yes Is it a feasible solution?
Random start of variables.
Clean the variables..
Particle Swarm Optimization
For the proper functioning of the PSO algorithm, the concepts of population that are usually called a swarm, and each of these solutions is called a particle. The essence of the algorithm is that each particle is guided by a combination of the best value particle obtained so far in the search space (maximum global) together with the best result obtained by the particle (local maximum). The optimization process is iterative until some termination condition is met.
Formally let f : R n → R corresponds to the fitness function to be optimized. This function considers a candidate solution that is represented by a vector in R n and generates a real value as output. This obtained value, represents the value of the objective function for the given candidate solution. The goal is to find a solution for which f (a) ≤ f (b) for all b in the search space, which would mean that a is the global minimum. The algorithm pseudo-code is shown in Procedure 1.
Algorithm 1 Particle swarm optimization algorithm
1: Objective function f(s) 2: Generate initial solutions of n particles. 3: Get the particle's best known position to its initial position: p i ← s i . 4: if f (p i ) < f (g) then 5: Update the swarm's best known position: g ← p i 6: end if 7: Initialize the particle's velocity: v i 8: while stop criterion are meet do 9: for each particle and dimension do 10: Pick random numbers: r p , r g 11: Update the particle's velocity: Update the particle's position: end for 14: if f (s i ) < f (p i ) then 15: Update the particle's best known position: p i ← s i . 16: if f (p i ) < f (g) then 17: Update the swarm's best known position: p i ← s i . 18: end if 19: end if 20: end while
db-Scan Operator
The solutions resulting from the execution of the PSO algorithm are grouped by the db-scan operator. We should note that the db-scan operator can be applied to any swarm intelligence continuous metaheuristics. The spatial clustering technique based on noise density of applications (db-scan), requires for the clustering, a set of points S within a vector space, and a metric, usually, the metric is the Euclidean. Db-scan groups the points of S that meet a minimum density criterion. The rest of the points are considered outliers. As input parameters db-scan requires a radius and the minimum number of neighbors δ. The main steps of the algorithm are shown below: • Find the points in the neighborhood of every point and identify the core points with more than δ neighbors.
•
Find the connected components of core points on the neighbor graph, ignoring all non-core points.
•
Assign each non-core point to a nearby cluster if the cluster is an neighbor; otherwise, assign it to noise.
In the db-scan (dbscanOp) operator, the db-scan grouping technique is used to make groups of points to which we will assign a probability of transition. This probability of transition will subsequently allow the discretization operator to discretize continuous solutions. The grouping proposal uses the movements obtained by PSO in each dimension for all the particles. Suppose s(t) is a solution in iteration t, then ∆ i (s(t)) represents the magnitude of the offset ∆(s(t)) in the i-th position, considering the iterations t and t + 1. After all the displacements were obtained, the grouping is carried out. To obtain the groups, the displacement module will be used, which is denoted by |∆ i (s(t))|. This grouping is done using the db-scan technique. Finally, a generic function P tr is used, which is shown in Equation (2) with the objective of assigning a probability of transition to each group and therefore to each displacement.
Then using the function P tr , a probability is assigned to each group obtained from the clustering process. In this article, we use the linear function given in Equation (2), where Clust (x i ) indicates the location of the group to which ∆ i (s) belongs. The coefficient α represents the initial transition coefficient and β models the transition separation for the different groups. Both parameters must be estimated. The pseudo-code of the discretization procedure is shown in Algorithm 2. P tr (x) = α + γx (2) where x represents the value of Clust(s i ). Also, because s i ∈ Clust(s i ), each element of Clust(s i ), is assigned the same value P tr . That is, P tr (s i ) = P tr (Clust(s i )). On the other hand, γ = α * β are constants that will be determined in the tuning of parameters, where α corresponds to the initial transition coefficient and β represents the transition probability coefficient.
Discretization Operator
The discretization operator receives the list lTransitionProbability (t + 1). This list contains the values of the transition probabilities which were the result delivered by the db-scan operator. Then given a solution s(t) ∈ ls(t), we select each of its components s i (t) and we proceed to determine through the transition probability if this component should be modified. In the case of large transition probabilities, there is a greater possibility of modification. Then a random number is obtained at [0, 1] and this number is compared with the value of the transition probability assigned to the component. In cases where the modification condition is satisfied, it can increase the value by 1 or decrease it by 1. Finally, the selected value is compared with the best value obtained by the algorithm and remains with the minimum of both. The pseudocode of the discrete procedure is shown in Algorithm 3.
Results and Discussion
The experiments developed with the objective of determining the performance of our hybrid algorithm applied to the counterfort retaining wall problem will be detailed in this section. In Section 5.1, we will explain the strategy used to perform the tuning of the parameters. Then, in the first experiment detailed in Section 5.2, we will study the contribution of the db-scan operator to the discretization process. This study will be carried out through a comparison with a random operator. Subsequently, in the second experiment, the db-scan technique will be compared with the k-means clustering technique. This comparison is detailed in Section 5.3. Finally, in Section 5.4 our db-scan PSO proposal will be compared with another algorithm in the literature that solved the same problem. Regarding the scenario of the experiments, each problem will be run 30 times. The value 30 is the minimum number appropriate to be able to obtain statistical conclusions on a population [64], in addition, the signed-rank Wilcoxon test [65] will be incorporated to determine if the difference between the results is statistically significant. For this test, the p-value used was 0.05. The software was built in Python 3.6 and run on a computer with the following configuration: Intel Core i7-4770 CPU and 16 GB of RAM.
Parameter Settings
In the methodology used to perform the adjustment of the algorithm parameters, heights of 8 and 12 m were considered. The selection of these heights was motivated by their difference in complexity. 8 represents walls of small size and 12 represents walls of a greater size where the satisfaction of stability restrictions has a greater difficulty. After defining the instances to use, each of the defined configurations was resolved 5 times for each height. The set of configurations used are detailed in Table 4. The value 5 was chosen with the intention of being able to execute all the combinations in a limited time. The range column in Table 4 represents the scanned values to perform the PSO adjustment. Obtaining these ranges was based on previous studies where the db-scan technique was applied to other combinatorial problems. For more details on the method, see the reference [26].
In order to find the best configuration, the method proposes using four measurements. The best solution (bs), the worst solution (ws), the average solution (as) and the convergence time (ct). These measures are defined in Equations (3)- (6). The best global value (bgv) corresponds to the best value obtained in all the configurations executed. Furthermore, the best local value (blv) represents the best value obtained by a particular configuration. The worst local value (wlv) represents the worst moment obtained for a given configuration. The average local value (alv) is the average value obtained by a configuration. The minimum global time (mgt) accounts for the minimum convergence time resulting from all evaluated configurations and the convergence local time (clt) the minimum time for a particular configuration. The minimum global time (mgt) corresponds to the best value getting for all configuration. Finally, the worst global value (wgv) accounts for the worst value obtained by all the configurations. Each of the measures defined, the closer to 1, the better performance that indicator has. On the other hand, the closer to 0, the worse performance. Since there are 4 measurements to be able to carry out the evaluation, we incorporate them into a radar graph and calculate their area. As a consequence of the measurement definition, the largest area corresponds to the configuration that has the best performance considering the 4 defined measurements.
Definition 1 ([10,26]). Measure definitions: 1. The deviation of the best local value obtained in five executions compared with the best global value: 2. The deviation of the worst value obtained in five executions compared with the best global value: 3. The deviation of the average value obtained in five executions compared with the best global value: 4. The convergence time for the average value in each experiment is normalized according to Equation (6).
For PSO, the coefficients c 1 and c 2 are set to 2. The parameter ω linearly decreases from 0.9 to 0.4. For the parameters used by db-scan, the minimum number of neighbors (minPts) is estimated as a percentage of the number of particles (N). The parameter settings are shown in Table 4 . In the table, the column labeled "Value" represents the selected value, and the column labeled "Range" corresponds to the set of scanned values.
db-Scan and Random Operators Comparison
In this first experiment, the contribution of the db-scan operator in optimizing costs and emissions for the design of the wall will be studied. To properly determine the contribution of the db-scan operator, a random operator was designed to replace the discretization performed by db-scan. In particular, the execution of the db-scan operator in Figure 6 is replaced by a random operator. This random operator assigns a fixed probability of 0.5 instead of assigning probabilities per group. Different heights of the wall were considered, starting at 6 (m) and ending at 14 (m) with increments of one meter. For a proper statistical comparison, 30 runs are executed for each height and operator. The results are then documented in tables and boxplots. Finally, the Wilcoxon signed-rank test is used to determine the significance of the results. Figure 7, in the case of the best value indicator, the operator that uses db-scan was Superior at all heights for both cost and emissions. Additionally, we observe the difference between the operators increases as the height increases. In the case of 6 and 7 m in the case of cost, the difference is 2.2% and 4.6% respectively. In the case of heights of 13 and 14 m, this difference is 30.0% and 34.5% respectively. When carrying out this same analysis for the emissions of CO 2 , we find that for the heights of 6 and 7 the differences are 3.0% and 1.2% respectively and at the heights of 13 and 14 we obtain 15.9% and 14.2% respectively. In the comparison of the average indicator, we see a very similar result to that reported in the best value analysis. The average indicator of the db-scan operator is higher for all heights than the random operator in both optimizations. Like the best value case, the difference increases as the height increases. In the case of averages, because the number of values is greater than in the case of the best value, we apply the Wilcoxon significance test. The result indicates that the difference is statistically significant in both cases, costs, and emissions. In Figure 7 we show the boxplots for the cost optimization results. In both cases, db-scan and random it is observed that the dispersion and the Inter-quartile range of the values obtained in the optimization increases as the height increases. However, from height 9 onwards this dispersion is more noticeable in the case of the random operator. When analyzing the results of the optimization of CO 2 , emissions, which are shown in Figure 8, the behavior is very similar to that of cost. In the case of emissions, the increase in dispersion and in the inter-quartile range is more noticeable in the random operator from the height of 11 m. Emissions (kg CO2 eq.) The convergence diagrams for heights 6, 9, 12 and 14 are shown in Figure 9a,b. These diagrams correspond to the results obtained in cost optimization using the random and db-scan operators. From the db-scan convergence chart, we see that height 6 has the best convergence, followed closely by 9. On the other hand, heights 12 and 14 have a similar convergence, being slower than the case of 6 and 9. At higher wall heights, complying with constraints becomes more complicated and therefore the optimization problem is more difficult to solve. In the case of the random operator, it shows a correspondence between the height and the speed of convergence. The lower the height, the better its convergence. On the other hand, we see that for the random case, already in the 500 iterations the slope tends to stabilize unlike in the db-scan case where for the most difficult problems the slope stabilizes after the 600 iterations.
db-Scan and k-Means Operators Comparison
This second experiment aims to compare the performance of the algorithm that uses db-scan, with another algorithm that uses k-means as a discretization method. This experiment is inspired by the fact that both techniques correspond to unsupervised learning algorithms that aim to cluster points. In the case of k-means, unlike db-scan, the number of clusters must be defined a priori. In this experiment, suggested by the articles [10,26], k was configured with the value 5. The Equation (2) was used to set the values of the probability of transition for each cluster. As in experiment 1, the db-scan module in Figure 6 is replaced in this case by k-means leaving the rest of the modules unchanged.
The results of this experiment are shown in Table 6 and Figures 10 and 11. When analyzing the results of the best value indicator shown in Table 6, we observed in the case of cost optimization, the results are similar, with k-means being higher in 5 of the 9 heights. In the case of optimization of CO 2 emissions, something similar occurs, with very close values between the two algorithms.
In the last case, k-means was higher in 3 cases, db-scan in 1, and in 4 heights their values coincided. When analyzing the average indicator, in the case of optimizing the cost of the wall, we observe that for small heights very close values are obtained in both algorithms, being k-means higher than db-scan. Particularly in the case of heights 6, 7, 8 the difference in percentage was 0.34%, 1.16%, and 1.83% respectively. On the other hand, when we analyze the values obtained in the highest walls, we find that db-scan is superior to k-means. Particularly for heights 12, 13, and 14, we have that the difference is 3.63%, 5.04%, and 3.46% respectively. Wilcoxon's statistical test when analyzing the total population indicates that the difference between both algorithms is not significant. In the case of emission optimization, the behavior of the average indicator is different from that of cost optimization. The db-scan algorithm is superior in 6 of the 9 heights in this indicator. Heights 13 and 14 stand out, achieving differences of 3.3% and 5.78% respectively. When analyzing the total distribution of points, the Wilcoxon test indicates that the difference is significant in favor of db-scan.Finally, when analyzing Figures 10 and 11 both algorithms have a similar behavior between heights 6 and 11. From height 12 onwards, the increase in dispersion and in the Inter-quartile range becomes much more notorious in the case of k-means. This last result shows that for more difficult problems, db-scan behaves more robust than k-means. Emissions (kg CO2 eq.) Figure 11. Emission box-plots, comparison between db-scan and k-means operators.
db-Scan PSO and HS Comparison
The third experiment aims to compare the hybrid db-scan PSO algorithm with results published in the literature. We particularly consider the results published in [18,58]. In these articles, a variant of the HS algorithm for the optimization of buttress retaining walls was developed. To carry out the evaluation, the same procedure as the previous experiments will be followed, i.e., through the best value and average indicators supplemented with boxplots and the Wilcoxon test for the statistical significance of the results.
The comparison of both algorithms is documented in Table 7 and in Figures 12 and 13. When analyzing the best value indicator, the db-scan PSO algorithm is superior in 8 of the 9 instances to HS. In optimizing emissions of CO 2 , in 9 of the 9 instances, db-scan performs better. When evaluating the average indicator, the situation is similar to reported by the best value indicator. In 8 of 9 heights of the wall, db-scan PSO has better performance for cost optimization and in the 9 heights, it is superior for emissions. Wilcoxon's test indicates that in both cases the difference is significant. When analyzing the Figures 12 and 13 we observe that from height 12 onwards, the dispersion and the inter-quartile range of HS solutions grow significantly concerning db-scan PSO.
Conclusions
To address the buttressed walls problem, a hybrid algorithm based on the db-scan clustering technique and the PSO optimization algorithm was proposed in this article. This hybridization was necessary because PSO works naturally in continuous spaces and the problem studied is combinatorial. A Db-scan was used as a discretization mechanism. The optimization functions considered were cost and emission of CO 2 . To measure the robustness of our proposal, three experiments were designed. The first evaluates the performance of the hybrid algorithm with respect to a random operator. Later in the second experiment, the performance was compared with respect to the k-means clustering technique. Finally, in the last experiment, we studied the performance of the hybrid algorithm when compared to an HS adaptation described in the literature. The first experiment concludes that the db-scan operator contributes to the quality of the solutions, obtaining better values than the random operator, in addition to reducing the dispersion of these. In comparison with k-means mixed results are obtained, in some cases, k-means is superior to db-scan and in other db-scan improves the solutions obtained by k-means. Regarding the dispersion in the different instances, we observed that from height 12 onwards db-scan obtained much smaller dispersions than k-means. Lastly, in comparison with HS, in general, db-scan surpass HS obtaining better results, especially in the instances where the height was over 12 m.
From the results obtained in this study, several lines of research emerge. The first line is related to population management. In the present work, the population was a static parameter. By analyzing the results generated by the algorithm as it iterates, it is possible to identify regions where search needs to be intensified or regions where further exploration is needed. This would allow for dynamic population management. Another interesting research line is related to the parameters used by the algorithm. According to what is detailed in Section 5.1, a robust method was used to get the most suitable configuration. However, this configuration is a static one and is not necessarily the best configuration throughout all execution. Proposing a framework that allows adapting the parameters based on the results obtained by the algorithm as it is executed, would allow generating even more robust methods than the current one. | 8,563.8 | 2020-05-26T00:00:00.000 | [
"Computer Science"
] |
A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples
Currently, deep-learning-based methods have been widely used in fault diagnosis to improve the diagnosis efficiency and intelligence. However, most schemes require a great deal of labeled data and many iterations for training parameters. They suffer from low accuracy and over fitting under the few-shot scenario. In addition, a large number of parameters in the model consumes high computing resources, which is far from practical. In this paper, a multi-scale and lightweight Siamese network architecture is proposed for the fault diagnosis with few samples. The architecture proposed contains two main modules. The first part implements the feature vector extraction of sample pairs. It is composed of two lightweight convolutional networks with shared weights symmetrically. Multi-scale convolutional kernels and dimensionality reduction are used in these two symmetric networks to improve feature extraction and reduce the total number of model parameters. The second part takes charge of calculating the similarity of two feature vectors to achieve fault classification. The proposed network is validated by multiple datasets with different loads and speeds. The results show that the model has better accuracy, fewer model parameters and a scale compared to the baseline approach through our experiments. Furthermore, the model is also proven to have good generalization capability.
Introduction
At present, bearings are an essential component of machine manufacturing equipment. The good or bad running conditions of bearings directly affects the operation of the equipment. However, complex real environments, including abnormal humidity, temperatures and current magnitudes, cause different degrees of damage to the bearings, resulting in the occurrence of faults. This produces high maintenance costs as well as delays of production progress to the factory and even threatens the personal safety of personnel. Therefore, the safety of bearings has become a crucial concern. The research on the bearing fault diagnosis algorithm is of great significance to the safety of equipment [1,2].
Thus far, the traditional bearing fault diagnosis technology is to manually analyze the vibration signal obtained by the accelerometer [3]. The corresponding methods are used to extract the characteristic information from the vibration signal, which mainly include fast Fourier transform (FFT) [4], wavelet transformation (WT) [5], empirical mode decomposition (EMD) [6], short-time Fourier transform (STFT) [7] and Wigner-Ville distribution (WVD) [8]. Furthermore, the advent of Hilbert transformation (HT) [9] made it possible to diagnose transient bearing faults. These methods have been shown to be effective in practice. In recent years, machine learning has been utilized in the study of bearing fault diagnosis.
The main methods are artificial neural networks (ANN) [10], principal component analysis (PCA) [11], K-Nearest Neighbors (K-NN) [12] and support vector machines (SVM) [13]. Machine learning as a branch of artificial intelligence is widely used in various fields. The use of machine learning has taught computers how to process data efficiently compared to traditional methods. The computer can find more subtle features to analyze, which improves the accuracy and intelligence of bearing fault diagnosis. However, with the rapid changes of current technology, the amount and types of data have also ushered in rapid growth. Feature selection, which we need to rely on experts to perform, becomes time-consuming and laborious. Deep learning not only has better accuracy and processing speed but also can solve problems end-to-end. Therefore, deep learning is gradually being widely adopted.
Deep learning has made great breakthroughs in the fields of computer vision, natural language and data mining. Typical methods, such as convolutional neural networks (CNN) [14,15], Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) [16] and Generative Adversarial Networks (GAN) [17], have obvious effects in dealing with problems in these fields. These methods simplify the step of feature extraction at the same time. Furthermore, deep learning has good application prospects in the field of bearing faults. Compared with the traditional diagnosis methods, the deep-learning method realizes the automatic extraction of features and has a good effect on the accuracy of diagnosis.
A fault diagnosis method based on CNN multi-sensor fusion was proposed in the literature [18]. An automatic recognition architecture for rolling bearing fault diagnosis based on reinforcement learning was also proposed in the literature [19]. With the use of real-life scenarios, the problem of insufficient training samples has been noticed and studied. In recent years, excellent progress has been made in the study of neural networks based on small samples [20,21]. Fang, Q. et al., proposed a denoised fault diagnosis algorithm with small samples that can solve the problem of bearing fault diagnosis under small samples [22]. However, a model with complex structures often requires a large number of parameters. This leads to a higher level of operational equipment. Too many parameters make it far from practical in real world scenarios. Furthermore, this may also affect the computational speed. Hence, controlling the number of model parameters is extremely important in practical applications. Fang, H. et al. proposed a lightweight fault diagnosis model that can solve the problem of too many model parameters [23]. However, it cannot perform fault diagnosis when there are insufficient samples. This shows that the recently proposed models are unable to achieve a better trade-off between accuracy and lightweight [24,25].
To overcome the problems of few samples and a huge amount of parameters, an end-toend multi-scale and lightweight Siamese network with symmetrical architecture (MLS-net) is proposed in this paper. MLS-net not only maintains good accuracy in bearing fault diagnosis under small samples but also has fewer parameters to reduce resource consumption and a good generalization ability. The main contributions are summarized below.
•
We construct a novel fault diagnosis network architecture by combining an improved Siamese network and few-shot learning for the case of small samples. • A multi-scale feature extraction module is designed to improve the feature extraction capability of the model. Furthermore, we use the dimensionality reduction method to compress the parameters of the model to conserve device resources.
•
Extensive experiments are conducted on multiple datasets to demonstrate the efficiency and generalization of the proposed architecture.
The rest of the paper is organized as follows: Section 2 introduces the mentioned basic theory. Section 3 describes the proposed network structure. Section 4 presents the details and results of the experiments. Section 5 concludes the paper.
Inception
The inception module has an important role in model compression and feature extraction [26]. The key to the module improvement is the introduction of 1 × 1 convolution and the construction of a multi-scale convolution structure. The accuracy of the model on image classification was proven to be significantly improved through experiments. In addition, the model can utilize the computational resources more efficiently. More features are acquired with the same amount of parameters. The accuracy is also significantly improved. At the same time, the problems of overfit, gradient explosion and gradient disappearance due to the increased depth of the model are also improved.
There are four branches in the inception module. The first three branches use 1 × 1 convolution kernels for dimensionality reduction, which serves to optimize the problem of too many parameters caused by convolution operations. The reduction in dimensionality also brings a reduction in calculations. It is beneficial to the full utilization of computational resources. In addition, convolution kernels with different sizes are used to obtain different perceptual fields in the inception module. The branch contains 1 × 1, 3 × 3 and 5 × 5 sizes.
The different sizes of the convolution kernels allow for richer information to be extracted from the features. At the same time, multi-scale convolution uses the principle of decomposing sparse matrices into dense matrices to speed up the convergence. A comparison of the traditional convolution and inception modules is shown in Figure 1.
Siamese Network
A Siamese network [27] is a symmetrical architecture built from two neural networks. They are mainly applied in small sample cases. The inputs of the model are two samples from the same or different datasets. The main body consists of feature extraction and a similarity calculation module. The function of the feature extraction module outputs the feature vectors of the input samples. The similarity calculation module calculates the similarity of the two feature vectors. The similarity is compared between the predicted data and the prior knowledge using the model obtained from training. Thus, fault classification with small samples is achieved. Its emergence solves the problem of deep neural networks to obtain high accuracy and overfitting in the absence of a large number of data samples.
Few-Shot Learning
Few-shot learning [28] is the use of few samples for classification or regression. It is different from traditional supervised learning methods. Few-shot learning does not generalize the training set to the test set. It aims to make the model understand the similarities and differences of things and learn to distinguish between different things.
Few-shot learning generally consists of a support set (S), a query set (Q), a training set (T) and a judgment rule (R), where S contains a small amount of supervised information in Q.
The combination of S and Q is used for predictive classification. R is the procedure equipped to determine the similarities and differences of things. We use T as prior knowledge to train R. R is then used to determine the similarities and differences between samples in S and Q to achieve small sample classification.
Structure of MLS-Net
The overall architecture of MLS-net is shown in Figure 2. We fuse the improved subnetwork with a Siamese network. A multi-scale and lightweight bearing fault diagnosis architecture applied to the small sample situation is constructed. The whole structure body consists of two symmetrical branches. As we can see from the figure, the overall architecture contains four parts: data pre-processing, the sub-network, similarity classification and the few-shot learning test.
Data Pre-Processing
The data pre-processing part focuses on the construction of the dataset that needs to be input into the model. The intercepted fault data from the same class and different classes are randomly combined to form the same and different class sample pairs. In the input sample pairs, we input the bearing vibration data from the sample pairs into the two symmetrical feature extraction branches separately.
The whole network needs to input pairs of samples in the format (x 1 , x 2 , Y). Each sample pair contains a label Y. When Y is 1, it means that the input sample pairs are fault data of the same category. However, when Y is 0, it means that the input sample pairs are the fault data of different categories. The corresponding x 1 and x 2 represent the two vibration data to be input. Further details of the data pre-processing can be found in Section 4.1.
Sub-Network
The sub-network part has two feature extraction modules with shared weights. The shared weights ensure that the results obtained from the two branches are comparable during similarity classification. The main purpose of this part is to extract the feature vector of the bearing fault data after convolutional processing using an optimized subnetwork. The structure of the sub-network consists of multiple multi-scale and reduced dimensional feature extraction modules. The fused multi-scale feature information is used to enhance the model's ability to obtain information from the samples. The function of the dimensionality reduction module is as a reducing parameter.
Similarity Classification
The similarity classification part mainly uses the distance formula to calculate the similarity between the feature vectors of the two branches. A similarity percentage is given after normalization. This similarity percentage is used to determine whether the two input bearing fault vibration data are of the same class. We use the trained similarity calculation model in combination with the few-shot learning test method to realize bearing fault diagnosis.
To implement the similarity calculation module, we first use the distance formula to obtain the distance between two feature vectors. The closer the two feature vectors are, the more likely that we can assume that they are the same class. When far apart, they are different classes. We chose the Euclidean distance as the metric formula for the two feature vectors. The formula is as follows.
where x 1 and x 2 are the input samples. t(x 1 ) and t(x 2 ) are the feature vectors obtained after sub-network processing.
The output of the entire network indicates the similarity of the sample pairs. In fact, this problem has been transformed into a binary classification problem in the similarity classification module. This is to give a probability to determine whether two input samples are of the same class or different classes. We use the sigmoid function to map the distance between two feature vectors to the range of (0, 1). The probability is used to intuitively predict the magnitude of the distance between the two vectors. The formula to calculate the output is as follows.
where FC is full connection processing for Euclidean distance output. sigm is sigmoid function. p(x 1 , x 2 ) is the probability of the similarity of sample pairs. As the whole similarity calculation module is transformed into a binary classification problem. When the entire network is trained, binary cross entropy is used as the loss function of the network. The corresponding formula is as follows: where Y(x 1 , x 2 ) represents the corresponding label. The same is "1", and different is "0". Once the loss function is determined, a gradient descent function can be used to train the Siamese network. The model weights are fine-tuned over multiple cycles by using forward and backward propagation. A network model that can determine the similarity of the fault samples is trained. We can use the trained similarity classification model and few-shot learning test method for bearing fault diagnosis.
Few-Shot Learning Testing
The C-shot K-way testing is generally used to test the model under the situation of small samples. K classes are extracted from the existing dataset, and C samples from each class are taken to build the support set as the test criteria. The test set is called the query set. We use the similarity classification module to calculate the similarity probability between the support set and the query set. With the help of the similarity probability, we can easily determine the category of the test sample. The general testing methods include one-shot K-way testing and C-shot K-way testing.
In one-shot K-way testing, each class in the support set S contains only one sample. This test aims to calculate the probability of similarity for each sample pair consisting of a support set and a query set. The sample pairs with the highest probability are of the same kind. The definition of S and the formula for the highest score T are as follows.
In the C-shot K-way testing, each class in the support set S contains only C samples. Unlike one-shot K-way testing, this is performed by comparing the maximum of the sum of the probability. The specific formula is as follows.
Sub-Net Optimization
Siamese networks have been proven to be effective in dealing with small sample size problems. However, when the structure of feature extraction network is simple. The model cannot effectively extract enough features from the samples for classification. This article improves on the branches in the Siamese network to overcome the above problem. The sub-network is improved to a multi-scale and lightweight structure. The model extracts rich features through convolution kernels of different sizes. We use 1 × 1 convolution kernels to compress the model and reduce the calculations. This method can improve the model feature extraction capability and model accuracy. The optimization of sub-network module is shown in Figure 3.
The optimization for sub-network module is mainly inspired by the Inception module. The improvement of the sub-network is based on a convolutional network with a first layer of wide convolution [29]. As shown in Figure 3, the new model retains the wide convolutional layer of the first layer in the original network. This is to extract more feature information from the one-dimensional bearing vibration data, while other layers introduce multi-scale modules and 1 × 1 for sub-network optimization. The 3 × 3 and 5 × 5 convolution kernels generate a huge amount of computation when there are too many parameters in the input. We introduce a 1 × 1 convolution kernel to achieve dimensionality reduction of the data, thus, reducing the amount of calculation and model parameters.
(a)sub-network After the model is processed by the first layer of wide convolution, it continues to be processed by the multi-scale module to obtain richer feature information. The multi-scale module can have different perceptual fields compared to the conventional convolution module. There are two types of multi-scale convolution modules in the model. One is a combination of 3 × 3 convolution and 5 × 5 convolution named IncConv1. The other is the combination of 1 × 1 convolution and 3 × 3 convolution named IncConv2. Due to the larger scale of the data in the first stage. The IncConv1 is chosen for processing in the model. As the scale of the processed data decreases and the depth of the model increases, the IncConv2 is gradually chosen for processing. This is to reduce the parameters and computational effort.
The parameters of the convolutional layers in the sub-network are as follows: the input part is 2048 × 1 size data. The size of the convolutional layer in the first layer is 64 × 1 and contains a total of 16 convolutional kernels. The step size of the convolutional layers after this one is 1. The size of the second layer is 5 × 1 and contains 32 convolutional kernels. The third layer is the IncConv1 module mentioned above. It is a combination of convolutional kernels of sizes 3 × 3 and 5 × 5. The fourth and fifth convolutional layers are the IncConv1 modules. These are a combination of 1 × 1 and 3 × 3 size convolutional kernels.
Before the fully connected layer is a dropout layer with a parameter set to 0.5. It is used to prevent overfitting during model training and to accelerate the convergence of the model. The final output is a fully-connected layer with an output of 100. We add a maximum pooling layer after each convolutional layer. The size of each maximum pooling layer is 2 × 1, and the stride is 1. The maximum pooling is to reduce the model parameters and increase the computational speed while extracting features robustly.
Processing of the Network
The specific operations can be seen in Figure 4 in the following modules. • Preprocessing: The bearing fault vibration data is segmented using sliding windows to obtain the bearing fault samples. We construct a training set and a test set according to the requirement that the model input is a sample pair. • Model training: The training set is divided into sample pairs with equal proportions of the same and different classes of faults. We feed the training samples into the network. Then, the network is trained by using the Adam gradient descent algorithm and a binary cross entropy loss function. Finally, we save the model with the best training results. • Model testing: We first combine the samples from the test set and the training set in order to form a support set. The trained optimal model is used in the similarity probability calculation. The sample pair with the highest similarity probability among the obtained multiple similarities is selected as the fault class.
Data Set Preparation
We must understand the performance of the proposed network structure in the case of insufficient samples. Three datasets are used for validation in this experiment. They are the Case Western Reserve University (CWRU) bearing fault dataset [30], Mechanical fault Prevention Technology Institute (MFPT) bearing fault dataset [31] and Laboratory simulated bearing fault dataset.
(1) CWRU bearing fault dataset For this experiment, the 12 kHz bearing fault on the drive side from the Case Western Reserve University bearing dataset is used as the experimental data. The fault types are divided into four categories: normal, ball fault, inner ring fault and outer ring fault. Each fault, in turn, contains three fault categories of 0.007, 0.014 and 0.021 inch dimensions; therefore, the total number of fault categories is 10. The specific classification is in Table 1.
(2) MFPT bearing fault dataset The MFPT dataset is provided by the Mechanical Prevention Technology Association. The dataset contains data from the experimental bench and three real-world fault data. Fault types are divided into three categories: baseline conditions, outer race fault conditions and inner race fault conditions. The sampling frequency of the data set is 25 Hz. We selected seven types of data from MFPT to construct the experimental dataset. The fault types are classified into three categories: normal, outer ring fault and inner ring fault. Each fault class data is selected with load conditions of 50, 200 and 300 lbs. The total number of classes of the fault categories in the experimental dataset is seven. The specific classification is in Table 2.
(3) Laboratory bearing fault dataset The main structure of the test bench is shown in the diagram below. The components are the following: accelerometer, bearings, motors, acquisition cards, frequency converter and external computers and other key devices. The positions of the individual devices are marked in Figure 5. The entire experimental equipment is rotated by motors driving the bearing parts. The accelerometers collect the vibration signal in real time. The vibration signal is then transferred to the computer for storage and analysis by means of an acquisition card.
The experiments conducted in this case are set up for three fault situations. The faults are outer race fault, inner race fault and ball fault. All three faults are set as scratch faults. Three faults are set to penetrate in the axial. The width of the fault is 1.2 mm, and the depth is 0.5 mm. All three faults are tested twice at 1800 and 3000 r/min, respectively. Therefore, all fault categories are divided into six categories. Details of the corresponding health conditions are shown in the following Table 3. Table 3. Laboratory bearing dataset classification description.
Fault Location (r/min) Status Labels Number of Training Samples Number of Test Samples
Out race ( Each type of fault data is a vibration signal collected by an accelerometer. To ensure consistent conditions with the comparison schemes, the dataset is constructed based on the method in [29]. The detailed schematic diagram for building the training and test sets is shown in Figure 6. We build the training set from the first half of the vibration signal and the test set from the second. Each training sample is 2048 points in length. We use a sliding window with a step size of 80 to intercept the training samples sequentially backwards. The data intercepted by the sliding window is the training set. The second half of the vibration signal is divided into multiple non-overlapping test samples, and each test sample also contains 2048 points.
Training samples
Test samples
Experimental Setup
The training samples are divided into the training set and validation set. By comparing the loss rate under different ratios in Figure 7, the ratio of the training set and validation set is configured to be 4:1 for better convergence performance. In addition, the model is implemented using the Keras library and Python 3.6. The total epoch of model training is 15,000, and the small batch size is 32. The optimal model is saved after 20 training sessions have been conducted in the experiment. To validate the performance of the models obtained by training under different samples, the quantities 60, 90, 120, 200, 300, 600 and 900 are randomly selected on the CWRU and Laboratory datasets. The number of fault types selected in MFPT is seven. For the sake of balance of the training data, we randomly select the quantities 70, 105, 140, 210, 280, 490 and 700.
The sample pairs we input each time are randomly selected from the above training set. When they belong to the same class, they are labeled as positive samples; otherwise, they are negative samples. We also need to ensure that the number of positive and negative sample pairs is equal to ensure a balanced sample.
In Experiment 1, we vary the number of multi-scale modules in the model to determine the optimal model structure. In Experiment 2, we test the model on three datasets to verify the performance of MLS-net. In Experiment 3, we visualize the model performance using visualization tools. In Experiment 4, we calculate the model size and parameters.
The following three methods will be tested on the three datasets to compare with the new proposed model.
1.
Support Vector Machine (SVM): SVM is a classical machine learning method for dichotomous classification problems. We use an SVM between any two classes to implement a multi-classification task.
2.
One-Dimensional Convolutional Neural Network (1D-CNN) [29]: 1D-CNN, which is a five-layer DCNN. It uses 64 × 1 convolutional kernels for the first layer and 3 × 1 convolutional kernels for the next four layers. The corresponding number of convolutional kernels is 16, 32, 64, 64 and 64. We add a maximum pooling layer of size 2 × 1 after each convolutional layer. The final layer is a fully connected layer with an output of 100. 3.
The baseline Siamese network (BS-net) [32]: BS-net has the same structure as the proposed Siamese network. However, the structure of sub-network in the Siamese network is the 1D-CNN mentioned above.
Determination of the Number of Multi-Size Modules
We want to determine the optimal number of multi-size modules in the model. The multi-size modules are divided into two categories by the introduction of the sub-network. The larger size is a fusion of 5 × 5 and 3 × 3, which we call IncConv1. The smaller size is a fusion of 3 × 3 and 1 × 1, which we call IncConv2. Under the premise that the sample size is set to 60, we will vary the number of these two modules to determine the optimal number of modules.
The comparison between Tables 4 and 5 shows that the trend of the accuracy of the model decreases as the number of the IncConv1 increases. This shows that the number of modules for IncConv1 should be 1. Figure 8a,b shows the variation of accuracy on each data set and the mean of the three types of data. The mean lines in both plots show that, as the number of modules increases, the accuracy rate decreases. However, our previous analysis shows that the number of IncConv1 should be 1.
Therefore, we only need to observe Table 4 to determine the number of IncConv2. We find that the accuracy rate decreases as the number of modules increases.The accuracy of the models is similar at number 1 and 2; however, the total number of parameters is different. To balance the accuracy and the total number of parameters, we finally decided to set the number of IncConv2 to 2.
Model Effects with Different Sample Sizes
In this section, we want to verify that the proposed method performs well in the case of insufficient samples. We chose the methods described above: (SVM), 1D-CNN, BS-net and MLS-net for performance comparison. Several models are tested on three bearing fault datasets. Table 6 and Figure 9 show the results of the experiments. It is clear that the MLS-net shows the most excellent results. We analyze the results of each dataset and see that the SVM method has a significant difference in accuracy compared to the other methods. The accuracy of the SVM differs from other methods by nearly 20% or more when the sample is insufficient. There is also a 10% difference in accuracy with a large number of samples. It can be seen that the deep-learning approach is far superior to SVM. Compared with 1D-CNN, the Siamese network model is more complex in structure. The model cleverly uses metrics for similarity calculation and incorporates few-shot learning methods. This makes the ability of fault classification significantly better than 1D-CNN in the case of small samples. The MLS-net is compared with BS-net by experimental data. When the samples are insufficient, the accuracy of the MLS-net is improved in all cases. It can be seen that the introduced multi-size convolutional module can obtain richer information. With the increase of samples, the accuracy of both tends to be the same. Sometimes the accuracy of the old model is higher than that of the MLS-net. The difference between the two models is within 1%. It can be seen that there is minimal loss of accuracy when the sample is sufficient.
Visualization Analysis
We attempted to obtain a better understanding of how well the model performs in the presence of insufficient samples. We would like to make further proof by using the feature visualization method of t-SNE and the confusion matrix of the test results. In Figure 10, we show the visualization of the last layer of the fully connected layer on the CWRU dataset and Laboratory dataset. The number of samples for model training is set to 90. In Figure 11, the confusion matrix plot of the test results on these two datasets is also shown. The comparison methods used in both plots are the BS-net and the MLS-net proposed in this paper.
In Figure 10, the Figure 10a,b are of the CWRU dataset. Figure 10c,d are the Laboratory dataset. As can be seen in the figure on the CWRU dataset, the MLS-net can be clearly seen on categories 1, 2 and 3 with a good distinction. Whereas, on the BS-net it shows that the three categories are mixed together and cannot be clearly distinguished. It can be seen that the BS-net is not as good at classifying as the MLS-net. This problem is more apparent in the Laboratory data set. Multiple classes are mixed together and all data distribution is discrete on the BS-net. This problem is well resolved in the plots of the MLS-net. It can be seen that the MLS-net has a better ability to classify samples with small samples.
In Figure 11, Figure 11a,b are the CWRU bearing dataset and Figure 11c,d are the Laboratory bearing dataset. As we can see in both Figure 11a,b, the number of accurate judgements for each category in Figure 11a is greater. Whereas, in Figure 11b, it is clear that the accuracy of per category is much lower. In Figure 11c,d, the comparison of the two models is also the same. This shows that the new model also has a superior performance in prediction compared to the BS-net. At the same time, the performance of the MLS-net is consistent across the different dataset. It means that the MLS-net can be applied to practical bearing fault diagnosis.
Model Lightweight Comparison
In this subsection, the main purpose is to analyze the comparison of model size under different models and datasets. The results of the experiment mainly contain the total number of model parameters and model sizes for SVM, 1D-CNN, BS-net and MLS-net under the three dataset. The recently proposed bearing fault diagnosis models ANS-net [22] and LEFE-net [23] are also compared. We jointly determine the merit of a model based on the parameters and the accuracy rate. A model that has fewer parameters while having the higher accuracy will have superior performance. The system cost and the speed of computation will be greatly increased under fewer parameters. The details are shown in the following Figure 12 and Table 7.
In Figure 12, we mainly depict the relationship between model accuracy and total number of parameters. The horizontal coordinate indicates the model parameters. The vertical coordinate indicates the accuracy rate. The accuracy of each model is obtained from the experiment when the sample size is set as 900 for the CWRU dataset. As we can see from Figure 12, MLS-net shows the better performance in terms of the model parameters and accuracy compared with 1D-CNN and BS-net.
Although ANS-net has similar accuracy with MLS-net, the number of MLS-net parameters is only 41449, which is greatly reduced. The ANS-net, on the other hand, has far more than 100,000 parameters. LEFE-net has fewer parameters than ANS-net. However, its accuracy is lower than ANS-net and MLS-net when the training sample size is 900. When the sample drops to 60, the accuracy of LEFE-net will be further greatly reduced. Overall, MLS-net is able to run efficiently with less computation cost as well as guaranteed accuracy. From Tables 6 and 7 and Figure 12, it is clear that MLS-net has a significant improvement in model size and accuracy. Specifically, SVM is 100-times larger than MLS-net in terms of model size, while its accuracy is only 50% of MLS-net under small samples. MLSnet is also more advantageous in terms of the parameters of the model. It compresses about 20% parameters in comparison with BS-net and 1D-CNN. However, MLS-net under small samples improves the accuracy compared to 1D-CNN with an improvement of 10-15% and improves the accuracy by about 2-5% compared with BS-net.
MLS
ANS-net was recently proposed as a bearing fault diagnosis model for the small sample case. Although it has a high accuracy rate under small samples, a large number of parameters (more than 100,000) are needed to ensure the accuracy. In addition, MLS-net performs better in accuracy and lightweight than the lightweight bearing fault diagnosis model LEFE-net. Through the above experiments, MLS-net is proven to have a lighter model structure and better accuracy under small samples, which can greatly improve the efficiency of bearing fault diagnosis.
Conclusions
In this paper, we proposed the MLS-net for the end-to-end bearing fault diagnosis problem. The model has a great ability to classify in the case of small samples. It also has a multi-scale feature fusion module to enable further feature information to be acquired. With dimensionality reduction, the model is also able to obtain comparable accuracy with fewer parameters. The model was mainly designed based on the idea of metrics. Two symmetrical sample feature extraction modules with shared parameters are contained. These are mainly used to extract the feature vectors of the two sample pairs of the input. The similarity calculation module is used to calculate the similarity of the two extracted feature vectors. Thus, the trained model has the ability to compare the similarity probability between the standard samples and predicted samples. This enables the classification of the bearing fault.
To better validate the proposed model MLS-net, we tested it on three datasets to demonstrate its performance. The results show that the model had higher accuracy with fewer parameters when the sample was insufficient compared to recently proposed methods. This proves that MLS-net as proposed in our paper makes a good tradeoff between the accuracy and computing cost. In addition, the results were consistent across the three datasets tested. This indicates that the whole model has good generalization ability for different fault datasets.
The model showed good performance by retraining the method in this paper on multiple datasets. However, the need of retraining the model each time makes the operation cumbersome. In future work, we can focus our research more on the transfer scenarios of the model and fault diagnosis in noisy environments. | 8,192.8 | 2022-04-29T00:00:00.000 | [
"Computer Science"
] |
Hurricane impacts on a coral reef soundscape
Soundscape ecology is an emerging field in both terrestrial and aquatic ecosystems, and provides a powerful approach for assessing habitat quality and the ecological response of sound-producing species to natural and anthropogenic perturbations. Little is known of how underwater soundscapes respond during and after severe episodic disturbances, such as hurricanes. This study addresses the impacts of Hurricane Irma on the coral reef soundscape at two spur-and-groove fore-reef sites within the Florida Keys USA, using passive acoustic data collected before and during the storm at Western Dry Rocks (WDR) and before, during and after the storm at Eastern Sambo (ESB). As the storm passed, the cumulative acoustic exposure near the seabed at these sites was comparable to a small vessel operating continuously overhead for 1–2 weeks. Before the storm, sound pressure levels (SPLs) showed a distinct pattern of low frequency diel variation and increased high frequency sound during crepuscular periods. The low frequency band was partitioned in two groups representative of soniferous reef fish, whereas the high frequency band represented snapping shrimp sound production. Daily daytime patterns in low-frequency sound production largely persisted in the weeks following the hurricane. Crepuscular sound production by snapping shrimp was maintained post-hurricane with only a small shift (~1.5dB) in the level of daytime vs nighttime sound production for this high frequency band. This study suggests that on short time scales, temporal patterns in the coral reef soundscape were relatively resilient to acoustic energy exposure during the storm, as well as changes in the benthic habitat and environmental conditions resulting from hurricane damage.
Introduction
Ecosystems throughout the world are increasingly threatened by multiple natural and anthropogenic stressors, often leading to ecosystem shifts from desirable to less desirable states [1][2][3][4]. Coral reefs are the some of the most diverse ecosystems on Earth, and the transition between disturbance states is often observed through changes in coral reef community composition and ecosystem function [5][6][7]. The desired state is an environment that supports critical ecological processes and resulting patterns across space and time, such as overall system production, key predator-prey (or grazer) interactions, and reproduction across multiple functional a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 groups [8,9]. Disturbance states in coral reef ecosystems are often described as a shift from the desired, structurally complex coral dominated reef, to a less desirable macroalgal dominated state [10][11][12], as well as reductions in major functional groups such as herbivorous grazers, prey fish stocks, and apex predators [13,14].
Natural disturbance impacts to coral reef ecosystems vary from chronic events such as coral predation, bioerosion, and intermittent disease, to pulsed catastrophic events such as mass bleaching and hurricanes [15][16][17]. In certain geographic regions such as the Caribbean, Bahamas and Florida, coral reefs are prone to hurricanes that can cause massive structural damage [18,19]. Hurricanes can disrupt and reduce ecosystem functions and services [20][21][22][23][24]. Longterm habitat degradation and the persistent decline in the three-dimensional structure of coral reefs can have cascading consequences for reef fish diversity, fisheries, and ecosystem services [25][26][27]. The response of reef fauna to disturbed benthic habitats can lead to a shift in their spatial distribution as changes in prey densities or habitat specialists compete for sufficient refuge space [28]. Conversely, large-bodied, transient reef fish are more likely to survive an immediate decline in benthic cover because of their ability to relocate to a presumably more desirable habitat [29]. Hurricane impacts have been widely assessed in coral reef ecosystems as catastrophic events that not only promote long-term declines in habitat quality (e.g. algal regime shifts, sedimentation), but further hinder recovery processes from other chronic stressors such as coral disease, overfishing, pollution, and sedimentation [18,24,30,31]. Nevertheless, we know little about how extreme episodic impacts, such as hurricanes, alter the behavior of biological sound production, or biophony, of soniferous (sound-producing) species.
One of the most common quantitative methods to assess the magnitude of change within a population or community pre-and post-disturbance is to assess the temporal response of species composition using abundance or biomass indices [22,32,33]. Recent application of passive and active acoustic sampling techniques now allow studies of hurricane impacts to marine organisms using a before, during, and after impact approach [34][35][36]. For example, acoustic telemetry (active acoustic techniques) of tagged fish characterized the success or failure of nocturnal foraging reef fish (e.g. grunts, snapper) to find refugia, as well as their vulnerability to predation, following a severe disturbance [37]. A recent study on juvenile bull shark (Carcharchinus leucas) movements before and after hurricane Irma also described predatory behavioral responses related to shifting prey densities [38]. Although active acoustic studies provide information on individual animal movements, passive acoustic monitoring provides information on soniferous species assemblages that use sounds to communicate, and thereby can be sampled to reflect potential deviations in behavior in response to disturbances such as hurricanes [39][40][41].
Soundscape ecology
Soundscapes, the collection of biological, environmental, and anthropogenic sound sources within an ecosystem, can provide high resolution spatiotemporal information about ecosystem patterns and processes [42][43][44]. Critical information about habitat-specific biodiversity and environmental conditions can be derived from passive acoustic monitoring [44,45]. Additionally, soundscape analysis allows for the passive acquisition of species assemblage patterns without the influence of human interactions [46][47][48]. The presence of divers can alter fish distribution and behavior as a negative (i.e. avoidance) or positive (i.e. aggregate) association with human presence [49][50][51]. Soundscapes provide a collection of empirical data in a natural state without disrupting critical biological or ecological interactions, and allows for visualization of various temporal patterns in acoustic activity and inferred behavior (e.g. hourly, daily, annually, seasonally) for soniferous species. The ecological application of soundscapes is becoming more widely accepted as an indicator of species presence/absence, habitat associations, and complex biological interactions (e.g. territorial behavior, spawning aggregations, migratory patterns) with applications across a wide range of terrestrial (e.g. woodland forest, desert) [52-54] and aquatic (e.g. coral reefs, oyster reefs, seagrass beds, kelp forest) [55][56][57][58][59] ecosystems.
Application of soundscape ecology to disturbance impacts
Soundscape methods have been useful in distinguishing between healthy and degraded ecosystems largely by recording the presence and absence, as well as behavior of key soniferous taxa [44]. For example, distinct changes in important ecological behavior (e.g. foraging, mating) or daily activities across space and time can reflect noise avoidance or acoustic masking [60-62], with the former resulting in quieter areas due to a decrease in soniferous species abundance and diversity [63,64]. Landscape ecology studies are increasingly relying on bioacoustic monitoring to assess deleterious impacts resulting from human land use activities (e.g. clear cutting, forest fire, habitat destruction, noise pollution etc.), and to assess changes in biodiversity, spatial distribution, and animal behavior [65][66][67]. In a broader context, soundscape studies in terrestrial systems are proving to be instrumental in rapidly assessing biodiversity and informing management recommendations for ecological conservation in the aftermath of detrimental anthropogenic and natural disturbances [68,69]. Thus, understanding the interaction between disturbance states due to natural or anthropogenic impacts and changes in a soundscape are becoming increasingly relevant to management in terrestrial [70, 71] and aquatic ecosystems [72,73].
Underwater soundscape monitoring is unique in its access to sound-producing invertebrates and resident reef fish assemblages. In underwater environments, sound is an important indicator of habitat quality [74][75][76], where relatively high densities of soniferous species may indicate high ecosystem health or structural complexity via an abundance of refugia [77]. For example, Freeman & Freeman [78] used coral reef soundscapes to quantitatively assess the correlation between dominant biological frequencies and habitat quality, in which macroalgal dominated reefs are an indicator of reef degradation and were dominated by high frequency sounds produced by benthic invertebrates. Underwater soundscape studies have been successful in characterizing critical spawning habitats, estimating soniferous species abundance, and characterizing community-level interactions [79][80][81] by collecting semi-continuous, non-invasive information when traditional sampling methods, such as use of nets or diver surveys, are logistically not feasible (e.g. at night, during extreme storm events). Recent work on coral reef soundscapes have provided baseline data on spatiotemporal variation of coral reef soundscapes across various disturbances states such as dead coral cover, high crustose coralline algae cover, and other degraded habitats [82][83][84][85].
The presence of rainfall, wind and wave activity on the ocean's ambient soundscape is well established [86,87]; however, few studies have focused specifically on modification in the soundscape during extreme weather events, such as hurricanes. Weather and climate may also indirectly influence abiotic sound production by controlling the distribution of ice at high latitudes [88], with the collapse of large ice sheets in Antarctica elevating sound levels throughout the southern Pacific and Indian Oceans [89]. In some ocean basins, the soundscape may be disrupted by large earthquakes that generate high amplitude sounds over time scales of minutes, or by intense episodes of submarine volcanism, which may extend for periods of days-toweeks [90][91][92][93]. The potential ecological significance of these transient natural sound sources is not well understood; however, like hurricanes, they dominant the low-frequency portions of the acoustic spectrum that can be critical in the communication of many marine fauna.
Hurricane Irma and objectives
On September 2017, Hurricane Irma (Category 4) traveled across the Lower Florida Keys with sustained hurricane force winds (>64kts) extending 130 km from the center [94]. Hurricane Irma passed directly over the Florida Keys National Marine Sanctuary (FKNMS) nearshore marine habitats before making landfall near Cudjoe Key, Florida (USA) [94,95]. Short-term impacts by large freshwater inflows resulted in changes in the phytoplankton community in nearby coastal canals, with phytoplankton communities returning to normal seasonal patterns within 3 months after the hurricane [96]. The impacts to the Lower Florida Keys seagrass communities from Irma were generally localized, with species-specific beds of seagrass uprooted, and loss of seagrass from storm water runoff resulting in low dissolved oxygen and persistent hyposalinity, similar to historical datasets [97,98]. Coral reefs in the Middle and Upper Keys showed a significant decline in abundance of the keystone urchin grazer Diadema antillarum, as well as loss of sponges and hydrocorals due to high sedimentation [99].
During October 2017, NOAA science divers and partners surveyed more than 50 coral reef sites from Biscayne Bay (near Miami) to the Marquesas (southwest of Key West) and described severe damage in the Middle and Lower Florida Keys sponge and coral communities from storm force waves, fast-moving debris, and heavy sediment deposits [100]. Sedimentation was the most common impact among sites, resulting in poor visibility and high amounts of marine debris [100]. In December 2017, NC State science divers surveyed eight fore-reef sites, including ESB and WDR, and observed poor visibility (<3m), loose rubble, collapsed reef ledges with a mix of schooling species, as well as sedimented and fragmented sub-massive reef-building corals (Fig 1, personal observation K. Simmons). The short-term disturbance in environmental conditions and the remaining fractured reef habitat structure likely impacted marine faunal interactions and behavior; however, little is known about how these changes in the coral reef habitat are reflected in the sound production of coral reef animals that are mobile.
Passive acoustic recordings were used to characterize the underwater soundscape of the coral reef tract in the lower Florida Keys, USA before, during and after Hurricane Irma. In the weeks following the storm, the biological sounds produced by fish exhibited similar pre-disturbance temporal patterns, and the high frequency noise associated with snapping shrimp showed only a small shift in its diurnal patterns. This opportunistic study investigates the utility of soundscapes in assessing disturbance impacts to the coral reef soundscape generated by soniferous reef fishes and snapping shrimp within a track of the Florida Keys reef system impacted by Hurricane Irma. The main objectives of this study were to (i) quantify the cumulative acoustic exposure associated with the passage of hurricane Irma, and (ii) identify and quantify temporal changes within the biophony in response to Irma with emphasis on daily and diurnal soundscape patterns.
Study system
Underwater soundscape characterization was conducted within the lower (Zone D) FKNMS, which comprises a network of marine reserve types and regulated fishing habitats designated in 1990 [101] (Fig 2). This region is part of the Florida Keys Coral Reef Tract, a large bank-barrier reef system that extends 350 km from the Florida Straits northward to St. Lucie Inlet, Martin County [102]. The lower FKNMS habitat includes a mosaic of shallow, marginal reef systems with spur-and-groove reef formations, reef rubble and a diverse array of hardbottom habitat (e.g. stony corals, soft corals, sponges, macroalgae, adjacent seagrass beds). No-take, marine reserves within the FKNMS vary in size, yet most are relatively small (~0.2 to 0.5km 2 ).
The Lower Keys often have higher salinity and turbidity relative to the Middle and Upper Keys region due to nearshore transport of nutrient-rich deep water [103] facilitated by the Florida Current, gyre system [104,105].
As a part of a larger study by our research group, eight hydrophones were deployed in July 2017 across several marine reserve zones. (Fig 2). The other hydrophones were lost, presumably due to wave action and surge from the hurricane. The hydrophone at Western Dry Rocks was recovered after the hurricane lying in sand near the mooring, which removed our ability to use these data to quantitatively assess the post-disturbance soundscape. Western Dry Rocks (WDR-24.445˚N, 81.926˚W) is a regulated fishing site~22 km southeast of Key West, FL within the FKNMS. This reef is characterized by wide spur-and-groove sand channels with high relief ledges and a mean depth of 6m. Although live coral cover is relatively low compared to protected reefs, the benthos consists of gorgonians, coral rubble, and hard-bottom substrate. Eastern Sambo (ESB-24.491˚N, 81.664˚W) is one of four Special-Use Areas (SUA) or no-entry/no-take zones within the FKNMS, and is no-access except for permitted scientific research, restoration, monitoring, or educational purposes. This reef is characterized as a spur-and-groove bank reef with a mean depth of 5m comprised of massive reef building corals, sponges, and gorgonians.
Environmental data collection
Hurricane Irma's track, wind swath and landfall data were accessed from NOAA's National Hurricane Center report on Irma (https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf). Hurricane Irma made landfall near Cudjoe Key in the lower Florida Keys at 08:00 Eastern Standard Time (EST) on September 10, 2017 (Fig 2) before continuing north toward central Florida. Maximum wind speeds reached 115kts with a minimum barometric pressure of 931 hPa at landfall. Wind swath radii were defined as maximum sustained 1-minute wind speed values for tropical storm force winds (34kts), storm force winds (50kts) and hurricane force winds (64kts).
Barometric pressure data were used, independent of the acoustic time series, to delineate the passage of the storm over the reef. Storm duration was defined as the time window over which the pressure fell and remained below its 2.5% quantile level for data collected between July and October 2017. Barometric pressure data were obtained from the Sand Key Lighthouse, Buoy Station ID SANF1 (24.456˚N, 81.877˚W), located~5km from WDR. These data were recorded hourly with a standard barometer elevation at 14.6 m above the mean sea level. Because underwater acoustic time series are non-stationary (i.e., have time dependent mean and variance), soundscape changes must be evaluated within time windows before and after the storm that minimize the effect of processes, such as lunar phase (e.g., [85,106,107]), that are likely to influence biological sound production on relevant timescales. To account for this potential influence, 18-day and 24-day periods spanning the same portion of the lunar cycle around the New Moon were identified before (New Moon on August 21st) and after (New Moon on September 20th) the hurricane. These time periods were constrained by the timing of the storm and length of our acoustic time series. Astronomical data were obtained from the US Naval Observatory Portal (www.usno.navy.mil/USNO).
Acoustic data collection and analysis
The coral reef soundscape was monitored via bottom-mounted hydrophones (Soundtrap ST300, Ocean Instruments NZ) suspended~0.15m above the sandy bottom of the fore-reef at each reef site. Both hydrophones began recording on 14 July 2017 and ended on 01 October 2017 (WDR) and 17 October 2017 (ESB). Both recorders were recovered by a dive team on December 2017. The WDR hydrophone was found lying flat in the sand and detached from the mooring. A spectrogram of the WDR data ( Fig 5) indicates a change in acoustic coupling after the hydrophone came into contact with the seabed. Although fish chorusing and snapping shrimp activity are still evident in the time series, the post-storm WDR data were excluded from our quantitative before-after comparisons of the soundscape.
The hydrophone recorders were calibrated with a flat frequency response over the~0.02-40kHz band. Hydrophones were programmed to record 2 minutes of acoustic data every 20 minutes (72 files/day) with 16-bit A/D conversion and at a sample rate of 48kHz. Acoustic recordings were processed in MATLAB using purpose-written code. The mean spectrum of the acoustic data was calculated for each 2-minute recording using the fast Fourier transform, with a window length (NFFT) of 2 14 samples and a frequency resolution (Δf) of 2.93 Hz. Hydrophones were also equipped with a temperature sensor that recorded once during each acoustic sampling period.
Fish sounds occupy the low-frequency spectrum (<50Hz to several kHz), often competing with background environmental noise (i.e. wind, wave action) in similar frequency bands [86,108]. Sound Pressure Levels (SPLs) were calculated at several frequency bands of ecological interest: (1) a low frequency band L1 (50-300Hz) representative of the fish families Serranidae [109][110][111], Holocentridae [112], and Pomacentridae [113], (2) a low frequency band L2 (1.2-1.8kHz) representative of Haemulidae [114,115], Lutjanidae [116], Scaridae [81], Sciaenidae [116,117], and (3) a high frequency band H (7-20kHz) representative of snapping shrimp (Alpheidae), which are a dominant sound producer in coral reef habitats [82,83,118,119]. See S1 Fig To minimize the potential influence of anthropogenic noise and "fish bump" signals in the acoustic time series, SPL data before and after the storm were trimmed to exclude files constituting the loudest 2% of the data over these combined intervals (S2A Fig). Incidental fish bumps are transient signals caused by the physical interaction of an animal with the hydrophone or hydrophone mooring, generating artifacts in the acoustic data [120][121][122]. Trimming excludes those files containing large amplitude fish bumps (S2B and S2C Fig), as well as files with anomalously large SPLs due to transient boat noise (S2D Fig). For a given recording window (i.e. 00:00, 00:20, . . ..23:40), the trimmed mean SPLs before and after the storm were calculated for each band (L1, L2, and H). Uncertainty (68% confidence interval) was estimated using a bootstrap resampling procedure (see below).
Generally, for reefs in the South Atlantic and Caribbean, as well as in other coral reef systems, biologic sound production varies diurnally. These patterns often reflect the abundance or acoustic behavior of multiple species, with times of peak acoustic activity in a given frequency band varying between reef systems [e.g., 57, 83,106,123,124]. Because these daily acoustic patterns tend to persist, even as average SPLs may rise and fall, the disruption of this pattern following a disturbance event may indicate changes in the abundance or acoustic behavior of the impacted species. We therefore investigated the daily SPL patterns (over the 72 recordings made each day), as well as the diurnal (daytime vs. nighttime) differences in SPLs within each of the ecologically relevant frequency bands. The decibel difference between daytime and nighttime SPL provides way to normalize for the non-stationarity of the acoustic time series on longer time scales-as opposed to making inferences based on small changes in the absolute SPL before and after the storm. Daily and diurnal patterns are absent or masked during the storm, and therefore not discussed.
For each 24-hour period, daytime and nighttime mean SPLs were also calculated from the trimmed SPL time series. Daytime was defined as the period between local sunrise (05:48-06:19 EST) and sunset (18:15-19:18 EST), whereas nighttime was defined as the period between sunset and sunrise. Uncertainties (68% confidence interval) in the means were estimated using a bootstrap resampling (5000 draws). The probability that the mean daytime SPL was higher than the mean nighttime SPL on each day was estimated from the portion of resampled outcomes with SPL day > SPL night . Values of p � 1 indicate significantly higher daytime sound levels, and values of p � 0 indicate significantly higher nighttime sound levels on a given day.
For the 18-and 24-day windows assessed before and after the hurricane, the mean difference between daytime and nighttime SPLs, and the confidence intervals for this difference, were estimated using a paired resampling (5000 draws) of the nighttime and daytime means for each 24-hour period. The probability that daytime SPL was greater than nighttime SPL was calculated from the resampled differences, where p � 1 indicates significantly higher daytime sound levels, and p � 0 indicates significantly higher nighttime sound levels over the assessment window.
Hurricane acoustic energy exposure
Hurricanes represent broadly distributed acoustic sources, with the sounds recorded at each hydrophone arriving from a range of azimuths and incidence angles. However, to place the acoustic exposure at these reef sites in context and make comparisons with other sound sources, we quantified the acoustic exposure by representing all storm related noise as being sourced from a point at the sea surface directly above each hydrophone and calculating the equivalent energy.
Over the four-day duration of the storm, the received root mean square SPLs calculated for each file were corrected to acoustic source levels (referenced @ 1m) assuming spherical spreading loss between the sea surface and seafloor. The equivalent acoustic power (J/s) that radiated into the water column (i.e., across a 1 m radius hemisphere with surface area 2π) was then estimated assuming a constant water density (1030 kg/m 3) and sound velocity (1485 m/s) [125]. The acoustic energy was determined by integrating these power values over the duration of the storm, assuming each two minute file is representative of a surrounding 20 minute time window, and then subtracting the energy that would be calculated if the procedure was repeated using the mean background (pre-storm) noise levels. This energy exposure value can then be compared to the equivalent energy that would be associated with common natural and anthropogenic sources (e.g. fishing vessels) operating over a set duration (e.g., [126,127]) if these sources were fixed in position at the sea surface directly above the hydrophone. This value, however, does not represent the total acoustic energy imparted by the storm.
Environmental conditions
Barometric pressure data exhibited semidiurnal oscillations characteristic of the Florida Keys region (Fig 3A). The passage of the storm is marked by a period of low (< 1011 hPa) barometric pressure, which extends from~12:00 on September 8, 2017 to~12:00 September 12, 2017 (4 days), reaching a trough at 966 hPa on September 10, 2017 at 06:50 (all times EST). In analyzing the soundscape during the pre-and post-storm windows, a 1-day buffer was applied on either side of the hurricane, such that the pre-storm period ends on September 7 th at 12:00 and the post-storm period begins on September 13 th at 12:00.
Before and after the storm, daily bottom temperatures at WDR and ESB varied between 26-28˚C, except for a short period of slightly increased temperatures at ESB between August 15 to August 19, 2017, which was likely influenced by the lunar spring tide. Both sites exhibited a sharp decline in bottom temperature reaching 25˚C shortly after the hurricane made landfall. (Fig 3B). Post-hurricane, cooler water temperatures remained a few days longer at ESB than WDR before returning to pre-disturbance daily temperature oscillations.
Acoustic spectra
The acoustic spectra were assessed over the peak of the storm period (September 9 th -10 th ) and compared with the spectra over four-day periods immediately before (September 3 rd -7 th ) and after (September 13 th -17 th ) the storm (Fig 3). Over the four days before the hurricane, the spectra at each site was elevated broadly across the 50-300 Hz frequency range, with additional low amplitude spectral peaks in the frequency ranges of 600-900Hz and 1600-1900Hz being observed most clearly at ESB (Fig 4A). During the peak of the hurricane, the low frequency component of the soundscape was impacted most dramatically and median spectral power increased by 40-50 dB over pre-storm levels in the~10-100 Hz frequency range, and with multiple narrow band spectral peaks at frequencies of 100's to 1000's Hz observed at both sites (Fig 4C and 4D). Within the four-day window after the hurricane, spectra at ESB remained elevated in the 50-300 Hz and 1600-1900Hz frequencies (Fig 4E), yet the pre-storm peak between 600-900Hz was absent.
Acoustic energy exposure
WDR experienced a higher cumulative energy exposure than ESB estimated at 9.9 x 10 3 J and 4.8 x 10 3 J, respectively. In comparison to other acoustic energy disturbances commonly experienced in the lower Florida Keys region, the exposure over the duration of Hurricane Irma was comparable to small vessel (SL = 153 dB re 1μPa @ 1m) operating continuously [128][129][130] directly overhead for 1 week (ESB) to 2 weeks (WDR). The WDR hydrophone presumably detached from its mooring at some point during the storm; however, the exact timing of this event was not readily identifiable, and no corrections were made to account for potential changes in sensitivity of the instrument. Estimates of acoustic exposure also do not account for the signals produced by debris impacting the hydrophone and mooring, since this effect is not easily disentangled from the acoustic wavefield.
Biophony
Both sites showed temporal patterns in the biophony evident with their long-term spectrograms (Figs 5 and 6). A daily pattern of fish vocalizations within the L1 frequency band was apparent at WDR and ESB over the~2 month recording period before the hurricane, with increased sound levels around the full moons in August and September (Fig 5). Fish calls within both low frequency bands were masked or absent during the storm, before reappearing immediately after the storm (Figs 5 and 6). The apparent post-storm shift in high-frequency sound levels at WDR likely reflected a change in sensitivity of the hydrophone after it became detached from the mooring (Fig 5A). The low frequency bands at ESB diminished in intensity during the waning part of the lunar cycle and became more pronounced approaching the October full moon (Fig 5B). Additionally, the less pronounced, yet persistent fish calling evident in the L2 band was observed before and after the storm at ESB (Figs 5B and 6). The L2 band captured broadband fish calls, including the upper range of pulsated "grunts" (>1000Hz) and aggregated "knocks" between 1200-2500Hz, as well as including the lower range of snapping shrimp sound production in the high frequency band. Snapping shrimp activity within the H band persisted before and after the storm at both sites (Fig 5).
Temporal soundscape patterns
The daily patterns in SPLs before and after the storm were examined for the ESB site. Within the three frequency bands, trimmed means were calculated for each recording interval (00:00, 00:20. . . 23:40) over the 18-and 24-day windows capturing the same portion of the lunar cycle before and after the storm. The results for the 18-day windows are displayed in Fig 7, along with their bootstrapped confidence intervals. The dominant temporal pattern was a diurnal rhythm (day vs. night) in sound production, along with a small increase in high frequency noise during crepuscular periods. The daily pattern of low and high frequency sound production was largely maintained after the storm, with only small shifts in the average loudness. Within the L1 band, a small decrease in the average SPL is observed during the nighttime hours, with little change in the average level during the daytime hours. For the L2 band, a small decrease in the average SPL is observed during the daytime hours, with little change observed at night.
To investigate the diurnal patterns in more detail, the mean daytime and nighttime SPLs, along with their bootstrapped confidence intervals, were calculated daily for each frequency band (Fig 8). For each 24-hour period, the probability that the mean daytime SPL is greater than the mean nighttime SPL was estimated from the resampled means. Within the L1 frequency band, daytime SPL was consistently higher than nighttime SPL (p � 1), except for the time window when the storm passed over the reef (Fig 8A). Within the L2 frequency band, daytime SPL was consistently higher than nighttime SPL (p � 1) before the storm; however, there was no consistent diurnal pattern after the storm (Fig 8B). Within the H frequency band, Fig 6. Short-duration spectrograms from Eastern Sambo. Spectrograms displaying the low frequency patterns of sound production during 5-day windows around the full moons that occurred (a) before and (b) after the passage of Hurricane Irma. Spectrograms are derived using the average spectra with each two minute recording. Time-axis ticks indicate midnight EST. Sound pressure levels are elevated during daytime hours, relative to the nighttime hours. The daily pattern of sound production reflects the acoustic activity and/or presence of multiple species (see call example in S1 Fig). The diurnal pattern in low-frequency (L1) sound production is present before and after the storm. The diurnal pattern of mid-frequency (L2) sound production is a less pronounced, and appears to weaken after the passage of the storm. Panels on the right show average sound pressure levels during daytime and nighttime recordings averaged over the 5-day windows.
https://doi.org/10.1371/journal.pone.0244599.g006 there was no persistent diurnal pattern before the storm, yet daytime sound production decreased slightly after the storm creating a persistent pattern of higher nighttime SPL relative to daytime SPL (p � 0) (Fig 8C).
PLOS ONE
The magnitude and significance of these diurnal patterns in SPLs were quantified further by resampling the paired daytime and nighttime means over the 18-and 24-day windows before and after the storm. Fig 9 summarizes these results, reporting the mean diurnal difference (Δ avg ) and its 95% confidence interval. Time windows with p � 1, and positive confidence intervals, exhibited significantly higher daytime SPL, relative to nighttime SPL; or conversely, time windows with p � 0, and negative confidence intervals, exhibited significantly higher nighttime SPL, relative to daytime SPL.
Before the hurricane, daytime SPL over the 18-day window was higher than nighttime SPL in the L1 band at both WDR (Δ avg = 6.42dB) and ESB (Δ avg = 3.02dB), with p � 1. This diurnal difference was maintained with similar amplitude at ESB after the storm (Δ avg = 3.78dB, p � 1). The same pattern of higher daytime SPL than nighttime SPL was observed over the 24-day windows. Within the L2 band, before the storm daytime SPL also was higher than nighttime SPL at both WDR (Δ avg = 2.31dB) and ESB (Δ avg = 1.47dB), with p � 1. This pattern weakened slightly at ESB after the storm, within both the 18-(Δ avg = 0.47dB) and 24-day (Δ avg = 0.25dB) windows, p =~0.98. Within the H band prior to the storm, a small diurnal difference was observed only at WDR (Δ avg = 0.15dB, p = 0.98). After the storm, however, the nighttime SPLs were elevated slightly relative to daytime SPL within both the 18-(Δ avg = -0.93dB) and 24-day (Δ avg = -0.89dB) windows, p � 0 at ESB.
Discussion
This study used passive acoustics to characterize the impacts of a major hurricane on a coral reef soundscape and the underlying temporal changes within the biophony that reflect biological behavior and activity. Observing changes in temporal patterns at hourly and daily scales for both the high and low frequency band, representative of ecologically important soniferous Pairwise bootstrap (n = 5000) of mean differences, 95% confidence, and probabilities (p) daytime mean SPL > nighttime mean SPL for 18-day observation window at Western Dry Rocks (A) and Eastern Sambo (B). An additional pairwise analysis is given for Eastern Sambo for 24-day observation window (C). Frequency bands are denoted as follows: L1 low frequency (50-300Hz); L2 low frequency (1,200-1,800Hz); H, high frequency (7,000-20,000Hz). The color-bar represents the change in SPL (dB) between daytime-nighttime paired SPLs, with the 95% confidence range for decibel differences given in brackets. High p values and positive changes in decibel levels indicate periods when the average daytime SPL was higher than average nighttime SPL. Low p values and negative changes in decibel levels indicate periods when the average nighttime SPL was higher than average daytime SPL. https://doi.org/10.1371/journal.pone.0244599.g009
PLOS ONE
taxa, provided evidence that coral reef soundscapes may be resilient to a natural, acute disturbance despite experiencing physically destructive conditions. The extent to which a coral reef soundscape recovers to pre-disturbance patterns in sound pressure levels may depend on known characteristics of resilience in coral reef ecosystems, such as structural complexity or relative abundance of herbivorous species [131 and references therein], as well as characteristics of the storm itself, such as wind-speed, direction and duration.
The influence of abiotic factors on the underwater soundscape during a disturbance
The expanse of the hurricane wind swath (64kt radius) exposed both sites (WDR and ESB) to high levels of acoustic energy, yet the potential of inflated exposure estimates from WDR's hydrophone as it detached and presumably came into contact with fast moving sediment and debris did not allow for uncertainty estimates. Spectral densities during the hurricane did increase and produced narrow, wave-like peaks at high frequencies that may be explained by hydrodynamic processes. For example, previous studies indicate air-sea interactions can generate bubble formations that vary with wind speed intensity [132,133], and bubble formation and dissipation can be produced via wave action [134,135]. Moreover, air-sea interactions and the resulting swell of waves can vary depending on water depth and the structural complexity of the reef as current velocities can change when interacting with physical features. As previously described, WDR is characterized by relatively wide spur-and-groove channels, whereas ESB is dominated by a matrix of massive reef building corals and micro-patch reefs with relatively low sloping sand channels. Down-welling current velocities can intensify along the reef slope (spur) and increase vertically over the grooves of spur-and-groove reef formations [136]. Physical attributes of reef formations also drive other hydrodynamic processes such as refraction, dissipation, and shoaling-attributes that dictate the force and momentum of flow within the water column [137]. Therefore, the difference in sound spectral densities at each site during the storm passage may relate to stronger circulatory flows of wave action funneling into deep groove channels at WDR compared to ESB. Environmental changes during the hurricane, such as a decrease in bottom temperature and barometric pressure, may have had minimal impacts on the biophony as the presence of resident soniferous species immediately following the storm provides an alternative perspective on pre-storm migration patterns of fish and sharks seen in related studies [138,139].
Hurricane impacts to coral reef soundscapes
There are very few examples of how coral reef soundscapes respond to hurricane impacts, and of those, there is little quantitative information on specific impacts to soniferous reef fish groups. In contrast with previous coral reef soundscape studies that observe temporal patterns in the low frequency band across a wide frequency range (e.g. 0-3kHz), our results partitioned the low band to distinguish between reef fish families such Serranidae, Holocentridae, and Pomacentridae (represented by the L1 frequency band), and Haemulidae, Lutjanidae, Scaridae, and Sciaenidae (represented by the L2 frequency band). Nonetheless, the increased SPL at low frequencies during the daytime, relative to the nighttime, can likely be viewed as the integrated signature of multiple soniferous species with varying abundances and acoustic behaviors.
Reef fish chorusing around lunar phases were more prominent at ESB than WDR, and the presence of both the L1 and L2 frequency bands suggest the presence of a range of fish families during the same lunar phase despite impacts from Hurricane Irma. Fish chorusing was sometimes indicative of spawning behavior, and essential spawning locations are commonly populated by multiple species [140][141][142][143]. In a related study, Hurricane Charley (category 4) passed directly over Charlotte Harbor, Florida, USA yet nightly fish chorusing during spawning events yielded louder SPLs during and a few days after the hurricane than before, suggesting fish distribution patterns or behavior may not have been impacted by the hurricane [144]. Although the magnitude of change in low frequency-band sound pressure levels within the Irma observation windows (18-and 24-days) tested in this study were not significant, increased spatial coverage of soundscape characterization within a site using multiple hydrophones may have revealed different results.
The magnitude of change in diurnal sound pressure levels varied for each frequency band across the observation windows in this study. The L1 band at ESB was most resilient to change as average daytime sound levels maintained louder sound levels than paired nighttime sound levels during both observation windows. The L2 band also followed a similar diurnal trend as the L1 band at both sites; however, diurnal patterns in ESB's L2 band weakened post-hurricane due to a decrease in daytime SPLs. This result differs from observations within the coral reefs Puerto Rico, where nighttime chorusing is reported to have increased following the passage of Hurricanes Irma and Maria [145,146].
Diel migrations and nocturnal activity documented by acoustic tagging (telemetry), represented by the L2 band in this study, has been observed for grunts [147] and snappers [148,149]. These species typically form mixed-species schools in refuge space underneath reef outcroppings or ledges during daylight hours before migrating to forage on adjacent seagrass beds around dusk [150][151][152][153]. The reductions in habitat quality (e.g. habitat degradation, turbidity) following a hurricane may have provided enhanced opportunities for cryptic or nocturnal species to forage or find mates during low visibility conditions and presumably low predation risk, which could promote a relatively broad range of vocalizations among reef fish [146,154].
Diurnal snapping shrimp activity, as characterized by the H-frequency band, appeared resilient to the hurricane disturbance, with little change in snapping shrimp activity in the weeks following the storm. The H band at ESB did not show any significant difference between day-night SPLs before Irma, with only a small (~0.2 dB) difference developed in the weeks after the storms as daytime SPL decreased slightly. Recent studies in Puerto Rico revealed snapping shrimp inhabiting a shallow reef were silenced or masked during Hurricane Maria and did not return to crepuscular peaks in sound production until several days after hurricane passage [145,146].
In this study, the coral reef soundscape post-Irma reflected the response of both fish and invertebrate behavior (e.g. daily, diurnal chorusing patterns) to a large-episodic disturbance in the form of a Category 4 Hurricane. Temporal patterns in the biophony at ESB appeared resilient to the acoustic energy exposure, change in environmental variables, and physical damage caused by Hurricane Irma. There are very few studies of how the soundscape of an ecosystem responds to a major environmental disturbance. Gasc et al. [52] highlighted changes in acoustic composition of an isolated desert after a wildfire event in which not only was acoustic activity diminished at burned sites, but the soundscape also reflected a change in taxonomic species distribution (e.g. insects, birds) and vegetative response (e.g. floral regeneration) post-disturbance. Their results are supported by traditional disturbance ecology studies where the resulting ecosystem reflected the severity of the disturbance and revealed which biological legacies (i.e. taxa-specific traits of survivors, remaining habitat structure) contribute to the re-establishment of an ecosystem [15,155,156].
In conclusion, this study characterized environmental variables associated with the passage of a Category 4 hurricane on a coral reef, and the associated temporal patterns in the biophony before, during, and after a natural disturbance. The short-term response of Eastern Sambo's coral reef soundscape appeared resilient to the acoustic energy exposure, change in environmental variables, and physical damage caused by Hurricane Irma. Underwater soundscapes can be a complimentary ecological tool useful in characterizing small, yet important shifts in ecological communities during disturbances with localized impacts.
Supporting information S1 Fig. Fish call spectrograms. Representative waveforms (top) and spectrograms (bottom) for the L1 low frequency band 50-300Hz: (A) Serranid growl, (B) fish "chirps"; and the L2 low frequency band 1200-1800Hz: (C) Haemulid "grunts", (D) rapid aggregated "knocks". Mean amplitudes were calculated using a bandpass filter 30-3000Hz and a steepness of 0.65. Spectrograms were calculated using a window length of 2048Hz with 50% overlap. These trimmed data were used in calculating daytime and nighttime means. The largest amplitude spikes removed by this process are associated with files that contain one or more fish bumps. These signals do not represent sound, but can have a major influence on the calculated sound pressure levels. For example, the sound pressure level of the file shown in panel B) has a value of 113 dB rms re 1 μPa when averaged over the first 90 seconds of the file; this is consistent with expected background noise levels. However, when the series of fish bumps are included in the calculation, the amplitude rises by more than 30 decibels. Panel C) shows an individual bump signal. These signals are often clustered temporally, but typically occur in no more than 1 or 2 files per day. The trimming of the time series also removes a handful of files (3-4 per week) containing the sounds of a nearby small boat, as shown in panel D). The resulting trimmed time series better represents the underlying diurnal pattern of acoustic noise with the environment and is used to assess patterns of biophony. (TIF) | 9,595.6 | 2021-02-24T00:00:00.000 | [
"Environmental Science",
"Physics"
] |
Numerical Solution of MHD Convection and Mass Transfer Flow of Viscous Incompressible Fluid about an Inclined Plate with Hall Current and Constant Heat Flux
The present numerically study investigates the influence of the Hall current and constant heat flux on the Magneto hydrodynamic (MHD) natural convection boundary layer viscous incompressible fluid flow in the manifestation of transverse magnetic field near an inclined vertical permeable flat plate. It is assumed that the induced magnetic field is negligible compared with the imposed magnetic field. The governing boundary layer equations have been transferred into non-similar model by implementing similarity approaches. The physical dimensionless parameter has been set up into the model as Prandtl number, Eckert number, Magnetic parameter, Schmidt number, local Grashof number and local modified Grashof number. The numerical method of NactsheimSwigert shooting iteration technique together with Runge-Kutta six order iteration scheme has been used to solve the system of governing non-similar equations. The physical effects of the various parameters on dimensionless primary velocity profile, secondary velocity profile, and temperature and concentration profile are discussed graphically. Moreover, the local skin friction coefficient, the local Nusselt number and Sherwood number are shown in tabular form for various values of the parameters.
Introduction
Hall current has important contribution in the study of MHD viscous flows.It has many applications in problems of the Hall accelerators as well as in the flight MHD.The current trend is on the application of MHD towards a strong magnetic field and a low density of gas.For this reason, the Hall current and ion slip become important.Hydrodynamic flow of a viscous liquid through a straight channel in presence of Hall Effect is examined by Sato [1], Yamanishi [2], and Sherman and Sutton [3].The Hall current effects on the boundary layer flow past a semi-infinite plate are studied by Katagiri [4].Free convection flow of a conducting fluid permeated by a transverse magnetic field in the presence of the Hall effects and uniform magnetic field is analyzed by Pop and Watanabe [5].Aboeldahab and Elbarbary [6] studied the effect of the Hall current on the MHD free convection flow in the presence of foreign species over a vertical surface, where the flow is subjected to a strong external magnetic field.Eichhorn [7] investigated the similarity solution by considering the power-law variations in the plate temperature and transpiration velocity.Vedhanayagam et al. [8] worked on the free convection flow along a vertical plate with the arbitrary blowing and wall temperature.Lin and Yu [9] investigated the free convection flow over a horizontal plate.Recently, Hossain et al. [10] investigated the natural convection flow from a vertical permeable flat plate with the variable surface temperature, considering the temperature and transpiration rates to follow the power-law variation.Saha et al. [11] studied the effect of Hall current on the steady laminar natural convection boundary layer flow of MHD viscous and incompressible fluids.Lately, Saha et al. [12] examined the effect of Hall current on MHD natural convection flow from vertical permeable flat plate with uniform surface heat flux.In recent years a number of studies of MHD convective heat and mass transfer boundary layer flow of viscous incompressible fluid were reported in the literature [13]- [25].However, the effect of hall current and constant heat flux is still not getting promising attraction to the researchers.In this study MHD Free Convection and Mass Transfer Flow of Viscous Incompressible Fluid about an inclined Plate with Hall Current and Constant Heat Flux is investigated.
Mathematical Analysis
Steady natural convection boundary layer flow of an electrically conducting and viscous incompressible fluid from a semi-infinite heated permeable inclined flat plate with a uniform surface heat flux and transverse magnetic field with the effect of the Hall current is considered.Here x axis is taken along the vertically upward direction and y axis is normal to it.The leading edge of the permeable surface is taken along z axis.The uniform heat is supplied from the surface of the plate to the fluid, which is maintained uniformly throughout the fluid flow.The temperature and concentration at the wall are w T and w C respectively.The temperature and con- centration outside the boundary layer are T ∞ and C ∞ respectively.Uniform magnetic field of magnitude 0 B is imposed to perpendicular to the flow along the y axis.Let the angle of inclination of the plate is γ and the plate is semi finite.The x component momentum equation reduces to the boundary layer equation if and only if body force is made by gravity, then the body force per unit mass is 0 cos , We have the generalized ohm's law in the absence of electric field to the case of short circuit problem is of the form ( ) where, e µ is the magnetic permeability, e τ is the electron collision time, σ is the electrical conductivity, e ω is the cyclotron frequency, 0 B is the applied magnetic field.Since no applied or polarized voltage exist, so the effect of polarization of fluid is negligible, i.e.
( ) e e e J J B q B B ω τ σµ If is assumed that induced magnetic field generated by fluid motion is negligible in comparison to the applied one i.e.
( ) . This assumption is valid because magnetic Reynolds number is very small for liquid metals and partially ionized fluids.
Since the Hall coefficient is e e m ω τ = , so the Equation ( 2) we can write ( ) ( ) where 0 y J = .The fundamental equations for the steady incompressible MHD flow with the generalized Ohm's law and Maxwell's equations, under the assumptions that the fluid is quasi-neutral, and the ion slip and thermoelectric effects can be neglected.Since the plate is semi-infinite and motion is steady, all physical equations will be the functions of x and y.Thus mathematically the problem reduces to a two dimensional problem given as follows: Subjected to the boundary conditions 0, 0, 0, , at 0 0, 0, , as where , , u v w are the velocity components in the , , x y z direction respectively, υ is the kinematics viscosity, ρ is the density.T, w T and T ∞ are the temperature of the fluid inside the thermal boundary layer, the plate temperature and the fluid temperature in the free stream, respectively, while C, w C , C ∞ are the corresponding concentrations.Also, σ is the electric conductivity of the medium, k is the thermal conductivity of the medium, m D is the coefficient of mass diffusivity, p c is the specific heat at constant pressure, Q is the constant heat flux per unit area and other symbols have their usual meaning.
In order to solve the above system (Figure 1) of Equations ( 6)-( 9) with the boundary conditions (10), we adopt the well-defined similarity analysis to attain similarity solutions.For this purpose, the following similarity transformations are now introduced; Thus, Equations ( 6)-( 10) becomes; ( ) ( ) The corresponding boundary conditions are where is the local modified Grashof number.Similarity transformations expressions also satisfy the continuity Equation (5).
Skin-Friction Coefficients, Nusselt Number and Sherwood Number
The quantities of chief physical interest are the skin friction coefficients, the Nusselt number and the Sherwood number.The equation defining the wall skin frictions are , hence we have ( ) . The numerical values of the skin-friction coefficients, the Nusselt number and the Sherwood number are sorted in Tables 1-8.
Results and Discussions
In this study the MHD Free Convection and Mass Transfer Flow of Viscous Incompressible Fluid about an inclined Plate with Hall Current and Constant Heat Flux have been investigated using the Nachtsheim-Swigert shooting iteration technique.To study the physical situation of this problem, we have computed the numerical values of the velocity, temperature, and concentration within the boundary layer and also find the skin friction coefficient, Nusseltnumber, Sherwood number at the plate.It can be seen that the solutions are affected by the parameters, namely suction parameter w f , Grashof number r G , modified Grashof number m G , magnetic parameter M , Prandtl number r P , Eckert number c E , Schimidt number.The values of M and r G are taken to be large for cooling Newtonian fluid keeping the plate at different angle.The values 0.2, 0.5, 0.73, 2, 3, 4, 5 are considered for r P .The values 0.1, 0.5, 0.6, 1.0, 2.0, 3.0, 4.0 also considered for c S .The values of other parameters are however chosen arbitrarily.
Figures 2-5, respectively, show the primary velocity, secondary velocity, temperature and concentration profiles for different values of suction parameter w f .Here 0 w f > corresponds to suction and 0 w f < cor- responds to injection at the plate or blowing.From Figure 2-5, it can be seen that the primary velocity, secondary velocity, temperature and concentration profiles decreases with the increase of suction parameter w f .Figures 6-9, respectively, show the primary velocity, secondary velocity profiles decreased and temperature and concentration profiles increases for different values of M .Figure 10 & Figure 11, respectively, show the cross-flow of primary velocity and secondary velocity, at first increases then decreases with the increase of c E .Figure 12 & Figure 13 shows that the temperature profile increase and concentration profile decreas- es with the increase of c E .Figures 14-17 show that the primary velocity, secondary velocity profile and con- centration profile decreases and temperature profile increases with the increase of c S .Figure 18 & Figure 19, respectively, shows the cross flow of the primary velocity and secondary velocity with the increase of r P both of the profile is decrease then increase.Figure 20 & Figure 21 shows that temperature decrease and concentration profile increase with the increase of c S .Figure 22 & Figure 23, show the cross flow of the primary velocity and secondary velocity with the increase of γ both of the profile is decrease then increase.Finally the effect of various parameters on the skin friction coefficients ( x τ , w τ ), Nusselt number ( u N ) and Sherwood ( h S ) are tabulated in Tables 1-8.Table 1 shows that the skin friction coefficient ( x τ , w τ ) decreases and Nusselt number ( u N ) and Sherwood number ( h S ) increase with the increase of w f .Table 2 shows that the skin friction coefficient x τ decreases and w τ increases and Nusselt number ( u N ) and Sherwood number ( h S ) decreases with the increase of M .Table 3 shows that the skin friction coefficient ( x τ , w τ ) and Sher- wood number ( h S ) increases and Nusselt number ( u N ) decreases with the increase of c E .Table 4 shows that the skin friction coefficient ( x τ , w τ ) and Sherwood number ( h S ) decreases and Nusselt number ( u N ) in- creases with the increase of r P .Table 5 shows that the skin friction coefficient ( x τ , w τ ) and Nusselt number ( u N ) decreases and Sherwood number ( h S ) increases with the increase of c S .Table 6 shows that the skin friction coefficients ( x τ , w τ ), Nusselt number ( u N ) and Sherwood number ( h S ) decreases with the increase of γ .Table 7 & Table 8 shows that the skin friction coefficients ( x τ , w τ ), Nusselt number ( u N ) and Sher- wood number ( h S ) increases with the increase of r G and m G .
Conclusions
The effect of viscous incompressible fluid flow about an inclined plate with hall current is analyzed in the present study for constant heat flux.A range of physical parameter values tested over the boundary layer flows.The variation of different physical parameters instigated different flow pattern as increasing, decreasing and cross flow in the dimensionless primary and secondary velocity, temperature and concentration distribution as well as in the profile of skin friction coefficients, Nusseltand Sherwood number.The findings of the present investigation are briefly: • As suction parameter increases, the primary velocity, secondary velocity, temperature and concentration profiles decrease gradually.However for the same parameter effects, the skin friction coefficient decreases and Nusselt and Sherwood numbers increase.• The primary velocity, secondary velocity profiles as well as the skin friction coefficient, Nusselt and Sherwood numbers decreased as magnetic parameter increased whereas the reverse effects found in the profile of temperature and concentration.• The cross-flow of primary and secondary velocity observed as Eckert number increases whereas temperature profile increases and concentration profile decreases for the same parameter effects.Also for the similar parameter effects skin friction coefficient and Sherwood number increase whereas Nusselt number decreases.• The increasing effect of Schmidt number causes primary and secondary velocity profile and concentration profile as well as the skin friction coefficient and Nusselt number decrease whereas the reverse situation observed in the temperature and Sherwood number profiles.• The cross flow of the primary and secondary velocity with the increase of Prandlt number has been observed, where both of the profiles first decrease and near the layer the profiles increase.However within the same parameter effects, the skin friction coefficient and Sherwood number decrease and Nusselt number increases.
• As the parameter γ rises the cross flow pattern observed in primary and secondary velocity profiles whe- reas temperature and concentration profile increase.However for the same parameter effects the skin friction coefficient, Nusselt number and Sherwood number profiles decrease.• The increasing effect of Grashof and modified Grashof number causes the cross flow of the primary and secondary velocity whereas temperature and concentration profile decrease for the similar parameter effects.However the skin friction coefficient, Nusselt number and Sherwood number increase.
F
is the accele- ration due to gravity.Further no body force exists in the direction of y and z, i.e. = .The x component of pressure gradient at any point in the boundary layer must equal to the pressure gra- dient in the region outside the boundary layer, in this region 0 u = , 0 v = .Hence x component of pressure gra- is the density of the surrounding fluid at temperature T ∞ .The quantity ρ ρ ∞ − is related to the temperature difference T T ∞ − and concentration (or mass) differences C C ∞ − through the thermal volume expansion coefficient β and concentration volume expansion coefficient β * by the relation,
Figure 1 .
Figure 1.Physical configuration and co-ordinate system.
Figure 24 &
Figure 25, show that temperature and concentration profile increases with the increase of γ .
Figure 26 &
Figure 26 & Figure 27, show the cross flow of the primary velocity and secondary velocity with the increase of r G . Figure 28 & Figure 29, shows that the temperature and concentration profile decreases with the in- crease of r G . Figure 30 & Figure 31, show the cross flow of the primary velocity and secondary velocity with the increase of m G . Figure 32 & Figure 33, shows that the temperature and concentration profile decreases
Figure 2 .
Figure 2. Primary velocity profile for w f .
Figure 3 .
Figure 3. Secondary velocity profile for w f .
Figure 4 .
Figure 4. Temperature profile for w f .
Figure 5 .
Figure 5. Concentration profile for w f .
Figure 6 .
Figure 6.Primary velocity profile for M .
Figure 8 .
Figure 8. Temperature profile for M .
Figure 9 .
Figure 9. Concentration profile for M .
Figure 10 .
Figure 10.Primary velocity profile for c E .
Figure 11 .
Figure 11.Secondary velocity profile c E .
Figure 12 .
Figure 12.Temperature profile for c E .
Figure 13 .
Figure 13.Concentration profile for c E .
Figure 14 .
Figure 14.Primary velocity profile for c S .
Figure 15 .
Figure 15.Secondary velocity profile for c S .
Figure 16 .
Figure 16.Temperature profile for c S .
Figure 17 .
Figure 17.Concentration profile for c S .
Figure 18 .
Figure 18.Primary velocity profile r P .
Figure 19 .
Figure 19.Secondary velocity profile r P .
Figure 20 .
Figure 20.Temperature profile for r P .
Figure 21 .
Figure 21.Concentration profile for r P .
Figure 26 .
Figure 26.Primary velocity profile for r G .
Figure 27 .
Figure 27.Secondary velocity profile for r G .
Figure 28 .
Figure 28.Temperature profile for r G .
Figure 29 .
Figure 29.Concentration profile for r G .
Figure 30 .
Figure 30.Primary velocity profile for m G .
Figure 31 .
Figure 31.Secondary velocity profile for m G .
Figure 32 .
Figure 32.Temperature profile for m G .
Figure 33 .
Figure 33.Concentration profile for m G .
Figure 34 .
Figure 34.Primary velocity profile for m.
Figure 35 .
Figure 35.Secondary velocity profile for m.
Figure 36 .
Figure 36.Temperature profile for m.
Figure 37 .
Figure 37. Concentration profile for m.
Table 1 .
Numerical values of Skin friction coefficient x
Table 2 .
Numerical values of Skin friction coefficient x
Table 3 .
Numerical values of Skin friction coefficient x x τ
Table 4 .
Numerical values of Skin friction coefficient x
Table 5 .
Numerical values of Skin friction coefficient x
Table 6 .
Numerical values of Skin friction coefficient x
Table 7 .
Numerical values of Skin friction coefficient x
Table 8 .
Numerical values of Skin friction coefficient x | 4,090.8 | 2015-12-04T00:00:00.000 | [
"Engineering",
"Physics"
] |
18F-FDG PET as an imaging biomarker for the response to FGFR-targeted therapy of cancer cells via FGFR-initiated mTOR/HK2 axis
Rationale: The overall clinical response to FGFR inhibitor (FGFRi) is far from satisfactory in cancer patients stratified by FGFR aberration, the current biomarker in clinical practice. A novel biomarker to evaluate the therapeutic response to FGFRi in a non-invasive and dynamic manner is thus greatly desired. Methods: Six FGFR-aberrant cancer cell lines were used, including four FGFRi-sensitive ones (NCI-H1581, NCI-H716, RT112 and Hep3B) and two FGFRi-resistant ones (primary for NCI-H2444 and acquired for NCI-H1581/AR). Cell viability and tumor xenograft growth analyses were performed to evaluate FGFRi sensitivities, accompanied by corresponding 18F-fluorodeoxyglucose (18F-FDG) uptake assay. mTOR/PLCγ/MEK-ERK signaling blockade by specific inhibitors or siRNAs was applied to determine the regulation mechanism. Results: FGFR inhibition decreased the in vitro accumulation of 18F-FDG only in four FGFRi-sensitive cell lines, but in neither of FGFRi-resistant ones. We then demonstrated that FGFRi-induced transcriptional downregulation of hexokinase 2 (HK2), a key factor of glucose metabolism and FDG trapping, via mTOR pathway leading to this decrease. Moreover, 18F-FDG PET imaging successfully differentiated the FGFRi-sensitive tumor xenografts from primary or acquired resistant ones by the tumor 18F-FDG accumulation change upon FGFRi treatment. Of note, both 18F-FDG tumor accumulation and HK2 expression could respond the administration/withdrawal of FGFRi in NCI-H1581 xenografts correspondingly. Conclusion: The novel association between the molecular mechanism (FGFR/mTOR/HK2 axis) and radiological phenotype (18F-FDG PET uptake) of FGFR-targeted therapy was demonstrated in multiple preclinical models. The adoption of 18F-FDG PET biomarker-based imaging strategy to assess response/resistance to FGFR inhibition may benefit treatment selection for cancer patients.
Introduction
Tyrosine kinase receptor fibroblast growth factor receptors (FGFRs) consist of 4 members including FGFR1, 2, 3, and 4, which play critical and diverse roles in early embryonic development and maintaining body metabolic balance. FGFR aberrant activation via gene fusion, activating mutation and amplification as well as ligand stimulation can promote tumor initiation and development in a variety of cancers [1][2][3]. For example, FGFR1 amplification was found in approximately 6% of lung cancer cases, mainly in squamous non-small cell lung carcinoma subtype without effective treatments [1,4]. FGFR2 was amplified in less than 10% of gastric cancer cases, associated with bad prognosis [1,2]. FGFR2 fusion occurred in 45% of intrahepatic cholangiocarcinoma cases [5]. Notably, fusion and activating mutations of FGFR3 frequently occurred in urothelial bladder carcinomas and predominantly in non-muscle invasive urothelial cell carcinoma type (occurring in 75% of cases) [2,6,7]. FGF19 amplification-induced FGFR4 activation was observed in hepatocellular carcinomas and might represent FGFR4-dependent cancer subtype [1,2,[8][9][10]. FGFRs thus become attractive targets for anti-cancer drug development. The pan-FGFR inhibitor (FGFRi) Erdafitinib, active against FGFR1-4, is the first FGFRi approved by FDA to treat patients with locally advanced or metastatic urothelial carcinoma with susceptible FGFR3 or FGFR2 genetic alterations, which has progressed during or following platinum-containing chemotherapy in 2019 [11,12]. Immediately afterward, FDA granted the approvals of FGFR1-3 inhibitors (Pemigatinib and Infigratinib) for patients with previously treated, unresectable locally advanced or metastatic cholangiocarcinoma with FGFR2 fusion or other rearrangements in 2020 [13] and in 2021 [14], respectively. Besides these three approved inhibitors, numerous FGFRi inhibitors, such as Rogaratininb, AZD4547, and Futibatinib, are still in phase I-III clinical trials in various malignancies [15,16].
Despite a promising prospect of FGFRi in certain cancer patients, its clinical efficacy is far from satisfactory with an overall response rate of 20-40% in the approved indication [17][18][19]. Even much lower response rate was observed in other cancer types [1,15,20]. Primary and acquired resistances to FGFR therapy due to the secondary mutations and the feedback activation of alternate pathways may decrease their clinical benefits [1,15]. It is indicated that the patients stratifying strategy based on FGFR aberration alone is very limited and cannot guarantee the patients' response to FGFRi. Exploring the therapeutic response biomarkers of FGFRi is therefore an urgent need. We have identified c-Myc, a fundamental downstream effector of FGFR signaling, could determine the therapeutic response to FGFRi in FGFR-addicted cancers [21]. Usually, c-Myc expression levels are examined in the tumor samples via biopsy or surgery, as well as in the circulating tumor cells (CTCs). However, sample availability, sensitivity on CTCs-based assays and tumor heterogenicity are unable to guarantee the accurate assessment of c-Myc. A novel biomarker to evaluate the therapeutic response to FGFRi in patients in a non-invasive, real-time and quantitative manner is greatly desired.
The identification of several FGFs, such as FGF1, FGF15/19, FGF21 and FGF23, with high relevance to metabolic regulation [22][23][24][25] attracted our attention. In fact, deregulating cellular metabolism, the hallmark of cancer, is required by the tumor cells to meet energy and structural requirements for rapid proliferation [26,27]. Notably, these metabolic changes are indispensable for certain cancers, making such tumors with metabolic vulnerability [26]; therefore, they can be exploited as therapeutic intervention or monitoring therapeutic response. We noticed that aberrant FGFR1 could enhance the Warburg Effect to drive prostate cancer progression by reprogramming LDH isoform expression and activity [28]. Meanwhile, the biologic basis for 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) is the Warburg Effect [29]. 18 F-FDG is a radiolabeled analogue of glucose whereby the 2' hydroxyl group is substituted with 18 F. FDG passes the cellular membrane mediated by the glucose transporters (GLUTs) and is phosphorylated by hexokinases (HKs) to FDG-6-phosphate, which cannot be further metabolized to participate the tricarboxylic acid cycle [30]. Dephosphorylation of FDG-6-phosphate back to FDG by glucose-6phosphatase is the only way to exit the cells. The enhanced levels of GLUTs and HKs, along with the reduced level of glucose-6-phosphatase in tumors lead to FDG trapping in cancer cells [31]. 18 F-FDG PET imaging has thus been most widely applied in clinical practice and become the gold standard for oncology [32].
Considering the huge clinical translation potential, we investigated in this study whether 18 F-FDG PET could be used as a biomarker candidate for the therapeutic response to FGFRi in oncology. In different preclinical tumor models in vitro and in vivo, we found FGFR-targeted therapy decreased the accumulation of 18 F-FDG only in FGFRi-sensitive tumors, but not in FGFRi-resistant ones. We then demonstrated that downregulation of hexokinase 2 (HK2) via mTOR pathway by FGFR inhibition leading to the decrease of 18 F-FDG uptake in FGFRi-sensitive cells. A novel application of the well-established 18 F-FDG PET imaging to functional assessment of the treatment response to FGFR-targeted therapy in cancer patients as well as the underlying molecular mechanism are suggested.
Cell culture and reagents
NCI-H1581, NCI-H716, NCI-H2444 and Hep3B cells were obtained from the American Type Culture Collection (USA). RT112 cell was obtained from Deutsche Smmlung von Mikroorganismen und Zellkulturen GmbH (Germany). All cell lines in this study were maintained in the appropriate medium as suppliers suggested and were authenticated via short tandem repeats (STR) analysis with the latest test in 2020 (Genesky Biotechnologies, China) or single-nucleotide polymorphism (SNP) analysis with the latest test in 2021 (Crown Bioscience, China). All cells were routinely tested for mycoplasma by Mycoplasma Detection Kit-QuickTest (B39032; Biotool, China) and found to be free of contamination.
FGFR inhibitors (Erdafitinib, AZD4547, and BLU9931), AKT inhibitor MK2206, mTOR inhibitor AZD8055, proteasome inhibitor MG132 and lysosome inhibitor Leupeptin were purchased from Selleck Chemicals (China) and dissolved in DMSO at the concentration of 10 mM as a stock solution for in vitro study. AZD4547 was dissolved in 1% Tween-80 and BLU9931 was formulated in 0.5% carboxymethylcellulose/1% Tween-80 respectively for in vivo study.
To generate NCI-H1581 cells with acquired resistance to FGFRi, NCI-H1581 cells were treated by AZD4547 with the increasing concentration in a stepwise manner (from 30 nM to 1 μM). After approximate 6 months of induction, the NCI-H1581/AR cell line was obtained till its growth kinetics was similar to that of the parental NCI-H1581 cell line [33].
Mass spectrum
Protein extraction, digestion, Tandem Mass Tag (TMT) labeling and high pH reversed-phase liquid chromatography peptides fractionation were performed as described previously [34]. Detailed methods were available in the Supplementary Materials. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [35] partner repository with the dataset identifier PXD032227.
F-FDG uptake in vitro
For adherent cells, 2.0×10 5 cells/well were seeded in 12-well plates and 12 h FGFRi treatment at indicated concentrations in normal medium was started next day, followed by another 12 h FGFRi treatment in the glucose-free starvation medium with 5% fetal bovine serum. For non-adherent cells, 3.0×10 5 cells/well were seeded in 12-well plates in normal medium with FGFRi at indicated concentrations for 12 h, followed by another 12 h FGFRi treatment in the above starvation medium. After starvation, 1 µCi 18 F-FDG/well was then added and incubated at 37°C for 1 h. The radioactivity from both cell-accumulated and free 18 F-FDG were measured by an automatic gamma counter (Wizard 2470; PerkinElmer, USA). Relative 18 F-FDG uptake rate was normalized by cell number analyzed using Countstar BioTech (China). All samples were tested in triplicate.
Cell viability assay
Cells were inoculated in 96-well plates overnight and incubated with FGFRi at indicated concentrations or vehicle (as a negative control) for 72 h. Cell Counting Kit-8 (Dojindo Molecular Technologies, China) was used to assess cell viability as the instruction described. The normalized cell viability (%) was calculated as 100 × (ODFGFRi/ODvehicle).
Gene silencing by siRNA
Cells were inoculated in 6-well plates overnight and transfected with siRNAs as below by Oligofectamine RNAiMAX reagent (Invitrogen, USA) according to the manufacturer's instructions. After 48 h, cells were harvested for further analysis.
Animal studies
All animal studies were approved by the Institutional Animal Care & Use Committee of Shanghai Institute of Materia Medica, Chinese Academy of Sciences. 4-to 6-week-old female athymic nude mice nu/nu or SCID mice were provided by Shanghai Institute of Materia Medica or purchased from Beijing HFK Bioscience (China). 1×10 7 tumor cells, including NCI-H1581, Hep3B, NCI-H2444, or NCI-H1581/AR cells, were suspended in 200 μl ice-cold sterile PBS and subcutaneously injected into right flank of the mouse. Tumor-bearing mice were divided into the vehicle group and FGFRi treatment group randomly when the tumor volume reached approximately 100 mm 3 . For NCI-H1581, NCI-H1581/AR, and NCI-H2444 xenograft-bearing mice, AZD4547 (12.5 mg/kg, p.o., once a day) was given for 4 or 5 days. For Hep3B xenograft-bearing mice, BLU9931 (30 mg/kg, p.o., twice a day) was given for 6 days. Tumor size was measured by caliper every day and tumor volume (TV) was calculated with the formula: TV = (width 2 × length) / 2. The relative tumor volume was normalized by the TV immediately before FGFRi treatment.
PET/CT imaging
The tumor-bearing mice were fasted for 8 h before injection of 100-200 µCi 18 F-FDG via tail vein. During the uptake period (40-60 min), the mice were anesthetized under 1.5% isoflurane. Ten-min static data of PET imaging were recorded, followed by 10-min CT scan, using a microPET/CT scanner (Inveon; Siemens, Germany). PET data were reconstructed using the microQ Viewer software (Version 1.7.0.6; Siemens). Region of interest (ROI) delineating the tumor was drawn and Mean Standardized Uptake Value (SUVmean) of the tumor was obtained for 18 F-FDG uptake in vivo.
Statistical analysis
Data were presented as mean ± SD, except the data for xenograft growth curve, which were presented as mean ± SEM. The differences between two groups were analyzed by an unpaired Student's t-test using GraphPad Prism 8.0 software (USA). FGFR4-HK2 signature score was determined by Cox model [36]. Briefly, this score of each patient was calculated as follows: FGFR4-HK2 signature score = XFGFR4βFGFR4 + X HK2 β HK2 (X and β indicated the mRNA level and the risk coefficient of Cox model by survival analysis in R version 3.5.3, respectively). The correlations between the levels of FGFR4 mRNA/HK2 mRNA/FGFR4-HK2 signature score and overall survival (OS) were analyzed by the Kaplan-Meier method. The differences in the survival rates between curves were assessed by the log-rank test. p < 0.05 was considered statistically significant.
FGFR inhibition led to 18 F-FDG uptake reduction in the FGFRi-sensitive cancer cells
To identify novel biomarkers for evaluating the therapeutic response to FGFRi, TMT-labeled mass spectrometry-based proteomics was carried out in a FGFRi-sensitive cell line (NCI-H1581, a lung cancer cell line with FGFR1 amplification) upon AZD4547 treatment ( Figure 1A). As expected, proteins associated with cell cycle regulation were identified among the significantly differentially expressed ones (fold change > 1.2 or < 0.8, with p < 0.05; Table S1), which was consistent with our previous report [21]. Notably, protein levels of HK2 and GLUT3/14 (alias SLC2A3/14), the key factors highly related to glucose metabolism, especially to FDG trapping, were significantly decreased in AZD4547-treated NCI-H1581 cells than those in vehicle-treated ones ( Figure 1A). Therefore, 18 F-FDG uptake was tested in NCI-H1581 cells with FGFRi treatment in vitro. Two selective inhibitors, AZD4547 targeting FGFR1-3 and Erdafitinib targeting FGFR1-4 were used. We observed that both AZD4547 (0.1 μM) and Erdafitinib (0.01 μM) not only inhibited the cell proliferation (p < 0.001) but also reduced the 18 F-FDG uptake (p < 0.05) ( Figure 1B).
Whether FGFR inhibition leading to 18 F-FDG uptake reduction is a common effect on FGFR-aberrant tumor cells was further investigated. Four other cancer cell lines, the FGFRi-sensitive ones including NCI-H716 colon cancer cell line with FGFR2 amplification, RT112 bladder cancer cell line with FGFR3 amplification, and Hep3B liver cancer cell line with FGF19 amplification-induced FGFR4 activation, along with the FGFRi-primary resistant one (NCI-H2444 lung cancer cell line with FGFR1 amplification), were examined. Another FGFR4 selective inhibitor, BLU9931 was chosen for tests in Hep3B cells. Consistently, FGFRi-induced significant cell proliferation inhibition was still accompanied with 18 F-FDG uptake decrease in NCI-H716 cells (p < 0.001; Figure 1C), RT112 cells (p < 0.01; Figure 1D), and Hep3B cells (p < 0.01; Figure 1E). However, in NCI-H2444 cells both AZD4547 and Erdafitinib did not show the inhibitory effects on cell proliferation and 18 F-FDG uptake even at the concentration of 1 μM ( Figure 1F). These data implied that 18 F-FDG uptake might be correlated with the drug sensitivity/ resistance to FGFRi in FGFR-aberrant cancer cells.
FGFR inhibition downregulated HK2 gene via mTOR pathway
Since mass spectrometry-based proteomics identified that AZD4547 could decrease the protein levels of HK2 and GLUT3/14 ( Figure 1A), which are the main mediators of 18 F-FDG uptake, we tested the expression levels of these two molecules and other members in HK and GLUT families by Western blot analysis in NCI-H1581 cells for confirmation ( Figure 2A). Due to the subtle change of GLUT3 and the specific expression of GLUT14 major in testis [37], only the significant inhibitory effects of FGFRi on HK2 expression levels were further investigated. Three FGFR inhibitors (Erdafitinib, AZD4547 or BLU9931) were tested correspondingly in five FGFR-aberrant cancer cells. In the FGFRi-sensitive cells, including NCI-H1581 ( Figure 2B), NCI-H716 ( Figure 2C), RT112 ( Figure 2D), and Hep3B cells ( Figure 2E), FGFRi reduced the phosphorylated levels of FGFR (p-FGFR) or FRS2 (p-FRS2), which is the FGFR key adaptor protein as the well-recognized surrogate for FGFR activation [10,11,21,38], as well as decreased the protein levels of HK2. But in the FGFRi-resistant cells (NCI-H2444 cells), even p-FRS2 was suppressed by AZD4547 or Erdafitinib treatment, HK2 protein did not show significant changes ( Figure 2F). It was suggested that FGFRi could inhibit HK2 only in the FGFRi-sensitive cells.
How FGFR inhibition downregulated HK2 was then studied. Considering the FGFR aberration usually activates AKT-mTOR, PLCγ, and MEK-ERK pathways in cancer [1,2], we used the selective inhibitors or specific siRNAs to block these downstream signalings to test which could downregulate HK2. As Figure 2G-H shown, both the AKT inhibitor (MK2206) and the mTOR inhibitor (AZD8055) reduced the HK2 levels and 18 F-FDG uptake, as same as the FGFRi. However, neither knockdown PLCγ by siRNAs in NCI-H1581 cells ( Figure 2I) nor MEK inhibition by PD0325901 in NCI-H1581 and NCI-H716 cells ( Figure 2J) affected the HK2 levels. We also knockdown c-Myc, which was the downstream effector of FGFR via MEK-ERK signaling in FGFR aberrant cancer [21], and no obvious HK2 expression change was exhibited in NCI-H1581 and Hep3B cells ( Figure S1), indicating the different regulatory mechanisms by FGF/FGFR for HK2 and c-Myc. Herein, function of FGFRi's tumor inhibition may not be actioned simply by FGFR pathway; FGFR inhibition induced HK2 reduction via AKT-mTOR signaling to regulate glucose metabolism was indicated in the FGFRi-sensitive cells.
Whether FGFR inhibition downregulated HK2 expression at posttranslational level was next addressed. Neither the proteasome inhibitor MG132 nor the lysosome inhibitor Leupeptin could reverse HK2 downregulation induced by FGFR inhibition in NCI-H1581 ( Figure 2K), NCI-H716 ( Figure 2K) and Hep3B cells ( Figure S2), suggesting this downregulation was not greatly dependent on protein degradation. mRNA levels of HK2 gene in NCI-H1581, NCI-H716, and NCI-H2444 cells were then detected at different time points (0-18 h after FGFRi treatment). (E) and NCI-H2444 (F) cells were incubated with indicated FGFR inhibitors (AZD4547, Erdafitinib, or BLU9931) at different concentrations. Cell viability (upper panels) and 18 F-FDG uptake (lower panels) were examined after 72 h and 24 h, respectively. Cells treated with vehicle were used as the normalization controls. Relative 18 F-FDG uptake was normalized by cell number. Data were shown as mean ± SD. *, p < 0.05; **, p < 0.01; ***, p < 0.001 vs vehicle group, using Student's t-test.
The significant decrease of HK2 mRNA was observed in FGFRi-sensitive cells starting from ~6 h after FGFR inhibition ( Figure 2L). Accordingly, HK2 protein level exhibited a slight reduction beginning from 6 h after FGFRi treatment and achieved a significant decrease after 12-or 24-h treatment in those cells ( Figure 2M). In FGFRi-resistant NCI-H2444 cells, no significant change of HK2 mRNA was detected under the treatment of AZD4547 or Erdafitinib ( Figure 2L). Furthermore, mTOR inhibitor (AZD8055) decreased expressional levels of HK2 mRNA ( Figure S3A) and HK2 protein in both FGFRi-sensitive ( Figure 2G) and -resistant cells ( Figure S3B). However, the mTOR signaling ( Figure S3C) and HK2 protein ( Figure 2F) did not show significant changes by AZD4547 or Erdafitinib treatment in NCI-H2444 cells, implying that FGFR might lose its regulation on mTOR signaling, but mTOR could still modulate the expression of HK2 gene in this FGFR-resistant cell line.
The correlation between FGFR/HK2 signaling and liver cancer patients' prognosis was tested in TCGA-LIHC (liver hepatocellular carcinoma) dataset. The patients with OS information (n = 373) were classified into two groups based on the levels of FGFR4 mRNA, HK2 mRNA and FGFR4-HK2 signature score, respectively (median value as the cutoff). The Kaplan-Meier survival analysis showed that high levels of FGFR4 mRNA (p = 0.0232), HK2 mRNA (p = 0.0057) and FGFR4-HK2 signature score (p = 0.0027) were associated with poor OS of LIHC patients ( Figure S4). The importance of FGFR/HK2 signaling in the prognosis of LIHC patients was indicated.
Taken together, above results suggested that FGFR inhibition downregulated HK2 gene transcription via AKT-mTOR signaling, leading to the decrease of glucose uptake in the FGFRi-sensitive tumor cells.
F-FDG PET as an imaging biomarker for the therapeutic response to FGFRi in vivo
In order to investigate whether 18 F-FDG PET could be used as an imaging biomarker for the therapeutic response to FGFRi in vivo, the FGFRi-sensitive xenografts, NCI-H1581 ( Figure 3A) and Hep3B ( Figure 3B), as well as the FGFRi-primary resistant xenografts (NCI-H2444; Figure 3C) were generated for FGFRi treatment with visualization by 18 F-FDG PET/CT imaging in vivo. NCI-H1581 and NCI-H2444 xenograft-bearing mice were treated with AZD4547 (12.5 mg/kg, daily) for 5 days; and Hep3B xenograft-bearing mice were treated with BLU9931 (30 mg/kg, twice a day) for 6 days. Upon FGFRi treatment, SUVmean for 18 F-FDG probe was significantly reduced in NCI-H1581 xenografts (p < 0.01; Figure 3D) and in Hep3B xenografts (p < 0.001; Figure 3E); meanwhile, vehicle could not induce such decrease. In NCI-H2444 xenografts, marked change of 18 F-FDG probe was not detected in both AZD4547and vehicle-treatment groups ( Figure 3F). The following IHC analysis confirmed that FGFRi treatment led to the reductions of HK2 and Ki67 (a cell proliferation marker) only in the FGFRi-sensitive xenografts ( Figure 3G-H), but not in the FGFRi-resistant xenografts ( Figure 3I).
These data encouraged us to explore whether 18 F-FDG uptake in the FGFRi-acquired resistant tumors would be similar to that in the FGFRi-primary resistant tumors. NCI-H1581/AR cell line, which was previously generated from NCI-H1581 parental cell line by exposure to AZD4547 at concentrations increasing stepwise [33], was tested in vitro and in vivo. Its resistance to AZD4547 and Erdafitinib was validated ( Figure 4A). Consistent with the results from NCI-H2444 cells, 18 F-FDG uptake ( Figure 4B) and HK2 protein level ( Figure 4C) in NCI-H1581/AR cells did not show the remarkable changes in presence of AZD4547 or Erdafitinib. NCI-H1581/AR subcutaneous xenograft model was then created. Using the same dosage of AZD4547 as that in NCI-H1581 xenograft-bearing mice for 4 days, tumor growth could not be inhibited in NCI-H1581/AR xenografts ( Figure 4D). 18 F-FDG-based PET/CT imaging could not detect significant alterations of the probe accumulation in NCI-H1581/AR xenografts, responding to AZD4547 treatment ( Figure 4E). FGFRi treatment in vivo could not alter the protein levels of HK2 and Ki67 in NCI-H1581/AR xenografts ( Figure 4F). It was implied that the acquired resistance to targeted FGFR therapy might accompany by the disability of FGFRi-induced FDG uptake reduction.
Application of 18 F-FDG PET imaging to monitor the therapeutic response to FGFRi in vivo dynamically
To mimic the clinical practice of targeted FGFR therapy, we generated a NCI-H1581 xenograft model with the treatment regimen as a 5-day FGFRi treatment followed by a 4-day interval. 18 F-FDG PET/CT imaging was applied to assess the glucose uptake at three time points: 1) right before FGFRi treatment (Day 1); 2) right after FGFRi treatment (Day 6); 3) at the endpoint (Day 10; 4 days without FGFRi treatment) ( Figure 5A). Using this model, we wanted to investigate whether 18 F-FDG PET could reflect the tumor response to FGFRi in a dynamic and quantitative manner. NCI-H716 cells (lower panel) with AZD4547 treatment. β-Actin was used as the loading control. Data were shown as mean ± SD. **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05. Relative band intensity of target protein was normalized to its corresponding loading control as fold of the vehicle-, or control-, or non-treated group.
The tumor growth curves measured by calipers ( Figure 5B) showed that in the vehicle group, tumors kept growing with ~13.6-fold increase in tumor volume; in the AZD4547 group, tumor growth was significantly inhibited within the first phase (5-day with AZD4547), whereas tumor volume was slightly increased with ~1.9-fold increase in the second phase (4-day without AZD4547). As shown in Figure 5C, 18 F-FDG PET images demonstrated that vehicle treatment did not induced obvious changes in 18 F-FDG uptake by the tumor; and that 5-day FGFRi treatment resulted in a marked decrease of 18 F-FDG uptake by the tumor (p < 0.05). Notably, 4-day FGFRi withdrawal led to more 18 F-FDG accumulation in tumor (~2.3-fold increase in SUVmean on Day 10 compared with that on Day 6; p < 0.05), as well as the increased HK2 ( Figure 5D) and Ki67 levels ( Figure 5E). 18 F-FDG PET enabled the assessment of FGFRi therapeutic efficacy dynamically in vivo was indicated.
Discussion
FGFRs are clinically validated anticancer targets with pan-tumor potential, especially in the tumors lacking effective treatments. However, the clinical benefit in cancer patients with FGFR alterations is quite limited [39][40][41]. Moreover, even FGFR-aberrant patients attain an optimal response at an early stage, tumor relapse occurs eventually due to acquired resistance by the activation of bypass and downstream signalings or the development of FGFR secondary mutations [1]. Biomarkers or strategies with immediate translational potentials to evaluate the therapeutic response and to monitor the acquired resistance to FGFR-targeted therapy are urgently needed, particularly in a noninvasive and dynamic manner.
In the present study, TMT-labeled mass spectrometry-based proteomics suggested that FGFR inhibition regulated glucose metabolism in a FGFRi-sensitive cancer cell line NCI-H1581 with FGFR1 amplification. Interestingly, our previous report showed that cancer cells with FGFR-aberrant activation per se exhibited high glucose consumption into glycolytic pathway and resultant lactate production [42]. The critical role of glucose metabolism in FGFR-aberrant cancers is indicated, no matter with FGFRi treatment or not. In TCGA lung adenocarcinoma database (n = 740), upregulation of some glycolytic enzymes including HK2 gene was reported in FGFR-amplificated cancers, comparing with diploid cancers [42]. These findings encouraged us to further investigate whether FGFR kinasetargeted therapy was able to regulate HK2 expression and thereby inhibit glycolysis herein. Mechanistically, for the first time, we revealed that FGFR inhibition suppressed HK2 gene transcription via inhibiting mTOR in FGFRi-sensitive in vitro and in vivo cancer models with different FGFR1-4 anomalies ( Figure 6). In the FGFRi-sensitive cells, we found only mTOR inhibition could suppress HK2 expression; while inhibitions of other FGFR key downstream molecules (PLCγ and MEK/ERK) did not show the same inhibitory effect on HK2. Considering that c-Myc functioned as a key downstream effector in aberrantly activated FGFR signaling in cancer [21] and that FGF-induced vascular development was dependent on endothelial glycolysis via MYC/HK2 [43], we also tested whether HK2 downregulation by FGFRi was c-Myc dependent in this study. However, we found c-Myc knockdown had nonsignificant influence on HK2 expression level. Additionally, since HK2 can be regulated by several factors including epigenetic factors [44], perhaps HK2 expression might serve as a biomarker independently of FGFR aberration. Such different regulatory mechanisms on HK2 may partially be owing to the differences in biological context and cell lineage. The novel FGFR/mTOR/HK2 axis-mediated glucose metabolic regulation to assess the response to FGFR-targeted therapy is suggested. Nude mice bearing NCI-H1581 xenograft were orally treated with AZD4547 at 12.5 mg/kg once a day for 5 days, then AZD4547 was withdrawn for 4 days. 18 F-FDG PET/CT imaging was performed on Day 1 (before treatment), Day 6 (right after treatment) and Day 10 (after treatment interval). B, Relative tumor growth curves of NCI-H1581 xenografts (n = 3 for each group) within the indicated periods. Blue arrows, time points with 18 F-FDG PET/CT imaging; red arrows, time points with AZD4547 treatment.
Xenograft volumes at the starting point (0 day) were used as the normalization controls. Data were shown as mean ± SEM. C, 18 F-FDG PET/CT imaging on the NCI-H1581 xenograft-bearing animals upon FGFRi or vehicle treatment. Representative 18 F-FDG PET/CT images for SUVmean (left panel; xenografts were indicated with white dashed lines) and values for SUVmean from the xenografts (right panel) at the indicated time points were shown. Data were shown as mean ± SD. ns, p ≥ 0.05; *, p < 0.05. D-E, IHC staining for HK2 (D) and Ki67 (E) of NCI-H1581 xenografts on Day 6 (right after treatment) and Day 10 (after treatment interval). Scale bar, 50 μm. The disturbed glucose metabolism, as metabolic vulnerability for FGFR-addicted cancers, especially the corresponding expressional change of HK2, the key rate-limiting glycolytic enzyme, allows us trying to monitor the FGFRi-response by 18 F-FDG (an analog of glucose) PET imaging. We demonstrated that FGFR inhibition reduced 18 F-FDG in vitro uptake in four FGFRi-sensitive cancer cells, but not in primary and acquired resistant cancer cell lines to FGFRi. 18 F-FDG PET/CT imaging and IHC analysis on the tumor xenograft-bearing animals further confirmed the in vivo decreases of HK2 expression, 18 F-FDG tumor accumulation and tumor proliferation upon FGFRi treatment only in FGFRi-sensitive tumors; whereas these decreases were not observed in the FGFRi-de novo and acquired resistant tumors. The change of 18 F-FDG tumor uptake at early treatment stage might be used to identify the primary FGFRi-resistant patients noninvasively, despite the presence of FGFR activating mutations, to avoid unnecessary ineffective treatment and spare costs. Furthermore, both 18 F-FDG tumor accumulation and HK2 expression could respond the administration/withdrawal of FGFRi in NCI-H1581 xenografts correspondingly, which in turn suggested that the loss of 18 F-FDG tumor uptake response to FGFRi treatment might be associated with the acquired FGFRi-resistance. Certainly, using an inducible HK2 xenograft model monitored by 18 F-FDG PET imaging will further strengthen our novel finding, the association of FGFRi-regulated HK2 with 18 F-FDG uptake. 18 F-FDG PET as a novel biomarker for the response/resistance to FGFR-targeted therapy in cancers is thus indicated.
Since 18 F-FDG PET/CT imaging can provide both metabolic and anatomical information, it has been widely used in diagnosis, staging, molecular stratification and monitoring of the therapeutic effects and prognostic evaluation of cancer patients [45,46]. 18 F-FDG PET/CT imaging has been reported to monitor the therapeutic response to tyrosine kinase inhibitors (TKIs), such as EGFR- [47,48], VEGFR- [49], and ALK-TKIs [50]. The most successful application example of 18 F-FDG PET/CT imaging is prediction the survival outcomes and guidance the targeted therapy in thousands of non-small cell lung cancer with EGFR mutations involving hundreds of research articles [51][52][53]. However, no paper on 18 F-FDG PET/CT imaging for the drug sensitivity/resistance of FGFR-TKIs is published till now. Further literature search revealed that one related report on 18 F-FDG PET imaging was used to determine whether Dovitinib (a multitarget-tyrosine kinase inhibitor targeting FGFRs 1-3, VEGFRs, FLT3, c-Kit, PDGFR, and other receptor tyrosine kinases) altered tumor glucose metabolism and subsequent clinical outcome in a phase II study of 15 patients with recurrent or metastatic adenoid cystic carcinoma. 18 FDG-PET scans detected an early metabolic response only in 3 of 15 patients, but it did not correlate with RECIST (Response Evaluation Criteria in Solid Tumors) response. The authors claimed that they could not determine whether the observed effects were due to the specific inhibition of FGFR or other target receptors, or a combinatorial effect, because the enrolled patients were not selected for FGFR aberrance and Dovitinib was a multitarget kinase inhibitor [54]. Selective FGFR inhibitors in the selected patients with right drug target would be required to determine whether 18 FDG-PET could respond to the FGFR signaling inhibition in this rare cancer type. | 6,966.4 | 2022-08-29T00:00:00.000 | [
"Biology"
] |
The Safety of Long-Term Proton Pump Inhibitor Use on Cardiovascular Health: A Meta-Analysis
Introduction: Proton pump inhibitors (PPIs) are one of the most prescribed classes of drugs worldwide as a first-line treatment of acid-related disorders. Although adverse effects are rare and rapidly reversible after a short exposure, concerns have been recently raised about a greater toxicity on cardiovascular health after a longer exposure, especially when combined with clopidogrel. We aimed to evaluate the safety of long-term PPI use on cardiovascular health in patients with known atheromatous cardiovascular disease. Methods: A literature search was conducted in the PubMed, Embase, and Cochrane Library databases and grey literature in April 2022. Articles published between 2014 and 2022 were considered relevant if they were designed as randomized controlled trials (RCTs) that included post hoc analyses or prospective observational studies and if they investigated clinical cardiovascular outcomes associated with PPI use for 6 months or more in patients suffering from cardiovascular disease requiring antiplatelet agent therapy and/or coronary angioplasty. Statistical analyses were performed using RevMan 5.4 software (Computer program, the Cochrane Collaboration, 2020, London, UK). The risk of bias was assessed using the Cochrane risk-of-bias tool for the RCTs and the Newcastle–Ottawa scale for the observational studies. Results: A total of 10 full-text articles involving 53,302 patients were included. Substantial heterogeneity was found among the 10 included studies. The primary analysis showed no significant differences between the PPI group and the control group for the risks of major adverse cardiovascular events (MACEs), all-cause death (ACD), or target vessel revascularization (TVR) using a random-effects model (OR 1.15, 95% CI 0.98–1.35, p = 0.08, I2 = 73%; OR 1.24, 95% CI 0.94–1.65, p = 0.13, I2 = 63%; and OR 1.19, 95% CI 0.76–1.87, p = 0.45, I2 = 61%, respectively). The primary analysis yielded similar results for the risks of myocardial infarction (MI), stroke, and cardiovascular death (CVD) using a fixed-effects model (OR 0.98, 95% CI 0.88–1.09, p = 0.66, I2 = 0%; OR 1.02, 95% CI 0.90–1.17, p = 0.73, I2 = 0%; and OR 1.04, 95% CI 0.94–1.16, p = 0.44, I2 = 35%, respectively). Likewise, a subgroup analysis based on eight randomized controlled trials failed to identify any association between PPI use and the risks of MACEs, MI, stroke, TVR, ACD, or CVD using a fixed-effects model (overall pooled OR 1.01, 95% CI 0.96–1.06; p = 0.66; I2 = 0%). The pulled data from the two included observational studies (OS) demonstrated a significantly increased risk of MACEs in the PPI group (OR 1.42, 95% CI [1.29–1.57], p <0.001; I2 = 0%). In another subgroup analysis, no evidence of an increased risk of adverse cardiovascular events in the co-therapy PPI/clopidogrel versus clopidogrel alone groups was found with the exception of the risk of ACD (OR 1.50, 95% CI 1.23–1.82, p = 0.001, I2 = 0%). Nevertheless, after performing a sensitivity analysis reaching heterogeneity I2 = 0%, the co-prescription of PPIs and clopidogrel was at increased risk of MACEs (p < 0.001), CVD (p = 0.008), and TVR (p < 0.001) but remained statistically non-significant for the risk of MI (p = 0.11). Conclusions: The overall results of this meta-analysis showed that long-term PPI use was not associated with an increased risk of adverse cardiovascular events. However, inconsistent results were found for combined PPI/clopidogrel therapy. These results should be considered with caution in light of the significant heterogeneity, the limited number of included studies, and the lack of adjustment for potential confounders.
Introduction
Proton pump inhibitors (PPIs) are one of the most prescribed classes of drugs worldwide [1]. This phenomenon is largely due to their effectiveness in the management of acid-related diseases such as gastroesophageal reflux disease (GERD), peptic ulcer, gastrointestinal bleeding, and Helicobacter pylori infection and the prevention of gastric ulcers in patients on aspirin or non-steroidal anti-inflammatory drugs [2]. Presumed safe, PPIs have been available over the counter since 2003, and previous data reported a significant amount of off-label PPI use, with up to 65% of prescriptions having no appropriate indication in the United States [3]. Omeprazole alone was dispensed more than 70 million times in 2016 [3], and PPIs account for over $10 billion in health care costs, with a global cost exceeding $25 billion per year [4]. Moreover, rebound acid hypersecretion may occur after stopping PPI therapy, leading to the recurrence of gastric symptoms and thus to drug dependency [5]. This PPI overuse raises concerns about the potential risks it could cause, especially in the elderly affected by multiple comorbidities and taking multiple medications [6]. Indeed, recent studies, mostly observational studies, reported various adverse events related to long-term PPI therapy, including the risks of cardiovascular diseases [7], fractures, pneumonia, Clostridium difficile infection, impaired absorption of micronutrients, kidney disease, dementia, gastric neoplasia [4,8], and drug-to-drug interactions [3]. Regarding the cardiovascular risk, the concomitant use of clopidogrel and PPIs has been specifically investigated in several studies as clopidogrel and PPIs are both metabolized by the cytochrome P450 isoenzyme 2C19, leading to drug-drug interaction due to competition at the binding site [9].
In this meta-analysis, we aim to evaluate the association between long-term PPI use (defined as exposure ≥ 6 months) and the risk of adverse cardiovascular events in patients with known atheromatous cardiovascular disease using studies with evidence levels I or II according to the evidence-based clinical practice guidelines [10]. The primary endpoint was the overall safety of PPIs. The secondary endpoints were defined as the safety of combined PPI/clopidogrel therapy and the overall safety of PPIs according to study design.
Protocol
This systematic review was conducted in compliance with the Cochrane Handbook for Systematic Reviews of Interventions [11] and PRISMA [12] guidelines.
Search Strategy
The literature search was conducted in PubMed, EMBASE, and the Cochrane Library in April 2022. In order to reduce publication bias, we also conducted a search of the grey literature through the Data Archiving and Networked Services and Grey Literature Report databases. The following keywords: ("proton pump inhibitor" OR "proton pump inhibitors" OR "PPIs") AND ("cardiovascular disease" OR "anti-platelet therapy" OR "clopidogrel" OR "aspirin") AND ("adverse effect" OR "adverse drug reaction" OR "risk") were searched. Detailed search terms and combinations used for the literature search are available in online Supplementary Table S1. For the grey literature, we only used the keyword "proton pump inhibitors". Hand searching of references lists was performed to find any additional appropriate article.
Study Selection Criteria
We limited the searches to articles published from January 2014 to April 2022 written in English or French. We selected randomized controlled trials (RCTs) including post hoc analyses and prospective observational studies reported as full text and published by highly influential journals according to the eigenfactor metrics [13].
Articles were included if patients were aged 18 years or older with atheromatous cardiovascular disease at baseline; the experimental intervention was PPI use for 6 months or longer; PPI use was compared with another PPI (established in the study protocol as not at risk of cardiovascular events), another antacid (established in the study protocol as not at risk of cardiovascular events); placebo treatment; or no treatment. All PPIs were assessed as one drug class considering that all PPIs were sufficiently similar to be combined relevantly as one interventional group. Articles were excluded if the study was designed as a retrospective study or a case-control study; the study involved the general population, an isolated case, pediatric population, or animals; or the study consisted of a meta-analysis or a systematic review (references lists were screened to provide additional citations).
The clinical endpoints were major adverse cardiovascular events (MACEs), myocardial infarction, stroke, target vessel revascularization (TVR), cardiovascular death (CVD), and all-cause death (ACD). The MACEs included cardiovascular death, myocardial infarction, and/or stroke, and/or TVR.
Data Extraction
Data extraction was performed by one reviewer (DJ), and the correctness of the extracted data was verified multiple times. In cases of uncertainty, a second reviewer could be requested. Study characteristics and patients' characteristics at baseline were collected. The following data were extracted for each included study: study design, first author, year of publication, number of centers, total duration of follow-up, number of patients, mean age, gender, percentage of gender, intervention, comparison, concomitant medications (aspirin, clopidogrel), comorbidities (hypertension, dyslipidemia, overweight/obesity according to body mass index, prior myocardial infarction, prior stroke, smoking status as a major risk factor for cardiovascular diseases), and primary and secondary endpoints in accordance with the clinical endpoints of our meta-analysis (major adverse cardiovascular events, myocardial infarction, stroke, target vessel revascularization, cardiovascular death, all-cause death) ( Table 1).
Statistical Analysis
Meta-analysis was performed by calculating the pooled odd ratios with 95% confidence intervals (CI) using Revman 5.4 software. Study results were considered statistically significant for p-value < 0.05 and CI excluding 1. Heterogeneity among included studies was assessed using the I 2 statistic and was considered low if I 2 < 50% and high if I 2 ≥ 50% [22]. The pooled effect size was estimated using a fixed-effects model for I 2 < 50%, while a random effects model was used for I 2 ≥ 50%. We conducted subgroup analyses to assess the influence of concurrent use of clopidogrel and PPIs on cardiovascular adverse events, as well as the influence of study design on the results obtained. We also performed a post hoc subgroup analysis that investigated the potential adverse effects related to the specific PPI. Sensitivity analyses were also performed to determine the impact of heterogeneity on the original results. Funnel plots were used to investigate potential publication bias.
Study Selection
The initial search identified 1717 relevant studies. After title and abstract screening, 1665 studies were excluded. Of the 52 studies remaining, all retrieved from PubMed (n = 29), Embase (n = 20), and the Cochrane Library (n = 3), 22 duplicates and 9 articles only available as abstracts were excluded. Of the 21 articles selected for full-text review, 10 studies met all eligibility criteria and were therefore included in this meta-analysis. Figure 1 shows the PRISMA flow diagram for the study selection. events, as well as the influence of study design on the results obtained. We also performed a post hoc subgroup analysis that investigated the potential adverse effects related to the specific PPI. Sensitivity analyses were also performed to determine the impact of heterogeneity on the original results. Funnel plots were used to investigate potential publication bias.
Study Selection
The initial search identified 1717 relevant studies. After title and abstract screening, 1665 studies were excluded. Of the 52 studies remaining, all retrieved from PubMed (n = 29), Embase (n = 20), and the Cochrane Library (n = 3), 22 duplicates and 9 articles only available as abstracts were excluded. Of the 21 articles selected for full-text review, 10 studies met all eligibility criteria and were therefore included in this meta-analysis. Figure 1 shows the PRISMA flow diagram for the study selection.
Study Characteristics
The general study and patient characteristics at baseline are summarized in Table 1. The pooled analysis included a total of 53,302 patients (18,495 patients in the PPI group
Study Characteristics
The general study and patient characteristics at baseline are summarized in Table 1. The pooled analysis included a total of 53,302 patients (18,495 patients in the PPI group and 34,807 patients in the control group). The average age was 64.5 years. Both genders were included in all studies, with an average female rate of 29.9%.
Study Quality Assessment
The included studies were of moderate to high methodological quality, except for the PHA, which evaluated the RCTs of low methodological quality, as expected ( Table 1). The methodological quality assessment was based upon the Cochrane risk-of-bias [25] assessment tool for the included RCTs and post hoc analyses ( Figure 2) and the Newcastle-Ottawa scale [26] (NOS) for the observational studies ( Table 2). The Cochrane risk-of-bias tool covers six domains of bias: selection bias, performance bias, detection, attrition bias, reporting bias, and other. Each domain can be assessed as low risk, unclear risk, or high risk. We attributed 0 points, 0.5 point, and 1 point for each domain considered as low risk, unclear risk, and high risk, respectively (Table 1). Three RCTs showed no risk of bias, three RCTs showed one to three risks of bias (selection, performance, and detection), and the two post hoc analyses demonstrated a high risk of bias in all domains except for the risks of attrition and selection bias, which remained unclear ( Figure 2). The NOS evaluates the risk of bias in three domains: selection, comparability, and outcome. A score of 6 to 9 was regarded as good, 3 to 5 as fair, and 0 to 2 as poor, according to the NOS interpretation established by Wells et al. [26]. The two included observational studies reached scores of 7 and 8 (
Primary Analysis
The results of the primary analysis are presented in Figure 3. We found no significant differences between the PPI group and the control group for the risks of MACEs, ACD, or TVR using a random-effects model (OR 1.15, 95% CI 0.98-1.35, p = 0.08, I 2 = 73%; OR 1.24, 95% CI 0.94-1.65, p = 0.13, I 2 = 63%; and OR 1.19, 95% CI 0.76-1.87, p = 0.45, I 2 = 61%, respectively; Figure 3A). Similar results were found for the risk of myocardial infarction
Subgroup Analysis
In the first subgroup analysis assessing the cardiovascular risk associated with the concomitant use of clopidogrel and PPIs, we found that this co-therapy heightened the risk of ACD (OR 1.50, 95% CI 1.23-1.82, p < 0.001) but did not raise the risks of MACEs (OR 1.20, 95% CI 0.97-1.48, p = 0.09), MI (OR 1.03, 95% CI 0.80-1.32, p = 0.81), stroke (OR
Heterogeneity and Sensitivity Analyses
In order to evaluate the influence of high heterogeneity on the results obtained, we performed sensitivity analyses by excluding each study successively until I 2 = 0. No sensitivity analyses were performed for the randomized controlled trial subgroup, the observational study subgroup, or the omeprazole subgroup as original heterogeneity I 2 was nil for these three analyses. PPI use became significantly at risk for TVR in the primary analysis (p < 0.001) after excluding one study. All other results remained stable (Table 3). After the exclusion of one to two studies for each outcome, combined PPI/clopidogrel therapy was at increased risk of MACEs (p < 0.001), TVR (p < 0.001), and CVD (p = 0.008). The risk of MI remained insignificant (p = 0.29) ( Table 3).
Heterogeneity and Sensitivity Analyses
In order to evaluate the influence of high heterogeneity on the results obtained, we performed sensitivity analyses by excluding each study successively until I 2 = 0. No sensitivity analyses were performed for the randomized controlled trial subgroup, the observational study subgroup, or the omeprazole subgroup as original heterogeneity I 2 was nil for these three analyses. PPI use became significantly at risk for TVR in the primary analysis (p < 0.001) after excluding one study. All other results remained stable (Table 3). After the exclusion of one to two studies for each outcome, combined PPI/clopidogrel therapy was at increased risk of MACEs (p < 0.001), TVR (p < 0.001), and CVD (p = 0.008). The risk of MI remained insignificant (p = 0.29) ( Table 3).
Publication Bias
Visual inspection of the funnel plots performed for the primary analysis and RCT subgroup analysis found no evidence of publication bias ( Figure 7A,B,D). Conversely, the funnel plot of the clopidogrel/PPI subgroup analysis suggested the existence of publication bias as the PPI effects estimated in each included study scatter asymmetrically around the summary effect ( Figure 7C). The number of studies involved in the omeprazole subgroup analysis was too small to expect relevant interpretation of a funnel plot. Therefore, we did not calculate a funnel plot for this specific analysis. Table 3. Sensitivity analysis summary (I 2 = 0%).
Publication Bias
Visual inspection of the funnel plots performed for the primary analysis and RCT subgroup analysis found no evidence of publication bias ( Figure 7A,B,D). Conversely, the funnel plot of the clopidogrel/PPI subgroup analysis suggested the existence of publication bias as the PPI effects estimated in each included study scatter asymmetrically around the summary effect ( Figure 7C). The number of studies involved in the omeprazole subgroup analysis was too small to expect relevant interpretation of a funnel plot. Therefore, we did not calculate a funnel plot for this specific analysis.
Discussion
We conducted this meta-analysis with the aim of asse ovascular events associated with long-term PPI use amon atous cardiovascular disease. Overall, the main results of o no evidence that PPIs as a drug class were associated w cardiovascular events. However, conflicting results were PPIs and clopidogrel. Overall, the subgroup analysis invol bined therapy was safe, while the sensitivity analysis that results. Nevertheless, the PPI/clopidogrel co-therapy sub to potential publication bias according to the visual in which entails a greater risk of publication bias in the sen smaller number of studies. When considering specific PP of omeprazole's effects on cardiovascular health found it The potential cardiovascular risk associated with PPI authors, mostly in retrospective observational studies, a s
Discussion
We conducted this meta-analysis with the aim of assessing the risks of various cardiovascular events associated with long-term PPI use among patients with known atheromatous cardiovascular disease. Overall, the main results of our meta-analysis demonstrated no evidence that PPIs as a drug class were associated with an increased risk of adverse cardiovascular events. However, conflicting results were found for the combined use of PPIs and clopidogrel. Overall, the subgroup analysis involving high I 2 found that this combined therapy was safe, while the sensitivity analysis that controlled for I 2 found opposite results. Nevertheless, the PPI/clopidogrel co-therapy subgroup analysis was susceptible to potential publication bias according to the visual interpretation of the funnel plot, which entails a greater risk of publication bias in the sensitivity analysis that included a smaller number of studies. When considering specific PPIs, the independent assessment of omeprazole's effects on cardiovascular health found it to be safe.
The potential cardiovascular risk associated with PPI use has been studied by several authors, mostly in retrospective observational studies, a study design most likely to lead to selection, confusion, and information bias. It seems that a causal link between PPI exposure and adverse events can hardly be established if there are uncertainties in the measurement of the exposure to PPIs. Moreover, target populations differ from one study to another, which might have resulted in considerable meta-analytic heterogeneity in patients' baseline characteristics (patients with chronic heart disease +/− acute coronary syndrome +/− post-percutaneous coronary intervention +/− dual antiplatelet therapy including clopidogrel and/or aspirin +/− heart failure; overall population). Several pathophysiological mechanisms have also been put forward to support and justify the study of this risk. However, it must be noted that these articles report conflicting results for both clinical and biological outcomes. Furthermore, we could observe that performing randomized controlled trials versus cohort studies led to diametrically opposite results in most published studies. While cohort studies, prospective or retrospective, tend to support the hypothesis of an increased cardiovascular risk during long-term PPI exposure, randomized controlled trials tend to refute this hypothesis. The same is true for meta-analyses including mostly cohort studies and those including exclusively or almost exclusively randomized controlled trials. Therefore, a significant association between PPI use and cardiovascular events could be more likely related to unmeasured potential confounders than related to a PPI's proven toxicity.
Finally, most published meta-analyses pulled data from different study designs, which is expected to lead to differences in the observed intervention effects, increasing heterogeneity and weakening the accuracy of the results. [22] The potential drug-drug interaction between PPIs and clopidogrel that may increase the incidence of cardiovascular ischemic events was the hypothetical case most studied. The increased cardiovascular risk associated with the combined use of clopidogrel and PPIs could result from a competitive interaction between clopidogrel and PPIs with cytochrome P450 isoenzyme 2C19 (CYP2C19), affecting the clopidogrel-specific inhibition of ADP-induced platelet aggregation. Moreover, the conversion of clopidogrel to its active metabolite varied depending on CYP2C19 genetic polymorphisms [27], with 4% to 30% of people being low metabolizers or non-metabolizers, while the others are described as rapid metabolizers [9]. An affinity to CYP2C19 also differed from one PPI to another, with the highest affinity found for omeprazole and the lowest affinity or no affinity found for pantoprazole depending on the study [24,[27][28][29][30].
In a meta-analysis of 31 observational studies and 4 RCTs assessing PPI/clopidogrel cardiovascular risk within patients in the post-discharge treatment of unstable angina/non-ST segment elevation myocardial infarction, Melloni et al. [31] found an increased risk of cardiovascular outcomes and stroke in observational studies, while no differences between omeprazole and placebo were found in four RCTs, despite reducing upper gastrointestinal bleeding. Another meta-analysis involving 18 cohort studies (Shi et al. [32]) reflected a higher risk of MACEs and cerebrovascular events (p < 0.001), ACD (p < 0.001), cardiac death (p < 0.001), myocardial infarction (p < 0.001), stent thrombosis (p < 0.001), TVR (p = 0.005), and stroke (p = 0.003), with moderate to high I 2 , within patients taking clopidogrel and PPI after stent implantation. In a pooled analysis of 39 studies (31 cohort studies, 8 RCTs and propensity-matched studies), Cardoso et al. [33] found that the concomitant use of PPIs and clopidogrel heightened the risks of ACD (p < 0.001), MI (p < 0.001), stent thrombosis (p = 0.02), acute coronary syndrome (p = 0.004), and cerebrovascular accident (p < 0.001). Similar results were found in an analysis restricted to cohort studies. However, in a separate pooled analysis of eight RCTs and propensity-matched studies, Cardoso et al. found that combined PPIs and clopidogrel use had no impact on the occurrence of cardiovascular outcomes (ACD p = 0.66; ACS p = 0.35; MI p = 0.65; CVA p = 0.34; TVR OR 0.88; p = 0.01) while significantly reducing the risk of gastrointestinal bleeding (OR 0.24, p = 0.003). After pulling data from 22 cohort studies and 6 RCTs, Lee et al. [34] found that the concomitant use of PPIs and clopidogrel increased the risk of MACEs (p < 0.001), CVD (p < 0.001), and MI (p < 0.001), with high heterogeneity for most analyses up to 90%. Nevertheless, the pulled data from the six RCTs showed no significant association between PPI/clopidogrel co-therapy and the risk of MACEs (p = 0.96, I 2 = 90%). When considering each specific PPI separately in adjusted analyses (I 2 ranging from 0% to 85%), omeprazole, pantoprazole, and lansoprazole were at increased risk for MACEs, while esomeprazole and rabeprazole were not (p = 0.19 and p = 0.40, respectively). PPI use was found to be a protective factor against gastrointestinal bleeding (RR = 0.29, p < 0.001; I 2 = 0%). The meta-analysis by Bundhun et al. [35] including nine cohort studies and two RCTs showed that the combination of clopidogrel and PPIs increased the risks of MACEs, MI, stent thrombosis, and TVR but not the risk of mortality for a PPI exposure greater than one year. In a meta-analysis of seven observational studies, Kwok et al. [36] found an elevated risk of MACEs independent of clopidogrel use. Kwok et al. also found an increased risk of MACEs in association with lansoprazole, omeprazole, esomeprazole, and pantoprazole individually when used with clopidogrel.
With regard to biological investigations, Gu et al. [14], Zhang et al. [9], and Lin et al. [37] did not find a significant risk of higher platelet reactivity (p = 0.17; p > 0.05; p = 0.4315 respectively) after measuring platelet reactivity in the blood samples of patients receiving clopidogrel, while Weisz et al. [21] found an opposite result (OR 1.38, 95% CI 1.25-1.52, p = 0.001). Sibbing et al. [38] found that omeprazole was significantly associated with a higher platelet aggregation when combined with clopidogrel, while pantoprazole and esomeprazole were not. In relation to CYP2C19 polymorphisms, Furuta et al. [29] reported that omeprazole and rabeprazole significantly lowered the mean inhibition of platelet aggregation (IPA) induced by clopidogrel in rapid metabolizers, while the decreased metabolizers (low and non-metabolizers) were more likely to convert from "responders" (IPA ≥ 30%) to "non-responders" (IPA < 30%) when using a concomitant PPI. They also found that taking PPIs and clopidogrel at two separate times of the day did not prevent the drug-drug interaction between clopidogrel and a PPI. Furuta et al. [29] did not bring to light any difference between omeprazole, rabeprazole, and lansoprazole combined with clopidogrel versus clopidogrel alone regardless of CYP2C19 polymorphisms. In a study enrolling 174 patients, Hokimoto et al. [39] found significantly lower platelet reactivity in patients on clopidogrel and carrying CYP2C19 normal function alleles (extensive metabolizers, EM) compared with patients carrying one (intermediate metabolizers, IM) or two (poor metabolizers, PM) loss-of-function alleles. In line with these results, the cardiovascular event rate was higher in the IM and PM groups than in the EM group. The specific assessment of rabeprazole, a PPI known for having less affinity for CYP2C19, demonstrated no significant differences in residual platelet aggregation or in cardiovascular event rate when combined with clopidogrel versus clopidogrel alone. In a meta-analysis involving four cohort studies and one RCT, Biswas et al. [40] claimed that patients bearing the dual burdens of carrying CYP2C19 loss-of-function alleles and taking PPIs and clopidogrel concomitantly faced a higher risk of major adverse cardiovascular events. However, in studies assessing the influence of CYP2C19 polymorphisms on cardiovascular outcomes, sample sizes appear to be too small for detecting a reliable difference in biological and clinical outcomes.
Independent of clopidogrel use, Dahal et al. [41] demonstrated that PPI use alone was not at increased risk for cardiovascular mortality, all-cause mortality, myocardial infarction, or stroke in a meta-analysis of nine RCTs including patients taking aspirin for the prevention of cardiovascular diseases and stroke. Zhai et al. [42] sought to examine the safety of PPIs for cardiac and vascular health using the Food and Drug Administration Adverse Event Reporting System (FAERS). PPIs were not associated with more cardiac and vascular events compared with the whole database. However, the authors reported a wide range of vascular signals and to a lesser extent cardiac signals. Pantoprazole and esomeprazole showed the broadest spectrums of signals. However, there is no certainty that the reported adverse events are due to the PPIs involved.
Another hypothetical biological mechanism advanced by some authors involves a dysfunction of the vascular endothelium. Endothelial nitric oxide synthase (NOS) is an enzyme that produces the vasoprotective and vasodilator molecule nitric oxide (NO) [43]. Plasma asymmetrical dimethylarginine (ADMA) is an endogenous inhibitor of nitric oxide synthase. Thus, elevated plasma ADMA levels might increase the occurrence of cardiovascular events. In 2013, Ghebremariam et al. [7] published a paper explaining that PPIs elevated plasma ADMA levels by inhibiting an enzyme (dimethylarginine dimethylaminohydrolase) that degrades ADMA. They also found that PPIs reduced nitric oxide levels and endothelium-dependent vasodilatation in a murine model and in ex vivo human tissue. In a cross-over pilot study of 21 adults published in 2015, Ghebremariam et al. [44] found increased plasma ADMA levels in vivo in patients on lansoprazole versus placebo and in patients with a history of cardiovascular disease versus healthy patients. However, these differences were not statistically significant.
Strengths and Limitations
Our meta-analysis has various strengths. One, unlike most published meta-analyses, we selected studies with evidence levels I or II according to the evidence-based clinical practice guidelines, which aim to reduce methodological heterogeneity as well as ensure data accuracy, internal validity, and relevant results. Two, all included studies were recently published and retrieved from journals of high scientific influence. Three, of the ten studies included, eight were conducted in numerous centers, reinforcing external validity. Four, of the ten included studies, six were randomized controlled trials, of which four were doubleblind. Five, the unbalanced distribution of patients' baseline cardiovascular risk factors did not disserve the main results of our study, as we found an insignificant association between PPI use and the occurrence of adverse cardiovascular events in most conducted analyses. However, heterogeneity within the cardiovascular risk factors could have caused confusion in the sensitivity analysis of the influence of clopidogrel associated with PPIs, which showed evidence of a statistically significant higher risk of cardiovascular outcomes. Our meta-analysis also has several limitations that may have resulted in information, selection, and publication bias. One, the selection criteria that defined the eligible articles favored the quality and the relevance of the included studies but also involved the exclusion of a significant number of references. Two, the number of included studies was small, and the overall heterogeneity between combined studies proved to be substantial. Three, the included studies presented notable differences in terms of patients' characteristics, followup duration, and PPI used. We did not perform adjusted analyses for patients' baseline characteristics that were not comparable between two groups. Four, in one study [14], the control group took a PPI (pantoprazole), while the control groups in other included studies were given a placebo or called "no PPI". However, the exclusion of this study from the performed pooled analyses did not change the results obtained. Five, we did not assess the potential dose-related and time-related effects due to the lacks of available data on dosage, frequency, and indication for PPI therapy. Six, we did not corroborate the results of clinical outcomes with the biological mechanisms argued in previous studies. Seven, we did not investigate the potential difference in the rate of gastrointestinal bleeding between PPI use and no-PPI use due to the lack of available data. Eight, the definition of MACEs was different among studies, which may partly explain the high heterogeneity and conflicting results across the analyses we performed for this particular outcome. Nine, we cannot exclude residual confounding variables, mainly in the non-randomized studies, that could affect the comparability between the two groups. Ten, the included post hoc analyses and observational studies collected information on PPI use and outcomes of interest via interviews at baseline and follow-up visits or via medical records, which entails information and confusion bias to varying degrees. Eleven, the visual inspection of the funnel plots alone may lead to the misevaluation of the publication bias of a meta-analysis, especially when combining a small number of studies [33]. Given the aforementioned limitations, the results of this meta-analysis should be taken with caution.
Conclusions
The overall results of this meta-analysis support the hypothesis that there is no significantly increased risk of cardiovascular events in association with PPI use alone, suggesting that PPIs can be safely used in appropriate clinical settings. The association between the combined use of PPI/clopidogrel and adverse cardiovascular events remained unclear due to substantial bias and inconsistent results across the analyses of the pulled data. These results must be interpreted with caution given the lack of adjustment for known confounders, unmeasured confounders, high heterogeneity, and small number of included studies. Further large-scale randomized controlled trials are required to provide a reliable statement on the safety of PPIs regarding cardiovascular events in association with clopidogrel or not.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/jcm11144096/s1, Table S1: Results of literature research for each database. | 7,254.8 | 2022-07-01T00:00:00.000 | [
"Biology",
"Medicine"
] |
Inducible LGALS3BP/90K activates antiviral innate immune responses by targeting TRAF6 and TRAF3 complex
The galectin 3 binding protein (LGALS3BP, also known as 90K) is a ubiquitous multifunctional secreted glycoprotein originally identified in cancer progression. It remains unclear how 90K functions in innate immunity during viral infections. In this study, we found that viral infections resulted in elevated levels of 90K. Further studies demonstrated that 90K expression suppressed virus replication by inducing IFN and pro-inflammatory cytokine production. Upon investigating the mechanisms behind this event, we found that 90K functions as a scaffold/adaptor protein to interact with TRAF6, TRAF3, TAK1 and TBK1. Furthermore, 90K enhanced TRAF6 and TRAF3 ubiquitination and served as a specific ubiquitination substrate of TRAF6, leading to transcription factor NF-κB, IRF3 and IRF7 translocation from the cytoplasm to the nucleus. Conclusions: 90K is a virus-induced protein capable of binding with the TRAF6 and TRAF3 complex, leading to IFN and pro-inflammatory production.
Introduction
The innate immune system senses a variety of pathogen-associated molecular patterns (PAMPs) via specific pattern-recognition receptors (PRRs), including the Toll-like receptor (TLR) and the RIG-I-like receptor (RLR) [1]. All TLRs, with the exception of TLR3, recruit myeloid differentiation primary response 88 (MyD88) to their receptor complex [2,3]. The TLR/MyD88 complex in turn recruits members of the interleukin-1 (IL-1) receptor-associated kinase (IRAK) family. Activated IRAK family members dissociate from MyD88 and interact with tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6). TRAF6 forms a ubiquitin-conjugating enzyme complex that recruits transforming growth factor beta-activated kinase 1 (TAK1), leading to the I kappa B kinase (IKK) complex, composed of IKKα, IKKβ, and NEMO. As result, transcription factors, including nuclear factor-κB (NF-κB), interferon regulatory factor 3 (IRF3) and IRF7, translocate from the cytoplasm to nucleus [4,5]. By contrast, TLR3 uses TIR domain-containing adaptor protein inducing IFNβ (TRIF) as the adapter protein. Activated TRIF recruits TRAF3, leading to TRAF family member-associated NF-κB activator (TANK) binding to TRAF3. Then, TANK recruits TBK-1 and/or IKKε to form a complex, causing IRF3 to translocate from the cytoplasm to the nucleus [6,7]. After transcription factors migrate to the nucleus and assemble on the promoter, there is induction of type I interferons (IFN) and pro-inflammatory cytokines. These cytokines subsequently induce transcription of a wide range of antiviral and inflammatory genes that mediate innate antiviral immune and inflammatory responses [8,9].
90K, also known as LGALS3BP, galectin-3-binding protein (Gal-3BP) and Mac-2-binding protein, is a ubiquitous multifunctional secreted glycoprotein originally studied in the context of neoplastic transformation and cancer progression [10,11]. However, recently, several lines of evidence show that 90K expression is induced by various kinds of viral infections, including human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), hantavirus and dengue virus [12,13]. Furthermore, 90K contains a scavenger receptor cysteinerich (SRCR) domain, a BTB/POZ domain, a BACK (BTB and C-terminal Kelch) domain and an approximately 200 amino acid (aa) C-terminus with no significant similarity to other human proteins [14]. The SRCR domain is found in soluble or membrane-associated innate immunity-related proteins [15]. Many studies proposed intracellular and extracellular innate immunity functions for 90K. For example, 90K reduced the secretion of IL-4, IL-5, and IL-13 in PBMCs [16]; 90K treatment lead to CD14+ cell (monocytes/macrophages) induction by IL-1, IL-6 and TNF-α [17]; exogenous Gal-3BP has also been shown to activate THP-1 cells alone and additively with IFN-γ [18]. Although several functions of 90K in cytokine expression have been described, a clear role for 90K in innate immune responses to viral infections has not been established.
In this study, we show that many viruses, including influenza A virus (IAV), vesicular stomatitis virus (VSV) and herpes simplex virus (HSV) induce 90K expression. In turn, 90K limits viral expression and replication by inducing IFN and pro-inflammatory cytokines. Further experiments demonstrate that 90K functions as a scaffold protein to interact with TRAF6, TRAF3, TAK1 and TBK1, potentiates TRAF6 and TRAF3 ubiquitination and serves as a specific ubiquitination substrate of TRAF6. Taken together, our results suggest a novel mechanism for induction of IFN and pro-inflammatory cytokines during viral infections.
Viral infection induces 90K expression
A previous study compared global gene expression differences in the liver in wild-type and HCV-infected chimpanzees by DNA microarray analysis [19,20]. They found a number of gene expressions were changed during HCV infection. In this study, we investigated whether those genes affected HBV replication. As shown in S1 Fig, 90K and MAVS significantly inhibited the expression of HBeAg. Because the relationship between MAVS and virus has been widely reported, 90K was chosen for subsequent experiments.
Since 90K was found elevated in patients infected with HIV, HBV, HCV, hantavirus and dengue virus, we determined whether other virus induce 90K expression. Results showed that influenza A virus (IAV), vesicular stomatitis virus (VSV) and herpes simplex virus (HSV) infection upregulated mRNA expression of 90K in mouse embryonic fibroblasts (MEFs) ( Fig 1A-1C). IFN-α detection was included as a positive control for comparison (Fig 1A-1C). Similarly, poly(I:C), a synthetic dsRNA analog, also induced 90K expression ( Fig 1D). 90K induction was also observed in IAV-infected murine lungs ( Fig 1E). We next generated IFNAR1 knockout (IFNAR1 -/-) cells to investigate the role of virus on 90K expression. As shown in Fig 1F, the protein levels of 90K were significantly reduced in IFNAR1 -/cells compared with wildtype (WT) cells. Interestingly, IFN-α also induced 90K expression in A549 cells ( Fig 1G). These data together suggested that these viral infections induce upregulation of 90K.
90K constrains viral replication in vitro and in vivo
The finding that 90K was induced by various virus prompted us to investigate whether 90K influenced universal cellular antiviral activity. We first assessed the effect of 90K on IAV replication by measuring the production of three different forms of IAV RNA (mRNA, cRNA, and vRNA) using an approach described previously. Real-time RT PCR analyses showed that the levels of NP-specific mRNA, complementary RNA (cRNA) and vRNA were suppressed by 90K overexpression (Fig 2A). To confirm the effects of 90K on viral replication, we designed three specific shRNAs for 90K (shRNA-90K #1, #2, and #3) and tested their efficiency as shown in S2A Fig. ShRNA-90K #2 was selected for the experiments described below, in which we found that 90K knockdown increased IAV transcription and replication ( Fig 2B). We next assessed the effects of 90K on HBV replication. The results indicated that 90K overexpression reduced HBeAg/HBsAg expression and HBV DNA replication, whereas 90K knockdown induced HBeAg/HBsAg expression and HBV DNA replication (Fig 2C and 2D). Furthermore, the effect of 90K on VSV production was also evaluated. As expected, VSV viral titers were significantly lower in 90K-overexpressing cells and VSV RNA levels were augmented when 90K was knocked down (Fig 2E and 2F). Similar results were also obtained in VSV-GFP infected cells by using flowcytometry and fluorescence microscopy (S2B and S2C Fig). We also investigated the effect of 90K on EV71 replication in RD cells. 90K overexpression effectively suppressed viral VP1 mRNA ( Fig 2G) and protein (S2D Fig) levels. Conversely, 90K knockdown enhanced VP1 mRNA levels ( Fig 2H). Similar results were also obtained in HSV-infected Hela cells (Fig 2I).
Mice deficient in the 90k gene (90k -/mice) generated by standard knock-out embryonic stem cell system technology [21], with schematic diagram shown as S2E Fig, were used to further confirm the role of 90K in viral replication in vivo. We infected WT or 90k -/mice with the A/FM/1/47 (H1N1) strain of influenza virus and monitored body weights. The 90k -/mice exhibited lower body weights than WT mice during IAV infection ( Fig 2J). Moreover, in response to the IAV infection, 90k -/mice displayed an abbreviated survival time and lower survival rate than those of WT mice (Fig 2K). This increase in mortality was associated with higher lung/body index and significantly increased pulmonary viral load (Fig 2L and 2M). In line with the increasing in viral loads in 90k -/mice, hematoxylin-and-eosin (H&E) staining The protein level of 90K was analyzed by western blot. All experiments were repeated at least three times with consistent results. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, n = 3 ( �� P < 0.01; � P < 0.05), n.s., not significant.
90K plays an important role in virus-induced proinflammatory cytokines and chemokines expression in vitro and in vivo
IFN and cytokines regulate a broad range of viral infections. Since 90K also affects replication of many viruses, we investigated whether 90K regulated IFN and pro-inflammatory production. Using luciferase activity reporter assays, we showed that 90K overexpression stimulated ISRE, NF-κB, IFN-β and IFN-λ promoter (S3A Fig). Elevated mRNA levels of IFN-α, IFN-β, IFN-λ1 and IFN-λ2/3 were also observed in HSV-infected HeLa cells when 90K was overexpressed (S3B Fig). Several lines of evidence support the notion that some kinds of IAV infection induce massive release of inflammatory cytokines and chemokines, called the 'cytokine storm'. Thus, we examined the role of 90K in IAV-induced cytokine storm. As shown in Fig 3A, 90K overexpression enhanced IAV-induced IFN and pro-inflammatory production in A549 cells. We next investigated whether the production of proinflammatory cytokines and chemokines was altered in 90k -/mice during the IAV infection. Real-time RT-PCR showed that the mRNAs levels of proinflammatory cytokines and chemokines were significantly lower in MEFs, splenocytes, PBMCs and lungs of 90k -/mice than in WT mice during IAV infection (Fig 3B-3E). Similar results were also obtained in poly (I:C) treated A549 cells or 90k -/mice and WT mice MEFs, splenocytes and PBMCs. (S3C- S3F Fig). We also examined the role of 90K on other virus-regulated cytokine expressions. Lower mRNA levels of cytokines were observed in MEFs and PMBCs of 90k -/mice than in WT mice during VSV, EV71, ZIKV and HSV infections (S3G and S3H Fig). These data collectively indicate that 90K positively regulated virus-triggered proinflammatory cytokine and chemokine expression.
90K potentiates transcription factors translocation and ISG induction
Because we found that 90K potentiated pro-inflammatory cytokine expression, we speculated that 90K was involved the upstream and downstream events of pro-inflammatory cytokine measured by ELISA (left panel) and the amount of HBV capsid-associated DNA (right panel) was determined by realtime RT-PCR assay. (D) Experiments were performed as in (C) except cells were transfected with indicated shRNAs. (E) A549 cells were transfected with indicated plasmids for 24 hours followed by infection with VSV (MOI = 1) for 24 hours prior to plaque assay (upper panel). The protein levels of 90K were quantified by western blot (lower panel) (F) A549 cells were transfected with indicated shRNAs for 24 hours followed by infection with VSV (MOI = 1) for 8 hours prior to real-time RT-PCR assay (upper panel). The protein levels of 90K were quantified by western blot (lower panel). (G) RD cells were transfected with indicated plasmids for 24 hours followed by infection with EV71 (MOI = 1) for 2 hours prior to real-time RT-PCR assay. (H) Experiments were performed as in (G) except cells were transfected with indicated shRNAs. (I) Hela cells transfected with indicated plasmids for 24 hours followed by infection with HSV-1 for 24 hours prior to real-time RT-PCR assay. (J-M) WT (black square, n = 9) and 90k -/-(red circle, n = 9) mice were intranasally infected with 10 4 TCID 50 of IAV, and body weights were recorded daily (J). Survival curves show data collected until day 14 post-infection (K). The statistical analysis was performed using a log-rank test. Lung/body weight normalized (L) and viral titers (M) in lung tissues were evaluated on indicated times after influenza viral infection. (N) Histological analysis of the lung tissue of WT (n = 3) and 90k -/mice (n = 3) stained with H&E on days 0, 2, 4, and 6 after intranasal infection with 10 4 TCID 50 IAV. (O) WT and 90k -/splenocytes were infected with VSV (MOI = 1), ZIKV (MOI = 1), HSV (MOI = 1) or EV71 (MOI = 1) for 12 hours, respectively. The relative levels of nucleocapsid protein (VSV), envelope protein (ZIKV), ICP0 (HSV) or VP1 (EV71) were quantified by real-time RT-PCR assay. All experiments were repeated at least three times with consistent results. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, n = 3 ( �� P < 0.01; � P < 0.05), n.s., not significant. expression. To test this hypothesis, we investigated the effect of 90K on the IKK complex. Western blot experiments indicated that overexpression of 90K increased the phosphorylation of IκBα, IKKα/β and TAK1 ( Fig 4A). Because induction of pro-inflammatory cytokines requires coordinated and cooperative actions of the transcription factors IRF3/7 and NF-κB, we wondered whether 90K was involved this process. In Western blot assays, 90K overexpression clearly enhanced the translocation of the NF-κB subunits p50/p65 as well as IRF3/7 from the cytosol to the nucleus after SeV infection, whereas total protein levels of p65/50 and IRF3/7 were not affected (Fig 4B and 4C). Similar results were obtained by immunofluorescence assays in A549 cells and MEFs (S4A and S4B Fig).
We further examined whether the expression of IFN-stimulated genes (ISGs) was affected by 90K. Results showed that ISG levels were significantly lower in the MEFs, splenocytes, PBMCs and lungs of 90k -/mice than in WT mice during the IAV infection (Fig 4D-4G). Similar results were obtained of poly (I:C)-treated 90k -/mice and WT mice MEFs, splenocytes and PBMCs. (S4C-S4E Fig). The role of 90K on ISG expression was also investigated in vitro using IAV, poly (I:C) and SeV. Consistently, overexpression of 90K enhanced viral-and poly The relative levels of inflammatory cytokine in mice lung were quantified by real-time RT-PCR assay. All experiments were repeated at least three times with consistent results. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, n = 3 ( �� P < 0.01; � P < 0.05), n.s., not significant. (I:C)-induced ISG expression (Fig 4H-4J). These data collectively indicate that 90K positively regulates virus-triggered signaling and ISG expression.
90K interacts with TRAF6 and TAK1
Because we found that 90K induced phosphorylation of IκBα, IKKα/β and TAK1, we speculated that 90K associated with some components upstream of the IKK complex. To test this hypothesis, we investigated the relationship between 90K and some complexes that lie -tagged 90K interacted with Flag-tagged TRAF6 and TAK1, but not with other components, including MyD88, IRAK4, IκBα, RIG-I, MAVS, TAB1, TAB2 and TAB3 (Fig 5A-5C). Using HA-tagged 90K, we further confirmed the interaction of 90K with TRAF6 and TAK1 via Co-IP and reverse Co-IP experiments (S5A and S5B Fig). These results were further supported by confocal microscopy observations that 90K and TRAF6 and TAK1 co-localized in 293T and A549 cells during SeV infection (S5C- S5E Fig).
We further performed endogenous Co-IP experiments, and the results indicated that 90K was weakly associated with TRAF6 and TAK1 in unstimulated cells, and this association increased after stimulation with SeV ( Fig 5D). To map the region of 90K that interacted with TRAF6 and TAK1, we constructed a series of truncations of 90K, TRAF6 and TAK1 (Fig 5E-5H, upper panel). We further demonstrated that N-terminus kinase domain (aa 1-303) and C-terminus domain (aa 480-579) of TAK1 were required for its binding to 90K, while 90K interacted with all truncations of TRAF6 (Fig 5E and 5F). Next, we found that the BTB domain (aa 125-259) and BACK domain (aa 260-585) of 90K were required for association with both TRAF6 and TAK1 (Fig 5G and 5H).
Because 90K interacts with both TRAF6 and TAK1, we reasoned that 90K might enhance the interaction between TRAF6 and TAK1. Results from Co-IP and reverse Co-IP assays indicated that the interaction between TRAF6 and TAK1 was substantially enhanced by 90K ( Fig 5I and 5J). We further performed endogenous Co-IP experiments, and the results indicated that VSV-induced TRAF6 and TAK1 association, and this association increased after transfected with 90K overexpression plasmid ( Fig 5K). Collectively, these data indicate that 90K reinforces the recruitment of TAK1 to TRAF6.
90K potentiates TRAF6 auto-ubiquitination, and TRAF6 in turn ubiquitinates 90K
TRAF6 has been shown to undergo lysine-63 (K63)-linked auto-ubiquitination. Because we found that 90K interacts with TRAF6, we speculated that 90K regulates TRAF6 ubiquitination. We performed ubiquitination assays to determine whether 90K altered TRAF6 polyubiquitination. Co-expression of 90K in the presence of exogenous ubiquitin clearly accelerated TRAF6 ubiquitination (Fig 6A). Another experiment suggested that 90K enhanced K63-linked, but not K48-linked, polyubiquitination of TRAF6 ( Fig 6B). Consistently, knockdown of 90K did not affect the expression of TRAF6 and TAK1 (S5F Fig). We next assessed the levels of endogenous TRAF6 polyubiquitination in 90K-overexpressing 293T cells infected with VSV. Results showed that significantly higher levels of TRAF6 polyubiquitination were obtained in the presence of 90K in VSV infected cells (Fig 6C). Similar results were also obtained using ubiquitin antibody for immunoprecipitation and TRAF6 antibody for immunoblot (Fig 6D). TRAF6 consists of an N-terminal RING finger domain, a series of four internal zinc finger motifs, an α-helical coiled-coil domain, and a C-terminal TRAF-C domain. We next investigated which domain of TRAF6 underwent polyubiquitination enhanced by 90K. Notably, 90K accelerated polyubiquitination of full-length and the RING/ZnF domain of TRAF6 (Fig 6E). It has been reported that the TRAF6-C70A mutation in the RING domain abolishes the ligase activity of TRAF6 and Lys-124 is the predominant ub acceptor site for TRAF6-mediated auto-ubiquitination [22,23]. We next investigated the role of those two residues in 90K regulated TRAF6 ubiquitination. Results showed that 90K failed to induce polyubiquitination of TRAF6 C70A and K124R mutants (Fig 6F).
Because TRAF6 also facilitates a number of signaling pathways by catalyzing K63-linked ubiquitination of specific substrates, we speculated that 90K may be a specific substrate of TRAF6 involved in the anti-viral signaling pathway. In an overexpression system, TRAF6 enhanced polyubiquitination of 90K (Fig 6G). Further study indicated that TRAF6 enhanced K63-linked, but not K48-linked polyubiquitination of 90K (Fig 6H). We next sought to determine which domain of 90K undergoes polyubiquitination. As shown in Fig 6I, TRAF6 induced polyubiquitination of the BTB and BACK domains, but not the SRCR domain. We next determined the antiviral activity of these three mutants. Consistently, the BTB and BACK domains of TRAF6 inhibited IAV replication (S6 Fig). In contrast, the SRCR domain of TRAF6 did not affect IAV replication (S6 Fig). These results suggest that 90K, a substrate of TRAF6, promotes TRAF6 ubiquitination.
90K interacts with TRAF3 and TBK1 and potentiates TRAF3 ubiquitination
Given that TRAF3 also plays an important role in TLR an RLR signaling, we also investigated the role of 90K in TRAF3-mediated signaling. Co-IP experiments indicated that Myc-tagged 90K interacted with Flag-tagged TRAF3 and HA-tagged TBK1 (Fig 7A and 7B). And the interaction between TRAF3 and TBK1 was substantially enhanced by 90K (Fig 7C and 7D). Further study indicated that 90K elevated polyubiquitination of TRAF3 (Fig 7E). Furthermore, overexpression of 90K potently promoted K63-linked polyubiquitination of TRAF3 but had only a minimal effect on K48-linked polyubiquitination of TRAF3 (Fig 7F). Different from TRAF6, 90K didn't serve as a specific ubiquitination substrate of TRAF3 (Fig 7G). Interestingly, 90K failed to mediate polyubiquitination of TAK1 and TBK1 (Fig 7H and 7I). These results suggest that 90K associates with TRAF3 and TBK1, reinforces the recruitment of TBK1 to TRAF3 and mediates K63-linked polyubiquitination of endogenous TRAF3.
Discussion
We identified a previously-unrecognized role for inducible 90K in IFN and pro-inflammatory cytokine production in response to viral infections. 90K exhibited strong antiviral activity toward a broad range of viral infections. Our study provided several lines of evidence that 90K is a novel activator of NF-κB and IRF3/7 signaling by positive regulation of TRAF6 and TRAF3 function.
Currently, several reports have shown that both chronic and acute viral infections induce 90K expression. One group observed that high 90K concentrations in HIV-infected individuals were associated with faster progression towards acquired immune deficiency syndrome (AIDS) [24]. Other investigators observed high levels of 90K expression in the serum of HBV and HCV patients, and correlated with the severity of liver damage [25]. Similar results were also observed in acute viral infections. DENV, hantavirus also induced 90K expression [12,26]. In the current study, we investigated the relationship between 90K and other three viruses, including IAV, VSV and HSV. These viruses also induced 90K expression. Intriguingly, 90K inhibits replication of many kinds of viruses, including IAV, HBV, ZIKV, EV71, VSV and HSV. Further studies demonstrated that it plays an important role in virus-induced IFN and pro-inflammatory cytokine production in vitro and in vivo. In light of previous studies and our current results, we propose that 90K is a novel virus-induced host factor that exhibits antiviral activity toward a broad range of viral infections. This character of 90K is similar to that of major vault protein (MVP), a novel virus-induced host factor identified by our team recently that also inhibited replication of many viruses by up-regulating type-I interferon production [27].
Scaffold proteins interact and/or bind with several members of a signaling pathway, tethering them into complexes. Adaptor proteins are accessories to main proteins in a signal transduction pathway. Adaptor proteins include a variety of protein-binding modules that link protein-binding partners together and facilitate the creation of larger signaling complexes. In innate immunity, there are some well-known scaffold/adaptor proteins. For example, MyD88 and TRIF are crucial adaptor proteins that lie downstream of TLRs [28]. Those adaptor proteins interact with other signaling molecules, including members of the IRAK and TRAF families. TRAF6 is essential for the activation of most known MyD88-mediated effector pathways, but are dispensable for TRIF-dependent NF-κB, IRF3 and IRF7 activation [29]. TRIF-dependent NF-κB activation is still not fully understood. Several studies confirmed that the adaptor protein TNFR1-associated death domain protein (TRADD) and the serine/threonine kinase receptor-interacting protein 1 (RIP1) play important roles in TRIF-mediated NF-κB activation [30,31]. On the other hand, TRAF3 is recruited to both the MyD88-and TRIFassembled signaling complexes and positively control IRF3 and IRF7 activation [7,32]. In this study, we further investigated the mechanisms of 90K in cellular antiviral responses. To our surprise, 90K interacts with both TRAF6 and TAK1, but not with MyD88 and IRAKs, and promotes the association between TRAF6 and TAK1. Therefore, we believe that 90K may function as a scaffold/adaptor protein, downstream of MyD88 and IRAKs, connecting TRAF6 and TAK1 interaction. Interestingly, in this study, we also observed the interaction between 90K and TRAF3 and TBK1, indicating that 90K may function as another candidate for a scaffold/ adaptor protein connecting TRAF3 and TBK1. Further study verified that 90K potentiates NF-κB, IRF3 and IRF7 translocation. These results provide a detailed understanding of TRIFdependent NF-κB activation.
Apart from their role as adaptor proteins, TRAF proteins also act as E3 ubiquitin ligases, a function that is crucial for the activation of downstream signaling events [33,34]. In this study, we showed that 90K is a specific substrate of TRAF6. Moreover, 90K possesses a BTB domain that defines a recognition motif for the assembly of substrate-specific RING/cullin 3/BTB ubiquitin ligase complexes [35]. We suspect that 90K may affect TRAF6 and TRAF3 ubiquitination. Interesting, 90K not only interacts TRAF6 and TRAF3, but also promotes TRAF6 and TRAF3 ubiquitination. Nevertheless, we still do not know whether 90K is an E3 ubiquitin ligase or whether 90K recruits E3 ubiquitin ligases to TRAF6 and TRAF3. When considering the next step, studies exploring these questions would be of great help in further clarifying the role of 90K in innate immunity.
We propose a working model describing the role of 90K in innate immunity regulated by virus (Fig 8). In this model, viral infection strongly induces 90K expression. Subsequently, 90K interacts with TRAF6 or TRAF3, leading to TRAF6 or TRAF3 ubiquitination. This complex in turn recruits TAK1 and TBK1, that signals to translocate the transcription factors NF-κB, IRF3 and IRF7,from the cytoplasm to the nucleus for subsequent production of IFN and inflammatory cytokines. In conclusion, the results of this study reveal a previously-undescribed role for 90K in regulating viral replication and antiviral responses to advance our knowledge of the host immune response to viral infection.
Ethics statement
All animal experiments in this study were in accordance with protocols and procedures approved by the Institutional Animal Care and Use Committee of Wuhan University (approval number WDSKY0200902-2). The animal care and use protocol was adhered to the Chinese National Laboratory Animal-Guideline for Ethical Review of Animal Welfare.
Experimental animals and in vivo virus infection
The C57BL/6 90K -/mice were kindly provided by Prof. Klaus Ley (La Jolla Institute for Allergy & Immunology, La Jolla, CA). Age-and sex-matched wild-type C57BL/6 mice were purchased from the Center for Animal Experiment of Wuhan University. All mice were housed in the specific pathogen-free animal facility at Wuhan University.
8-12-week-old mice were infected with mouse-adapted influenza virus A/FM/1/47 (H1N1) with 1000-fold tissue culture-infective dose at 50% (TCID 50 ) per mouse by intranasal instillation. Body weights and survival were monitored for 14 days. Pulmonary virus quantitation was previously described [36]. Briefly, mice were euthanized and dissected; mice lungs were homogenized in influenza virus growth medium (DMEM supplemented with 0.2% BSA solution, 100 U/ml penicillin, 100 mg/ml streptomycin, 2 mM L-glutamine, 25 mM HEPES buffer, 2 mg/ml TPCK trypsin) on days 0, 2, and 6 post-infection, and lung viral titers were determined by a modified TCID 50 assay reported by the World Health Organization and calculated according to the Reed-Muench method. Mice lungs were fixed in 10% neutral buffered formalin, embedded in paraffin, sectioned, and stained with hematoxylin-eosin and examined by light microscopy at 200× magnification for histological changes. For measurement of lung/ body index, mice were euthanized and the body and lung tissues were weighed on days 0, 2, and 6 post-infection and the corresponding ratio was calculated. For cytokine studies, mice were sacrificed and lungs were harvested for qRT-PCR at day 0, 2 and 6 after infection.
Fig 8. Model of the biological effect of 90K in virus-induced inflammatory cytokine expression.
After virus infection, TRAF6 recruits the newly-expressed 90K through its RING/ZnF domain. Association of the TRAF6 and 90K forms a ubiquitinconjugating enzyme complex that recruits TAK1. Induced 90K also interacts with TRAF3 and TBK1 to form a complex. Two complexes signal to translocate the transcription factors IRF3/7 and NF-κB, from the cytoplasm to the nucleus for subsequent production of IFN and inflammatory cytokines.
Preparations of MEFs, splenocytes, and PBMCs
MEFs were prepared from day 13.5 C57BL/6 mice embryos and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum 1% penicillin-streptomycin. Murine PBMCs were isolated from blood using mouse lymphocyte separation medium (TBD, Tianjin, China) and cultured in RPMI 1640. Murine splenocytes were obtained by processing spleens through a disposable cell strainer and erythrocytes were removed by incubation in hypotone Tris/NH 4 Cl red cell lysis buffer and were cultured in endotoxin-free DMEM.
Purification and quantification of HBV capsid-associated DNA
Capsid-associated DNA was extracted as described previously [38], with modifications. At 72 h post-transfection of indicated plasmids together with pHBV1.3, Huh7 cells (obtained from China Center for Type Culture Collection) were lysed in 1 ml of lysis buffer 50 mM Tris, pH 7.5, 0.5% NP-40, 1 mM EDTA, and 100 mM NaCl) and mixed gently at 4˚C for 1 h. and subsequently incubated with 10 μl of 1 M MgCl 2 and 10 μl of DNase I (10 mg/ml, Takara) at 37˚C for 2 h. DNA that was not protected by HBV core protein was digested with DNase I. Viral cores were then precipitated by adding 35 μl (0.5 M) of EDTA and 225 μl of 35% polyethylene glycol and incubating them at 4˚C for at least 30 min, after which the cores were concentrated by centrifugation and the pellets were resuspended in 10 mM Tris, 100 mM NaCl, 1 mM EDTA, 1% sodium dodecyl sulfate (SDS), and 20 μl of proteinase K (25 mg/ml) and incubated overnight. Viral DNAs released from lysed cores were extracted with phenol and chloroform, precipitated with isopropanol and resuspended in Tris-EDTA. Absolute quantitative RT-PCR was performed using pHBV1.3 dilutions as standards with a set of primers for HBV DNA detection as follows: 5 0 -AGAAACAACACATAGCGCCTCAT-3 0 (sense), 5 0 -TGCCCCAT GCTGTAGATCTTG-3 0 (antisense).
VSV plaque assays
A549 cells cultured in 12-well plates were transfected with the indicated plasmids for 24 hours followed by infection with VSV-GFP (MOI of 1). After one hour, the cells were washed with warm PBS and fresh medium was added. Twelve hours later, the supernatants were collected and diluted to 10 −6 , 10 −5 , 10 −4 , 10 −3 and 10 −2 with DMEM and used to infect confluent Vero cells (obtained from China Center for Type Culture Collection) cultured in 24-well plates. One hour later, the cells were washed with PBS twice and cultured in a mixture of warm 3% lowmelting point agarose and DMEM containing 10% FBS, 1% methylcellulose and 1% streptomycin and penicillin for 72 hours. Cells were stained with 0.2% crystal violet for 2 hours, and the overlays were removed. The numbers of plaques were counted, averaged and multiplied by the dilution factor to determine the viral titer (PFU/ml).
Luciferase assays
A549 cells were seeded in 24-well plates and transfected with the indicated plasmid and luciferase reporter plasmid at an appropriate ratio together with pRL-TK (Renilla luciferase plasmid) as internal control and empty vector was used to equalize the total amount of DNA. Cells were disrupted with lysis buffer (Promega), and luciferase activity was measured by the Dual-Luciferase Reporter Assay (Promega) 24 hours post-transfection according to the manufacturer's instructions. The firefly luciferase enzyme activity was normalized to Renilla luciferase enzyme activity and expressed as fold-expression.
Nuclear extractions were harvested by centrifugation at 13,000 g for 10 min. All fractions were snap-frozen in liquid nitrogen and stored at -70˚C until use.
Quantitative real-time PCR and ELISA
Total RNA was isolated with the TRIzol reagent (Invitrogen) and cDNA was synthesized using the TRUEscript H Minus M-MuLV Reverse Transcriptase (Aidlab Biotechnologies). Data shown are the relative abundances of the indicated mRNA derived from human or mouse cells normalized to those of GAPDH or HPRT, respectively. Gene expression was examined with a Bio-Rad CFX connect system with iTaq Universal SYBR Green Supermix (Biorad). Gene-specific primer sequences were as described or reported in S1 Table. Protein levels of HBV e/s antigen in supernatants were measured by ELISA kit (Shanghai Kehua Bio). For HSV DNA qualification, cells were lysed and DNA was isolated using genomic DNA extraction kits (Aidlab Biotechnologies) followed by absolute quantitative RT-PCR with the indicated primers (S1 Table).
Immunoblotting and immunoprecipitation
Cells were lysed in Nonidet P-40 lysis buffer containing 150 mM NaCl, 1 mM EDTA,1% Nonidet P-40, and 1% protease and phosphatase inhibitor cocktail (Roche). Protein concentration was evaluated by Bio-Rad Protein Assay (BioRad). Western blot analysis was performed with the indicated antibodies. For immunoprecipitation, IgG or indicated antibodies (2 μg) were added to cell lysates (1-5 mg) for 4 h at 4˚C and captured by the addition of protein A/G agarose (Pierce) and the precipitates were washed three times with lysis buffer containing 500 mM NaCl as described above. The immune complexes were recovered and subjected to SDS-PAGE and detected by immunoblotting with the appropriate antibodies.
Site-directed mutagenesis
The template plasmid was amplified with a pair of primers containing a point mutation by TransStart FastPfu Fly DNA Polymerase (Transgen), and the products were subsequently digested with 10 U of DpnI (Takara) at 37˚C for 3 h and were transfected into DH5α competent cells (purchased from Takara Biomedical Technology). Plasmids were extracted with the E.Z.N.A. Plasmid Mini Kit I (Omega).
Statistical analysis
All data points are expressed as mean values ± standard deviation. Statistical analysis was performed using Origin 9.0 software. All data were analyzed using one-way analysis of variance (ANOVA) with Tukey's test to determine differences between groups. A value of p < 0.05 was considered statistically significant. followed by infection with VSV (MOI = 1) for 24 hours prior to flow cytometry analysis (B) and fluorescent microscopy analysis (C). (D) RD cells were transfected with indicated plasmids for 24 hours followed by infection with EV71 (MOI = 1 or MOI = 10) for 8 hours prior to western blot analyses. (E) Schematic diagram of 90K knockout mice. Murine 90K cDNA includes six exons spanning approximately 9.5 kb. A 7.4-kb fragment of the 90K genomic sequences was replaced with the neomycin resistance gene (neo), leaving 90K exon 1, exon 2, and 30 nt of exon 3 that code for the first 27 amino acids of 90K. (F) WT and 90k -/-MEFs were infected with EV71 (MOI = 1) for 12 hours. The relative protein levels of VP1 (EV71) were quantified by western blot analyses. All experiments were repeated at least three times with consistent results. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, n = 3 ( �� P < 0.01; � P < 0.05), n.s., not significant. (A) A549 cells were transfected with indicated plasmids for 24 hours prior to immunofluorescence assays. The total percentage of p65/50 and IRF3/7 nuclear localization of the whole cells was quantified using ImageJ software and shown as relative percentage of nuclear fluorescence intensity. (B) MEFs of WT and 90k -/mice were infected with IAV (MOI = 1) for 6 hours prior to immunofluorescence assays. The total percentage of p65/50 and IRF3/7 nuclear localization of the whole cells was quantified using ImageJ software and shown as relative percentage of nuclear fluorescence intensity. (C-E) The relative levels of ISGs in WT and 90k -/-MEFs (C), splenocytes (D) and PBMCs (E) treated with poly (I:C) for indicated times were quantified by real-time RT-PCR. All experiments were repeated at least three times with consistent results. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, n = 3 ( �� P < 0.01; � P < 0.05). (TIF) S5 Fig. Related to Fig 5. 90K co-localized with TRAF6 and TAK1. (A and B) 293T cells were transfected with the indicated plasmids for 48 hours. Coimmunoprecipitation and immunoblots were performed with the indicated antibodies. (C and D) 293T cells were uninfected or infected with SeV (MOI = 1) for 6 hours prior to immunofluorescence assays. (E) A549 cells were infected with or without SeV (MOI = 1) for 6 hours prior to immunofluorescence assays. (F) A549 cells were transfected with shRNA-ctrl or shRNA-90K plasmid for 24 hours followed by stimulated with poly(I:C) for 12 hours prior to western blot assay. All experiments were repeated at least three times with consistent results. (TIF) S6 Fig. Related to Fig 6. The BTB and BACK domains of 90K inhibited IAV replication. (A) A549 cells were transfected with indicated truncated 90K constructs for 24 hours followed by infection of IAV (MOI = 1) for 24 hours. The relative levels of NP-specific mRNA was quantified by real-time RT-PCR assay. In the real-time RT-PCR experiments, the control was designated as 1. Bar graphs present means ± SD, ( �� P < 0.01; � P < 0.05). (TIF) S1 Table. Primers used for RT-PCR analysis. | 8,153.6 | 2019-08-01T00:00:00.000 | [
"Biology"
] |
Design and Implementation of Distress Prevention System using a Beacon
It was proven that human accidents due to mountain climbing occupy large proportion of recently occurred man-made disasters. This paper designed and implemented an application that tells users about a variety of accidents that frequently occur in mountain and shows dynamic information about mountain climbing. It designed ‘distress prevention system using beacon’. When using beacon, it can dynamically express information without using GPS. It has advantage of supporting BLE and has little Smartphone battery consumption. The application was developed based on Android as a prototype. We expect that the application developed in this paper can contribute to the protection of precious lives by helping quick rescue in case of emergency such as distress. Manager of mountain can control data regarding the mountain climbing accident where the related functions are provided through view and user authority setup in DBMS.
Introduction
As perception about the importance of health gets stronger, the number of people interested in mountain climbing is increasing. Accordingly, mountain climbing accidents or distress in mountain is becoming more frequent. '2013 Disaster Yearbook' by the Ministry of Public Safety and Security regarding the situation of human disaster occurrence indicates very high frequency of human damage from mountain climbing, along with car accidents, falling and bicycle accidents [1]. However, national point number system that marks location information with signs is the best response to mountain climbing accidents as of now which has location limits as it has a form of sign [2]. In this situation, prompt response is almost impossible when accidents or critical situation happens. Hence, this paper attempts to provide dynamic and quick information regarding mountain climbing by using beacon to decrease accidents occurrence and help quick rescue in case of critical situation. Moreover, we believe that mountain climbing information can be made to data to be accumulated when using this application and that they can be used as other contents based on Big Data. Implementation in this paper a test application developed based on Android as a prototype.
Related Application
'Rambler' by Bientus and 'Sanddara Baramddara' by Baramgaebi are the representative domestic applications that are related to mountain climbing [3] [4]. Figure 1 shows the implementation look of 'Rambler' and 'Sanddara Baramddara'.
'Rambler' is a service that marks users' record on mountain climbing activity on the map by using GPS [5].
Users can obtain information about mountain trail through map and can also see information about mountain trail that other users recorded alongside media. The recorded information can be used as diverse statistics data and they are linked to web service. 'Sanddara Baramddara' is an application that provides information about nationwide mountains, similar to most of the mountain-related applications. Without special function, it helps users search for desired mountains easily and supports communication such as comment.
In this paper, basic information about domestic mountains is provided and records on mountain climbing activity are implemented. As a major function, mountain climbing information is shown to users in real time through beacon.
Major Function and Restriction Element
Major function of the system implemented in this paper contains the following. First is information notification service through beacon. Second is record on mountain climbing activity. Third is record on distress. Figure 2 shows the principles of service function of beacon [6].
Figure 2. Beacon Service
Beacon is a location-based communication technology that attempts Bluetooth communication in one direction to user application SDK as shown in Figure 2 [7]. Beacon is a simple device that delivers only id values to users. Id value is identified in SDK and communication with server is made by using this value as a factor. The role of beacon is to prepare a chance for communication with server when user enters into a relevant range. Here, the relevant range is the Bluetooth communication distance, which is up to 70m. In this paper, beacon signal captured by Bluetooth function while mountain climbing is analyzed to extract id values and the corresponding data are retrieved from DB to provide adequate application table and push message.
The second major function is record on the mountain climbing activity. The application implemented in this paper shows information about mountain, date and time of mountain climbing and passage.
Finally, the most important function, that is, the function in case of distress is as follows. Beacon is installed in locations that is not mountain trail or is a zone susceptible to distress and warning is given when user enters this point. Here, the previously stored GPS coordinate of beacon is expressed on map to indicate the user's current location and direction for the route back to mountain trail is provided along with a compass. As this function is map information that is provided with the stored data, no GPS sensor is necessary. In case of injury or when call is required, user can easily contact management office with the provided calling connection function. If communication is not possible, call can be made through server by using call button. Moreover, if there is no movement at the spot for a certain period of time or if one crosses the distress point more than twice, it is regarded as emergency situation and user information and location information are transferred to the server. It is expected that management office can easily recognize the information and promptly respond to the situation. To use this kind of function, users should always turn on Bluetooth while mountain climbing. The current BLE beacon has a feature of little battery consumption of Smart mobile devices compared to previous Bluetooth protocols [8]. Table 1 below presents the limiting factors in the process of system design in this paper. First, users apply for membership with simple personal information that can be helpful in case of distress and log on. The log-in information continuously maintains session in local domain. Afterward, beacon function is turned on at entrance and entrance beacon is recognized with an entrance finding button. From here, beacons in the relevant mountain can be recognized and information is provided. It is designed such that Bluetooth is automatically turned on and turned off once mountain climbing finishes.
Flow Chart and Scenario
Next, there are six types of beacon that is recognized in case of mountain climbing, which include 'peak', 'photo zone', 'hazard zone', 'distress point', 'exit' and 'entrance'. 'Entrance' provides the relevant entrance information and begins counting the mountain climbing time. 'Peak' provides relevant peak information and notifies peak arrival time. 'Photo zone' provides information about the surrounding landscape with image and recommends spots for taking pictures. 'Hazard zone' tells risk of relevant location such as frozen road, falling rock zone and steep slope to prevent accidents in advance. As was mentioned earlier, 'distress point' provides map information along with compass and has a calling function. Finally, when the user arrives at exit, 'exit' beacon is perceived to store the record on the mountain climbing activity so far and lists them to display. A push message is also serviced which connects to a relevant table once a beacon is perceived. Even when user climbs mountain while shutting the Smartphone screen off, he/she can dynamically receive mountain climbing information from the push message.
Entity Relationship Diagram (ERD)
The application in this paper consists of a total of nine tables whose relationship is depicted in Figure 6. This system provides data on mountain climbing accident through view and user authority setup in DBMS. Figure 11 shows the implementation of 'user view' and 'mountain climbing information view' of DBMS. View table is created to provide only the information that can be given to administrator. Figure 11. 'User View' and 'Mt. Climbing View' Figure 12 implements the authority given to administrator so that administrator can directly use the information coming to web server table in a limited manner.
Conclusion
This paper designed 'distress prevention system using beacon' and implemented it in a form of application. When using beacon, it can dynamically express information without using GPS. Moreover, it has advantage of supporting BLE (Bluetooth Low Energy) and has little Smartphone battery consumption. Moreover, manager of mountain can control data regarding the mountain climbing accident where the related functions are provided through view and user authority setup in DBMS.
Using the system implemented in this paper, developing contents based on Big Data is also possible by accumulating information about when and how a certain age group went to mountain climbing. Moreover, it is expected to grow to be ICT convergence contents that can replace the national point number system. | 1,986.6 | 2016-01-01T00:00:00.000 | [
"Environmental Science",
"Engineering",
"Computer Science"
] |
An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study
The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.
Machine Learning
One of the biggest challenges that has arisen as part of the recent advances in the field of bioinformatics, is the vast amount of data that is being generated at an ever-increasing pace [1][2][3]. Utilising techniques such as next generation RNA and DNA sequencing, researchers have been able to provide access to exceptionally precise information on entire genomes [4]. This massive volume of data has created a problem of complexity, making it impossible to interrogate the data with traditional methodologies and provide answers with the desired degree of accuracy.
Machine learning is an interdisciplinary field of bioinformatics that involves a data-driven class of algorithms that seek to find solutions to a given problem by studying patterns in datasets based on factors such as gene expression and clinical information across a multitude of cases. These approaches have been widely and successfully used in biology, particularly in biomarker discovery studies [5,6], due to the versatility and power afforded by them and has resulted in a wide variety of machine learning algorithms and methodologies. This review seeks to explore the potential of an Artificial Neural Network (ANN) based pipeline to discover, analyse and validate novel biomarkers in diverse diseases. For this purpose, Alzheimer's disease (AD) will be used since the cause of the condition is poorly understood and there is no widely available cure or treatment.
Supervised Learning
Supervised learning approaches, the mechanisms of which are further discussed in chapter 3, are widely applied and use source features to predict a target class [7]. The supervised approach allows the algorithm to train itself by detecting patterns in large data sets that are predictive of the target class, such as highlighting the variance at the genetic level between AD and cognitively normal individuals. We can also make use of previous studies and adjust the algorithm parameters so that it accounts for this information, which allows the power of this approach to increase over time and produce more accurate and robust results. One major advantage of supervised learning is that such approaches are tolerant of the highly complex, nonlinear and noisy data that are often found in biological systems. and adjust them as they sample the data, effectively learning the optimal solution. The main advantages of using ANNs include their high fault and failure tolerance, scalability and consistent generalisation ability, which allows them to predict or classify well for new, fuzzy and unlearned data [8,9]. This makes the ideal for biomarker studies which resulted in their use in generating panels of biomarkers that can be used as predictors in conjunction with each to aid prognosis in diseases such as breast cancer [10].
ANN architecture is based on the perceptron, coined by Rosenblatt in 1958, which is composed of a single artificial processing neuron with an activation threshold, adjustable weights and bias, but only usable for the classification of linearly separable patterns, as learning is achieved when an error occurs during testing. This is rarely the case with complex conditions such as AD, cancer or diabetes, as patients rarely fall in a standard distribution and the variance between them is potentially significant. Typically, ANNs make use of a Multi-Layer Perceptron (MLP) which is made up of multiple perceptrons arranged in layers of three or more, consisting of input, hidden and output layers, which consider predictor variables, perform feature detection through an activation function and output the results of the algorithm respectively.
Alternative ANN architectures include Recurrent Neural Networks, Radial Basis Function, Kohonen's self-organizing maps and Adaptive Resonance Theory but the focus of this review will be on the MLP. ANNs have seen widespread success in predicting and classifying data in multiple cancer subtypes such as early detection [11], prediction of long term survival [12] and biomarker discovery in breast cancer [10,13], classifying colorectal cancer tissues [14] and discriminating between benign and malignant endothelial lesions [15]. Thus, we are confident that they will see similar success in AD.
The main ANN disadvantage is their liability to overfit when the parameters have not been optimised and often receive criticism for their "black box" approach that allows for little interpretation of the results and process.
Alzheimer's Disease
Alzheimer's disease is recognised as the most common form of dementia worldwide. This chronic neurodegenerative disease usually starts slowly, with the common early symptom being difficulty to remember short-term events and progressively getting worse, with severe degeneration of multiple brain regions including the hippocampus, entorhinal cortex, neocortex, nucleus basalis, locus coeruleus and raphe nuclei ( Fig. 1), leading to disruption in mental functions such as comprehension, judgement, language and calculation. Moreover, due to slow progression that characterises the disease as well as common misconceptions, it is common for patients and their families to assume that this degeneration is a normal part of ageing, thus delaying early prognosis. It is crucial to emphasise that AD is the abnormal degeneration of mental faculties and while age is indeed the biggest risk factor, it is far from the only one.
In addition to the enormous emotional cost the disease exerts on patients and their families, it has become a major public concern due to the high healthcare costs which, in combination with the overall rise in the elderly population has classified AD as a priority condition [16]. According to the World Health organisation, in 2015 there were over 40 million people with dementia in the US, 15 million of which suffered from Alzheimer's disease. Healthcare costs have spiralled to over USD 900 billion, whereas in Europe the costs have risen to nearly 250 billion euros, a rise of almost 40% from 2008. Moreover, it is projected that by 2050, 22% of the world's population will be over the age of 60, and therefore at increased risk, with patients in third world countries accounting for 80% of the total.
Theories and Treatments
Compounding the social and economic challenges presented by the disease is the fact that its root causes are unknown and there is no cure or effective treatment. While there is a small percentage of the population, 1-5% of all cases, that suffer from early onset AD, which is caused by mutations in the amyloid precursor protein gene (APP) and the two presenilin genes PSEN-1 and PSEN-2, the cause for the majority of late onset Alzheimer's cases is still unknown. In the last decade, clinically approved drugs for AD such as Cholinesterase inhibitors like Donepezil, Galantamine and Rivastigmine as well as N-methyl-Daspartate antagonist Memantine [17] have not been able to make significant progress with the disorder.
Cholinesterase inhibitors, which target the cholinergic systems in the basal forebrain, where developed based on the theory that the loss of acetylcholine neurons during the early development of the disease inhibit the synthesis and degradation of acetylcholine, one of the major neurotransmitters in the brain. Therapy was targeted at patients with mild, moderate and severe AD but improvement of cognitive Fig. 1. Physiological differences between a healthy and AD brain section, demonstrating white matter shrinkage in the hippocampus and cerebral cortex. Source: www.alz.org. functions was noticeably better in patients that started treatment early [18]. N-methyl-D-aspartate antagonist on the other hand, is an uncompetitive moderate affinity antagonist, targeted at moderate to severe AD cases, with the purpose of protecting neurons from excitotoxicity. Other forms of therapy have focused on combinations of these drugs and treatment of the behavioural and psychological symptoms of the disease.
More recently, therapeutic approaches have been based on the amyloid hypothesis, attempting to slow, stop and reverse the development of amyloid plaques by inhibiting production of beta amyloid, as well as the hyperphosphorylation and deposition of tau protein.
Finally, further research has been focusing on the effects of oxidative damage and chronic inflammation in the brain to determine their effects in the development and progression of AD. It is evident by the variety of approaches as well as the failure of most forms of therapy to reverse or even significantly slow the disease progression, that a deeper understanding of the pathogenesis of AD is urgently needed to effectively combat it.
Physiology of Alzheimer's Disease
Historically, identification of AD could only be performed post mortem upon examination of the brain tissue. As a result, the physiological hallmarks of AD have been widely considered to be the presence of amyloid plaques, extracellular deposits of insoluble beta-amyloid (Aβ) in the parenchyma of the brain as well as neurofibrillary tangles (NFT), intracellular deposits of hyper-phosphorylated tau protein which fill the neuron and take its shape, preventing it from functioning correctly (Fig. 2).
Amyloid plaques consist of a solid core of defective Aβ and are surrounded by degenerate axons and dendrites, activated microglia and astrocytes. This defective protein is a result of the cleaving of the amyloid precursor protein (APP) by secretases beta (β) and gamma (γ). The location APP is cleaved by γ-secretase determines whether Aβ will be the long or short form. The short form is the most common (~90%) but the long form is found as often as 40% in the brains of AD patients [19], and while small amounts can be cleared easily, the high rate of production leads to the system being unable to keep up. Moreover, soluble forms of the protein have been shown to be neurotoxic and synaptotoxic [20].
Neurofibrillary tangles are a result of the hyperphosphorylation of tau, a microtubule associated protein (MAP) whose role is to bind to tubulin and stabilise the structure of neurons to maintain their function. When hyperphosphorylated due to excessive amounts of phosphate ions, it changes from its normal soluble form to oligomeric and fibrillized forms, does not bind to tubulin, inhibits microtubule structure and assembly and has been shown to have a neurotoxic effect [21].
The Amyloid Cascade Hypothesis
The leading theory for the cause of Alzheimer's disease is the amyloid cascade hypothesis, first proposed in 1992 and its influence on AD research cannot be understated. The hypothesis posits that mutations in the APP and presenilin genes PSEN1 and PSEN2 leads to the deposition of Αβ in the brain which subsequently leads to the formation of NFTs, cell death and dementia. Experiments in animal models have shown that chemically or damage induced lesions lead to an increase in APP levels and accelerate the development of AD [22,23]. Unfortunately, all approaches based on the amyloid cascade have failed at Phase III clinical trials -tramiprosate, tarenflurbil and semagacestatand research has not been able to conclusively link the build-up of Aβ to the formation of NFTs (Fig. 3) [24].
While it has been made clear that the amyloid cascade hypothesis is not enough to sufficiently explain the development of AD or aid in its detection and consequently, is currently under heavy scrutiny, it is also not possible to accept the null hypothesis, as autosomal dominant mutations in the aforementioned APP, PSEN1 and PSEN2 genes along with the apolipoprotein E4 (APOE4) allele have been proven to be the key components in familial, or early onset, Alzheimer's disease. Instead, the amyloid cascade hypothesis has to be modified to account for the rate of Aβ deposition and clearance, the connection with the development of NFTs and the effect of inflammation in the development of AD. Karran et al. [25] have attempted to update the hypothesis for use in therapeutics by presenting four distinct scenarios describing the role of Aβ in AD. These scenarios are: 1. Aβ could trigger development of the disease and further accumulation has little to no effect 2. development starts once Αβ reaches a certain, as yet unknown, threshold 3. Aβ is a key driver of AD and its continued deposition accelerates the effect 4. Aβ is irrelevant and the presence of plaques and increased levels of Aβ are a side effect of a different cause.
It should be noted that a major limitation of this hypothesis is that it fails to account for AD patients with little to no AD pathology [26] and thus amyloid plaques as identified by PET scan. In recent years, mice studies have shown that Aβ deposition is a potential driver for tau hyperphosphorylation, fixing one the major limitations of the amyloid hypothesis. Crossing APP transgenic mice with tau knockout mice, resulted in offspring with significantly fewer behavioural deficits [27] while other studies have shown that soluble oligomers of Aβ can lead to alterations in tau, potentially cascading to AD [28] although the mechanisms are still unclear. Strooper and Karran [29] attempted to provide alternatives including proteostatic stress during the biochemical phase when Aβ aggregates at an abnormally fast pace, defections in the amyloid and tau clearance mechanisms and a decrease in synaptic plasticity. As Selkoe and Hardy [27] suggest, the amyloid hypothesis, for all it limitations, is essential for therapeutics due to the fact that the complexity of the disease increases drastically after initiation due to the rise in complexity of downstream pathogenic processes, the most likely point of the disease where treatment will be at its most successful.
Inflammation in Alzheimer's Disease
Recent research has also been focused on investigating the role of inflammation in AD in an attempt to explain the development of the disease. The inflammation hypothesis posits that deposition of Aβ causes chronic activation of the immune system and disrupts microglial clearance functions. Microglia are immune cells located in the parenchyma of the brain, making up 20% of the total glial population. Their functions include phagocytosis, induction of inflammation, and antigen presentation to lymphocytes [30]. However, their roles also include clearance of extracellular deposits of Aβ, and microglial receptors TLR2, TLR4, TLR6 and co-receptors CD36, CD14 and CD47 activated upon detection of the protein. These receptors can also sense pathogenassociated molecular patterns such as bacterial lipopolysaccharides and viral surface proteins and thus are instrumental for mediating the immune response. Certain bacteria have similar surface amyloids, such as curli fibers, which resemble Aβ aggregates and thus activate toll-like receptors (TLR) and CD36, which in turn triggers the formation of a TLR4-TLR6 heterodimer and results in signalling activation via the transcription factor NF-κB. This leads to a cytokine cascade which further attracts immune cells to the site of the perceived infection (Fig. 4).
Moreover, certain cytokines such as IL-1β, damage the synaptic plasticity by disrupting the formation of dendritic spines, with high cytokine expression being able to disrupt normal hippocampus function. This lead to the hypothesis that chronic activation of the immune systems leads to chronic inflammation and microglial cell death, resulting in increased proliferation and accelerated senescence.
Artificial Neural Networks
As explained previously, ANNs are a form of machine learning, statistical models emulating the function of a neuron, able to identify patterns and linearly separate them by assigning a numerical weight value to each input and adjust them as they sample the data, effectively learning the optimal solution. They can make use of parallel processing in order to predict solutions to complex and non-linear data (Fig. 5) [31].
The ANN used for this project is a Multi-Layer Perceptron (MLP) with a back-propagation (BP) algorithm. It is organised in several layers, each with a number of mathematical processing elements depending on the complexity of the problem and the BP algorithm is responsible for feeding the error back through the model, allowing it to adjust the training weights accordingly and stop early if no gains can be made.
Stepwise Analysis
The stepwise ANN approach developed by Lancashire [33] allows for the identification of a gene or set of genes with the best predictive performance to classify samples based on a certain question by data mining the complete transcriptome. The ANN model functions by modifying the network weights and subsequently adding variables in an iterative manner to find a model with the lowest predictive error. The architecture consists of a single hidden layer, feed forward MLP with a variable number of hidden nodes and a sigmoidal transfer function, using a back-propagation algorithm incorporating supervised learning for updating the network weights. A Monte Carlo Cross Validation (MCCV) strategy was applied to produce a more generalized model with an improved predictive ability for unseen or future cases. The MCCV randomly divides the samples into training, test and validation subsets in 60:20:20 proportion for 50 iterations to provide the most consistent models. The parameters selected for this series of tests are 1 step, 10 loops with a momentum of 0.5, learning rate of 0.1 and threshold of 0.01 [34]. These parameters have been thoroughly tested and successfully used in other studies [10]. The dataset used for this experiment is [dataset] E-GEOD-48350 [35].
The dataset is publicly available and has been accessed using ArrayExpress [36] as well as the Gene Expression Omnibus (GEO) [37]. It was selected based on the following parameters to ensure high quality results: • Human samples only • Patient size of N80 • Genes in array N40,000 • A minimum of four brain region samples • Healthy controls between 33% and 66% of the dataset • Recent Publication • Raw data in the form of CEL files available.
The methodology flowchart is included in the Supplemental Fig. 1. The outcome of the stepwise analysis is a list of genes, ordered from the most to least likely to explain the variance in the population based on AD status.
Categorical and Continuous
It is worth noting that two distinct versions of the algorithm were usedcategorical and continuous. The categorical version seeks to interrogate the dataset using two predictors 0 and 1 for two distinct possibilities. This is based on known clinical information and a multitude of questions were considered. These questions include examining the differences between a healthy and an AD brain based on the overall gene expression as well as the differences between different regions in the brain, most notably the hippocampus. The continuous version of the algorithm allows us to consider every gene as its own independent predictor. This was used to examine the currently accepted biomarkers for AD [38,39] APP (amyloid beta precursor protein), MAPT (microtubule associated protein tau) and APOE (apolipoprotein E) and compare them to biomarkers discovered by the categorical algorithm.
Network Inference
The results obtained from the stepwise ANN approach were further analysed with an interaction algorithm developed by Lemetre et al. [34] to perform network inference. The interaction algorithm allows for the iterative quantification of the influence that multiple genes might have on the expression level of a single gene, until all the genes within the data have been quantified this way, using the same parameter values as those utilized for the ANN stepwise algorithm [34]. This allows for the determination of the central role of the most influential genes selected by the stepwise ANN within a system. The interaction algorithm predicts a single probe and assigns a weighted score which is directly proportional to the intensity of linkage between itself and the expression values of all other gene probes [35], while the intensity and directionality of the interaction between a source and target are determined based on the sum of the weights from an input to an output. The association between gene pairs can be bi-or unidirectional and be either stimulatory or inhibitory. This process was repeated until all gene probes were used as an output iteratively and a large matrix of interaction scores was generated by averaging values across 10 iterations. The results were visualised using Cytoscape. The methodology, proposed by Tong et al. [40], is a novel ANN designed to infer directed gene-gene interactions in a pairwise manner, allowing the user to observe how changes in a given genes leads to changes in other genes and the network as a whole. The flowchart is included in the Supplemental Fig. 2.
Interaction Matrix
One of the greatest problems encountered during the previous approach when they are used to predict a single best marker is the fact that the selection process is stochastic; there is a random probability element and while the results can be statistically significant, it makes the process imprecise. To counter that effect and increase the power of this method, the top 500 genes selected by the stepwise process were split into 5 datasets of 100 genes each and combined into 16 sets of 200 genes each for network inference. This specific number was selected as the stepwise algorithm performance started to plateau after the first 400 genes indicating that the differentiation between the given conditions -AD and healthywas decreasing. Once the network inference was completed, the data was consolidated and the top 1000 strongest interactions were selected and visualised with Cytoscape.
The reasoning behind developing this technique is that the normal single marker approach only focuses on a small subset (~0.1%) of the genes actively influencing a given condition. Moreover, by only selecting the 100 strongest interactions, it is guaranteed that in the resulting network, the biggest hubs, hence the most like drivers of the disease and targets for therapy, will be kept to a minimum and will be biased towards the most differentiated genes as seen in Fig. 6. It is important to note however, that for a highly focused system such as studying a specific subset of genes in a subset of a disease, such as proliferation markers in untreated breast cancer patients, the very nature of the data would result in a network where all the hubs are equally important. Thus, in such cases, identifying key markers and drivers using the strongest interactions is still the superior choice.
As seen in Fig. 6, upon separating the data to only include gene expression data exclusively from the hippocampus from AD patients only, selected as it is the area most strongly affected in AD, a rarely seen duality presents itself. In most complex diseases such as cancer, the dysregulation that is represented in such interactomes is a direct result of the mechanisms of the disease. Successful cancers can highjack the body's immune response, avoid detection and proliferate uncontrollably. This in turn, leads to the body mounting a very strong response by attempting to upregulate anti-tumour factors and suppress proliferation factors among others in order to prevent the abnormal cells from disrupting the function of crucial organs [10]. Diabetes is similarly represented, as due to chronically high sugar levels the function of the organs affected get significantly damaged [41]. This leads to interactomes that are either mostly up-or down-regulated.
However, irrespective of the cause, non-familial AD is a direct result of the failure to regenerate damaged cells and clean away debris over a long period of time. Moreover, the isolated nature of the brain, the increased regulation of substances that can cross the blood brain barrier and most importantly the brain's plasticity, are crucial defence factors other organs lack. Plasticity is especially important as the brain can tolerate extensive damage before showing significant dysregulation, which is why AD is so hard to identify early [42]. As a result, the interactomes of affected regions show both up-and downregulation as it is possible to observe both suppression factors that could potentially be the direct cause of the disease and healing factors that are attempting to restore balance, as the mechanisms for it are still present and functional. In fact, dysregulation in the mechanisms involved in immune response and debris clearance could be used as predictors for early prognosis of AD as they are still functional, but increasingly ineffective.
This duality in the interactome however, reveals an interesting pattern within the data. Based on a fold change analysis of the original microarray data for AD in E-GEOD-48350, the genes that are overexpressed are downregulated overall. Conversely, underexpressed genes are predicted to be mostly downregulated. It is a fact that the hippocampus is the most dysregulated brain region in AD, so this is possible proof that the system is attempting to restore balance by suppressing the high expression of factors such as HIPK1 [43], a kinase which plays an important role in senescence, ITPKB, a kinase that regulates inositol polyphosphates or BCL2, a protein phosphatase which is a crucial apoptosis factor. In short, the system is attempting to decrease the effect of genes involved in cell death.
The factors that are underexpressed on the other hand, appear to be upregulated and significantly more dysregulated, with an overall larger number and stronger individual interactions. The largest hub is PPM1H, another protein phosphatase which dephosphorylates CDKN1B, a CD kinase inhibitor involved in diseases such as Type IV Multiple Endocrine Neoplasia and familial Primary Hyperparathyroidism. Another such gene is FRS3, a fibroblast growth factor receptor substrate which is involved in regulation of RAS signalling.
While these genes and others like them seem to indicate that there is a significant effort to re-establish homeostasis, of further interest are the genes that do not fall inside these clearly defined categories. These genes include multiple tubulins such as TUBA1B and TUBB2A which are underexpressed but being simultaneously up-and downregulated, TGFBR3 which encodes for the transforming growth factor beta, type III receptor and plays a crucial role in cell adhesion and is associated with diseases such as familial cerebral saccular aneurysm. TGFB itself activates transcription factors of the SMAD family, which in turn, regulates gene expression. ATP2C1 is an ATPase which catalyses the hydrolysis of ATP and is underexpressed while still attempting to downregulate CARD8. CARD8 itself is caspase recruitment domain containing family of proteins and is involved in pathways negatively regulating the activation of NFKB, which as explained during the introduction, has a key role in the theory of neuroinflammation, and is quite likely an attempt to slow down or stop the chronic immune response leading to said neuroinflammation. Other irregularities include MAP1LC3A and MPP2 explained earlier and CD44, a cell-surface glycoprotein involved in cell-cell interactions, cell adhesion and migration and interacts with, among other things, matrix metalloproteinases (MMPs). MMPs, and MMP-9 in particular have long been suspected in playing a key role during AD and have been shown neuroprotective capabilities [44]. Finally, one of the most highly underexpressed and downregulated genes is C1QTNF4, a complement-C1q tumour necrosis factor-related protein whose role is not clearly defined but has been suspected of acting like a pro-inflammatory cytokine, leading to the activation of NFKB and upregulate production of IL6.
Additionally, one of the major advantages of this method is the that it generates a large and complex interactome that can be used to further examine a gene of interest as seen in Fig. 8.
In this example tubulin 2 beta (TUBB2A), a structural component of microtubules and a gene closely associated with tau, has consistently been in the top genes identified in AD across multiple tests. Due to the size of the previous interactome, there is enough complexity to be able to further analyse the way it interacts with other genes without having to use the algorithm again. If enough genes are identified as relevant to the question, then they can be used as predictors in the continuous ANN and then used for network inference. This also solves the major disadvantage of this methodology; it is computationally expensive and slow.
In Fig. 8 we can observe that TUBB2A is underexpressed but also downregulated by the clear majority of predicted interactions, including by other tubulin variants such as TUBB3 and TUBB4B as well as BRE which was discussed earlier. It is interesting however that both CASC3 and NFKBIA, both of which are overexpressed in this case, are attempting to upregulate TUBB2A, weakly in the case of NFKBIA but relatively strongly in the case of CASC3. CASC3 also appears to be very strongly downregulated by TUBB4B, MRPS25 a mitochondrial ribosomal subunit involved in mitochondrial translation and organelle maintenance and biosynthesis, and FARSB, a Phenylalanyl-TRNA Synthetase Beta Subunit involved in tRNA aminoacylation and has been found to be associated with muscular dystrophy. Thus, it is possible to surmise that the dysregulated state of the TUBB2A gene in the network is directly correlated with mechanistic dysregulations in other genes that in turn affect genes responsible for regulation of TUBB2A itself. CASC3 and NFKBIA are failing to significantly upregulate TUBB2A back to normal levels due to dysregulation within themselves.
Driver Analysis
One of the challenges faced when trying to elucidate a marker, driver or therapy target is the selection criteria used. It is crucial to point out that the data used in these experiments presents us with a "snapshot" of the condition investigated, a generalized picture of how each gene is affected by every other gene, while the biological system is in a state of imbalance. As a result, the biggest hubs of most interactomes tend to be either the genes most up-or down-regulated in the network at the time. This has two potential interpretations. The hub is the source of the imbalance and thus, the most likely driver of the disease and target for therapy, and the downregulation is a result of the system attempting to restore balance, or that the hub is the factor preventing the imbalance by working against the disease and is being upregulated in an effort to restore the system to its original state.
The purpose of the driver analysis is to provide a non-biased selection condition based on the sum of the weights each gene exerts on the network, quantifying the amount of influence on a target and the amount of influence of a target. As explained in Section 2.4 the Fig. 8. Focused Tubulin interactome based on Fig. 7. Tubulin beta 2A interactions in AD. Of note is its positive regulation by an NFKB inhibitor. interaction algorithm analyses the selected genes in a pairwise manner and assigns each of those pairs a value predicting how strongly their genes interact. Hence, by summing the weight that each source gene exerts on each target and vice versa, it becomes possible to rank them by which ones have the greatest overall effect on the network and which ones are the most affected.
The advantage of this method is the fact that it considers and gives equal importance to non-hubs as it only measures the total effect each gene has on the totality of the network. As such, it is possible to draw attention to genes with a multitude of weak interactions rather than only a few strong ones, which might otherwise not be visible. It is reasonable to assume that such genes may not be the greatest drivers of the disease, but crucial components of the system, and this method allows us to analyse those genes without them being obscured by the hubs and most likely drivers, thus giving a wider and impartial view of the condition. Moreover, the driver analysis is not affected by the complexity of the question, being able to provide comparable results across multiple datasets, in both focused and general conditions. The driver analysis was carried out on the 500 selected genes of the matrix interaction. The most influential source genes showed significant similarities and differences to the results of previous analyses on AD (Table 1). Genes identified in the interactome such as a ITPKB and CASC3 as well as trafficking proteins like TRAK1 and kinases like PRKD3 are expected. Of note is the disproportionate presence of BCL2 when compared to the interactome. However, the sources of interest include RHOBTB3, a member of the highly conserve family of Rho GTPases similar to RHOQ discovered during Table 1 Diver analysis showing the top 50 most influential and most influenced genes according to their unbiased impact on the network in the hippocampus in AD. The influence amount is the sum of all weights calculated by the interaction algorithm and is relative to the rest of the values. Probe IDs in red have not been annotated as of January 2017. earlier testing, as well as SRGAP1. SRGAP1 encodes for a GTPase activator and works in conjunction to CDC42, a GTPase of the same family, to negatively regulate neuronal cell migration. Moreover, when combined with receptor ROBO1, it can deactivate CDC42. Its presence so high on the source list as a downregulating factor, indicates that its function is being stronger than expected, resulting in slower cell migration and impediment of the regeneration process. CARD8, discussed earlier, has a strong, negative effect on the network, suppressing the expression of related genes. Meanwhile, the most targeted genes on the network include PPM1H, a protein phosphatase, TGFBR3, multiple kinases, and an alpha-tubulin TUBA1B. More beta tubulins are included in the complete list. Also, although rarely seen, ATAT1, an alpha tubulin acetyltransferase, a neuronal cell component crucial to the microtubule growth appears to be negatively regulate. ATAT1 is involved in coenzyme binding and tubulin N-acetyltransferase activity and only acetylates older microtubules, being unable to act on unstable ones. Genes such as APGAT1 which fulfil similar purposes have been discovered in previous test, suggesting that slower/weaker acetylation of older microtubules could play a key role in the development of AD. Curiously, one of the upregulated factors is AREL1, apoptosis resistant e3 ubiquitin protein ligase 1, which inhibits apoptosis. It is possible that it is being upregulated in an attempt to keep the neurons alive and functioning to prevent further damage. Finally, the presence of ITPKB as both a significant source and target indicate that it is a crucial component of the system regardless of disease state. The results will be used for a functional analysis via the Bioconductor R package [45]. A second table regarding the driver analysis of the cohort of cognitively normal controls is available in the Supplemental Table 2 for comparison.
Conclusions and Future Developments
In conclusion, the results obtained by this series of experiments show promise for a greater understanding of the biology behind Alzheimer's disease, its progression and the mechanisms involved. By expanding to other brain regions and datasets and focusing the questions on the most relevant genes, it is possible to identify new markers and drivers of the disease that can be used alongside the current ones to improve prognosis and provide more targets for therapy.
It is worth noting that the results obtained and analysed with this pipeline have been generated without using a null hypothesis, in a non-parametric manner. The only question was the difference between AD and healthy brains and was expanded to include predictors as general as the presence of the disease down to the expression of individual genes. It is evident by the results that by reducing the bias introduced by datamining for very focused questions and increasing the variance, we are presented with multiple potential biomarkers as well as new discovery routes such as further evidence of the role on inflammation and microtubule stabilisation. The pipeline has thus managed to generate unbiased, varied and novel information that can be used to guide further, more targeted research as well as validation of these results experimentally.
Future development will focus on improving the speed and power of the algorithms and increase the interpretability of the results. Using general-purpose computing on graphics processing units, it is possible to reduce the time requirements by up to 75% at the cost of computational power, though recent advances in the field have made it significantly more likely and affordable. Further tests are being focused on the variance between different brain regions as well as the effect of individual genes on the system. Moreover, this series of tests is being repeated in RNA-seq and proteomic datasets in order to study the effect of AD pre and post translation, as well as other gene expression datasets to ensure consistency in the results. | 8,429.4 | 2018-02-21T00:00:00.000 | [
"Medicine",
"Computer Science"
] |
Sulfhydryl Functionalized Magnetic Chitosan as an Efficient Adsorbent for High-Performance Removal of Cd(II) from Water: Adsorption Isotherms, Kinetic, and Reusability Studies
In this study, dimercaptosuccinic acid-functionalized magnetic chitosan (Fe3O4@CS@DMSA) was synthesized via in situ coprecipitation process and amidation reaction, aiming to eliminate cadmium (Cd(II)) ions from an aqueous environment. The structure, morphology, and particle size of the Fe3O4@CS@DMSA adsorbent were investigated using FTIR, TEM, EDX, TGA, zeta potential, and XRD techniques, and the obtained results approved the successful synthesis of the Fe3O4@CS@DMSA nanocomposite. The influence of external adsorption conditions such as pH solution, adsorbent mass, initial Cd(II) concentration, temperature, and contact time on the adsorption process was successfully achieved. Accordingly, pH: 7.6, contact time: 210 min, and adsorbent mass:10 mg were found to be the optimal conditions for best removal. The adsorption was analyzed using nonlinear isotherm and kinetic models. The outcomes revealed that the adsorption process obeyed the Langmuir and the pseudo-first-order models. The maximum adsorption capacity of Fe3O4@CS@DMSA toward Cd(II) ion was 314.12 mg/g. The adsorption mechanism of Cd(II) on Fe3O4@CS@DMSA nanocomposite is the electrostatic interaction. The reusability test of Fe3O4@CS@DMSA nanocomposite exhibited that the adsorption efficiency was 72% after the 5th cycle. Finally, this research indicates that the Fe3O4@CS@DMSA exhibited excellent characteristics such as high adsorption capacity, effective adsorption-desorption results, and easy magnetic separation and thus could be an effective adsorbent for removing Cd(II) ions from aqueous solutions.
Introduction
Water contamination by a toxic cadmium (Cd(II)) metal is a widespread environmental issue owing to its long-term adverse effects on humans and ecosystems. Cd(II) is one of the most dangerous metal ion due to its nondegradable, strong bioaccumulate, and highly toxic even at low concentrations, which leads to a serious threat to human health [1,2]. Cd(II) pollution can cause kidney, liver, and bone damage to humans with a long time exposure. Cd(II) has excellent solubility which can be easily released into the aqueous systems through industrial production processes such as alkaline batteries, electroplating, textile printing industries, and pigment [3]. Cd(II) is classified as a category one carcinogen by U.S. EPA, and the maximum concentration of Cd(II) in drinking water is 5 μg/L [4,5]. Thus, the removal of extremely toxic cadmium from an aqueous environment is essential to avoid pollution to the environmental systems. Several techniques, namely, chemical precipitation [6], adsorption [7], membrane separation [8], ion exchange [9], and electrodeposition [10,11], have been applied to treat the toxic metals from wastewater. Among them, the adsorption technique has been proven economical, simple, easy operation and ecofriendly, cost-effective, versatile in nature, and highly efficient for metal removal [12].
Many adsorbents have been applied to adsorption of Cd(II) from aqueous medium like sulfonated biochar [13], functionalized cellulose derived [14], amino-functionalized lignin [15], metal-organic framework (MOF) ZIF-8 [16], EDTA/mGO [17], and para-aminobenzoic acid-functionalized activated [18]. These adsorbents suffer from the difficulty of recovering metal after adsorption using traditional methods such as centrifugation and filtration, which may result in secondary pollution and loss of the amount of adsorbents [19]. Magnetic nanocomposite has received great attention as an efficient adsorbent owing to its many advantages such as easy magnetic separation, high surface area, low toxicity, biocompatibility, and the existence of a large number of surface hydroxyl groups that use them in surface modification. To improve the stability of Fe 3 O 4 nanoparticles under acidic conditions and reduce the agglomeration of the nanoparticles, the surface of Fe 3 O 4 nanoparticles can be modified with some materials like activated carbon [20], graphite oxide [21], and carboxylated MNP nanoparticles [22].
Naturally abundant polysaccharides such as chitosan are considered as one of the most promising surface stabilizing materials for magnetite nanoparticles due to their multifunctionality, nontoxicity, biocompatibility, and renewability [23]. Chitosan has a strong affinity with metal ions because of the existence of NH 2 and OH groups which can serve as the active adsorption sites for the removal of metals [24][25][26]. To improve the number of active adsorption sites for adsorption on magnetic chitosan, it needs to be surface modified to provide specific functional groups. Meso-2,3dimercaptosuccinic acid (DMSA) is a suitable candidate for enhancing the adsorption process owing to DMSA having carboxyl and thiol groups, which can be used for the capture of heavy metals [27,28]. In addition, DMSA acid is a nontoxic chelating agent and FDA approved drug which has been used to treat heavy metal poisoning in the human body [29][30][31]. To the best of our knowledge, the Fe 3 O 4 @CS@DMSA nanocomposite has not been used for the elimination of pollutants.
In this study, Fe 3 O 4 @CS@DMSA nanocomposite was synthesized by an in situ coprecipitation method followed by a covalent functionalization of Fe 3 O 4 @CS with DMSA acid via amidation reaction. The synthesized Fe 3 O 4 @CS@DMSA adsorbent was applied to eliminate Cd(II) ions from water. The synthesized Fe 3 O 4 @CS@DMSA was characterized using zeta potential, FTIR, XRD, TGA, TEM, and EDX techniques. The impact of external adsorption conditions such as pH solution, adsorbent mass, initial Cd(II) concentration, temperature, and contact time on the adsorption process was successfully achieved. To achieve the adsorption capacity and mechanisms of Cd(II) adsorption onto Fe 3 O 4 @CS@DMSA nanocomposite, the equilibrium kinetic and isotherm were studied. Thermodynamic parameters were also studied. The reusability test of Fe 3 O 4 @CS@DMSA nanocomposite was performed by carrying out five cycles of adsorption-desorption studies. 2.5. Batch Adsorption Experiments. The removal efficiency of Cd(II) ions by Fe 3 O 4 @CS@DMSA from water was studied by batch method to achieve the impact of various process factors such as adsorbent mass, contact time, pH solution, temperature, and initial Cd(II) concentration on adsorption process. In this work, contact time, solution pH, and adsorbent mass were achieved in the range of 5-350 min, 1.8-9.1, and 5-30 mg whereas temperature and initial Cd(II) concentration were varied from 25 to 45°C and 25 to 300 mg/L. A known amount of Fe 3 O 4 @CS@DMSA was put into an Erlenmeyer containing 25 mL of known Cd(II) concentration, and the sample was then adjusted to the desired pH at 25°C. After that, the sample solution was shaken for 24 h. Then, the sample was isolated by a magnet, and the residual concentration of Cd(II) ions has been determined using AAS. The adsorbed amount (q e (mg/g)) and percentage adsorption of Cd(II) were calculated using Equations (1) and (2), respectively.
Experimental
where C o and C e refer to initial and equilibrium Cd(II) concentration in the solution (mg/L), respectively; V (L) refers to the volume of the Cd(II) solution; m (g) is the weight of Fe 3 O 4 @CS@DMSA nanocomposite. The adsorption capacities for Fe 3 O 4 @CS and Fe 3 O 4 @CS@DMSA adsorbents toward Cd(II) ions were 52.5 mg/g and 58.8 mg/g, respectively, at condition parameters at constant adsorbent mass (0.01 g), initial Cd(II) concentration (25 mg/L), temperature (25°C), pH (7.6), stirring rate (100 rpm), and contact time (1440 min).
Adsorption Science & Technology
to the presence of the ν(-OH) group overlapping with the -NH group. The characteristic band for (-SH) appeared at 2550 cm -1 [34]. Besides, the bands at 1670 and 1554 cm -1 are due to amide I and amide II or δ(N-H) groups, respectively [36], which indicates the formation of amide bonds between Fe 3 O 4 @CS and DMSA [37]. The Fe-O band was decreased to 552 cm −1 , confirming the presence of magnetic nanoparticles. The bands at 1481, 1355, 1274, and 1044 cm -1 are due to ν(-COO-), ν(-C-N), ν(C-O), and ν(O-C-O) stretching vibration, respectively [38,39]. After adsorption of Cd(II), the spectra showed the bands were slightly shifted and decreased in intensity due to the binding of COOH, SH, and OH onto Fe 3 O 4 @CS@DMSA surface with Cd(II) ions through electrostatic attractions. In detail, the bands at 3393 and 2550 cm -1 decrease in intensity owing to the interaction between Cd(II) and carboxyl (COOH), hydroxyl (OH), and SH groups, respectively, on the Fe 3 O 4 @CS@DMSA surface by electrostatic interaction. In addition, the band at 1274 cm -1 for ν(C-O) disappeared after Cd(II) adsorption onto the Fe 3 O 4 @CS@DMSA surface. The decreased intensity of the band at 1481 cm -1 for ν(COO -) indicates the adsorption of Cd(II) onto the Fe 3 O 4 @CS@DMSA surface. Transmittance (%) Adsorption Science & Technology The XRD patterns of magnetite nanoparticles and Fe 3 O 4 @CS@DMSA are indicated in Figure 1 , (511), and (400) crystal planes of cubic phase magnetite, which was consistent with a previous report [40]. Compared with magnetite, the XRD pattern of Fe 3 O 4 @CS@DMSA appeared a new broad reflection at a 2θ value of 22.5°, confirming the Fe 3 O 4 nanoparticles covered by DMSA and chitosan [41,42]. Using the Scherer equation (3), the average crystal size (D) of Fe 3 O 4 @CS@DMSA nanocomposite was calculated: [43,44]. The thermal stability of Fe 3 O 4 @CS@DMSA exhibited a high loss in mass of approximately 17% with two stages. In the first one, the weight loss was~3% in low temperature up to 200°C owing to elimination of adsorbed water and solvent absorbed onto the surface Fe 3 O 4 @CS@DMSA nanocomposite. In the second one,~15% weight loss at around 200-700°C ascribes to the thermal decomposition of an organic part of CS and DMSA [45], confirming the successful synthesis of Fe 3 O 4 @CS@DMSA nanocomposites.
To determine the point of zero charge (PZC) of Fe 3 O 4 @CS@DMSA, the surface charge of Fe 3 O 4 @CS@DMSA was measured under different pH values. The outcomes are displayed in Figure 2(b). It was seen that the zero of point charge value (pH pzc ) of Fe 3 O 4 @CS@DMSA nanocomposite was~5.2. This value is lower than~7.1 for Fe 3 O 4 nanoparticles [46]. This behavior of the Fe 3 O 4 @CS@DMSA nanocomposite is mainly assigned to the existence of -OH, COOH, and -SH groups, which are being protonated at lower than~5.2.
The size and morphology of the Fe 3 O 4 @CS@DMSA were studied by TEM, and the outcomes are displayed in . It is clear that the nanoparticles were uniform spherical morphology with a bright of amorphous CS and DMSA over the dark spot crystalline core of magnetite nanoparticles [47]. The value particle size of Fe 3 O 4 @CS@DMSA was~11.5 nm confirming the surface modification of magnetite nanoparticles with CS and DMSA (Figure 3(b) [48]. The effect of different initial pH values (1.8-9.1) on Cd(II) adsorption by Fe 3 O 4 @CS@DMSA nanocomposite was studied as shown in Figure 4(a). The other parameters were kept constant as initial Cd(II) concentration (25 mg/L), temperature (25°C), contact time (210 min), adsorbent mass (10 mg), and agitation speed (100 rpm). As implied in Figure 4(a), the adsorption capacity of Fe 3 O 4 @CS@DMSA toward Cd(II) was increased from 0.75 to 58.75 mg/g as the pH increased from 1.8 to 7.6, respectively. After that, it is slightly reduced and may be owing to the formation of Cd(II) hydroxide precipitate such as Cd(OH) + and Cd(OH) 2 , (1)) [38,52]. After the adsorbent mass of 10 mg, no significant change in adsorption capacity was observed.
Effect of Contact Time.
To find out the optimum contact time, experiments were conducted at various time intervals between 5 and 350 min at constant adsorbent mass (10 mg), initial Cd(II) concentration (25 mg/L), pH (7.6), stirring rate (100 rpm), and temperature (25°C) as presented in Figure 4(c). It was noticed that the amount of Cd(II) adsorbed onto the Fe 3 O 4 @CS@DMSA increased rapidly with increasing equilibrium time and the maximum adsorption capacity and removal efficiency reached up to 58.0 mg/g and 92.8%, respectively, at 210 min. In the initial stage of the adsorption process, the Cd(II) ions easily interacted with active sites of Fe 3 O 4 @CS@DMSA nanocomposite owing to the abundance of the active adsorption sites on the Fe 3 O 4 @CS@DMSA surface. After 210 min, no significant change in the adsorption capacity owing to the active sites of Fe 3 O 4 @CS@DMSA tended to saturate and could not easily adsorb the Cd(II) ions. to 295 mg/g with the rising initial Cd(II) ion concentration from 25 to 300 mg/L at 298 K. This phenomenon can be explained that a higher Cd(II) concentration rises the driving force and provides more collisions between Cd(II) ions and active sites of Fe 3 O 4 @CS@DMSA which could improve the adsorption rate. The influence of temperature on the adsorption process is presented in Figure 4(b). The adsorption capacity of Fe 3 O 4 @CS@DMSA toward Cd(II) was decreased from 295 to 195 mg/g when the temperature was improved from 298 K to 318 K at 300 mg/L, suggesting that the adsorption of Cd(II) on Fe 3 O 4 @CS@DMSA is exothermic. This could be explained by the weakening of the adsorptive forces between the active sites of Fe 3 O 4 @CS@DMSA nanocomposite and the Cd(II) ions [53], which is consistent with the previous report on Cd(II) adsorption by MGO-Trp [54] and CSAP [55].
where C e (mg/g) is the equilibrium aqueous-phase Cd(II) concentration. q e and q m refer to the equilibrium and maximum amount of Cd(II) adsorbed (mg/g), respectively; K DR (mol 2 /kJ 2 ), K F , and K L are the constants of D-R, Freundlich, and Langmuir models, respectively; E (kJ/mol) and ɛ are the average free energy and the Polanyi potential, respectively; n is the adsorption intensity. Figure 5 displays the three nonlinear fitting parameter results of the adsorption isotherm models for the Cd(II) adsorption on Fe 3 O 4 @CS@DMSA nanocomposite. By comparison, it was observed that the R 2 values were 0.96215, 0.97072, and 0.7983 for Langmuir, Freundlich, and D-R isotherms, respectively. Thus, the experimental data was better described by the Freundlich (R 2 = 0:97072) model than those of the Langmuir and D-R models, which indicated the heterogeneous nature of Fe 3 O 4 @CS@DMSA and a contribution of electrostatic interaction to the Cd(II) adsorption on Fe 3 O 4 @CS@DMSA (physisorption nature) [59]. By applying the Freundlich equation, the values of n (adsorption intensity) were in the range of 2.2381-3.2730, indicating that multilayer adsorption occurred onto the heterogeneous surface of Fe 3 O 4 @CS@DMSA. In addition, the Cd(II) adsorption is a favorable process. The higher value of n = 3:2730 at 298 K indicates that the Fe 3 O 4 @CS@DMSA nanocomposite has better adsorption performance [59,60]. According to the Langmuir model, the maximum amount of Cd(II) adsorbed was 314.12 mg/g. This value is higher than other material adsorbents for adsorption of Cd(II) like Fe 3 O 4 /chitosan-glycine-PEGDE (171.06) [61], polyethyleneimine-modified magnetic porous cassava (143.6) [62], chitosan-modified kiwi branch biochar (126.58) [63], sulfhydryl-modified chitosan beads (183.1) [64], MNP-DMSA (25.44) [65], chitosanpectin gel beads (177.6) [66], chitosan-iron oxide (CS-Fe 2 O 3 ) (204.318) [67], and thiocarbohydrazide-chitosan gel (81.26) [68] (Table 3). According to the D-R isotherm, the values of means free energy (E) were found to be in the range of 0.0396-0.1773 kJ/mol), which indicated that the Cd(II) adsorption onto Fe 3 O 4 @CS@DMSA nanocomposite classified as a physical adsorption process due to the E value is less than 8 kJ/mol [69].
q t = q 2 e k 2 t 1 + q e k 2 t , ð11Þ
Adsorption Science & Technology
where q e and q t (mg/g) refer to the amounts of Cd(II) adsorbed on Fe 3 O 4 @CS@DMSA at equilibrium and time t, respectively; k 1 is the PFO rate constant; k 2 represents the PSO rate constant; β (mg/g) is the Elovich kinetic parameter; α refers to the desorption constant.
Based on R 2 values, PFO displays a better correlation coefficient (R 2 = 0:97515) than the PSO (R 2 = 0:9567) and Elovich (R 2 = 0:93427) models, suggesting the rate-limiting step for Cd(II) is physisorption involving electrostatic interaction between Cd(II) and Fe 3 O 4 @CS@DMSA nanocomposite. The value of q e:cal (61.45 mg/g) calculated is close to the experimental equilibrium adsorption capacities (q e,exp = 58:75 mg/g). By applying the Elovich equation, the values of α and β were 0.877 mg/g min and 0.043 mg/g with R 2 (0.93427), respectively. The low value of R 2 indicates the absence of a chemisorption mechanism.
3.3.3. Adsorption Thermodynamics. The thermodynamic parameters, namely, enthalpy change (ΔH°) (Equation (12) and entropy change (ΔS°) (Equation (12)), can be obtained from the slope and intercept of the Van't Hoff plot of ln K e vs. 1/T (Figure 6(a)) at different temperatures (298-318 K), and the free energy change (ΔG°) can be estimated from the equation (Equation (13)): where K 0 e (Equation (14)) is the thermodynamic equilibrium constant at a certain temperature [59][60][61] and [adsorbate]°, γ, and K L are the standard concentrations of the adsorbate (1.0 mol/L), activity coefficient, and Langmuir constant, respectively. The thermodynamic parameters for Cd(II) adsorption onto Fe 3 O 4 @CS@DMSA are summarized in Table 5. The negative values of free energy (ΔG°) was noticed, indicating that the Cd(II) adsorption onto Fe 3 O 4 @CS@DMSA is a spontaneous reaction and the values of ΔG°were increased from -22.36 to -19.66 kJ/mol with rising temperature from 298 to 318 K which demonstrates the favorability of the adsorption of Cd(II) at a lower temperature. Negative values of ΔH°and ΔS°indicated that the Cd(II) adsorption onto Fe 3 O 4 @CS@DMSA was exothermic and the decreased the reaction randomness. Figure 7. Based on the adsorption kinetic results, the adsorption process followed the pseudo-firstorder model, suggesting a physical interaction through electrostatic attraction between the Cd(II) ions and the Fe 3 O 4 @CS@DMSA nanocomposite. According to the FTIR analysis (Figure 1(a)), the position peaks of functional groups declined in intensity and slightly shifted to a lower wavenumber. In detail, the bands at 3393 and 2550 cm -1 decrease in intensity owing to the interaction between Cd(II) and carboxyl (COOH), hydroxyl (OH), and SH groups, respectively, on the Fe 3 O 4 @CS@DMSA surface by electrostatic interaction. In addition, the band at 1274 cm -1 for ν (C-O) disappeared after Cd(II) adsorption onto the Fe 3 O 4 @CS@DMSA surface. The decreased intensity of the band at 1481 cm -1 for ν(COO -) indicates the adsorption of Cd(II) onto the Fe 3 O 4 @CS@DMSA surface. Besides, the (15)) was estimated by the equation:
Adsorption Mechanism. The proposed adsorption mechanism is shown in
where C m and C e (mg/L) refer to the concentration of Cd(II) ion released in the solution and the initially adsorbed Cd(II) concentration, respectively. The results of the Cd(II) adsorption/desorption test on Fe 3 O 4 @CS@DMSA nanocomposite using eluents are indicated in Figure 6(b). It was observed that the percentage desorption was found to be CH 3 COOH (47.48%), HNO 3 (87.26%), and HCl (91.3%), which indicate the best eluent for desorption of Cd(II) was 0.01 M HCl owing to the smaller ionic size of the Clion compared to CH 3 COOand NO 3 -. For reusability of the Fe 3 O 4 @CS@DMSA study, the Cd(II)-loaded Fe 3 O 4 @CS@DMSA was isolated using a magnet and then the solid adsorbent was washed with deionized water, dried, and regenerated with 25 mL of 0.01 M HCl. After that, the sample was shaken at room temperature for 210 min. The solid/solution phase is separated via a magnet, and the supernatants are analyzed by the AAS method. After desorption, the Fe 3 O 4 @CS@DMSA nanocomposite was reused to Cd(II) adsorption and five adsorption/desorption cycles were applied. The results obtained are presented in Figure 6(c). It is seen that up to five cycles about 72% of Cd(II) were successfully removed. The reduction in removal efficiency of Cd(II) after five cycles may be owing to incomplete desorption of the Cd(II) ions on Fe 3 O 4 @CS@DMSA.
Conclusion
Fe 3 O 4 @CS@DMSA nanocomposite was synthesized via the in situ coprecipitation method followed by a covalent functionalization of Fe 3 O 4 @CS with DMSA acid by amidation reaction. The synthesized Fe 3 O 4 @CS@DMSA nanocomposite was characterized using zeta potential, FTIR, XRD, TEM, EDX, and TGA techniques. These techniques confirmed the formation of adsorbent successfully. After characterization, the Fe 3 O 4 @CS@DMSA was used to eliminate Cd(II) ions from aqueous systems. The Fe 3 O 4 @CS@DMSA adsorbent exhibited a high adsorption capacity (314.12 mg/g at the optimum condition pH: 7.6, contact time: 210 min, temperature: 298 K, adsorbent mass:10 mg, and stirring rate: 100 rpm). The FTIR and EDX results confirmed the existence of Cd(II) ions after adsorption on Fe 3 O 4 @CS@DMSA nanocomposite. The Freundlich isotherm data and pseudofirst-order kinetic data displayed more compatibility with the equilibrium data than that of other models. The mecha-nism of Cd(II) adsorption on Fe 3 O 4 @CS@DMSA nanocomposite is electrostatic attraction. The thermodynamic results confirmed the spontaneous and exothermic nature of adsorption. The reusability test of Fe 3 O 4 @CS@DMSA nanocomposite exhibited that the adsorption efficiency was 72% after five cycles. The results indicate that the Fe 3 O 4 @CS@DMSA has a good potential for the elimination of Cd(II) from an aqueous solution.
Data Availability
Anyone who wants to request research article data can contact me directly via the following email<EMAIL_ADDRESS><EMAIL_ADDRESS>Chemistry Department, College of Science, King Saud University.
Conflicts of Interest
There are no conflicts to declare. | 4,999.4 | 2022-06-13T00:00:00.000 | [
"Engineering",
"Materials Science"
] |
Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models
Researchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such as language modelling. Such a procedure is possible due to the design of the softmax classification layer, which previous work has shown to comprise a viable set of semantic representations for the model vocabulary, and these these output embeddings are known to perform well on word similarity benchmarks. In this paper, we make meaningful comparisons between the input and output embeddings and other SOTA distributional models to gain a better understanding of the types of information they represent. We also construct a new set of word embeddings using the output embeddings to create locally-optimal approximations for the intermediate representations from the language model. These locally-optimal embeddings demonstrate excellent performance across all our evaluations.
Introduction
Neural Language Modelling has recently gained popularity in NLP. A Neural Network Language Model (NNLM) is tasked with learning a conditional probability distribution over the occurrences of words in text (Mikolov et al., 2011). This language modelling objective requires a neural network with sufficient capacity to learn meaningful linguistic information such as semantic knowledge and syntactic structure. Due to their ability to learn these important linguistic phenomena, NNLMs have been successfully employed as an effective method for generative pretraining (Dai and Le, 2015) and transfer learning to other natural language tasks (Peters et al., 2018a;Howard and Ruder, 2018;Radford et al., 2018). As previously suggested by Bengio et al. (2003), Mnih and Hinton (2007) and Mnih and Teh (2012), the weights of the final fully-connected output layer, or output embeddings, which compute the conditional probability distribution over the lexicon, also constitute a legitimate set of embedding vectors representing word meaning, as is the case for the input embeddings. This commonality between the input and output layers of the NNLM has motivated researchers to tie these representations together during training, improving performance on language modelling tasks (Inan et al., 2016;Press and Wolf, 2017). Furthermore, such a procedure is intuitive, since both the input and output embeddings of the network would appear to be performing a similar task of encoding information about lexical content. As described by Inan et al. (2016), they clearly live in an identical semantic space in language models, unlike other machine learning models were the input and output embeddings have no direct link.
On the other hand, it would also be reasonable to assume that the output representations require highly task-specific features (Peters et al., 2018a,b;Devlin et al., 2019). Despite their utility in language modelling, in-depth analysis of these input and output vector representations remains limited. The goal of this work is to gain a deeper understanding of the aspects of language captured in these contrasting representations. Our two main contributions 1 are as follows: 1. We perform an investigation to uncover both the broad types of semantic knowledge and fine-grained linguistic phenomena encoded within each set of word representations.
2. We propose a simple method for constructing locally-optimal approximations that we use to extend our analysis to the intermediate representations from the network.
1 Code available at https://github.com/ stevend94/CoNLL2020 Though generally considered task-agnostic, by making extensive comparisons between these neural representations we may reason about the type of information most salient in the representations in each semantic space. Our results demonstrate that the input and output embeddings share little in common with respect to their strength and weaknesses, while the locally-optimal embeddings generally perform the best on most downstream tasks.
Related Work
Recent trends in NLP has seen a focus towards building generative pretraining models, which have achieved state-of-the-art performance on downstream tasks (Peters et al., 2018a;Radford et al., 2018;Devlin et al., 2019;Lan et al., 2019;Liu et al., 2019;Yang et al., 2019). These sequencebased autoencoder models have almost universally adopted the convention of weight tying in their input and output layers, which has been shown to improve training and decrease perplexity scores on language modelling tasks (Inan et al., 2016;Press and Wolf, 2017). Motivated by these results, researchers have proposed a number of modifications to these networks in relation to the output classification layers. For example, Gulordava et al. (2018a) combine weight-tying with a linear projection layer in the penultimate stage of the network to both decouple hidden state representations from the output embeddings and control the size of the embedding vectors. Takase et al. (2017) suggest modifying the architecture of the network by adding a gating mechanism between the input layer and the final classification layer of NNLMs. Focusing solely on the final classification layer, Yang et al. (2017) propose using a number of weighted softmax distributions, called a Mixture of Softmaxes, to overcome the bottleneck formed by their limited capacity. Takase et al. (2018) extend this approach by adding what they call a Direct Output Connection, which computes the probability distribution at all layers of the NNLM. Other work has focused on weight tying such as with the Structural Aware output layer (Pappas et al., 2018;Pappas and Henderson, 2019). Despite their importance, there is limited work which attempts to further analyse these output embeddings beyond the work of Press and Wolf (2017), who show that these representations outperform the input embeddings on word similarity benchmarks. In recent years, such analyses has gained popularity in the NLP community as researchers have shifted their focus towards interpretability in neural networks (Alishahi et al., 2019;. Examples include probing tasks, which are supervised machine learning problems that look to decode salient linguistic features from embedding vectors (Adi et al., 2016;Wallace et al., 2019;Tenney et al., 2019). Other work has focused on determining whether more cognitive aspects of meaning are adequetely encoded within these representations, through probing (Collell and Moens, 2016;Li and Gauthier, 2017;Derby et al., 2020) or using cross-modal mappings (Rubinstein et al., 2015;Fagarasan et al., 2015;Bulat et al., 2016;Derby et al., 2019;Li and Summers-Stay, 2019). Moving beyond basic linguistic phenomena, researchers have also investigated more complex aspects of language such as syntactical structure using probing methods (Linzen et al., 2016;Bernardy and Lappin, 2017;Gulordava et al., 2018b;Marvin and Linzen, 2018).
Research Context and Motivation
In this section, we first discuss some background about the input and output embeddings in NNLMs. Then, we briefly discuss how to compute new representations that are locally-optimal to the prediction step from the fully-connected softmax layer of the NNLM, by using stochastic gradient descent.
Neural Network Language Model
Consider a sequence of text (y 1 , y 2 , . . . y N ) represented as a list of one-hot token vectors. The goal of a neural network language model is to maximize the conditional probability of the next word based on the previous context. For a vocabulary V , at the time step t − 1 the network computes the probability distribution y * t of possible target words as follows: where f consists of one or many temporally compatible layers, such as LSTMs (Hochreiter and Schmidhuber, 1997) or masked transformers (Vaswani et al., 2017). The function f takes in a previous state as contextual information h t−1 ∈ R d f and embeddings e t from the look-up table E ∈ R de×|V | , and produces a new hidden state h t which the fully-connected output layer uses to compute the probability distribution y * t . We then compute the cross-entropy loss L(y t , y * t ) between the predicted distribution and the actual distribution, and minimize the loss with gradient descent.
To consider the case of weight-tying, we first note the fact that the size of the predicted probability distribution must span the length of the lexicon V . Then, disregarding the bias term, as W ∈ R |V |×d f it is easy to see how we can set E = W T if we set d f = d e . Weight tying has several advantages, including less training parameters and improved perplexity scores on language modelling objectives (Inan et al., 2016;Press and Wolf, 2017). However, the information that both the input and output embeddings must individually learn in order to predict the correct target concept may be entirely different.
Hidden State Word Representations
While these output embeddings can function as a set of semantic representations, their real goal is to instead compute the conditional probability distribution over the lexicon using context information from the hidden layers of the network. As such, the output embeddings may contain certain features that are specific to the language modelling objective, allowing them to identify information from the hidden layers that is relevant to predicting the target word. In addition to considering the input and output embeddings, we also consider the activation vectors from the latent layers of the language model in order to extend the scope of our analysis. From the perspective of how these layers represent lexical information, we are interested in the activation vectors in the hidden layers that lead to high prediction probabilities for the target words.
Intuitively, in order to find some activation vector from the latent layers that best represents a particular word, we would like to generate a sentence fragment that is optimal with respect to predicting that word (i.e. the hidden state h t for the sentence fragment yields the highest possible probability value for the target word being the next word in the sequence, given the calculations in Eqn. 3.1). We could then use these hidden state activations for each word as an additional embedding space, similar to Bommasani et al. (2020). However, we lack an efficient generative process for finding such optimal sentence fragments. We could sample a large number of sentence fragments from a corpus and record which sentence fragments give the high-est output probability for each word in our lexicon, but this will be highly inefficient and moreover will not guarantee that we have found the best hidden state activation vector for each word.
In the next section, we present a procedure to identify such optimal hidden states, which we refer to as locally-optimal vectors.
Locally-optimal Vectors
To find a latent representation that maximally predicts the target word from the final classification layer of the NNLM, we build a gradient-based approximation for each word. To achieve this, we employ a similar technique to Activation Maximization in computer vision (Simonyan et al., 2013). For a pretrained NNLM, let W ∈ R d f ×|V | be the weight matrix (i.e. output embeddings) and let b ∈ R |V | be the bias vector of the final prediction layer of the network. For each word in w ∈ V , we want to find the corresponding input I ∈ R d f that maximizes the probability of the word w. Let S w be the score function for the word w ∈ V , which takes an input and gives the probability output of the target class w. We can then formulate the problem as arg max where λ is a regularisation parameter. As described by Simonyan et al. (2013), maximizing the class probability can be achieved by minimizing the score for incorrect classes. This is undesirable for visualization purposes (see Simonyan et al., 2013), which is the reason why softmax normalization is usually omitted, though in our case, finding the most probable class is desirable. The regularisation term stops the magnitude of the vectors growing too large and instead focuses on the angular information between representations. We refer to these representations as AM Embeddings. Although these embeddings have the same dimensionality as the hidden states h t in the NNLM and play the same role in the softmax calculation, we note that they are not derived from any particular text sequence input to the NNLM and indeed there may not exist any sentence fragment that produces these hidden state activations. we may analyse the input and output embeddings as separate entities. We use the freely-available language model of Jozefowicz et al. (2016), which we refer to as JLM. The JLM network consists of a character level embedding input and two LSTM layers of size 8192, which both incorporate a projection layer to reduce the hidden state dimensionality down to 1024. The softmax output of the model has a word-level vocabulary of 800K word classes, and the model is trained on the one billion word news dataset (Chelba et al., 2013).
Pretrained NNLM Embeddings
We first acquire the input and output embeddings by extracting the appropriate matrices from their respective locations in the JLM network, with the input embeddings generated using the character-level layers. We then construct the AM embeddings, first by randomly initialising a set of |V | vectors before optimising using the Adam optimiser with a learning rate of 0.001 and regularisation term λ=10 −5 . We train for 100 epochs, with a batch size of 1024 using Keras. Due to the enormous size of the lexicon of the JLM language model, we downsample the 800K word vocabulary by taking the first 20K most frequently occurring words, which gives good coverage over the evaluation datasets.
Distributional Semantic Models
We also want to compare these embeddings with state-of-the-art distributional semantic models in order to make meaningful comparisons. For this, we use the skip-gram implementation of Word2Vec and FastText (Bojanowski et al., 2017) using the gensim package 2 and the Python implementation of Facebook's FastText 3 re-spectively. Word2Vec was trained with embeddings of size 300 and a context window of 5, while Fast-Text uses the default settings with embedding size 100, window size 5, and ngrams of sizes from 3 to 6. We also train a Python implementation of GloVe (Pennington et al., 2014) for 100 epochs with a learning rate of 0.05 to construct word embeddings of size 300. For a fair comparison, all models are trained on the same billion-word dataset (Chelba et al., 2013) as JLM.
Experiments
To assess these representations for both taskspecific effectiveness and fine-grained linguistic knowledge, we perform a broad range of experiments. These assessments include comparison with human understanding on word relations (Intrinsic Evaluations), analysing performance on supervised machine learning tasks (Extrinsic Evaluations), and using probing tasks to isolate linguistic phenomena. We hypothesise that the input and output embeddings should perform quite well on the intrinsic benchmarks, while the AM embeddings should give the best results on downstream prediction tasks, which we would similarly expect with the hidden representations from the intermediate layers of the network (Peters et al., 2018a).
Intrinsic Evaluations
We first compare the word embeddings with human semantic judgements of word pair similarity.
The rationale is that a good semantic model should correlate with semantic ground-truth information elicited from humans, either from conscious judgments, or from patterns of brain activation as people process the words (Bakarov, 2018). Similarity Benchmarks A traditional method for evaluating word embeddings uses the intuition of human raters about word semantic similarity. Word similarity benchmarks can, in general, be partitioned into two types: semantic similarity and semantic relatedness. Here, semantic relatedness refers to the strength of association between words (e.g. COFFEE and CUP), while semantic similarity reflects shared semantic properties (e.g. COFFEE and TEA). For benchmarks focusing on semantic relatedness/association, we use MEN (Bruni et al., 2012), MTurk (Radinsky et al., 2011) and WordSim353-Rel (Agirre et al., 2009), and for semantic similarity we use SimLex-999 (Hill et al., 2015), and WordSim353-Sim (Agirre et al., 2009). We also include two datasets whose judgement scores do not fall into either category, Word-Sim353 (Finkelstein et al., 2002) and RareWords (Luong et al., 2013). For the embedding vectors, similarity is computed using the cosine between pairs of word vectors, with Spearman's ρ used to measure the correlation between human scores and the cosine similarities. We perform our analysis using the Vecto python package (Rogers et al., 2018) 4 .
Predicting Brain Data
We also evaluate these embeddings on another intrinsic evaluation task that does not directly employ human semantic judgement. Instead, this evaluation asks whether the embedding models can reliably predict activation patterns in human brain imaging data as participants processed the meanings of words. For this, we use BrainBench (Xu et al., 2016) 5 , a semantic evaluation platform that includes two separate neu-roimaging datasets (fMRI and MEG) from humans for 60 concept words. This benchmark evaluates how well the embeddings can make predictions about the neuroimaging data using a 2 vs. 2 test, with 50% indicating chance accuracy.
Intrinsic evaluation results
In general, the output embeddings perform better than the input embeddings (Table 1), similar to (Press and Wolf, 2017). The only case where the input embeddings yield higher correlations than the output embeddings are on Rare Words. We can attribute this to the fact that the input embeddings are constructed from character-level representations. In comparison to the SOTA distributional models, the output embeddings tend to only beat FastText on Sim-Lex999 and BrainBench, while also struggling in comparison to Word2Vec on semantic relatedness and hybrid tasks. On the other hand, our AM embeddings perform very well in all evaluations, being the top-preforming model in most evaluations and performing quite similarly to FastText on MEN and Rare Words. While we hypothesised that the AM embeddings should perform quite well on downstream tasks, the ability of these novel word embeddings to explain human semantic judgement and reliably decode brain imaging data is surprising and interesting.
Extrinsic Evaluations
Next, we evaluate these representations by analysing their performance on a number of downstream tasks. Each task may demand a certain set of features relevant to the task, requiring these representations to encode a wide range of linguistic knowledge. We expect the output embeddings to perform better than the input embeddings and other SOTA semantic models based on previous research, which demonstrates that representations from the upper layers of the NNLM tend to perform better at prediction tasks (Peters et al., 2018a,b;Devlin et al., 2019). Since the AM embeddings represent a locally-optimal instance for the penultimate layer of the network, we also expect them to perform well.
Transfer Learning Tasks We make use of Sen-tEval (Conneau et al., 2017), an evaluation suite for analysing the performance of sentence representations. Though we are working with word embeddings, applications rarely require words in isolation.
To build sentence embeddings, we take the average embedding vector of all words in the sentence.
SentEval includes a number of binary classification datasets, including two movie review sentiment datasets (MR) (Pang and Lee, 2005) and (SST2) , a product review dataset (CR) (Hu and Liu, 2004), subjectivity dataset (Subj.) (Pang and Lee, 2004) and an opinion polarity dataset (MPQA) (Wiebe et al., 2005). It also includes two multiclass classifications tasks, a question type classification dataset (TREC) (Voorhees and Tice, 2000) and a movie review dataset with five sentiment classes , as well as an entailment dataset (SICK-E) (Marelli et al., 2014) and paraphrase detection dataset (MRPC) (Dolan et al., 2004). For classification, we use a one-layer PyTorch GPU model with default parameters and Adam optimisation.
The results (Table 2) show that, on binary classification tasks, the input and output embeddings perform quite similarly, while both provide better results than the distributional models in almost all cases. Taking a closer look, we can see that the out- put embeddings perform best at predicting movie review sentiment (MR, SST2) and opinion polarity (MPQA), while the input embeddings provide the highest scores when predicting product review sentiment (CR) and subjectivity (Subj.). When predicting multiple classes (TREC, SST5), the input embeddings perform marginally better than the output embeddings, though the AM embeddings perform best overall on both binary and multiclass datasets. Interestingly, the input embeddings are much better at both predicting entailment (SICK-E) and paraphrase detection (MRPC) than all other models.
Semantic Text Similarity To further evaluate how well these embeddings perform at judging sentence relations, we also employ transfer learning to the semantic relatedness tasks from SemEval, in particular SICK-R (Marelli et al., 2014) and STS B (Cer et al., 2017). The task consists of sentence pairs with scores ranging from 0 to 5, indicating the level of similarity between the sentences. We see from the results ( Table 3) that the input embeddings again give the highest correlation with semantic relatedness scores, similar to the previous results. Furthermore, the AM embeddings perform worse at judging relatedness than the output embeddings, though the differences are quite small. Our AM embeddings still outperform all SOTA distributional models.
We also perform transfer learning on a set of Semantic Textual Similarity (STS) benchmarks, (Agirre et al., 2016) semantic similarity tasks. Each dataset contains sentence pairs similar to the relatedness tasks, though each is taken from different sources such as news articles or forums. Here, we record performance using the average Pearson and Spearman correlation for each STS dataset, with results displayed in Figure 1. The input embeddings again give the best performance on all datasets, similar to previous results on sentence relatedness. Furthermore, the AM embeddings perform better than the output embeddings on all datasets, in contrast to the previous findings. The results demonstrate that the input embeddings are much more suited to sentence comparison tasks than the other pretrained NNLM embeddings.
Probing Tasks
We next examine whether the embedding vectors capture certain linguistic properties when utilised as sentence representations. These probing tasks are formulated as a supervised classification problem, with strong performance indicating the presence of an isolated characteristic such as sentence length. Similar to the transfer learning tasks, we take the average embedding vector of all words to generate the sentence embedding. These tasks are taken from Conneau et al. (2018), which includes probing tasks partitioned into three separate categories.
• Surface Information: The tasks include sen-tence length prediction (SentLen) and deciding whether a word is present in the representations (WordContent).
• Syntactical Information: Focusing on grammatical structure, these include tasks for predicting the maximum length of a node to the root (TreeDepth) and predicting the top constituent below the <S> node (TopConsts).
• Semantic Information: Focusing on dependency knowledge, these include tasks for predicting the tense of the main verb (Tense), the number of subjects of the main clause (SubjNum) and the number of objects of the main clause (ObjNum).
We exclude other probing tasks that rely on word position in the sentence, since these averaged word embeddings are invariant with respect to word order 6 . The results are displayed in Figure 2. The SOTA distributional models tend to perform worse than the pretrained NNLM representations when predicting SentLen and WordContent, though the output models perform poorly compared to the input and AM embeddings. The AM embeddings perform well, perhaps because of their training objective which incentivises linear separability. When predicting syntactic information, the input and output embeddings perform similarly at classifying TreeDepth and TopConsts, with the AM embeddings performing best. Finally, when predicting Tense, SubjNum and ObjNum, the output embeddings are superior, which may be due to the output embeddings heavily encoding dependency information that is relevant to predicting the upcoming word during language modelling. Indeed, LSTMs are particularly good at learning dependency information such as subject-verb agreement (Linzen et al., 2016).
Neural Language Modelling
We have demonstrated that the linguistic knowledge captured by the input and output embeddings are moderately distinct. These results may imply that the input and output embeddings of the NNLM require a particular set of non-overlapping characteristics that are important to their respective roles in the NNLM. To further understand whether and how these representations are distinctive to their particular functions in the input and output layers, we perform domain transfer on the language modelling objective. For our evaluation, we test each set of embedding vectors when fixed as certain weights in the network: 1. NNLM In : Fixing our embedding vectors as the lookup table input to the language model. 2. NNLM Out : Fixing the softmax output layer by using the transpose of the stacked embedding vectors as the matrix of dense weights, without a bias vector. 3. NNLM Tied : Fixing the embedding inputs and softmax output by using our embeddings as the tied weights.
Here we expect the input embeddings and output embeddings to perform well in the case of NNLM In and NNLM Out respectively, since in these cases their role is congruent with their origi-nal role in JLM. We also expect the other distributional models to perform well as input embeddings based on previous research. It will also be interesting to see how the AM representations perform since they are trained using output embeddings and thus should share a lot of their linguistic knowledge. If the input and output embeddings perform similarly, we can infer that these representations contain considerable overlap in lexical information. However, if they perform poorly when their roles are switched, we can conclude that these representations must learn some role-specific features not encoded in the other semantic spaces. See the appendix for training details, which closely follow the medium-sized LSTM model presented by Zaremba et al. (2014) with the Penn Treebank dataset (Marcus et al., 1993).
Perplexity Results
Results are displayed in Table 4. In the NNLM In models, we see that the AM embeddings provide the best performance, even outperforming the input embeddings, with the output embeddings and SOTA distributional models performing quite well. We also note that the input embeddings still provide slightly better performance than the output embeddings in this analysis. In the case of the NNLM Out networks, most of the distributional models perform poorly. The NNLM struggles when the distributional models are utilised as fully-connected classification weights, while the output embeddings, which were trained for this task, perform best, though the AM embeddings also perform well. The input embeddings perform poorly in the NNLM Out model, indicating that the output embeddings do encode role-specific knowledge not captured by the other distributional models. Finally, when we tie and fix the weights, the SOTA distributional models and input embeddings do not improve the performance much in the NNLM Tied model. Both the output embeddings and AM embeddings have good performance, and our AM embeddings surprisingly give the best results.
Discussion
We can draw several conclusions from these results. As expected, the type of semantic knowledge these representations capture is dependent on their position in the network.
Semantic Knowledge
The input embeddings struggle with representing word-level semantic relationships though perform well at estimating relatedness between sentences and paraphrase detection. The input embeddings also seem to encode several aspects of surface-level information such as sentence length, which is behavior more expected of contextualised representations of meaning. Indeed, the input embeddings seem to contain at least some qualities that make them suitable for building sentence-level representations. On the other hand, the output embeddings struggle as sentence-level representations. This is not so surprising, since these embeddings are the input components used to construct contextual representations in the intermediate layers, unlike the output embeddings.
The output embeddings seem to correlate more closely with human judgment on the word-level association and neuroimaging data for isolated concept words than the input embeddings. Furthermore, the output embeddings are highly taskspecific to language modelling. Though other distributional semantic models estimate representations of meaning through somewhat similar language modelling objectives, they fail to learn any meaningful knowledge that is transferable to the output classification layer of the language modelling task.
Weight Tying
There are a number of characteristics that each set of representations seem to capture quite well given their position in the architecture of the NNLM. In a tied representation, we would expect the network to learn a set of embedding vectors that encode all such knowledge, though the contribution from each layer may not be entirely equal. Press and Wolf (2017) noted that, due to the update rules that occur when using weight tying between these layers, the output embeddings get updated at each row after every iteration, unlike the input embeddings. This implies a greater degree of similarity of the tied embedding to the untied model's output embedding than to its input embedding. From the perspective of this work, we would also add that a tied representation would be more similar to the output embeddings since the information they capture is more important to the overall learning objective. Based on our results, while the output embedding knowledge is quite transferable to the input embeddings, the converse is false.
Transfer Learning
In recent years, representations from pretrained neural language models have become a popular choice for transfer learning to other tasks. Generally, the intermediate representations from the layers of the network are preferred, since they are contextualised over the sentence and generally perform better in downstream tasks. In our work, we use the AM embeddings to behave as a stand-in for the intermediate layers' hidden states that are locallyoptimal to each particular target word. Similar to these intermediate representations, our AM embeddings perform quite well on downstream NLP tasks. While this is to be expected, the results on the intrinsic evaluations and language modelling tasks are surprising. We would expect these embeddings to learn quite a bit of knowledge from the output embeddings, though the increase in performance on some tasks is striking. This may be due to the activation maximisation training objective that we employ, which forces linear separability between words in the lexicon whilst preserving the semantic information about each word (see Appendix).
Conclusion
We perform an in-depth analysis of the input and output embeddings of neural network language models to investigate what linguistic features are encoded in each semantic space. We also extend our analysis by constructing locally-optimal vectors from the output embeddings, which seem to provide overall better performance on both intrinsic and extrinsic evaluation tasks, beating wellestablished distributional semantic models in almost all evaluations. | 6,925.8 | 2020-11-16T00:00:00.000 | [
"Computer Science"
] |
RURAL WOMEN FARMERS ’ ASSESSMENT OF CREDIT ORIENTED SELF-HELP GROUPS IN DELTA STATE , NIGERIA
This study was conducted to analyze the perception of the rural woman about credit oriented self-help group in Delta State, Nigeria. A sample size of 110 respondents was used for the study and data were collected from them with the use of structured interview schedule and questionnaires. Data were analyzed using descriptive statistics and linear regression equation model as the lead equation. It was revealed that the women farmers subscribed to self-help groups in order to be able to have access to credit (mean = 3.78), information (mean = 3.55), extension services (mean = 3.45). The respondents were satisfied with their respective self-help groups. However, they had some challenges such as inadequate access to extension services (mean = 3.55) and lack of commitment by the leaders (mean = 3.22) and members (mean = 3.19). Educational level and frequency of extension contact of the respondents were found to influence their perception on self-help groups at 5% level of significance. It is recommended that governmental and non-governmental organizations, and university agricultural extension departments should carry out a campaign on workshops for these groups on commitment and extension agencies should diversify their focus to include selfhelp groups and activities.
Introduction
Self-help, according to American Psychological Association (APA), refers to self-guided improvement economically, intellectually, or emotionally, most frequently with a substantial psychotic or spiritual basis.Self-help often takes place based on self-reliance or support groups where people who find themselves in similar situation group together (VandenBos, 2007).Often people who find themselves in similar conditions come together to form self-help group such as co-operative societies and farmers' associations for the purpose of harnessing their financial resources as savings for the group and using such societies and associations to help themselves have access to credit facilities from the group.This is very common among rural women who according to Prakash (2003) are more involved in arable farming than men in the developing nations of the world.These women hardly own any land, and have difficulties in acquiring credit, particularly in sub-Sahara Africa and Caribbean countries, in spite of the fact that they produce up to 80% of basic food stuffs (Prakash, 2003).
Another problem rural women face is dearth of information by agricultural advisers and projects since extension agents do not often pay attention to them.Since most agricultural extension agents are men, it is difficult for them to interact with female farmers frequently.Ofuoku (2011) found that husbands of the female farmers react negatively to frequent contact between their wives and male extension agents.Okorodudu and Okobiah (2004) observe that in Nigeria incidence of spouse abuse involving wife beating by the husband has been reported.
The self-help groups like the farmers' association and co-operative societies are supposed to be panacea to the problems encountered by these women.But the attitude of these women towards their self-help groups determines the success or goal attainment through them.
Through self-help groups, the women are expected to be able to harness their resource and have easy access to credit and extension services since the ratio of extension agents to farmers is disproportionate.According to Ofuoku (2011), there is a negative disposition of their spouse to frequent male extension contact when singly visited by male extension agents, who are more numerous than female extension agents among field extension agents in Delta State.
Food security means having sufficient food all year round, but more than three million people in Africa currently face food insecurity and the challenges to meet their food and nutrition need are likely to become greater in the years ahead (Meludu et al., 1999).According to Prakash (2003), women grow about half of the world's food, but hardly own any land, have difficulty in attaining credit and are overlooked by agricultural advisors and projects.In Africa and Nigeria in particular, three quarters of the agricultural work are done by women especially in the area of food crop production.
Considering the aforementioned and in spite of the existence of self-help groups, rural women still suffer in terms of access to credit and extension services.Self-help groups such as co-operative societies, farmers' associations and traders' association are formed to empower individuals to carry out their productive activities and for self development.Therefore, it is imperative to understand the dynamics of the psychological process involved in the interaction of rural women and self-help groups in the process of empowering themselves.This would likely result into a solid policy foundation block for the Ministry of Commerce and Industry which regulates cooperative society activities for effective and efficient self-help system in the study area in particular and similar ministries in other states of Nigeria.
The major objective of the study was to analyze the perception on satisfaction of rural women towards credit oriented self-help groups in Delta State.Specifically, the objectives were to determine: (i) the socio-economic characteristics of rural women engaged in self-help groups in Delta State; (ii) the types of self-help groups in the study area; (iii) the level of satisfaction of rural women toward self-help groups in Delta State; (iv) the constraints encountered by rural women in self-help groups in the study area.
The hypothesis was that the socio-economic characteristics of the rural women do not influence their perception of credit oriented self-help groups in the study area with respect to need satisfaction.
Material and Methods
The study area is Delta State, Nigeria.The state is located roughly between longitude 5°00' and 6°45' East of Greenwich Meridian and latitude 5°00' and 6°30' North of the Equator.The state shares boundary with Edo State to the north, Bayelsa State and the Atlantic Ocean to the south, Anambra State to the east and Ondo State to the west.
The state lies in the forest vegetation belt.Tree and arable crops are predominantly grown by farmers who form about 75% of the population and are mostly small scale farmers, the majority of whom are women.
The state is divided into three agricultural zones -Delta North, Central and South Agricultural Zones by the Delta State Agricultural Development Program with Agbor, Effurun and Warri as the Zonal headquarters respectively.Delta South and Central Agriculture Zones have 8 extension blocks each, while Delta North Agricultural Zone comprises 9 extension blocks.
Four agricultural extension blocks were randomly selected from each of the three agricultural zones.From each of the blocks selected, 12 women farmers were randomly selected using lottery method from the register of the self-help groups.This will give us 144 respondents at the end, but only 110 structured interview schedule and questionnaire copies could be retrieved.
Data for the study were generated from primary sources.The primary data were generated from the selected respondents, using questionnaires for those with high level education and structured interview schedules for those who have low level of education and those who are not formally educated.The instrument was administered by the researcher and trained enumerator that were hired from among the extension agents.The level of satisfaction of the farmers was measured using Likert's scale.
The data collected for study were analyzed by using descriptive statistics such as frequency counts, percentages and means derived from 5-point Likert's scale.The hypotheses were analysed with the use of linear regression.
The 5-point Likert's scale was calibrated as follows: strongly agree (SA) = 5; agree (A) = 4; undecided (UD) = 3; disagree (DA) = 2; and strongly disagree (SD) = 1.The multiple regression model was implicitly specified as follows: where: Y = level of satisfaction, X 1 = levels of education (years), X 2 = age of women (years), X 3 = extension visits (number of visits by extension agents in a month), X 4 = household size (number of persons in the household).
Four functional forms of the model -linear double log, semi log and exponential were fitted to determine the function with the best fit and the linear model proved to be the best fit.
Socio-economic characteristics of respondents
Table 1 indicates that most (29.10%) of the rural women were above 60 years of age.Those aged 41-50 years reached 24.55%, while those aged 51-60 years reached 20% of the respondents.The implication is that since the younger ones were not as many as those aged 41 to above 60 years, they (younger ones) have either relocated to urban areas in search of white collar jobs or are in various tertiary institutions.Tadaro (1976) as cited by Ekong (2003) hypothesized that the greater the difference in economic opportunities between urban regions the greater is the flow of migrants from rural areas.
Most women (38.18%) were married while 26.36% were widowed; 18.18% were single and 15.45% were divorced.Most of them were saddled with responsibilities, particularly the married, divorced and windowed.
Most (31.81%) of the rural women had tertiary education.Those that had secondary education accounted for 30.91% and those with primary education 27.27%.Very few (10%) had no formal education.
This means that most of the rural women had one form of formal education or the other.Most (19.09%) had 6-person household.This was followed by those (15.45%)who had 5-person household.This is at variance with campaign on birth control and the biting economic situation; many household heads have resolved to control birth.Most (29.10%) of the women had farms of the size of ½ ha.This was followed by those (12.72%)who had farms of the size of 3½ ha and those (10.91%)who had farms of the size of 6 ha and above.This implies that most of the women are smallholder farmers.Most (21.81%) of the farmers had an annual income of between N50,000 and 100,000; 16.36% had an annual income of between N 101,000 and N150,000; 11.82% had an annual income of between N151,000 and N200,000; 3.63% had an annual income of N451,000 and N500,000 and 4.54% had an annual income of above N500,000.This implies that most of the women have very poor income.This can be attributed to their levels of farm operation.
Most (61.81%) of them opined that they had no contact with extension agents while 20% had one contact with them monthly; 4.45% twice monthly; 6.36% three times in a month and 6.63% four times monthly.This means that most of the women have no access to extension agents.This agrees with Fadiji et al. (2006), who discovered that access to extension agents is related to the socio-economic status of farmers.This is so because the above information indicates that most of the women received low level of income and had small-sized farms.This is also attributed to sociological reasons among which is that in Africa, the society frowns at frequent interaction between men and married women, and as a result of this, male extension agents, who make the majorityof extension agents in Delta State, find it difficult to interact with the women farmers (Ofuoku, 2011).
Most (53.63%) of the respondents subscribed to various farmers' cooperative societies, while 18.18% registered their membership with Fish farmers' association; 15.45% of the respondents subscribed to Cassava Farmers Association and 12.72% registered with All Farmers' Association of Nigeria.These various self-help groups have been formed by the women and other farmers for their benefit with respect to their farming business which translates to higher level of living among the members and the women in particular.
Reasons for subscribing to self-help groups Table 2 indicates that all the reasons for subscription to the various self-help groups were very important to the respondents and they include access to: information (mean = 3.55), inputs (mean = 3.45) credit (mean = 3.78) and extension services (mean = 3.25).The most important reasons include access to credit information.The results are congruent with the findings of Ofuoku et al. (2008) as they discovered the same set of reasons being behind subscription to cooperative societies and other self-help groups among fish farmers in southern Nigeria.88) 10 (30) ( 28) 27 ( 27 Credit and information are very crucial to successful business transaction and farming in particular.Ike and Ajieh (2009) opined that part of the philosophy of a cooperative organization like women's self-help groups is fulfilment of the credit and information needs of their members.
Level of satisfaction with self-help groups' operations among respondents
Table 3 indicates that the respondents were very satisfied with their various self-help groups.This is evident as Cassava farmers' association's operation was rated as very satisfactory (mean = 4.47), All Farmers' Association of Nigeria members rated its operations as being very satisfactory (mean = 3.92); farmers' cooperative society was rated as very satisfactory (mean = 4.45) and Fish farmers' association was also rated as very satisfactory (mean = 4.40).This is congruent with part of the philosophy of cooperative organizations which is fulfilling of the needs of their members, who are conscious of the benefits of collective action as stated by Ike and Ajieh (2009).Ofuoku and Urang (2012) found that farmers were satisfied with the rate of release of credit to them by their various cooperative groups.However, Ugbomeh et al. (2008) found that members of women self-help groups in Bayelsa State, Nigeria were not satisfied with their groups.Ike (2009) discovered that there was a disparity between the amounts of credit applied for by members of self-help groups in Enugu State, Nigeria.The members of the various groups have decided to join the groups in a desire to derive mutual benefits and achieve economic independence.The greater the extent to which a group fulfills the needs of members, the more cohesive the group will be (Ogionwo and Eke, 1999).Ofuoku et al. (2008) also discovered that there is a relationship between satisfaction of the needs of the members and cohesiveness of the fish farmers' groups in southern Nigeria.
Constraints to self-help groups
Table 4 shows that inadequate access to extension services (mean = 3.55); lack of commitment by leaders (mean = 3.22) and lack of commitment by members (mean = 3.19) were the major constraints to the operations of self-help groups in the study area.Inadequate access to extension service was also discovered by Ofuoku and Ajieh (2005) to be a serious constraint among poultry farmers in Delta State.This was attributed to the inadequate population of extension agents in the study area.Lack of commitment by leaders is evidenced by irregular attendance at meetings, irregular payment of subscription and autocratic leadership style by leaders.According to Ogionwo and Eke (1999), there is a relationship between groups' leader and cohesiveness of the group.This implies that uncommitted leadership can upset the cohesiveness of a group as people do not like autocratic leadership style in a group that is supposed to be democratic one.
The leadership style used by the group leaders matters a lot (Shea, 1999).Members also require that leaders should lead by example by paying their subscriptions regularly.Membership's lack of commitment is attributed to irregular attendance to meetings and irregular payment of subscription and irregular refund of loans.This is congruent with the findings of Ofuoku and Agbamu (2013), who found that leadership of many self-help groups in Delta State was not effective.Attributes like this can also upset cohesiveness of group.These issues should be typified by such norms as honesty, fairness, equity, democracy and mutual fellow feelings (Ofuoku et al, 2006).
Test of hypothesis
In Table 5 only the lead equation which is the linear functional form is presented for the women.The choice of the linear equation was based on the magnitude of the coefficient of determination (R 2 ), the number of significant variables and the conformity of the sign the variables bear to a priori expectation.The table indicates an R 2 value of 0.611.This implies that 61.1% of the variations in the perception of the women were explained by the independent variable included in the model.The F-ratio is also statistically significant which attests to the fact that the models fit the data.Respondent's educational level and frequency of extension contact were statistically significant and positively related to the level of satisfaction with self-help groups.
Table 5.Estimated relationship between socio-economic characteristics of the rural women and their perception of credit oriented self-help groups in the study area with respect to need satisfaction.These imply that increase in variables ceteris paribus would result to increase in appreciation of the self-help groups by the women.These variables (education and frequency of extension contact) can therefore be said to influence the women's perception of self-help groups.The results on level of education and frequency of extension contact are not surprising as it was expected that farmers with higher level of formal education and higher frequency of extension contact would have higher perception about self-help groups than those with lower level of formal education and lower frequency of extension contact.This is in consonance with the results obtained by Adekoya and Ajayi (2000) and Ajayi and Banmeke (2007).Education and frequency of extension contact are therefore, two salient variables that influence satisfaction as perceived by the respondents.
Conclusion
The rural women belong to various self-help groups.They subscribed to these self-help groups in order to have access to information on their farming activities, inputs, and credit and extension services.They were very satisfied with their various self-help groups, but still have some challenges ranging from inadequate access to extension services and inputs to lack of commitment by the leaders and members.Conclusively, they were satisfied with the benefit they gained from selfhelp groups, especially with respect to access to credit and information.
Considering the findings, it is recommended that: i) Extension agents should expand their focus to include self-help groups' activities.This will take into account the challenges of inadequate extension services and inadequate access to inputs.
ii) Governmental agencies, non-governmental organizations and members in self-help groups should interact closely in order to appreciate the problems involved in the group process.
iii) The leaders that are obstinate should be voted out of office and committed members should be elected by members of the respective groups to replace them.
Table 1 .
Socio-economic characteristics of respondents.
Table 2 .
Reasons for subscribing to self-help groups among respondents.
Table 3 .
Level of satisfaction with self-help groups operating among respondents.
Table 4 .
Constraints to self-help groups. | 4,159.4 | 2014-01-01T00:00:00.000 | [
"Agricultural and Food Sciences",
"Economics"
] |
Deconfined critical point in a doped random quantum Heisenberg magnet
We describe the phase diagram of electrons on a fully connected lattice with random hopping, subject to a random Heisenberg spin exchange interactions between any pair of sites and a constraint of no double occupancy. A perturbative renormalization group analysis yields a critical point with fractionalized excitations at a non-zero critical value $p_c$ of the hole doping $p$ away from the half-filled insulator. We compute the renormalization group to two loops, but some exponents are obtained to all loop order. We argue that the critical point $p_c$ is flanked by confining phases: a disordered Fermi liquid with carrier density $1+p$ for $p>p_c$, and a metallic spin glass with carrier density $p$ for $p<p_c$. Additional evidence for the critical behavior is obtained from a large $M$ analysis of a model which extends the SU(2) spin symmetry to SU($M$). We propose that key aspects of cuprate phenomenology are realized by the vicinity of this deconfined quantum critical point.
However, it appears that the restoration of the broken symmetry cannot be the driving mechanism for a quantum phase transition at p = p c : the broken symmetries are weak and differ among the cuprates, and the transport [5], thermodynamic [6][7][8], electronic [2][3][4]10], and spin dynamics [11][12][13] signatures are strong. This paper will study a model with all-to-all randomness (see (1.1) below) which exhibits a deconfined quantum critical point [14] with many similarities to the mysterious cuprate phenomenology. Our model has a quantum critical point at p = p c with fractionalized excitations, separating metallic states with carriers densities of p and 1 + p (see Fig. 1). The overdoped state is a conventional disordered Fermi liquid, the underdoped 'pseudogap' phase with carrier density p has spin glass order, but the quantum critical point is described by fractionalized excitations. Our critical theory is distinct from a Landau-Hertz-Millis-type theory [15,16] of the quantum fluctuations of the spin glass order in a metal; the latter theory has no fractionalization at criticality and does not exhibit a change in carrier density across the transition. Moreover, our p = p c critical theory is expected to maximally chaotic [17], similar to the Sachdev-Ye-Kitaev (SYK) [18,19] models, and unlike the Landau theories [20].
Our results provide a simple rationale for the existence of a quantum phase transition in correlated metals with 'Mottness'. Broken symmetries are not essential, and only play a secondary role. At low doping, we have fermionic 'holons' of density p, moving in a background of condensed bosonic 'spinons' (see Fig. 1). At higher doping, we have condensed bosonic holons, so that the fermionic spinons behave like a Fermi liquid of hole-like carrier density 1 + p. This statistical transmutation, and corresponding transformation in the many-body state, is accomplished by a strongly coupled deconfined critical point which exhibits a boson-fermion duality. Note that, because of the presence of the Higgs-like condensates on both sides of the critical point, there is no true fractionalization for either p > p c or p < p c .
There have been discussions [21] of deconfined critical points between a magnetic metal with 'small' Fermi surfaces, and a non-magnetic heavy Fermi liquid with a 'large' Fermi surface (a review of related ideas is in Ref. 22). However, to date, no tractable realization of this scenario has been found in non-random systems. Our results show that a similar scenario is naturally realized in models with random couplings. We also note the study of Ref 23, which found an evolution between small and large Fermi surface across optimal doping in a plaquette dynamical mean field theory.
Our model is in the class of SYK models [18,19] with random all-to-all couplings, which have been extensively exploited recently for descriptions of strange metals and the quantum information theory of black holes. Specifically, we generalize the insulating random Heisenberg magnet originally studied in Ref. 18 to metallic states of a t − J ij model at non-zero doping, along the lines of Ref. 24. We consider electrons, annihilated by c iα , spin α =↑, ↓ on sites i = 1 . . . N with double occupancy prohibited α c † iα c iα ≤ 1. The Hamiltonian is the familiar t-J model with where P is the projection on non-doubly occupied sites, µ is the chemical potential and S i = (1/2)c † iα σ αβ c iβ is the spin operator on site i, with σ the Pauli matrices. The complex hoppings t ij = t * ji and the real exchange interactions J ij are independent random numbers with zero mean and mean-square values |t ij | 2 = t 2 and J 2 ij = J 2 . We will work at a variable hole density p, defined by 1 N i c † iα c iα = 1 − p . (1. 2) The insulating p = 0 case of H tJ was studied in Ref. 18, and in Ref. 24 for non-zero p, after generalizing the SU(2) spin symmetry to SU(M ) and taking the large M limit (see Appendix C). A gapless critical ground state was found [18] at large M for p = 0. However, subsequent numerical studies [25,26] of the insulating SU(M = 2) case found a spin glass ground state, and such insulating spin glass states had also been examined in the large M limit [27,28]. At non-zero p,
Disordered
Fermi liquid. Condense holon b, f ↵ carrier density 1 + p < l a t e x i t s h a 1 _ b a s e 6 4 = " 5 a L W m L R L Y L d I h s s a R L f s 5 y 6 H B + k = " > A A A C Y 3 i c d V B d a x Q x F M 2 M r d a p H 2 v 1 T Y T g j i B Y h p n 9 q P a t t C I + V n D b w m Z Z M s m d 3 d B M E p N M Y R n 2 Z / r g Q x / 9 F b 4 0 u 9 2 C i h 4 I H M 4 5 N z c 5 p Z H C + T z / E c X 3 t r b v P 9 h 5 m O w + e v z k a e f Z 3 p n T j W U w Y l p q e 1 F S B 1 I o G H n h J V w Y C 7 Q u J Z y X l y c r / / w K r B N a f f U L f carrier density p < l a t e x i t s h a 1 _ b a s e 6 4 = " C z T 5 Z 3 9 M p v 1 M j B F q j U k D 8 N 1 M D 4 e f 0 g 9 m E 5 H 2 X G G s y T t a 4 h 2 d b Y c / C C F Y n X l 7 a x b b p 6 l 2 i 0 a a h x n A t q I 1 B Y 0 Z R u 6 g r m H k l Z g j 4 o r r m 0 P F 0 0 f W o s P v V j g U h n / p M M 9 e 9 v c 0 M r a b Z X 7 z u 4 g + 7 f W k f / S 5 r U r 3 y 8 a L n X t Q L L r Q W U t s F O 4 + w F c c A P M i a 0 H l B n u 1 8 Z s T Q 1 l P g 4 b + T x u j s b / B + e j J B s n 0 y + j 4 c n H X T J 7 6 B V 6 g 9 6 i D L 1 D J + g T O k M z x N D P Y D 9 4 E b w M f o W v w 2 F 4 e N 0 a B j v P c / R H h c l v 4 g y + z g = = < / l a t e x i t > b † " |vi < l a t e x i t s h a 1 _ b a s e 6 4 = " v E C y c C T t e y k y w P j 6 S C P C L A B 1 c / k h n r 3 P g O 9 L t 7 g Z 7 A Q / a / g I t t s T x s P k r S g p R 5 a h J K L C 2 H / g l D W o w J I X C W S O q L J Y g x p B h 3 1 E N O d q P y U S W d k E H 9 S L o j L 9 3 Z s L T w r i n i S / U f 5 d r y K 2 d 5 r G b n M e y j 7 2 5 + J T X r y j 9 N K i l L i t C L e 4 P p Z X i V P B 5 a z y R B g W p q S M g j H T f 5 m I E B g S 5 b h u u j 7 + h + f / J y W 4 7 6 L S 7 X 8 P W 4 e d l M + t s h 7 1 j H 1 j A 9 t k h O 2 L H r M c E u 2 Q / 2 E 9 2 7 V 1 5 N 9 6 t d 3 c / u u I t d 7 b Z A 3 i / / w B t R a g 6 < / l a t e x i t > b † # |vi < l a t e x i t s h a 1 _ b a s e 6 4 = " z 0 N n q 6 d M e H I M T M Z k u 1 J t g F u 2 L X 7 M a 7 9 G 6 9 O + / + c X T F W + 5 s s D / g / X 4 A L B C p I Q = = < / l a t e x i t > f † |vi < l a t e x i t s h a 1 _ b a s e 6 4 = " l X g T R A 1 N H c D D R U e Z o n / r k M t K M n 4 = " > A A A C H 3 i c d V D L a t t A F B 2 l z a P O y 2 m X 3 Q w x h U C C k f x I 2 l 1 o N 1 2 m E C c B y z F X o y t 5 8 G g k Z q 4 M R v V H 9 B P 6 F d 2 2 q + 5 C t l n 0 X z p 2 H G h C e m D g c M 6 9 3 D k n K p S 0 5 P t 3 3 s q L l 6 t r 6 x u v a p t b 2 z u 7 9 b 3 X F z Y v j c C e y F V u r i K w q K T G H k l S e F U Y h C x S e B m N P 8 3 9 y w k a K 3 m p s k y S F F 4 V B y C K F 5 9 H l 4 d g / H 6 C x M t e n N C y w m 0 G q Z S I F k J N 6 1 W 9 J L 4 z z K w 3 G 5 F c / w h j S F A 0 P F S Z 0 z Q c 8 N D L t U 2 h A p w p 7 1 Z p f 9 1 v N R r P J H T R 2 G t 9 9 B 6 3 W d r A T 8 K D u T 6 r G p n X c q / 5 z d 4 s y Q 0 q v 2 k l X 6 a b 0 a z 8 0 W F k p Z 8 / 6 e 3 s r r 2 7 P n 6 x m Z l 6 8 X L V 9 v V 1 z u n N i numerical studies of multi-orbital Hubbard models [29,30], and at the metal-insulator transition of a disordered Hubbard model at half-filling (p = 0) [31].
are all physical observables which realize the superalgebra SU(1|2) [32] (see Appendix A). We are interested in the 3-dimensional representation of physical states obeying Hence, the physical states are invariant under the U(1) gauge transformation f α → f α e iφ , b → be iφ , while individual spinon and holon excitations carry U(1) gauge charges.
Alternatively we can use a representation with bosonic spinons b α and fermionic holons f. Now the gauge-invariant operators are This realizes the same superalgebra SU(2|1) ≡ SU(1|2) as (1.3), and the same 3-dimensional representation is obtained by the constraint Note that while we find it convenient to refer to the superalgebra, there will be no supersymmetry in our results: H tJ does not commute with all SU(1|2) generators.
We can now describe the structure of our main results illustrated in Fig. 1. We find a deconfined critical point p = p c at which the 3 spinon and holon states are nearly degenerate. Assuming all three states are equally probable at criticality, we obtain a critical density p c = 1/3. Indeed, as in the 2+1 dimensional theories, we will find that a Wess-Zumino-Witten [32,[35][36][37][38] term (S B in (2.4) and (2.6) below) plays a central role in the criticality.
Away from the critical point, there is a runaway RG flow to states in which either the spinon or holon states are lower in energy. As illustrated in Fig. 1 We will describe the nature of the infinite volume (N → ∞) limit of H tJ in Section II, and map possible critical states of the large N limit to quantum impurity models in Section II A. The II. LARGE VOLUME LIMIT The limit of large volume (N → ∞) of H tJ is obtained by the methods described in Refs. [18,27,28] for the insulating model at p = 0. We introduce field replicas in the path integral, and average over t ij and J ij . At the N = ∞ saddle point, the problem reduces to a single site problem, with the fields carrying replica indices. The replica structure is important in the spin glass phase [27,28], which we will explore in subsequent work. In the interests of simplicity, we drop the replica indices here as they play no significant role in the critical theory and the RG equations.
Within the imaginary time path integral formalism (with τ ∈ [0, 1/T ], with T the temperature), the solution of the model involves a local single-site effective action which reads: In this expression, µ is the chemical potential determined to satisfy (1.2) and S ∞ is the action associated with the constraint of no double occupancy (U = ∞). Decoupling the path integral introduces fields analogous to R and Q which are initially off-diagonal in the spin SU(2) indices.
We have assumed above that the large-volume limit is dominated by the saddle point in which spin rotation symmetry is preserved on the average, and so R and Q were taken to diagonal in spin indices. The path integral Z is a functional of the fields R(τ ) and Q(τ ), and we define its In the thermodynamic (N → ∞) limit, the solution of the model is obtained by imposing the two self-consistency conditions: These equations and the mapping to a local effective action are part of the extended dynamical mean-field theory framework (EDMFT), which becomes exact for random models on fully connected lattices [16]. They can also be viewed as an EDMFT approximation to the non-random t-J model [23,[39][40][41]. To make contact with notations often used in the (E)DMFT literature, we note This path-integral representation can be formulated in the SU(1|2) representation (1.3) as: where S(τ ) is to be represented by (1.3). Here S B is the kinematic Berry phase (i.e. the Wess-Zumino-Witten term [35]) of the SU(1|2) superspin at each site [32], S tJ is the action containing the terms arising from H tJ , λ is the Lagrange multiplier imposing Eq. (1.4) and the chemical potential s 0 is determined to satisfy (1.2), which now becomes Note that Z is a U(1) gauge theory, and under the U(1) gauge transformation λ → λ − ∂ τ φ.
Let us also present the exactly equivalent formulation of the large N saddle point in terms of the SU(2|1) superspin. Now the Berry phase S B in (2.4) is replaced by while S tJ has the same form, apart from representing c α (τ ) and S(τ ) by (1.5), and replacing the The density constraint determining s 0 in (2.5) is replaced by Appendix C analyzes the path integral (2.4) using a large M expansion in an approach which generalizes the SU(2) spin symmetry to SU(M ); a similar large M method has been used previously for a Hubbard model [42][43][44] and other phases of a disordered t-J model [45].
The body of the paper will focus on an RG analysis of Z. This is performed by expressing it in terms of an auxiliary quantum impurity problem, which we will now set up.
A. Mapping to a quantum impurity problem In our RG analysis, we find it useful to consider the path integral as a functional of the fields R(τ ) and Q(τ ) with an arbitrary time dependence, and to defer imposition of the self-consistency conditions in (2.3). As we are looking for critical states, we assume that these fields have a power-law decay in time with where, for now, d and r are arbitrary numbers determining exponents, which will only be determined after imposing (2.3). Our analysis exploits the freedom to choose d and r: we will show that a systematic RG analysis of the path integral Z is possible to all orders in andr, where (2.9) The analysis assumes andr are of the same order, and expands order-by-order in homogeneous polynomials in andr. Such RG analyses were carried out in Refs. [46][47][48] for an insulating spin model in which t = 0, and by Fritz and Vojta [49][50][51] for a pseudogap Anderson impurity model in which J = 0 (see also Refs. [52,53]); we note that ther expansion of Refs. [49][50][51] was in good agreement with numerical studies [54]. We will combine these analyses here, and our notations here for andr follow these earlier works.
We proceed by decoupling the last two terms in S by introducing fermionic (ψ α ) and bosonic (φ a , a = x, y, z) fields respectively, and then the path integral Z reduces to a quantum impurity problem. The 'impurity' is a SU(1|2) superspin realizing the 3 states on each site of the t-J model, and this is coupled to a 'superbath' of the ψ α and φ a excitations. The quantum impurity problem is specified by the Hamiltonian For completeness let us also explicitly present the Hamiltonian using a SU(2|1) impurity of bosonic spinons and fermionic holons We note several features of H imp , which apply equally to (2.10) and (2.11): • The bosonic bath is realized by a free massless scalar field in d spatial dimensions, as in Refs. [46][47][48]. The field π a is canonically conjugate to the field φ a . The impurity spin S couples to the value of φ a at the spatial origin, φ a (0) ≡ φ a (x = 0, τ ). It is easy to verify that upon integrating out φ a from H imp , we obtain the J term in S tJ , with Q(τ ) obeying (2.8).
• The fermionic bath is realized by free fermions ψ kα with energy k and a 'pseudogap' density of states ∼ |k| r . The impurity electron operator c α is coupled to ψ α (0) ≡ |k| r dk ψ kα .
Integrating out ψ kα from H imp yields the t term in S tJ , with R(τ ) obeying (2.8).
• We have replaced the path integral over the Lagrange multiplier iλ in S B by a constant real λ. The constraint (1.4) can be conveniently and exactly imposed by the Abrikosov method of sending λ → ∞ [48][49][50][51], as we will see in Section III. So the consequences of S B will be accounted for exactly, and that is also the case for the alternative analysis in Appendix B, where S B is accounted for by the exact implementation of the superalgebras.
• The two formulations of H imp in (2.10) and (2.11) are equivalent, and lead to identical RG equations. This is because, ultimately, the quantum dynamics depends only upon the superspin algebra and the representation of the superspin on each site, and these are the same for SU(2|1) formulation by (1.3,1.4) and SU(1|2) formulation by (1.5,1.6). An explicit derivation of the one-loop RG equations using only the superspin algebra and representation appears in Appendix B.
• The model is now characterized by 3 couplings constants, s 0 , γ 0 , and g 0 , we will derive the RG equations for these couplings in Section III. The coupling of the superspin to the fermionic bath is g 0 , and to the bosonic bath is γ 0 : we will see that the RG flow of these couplings is marginal for small andr, and they are attracted to a deconfined critical point.
• The coupling s 0 acts like a 'Zeeman field' on the superspin, which breaks the degeneracy between the spinon and holon states. The flow of s 0 is strongly relevant at the deconfined critical point, and this drives the system into one of the two phases in Fig. 1.
We note that impurity models with both fermionic and bosonic baths have been considered earlier by Sengupta [55], and by Si and collaborators [56][57][58], but not for the 'superspin' case with significant particle-hole asymmetric charge fluctuations on the impurity site. Specifically, we fully project out doubly occupied states, while keeping holon states low energy, and these features are crucial to the structure of our critical theory, as in Refs. [49][50][51]. Also, the effect of a Zeeman field in an impurity spin model with a bosonic environment was studied in Refs. [59][60][61] in the context of the superfluid-insulator transition.
III. RG ANALYSIS
This section will present the RG analysis of the impurity model defined by (2.10). The RG analysis will initially not account for the self-consistency conditions (2.3). We will apply them later in Section III E.
We will employ the SU(1|2) superspin formulation, although essentially the same analysis can be applied to the SU(2|1) superspin, with exactly the same results. A key feature of the computation is that we impose the local constraint (1.4) exactly. This implemented by the Abrikosov method of taking the λ → ∞ limit, as described in earlier analyses [48][49][50][51].
An alternative approach to obtain the RG equations generalizes the method of Refs. [46,47] for SU(2) spins to superspins in either SU(2|1) or SU(1|2). This method utilizes only gaugeinvariant information contained in the superspin algebra and its representation, and is presented in Appendix B. The RG equations are identical to those obtained in this section.
At the tree-level, we can identify the scaling dimensions from (2.10): This establishes r = 1 and d = 3 as upper critical dimensions.
We define the following renormalized fields and couplings, The renormalization factors are to be obtained from self-energy and vertex corrections, as we will show below. We will work with s 0 = 0 and subsequently derive the flow away from it. Also, we work at zero temperature, i.e., β → ∞. coupling. Here we have two relevant diagrams and we quote the self-energy below: with γ E being the Euler's constant.
For the bosonic self energy there is only one diagram (Fig. 2(c)) at the one-loop level. The self energy is evaluated as follows: A factor of 2 is due to the spin index of internal f and ψ-line.
The expressions for Σ f 2(a) and Σ f 2(c) agree with those in Refs. [49][50][51], while that for Σ f 2(b) agrees with that in Ref. 48. Similarly, the self-energy diagrams at two-loop level are evaluated in a straightforward manner, as shown in Appendix D.
B. Vertex correction
There is no one-loop vertex correction to the fermionic bath coupling g 0 . However, it does acquire corrections at two-loop level and the corresponding diagrams are shown in Fig. 12. The bosonic bath coupling γ 0 has vertex corrections both at the one-loop and two-loop level. The one-loop diagram is shown in Fig. 2(d), while the two-loop diagrams are shown in Fig. 13. Here we explicitly evaluated the one-loop vertex correction to γ 0 , This expression agrees with that in Ref. 48. We can similarly evaluate the two-loop level corrections and these are quoted in the Appendix D.
C. Beta functions and fixed points
We now demand the cancellation of poles in the expression for the renormalized vertex and the f /b Green's functions at the external frequency, iν − λ = µ. This leads to the following expressions of the renormalization factors. Note that Z φ = 1 exactly, owing to the absence of bulk interaction terms such as φ 4 . For the rest we have, .
Using Eqns. (D3) and (D4), we obtain the beta functions as follows: We can find the fixed points to two-loop order by setting the beta functions to zero. This gives us four fixed points (g * 2 , γ * 2 ): The stability of the fixed points can be analyzed by looking at the eigenvalues of the stability matrix. We find that the Gaussian fixed point is always unstable. Importantly, we find that the non-trivial fixed point, F P 4 , with g * = 0 and γ * = 0 is stable for a range of values in the parameter space of andr. In Fig. 3 we plot the RG flow in the g − γ plane at one-loop level and show the different fixed points.
These fixed points corresponds to the underlying t-J model at a non-zero doping density p.
However, the precise value of p depends upon high energy details, and cannot be deduced from the fixed point couplings, as we discuss in Appendix D 2. With the beta function at hand, it is straight forward to calculate the anomalous dimension of the fermion and boson propagators, defined as follows: Note that these anomalous dimensions are gauge-dependent, and not physically observable. We have defined them in the gauge λ = constant. In terms of the coupling constants, At the fixed points, we obtain the following expressions for the anomalous dimension, (3.26)
E. Anomalous dimensions of the electron and spin operators
We now calculate the anomalous dimensions η c and η S of the physical and gauge-invariant composite operators, the electron c α and the spin S specified in (1.3), defined such that at large τ . We will show that it is possible to determine these anomalous dimensions to all orders in the andr expansions, as was also the case in previous analyses [46][47][48][49][50][51].
To compute these scaling dimensions, we add source terms to the action Within the field-theoretic RG scheme, we have are renormalized as follows: It turns out that the diagrams contributing to the vertex corrections to Λ S and Λ c are exactly those we encountered while evaluating Z γ and Z g respectively. Thus we have, It is these identities which enable use to compute the anomalous dimensions exactly. We evaluate the required anomalous dimensions as, We can now make an exact statement for η S for fixed points with γ = 0. From Eqns. (3.32) and (3.31) we obtain, Substituting the above equation in Eqn. (D4), we obtain, which leads to The first term on the r.h.s. of (3.35) arises from the tree-level scaling dimension, while the second term contains potential corrections higher order in . However, at the fixed point where γ = γ * = 0, we have β(γ * ) = 0 and therefore, η S = , to all orders in andr. (3.36) The same value of η S is also obtained in the large M expansion in (C44) and (C47).
Similarly, using Eqns. (3.32) and (3.31) in combination with Eqn. (D3) we obtain the following relation: Thus at the fixed point, β(g * ) = 0, such that g * = 0, we obtain η c = 2r , to all orders in andr. (3.38) The same value of η c is also obtained in the large M expansion in (C34) and (C36).
We can now state the main result of this subsection: at the non-trivial fixed point F P 4 (g * = 0, γ * = 0) we have η S = and η c = 2r to all orders in andr.
Finally, we can impose the self-consistency condition, Eq. c α ∼ f α , and the hopping term t ij in H tJ in (1.1) reduces to a renormalized hopping term for the f α spinons. Indeed, the resulting theory for the f α fermions is similar to that studied by Parcollet and Georges [24], and more recently in SYK-like extensions [62][63][64][65][66].
Note that this disordered Fermi liquid has a hole carrier density of 1+ p. This follows from c α ∼ f α , and the density of f α fermions obtained from (1.4) and (2.5).
As we approach the critical point, with p p c , we expect E c and b to both vanish algebraically.
However, we do not expect the large M theory of Ref. 24 to properly describe the approach to the critical point: in this large M theory, we obtain an insulating state as b vanishes, whereas our p = p c critical point is metallic. Indeed, as b is gauge-charged field, the value of b is not a gauge-invariant quantity which can be directly compared between different approaches. However, the crossover scale E c is better defined, and we can deduce the behavior of E c near p = p c by the RG analysis of Section III. We expect where λ s is the relevant RG eigenvalue with which s flows away from the F P 4 fixed point, specified in (D13).
B. Pseudogap region
For p < p c , we use the SU(2|1) theory, and condense the b α spinons to obtain spin glass order, as described in Refs. [27,28]. The presence of the mobile f fermions will make this a metallic spin glass with carrier density p, as determined by (2.7).
We need to extend the insulating spin glass theory of Refs. [27,28] to the metallic spin glass, and this will be studied in greater detail in future work. Here we note that a systematic description Right at p = p c , the critical theory is expected [28] to have a non-vanishing extensive entropy S 0 as T → 0. This follows from the similarity of the random insulating magnet [18], and many other models in the SYK class.
Away from p = p c , we expect that the entropy follows the behavior of the critical point at temperatures above the coherence scale E c , before vanishing lineary with T at temperatures below E c , as shown in Fig. 4. We can therefore estimate that the linear-in-T coefficient of the specific So we expect γ to diverge as |p − p c | −1/λs in the infinite range model H tJ . It is notable that this behavior resembles experimental observations [8]. ; a linear-in-T resistivity was also found in numerical studies [40,41] of lattice models without disorder described by equations closely related to those of the large M limit of Appendix C. And we note that there is a recent report of spin glass correlations in the pseudogap phase [13], extending earlier impurity-induced observations [11,12].
l a t e x i t s h a 1 _ b a s e 6 4 = " S 2 E m c x 5 f H z H c + C E L O u G A t 2 X Z T b A = " > A A A C G H i c d Z C x T h t B E I b 3 C A Q w S T B J S b P C i k S R n P b w w b m K U G h S E i k G J N u y 9 v b G 9 o q 9 v d X u H G A d f o w 0 F P A q d C g t H W + S M m v j S A G R X x r p 1 / w z m t G X G i U d M v Y Q L L x a X H q 9 v L J a W 3 v z 9 t 1 6 f e P 9 k S t K K 6 A t C l X Y k 5 Q 7 U F J D G y U q O D E W e J 4 q O E 5 P D 6 b 5 8 R l Y J w v 9 A 8 c G e j k f a j m Q g q N v d S 7 N Z 9 M X l / Q L Z f 1 6 g 4 V J 0 t z Z a 1 I W R i x J W s w b F s f x b o t G I Z u p Q e Y 6 7 N d / d 7 N C l D l o F I o 7 1 4 m Y w V 7 F L U q h Y F L r l g 4 M F 6 d 8 C B 1 v N c / B f c r O p H E z 2 6 t m 3 0 / o R x 9 m d F B Y X x r p r P v v c s V z 5 8 Z 5 6 i d z j i P 3 P J s 2 X 8 o 6 J Q 5 a v U p q U y J o 8 X h o U C q K B Z 2 i o J m 0 I F C N v e H C S v 8 2 F S N u u U A P r N Z 1 4 G n q I Y 6 q L s I F n s v M 3 6 l i q S c e 1 V 8 e 9 P / m a C e M m u H u 9 7 i x / 3 U O b Y V s k i 2 y T S K S k H 3 y j R y S N h G k I D / J N b k J r o L b 4 C 7 4 9 T i 6 E M x 3 P p A n C u 7 / A L F n o W
It is useful to compare the structure of H tJ in (1.1) with that of SYK lattice models [62][63][64][65].
The SYK models have a random 4-fermion interaction term, and a random 2-fermion hopping term of strength t, but no on-site fermion constraint. At the lowest energies, the 2-fermion term is always relevant and drives the system away from SYK criticality to a Fermi liquid state. In Consequently, we find p c = 1/3 at zeroth order (see Fig. 1). Finally, we comment on the extent to which a model with all-to-all randomness can be mapped to the cuprates. Randomness is present in the experimental systems, and also serves important simplifying purposes in our theoretical analysis. Moreover, certain approximations to models without randomness lead to closely related saddle point equations [23,[39][40][41]. Several of the broken symmetries do not exist in the random model, and subtle questions [74] about the structure of the Fermi surface in momentum space can be avoided. However, the central issues of carrier density, fractionalization, emergent gauge charges, and associated quantum phase transitions remain well defined even in the presence of randomness. Given the recent spin glass observations [13], and as we noted in Section IV B, it will be useful to study the metallic pseudogap state, and the interplay between the spin glass order and charge transport. A possible approach is the extend the large M theory of Refs. [27,28] to include fermionic holons, as well as numerical studies for M = 2.
Appendix C presents
The constraint (1.4) commutes with all operators of the superalgebra. Imposing this constraint yields the fundamental representation of SU(1|2).
Alternatively, we can use the operators in (1.5). These realize the SU(2|1) algebra, and it can be verified that these operators also obeys the superalgebra in Eq. (A1). The constraint projecting to the fundamental representation is now (1.6).
Larger symmetries
We consider the model for general M and M , with the electron operator operator where the matrices T a obey The operators c α , S a , the operator A general constraint fixing the representation is with P a positive integer, and our interest in the case P = M/2 which realizes the representation in which the SU(M ) subalgebra is self-conjugate. Note that the fundamental representation of the superalgebra is P = 1, but this does not lead to a convenient large M limit.
We can also consider a bosonic spinon and fermionic holon decomposition for general M , M The analogous steps will lead to a realization of the SU(M |M ) superalgebra, which is identical to the SU(M |M ) superalgebra. However, the constraint now leads to a different representation of the superalgebra from (A6) for P = 1 [75].
Operator expectation values
We will compute the only RG equations for the SU(M |M ) theory in Appendix B following the method in Appendix C of Ref. 47. After using the identity (A4), the computations in Appendix B can be reduced to the following operator traces.
First, let us compute the dimension, D(M, M , P ), of the superspin Hilbert space. To compute this, it is useful to compute the grand-canonical partition function, while ignoring the constraint (A6).
where z is the common fugacity. Then we can impose the constraint (A6), and the dimension of The values for M = 2, P = 1, and M = 1 case of interest to us are simple: This simplicity is the reason Section III was able to compute the RG equations using Feynman diagrams and the Abrikosov method. This appendix generalizes the method of Refs. [46,47] for SU (2) spins to superspins in SU(M |M ). This method utilizes only gauge-invariant information contained in the superspin algebra and its representation; thus the Berry phase S B (see (2.4) and (2.6)) of the supergroup [32] is exactly accounted for by the commutation and anti-commutation relations. The RG equations obtained here reduce to those of Section III at M = 2, P = 1, M = 1.
Some other random values
We consider here the Hamiltonian where c α and S a are defined in (A2) The setup of the renormalization factors in the present perturbation theory is somewhat different from (3.2). We now write, using the operators defined in (A2) and (A3), The renormalization constants Z S and Z c are the same as those defined in (3.30), but we will now compute them in a different manner. The notation of our renormalization constants also differs from that in Ref. 48, and we provide a translation in Table I interactions of the bosonic bath field φ, and hence we have Z φ = 1, as we noted in Section III C.
For the same reasons, it was argued in Refs. [46][47][48] that (in our notation) Z γ = 1 in the absence of bulk interactions. The reasoning extends also to the fermionic bath, and so we have Z γ = 1 and Z g = 1 exactly. These identities can also be understood from the statement below (3.30) that the vertex corrections Λ S,c arise from the same diagrams as Z γ,g . We will now compute Z S and Z c by renormalizing the two-point correlators of S a and c α , and this will sufficient to obtain the needed beta functions. Also, Note we evaluate the above integrals at T = 0, by extending the integrals appropriately as explained in Ref. 47. Here, Similarly, From (B3) and (B4) we obtain, It is then straightforward to write, where , Note that for M = 2 , M = 1, we obtain L γ = L g = 2 which agrees with the result that can be obtained from (3.31) and the results in Section III.
Electron correlator
Next we evaluate the electron correlation, O 2 ≡ c(τ )c † (0) = N 2 /D. The diagrams contributing to the numerator are shown in Fig. 7, while those contributing to the denominator have been already evaluated in (B3). Thus we obtain, where, Thus we have, Similarly, it is the straightforward to write, where Note that for M = 2 , M = 1, we obtain P g = 3 and P γ = 3/4 which agrees with the result that can be obtained from (3.31) and the results in Section III.
RG flow
We are now in a position to write the beta functions for the coupling constants. Using (B2) we find two equations, We have used the exact relations Z g = Z γ = 1 in obtaining these equations. Solving these two equations and using the expressions for the renormalization factors found above we obtain the following one-loop beta functions, Recall that at M = 2, M = 1, we have P g = 3, P γ = 3/4, L g = 2, and L γ = 2. With this the above expressions match the one-loop beta functions derived earlier in Sec. III.
We can also calculate the anomalous dimension for the spin and electron operators, defined in (3.32). From (B47) and (B48) we obtain exactly the same equations derived before, i.e., (3.35) and (3.37). Thus at the non-trivial fixed point where γ * = 0, g * = 0 we obtain η S = and η c = 2r to all orders in andr.
Appendix C: Large M limit
In this appendix we consider the large M limit examined originally in the insulating spin model in Ref. 18. To extend the large M limit to the t-J model, we also need to endow the electron with an additional orbital index = 1 . . . M as in (A2), and take the large M limit at fixed using SU(M |M ) superspin formulation of Appendix A 1 while imposing the constraint (A6) at P = M/2. Similar large M limits were taken in particle-hole symmetric models in Refs. [42][43][44][45] and for a non-random t-J model in Refs. [40,41].
A sketch of our proposed large M phase diagram is shown in Fig. 8. This applies to the theory < l a t e x i t s h a 1 _ b a s e 6 4 = " v W 6 V 0 A e t X n V 6 s n p s g v n E K e U V M i g = " > A A A C J n i c d V D L S g M x F M 3 4 r O O r 6 t J N s A g u Z J j p w 8 e u 6 M a N U M G q 0 C k l k 7 l T g 5 n M k G Q K Z e h / + A l + h V t d u R N x I f g p p m M F F T 0 Q O J x z b p J 7 g p Q z p V 3 3 1 Z q a n p m d m y 8 t 2 I t L y y u r 5 b X 1 C 5 V k k k K b J j y R V w F R w J m A t m a a w 1 U q g c Q B h 8 v g 5 n j s X w 5 A K p a I c z 1 M o R u T v m A R o 0 Q b q V e u + g H 0 m c g p C A 1 y Z J + C J p w z 6 v t Y p U z g P i d K O b Y P I v z K 9 M o V 1 3 E b 9 V q 9 j g 2 p 7 d U O X U M a j a q 3 5 2 H P c Q t U 0 A S t X v n N D x O a x W a c j q / r e G 6 q u z m R m l E O I 9 v P F K S E 3 p A + d A w V J A a 1 G w 5 Y q g r a z Y s 1 R 3 j b m C G O E m m O 0 L h Q v w / n J F Z q G A c m G R N 9 r X 5 7 Y / E v r 5 P p 6 K C b M 5 F m G g T 9 f C j K O N Y J H n e G Q y a B a j 4 0 h F D J z L c x v S a S U F O H s k 0 f X 0 v j / 8 l F 1 f H q T u O s W m k e T Z o p o U 2 0 h X a Q h / Z R E 5 2 g F m o j i m 7 R P X p A j 9 a d 9 W Q 9 W y + f 0 S l r M r O B f s B 6 / w C T y q a p < / l a t e x i t > hb ↵ i 6 = 0 < l a t e x i t s h a 1 _ b a s e 6 4 = " V / N L 9 Z E E 2 0 c o L 3 8 n N X 5 T J 7 X 7 K C U = " > A A A C K X i c d V D B T t t A F F x T K D S F E t o j l x V R p R 5 Q Z B M 7 w C 2 i l x 5 T q Q G k O I q e N 8 / J K u u 1 2 X 1 G i q x 8 C Z / Q r + g V T t z a S p z 6 I 9 2 E V I K q H W m l 0 c x 7 e j u T F E p a 8 v 0 f 3 t q L 9 Y 2 X m 1 u v a q + 3 d 9 7 s 1 v f e n t u 8 N A J 7 I l e 5 u U z A o p I a e y R J 4 W V h E L J E 4 U U y / b j w L 6 7 R W J n r L z Q r c J D B W M t U C i A n D e t R r D C l W I E e K + R x B j R J D U y r Z D 6 M Q R U T 4 L G R 4 w n F Z j W h 8 Y r 7 w 3 r D b / p R 2 A p D 7 k i r 3 T r 1 H Y m i o 6 A d 8 K D p L 9 F g K 3 S H 9 Y d 4 l I s y Q 0 1 C g b X 9 w
r M = " > A A A B + n i c d V D L T g I x F O 3 g C / E F u n T T C E b c T D q 8 l 0 Q 3 b k g w O k A C h H R K g Y b O I 2 1 H Q w Y + x Y 0 L j X H r l 7 j z b y w P E z V 6 k p u c n H N v 7 r 3 H C T i T C q E P I 7 a 2 v r G 5 F d 9 O 7 O z u 7 R 8 k U 4 c N 6 Y e C U J v 4 3 B c t B 0 v K m U d t x R S n r U B Q 7 D q c N p 3 x 5 d x v 3 l E h m e / d q k l
A u y 4 e e m z A C F Z a 6 i V T N 3 Y 2 U z u b 1 j L n U I 2 o L y a 9 Z B q Z V q W U L 5 Q g M o s W Q u W 8 J r k c q l h l a J l o g T R Y o d 5 L v n f 6 P g l d 6 i n C s Z R t C w W q G 2 G h G O F 0 l u i E k g a Y j P G Q t j X 1 s E t l N 1 q c P o O n W u n D g S 9 0 e Q o u 1 O 8 T E X a l n L i O 7 n S x G s n f 3 l z 8 y 2 u H a l D p R s w L Q k U 9 s l w 0 C D l U P p z n A P t M U K L 4 R B N M B N O 3 Q j L C A h O l 0 0 r o E L 4 + h f + T R s 6 0 C m b x O p e u X q z i i I N j c A K y w A J l U A V X o A 5 s Q M A 9 e A B P 4 N m Y G o / G i / G 6 b I 0 Z q 5 k j 8 A P G 2 y d l t J L U < / l a t e x i t > SU(M |M 0 ) theory < l a t e x i t s h a 1 _ b a s e 6 4 = " 4 g 6 n r o G H R Q 5 G g V u 3 W G 0 p + 7 b H d J A = " > A A A B + n i c d V D L T g I x F O 3 g C / E F u n T T C E b c T D q 8 l 0 Q 3 b k g w O k A C h H R K g Y b O I 2 1 H Q w Y + x Y 0 L j X H r l 7 j z b y w P E z V 6 k p u c n H N v 7 r 3 H C T i T C q E P I 7 a 2 v r G 5 F d 9 O 7 O z u 7 R 8 k U 4 c N 6 Y e C U J v 4 3 B c t B 0 v K m U d t x R S n r U B Q 7 D q c N p 3 x 5 d x v 3 l E h m e / d q k l A u y 4 e e m z A C F Z a 6 i V T N 3 Y 2 U 5 v W z j L n U I 2 o L y a 9 Z B q Z V q W U L 5 Q g M o s W Q u W 8 J r k c q l h l a J l o g T R Y o d 5 L v n f 6 P g l d 6 i n C s Z R t C w W q G 2 G h G O F 0 l u i E k g a Y j P G Q t j X 1 s E t l N 1 q c P o O n W u n D g S 9 0 e Q o u 1 O 8 T E X a l n L i O 7 n S x G s n f 3 l z 8 y 2 u H a l D p R s w L Q k U 9 s l w 0 C D l U P p z n A P t for details of a similar computation with particle-hole symmetry. In our case, both the boson and fermion Green's functions will have to be particle-hole asymmetric, as in Refs. [18,27,28,76].
We will perform an analytic low energy analysis of the large M equations, and find a critical solution which is in close correspondence with the RG fixed point F P 4 in Section III. However, the large M solution appears to be present for a range of dopings, and not at a critical doping as in the RG analysis. We expect that either constraints from the higher energy structure of the large M theory, or corrections higher order in 1/M , will convert the critical phase to a critical point.
It appears that the numerical studies of Haule et al. [40,41] examined the finite temperature behavior about the critical phase described here as their model of the pseudogap. This contrasts with our model of the pseudogap in Fig. 1 as a metallic spin glass flanking a critical point. We also note that the present large M limit, with fermionic spinons, cannot obtain a metallic spin glass; instead we have to use the bosonic spinon approach outlined in Section IV B.
Green's functions
We follow the condensed matter notation for Green's functions in which We drop indices α, , and all Green's functions are diagonal in these indices. It is useful to make ansatzes for the retarded Green's functions in the complex frequency plane, because then the constraints from the positivity of the spectral weight are clear. At the Matsubara frequencies, the Green's function is defined by So the bare Green's functions are The Green's functions are continued to all complex frequencies z via the spectral representation For fermions, the spectral density obeys for all real Ω and T , and for bosons the constraint is The retarded Green's function is G R (ω) = G(ω + iη) with η a positive infinitesimal, while the advanced Green's function is G A (ω) = G(ω − iη). It is also useful to tabulate the inverse Fourier transforms at T = 0 where C +R > 0 and C −R > 0, but they need not be equal. This corresponds to the ansatz in (2.8) which has C +R = C −R . We can allow these amplitudes to be distinct in the large M limit. We also examined generalization of the RG analysis in Section III to the case C +R = C −R ; we found that the perturbative RG then gave inconsistent renormalizations of the coupling g, and so C +R = C −R in the context of the andr expansion.
For the boson Green's function, we write at a complex frequency |z| J expressed in terms of the three real parameters, C b , ∆ b and θ b . The constraint (C9) becomes Using (C10) we obtain in τ space for |τ | 1/J Finally, we can write expressions similar to (C18) for Q(τ ) for |τ | 1/J (C22) where C Q > 0. This corresponds to the ansatz for Q(τ ) in (2.8) We can relate the parameters in the ansatzes for the bosonic bath Q(τ ) and the fermionic bath R(τ ) to the parameters in the ansatzes for the Green's functions G f and G b , by using the selfconsistency conditions (C13). This yields expressions forr, , C ±R , and C Q in terms of ∆ f,b , θ f,b , and C f,b :r However, in keeping with RG computation, we will defer application of the relations in (C23).
Now we see that the large M result (C34) is precisely the result (3.38) for the electron anomalous dimension obtained to all orders in the andr expansions.
Comparing the amplitudes of (C31), (C32) and (C33) we obtain The comparison of this with (C29), (C30) leads to two possible solutions, appearing as the two intermediate critical phases in Fig. 8.
The second J 2 terms in (C29) and (C30) are much smaller than the t 2 terms when ∆ f − /2 > ∆ b −r; using (C34), we obtain the condition ∆ f > /4. So the J 2 terms can be neglected. Indeed, the low energy solution is then entirely independent of the strength of the exchange interaction, which is rather different from the structure of the F P 4 fixed point in Section III with both g * and γ * non-zero. Instead, it is the F P 3 fixed point, with γ * = 0, which matches the structure of the present large M solution, and this fixed point was found to be unstable in the RG analysis for M = 2, M = 1. We will therefore only write down the saddle point equations here, and not consider this case further.
We can also apply the self-consistency relations in (C23) to the exponents, and obtainr = 1/2 Now the t 2 and J 2 terms in (C29) and (C30) are equally important, and we will see that the structure of this large M solution is very similar to that of the critical point found in the RG analysis in Section III.
In the large M limit, the spin correlator is given by and so the anomalous dimension of the spin operator is We now see that the spin anomalous dimension implied by the large M equations (C44) and (C47) is consistent with the result (3.36) obtained to all orders in the andr expansion.
Note that the values of ∆ f and ∆ b above, combined with (C36) and (C47) yield the self-consistent values in (3.39). For the last two equations in (C50), notice the bounds |θ f | < π/4 and π/4 < θ b < π/2 below (C24); so all the co-efficients on the left hand sides of (C50) are positive, and the last two equations determine the values of C f and C b . The values of θ f and θ b are then determined by the particle density p from (C24). So this low energy solution can exist at a variable particle density, and the present low energy M = ∞ theory describes a potential critical phase, rather than a critical point.
Finally, let us note the form of the electron Green's function from (C35) , for τ > 0 and T = 0 The exponent and signs of (C51) agree with the self-consistent electron Green's function obtained in (3.40) (recall (C16) and (C20)), but it appears that the magnitudes of the amplitudes in (C51) can be different between τ > 0 and τ < 0. This is a subtle feature of the large M theory which is not reproduced by the andr expansion in the body of the paper. This is related to the discussion below (C18).
Also note that the 1/τ decay of (C51) is similar to that of a Fermi liquid. Nevertheless, this Similarly, we have
Flow away from criticality
For the flow equation of s at one-loop, we will follow the momentum-shell RG procedure, where the cut-off D is kept explicitly. In this case, we introduce masses for bosons and fermions, but keeping in mind that only their difference is physically relevant. To this end we consider the Fourier-transformed action, where the self energies are evaluated as follows: with Σ F = Σ a + Σ b and Σ B = Σ c . The scaling factor is l = 1 + δD/D such that under the scaling k = lk and iω = liω. Thus we have, Thus we have the following expressions for the renormalized masses: Note that along with this the fermionic and bosonic operators, bosonic field and the coupling constants are also renormalized. For instance, f = l −1+g 2 /2+3γ 2 /8 f and b = l −1+g 2 b. In addition to the self-energy corrections there is also a vertex correction to γ at this order. However, this does not influence the mass renormalization and thus we can already proceed to calculate the flow equation for the mass. In our notation introduced earlier, We can compute the relevant eigenvalue associated with the flow of s at the fixed points of the beta functions, and find at the non-trivial fixed point F P 4 . At the self-consistent values, i.e., = 1 andr = 1/2 we have λ s = 1/6, although we cannot trust the result at such large values of andr. Similarly, at F P 1 , F P 2 , and F P 3 we find λ s to be 1, 1, and 1 − 2r respectively.
Within the momentum-shell RG, we get the same beta functions for g and γ, after considering the vertex correction as well. We can also calculate the particle densities (n f /b ). This can be done at any s. We will first make the following identification: s =s/β, such thats is small. This will facilitate us to do a q(λ) = q f (λ) + q b (λ), such that,
Particle density
So, we have, Note that we still satisfy the particle density constraint n f + n b = 1 exactly. It is also interesting to note that n b = 1/3 at zeroth order, which corresponds to p c = 1/3.
Two-loop self energy
We first evaluate the fermionic self energies to two-loop order. The relevant Feynman diagrams are shown in Fig. 10.
Σ f 10(c) (iν) = −3 16 We will now evaluate the bosonic self energies to two-loop order. The relevant Feynman diagrams are shown in Fig. 11.
Two-loop vertex corrections
Let us first evaluate the vertex correction to the fermionic bath coupling g 0 at two-loop level.
The relevant Feynman diagrams are shown in Fig. 12,.
We will now evaluate the two-loop vertex correction to the bosonic bath coupling γ 0 . The corresponding diagrams are shown in Fig. 13.
We get C sf = 0. Using the results in this appendix we obtain the renormalization factors and beta | 15,593.2 | 2019-12-18T00:00:00.000 | [
"Physics"
] |
A peek into cancer-associated fibroblasts: origins, functions and translational impact
ABSTRACT In malignant tumors, cancer cells adapt to grow within their host tissue. As a cancer progresses, an accompanying host stromal response evolves within and around the nascent tumor. Among the host stromal constituents associated with the tumor are cancer-associated fibroblasts, a highly abundant and heterogeneous population of cells of mesenchymal lineage. Although it is known that fibroblasts are present from the tumor's inception to the end-stage metastatic spread, their precise functional role in cancer is not fully understood. It has been suggested that cancer-associated fibroblasts play a key role in modulating the behavior of cancer cells, in part by promoting tumor growth, but evolving data also argue for their antitumor actions. Taken together, this suggests a putative bimodal function for cancer-associated fibroblasts in oncogenesis. As illustrated in this Review and its accompanying poster, cancer-associated fibroblasts are a dynamic component of the tumor microenvironment that orchestrates the interplay between the cancer cells and the host stromal response. Understanding the complexity of the relationship between cancer cells and cancer-associated fibroblasts could offer insights into the regulation of tumor progression and control of cancer. Summary: Cancer-associated fibroblasts constitute a functionally heterogeneous mesenchymal cell population in the tumor microenvironment. This ‘At a glance’ article reviews their origin and their pro- and antitumor properties.
The origin and functions of CAFs are likely as diverse as the markers used for their identification (see poster), yielding a complex picture of their composition, dynamic lineage evolution, and functional roles at various stages of cancer progression (Augsten, 2014;Cortez et al., 2014;Kalluri, 2016;Madar et al., 2013;Öhlund et al., 2014). Here, we summarize the complex features of CAFs to inform on their origin, activation, accumulation, heterogeneity and function. Much like the complexity of the tumor immune response, CAFs also exhibit complex tumor-associated phenotypes, suggestive of their distinct functions (Augsten, 2014;Cirri and Chiarugi, 2012;Ishii et al., 2015;Kalluri, 2016;Luo et al., 2015;Madar et al., 2013;Öhlund et al., 2014). We also discuss the distinct functions of CAFs in promoting and restraining cancer. We summarize their roles in cancer progression, which are wideranging and include the production of ECM components and remodeling enzymes, as well as the secretion of metabolites, cytokines, and growth factors that signal to cancer cells (see poster) and influence tumor angiogenesis and immune infiltration. We also discuss their less-known cancer-restraining functions, which are predominantly associated with the regulation of early antitumor response and tumor metabolism. CAFs also express a number of signaling receptors that are engaged in maintaining or changing the CAF phenotypes during cancer progression. These receptors might also be involved in integrating signals from various cell types within the TME, thus further influencing the functioning of CAFs, and are discussed both in this article and in the accompanying poster.
Origins and characteristics of CAFs
A significant proportion of CAFs likely emerge from a mesodermderived precursor cell, although the precise origin of all CAFs in a given tumor bed is still not fully understood and is likely mixed (Madar et al., 2013) (see poster). Gaining further insights about the origin of CAFs could offer novel understanding of their plasticity, identifying markers, signaling cues that lead to their activation, and means to target their pro-tumorigenic and/or enhance their antitumorigenic functions. When a cancer arises in the adult organ, the dominant niche likely includes the expansion of quiescent fibroblasts residing in the host tissue in response to the injury caused by the developing neoplasm (reviewed in Kalluri, 2016). Additionally, CAFs can be recruited to the tumor from a distant source, such as the bone marrow (reviewed in Kalluri, 2016;Shiga et al., 2015). The trans-differentiation of pericytes (Box 1), endothelial and epithelial cells can also give rise to a CAF-like hybrid cell population when the latter two undergo the endothelialto-mesenchymal transition (EndMT; Box 1) (Potenta et al., 2008) and the epithelial-to-mesenchymal transition (EMT; Box 1) (Kalluri and Weinberg, 2009) programs, respectively. The notion that CAFs can, similarly to cancer cells, disseminate into the circulation and to distant metastatic sites, suggests that CAFs have additional complex roles in metastasis (Cirri and Chiarugi, 2012;De Wever et al., 2014).
Despite the technical advances in genetic lineage tracing (also known as fate mapping) and in fluorescent tagging to elucidate the origin(s) of CAFs in tumor-bearing mice (LeBleu et al., 2013;O'Connell et al., 2011;Ozdemir et al., 2014), the inherent difficulty in clearly identifying their biological origin is due to the lack of specific markers for fibroblasts. In microscopic analyses of tissue sections, CAFs can be identified based on their spindle shape and elongated cytoplasmic processes (Hematti, 2012;Ishii et al., 2015;Kalluri, 2016). Notably, they were experimentally found to be easy to adapt to tissue culture conditions, and expand in vitro as spindleshaped cells (see poster). They can be distinguished from other cell types within the tumor by exclusion criteria defined by Box 1. Glossary Desmoplastic reaction: Secondary to an initial tissue injury, it is the collective response of stromal cells, including activated fibroblasts and recruited immune cells, in generating scar tissue. Endothelial-to-mesenchymal transition (EndMT): a cellular program wherein endothelial cells lose some of their features and gain mesenchymal-like characteristics (reviewed in Potenta et al., 2008;Yu et al., 2014a). Epithelial-to-mesenchymal transition (EMT): a cellular program wherein epithelial cells lose some of their features and gain mesenchymal-like characteristics (reviewed in Kalluri, 2009;Kalluri and Weinberg, 2009). Extracellular matrix (ECM): the secreted fibrous proteins and proteoglycan assembling into a supportive network that enables tissue organization, cellular adhesion, proliferation and migration (ECM in cancer reviewed in Lu et al., 2012). Mesenchymal stromal cells (MSCs): historically defining a population of bone marrow-derived cells that present as adherent, fibroblast-like cells following their isolation. This population of cells may include cells with multipotent properties, also referred to as mesenchymal stem cells. Mesoderm: the middle germ layer in the developing embryo that emerges during gastrulation and is in between the other two germ layers, namely, the ectoderm and endoderm. Metronomic chemotherapy: a low-dose, continuous chemotherapeutic regimen aimed to target tumor angiogenesis together with cancer cells. Paracrine signaling: a form of communication between cells where signaling factors (such as growth factors) are secreted by a cell to elicit a change in the nearby recipient cell that responded to the signaling factor. Pericytes: or perivascular cells, the cells lining the abluminal (outer) surface of microvessels (reviewed in Armulik et al., 2011). Tumor microenvironment (TME): noncancer cells and ECM found in a tumor, which includes CAFs, blood vessels and immune cells (reviewed in Balkwill et al., 2012;Chen et al., 2015;Quail and Joyce, 2013). their morphological features and a lack of expression of nonmesenchymal markers, such as those expressed by endothelial, epithelial, immune and neuronal cells; and based on inclusion criteria defined by the expression of a slew of posited mesenchymal markers, although none of these has absolute specificity (Gascard and Tlsty, 2016;Kalluri, 2016;Rasanen and Vaheri, 2010;Shiga et al., 2015).
So far, researchers have identified an exhaustive list of candidate markers for CAFs (Ishii et al., 2015;Kalluri, 2016), noting that their relative expression and abundance, and distinct overlapping expression patterns in different tissue types (Liao et al., 2018;Roswall and Pietras, 2012;Sugimoto et al., 2006) all contribute to the challenge in determining the biological origin of CAFs in growing tumors. Some of the most commonly utilized markers, possibly due to their overlapping expression amongst a large population of CAFs, are discussed below and listed in the second panel and the centered schematic of the poster. Although this is still an ongoing area of investigation in many laboratories, distinct tumor types can present with different abundance and overlap in a given set of CAF markers. The abundance of a given CAF marker in a tumor type might represent features of activation of the dominant type of resident fibroblasts in the impacted tissue. For example, αSMA + CAFs (see Abbreviations, Box 2) are dominantly found in pancreatic carcinoma and might reflect the activation of resident stellate cells (Ferdek and Jakubowska, 2017;Ozdemir et al., 2014), whereas PDGFRα + CAFs (Box 2) in melanoma might reflect the activation and expansion of resident dermal fibroblasts that express this marker (Anderberg et al., 2009;Lynch and Watt, 2018). Comparative analyses of Rip1Tag2 pancreatic carcinoma and 4T1 orthotopic breast carcinoma in mice showed distinct overlap of CAF markers, with as many as 43.5% of FSP1/S100A4 + (Box 2) fibroblasts showing co-expression of αSMA in pancreatic carcinoma, whereas only 10.9% of FSP1/S100A4 + fibroblasts showed co-expression of αSMA in breast carcinoma (Sugimoto et al., 2006).
To define and identify the origin of fibroblasts, it is crucial to consider that CAFs are 'activated fibroblasts', which, in contrast to nonactivated (quiescent) tissue-resident fibroblasts, are an expanding population of cells that either proliferates in situ or is recruited to the tumor (Kalluri, 2016;Ozdemir et al., 2014;Rasanen and Vaheri, 2010). The key features of CAFs, distinguishing them from quiescent fibroblasts, include metabolic adaptations to support their need for enhanced proliferation and biosynthetic activities, such as production of ECM components and enzymes to remodel the ECM, growth factors and cytokines (Alexander and Cukierman, 2016;Erez et al., 2010;Han et al., 2015;Harper and Sainson, 2014;Kalluri, 2016;Marsh et al., 2013;Öhlund et al., 2014;Rasanen and Vaheri, 2010;Raz and Erez, 2013;Wu et al., 2017). Although the distinct functions of CAFs could inform on their origins, these functions might dynamically shift during cancer progression, likely reflecting the flexibility of CAFs in adapting to a changing (tumor) microenvironment.
Activation and heterogeneity of CAFs
As their appellation infers, CAFs are defined by their association with cancer cells within a tumor. In carcinomas, their biology is generally studied in relation to the biology of genetically aberrant neoplastic epithelial (cancer) cells. It is therefore critical to appreciate that CAFs emerge as part of the host's response to epithelial injury caused by the growing tumor (Ishii et al., 2015;Kalluri, 2016). The initial recruitment of CAFs to the nascent neoplastic lesions might thus reflect their role in the early antitumor response (Kalluri, 2016;Marsh et al., 2013). In wounds, activated fibroblasts accumulate and facilitate many aspects of the tissue remodeling cascade to initiate the repair process and to control and prevent further tissue damage (Bainbridge, 2013;Kalluri, 2016;Öhlund et al., 2014). Activated fibroblasts also induce an intrinsic program, likely influenced by other cells, to limit an excessive scarring response, which would otherwise further injure the tissue (Duffield et al., 2013;Kalluri, 2009;Klingberg et al., 2013;Zeisberg and Kalluri, 2013). An example of the detrimental action(s) of fibroblasts in response to epithelial damage is organ fibrosis, a condition associated with unabated fibroblast activation that results in chronic inflammation and impaired functional regeneration of the impacted tissue (Duffield et al., 2013;Zeisberg and Kalluri, 2013). The mechanisms underlying this unabated activation of fibroblasts remain largely unknown, although epigenetic reprogramming might, at least in part, contribute to this sustained activated state (Albrengues et al., 2015;Bechtel et al., 2010;. For example, hypermethylation of the RASAL1 promoter leads to its transcriptional suppression, increased Ras-GTP activity and perpetuated activation of fibroblasts, which is promoted in renal fibrosis (Bechtel et al., 2010). Interestingly, a global hypomethylation of the genomes of CAFs was also reported (Jiang et al., 2008), possibly driving the upregulation of genes associated with the CAF secretome. Moreover, biological aging or senescence of fibroblasts are associated with the secretion of various pro-tumorigenic factors that can contribute to CAF activation in oncogenesis. The concomitant downregulation of the NOTCH protein effector CSL (Box 2) and p53 overcomes the senescence failsafe mechanism and enables CAF activation and proliferation (Procopio et al., 2015).
Box 2. Abbreviations
αSMA alpha smooth muscle actin BMPRI/II bone morphogenetic protein receptor type I/II CAV1 caveolin-1 CSL CBF1/Su(H)/Lag-1 transcription factor complex CTGF connective tissue growth factor DDR2 discoidin domain-containing receptor 2 EGFR epidermal growth factor receptor FAP fibroblast activation protein FGF2 fibroblast growth factor 2 FGFR fibroblast growth factor receptor FSP1/S100A4 fibroblast-specific protein 1, also known as S100A4 IL-10 interleukin 10 IL-6 interleukin 6 INFγ interferon gamma LIF leukemia inhibitory factor LOX lysyl oxidase LOXL1 lysyl oxidase-like 1 MMPs matrix metalloproteinases PDGF platelet-derived growth factor PDGFRα/β platelet-derived growth factor receptor alpha/beta PGE2 prostaglandin E2 SDF-1 (CXCL12) stromal cell-derived factor 1 SHH sonic hedgehog TGFβ transforming growth factor beta TGFβRI/II transforming growth factor beta receptor I/II TIMPs tissue inhibitors of metalloproteinases TNFα tumor necrosis factor alpha VCAM1 vascular cell adhesion protein 1 VEGF vascular endothelial growth factor WNTs wingless-related integration site, protein ligands in the WNT signaling pathways CTLA-4 (CD152) cytotoxic T-lymphocyte protein 4 PD-L1 programmed death-ligand 1 We speculate that fibroblasts become activated during the initial stages of oncogenesis, giving rise to CAFs, which then remodel the tumor microenvironment to elicit tissue repair, thereby possibly exerting antitumor functions. However, as the tumor grows, this repair process might, in turn, promote tumor growth, as cancer cells utilize the CAF-secreted growth factors to facilitate their own survival and proliferation. A precise tipping point between the functions of CAFs in tissue repair and in promoting tumors might not exist. Rather, the pro-tumorigenic activity of CAFs may evolve gradually (see poster). It is, however, conceivable that the kinetics of such changes in CAF action(s) might be different in different tumor types, in part because the resident fibroblasts exhibit different organ-specific transcriptomic profiles (Rinn et al., 2006). Even within an individual tumor type, for example, in pancreatic cancer, different subtypes of CAFs can exert distinct paracrine actions (Box 1) that could impact tumor-enhancing inflammation (Öhlund et al., 2017).
The activation of fibroblasts was initially studied in the context of wound healing (Bainbridge, 2013;Klingberg et al., 2013). When damage occurs in normal tissue, the damaged epithelial cells and the immune cells recruited to the damage site release chemical mediators that initiate the activation of resident fibroblasts. These include damage-associated molecular patterns, as well as secreted growth factors (e.g. TGFβ proteins, PDGFs, FGF2; Box 2) and cytokines [INFγ (IFNG), TNFα (TNF), interleukins; Box 2] Rasanen and Vaheri, 2010) (see poster). With respect to CAFs, the transition from quiescent fibroblasts to activated CAFs might depend on additional chemical mediators, including growth factors, cytokines and metabolites aberrantly produced by the malignant cells and by the recruited immune cells (Harper and Sainson, 2014;Kalluri, 2016;Roy and Bera, 2016). As mentioned above, the activated state of CAFs requires metabolic reprogramming (Martinez-Outschoorn et al., 2014;Roy and Bera, 2016;Wu et al., 2017;Yang et al., 2016;Zhang et al., 2015), presumably to enable their enhanced proliferation and increased biosynthetic functions, such as the production of extracellular proteins like collagens, laminins, elastin and others (Alexander and Cukierman, 2016).
It is often presumed that in a growing tumor, CAFs are the dominant producer of ECM proteins, in part reflecting the close proximity of CAFs to the areas of ECM remodeling (Alexander and Cukierman, 2016;Kalluri, 2016;Lu et al., 2011). However, emerging evidence suggests that the cancer cells themselves might also produce ECM components (Ozdemir et al., 2014), and acquired features of cancer cells, such as the loss of TGFβ signaling, specifically result in increased ECM production (Laklai et al., 2016). The desmoplastic reaction (Box 1) and accumulation of CAFs is often associated with cancer progression (Kalluri, 2016). Upon histological evaluation, such an abundance of CAFs and ECM in a tumor specimen might, however, simply reflect a more advanced stage of tumor progression, rather than being causally associated with a poor clinical outcome.
Several studies attempted to identify activated CAFs by examining a number of biological markers and transcriptional changes, with a number of groups attempting to characterize specific CAF markers. But, as discussed below, these attempts were rarely successful. The markers that are most commonly used to identify CAFs in in vivo pre-clinical and in clinical studies (reviewed in Cortez et al., 2014;Criscitiello et al., 2014;Hematti, 2012;Kalluri, 2016;Madar et al., 2013;Marsh et al., 2013;Rasanen and Vaheri, 2010;Shiga et al., 2015) include (see poster and Box 2): (1) ECM components, such as collagen I, collagen II, fibronectin, tenascin C (TN-C) and periostin, and remodeling enzymes, such as LOX, LOXL1, MMPs and TIMPs (De Wever et al., 2004;O'Connell et al., 2011); (2) growth factors and cytokines, such as TGFβs, VEGFs, PDGFs, EGF, FGFs, PGE2, CTGF, SDF-1 (CXCL12) and WNTs (Erez et al., 2010;O'Connell et al., 2011;Orimo et al., 2005); (3) receptors and other membrane-bound proteins, such as PDGFRα/β, VCAM1, DDR2, TGFβRI/II, EGFR, FGFRs, BMPRI (BMPR1A/B)/BMPRII, podoplanin and FAP, and a decreased expression of CAV1 (Quail and Joyce, 2013;Rasanen and Vaheri, 2010;Sotgia et al., 2009); (4) cytoskeleton components and other cytoplasmic proteins, such as desmin, vimentin, αSMA and FSP1/S100A4 (Quail and Joyce, 2013;Sugimoto et al., 2006). The heterogeneity of such markers in distinct tumor types (Cortez et al., 2014;Sugimoto et al., 2006), and expression of some of these markers in normal tissues (Council and Hameed, 2009), pose a significant challenge when studying the role of CAFs and their biological properties in cancer. For example, distinct overlap in FSP-1/S100A4 and αSMA expression in CAFs from breast tumor compared with pancreatic tumors (as detailed above, Sugimoto et al., 2006) add an additional level of complexity when attributing functions of CAFs defined by either of these individual CAF markers in a given tumor type. In addition, analyzing the signaling pathways that occur in CAFs as opposed to the malignant cells in the tumor is challenging, because receptors such as PDGFRα/β, TGFβRI/II, EGFR, FGFR, BMPRI/II and others can be expressed by both CAFs and the malignant cells. Therefore, it is likely that studying CAFs will require the use of multiple identifying markers in parallel.
That said, genetically engineered mouse models (GEMMs) are offering new insights on the functional heterogeneity of CAFs, including the definition of CAF markers in relation to their function in the tumors (O'Connell et al., 2011;Ozdemir et al., 2014;Rhim et al., 2014). For example, the study of GEMMs designed to limit the accumulation of CAFs in growing pancreatic tumors (Ozdemir et al., 2014), or to conditionally delete the pro-angiogenic growth factor VEGFs in breast CAFs (O'Connell et al., 2011), revealed that there are distinct functional subtypes of CAFs. Additionally, the use of defined gene promoter-driven expression of viral thymidine kinase (TK) proteins in GEMMs to study CAFs has enabled researchers to deplete distinct populations of proliferating CAFs using ganciclovir, a compound that is only toxic to cells that express viral TK. This system is described in Cooke et al. (2012), LeBleu et al. (2013) andO'Connell et al. (2011), and is being actively used to determine the functions of CAFs in various tissues. For example, in breast cancer, the ganciclovir-mediated depletion of proliferating FSP1/S100A4 + stromal cells did not impact primary tumor growth, but it resulted in suppressed metastasis (O'Connell et al., 2011). In this context, it is possible that FSP1/S100A4 + cells promoted metastatic disease via the secretion of VEGFA and TN-C, which remodel blood vessels and can provide protection from apoptosis, respectively (O'Connell et al., 2011). In contrast, a similar approach used to deplete CAFs expressing αSMA, a dominant CAF population in the pancreatic desmoplastic reaction, suggested that αSMA + stromal cells were predominantly acting to restrain, rather than to promote, cancer progression. Thus, their depletion resulted in more aggressive tumors, suggesting that αSMA + CAFs might play a role in controlling the tumor immune response and that their depletion results in a more immunosuppressive tumor microenvironment (Ozdemir et al., 2014). Although more studies are needed, these results support the hypothesis that distinct CAFs, as defined by their expression of specific markers, exert either anti-or pro-tumor functions, and that these might also be tumor type dependent.
The pro-and antitumor functions of CAFs
As indicated by the GEMM studies discussed above, distinct subsets of CAFs present with cancer-restraining or cancerpromoting functions. The interplay of CAFs and cancer cells within the TME can be depicted as a highly complex signaling network, with dynamic axes of signaling that can oppose or synergize to influence each other's function and impact on cancer progression and metastasis (Gascard and Tlsty, 2016;Ishii et al., 2015;Kalluri, 2016;Luo et al., 2015;Marsh et al., 2013;Mezawa and Orimo, 2016).
Early studies using ad-mixing experiments, wherein cultured CAFs and cancer cells were mixed together prior to their injection in mice, largely investigated the pro-tumorigenic influence of CAFs on cancer cells. This work supports the notion that CAFs have protumorigenic effects, as indicated by the more aggressive formation of tumors in mice or enhanced proliferation or migration of cancer cells in vitro (Berdiel-Acer et al., 2014;Erez et al., 2010;Karnoub et al., 2007;Orimo et al., 2005;Tyan et al., 2011). However, fibroblasts are often referred to as 'easy to culture' and are indeed a cell type that has demonstrated robust adaptation to ex vivo expansion on plastic (see poster). Tumor-promoting CAFs, secreting pro-survival factors, might have a selective advantage over tumor-restraining CAFs when propagated in vitro. This could thus have biased ad-mixing studies in which tumor cells were selectively mixed with a CAF population that became enriched for their tumor-promoting properties. Thus, the interpretation of early ad-mixing studies should consider the possibility of a preferential culture enrichment of pro-tumorigenic CAFs (Kalluri, 2016). Activated fibroblasts, which have similar features to mesenchymal stromal cells (MSCs, Box 1) (Hematti, 2012), have been shown to possess intrinsic cellular plasticity, challenging their functional characterization as being capable of reprogramming into distinct lineages, including endothelial cells, adipocytes and chondrocytes (Gascard and Tlsty, 2016;Kalluri, 2016;Lorenz et al., 2008;Ubil et al., 2014). If the same is true for CAFs, such multi-lineage differentiation potential might then also be associated with a change in their tumor-promoting or -restraining functions. To discern the precise roles of CAFs in tumors, multiple approaches will be needed to overcome the experimental limitations in the systems studied, as well as to overcome the heterogeneity of CAF markers. The current experimental limitations include a lack of precise in vivo (mouse) modeling and imaging tools to dissect the molecular determinants of CAF functions during cancer progression, to track their heterogeneous marker expression over time, and to mechanistically probe their functional relationship with other components of the TME, such as the immune cell infiltrate, ECM, and intratumoral hypoxia and angiogenesis. Comparative analyses between tumor models and tumor types that would be aimed at determining the overlap (or lack thereof ) of distinct CAF markers, used concomitantly with putative non-CAF markers and lineage tracing analyses, could help with the correct interpretation of existing studies, such as those cited in this Review, that remain limited by a lack of in-depth knowledge of the heterogeneous CAF markers. Further, the study of CAF functions will also need to consider the distinct stages of cancer progression, and a likely evolution of the co-dependency between CAFs and cancer cells in their dynamic microenvironment. The impact of CAFs on cancer progression is not limited to their direct influence on cancer cells, but also extends to other cellular components of the primary and metastatic lesions that regulate tumor-mediated reprogramming of the vasculature and of the immune system (Barnas et al., 2010;Erez et al., 2010;Fukumura et al., 1998;Guo et al., 2008;Gyotoku et al., 2001;Liao et al., 2009;Raz and Erez, 2013;Tang et al., 2016). The complexity of the functional relationships of CAFs to cancer cells and other cellular populations in the TME further implicates that CAFs can serve as both tumor-promoting and tumor-restraining entities during cancer progression: for example, a given population of CAFs exerts tumor-promoting functions onto cancer cells, but can exert tumor-restraining functions by remodeling the TME (Augsten, 2014;Gascard and Tlsty, 2016;Han et al., 2015;Harper and Sainson, 2014;Mezawa and Orimo, 2016). A more precise understanding of the overall implications of CAFs in relation to multiple components of the TME, as well as to cancer cells, could enable a better future therapeutic design to limit tumor-promoting CAFs functions while enhancing their tumor-restraining functions.
Pro-tumorigenic functions of CAFs
The pro-tumorigenic functions of CAFs (see poster) are generally driven by their altered secretome (Erez et al., 2010;Mezawa and Orimo, 2016;Orimo et al., 2005;Raz and Erez, 2013). The paracrine signaling between CAFs and cancer cells, wherein CAFs secrete growth factors and cytokines such as CXCL12 (Orimo et al., 2005;Yu et al., 2014b), CCL7 (Jung et al., 2010), TGFβs Yu et al., 2014b;Zhuang et al., 2015), FGFs (Bai et al., 2015;Henriksson et al., 2011;Sun et al., 2017), HGF (De Wever et al., 2004;Jedeszko et al., 2009;Tyan et al., 2011), periostin (POSTN) (Kikuchi et al., 2008;Ratajczak-Wielgomas et al., 2016) and TN-C (De Wever et al., 2004;O'Connell et al., 2011), might directly and positively impact tumor progression by enhancing the survival, proliferation, stemness, and the metastasis-initiating capacity of cancer cells, ultimately promoting cancer progression, but also enhancing resistance to therapy. In light of these studies, the paracrine signaling between CAFs and cancer cells has been characterized as a reciprocal and convergent set of signaling activities that promote tumor growth and cancer invasion and metastasis (Alexander and Cukierman, 2016;Cirri and Chiarugi, 2012;De Wever et al., 2014;Han et al., 2015;Mezawa and Orimo, 2016). CAFs are also effective in the remodeling of the tumor vasculature through the secretion of VEGFs, FGFs and IL-6, and of the ECM through the secretion of MMPs and ECM proteins, and in modulating pro-tumorigenic inflammation through the secretion of IL-1 (IL1A), IL-6, TNFα, TGFβs, SDF-1 and MCP-1 (CCL2). These represent the indirect influences of CAFs in promoting tumor growth, wherein the CAF secretome enhances angiogenesis and ECM stiffness to promote the survival, proliferation and migration of cancer cells, and generates an immunosuppressive microenvironment that limits antitumor immunity (reviewed in Gascard and Tlsty, 2016;Han et al., 2015;Harper and Sainson, 2014;Kalluri, 2016;Marsh et al., 2013;Raz and Erez, 2013). CAFs were also reported to exert a physical force, transmitted by CAFcancer cell adhesion, to promote a cooperative collective invasion or co-migration of CAFs and cancer cells (Labernadie et al., 2017), supporting the notion that a direct cell-cell contact between CAFs and cancer cells promotes cancer cell invasion.
The tumor immunity and the intratumoral vascular program are regulated by cytokines and chemokines that are secreted by CAFs (Erez et al., 2010;Fukumura et al., 1998;Guo et al., 2008;Liao et al., 2009;Tang et al., 2016). However, a mechanistic understanding of how CAFs co-regulate their own signaling network with the signaling networks of immune cells and blood vessels will require more studies. Indeed, many of the CAF-derived chemokines and cytokines that were mentioned above also function in a positive feedback loop to enhance or perpetuate CAF activation (Kalluri, 2016;Rasanen and Vaheri, 2010). Furthermore, whether cancer cells directly influence the CAF secretome to promote tumor growth remains to be determined with further in vivo functional studies. It is conceivable that the tumor-promoting functions of CAFs are due to 'collateral damage' from their otherwise protective, wound repair activities, and that cancer cells merely benefit from a CAF secretome that was originally intended for wound repair. We postulate that this could possibly occur in the early stages of oncogenesis, which might then be followed by a cancer cellmediated reprogramming of CAFs to enhance tumor progression and facilitate metastasis. For example, the pro-inflammatory cytokine LIF (Box 2), secreted by both CAFs and cancer cells, was found to mediate the epigenetic modifications of CAFs in order to enhance their pro-tumorigenic functions, namely by enhancing the CAF acto-myosin contractility that enabled the CAFs to form ECM tracks, which were then used by the cancer cells in a collective invasion (Albrengues et al., 2015).
Finally, the pro-tumorigenic functions of CAFs could be attributed to their role in reprogramming and shaping the metabolic microenvironment of tumors (Kalluri, 2016;Lisanti et al., 2013) (see poster). Several lines of investigation support that metabolites, such as lactate and ketone bodies, are produced by CAFs and can support the growth and proliferation of the cancer (Martinez-Outschoorn et al., 2014;Yang et al., 2016) and the immune cells in the TME, specifically T cells (Ghesquiere et al., 2014;Molon et al., 2016).
Antitumor functions of CAFs
While the pro-tumorigenic functions of CAFs are likely to be based on their production of pro-survival factors, which in turn enhance cancer cell proliferation and metabolic adaptation, their antitumor properties are predominantly associated with their functions as regulators of antitumor immunity (Kalluri, 2016) (see poster). Some of the clinical efforts to target CAFs, supported by preclinical studies, have offered novel insights into the heterogeneous function(s) of CAFs in cancer progression, and in some cases, as discussed in detail below, highlighted their antitumor properties (Öhlund et al., 2014Ozdemir et al., 2014;Rhim et al., 2014). The depletion of CAFs using genetic strategies in GEMMs of pancreatic cancer revealed that proliferating αSMA-expressing CAFs do limit tumor progression rather than promoting it. Their depletion yielded a more invasive tumor with enhanced intratumoral hypoxia, as well as increased proportions of regulatory T cells (Ozdemir et al., 2014). A reduction in CAFs in GEMMs of pancreatic tumors harboring a genetic deletion of SHH (Box 2) in the cancer cells also resulted in more aggressive tumors with increased cancer cell proliferation, which was possibly mediated by an enhanced tumor vascularity (Rhim et al., 2014). Notably, in patient-derived pancreatic cancer samples, the abundance of αSMA + CAFs did not correlate with a diminished intratumoral T cell infiltration, suggesting that these CAFs might promote T cell accumulation in the proximity of cancer cells in vivo (Carstens et al., 2017). Indeed, the CAF secretome might also exert antitumor functions; for instance, IL-10, TGFβs, IFNγ and IL-6 participate in the recruitment and polarization of macrophages, NK cells and T cells, which promote an immune control of cancer cells (reviewed in Harper and Sainson, 2014;Kalluri, 2016). Thus, the net effect of the CAF secretome must be considered as bimodal and dynamic. The use of GEMMs and sophisticated experimental methodologies to determine the functional heterogeneity of CAFs, thereby linking defined cellular markers to specific CAF functions, will help to further discern the pro-and antitumor functions of CAFs in distinct tumor types.
Therapeutic targeting of CAFs
The development of anticancer therapies to target CAFs has largely focused on their pro-tumorigenic functions. Most conventional anticancer therapeutic approaches are likely to affect CAFs as well, because highly proliferating cells are more sensitive to agents that affect generic signaling networks, induce DNA damage, impede DNA/RNA synthesis and block the cytoskeletal remodeling necessary for cell division. Although the potency of chemo-and radiotherapy is based on the premise that cancer cells will have enhanced sensitivity, as they are more proliferative, the unintended impact of such therapeutic interventions on the function or accumulation of CAFs is largely unknown. Depletion of FAP + cells using genetic strategies resulted in a cachexia and anemia phenotype in mice , underscoring that strategies to target CAFs for anticancer therapies must also take into consideration the systemic side effects, such as the risk of developing cachexia, anemia and other paraneoplastic syndromes. Nonetheless, depletion of FAP + CAFs in mice with pancreatic cancer enabled the antitumor efficacy of immune checkpoint blockade, namely anti-CTLA4 and anti-PD-L1 (CD274) antibodies (Box 2) . Depleting FAP + CAFs in mice with melanoma also reduced the activity of immunosuppressive cells and improved antitumor activity of CD8 + tumor-infiltrating T cells (Zhang and Ertl, 2016). Although these studies support a functional role of FAP + cells in immunosurveillance, the targeting of FAP + CAFs, via adoptive transfer of FAP-targeted chimeric antigen receptor (CAR) T cells, can also suppress pancreatic cancer growth in mice by suppressing tumor angiogenesis (Lo et al., 2015).
CAFs have been implicated in promoting resistance to therapy, so there is an interest in devising a targeted anti-CAF therapeutic approach (Hale et al., 2013) (see poster). The cancer therapies currently used in the clinic can activate or modulate CAF functions. For example, targeting BRAF in melanoma was reported to activate CAFs to remodel the tumor ECM, thereby providing protumorigenic signals that supported residual disease (Hirata et al., 2015). Furthermore, genotoxic stress and the associated damage induced by chemotherapeutic agents (e.g. mitoxantrone) caused transcriptomic changes in the CAFs, resulting in the secretion of WNT16B (WNT16), which signals to enhance survival and EMT in prostate cancer cells (Sun et al., 2012). There is also evidence that the CAF secretome and their ECM-remodeling properties could mediate resistance to chemotherapy by promoting invasion and dissemination of cancer cells via ECM degradation and vascular remodeling (reviewed in Kalluri, 2016;Kharaishvili et al., 2014). Resistance to chemotherapy could also be mediated by direct CAFcancer cell signaling that promotes cancer cell survival when exposed to the cytotoxic effects of the chemotherapeutic agent cisplatin (Li et al., 2001). A recent study by Su et al. identified that CD10 + (MME + ) GPR77 + (C5AR2 + ) CAFs promote breast cancer stem cell survival and resistance to chemotherapy through secretion of IL-6 and IL-8 (CXCL8) (Su et al., 2018). Although these findings support a role for CAFs in chemoresistance, the likely functional heterogeneity of CAFs, as discussed above, means that researchers should exercise caution when generalizing their pro-tumorigenic actions in the context of drug resistance studies (Kharaishvili et al., 2014;Öhlund et al., 2014).
A more effective approach to target CAFs could lie in delineating the regulatory pathways that lead to the activation of fibroblasts. In pancreatic cancer, the vitamin D analog calcipotriol was capable of reprogramming the CAFs to acquire the nonactivated phenotype of pancreatic stellate cells, the resident mesenchymal cells of the pancreas (Sherman et al., 2014). Clinical trials are ongoing to test whether such CAF reprogramming enhances the efficacy of gemcitabine, a chemotherapeutic drug used commonly in pancreatic cancer. Moreover, using JQ1, an inhibitor of the BET family of bromodomain chromatin-modulating proteins, in patientderived xenografts of pancreatic cancer resulted in reduced activation of CAFs and attenuated tumor growth (Yamamoto et al., 2016). Finally, although conventional maximum-tolerated dose treatment is known to activate CAFs, applying metronomic chemotherapy (Box 1) was recently reported to limit such chemotherapy-induced activation of CAFs. Although maximumtolerated dose chemotherapy enhanced CAF pro-tumorigenic functions, metronomic chemotherapy restricted the CAF protumorigenic functions by decreasing the expression of chemokines, thereby limiting the expansion of the stem-like tumor-initiating cells following therapy (Chan et al., 2016).
The approaches summarized here will not only inform on the impact of targeting CAFs during cancer therapy, but can also provide additional insights into the biology of this important player of the TME. These novel insights could, in turn, impact novel and promising therapies, including future combination strategies that also aim to remodel the TME, such as antiangiogenic therapy and immunotherapy.
Conclusions
The next decade will likely bring about many more discoveries regarding the biology of CAFs, informed by the development of new experimental tools that could more precisely define their functional contribution to cancer progression and therapy. Ongoing and future studies, employing novel approaches to monitor and functionally alter CAFs in vivo, will likely unravel new regulatory pathways involving CAFs in cancer progression. The precise definition of the heterogeneous CAF populations at distinct stages of cancer progression, with markers that inform on their functions, remains the most challenging aspect in the study of CAFs. Building on the precise knowledge of CAF markers to elucidate which CAF subpopulations exert a pro-versus antitumor effect will likely be beneficial for cancer treatment. Results from such studies could ultimately offer insights into novel combination therapies aimed at exploiting the therapeutic vulnerabilities of the TME, and at reprogramming the CAFs and other components of the TME to control cancer progression and enable efficient therapeutic responses. | 8,155 | 2018-04-01T00:00:00.000 | [
"Biology",
"Medicine"
] |
Platelet-Derived Growth Factor Induces Rad Expression through Egr-1 in Vascular Smooth Muscle Cells
Background Ras associated with diabetes (Rad) inhibits vascular lesion formation by reducing the attachment and migration of vascular smooth muscle cells (VSMCs). However, the transcriptional regulation of Rad in VSMCs is unclear. Methodology and Principal Findings We found that Platelet-Derived Growth Factor (PDGF)induced Rad expression in a time- and dose-dependent manner in rat aortic smooth muscle cells (RASMCs) using quantitative real-time PCR. By serial deletion analysis of the Rad promoter, we identified that two GC-rich early growth response-1 (Egr-1) binding sites are essential for PDGF-induced Rad promoter activation. Overexpression of Egr-1 in RASMCs strongly stimulated Rad expression while the Egr-1 corepressor, NGFI-A binding protein 2 (NAB2), repressed PDGF-induced Rad up-regulation in a dose-dependent manner. Direct binding of Egr-1 to the Rad promoter region was further confirmed by chromatin immunoprecipitation assays. Conclusions Our results demonstrate that Rad is regulated by PDGF through the transcriptional factor Egr-1 in RASMCs.
Introduction
Ras associated with diabetes (Rad) is a member of the RGK family which is composed of Rad, Gem/Kir, Rem and Rem2 [1]. It is expressed in the heart, skeletal muscle and lung [2]. Rad is highly expressed in the skeletal muscle of some type II diabetic patients [2], which suggests that Rad is related with glucose metabolism and insulin resistance. Our previous studies demonstrate that Rad is critical in maintaining normal cardiac functions. Rad expression decreases significantly in human failing hearts and Rad knockout (KO) mice are more susceptible to cardiac hypertrophy with increased CaMKII phosphorylation compared with their littermate controls [3]. Our findings as well as the others' indicate that Rad inhibits myocardium L-type calcium channel activity and attenuates the b-Adrenergic Receptor (b-AR) activity [4,5]. Dominant negative suppression of endogenous Rad in the heart up-regulate L-type Ca 2+ channel expression on the plasma membrane, leading to I Ca,L increase and action potential prolongation [6]. Rad is upregulated in vascular smooth muscle cells (VSMCs) during the formation of vascular lesions and overexpression of Rad attenuated neointimal formation by strongly inhibiting smooth muscle migration [7]. However, the molecular mechanism for the induction of Rad during vascular lesion formation is unknown.
Platelet-derived growth factor (PDGF) plays an important role in normal tissue growth and the patho-physiological processes of vascular diseases like atherosclerosis and restenosis [8]. During the initiation and progression of atherosclerosis, VSMCs are activated by growth factors like PDGF or cytokines, then proliferate and migrate from the media into the intimal surface of the vessel, thus facilitating neointimal formation [8]. Egr-1 is a zinc-finger transcription factor that regulates cell proliferation and differentiation [9]. It is an immediate-early response protein that is rapidly and transiently stimulated by various growth factors including PDGF [10]. Egr-1 regulates gene transcription by the specific binding of its DNA binding domain, which consists of three zinc fingers, to the consensus GC-rich regions in the promoter of its target genes [11]. Structure analysis of Egr-1 identified a 34 amino acids inhibitory domain (R1) at the 59 zinc finger binding region [12]. Two corepressors, NGFI-A-binding proteins 1 and 2 (NAB1 and NAB2) can markedly decrease Egr-1 transcriptional activity by binding to this domain [13,14].
In the present study, we set out to explore how Rad is transcriptionally regulated in VSMCs. We found PDGF induced Rad expression in a dose-and time-dependent manner, which Egr-1 and its partners mediated this induction.
PDGF induces Rad expression in RASMCs
To determine the effects of the growth factor PDGF on Rad expression in RASMCs, we treated cultured RASMCs with PDGF (20 ng/ml) for 0, 0.5, 1, 2 and 6 hours. Quantitative real-time PCR revealed that expression of Rad increased 0.5 hours after PDGF stimulation, peaked at 1 hour, and returned to the baseline levels 6 hours later ( Figure 1A). Rad was induced by PDGF in a dose-dependent manner ( Figure 1B). One hour of 20 ng/ml PDGF treatment resulted in a 3.5-fold increase in Rad mRNA, compared to untreated cells ( Figure 1B).
To identify the mechanisms by which PDGF activates Rad expression, we isolated Rad promoter and made reporter constructs containing different length Rad promoters. RASMCs were transfected with these constructs and then treated with PDGF. PDGF stimulated Rad promoter activity in all Rad promoter constructs except pRad-57, which also lacked basal promoter activity ( Figure 1C).
Egr-1 binding sites in the Rad promoter are required for PDGF-induced Rad promoter activation Computer analysis revealed two GC-rich regions that may serve as Egr-1 binding sites within the 2155 to 257 bp region of the Rad promoter ( Figure 2A). To investigate whether these are functional Egr-1 binding sites, we over-expressed a constitutively active Egr-1 (Egr-1*) and tested whether it could transactivate Rad promoter. Egr-1* over-expression resulted in a ,25-fold increases of the pRad-155bp promoter activity compared with pcDNA3.1 vector transfection( Figure 2B). Then we mutated one or both of the predicted Egr-1 binding sites in the pRad-155 construct and co-transfected wild-type or mutant reporter constructs with a constitutive active Egr-1* expression construct into 293A cells. Disruption of the first Egr-1 binding site in position -74 (pRad-155m1) caused a slight decrease in the Rad promoter activity but the induction upon Egr-1* overexpression remain unchanged ( Figure 2B). However, disruption of the second Egr-1 binding site in position -62 (pRad-155m2) resulted in a weaker induction of the Rad promoter activity by Egr-1* (,7 folds) compared with the 25fold activation in the pRad-155 construct, whereas the basal level remained unchanged. However, mutations on both Egr-1 binding sites (pRad-155m3) impaired both basal and Egr-1*-induced transactivation of the Rad promoter ( Figure 2B).
To further test whether Egr-1 binding site mutation affects PDGF-induced Rad promoter activation, we transfected RASMCs with wild-type (pRad-155) or Egr-1 binding site mutant constructs and then treated the cells with PDGF. PDGF triggered a nearly 3 fold promoter activity induction on pRad-155, and the induction dropped to ,1.8 fold in pRad-155m1, and no PDGF induction could be observed in pRad-155m2 construct meanwhile the basal activity of these constructs remained consistent. When both Egr-1 binding sites were disrupted as in pRad-155m3, the basal level of the Rad promoter activity dropped dramatically and no induction by PDGF was found ( Figure 2C). These results are consistent with the above experiments performed in 293A cells overexpressing a constitutive active Egr-1*. Furthermore, mutation of both Egr-1 binding sites in the pRad-3050 reporter construct caused , 54% decrease in the basal level of pRad-3050 promoter activity and PDGF-induced promoter activation was also completely abolished in pRad-3050m ( Figure 2D). Taken together, the Egr-1-responsive regions located at 262 and 274 bp of Rad promoter are essential for PDGF-induced Rad promoter activation.
Egr-1 mediates PDGF-induced Rad expression in RASMCs
Egr-1 was activated by various growth factors including PDGF, and we also found a remarkable induction of Egr-1 by PDGF in RASMCs (data not shown). Real-time quantitative PCR and Western blot demonstrated that Egr-1 markedly induced Rad expression at both mRNA and protein levels ( Figure 3), which is consistent with Egr-1 transactivation of the Rad promoter. These results suggest that Egr-1 is a potent transcriptional activator for Rad. We hypothesized that PDGF induced Rad expression via the activation of Egr-1. NAB2 functions as a corepressor of Egr-1 [14], therefore we asked whether over-expresion of NAB2 inhibits PDGF-induced Rad expression. RASMCs were infected with a recombinant adenovirus expressing NAB2 (Ad-NAB2) followed by PDGF treatment for 1 hour. Ad-GFP was used as a control in this experiment. We found that Rad expression increased in response to PDGF in Ad-GFP-infected RASMCs, and overexpression of NAB2 resulted in a dose-dependent abrogation of PDGF-induced Rad expression ( Figure 4A). The induction of Rad mRNA was completely abolished when Ad-NAB2 reached a concentration of 250 MOI (multiplicity of infection). Furthermore, NAB2 inhibited Rad promoter activity elevated by PDGF-BB ( Figure 4B). These results support that Egr-1 is a key mediator involved in PDGFinduced Rad expression in RASMCs.
PDGF enhances Egr-1 binding to Rad promoter
ChIP assay was performed to further confirm the physiological relevance and functionality of Egr-1 through its putative binding sites in the Rad promoter. RASMCs untreated or treated with PDGF for 1 hour were incubated with formaldehyde to cross-link protein and binding sites in DNA. The -213 to -2 region of the Rad promoter region was amplified by PCR. After PDGF treatment, Egr-1 was found to bind to the Rad promoter. As for DNA from untreated cells or DNA precipitated by control IgG, we did not find any PCR amplifications ( Figure 5). Our data strongly support that Egr-1 bound to the proximal Rad promoter following PDGF treatment in RASMCs.
Discussion
In the present study, we provide the first evidence that PDGF induces Rad expression in a time-and dose-dependent manner in rat aortic smooth muscle cells, and that Egr-1 is a key transcriptional factor to mediate PDGF-induced Rad expression.
Rad is a RGK-family small GTPase initially identified by subtractive cloning and found to be over-expressed in skeletal muscle of a group of patients with type II diabetes [2]. Rad possesses a structurally unique Ras-related core and COOH-and NH2-terminal extensions but lacks the CAAX-like prenylation motif at the COOH terminus possessed by Ras [15,16]. In atherosclerotic lesions, PDGF released from inflammatory and immune cells promotes VSMCs proliferation and attracts VSMCs to migrate from media to intima [17]. In different animal models of acute arterial injury, VSMCs accumulation in lesions is inhibited by the administration of various PDGF pathway inhibitors, including neutralizing PDGF antibodies [18], PDGFR kinase inhibitors [19], and PDGFR-neutralizing antibodies [20]. Here we demonstrated that PDGF stimulated Rad expression in RASMCs in a time-and dose-dependent manner. Our previous study indicated that Rad is a critical mediator that reduces vascular lesion formation by suppressing the attachment and migration of VSMCs via inhibition of Rho/ROK activity [7]. Rad induced by PDGF may serve as a suppressor for PDGF induced smooth muscle cell migration.
We have identified that normal Rad level is critical in maintaining cardiac and blood vessel functions. Rad expression decreases in human failing hearts and Rad knockout (KO) mice Ad-GFP was added to keep the amount of adenovirus consistent. Rad mRNA was quantified using real-time PCR (n = 3, *p,0.05, **p,0.01); (B) RASMCs were transfected with pRad-155 and infected with Ad-NAB2 or Ad-GFP for 24 hours and then stimulated with PDGF. Luciferase activity was measured 1 hour after PDGF stimulation (n = 4, *p,0.05, ** p,0.01). doi:10.1371/journal.pone.0019408.g004 Figure 5. Egr-1 binds to Rad promoter after PDGF treatment. ChIP assay was performed in RASMC stimulated with or without PDGF (20 ng/ml) for 1 hour. After formaldehyde cross-linking, the protein-DNA complexes were recovered using anti-Egr-1 antibody or nonspecific IgG. PCR was performed to detect the proximal Rad promoter. PCR products were detected by 2% agarose gel electrophoresis. doi:10.1371/journal.pone.0019408.g005 are more susceptible to cardiac hypertrophy [3]. Rad inhibits myocardium L-type calcium channel activity and attenuates the b-Adrenergic Receptor (b-AR) activity [4,5]. In blood vessel, Rad expression increases significantly after balloon injury. Unlike other small GTPase, the RGK family proteins are regulated at the transcriptional level. However, the understanding for Rad transcriptional regulation is very limited. Potential E box sequences (CANNTG), which could serve as binding sites for the HLH family transcription factors like myf5, MyoD, myogenin and MRF4, are predicted in the Rad proximal promoter region [21]. Rad expression increases significantly during skeletal muscle regeneration. Myogenic transcriptional factors like MEF2, MyoD and Myf-5 can increase the transcriptional activity of Rad promoter [22].
Until now, the molecular mechanism by which Rad is activated during vascular lesions formation is not clear. Rad promoter activity increased after PDGF treatment, suggesting that PDGF induces Rad expression at transcriptional level. Deletion and mutation analysis of Rad promoter revealed that potential Egr-1 binding sites would be critical for PDGF-induced Rad expression. It has been well documented by us and other groups that PDGF induces dramatic expression of Egr-1 [23]. Our data showed that Egr-1 activated the Rad promoter in RASMCs, and overexpression of Egr-1 resulted in a marked increase at Rad mRNA and protein levels in RASMCs. Egr-1 activity is negatively regulated by NGFI-A-binding proteins 1 and 2 (NAB 1 and NAB 2). Binding of NAB2 to Egr-1 through interaction between the NCD1 (NAB conserved domain 1, NCD1) and the R1 domain of Egr-1 represses the transcriptional activity mediated by Egr-1 [14]. We found that up-regulation of Rad induced by PDGF-BB was inhibited by the co-repressor NAB2 in a dose-dependent manner. Together, these data support that Egr-1 and its interacting partner(s) are the key mediators responsible for PDGF-induced Rad expression.
Egr-1 expression is strikingly elevated in the VSMCs of atherosclerotic lesions [24] and plays critical roles in regulating VSMCs growth and intimal thickening after vascular injury [25]. Egr-1 binds preferentially to GC-rich regions of the promoters of its target genes [26]. ChIP assay indicated that after PDGF stimulation, Egr-1 binded to the proximal Rad promoter region. Serial deletions of the Rad promoter also defined a 2155 to 257 bp region responsible for both basal and Egr-1-inducible promoter activity. Two GC-rich motifs were predicted in this region as potential binding sites for Egr-1. Mutation of both Egr-1 binding sites in pRad-3050 and pRad-155 resulted in dramatic reduction in Rad basal promoter activity and complete abolishment of PDGF-induced Rad promoter activation. Egr1-activated Rad promoter activity was attenuated when the putative Egr-1 binding site at position -62 or both binding sites were mutated. Consistent with this, PDGF-stimulated Rad promoter activation was abolished when the putative Egr-1 binding site in position -62 or both binding sites were mutated. These results suggest that the Egr-1 response element located at 262 bp plays the crucial role in PDGF-induced Rad promoter activity.
Here we identified Rad as another target gene of transcriptional factor Egr-1. After vascular cell injury, Egr-1 is expressed primarily in the nucleus and is capable of altering the transcription of several genes implicated in the pathogenesis of vascular disease, including PDGF, FGF-2, TNFa, tissue factor, ICAM and p53. Genes activated by Egr-1 play important roles in VSMC proliferation, neointima formation and contribute to the development of vascular diseases. Many of those genes like PDGF are activated by Egr-1 and further induce Egr-1 expression. Osteopontin and Egr-1 also positively regulate each other in VSMCs, which may play an important role in controlling inappropriate remodeling of vessel walls [27]. These positive feedback loops amplify gene transcription activated by Egr-1. On the other hand, the negative feedback loop between Egr-1 and other genes like NAB2 prevents the permanent activation of Egr-1 target genes. In response to extracellular stimuli Egr-1 induces the expression of NAB2, which in turn represses the activity of Egr-1 through binding to the R1 domain of Egr-1 [28]. Gene expression profiling by microarrray analyses revealed enhanced expression of several Egr family members in the hearts of Rad Knockout mice compared with their wild-type littermates. These raised the possibility that there is a negative feedback loop between Rad and Egr-1, i.e. Egr-1 activation induced Rad expression and Rad inhibited the further activation of Egr-1. Furthermore, the effect of Rad on other transcription factors involved in neointima formation and VSMC migration remains to be further investigated.
Cell Cuture
Rat aortic smooth muscle cells (RASMCs) were isolated from male Sprague-Dawley rats as described previously [23] and cultured in Dulbecco's modified Eagle's medium supplemented with 10% FBS in a 5% CO2 humidified atmosphere at 37uC. Early passages (5 to 9) of cells grown to 80-90% confluence were used for all experiments. Cells were placed in serum-free medium for 24 hours before treatment with PDGF-BB (Sigma).This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Experiments were approved by the Peking University Animal Care and Use Committee (Permit Number: IMM-XiongJW-2).
RNA Isolation and Quantitative Real-time PCR
Total RNA was extracted from cultured RASMCs with Trizol (Invitrogen) and 1 mg was used for cDNA synthesis using a first strand cDNA synthesis kit according to the manufacturer's instructions (Invitrogen). Rad mRNA level was assessed by realtime quantitative RT-PCR as previously described [7] and 18S level was measured for normalization of variations in RNA input and cDNA synthesis.
Western Blot Analyses
Cells were lysed with lysis buffer (Cell Signaling Technology). The lysates were resolved on 12% SDS-polyacrylamide gels, and proteins were then transferred to PVDF membranes (Bio-Rad). The membranes were blocked for 1 hour at room temperature and incubated overnight at 4uC with anti-Rad antibody (1:2000). Rabbit anti-Rad polyclonal antibody was a kind gift from Dr. Ronald Kahn [30]. Blots were then incubated with anti-rabbit secondary antibody (1:2000) for 1 hour at room temperature and the Rad/antibody complexes were visualized by chemiluminescence (Bio-Rad) according to the manufacturer's recommendations.
Transient Transfection and Luciferease Assays
Transfection of RASMCs or 293A cells were performed using LipofectamineTM2000 (Invitrogen) according to the manufacturer's instructions. The pRL-CMV plasmid was cotransfected as the control for transfection efficiency. Luciferase activity was measured by the Dual Luciferase Reporter System using a TD20/20 Luminometer (Turner Biosystems).
Chromatin Immunoprecipitation (ChIP)
ChIP assay was performed using a chromatin immunoprecipitation assay kit (Upstates). Cultured RASMCs were treated with PDGF or vehicle for 1 hour. Proteins were cross-linked with DNA by 1% formaldehyde treatment for 10 minutes at 37uC. Cells were then lysed and DNA was sheared by sonication (MicrosonTM, strength 40%, pulse 10 seconds, 4 times). The sonicated DNA was diluted in ChIP dilution buffer. The diluted cell supernatant was pre-cleared with protein A agarose before immunoprecipitation with anti-Egr-1 antibody (Santa Cruz) or non-specific IgG. The immune complexes were collected by adsorption to protein A agarose, precipitated by gentle centrifugation at 3,000 rpm of 4uC for 1 min, and the supernatant containing unbound nonspecific DNA was discarded. The precipitate was washed and the immune complexes were eluted, adjusted to 200 mM NaCl and incubated at 65uC for 5 hours to reverse the crosslinks. After successive treatments with RNase A and proteinase K, the DNA was extracted with phenol-chloroform, precipitated with ethanol and resuspended in H 2 O. The immunoprecipitated DNA were analyzed by PCR with primers spanning -213 to -2 of the Rad promoter. The primer sequences were as follows: forward: 59-TCGCTCTCTCTCTCCTTCTCACAC-39 and reverse: 59-AC-CCTCTTCCTCGGACCTTACATC-39. An aliquot of the sonicated DNA was used as the input. PCR products were detected by 2% agarose gel electrophoresis.
Statistical Analysis
Each experiment was repeated for a minimum of three times. Statistics were analyzed using either ANOVA (for multiple comparisons) or Student's 2-tailed t test for comparing two means. Data are presented as Means6SD. In all cases p ,0.05 was considered statistically significant. | 4,312 | 2011-04-29T00:00:00.000 | [
"Biology",
"Medicine"
] |
TRANSFORMATIONAL PROCESSES IN RESOURCE-RICH COUNTRIES: FROM NATURAL RESOURCES TO INNOVATION AND TECHNOLOGY -BASED ECONOMY
This paper aims to assess the development pattern of resource - rich countries like Norway, Kuwait, Saudi Arabia, and the Republic of South Africa by analyzing their stock markets. As resource - rich countries have profound implications for the world's sustainable development, it is essential to investigate whether they transform natural resource wealth into sustained growth by reorientation from related to raw material industries to modern ones based on information and technologies. The analysis of resource - rich countries' stock markets allowed us to conclude that partially these countries are on the way to an economy dominated by intangible assets. Nevertheless, despite the declared intentions, some traditional attachments to raw materials are still present. At the same time, most companies in the optimal portfolios of these markets have a long -term non-linear strategy. The impact of the pandemic COVID - 19 turned out to be significant, but short - lived. The research was carried out based on R and Python packages.
INTRODUCTION
The abundance of natural resources is no longer a guarantee of the successful economic development of any country.Although, natural resources have helped bolster prosperity in some resource-rich countries, at the same time they have made these countries vulnerable to high volatility in natural resources prices, and dependent on revenues from raw materials exports.Moreover, an abundance of natural resources can stimulate the development of extraction industries but hinder the development of hi-tech manufacturing sectors.As a result, many resource-rich countries went through multiple economic crises and experienced so-called "Dutch disease".
It is a common fact, that to gain sustained economic growth, resource-rich countries should use natural resources in a way that is truly a comparative advantage, but not an obstacle to their economic development.The transition to an innovative economy based on intangible assets can be essential to such growth.Reducing dependence on revenues from natural resources exports and strengthening innovative industries will expand the country's economic potential and sustainability.
The stock market can play a crucial role in it by transferring the revenues from natural resources exports to productive investments.With its help, financial resources can be attracted to hi-tech manufacturing sectors to expand the production of their goods and services.
We assume that the stock market can reflect the structure of the economy or at least its transition priorities.The analysis of the stock market structure provides us with information about the share of natural resources-based and high-tech companies; the dynamics of companies' capitalization in new industries; investment in research and development, etc.By analysing it, we can make some assumptions about the country's present and future development patterns.
In this article, we want to follow the development patterns of resource-rich countries like Norway, Kuwait, Saudi Arabia, and the Republic of South Africa by analysing their stock markets in detail.In other words, we want to find out, based on stock market analysis, whether these countries are switching from dominance related to raw material industries to industries with intangible assets.
LITERATURE REVIEW
The idea of the impact of natural resources on the economy and its structure has evolved.While classical ideas from the time of mercantilism viewed natural resources as a factor of economic growth and an opportunity to increase exports and limit imports through significant government intervention, new ideas have emerged over time.In the 20th century, the phenomenon of the "Dutch disease" (Goujon & Mien, 2021); and the "resource curse" (Sachs & Warner, 1995;Ross, 1999Ross, , 2015;;Adams et al., 2019;Jiang et al., 2021) were actively studied.Another area of research was the study of the development of resource-rich countries in terms of institutional factors (Bhattacharyya & Hodler, 2014;Abdulahi et al., 2019).They emphasized the problems of corruption and military escalation.However, the focus of our attention is on the innovative factors of such countries' development.In the era of digitalization and artificial intelligence, we cannot ignore the impact of innovation and technology on the structural transformation of modern economies (Dwumfour & Ntow-Gyamfi, 2018;Erdoğan et al., 2020;Amin et al., 2024).
Since we are studying resource-rich countries, it is important to define the criteria for their classification.The International Monetary Fund (IMF) defines a country to be 'resource-rich' when exports of non-renewable natural resources such as oil, minerals and metals account for more than 25 % of the value of the country's total exports (Lundgren et al., 2013, p. 6).Lashitew et al. (2021) emphasized the importance of measuring resource wealth.They used such indicators as resource dependence (the share of resources in exports or GDP) and resource abundance (resource rents per person).According to their results, the correlation of indicators of resource dependence and resource abundance with indicators of competitive ability gave different results.The correlation of resource dependence with the indicators of human capital attainment, R&D expenditure, innovation output, and financial access was negative, while the correlation of resource abundance was positive.They also showed that improvements in diversification had rarely been accompanied by a strengthening in competitiveness, especially among extremely resource-rich countries.The study was based on a sample of 42 countries that were the most resource-dependent in the 1970s, for a period of 1981-2014.Amin et al. (2020) investigated 13 resource-rich countries and 15 resource-scarce countries dividing all of them into subgroups: resource-rich African countries, resource-rich OPEC countries, resource-scarce Asian countries and other resourcelimited countries.The authors analyzed the relationship between technology and long-term economic growth in these countries from 1994 to 2019.The authors found evidence of a "resource curse" symptom between subgroups, as the impact of technology on long-term economic growth was greater in subgroups with limited resources.
"The new economy is not an appearance once an invention or an innovative "break-through" but rather the result of processing the current economy, in a quasi-continuity of physical and human-dominated by knowledge and globally" (Gâf-Deac, 2017).Based on Romania's experience, Gâf-Deac (2017) posed a desirable trend for humanity in transitioning to a new economy oriented on intangible assets.Earlier, Gioacasi (2015) identified the 80s as an industrial period, which "flourished, having played on the importance of tangible resources and easy access to markets and raw materials."The enterprise theoretical resource approach says that asset competitive advantage is the "fulfilment by it of four conditions: it is valuable, rare, imperfectly imitable and nonsubstitutable".
The new information market has demonstrated that resources are not creating economic benefits by themselves, but rather the ability of human capital to make use of enterprise resources.
Currently, we are witnessing the development of a new vision for the resources of an enterprise that distinguishes between physical assets and non-financial resources.Modern businesses invest in motivating and training employees to develop innovative products (Gioacasi, 2015).Labra et al. (2016) concentrated on the essential role of "openness and foreign direct investment to access foreign technologies" as key driving factors.The case of Chile confirmed the importance of intangibles for a country's growth.The weak innovation capability can become a severe blockage for sustained progress.
"Not only absorptive capacities but also innovative capabilities are required in these economies for keeping in the path of a sustainable development" (Labra et al., 2016).Blomstrüm and Kobbo (2007) considered an industry's success as a mix of "systematic knowledge creation and random technological innovation."According to them, it is not possible to continuously generate breakthrough technologies; however, it is possible to create an environment where winning based on innovations, new markets, and new visions will become the norm.
For raw material-based industries, the innovations are likely to be incremental, and "a large share may be related to changes in demand or international competition, rather than major changes in production technology."In younger industries, fundamental changes in technology will be more common, and the "main challenges are related to the ability to acquire the technical skills necessary to remain competitive" (Blomstrüm & Kobbo, 2007).
The reorientation of the economy has become especially interesting considering rising oil prices in recent years.For some countries, this situation gives incredible opportunities.The government of Saudi Arabia has announced plans to transform the country into an industrial power by 2024.Niblock (2018) paid the most attention to what happened in the first oil boom (1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982) in the Saudi Arabian economy.It failed to lead to sustainable non-oil development.
Similarly, Kuwait's economic policy target is to overcome its heavy dependence on oil and the dominance of the public sector.The authorities tried to achieve a two-pronged development strategy: diversifying the country's economic base away from oil and promoting private sector development.The study by Gelan et al. (2021) revealed that diversification and privatization could be possible instruments but in interaction and unity."The findings indicated that the strength of the primary and secondary oil sectors lies in their forward linkages, supplying other sectors with their outputs, but their backward linkages, rooted in buying inputs from other industries, are not as strong" (Gelan et al., 2021).
The digital economy is the best environment for such a transformation.From the experience of Norway, the fundamental pillars are: "trust-based cooperation across social partners and public and private sectors; the public sector's driving role; cross-organization consolidations and consortia; and application-oriented initiatives."But it also contains several unresolved challenges, such as "better inclusion of local districts, particularly in northern Norway; spectacular failures of digitalization projects; and uneven digitalization in the public sector, where data silos still affect service efficiency" (Parmiggiani & Mikalef, 2020).
Norway led the 2017 Inclusive Development Index, a study of which countries were the best "at delivering growth that lasts for decades, was broad-based across sectors, created jobs for the vast majority of the population and reduced poverty."It had the lowest income inequality in the world, helped by a mix of policies that support the modern education system and innovation process.It also created the world's largest sovereign wealth fund, which manages oil and gas revenues, moving the whole country to long-term economic planning (World Economic Forum [WEF], 2017).2019) suggested how to move in the right direction in the development of the built-environment transformation strategy for the South African Republic.Trans-formation should be "conceptualized with a view to redressing historical imbalances and providing expanded opportunities for worker education and skills development from the grassroots, with attention to the previously disadvantaged" (Musonda et al., 2019).
Musonda et al. (
Summarizing the proposed detailed analysis of the general situation and within the framework of individual countries, there is a necessity to transform the economic development of Norway, Kuwait, Saudi Arabia, and the Republic of South Africa into more innovative, and less dependent on natural resource extraction.
AIMS AND OBJECTIVES
This study aims to analyse the transformation of resource-rich countries' economic structure from resource-based to innovative with the help of their stock markets.The best tool to verify this statement is the analysis of optimal portfolios in chosen developed and emerging markets.
The hypothesis of the study is that raw material-rich countries build their strategies based on reducing gradually the raw material character of their economy and moving to industries with a predominance of intangible assets.
The study consists of two parts.In the first part, we analyse each of the created portfolios based on the following criteria: return, risk, and predictability.The next step of the research was to identify a long-term strategy based on the 5-factor Fama-French model.This model provides a differentiated approach based on the factors for developed and emerging markets.The analysis of the dynamics of risks and investment priorities allows us to make assumptions about the transformation processes in chosen countries.The final stage of the study was modelling based on neural networks.This method allows us to detect nonlinear processes.Since our study is an attempt to explain changes in the structure of economies through stock markets, we use both macroeconomic and microeconomic theoretical approaches to explain our results.
METHODS
In order to test our hypothesis, we make the assumption that the share of intangible assets in the balance sheet is insignificant for raw materials-based industries.Indeed, if the basis of the company's activity is raw material extraction, then it is not patents, skilled labour, or in-process R&D that prevails, but fixed assets, land plots, the object of a concession, etc. Add to this the fact that the market for intangible in the raw materials-based economies is very weak, and therefore companies mostly disclose such transactions as expenses.This fact significantly complicates the auditor's work.In the approach based on the optimal portfolio, according to the weighting coefficients, we determine the industry's leaders.Next, we do the same ranking assessment by cumulative return.
On the basis of the daily change in return, we test the nature of the stable incomes of such companies from the risk side.It is important to analyze the state of the business precisely in view of the ratio between return and risk (the well-known Sharpe ratio, which we use to build an optimal portfolio).
Machine learning techniques allow us to answer the question of whether these businesses are predictable.On the other hand, the Fama-French model allows us to reveal economic strategies.The most important thing for us is the fact itself of having a working model, which we determine on the basis of the F-criterion (smaller or at least close to 5%).Analysis of individual factors is not the focus of this study.Taken together, these two approaches give us a basis for asserting that our estimates are not a short-term outcome.We expect that the Fama-French model for developing countries will not always work for such a differentiated sample.That is why we have tested the existence of a long-term nonlinear strategy based on a modified Fama-French approach using neural networks.We use, for example, the designations c (6,2) and c (2), respectively, for two hidden layers with 6 and 2 neurons and only one layer with two neurons.
Creating portfolios
First, we create four portfolios based mainly on the ingredients of appropriate stock market indices: MSCI TADAWUL 30, Kuwait Main Market 50, OSE Benchmark, and South Africa Top 40.We analyze these portfolios in relation to profitability, risk, and their ingredients regarding the predictability of their price.
Machine learning methods
Machine learning methods are chosen to estimate business predictability.A random forest is a capable estimator that uses averaging to improve predictive accuracy and control overfitting (Fawagreh et al., 2014).In the case of gradient boosting, in each stage, a regression tree is fitted on the negative gradient of the given loss function (Buhlmann, 2006).The Support Vector Machine Regressor helps us because of our interest in the radial kernel.
Using Fama-French model Further, we intend to reveal the presence or absence of a long-term strategy based on the 5-factor model of Fama-French (maximum possible period, factors for developed or emerging markets).A five-factor model is aimed at determining at capturing the size, value, profitability, and investment patterns in average stock returns (Fama & French, 2014).
The five-factor model time series regression traditionally has the equation below: where Rit is the return of one of portfolio i in month t; RFt is the riskfree return; RMt is the return on the value-weight market portfolio; SMB is the return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stocks (i.e. the size effect); HML is the return spread of cheap minus expensive stocks (i.e. the value effect); RMW is the return spread of the most profitable firms minus the least profitable; CMA is the return spread of firms that invest conservatively minus aggressively; ai, bi, si, hi, ri, ci -some coefficients; eit is a zero-mean residual.
If the exposures to the five factors, bi, si, hi, ri, and ci, capture all variation in expected returns, the intercept ai in (1) is zero for all securities and portfolios i (Fama & French, 2014).
Using modelling based on neural networks
If the approach based on the 5-factor model of Fama-French does not show a result (the necessary criteria are not met), we move on to modelling based on neural networks, rejecting linearity (Tronto et al., 2008).
A modification of the approach based on neural networks used in this study is an attempt to simulate a crisis on the market by adding a hidden layer with an increased number of neurons (factors) as a sign of a crisis on the market.As the object of such analysis, the Fama-French matrix of factors and the adjusted price of shares of the analyzed company are chosen.
Data
This study focuses on several countries: South Africa, Kuwait, Saudi Arabia, and Norway for the period 2019-2023.The last was chosen by the authors due to its unique role in providing oil to European countries regarding Russian aggression against Ukraine and related sanctions against the Russian energy sector.Also, Norway is number one in the Inclusive Economies Index.
We create a portfolio for each country based on the respective stock indices.
All data are derived based on the company's ticker and the corresponding programming language packages.
We get all the data for calculations from the website: yahoo.finance,investing.com,and focus-economics.combased on the tickers of the respective companies and the corresponding Python and R language packages.In most cases, the maximum possible period for analysis is chosen, based on the time the company has been on the stock market.Sometimes this restriction applies to a group of companies.All the markets we offered turned out to be quite risky (analysis for ingredients of optimal portfolios based on the Efficient Frontier method, Table 1).Except for some marginal cases, the level of risk can be limited to the level of 20%.It is obvious that the greatest outbreaks of risk occurred at the beginning of the pandemic.At the same time, some markets didn't fully recover from that shock (Figures 1-2).Traditionally, such estimates are made on the basis of standard deviation; at the same time, the chosen type of analysis (daily return-based) allows us to assess the dynamics of the process.Interestingly, the situation in all four markets seems to be similar (Figures 1-4).
Investm ent grow th analysis
For investment growth analysis we also use only ingredients of optimal portfolios for chosen markets (Table 1).Among the leading branches for the Norwegian market, we recognized ABL Group ASA (ABL.OL) -oil and gas products; Navamedic ASA (NAVA.OL) -pharmaceutical; 5th Planet Games A/S (5PG.OL) -toys and games and Jaeren Sparebank (JAREN.OL)banking (Figure 5).7).In this last case, our analysis supported trends revealed by Niblock (2018).Now we agree that the Saudi Arabian aim was at least partly achieved.The movement in the same direction could be recognized in the case of Kuwait -KFOUC.KW -iron/steel; Gulf Cable and Electrical Industries (CABLE.KW) -electrical equipment and parts; Jazeera Airways (JAZEERA.KW) -passenger airlines; Tamdeen Investment (TAMINV.KW) -investment advisors (Figure 8).
It seems that Norway, as one of the world leaders in the field of innovative economic development, will stand out completely in comparison with the countries of Asia or South Africa.At the same time, the presence of businesses in such industries as software, telecommunications, and healthcare in the optimal portfolios of all these states suggests a common trend towards the dominance of the economy based on information and knowledge.South Africa stands out somewhat in this group.But when we analyse the smallest participants in the portfolio, an understanding emerges that the same processes are taking place there, but with a certain lag.
Indeed, if we were to turn our attention to South Africa's current commitment to the BRICS association and even the peculiar sprouts of disengagement from developed markets, we might expect that a reverse transformation trend was also quite likely.This is not yet visible in the reaction of the stock market.A similar testing to this one in the next decade will answer this question.Analysing the price charts of companies' shares for the next 50 days (Figures 9-12) we note the obvious absence of clear trends.Such methods of machine learning as random forest (rf), gradient boosting (gb), and support vector regressor (svr) are a very much suitable instrument for it.The argument for such a conclusion is a high coefficient of determination, or in other words, good accuracy of the models (Table 2).Machine learning based approach allows to indicate the absence of a clear transformational strategy in the market (like traditional industries are steadily losing, new industries are steadily gaining, or vice versa), or such a process exists, but at the same time, it is not linear.Perhaps the explanation is a certain jump in the market's interest in business based on Internet platforms, online learning, and software during the pandemic, which was largely minimized in the post-pandemic period.On the other hand, let's not forget about the fleeting impact of pandemic restrictions.At the same time, for the selected companies, we have shown the short-term impact of the pandemic on these companies, based on risk and investment growth.In this way, we approached the possibility and necessity of describing the strategies of the elements of our sample, which can traditionally be done based on the Fama-French analysis.
Fam a-French m odel
In Fama -French analysis we take the factors for Norway as for developed markets, and for other participants as for emerging markets (Table 3).
As we can see, the Fama-French model is not functional in most cases.Only for some businesses we can find out some details about growth or value stock (HML), big or small (SMB), conservative or aggressive (CMA).Perhaps it would be possible to improve the result somehow, using not monthly, but daily statistics.However, it is unlikely that this would solve the problem since it is practically impossible to bring such different markets under a single average model.
Neural netw ork-based approach
Let's try to abandon linearity and use a non-linear mechanism.By manipulating additional hidden layers with different sets of neurons and algorithms provided by R packages (neural net and others), this can be done.We can see that the pandemic crisis had an impact on most of the identified portfolio leaders but did not change crucially their long-term strategy.For some businesses, it is not even useful to enter the crisis identifier additional variable (6th neuron) into the model.
So, we try to influence the business strategy based on the traditional set of factors with a financial crisis, which is simulated by an additional hidden layer with a set of neurons that is more painful than the original set of factors.If it affects the business significantly (decrease in model steps, increase in model accuracy), it means that the crisis could significantly change the company's strategy.We consider the company's strategy to be the interaction of factors obtained from the 5factor Fama-French model.Only for JAZEERA.KW, NAVA.OL, JAREN.OL and 4009 SR (Middle East Healthcare Company) the introduction of the crisis into the model damaged it, and therefore it turned out to be inappropriate.The chosen approach is based on an algorithm slr (smallest learning rate).
The most significant conclusion of the last model is the presence of a long-term non-linear strategy for the majority in our sample.Only for two enterprises, such an analysis did not give results due to the too short period of the presence on the stock market.
DISCUSSION
The basis of the discussion is the choice between the improvement of the traditional Fama-French model with the use of daily statistics, smoothing of curves, input into the model of the additional factors or rejection of linearity and the analysis of applied algorithms based on neural networks.Both approaches have their pros and cons.In our opinion, the non-linear nature of the interaction between the Fama-French factors and the verified price of the company's shares is a more accurate method.A special advantage of this method is the inclusion of additional hidden layers in the model with a number of neurons (factors) manipulation.
In this way, the rejection of hypotheses or restrictions regarding the linearity of processes, strategies, and trends allow to significantly increase the viability of theoretical models, making it difficult to assess the influence of individual factors.
Based on the Fama-French method, it was possible to analyse long-term strategies for only four companies in our sample.At the same time, it does not mean that companies do not have such a strategy.It can be obtained based on nonlinear models using neural networks.The impact of the crisis caused by pandemic restrictions is proposed to be embodied with the help of an additional hidden layer using a larger number of neurons than at the initial layer.The absence of such influence was found only in industries which are capable of effective local development (regional banking, medical facilities, etc.).The contradictory case of the Jazeera airline is an exception, which can be explained by the very quick resuscitation of the company after certain losses related to the pandemic.
Expectations regarding Norway and its special role in providing Europe with oil and gas in critical situations turned out to be exaggerated.This significant change in external factors obviously affected the weighting coefficients of representatives of the extractive industry in the optimal portfolio but did not transfer them to the status of clearly prevailing.
The next debatable issue is accepting the stock market environment as a reflection of the country's economy.It proved to be true for developed countries, but for emerging markets, it is partially correct.
CONCLUSIONS
According to the machine learning-based approach, there is no clear transformational strategy on the market of Norway, Kuwait, Saudi Arabia, and the Republic of South Africa or that such a process exists, but at the same time it is not linear.The analysed resource-rich countries are gradually switching to intangible assets although some traditional attachments to raw materials are still present.In any of the proposed optimal portfolios natural resources companies are not dominant.
The transformation processes on the stock markets of Norway, Kuwait, Saudi Arabia, and the Republic of South Africa turned out to be quite similar in the aspect of risk dynamics and investment priorities.
With rare exceptions, most of the companies in the optimal portfolios of these countries are predictable businesses with a long-term nonlinear strategy according to machine learning methods.The impact of the pandemic COVID-19 on them turned out to be significant, but short-lived.
Figure 1 .
Figure 1.Daily simple return on the Norwegian market.(Source: authors' processing in Python)
Figure 2 .
Figure 2. Daily simple return on the South African market.(Source: authors' processing in Python)
Figure 3 .
Figure 3. Daily simple return on the Kuwaiti market.(Source: authors' processing in Python)
Table 4 .
Neural network-based analysis.Note: * Jazeera Airways returns to profitability in record time.The companies (strategies) least affected by the crisis according to our approach are highlighted.(Source: authors' processing in Python) | 6,081.6 | 2024-04-30T00:00:00.000 | [
"Economics",
"Environmental Science"
] |
A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications
The data-driven (DD) is a systematic approach to improve the data and model by deriving/adding features to address the problem identified during the iterative loop of forecasting model development. This article proposes a DD framework for forecasting short-term PV generation and load demand. A framework of three stages with a unique contribution in each stage, such as generalizing data preprocessing steps (stage-1), multivariate feature generation and selection (stage-2), and model hyperparameter tuning (stage-3) for further improvement in forecasting is proposed. It focuses on data as well as forecasting models. The whole process is analyzed using the time-series measured data collected from a real-life demonstration project in Ireland. Data preprocessing is generalized for both generation and demand forecasting under the same framework. The relevant features are selected with the help of the proposed random forest sequential forward feature selection algorithm. Hyperparameters are tuned through tree-structured Parzen estimator algorithm for further improvement. In addition, the performance of the classical autoregressive integrated moving average model is compared with the machine learning-based gate recurrent unit, long short term memory, recurrent neural network, and convolutional neural network models. Results show that the data-driven forecasting model framework systematically improves the model performance. The seasonal variation has also a high impact on the model performances.
I. INTRODUCTION
F ORECASTING is an essential element in the context of microgrids control and operation. Over the recent years, consumers have shown interest in installing distributed energy resources (DERs), especially photo voltaics (PV) and energy storage and this has populated several scattered DERs at low voltage networks. A challenge has emerged for utilities/microgrid controllers to allow high penetration of DERs and at the same time maintain the grid stability and integrity of the entire electrical system. Accurate forecasting, therefore, can help make the utilities aware of the uncertainty related to production from DERs and the varying load demand so that Manuscript utilities can anticipate the risk and take the necessary action to circumvent the problem. The individual residence with/without PV generation inside a microgrid must be taken into account and exactly match the individual profiles of users in a microgrid, forecasting of generation and load demand at the user level becomes prominent. To be precise, the forecasting horizon has a very crucial role in a decision-driven microgrid system. Depending upon the applications, the prediction range can differ significantly. For example, the long-term forecast can help in planning microgrids, the mid-term forecast is useful for resource security and allocation and the short-term forecast (STF) can be instrumental for microgrid control, real-time scheduling of generation and energy market-related operations. The focus of this article is on the short term forecast (from minutes to day-ahead) to augment the control and energy market segment in microgrids.
A. Literature Review
In general, STF models can be classified into two major categories: classical methods and artificial intelligence (AI) based methods. Classical methods include statistical time series [1] and regression-based models [2]. The simple mathematical equations involved in these methods makes them efficient for linear time-series problems but to deal with complex nonlinear forecasting, these methods are inefficient [3]. Hence, AI-based methods that consist of machine learning (ML) enabled models with learning capability have been applied in nonlinear forecasting problems [4]. The application of AI in microgrid control environments has also been reviewed in [5]. Some of the most popular ML models are artificial neural networks (ANNs) [6], random forest (RF) [7], support vector machines [3], and support vector regression (SVR) [8]. Most of the above-mentioned forecasting approaches are model-centric in which emphasis is given to improving forecasting models rather than data. However, datadriven (DD) approaches that focus more on the data might aid in improving the prediction accuracy. To fix this model-centric trend, in recent time, DD approaches have been considered for forecasting PV generation [9], [10] and load demand [3], [8], [11], [12]. Laouafi et al. [8] have focused on improving forecasts by combining different forecasting models.
1) Data-Driven PV Forecasting: Rather than stressing multicombination forecasting models, Kang et al. [13] have proposed a DD approach that compares the target time series to a set of reference time series and predicts future value based on This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ an average of their future paths. González Ordiano et al. [14] have suggested a DD forecasting model by only considering the data processing that includes missing values, outlier detection, and imputation for missing values. It does not consider any derived feature to validate any possibility of improvement in the forecasting model. Shi and Eftekharnejad [9] have proposed a DD approach in which post and preprocessing the data, features like resolution and weather parameters are derived and added to the dataset. A day-ahead PV forecasting with two features for lagged and newest observations process is proposed in [15]. It improves the accuracy of prediction by implementing SVR model. To target the relevant lagged features, a least absolute shrinkage and selection operator based algorithm is discussed in [10], which derives the important lagged values of historical PV time-series data and use those features to predict the day-ahead PV generation using a feed-forward neural network (NN) method. Similarly, Rafati et al. [16] derive the important lagged values by RReliefF (RRF) algorithm and utilize them to predict 15 min PV generation through a single hidden multilayer perceptron (MLP) model.
2) Data-Driven Demand Forecasting: A dynamic mode decomposition method for abstracting features in the load demand time-series data is discussed in [17] which selects particular days of similar load patterns from past values and creates a new input time series to predict future values. A similar-day feature is derived using meteorological data to forecast load demand in [18]. The blend of weather data with load demand is tested against the classical autoregressive integrated moving average (ARIMA) model and found that this hybrid method achieves better accuracy on both ordinary and unordinary (i.e., huge meteorological change in a day) days. Dong et al. [12] utilize the temporal time-series features like the year, month, season, temperature to forecast the load demand with an ensemble ANNbased algorithm which performs better than a backpropagation NN model.
Based on daily and hourly load change (load tracking), new features like load difference of two consecutive hours and a change between the load of the specific hour and a day before were derived in [19] and the important features were ranked according to RRF algorithm. The forecasting model was developed using MLP NN to forecast hour-ahead demand. A two-stage load demand prediction methodology is proposed in [20]. The first stage selects the input features of lagged variables and the second stage forecasts the load. The feature selection method might enhance the performance but the hyperparameters like the number of layers and neurons are randomly selected for the MLP model.
3) Research Gap on DD Approach: Inappropriately, preprocessing the data for forecasting is sometimes misunderstood as a DD approach [21]. In fact, processing the data is an essential step for every forecasting problem and should not be confused with. There is a lack of general data preprocessing methods that can be applicable for both PV generation and load demand forecasting.
In the context of feature selection techniques, mutual information (MI), RRF, correlation-based selection (CFS), and autocorrelation (AC) methods are becoming popular for load demand forecasting problems. While AC and CFS detect only linear correlation, MI and RRF can easily observe nonlinear correlations [20]. Liu et al. [11] have developed a wrapper based greedy forward feature selection technique for selecting features from a pool of features which can be implemented using linear regression (LR) as an estimator. But this algorithm may not work well for nonlinear variables relation and can be computationally expensive due to the involvement of multiple stages of determining feature correlation. Hence, in this article, we propose RF as an estimator instead of LR in the sequential forward feature selection (RF-SFS) algorithm as it can deal with non-linearity and multiple features.
Due to the popularity of the classical ARIMA model and ML models like convolutional neural network (CNN), recurrent neural network (RNN), long short term memory (LSTM) and gate recurrent unit (GRU), [22] these models have further been considered in this article to assess the individual model performance following the DD approach.
ARIMA model works well on stationary data. For forecasting time series, CNN can be a 1-D model containing a hidden convolutional layer working over a 1-D sequence. The following pooling layer consists of capturing the pattern in the data and a dense layer interprets the patterns extracted from its preceded layer. RNN, unlike traditional feed-forward NN, stores the output of hidden layers which is then fed back to the input layer to predict the next value. LSTM has an advanced memory feature over RNN consisting of additional input and output gates with many cells in LSTM that solves the exploding or vanishing gradient problems present in deep NN. GRU is a variant of RNN similar to LSTM. Unlike LSTM, there is no output gate in GRU and consists of an update and a reset gate that facilitate the effective control and flow of information in the cells of the NN. More details of these models can be found in [22].
To mitigate the manual hit and trial approach of hyperparameter tuning of ML models, some algorithms are quite popular such as Grid search along with manual search [23], Random search [23], and Gaussian process (GP) based Bayesian optimization [24]. Of these, grid search suffers from the curse of dimensionality and random search struggles with its nonadaptiveness to different experiments [23]. Bayesian method is one of the advanced sequential search tuning algorithms but originally not applicable for a conditional search space, categorical, or integer values [25]. The ML models with tree-based structures can solve this problem. Hence, tree-structured Parzen estimator (TPE) [26] is used in this article to tune the hyperparameters.
B. Key Contributions
This article discusses a DD forecasting framework that contains data preprocessing steps generalized (bringing both PV and load data pre-processing and forecasting under the same framework), followed by a feature selection through the RF-SFS algorithm and completes with hyperparameter tuning by the TPE method. The foremost contributions are as follows.
A systematic DD forecasting framework: The proposed framework systematically presents the steps required in time-series forecasting that include data collection, processing, feature generation, selection, and hyperparameter tuning.
A generalized data preprocessing for both PV and load forecast:
The presented preprocessing steps eliminate the need for physical parameters like inverter rating, voltage, short-circuit current, performance ratio, etc., and are valid for load demand data as well. It emphasizes DD modeling rather than physics-driven modeling. A unique RF-SFS algorithm for feature selection: The proposed SFS-RF combination is a novel contribution to PV generation and demand forecasting problems. According to the enhanced findings reported in this article, the performance of this combination cannot be overlooked. Implementation of TPE for hyperparameter tuning: TPE is one of the advanced algorithms compared to grid search and random search techniques. The TPE technique has not been previously developed for Load and PV forecasting applications. The findings show that TPE greatly increases the accuracy by picking the most appropriate hyperparameters for this situation.
II. PROPOSED METHODOLOGY
The proposed data-driven forecasting model framework shown in Fig. 1 comprises three stages as a workflow package.
1) Stage-1: Data collection, preprocessing, and data selection (generalized data preprocessing). 2) Stage-2: Feature generation and selection (implement RF-SFS algorithm, combine the relevant attributes). 3) Stage-3: Best-performing models identification (Implement HT). Forecasting models and accuracy evaluation are performed in each stage to obtain the improvement. The overall methodology is explained step by step in the following sections with a case study example, where the time-series measured data has been collected from a real-life pilot project, StoreNet [27].
A. Stage -1: Data Collection, Preprocessing, and Data Selection (DPS)
Data is processed initially to improve the quality of data. Identifying format, timestamps interval, consistency, dealing with missing data, imputation and resampling with final statistics analysis are key things to consider during the analysis. The data preprocessing steps as suggested in [28] have been improved and generalized for both PV generation and demand profile as shown in Fig. 2. The steps are elaborated s follows.
Step-1: Initial Data Identification
Post data collection, recording interval, feature labels and the timestamp format should be observed. For example, in this work, the data was recorded at 1 min interval and the timestamps were in two formats, unix and dd.mm.yyyy hh:mm:ss, which is then converted to dd/mm/yyyy HH:MM:SS format to maintain the consistency. Feature labels were also updated as there were unexpected blank spaces.
Step-2: Consistency Examination
The collected data is reviewed for gaps, repetitiveness, and duplication. Several repeated little gaps are identified but no duplicate entries.
Step-3: Invalid and missing data identification
The values for PV and load demand are missing only in the case when the timestamp is not recorded. No invalid data is found during the analysis process. The values are in line with the capacity of PV installation and consumption profiles.
Step-4: Data imputation and resampling
The missing timestamps are first resampled with the 1 min interval, which is the original resolution of the collected dataset. The variation from previous to next values is not extremely high and the mean value with linear interpolation is valid. Hence, the missing values are imputed using the mean and linearly interpolated to make the observations uniform and consistent. However, other missing data treatment techniques depending upon the missing rate and timestamps can be found in [29].
Step-5: Data verification, aggregation, and statistics
Finally, the dataset is cross-checked for any invalid/corrupted data which is unexpected in the given time series, for example, sudden zero value or extremely high value that is beyond the installed PV capacity and peak load demand. The processed data is now ready to forecast but to follow the benchmark models, some exogenous parameters are required. So the weather parameters like solar irradiation, dry bulb, and grass temperature are downloaded from the Irish Meteorological site [31] and fed to the forecasting models and then the accuracy (error) is calculated.
B. Stage-2: Feature Generation and Selection (FGS)
This stage processes multivariate forecasting by implementing the reference benchmark models developed in [22]. With the available PV generation and load demand data, several features are derived and explained as follows: Step-1: Feature generation First, the entire one-year dataset is split into seasons of the year (Spring, Summer, Autumn, Winter). The features considered here for STF are based on the literature [32], [33] and are categorized into three classes. Their development mechanism is described in the following sections. Encoded cyclic features: A polar coordinate system is used to encode calendar effect features. A periodical cycle in a polar coordinate system is regarded as a unit circle. The periodical features in this system are encoded as a unit circle and their coordinates specify the time. The process of encoding is described by the following: where t is the time and p represents the cycle length. The cycle length p = 24 for the time of the day, p= 7 for the day of the week, and p = 12 for the month of the year. The nomenclature of encoded variables is presented in Table I. Historical time-series features: For a given time t, load in 24 h is denoted by L t+48 and L t is the current load. Similarly, for PV, it is P t+48 and P t . In terms of historical time series, which is denoted by L t−i and P t−i , i = 0, 1, . . . , K, where K is the number of past values and can be regarded as candidate features to forecast value at L t+48 and P t+48 . However, considering all the previous 48 lagged points will increase the input dimension and computational complexity of the model. Thereby, to determine the optimal number of lagged values as candidate features, AC and partial AC (PAC) methods are used. Weather features: Weather is one of the important factors when forecasting PV generation and load demand. Similar to [34] and [35], three parameters that are frequently used as exogenous variables are considered here. It is also compelling to assess their impact on forecasting L t+48 and P t+48 . These are hence, considered as candidate features and since the weather parameters at t + 48 are unknown, the parameters are recorded at time t following the model as presented in [11]. All considered candidate features with their description are presented in Table I.
Step-2: Feature selection Sequential selection algorithms belong to the family of greedy search algorithms and are mainly used to reduce the ddimensional feature set to a k-dimensional subset, where k ≤ d. The ultimate goal of using feature selection algorithms is to determine the optimum number of features relevant to the problem which further helps in improving the computational efficiency or decreasing the generalization error of the forecasting model by eliminating irrelevant features [36]. Mathematically, for a given set Selecting the most optimal features means deriving a new subset that contains all the essential parameters i.e., Sequential selection algorithm can work in two different ways, forward (SFS) and backward (SBS) to eliminate the relevant and redundant features. SFS begins with a single feature and makes the data model using the given structure. Then it sequentially selects the features that provide higher performance (depending upon the performance metric defined in the structure) and this process is repeated until an optimum number of features is selected. In BFS, the algorithm starts with all features and sequentially eliminates the features that provide the least reduction accuracy. It repeats the process until a set of optimum features is not obtained. BFS has a limitation of feature reevaluation, i.e., once the feature is removed, it is not possible to reevaluate its usefulness and cannot be included in the next iteration [37]. On the other hand, SFS does not have this problem and hence, we consider here for this work. The pseudocode for SFS is extracted from [38] and discussed below. A RF algorithm is used to make it work as an estimator for selecting suitable features. For the RF regression number of estimators considered is 10 with a coefficient of determination (R 2 ) as the performance metric in SFS. Thus, we combine the RF estimator and SFS algorithm and propose the RF-SFS algorithm here to deal with nonlinearity and multiple features.
As shown in Fig. 3, the whole training set is divided into six partitions and from the second partition onward, one partition is considered as a test set each time and all previous partitions comprise the training set. The performance considered is a crossvalidation score and calculated by averaging the performance of Algorithm: Pseudo code for SFS algorithm.
Data: F, k Result: X k Initialisation: each test set The SFS adds features from the 14 derived features and forms a feature subset in a greedy manner. In each stage, the RF estimator selects the best feature to add based on the cross-validation (CV) score obtained by the estimator. After performing the feature selection experiment separately for PV generation and load demand, as shown in Figs. 4 and 5 for each season, the different combinations of optimal features are selected based on the highest CV score obtained over multiple iterations. For instance, PV generation as a target variable in the spring season, a set of five features has achieved the highest score of 0.9810. Similarly, for load demand, a set of six features has achieved the highest CV score of 0.8816. The CV score with selected feature names is tabulated in Table II.
C. Stage-3: Best-Performing Model Identification (BMI)
Recent advances in configurations of existing forecasting techniques have brought a shift in dealing with traditional classification and regression tasks. Hyperparameter optimization has been a manual process of selecting the optimal number of parameters within an ML model for so long. This hit and trial approach is time-consuming and cumbersome. To eliminate this, we adopt a TPE algorithm, which is a greedy sequential method and computationally more efficient than conventional tuning methods, based on the expected improvement criterion [26].
For a given configuration space X, the TPE models p(x|y) by transforming the graph-structured generative process (selecting the number of layers first and then choosing the parameters for each), replacing the configuration distribution with nonparametric densities. By utilizing the different observations {x (1) , x (2) . . . x (k) } in the nonparametric densities, the replacement indicates that the learning algorithm can generate many different densities for the given configuration space X. Making use of two such densities, the TPE algorithm defines p(x|y) as [26] given by the following: where l(x) represents the density generated by using observations {x (i) } in a way that corresponding loss f (x (i) ) is less than y * . The density g(x) is generated by the remaining observations. Unlike the GP algorithm, the TPE selects the y * as some quantile γ of observed values y such that the p(y < y * ) = γ that does not require any model for p(y).
We utilize the open-source hyper-Opt [39] software for hyper-parameter tuning. The ML models are implemented and simulated using the open-source library TensorFlow [40]. The HyperOpt has four essential elements to optimize the hyperparameters, namely, search space, a loss function, optimization algorithm (TPE here) and a database of score and configuration history as shown in Fig. 6. Initially, the user defines the search space with a given set of parameters to be tuned, mathematically it is determined by a continuous and convex function. A loss function is to be evaluated for each configuration setting using the number of observations. The optimization algorithm is based on sequential model-based global optimization that finds the best solution for convex optimization problem and the scores for each configuration through the iterative process are stored in the database in a set of tuples (score, configuration). The search space with the highest score is extracted and the new space is redefined for further sampling. This process is repeated until either the overall highest score is achieved or early stopping is enabled.
III. MODEL EVALUATION
Since the dataset is categorized according to the seasons, the training and testing are split in a way that the last day of each season is considered for testing and the remaining samples are considered for training the models out of which 10% are reserved for validation to achieve a day-ahead (short-term) forecast as described in Table III.
The models are evaluated on normalized RMSE (nRMSE) values [41]. For PV forecast, the formula is given by the following: Similarly, for load demand forecast, it is given by the following: whereP i and P i represent predicted and measured power at time i, P installed is the total installed PV capacity, N is the total number of samples, andP is the mean of all observations. There are two more metrics included to better understand the performance of the DD approach in different models, namely, normalized mean bias error (nMBE) and the forecast skill score. The nMBE metric indicates if there is a significant tendency to systematically underforecast or overforecast, which is termed as bias [42]. The positive and negative values imply over and underforecast, respectively. It is indeed useful for network operators as the understanding will allow them to better allocate the resources in the dispatch process and compensate for the errors and is given by the following [43]: where P i, max is the maximum power among all observations. Skill score (SS) [44] represents the fractional improvement in the new method/model compared to the benchmark model for the considered metrics. SS can range from 0 to 1, where 0 means no improvement, 1 indicates perfect forecast, and negative SS means that the new method/model performs worse than the reference. SS is given by the following [42]: Skill Score (%) = 100 Metric base − Metric forecast Metric base .
IV. RESULTS AND DISCUSSION Statistical and ML-based models are considered and evaluated here for each stage. The entire three-stage framework is simulated for each season of the year and the errors are calculated for every stage.
nRMSE: Fig. 7 shows the performance and evaluation through nRMSE. ARIMA being the classical model, there are no hyperparameters like other advanced ML algorithms. Hence, the results for ARIMA are considered only until stage 2. Following the forecasting framework, the error is sequentially reduced in each stage and for both forecastings. Significant improvement appears for PV generation forecasting [see Fig. 7(a)] and mainly during the summer months. This is also followed by spring. This confirms the initial validation of the effectiveness of the proposed framework. For example, GRU shows the best performance for both PV and load forecasting in spring [see Fig. 7(b)]. The error, which was 13.9% in stage 1, is decreased to 9% in stage 3 for PV forecasting. For load forecasting, the error is reduced by 6.4% in stage 3 compared to stage 1. For summer, an improvement of 14.16% is observed in the LSTM model giving only 5.34% error in the final stage and performing the best for PV forecasting. For load prediction, RNN (12.6%), LSTM (12.5%), and GRU (12%) are quite close to each other. Similarly, in autumn, RNN performs best for PV and LSTM for load forecasting. For winter, the LSTM model outperforms all the other models giving only 0.56% of error for predicting PV output whereas RNN surpasses other models by giving the lowest 9% error to forecast day-ahead load demand.
From the training time perspective, as shown in Fig. 8, it can be observed that GRU and LSTM have the least training time among all the models. In contrast, RNN has taken the most time to train the model for both PV and load forecasting.
Forecast plots are shown in Fig. 9 for the summer season. Inferred from the above analysis, LSTM is the best-performing PV forecast model and hence three consecutive stages are shown in Fig. 9(a). Similarly, GRU is the best-performing model for load forecasting in the summer season. The performance of these three stages is shown in Fig. 9(b). Thus, it further validates the effectiveness of the proposed forecasting framework. nMBE: Fig. 10(a) shows the nMBE in the case of PV forecast. Like nRMSE, the bias is reducing here as progress from stage 1 to 3. More specifically, the autumn season has the lowest bias tendency. However, it is interesting to note that the bias in the summer (overforecast) and winter (underforecast) seasons is the opposite. One of the reasons could be that the summer (winter) months have very high (low) PV production and thus prediction models track these dynamics and bias follow the trends according to the historical and independent features' time-series values. For all cases, the stage 3 process can significantly reduce the bias error. The bias for the best model (LSTM) in summer and winter is 0.96% and -11.1%, respectively. For the spring (GRU) and autumn (RNN), the bias is 2.46% and 0.65%, respectively.
In the case of load forecast [shown in Fig. 10(b)], the bias is also gradually reducing from stages 1 to 3. Similar trends appear here, as is found for the PV forecasting. Demand is high (low) in winter (summer) months, thus the forecast models follow the trend and result in an over (under) forecast. The bias in the winter for the best model (RNN) is 5.78%, which is an overforecast. Models in spring (GRU), summer (GRU), and autumn (RNN) seasons have the bias of 2.33%, -2.3%, and 0.67%, respectively. The bias is mostly independent of forecasting models and depends upon the seasons or specifically the training data given to the models.
Skill Score: Considering ARIMA as the least-performing baseline model, the SS of other models has been presented for different seasons, as shown in Fig 11. It depicts the accuracy improvement in other models compared to ARIMA. Similar to nRMSE and nMBE, successful improvement appears in SS for both PV and load forecasting methods. These significant improvements again validate the importance of the proposed forecasting framework.
For PV forecast, all models have improved [see Fig. 11(b)] accuracy however, surprisingly, in load forecast CNN and LSTM have a negative improvement over the baseline model. It is noteworthy that the neural network model performance (CNN and LSTM) during load forecast has degraded as shown in Fig. 11(b). It also indicates the sensitivity of the models. It might happen due to the complexities within the internal architecture like model type, size, optimization process, and data complexity which is a further point of investigation in future research.
V. CONCLUSION
In this article, a DD forecasting framework has been proposed that does not only focus on data but also on models. The approach also brings the generation and demand forecastings under the same framework. Data preprocessing steps have been generalized for stage-1. The error is evaluated for each model and individual application. Stage-2 focuses on carefully selecting features through the proposed RF-SFS algorithm. The RF works as an estimator and it sequentially simulates to find specific combinations of features and selects a set with the best performance. Stage-3 tunes the hyperparameters in the models by the proposed TPE algorithm mainly focusing on optimizing the model performance. The numbers of neurons, layers, window length, and batch size are selected for each forecasting task and benchmark models are updated with new parameters. Forecasting on new models is performed and errors are recorded. In concluding the results, the following are the key takeaways from this article: 1) The proposed data-driven forecasting model framework systematically improves the model performance. Baseline model evaluation, feature development, selection, and hyperparameter tuning gradually reduce the error. 2) For each season and application, RF-SFS algorithm selects a different set of independent variables and thus, the appropriate selection of features improves in forecasting models. 3) The model performance also varies and can be biased depending upon the seasons. One universal forecasting model cannot be fixed for the entire year and seasonal/variable models should be developed for better and more accurate forecasting. 4) Newly installed PV in the geographical region in which this forecasting research has been conducted can get benefit from the results and responsible control entities can better match the supply and demand. 5) Enabling graphics processing unit support during model training and forecasting can significantly reduce the training time. However, it still depends on the training data, model complexity and different hyperparameters involved in it. 6) Future research intends to focus on making the AI explainable by utilizing models' interpretability or explainable modeling.
ACKNOWLEDGMENT
This work is a part of MiFIC project and the authors in IERC thankfully acknowledge the support from the Department of the Environment, Climate and Communications. | 7,201.6 | 2022-10-01T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
The incredible shrinking puffin: Decreasing size and increasing proportional bill size of Atlantic puffins nesting at Machias Seal Island
Climate change imposes physiological constraints on organisms particularly through changing thermoregulatory requirements. Bergmann’s and Allen’s rules suggest that body size and the size of thermoregulatory structures differ between warm and cold locations, where body size decreases with temperature and thermoregulatory structures increase. However, phenotypic plastic responses to malnutrition during development can result in the same patterns while lacking fitness benefits. The Gulf of Maine (GOM), located at the southern end of the Labrador current, is warming faster than most of the world’s oceans, and many of the marine species that occupy these waters exist at the southern edge of their distributions including Atlantic puffins (Fratercula arctica; hereafter “puffin”). Monitoring of puffins in the GOM, at Machias Seal Island (MSI), has continued annually since 1995. We asked whether changes in adult puffin body size and the proportional size of bill to body have changed with observed rapid ocean warming. We found that the size of fledgling puffins is negatively related to sea surface temperature anomalies (warm conditions = small fledgers), adult puffin size is related to fledgling size (small fledgers = small adults), and adult puffins have decreased in size in recent years in response to malnutrition during development. We found an increase in the proportional size of bill to wing chord, likely in response to some mix of malnutrition during development and increasing air temperatures. Although studies have assessed clinal variation in seabird morphology with temperature, this is the first study addressing changes in seabird morphology in relation to ocean warming. Our results suggest that puffins nesting in the GOM have morphological plasticity that may help them acclimate to ocean warming.
Introduction
Environmental changes often lead to physiological challenges as organisms struggle to meet their basic metabolic and thermoregulatory needs [1][2][3][4].As a result, many organisms have experienced changes to their phenology and distribution resulting from climate change [5].
Warming, particularly in the marine realm, may have particularly acute impacts on ectotherms (e.g., crustacea, fish, squid) that seabirds eat, as many live close to their thermal maximum [6]; this can lead to cascading impacts up the food chain.In general, phenotypic plasticity allows organisms to meet changes in their physiological needs that can lead to changes in body size and allometry [but see 7,8].In fact, decreasing body size has been termed the "third universal response to climate change" [9,10] and there are many documented accounts of decreasing body size with increasing temperature [e.g., [11][12][13][14][15]. Changing body size may be a result of a phenotypic plastic response to malnutrition during development [16], that leads to reduced growth rates and decreased adult size in many birds [17][18][19].Alternatively, decreasing body size and allometric changes may be a genetic microevolutionary response to warming as explained by Bergmann's and Allen's rules.Bergmann's Rule generalizes an increase in body size with decreasing temperature due to thermoregulatory benefits of decreased surface-area to volume ratio [20,21].Allen's Rule predicts larger appendage size in warmer locations due to thermoregulatory benefits of proportionally larger surface area over which to dissipate heat [22,23].Allometric changes predicted by Allen's Rule have been observed across many taxa and linked with climatic warming [24][25][26].Regardless of the driver of change in body size and allometry, whether decreased adult size confers a fitness benefit will depend on whether environmental conditions remain favorable for the phenotype in question [19].
Seabirds are highly mobile, colonial nesting species that sit near the top of many marine food chains [27].They are often used as indicators [28,29] or sentinels [30] for marine ecosystems and have shown many responses to ocean warming, including changes in phenology, foraging behaviours, and reproductive performance [31].Many long-term, colony-based studies have documented changes in prey quality and quantity [e.g., 32,33], and decreases in nestling growth rate and fledgling size [34].But because of the size of most seabird colonies, measuring individuals when they depart their natal site as fledgers and again as breeding adults is difficult.Similarly, it is not normally possible to age individuals accurately when they are captured as adults.Thus, we are unaware of any seabird study assessing phenotypic changes at the adult stage related to environmental conditions during development.
The Gulf of Maine (GOM) sits at the southern edge of many marine species ranges, including the copepod Calanus finmarchicus and Atlantic puffins (Fratercula arctica; hereafter "puffin").The GOM is warming faster than most of the world's oceans [35,36] and experienced a major regime shift in 2010, followed by numerous heat waves and changes in circulation that together resulted in a reduction in Calanus [37].Calanus and other copepods are important prey items for many species of planktivorous fish (e.g., Atlantic herring Clupea harengus and American sandlance Ammodytes americanus [38,39]) that are important prey for seabirds and other top predators [40][41][42].This decline of copepods was accompanied by decreases in herring abundance in both fisheries data and puffin chick diet [33,35].As a result, puffin chicks in the GOM have experienced reduced growth rates, nestling survival, and size at fledging [31,33,43,44].
Machias Seal Island (MSI) is a small island located at the junction between the Bay of Fundy and the GOM and hosts the largest nesting colony of puffins in the GOM.MSI is the site of a long-term annual seabird monitoring program since 1995.This program includes an extensive capture-mark-recapture (CMR) program, in which both adults and chicks are captured/recaptured, measured, and released.A unique feature of MSI is the light station surrounded by a mown lawn where puffin fledglings congregate on the night of fledging and are easily captured and measured.This exceptional dataset records the size of fledgling puffins the night they depart the island for their first winter at sea and later measurements of these same individuals when they return as adults.
Atlantic puffins are a small (~430 g), long-lived, pelagic seabird, their global nesting range extending from Spitsbergen, Norway and Novaya Zemlya, Russia, to the Gulf of Maine, USA at its southernmost extent [45].At MSI, puffins return to the colony to begin breeding from three years after fledging, 96% have returned by age seven [43].Puffins lay one egg each year and both adults incubate the egg and provision the chick.Puffin adult and chick diet at MSI is composed mainly of adult and larval sandlance, and juvenile white hake (Urophycis tenuis) and Atlantic herring [33].Proportions of prey species fed to chicks can vary substantially both within and between years [31,33,46].During years when chicks are nutritionally stressed (i.e., when prey quality, as characterized by Scopel et al. [33], and availability are low) chick growth rates and fledging size are reduced [31].At MSI, as SST anomalies increase, the proportion of low-quality prey fed to chicks increases (see Fig 1), often resulting in slow chick growth rates and small size at fledging [44].Puffin body size is indexed chiefly by wing chord length, and the puffin bill has recently been shown to dissipate heat [47] as is the case in many birds [48,49].Our long-term dataset in the rapidly warming GOM provides an important opportunity to detect phenotypic changes in relation to ocean warming.Our study colony is among the furthest south of any regularly monitored site within the species' range, in waters that are warming extremely rapidly, so puffins here are likely to be close to the limit of their thermal tolerance.Puffins are known to show considerable phenotypic plasticity in chick growth rates and fledging periods in relation to nesting habitat and associated vulnerability to predation [50,51] and food availability [52,53].Our objective was to assess changes in adult body and bill size at MSI in relation to observed environmental conditions (i.e., warming and associated ecosystem changes) in the GOM (see Fig 1A).Here, we use sea surface temperature (SST) anomalies as a proxy of ecosystem-wide changes that have occurred in the GOM (i.e., reduced copepod and forage fish abundance and warming), proportion of low-quality prey fed to chicks as an indication of malnutrition during chick development, and mean maximum air temperature during chick rearing as an assessment of thermoregulatory environment during development.We note that changes in ambient temperature inside nesting burrows (where nestlings remain) may be buffered against the increases observed outside the burrows.We developed three competing hypotheses for change in body and bill size of Atlantic puffins 1) phenotypic plastic response to malnutrition during development; 2) genetic microevolutionary response to warming as explained by Bergmann's and Allen's rules; or 3) result of the interplay between malnutrition and genetic microevolutionary response to warming and resulting ecosystem-wide changes.When testing for a change in body size, we used a tiered approach; first, testing whether fledger body size has changed and is a function of environmental conditions (i.e., SST anomalies, prey quality fed to chicks, and maximum air temperature during chick rearing; see Fig 1).We then asked if fledger body size predicts adult body size, and finally whether observed changes in adult body size are the result of malnutrition, warming, or some combination of both.We predict that change in body size is a phenotypic plastic response to malnutrition during development, as suggested by previous studies [31,33,46].Avian bills are highly vascularized, poorly insulated, and used for thermoregulation by many species [48,49] including puffins [47], and given the heat-dispersion hypothesis embedded in Allen's rule, we predict that a warming environment increases the demand for heat dissipation and larger thermoregulatory structures would be selected for during development.Similar to body size, many birds show clines in bill size [see review in 23,48], changes in bill size in relation to warming have been documented for many bird species [24,25], though not, to our knowledge, for seabirds.We predict that if puffins use their bills for thermoregulation, adult bill size will increase in proportion to body size as temperatures increase, as predicted by Allen's Rule.
Study site
Machias Seal Island (MSI) is a small island (~9.5 hectares) located at the mouth of the Bay of Fundy and the edge of the GOM that is designated as a migratory bird sanctuary.The seabird nesting colony at MSI includes nesting populations of Atlantic puffin, razorbill (Alca torda), common murre (Uria aalge), Arctic tern (Sterna paradisaea), common tern (S. hirundo), Leach's storm-petrel (Hydrobates leucorhous), and common eider (Somateria mollissima).Members from the Atlantic Laboratory for Avian Research (ALAR) at the University of New Brunswick have been studying the seabird populations at MSI since 1995.Beginning in 1980, personnel from the Canadian Wildlife Service banded and resighted puffins at MSI, but field work at MSI before 1995 was not annual.All research conducted at MSI since 1995 has occurred under approved annual animal use protocols from the University of New Brunswick, and annual scientific, migratory bird sanctuary, and bird banding permits from Environment and Climate Change Canada.All data included in our analyses were collected as part of the long-term monitoring program at MSI during 1995-2022, methods are detailed in island protocols that are available online: https://msialar.wixsite.com/alar-msi/msi-protocols.
Our analyses rely on data collected for individuals at both the fledger and adult stage, because individuals begin attending the colony in their third to fifth years after fledging and recapture rates of previous banded adults is small (~18% of our captures each year are previously banded individuals), sample sizes of adults banded as fledgers (~4% of our captures each year are adults that were banded as fledgers) decreases rapidly for birds that fledged in the most recent ten to fifteen years and is < five after 2011 and zero after 2015.Due to this limitation and limited sex information (see below) our sample sizes and the fledge years included vary across our four main analyses.
Data collection
Lighthouse fledger body size.Beginning in mid-to late-July each year, puffin fledglings depart MSI at night for the non-breeding season when they remain at sea.Many are attracted to the light from the light station located near the middle of MSI and congregate on the mown lawn in front of it.Every half hour during the hours of darkness, ALAR researchers patrol the lawn checking for puffin fledgers.Each fledger encountered is captured by hand, banded with a Bird Banding Laboratory (BBL) band and an alpha-numeric field readable band (FRB), measured (mass [grams], natural wing chord length [mm], culmen [mm], and head + bill [mm]), and then released into the water.
Banding and recapture.May through August of each year, adult puffins are captured at their nest sites, by grubbing nesting burrows and using box traps (a trap placed in the colony with a swivel-top lid that when a puffin lands on the surface deposits the bird into the box and closes again).All birds captured are banded with a BBL and FRB, measured (mass [grams], natural wing chord length [mm], culmen [mm], bill depth [mm], and head + bill [mm]), and released.Birds that were previously banded are measured as above, bands read, and released.
As part of a larger monitoring program on MSI, approximately 70-100 puffin nesting burrows are monitored each year to measure reproductive success.All chicks in these burrows that survive to 30-35 days are banded with a BBL and FRB.For burrow-banded puffin chicks and lighthouse fledgers, hatch year (here termed "fledge year") is known.When one of these birds is recaptured as an adult, its age in years is known, and these individuals are included in our sample of known-age adults.For our analyses, we consider only individuals that were banded as chicks and recaptured as adults (hereafter termed "known-age").
Sex determination.Male and female puffins cannot be distinguished visually; we used two methods, genetic sex determination and discriminant function analysis, to determine the sex of the individuals in this study.During banding, 5-6 breast feathers are plucked for genetic analysis.Over the years different ALAR researchers have conducted genetic analyses for puffins on MSI for different projects.We collated that information and have genetic sex information for a total of 1,529 individual puffins.In all cases, we used the methods outlined in Fridolfsson and Ellegren [54] Devlin et al. [55], and Friars and Diamond [56].For individuals that did not have genetic sex information we used the discriminant functions developed by Friars and Diamond [56], using bill length and depth.Specifically, we used equations 2 (for birds with fewer than 1.5 bill grooves or no bill groove information) and 4 (for older birds with more than 1.5 bill grooves) to assign individuals as male or female.Our approach for sex determination was conservative; for equation 2 we assumed birds were female if the discriminant score was < -0.612, or male if the discriminant score was > 0.960, all other birds were classified as unknown.For equation 4 we assumed birds were female if the discriminant score < -0.605, or male if the discriminant score was > 0.838, again all other birds were classified as unknown.To test the efficacy of the discriminant function, we applied it to all birds with genetic sex information and calculated the proportion of those birds that were designated male or female by both methods and those that were not.Assuming that the genetic analysis was the correct sex designation, we found that 85% (88% correct assignments for females and 83% correct assignments for males) of the individuals were assigned the same sex using both methods, providing support for including birds sexed using the discriminant function.
Environmental conditions
SST data was downloaded from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory website (https://www.psl.noaa.gov).We extracted SST monthly mean data from the NOAA Optimum Interpolation (OI) SST V2 high resolution dataset for all years between January 1, 1981 -December 31, 2022 for 18 1˚×1˚cells that lie within the GOM, which encompasses the full extent of puffin foraging range during the breeding season [57,58].We calculated annual means for each year in our dataset and an overall mean, to use as our reference period, for the period January 1, 1981 -December 31, 2011.Anomalies were then calculated as the difference between the annual mean and the reference period.
Air temperature (degrees Celsius; daily min and max) is recorded every day during the puffin nesting season at 2100 h Atlantic Daylight time (ADT).Here, we calculated the mean maximum air temperature for June 1 -July 31 in each year and used that as an index of air temperature during chick development.
Prey quality, or the proportion of low-quality prey fed to chicks, is calculated as a proportion of the total biomass of prey considered "low-quality" [see 33] recorded during multiple 3-hour prey watches that occur each year during chick rearing.For detailed methods see [33,59].
Statistical analyses
All statistical analyses were completed in the RStudio environment (R version 4.2.0 [60,61]) using an information theoretic approach.Specifically, we used the R package "MuMIn" [62] and ranked models using Akaike's Information Criterion (AIC) for small sample sizes (AICc), and AICc weights were used to evaluate model likelihood [63].We used parameter estimates and standard errors to draw inference from our data.When the top model received less than 90% of the total weight among models, we used model averaging (from the "MuMIn" package) and the "conditional average" method [see 63,64].We then used weighted parameter estimates and unconditional standard errors to draw inference, as above.When testing among models that include SST anomaly, mean maximum air temperature, and prey quality, we standardized our effect sizes on two standard deviations following Gelman [65].Summary data are presented as means ± 95% CI, unless otherwise noted.
Change in lighthouse fledger size
To evaluate whether lighthouse fledger size (i.e., wing chord length) is correlated with environmental conditions, we used the "nlme" package in R [66] and generalized linear mixed models with a gaussian distribution.Lighthouse fledger data included all fledgers captured on the mown lawn in front of the light station during 1995-2019 that were of known sex (n = 1,020).Male puffins average slightly larger than females in all dimensions [53,56] and we expect annual variation, thus our candidate models all had Sex and Year included as random effects.First, we ran the most parameterized model (i.e., global model) fit by restricted maximum likelihood (REML) and checked model diagnostics.Our diagnostics revealed assumptions of linearity were met but temporal autocorrelation was present.We defined autocorrelation structure using autoregressive moving average (ARMA) serial correlation structure.To find the optimal ARMA structure, we ran models with all combinations of p and q between zero and three and compared them with AIC.The model with the lowest AIC value was taken as the model containing the optimal ARMA structure (see [67] for more information); that ARMA structure was then incorporated into our a priori candidate model set that was run as above but fit with maximum likelihood (ML).
Change in adult body size
Relationship between fledger and adult body size.To assess the relationship between fledger and adult body size (i.e., whether a small fledger becomes a small adult) we ran a series of linear regressions with no autocorrelation structure evaluating the relationship between structural measurements (i.e., wing chord length, culmen, and head+bill) taken from a lighthouse fledger and from the same individual captured as an adult, following the same AIC procedure detailed above but including only Sex as a random term.Our dataset included all individual puffins that were measured as both fledgers and adults from fledge years 1995-2015, and that have sex information (n = 285).In some instances (n = 54) individual adults were captured and measured in multiple years; here, we used the average of all measurements taken for that individual, so each individual is represented only once.
Change in adult size.Using all known-age adults with measurements, we tested whether aspects of adult puffin body size (i.e., wing chord length, head+bill length, and proportional bill size to wing chord length) have changed as a function of environmental conditions during their fledge year.We used two indices to represent bill size: bill depth and bill area, treating the bill as an isosceles triangle and using the following equation for the area of a triangle: Bill Area ¼ 0:5 � ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi We used the same statistical procedures as outlined above for lighthouse fledgers.This analysis is a direct test assessing 1) reduction in the structural size of adult puffins and 2) increase in proportional bill size to body size.Here our dataset includes data for all adults banded as both lighthouse fledgers and as chicks prior to fledging, and that have sex information for years 1995-2011 (n = 286), as our sample size for known-age adults with measurements for fledge years after 2011 is low (< 5) and reaches zero by fledge year 2016.Similar to our dataset assessing correlation between fledger and adult size, if an individual was captured and measured more than once, we used the average of all measurements taken for that individual, so each individual is represented only once.
Change in lighthouse fledger size
Between 1995-2022 we have measurement data for 5,348 lighthouse fledgers (average 163 ± 35 individuals measured per year).Of those, we have sex designation for 1,020 individuals (505 female and 515 male).Male lighthouse fledgers (wing chord length 140 ± 0.56 mm) tend to be larger than females (138 ± 0.55 mm), and on average we have observed a decrease in the size of both males and females as SST anomalies, mean maximum air temperature, and proportion of low-quality prey fed to chicks all increase (Fig 2 1).Our model averaged parameter estimates and standard errors suggest an important negative correlation between annual SST anomalies and fledger wing chord (Table 2).Our parameter estimates and unconditional standard errors also suggest a weak negative correlation between fledger body size and mean maximum air temperature but no discernable relationship with prey quality (Table 2).
Change in adult body size
Relationship between fledger and adult body size.At MSI, a total of 285 known-age adult puffins were measured at least once after being measured as a lighthouse fledger.Measurements of culmen and head+bill were not as consistently recorded as wing chord measurements, particularly on fledgers; sample sizes for known-sex culmen, head+bill, and wing chord are 221 (111 male, 110 female), 122 (63 male, 59 female), and 251 (129 male, 122 female), respectively.In all cases, average measurements of structural size were smaller for fledgers than for adults and for females than for males (Fig 3A).Adult culmen, head+bill, and wing chord all show a general trend suggesting a positive correlation between fledger and adult size (Fig 3B).Our statistical analyses support this trend for all three variables and show positive correlations between each set of measurements (Tables 3 and 4).
Change in adult size-Wing chord length and Head+Bill length.Over our 28-year time series at MSI we have measured 363 adults of known age (153 female, 165 male, and 45 of unknown sex).We note variability in the number of individuals measured in each fledge year, here we included only birds from fledge years between 1995-2011 (n = 276; 132 female, 144 male).The total number of individuals measured as adults from each of these fledge years averaged 25 ± 7. We note variability in changes in size of adult wing chord and head+bill with our different environmental conditions, ranging from no discernable change, to both positive and negative changes (Fig 4A -4F).Our statistical analyses show a negative relationship between both wing chord and head+bill, and proportion of low-quality prey (Tables 5 and 6).We also note weak positive relationships between both structures and mean maximum air temperature, and negative (wing chord) and positive (head+bill) relationships with SST anomaly (Table 6).
Change in adult size-Proportional bill size
On average, the proportional size of bill depth to body size (i.e., wing chord length) was 0.23 ± 0.002 (range 0.18-0.29).Overall, we see a trend towards a larger proportional size (for both proportional bill size and proportional bill area) in years with higher SST anomalies (Fig 4G and 4H) and mean maximum air temperature (Fig 4J and 4K).We observe little change in proportional bill size with proportion of low-quality prey (but note negative relationship for male bill area; Fig 4I and 4L).We note that the top candidate model for these two metrics of proportional bill size differ, with SST anomaly being the top supported model for proportional bill size and the top supported model for proportional bill area being mean maximum air temperature (Table 5).We also note weak positive and negative support for SST anomaly and proportion of low-quality prey, respectively, for proportional bill area (Table 6).
Discussion
Both Bergmann's and Allen's rules predict that body/appendage size is mediated by temperature; with warming, body size should decrease (Bergmann's rule) and thermoregulatory structures should increase in proportion to body size (Allen's rule), but changes to body size can also be the result of malnutrition during development with no genetic microevolutionary change.We developed three competing hypotheses to explain observed changes in body and bill size of Atlantic puffins at Machias Seal Island, where rapid ocean warming and changes in prey quality are occurring: 1) phenotypic plastic responses to malnutrition during development; 2) genetic microevolutionary responses to warming as explained by Bergmann's and Allen's rules; and 3) result of the interplay between malnutrition and genetic microevolutionary responses to warming from ecosystem-wide changes.We did not find consistent support for any single hypothesis solely.We found that fledger body size is related to SST anomaly, supporting our third hypothesis; and that fledger and adult body and bill size are related.Thus, we would predict that adult body size should also be related to SST anomalies, but our analysis for adult body size found relatively strong support for our first hypothesis, suggesting that even though ecosystem-wide changes are most important for fledger size, the proportion of low-quality prey fed to chicks prior to fledging is most important for determining adult body size.Further, we found that proportional bill size/area support hypotheses 2) and 3), depending on the metric used.In both cases proportional bill size is larger with warming (i.e., SST and mean maximum air temperature).The differences among structures is intriguing and may suggest that we did not include an important predictor of adult size, or that our third hypothesis (some combination of hypotheses 1 and 2) is the best explanation for changes in puffin body and bill size.We note that responses of male and female puffins differed, and females seem to generally have a stronger response to warming than males, particularly in proportional Table 1.Candidate models assessing correlation between Atlantic puffin lighthouse fledger wing chord length (WC) and environmental conditions at Machias Seal Island during 1995-2019.All candidate models included autoregressive moving average autocorrelation structure p = 1, q = 1, and the random terms Sex and Fledge Year (n = 1,020).).For proportional bill size we found that SST anomaly is important for both sexes, but for proportional bill area, prey quality is most important for males and mean maximum air temperature for females (S1 and S2 Tables).We postulate that differences may result from the different energetic demands faced by males and females, particularly during egg production, but future work is needed to investigate these differences.Atlantic puffins are considered a cold-adapted species, their global nesting range extending from Spitsbergen, Norway and Novaya Zemlya, Russia, to the Gulf of Maine, USA at its southernmost extent [45].A cline in body size has been documented, with the largest puffins (with largest bill) nesting in the coldest regions [45,68], and among the different puffin sub-species, the largest, F. arctica naumanni, nesting in the high Arctic and the smallest, F. arctica grabae, nesting in the southeast Atlantic (those nesting in the GOM (F.a. arctica) are intermediate in size [53,68]).Although puffins with the largest bills nest in the most northern (i.e., coldest) regions and Lowther et al. [45] state that proportionally the naumanni sub-species has the largest bill, evidence from published accounts of morphometrics suggest this is not the case (see S3 and S4 Tables, and S1 and S2 Figs).Rather a cline exists where the proportional size of the bill to body is largest at the most southern colonies where the grabae and arctica sub-species nest.Thus, variability in body and bill size is present, and given differences in metabolic and thermoregulatory requirements in different regions, local acclimation is expected.Our results support that local acclimation is occurring in Atlantic puffins nesting in the GOM and may help them adapt to the rapidly warming environment.We note that we would expect proportional bill size to be larger if body size is decreasing and bill size remains the same.To test, post hoc, whether the change in proportional bill size is an artefact of changing body size, we assessed whether absolute bill size followed the same trends as proportional bill size.Here, we found the relationship holds and that mean maximum air temperature is positively related to absolute bill size (i.e., absolute bill size and proportional bill area both increase with increasing air temperatures; S5 and S6 Tables), lending support to our conclusion that proportional bill size is changing with environmental conditions.
Model
Our results are consistent with both a phenotypic plastic response and genetic microevolutionary changes with warming.Van Gils et al. [16] suggest that trophic disruptions that result in a size change are separate from Bergmann's and Allen's rules and we have not tested for a genetic change or whether observed changes are adaptive.We suggest that ecosystem-wide changes resulting from warming can lead to variable responses and that these two rules are not mutually exclusive.We postulate that changing size of puffins nesting at MSI is the result of both trophic disruption and warming.Further research is necessary to understand if the changes we have observed confer a fitness benefit and whether malnutrition, as the proximate cause of change in size, can lead to an adult that is better adapted to their warming environment.
All individual puffins measured were larger in the adult stage than as fledgers, providing evidence of continued growth post fledge and Georgantopoulos [44] found that large fledgers at MSI grew less after fledging than smaller fledgers, suggesting compensatory growth.The impact of compensatory growth often means lower survival, as individuals allocate energy to growth instead of to improving their condition [69].A study of tufted puffins (Fratercula cirrhata) at Triangle Island, BC found that small fledgers are less likely to return to the island as adults than large fledgers [70]; if the same is true for Atlantic puffins at MSI, our sample is biased against finding what we predicted.
Our finding of smaller bodies and larger bills for individuals that fledged during years where environmental conditions were warmer/prey quality was low is particularly interesting given that our analysis did not include the most recent years when all environmental variables are highest.This is particularly true for proportional bill size/area.If puffin bills are used as thermoregulatory structures and their size is determined during development, it is possible that phenotypic plasticity and the ability to alter the size of the bill depending on environmental conditions during development may be crucial in the long-term persistence of puffins in the GOM.However, much research is needed to fully understand the importance of bills as thermoregulatory organs, how bill size is determined, and the phenotypic limits of variability in this trait.
Puffins nesting in the GOM are experiencing some of the most rapid warming occurring around the globe.Documented changes to the quality of food fed to chicks, and decreases in chick growth rate, fledgling body condition, and reproductive success [31,33,46] suggest that environmental changes are negatively affecting this population.To date, we have not observed a decrease in the nesting population of Atlantic puffins at MSI, or a decrease in annual survival (although research updating our survival estimates is ongoing) suggesting that, for now, puffins have been able to cope with warming.Exactly how they are coping, the mechanisms involved, and the fitness consequences remain unknown.Here, we have used our MSI dataset of measurements of known-age puffins to complete the first assessment of seabird responses to climate change with respect to the malnutrition hypothesis, and Bergmann's and Allen's rules.Further research is needed to assess whether the changes we have observed confer a fitness benefit and are heritable.
Nutritional stress during development, particularly during the period of adult provisioning, has been shown to result in reduced growth rates and decreased adult skeletal size in song sparrows (Melospiza melodia) and other birds [17][18][19]71].In a study of growth allocation of Atlantic puffin chicks, Øyan and Anker-Nilssen [72] found that when stressed for food, Atlantic puffin chicks allocate more energy to growing their head (including their bill) over that of their wings.Thus, during years of reduced prey quality we would expect puffin wings to be smaller at fledge and bills to be proportionally larger, as our results show.However, we would not expect to see an absolute increase in bill size as observed, rather a proportional increase resulting from smaller wings.Assuming thermoregulatory constraints are an important component of Atlantic puffin fitness, we hypothesize that the fitness benefits conferred result in higher survival and recruitment rates for individuals with larger bills and smaller bodies.Thus, the impact of nutritional stress on Atlantic puffin chicks on MSI may help the population adapt to rapid warming in the GOM.However, future work is required to assess the mechanistic processes responsible for determining body size and allometry, how these processes change in relation to nutritional stress and/or changing environmental conditions, and whether the observed changes in body and proportional bill size confer a fitness benefit. .Model averaged parameter estimates, unconditional standard errors, and relative likelihoods for the candidate model set evaluating the relationship between male and female Atlantic puffin adult bill depth and environmental conditions (i.e., SST anomaly, mean maximum air temperature, and prey quality fed to chicks) at Machias Seal Island during 1995-2011.Parameters in bold font are those that do not bound zero.Model averaging was completed using the "MuMIn" R package in the RStudio environment.(DOCX)
Fig 1 .
Fig 1. SST anomalies and proportion of high-quality prey in Atlantic puffin chick diets.Comparison of annual SST anomalies (A) in the Gulf of Maine, mean maximum air temperature in June and July (i.e., during chick rearing); B) and proportion of low-quality prey in chick diets (C) by year at Machias Seal Island during 1995-2022.Blue line is linear regression.High prey quality is defined as the percentage by biomass of Atlantic herring (Clupeus harengus) and sandlance (Ammodytes hexapterus) fed to chicks in each year [see 33].https://doi.org/10.1371/journal.pone.0295946.g001
Fig 2 .
Fig 2. Atlantic puffin lighthouse fledger wing chord length and environmental conditions.Correlation between mean (± 95% CI) Atlantic puffin lighthouse fledger wing chord length at Machias Seal Island during 1995-2019 and (A) annual SST anomalies in the Gulf of Maine (SST anomalies were calculated using 1981-2011 as the reference period), (B) proportion of low-quality prey fed to chicks, and (C) mean maximum air temperature during chick development for females and males.Data from 2011-2019 are in red.https://doi.org/10.1371/journal.pone.0295946.g002
Fig 3 .
Fig 3. Size difference and correlation between Atlantic puffin fledgers and adults.Comparison of (A) difference in size at the fledger and adult stage and (B) correlations between adult and fledger Atlantic puffin (male [white] and female [dark grey]) culmen, head+bill, and wing chord lengths at Machias Seal Island between 1995-2015.Box plots in (A) show the median (black line), first and third quartiles, and the whiskers extend to 1.5 interquartile range (IQR), the black dots are outliers; lines in (B) are linear regressions with 95% confidence intervals.https://doi.org/10.1371/journal.pone.0295946.g003
Fig 4 .
Fig 4. Difference in structural size of male and female Atlantic puffins in relation to environmental conditions.Comparison of wing chord (A, B, & C), head+bill (D, E, & F), and proportional size of bill depth to body size (G, H, & I) and bill area to body size (J, K, & L) of male and female known age Atlantic puffins (Fratercula arctica) from Machias Seal Island in relation to SST anomaly (A, D, G, & J), Mean maximum air temperature (B, E, H, & K), and proportion of low-quality prey (Prey Quality; C, F, I, & J) in the year of fledging during 1995-2011.
).All anomalies higher than 0.92 occurred after 2009.Similarly, mean maximum air temperature and the proportion of low-quality prey fed to chicks varied with year and ranged between 13.4˚C-18.9˚C(Fig 1B) and 0.0002-0.55(Fig 1C).Years with mean maximum air temperature > 18.0˚C have all occurred after 2010, years with the highest proportion of lowquality prey fed to chicks (i.e., > 0.40) all occurred after 2004.
Table 2 . Parameter estimates, standard errors, and relative likelihoods for the top supported candidate model evaluating difference in Atlantic puffin lighthouse fledger wing chord length (WC) and annual SST anomalies between 1995-2019 at Machias Seal Island.
Parameters in bold font do not bound zero. https://doi.org/10.1371/journal.pone.0295946.t002
Table 3 . Candidate models assessing whether Atlantic puffin adult culmen (CUL), head+bill (HB), and wing chord (WC) are a function of fledgling culmen, head +bill, and wing chord length.
All data are from Machias Seal Island, 1995-2015, the term Sex is included as a random effect in all models.post hoc test conducted on each sex separately, shows variability in responses to environmental conditions by females and males for each structure measured (see S1 and S2 Tables https://doi.org/10.1371/journal.pone.0295946.t003bill size.A
Table 4 . Model averaged parameter estimates, unconditional standard errors, and relative likelihoods for the candidate model sets evaluating the relationship between Atlantic puffin adult and fledger culmen (CUL), head+bill (HB), and wing chord (WC) at Machias Seal Island during 1995-2015.
Parameters in bold font are those that do not bound zero.
Table 5 . Candidate models assessing whether Atlantic puffin adult head+bill (HB), wing chord (WC), ratio between bill depth and wing chord (BD:WC), and the ratio between bill area and wing chord (BA:WC) are a function of environmental conditions.
All data are from Machias Seal Island, 1995-2011, the terms Sex and Fledge Year were included as random effects in all models.
Table 6 . Model averaged parameter estimates, unconditional standard errors, and relative likelihoods for the candidate model sets evaluating the relationship between Atlantic puffin adult head+bill, wing chord, ratio between bill depth and wing chord, and ratio between bill area and wing chord, and SST anomalies at Machias Seal Island during 1995-2011.
Parameters in bold font are those that do not bound zero. https://doi.org/10.1371/journal.pone.0295946.t006 | 8,850.8 | 2024-01-17T00:00:00.000 | [
"Environmental Science",
"Biology"
] |
Novel Mixed NOP/Opioid Receptor Peptide Agonists
The nociceptin/orphanin FQ (N/OFQ)/N/OFQ receptor (NOP) system controls different biological functions including pain and cough reflex. Mixed NOP/opioid receptor agonists elicit similar effects to strong opioids but with reduced side effects. In this work, 31 peptides with the general sequence [Tyr/Dmt1,Xaa5]N/OFQ(1-13)-NH2 were synthesized and pharmacologically characterized for their action at human recombinant NOP/opioid receptors. The best results in terms of NOP versus mu opioid receptor potency were obtained by substituting both Tyr1 and Thr5 at the N-terminal portion of N/OFQ(1-13)-NH2 with the noncanonical amino acid Dmt. [Dmt1,5]N/OFQ(1-13)-NH2 has been identified as the most potent dual NOP/mu receptor peptide agonist so far described. Experimental data have been complemented by in silico studies to shed light on the molecular mechanisms by which the peptide binds the active form of the mu receptor. Finally, the compound exerted antitussive effects in an in vivo model of cough.
■ INTRODUCTION
Nociceptin/orphanin FQ (N/OFQ; FGGFTGARKSAR-KLANQ) is the endogenous ligand of the N/OFQ peptide (NOP) receptor. 1,2 N/OFQ and the NOP receptor display high structural homology with peptides and receptors of the opioid family but distinct pharmacology. 3 The N/OFQ-NOP receptor system controls several biological functions at both central and peripheral levels including pain transmission, mood and anxiety, food intake, learning and memory, locomotion, cough and micturition reflexes, cardiovascular homeostasis, intestinal motility, and immune responses. 4 The effects of N/OFQ and selective NOP agonists in analgesiometric assays are complex depending on the dose, administration route, type of pain, and animal species. 5,6 On the contrary, strong and consistent experimental evidence suggests that the simultaneous activation of NOP and opioid receptors elicits synergistic analgesic effects. 6,7 On these bases, mixed NOP/opioid receptor agonists (cebranopadol, 8,9 AT-121, 10 BU10038, 11 and BPR1M97 12 ) have been developed and investigated for their antinociceptive properties. It was consistently demonstrated that these drugs elicit similar analgesic effects to strong opioids but with substantially reduced side effects including respiratory depression, tolerance, and abuse liability (see the recent review by Kiguchi et al. 13 ).
Other ligands targeting multiple opioid receptors have been studied. 14 For example, dual-acting mu agonist/delta antagonist peptidomimetics demonstrated to produce antinociception in vivo with reduced tolerance liability compared with morphine. 15,16 Moreover, mixed kappa agonist/mu partial agonist ligands have been investigated as potential treatment agents for cocaine and other psychostimulant abuses. 17 Finally, mixed kappa agonist/delta antagonist ligands have been developed as tools for the characterization of delta and kappa-opioid receptor phenotypes. 18 With the aim of generating a peptide acting as a nonselective NOP/opioid agonist, we investigated different approaches. On one hand, the peptide [Dmt 1 ]N/OFQ(1-13)-NH 2 has been identified as a nonselective agonist for NOP and opioid receptors 19 and its tetrabranched derivative, generated using the peptide welding technology (PWT), 20 was demonstrated to produce a robust analgesic effect after spinal administration in nonhuman primates. However, this action was sensitive to NOP but not opioid receptor antagonists. 21 On the other hand, N/ OFQ and dermorphin-related peptides were linked together to generate the hetero-tetrabranched derivative H-PWT1-N/ OFQ-[Dmt 1 ]dermorphin 22 or the dimeric compound DeNo. 23 Despite its promising in vitro pharmacological profile as a mixed NOP/opioid agonist, DeNo was not effective as a spinal analgesic. 23 In the present study, we further investigate the possibility of generating a mixed NOP/opioid agonist based on the following evidence: (i) mixed NOP/kappa ligands can be obtained combining the C-terminal sequence of N/OFQ with the N-terminal of dynorphin A, where amino acids in positions 5 and 6 were particularly important for receptor selectivity; 24 (ii) Thr 5 in N/OFQ(1-13)-NH 2 can be replaced with several different residues without loss of peptide efficacy and potency at the NOP receptor; 25 (iii) the substitution of Phe 1 in N/OFQ with Tyr 26 and particularly with Dmt 19,27,28 increases affinity/ potency at classical opioid receptors. Thus, in the present study, 31 peptide derivatives with the general sequence [Tyr/ Dmt 1 ,Xaa 5 ]N/OFQ(1-13)-NH 2 were generated and tested for their action at NOP and opioid receptors ( Figure 1).
Experimental data have been complemented by an in silico study of the binding of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 to the mu receptor. This non-natural peptide has been compared with the agonist peptide DAMGO ([D-Ala 2 , N-MePhe 4 , Gly-ol]enkephalin) and the N-terminal fragment of N/OFQ (N/ OFQ(1-9)-NH 2 ). The starting point of the computational study was the structure of the activated mu receptor in complex with the agonist peptide DAMGO that has been previously reported by X-ray diffraction and cryo-electron microscopy. 29,30 The last structure of the complex DAMGO-mu receptor was used as a model, allowing the setup of the two unknown complexes with the selected peptides by molecular docking. Specifically, docking of a flexible ligand to multiple receptor conformations as already applied to the study of NOP agonists and antagonists 31,32 was
■ RESULTS
Chemistry. The peptide derivatives reported in Tables 1−4 were prepared through automated Fmoc/tBu-based solid-phase peptide synthesis (SPPS) on a Rink amide MBHA resin. Commercially available protected amino acids were employed as synthetic precursors of the target peptides except for Fmoc-2′,6′-dimethyl-tyrosine (Fmoc-Dmt-OH) that was instead synthesized in analogy to an approach previously published by Wang et al. 39 (Scheme S1 of the Supporting Information). Specifically, H-Tyr-OH was first esterified to H-Tyr-OMe under standard conditions, and then, the phenolic hydroxyl was protected with a tert-butyldimethylsilyl ether moiety before the following coupling with picolinic acid. The latter function worked as a directing group for the subsequent Pd(OAc) 2catalyzed C−H alkylation with CH 3 I and K 2 CO 3 allowing the simultaneous and regioselective introduction of two methyl groups at the ortho-positions of the aromatic ring. Then, full deprotection under strongly acidic conditions, followed by treatment with Fmoc-Cl, led to the desired 2′,6′-dimethyl Table S1.
In Vitro Structure−Activity Relationship. N/OFQ(1-13)-NH 2 stimulated calcium mobilization with high potency and maximal effects in cells coexpressing NOP receptors and chimeric G proteins, while being inactive in cells expressing the mu opioid receptor. On the contrary, dermorphin stimulated calcium mobilization with high potency and maximal effects in mu expressing cells, while it was inactive in NOP cells ( Table 1). The substitution of Phe 1 with Tyr as in [Tyr 1 ]N/OFQ(1-13)-NH 2 did not affect NOP potency while promoting a minor increase in mu potency. Thr 5 in [Tyr 1 ]N/OFQ(1-13)-NH 2 was replaced with a series of both proteinogenic and nonproteinogenic amino acids with different polar/nonpolar, aliphatic/aromatic, linear/branched side chains with the aim to explore the effect of several structural parameters on the biological activity. The substitution of Thr 5 with Asn, Val, Lys(Ac) caused a slight (<10-fold) reduction in NOP potency and no modification of mu potency. The same substitution with Abu, Lys, Dap, and Dab induced a larger loss (>10-fold) of NOP potency. The introduction in position 5 of Leu, Nle, and Nva promoted a moderate decrease in NOP potency associated with a significant increase in mu potency. A similar increase in mu potency was achieved with [Tyr 1,5 ]N/OFQ(1-13)-NH 2 , which however displayed a larger decrease in NOP potency; thus, the NOP/mu concentration ratio for this peptide was near 1 ( Table 1). None of the amino acid substitutions evaluated in Table 1 modified ligand efficacy at both NOP and mu receptors. Based on these results, aromatic residues were selected for further modifications of position 5 of [Tyr 1 ]N/OFQ(1-13)-NH 2 .
As shown in Table 2, 14 compounds with an aromatic residue substituting Thr 5 in [Tyr 1 ]N/OFQ(1-13)-NH 2 were assayed in NOP and mu receptor expressing cells. The different amino acids did not modify ligand efficacy but produced different effects on NOP and mu potency. In particular, the NOP potency of these derivatives was in the range of 8.70−6.61, while the mu potency of these compounds was <6 with the exceptions of peptides substituted with Phe, Phg, 1Nal, (pNH 2 )Phe, and Dmt (range 6.08−6.81). Then, for further investigation, we selected those sequences showing pEC 50 values >7 for the NOP receptor and >6 for the mu receptor associated with an NOP/mu concentration ratio >0.05. These criteria were matched by [Tyr 1 ]N/OFQ(1-13)-NH 2 derivatives substituted in position 5 with Tyr, Phe, Phg, 1Nal, (pNH 2 )Phe, and Dmt.
The third series of peptides was generated by substituting Tyr 1 with Dmt that is known to increase opioid receptor potency. 40 In fact, as shown in Table 3, [Dmt 1 ]N/OFQ(1-13)-NH 2 displayed a moderate (10-fold) decrease in NOP potency compared to [Tyr 1 ]N/OFQ(1-13)-NH 2 associated to a more pronounced increase (approx. 40-fold) in mu potency. The substitution of Thr 5 of [Dmt 1 ]N/OFQ(1-13)-NH 2 with the above-mentioned amino acids generated results similar to those obtained with [Tyr 1 ]N/OFQ(1-13)-NH 2 , that is, a slight to moderate decrease in NOP potency associated to a large increase in mu potency. The most exciting result has been obtained with [Dmt 1,5 ]N/OFQ(1-13)-NH 2 that displayed similar and high potency at both NOP and mu receptors.
[Dmt 1,5 ]N/OFQ(1-13)-NH 2 was further evaluated in dynamic mass redistribution (DMR) experiments performed on CHO cells expressing the human NOP and mu receptors. As summarized in Table 4, N/OFQ elicited a concentrationdependent positive DMR signal in cells expressing the NOP receptor being inactive in mu expressing cells. Opposite results were obtained with dermorphin that behaves as a mu-selective agonist. [Dmt 1,5 ].N/OFQ(1-13)-NH 2 elicited a robust DMR response in both cell lines with similar maximal effects to standard agonists. [Dmt 1,5 ].N/OFQ(1-13)-NH 2 displayed nanomolar potency at both NOP and the mu receptor with a mu/NOP potency ratio of 8.51 (Table 4).
Molecular Dynamics. As explained in the Experimental Section, MD simulations have been performed setting up nineresidue long peptides (i.e., [Phe/Dmt 1 ,Thr/Dmt 5 ]N/OFQ(1-9)-NH 2 ) due to the fact that longer peptides lack reliable starting conformations by molecular docking. Moreover, in the following, we will focus on the first five residues of the peptides, those entering the mu opioid receptor orthosteric site, as residues 6−9 represent the more flexible part of the peptides along the MD simulation. The results obtained for [Dmt 1,5 ]N/ OFQ(1-9)-NH 2 were compared with those obtained by similar simulations performed on the mu agonist peptide DAMGO and also on N/OFQ(1-9)-NH 2 as a sort of negative control since this peptide lacks mu receptor affinity. 26 In Figure 3A, the 3D conformation obtained after docking and MD for [Dmt 1,5 ]N/OFQ(1-9)-NH 2 (colored purple) is superimposed to the known one reported for DAMGO (colored yellow, PDB code 6DDF). 30 displayed and superimposed, that is, hydrophobic and polar average number of contacts ( Figure 4A,B), percentage of formation of hydrogen bonds, and average "strength" of π−π stacking and π−cation interaction ( Figure 4C−E, respectively). Accordingly, the representative conformation of [Dmt 1,5 ]N/ OFQ(1-9)-NH 2 in the orthosteric site largely overlaps with that of DAMGO ( Figure 3A). The N-terminus of Dmt 1 forms salt bridge/hydrogen bond contacts with D 147 (a residue conserved all along the opioid family) similar to both DAMGO and the morphinan agonist BU72 (PDB code 5C1M). 29 MD simulations show that this important interaction is strongly stabilized by the presence of another conserved residue, Q 124 (TM2), whose nitrogen and oxygen side-chain atoms reinforce the hydrogen bond network by contacts with both the carboxyl oxygen of D 147 and the N-terminus of Dmt 1 . Moreover, water bridges fill the small remaining volume between the D 147 and Q 124 side chains and the backbone donor/acceptors of Dmt 1 , Gly 3 , and Gly 2 , with the latter being in direct H-bond with W 318 of TM7. 1,5 ]N/OFQ(1-9)-NH 2 (colored purple) in the active mu receptor, according to "in silico" docking and MD (starting receptor structure from PDB code 6DDF). Only the first five residues are shown. The reported DAMGO conformation (the same PDB code) is superimposed (yellow). (B) Hydrophobic contacts between Dmt 1 , Phe 4 , and Dmt 5 with their neighboring residues. (C−E) Interaction histograms of residues Dmt 1 , Phe 4 , and Dmt 5 , respectively, including hydrophobic, polar, H-bonds, water bridges of first and second order, salt bridges, and π−cation and π−π stacking as derived from long-lasting MD.
Journal of Medicinal Chemistry pubs.acs.org/jmc Article Dmt 1 is also in direct hydrophobic contact with TM6 residues (W 293 , H 297 , and especially I 296 , Figure 3B,C). Along the MD trajectories, its aromatic head moves alternating firstand second-order water bridges with H 297 of TM6 ( Figure 3C). Partial π−π stacking between the Dmt 1 and H297 rings is also observed during the simulations. Hydrophobic, π-stacking, and water bridge contacts between Dmt 1 and Y 148 (TM3), H 297 and W 293 (TM6) frequently occur ( Figure 3C). The latter residue, in the so-called receptor polar cavity, is thought to be very important for the activation mechanism in many class A GPCRs, and these interactions, although not fully stable, could contribute to stabilize the receptor active state.
The formation of alternating second-order water bridges (along 78% of the trajectory) shows that the N-terminus of Dmt 1 , together with D 147 , is also in contact with N 150 ( Figure 3C), an important conserved residue that in the reported high- Journal of Medicinal Chemistry pubs.acs.org/jmc Article resolution structure of the inactive delta receptor 41 is shown to connect the orthosteric site to the sodium pocket in the central part of the receptor. While Dmt 1 interacts with both TM3 (more than 40 contacts with residues Y 148 and M 151 ) and TM6 (about 80 contacts with residues W 293 , I 296 , and H 297 ), Phe 4 is immersed in the same hydrophobic pocket as the phenyl group of DAMGO between TM2 (residues F 123 and Q 124 ) and TM3 (residues V 143 and I 144 ) ( Figure 3B,D), still participating with its amidic nitrogen and water bridges to the main hydrogen bond network linking the peptide to D 147 and Q 124 ( Figure 3D).
The Dmt 5 peptide residue mainly interacts with residues not conserved within the opiate family, that is, E 229 and K 233 of TM5, V 300 , and K 303 of TM6, and W 318 of TM7. Movements of this ring allow an alternation of nonpolar interactions with the aliphatic chains of K 303 (TM6) and K 233 (TM5) ( Figure 3B) and of possible π−cation interactions with the positively charged amine of both the same K residues ( Figure 3E). Similarly, the amidic oxygens of Gly 3 , Dmt 5 , and Gly 6 alternate in H-bond or water bridge contacts with R 211 (ECL2) and E 229 (TM5) on two opposite sides of the receptor.
Details on the MD simulations of DAMGO and [Dmt 1,5 ]N/ OFQ(1-9)-NH 2 in complex with the mu receptor are given in Figures S1 and S2, reporting the root-mean-square deviation (RMSD) analyses and clustering outcomes for each of the investigated peptides. Moreover, the RMSD analysis ( Figure S3) and the representative conformation of residues 1−5 of N/ OFQ(1-9)-NH 2 (purple) are shown, compared to DAMGO (yellow). MD shows that the interactions of N/OFQ(1-9)-NH 2 with TM6 are strongly diminished; in addition to the absence of polar contacts and the water density between residues 1−5 of the peptide and TM6, there are only about 20 nonpolar contacts (between Phe 1 and W 293 and between Phe 1 and F 236 ), while both polar and nonpolar interactions with TM3 increase.
In Figure 4, the maps of hydrophobic, polar, hydrogen bond, π−π stacking, and π−cation interactions for the peptides under study are superimposed for an overall immediate comparison. The hydrophobic and polar interaction maps are widely superimposable on all peptides ( Figure 4A,B), attesting the similarity of their conformation inside the orthosteric site, with an increase of nonpolar contacts between [Dmt 1,5 ]N/OFQ(1-9)-NH 2 and TM6 (I 296 , V 300 , and K 303 ) essentially due to the aromatic ring of Dmt 5 . The N-terminus of all three peptides forms hydrogen bonds with D 147 and Q 124 ( Figure 4C). More interestingly, according to our simulation, the H-bond contact reported in the crystal structure between the amidic oxygen of Gly 3 of DAMGO and the indole nitrogen of W 318 is not fully stable; in the same time, the phenolic head of Tyr 1 tends to extend toward the so-called "polar cavity" between Y 326 and W 293 in the intracellular side ( Figure S1C), with the possibility to form π-stacking with W 293 ( Figure 4D) and a H-bond besides a π−cation contact between its N-terminus and the phenol group of Y 326 . On the other hand, the H-bond between Gly 2 of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 and W 318 remains quite stable, as reinforced by partial π-stacking between W 318 and Dmt 5 ( Figures 3E and 4D), while the Dmt 1 phenolic head, sterically hindered by the two methyl groups, does not extend toward the polar cavity. Concerning N/OFQ (1-9)-NH 2 , Phe 1 has negligible hydrogen and water bond contacts with the inner side of the receptor, and the contacts between Phe 4 , Thr 5 of the peptide and T 218 , L 219 of extracellular loop 2 (ECL2) are stronger ( Figure 4C). The N-terminus of the three peptides can form π−cation interactions with the aromatic ring of Y 326 , while as mentioned above, π−cation contributions due to interactions of K 233 and K 303 are exclusive of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 ( Figure 4E). In
■ DISCUSSION
This structure activity investigation was aimed at the identification of novel peptides acting as mixed NOP/mu receptor agonists. To this aim, we substituted Phe 1 of N/ OFQ(1-13)-NH 2 with amino acids containing a phenol moiety and Thr 5 with several proteinogenic and nonproteinogenic residues. Novel peptides were investigated in calcium mobilization experiments performed in cells expressing the human recombinant receptors and chimeric G proteins. The structure activity investigation led to the identification of the potent mixed agonist [Dmt 1,5 ]N/OFQ(1-13)-NH 2 whose NOP and mu agonist properties were confirmed in DMR studies. Moreover, [Dmt 1,5 ]N/OFQ(1-13)-NH 2 was also able to potently stimulate kappa but not delta opioid receptors. The capability of this peptide to bind the mu receptor has been also investigated in MD studies that suggested a similar active conformation for [Dmt 1,5 Previous studies demonstrated that the substitution of Phe 1 in N/OFQ with Tyr reduces NOP selectivity over opioid receptors 26,42 and that Thr 5 of N/OFQ(1-13)-NH 2 can be substituted with different amino acids with no changes in peptide efficacy and relatively little modifications of potency. 25 Thus, we selected a series of amino acids to substitute Thr 5 in [Tyr 1 ]N/OFQ(1-13)-NH 2 in order to increase the mu receptor activity of the peptide derivatives. The results obtained with [Tyr 1 ]N/OFQ(1-13)-NH 2 derivatives were similar to those previously obtained with N/OFQ(1-13)-NH 2 derivatives in terms of NOP receptor activity. As far as the mu receptor is concerned, an increase in potency has been obtained with Leu, Nle, Nva, and Tyr. These results are not unexpected since Leu in position 5 is found in naturally occurring opioid ligands (Leuenkephalin and dynorphin) and Nle (and possibly Nva) may mimic methionine, which is also present in position 5 of other endogenous opioid peptides (Met-enkephalin, beta-endorphin). In addition, the same can be said for Tyr 5 , which is found in amphibian opioid peptides such as the mu-selective agonist dermorphin. Moreover, previous studies demonstrated that position 5 of enkephalin can be replaced with aromatic residues 43 or non-natural aliphatic residues 44 with no major changes of bioactivity. Interestingly, [Tyr 1,5 ]N/OFQ(1-13)-NH 2 displayed very similar potency at NOP and the mu opioid receptor; thus, with the aim to identify potent mixed NOP/mu agonists, further studies were performed substituting position 5 with aromatic amino acids.
Despite the investigation of 14 chemically different aromatic residues, no clear structure activity information was obtained. In fact, with the exception of hPhe 5 , little changes in NOP potency were measured and the same can be said for mu receptor activity. Thus, for further studies, we selected compounds matching the following criteria: pEC 50 > 7 for the NOP receptor and >6 for the mu receptor, with NOP/mu ratio > 0.05. This let us to select Tyr, Phe, Phg, 1Nal, (pNH 2 )Phe, and Dmt to be substituted in The calcium mobilization assay used in the present study has been previously set up 45,46 in our laboratories and then validated by investigating a large number of NOP and opioid receptor ligands. 19,23,47 However, this assay is based on the aberrant signaling generated by the expression of chimeric G proteins; therefore, we reassessed the pharmacological effects of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 with the DMR assay. This test measures the physiological G i -dependent signaling of NOP and opioid receptors as demonstrated by its sensitivity to pertussis toxin treatment. 48,49 DMR studies confirmed the mixed mu/ NOP full agonist properties of [Dmt 1,5 Finally, the effects of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 at kappa and delta opioid receptors were investigated. At delta receptors, [Dmt 1,5 ]N/OFQ(1-13)-NH 2 displayed low potency and efficacy, while it behaved as a potent full agonist at the kappa receptor. Of note, the kappa potency of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 was similar to that shown at NOP and mu receptors. These results were not unexpected. In fact, binding experiments performed in guinea-pig brain membranes demonstrated the following rank order of affinity for [Tyr 1 ]N/OFQ(1-13)-NH 2 : NOP > mu = kappa > delta. 26 Moreover, similar results have been previously obtained in functional studies performed with human recombinant receptors with [Dmt 1 ]N/OFQ(1-13)-NH 2 that displayed the following rank order of potency: NOP = mu > kappa > delta. 19 Collectively, these findings indicate that modifications of position 1 of N/OFQ such as Tyr and Dmt are sufficient for increasing mu and kappa but not delta receptor binding. Most probably, this is due to the fact that the Cterminal portion of N/OFQ is enriched in positively charged Journal of Medicinal Chemistry pubs.acs.org/jmc Article residues that may favor mu and kappa interactions but are detrimental for delta receptor binding. 50 To get insights into the mechanisms by which [Dmt 1,5 ]N/ OFQ(1-13)-NH 2 binds the mu receptor, MD studies were performed using the recently solved DAMGO-mu receptor-G i complex. 30 The results obtained with [Dmt 1,5 ]N/OFQ(1-9)-NH 2 were compared with those of DAMGO and N/OFQ(1-9)-NH 2 used as the positive and negative control, respectively. These studies show that beyond the pivotal and expected interaction between the [Dmt 1,5 ]N/OFQ(1-9)-NH 2 N-terminus and D 147 , the phenol oxygen of Dmt 1 can make firstor second-order water bridges with H 297 (Gln in NOP) of TM6 of the mu receptor. This is in good agreement with the observation of a water bridge between the agonist BU72 and H 297 in the active mu receptor and other small molecules or peptide mimetic agonists of kappa and delta receptors 30 and can account for the reduction of NOP selectivity and the increase of mu potency as simply induced by the presence of the phenol groups of Tyr 1 or Dmt 1 in N/OFQ instead of the phenyl group of Phe 1 . Partial π−π stacking between Dmt 1 (or Tyr 1 ) and H297 could further contribute to peptide stabilization in the orthosteric site, thus enhancing these effects. Analogous contacts have been reported for cocrystallized mu 51 and delta 52 but not NOP 53 antagonists. Importantly, Phe 1 of the N/OFQ sequence cannot form water bridges with H 297 , and this is most probably the reason for the lack of mu affinity of the peptide.
Interestingly, as stated above, Dmt 5 mainly interacts with residues that differ within the opiate family, that is, E 229 (G in NOP, D in kappa and delta) and K 233 (A in NOP) of TM5, V 300 (I in kappa), and K 303 (W, E, and Q in delta, kappa, and NOP receptor, respectively) of TM6, and W 318 (L in NOP) of TM7. While the carbonyl oxygen of Dmt 5 is in water bridge contact with E 229 , its aromatic bulky head is stacked between the aliphatic chains of K 303 and K 233 , making possible π−cation interactions with the positively charged amine of both lysines ( Figure 3B,E). In the reported crystal structure of mu-DAMGO 30 (PDB code 6DDF), the K 303 positive charge is found at a 3.3 Å distance of the carbonyl oxygen of N(Me)-Phe of DAMGO, compatible with a weak H-bond, whereas K 233 does not appear to contribute to the stabilization of the mu active state induced by both DAMGO and BU72; 29 the K 233 amine group is found covalently linked to the antagonist β-funaltrexamine in the crystal structure of the inactive mu receptor. 51 As K 303 and K 233 are present in the mu but not the NOP receptor, the above-mentioned interactions between Dmt 5 and the two lysine residues could contribute to explain the mu-selective increase of affinity of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 compared to [Dmt 1 ]N/OFQ(1-13)-NH 2 , thus making [Dmt 1,5 ]N/OFQ(1-13)-NH 2 a mixed mu/NOP agonist. Last, as observed along the MD runs, the indole nitrogen of W 318 in TM7 does not interact with N/OFQ but can form H-bond contact with Gly 2 of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 as well as with DAMGO (49 and 63% of the trajectory, respectively). Thus, beyond differences in steric hindrance of Tyr 1 and Dmt 1 that may generically contribute to a larger hydrophobic core for the last one, the entity of the interaction between W 318 and Gly 2 could also contribute to explain the enhanced potency of [Dmt 1,5 ] N/ OFQ(1-13)-NH 2 . Data obtained from this molecular modeling investigation are in agreement with those reported by recent studies performed on a series of cyclic opioid peptides. 54 The role of the N/OFQ-NOP receptor system has been widely reported in several biological functions at the central and peripheral levels, including the cough reflex. 4 Previous studies showed that NOP receptor agonists given centrally or peripherally suppress capsaicin and acid inhalation-induced cough in guinea pigs. 33−37 Moreover, opioid drugs are widely used as antitussive agents, 38 and inhalation of encephalin was shown to be effective in reducing cough reflex in vivo. 55 The novel mixed NOP/opioid agonist [Dmt 1,5 ]N/OFQ(1-13)-NH 2 showed an inhibitory activity against citric acid-induced cough in guinea pigs, thus demonstrating the in vivo activity of the compound. However, further studies are needed to investigate the receptor mechanism involved in the antitussive action of the molecule.
■ CONCLUSIONS
In this study, starting from the NOP-selective sequence of N/ OFQ(1-13)-NH 2 , we developed a structure activity investigation focused on positions 1 and 5. Regarding position 1, a phenol moiety is required to increase mu receptor binding, and regarding position 5, aromatic residues generated the best results in terms of similar potency at NOP and mu receptors. This study led to the identification of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 as the most potent mixed peptide agonist for NOP and mu receptors so far described in the literature. MD studies shed light on the molecular mechanisms adopted by this peptide to bind the active form of the mu receptor: some features of the mode of binding of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 are superimposable to those of DAMGO, that is, the ionic bond with D 147 of TM3 and the H-bond network with H 297 of TM6, while others are peculiar of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 , that is, polar interactions of Dmt 5 with K 223 and K 303 of TM5 and TM6, respectively.
[Dmt 1,5 ]N/OFQ(1-13)-NH 2 is a novel mixed agonist for NOP and mu receptors that exerted antitussive effects in an in vivo model of cough. The compound will be evaluated in future studies for its antinociceptive properties. In fact, mixed NOP/ mu agonists of both peptide and nonpeptide structures have been consistently demonstrated in preclinical studies to promote antinociceptive effects similar to those of morphine being however better tolerated particularly in terms of respiratory depression, tolerance, and abuse liability. 13 Importantly, phase II and III clinical studies performed with the mixed NOP/mu agonist cebranopadol have confirmed this favorable profile in pain patients. 9,56 Nowadays, the availability of safer analgesic drugs is particularly needed for facing the opioid epidemic that leads to a progressive increase of fatal overdoses over the past 2 decades. 57 ■ EXPERIMENTAL SECTION Chemistry. Materials and Methods. All solvents and reagents were purchased from Sigma-Aldrich and Fisher Scientific. Enantiopure Fmoc-protected amino acids and the resins for SPPS were purchased from AAPPTec. Peptides were synthesized using a standard Fmoc/tbutyl strategy 58 with a Syro XP multiple peptide synthesizer (MultiSynTech GmbH, Witten Germany) on a Rink amide MBHA resin (4-(2′,4′-dimethoxyphenyl-Fmoc-aminomethyl)-phenoxyacetamido-norleucyl-MBHA resin; loading 0.55 mmol/g). Fmoc-amino acids were used with a 4-fold excess on a 0.11 mM scale of the resin and coupled to the growing peptide chain using N,N′-diisopropylcarbodiimide and 1-hydroxybenzotriazole (DIC/HOBt, 4-fold excess) for 1 h at room temperature. Each Fmoc removal step was performed using 40% piperidine in N,N-dimethylformamide, and all the subsequent couplings were repeated until the desired peptide-bound resin was completed. The cleavage cocktail to obtain the peptides from the resin consisted of 95% trifluoroacetic acid, 2.5% water, and 2.5% triethylsilane, and cleavages were conducted for 3 h at room temperature. After filtration of the resin, diethyl ether was added to the filtrate to promote precipitation of the peptide products that were finally isolated by centrifugation. Reverse-phase purification of crude peptides was carried out on a Waters Prep 600 high-performance liquid chromatography (HPLC) system with a Jupiter column C18 (250 × 30 mm, 300 Å, 15 μm spherical particle size) using a gradient, programmed time by time, of acetonitrile/water [with 0.1% trifluoroacetic acid (TFA)] at a flow rate of 20 mL/min. Nonpeptide derivatives were purified through flash column chromatography using a Biotage System Isolera One. Analytical HPLC was performed with a Beckman 116 liquid chromatograph furnished of a UV detector. The purity of peptides in Table 1 was assessed with a Symmetry C18 column (4.6 × 75 mm, 3.5 μm particle size, SYSTEM GOLD) at a flow rate of 0.5 mL/ min using a linear gradient from 100% of A (water + 0.1% TFA) to 100% of B (acetonitrile + 0.1% TFA) over a period of 25 min. The purity of peptides in Tables 2 and 3 was assessed with an Agilent Zorbax C18 column (4.6 × 150 mm, 3.5 μm particle size, KARAT32) at a flow rate of 0.7 mL/min using a linear gradient from 100% of A (water + 0.1% TFA) to 100% of B (acetonitrile + 0.1% TFA) over a period of 25 min. All final compounds were monitored at 220 nm showing ≥95% purity, and their molecular weights were confirmed using an ESI Micromass ZQ, Waters (HPLC chromatograms and ESI mass spectra of the final peptide derivatives have been reported in the Supporting Information). 1 H and 13 C NMR spectra were recorded for nonpeptide derivatives on a Varian 400 MHz instrument, and all experiments were performed in deuterated DMSO using its residual shifts as reference (s: singolet, d: doublet, dd: double doublet, t: triplet, m: multiplet).
In Calcium Mobilization Assay. CHO cells stably coexpressing the human NOP or kappa or the mu receptor and the C-terminally modified G αqi5 and CHO cells coexpressing the delta receptor and the G αqG66Di5 protein were generated and cultured as described previously. 45,46 Cells were maintained in Dulbecco's modified Eagle's medium/nutrient mixture F-12 (DEMEM/F-12) supplemented with 10% FBS, 100 U/mL penicillin and 100 μg/mL streptomycin, 100 μg/ mL hygromicin B, and 200 μg/mL G418 and cultured at 37°C in 5% CO 2 humidified air. Cells were seeded at a density of 50,000 cells/well into 96-well black, clear-bottom plates. The following day, the cells were incubated with Hanks' balanced salt solution (HBSS) supplemented with 2.5 mM probenecid, 3 μM of the calcium-sensitive fluorescent dye Fluo-4 AM, and 0.01% pluronic acid for 30 min at 37°C. After that time, the loading solution was aspirated and 100 μL/well of HBSS supplemented with 20 mM HEPES, 2.5 mM probenecid, and 500 μM brilliant black was added. Serial dilutions were carried out in HBSS/ HEPES (20 mM) buffer (containing 0.02% BSA fraction V). After placing both plates (cell culture and master plate) into the fluorometric imaging plate reader FlexStation II (Molecular Devices, Sunnyvale, CA), fluorescence changes were measured. On-line additions were carried out in a volume of 50 μL/well. To facilitate drug diffusion into the wells, the present studies were performed at 37°C. Maximum change in fluorescence, expressed as percent over the baseline fluorescence, was used to determine agonist response.
DMR Assay. CHO cells stably expressing the human NOP and mu receptors were kindly provided by D.G. Lambert (University of Leicester, UK). Cells were cultured in DMEM/F-12 medium supplemented with 10% FBS, 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mmol/L L-glutamine. The medium was supplemented with 400 μg/mL G418 to maintain expression. Cells were cultured at 37°C in 5% CO 2 humidified air. For DMR measurements, the label-free EnSight Multimode Plate Reader (Perkin Elmer, MA, US) was used. Cells were seeded 15,000 cells/well in a volume of 30 μL onto fibronectin-coated 384-well DMR microplates and cultured for 20 h to obtain confluent monolayers. Cells were starved in the assay buffer (HBSS with 20 mM HEPES, 0.01% BSA fraction V) for 90 min before the test. Serial dilutions were made in the assay buffer. After reading the baseline, compounds were added in a volume of 10 μL; then, DMR changes were recorded for 60 min. Responses were described as picometer (pm) shifts over time (sec) following subtraction of values from vehicle-treated wells. Maximum picometer (pm) modification (peak) was used to generate concentration response curves. All the experiments were carried out at 37°C.
Data Analysis and Terminology. The pharmacological terminology adopted in this paper is consistent with IUPHAR recommendations. 59 All data are expressed as the mean ± standard error of the mean (SEM) of at least three experiments performed in duplicate. For potency values, 95% confidence limits (CL 95% ) were indicated. Agonist potencies are given as pEC 50 , that is, the negative logarithm to base 10 of the molar concentration of an agonist that produces 50% of the maximal effect of that agonist. Concentration-response curves to agonists were fitted to the classical four-parameter logistic nonlinear regression model: E ff e c t = B a s e l i n e + ( E m a x − B a s e l i n e ) / ( 1 + 10 (LogEC 50 − Log[compound]) × Hillslope ). Curve fitting was performed using PRISM 6.0 (GraphPad Software Inc., San Diego).
Molecular Dynamics. The setup of an in silico model of the nonnatural peptides [Dmt 1,5 ] N/OFQ(1-9)-NH 2 and N/OFQ(1-9)-NH 2 in complex with the human mu receptor has been described in the Supporting Information. Classical MD simulations of these two receptor-peptide complexes were performed and compared with an MD simulation of the experimental system DAMGO-mu receptor-G i protein complex as derived by the PDB file 6DDF. 30 The GROMACS 2018.3 package 60 was used under the AMBER parm99sb force field 61 at the full atomistic level using a TIP3P water solvent and an explicit preequilibrated phospholipid bilayer of 128 POPC (1-palmitoyl-2-oleoylsn-glycero-3-phosphocholine) molecules obtained by the Prof. Tieleman website (http://moose.bio.ucalgary.ca). All the MD sessions were performed in a water−membrane system prepared as previously described. 31,32 The receptor-peptide-membrane systems were solvated in a triclinic water box (having basis vector lengths of 7, 7.4, and 9.3 nm) under periodic boundary conditions for a total number of about 45,000 atoms (6400 solvent molecules). The total charge of the system was neutralized by randomly substituting water molecules with Na + ions and Cl − ions to obtain neutrality with a 0.15M salt concentration. Following a steepest descent minimization algorithm, the system was equilibrated under canonical ensemble (NVT) conditions for 300 ps using a V-rescale, modified Berendsen thermostat with position restrains for both the receptor-peptide complex and the lipids and thereafter in a isothermal−isobaric ensemble (NPT) for 500 ps, applying position restraints to the heavy atoms of the protein-peptide complex, and using a Nose−Hoover thermostat and a Parrinello-Rahman barostat at 1 atm with a relaxation time of 2.0 ps. The MD simulation of the mu receptor-DAMGO-G i protein was carried out on the whole ternary complex without positional restraints. On the other hand, in order to reduce the computational time, in the two mu receptor-peptide complexes, the G i protein was not included in the system, but all residues within 5 Å of the G i protein interface were restrained to the initial structure of the activated receptor using 5.0 kcal mol −1 Å −2 harmonic restraints applied to non-hydrogen atoms. Using such restraints ensures that the receptor maintains an active conformation throughout the simulation. MD runs were performed under NPT conditions at 300 K with a T-coupling constant of 1 ps. van der Waals interactions were modeled using a 6−12 Lennard-Jones potential with a 1.2 nm cutoff. Long-range electrostatic interactions were calculated, with a cutoff for the real space term of 1.2 nm. All covalent bonds were constrained using the LINCS algorithm. The time step employed was 2 fs, and the coordinates were saved every 5 ps for analysis.
The MD analysis of the DAMGO-mu receptor-G i protein complex ( Figure S1) shows an overall stability of the starting configuration (corresponding to the crystal structure) with some motion of the phenolic head toward the intracellular side of the receptor, still conserving the water bridge contact with H 297 . A non-negligible rearrangement is observed (Figures S2 and S3) along the MD sessions, starting from the docked conformations of [Dmt 1,5 ]N/OFQ(1-9)-NH 2 and N/OFQ(1-9)-NH 2 , probably due to the limitations of the docking procedures applied to molecules with a large number of torsions, and confirms the importance of performing long-lasting MD sessions. Analysis of MD trajectories was performed using state-of-the-art computational tools, as described in the Supporting Information.
Artwork. 3D images of peptide-receptor structures were obtained by the Chimera software. 62 In Vivo Pharmacological Studies. Animals. Guinea pigs (Dunkin Hartley, male, 400−450 g, Charles River, Milan, Italy) were used. The group size of n = 6 animals was determined by sample size estimation using G*Power (v3.1) 63 to detect the size effect in a post-hoc test with type 1 and 2 error rates of 5 and 20%, respectively. Allocation concealment to the vehicle(s) or treatment group was performed using a randomization procedure (http://www.randomizer.org/). The assessors were blinded to the identity (allocation to the treatment group) of the animals. Guinea pigs were housed in a temperature-and humidity-controlled vivarium (12 h dark/light cycle, free access to food and water) for at least 1 week before the start of the experiments. Cough experiments were done in a quiet, temperature-controlled (20−22°C) room between 9 am and 5 pm and were performed by an operator blinded to the treatment. All experiments were carried out according to the European Union (EU) guidelines for animal care procedures and the Italian legislation (DLgs 26/2014) application of the EU Directive 2010/63/EU. All animal studies were approved by the Animal Ethics Committee of the University of Florence and the Italian Ministry of Health (permit #450/2019-PR) and followed the animal research reporting in vivo experiment (ARRIVE) guidelines.
Measurement of Cough in Conscious Guinea Pigs. Cough experiments were performed using a whole-body plethysmography system (Buxco, Wilmington, NC, USA, upgraded version 2018). 64 The apparatus consists of four plethysmographs (four transparent Perspex chambers) ventilated with a constant airflow and each provided by a nebulizing head (Aerogen) and adjustable bias flow rates for acclimation and nebulization. The particle size presents an aerodynamic mass median diameter of 6 μm, and the output of the nebulizing heads can be set in the range between 0 and 0.4 mL per minute. The number of elicited coughs was automatically counted using the instrument. The nebulization rate used in the following experiments was 0.15 mL/min, and the air flows were 1750 mL/min during the acclimation phase and 800 mL/min during nebulization. These rates were previously found in our lab to elicit a significant number of cough events in the citric acidinduced cough model.
On the day of experiments, guinea pigs were individually placed into the chambers and let to acclimate for 10 min. To test the antitussive effect of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 , two different protocols were used. Protocol 1: after acclimation, a mixture of [Dmt 1,5 ]N/OFQ(1-13)-NH 2 (1 mM) or its vehicle (0.9% NaCl) and the tussive agent, citric acid (0.4 M), was nebulized for 10 min. During the 10 min of nebulization and for 5 min immediately post challenge (recovery period), the number of elicited coughs was automatically recorded using the BUXCO system. Protocol 2: after acclimation, [Dmt 1,5 ]N/ OFQ(1-13)-NH 2 (1 mM) or its vehicle (0.9% NaCl) was nebulized for 10 min. After 20 min of recovery, the tussive agent, citric acid (0.4 M), was delivered by aerosol via a nebulizer for 10 min. During the 10 min of the citric acid challenge and 5 min immediately post challenge (recovery period), the number of elicited coughs was automatically recorded using the BUXCO system.
For the in vivo experiment, the statistical significance of differences between groups was assessed using Student's t-test. The authors declare the following competing financial interest(s): S.P., V.A., D.I., C.T., E.M., C.R., D.P., G.C., and R . G . a r e i n v e n t o r s o f t h e p a t e n t a p p l i c a t i o n (102020000025972) focused on NOP/mu mixed agonists. G.C. and R.G. are founders of the University of Ferrara spin off company UFPeptides s.r.l., the assignee of such patent application. C.R. is CEO of UFPeptides s.r.l.
■ ACKNOWLEDGMENTS
FAR (Fondo di Ateneo per la Ricerca Scientifica) grants from the University of Ferrara support D.P., C.R., G.C., and R.G. C.R. is supported by an FIR (Fondo per l'Incentivazione alla Ricerca) grant from the University of Ferrara. S.P., F.F., D.P., G.C., and R.G. are supported by the grant PRIN 2015 (Prot. 2015WX8Y5B_002) from the Italian Ministry of Research and Education. | 9,909.2 | 2021-05-17T00:00:00.000 | [
"Medicine",
"Chemistry"
] |
Depolarization of Vector Light Beams on Propagation in Free Space
: Nonparaxial propagation of the vector vortex light beams in free space was investigated theoretically. Propagation-induced polarization changes in vector light beams with different spatial intensity distributions were analyzed. It is shown that the hybrid vector Bessel modes with polarization-OAM (orbital angular momentum) entanglement are the exact solutions of the vector Helmholtz equation. Decomposition of arbitrary vector beams in the initial plane z = 0 into these polarization-invariant beams with phase and polarization singularities was used to analyze the evolution of the polarization of light within the framework of the 2 × 2 coherency matrix formalism. It is shown that the 2D degree of polarization decreases with distance if the incident vector beam is not the modal solution. The close relationship of the degree of polarization with the quantum-mechanical purity parameter is emphasized. orbital momentum and annular intensity distributions.
Introduction
The polarization of light reflects the vector nature of electromagnetic fields and plays a very important role in optics and physics of the light-matter interaction [1][2][3]. The polarization properties of fields should be taken into account in many problems of light propagation in various media. It is known that the polarization of a plane wave in a homogeneous and isotropic non-dispersion medium does not change during propagation. Although a polarized plane wave propagates in free space without changing its polarization state, structured light beams undergo depolarization and a change in the polarization state during propagation due to diffraction and spin-orbit interaction. Significant changes in the state of polarization and the degree of polarization occur during the propagation of radiation in inhomogeneous media. Physically, depolarization occurs due to a decrease in the degree of correlation between the different field components. In [4,5] depolarization of light in randomly inhomogeneous media was investigated. Two mechanisms of depolarization were analyzed: diffractional and geometrical, which are caused by diffraction and the Rytov rotation [6,7] of the polarization vector, respectively. In single-mode optical fibers, the change in the input polarization is usually due to nonlinearity and birefringence of the medium [8][9][10]. However, depolarization takes place also in optical waveguides without birefringence. In particular, it was shown in [11] that the polarization degree of the linearly polarized light in a graded-index isotropic optical fiber decreases with increasing distance. In [12], it was shown theoretically that the polarization degree of the linearly polarized light in an isotropic optical fiber with parabolic distribution of refractive index decreases with increasing distance due to the Rytov rotation of the polarization vector, but the degree of polarization of circularly polarized light is retained with increasing distance. However, in the experiments [13], preservation of the polarization degree for circularly polarized light in an isotropic optical fiber has been not observed. In [14][15][16], the quantum-mechanical formalism of coherent states was applied to study the evolution of polarization in a multimode isotropic graded-index medium. It was shown that the effects of diffraction and spin-orbit interaction are responsible for depolarization of light in an isotropic graded-index medium. The rotation of the plane of linear polarization during propagation in an optical fiber is considered. It is shown that the axial displacement and the angle of tilt of the incident beam to the fiber axis influence on the rotation angle of the polarization plane. Intrafibre rotation of the plane of polarization was also demonstrated in [17].
In past decades, there has been considerable interest in studying changes in the degree of polarization of electromagnetic beams on propagation in free space [18][19][20][21][22][23][24][25][26]. It has been shown that, in general, the degree of polarization varies with propagation even in free space [18,19]. In [25], the possibility of controlling the degree of polarization of light during propagation in free space by changing the coherence of the light source was demonstrated experimentally. The results were in good agreement with the theory [24]. In some cases, the spectral degree of polarization remains propagation-invariant along the axis of a Gaussian Shell-model beam [27,28]. Recently, the study of the propagation and focusing of nonuniformly totally polarized beams has been of great interest [29][30][31][32][33][34]. Of particular interest is the study of the invariance on propagation of the characteristics of vector beams, such as the state and degree of polarization [35][36][37][38]. In [27,36,37], the conditions under which the polarization invariance is observed in the propagation of partially coherent electromagnetic beams are analyzed. It is known that the spirally polarized beams [29,30,39,40] remain invariant in polarization during propagation. Note that the radial and azimuthal polarizations are the particular cases of the spirally polarized beams.
In recent years, the study of the vector vortex beams with the phase and polarization singularities has been of particular interest [41][42][43][44][45][46]. Various types of cylindrical vector beams (CVBs) with helical wavefronts and spatially nonuniform state of polarization, such as Laguerre-Gauss (LG) beams which combine spin and orbital angular momentum (OAM), were proposed. It should be noted that cylindrically polarized LG beams can be considered as modal solutions in free space only in the paraxial approximation. Paraxial and nonparaxial propagation properties of cylindrically polarized beams have been considered in [47,48]. In [49][50][51], generation of arbitrary vector vortex beams on hybrid-order Poincaré sphere was investigated. In [52], an overview of the latest results on the generation and observation of polarization singularities in metaphotonics is presented. A review on polarization optics and polarimetry for recent biomedical and clinical applications is presented in [53]. In [54], the phase and polarization singularity sheets using metasurfaces experimentally are realized. Recent developments of wave field multidimensional manipulations based on artificial microstructures are presented in [55]. In [56] the spin-decoupled metasurface for simultaneous detection of SAM (spin angular momentum) and OAM via momentum transformation was proposed.
Various approaches to generate vector vortex beams have been proposed [31], such as interferometry, subwavelength gratings, conical Brewster prism, twisted nematic liquid crystals, etc. In [51], to generate an arbitrary vector vortex beam on the hybrid-order Poincaré sphere, a simple approach using the combination of an inhomogeneous birefringent q-plate and a spiral phase plate is proposed. A passive device consisting of a pair of polarization-selective cylindrical lenses for generating cylindrical vector beams with arbitrary azimuthal and radial topological charges is demonstrated in [44].
The usual description of polarization is based on the Stokes parameters or on 2 × 2 polarization matrices. In [57,58] the unified theory of coherence and polarization is formulated which can be used to study the changes of polarization at propagation of random electromagnetic beams. Usually, the calculations of the coherence matrix elements are carried out in a paraxial approximation. However, for tightly focused beams, nonparaxial effects become significant.
In this paper, a theoretical analysis of the propagation of polarized vortex light beams in free space is carried out using the vector mode decomposition method. It is found that the hybrid vector Bessel modes with radial and azimuthal indices are the solutions of the Maxwell equations. Polarization-invariant vector Bessel beams with phase and polarization singularities which combine spin and OAM are proposed. Change in the polarization state and the degree of polarization on propagation is shown for an arbitrary incident beam that is not a modal solution of the Maxwell equations.
Problem Formulation
The equation describing the propagation of monochromatic light in free space for a vector electric field → E can be obtained from Maxwell's equations as where e ⊥ , k = 2π/λ is the wavenumber, and β is the propagation constant.
The radial, azimuthal and longitudinal components of the electric field, defined in cylindrical polar coordinates r, ϕ, z, are described by the equations: Below we consider the beams with spatial transverse dimensions significantly exceeding the wavelength, so the longitudinal field component e z e ⊥ can be neglected because the electromagnetic field in this case is transverse. For such fields, the polarization is well described by Stokes parameters determined from the 2 × 2 coherency matrix [1].
Cylindrical hybrid vector Bessel beams with phase (scalar) and polarization (vector) singularities are the exact solutions of the vector two-dimensional Helmholtz Equation (2): where ψ pl (r) = AJ l µ pl r/R 0 , that the normalized functions ψ pl (r) satisfy the equation These solutions form a complete set of mutually orthogonal functions in the given interval [0, R 0 ]. Hence, any field in the initial plane z = 0 can be decomposed into these modal solutions.
Solutions (3) are propagation-invariant in free space, i.e., they are non-diffractive Bessel vector beams with radial and azimuthal indices possessing polarization and phase singularities. Note that the non-diffractive scalar Bessel beams in free space were proposed in [60]. In [61], three-dimensional self-imaging electromagnetic fields that are exact solu-tions of Maxwell's equations were analyzed. In [62], the amplitude components of the vector non-diffractive beams were obtained as solutions to the vector Helmholtz wave equation. It was shown in [63] that the normalized Bessel beams with radial indices are the solutions of the scalar Helmholtz equation.
When l = 0, Equation (5) reduces to the electric field for the linearly polarized Bessel beams with non-zero intensity in the center. When l = 1, Equation (5) describes the electric field of a radially or azimuthally polarized beam with annular intensity distribution. Light beams with radial and azimuthal polarizations are the most well-known cylindrical vector beams with phase (scalar) and polarization (vector) singularities. The phase singularity is associated with a phase (scalar) vortex of a beam or with the helical wavefront. The polarization singularity occurs in beams with spatially varying polarization distribution [45,46].
Cartesian components of the field are related to polar by the relationship: Thus, the vector mode represents the superposition of beams in orthogonal polarization (spin) and OAM states. A spin part is associated with polarization and an orbital part is associated with spatial distribution. There is an entanglement or non-separability between spatial, polarization and orbital angular momentum degrees of freedom.
Arbitrary incident beam at z = 0 with spatial transverse dimensions significantly exceeding the wavelength can be decomposed in the series of modal solutions: where The evolution of the incident field is determined by the expression where β pl are the propagation constants of the modes.
Degree of Polarization
The degree of polarization is determined from the calculations of the coherency matrix [1]: The angular brackets define the ensemble average taken over the statistical ensemble representing the randomly varying electromagnetic field. The degree of polarization is determined by the expression [1]: where In the experiments, the degree of polarization P can be determined from the measurements of the Stokes parameters [1,2]: Note that, at the polarization singularities, the Stokes phase φ 12 is the undefined quantity [45].
There is an analogy between the coherency matrix and the quantum-mechanical density matrix [64]: where ρ is the density matrix corresponding to the normalized coherence function This analogy allows such thermodynamic parameters as entropy, temperature, etc. for the optical radiation to be introduced [64]. The entropy of the optical beam can be determined as where w n = n|ρ|n Spρ is the probability of excitation of a given mode, i.e., the fraction of radiation energy carried by this mode.
It is known that the entropy is the measure of the lack of information about the system. The entropy reaches its minimum (S = 0) in the case of totally polarized pure state and has a maximum (S = ∞) for unpolarized and incoherent radiation.
The density matrix operator has a trace Spρ = 1 and for the mixed (partially polarized) state Spρ 2 ≤ 1.
The value Spρ 2 is a measure of quantum-mechanical purity. The purity parameter or impurity can be introduced to describe the partially polarized beams [65]: where There is a close relationship between the degree of polarization and the quantum purity parameter of the light beam, so the degree of polarization can be represented by [65]: It follows from (15) that the trace of the matrix ρ 2 is given by the expression and, hence, the equality Spρ 2 = 1 is the necessary and sufficient condition for finding a beam in a pure state. The degree of polarization P = 1 corresponds to a pure state and P = 0 corresponds to an unpolarized beam. Note that we use here the term "purity" with respect to coherent fields. We show that the purity of a beam is preserved during propagation if only this beam is a polarization-invariant modal solution of Maxwell's equations.
Simulation Results
Consider the incident vector beam at z = 0 with Gaussian and Bessel-Gauss (BG) spatial distributions of the intensity: where a 0 is the radius of a Gaussian beam, These beams are linearly polarized, so they can be represented by the decomposition into the modal solutions with zero OAM (l = 0). The modal amplitude coefficients for these incident beams can be calculated analytically. The expressions for modal coefficients have the form: where µ p are the positive zeros of the Bessel function J 0 (z), and I 0 (z) is the modified Bessel function of the first kind. Note that polarization-invariant linear and circular polarized vector beams have no vortices (l = 0), i.e., such beams have non-zero intensity on the axis and no OAM. On the contrary, polarization-invariant radially and azimuthally polarized vector beams have non-zero angular orbital momentum and annular intensity distributions.
Purity
The quantum-mechanical purity of a beam can be calculated using (14). Initial density matrix operator for a pure state is given by the incident vector beams:
Purity Parameter
In Figure 1, the dependences of the quantum-mechanical purity Spρ 2 and the purity parameter (impurity) ζ = 1 − Spρ 2 on the propagation distance are presented for different widths of linearly polarized Gaussian beam with zero OAM. Figure 1b shows the dependence of the quantum purity parameter of the beam on the distance. It can be seen that if the beam is in a pure state in the initial plane, then, during propagation, the beam transforms into a mixed state. Purity Parameter In Figure 1, the dependences of the quantum-mechanical purity and the purity parameter (impurity) = 1 − on the propagation distance are presented for different widths of linearly polarized Gaussian beam with zero OAM. Figure 1b shows the dependence of the quantum purity parameter of the beam on the distance. It can be seen that if the beam is in a pure state in the initial plane, then, during propagation, the beam transforms into a mixed state. In Figure 2a, the dependence of the impurity on distance is shown for a beam radius = 15 μm. In Figure 2b, the dependence of an entropy of a Gaussian beam as function of propagation distance is shown. It can be seen that there is a significant change in entropy at a length of the order of the diffraction length.
Linear Polarization
The degree of polarization can be calculated using the expression (15). Figure 3a shows the change in the degree of polarization of linearly polarized Gaussian light beams with a distance for different beam radii. It can be seen from the figure that a strong change in the degree of polarization occurs at distances of the order of the diffraction length = 2 ⁄ . This means that the focused beams are depolarized at a shorter distance. Figure 3b shows the change in the degree of polarization of a linearly polarized Gaussian light beam with a distance for the beam radius = 15 mm. In Figure 2a, the dependence of the impurity on distance is shown for a beam radius a 0 = 15 µm. In Figure 2b, the dependence of an entropy of a Gaussian beam as function of propagation distance is shown. It can be seen that there is a significant change in entropy at a length of the order of the diffraction length. Purity Parameter In Figure 1, the dependences of the quantum-mechanical purity and the purity parameter (impurity) = 1 − on the propagation distance are presented for different widths of linearly polarized Gaussian beam with zero OAM. Figure 1b shows the dependence of the quantum purity parameter of the beam on the distance. It can be seen that if the beam is in a pure state in the initial plane, then, during propagation, the beam transforms into a mixed state. In Figure 2a, the dependence of the impurity on distance is shown for a beam radius = 15 μm. In Figure 2b, the dependence of an entropy of a Gaussian beam as function of propagation distance is shown. It can be seen that there is a significant change in entropy at a length of the order of the diffraction length.
Linear Polarization
The degree of polarization can be calculated using the expression (15). Figure 3a shows the change in the degree of polarization of linearly polarized Gaussian light beams with a distance for different beam radii. It can be seen from the figure that a strong change in the degree of polarization occurs at distances of the order of the diffraction length = 2 ⁄ . This means that the focused beams are depolarized at a shorter distance. Figure 3b shows the change in the degree of polarization of a linearly polarized Gaussian light beam with a distance for the beam radius = 15 mm.
Linear Polarization
The degree of polarization can be calculated using the expression (15). Figure 3a shows the change in the degree of polarization of linearly polarized Gaussian light beams with a distance for different beam radii. It can be seen from the figure that a strong change in the degree of polarization occurs at distances of the order of the diffraction length l d = ka 2 0 /2. This means that the focused beams are depolarized at a shorter distance. Figure 3b shows the change in the degree of polarization of a linearly polarized Gaussian light beam with a distance for the beam radius a 0 = 15 mm. Note that a coherence-induced decrease in the degree of polarization with the propagation distance for partially coherent beams in free space was shown in [19,25,26]. It was found that for a completely spatially coherent beam, its degree of polarization does not change during propagation [19,25,26]. The spectral degree of polarization remains propagation-invariant along the axis of a Gaussian Shell-model beam [27,28]. Here, we show that the decrease in the degree of polarization occurs also for spatially coherent beams. Unlike the coherence-induced mechanism of polarization changes, the decrease in the degree of polarization with distance arises also due to the interference between the modes having different propagation constants. A decrease in the degree of correlation between different modes takes place due to a difference in propagation velocities of Bessel vector modes with different radial indices.
Note that for very small radii of the incident beam ( << λ), the two-dimensional description of polarization is incomplete. It was shown in [66,67] that knowledge of the vector polarization of radiation alone is not sufficient to fully determine the polarization state of a focused light beam. Additional parameters (tensor polarization components) should be taken into account. It is shown in [66] that the vector and tensor degrees of polarization are sufficient for a complete description of the three-component field of a light beam. In Figure 4, the dependences of the degree of polarization on the propagation distance are shown for Gaussian and BG beams with approximately similar effective widths. Note that a coherence-induced decrease in the degree of polarization with the propagation distance for partially coherent beams in free space was shown in [19,25,26]. It was found that for a completely spatially coherent beam, its degree of polarization does not change during propagation [19,25,26]. The spectral degree of polarization remains propagation-invariant along the axis of a Gaussian Shell-model beam [27,28]. Here, we show that the decrease in the degree of polarization occurs also for spatially coherent beams. Unlike the coherence-induced mechanism of polarization changes, the decrease in the degree of polarization with distance arises also due to the interference between the modes having different propagation constants. A decrease in the degree of correlation between different modes takes place due to a difference in propagation velocities of Bessel vector modes with different radial indices.
Note that for very small radii of the incident beam (a 0 << λ), the two-dimensional description of polarization is incomplete. It was shown in [66,67] that knowledge of the vector polarization of radiation alone is not sufficient to fully determine the polarization state of a focused light beam. Additional parameters (tensor polarization components) should be taken into account. It is shown in [66] that the vector and tensor degrees of polarization are sufficient for a complete description of the three-component field of a light beam. In Figure 4, the dependences of the degree of polarization on the propagation distance are shown for Gaussian and BG beams with approximately similar effective widths. Note that a coherence-induced decrease in the degree of polarization with the propagation distance for partially coherent beams in free space was shown in [19,25,26]. It was found that for a completely spatially coherent beam, its degree of polarization does not change during propagation [19,25,26]. The spectral degree of polarization remains propagation-invariant along the axis of a Gaussian Shell-model beam [27,28]. Here, we show that the decrease in the degree of polarization occurs also for spatially coherent beams. Unlike the coherence-induced mechanism of polarization changes, the decrease in the degree of polarization with distance arises also due to the interference between the modes having different propagation constants. A decrease in the degree of correlation between different modes takes place due to a difference in propagation velocities of Bessel vector modes with different radial indices.
Note that for very small radii of the incident beam ( << λ), the two-dimensional description of polarization is incomplete. It was shown in [66,67] that knowledge of the vector polarization of radiation alone is not sufficient to fully determine the polarization state of a focused light beam. Additional parameters (tensor polarization components) should be taken into account. It is shown in [66] that the vector and tensor degrees of polarization are sufficient for a complete description of the three-component field of a light beam. In Figure 4, the dependences of the degree of polarization on the propagation distance are shown for Gaussian and BG beams with approximately similar effective widths. It can be seen that a faster decrease in the degree of polarization occurs for the Gaussian beam (17). The degree of polarization of the BG beam (18) decreases more slowly than for a Gaussian beam if w 0 w B . Indeed, in this case, the BG beam becomes more identical to the Bessel beam. The intensity profiles of the incident Gaussian (17) and Bessel-Gauss (18) beams are shown in Figure 5.
It can be seen that a faster decrease in the degree of polarization occurs for the Gaussian beam (17). The degree of polarization of the BG beam (18) decreases more slowly than for a Gaussian beam if ≫ . Indeed, in this case, the BG beam becomes more identical to the Bessel beam. The intensity profiles of the incident Gaussian (17) and Bessel-Gauss (18) beams are shown in Figure 5.
Radial Polarization
Consider the radially polarized incident vector beams at z = 0 with annular Gaussian and Bessel-Gauss spatial distributions of the intensity and OAM l = 1: where is the radius of a Gaussian beam, = ⁄ , and / = is the effective width of the BG beam. For the modal coefficients we have where are the positive zeros of the Bessel function ( ), and ( ) is the modified Bessel function of the first kind.
In Figure 6a, the dependences of the degree of polarization on the propagation distance are presented for the beams with annular Gaussian intensity distribution (21) and OAM l = 1.
It can be seen that the smaller the radius of the beam, the smaller the distance at which the noticeable depolarization of the beam occurs. In Figure 6b, the degrees of polarization as function of distance are presented for Gaussian (21) and BG (22) beams with OAM l = 1 and with approximately similar effective widths (Figure 7).
It follows that the decrease in the degree of polarization of the BG beam (22) occurs much slower than for a Gaussian beam (21) if the value exceeds the effective radius of the BG beam . In this case, the behavior of the BG beam is similar to the vector Bessel beam, which is a modal solution. The closer the beam is to the modal solution, the weaker the beam depolarization with distance.
Radial Polarization
Consider the radially polarized incident vector beams at z = 0 with annular Gaussian and Bessel-Gauss spatial distributions of the intensity and OAM l = 1: where a 0 is the radius of a Gaussian beam, A 0 = , and w B /µ 1 = γ −1 is the effective width of the BG beam. For the modal coefficients we have where µ p are the positive zeros of the Bessel function J 1 (z), and I 1 (z) is the modified Bessel function of the first kind. In Figure 6a, the dependences of the degree of polarization on the propagation distance are presented for the beams with annular Gaussian intensity distribution (21) and OAM l = 1.
It can be seen that the smaller the radius of the beam, the smaller the distance at which the noticeable depolarization of the beam occurs. In Figure 6b, the degrees of polarization as function of distance are presented for Gaussian (21) and BG (22) beams with OAM l = 1 and with approximately similar effective widths (Figure 7).
It follows that the decrease in the degree of polarization of the BG beam (22) occurs much slower than for a Gaussian beam (21) if the value w 0 exceeds the effective radius of the BG beam w B . In this case, the behavior of the BG beam is similar to the vector Bessel beam, which is a modal solution. The closer the beam is to the modal solution, the weaker the beam depolarization with distance. It should be noted that the simulation results presented above correspond to the degree of polarization, which is averaged over the cross section of the beam. The values of the local degree of polarization determined at a given point of the transverse plane are usually considered. It is known that the local degree of polarization varies with displacement from the axis of the beam, i.e., the polarization is inhomogeneous in cross section [19]. However, the average degree of polarization is usually determined during measurements.
Discussion
Thus, polarization-invariant hybrid vector Bessel beams with phase and polarization singularities are proposed, which combine spin and OAM and are the solutions of Maxwell's equations in free space. These modal solutions with discrete azimuthal and radial indices form a complete set of mutually orthogonal functions. Consequently, an arbitrary vector beam in the initial plane = 0 can be decomposed into these modal solutions, the evolution of which is determined by the propagation constant . The modal approach provides clear physical insight into the depolarization mechanism of vector beams in free space and computational simplification in the analysis.
One of the important properties of hybrid vector beams, which are the modal solutions, is the entanglement of phase and spin degrees of freedom describing the orbital angular momentum and the polarization vector in orthogonal basis vectors of circular It should be noted that the simulation results presented above correspond to the degree of polarization, which is averaged over the cross section of the beam. The values of the local degree of polarization determined at a given point of the transverse plane are usually considered. It is known that the local degree of polarization varies with displacement from the axis of the beam, i.e., the polarization is inhomogeneous in cross section [19]. However, the average degree of polarization is usually determined during measurements.
Discussion
Thus, polarization-invariant hybrid vector Bessel beams with phase and polarization singularities are proposed, which combine spin and OAM and are the solutions of Maxwell's equations in free space. These modal solutions with discrete azimuthal and radial indices form a complete set of mutually orthogonal functions. Consequently, an arbitrary vector beam in the initial plane = 0 can be decomposed into these modal solutions, the evolution of which is determined by the propagation constant . The modal approach provides clear physical insight into the depolarization mechanism of vector beams in free space and computational simplification in the analysis.
One of the important properties of hybrid vector beams, which are the modal solutions, is the entanglement of phase and spin degrees of freedom describing the orbital angular momentum and the polarization vector in orthogonal basis vectors of circular It should be noted that the simulation results presented above correspond to the degree of polarization, which is averaged over the cross section of the beam. The values of the local degree of polarization determined at a given point of the transverse plane are usually considered. It is known that the local degree of polarization varies with displacement from the axis of the beam, i.e., the polarization is inhomogeneous in cross section [19]. However, the average degree of polarization is usually determined during measurements.
Discussion
Thus, polarization-invariant hybrid vector Bessel beams with phase and polarization singularities are proposed, which combine spin and OAM and are the solutions of Maxwell's equations in free space. These modal solutions with discrete azimuthal and radial indices form a complete set of mutually orthogonal functions. Consequently, an arbitrary vector beam in the initial plane z = 0 can be decomposed into these modal solutions, the evolution of which is determined by the propagation constant β pl . The modal approach provides clear physical insight into the depolarization mechanism of vector beams in free space and computational simplification in the analysis.
One of the important properties of hybrid vector beams, which are the modal solutions, is the entanglement of phase and spin degrees of freedom describing the orbital angular momentum and the polarization vector in orthogonal basis vectors of circular polarization. These complex modes cannot simply be expressed through the TE and TM modes. The entanglement property is inherent in quantum systems and may have implications for future quantum networks [68][69][70][71]. In [70], it was demonstrated that the entanglement, or non-separability, between the spatial and polarization degrees of freedom also experiences self-healing.
Hybrid vector modes have non-separable spatial, phase and polarization degrees of freedom. Hence, there is an analogy between the non-separability of such modes and entanglement of quantum states. Here, we found that quantum-mechanical purity is a measure of the average degree of polarization. In turn, the purity is related to the entropy.
Note that the vector beam with separable spin and OAM degrees of freedom in the initial plane z = 0 transforms into the polarization-OAM entanglement beam during propagation [72,73].
Degree of polarization of pure states, which are the modal solutions of the Maxwell equations, remains invariant during propagation, i.e., it does not change on propagation. This indicates that the purity parameter and entropy for the modal solutions remain unchanged. A decrease in the degree of polarization with distance arises due to the interference between the modes having different propagation constants. Due to differences in propagation velocities of Bessel vector modes with different radial indices, a decrease in the degree of correlation between the different modes takes place. The more propagating modes are excited, the more the degree of polarization decreases with distance.
Note, it was shown in [24][25][26][27][28], that the change in the degree of polarization of the beam is influenced by the coherence properties of the field in the source plane. Here, we have demonstrated that, unlike the depolarization of partially coherent beams, the depolarization of coherent beams occurs due to interference between propagating modes.
Here we considered 2 × 2 matrices to describe the polarization of vector beams. Indeed, in conventional paraxial optics, the fields are transverse, so the polarization is well described by Stokes parameters determined from the 2 × 2 coherency matrix [1][2][3]. Although the electromagnetic field in the far zone is transverse, many problems of near-field optics, data storage, optical microscopy, and focusing and scattering need to be analyzed, taking into account the longitudinal component of the field E z . For strongly focused beams, the Stokes 2 × 2 formalism should be extended to 3 × 3 coherency matrices [74][75][76][77][78][79]. In [67,72,73], the evolution of three-dimensional electromagnetic fields in a graded-index medium was studied. The polarization state of the most general two-dimensional field can be completely described by three parameters. This is related to as vector polarization. For a three-dimensional field except for 3 components of the vector polarization, there are 5 components of tensor polarization. The complexity of 3D electromagnetic field arises from the tensor polarization [66,67]. In contrast to the 2D case, where any beam can be represented as the superposition of totally polarized and unpolarized light, in the 3D case, an arbitrary beam cannot be represented as a mixture of an unpolarized beam (one parameter) and a polarized beam (5 parameters) because nine independent parameters are needed to characterize the coherence matrix.
Recently, new developments based on the transverse spin phenomenon, which occurs in focused fields, have been of great interest [114][115][116][117][118]. New optical phenomena associated with the angular momentum have been demonstrated in [119]. It was shown in [115] that transverse spin manifests itself even in completely unpolarized fields. There is a connection between 3D polarization in the transverse spin [117] and non-paraxial fields [118] owing to the spin-orbit interaction of light. These phenomena can be used in telecommunications, nanoelectronics, optical imaging, etc. [119,120].
Conclusions
In summary, new hybrid vector Bessel beams with polarization-OAM entanglement, which are the modal solutions of the Maxwell equations, are proposed to study the evolution of vector beams in free space. Non-separability of degrees of freedom associated with the spatial coordinates, spin (polarization) and OAM (phase) is the main feature of the vector vortex beams which are the modal solutions.
The vector mode decomposition method was developed for the analysis of the evolution of the polarization of arbitrary transverse incident beams with different spatial intensity distributions. Using the decomposition of the field of an arbitrary incident vector beam into the hybrid modal solutions, the change in the 2D degree of polarization, the quantum purity parameter and the entropy of the beam with distance was investigated.
The change in the degree of polarization of vector vortex beams on propagation in free space due to interference between different propagating modes has been demonstrated. A significant decrease in the degree of polarization occurs at the diffraction length l d for incident beams that are not vector modal solutions.
Close connection of the degree of polarization with the quantum-mechanical purity parameter is emphasized.
The results obtained may be useful in free-space optical communications, singular optics, photonics, optical coherence tomography, imaging, and may have implications for future quantum networks. | 8,167.6 | 2022-03-06T00:00:00.000 | [
"Physics"
] |
Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in TurboGenerator Rotor Windings
Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil method. Compared with dynamic eccentricity faults, the characteristics of the variations in the main magnetic field after a turn-to-turn short circuit in rotor windings were analyzed and the unique characteristics were extracted. We propose that the degree of a turn-to-turn short circuit can be judged by the difference in the induction voltage of the double U-shaped detection coils mounted on the stator core. Here, the faulty slot position was determined by the local convex point formed by the difference in the induced voltage. Numerical simulation was used here to determine the induced voltage characteristics in the double U-shaped coils caused by the turn-to-turn short circuit fault. We analyzed the dynamic eccentricity fault as well as combined the fault of a turn-to-turn short circuit and dynamic eccentricity. Finally, we demonstrate the positive anti-interference performance associated with this fault detection method. This new online detection method is satisfactory in terms of sensitivity, speed, and positioning, and overall performance is superior to the traditional online detection methods.
Introduction
Small-and medium-capacity turbo-generator units in China's power system have gradually been eliminated except for the power stations used for urban heat supply.Some 300 MW units, as well as large-capacity turbo-generator units of 600 MW, 1000 MW, or above, have been retained, which have been adopted at most thermal power stations and nuclear power stations.These units have large electromagnetic loads, especially the rotor windings, and they must bear large mechanical and thermal stresses under strong vibration conditions.If turn-to-turn insulation is weak, a turn-to-turn short circuit fault can easily occur.
The most troublesome problem for turbo-generator rotor windings caused by turn-to-turn short circuit faults is the deterioration that occurs in the vibration state.Sometimes, rotor grounding and shaft magnetization issues occur as well.Usually, 1-1.5 months are required to address and resolve this problem.This includes finding, processing, and repairing the turn-to-turn short circuit fault in the rotor winding.When this happens, serious economic losses are incurred by the power plants, including the loss of generating capacity, labor costs, and energy available for starting and stopping machines repeatedly, as well as service life loss.Therefore, serious problems caused by fault deterioration can be avoided if the turn-to-turn short circuit in the turbo-generator rotor winding can be assessed more accurately.If the short circuit can be found more effectively, timely warnings can be provided at the early stage so many problems can be avoided.Also, the accurate localization of a fault can reduce fault processing time and reduce economic losses to a great extent.In this case, work in this area has certain value and prospects [1][2][3].
For turbo-generators, various kinds of online detection methods capable of detecting turn-to-turn short circuits in rotor windings have been proposed.For example, Mladen Šašić et al. [4] detected the symmetry of each slot leakage flux in the rotor using a micro-probe.According to the symmetry, they determined if a turn-to-turn short circuit occurred.Rastko Fišer et al. [5] proposed installing a small magnetic flux probe on the stator core "tooth unit".This allowed users to confirm the short circuit's position in the rotor winding using the difference in induced voltages on the probe according to the corresponding slots in the normal pole and the fault pole.In Li Yonggang et al. [6], the turn-to-turn short circuit in rotor windings was judged by the relative deviation between the theoretical and actual values of the excitation current.In Wu Yucai, et al. [7], turn-to-turn short circuits in rotor windings were judged by the deviation between the actual electromagnetic power and expected electromagnetic power.In Wu Yucai, et al. [8], a detection method without the use of a sensor was proposed.Here, the turn-to-turn short circuit was judged by the induced voltage harmonic of piercing screw in the stator core.In addition, shaft voltage harmonics [9,10], no-load electromotive force deviation [11], end leakage flux harmonics [12], and parallel branch circulation in the stator winding [13] have all been used to confirm whether the existence of a turn-to-turn short circuit fault in a rotor winding.For nearly half of these detection methods, excitation current data must be acquired from the turbo-generator including the excitation current method, virtual power method, and expected electromotive force method.These types of methods are inapplicable to large-capacity nuclear power units that use brushless excitation technology as a general rule.For the detection methods independent of excitation current, the detection coil method has been put into practical use.With this method, there is small interference under no-load or short circuit conditions in the turbo-generator and the detection effect is relatively good.When working under load, the output voltage waveform in the detection coil becomes insensitive to the turn-to-turn short circuit fault.This occurs due to the influence of the armature field, so the sensitivity drops sharply.Therefore, for this method, units are adjusted to a no-load or short circuit condition to finish the test after estimating that the turbo-generator is having a problem with a turn-to-turn short circuit in the rotor winding.In this case, online performance cannot be completely assessed, and though it is a method that can improve sensitivity, real-time evaluation is not possible.Wu Yucai et al. [14] proposed installing a U-shaped magnetic field detection coil inside the turbo-generator.Then, the turn-to-turn short circuit fault in the rotor winding was judged using the even or fractional sub-harmonic in the induced voltage of U-shaped coil.This U-shaped detection coil method is based on the asymmetry of the north and south magnetic pole fields in the faulty turbo-generator.In this case, users are not limited by the excitation mode of the turbo-generator; however, t another fault like a turn-to-turn short circuit fault can occur in rotor windings.This is known as the rotor eccentricity fault, where the magnetic field asymmetry of the turbo-generator is caused by dynamic eccentricity, which is stronger on the small air gap side and weaker on the large air gap side.When the turn-to-turn short circuit for rotor windings are judged using the U-shaped coil method, the first step is to eliminate the interference of the dynamic eccentricity fault because dynamic eccentricity faults are common in turbo-generators.Here, success or failure depends on whether the eccentricity interference is eliminated.
In terms of the anti-interference performance, the same shortcomings generally exist for all detection methods based on using the magnetic field.When the operating state of a turbo-generator changes rapidly (the reactive power also changes rapidly), the induced voltage waveforms of the sensor are not comparable at different times.In this case, it is possible to receive a false alarm signal when operating in a normal state, so an unnecessary shutdown may occur.In addition, most of the detection sensors have only a single function and they can only be used to detect turn-to-turn short circuit faults for rotor windings.If turn-to-turn short circuits in rotor windings and dynamic eccentricity faults can be detected simultaneously using the same kind of sensor, the monitoring systems for turbo-generators will be perfected and capable of simplifying the structure of detection sensors.
In this study, the influence of turn-to-turn short circuits in rotor windings on the main magnetic field of a turbo-generator was deduced.Similarity comparisons with dynamic eccentricity faults were conducted, and an online detection method for turn-to-turn short circuits in rotor winding based on double U-shaped coils was designed.We used to numerical simulation to prove that the fault features of turn-to-turn short circuit faults extracted from the new signal acquisition method are not covered by dynamic eccentricity faults, and the fault location can be determined.After the turn-to-turn short circuit fault is eliminated, the new detection method can be used to judge the rotor for a dynamic eccentricity fault.
Principle of Detection Methods
The rotor winding of a turbo-generator has a distributed structure.A certain number of excitation windings (generally 6-10 turns) are placed in each slot of the rotor and they are insulated by insulating layers, as shown in Figure 1.According to ampere circuital theorem, the excitation magneto motive force in a turbo-generator is a stepped wave, as shown in Figure 2. The amount of stepped wave in each slot of the rotor is related to the effective ampere turns of the slot.The larger the number of effective ampere turns, the larger the step amount.In the normal state of rotor winding, the number of winding turns for the rotor north and south poles is the same and the number of effective ampere turns is also the same.So, the excitation magnetic force is symmetrical.If a turn-to-turn short circuit fault occurs in a slot winding of the rotor, the current of the short circuited winding in the slot is zero, and the step amount of the excitation magnetic force in the slot decreases.In this case, the entire excitation magnetic force exists in an asymmetrical state.
where F i represents the amplitude of the ith harmonic magnetic potential, i is an odd number, θ r represents the spatial mechanical angle of the rotor, p represents the pole pair of the turbo-generator, a k represents the number of windings in the kth slot, γ is the angle at which the large gear of the rotor occupies the circumference of the rotor, I f represents the excitation current, m is the number of short circuit slots, and Q is the number of short circuit turns.
Energies 2019, 12, x FOR PEER REVIEW 3 of 20 systems for turbo-generators will be perfected and capable of simplifying the structure of detection sensors.
In this study, the influence of turn-to-turn short circuits in rotor windings on the main magnetic field of a turbo-generator was deduced.Similarity comparisons with dynamic eccentricity faults were conducted, and an online detection method for turn-to-turn short circuits in rotor winding based on double U-shaped coils was designed.We used to numerical simulation to prove that the fault features of turn-to-turn short circuit faults extracted from the new signal acquisition method are not covered by dynamic eccentricity faults, and the fault location can be determined.After the turn-to-turn short circuit fault is eliminated, the new detection method can be used to judge the rotor for a dynamic eccentricity fault.
Principle of Detection Methods
The rotor winding of a turbo-generator has a distributed structure.A certain number of excitation windings (generally 6-10 turns) are placed in each slot of the rotor and they are insulated by insulating layers, as shown in Figure 1.According to ampere circuital theorem, the excitation magneto motive force in a turbo-generator is a stepped wave, as shown in Figure 2. The amount of stepped wave in each slot of the rotor is related to the effective ampere turns of the slot.The larger the number of effective ampere turns, the larger the step amount.In the normal state of rotor winding, the number of winding turns for the rotor north and south poles is the same and the number of effective ampere turns is also the same.So, the excitation magnetic force is symmetrical.If a turn-to-turn short circuit fault occurs in a slot winding of the rotor, the current of the short circuited winding in the slot is zero, and the step amount of the excitation magnetic force in the slot decreases.In this case, the entire excitation magnetic force exists in an asymmetrical state.Ff is the excitation magneto motive force in a stepped wave after a turn-to-turn short circuit; Ff' is the excitation magneto motive force of the stepped wave when the winding is normal.
where Fi represents the amplitude of the ith harmonic magnetic potential, i is an odd number, θr represents the spatial mechanical angle of the rotor, p represents the pole pair of the turbo-generator, ak represents the number of windings in the kth slot, γ is the angle at which the large gear of the rotor occupies the circumference of the rotor, If represents the excitation current, m is the number of short circuit slots, and Q is the number of short circuit turns.
When the rotor winding is normal, the excitation magneto motive force waveform is symmetrical, and the stepped magnetic potential waveform contains only the fundamental wave and the odd harmonic after the Fourier decomposition.This is shown in Equation (1).
When the turn-to-turn short circuit in a rotor winding occurs, the asymmetric stepped excitation magnetic force contains even or fractional harmonics after the Fourier decomposition.The excitation magnetic force is shown in Equation ( 2) [15].Here, the blue item is the magnetic potential loss in the excitation winding which has short circuited.This is shown by the equation where j = 1, 2, 3, 4...: The eccentricity of the rotor can be expressed as: where g represents the largest or shortest air gap length of the turbo-generator and g0 represents the average air gap length of the turbo-generator.
It is assumed that the air gap in the turbo-generator is uniform, g(θr) = g0 and that the air gap permeability is constant.Therefore, this can be expressed as: where, μ0 represents the permeability of the vacuum.When the rotor winding is normal, the excitation magneto motive force waveform is symmetrical, and the stepped magnetic potential waveform contains only the fundamental wave and the odd harmonic after the Fourier decomposition.This is shown in Equation (1).
When the turn-to-turn short circuit in a rotor winding occurs, the asymmetric stepped excitation magnetic force contains even or fractional harmonics after the Fourier decomposition.The excitation magnetic force is shown in Equation ( 2) [15].Here, the blue item is the magnetic potential loss in the excitation winding which has short circuited.This is shown by the equation where j = 1, 2, 3, 4...: The eccentricity of the rotor can be expressed as: where g represents the largest or shortest air gap length of the turbo-generator and g 0 represents the average air gap length of the turbo-generator.
It is assumed that the air gap in the turbo-generator is uniform, g(θ r ) = g 0 and that the air gap permeability is constant.Therefore, this can be expressed as: where, µ 0 represents the permeability of the vacuum.The excitation field of the turbo-generator can be expressed as Equation ( 5): Equation (5) shows that some new harmonics appear in the excitation magnetic field after the turn-to-turn short circuit fault occurs in the turbo-generator rotor winding.For a 2-pole turbo-generator, even harmonics can occur 2, 4, and 6 times, and these will be generated in the excitation magnetic field; for the 4-pole turbo-generator, the 1/2, 3/2, 2, and 5/2 times harmonics do not exist when the rotor winding is normal.
The main magnetic field asymmetry of a turbo-generator can also be caused by the dynamic eccentricity of the rotor.In an abnormal state, the length of the air gap at any position changes periodically with time.If the slot effect, saturation effect, and higher harmonics are neglected, the length of the air gap in the synchronous rotating coordinate system can be expressed as [16]: Energies 2019, 12, 1378 5 of 18 where β represents the angle between the shortest air gap and axis d of the rotor.Accordingly, the air gap permeance of the turbo-generator can be expressed as: where ε is the relative eccentric ratio.
The air gap synthetic magnetic potential of the turbo-generator is equal to the superposition of excitation magnetic potential and armature reaction magnetic potential, which contains only odd harmonics.According to the air gap magnetic permeance method, the main magnetic field can be expressed as Equation ( 8): where α 1 and α 3 represent the phase of the fundamental and third harmonics magnetic potential, respectively; and F 1 and F 3 represent the amplitude of the fundamental and third harmonics magnetic potential, respectively.The blue item in Equation ( 8) appears after the fault, including the p + 1, p − 1, 3p + 1, and 3p − 1 harmonic components, which rotate synchronously with the rotor.For a 2-pole turbo-generator (p = 1), there is no direct current component, and 2nd and 4th harmonics are in the magnetic field before the fault; for a 4-pole turbo-generator (p = 2), there are no 1/2, 3/2, 5/2, and 7/2 harmonics in the magnetic field before the fault.
Compared with the magnetic field found with a turn-to-turn short circuit fault in the rotor winding, the frequency of the harmonics that appear in the main magnetic field of a turbo-generator are the same under dynamic eccentricity.It is impossible to judge a turn-to-turn short circuit fault in the rotor winding using the induced voltage spectrum of a single U-shaped coil.Even if these harmonics are detected, it is impossible to determine whether these harmonics are caused by dynamic eccentricity or by a turn-to-turn short circuit in the rotor winding.To address this shortcoming, we propose that two detection coils should be placed symmetrically at a circumferential distance of 180 • in the turbo-generator.The principle of this type of detection system is shown in Figure 3.
The voltages induced by the U-shaped coils can be expressed as e = B r Lv = B r Lω r R, where B r represents the radial component of the air gap flux density at the effective part of U-shaped coil, L is the effective part length of the U-shaped coil, v represents the moving speed of the radial magnetic field relative to the effective part of the U-shaped coil;, ω r is the mechanical angular speed of rotor rotation, and R is the distance between the effective part of the U-shaped coil and the center of the rotor.As L, ω r , and R are all constants, the induced voltage waveform of the U-shaped coil is similar to the radial component waveform of gap flux density at the effective part of the coil.Theoretically, during a fully symmetrical state of the turbo-generator air gap magnetic field, the induced voltages of both coils are the same.After decomposition, the harmonics with the faulty characteristics are not included.The result is zero if the induced voltage time domain waveforms of both coils are added together (two-pole generator) or subtracted (four-pole generator).If a turn-to-turn short circuit fault or dynamic eccentricity fault occurs, although the harmonic contents of the induced voltages for both coils are the same, the induced voltages are different due to the different positions of both detection coils.In this case, the voltage time domain waveform is not zero after operation.There is a significant advantage for adopting the use of double coils: the induced voltages of both coils change simultaneously with a change in the turbo-generator's operating state.Under a normal state, the output voltage waveforms of both coils will always be the same (four-pole generator) or the opposite (two-pole generator); therefore, the double coil method has excellent anti-interference abilities, which helps to reduce the probability of misjudgment.The voltages induced by the U-shaped coils can be expressed as e = BrLv = BrLωrR, where Br represents the radial component of the air gap flux density at the effective part of U-shaped coil, L is the effective part length of the U-shaped coil, v represents the moving speed of the radial magnetic field relative to the effective part of the U-shaped coil;, ωr is the mechanical angular speed of rotor rotation, and R is the distance between the effective part of the U-shaped coil and the center of the rotor.As L, ωr, and R are all constants, the induced voltage waveform of the U-shaped coil is similar to the radial component waveform of gap flux density at the effective part of the coil.Theoretically, during a fully symmetrical state of the turbo-generator air gap magnetic field, the induced voltages of both coils are the same.After decomposition, the harmonics with the faulty characteristics are not included.The result is zero if the induced voltage time domain waveforms of both coils are added together (two-pole generator) or subtracted (four-pole generator).If a turn-to-turn short circuit fault or dynamic eccentricity fault occurs, although the harmonic contents of the induced voltages for both coils are the same, the induced voltages are different due to the different positions of both detection coils.In this case, the voltage time domain waveform is not zero after operation.There is a significant advantage for adopting the use of double coils: the induced voltages of both coils change simultaneously with a change in the turbo-generator's operating state.Under a normal state, the output voltage waveforms of both coils will always be the same (four-pole generator) or the opposite (two-pole generator); therefore, the double coil method has excellent anti-interference abilities, which helps to reduce the probability of misjudgment.
Modeling and Simulation of Normal Operation Conditions
We chose the TA1100-78 turbo-generator as an example, which is produced by the motor factory of DongFang located in DeYang China; and its basic parameters are shown in Table 1.First, the two-dimensional transient electromagnetic field simulation model was built according to the geometric parameters of the turbo-generator.Then, simulation verification work was completed based on the state settings and fault settings shown in Figure 4.
Modeling and Simulation of Normal Operation Conditions
We chose the TA1100-78 turbo-generator as an example, which is produced by the motor factory of DongFang located in DeYang China; and its basic parameters are shown in Table 1.First, the two-dimensional transient electromagnetic field simulation model was built according to the geometric parameters of the turbo-generator.Then, simulation verification work was completed based on the state settings and fault settings shown in Figure 4.The induced voltage waveforms of both detection coils operating during the normal state of the turbo-generator rotor are shown in Figure 5. Depending on whether the turbo-generator was under no-load or rated load conditions, the induced voltage waveforms from both coils were almost completely coincident.The induced voltage waveforms of both detection coils operating during the normal state of the turbo-generator rotor are shown in Figure 5. Depending on whether the turbo-generator was under no-load or rated load conditions, the induced voltage waveforms from both coils were almost completely coincident.
Modeling and Simulation of Turn-to-Turn Short Circuit
A scenario was established where a short circuit fault in the rotor winding occurred in slot #3 separately for 0-2 turns.The induced voltage curves of the detection coils are shown in Figures 6 and 7 over one rotation period (0.04 s).
Modeling and Simulation of Turn-to-Turn Short Circuit
A scenario was established where a short circuit fault in the rotor winding occurred in slot #3 separately for 0-2 turns.The induced voltage curves of the detection coils are shown in Figures 6 and 7 over one rotation period (0.04 s).When the faulty magnetic pole sweeps over the detecting coils, the induced voltages in the coils were smaller than when the rotor winding was normal.If more turns short circuited, the voltage dropped considerably.This result occurred whether the turbo-generator was operating under no-load or with a rated load.The fault features did not change with a change in the turbo-generator's operating condition.
Modeling and Simulation of Turn-to-Turn Short Circuit
Harmonic analysis was also conducted on the induced voltage in the detecting coils and all the sub-harmonic contents were obtained as shown in Figure 8.When the faulty magnetic pole sweeps over the detecting coils, the induced voltages in the coils were smaller than when the rotor winding was normal.If more turns short circuited, the voltage dropped considerably.This result occurred whether the turbo-generator was operating under no-load or with a rated load.The fault features did not change with a change in the turbo-generator's operating condition.
Harmonic analysis was also conducted on the induced voltage in the detecting coils and all the sub-harmonic contents were obtained as shown in Figure 8. Figure 8 shows that the harmonics at both 25 Hz and 75 Hz in the induced voltage of the detecting coils increased significantly after the occurrence of a turn-to-turn short circuit fault in the rotor winding.This is consistent with the previous theoretical analysis.Figure 8 shows that the harmonics at both 25 Hz and 75 Hz in the induced voltage of the detecting coils increased significantly after the occurrence of a turn-to-turn short circuit fault in the rotor winding.This is consistent with the previous theoretical analysis.
Simulation of a Dynamic Eccentricity Fault
To verify the anti-interference characteristics of the double coil detection method, the induced voltage of the detection coils has to be observed when the dynamic eccentricity fault occurs.When the turbo-generator was under no load, the rotor dynamic eccentricity was set to 1% and the eccentric direction was 45 • away from the d axis of the rotor.Then, the induced voltage curves for the No. 1 and No. 2 detection coils were obtained as shown in Figure 9. Figure 9 shows that the induced voltage curves of both coils no longer coincided after the occurrence of a dynamic eccentricity fault.Within the half cycle, the induced voltage peak of the No. 1 detection coil was higher than that of the No. 2 detection coil.Within the other half cycle, the induced voltage peak of the No. 2 detection coil was higher than that of the No. 1 coil.
The induced voltage of both coils under rated load is shown in Figure 10.Without a dynamic eccentricity fault, the induced voltage waveforms for both coils were symmetrical; after the dynamic eccentricity fault occurred, the induced voltages became asymmetrical.Therefore, the change rule is similar to that under no-load.Figure 9 shows that the induced voltage curves of both coils no longer coincided after the occurrence of a dynamic eccentricity fault.Within the half cycle, the induced voltage peak of the No. 1 detection coil was higher than that of the No. 2 detection coil.Within the other half cycle, the induced voltage peak of the No. 2 detection coil was higher than that of the No. 1 coil.
The induced voltage of both coils under rated load is shown in Figure 10.Without a dynamic eccentricity fault, the induced voltage waveforms for both coils were symmetrical; after the dynamic eccentricity fault occurred, the induced voltages became asymmetrical.Therefore, the change rule is similar to that under no-load.By using the induced voltage of a single detection coil as an example, a Fourier decomposition was performed and the resulting spectrum is shown in Figure 11.By using the induced voltage of a single detection coil as an example, a Fourier decomposition was performed and the resulting spectrum is shown in Figure 11.By using the induced voltage of a single detection coil as an example, a Fourier decomposition was performed and the resulting spectrum is shown in Figure 11. Figure 11 shows that the harmonics of both 25 Hz and 75 Hz induced in the U-shaped coil increased after the occurrence of a dynamic eccentricity fault, and these were not specific to the turn-to-turn short circuit fault of rotor winding.Therefore, it is difficult to distinguish the turn-to-turn short circuit fault in the rotor winding from a dynamic eccentricity fault using the amplitudes of the characteristic harmonics.Here, a dynamic eccentricity fault may be accidently judged to be a turn-to-turn short circuit fault in the rotor winding.
Extraction Method for Turn-to-Turn Short Circuit Characteristics for Rotor Winding
After a turn-to-turn short circuit in the turbo-generator rotor winding occurs, the step amplitude of excitation magnetic field changes at the position of fault slot.The leakage flux of rotor faulty slot will be smaller than that of corresponding normal slot [4].For a rotor dynamic eccentricity fault, the change in the air gap length for the turbo-generator is asymptotic, so the change in main magnetic field is relatively subtle.It exists without an obvious or sudden change point, and the leakage flux of each slot in the rotor is not much different.The voltage change rate reflects the change details of the main magnetic field more accurately.Therefore, the induced voltages of both coils can be differentiated, and the voltage waveforms can be subtracted after the differential (they could also be added together if the turbo-generator is two-pole) for observing the sudden change points from the differential waveforms.Figure 12 shows the differential curve from the induced voltage change rates for both detection coils when a 0-2-turn short circuit fault occurs in the winding at the slot #3 in a turbo-generator rotor.Figure 11 shows that the harmonics of both 25 Hz and 75 Hz induced in the U-shaped coil increased after the occurrence of a dynamic eccentricity fault, and these were not specific to the turn-to-turn short circuit fault of rotor winding.Therefore, it is difficult to distinguish the turn-to-turn short circuit fault in the rotor winding from a dynamic eccentricity fault using the amplitudes of the characteristic harmonics.Here, a dynamic eccentricity fault may be accidently judged to be a turn-to-turn short circuit fault in the rotor winding.
Extraction Method for Turn-to-Turn Short Circuit Characteristics for Rotor Winding
After a turn-to-turn short circuit in the turbo-generator rotor winding occurs, the step amplitude of excitation magnetic field changes at the position of fault slot.The leakage flux of rotor faulty slot will be smaller than that of corresponding normal slot [4].For a rotor dynamic eccentricity fault, the change in the air gap length for the turbo-generator is asymptotic, so the change in main magnetic field is relatively subtle.It exists without an obvious or sudden change point, and the leakage flux of each slot in the rotor is not much different.The voltage change rate reflects the change details of the main magnetic field more accurately.Therefore, the induced voltages of both coils can be differentiated, and the voltage waveforms can be subtracted after the differential (they could also be added together if the turbo-generator is two-pole) for observing the sudden change points from the differential waveforms.Figure 12 shows the differential curve from the induced voltage change rates for both detection coils when a 0-2-turn short circuit fault occurs in the winding at the slot #3 in a turbo-generator rotor.
Figure 12 shows that after the turn-to-turn short circuit fault in the rotor winding occurred, four obvious pulses appeared in the differential waveform of the induced voltage change rates during one rotation period.There are two positive and two negative pulses, appearing alternately.The time that the pulses appeared was exactly the time when the faulty slot swept over the detection coils.
After comparison, the amplitudes of the positive and negative pulses in the waveform were found to be equivalent under no-load conditions with good symmetry.However, the amplitudes of the pulses in the waveform were no longer equal to a rated load.This was caused by the armature reaction.The magnetic field saturation of two slots of the faulty coils was different due to the armature's magnetic field and the leakage flux of the rotor slots was affected further.In Figure 12, the increased amplitude of pulses is positively related to the number of short circuit turns.This shows that the severity and development trend of turn-to-turn short circuit faults in the excitation winding can be reflected by the difference in the induced voltage change rates.Figure 12 shows that after the turn-to-turn short circuit fault in the rotor winding occurred, four obvious pulses appeared in the differential waveform of the induced voltage change rates during one rotation period.There are two positive and two negative pulses, appearing alternately.The time that the pulses appeared was exactly the time when the faulty slot swept over the detection coils.
After comparison, the amplitudes of the positive and negative pulses in the waveform were found to be equivalent under no-load conditions with good symmetry.However, the amplitudes of the pulses in the waveform were no longer equal to a rated load.This was caused by the armature reaction.The magnetic field saturation of two slots of the faulty coils was different due to the armature's magnetic field and the leakage flux of the rotor slots was affected further.In Figure 12, the increased amplitude of pulses is positively related to the number of short circuit turns.This shows that the severity and development trend of turn-to-turn short circuit faults in the excitation winding can be reflected by the difference in the induced voltage change rates.One turn of a short circuit fault was set separately in the winding of slot #3, slot #6, and slot #9 on the rotor.The differential curve of the induced voltages for both detection coils is shown in Figure 13.
In Figure 13, when any of the detection coils swept over slots #3#, #6, and #9 of the rotor, there was an obvious pulse on the differential waveform of the induced voltage change rates.There were 12 pulses during one rotation period.This shows that the fault positions and number of faulty slots can still be judged effectively using this method when a turn-to-turn short circuit occurs simultaneously in the winding of multiple slots on the rotor.One turn of a short circuit fault was set separately in the winding of slot #3, slot #6, and slot #9 on the rotor.The differential curve of the induced voltages for both detection coils is shown in Figure 13.In Figure 13, when any of the detection coils swept over slots #3#, #6, and #9 of the rotor, there was an obvious pulse on the differential waveform of the induced voltage change rates.There were 12 pulses during one rotation period.This shows that the fault positions and number of faulty slots can still be judged effectively using this method when a turn-to-turn short circuit occurs simultaneously in the winding of multiple slots on the rotor.
Combined Fault Simulation for a Turn-to-Turn Short Circuit in the Rotor Winding and Dynamic Eccentricity
Using a turbo-generator with rated load as an example, turn-to-turn short circuit faults and dynamic eccentricity faults were set to occur simultaneously in the rotor winding to verify the effectiveness and anti-interference levels of the new method.Here, the turn-to-turn short circuit in the rotor winding occurred in slot #3 with one turn; the degree of dynamic eccentricity was 1%, with an eccentric direction of 0 • .The difference in the induced voltage change rates for two the detection coils is shown in Figure 14.
Figure 14 shows that when the turn-to-turn short circuit fault for the rotor winding and dynamic eccentricity fault occurred simultaneously, there were obvious convex points on the differential waveform for the induced voltage change rates for both detecting coils.This corresponded to the faulty slots on the turn-to-turn short circuit, showing that dynamic eccentricity has little effect on the sudden change point in the local magnetic field caused by a turn-to-turn short circuit.Dynamic eccentricity does have a certain influence on the harmonics of the main magnetic field of the turbo-generator.Therefore, the turn-to-turn short circuit fault in the rotor winding can still be detected effectively using the new detection method.
Combined Fault Simulation for a Turn-to-Turn Short Circuit in the Rotor Winding and Dynamic Eccentricity
Using a turbo-generator with rated load as an example, turn-to-turn short circuit faults and dynamic eccentricity faults were set to occur simultaneously in the rotor winding to verify the effectiveness and anti-interference levels of the new method.Here, the turn-to-turn short circuit in the rotor winding occurred in slot #3 with one turn; the degree of dynamic eccentricity was 1%, with an eccentric direction of 0°.The difference in the induced voltage change rates for two the detection coils is shown in Figure 14. Figure 14 shows that when the turn-to-turn short circuit fault for the rotor winding and dynamic eccentricity fault occurred simultaneously, there were obvious convex points on the differential waveform for the induced voltage change rates for both detecting coils.This corresponded to the faulty slots on the turn-to-turn short circuit, showing that dynamic eccentricity has little effect on the sudden change point in the local magnetic field caused by a turn-to-turn short circuit.Dynamic eccentricity does have a certain influence on the harmonics of the main magnetic field of the turbo-generator.Therefore, the turn-to-turn short circuit fault in the rotor winding can still be detected effectively using the new detection method.
Conclusions
In this article, the characteristics of turn-to-turn short circuit faults in turbo-generator rotor windings were analyzed.For the first time, dynamic eccentricity interference was considered and we proposed that turn-to-turn short circuit faults in rotor winding can be judged in combination with double coils for improving the overall anti-interference performance.Our conclusions are as follows: (1) Two U-shaped detection coils were installed separately at two positions on the stator yoke of the turbo-generator at an interval of 180°.The fault position and short circuit degree of a turn-to-turn short circuit in the rotor winding can be judged exactly using the difference in the induced voltages from the coils.(2) The interference from dynamic eccentricity faults can be effectively eliminated using the double coil detection method.In the case of combined faults (i.e., a turn-to-turn short circuit and dynamic eccentricity), the turn-to-turn short circuit fault can be still located exactly.
Conclusions
In this article, the characteristics of turn-to-turn short circuit faults in turbo-generator rotor windings were analyzed.For the first time, dynamic eccentricity interference was considered and we proposed that turn-to-turn short circuit faults in rotor winding can be judged in combination with double coils for improving the overall anti-interference performance.Our conclusions are as follows: (1) Two U-shaped detection coils were installed separately at two positions on the stator yoke of the turbo-generator at an interval of 180 • .The fault position and short circuit degree of a turn-to-turn short circuit in the rotor winding can be judged exactly using the difference in the induced voltages from the coils.(2) The interference from dynamic eccentricity faults can be effectively eliminated using the double coil detection method.In the case of combined faults (i.e., a turn-to-turn short circuit and dynamic eccentricity), the turn-to-turn short circuit fault can be still located exactly.(3) The double-coil detection method can be used to determine whether there is a turn-to-turn short circuit fault in in the turbo-generator rotor winding, and can also be used as a detection sensor for turbo-generator dynamic eccentricity faults.This means that the method can also assist in judging dynamic eccentricity faults and have a good detection effect for slight eccentricity.
Figure 1 .
Figure 1.End structure of the excitation winding.
Figure 1 .
Figure 1.End structure of the excitation winding.
FFigure 2 .
Figure 2. Waveform of excitation magneto motive force.Ff is the excitation magneto motive force in a stepped wave after a turn-to-turn short circuit; Ff' is the excitation magneto motive force of the stepped wave when the winding is normal.
Figure 2 .
Figure 2. Waveform of excitation magneto motive force.F f is the excitation magneto motive force in a stepped wave after a turn-to-turn short circuit; F f ' is the excitation magneto motive force of the stepped wave when the winding is normal.
Figure 3 .
Figure 3. Schematic diagram of the detection system: (a) Structure of a single coil; (b) Online detection system.
Figure 3 .
Figure 3. Schematic diagram of the detection system: (a) Structure of a single coil; (b) Online detection system.
Figure 4 .
Figure 4. Schematic diagram of the simulation.
AFigure 6 .Figure 6 .Figure 6 .Figure 7 .
Figure 6.Induced voltage curve for the No. 1 detection coil with different short circuit degrees in slot #3 under no-load conditions: (a) Global map; (b) Local map.Where, SC is the abbreviation of Short Circuit.
Figure 7 .
Figure 7. Induced voltage curve for the No. 1 detection coil with different short circuit degrees in slot #3 under rated load conditions: (a) global map; (b) local map.
Figure 8 .
Figure 8. Induced voltage spectrum for detection coils with different short circuit degrees: (a) no-load; (b) rated load.
Figure 8 .
Figure 8. Induced voltage spectrum for detection coils with different short circuit degrees: (a) no-load; (b) rated load.
Figure 9 .
Figure 9. Induced voltage curves for both detection coils at the dynamic eccentricity of 45 • under no-load: (a) global map; (b) local map 1; (c) local map 2; (d) local map 3; (e) local map 4.
Figure 10 .
Figure 10.Induced voltage curves for both detection coils at a dynamic eccentricity of 45 • with rated load: (a) global map; (b) map 1; (c) local map 2; (d) local map 3; (e) local map 4.
Figure 10 .
Figure 10.Induced voltage curves for both detection coils at a dynamic eccentricity of 45° with rated load: (a) global map; (b) local map 1; (c) local map 2; (d) local map 3; (e) local map 4.
Figure 12 .
Figure 12.Difference in the induced voltage change rates for both detection coils with different short circuit degrees in winding slot #3: (a) no-load; (b) rated load.
Figure 12 .
Figure 12.Difference in the induced voltage change rates for both detection coils with different short circuit degrees in winding slot #3: (a) no-load; (b) rated load.
Figure 13 .
Figure 13.Difference in induced voltage change rates for both detection coils with a one-turn short circuit occurring separately in slots #3#, #6, and #9: (a) no-load; (b) rated load.
Figure 13 .
Figure 13.Difference in induced voltage change rates for both detection coils with a one-turn short circuit occurring separately in slots #3#, #6, and #9: (a) no-load; (b) rated load.
Figure 14 .
Figure14.Difference in the induced voltage change rates for both detection coils with rated load when the dynamic eccentricity was 1% and a 1-turn short circuit occurred.
Figure 14 .
Figure14.Difference in the induced voltage change rates for both detection coils with rated load when the dynamic eccentricity was 1% and a 1-turn short circuit occurred. | 9,590 | 2019-04-10T00:00:00.000 | [
"Physics",
"Engineering"
] |
The cost-effectiveness of upfront point-of-care testing in the emergency department: a secondary analysis of a randomised, controlled trial
Background Time-saving is constantly sought after in the Emergency Department (ED), and Point-of-Care (POC) testing has been shown to be an effective time-saving intervention. However, when direct costs are compared, these tests commonly appear to be cost-prohibitive. Economic viability may become apparent when the time-saving is translated into financial benefits from staffing, time- and cost-saving. The purpose of this study was to evaluate the cost-effectiveness of diagnostic investigations utilised prior to medical contact for ED patients with common medical complaints. Methods This was a secondary analysis of data from a prospective, randomised, controlled trial in order to assess the cost-effectiveness of upfront, POC testing. Eleven combinations of POC equivalents of commonly-used special investigations (blood tests (i-STAT and complete blood count (CBC)), electrocardiograms (ECGs) and x-rays (LODOX® (Low Dose X-ray)) were evaluated compared to the standard ED pathway with traditional diagnostic tests. The economic viability of each permutation was assessed using the Incremental Cost Effectiveness Ratio and Cost-Effectiveness Acceptability Curves. Expenses related to the POC test implementation were compared to the control group while taking staffing costs and time-saving into account. Results There were 897 medical patients randomised to receive various combinations of POC tests. The most cost-effective combination was the i-STAT+CBC permutation which, based on the time saving, would ultimately save money if implemented. All LODOX®-containing permutations were costlier but still saved time. Non-LODOX® permutations were virtually 100% cost-effective if an additional cost of US$50 per patient was considered acceptable. Higher staffing costs would make using POC testing even more economical. Conclusions In certain combinations, upfront, POC testing is more cost-effective than standard diagnostic testing for common ED undifferentiated medical presentations – the most economical POC test combination being the i-STAT + CBC. Upfront POC testing in the ED has the potential to not only save time but also to save money. Trial registration ClinicalTrials.gov: NCT03102216.
Introduction
Point-of-Care (POC) testsdiagnostic tests that are performed at or near the patient's bedsidehave been touted as potential time-saving interventions to decrease waiting times in the Emergency Department (ED) [1][2][3]. These tests can decrease the turnaround time of special investigations thereby reducing delays which can cause prolonged patient times in the ED [2,4]. While these POC time-savers are mostly reported in the literature as being cost-prohibitive to implement when their direct costs are compared to the traditional diagnostic testing, the POC system costs have conversely also been reported as being less expensive than central laboratory costs in other studies [2,[5][6][7]. Recouping the personnel costs from the time that is saved, however, may paradoxically mean that the more expensive POC tests have financial benefit and therefore become an economically viable option [2,5]. The improved overall processing of the patient as a result of the reduced turnaround times, more rapid diagnosis and disposition could potentially allow for fewer staff members to manage the same number of patients in the same time as using a conventional system [2,5,8]. This would be an important potential consideration when planning and optimising ED staffing.
Conventionally, special investigations such as blood tests, electrocardiograms (ECGs) and radiological investigations take place after an ED doctor has evaluated a patient. Both the patient and the doctor then need to wait for the results of these tests before the doctor can make a disposition decision for the patient. When conventional testing is replaced with POC tests performed upfront prior to doctor assessment, significant timesaving has been demonstratedthis was our randomised, controlled trial that provided the data on which this secondary analysis is based [9]. Whether the timesaving from this intervention could translate into money-saving is important to determine. This information would be useful for policy-and decision-makers with regards to deciding whether to implement upfront, POC testing in their EDs.
The aim of this study was to evaluate the costeffectiveness of common diagnostic investigations, in the form of POC tests, performed prior to doctor assessment, for patients presenting to the ED as a secondary analysis of data obtained from our randomised controlled trial [9].
Study design and setting
This was a secondary analysis of data from an investigator-initiated prospective, randomised, controlled trial. The original trial evaluated the time-saving potential of upfront, POC tests in the ED [9]. This secondary analysis was conducted in order to assess the cost-effectiveness of the upfront POC testing. The trial was conducted in the ED of a tertiary, academic hospital in a metropolitan area of Johannesburg, South Africa. The ED sees approximately 65,000 patients annually. The hospital is a government-funded, public sector hospital serving a region with a population of approximately one million medium-to low-income people.
The study took place between 13 February and 29 June 2017.
Permission to conduct the study was granted by the Research Ethics Committee of the Faculty of Health Sciences of the University of Johannesburg (REC-01-185-2016); the Human Research Ethics Committee of the University of the Witwatersrand (M171086); South African National Health Research Ethics Council (DOH-27-0117-5628); and was registered as a clinical trial with the South African National Health Research Database (GP_ 2017RP57_655) as well as with clinicaltrials.gov (NCT03102216). Written informed consent was obtained from all patients. Patients were not paid for their participation in this study nor did they incur any expenses related to the study.
Selection of participants
During weekdays, all adult patients older than 18 years who presented to the ED with various common medical symptoms were eligible for inclusion in the study. The medical symptom groups included were typical of the so-called undifferentiated patient that may present to the ED viz.
"abdominal group"patients who presented with any form of abdominal pain and/or vomiting "chest group"patients who presented with dyspnoea, chest pain, cough and/or syncope "generalized body pain/weakness group"patients who presented with generalized body pain and/or weakness "psychiatric group" -patients who presented with psychosis, aggression, hallucinations and/or having taken a drug overdose Patients who required immediate resuscitation or who were pregnant were not considered for inclusion. Figure 1 demonstrates the methodology followed in the study, showing the normal ED pathway compared to the eleven POC pathways utilised during the study period.
Block randomisation was done prior to study commencement using www.randomizer.organ online randomisation tool. Randomisation was independent of the nature of the patient presentation. Symptom categories were represented equally in each POC block and all twelve test pathways were assigned to each of the above symptoms groups (Fig. 1). Based on the block randomisation, data collection sheet sets were placed upside-down in the order generated. After the patient signed consent, either the research doctor or the research assistant took the next data collection sheet in the order supplied.
The patients were randomised to receive either the normal ED workflow pathway (i.e. the control) or one of the other eleven intervention POC pathways with various combinations of one, two, three or four POC tests (see Fig. 1). This was done in order to ascertain whether any particular individual POC test or if certain combinations of POC tests could provide the most benefit.
In the control pathway, after triage, consent and randomisation, a doctor evaluated the patient. If diagnostic tests were required, they were ordered as indicated. All blood tests were performed according to standard procedures in the on-site hospital laboratory and if the patient required a blood gas analysis, the doctor would perform this on one of two blood gas analysers available in the ED (Cobas B 221 POC system, Roche Diagnostics or ABL800 Flex, Radiometer). X-rays were performed in the radiology department and the ED staff performed ECGs as required.
The doctor would review the patient a second time once the results of the diagnostic tests were available. This was followed by the disposition decision.
In the enhanced, intervention POC pathways, if the doctor deemed additional investigations over and above the POC tests necessary, those tests were then performed according to the ED standard procedures. Once the additional results were subsequently available, the patients were reviewed.
Patients were not subjected to any form of diagnostic investigation that they would not most likely have received by following the control pathway for each particular symptom group. The main difference between the control workflow pathway and the enhanced, intervention POC workflow permutations was that the tests were performed in the ED at the so-called point-of-care prior to the patient seeing the doctor for the first time.
Patient throughput time in the ED consists of administrative time and treatment time [10]. Table 1 contains the definitions, possible confounders and solutions employed in this study to overcome them in order to accurately evaluate the effects of the POC tests on patient time in the ED and therefore the impact on the costeffectiveness.
POC tests
The POC equivalents of commonly used special investigations in the ED were chosendetails are provided in Table 2. The POC testing was performed in a private cubicle where the LODOX® (LOw-DOse X-ray) machine was located within the ED. All other testing was done as per standard procedure in the ED. Details of the direct cost comparisons of the diagnostic tests and their POC equivalents are depicted in Table 2.
POC costs
Costs for the POC blood tests were obtained from the supplier. Capital and maintenance costs of equipment were included in the prices of all tests whether POC or control diagnostic tests therefore no indirect costs were added. Discounting was not applied. Prices for the control pathway investigations were obtained from the hospital laboratory (blood tests) and radiology department (X-rays/LODOX®). The cost of the ECG was equivalent in both pathways.
When comparing the costs of the intervention permutations to the control group, the costs were calculated as follows:
Control group
All tests as ordered by the doctors were included e.g. if the doctor ordered an ECG and a blood gas, only the costs of those two tests were included for that particular patient.
Intervention POC permutation groups
The costs of the POC tests specific to the group PLUS any additional tests that were ordered by the doctors were included in the total cost e.g. If the doctor ordered an x-ray for a patient who was in the i-STAT + CBC group, the cost of the x-ray was then added to the total cost for that patient.
Staffing costs
The cost calculations were performed as per Schilling's recommendation [5]. Staffing was considered as evenly distributed throughout the year and calculated using doctor and nursing costs only. Using this method, the cost of one minute of ED staffing was calculated to be US$5.37, which is equivalent to US$0.75 per patient per minute in our ED.
Sample size calculation
The sample size estimation was based on the determination of the effect of workflow pathways within each symptom group that were initially analysed. This required a two-way Analysis of Variance (ANOVA). Based on the detection of at least a medium effect size (f = 0.25 or a 20% difference in times between groups) with 80% power at the 5% significance level, a sample size of 864 patients was required. All patients go through the same process of opening a file and registering on the hospital system in our ED.
After a disposition decision is made, the patient may or may not timeously leave the ED.
The administrative process can be substantially longer on some days than on other days. This would change the wait-times for the patients prior to them presenting to the doctor and would confound the time measurements overall and therefore impact on the cost analysis.
Exit block may lead to a delay in the patient leaving the ED even if a timeous disposition decision is made.
Only treatment time was evaluated. The disposition decision time was utilised.
Outcomes
Treatment time was the main outcome measure for assessing the effectiveness of POC interventions. A difference in treatment time of 20% was considered to be clinically significantthis is higher than that utilised in previous studies (9-18%) [2,9]. The main outcome measure for the cost-effectiveness of upfront, POC tests was the incremental cost effectiveness ratio (ICER).
The ICER was expressed as where C1 and E1 are the cost and effect (time) in the intervention group and C2 and E2 are the cost and effect in the control group [13].
Statistical analysis
A cost-effectiveness plane was constructed by plotting the effects on the horizontal axis and costs on the vertical axis. Further analysis utilised a non-parametric bootstrapping model. This model used the observed data for each permutation which was inserted into an excel template supplied by Barton et al [14]. For each bootstrap sample, the mean incremental costs and effects were calculated and repeated 1000 times. Incremental costeffectiveness acceptability curves were then calculated from the bootstrap data across a range of increasing potentially acceptable costs. This analysis excluded the effects of potential cost-saving related to staffing expenses. Data analysis was carried out using SAS (version 9.4 for Windows). The 5% significance level was used for all statistical analyses.
Results
There were 1134 patients enrolled in the trial. Consecutive patients were included during the patient enrolment periodsthere was no patient selection. Five patients refused to participate in the study. After exclusions, 1044 patients were randomised. Figure 1 summarises the patient flow.
During data collection for the primary study, the outcomes in the "psychiatric group" (n = 147) were found to be very different from the other three symptom groups in an interim analysis. The psychiatric patients were seen almost immediately in most cases based on their "orange" triage scores and commonly only needed a single investigation viz. a blood gas analysis. From an EDthroughput perspective it was already functioning optimally and the extra testing was not required. Their data was therefore excluded as it would have skewed the results from both a time-and cost perspective. Therefore, 897 patients were included in the analysis.
Ten patients presented to the ED on more than one occasion during the study period and agreed to be enrolled a second time. They were treated no differently from patients who were seen for the first time. They all signed a new consent form and were randomised yet again. Their inclusion was therefore unlikely to have influenced the study outcomes.
Patient characteristics
A comparison of patient characteristics based on workflow allocation is tabulated in Table 3. There were no significant differences in age, triage category or The i-STAT System utilises single-use i-STAT test cartridges (i-STAT, Abbott Point of Care, Princeton, NJ, USA) with a handheld POC blood analyser. The CHEM8+ (sodium, potassium, chloride, total carbon dioxide, ionised calcium, glucose, urea, creatinine, haematocrit, haemoglobin and anion gap) and CG4+ (Lactate; pH; partial pressure carbon dioxide (PCO 2 ); partial pressure of oxygen (PO 2 ); total carbon dioxide; bicarbonate; base excess and oxygen saturation) were performed on venous blood specimens.
Abbott CEL-DYN Emerald 22 benchtop haematology system
The CEL-DYN Emerald 22 benchtop haematology system was used. It is capable of providing a POC Complete/Full Blood Count as well as a white blood cell differential count.
ECG
Philips Pagewriter TC30 ECG machines were utilised to obtain the ECGs. All patients randomised to receive an ECG received a standard 12-lead ECG as well as a right-sided (V1R-V6R) and posterior (V7-V9) ECG. The cost of the ECG was the same in both the control and the intervention groups.
LODOX®
A Lodox Xmplar-dr was used by a radiographer to perform the LODOX® (LOw-DOse X-ray) radiographs (chest and abdomen, antero-posterior and lateral). The radiation exposure was approximately 339uGy per patient versus a standard chest and abdomen radiograph of approximately 5200uGy [11]. LODOX® is the radiological equivalent of a POC test as it can provide a full body X-ray within 19 s without the patient leaving the ED. Its utility in trauma patients is reasonably well-known but its use as a diagnostic tool for non-trauma patients in the ED has not been evaluated previously [12]. disposition between the patients enrolled into the control or POC intervention groups.
Treatment times
A 20% reduction in treatment time was exceeded by all POC workflow permutations, except ECG alone and LODOX® alone groups. With regards to disposition decision, there were no significant differences in treatment times between patients who were ultimately admitted or discharged within particular workflows, or between admission and discharge within particular symptom groups (p = 0.091).
Time taken for POC testing and patient waiting times
The patient waiting time to see a doctor after arrival in the ED was on average between 57 and 152 min. It took between 4 and 23 min to obtain the results of the POC tests. This included the time taken for phlebotomy, specimen processing and results printing for the i-STAT and CBC permutations. The blood tests could generally be performed concurrently, however, the LODOX® and ECGs had to be performed sequentially.
Investigation utilisation in the control pathway
There were 78.7% (59/75) patients in the control group who had blood tests and/or a blood gas analysis. Twenty-four per cent (36/75) had a blood gas analysis only. X-rays were performed in 58.7% (44/75) of patients and 64% (48/75) had ECGs performed. Table 2 lists the costs for the individual investigations. Overall, POC equivalent tests cost US $9.93 less than the standard control investigations if all the tests were performed in a patient.
Costs of investigations
The time-saving and costs for each workflow is presented in Table 4. LODOX®-containing permutations (dashed lines) and non-LODOX®-containing pathways (solid lines) are demonstrated using different values for funder willingnessto-pay (λ). Non-LODOX® permutations were virtually 100% cost-effective if an additional cost of US$50 per patient was considered acceptable. The overall admission rate for this ED is usually 30-35%. This includes all patient presentations e.g. trauma, general surgery, orthopaedics, otorhinolaryngology etc. The medical subgroup of patients typically has a higher admission rate than other patients
Discussion
Saving time is an ever-present goal in the ED. However, for upfront POC testing to be viable in the ED, the time-saving benefit needs to be weighed against the cost.
Costs of investigations and cost effectiveness analysis
Variations have been reported with respect to the net cost of POC testing [5,8,13]. In Sweden, POC was found to be substantially cheaper than the costs of similar tests performed in a laboratory [5]. In Australia, however, test costs were higher in the POC group [6]. Costs have previously been calculated using the direct differences between that of the POC tests and the laboratory costs without taking the expense of personnel into account [8]. The cost of staffing needs to be taken into account as decreases in test turnaround time could be translated into savings in staffing due to an improved overall processing of the patient from decreased turnaround times as well as quicker diagnosis and ultimately more rapid patient disposition [2,5,8].
In our study, direct head-to-head cost comparison between the POC tests compared to standard laboratory and radiological expenses in our study surprisingly showed a saving of US$9.93 if all the tests had been performed on all patients compared to using standard diagnostic tests. This was mainly as a result of the lower cost of the LODOX® compared to the x-ray and the lower These are the average total actual costs that were incurred for the patients in each permutation. In the Control group, the only tests that were included were those selected by the doctors as they saw fit. In other groups, the average costs appear to be higher than would be expected as extra diagnostic tests may have been performed in those groups over and above those which were assigned (e.g. additional blood tests such as serum amylase or lipase). This principle extends across each of the groups cost of the POC CBC compared to the laboratory CBC. Although this comparison of total costs appeared promising, it was necessary to look at the cost-effectiveness of the individual permutations.
When evaluating the cost:benefit ratio for POC testing, it is essential to include the disbursements on staffing. The time a doctor spends with the patient has a costif this time can be decreased with POC testing then the Fig. 2 a Cost Effectiveness Plane. Permutations in the south-east quadrant were less costly and more effective (also referred to as dominant) [13]. Permutations in the north-east quadrant were still more effective but were also costlier. b Cost-Effectiveness Acceptability Curve. Costeffectiveness acceptability curves for each of the permutations. The proportion of the bootstrap datapoints achieving cost-effectiveness at each increment of potentially acceptable cost is shown. Permutations which included LODOX® are shown with dashed lines. The dotted lines represent two potential willingness-to-pay thresholds. For example, at US$50, virtually all the non-LODOX® permutations have a high probability of being cost-effective. On the other hand, at a willingness-to-pay threshold of US$30, only the iSTAT and the ECG permutations have a high probability of being cost-effective. This graph allows the funder to weigh the relative cost of each of the permutations against their known effectiveness. CBC Complete Blood Count, ECG electrocardiogram, i-STAT i-STAT POC tests, LODOX® Low-dose x-ray cost of the doctor needs to be included in the costeffectiveness evaluation [2,5,8]. When personnel costs and time-saving were both considered, the net additional savings increased further, with the true benefit of certain test combinations being highlighted. In Fig. 2, it can be seen that all LODOX®-containing permutations fell into the north-east "more costly but more effective" quadrant. X-ray and LODOX® costs formed the bulk of the expenses related to the diagnostic testing combinations making all the LODOX®-containing options more costly. LODOX® therefore added substantially to the cost, without much additional time-saving. Also, only 58.7% of the control group had an x-ray performed while 100% of the participants in the respective LODOX® permutations received an x-ray. This lead to an overall additional comparative cost per patient compared to the control group despite LODOX® being more inexpensive than a standard x-ray. The addition of a LODOX® in a protocolised fashion may need to be reevaluated and may perhaps be more valuable if introduced only after an admission decision is made. Indiscriminate use of LODOX® on all patients irrespective of whether they require hospital admission would lead to over-testing and unnecessary radiation exposure even if it is relatively low-dose radiation. Some patients also received a formal x-ray in addition to their LODOX® which increased costs and so was a confounder for the LODOX®-containing groups overall.
While the ECG only group was cost-effective because of a direct saving of US$5.90 per patient, the lack of significant time-saving makes it ineffectual to assist with ED throughput.
The most cost-effective combination, which ultimately would save money based on the time-saving, was the i-STAT + CBC permutation. It was second in time-saving to i-STAT + ECG + LODOX® by one minute and equivalent in saving time to the combination where the patients had all the tests performed. The latter two permutations would require additional spending in order for them to be implemented. With one-third of patients having laboratory testing in general in the ED, the i-STAT + CBC option would fulfil the dual purpose of demand and cost-effectiveness [15,16].
The impact of staffing costs
Staffing costs play a significant role in the calculation of cost effectiveness. In a Swedish ED, Schilling showed a significantly higher cost saving than in our study. This was largely due to their higher cost of staffing (US$24.08/min versus our US$5.37/min) [5]. A higher staffing cost would mean that time saved using POC testing is ultimately even more economical. The timesaving could potentially also be used to offset staffing costs. There may be an opportunity to reduce staffing levels based on reduced treatment times offered by the POC tests. Optimisation of patient processing means that the costs of staffing need to be taken into consideration [5].
Value for moneycost-effectiveness acceptability Permutations in the north-east quadrant of the costeffectiveness plane were more effective than the control but were also costlier. The determination of whether an intervention offers "good" value for money depends on the funder's willingness to pay (λ) [17]. The range of potential amounts that the funder may be considering are displayed on the x-axis of the cost-effectiveness acceptability curve and can be judged according to the relative probability that an intervention will be cost-effective shown on the y-axis. Figure 2B demonstrates this concept with the majority of the permutations still most likely to be cost-effective at a willingness to pay threshold of US$50, except for those permutations containing LODOX®.
Non-LODOX® permutations were virtually 100% costeffective if an additional cost of US$50 per patient was considered acceptable by funders.
This model has been used in previous studies on healthcare cost-effectiveness [13,14,17]. It is tool that allows decision-makers to balance up costs against nonquantifiable benefits. For example, a reduced waiting time might not have any direct cost implications, but will increase patient satisfaction. A funder might be willing to pay a small additional amount for this but not a large amount. This tool therefore allows the potential funder to better balance the benefits and costs. It also allows the funder to balance quantifiable costs e.g. the decision whether to close a diagnostic laboratory at night in favour of utilising POC tests.
Waiting times and special investigation use in the ED
Waiting for the results of special investigation such as blood tests, ECGs and radiographs commonly takes twothirds of a patient's entire ED length of stay [15]. A substantial amount of time could potentially be saved if these test results were available prior to the doctor's initial evaluation of the patient. In this study, waiting for results of the intervention POC tests was concurrent with the patients' wait to be seen by a doctor (minimum waiting time 57 min). This meant that the time taken to perform the POC tests (maximum 23 min) did not cause any significant delays for the patients as it took place during non-valued added time when the patients were waiting to see the doctor.
In Yoon's analysis of factors increasing length of stay in the ED in Canada, 38.4% of patients had laboratory tests and 44% underwent some form of X-ray imaging.
These interventions were associated with longer lengths of stay [15]. In the USA, Gardner et al. found that 33% of patients had laboratory investigations (increasing length of stay by 35.4 to 40.1 min) and 36% had x-rays (increased by 5 to 15 min) [18]. Thirty per cent of discharged patients in a Finnish study by Kankaanpää et al. needed laboratory testing [19]. The laboratory usage in our control group of 30% (excluding patients who had blood gas analyses alone) is similar to the utilisation in these other EDs. The x-ray utilisation rate was higher, however. This may have been due to the higher admission rate at our hospital of 42.7% (versus 11% in the Gardner study) as all patients admitted to the internal medicine service receive an x-ray.
All patients in an i-STAT-containing subgroup in our study showed a decreased treatment time. Although the performance of a LODOX® scan took only on average four and a half minutes, time-saving was only achieved when it was combined with other POC tests. This was similar to the time-saving gained by the performance of an upfront ECG. Gardner et al. found that ECGs only saved time (2.7 min) in those patients who were admitted but added time in patients who were ultimately discharged [18]. In our study, there was no difference in the number of tests performed regardless of disposition decision i.e. whether a patient was ultimately admitted to the hospital or discharged.
Standing orders versus upfront POC testing and "overtesting" In the ED, standing orders have been shown to improve patient throughput by reducing disposition time by up to 16.9% [20]. However, these orders are usually only actioned if the ED is full; have had variable uptake by the nursing staff resulting in both over-and undertesting and have not made use of POC devices [16,20]. Over-testing is frequently quoted as a danger when standing orders are in place or when POC testing is made easily available. There is, however, no evidence to support this [16,[21][22][23]. In Retezar's study evaluating triage standing orders, those patients who received the full gamut of tests had a 16% reduction in their mean treatment times. The hypothesis that upfront, protocolised testing leads to over-testing is nullified by her study findings where 98 % of the patients who did not receive the standing orders went on to receive similar investigations once they were seen by a doctor [24]. The cost of POC usage would therefore be unlikely to be exaggerated compared to standard diagnostic test utilisation. In our study, over-testing was possible in the patients who were ultimately admitted to the internal medicine service. Blood tests are commonly performed as a courtesy for those patients even if the results do not impact on the ED disposition decision. These were extra standard blood tests and not POC tests.
Other potential cost implications
Besides these direct costs, there is also the potential for further cost-saving that may be possible by reducing admission rates. In Fitzgerald et al's RATPAC trial, which focussed on patients with chest pain in suspected myocardial infarction, POC testing was associated with higher ED costs but lower general inpatient costs [7].
Other non-fiscal "cost savings" should also be evaluated in future POC cost-effectiveness analyses. Although we did not collect data on the patient experience, we acknowledge that their input would have been useful as part of the overall impact of the intervention. The very low refusal rate may have suggested that patients favoured this system, but no direct data were collected.
The doctors' perceptions of the effectiveness and appropriateness of the upfront POC testing were evaluated as part of this study. They were strongly supportive of the intervention [25].
Further possible positive effects which need to be quantified include the beneficial knock-on effects of decreases in patient complaints due to excess waiting times, increases in staff satisfaction, and the potential for fewer patients leaving the ED without being seen. This will require future investigation.
Patient sub-groups that could benefit from upfront POC testing
Although the symptom groups originally included in the study characterised typical categories of undifferentiated patients that present to the ED, interim analysis highlighted that the "psychiatric group" was already functioning optimally based on their high acuity triage scores as well as the limited special investigations that they required for safe patient disposition. The use of upfront POC testing in this group of patients would therefore have no time-or cost benefit. Upfront testing appeared to be most appropriate for the undifferentiated medical patient and the ultimate cost-effectiveness in any ED would depend on the case mix presenting to that ED.
Hospital admission rates and patient acuity
There was no difference between the patients who were admitted to the hospital (sicker patients) compared to those who were discharged from the ED (less ill patients). Both groups of patients benefited from the upfront testing. The overall percentage of patients admitted from the ED was higher than the usual admission rate of the ED of 30-35% (Table 3). These higher admission rates are likely related to the fact that only medical patients were included who, in general, are more ill than the non-medical patient population. They are also the patient group that would potentially benefit most from upfront POC testing. Therefore, it is unlikely that the high level of significant illness was an important source of bias in this study. There was a range of triage categories within each group (not significantly different) that further suggests that this was not an important bias. Furthermore, upfront POC testing is not suggested to be used in all patients presenting to the ED. Clearly some patients would not benefit (e.g. minor orthopaedic injuries), but it would be best applied to patients with undifferentiated medical presentations. This does mean, however, that EDs that see very few sick patients would benefit less from upfront POC testing.
Limitations
This single-centre study evaluated the impact of POC on the treatment time but there was no data collected nor assessment of the effect on patient outcome and potential adverse effects of universal testing. However, in previous POC studies, there has been no evidence to support the theory of over-testing [16,[21][22][23]. The patient medical complaints were heterogeneous. Whilst they were common symptoms in our ED, they may not be representative of other EDs. This was notable with regards to the low incidence of acute coronary syndrome-related chest pain. POC troponin was originally included as one of the i-STAT tests used in our study. Although troponin has been shown to be useful for patients with chest pain or suspected acute coronary syndrome in the ED as well as the presence of raised troponin levels having an association with worse short-term clinical outcomes, we ultimately excluded it for the costeffectiveness analysis as there would be no benefit for our ED population and would have resulted in over-testing [26,27]. Similarly, the indiscriminate use of D-dimer testing in this heterogenous population without employing pre-test probability scoring could potentially have been harmful and could also have resulted in over-testing. Therefore D-dimer testing was not included in the upfront testing. As the ED doctors were not blinded to which patients received the upfront POC tests, a Hawthorne-type effect was considered, but there was no evidence to support it. However, as the doctors themselves were recording all the times (and not an impartial observer), this could have been a potential source of error. Due to the funding of allied hospital staff being managed separately, staffing costs were calculated using doctor and nursing costs only. The costs related to xrays and LODOX® were based on the standard prices charged per patient per investigation as opposed to calculations based on the equipment amortisation costs. The different setup costs of a laboratory and of a POC system were also not taken into account. The performance of "admission tests" for the internal medicine service may have also confounded the diagnostic test utilisation. This may have lead to duplication of tests if the patient was admitted.
Conclusion
POC testing in the ED was more cost-effective, in certain combinations, than standard diagnostic tests when utilised upfront for patients with undifferentiated common medical complaints in non-resuscitation triage categories. The most economical POC test combination was i-STAT + CBC, which not only saved time, but, also saved the most money per patient. Besides these direct costs, there is also the potential for further cost-saving that may be possible by reducing hospital admission rates as well as the other non-fiscal "cost savings". These should be evaluated in future POC cost-effectiveness analyses. | 8,031.8 | 2019-12-01T00:00:00.000 | [
"Medicine",
"Economics"
] |
An Affective-Motivational Interface for a Pedagogical Agent
This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedagogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent learning system. This work focuses on the design of the inference engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is based on an affective-motivational model. This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.
Introduction
Intelligent Learning Systems (ILS) conceive the teaching-learning process (TLP) as a partnership between the module tutors, whose interactions with the user are represented by a pedagogical agent [1,2].Therefore, it is necessary to consider a psychological theory of emotion that sustains the necessary motivational affective model to obtain the affective-motivational state of the user.In the psychology of emotion, there are several theories whose fundamental differences relate to the definition and conceptualization of emotion [3].However, the elements of the emotion definition show some degree of convergence among the different theoretical.The theory of Ortony, Clore, Collins [4], known as OCC theory, specifies a psychological structure of emotions according to personal and interpersonal descriptions of events.Therefore, based on OCC theory, an affective-motivational cognitive structure is developing [5][6][7][8][9][10] and used in systems with artificial intelligence (AI).In this case, reasoning system is tied to tutor module, so that the pedagogical agent improves the intelligent learning system (ILS) performance.
In order to achieve it, the integration of affective-motivational cognitive structure efficiently binding to the TLP is necessary.
This paper is organized in sections.Section 2 explains the OCC theory underlying the affective-motivational cognitive structure, Section 3 explains the AI technique called Fuzzy Cognitive Maps (FCM), which are used to represent the affective-motivational model (based on the affective-motivational cognitive structure), Section 4 explains the pedagogical agent interface of ILS, Section 5 explains the affective-motivational cognitive structure design, Section 6 explains the ILS tests and results, finally we find Section 7 with conclusions.
Emotions According to the Theory of Ortony, Clore and Collins (OCC)
OCC theory proposes a general structure which specifies that there are three main kinds of emotions, the result of focusing on each of the three highlights of the world: • Events and their consequences • Agents and their actions.
• Pure and simple objects.This establishes the evaluation criteria: • Goals to evaluate events.
• Rules for evaluating the action of the agents.
• Attitudes for evaluating the objects.
There are three main kinds of emotions specified: • Emotions based on events: specifying the goals related to the events.• Emotions of attribution: attributed responsibility to the agents about their actions based on rules.• Emotions of attraction: based on attitudes toward objects.
The intensity of emotions can be affected by so-called local and global variables.Thus cognitive representations of emotions are also modified.
The local variables are variables that affect only one kind of emotion, for example, in the case of emotions based on events; the local variable that affects its intensity is the desirability of events and their consequences in relation to the goals.For attribution emotions, the corresponding local variable is the plausibility (approval or disapproval) of the agent's actions according to the rules.Finally Attraction emotions are affected in their intensity by the attraction of the objects.
Global variables, as the name implies, are variables that affect the intensity of all kinds of emotion and therefore, cognitive representation.These variables are: 1) proximity: it attempts to reflect the psychological proximity (in time or space) of event, object or agent that induces emotion, 2) sense of reality refers to the degree to which the event, agent or object underlying the affective reaction seems real to the person experiencing the emotion, 3) excitation: the existing level of arousal affects the intensity of emotions and thus their affective reaction and 4) the unexpected: refers to unexpected positive things are evaluated more positively than expected and unexpected negative things more negatively than expected.
Goals are classified according to OCC theory [4] in active pursuit goals (AGs), goals of interest (IGs) and filling goals (FGs).AGs represent the kind of things you want to get done and include goals set by Schank and Abelson (1977) named achievement goals (for obtain certain things), entertainment goals (to enjoy certain things), instrumental goals (are the instrument for other goals) and goals of crisis (to handle crises when preservation goals are threatened).IGs represent the kind of thing you want to happen and thus can generate AGs to encourage happen.IGs include preservation goals (to preserve certain states of affairs).FGs are cyclical, even if achieved, not abandoned.These goals include those established by Schank and Abelson (1977) like satisfaction goals (meet certain requirements).In case OCC theory, needs can be biological or otherwise be cyclical.
To emulate the perception of emotions during the ILSuser interaction, it is considered a pedagogical agent designed with an affective-motivational cognitive structure.Integrated, this latter, to the ILS inference engine [5][6][7][8][9][10].The affective-motivational cognitive structure is built according to the OCC theory and the implementation of this structure is realized through FCM [11][12][13][14][15].
Fuzzy Cognitive Maps (FCM)
The fuzzy cognitive maps (FCM) were introduced by Bart Kosko [16] to describe the behavior of a system in terms of concepts and causal relationships between these concepts.Digraphs FCM are used to represent causal reasoning in which the nodes are concepts that describe the main features of the system, and the edges between nodes establish causal relationships between the concepts.The diffuse part allows degrees of causality in relationships.FCMs are used as representation technique, due to its ability to handle inherent uncertainty in the decision making processes complex, and having a parallel and distributed reasoning [17].
The qualitative approach of the relationship matrix allows us to observe the behavior of the system.However, you must have a quantification and interpretation with respect to causation of FCM.This quantification allows for the next state of each node, by adding effects to all nodes on the particular node [13,14,18].
Causality Relationships
Causality relationships refer to the effect that a concept has on the rest of the concepts involved in the description of an environment.The effect is to increase or decrease the likelihood of occurrence of another concept.Therefore, there are two types of relationships: negative and positive.
• Negative: the negative relationship is one in which the increase in the likelihood of occurrence of an element causes the proportional decrease in the likelihood of occurrence of another element.And the decrease in one causes the proportional increase of other.Is expressed numerically by taking a value within the range [-1,0).• Positive: the positive relationship is one in which an increase in the likelihood of occurrence of an element causes proportional increase in the likelihood of occurrence of another element and a decrease in the likelihood of one causes the proportional decrease in the likelihood of occurrence of other.For example, increasing errors originates increased likelihood of occurrence of frustration.Is expressed numerically by taking values in the range (0, 1].• If there is no effect or it is neutral, the relationship is expressed as 0 (zero).
Interface Design: A Pedagogical Agent
Emotive pedagogical agents are the last generation to design human-computer interfaces.This kind of agents is different because their appearance more accurately simulates an animated character or even a human.They have a guide on how applications and conventions should be their personality and appearance.Pedagogical agents are created to support learning by interacting with students in interactive learning environments [19,20].The credibility of these agents build trust relies on the visual quality of the agent and the behaviors that emulate humans [21,22].
Animated pedagogical agents facilitate learning in computer environments.These agents represent animated characters that respond to actions taken by the user.Furthermore, there is the mode where the latter have the ability to move within the context of learning, thus providing useful functions within learning environments [23].
A pedagogical agent can be invaluable for the user knowing whether their actions are inappropriate or incorrect, in which case the agent can intervene.Pedagogical agents show entertaining speeches during the teaching-learning process and can intervene with tips, tactics didactic introductions and even attention calls [1,24].
The layout design consists of six phases: the Case, is where you define the user profile and from it creates the most suitable interface design, including pedagogical agent prototype, in Problem analyzing user requirements individually, in this case we use learning styles as a guideline, in Hypothesis are the profiles created by combining learning styles and color and how these can be represented graphically, for the Project is selected the most viable option and create models according to the specifications in a program for this purpose, in Performing, animations are developed and inserted into the interface, and finally in the Evaluation end user is used for use the interface and provide their comments [25].
Affective-Motivational Interface of a Pedagogical Agent
Figure 1 shows the affective-motivational interface of agent designed according to user preferences.These preferences are identified through a questionnaire which asked users about the physical characteristics of a pedagogical agent.The actions undertaken by the pedagogical agent are based on actions of the inference engine designed on an affective-motivational cognitive structure representing the TLP [10,12].
Affective-Motivational Structure
The affective-motivational structure (Figure 2) shows the concepts involved in the TLP, such as goals, events, actions of user or agent, rules and affects.These concepts are tied to the TLP elements through facets of motivation such as: effort, latency, persistence, and choice.
Interest and desire, for example, are especially related to persistence and effort.Joy, admiration, pride, and the like, for their part, provide the energy of motivation.Help is related to choice, and relief affect feeds this choice.
Strategies are actions based on an instructional goal emerged as a top goal in the affective-motivational structure.Therefore, these strategies are called instructional strategies.Instructional strategies can be cognitive and operative.Cognitive strategies are actions of the cognitive diagnosis and Operative strategies are appropriate actions to drive the instruction, so include strategies to contextualize, to guide, motivate and retain the user's attention [24].
To link operative strategies to the affective-motivational structure is necessary to define the actions to take in each instructional level related to the type of knowledge or skill involved in the proposed task to achieve instructional objectives [12].The actions for each instructional level are listed according to information and portrayal that are consistent for each category of generalizable skill (learning category).The strategy is chosen according to the type of user error, instructional level, learning category and inferred affect.
Causality Relationships in the Affective-Motivational Structure
TLP elements are interrelated through causality relationships that indicate the effect that an element has on the rest of the elements involved in the description of an environment.The effect is to increase or reduce the likelihood of another element appearing.So there are negative and positive relationships (Table 1).
For example, the ID, according to affective-motivational model, is positively related to JP, AL and P.This means that an increase in the likelihood of occurrence of ID causes the proportional increase in the likelihood of occurrence of JP, AL and P. Otherwise, a decrease in the likelihood of occurrence of ID causes the proportional decrease in the likelihood of occurrence of JP, AL and P.
On the other hand, ID is negatively related to Q, E and DR.This means that an increase in the likelihood of occurrence of ID causes the proportional decrease in the likelihood of occurrence of Q, E and DR.Otherwise, a decrease in the likelihood of occurrence of ID causes the proportional increase in the likelihood of occurrence of Q, E and DR.
Causality Matrix
The relationships are represented in a matrix of causali- 2) based on the description of the positive and negative relationships.Causality matrix forms the agent's inference engine.The inference engine response is the next state of each of the elements of the model and is obtained by multiplying an input vector (state values of the elements that constitute the affective-motivational model) by the matrix of causality.The resulting vector (output vector) is evaluated using the logistic function (Equation ( 1)) as threshold function [16].This is repeated until a stable output vector.
x = output vector resulting from multiplying the input vector by the causality matrix and represents the sum of effects between the elements of the motivational-affective model.c = scaling constant = 5.The logistic function takes the dimension of the result in the range [0,1].So that it enables us to interpret each of the vector values as the likelihood that respective motivational-affective model element is present [13,14,18].
Tests and Results
The prototype of intelligent learning system (ILS) is designed with an inference engine that includes the affective-motivational model (based on affective-motivational structure).
The ILS is tested a first group of college students enrolled in structured programming (application domain).The tests consist of solving scenarios representative of application domain teaching-learning process.
Scenarios are tasks classified according to learning category and instructional level corresponding with the instructional objectives of structured programming.
Performance results are compared with those obtained by a second group of college students who used the ILS designed with an inference engine excluding the affective-motivational model.The results are summarized in the graph of Figures 3(a) and (b).
The results obtained with the ILS which included an affective-motivational model, improved by 6% compared to those obtained with the ILS did not include the model.
Conclusions
The contribution of this work to affective computing lies in the model used to choose strategies (FCM).This model can provide approximate answers to what happens in the environment with the cognitive and affective state of the user due to the parallel distribution of causality.This improves the user interaction with the system.
FCM modeling the behavior exhibited in the strategies related to the cognitive affective-motivational structure.
This structure feeds the student and the tutor modules, to which it provides clues to the user's emotional state.This helps in choosing the affective-cognitive strategy that the pedagogical agent will deploy, thereby maximizing the effectiveness of the intervention. | 3,257.8 | 2014-01-01T00:00:00.000 | [
"Computer Science",
"Education"
] |
Targeting Human α-Lactalbumin Gene Insertion into the Goat β-Lactoglobulin Locus by TALEN-Mediated Homologous Recombination
Special value of goat milk in human nutrition and well being is associated with medical problems of food allergies which are caused by milk proteins such as β-lactoglobulin (BLG). Here, we employed transcription activator-like effector nuclease (TALEN)-assisted homologous recombination in goat fibroblasts to introduce human α-lactalbumin (hLA) genes into goat BLG locus. TALEN-mediated targeting enabled isolation of colonies with mono- and bi-allelic transgene integration in up to 10.1% and 1.1%, respectively, after selection. Specifically, BLG mRNA levels were gradually decreasing in both mo- and bi-allelic goat mammary epithelial cells (GMECs) while hLA demonstrated expression in GMECs in vitro. Gene-targeted fibroblast cells were efficiently used in somatic cell nuclear transfer, resulting in production of hLA knock-in goats directing down-regulated BLG expression and abundant hLA secretion in animal milk. Our findings provide valuable background for animal milk optimization and expedited development for agriculture and biomedicine.
Introduction
Human α-lactalbumin (hLA), the main whey protein in human milk, accounting for 41% of the whey and 28% of the total protein, has a primary function in regulating the synthesis of lactose which plays an important role in milk production [1,2]. hLA contains a high proportion of the essential amino acids of tryptophan and cysteine [3,4]. Due to its balanced nutrient, α-lactalbumin has been added as a new bioactive ingredient in infant formula [5]. Since it can indirectly activate the galactose to the receptor sugar glucose with high specificity, hLA is thus considered to be a valuable constituent of diets for patients whose protein intake must be restricted. In addition to the function of increasing iron absorption and exerting bactericidal activity in the neonatal digestive track [6,7], the complex of hLA with oleic acid can also induce tumour cell apoptosis and be confirmed effective for prevention and treatment of colon cancer [8][9][10][11].
β-Lactoglobulin (BLG), which is scarce in human breast milk, accounts for 53.7% of the total whey protein in goat milk and is a primary milk allergen that causes infant hypersusceptibility [12,13]. The presence of 2 disulfide bonds is suspected to be responsible for BLG allergic effects [14]. At present, goat milk and its by-products of yoghurt, cheese and powder have great significance in human nutrition and are widely appreciated around the world [15,16]. Several approaches such as heat treatment, enzymatic hydrolysis, fermentation and glycation have been applied to reduce the allergenic potential of BLG protein. However, these methods are generally cost and may affect other valuable nutrients. Thus, to extend the use of goat milk as a nutrient for human beings or to "humanize" goat milk, we applied TALENs to knock out BLG gene and simultaneously increase hLA component to improve the nutritional quality of goat milk, which is unlike the prime pharmaceutical interest to generate animal bioreactors in livestock [17,18].
Homology recombination (HR) which faithfully restores the original sequence by copying it from the sister chromatid for repairing double-strand breaks (DSBs) is low in its frequency for precise genomic modification. Engineered endonucleases including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and RNA-guided DNA endonucleases (RGEN) are programmable genome editing tools that can generate DSBs at preferred genomic regions and improve HR efficiency by several orders of magnitude [19]. ZFNs have been applied in cattle production to disrupt the BLG gene [20]. TALENs mediated disruption of BLG and insertion of human lactoferrin in BLG locus have also been successfully produced in our previous study [21]. Though TALENs have been widely used in gene-targeting research in cows, pigs [22], and human somatic and pluripotent stem cells [23], the production of genetically modified goat with hLA inserted in BLG locus using TALENs has not been reported.
Since a TALEN pair corresponding the exon1 of BLG locus had been efficiently applied in our previous report, our objective here is to utilize this gene-targeting system in goat fibroblasts by inserting the exogenous hLA gene into the BLG locus and subsequently using the targeted cell clones as donors for somatic cell nuclear transfer (SCNT). Considering that production of transgenic animals requires vast numbers of surrogates, immense labour and substantial funding, the precise identification of targeted clones, evaluation of tissue-specific expression of the transgene in goat mammary epithelial cells (GMECs) and assessment of the reconstructed embryos in vitro were carried out in present research. Milk assays of transgenic founder were performed to verify the feasibility for our procedure. Furthermore, to ensure that the genetic modification can be transmitted by germline, the gene targeting analysis for F 1 offspring was carried out in current study.
Ethics statement
All experiments were approved by the Care and Use of Animals Centre, Northwest A&F University. This study was performed in strict accordance with the Guidelines for the Care and Use of Animals of Northwest A&F University. Goat ovaries were collected from the Tumen abattoir, a local slaughterhouse in Xi'An, P.R. China. Recipient goats were obtained from Yangling Keyuan Cloning Co., Ltd. Every effort was made to reduce the number of animals used, and all surgeries were performed under anaesthesia via the intravenous injection of Sumianxing, a compound anaesthetic containing dimethylaniline thiazole, dihydroetorphine hydrochloride, ethylenediaminetetraacetic acid, and haloperidol (0.01 mL/kg; Veterinary Research Institute, Jilin, China).
Generation of the targeting vector pLoxpII-hLA-neo and a TALEN pair
The two homologous arms 5' (738-bp) and 3' (714-bp) were produced by PCR amplification from the goat genome. The 2,100-bp hLA DNA sequences (from signal peptide to stop codon) were acquired from the human blood genome. The neomycin resistance gene (neo), driven by a phosphoglycerol kinase (PGK) promoter, was designed for positive clone selection and was flanked by two loxP sites which would resulted in the removal of neo sequence by cre-mediated recombination. The hLA-neo cassette was flanked by homologous arms in the PloxpII-hLAneo vector (Fig 1A). A TALEN1/2 pair targeting exon1 of the BLG gene (GenBank: Z33881) was designed as our previous study [21]. Briefly, the binding sites were selected by the "TAL Effector-Nucleotide Targeter" [24] and assembled by the "unit assembly" method [25]. The assembled TALEN vectors were linearized by NotI to be used as templates for the in vitro TALEN mRNA transcription with the AmpliCap™ SP6 High Yield Message Maker Kit (Epicentre).
Transfection and selection of the primary cells
The goat fetal fibroblasts (GFFs) and goat ear fibroblasts (GEFs) were established from 40-dayold foetuses and the ear skin of approximately two-month-old kids, respectively. The early-passage (P1-P3) primary fibroblast cell lines were used for transfection with the BTX Electro cell manipulator ECM2001 (BTX Technologies). The plasmid pLoxPII-hLA-neo (12 μg) and the two TALEN plasmids or mRNA (8 μg) were co-electroporated into cell suspensions under the condition of 510 V, 2 ms, and 1 pulse. Transfected cells were then seeded into 90-mm plates with DMEM/F12 amended with 15% FBS under 37°C in a 5% CO 2 environment. 48 h later, the cells were screened with 700 mg/ml G418 (Sigma). G418-resistant cell clones were picked into a 48-or 24-well cell culture plate according to clone size and cell concentration.
Detection of gene insertion by PCR analysis
A small part cells of each clone were resuspended in 15-20 μL cell lysis buffer (40 mM Tris-HCL, pH 8.9; 0.9% Triton X-100, 0.9% Nonidet P-40, 0.4 mg/ml proteinase K [26]). The lysates were then incubated at 65°C for 15 min and heated to 95°C for 10 min. 2-4 μL DNA lysates were used as template in junction PCR reaction with primers B31/B32 (3' end, 2,300-bp product) or with primers B51/B52 (5' end, 1800-bp product). The genomic DNA of each cell colony was extracted using the TIANamp genomic DNA kit (Tiangen Biotech) for long-range PCR with LA Taq polymerase (TaKaRa); The purified PCR fragments above were cloned into the pMD19-T vector (TaKaRa) and sent for sequencing (GenScript Co., Ltd). Primers used for targeting analysis are presented in S1 Table. Expression analysis on the mRNA and protein levels in vitro The GMECs were screened with 500 mg/ml G418 (Sigma). A TranScript First-Strand cDNA Synthesis SuperMix Kit (Transgen) was used for reverse-transcription PCR analysis on total RNA samples. The primers BcF/BcR and hcF/hcR which were specific for BLG and hLA partial cDNAs were shown in S1 Table. Supernatants from the induced mammary epithelial cells were collected every 12 hours. The recovered supernatants were then vacuum freeze-dried and subjected to western blot analysis. The primary rabbit anti hLA antibody (1:1,000) used to detect hLA was from Santa Cruz Biotechnology.
Nuclear transfer
The SCNT procedure was performed according to a previously described report [27]. Briefly, the Cumulus-oocyte complexes (COCs) were cultured to maturation for 22-24 hours. The first polar bodies and chromosomes of the oocytes were suctioned out and the targeted donor cells were injected into the perivitelline space of the enucleated oocytes. Karyoplast-cytoplast couplets were fused by electrofusion. The couplets were incubated for 2-3 h in TCM-199. Fused embryos were activated in 5 mM ionomycin for 4 min and then cultured in 2 mM 6-dimethylaminopyridine in mSOF medium for 4 h. The embryos were then washed
Southern blot analysis
Southern blot analysis was performed according to standard procedures with the DIG High Prime DNA Labelling and Detection Starter Kit II (Roche). A 3' external hybridization probe (800-bp) and a neo probe (500-bp) were labelled for gene insertion (Fig 1A). The diagnostic fragments should were a 5.5-kb BamHI fragment extending across the 3' arm of the targeted allele and a 4.4-kb BamHI fragment of endogenous DNA extending across the 3' and 5' arms at the site of the wild-type allele.
Isolation of whey protein in goat milk
Milk samples were centrifuged at 10,000 g for 15 min to remove the fat fractions and diluted with an equal amount of PBS. The primary fat-free supernatant liquids were then adjusted to pH 3.8-4.6 with 1 M HCl and centrifuged at 10,000 g for 15 min to eliminate the casein fraction. The secondary casein-free supernatant liquids were then adjusted to pH 6.8 with 1 M NaOH for SDS-PAGE detection and western blot analysis.
Statistical analysis
Data presented are derived from at least three independent experiments. Statistical significances were analyzed using the One-Way ANOVA. A value of P<0.05 was considered significant.
Generation of targeting vector PloxpII-hLA-neo
Based on the target site of TALENs (Fig 1A), we generated a targeting vector PloxpII-hLA-neo to introduce hLA-neo cassette to exon 1 of the goat BLG locus. Two homology arms flanking hLA-neo cassette were designed for introducing HR which can repair the DSBs induced by TALEN pair. Upon the successful integration, the 90 bp fragment containing the ATG start codon to partial exon1 sequence of the BLG gene would be deleted and replaced with the hLAneo cassette, which would result in BLG protein loss and hLA expression driven by endogenous BLG promoter.
Preparation of gene-targeting clones with TALENs
To investigate the efficient TALEN-induced exogenous gene integration into BLG locus, the targeting vector PloxpII-hLA-Neo (S1 Fig), along with TALEN plasmids were engineered to introduce a DSB in exon1 of BLG gene in goat fibroblasts (Fig 1A). After 48 h of culturing the cells in normal medium without any drug selection, we harvested genomic DNA and measured the integration of the exogenous gene by 3' junction PCR. As expected, no measurable exogenous gene integration into the chromosome was observed in the absence of TALENs. By contrast, the junction region between endogenous and exogenous DNA was amplified by primers B31 and B32 in cells that were exposed both to the donor plasmid and TALENs (Fig 1B; Lane 1).
Approximately 4×10 6 cells containing GFFs and GEFs were transfected with targeting plasmids and the TALEN pair (Table 1). A total 741 colonies were obtained from four transfections with 10 days of G418 selection (700 mg/ml). To exclude the random DNA integration, the DNA lysates from each clone were used for the initial 3' junction PCR (Fig 1C). Products with a size consistent with this process were readily detected in 13.5% (n = 741) of the PCR samples. Subsequent 5' junction PCR was performed to amplify the left-hand junction between the endogenous BLG gene and hLA DNA ( Fig 1D). As evidence of this fact, approximately 88% of the 3' junction PCR-positive clones were confirmed correct during this procedure ( Table 1).
Screening of BLG bi-allelic targeted clones from that of mono-allelic clones
The long-range (LR) PCR primers LRF and LRR, located at the recombination site, were used to confirm correct targeting and to exclude false positive clones. We can see that in some clones there were only 4.7-kb targeted bands existing without endogenous 0.5-kb bands (Fig 2A; Lanes 1 and 19), this may illustrated that these clones are targeted at two alleles. Southern blot analysis demonstrated that these clones were targeted at two alleles of BLG gene (Fig 2B; Lanes 1-8). Totally, we found mono-and bi-allelic frequency of 10.1% and 1.1%, respectively, in all G418-resistant colonies through LR-PCR analysis ( Table 1).
Expression of the hLA in goat mammary epithelial cells in vitro
To verify that BLG expression levels was reduced by hLA insertion and the endogenous BLG promoter was driving expression of hLA, the targeting construct and the TALEN pair were cotransfected into GMECs by FugenHD. Screening GMEC clones with 500 mg/ml G418 and then performed PCR analysis for gene insertion. PCR-positive GMECs were treated with inductive medium (DMEM/F12 amended with 10 μg ml -1 EGF, 1% ITS, 5 μg ml -1 prolactin, and 1 g ml -1 hydrocortisone). Simultaneously, non-transfected cells were cultured in the same condition as controls. After 48-72 h of induction, total RNA was extracted from the experimental cells; hLA and BLG partial cDNAs were successfully generated from RT-PCR on the total RNAs (Fig 3A and 3B). Quantitative real-time PCR (qPCR) was performed to detect BLG and hLA gene expression levels in single clone at the transcriptional level. BLG mRNA level was down-regulated by 36.0%-43.0% and 99.6-99.9%, respectively, in BLG mono-allelic and bi-allelic knock-in clones. Moreover, hLA exhibited high mRNA expression levels both moallelic and bi-allelic modification clones (Fig 3C). To further confirm mammary-specific expression of the hLA transgene at the protein level, the supernatants of transfected GMECs treated with the inductive medium were collected and processed via western blot analysis (Fig 3D).
Production of gene-targeted goats by SCNT
To eliminate the potential integration of TALEN-DNA constructs on genome, we applied TALEN-encoding mRNAs to generate targeted cells to be used as donors for SCNT. In total, PCR analysis showed the targeting efficiency of 8.35% (n = 1032) by TALEN-mRNA mediated foreign gene integration (S2 Table). Subsequently, We have carefully examined several most potential off-target sites of this TALEN pair in the gene-edited fibroblasts via sequencing before SCNT, and no detectable off-target mutations were found in the genome of the clones (S3 Table). A total of 4 mono-allelic hLA knock-in colonies (GEF21, GEF94, GEF97, GFF86) and 1 bi-allelic hLA knock-in clone (GFF31) ( Table 2) with normal chromosome numbers, compact spindle-like cell morphology, and rapid growth were considered suitable for SCNT. The in vitro developmental experiment was carried out to evaluate blastocyst formation after SCNT (Fig 4A). In order to rule out mixed colonies (colonies contained non-targeted cells), 7 cloned embryos were used for 5' junction PCR and long-range PCR analysis. Fig 4B showed that there were heterozygous or homozygous embryos for the hLA gene knock-in at the BLG locus, and thus we would have one or two targeted copies of the BLG gene. Ultimately, 1112 embryos were reconstructed, and 717 embryos developing to 2-4 cells were surgically transferred to 50 Table 2). Seventeen pregnancies were detected by ultrasonography at day 60, and five were carried to term, resulting in six live births (Fig 4C). The birth weight for six cloned goats is shown in S4 Table, the one bi-allelic targeted goat deriving from GFFs died soon after one month due to pneumonia and anemia, other five goats deriving from GEFs have grown to adulthood. The other pregnancies were lost for spontaneous abortion or unknown reason.
Finally, junction PCR screening for each locus revealed integration of the transgene in the cloned goats (Fig 5A). DNA sequencing indicated that the 90-bp BLG fragment had been replaced with the exogenous 4.2-kb fragment of hLA-neo cassete. There were no point mutations or micro-rearrangements, such as base substitutions or insertions/deletions, on either side of the two homology arms either (Fig 5B). Southern blot analysis of the genomic DNA from these cloned goats suggested that five goats harboured one targeted allele and one harboured a two-allele insertion (Fig 5C).
Analysis of the milk from animals carrying the hLA allele
To investigate whether the recombinant hLA can secrete in goat milk, five pubertal BLG hLA/+ goats were crossed with wild-type goats for natural lactation. After parturition, milk whey proteins from BLG hLA/+ goats were analyzed by 15% SDS gel electrophoresis and Coomassie blue staining. As shown in Fig 5D in the upper panel, the 18-KD BLG bands from targeted goat milk were weaker than that of wild-type goat, this illustrate that BLG reduced in transgenic goat's milk. As shown by western blot analysis in the lower panel, hLA was highly expressed in BLG +/hLA goat milk compared with that of normal goat. The results indicate that the insertion of hLA in BLG locus is disrupting BLG expression and endogenous BLG promoter is driving the hLA expression in transgenic goat milk. As measured with an enzyme-linked immunosorbent assay (ELISA), the hLA concentration for BLG hLA/+ goat milk was 1.2 mg/ml. A milk composition analysis of fat, protein, lactose and milk solids revealed insignificant differences between transgenic and non-transgenic goats ( Table 3).
The BLG +/hLA heterozygous goats were normal in appearance and behaviour. Seven F 1 offspring were monitored via PCR and southern blot analysis (S2 Fig). As expected, two progenies were inherited one mutant allele from their mothers (S2 Fig and S5 Table). These data indicated that the site-specific insertion for exogenous hLA in BLG locus is heritable.
Discussion
In the current study, we demonstrate the feasibility of a TALEN-stimulated gene addition at the endogenous BLG loci and the successful production of cloned goats by SCNT. Our usage of TALENs, LR-PCR for bi-allelic targeting identification and in vitro hLA expression evaluation resulted in an effective procedure for preparing transgenic donor cells for SCNT. Milk production of transgenic goats will enable the direct evaluation of the allergenicity of BLG protein as well as the full investigation of hLA biological function in animal or human experiments.
Novel analogous technologies such as ZFNs, TALENs and RGEN system are programmable, site-specific engineering technologies enabling precise manipulation in natural chromosomal contexts. A previous study on the mouse Rosa26 gene demonstrated that specific TALENs exhibit superior targeting efficiency to that of ZFNs specific for the same targeting sequence [28]. Furthermore, ZFNs suffering from cytotoxicity and limited target sites [29,30] have restricted their applications. For RGEN technique, a rising number of researches have revealed high potential off-target cleavage due to three or fewer mismatches in their guide RNAs and the difficulty of controlling the concentrations of Cas9:guide RNA complexes [31,32]. These data document the superiority of TALENs to other two gene editing tools in geneinsertion applications at present.
TALEN-mediated isolation of colonies with mono-and bi-allelic modification by nonhomologous end-joining (NHEJ) around cleavage sites or by HR in livestock has been reported [22,33]. Nevertheless, few studies report bi-allelic LR-PCR analysis in targeted clones selection [26,34,35]. In general, time-consuming backcrossing of founder goat or sequential gene targeting with another targeting construct into the second allele is performed. Undoubtedly, LR-PCR analysis for the efficient detection of double-allelic clones based on exogenous integration between two arms and DNA cleavage site in our study reduced the time and labour costs. Furthermore, distance between the homology arms and the DNA cleavage site can affect TALEN-induced HR efficiency. In this report, the efficacy of HR using homologous arms contiguous to the DSB site (~64 bp for 5'arm and~10 bp for 3'arm) was comparative to that in previous study when the homology was~17 bp or~80 bp from the DSB site in vitro [21]. These results further suggested that the precise TALEN-mediated deletion or insertion with DNA templates may have more extensive application when refer to DSB sites.
Though foetal fibroblasts are ideal donors for cloning livestock [26,35,36], the weakness for this selection is the inability to confirm the presence of heritable diseases in the foetus. As shown in Table 2, our method revealed a superior cloning efficiency of GEFs (0.92%-1.45%) than GFFs (0-0.68%). In present study, we found that the GEF cells have a more typical fibroblast morphology and a smaller cell individuals than GFF cells before or after G418 selection (S3 Fig). This may result in higher cloning efficiency of GEFs and demonstrate that GEFs are excellent substitute for GFFs for cloning goats.
To avoid the occurrence of silencing or aberrant expression of transgene [37,38], the inductive in vitro expression of exogenous transgene in GMECs was performed. BLG expression reduction by almost 36-43% and 99% in mono-and bi-allelic targeted GMECs, rather than the expected 50% and 100%, in vitro, was consistent with our previous speculation that the expression of the intact BLG allele was up-regulated to compensate for the missing allele in monoallelic targeted goats [21]. Our observation of high hLA expression in BLG +/hLA goat milk indicates that all elements necessary for high α-lactalbumin expression are contained within the endogenous BLG fragment. High hLA expression may also balance milk protein synthesis. A water increase caused by lactose may be responsible for the insignificant change on lactose content between transgenic goats and non-transgenic goats. This would in return indicate that the volume of transgenic goats has risen in transgenic goats. Our data in Table 3 revealed that hLA insertion have not influence the mammary system and milk secretion mechanism balance itself well in transgenic goats.
Compared to the hLA random integration in goat genome and being driven expression specifically in goat mammary gland by synthetic BLG promoter [18], our study introduced the hLA gene into specific BLG locus and exploited the endogenous BLG regulatory fragment to direct abundant hLA secretion in goat milk as well as disrupt BLG expression. The current procedure is superior in avoiding position effect, important unknown endogenous gene silencing and the transgenic animal safety issue which may be caused by random integration [39,40]. Furthermore, rather than simply adding hLA protein without reducing other components in goat's milk which would affect the balance of milk composition [41], the BLG protein loss in present research can be complemented by hLA secretion. Further experiments on milk safety and functional analysis have to be carried out in future. Importantly, the germline transmission of targeting mutations offers us a opportunity for mass production of targeting animals.
In conclusion, we have demonstrated that TALEN-mediated HR can be used to create both mono-and bi-allelic insertion in fibroblasts, leading to the production of BLG mono-and biallelic targeted goats directing reduced BLG content and abundant hLA secretion in goat milk. The production of this goat strain is great progress for people who are allergic to goat or cow milk. We also expect that hLA presence in transgenic goat's milk could exhibit its function of increasing milk production and protecting the body from bacterial invasion as well as tumorigenesis in future study. We anticipate that our success on germline-transmittable production of hLA knock-in goats through TALEN-mediated genome engineering would expand the field of goat milk application in health care and therapeutic supplies. Table. Sequencing results of gene targeted clones in nine potential off-target sites. The potential binding sites of TALENs were in uppercase and the spacers were in lowercase. The mismatches in off-target sites were highlighted in yellow. | 5,489.4 | 2016-06-03T00:00:00.000 | [
"Biology",
"Agricultural And Food Sciences"
] |
Enhancement of Ceramics Based Red-Clay by Bulk and Nano Metal Oxides for Photon Shielding Features
We prepared red clays by introducing different percentages of PbO, Bi2O3, and CdO. In order to understand how the introduction of these oxides into red clay influences its attenuation ability, the mass attenuation coefficient of the clays was experimentally measured in a lab using an HPGe detector. The theoretical shielding capability of the material present was obtained using XCOM to verify the accuracy of the experimental results. We found that the experimental and theoretical values agree to a very high degree of precision. The effective atomic number (Zeff) of pure red clay, and red clay with the three metal oxides was determined. The pure red clay had the lowest Zeff of the tested samples, which means that introducing any of these three oxides into the clay will greatly enhance its Zeff, and consequently its attenuation capability. Additionally, the Zeff for red clay with 10 wt% CdO is lower than the Zeff of red clay with 10 wt% Bi2O3 and PbO. We also prepared red clay using 10 wt% CdO nanoparticles and compared its attenuation ability with the red clay prepared with 10 wt% PbO, Bi2O3, and CdO microparticles. We found that the MAC of the red clay with 10 wt% nano-CdO was higher than the MAC of the clay with microparticle samples. Accordingly, nanoparticles could be a useful way to enhance the shielding ability of current radiation shielding materials.
Introduction
Most countries around the world consider nuclear technology to be an alternative energy source to solve the problem of nonrenewable energy, which will run out one day. Due to the increased use of radioisotopes and radiation-emitting devices in various medical and industrial fields, it is necessary to study the ability of some readily available materials for use in construction, such as concrete, rocks and clay, to protect against gamma rays [1][2][3][4].
It is well known that materials with a high atomic number and density are very useful as ionizing radiation shields. The most common materials used for these purposes are lead, alloys, glasses, composites, some types of concrete, and clay materials [5][6][7][8].
Clay has been used since antiquity, in Mesopotamia, Egypt, Africa, and the Middle East; and more recently in Roman and Islamic civilizations in Asia, North America, Me-dieval Europe, and so on. Civilizations have built entire cities out of clay materials. Clay products are now used by more than a third of the world's population due to their high quality and resistance to weathering. Clay material in architecture is a part of the heritage of almost every nation on every continent.
In many developed and developing countries, clay materials are used for building and construction. Clay products, such as ceramic pots, fired bricks, and tiles (for ceilings and floors) are less expensive and more durable than cement, and they are environmentally friendly and safe building materials that are widely available at low prices in various regions [9][10][11]. Furthermore, clay has refractory properties such as a high melting point, thermochemical stability, abrasion resistance, and thermal shock resistance. One of the most important characteristics that distinguishes clay and from other materials is that it is non-toxic. Due to these characteristics, clay materials are suitable for use as shielding materials (designing radiation shields from clay materials) [12][13][14].
With the emergence of nanotechnology as a progressive branch of science in recent years, various types of nanoparticles have been used to design radiation shields. The advantage of using nanomaterials in this field is that the distances between molecules are very small, increasing the possibility of photon collisions with atoms of the material, and thus improving the material's ability to attenuate photons [15][16][17][18].
Nuclear engineers have placed a high value on nanocomposites containing metals or oxides of heavy elements, and their research has focused on developing these nanocomposites for use as an alternative to traditional radiation shields due to their promising properties, such as their lightweight, and desirable mechanical, chemical, and physical properties [19,20]. The majority of previous research has focused on developing some types of clay mixed with some heavy oxides for use as radiation shields, but very few studies have focused on developing clays mixed with nano-scale particles of heavy oxides [21].
Some types of Egyptian clay, which are natural building materials that may be considered for use as a radiation-shielding materials, will be investigated in this study. Clay can be found in relatively large reserves northeast of the city of Aswan in Egypt. Many companies produce it for the local ceramics and tile industries, mainly in Wadi Abu Sabira and Wadi Abu Ajaj. Due to the industrial importance of Aswan clay, some technical studies have been conducted to investigate its physical properties, either in its raw state as used for the manufacture of ceramics and tiles, or as a mixture with other raw materials [22].
Red clay is a clean and environmentally friendly building material that can be used as a radiation shield in radiation protection applications, or it can be added to concrete mixtures in certain proportions as an alternative to sand, resulting in an increase in its density, which leads to an increase in gamma ray attenuation. This form of clay's high melting point is indicative of its potential thermal stability in the case of prolonged exposure to high-energy radiation, and its compressive strength is appropriate for the production of high-strength shielding materials [23].
However, to the best of the authors' knowledge, studies related to the radio protective properties of these clays are almost non-existent, which prompted the researchers in this work to study the radio protective properties of red clay found in the Aswan region of Egypt, after adding a group of heavy-metal oxides as both micro-and nano-scale particles. In this work, some red clay originating from ceramic samples was prepared and the chemical composition was deduced by EDX analysis. The attenuation parameters of these samples were experimentally determined and compared with theoretical values produced by the XCOM software. The effective atomic number (Zeff ) was calculated for a broad energy range.
Materials and Methods
First, the red clay samples were collected from Aswan city, Egypt, then dried, crushed, and sieved using a sieve with a hole diameter of 100 µm. Secondly, micro-scale metal oxides (PbO, Bi 2 O 3 and CdO) were purchased from the El-Gomhouria Company in Egypt. The average particle size of these oxides ranged from 50 to 100 µm, and their purity was up to 99%. Meanwhile, nano-scale cadmium oxide (CdO) particles (average size 40 nm) were purchased from the NanoTech Company in Egypt, where they were chemically prepared. The red clay was mixed with the proportions of oxides shown in Table 1, and blended well by a mixer to obtain a homogeneous mixture. This mixture of powders was added to a proportion of water (mixture: water = 3:1) to form the compound, then put in a plastic container and allowed to dry for two weeks. Thus, 10 samples were prepared. These samples were left for two weeks to dry, and a sample of each type was then taken to measure its chemical composition by energy dispersive X-ray (EDX) analysis, as shown in Table 1. From knowledge of their compositions, the MAC could be theoretically calculated using the WinXCom program [24][25][26]. The radiation shielding parameters were experimentally determined by the narrow-beam method. A high-purity germanium (HPGe) detector was used, alongside point sources of different energies in cases where their activities and other specifications could be found (Table 2) [27][28][29][30][31][32][33]. The sample was placed between the source and the detector using a collimator and lead shield. The schematic diagram of the experimental measurement technique is shown in Figure 1. Measurements were undertaken for a time sufficient for the statistical uncertainty of the area under the peak to be less than 1%, and the count rate was calculated in the presence and absence of the sample. The MAC is calculated according to the following equation [34][35][36]: where A and A 0 represent the areas under the peak, and the count rates obtained from the spectrum in the presence and absence of the absorbing sample, respectively, × (cm) represents the thickness of the measured clay sample, and ρ (g/cm 3 ) the density. The linear attenuation coefficient or LAC is defined as the probability of photons interacting with matter per unit path length, and was calculated to determine other important shielding parameters (such as HVL and TVL) where the LAC equals MAC*ρ. The HVL and TVL represent the thicknesses needed to attenuate 50% and 90% of the initial photon intensity, respectively, and can be evaluated by the following equations [37,38]: The effective atomic number (Zeff) is another useful radiation interaction factor that is used to describe the attenuating properties of mixtures or compounds in terms of the elements present, and depends on the incoming photon energy. Zeff values for the studied polymers can be obtained using Equation (3) [39]: where fi, Ai, and Zi refer to the mole fraction, atomic weight, and an atomic number of each constituent element in the selected polymer, respectively. Figure 2 shows the mass attenuation coefficient (MAC) for the tested clays with different micro-samples as a function of energy between 0.015 and 15 MeV. The values were calculated using the XCOM software. In this work, red clay, which is used as a building material, was prepared using three oxides: PbO, CdO, and Bi2O3. In Figure 2, 10 wt% PbO, CdO, and Bi2O3 was added to the red clay, and the figure presents the effect of this addition on MAC. In the low-energy region (energies less than 70 MeV), it can be seen that the red clay with 10 wt% CdO has a greater MAC than the red clay with PbO and Bi2O3. This difference is due to the k-absorption edges of Cd, Pb, and Bi, which occur at 26.71, 88, and 90.53 keV, respectively. Due to Cd's k-absorption edge, it has a high attenuation ability, near 20-30 keV, causing it to have a higher MAC value than PbO and Bi2O3. Meanwhile, as the energy approaches 80 keV, the k-absorption edges of Pb and Bi cause the clays with these two elements to have a higher MAC than the clay with CdO. The effective atomic number (Z eff ) is another useful radiation interaction factor that is used to describe the attenuating properties of mixtures or compounds in terms of the elements present, and depends on the incoming photon energy. Z eff values for the studied polymers can be obtained using Equation (3) [39]:
Results and Discussion
where f i , Ai, and Z i refer to the mole fraction, atomic weight, and an atomic number of each constituent element in the selected polymer, respectively. Figure 2 shows the mass attenuation coefficient (MAC) for the tested clays with different micro-samples as a function of energy between 0.015 and 15 MeV. The values were calculated using the XCOM software. In this work, red clay, which is used as a building material, was prepared using three oxides: PbO, CdO, and Bi 2 O 3 . In Figure 2, 10 wt% PbO, CdO, and Bi 2 O 3 was added to the red clay, and the figure presents the effect of this addition on MAC. In the low-energy region (energies less than 70 MeV), it can be seen that the red clay with 10 wt% CdO has a greater MAC than the red clay with PbO and Bi 2 O 3 . This difference is due to the k-absorption edges of Cd, Pb, and Bi, which occur at 26.71, 88, and 90.53 keV, respectively. Due to Cd's k-absorption edge, it has a high attenuation ability, near 20-30 keV, causing it to have a higher MAC value than PbO and Bi2O3. Meanwhile, as the energy approaches 80 keV, the k-absorption edges of Pb and Bi cause the clays with these two elements to have a higher MAC than the clay with CdO.
Results and Discussion
In order to understand the influence of introducing PbO, Bi 2 O 3 , and CdO into red clay on its attenuation ability, the MAC and LAC of the clays were experimentally measured in a lab, and from these experimental values the HVL, TVL, and MFP ewere determined. Before analyzing the shielding ability of the clays, it is important to verify the accuracy of the experimental results, as all the conclusions rely on it. For this, the theoretical shielding capability of a material is obtained using XCOM, and then these results are compared with the experimental data. The theoretical results of red clay (no additives), red clay with 10 wt% PbO, red clay with 10 wt% Bi 2 O 3 , and red clay with 10 wt% CdO were compared at four different energies, as shown in Figure 3. All four tested parameters (MAC, lAC, HVL, and MFP) had a good level of agreement between their experimental and theoretical results, at all energies, and for all tested samples. For instance, the difference between the experimental and theoretical MAC for red clay with 10 wt% CdO at 0.0596 MeV is negligible, meaning that the two values agree to a very high degree of precision. The same results were found for the other samples and the other tested parameters. This result proves that the experimental setup used in this study can be reliably used to determine the shielding ability of the investigated clays. In order to understand the influence of introducing PbO, Bi2O3, and CdO into red clay on its attenuation ability, the MAC and LAC of the clays were experimentally measured in a lab, and from these experimental values the HVL, TVL, and MFP ewere determined. Before analyzing the shielding ability of the clays, it is important to verify the accuracy of the experimental results, as all the conclusions rely on it. For this, the theoretical shielding capability of a material is obtained using XCOM, and then these results are compared with the experimental data. The theoretical results of red clay (no additives), red clay with 10 wt% PbO, red clay with 10 wt% Bi2O3, and red clay with 10 wt% CdO were compared at four different energies, as shown in Figure 3. All four tested parameters (MAC, lAC, HVL, and MFP) had a good level of agreement between their experimental and theoretical results, at all energies, and for all tested samples. For instance, the difference between the experimental and theoretical MAC for red clay with 10 wt% CdO at 0.0596 MeV is negligible, meaning that the two values agree to a very high degree of precision. The same results were found for the other samples and the other tested parameters. This result proves that the experimental setup used in this study can be reliably used to determine the shielding ability of the investigated clays. experimental results, the accuracy of the obtained values was determined for the red clay samples with 30 wt% PbO, Bi 2 O 3 and CdO instead of 10 wt%, to test whether increasing the amount of additives affected the reliability of the results. This figure has similar trends to the previous figure; namely, the difference between the XCOM and the experimental results was extremely small (within an acceptable experimental error). This once again proves that the experimental setup used in this work provides accurate data for red clay with both low and high amounts of PbO, Bi 2 O 3 , and CdO. The effective atomic number (Zeff) of pure red clay, and red clay with 10 wt% PbO, Bi2O3 and CdO is illustrated in Figure 5a, while Figure 5b shows the results for red clay with 30 wt% PbO, Bi2O3, and CdO, as well as for a red clay sample with 10 wt% of PbO, Bi2O3, and CdO (totaling 30 wt% metal oxide). Figure 5a demonstrates that pure red clay has the lowest Zeff out of the tested samples, which means that introducing any of these three oxides into the clay will greatly enhance its Zeff, and, consequently, its attenuation capability. In addition, the figure shows that the Zeff for red clay with 10 wt% CdO is lower than the Zeff of red clay with 10 wt% Bi2O and PbO, which is expected as Cd has a lower atomic number than Bi and Pb. Meanwhile, pure red clay has a Zeff value of about 10-15, 15 at the lowest tested energy and then smoothly decreasing down to a constant value. The first subfigure also revealed a peak for the CdO clay and two peaks for both the PbO and Bi2O3 clays. These peaks can be attributed to the k-absorption edges of Cd, Pb, and Bi. In Figure 5b, the Zeff for red clay with 30 wt% CdO is once again lower than the Zeff of the clays with 30 wt% PbO and Bi2O3, as well as the clay with 10 wt% CdO, 10 wt% PbO, and 10 wt% Bi2O3. One peak was observed for the red clay with 30 wt% CdO and two peaks for the other samples, which confirms the conclusion that these peaks occur because of the presence of Pb and Bi. In both figures it can be seen that the maximum Zeff occurs at the lowest tested energy, and that the minimum values occur in the moderate-energy range (which is due to the Compton scattering effect). When comparing the Zeff values for the clay sample with 10 wt% PbO to the sample with 30 wt% PbO, it can be seen that Zeff increases with an increase in PbO content, meaning that the Zeff of the red clay with 30 The effective atomic number (Z eff ) of pure red clay, and red clay with 10 wt% PbO, Bi 2 O 3 and CdO is illustrated in Figure 5a, while Figure 5b shows the results for red clay with 30 wt% PbO, Bi 2 O 3 , and CdO, as well as for a red clay sample with 10 wt% of PbO, Bi 2 O 3 , and CdO (totaling 30 wt% metal oxide). Figure 5a demonstrates that pure red clay has the lowest Z eff out of the tested samples, which means that introducing any of these three oxides into the clay will greatly enhance its Z eff , and, consequently, its attenuation capability. In addition, the figure shows that the Z eff for red clay with 10 wt% CdO is lower than the Z eff of red clay with 10 wt% Bi 2 O and PbO, which is expected as Cd has a lower atomic number than Bi and Pb. Meanwhile, pure red clay has a Z eff value of about 10-15, 15 at the lowest tested energy and then smoothly decreasing down to a constant value. The first subfigure also revealed a peak for the CdO clay and two peaks for both the PbO and Bi 2 O 3 clays. These peaks can be attributed to the k-absorption edges of Cd, Pb, and Bi. In Figure 5b, the Z eff for red clay with 30 wt% CdO is once again lower than the Z eff of the clays with 30 wt% PbO and Bi 2 O 3 , as well as the clay with 10 wt% CdO, 10 wt% PbO, and 10 wt% Bi 2 O 3 . One peak was observed for the red clay with 30 wt% CdO and two peaks for the other samples, which confirms the conclusion that these peaks occur because of the presence of Pb and Bi. In both figures it can be seen that the maximum Z eff occurs at the lowest tested energy, and that the minimum values occur in the moderate-energy range (which is due to the Compton scattering effect). When comparing the Z eff values for the clay sample with 10 wt% PbO to the sample with 30 wt% PbO, it can be seen that Z eff increases with an increase in PbO content, meaning that the Z eff of the red clay with 30 wt% PbO is greater than that of the sample with 10 wt% PbO. Therefore, adding more PbO to the clay samples improves the shielding ability of the red clay. This same conclusion applies to increased amounts of both CdO and Bi 2 O 3 . The MAC for red clay with 10 wt% PbO, Bi2O3, and CdO microparticles were compared with the MAC of red clay with 10 wt% CdO nanoparticles at four different energies (Figure 6a). This comparison tested the effect of decreasing particle size on the MAC of the red clays. The figure shows that the MAC of the red clay with 10 wt% nano-CdO was higher than the MAC of the clay containing microparticle samples. This difference is most evident at the lowest tested energy, and decreases as the energy increases. This result suggests that nanoparticles could be a useful way to enhance the shielding ability of current radiation-shielding materials.
Since the red clays containing nanoparticles outperformed the clays with microparticles, another red clay with 30 wt% nano-CdO was prepared, and the MAC for this sample was compared with that of the 30 wt% micro-PbO, Bi2O3, and CdO, as well as to that of a sample with 10 wt% micro-PbO, 10 wt% micro-Bi2O3, and 10% wt% micro-CdO. The results for these samples are graphed in Figure 6b. This figure shows that the MAC for the red clay with nano-CdO is higher than the MAC of the clay containing micro-PbO, CdO and Bi2O3, which is especially apparent at the first tested energy. Therefore, it can be concluded that one method to improve the shielding ability of materials is to introduce nanoparticles rather than using microparticles. Additionally, it can be said that nano-CdO can be used as an alternative to PbO to create a more environmentally friendly shielding material. The MAC for red clay with 10 wt% PbO, Bi 2 O 3 , and CdO microparticles were compared with the MAC of red clay with 10 wt% CdO nanoparticles at four different energies (Figure 6a). This comparison tested the effect of decreasing particle size on the MAC of the red clays. The figure shows that the MAC of the red clay with 10 wt% nano-CdO was higher than the MAC of the clay containing microparticle samples. This difference is most evident at the lowest tested energy, and decreases as the energy increases. This result suggests that nanoparticles could be a useful way to enhance the shielding ability of current radiation-shielding materials.
Since the red clays containing nanoparticles outperformed the clays with microparticles, another red clay with 30 wt% nano-CdO was prepared, and the MAC for this sample was compared with that of the 30 wt% micro-PbO, Bi 2 O 3 , and CdO, as well as to that of a sample with 10 wt% micro-PbO, 10 wt% micro-Bi 2 O 3 , and 10% wt% micro-CdO. The results for these samples are graphed in Figure 6b. This figure shows that the MAC for the red clay with nano-CdO is higher than the MAC of the clay containing micro-PbO, CdO and Bi 2 O 3 , which is especially apparent at the first tested energy. Therefore, it can be concluded that one method to improve the shielding ability of materials is to introduce nanoparticles rather than using microparticles. Additionally, it can be said that nano-CdO can be used as an alternative to PbO to create a more environmentally friendly shielding material. Figure 7. The HVL of the studied materials compared with ordinary concrete and white casting mud.
Conclusions
In summary, this work started by collecting red clays from Aswan city in Egypt and blending them with various percentages of three oxides (PbO, Bi2O3 and CdO) with the
Conclusions
In summary, this work started by collecting red clays from Aswan city in Egypt and blending them with various percentages of three oxides (PbO, Bi2O3 and CdO) with the
Conclusions
In summary, this work started by collecting red clays from Aswan city in Egypt and blending them with various percentages of three oxides (PbO, Bi 2 O 3 and CdO) with the aim of fabricating novel clay materials with enhanced gamma-radiation-shielding features. The MAC for the prepared materials was experimentally measured and compared with the theoretical results determined by XCOM. The measured and XCOM data agree to a very high degree of precision. Accordingly, the experimental setup used in this study can be reliably used to determine the shielding ability of the investigated clays. The Z eff of pure red clay, red clay with 10 wt%, and red clay with 30 wt% PbO, Bi 2 O 3, and CdO is reported. The Z eff results demonstrated that introducing any of these three oxides into clay will greatly enhance its Z eff , and, consequently, its attenuation capability. The Z eff for red clay with 30 wt% CdO is lower than the Z eff of the clays with 30 wt% PbO and Bi 2 O 3 , and the clay with 10 wt% CdO, 10 wt% PbO, and 10 wt% Bi 2 O 3 . Additionally, we found that the Z eff increases with an increase in PbO content, meaning that the Z eff of the red clay with 30 wt% PbO is greater than that of the sample with 10 wt% PbO. We compared the MAC of red clay with nano-CdO, to that of red clay with micro-PbO and micro-Bi 2 O 3 , to understand the influence of particle size on the attenuation ability of the red clays. We found that the MAC for the red clay with nano-CdO was higher than the MAC of red clay wih micro-PbO, CdO, and Bi 2 O 3 . Therefore, it can be concluded that one method to improve the shielding ability of materials is to introduce nanoparticles rather than using microparticles. Additionally, it can be said that nano-CdO can be used as an alternative to PbO to create a more environmentally friendly shielding material. | 6,045.2 | 2021-12-01T00:00:00.000 | [
"Geology"
] |
PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants
More reliable and faster prediction methods are needed to interpret enormous amounts of data generated by sequencing and genome projects. We have developed a new computational tool, PON-P2, for classification of amino acid substitutions in human proteins. The method is a machine learning-based classifier and groups the variants into pathogenic, neutral and unknown classes, on the basis of random forest probability score. PON-P2 is trained using pathogenic and neutral variants obtained from VariBench, a database for benchmark variation datasets. PON-P2 utilizes information about evolutionary conservation of sequences, physical and biochemical properties of amino acids, GO annotations and if available, functional annotations of variation sites. Extensive feature selection was performed to identify 8 informative features among altogether 622 features. PON-P2 consistently showed superior performance in comparison to existing state-of-the-art tools. In 10-fold cross-validation test, its accuracy and MCC are 0.90 and 0.80, respectively, and in the independent test, they are 0.86 and 0.71, respectively. The coverage of PON-P2 is 61.7% in the 10-fold cross-validation and 62.1% in the test dataset. PON-P2 is a powerful tool for screening harmful variants and for ranking and prioritizing experimental characterization. It is very fast making it capable of analyzing large variant datasets. PON-P2 is freely available at http://structure.bmc.lu.se/PON-P2/.
Introduction
Rapidly advancing high-throughput sequencing technologies produce enormous amounts of genomic data. The increasing speed and decreasing cost of sequencing paves way for exomeand complete genome-based personalized medicine [1]. The major challenge to use genomics in personalized medicine is the same as in genetic diagnosis, namely the interpretation of effects and impacts of genetic variants [2].
Computational approaches are essential for screening harmful variations as the huge amounts of generated sequence data are practically impossible to analyze using experimental methods. For example, the Database for Short Genetic Variations (dbSNP) build 138 (released April 2013) [3] contains over 62 million human variants which is about 9 million more than in the previous build released 10 months earlier. Similarly, Catalogue Of Somatic Mutations In showed that it is significantly faster than other methods, thereby being able to analyze exome and genome wide datasets for identifying potentially harmful variants.
Dataset
Benchmark variation data was downloaded from VariBench [23], a database for variation datasets and consisted of 14,610 pathogenic and 17,393 neutral variations. A subset of the dataset containing 14,086 pathogenic variants in 1,082 proteins and 14,848 neutral variants in 6,598 proteins, for which all the features used in PON-P2 (excluding functional annotations) were available, was used for training and testing PON-P2. The dataset was divided into two parts i) one-tenth of the data was used as test dataset; ii) the remaining nine-tenths were used for feature selection and training. The dataset was divided in such a way that proteins in the same family were either in test or training dataset. The proteins were mapped to the protein families in Pfam database (Pfam 27.0) [24]. The training and test datasets are publicly available in Vari-Bench (http://structure.bmc.lu.se/VariBench/tolerance.php) along with Variation Ontology (VariO) annotations [25].
Features
Amino acid features. AAindex [26] contains three databases for altogether 685 physicochemical and biochemical properties of amino acids. 617 features were used after eliminating those with missing values. The features in AAindex1 have a numerical index for each amino acid while those in AAindex2 and AAindex3 are amino acid substitution matrices. For each variant, the difference between the indices for the reference and variant amino acid were calculated for AAindex1 features while the values were taken directly from AAindex2 and AAindex3 matrices.
Gene Ontology feature. The GO terms derived features have previously been used in variant classification [12,27]. The GO terms associated with each protein were extracted from Uni-ProtKB/Swiss-Prot. All the ancestors for each GO term were collected with R bioconductor tool GO.db (http://www.bioconductor.org/packages/2.13/data/annotation/html/GO.db.html). The GO terms were then filtered so that each protein had each term only once. Two separate sets of GO terms were created for each class (pathogenic and neutral). The summation of log ratio of the frequency of GO term in the pathogenic set to the frequency of GO term in the neutral set is calculated as: Where LR is the GO feature value for a protein; f(P i ) and f(N i ) are the frequencies of the i th GO term in pathogenic and neutral datasets, respectively. To avoid undetermined ratios, 1 was added to the frequencies.
Evolutionary conservation features. The ratio of non-synonymous substitution rate to synonymous substitution rate (ω) estimates selective pressure. Conserved sites are often structurally or functionally crucial and variations at such sites may be unfavorable. Synonymous variations are more common than non-synonymous variations and thus ω is higher for variable sites than for conserved sites. Orthologous protein and cDNA sequences for each human protein (translated from the longest transcript) were collected from Ensembl compara database [28] using perl application program interface (API). The orthologous protein sequences were aligned with ClustalW [29]. Based on the protein multiple sequence alignment, the codon alignment of cDNA sequences was generated using PAL2NAL [30]. The cDNA codon alignment was provided for selecton [31] to calculate codon-level ω. The human sequence was used as the reference sequence and the number of iterations was set to 1. Besides ω, other features that represent sequence profile including the proportions of reference and variant amino acids, and the number of sequences in the protein sequence alignment were used.
Functional and structural annotations. Site specific annotations were determined from UniProtKB/Swiss-Prot and PDB. The variations which occur at such sites were identified. The distribution of the annotations in the pathogenic and neutral datasets were calculated. The annotations, for which proportion of variations in either class was greater than 0.85, were selected.
Feature selection
The feature selection was performed in two steps. We combined two greedy feature selection approaches-backward elimination and forward selection [32]. In the first step, 10 feature subsets were selected by backward elimination method one from each 10-fold cross-validation set. The 10 feature subsets selected in the first step were combined together and a forward feature selection was performed in the second step. In the forward feature selection, the performance of each feature was evaluated by 10-fold cross-validation. The training data was split into 10 parts so that all variants in one protein family were strictly present in one of the partitions. 9 partitions were used for training and the remaining partition was used for testing. The first feature selection step included the following procedures: 1. A random forest classifier was trained using all 622 features.
2. The accuracy of the classifier was measured by using the cross-validation testing dataset and the features were ranked based on mean decrease in gini index.
3. The feature that obtained the least mean decrease in gini index was eliminated.
4. Another random forest classifier was trained using the remaining features.
5.
Steps 2 to 4 were repeated until there was only one feature left.
6. The accuracies of all the classifiers were compared and the features, used in the classifier with the highest accuracy, were selected.In the second step, we performed a forward feature selection to select the features that improve the performance by highest margin. First, a non-redundant feature set with all the features in the 10 subsets (from first feature selection) was obtained. An empty feature subset was initiated and was called selected feature set.
1.
A random forest classifier was trained using non-redundant feature set. The features were ranked by using random forest mean decrease in gini index. The highest ranked feature was added to selected feature set and eliminated from non-redundant feature set.
2. Another classifier was trained by using features in selected feature set and the accuracy was measured.
3. Features in selected feature set and one feature from non-redundant feature set was used to train a classifier and the accuracy was measured.
4.
Step 3 was iterated for all the features in non-redundant feature set.
5.
The feature that improved the accuracy by highest percentage was added to selected feature set and eliminated from non-redundant feature set.
6.
Steps 3, 4 and 5 were repeated until no improvement was achieved by addition of any of the features.
Then, the selected feature set was used to train PON-P2.
Random forest
PON-P2 uses randomForest package which is an R interface to the original random forest algorithm [33]. The number of features used to generate random feature subset was set to default value of 2. By stratified random sampling with replacement, 200 bootstrap samples, containing the same number of cases as the original training data, were generated and a classifier was trained on each bootstrap sample. The number of trees grown in each random forest was set to 300 as the prediction of random forest was reported to be stable at 300 when increasing the number of trees [14].
Using functional annotation information
The probability of pathogenicity for a variation occurring at a functionally annotated site is estimated from the probability predicted by random forest and proportion of variations (annotated as occurring in functional sites) in pathogenic class using following rule P c ðpÞ ¼ P a ðpÞ þ P rf ðpÞ À P a ðpÞ Â P rf ðpÞ ð 2Þ where, P c (p) is the combined probability of pathogenicity for the variation; P a (p) is the probability of variation to be pathogenic, which is derived from the proportion of pathogenic variations in training dataset for the annotation type and P rf (p) is the probability of pathogenicity of the variation predicted by random forest.
Determining the reliability
The variations predicted with high confidence are identified by using probabilistic method. Although we cannot determine the probability distribution function of bootstrap probabilities, we can apply Chebysev's inequality as it is applicable to any arbitrary distribution. For a random variable X with mean μ and standard deviation σ, Chebysev's inequality guarantees that at least 1-(1/k 2 ) values lie within k standard deviations from mean While 1-(1/k 2 ) is 0.95, if range of μ±kσ excludes 0.5, the prediction is labeled as reliable and is classified as either pathogenic or neutral. Otherwise, the variation is reported as unclassified.
Performance evaluation
The performance of PON-P2 and other prediction methods were evaluated by using six measures as recommended for binary classifiers [34,35]. The measures include positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC). These measures are defined mathematically as follows: where, TP and TN are the number of correctly predicted pathogenic and neutral cases, respectively, and FN and FP are the number of incorrectly predicted pathogenic and neutral cases, respectively. Performance cuboids were used to visualize the six major performance scores simultaneously in a 3-dimensional space. The overall performance measure (OPM) of a classifier is represented by normalized volume of the performance cuboid, which ranges from 0 to 1. Normalized MCC (nMCC) is calculated by rescaling the value of MCC from 0 to 1. The performance cuboids were obtained by plotting the six performance scores using rgl package (http://cran.r-project.org/web/packages/rgl/index.html) in R. Results
Feature selection and classifier design
PON-P2 is a random forest predictor for pathogenicity-association of amino acid substitutions ( Fig. 1). It is trained on annotated disease-causing variants as positive cases and variants with allele frequency > 0.01 in dbSNP as neutral cases. Extensive feature selection was performed to identify useful features for discrimination of disease-related variants from neutral ones. Eight useful features were selected from 622 features. The selected features were GO annotations, codon-level Ka/Ks, 3 features representing sequence profile and 3 physical and biochemical properties of amino acids including KOSJ950114 [36], RACS820113 [37] and TANS770104 [38] (S1 Table). From 10 rounds of feature selection, 5 features (GO, frequency of reference amino acid, KOSJ950114, Ka/Ks, number of sequences in MSA) were overlapping in all 10 selected feature sets. Random forest algorithm ranks the features based on mean decrease in gini index. A fraction of the training data is used to train a classifier and the remaining part is used to estimate the decrease in gini index [33]. The higher the decrease in gini index, the more important is the feature. The GO derived feature has high importance while the amino acid features are less important (S1 Table). The annotations of variations to functional and structural sites were collected from UniProtKB/Swiss-Prot and Protein Data Bank (PDB) (S2 Table). The distributions of variations in the pathogenic and the neutral datasets were computed to examine the disease-relation of variations at functional and structural sites. Five types of functional annotations were selected for which the proportion of variation in either class was greater than 0.85 (Fig. 2). This bias towards one class was utilized as additional information for the predictor. If a variation occurs at a site with functional annotations, the probability of pathogenicity of a variation at the functional site is combined with random forest probability to make final prediction (Fig. 1).
Performance of feature subsets
To estimate the contribution of each feature subset, we used combinations of features to train random forest classifiers and compared their performance on the test dataset. Sequence profile showed higher performance than selective pressure, and combination of sequence profile and selective pressure further improved the performance as well as the proportion of predicted variants (Table 1). Evolutionary conservation features show slightly lower performance but higher coverage than GO annotations and amino acid features together. Evolutionary conservation features perform even better when combined with GO derived feature and amino acid features (Fig. 3A). Although the performance contributions of individual features are small, the performance evaluation shows that each feature subset contributes to the performance of PON-P2 and elimination of any of the features results in poorer performance (Table 1).
Performance improvement by using annotations of functional and structural sites was estimated. A significant number of variants at functionally annotated sites were predicted with unreliable score by using the random forest. After combining the annotation information and random forest prediction, the rejection rate decreased considerably, however, with comparable accuracy (S3 Table). As the number of variants at functionally annotated sites is small, the contribution to the overall performance scores is relatively small; however, it is large for the variants at functionally annotated sites. PON-P2 was tested by using 10-fold cross-validation and an independent test dataset. In the 10-fold cross-validation, variants in the same protein and protein family were strictly placed in either training or test set. The accuracy, MCC and OPM of PON-P2 at confidence level 0.95 were 0.90, 0.80, and 0.73, respectively for 10-fold cross-validation and 0.86, 0.71 and 0.63, respectively, for the test dataset. PON-P2 showed highest performance scores when compared with other methods. The performance scores are higher than for the other tools even when the unreliable cases were classified as pathogenic or neutral based on the predicted probability (cutoff 0.5) ( Table 2). An independent analysis of bioinformatics tools for variations in Usherin protein showed that PON-P2 had the highest sensitivity (0.95) and specificity (0.98) among the predicted cases [39]. GO annotation is protein based feature which is the same for pathogenic and neutral variants in a protein.
To test the discriminative power of PON-P2 for pathogenic and neutral variants in the same protein, we retrieved amino acid substitutions from dbSNP with allele frequency > 0.01 for those proteins that contain pathogenic variants in the test data. 382 variants were identified in 62 proteins. Among 192 variants predicted with high confidence, 162 (84.4%) were classified as neutral by PON-P2. Thus, PON-P2 is not overfitted and it classifies both pathogenic and neutral variants correct in the same protein.
PON-P and PON-P2 reject the unreliable cases and classify the cases that are reliable at confidence level 0.95. To make a comparison of the performance of the methods using the same set of variants, we filtered out the variants rejected by PON-P2 and called the set of remaining variants c95-training and c95-test sets. c95-training set contains 61.7% of the training data while c95-test set contains 62.1% of the test data. The performance scores for all the methods (except PON-P as it automatically rejects unreliable cases) were computed. The methods show somewhat higher performance scores for both c95-training and c95-test sets. However, the other methods have still clearly lower performance than PON-P2 (S4 Table). These results show that rejection of the unreliable cases improves prediction performance significantly for all the methods. The performance scores for the different methods indicate that PON-P2 is the most balanced method in regards to the six performance scores. The real differences in performance (Fig. 3B,3C) are even larger as some of the methods have the benefit of being trained with cases in our dataset.
Recently, new predictors including MutationTaster2 [40] and Combined Annotation Dependent Depletion (CADD) [41] have been released. Because the tools have limited batch submission options, we compared the performance on the MutationTaster2 test dataset from http://www.mutationtaster.org/info/Comparison_20130328_with_results_ClinVar.html. We excluded the variations that were present in PON-P2 training dataset. The number of benign variants is higher than the number of deleterious variations, so we performed random sampling to select the same number of neutral and deleterious variations. The accuracy and MCC of PON-P2 were 0.95 and 0.90, respectively, which are higher than those for the other methods. The performance of PON-P2 and Mutation Taster2 were comparable when unreliable cases were predicted as pathogenic or neutral based on the predicted probabilities (cutoff 0.5) ( Table 3). Although this data seems to be biased as indicated by the data provider, the performance of the methods is still comparable as the results are biased on the same direction for all the methods. Most other tolerance prediction methods do not use classification with reject option. To check whether the performance of PON-P2 has improved only by rejecting the unreliable cases, we classified all the variants into binary classes. The probability cutoff of 0.5 was used above which the variants were predicted as pathogenic. The performance scores on such a binary classification showed that PON-P2 performs the best even when the unreliable cases are included (Tables 2 and 3). Hence, it clearly shows that the performance improvement is not solely due to the reject option but because of the robustness of the tool. SNAP was designed for predicting the functional effects of the variations and not optimized for prediction of diseaserelated variants. So, the performance comparison of SNAP with PON-P2 may not be optimal although SNAP has been widely used for pathogenicity prediction.
Performance cuboid and overall performance measure
In machine learning, Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC, also called for AROC) have been widely used to evaluate the performance of binary classifiers. A ROC curve shows the relative trade-off between true positive rate (TPR) and false positive rate (FPR) when different thresholds are set to distinguish between the two classes [42]. Classifiers like PON-P2, that are optimized to predict discrete classes, produce only a single point in the ROC curve thus being uninformative. For comprehending the full performance of a classifier, use of six performance measures has been recommended [35].
For the visualization and comparison of method performance, a novel projection to 3dimensional space was developed. Assuming that a cube centered at origin represents the performance of a perfect classifier, the six major performance scores are represented by the distance of six faces of the cube from the origin. The performance scores of an imperfect classifier do not always produce a cube. Hence, we name the visualization method as performance cuboid. The overall performance of a predictor is estimated by calculating the volume of the cuboid and normalizing it from 0 to 1, referred to as overall performance measure (OPM). Fig. 3 visualizes the comparison of different classifiers using performance cuboids. Only three faces of the cuboids are shown in full for better visibility. The classifier that gains the lowest performance scores is the closest to the origin i.e. has the smallest volume. The best performing predictor has its faces furthest away from the origin. For example, in Fig. 3B, SIFT and PolyPhen-2 achieve the lowest performance score and PON-P2 achieves the highest score. Therefore, the faces of the cuboids for SIFT and PolyPhen-2 are more visible while only small portion of the faces of the cuboid for PON-P2 are visible. The balanced overall performance of the predictor is given by OPM. OPMs for SIFT, PolyPhen-2, PON-P and PON-P2 are 0.41, 0.42, 0.65, and 0.73, respectively (Fig. 3B). The visualization and OPM scores show that PON-P2 performs better than the other predictors.
Prediction time
With increasing amounts of genomic data and increasing possibility of personalized medicine, it is clearly evident that fast computational tools are a necessity for identification of deleterious variations. PON-P2 utilizes computationally expensive features like codon-level selective pressure to improve the performance of classifier. Computing the feature vector takes longer time Variants with C-score greater than 15 were considered as deleterious and lower than 15 were considered as neutral as suggested by the method developers. c HumVar trained PolyPhen-2. The performance of this version was better than for HumDiv trained PolyPhen-2 (data not shown).
than the prediction. To allow fast run times, we collected the protein sequences (translated from the longest transcripts) for all the coding human genes in Ensembl database [43] and computed the feature vectors for each position in these sequences and stored in a relational database. When a user submits a query, PON-P2 extracts the feature vectors from the database and runs the prediction. Hence, the time required for making sequence alignment and preparing the feature values is skipped. The time required by PON-P2 and some other methods to complete a typical prediction task was compared. PON-P2 is significantly faster than any other method (S5 Table). The result shows that PON-P2 is capable of handling the huge amounts of genomic variation data generated by modern sequencing technologies.
Discussion
The handling of immense amount of variation data generated by next-generation sequencing technologies and relating them to diseases is a major challenge. Several computational tools based on different principles have been developed to rank and prioritize non-synonymous SNVs for experimental characterization. However, currently available tools are sub-optimal [16] and are not capable for fast interpretation of the amount of data being generated. SIFT [7], PolyPhen-2 [10] and some other tools provide precalculated scores and predictions for all possible variations in large number of human proteins. Therefore, these methods provide predictions faster if the precalculated predictions are used. However, our analysis showed that the performance of these methods is lower than for PON-P2 and some other existing methods. Hence, the need of more reliable and faster computational tools persists. To address the requirement we have developed a novel tool, PON-P2. It is based on evolutionary conservation, structural and functional annotations and properties of amino acids and predicts whether a variation is harmful or not. PON-P2 is trained on approximately equal numbers of disease-causing variations (positive dataset) and variations being relatively frequent (allele frequency > 0.01) in dbSNP (neutral dataset). Although the proportions of the harmful and benign variants in human are unknown, the best performance of binary classifiers are obtained by training with balanced dataset regardless of the composition of the true data [44]. The positive dataset was collected from databases and checked manually or automatically to be annotated as disease-causing. We feel that this provides the best starting point for developing variation tolerance predictor. Information about functional effects of variations have been used to train some other predictors. A problem emerges with such datasets because the functional effects are vaguely described e.g. in the widely used Protein Mutation Database (PMD) [45]. Secondly, there is not usually information about the biological effect. There is for example an extreme case of adenosine deaminase activity in severe combined immunodeficiency (SCID) where activity of 0.11% is sufficient for normal phenotype [46]. On the other hand, very minor change in activity (increase or decrease) can be harmful in other cases. Thus, changes in protein activity level are not necessarily sufficient to explain functional effects of variations.
We performed extensive feature selection to identify useful and non-redundant features. 8 features were selected from among 622 features. The attributes selected in PON-P2 are physical and biochemical properties of amino acids, GO and functional annotation, selective pressure and sequence profile. In a previous study, selective pressure together with sequence profile was observed to be more efficient than using them separately for classifying variants [19]. The analysis was performed with a comparatively small dataset consisting of about 11,000 variants. We evaluated the contribution of selective pressure and sequence profile using a more comprehensive variation dataset consisting of 28,934 variants. Both the selective pressure and the sequence profile improve the performance of classifier when combined with amino acid features and improves the prediction coverage when they are used together ( Table 1). The contribution of amino acid features, GO annotations, and conservation features were evaluated and elimination of any of these feature subsets decreases the performance of the predictor (Fig. 3A). Only 3 out of the 617 features in AAindex turned out to be useful for the prediction. These include one substitution matrix (KOSJ950114) and two protein structural features (RACS820113 and TANS770104). Thus, although AAindex mainly contains simple amino acid propensities, they are uninformative.
Using classification with reject option reduces the error rate of a classifier by making predictions only for reliable cases [21,22]. We use Chebysev's inequality and bootstrap method to determine the reliability of prediction. Using Chebysev's inequality, if the predicted probability is reliable at confidence level 0.95, the variation is classified as pathogenic or neutral. Otherwise, the variant is designated as unclassified. Increasing the confidence level further reduces the error rate but on the other hand, the rejection rate also increases. Therefore, we optimized the method at confidence level 0.95 where the error rate is comparatively low and a significant fraction of variants (62.1%) can be classified as pathogenic or neutral. The concept of classification with reject option has not previously been used in tolerance predictions apart from PON-P. The concept is relevant in tolerance prediction because the genetic variants cannot always be classified distinctly into pathogenic or neutral groups. There are variants with intermediate effects which may be deleterious or neutral depending on other parameters. The same variant, even in monozygotic twins, can cause different phenotype [47], thereby excluding the simple binary classification scheme utilized in most of the other predictors. Thus, it is essential to identify unreliable predictions and reject them to reduce the false predictions. This is further evidenced by the improvement in the performance scores of all the compared methods when excluding the unreliable cases identified by PON-P2 (S4 Table). The superior performance scores for PON-P2 when all the variants are predicted into binary classes indicate that the performance improvement is not solely due to the reject option but because the method is robust.
Although codon-level selective pressure was observed to improve the discrimination of disease-related variations from neutral [18,19], it has not been employed previously in prediction methods probably because of being computationally intensive. We computed all features including selective pressure for each position in proteins (translated from the longest transcript) of all coding human genes and stored in a database. Despite the fact that PON-P2 uses bootstrap method, that requires more computation time for prediction, PON-P2 is significantly faster than the other methods (S5 Table). The speed is essential for interpretation of variants in large scale sequencing projects e. g. for application to personalized medicine.
The human genome is not completely annotated. Therefore, some of the features used in PON-P2 may be unattainable for some variants. For example, GO feature cannot be calculated if there are no GO annotations for a protein. In such cases, PON-P2 provides prediction based on other selected features except GO. The selective pressure and sequence profile features are based on multiple sequence alignments of ortholog sequences. If the sequence is unique for human, PON-P2 does not make predictions as it would not be reliable.
PON-P2 is capable of predicting variation effects in 86% of human proteins with high accuracy. PON-P2 has both improved prediction performance and computation time, thus making it suitable for ranking, prioritizing and filtering of large scale variation datasets.
Supporting Information S1 | 6,561.8 | 2015-02-03T00:00:00.000 | [
"Computer Science",
"Biology"
] |
Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning
: The Laguerre-Gaussian (LG) beam demonstrates great potential for optical communication due to its orthogonality between different eigenstates, and has gained increased research interest in recent years. Here, we propose a dual-output mode analysis method based on deep learning that can accurately obtain both the mode weight and phase information of multimode LG beams. We reconstruct the LG beams based on the result predicted by the convolutional neural network. It shows that the correlation coefficient values after reconstruction are above 0.9999, and the mean absolute error (MAE) of the mode weights and phases are about 1.4 × 10 − 3 and 2.9 × 10 − 3 , respectively. The model still maintains relatively accurate prediction for the associated unknown data set and the noise-disturbed samples. In addition, the computation time of the model for a single test sample takes only 0.975 ms on average. These results show that our method has good abilities of generalization and robustness and allows for nearly real-time modal analysis.
Optics 2021, 2 88 of OAM beams. Even in propagation environments, such as atmospheric turbulence and underwater, CNNs have shown good accuracy performance [26][27][28][29]. However, most of these studies focus on identifying a single OAM beam mode or a combination of modes of multiple OAM beams. The phase information, which is unknown for optical intensity profile images, has been less studied.
In this paper, we propose a dual-output convolutional neural network (Y-Net) based modal analysis method for the multimode Laguerre-Gaussian (LG) beams [30], which is a kind of common beam that carries OAM. Our method not only outputs the weight of each mode based on the optical intensity profile of the input beams, but also obtains the phase information simultaneously. Moreover, we evaluated the method by optical field reconstruction and prediction errors at different mode numbers and propagation distances, and obtained superior results, which further demonstrate the advantages of the proposed scheme. Our approach has the potential to provide implications for accurate, robust and fast real-time modal analysis of OAM beams.
Materials and Methods
In the cylindrical coordinate system, a single-mode LG beam with zero radial index can be represented as [31]: where l represents the topological charge, r is the radiation distance, ϕ is the azimuth, z is the propagation distance, A |l| = 2 π|l|! , w(z)= w(0) (z 2 +z R 2 ) z R 2 1 2 , L |l| is Laguerre polynomial, z R = kw 0 2 2 is Rayleigh length and k is the wave vector. The superimposed optical field of LG beams with different l-quantum numbers, which are orthogonal to each other, can be expressed by the following equations: U(r, ϕ, z) = N ∑ n=1 a n e iθ n u l n (r, ϕ, z) (2) where N is the number of modes, u l n (r, ϕ, z) is the nth LG beam eigenmode, a n and θ n are the amplitude and phase of each eigenmode, respectively. a n 2 is the proportion of the nth eigenmode in the superimposed optical field and satisfies this expression N ∑ n=1 a n 2 = 1, which we call the mode weight. The optical intensity profiles of the multimode LG beams are shown in Figure 1.
In this paper, we propose a dual-output convolutional neural network (Y-Net) based modal analysis method for the multimode Laguerre-Gaussian (LG) beams [30], which is a kind of common beam that carries OAM. Our method not only outputs the weight of each mode based on the optical intensity profile of the input beams, but also obtains the phase information simultaneously. Moreover, we evaluated the method by optical field reconstruction and prediction errors at different mode numbers and propagation distances, and obtained superior results, which further demonstrate the advantages of the proposed scheme. Our approach has the potential to provide implications for accurate, robust and fast real-time modal analysis of OAM beams.
Materials and Methods
In the cylindrical coordinate system, a single-mode LG beam with zero radial index can be represented as [31]: where l represents the topological charge, r is the radiation distance, φ is the azimuth, z is is Rayleigh length and k is the wave vector. The superimposed optical field of LG beams with different l-quantum numbers, which are orthogonal to each other, can be expressed by the following equations: U r, φ, z = a n e iθ n u n l r, φ, z N n = 1 (2) where N is the number of modes, u n l r, φ, z is the n th LG beam eigenmode, a n and θ n are the amplitude and phase of each eigenmode, respectively. a n 2 is the proportion of the n th eigenmode in the superimposed optical field and satisfies this expression a n 2 N n = 1 = 1, which we call the mode weight. The optical intensity profiles of the multimode LG beams are shown in Figure 1. Furthermore, the weights of different modes can be expressed as a 1 2 , a 2 2 , · · · , a N 2 , while the expressing of the phase is a little different. Since the phases of each mode are relative, we define the first mode as the fundamental mode, which has a phase value of zero. The other modes are in relative phase, expressed as [θ 1 , θ 2 , · · · , θ N − 1 ]. It should be noted that the number of elements of the vector of relative phase is one less than the number of modes, and we also linearly scale the elemental values of this vector from [0, 2π] to [0,1]. In addition, we choose the optical field intensity profile of the multimode LG beams as the input, defined as I(x, y) = |U(r, ϕ, z)| 2 .
CNN is a typical deep learning method. In the ImageNet competition in 2012, Krizhevsky et al. proposed a CNN-AlexNet with ReLU as the activation function, which achieved a far better performance than other algorithms and gained wide attention from researchers [32]. A general CNN framework is composed of several layers with different functions connected in a certain order, and the output of each layer is used as the input features of the next layer and up to the output layer of the final model. In addition, a number of adjustable parameters are available for each layer for training. The core component of CNN is the convolutional layer, which extracts the features of the input image through convolutional operations and characterizes the obtained features into higher-dimensional feature spaces.
Other layers also have important roles, for example, the batch normalization (BN) layer normalizes the feature vector output from the convolutional layer. The max-pooling layer extracts a window from the feature map input and outputs the maximum value for each channel. The role of the fully connected (FC) layer is to map the learned features to the sample labeling space.
In order to obtain the amplitude and phase information of different modes in the LG beams simultaneously, we design a dual output convolutional neural network, as shown in Figure 2 below. The convolutional operation part of the model consists of 4 blocks, connected with 2 fully connected layers and the output layer, showing a Y-shaped structure. The branch of the dual output structure consists of the 2nd-4th blocks, the fully connected layer and the output layer. Each block contains 3 convolutional layers, 3 batch normalization layers and 1 max-pooling layer. The convolution kernels of the convolution layers in each block are of a size 3 × 3 and step size 1 × 1.
Optics 2021, 2, 9 89 Furthermore, the weights of different modes can be expressed as a 1 2 , a 2 2 , ⋯, a N 2 , while the expressing of the phase is a little different. Since the phases of each mode are relative, we define the first mode as the fundamental mode, which has a phase value of zero. The other modes are in relative phase, expressed as θ 1 , θ 2 , ⋯, θ N -1 . It should be noted that the number of elements of the vector of relative phase is one less than the number of modes, and we also linearly scale the elemental values of this vector from [0, 2π] to [0, 1]. In addition, we choose the optical field intensity profile of the multimode LG beams as the input, defined as CNN is a typical deep learning method. In the ImageNet competition in 2012, Krizhevsky et al. proposed a CNN-AlexNet with ReLU as the activation function, which achieved a far better performance than other algorithms and gained wide attention from researchers [32]. A general CNN framework is composed of several layers with different functions connected in a certain order, and the output of each layer is used as the input features of the next layer and up to the output layer of the final model. In addition, a number of adjustable parameters are available for each layer for training. The core component of CNN is the convolutional layer, which extracts the features of the input image through convolutional operations and characterizes the obtained features into higher-dimensional feature spaces.
Other layers also have important roles, for example, the batch normalization (BN) layer normalizes the feature vector output from the convolutional layer. The max-pooling layer extracts a window from the feature map input and outputs the maximum value for each channel. The role of the fully connected (FC) layer is to map the learned features to the sample labeling space.
In order to obtain the amplitude and phase information of different modes in the LG beams simultaneously, we design a dual output convolutional neural network, as shown in Figure 2 below. The convolutional operation part of the model consists of 4 blocks, connected with 2 fully connected layers and the output layer, showing a Y-shaped structure. The branch of the dual output structure consists of the 2nd-4th blocks, the fully connected layer and the output layer. Each block contains 3 convolutional layers, 3 batch normalization layers and 1 max-pooling layer. The convolution kernels of the convolution layers in each block are of a size 3 × 3 and step size 1 × 1. The advantages of this design are that, on the one hand, with limited hardware computing power, it takes less time to use one model to output both amplitude and phase than to use two models separately; on the other hand, the dual output structure is connected through block1 in Figure 2, and the two branch structures share the output feature map of block1. We believe that such dual-output structure will retain the link between amplitude and phase for the optical intensity profile. The advantages of this design are that, on the one hand, with limited hardware computing power, it takes less time to use one model to output both amplitude and phase than to use two models separately; on the other hand, the dual output structure is connected through block1 in Figure 2, and the two branch structures share the output feature map of block1. We believe that such dual-output structure will retain the link between amplitude and phase for the optical intensity profile.
The optical field intensity profile is limited to 128 × 128 image size, and the mode proportion of each mode is randomly and uniformly distributed between (0, 1) and normalized, and the relative phase values are randomly and uniformly distributed in [0, 2π] and linearly transformed. Other parameters are set as follows: the wavelength of the LG beam is 1064 nm, the beam waist radius is 15 mm. We generate a total of 100,000 samples (one input image, two label vectors) and divide them into three data sets: the training set, the validation set and the test set, which contain data in the ratio of 6:2:2. The model is trained using the samples in the training set and validated in the validation set, and we can pause to adjust the model parameters during the training process and finally test the model in the test set.
The performance of the model is closely related to the setting of hyper-parameters. We use a mini-batch of size 64 to speed up the computation and use the Adam function with an initial learning rate of 0.01 as an optimizer. Moreover, our model uses a decaying learning rate training method, in which the learning rate is halved every 4 epochs for the first 20 training epochs, and each epoch for the subsequent training epochs. As for the activation functions in the output layer, Softmax and Sigmoid [33] functions are used as activation functions for predicting the mode weights and relative phases, respectively. The loss function of the model considers the mean absolute error (MAE) function, and the weight ratio of the two vectors of mode weights and relative phase is set to 1:1, that is Loss = Loss A +Loss P , then the final loss function can be shown as: where N is the number of vector elements, y n is the element in the real label vector, and y n is the element in the predicted label vector. A, P in the corner scale represent the weight vector and the relative phase vector, respectively. All training and testing processes in this work are performed on a GPU server with RTX 2080ti graphics card, and the loss function values of the model converged after 30 training epochs, with the total process taking only about 25 min. The model is tested on a test set of 20,000 samples in 19.5 s, indicating an average computation time of 0.975 ms per sample, which demonstrates that our model allows for fast real-time modal analysis of multimode LG beams. It should be noted that the complexity of the model could be reduced by adjusting the hyperparameters and structure of the model to speed up the computation of the model. Using parallel computing to provide more computational resources is also a way to reduce computation time.
Results
When the weights of each mode in the multimode LG beams are known, as well as the relative phase, the input optical field intensity profile can be easily reconstructed, and the accuracy of the model prediction can be visualized by reconstructing the image. Here we have used the correlation coefficient to characterize the effect of the reconstruction [34], which is expressed as follows: where ∆I j (r)= I j (r) − I j (r), (j = m, r) and I j represents the mean value of the input optical intensity I m or the reconstructed optical intensity of I r . The value of C shows the similarity between the reconstructed image and the original image and is provided by Figure 3. Ideally, when the reconstructed image is the same as the original image, the correlation coefficient C is the maximum value of 1. The residual image profile [35] can be expressed by ∆I(x, y) = |I m − I r |, which is the absolute value of the difference between the reconstructed image and the original image at each pixel point.
by ∆I x, y = |I m -I r |, which is the absolute value of the difference between the reconstructed image and the original image at each pixel point.
( a) (b) We use a dual-output CNN to predict the weights and relative phases of multimode LG beams with 9 modes (l = 0, 1, ⋯, 8) superimposed, and put the reconstructed image according to Equation (2) together with the original input image and the residual image for visual comparison, and the results are shown in Figure 3. It can be seen that the correlation coefficient between the reconstructed image and the input image is above 0.999, while the intensity value of the residual image is almost 0, which indicates that our scheme is very feasible. It should be noted in Figure 3b that the residual phase images have several red points at which the phase values converge to 2π, indicating that the predicted values of the phase at these points may be opposite to the true values. Since the phase of optical wave is periodic, the phase difference converging to 2π can be considered as converging to 0, which is unavoidable when only one optical field profile is involved in the modal analysis [36].
We investigate the effect of the mode number of multimode LG beams on the mode analysis performance of the CNN, which is evaluated by the MAE function. The weight error and phase error are defined as ∆a 2 = 1 N a 2 p − a 2 t and ∆θ = 1 N -1 θ p − θ t , where the corner scale p and t denote the predicted and true values, respectively. As shown in Figure 4, the mode weight error and phase error gradually increase as the number of modes increases, and the phase error is always higher than the weight error, while the difference between them also becomes larger with the increase in the number of modes. Possible reason is that the optical intensity profile of the multimode LG beams is progressively more complex as the number of modes increases, which increases the difficulty of feature extraction and characterization of the CNN and leads to a gradual increase in the weights and phase error. However, this situation can be improved by training a larger number of [18] or higher resolution [35] samples. We can also use other methods that are common in the field of deep learning to reduce the prediction error of the model. For example, pre-training, hyperparameters adjusting, and so on.
Good generalizability is an important dimension when evaluating the performance of CNN. In practical application scenarios of optical communication, l-quantum number non-adjacent LG beams are used for multiplexing to avoid crosstalk of adjacent OAM modes during propagation. Our model is trained based on samples with adjacent l-quantum numbers, and samples with non-adjacent l are not considered. To verify whether the model performs better on unknown samples, we generate two datasets with mode composition of l = 1, 3, 5, 7, 9 and l = 1, 5, 9, respectively. We tested the CNN trained by the We use a dual-output CNN to predict the weights and relative phases of multimode LG beams with 9 modes (l = 0, 1, · · · , 8) superimposed, and put the reconstructed image according to Equation (2) together with the original input image and the residual image for visual comparison, and the results are shown in Figure 3. It can be seen that the correlation coefficient between the reconstructed image and the input image is above 0.999, while the intensity value of the residual image is almost 0, which indicates that our scheme is very feasible. It should be noted in Figure 3b that the residual phase images have several red points at which the phase values converge to 2π, indicating that the predicted values of the phase at these points may be opposite to the true values. Since the phase of optical wave is periodic, the phase difference converging to 2π can be considered as converging to 0, which is unavoidable when only one optical field profile is involved in the modal analysis [36].
We investigate the effect of the mode number of multimode LG beams on the mode analysis performance of the CNN, which is evaluated by the MAE function. The weight error and phase error are defined as ∆a 2 = 1 N a 2 p − a 2 t and ∆θ = 1 N − 1 θ p − θ t , where the corner scale p and t denote the predicted and true values, respectively. As shown in Figure 4, the mode weight error and phase error gradually increase as the number of modes increases, and the phase error is always higher than the weight error, while the difference between them also becomes larger with the increase in the number of modes. Possible reason is that the optical intensity profile of the multimode LG beams is progressively more complex as the number of modes increases, which increases the difficulty of feature extraction and characterization of the CNN and leads to a gradual increase in the weights and phase error. However, this situation can be improved by training a larger number of [18] or higher resolution [35] samples. We can also use other methods that are common in the field of deep learning to reduce the prediction error of the model. For example, pre-training, hyperparameters adjusting, and so on.
Good generalizability is an important dimension when evaluating the performance of CNN. In practical application scenarios of optical communication, l-quantum number nonadjacent LG beams are used for multiplexing to avoid crosstalk of adjacent OAM modes during propagation. Our model is trained based on samples with adjacent l-quantum numbers, and samples with non-adjacent l are not considered. To verify whether the model performs better on unknown samples, we generate two datasets with mode composition of l = 1, 3, 5, 7, 9 and l = 1, 5, 9, respectively. We tested the CNN trained by the dataset with mode composition of l = 1, 2, 3, 4, 5, 6, 7, 8, 9 on the above two new datasets to investigate the generalizability of the model. The test results are shown in Figure 5, the predicted values of mode weights basically agree with the actual values, indicating that CNN has good generalization ability for the associated unknown dataset, which can reduce the burden of device level and transfer it to the data processing category. This demonstrates the application value of the dual output CNN-based approach for modal analysis.
dataset with mode composition of l = 1, 2, 3, 4, 5, 6, 7, 8, 9 on the above two new datasets to investigate the generalizability of the model. The test results are shown in Figure 5, the predicted values of mode weights basically agree with the actual values, indicating that CNN has good generalization ability for the associated unknown dataset, which can reduce the burden of device level and transfer it to the data processing category. This demonstrates the application value of the dual output CNN-based approach for modal analysis. Our model is trained with images when the propagation distance is zero, but it is also suitable for modal analysis of samples with non-zero propagation distance. We used the model to test samples with different propagation distances and different mode combinations, and the results are shown in Figure 6. The weight error increases with the increase in beam propagation distance, but even for the most complex 9 modes multiplexed beams, the weight error of the model is only 5.6 × 10 −3 after propagating 120 m, indicating that our model can support the mode analysis work of multimode LG beams within a certain distance. It is worth mentioning that the prediction accuracy of CNN can be improved if the data samples after propagating a certain distance can be added to the data set. Optics 2021, 2, 9 92 dataset with mode composition of l = 1, 2, 3, 4, 5, 6, 7, 8, 9 on the above two new datasets to investigate the generalizability of the model. The test results are shown in Figure 5, the predicted values of mode weights basically agree with the actual values, indicating that CNN has good generalization ability for the associated unknown dataset, which can reduce the burden of device level and transfer it to the data processing category. This demonstrates the application value of the dual output CNN-based approach for modal analysis. Our model is trained with images when the propagation distance is zero, but it is also suitable for modal analysis of samples with non-zero propagation distance. We used the model to test samples with different propagation distances and different mode combinations, and the results are shown in Figure 6. The weight error increases with the increase in beam propagation distance, but even for the most complex 9 modes multiplexed beams, the weight error of the model is only 5.6 × 10 −3 after propagating 120 m, indicating that our model can support the mode analysis work of multimode LG beams within a certain distance. It is worth mentioning that the prediction accuracy of CNN can be improved if the data samples after propagating a certain distance can be added to the data set. Our model is trained with images when the propagation distance is zero, but it is also suitable for modal analysis of samples with non-zero propagation distance. We used the model to test samples with different propagation distances and different mode combinations, and the results are shown in Figure 6. The weight error increases with the increase in beam propagation distance, but even for the most complex 9 modes multiplexed beams, the weight error of the model is only 5.6 × 10 −3 after propagating 120 m, indicating that our model can support the mode analysis work of multimode LG beams within a certain distance. It is worth mentioning that the prediction accuracy of CNN can be improved if the data samples after propagating a certain distance can be added to the data set. Optics 2021, 2, 9 93 Figure 6. The relation between mode weight error and distance.
The performance of neural networks can also be affected by noise factors. We test on a dataset containing random noise to investigate the robustness of the model to noise. Each pixel value in the optical intensity profile image is multiplied by a factor f = 1 + N 0, 1 ·σ to generate the noisy image dataset, where N(0, 1) is the standard normal distribution and σ is the noise intensity [18]. As shown in Figure 7a, the images of the optical intensity profile of the 9-mode superimposed LG beams propagated for 100 m become gradually blurred with increasing σ values, and we also have selected a local region of the image to show this change in more detail. As shown in Figure 7b, the error value of the model prediction and the slope of the curve increase with the increasing noise intensity, and when the noise intensity reaches 0.12, the weighting error is still less than 1.4 × 10 −2 . It should be noted that it is difficult to reach this level of noise intensity in real situations [18], and the results in Figure 7b confirm that our model has a strong noise immunity. The performance of neural networks can also be affected by noise factors. We test on a dataset containing random noise to investigate the robustness of the model to noise. Each pixel value in the optical intensity profile image is multiplied by a factor f = 1 + N(0, 1)·σ to generate the noisy image dataset, where N(0, 1) is the standard normal distribution and σ is the noise intensity [18]. As shown in Figure 7a, the images of the optical intensity profile of the 9-mode superimposed LG beams propagated for 100 m become gradually blurred with increasing σ values, and we also have selected a local region of the image to show this change in more detail. As shown in Figure 7b, the error value of the model prediction and the slope of the curve increase with the increasing noise intensity, and when the noise intensity reaches 0.12, the weighting error is still less than 1.4 × 10 −2 . It should be noted that it is difficult to reach this level of noise intensity in real situations [18], and the results in Figure 7b confirm that our model has a strong noise immunity. The performance of neural networks can also be affected by noise factors. We test on a dataset containing random noise to investigate the robustness of the model to noise. Each pixel value in the optical intensity profile image is multiplied by a factor f = 1 + N 0, 1 ·σ to generate the noisy image dataset, where N(0, 1) is the standard normal distribution and σ is the noise intensity [18]. As shown in Figure 7a, the images of the optical intensity profile of the 9-mode superimposed LG beams propagated for 100 m become gradually blurred with increasing σ values, and we also have selected a local region of the image to show this change in more detail. As shown in Figure 7b, the error value of the model prediction and the slope of the curve increase with the increasing noise intensity, and when the noise intensity reaches 0.12, the weighting error is still less than 1.4 × 10 −2 . It should be noted that it is difficult to reach this level of noise intensity in real situations [18], and the results in Figure 7b confirm that our model has a strong noise immunity.
Conclusions
In summary, we propose a dual-output CNN mode analysis method that can quickly and accurately predict the mode weights and phase information of multimode LG beams simultaneously. The trained CNN can process a single input intensity image in less than 1 ms, and can also achieve more accurate predictions even for correlated unknown datasets and noise-disturbed samples. The performance of the model demonstrates that our method is accurate, robust and fast, which can reduce the burden of device level. In addition, our method may be applicable to the mode analysis of other OAM beams, such as the Bessel beam, which indicates that our method might be of general value to the practical application of OAM beams to optical communications. | 6,923 | 2021-05-24T00:00:00.000 | [
"Computer Science"
] |
Piezoelectric/Triboelectric Nanogenerators for Biomedical Applications
Bodily movements can be used to harvest electrical energy via nanogenerators and thereby enable self-powered healthcare devices. In this chapter, first we summarize the requirements of nanogenerators for the applications in biomedical fields. Then, the current applications of nanogenerators in the biomedical field are introduced, including self-powered sensors for monitoring body activities; pacemakers; cochlear implants; stimulators for cells, tissues, and the brain; and degradable electronics. Remaining challenges to be solved in this field and future development directions are then discussed, such as increasing output performance, further miniaturization, encapsulation, and improving stability. Finally, future outlooks for nanogenerators in healthcare electronics are reviewed.
Introduction
The ongoing development of nanogenerators in recent years has enabled the design of self-powered systems that can operate without external power supplies. Nanogenerators have the ability to harvest mechanical energy in different forms from a variety of sources, including human body motion and activities. This makes them particularly suitable for applications in the biomedical field. Nanogenerators can convert the tiny mechanical energy in body motion, muscle contraction/ relaxation, bone strain, and respiration into electrical energy [1][2][3][4]. The generated electrical energy can be used as a sustainable energy source for implantable biomedical devices, which would both reduce the volume of the powering unit and eliminate the need for battery replacement [5][6][7].
A great deal of work has been invested in the study of biomedical applications of nanogenerators, including self-powered sensors, pacemakers, and stimulators, and the results have shown that nanogenerators can be very promising in the biomedical field [8][9][10][11][12][13][14][15].
In this chapter, we first introduce the required characteristics of nanogenerator materials that can be used in the biomedical field. Generally, there are two main types of biomedical nanogenerators, piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG), which have different operating mechanisms. PENG are based on piezoelectric materials, such as polyvinylidene fluoride (PVDF) [8], poly(vinylidenefluoride-co-trifluoroethylene) [P(VDF-TrFE)] [9], BaTiO 3 (BTO) [10], ZnO [11], Pb(Zr x Ti 1−x )O 3 (PZT) [12], and (1 − x)Pb(Mg 1/3 Nb 2/3 )O 3 -xPbTiO 3 (PMN-PT) [13]. While TENG are based on triboelectric charges which are generated when dissimilar materials are in contact [14,15], their operating mechanism is a combination of tribo-electrification and electrostatic induction between the two contacted materials [14,15]. A broad range of materials exhibiting these effects can be selected, which make TENG ideal for biomedical applications. Besides the PENG and TENG nanogenerators, there are also other types of biomedical nanogenerators using biofuel cells (BFCs) or photovoltaics. BFCs transform chemical energy into electrical energy from molecules present in human body [16], which are very promising since there is >100 W of chemical energy in our body [17]. Flexible photovoltaic materials can meet the conformability requirements of e-skin, thus showing the possibility of solar-powered e-skin [18,19].
Next, we will provide some examples of important biomedical nanogenerator applications, including self-powered human activity sensors; pacemakers; cochlear implants; simulators for cells, tissues, and brain; and biodegradable electronics. After that, we will also discuss challenges and future outlooks for biomedical nanogenerators, including their miniaturization, stability, encapsulation, and output performance. We hope this book chapter will provide insight and inspiration to people who are interested in biomedical devices and nanogenerator development.
Nanogenerator materials for biomedical applications
Self-powered biomedical devices require nanogenerators that can directly harvest energy from their surroundings, in this case, from activities in the human body. This also requires the nanogenerators to have specific designs that respond to different mechanical stimuli with high sensitivity, since many bodily activities are subtle.
The materials used in biomedical nanogenerators should also be biocompatible. The primary conventional piezoelectric material is lead zirconate titanate (PZT). PZT has a high piezoelectric coefficient; however, the toxicity of Pb makes it unsuitable for application in the human body. Scientists have been searching for other materials in efforts to develop alternatives to lead-based nanogenerators. One of the emerging lead-free piezoelectric materials, 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 ) TiO 3 (BZT-BCT), has a piezoelectric coefficient comparable to PZT and also good biocompatibility, which makes it a promising candidate for applications in the biomedical field [10]. ZnO has also attracted great interest because of its favorable characteristics, which include piezoelectricity, biocompatibility, transparency, and large-area fabrication [11].
Finally, nanogenerators used in the biomedical field should have high sensitivity and efficiency because many bodily activities, such as respiration, heartbeat, muscle stretching, or blood circulation, are very gentle and render a small amplitude. Nanogenerators need high energy conversion efficiency and sufficient output power to be used in devices with comparable size [25][26][27]. Table 1.
Piezoelectric materials can be used for nanogenerators in biomedical field.
A cardiac sensor, used for heart-rate monitoring, is a critical component in personal healthcare management. Self-powered nanogenerators have been employed in self-powered cardiac sensors, as shown in Figure 1 [5]. Besides the merit of selfpowered, they are non-invasive, cost-effective and user-friendly. These implantable cardiac sensors can detect a number of arrhythmic symptoms and provide real-time feedback spontaneously [5]. Compared to current wearable heartbeat monitoring systems, the implantable cardiac sensors can provide both higher accuracy and greater reliability [39]. Self-powered wireless cardiac sensors have a great potential in the future heart healthcare monitoring market.
Physiological parameters such as respiration rate, blood pressure, and pulse rate are major concerns in clinical practice [40]. Failure to detect these signals timely can result in life-threatening conditions [40]. Scientists recently fabricated selfpowered TENG-based pressure sensors with a high sensitivity of 150 mV/Pa [41]. When the flexible pressure sensor was attached to the human body, respiration and pulse were accurately and spontaneously monitored [41]. The sensitivity, flexibility, and robustness of nanogenerators allow them to be used in wearable and wristbased pulse wave detectors [40][41][42][43].
Nanogenerators can be used in pacemakers
When a heart's natural pacemaker is not working properly, resulting in a heart-rate that may be too fast, too slow, or irregular, a doctor may implant a device called pacemaker to restore the heart's nature rhythm. Implantable battery-powered pacemakers, which use electrical impulses to stimulate the heart muscles and regulate heartbeat, have been in clinical use for more than 50 years [13,15]. Pacemakers have made significant contributions to the treatment of heart diseases such as sick sinus syndrome, heart blockage, and abnormal heart rate [13,15]. However, every 7-10 years, surgery is needed to replace the pacemaker battery [44,45]. Self-powered devices can prolong the pacemaker's operation and eliminate battery replacement surgery.
Both PENG and TENG have been investigated for cardiac pacemakers [46,47]. Generally, PENG are more robust and durable, but their outputs are relatively low.
Figure 1.
Illustration of heart-rate monitoring by a wireless self-powered cardiac sensor. Reprinted with permission from [5]. Copyright TENG materials show a higher output, but they need to be well encapsulated to prevent leakage. A schematic diagram of cardiac pacemaker without battery that can pace the porcine heart is shown in Figure 2 [31].
Nanogenerators can be used in cochlear implants
Cochlear implants are neural prosthetic devices that can restore a sense of hearing to people with hearing disability. Cochlear implants work by picking up sound using a microphone located externally above the pinna, and with an external processor, convert the microphone output into electrical pulses that are transmitted internally using a transmitter or receiver to finally stimulate the auditory neurons using an array of electrodes implanted in the cochlea [34]. The conceptual schematics of the cochlear and the basilar membrane are shown in Figure 3. However, current cochlear implants have limitations, because they require external components, which are inconvenient for patients. A totally implantable cochlear implant powered by a nanogenerator would address this issue [48]. Scientists have reported the fabrication and characterization of a prototype polyvinylidene fluoride polymer-based implantable microphone for detecting sound inside gerbil and human cochleae [34]. These results demonstrate the feasibility of the prototype devices as implantable microphones for the development of completely implantable cochlear implants. For patients, this will improve sound reception by utilizing the outer ear and will improve the use of cochlear implants. It should be noted that the development of nanogenerators in cochlear implants field is at the very early stage. They will need further design and innovation to achieve miniaturization, lowpower electronics, and an implantable microphone, before they meet the requirements of clinical applications.
Nanogenerators as stimulators for cells and tissues
Electrical signals play an instructive role in many cellular behaviors, including cell proliferation, differentiation and migration, and tissue wound healing and regeneration. Several examples and their required electrical fields are shown in Table 2.
Nanogenerators can provide electrical stimulation for cells and tissues [60][61][62][63]. A recent report shows that a self-powered well-aligned P(VDF-TrFE) piezoelectric nanofiber nanogenerator can be used as a piezoelectric stimulator for bone tissue engineering, as shown in Figure 4 [35]. The well-aligned piezoelectric P(VDF-TrFE) nanogenerators encouraged the MC3T3 cells to proliferate in vitro under a sustainable piezoelectric stimulus. This provides insights into the application of P(VDF-TrFE) piezoelectric nanofiber nanogenerators as a self-powered electrical stimulation system to assist tissue repair and regeneration.
Electrical muscle stimulation is clinically employed for rehabilitative and therapeutic purposes [60]. Figure 5 illustrates recent research using a stackedlayer triboelectric nanogenerator (TENG) through a flexible multiple-channel intramuscular electrode, which permitted electrical muscle stimulation [60]. Such a self-powered system could be potentially used for rehabilitative and therapeutic purposes to treat muscle function loss.
Nanogenerators have also been developed for skin wound healing. Scientists reported an efficient electrical bandage for accelerated skin wound healing [61]. From in vitro studies, they showed that accelerated skin wound healing could
Cells and tissues
Required electrical fields
Nanogenerators can be used in deep brain stimulators and neural stimulators
Deep brain stimulation is an effective treatment for a variety of neurological disorders, including Parkinson's disease, essential tremor, and epilepsy [64][65][66]. At present, it involves administering a train of pulses with constant frequency via electrodes implanted in the brain [67]. However, the implantable brain stimulator requires surgery to replace the battery every 3 to 5 years [68]. Self-powered deep brain stimulation is a future technology which does not need external power supply. Scientists have developed a flexible Pb(In 1/2 Nb 1/2 )O 3 -Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PIMNT) energy harvester that can be used in a self-powered deep brain stimulator [68]. More researches in this field open a new avenue for future deep brain stimulation using self-powered deep brain stimulator.
Modulation of neural signals using implantable bioelectronics is an emerging field in fields such as neuroprosthesis and bioelectronic medicine [69][70][71][72]. Triboelectric nanogenerators (TENGs) show a promising performance as a power source for neuro-stimulators. Recently, scientists have developed a novel water/ air-hybrid TENG that can be used for force-controlled direct stimulation [69]. In another research, scientists present an implanted vagus nerve stimulation system that is battery-free and can spontaneously respond to stomach movement [70]. These provide a concept in therapeutic technology using artificial nerve signal generated from coordinated body activities.
Nanogenerators as biodegradable electronics
Biodegradable electronics are quite a new scientific term but also an emerging area of research. The general goal is to create human-friendly electronics and enable the integration of electronic circuits with living tissue [73]. Biodegradable electronics, also called transient electronics, are built with degradable organic and inorganic materials, so that they can be integrated with living tissue and used for diagnostic and/or therapeutic purposes during certain physiological processes [74][75][76][77]. Once the therapeutic or diagnostic process is completed, the transient devices can be left behind in the body and will degrade and be absorbed gradually without any residue.
Reports show that a biodegradable triboelectric nanogenerator can degrade and be absorbed by the human body after completing its work cycle, so no operation is needed to remove them, leaving no long-term effects [76,77]. This demonstrates the potential of nanogenerators as a power source for transient medical devices.
Scientists have recently introduced a fully biodegradable nanogenerator based on gelatin film and electro-spun polylactic acid nanofiber membrane, which is fully Piezoelectric/Triboelectric Nanogenerators for Biomedical Applications DOI: http://dx.doi.org /10.5772/intechopen.90265 biodegradable in water [75]. The TENG are disposable and do not harm or pollute the environment.
In general, biodegradable triboelectric nanogenerators offer a promising green micro-power source for biomedical implants, by harvesting energy from body movements, and then dissolve with no adverse effect. The biodegradable medical device field is an emerging area, which shows a great potential for in vivo sensors and therapeutic devices.
Future development
The development of nanotechnologies can greatly advance healthcare systems. Nanogenerators can provide complementary or alternative power to traditional batteries in healthcare electronics. Autonomous biomedical devices might be realized with the development of nanogenerators, which will revolutionize the biomedical device and healthcare systems. We expect that autonomous self-powered biomedical systems with active sensing properties are the future development direction of medical devices.
Currently, the key challenges that need to be solved in the field of self-powered implantable medical devices are miniaturization, encapsulation, and stability. There is a strong demand for implantable medical devices with reduced size and weight, to minimize impact on daily activities and increase patient comfort. Also, TENG performance is greatly affected if moisture or liquid leaks into the device when applied in vivo. To avoid corrosion by body fluids, it will be necessary to develop durable and flexible encapsulation to protect the stability and working efficiency of TENG [5,6].
Future nanogenerator developments in this field are expected to address the following three aspects. Firstly, output performance and energy conversion efficiency should be increased to meet clinical requirements. Secondly, to be used in the human body, nanogenerators need to be highly flexible, sensitive, and durable. For example, many in vivo movements are gentle, and their amplitude is very small, so the nanogenerator must be sensitive enough to exploit small scale motion [7,14]. Thirdly, since the in vivo environment can be very complex and challenging, careful packaging is needed using biocompatible and soft materials.
In general, nanogenerators have many advantages, including high efficiency, low cost, light weight, and easy fabrication. Nanogenerators have an excellent potential for application in a variety of uses, to provide a sustainable power source for selfpowered biomedical electronics and healthcare monitoring systems. With further cutting-edge research and development in this field, a revolution in biomedical devices and healthcare system will be realized in the future.
Conclusion
In this chapter, we introduced typical nanogenerator materials that have been developed for biomedical applications. We summarized several examples of how nanogenerators can be used in the biomedical field. We included recent research on nanogenerators in self-powered pressure sensors; pacemakers; cochlear implants; stimulators for cells, tissues, and the brain; and biodegradable electronics. We also pointed out the challenges facing current research and future research directions for nanogenerators in medical devices. We hope this work provides insights and inspiration for future biomedical device and nanogenerator research.
Author details
Panpan Li, Jeongjae Ryu and Seungbum Hong* Department of Materials Science and Engineering, KAIST, Daejeon, Korea *Address all correspondence to: seungbum@kaist.ac.kr | 3,449.6 | 2019-12-27T00:00:00.000 | [
"Computer Science"
] |
The distribution of the ring current: Cluster observations
. Extending previous studies, a full-circle investigation of the ring current has been made using Cluster 4-spacecraft observations near perigee, at times when the Cluster array had relatively small separations and nearly regular tetrahedral configurations, and when the Dst index was greater than − 30 nT (non-storm conditions). These observations result in direct estimations of the near equatorial current density at all magnetic local times (MLT) for the first time and with sufficient accuracy, for the following observations. The results confirm that the ring current flows west-ward and show that the in situ average measured current density (sampled in the radial range accessed by Cluster ∼ 4– 4.5 R E ) is asymmetric in MLT, ranging from 9 to 27 nA m − 2 . The direction of current is shown to be very well ordered for the whole range of MLT. Both of these results are in line with previous studies on partial ring extent. The magnitude of the current density, however, reveals a distinct asymmetry: growing from 10 to 27 nA m − 2 as azimuth reduces from about 12:00 MLT to 03:00 and falling from 20 to 10 nA m − 2 less steadily as azimuth reduces from 24:00 to 12:00 MLT. This result has not been reported before and we suggest it could reflect a number of effects. Firstly, we argue it is consistent with the operation of region-2 field aligned-currents (FACs), which are expected to flow upward into the ring current around 09:00 MLT and downward out of the ring current around 14:00 MLT. Secondly, we note that it is also consistent with a possible asymmetry in the radial distribution profile of current density (resulting in higher peak at ∼ 4– 4.5 R E ). We note that part of the enhanced current could re-flect an increase in the mean AE activity (during the periods in which Cluster samples those MLT).
Introduction
The existence of the westward equatorial ring current around the Earth at geocentric distances of about 2-9 R E (R E is a mean Earth radius) was first suggested by Singer (1957).It is understood in terms of the gradient and curvature drifts of energetic particles (∼1 keV to a few hundreds of keV), trapped in the geomagnetic field.Le et al. (2004) examined the 20 years of magnetospheric magnetic field data from the ISEE, AMPTE/CCE, and Polar missions, and showed that there are two ring currents: an inner one flowing eastward at ∼3 R E , and the main westward ring current at ∼4-7 R E for all levels of geomagnetic disturbances.The ring current evolution is dependent on particle injections during geomagnetic activity and on loss mechanisms (Daglis et al., 1999).Because simultaneous magnetic field measurements at multiple, geometrically favorable positions were unavailable prior to Cluster, it had been impossible to obtain a precise idea about the current response to magnetospheric changes.Cluster (Escoubet et al., 2001) has provided us with a unique opportunity to directly survey the distribution of the ring current.
The Cluster mission is composed of an array of four spacecraft carrying identical payloads.The spacecraft were launched in pairs in July and August 2000 into similar elliptical, polar orbits, each with a perigee of ∼4 R E , an apogee of ∼19.6 R E , and identical orbital periods of 57 h.A typical orbital orientation with respect to the model field lines is shown in Fig. 1.Due to the Earth's orbital motion, Cluster's orbits precess in the solar-magnetospheric (SM) coordinate system, so that every year all magnetic local times (MLT) are covered.For the Cluster perigee crossings, 00:00, 06:00, 12:00, and 18:00 MLT are sampled, respectively, in February, May, August, and November (Escoubet et al., 2001).The spacecraft formed a tetrahedral configuration that evolved around each orbit.The orbits were adjusted approximately once every 6-12 months via a sequence of maneuvers to vary the spatial scales between 100 km and a few R E over the mission Published by Copernicus Publications on behalf of the European Geosciences Union.(Balogh et al., 2001).In-flight calibrations on FGM data routinely determine the maximum error in the data to within 0.1 nT.
The "Curlometer" technique has been developed to derive currents from four-point magnetic field measurements (Dunlop et al., 1988(Dunlop et al., , 2002;;Robert et al., 1998) based on Maxwell-Ampere's law where the second term on the right-hand side is negligible for a highly conducting plasma, and the measurement of ∇ × B assumes stationarity in the region of interest (i.e.assuming the field does not vary on the effective scales of the spacecraft motion).Moreover, this method assumes that all measurement points are situated inside or surround the same current sheet.This technique has been recently applied by Vallat et al. (2005), using Cluster 4-point magnetic field data to partially survey the ring current in the evening and postmidnight sectors.Their study was limited by the data taken during 2002, but suggested that the ring current can extend from −65 to 65 • in latitude all over the evening and postmidnight sectors about 9 h of MLT.The present paper ex-tends the study of Vallat et al. (2005) to survey the distributions of the ring current at all MLT and discuss the locations of the connecting region 2 field-aligned currents (FACs).
Observations from single pass
Figure 1 shows an example orbit of the Cluster spacecraft S/C1 between 05:00 and 18:00 UT on 6 February 2004.The plot shows the X-Z plane, in SM coordinates, and the configuration of all 4S/C (expanded by a factor of 80) at intervals along the track every 2 h.Geomagnetic field lines are drawn using the T96 model (Tsyganenko and Stern, 1996), for the average prevailing conditions: solar wind dynamic pressure, P dyn = 1.76 nPa, IMF B Y = −3.89nT, IMF B Z = −2.29 nT, and Dst = −18 nT.The spacecraft moved from the pre-midnight sector (21:00 MLT) and south of the magnetic equator through perigee at 11:31 UT (1.6 MLT) to the premidnight sector (13.8 MLT) and north of the equator.The spacecraft passed through or near the ring current near to perigee.The average separation between the four Cluster spacecraft was about 480 km and the configuration was a nearly regular tetrahedron.
Figure 2 shows an overview of the results from the curlometer technique (Dunlop et al., 2002) during this pass (09:31 to 11:31 UT). Figure 2 shows (a) the magnetic field magnitude, (b) the J X , J Y , J Z and J φ components of current density in cartestian and polar SM coordinates, (c) the ratio Div(B)/|Curl(B)|, and (d) the total current density; the horizontal axis is labelled by the MLT, latitude (LAT) and radial distance (R) of the spacecraft locations in SM coordinates, as well as the hours around perigee and UT.The approximate times of entry into and exit from the ring current region of Cluster 1 are highlighted by the grey region between the two red vertical dashed lines.These boundaries were determined by significant increase in the proton flux at higher energies (above ∼95 keV) observed by RAPID (Wilken et al., 2001) and by sharp decrease observed simultaneously by CODIF (Rème et al., 2001) at lower energy ranges (up to 40 keV) (data not shown).From Fig. 2, we find the 4 spacecraft observed almost the same magnetic field structures and that the results are stable within the marked region of the ring current encounter.The results using the curlometer technique are therefore reliable.From Fig. 2b, however, we find the three components of the current were highly variable during this pass before entry into and after exit from the ring current region, which Woodfield et al. (2007) and Zhang et al. (2010) explain in terms of the effect of the region-2 FACs.In the ring current, the current was stable, and J Z components in SM coordinates were near zero, J Y was mainly positive (duskward) with an average value of about 15 nA m −2 , and J X was mainly negative (tailward) with an average value of about 10 nA m −2 , while J φ was mainly negative (tailward) with an average value of about 20 nA m −2 (see Fig. 2b).These components show that the ring current lies mainly in the equatorial SM plane, directed duskward and tailward at 1.6 MLT.In Fig. 2c, the Div(B)/|Curl(B)| ratio is seen to have been highly variable and often >1 before entry into and after exit from the ring current region.Nevertheless, it was stable and mainly <0.5 (under red line) within the interval of the ring current.Dunlop et al. (1988) suggested that the ratio Div(B)/|Curl(B)| can provide a quality estimate for J calculated in place of the unknown error (J calculated − J real ) when the shape and orientation of the spacecraft configuration is regular tetrahedron, and the magnetic field structure is nearly isotropic within the tetrahedron.The use of Div(B) does not give a direct indication of the actual error in the current estimate and is less relevant for distorted tetrahedral configurations.Because a very long or a very flat tetrahedron (the elongation (E) or planarity (P) of the tetrahedron is greater than 0.9) will lead to an estimated error reaching 10 % and more, estimates of the absolute uncertainty in the calculation of curl B were also made (see discussion in Robert et al., 1998;Dunlop et al., 2002, andVallat et al., 2005).These authors showed that the curlometer results are reliable when the Div(B)/|Curl(B)| < 0.5, depending on the temporal sta-bility of the current.In fact, all values of the ring current above a few nA m −2 are, in principle, measurable by the curlometer and the use of Div(B)/|Curl(B)| is as a threshold indicator only.We use this as a criterion to select reliable results from all the passes in the year studied.The stability of the calculation can be independently tested by rotating the spacecraft order in the curlometer calculation, and we estimate the maximum error in |J | is below 20 % for the selected cases.The deduced |J |, ranged from 10 to 27 nA m −2 within the ring current, which is well above the measurable limit of the technique and in fact the results here depend only on J φ , which is the most accurate component of J .
Observations from one year of passes
Figure 3 shows the plots of the φ components of the current density in SM coordinates near Cluster perigee crossings between 14 July 2003(195/2003) and 27 April 2004(118/2004).Each vertical strip is a section of an orbitthe x-axis is the orbit number, y-axis is time relative to perigee, and the colour scale is the value of (a) J SM time between about −0.5 h before to 3 h after a perigee crossing (period A), while the ratio was mainly less than 0.5 between −1.2 and −0.5 h relative to perigee (period B) and was mainly near 1 between −3 and −1.2 h relative to perigee (period C).This is because the spacecraft were crossing region 2 and region 1 FACs and/or the cusp FACs in periods A and C, but traversing the central ring current region in period B. This confirms that the curlometer results are generally reliable in the ring current region, where the J φ component was almost always negative.These values show the expected westward ring current around the equator.The morphology of the ring current system suggested by Iijima et al. (1990) and Le et al. (2004) partially closes in the ionosphere via up and down Region 2 FACs, and the ring current can extend from −65 to 65 • in latitude all over the evening and post-midnight sectors (Vallat et al., 2005).Considering the effect of the FACs on the accuracy of the current calculations, we focus here on the results in the equatorial plane (−30 to 30 • ).
Using the criterion Div(B)/|Curl(B)| < 0.5 with a regular tetrahedron configuration, we selected all reliable results in the ring current when the Dst index is greater than −30 nT (more positive than −30 nT, i.e. non-storm conditions) and averaged them over 5-min intervals.The current vectors, shown in Fig. 4, are projected onto the XY plane in SM coordinates.The full azimuthal ring of current density observations at non-storm times has been obtained using almost a full year of data from 18 March 2002 to 14 June 2002 and from 14 July 2003 to 27 April 2004.Note, however, in Fig. 4 that there are a few white gaps due to missing data or lack of reliable data.In addition, the vectors are averaged over intervals of stable current vectors, which typically do not vary during each pass and therefore are only slightly dependent on the actual number of data points available.In fact, the fluctuation in current through the region during each pass is not significant compared to the general error in Curl(B).The basic error in Curl(B) is around 5-20 %, which is larger than the typical fluctuation in ring current values as most intervals within the ring current do not show large variations.Thus, the error arising from selection of a larger or smaller data interval is very small compared to the other uncertainties of measurement.In addition, we should note that the distribution of current vectors, in azimuth, is only a result of the Cluster spacecraft orbital constraints.
Discussion
Figure 4 shows the nearly full-circle distribution of the ring current for non-storm periods.The distribution is asymmetric, where the magnitudes are markedly enhanced between about 05:00 and 11:00 MLT and are reduced between about 12:00 and 17:00 MLT (between 17:00-24:00 MLT they are only slightly enhanced and appear to reduce again after 24:00 MLT along the nightside ring).This behaviour is notable since it is opposite to that reported by Jorgensen et al. (2004), for example, where the peak of the ring current occurs in the afternoon sector for quiet conditions.The current vectors between 05:00 and 11:00 MLT found here are, on average, a factor of 2 greater than in other sectors -a rather large asymmetry.We therefore investigate further what factors might drive the asymmetric distribution.These questions will be studied further in a later paper, but we attempt to show the plausible effects below.
Firstly, there may be a dependence on geomagnetic activity, which would also arise as a seasonal effect because the Cluster orbit samples MLT at different times of the year.Secondly, we note that Cluster samples the ring plane only in the radial range ∼4-4.5 R E , so that any adjustment of the radial profile of current density (which varies with MLT) is not well sampled.It is certainly possible that the higher current density seen here could result from a narrower density profile in that range of MLT.Other adjustments of the ring plane distribution could replicate these results without changing the overall, westward current in the ring.This scenario, however, does not itself suggest a source for the asymmetry.Thirdly, we can state that the asymmetry is at least consistent with a re-configuration of the whole current system into the polar ionosphere through connection to the region 2 FACs and ionospheric currents.Thus, the downward FACs (centered on 14:00 MLT) will naturally extract current (potentially) from the duskside ring plane, while the upward FACs (centered on 09:00 MLT) will deposit current into the dawnside ring plane.
In order to make a more quantitative investigation, we averaged the current density |J | and the corresponding Dst and AE indexes in every MLT one hour bin (or 15 • in XY plane) and we show comparisons of these parameters in Fig. 5. From Fig. 5a, we can confirm that the average magnitudes of the measured current density at the Cluster ring plane crossing (∼4-4.5 R E ) ranged from 9 to 27 nA m −2 , which are greater than the quiet time averages of ∼1-4 nA m −2 derived using the Parker equation from observed magnetic field and particle pressures (but then estimated over the range of L-values 2-9; see Lui andHamilton, 1992, andDe Michelis et al., 1999).This difference has been accounted for by Vallat et al. (2005) who also used the Curlometer technique, deriving similar values of 30 nA m −2 to our study.The profile of |J | also confirms that there is an enhancement in the morning sector and a dip in after noon.In fact, the trends shown there could be viewed as a steady growth of current density (from 10-27 nA m −2 ) in the MLT range from about 12:00-02:00 UT, and a less steady depletion of current density (from 20-10 nA m −2 ) in the MLT range from about 24:00-12:00 UT.The growth in current appears to increase at around 09:00 MLT and dips further at around 16:00 MLT.Furthermore, the dip in value between 00:00 and 02:00 MLT is also apparent.Although further work is needed to fully assess the effect of: (1) observation limitations in the observations (such as spatial gradient errors in the use of time series data); (2) the dependence of the current density values on Dst (generally regarded as a poor parameter), together with our use of non-storm (Dst >−30 nT) as opposed to quiet activity levels, and (3) the quality requirement Div(B)/|Curl(B)| < 0.5 which may reduce the averages slightly by removing the lowest J-values, we nevertheless feel a number of suggestions arise from the MLT trends revealed here.
For example, Fig. 5b and c, showing the average geomagnetic activities, confirm that the periods we investigated are under relatively quiet or non-storm conditions, although AE shows clearly enhanced activity between about 03:00 and 15:00 MLT and may account for part of the increased range of |J | values for those MLT values.This change in AE level however is a relatively small effect (changing from an average of about 200 nT to about 150 nT for other MLT), and we note that the variability seen in both AE and Dst from MLT bin to bin is large compared to the difference in mean values.Such variability is not reflected in the Cluster in situ sampling of |J | and therefore the current density does not respond significantly to changing activity.The trends in AE, furthermore, show a distinctly opposing effect to that seen in |J |: in the range 02:00-14:00 MLT, the running average of AE is slowly decreasing from 12:00 to 02:00 MLT while the current grows.Before 12:00 MLT, the correlation is less clear since AE shows little obvious trend here.Nevertheless, it is worth pointing out that the values in the MLT bins correspond to times of the year when Cluster samples that bin, hence the effect is also seasonal.The parameter Dst represents, in some sense, the overall current in the ring, although it is recognized that it is a rather poor parameter and contains effects from the time history of activity prior to its determination.The variability from bin to bin is large but the underlying trend does follow that of |J | from 12:00 to 06:00 MLT.The profile of Dst seen between 12:00 and 24:00 MLT is broken at about 18:00 MLT, however, and between 00:00 and 06:00 MLT the correlation is also poor.We therefore suggest that the trends seen in AE and Dst cannot fully account for the trends seen in the current density.As mentioned, it is possible that the asymmetry in |J | simply reflects an ordered change in the radial profile of the current density, so that the reduction in the local |J | at Cluster does not reflect a reduction in the total westward current flowing in the ring.However, we do not have an obvious mechanism to drive this change in mind, and in fact the enhanced current vectors are measured over the widest radial range for that range of MLT and suggest a well-defined, broad peak in |J |.Alternatively, we propose that the asymmetry is also consistent with the linkage to region 2 FACs, which map down to the ionospheric currents.For example, the growth of the current as it flows westward across the morning sector could indicate that the region 2 FACs, which are upward from the ionosphere, feed into the ring current around 09:00 MLT, and the decay as the current flows across the afternoon sector could reveal a downward FAC current around 14:00 MLT.These region 2 currents connect to Pedersen currents across the auroral oval (equatorward before noon and poleward afternoon) and are related to sunward Hall currents along the auroral oval (Untiedt and Baumjohann, 1993).Thus, the region 2 currents associated with the longitudinal gradients in the ring current are related to the auroral electrojets.The use of low Dst disturbance levels means that relatively nonstorm times have been studied here, so that the DP-2 current system will dominate.For the growth phase currents the region 2 FACs will be relatively close to noon (Cowley and Lockwood, 1992), consistent with the ring current decrease in Fig. 4 being localized relatively near noon.The question of how the whole current system resulting from the asymmetric ring current is configured in the polar ionosphere, and in particular the connection of the region 2 FACs with ionospheric currents, will be studied in a later paper.
Conclusions
We have investigated an almost full year of magnetic field data from the four Cluster spacecraft at times when they had small separations, nearly regular tetrahedral configurations, and Dst was greater than −30 nT (non-storm times).By using the multi-spacecraft curlometer technique, we have directly calculated the current distributions near the Cluster perigee crossings, confirming they have sufficient accuracy to obtain full-circle (all magnetic local times), unambiguous estimates of in situ ring current densities for the first time.The data are taken during non-storm periods, where all sam-ples reveal a westward current near the equator and indicate that this quiet time average westward ring current flow is asymmetric in magnetic local time (MLT) and has an average current density between 9 and 27 nA m −2 .
The direction of current is shown to be very well ordered for the whole range of MLT, in line with previous studies on partial ring extent (Vallat et al., 2005).The magnitude of the current density is sampled in the radial range accessed by Cluster (∼4-4.5 R E ), where the distinct asymmetry revealed grows from 10 to 27 nA m −2 as azimuth reduces from about 12:00 MLT to 03:00 MLT; and falls from 20 to 10 nA m −2 (less steadily than the growth) as azimuth reduces from 24:00 to 12:00 MLT.This result has not been reported before and we suggest it could reflect a number of effects.
Firstly, we argue it is at least consistent with the operation of region-2 field aligned-currents (FACs), which flow upward into the ring current around 09:00 MLT and downward out of the ring current around 14:00 MLT.This scenario, although unconfirmed and requires further study, does provide a possible mechanism driving the asymmetry, so that region 2 FACs connecting from the ring current into the auroral and ionospheric region help configure the current system in the polar ionosphere.In its favour, connectivity to the region 2 FAC system is one of the few mechanisms which can apparently achieve the particular asymmetry observed (particle injection and drift effects having the opposite contribution to the expected current).The scenario, however, assumes that the current density measured at the local crossings of Cluster does not substantially redistribute within the ring, as could be the case.
We therefore note that the effect is also consistent with an MLT asymmetry in the radial distribution profile of current density (which results in higher or lower peak values in MLT, centered on radial distances at ∼4-4.5 R E ), while maintaining the total flow of westward current.This second scenario, therefore, does not require connection via the FAC system.Nevertheless, it should be noted that inspection of Fig. 4 shows that the observed current densities which are enhanced are actually sampled from the widest radial range so that the peak in |J | in this range of MLT is actually broad and well defined.Moreover, this option does not provide an assumed mechanism to drive such a redistribution of current.Finally, it was noted in the Discussion (Sect.3) that part, but not all, of the current density enhancement could reflect an observed increase in the mean AE activity during the times when Cluster sampled those MLT, so that the effect is perhaps made more significant by changes in activity.54).The authors wish to express their gratitude to the UK research council NERC for funding this work through the GEOSPACE consortium, grant number NER/0/S/2003/00675.We thank the FGM Operations Team and FGM PI, E. A. Lucek for the data used.
Topical Editor R. Nakamura thanks three anonymous referees for their help in evaluating this paper.
Fig. 1 .
Fig. 1.Orbit plot in XZ plane in SM coordinates for night-side orientation orbit 555/556 on 6 February 2004.The orbit also shows the configuration of the Cluster spacecraft array as a tetrahedron (size scaled up by a factor of 80).Model geomagnetic field lines are drawn from the T96 model with the average inputting parameters: P dyn = 1.76 nPa, IMF B Y = −3.89nT, IMF B Z = −2.29 nT, and Dst = −18 nT.
φFig. 2 .
Fig. 2.An overview of the results calculated from 4 Cluster spacecraft technique (Curlometer) during 09:31 to 11:31 UT on 6 February 2004: (a) the magnitude of magnetic field observed by 4 Cluster spacecraft, (b) the J X , J Y , J Z and J φ components of current density in cartestian and polar SM coordinates, (c) Div(B)/|Curl(B)|, and (d) the total current density.
Figure3shows the plots of the φ components of the current density in SM coordinates near Cluster perigee crossings between14 July 2003 (195/2003) and 27 April 2004 (118/2004).Each vertical strip is a section of an orbitthe x-axis is the orbit number, y-axis is time relative to perigee, and the colour scale is the value of (a) J SM φ and (b) Div(B)/|Curl(B)|.From Fig.3b, we find that the ratio of Div(B)/|Curl(B)| was near or over 1 for most of the
Fig. 3 .
Fig. 3. Plots of φ components of the current density in SM coordinates around Cluster perigee crossings between 14 July 2003 (195/2003, orbit number 468) and 27 April 2004 (118/2004, orbit number 589).Each vertical strip is a section of an orbit -the x-axis is the orbit number, y-axis is time relative to perigee, and the colour scale is the value of J SM φ and Div(B)/|Curl(B)|, for the panels of (a) and (b), respectively.
Figure4shows the nearly full-circle distribution of the ring current for non-storm periods.The distribution is asymmetric, where the magnitudes are markedly enhanced between about 05:00 and 11:00 MLT and are reduced between about 12:00 and 17:00 MLT (between 17:00-24:00 MLT they are only slightly enhanced and appear to reduce again after 24:00 MLT along the nightside ring).This behaviour is notable since it is opposite to that reported byJorgensen et al. (2004), for example, where the peak of the ring current occurs in the afternoon sector for quiet conditions.The current vectors between 05:00 and 11:00 MLT found here are, on average, a factor of 2 greater than in other sectors -a rather large asymmetry.We therefore investigate further what factors might drive the asymmetric distribution.These questions will be studied further in a later paper, but we attempt to show the plausible effects below.Firstly, there may be a dependence on geomagnetic activity, which would also arise as a seasonal effect because the Cluster orbit samples MLT at different times of the year.Secondly, we note that Cluster samples the ring plane only in the radial range ∼4-4.5 R E , so that any adjustment of the radial profile of current density (which varies with MLT) is not well sampled.It is certainly possible that the higher current density seen here could result from a narrower density profile in that range of MLT.Other adjustments of the ring plane distribution could replicate these results without changing the overall, westward current in the ring.This scenario, however, does not itself suggest a source for the asymmetry.Thirdly, we can state that the asymmetry is at least consistent with a re-configuration of the whole current system into the polar ionosphere through connection to the region 2 FACs and ionospheric currents.Thus, the downward FACs (centered on 14:00 MLT) will naturally extract current (potentially) from the duskside ring plane, while the upward FACs (centered on 09:00 MLT) will deposit current into the dawnside ring plane.In order to make a more quantitative investigation, we averaged the current density |J | and the corresponding Dst and AE indexes in every MLT one hour bin (or 15 • in XY plane) and we show comparisons of these parameters in Fig.5. Figure 5a, b, and c shows the MLT distributions of the one hour average current density |J | (from Fig. 4) and the corresponding MLT distributions of the Dst and AE indices, respectively.Figure 5d, e, and f shows scatter plots of the average current density |J | against the Dst and AE indices, and AE against Dst, respectively.The red lines present linear fitted lines of the scatter points, but are primarily shown here to guide the eye, since the trends are not simply linear.From Fig.5a, we can confirm that the average magnitudes of the measured current density at the Cluster ring plane crossing (∼4-4.5 R E ) ranged from 9 to 27 nA m −2 , which are greater than the quiet time averages of ∼1-4 nA m −2 derived using the Parker equation from observed magnetic field
Fig. 5 .
Fig. 5.The MLT distributions of the one hour average current density |J | (from Fig. 4) and the corresponding MLT distributions of the Dst and AE indices, respectively, together with the scatter plots of this average current density |J | against Dst and AE indices, and AE against Dst, respectively. | 6,916 | 2011-09-28T00:00:00.000 | [
"Physics"
] |
Micro-FTIR and EPMA Characterisation of Charoite from Murun Massif ( Russia )
Combined micro-Fourier transform infrared (micro-FTIR) and electron probe microanalyses (EPMA) were performed on a single crystal of charoite fromMurun Massif (Russia) in order to get a deeper insight into the vibrational features of crystals with complex structure and chemistry. The micro-FTIR study of a single crystal of charoite was collected in the 6000–400 cm at room temperature and after heating at 100°C. The structural complexity of this mineral is reflected by its infrared spectrum. The analysis revealed a prominent absorption in the OH stretching region as a consequence of band overlapping due to a combination of H2O and OH stretching vibrations. Several overtones of the O-H and Si-O stretching vibration bands were observed at about 4440 and 4080 cm such as absorption possibly due to the organic matter at about 3000–2800 cm. No significant change due to the loss of adsorbed water was observed in the spectrum obtained after heating. The occurrence of well-resolved water bending vibration bands at about 1595 and 1667 cm accounts for more than one structural water molecule as expected by charoite-90 polytype structure model from literature. The chemical composition of the studied crystal is close to the literature one.
Introduction
Charoite, K 5 Ca 8 Si 18 O 46 (OH)•nH 2 O, is a high-valued semiprecious gemstone occurring uniquely in the alkaline rocks of the Murun Massif in Yakutiya, Russia [1].This rare silicate belongs to the group of the microporous minerals whose structure is based on a mixed tetrahedral-octahedral framework.The increased interest in these compounds came from their potential use as materials alternative to zeolites in different fields, from environmental protection to industrial applications.
The structure of charoite was only recently solved ex novo and "ab initio" in the P2 1 /m space group by electron diffraction data [2,3].Two-ordered polytypes with different cell parameters (i.e., the monoclinic "charoite-96" and orthorhombic "charoite-90") were identified inside the sample along with other partially ordered and disordered polytypes (i.e., "charoite-2a" and "charoite-d").The structure of both charoite-96 and charoite-90 polytypes consists of three different tubular drier silicate chains running along [001] having external and internal diameters comparable to those of carbon nanotubes.The silicate chains are bonded by their apical oxygen to bands of edge-sharing Ca-and Naoctahedra which also run along [001] forming cavities occupied by extraframework cations (K, Sr, Ba, and Mn) and H 2 O molecules.In the charoite-96, adjacent blocks of the three different chains are shifted by a translation of 1/2c whereas no shift occur in the charoite-90 polytype.The asymmetric unit of the charoite-90 contains 90 independent atomic positions among which three H 2 O sites and one OH site partially occupied by OH − and F − ions [2].For the monoclinic polytype, 87 atomic positions enclosing only one H 2 O and one OH/F sites were refined [3].A thermal study of charoite revealed that the dehydration (i.e., loss of adsorbed and structural H 2 O) occurs in the temperature range 80-290 °C whereas the dehydroxylation processes are observed from 290 to 480 °C [4].
Apart from the detailed structural characterization, few and incomplete spectroscopic data of charoite have been reported so far in the literature.The RRUFF database contains Raman spectra collected in the range ~1200-100 cm −1 whereas Buzatu and Buzgar [5] provided a wider range of Raman signal (from ~4000 to 200 cm −1 ).However, the last spectrum is characterized by strong fluorescence and noisy background which allow in distinguishing weak peaks mainly in the Si-O and Ca-O vibration region and in the position of N-H vibration (2367, 2403 cm −1 ) due to NH 4 + ions which probably substitutes for K + in the charoite structure.No signal was observed in the OH stretching region.The infrared spectrum of charoite was reported in comparison to that of canasite in [6].The authors observe absorption in the OH stretching and bending regions which accounts for the presence of water molecules in the charoite structure.However, no discussion or defined band assignment was reported being the structure of this mineral not yet clear at the time.
In the present study, micro-FTIR and EPMA were performed on the same single crystal in order to define the characteristic vibrational modes of charoite in the light of the recent structural determination of the mineral.
It is noteworthy that advances in the crystal chemical features of this mineral can help to determine their potential properties such as strengthening glass ceramics, ion exchange, and conductivity [7].
The crystal studied here was handpicked under a binocular microscope starting from a fragment of asbestoslike fibers elongated along the z-axis.The selected crystal is characterized by light-violet colour and 0.10 × 0.05 × 0.02 (mm 3 ) dimensions.
Infrared analysis of charoite was performed at room temperature before and after heating the sample at 100 °C for 4 hours.The measurements were performed on a single crystal mounted on glass capillary and laid on the cleavage plane.The spectra were collected over the range 6000-400 cm −1 using a Nicolet Avatar FTIR spectrometer with a nominal resolution of 4 cm −1 , equipped with a Continuum microscope, an MCT nitrogen-cooled detector, and a KBr beamsplitter.The observed IR-patterns resulted from the average of 128 scans.The OH stretching region (3700-3000 cm −1 ) of the spectrum was modelled using the program Origin Lab, assuming Gaussian functions to describe the peaks and a linear function to approximate the background.
Results and Discussion
The mean weight oxides (wt%) from EPMA analysis are SiO 2 , 56. [2], except for the low amount of Sr and the enrichment in Mg and Mn of the study crystal.
The typical single-crystal nonpolarized room temperature micro-FTIR spectrum of charoite in the 6000-400 cm −1 range is shown in Figure 1.The spectrum collected after heating at 100 °C is illustrated in Figures 2(a) and 2(b) together with that before heating.The results of fitting of the OH stretching region of the room temperature spectrum are displayed in Figure 3 whereas the main band positions observed for the studied sample are given in Table 1.The table also contains the literature infrared and Raman data of charoite.
The spectrum in Figure 1 evidences very strong absorption bands at about 600-800 cm −1 and ~1100 cm −1 which are assigned to stretching vibration of Si-O and Ca-O bonds [5,10].The group of bands in the 2077-1841 cm −1 range are due to the combination of Si-O absorption [11].The most notable feature of the spectrum in Figure 1 is a very broad and intense absorption extending from ~3650 to 3000 cm −1 , peaking at ~3450 cm −1 , as a result of internal stretching vibration modes (ν3 and ν1) and bending overtone (2ν2) of water molecules overlapping with absorptions of OH groups [12].The ν2 bending modes are centered essentially at about 1595 and 1667 cm −1 (Figure 2(b), Table 1).These findings together with the absence of significant changes in the OH absorption of the heated sample (compare spectra in Figures 2(a) and 2(b)) suggest that the spectrum of the studied charoite is not affected by the contribution of the adsorbed water.In addition, the two wellresolved bands in the water bending region of the spectrum in Figure 2(b) indicate that more than one water environment occurs in the charoite structure.This feature suggests that the analysed crystal consists essentially of the orthorhombic charoite-90 polytype which contains three H 2 O sites, that is, H 2 O(1), H 2 O(2), and H 2 O(3), other than one OH site [2].The OH stretching region of the acquired spectrum was fitted to the smallest number of peaks needed for an accurate description of the spectral profile.The 3)-O ≥ 2.99 Å.Generally, the shorter the H 2 O-O distance, the stronger is the hydrogen bond and the lower is the frequency of the O-H stretching band [13].Therefore, basing on the above crystal chemical considerations, the stretching vibrations of H 2 O(1), H 2 O(3), and H 2 O(2) molecules mainly contribute to the bands at 3285, 3385, and 3453 cm −1 , respectively.In addition, the band at 3530 cm −1 can be attributed to stretching vibrations of the OH groups which substitute for the O 2− at the vertex of the Na-octahedra in the charoite-90 polytype.Finally, the band at 3150 cm −1 well corresponds to the expected position of the Fermi resonance-enhanced overtone of the H 2 O bending mode.This approach in the OH-band assignment of the FTIR spectra of minerals is well consolidated in the literature [14][15][16].
The obtained micro-FTIR data slightly differ from those previously found for the powder charoite [6].Indeed, the authors identified, without a fitting analysis, four OH absorptions shifted to higher wavenumbers with respect to those here reported (see Table 1).In addition, absorption essentially centered at about 1620 cm −1 was generally related to water molecules with different energy [6].This discrepancy may be ascribed to the structural complexity of this mineral which leads to the crystallization of different polytypes sometimes intimately intergrown [2].
Note also that the spectrum in Figure 1 evidences a broad band at about 3000-2800 cm −1 and weak bands at about 1500-1450 cm −1 which are typical of the C-H vibrations [17] and may be ascribed to organic matter associated to the charoite fibers.In addition, two distinctive bands appear at about 4440 and 4080 cm −1 whose assignment is unclear.However, their frequency is very close to the combination modes involving the fundamental stretching of OH group coupled with the Si-O stretching.
As stated above, charoite crystallized jointly with other Ca-bearing alkaline minerals whose framework is based on Table 1: Band position (cm −1 ) and assignment for the bands in the infrared spectrum of the studied (see Figures 1 and 3) and literature charoite.
This study
Infrared data [6] Raman data [5] Band assignment , the hydrogen occurs only as hydroxyl groups.This is also confirmed by the absorption at ~3600 cm −1 and no signal in the water bending region of literature infrared spectra [6,19].Therefore, the application of the infrared spectroscopy, other than techniques such as X-ray or electron diffraction and microprobe analysis, contributes to discriminate associated minerals with similar structural framework but with different H speciation.
Conclusions
Micro-FTIR analysis was performed on a single crystal of charoite whose composition is similar to that reported in the literature [2].This study contributes in updating the infrared spectroscopic database of a complex mineral specimen such as the one at hand.Indeed, the data here reported complement those previously obtained on powder charoite [6].Decomposition of the hydrogen stretching region in several bands and analysis of the absorptions at low wavenumbers support the occurrence of OH groups and more than one H 2 O molecules in the charoite structure as recently found by electron diffraction of charoite-90 polytype [2].The not straightforward assignment of the overlapping bands in the OH stretching region is due to a combination of the O-H stretching vibration modes of hydroxyl groups and water molecules.This also reflects the structural complexity of this mineral which may crystallize in different polytypes with different hydrogen content and speciation [2].
Figure 2 :
Figure 2: Micro-FTIR spectra of Figure 1 (red line) compared with that on the same crystal after heating at 100 °C (green line) in the 4800-2400 cm −1 region (a) and in the 2400-1400 cm −1 range (b).
Figure 3 :
Figure 3: Decomposition of the hydrogen stretching modes region for the unheated charoite.
The chemical formula calculated on the basis of 45 oxygen atoms is (K 3.31 Sr 0.02 Ba 0.02 Mn 0.31 Mg 0.24 Fe 0.07 ) Σ = 3.97 (Ca 7.13 Na 1.10 ) Σ = 8.23 (Si 17.99 O 45 )F 0.10 , compatible with the ideal chemical formula K 5 Ca 8 Si 18 O 46 (OH)•nH 2 O.It is also in good agreement with that published in [18] Ca 5 [Si 12 O 30 ](OH)F 3 •H 2 O, and the miserite, K 3 Ca 10 (Ca,M 3+ ) 2 [Si 12 O 30 ][Si 2 O 7 ] 2 (O,F,OH) 2 •H 2 O, are characterized by the simultaneous presence of the H 2 O molecule and OH groups in the structure.Accordingly, the FTIR spectrum of frankamenite (registered with the R060100.1 code in the RRUFF database) and that of the miserite[18]exhibits two peaks centered at ~3600 and 3500 cm −1 which are, respectively, due to stretching vibrations of OH − groups and H 2 O molecules and a unique peak at the characteristic position (~1600 cm −1 ) of the water bending vibration.On the contrary, in canasite, K 3 Na 3 Ca 5 [Si 12 O 30 ](OH,O) 2.5 F 1.5 , and tokkoite, K 2 Ca 4 [Si 7 O 18 (OH)](F,OH) | 2,948.4 | 2018-04-03T00:00:00.000 | [
"Materials Science"
] |
Uncertainty analysis of the Limits to Growth model: sensitivity is high, but trends are stable
Since publication, the Limits to Growth model has received both praise and criticism. One criticism is the model’s sensitivity to input error. We have performed an uncertainty analysis to see in retrospect if the model’s sensitivity was of concern. The results show
that standard deviations of output variables are high. The general trends of the variables, however, are predictable, with very similarly shaped trend lines. Trajectories indicating a favourable future for humankind (i. e., without a severe decline in population and resources) stay in areas
of low probability.Uncertainty analysis is an important step in determining the reliability of a model. Models which are used to determine policies or guide decisions must be reliable to ensure sound choices are made. The Limits to Growth model by Donella Meadows and colleagues
was one of the first computer models to investigate global issues of population growth and resource constraints. The model received much attention and criticism, sometimes being accused of being too sensitive to variations in input parameters. This paper studies the model’s sensitivity
to input error through an uncertainty analysis, and examines if this sort of analysis could have affected the debate surrounding the model’s reliability and usefulness. Results showed that given the data used to calibrate the model, the output was susceptible to large variations, with
the population variable returning a normalised standard deviation of 0.43. However, despite input error, the trends of the variables remain predictable.
orrester's World Dynamics Model, which formed the basis of the book The Limits to Growth (Meadows et al. 1972), has received significant attention since the book's publication in 1972. The model is often referred to as the World3 (W3) model. It combines concepts from demography, economics, agriculture, and technology (Austin and Cottler 1977). The model's primary objective is to analyse the dynamic relationships between the world's human population and its resources; where the definition of resources is extended to man-made capital (Meadows et al. 1974). The model can be broken up into five main sections: population, capital, agriculture, resources, and pollution. All sectors are connected via feedback loops, making the model dynamic over time.
The purpose of the model was to provide qualitative projections of the dynamic behaviour of the world system. This was due to the unpredictable nature of social systems and also the lack of data available to researchers at the time. It was argued that while imprecise, the knowledge gained about the behaviour of the system would still be valuable for policy makers. The authors thought that decisions regarding population control, energy consumption, and investment in new technologies would be better informed by studying the model (Meadows et al. 1974).
The parameters in the model were derived by the collection and analysis of a large amount of published data. The details of the data, data analysis process, and justifications for parameter selection were published in a technical book (Meadows et al. 1974).
The results of the model showed current trends of that time would, if continued, lead to a sharp deterioration of the global community's welfare. Resources would become scarce and the carrying capacity would decline, in turn bringing about a sharp reduction in the human population (Meadows et al. 1972). This scenario was dubbed the standard run or business as usual.
To test the behaviour of the model, parameters were changed to simulate different social behaviours, resource conditions, environmental policies, or technological advances. A set of ten scenarios were presented in the book The Limits to Growth (Meadows et al. 1972). While each change in parameters resulted in different output, the general shape of trajectories and outcomes closely resembled the standard run, unless drastic changes were made in a specific manner. An example of a drastic change was to limit the number of children per family to two and to set capital investment to equal capital depreciation.
The W3 model underwent severe scrutiny when it was first published. Bardi describes the reaction to the study as "harsh", with many critics dismissing it as flawed (Bardi 2011). The debate surrounding the model quickly turned from scientific to political (Schoijet 1999). Often the sensitivity of the model would be used as an argument as small changes to parameters could produce large changes in the output (Trainer 1999).
Several sensitivity analysis (SA) studies were performed on the W3 model. The team that developed the W3 model intuitively tested it by manually varying parameters and inspecting the changes in output. Many other researchers conducted their own tests in the same fashion as Meadows' team (see Castro 2012 and references therein). The contributions of Austin and Cottler (1977), Vermeulen andde Jongh (1976) andde Jongh (1978) are clear examples of methodical local SA.
Vermeulen and de Jongh showed that small changes in parameters could produce drastic differences to the output of the W3 model (Vermeulen and de Jongh 1976). Their study noted that "[b]y changing three parameters by 10 % each in 1975 the world population collapse predicted by the model is averted", thus questioning the relevance of the W3 results. Austin and Cottler argued that if a model is sensitive to small variations in input parameters then the output is of "little practical interest" (Austin and Cottler 1977, p. 16).
In their study, Vermeulen and de Jongh found that some of the most sensitive parameters were industrial capital output ratio (ICOR), average lifetime of industrial capital (ALIC), fraction of industrial output allocated to consumption (FIOAC), life expectancy normal (LEN), reproduction lifetime (RL), desired complete family size normal (DCFSN), land yield factor (LYF), land fraction harvested (LFH), and inherent land fertility (ILF) (Vermeulen and de Jongh 1976, p.30). This was achieved through local SA, that is, altering each parameter by a small percentage and comparing the percentage change of the main variables. These parameters were then changed in combination and by ten percent to examine plausible responses of the model. Figure 1 shows some of the results obtained by Vermeulen and de Jongh (1976).
The study by Austin and Cottler again employed local SA to determine the sensitive parameters (Austin and Cottler 1977). Once identified, the authors discussed the potential of these parameters being changed in the real world to help alleviate problems. More recent work along these lines has been performed by Danós et al. (2017).
While the results of previous studies provided increased insights into the accuracy and behaviour of the model, the model's true uncertainty was largely unknown. An uncertainty analysis was required to fully understand the model's susceptibility to input error.
A very common method for testing the uncertainty of a model is via Monte-Carlo analysis. With this method Probability Density Functions (PDFs) are established for each model input. The model is executed a large number of times. Each time the input PDFs are sampled and the outputs recorded. Once all executions are completed, statistical measurement regarding uncertainty can be derived by analysing the recorded outputs (Saltelli et al. 2008).
A literature review, along with comments by Turner (2013), indicates that to date, an uncertainty analysis has not been performed on the W3 model. The purpose of this study is to undertake an uncertainty analysis of the W3 model to better determine its sensitivity to input error, and investigate the validity of past concerns about the model's sensitivity. In addition, we compare predicted with realised trajectories of output variables from 1970 to 2016.
Aim
The aim of this research is to evaluate the uncertainty of the W3 model due to input error. The results allow us to examine what could have been learnt by Meadows and his team in 1970 if the an alysis had been conducted at that time. A major criticism of the model is its sensitivity to input changes, and so an uncertainty analysis is useful to inform modellers and users about the inherent characteristics of the model.
Method
The W3 model was taken from the book Dynamics of Growth in a Finite World (Meadows et al. 1974, p. 549). The code was rewritten in the C++ language. To ensure the model was replicated correctly the standard run was executed and then plotted against the pub- Vermeulen and de Jongh (1976). Std run: standard run. Edit A: industrial capital out put ratio (ICOR) and fraction of industrial output allocated to consumption (FIOAC) increased by ten percent, and average lifetime of indus trial capital (ALIC) decreased by ten percent. Edit B: edit A plus reproductive life time (RL) decreased by ten percent, and desired complete family size normal(DCFSN)increased by ten percent. Edit C: ICOR and FIOAC decreased by ten percent, and ALIC increased by ten percent. Edit D: edit C plus RL increased by ten percent, and DCFSN decreased by ten percent.
lished results for the standard run (Meadows et al. 1974, p. 501) as shown in figure 2. The error between the results can be attributed to the low quality of the published plots and extracting this data to a digital form. From this plot it was concluded that the model had been reproduced to an acceptable standard for the purpose of this paper.
A Monte-Carlo analysis was used to conduct the uncertainty analysis. The input Probability Density Functions (PDFs) were sampled and passed to the model one million times. Independence is assumed between all parameters. The PDFs for the W3 model parameters were derived through analysis of the data presented in Meadows et al. (1974), as this data was used to calibrate the model. Where possible, the standard deviation was calculated using the same data. However, some of the data needed to calibrate the W3 model were sparse or not available, making it difficult to calculate the standard deviation. For these cases a reasonable standard deviation was assigned to the parameter. 1 For parameters based on intuition or a single source of data, a SD of 15 to 20 percent of the parameter's nominal value would be allocated. Parameters based on two or three sources received ten to 15 percent, and those based on three or more sources received five to ten percent.
An important aspect of the W3 model is its inclusion of some parameters as table functions, that is, a parameter depends on an input variable according to a predefined static transfer function. Tables are defined by a set of points on a graph. Linear interpolation is used to determine intermediate points. This presents a major challenge when conducting a global SA, as the tables need to be easily changed with each simulation run.
For tabular parameters, the standard deviation SD data was calculated according to equation 1, given a set of data points .
2
Here, n equals the number of data points, λ is the shape factor which we set to λ = , and . 2 W i represents a weighting factor for each data point. The further away a data point is from , the smaller its effect on the standard devia tion at . T is a table function. Figure 3 shows the calculated standard deviation for the table input ISOPC (indicated service output per capita).
To assign a PDF to a table parameter, a set of standard deviations was created. The th element of SD corresponds to the th element of . Figure 4 (p. 278) shows a hypothetical table parameter with standard deviation of . The four bell shaped curves show the probability density functions assigned to each element of . In this example, we can see that increases with , and the standard deviation assigned to this table parameter also increases with .
To sample the table parameter's PDFs and generate a new table T M , a number P is chosen at random from the set (0,1). If the ith element of T is then the i th element of T M is defined as,
3
where C is the cumulative distribution function such that with μ and σ denoting mean and standard deviation.
An example T M was generated with P = 0.2. The new table has been plotted in figure 4 as a dashed line. We can see that this new table is similar to the original table T but has been shifted slightly. As x increases, T M deviates from T due to the increasing SD. For every simulation, a new T M is generated.
1 The assigned standard deviation of these parameters is essentially arbitrary.
The values were chosen to ensure variation in parameters without changing them by orders of magnitude. To illustrate the quandary, imagine trying to assign a standard deviation to a set of data containing only one data point. 2 A value of λ = signifies that a variable xd from x will contribute of its value compared to a variable at x. This relationship is exponential with respect to x. The weighting function is an attempt to mathematically describe the SD of the table function. It is not meant to be a rigorous formulation. In the future we want to apply a suitable confidence measure for these generalized additive models. where N represents the number of data samples taken. Table 1 of the online supplement 3 summarises the standard deviations assigned to each parameter as a percentage of its nominal value. For W3 table parameters the average standard deviation is reported. The data for each parameter can be found at the indicated page number in Meadows et al. (1974).
Results
In this section the data from the study will be presented in a variety of methods, that is, trajectory lines, probability density maps, probability density functions, percentiles and averages, and standard deviations. Each method allows for a different understanding of the results.
The main variables examined from the W3 model were human population (POP), fraction remaining of non-renewable re sources (NRFR), industrial output per capita (IOPC), food per capita (FPC), crude birth rate (CBR), crude death rate (CDR), and persistent pollution normalised with 1970 levels (PPOLX). The units for these variables respectively are: people, unit-less, US dollars per person-year, vegetable equivalent kilograms per person, births per 1,000 people-year, deaths per 1,000 people-year, and unit-less. These variables will be the main focus of this paper.
Trajectories
Firstly, to understand the variety of outcomes, the trajectories of 100 simulations were plotted. This allows for a clear representation of how each trajectory behaves. This is limited to a relatively small number of examples (100 of the 1 million simulations) as the plot becomes overly crowded with the addition of more simulation trajectories. Figure 5 shows the trajectory of the population in the W3 model for 100 runs. It can be seen that there is a wide variation of trajectories, however most follow a similar path to that of the standard run shown by the light green line. Some trajectories manage to reach a maximum in excess of ten billion people. This is approximately three billion greater than the maximum of the standard run. Some trajectories have very high values in the year 2100 compared to the standard run.
One trajectory stays low for all years, showing how the model can react dramatically for some permutations of the variables. Two trajectories can be seen very slowly increasing for the whole simulation. This would indicate a favourable and stable future for humanity for the period simulated, that is, no sudden fall in population in the 21 st century.
Probability density maps
To overcome the crowding of the trajectory plot, probability density maps were produced. These maps show the concentration of trajectory lines, thus darker areas of the plot represent a higher probability of a trajectory passing through that point. Figure 6a shows the probability density of the population variable. Areas with a high density of lines in figure 5 correlate to darker areas in figure 6a. We can see that the probability is spread thinly after the year 2010, denoted by the lightening of the grey. The probability remains thinly spread for the first half of the 21 st century. We can note a slight converging after about 2070, denoted by a darkening, as most trajectories fall to lower levels of around 3.5 billion. Figure 6b shows the probability density of the fraction remaining of non-renewable resources. It is evident that the probability is spread thinly between the years 2000 and 2030. There is a clear increase in probability density for the final decades of the simulation for low values of remaining resources. The darker regions follow the trajectory of the standard run denoted by the thin black line.
Probability density functions
To better understand the evolution of the probability density it was plotted for individual times, that is, the initial year (1970) and each subsequent 20 years. Figure 7a (p. 280) shows the progression of the population's PDF over time. We can see a very concentrated PDF in 1970. This is representative of the limited uncertainty of world population data which was used to initialise the model. The PDFs widen with time, accompanied by a fall in peak probability.
The year 2050 has the lowest peak and greatest distribution of probability, indicating the least amount of certainty for the population.
The certainty of the model begins to increase after 2050, as evidenced by the peak probability increasing. The PDFs maintain a log normal shape for all times. The peak of each PDF loosely follows the standard run indicated by the vertical dashed line. Figure 7b (p. 280) shows the PDF over time for the fraction remaining of non-renewable resources variable. Again, there is a concentrat ed PDF for the year 1970. As the model progresses through time, the certainty quickly diminishes with the lowest peak in 2010. The peak begins to increase after 2010 as the fraction remaining of non-renewable resources variable converges to a low of around 0.2. Again the peak of the PDF follows the standard run.
Percentiles
A useful approach to analyse the results is with the use of percentiles. The percentiles chosen were the 5 th , 25 th , 50 th (median), 75 th , and 95 th . By examining the 5 th and 95 th percentiles the range of 90 percent of all trajectories can be easily determined. Likewise, the 25 th and 75 th percentiles show the range of 50 percent of all trajectories. Figure 8 (p. 281) shows the percentiles, mean, and standard run for six of the main variables of the W3 model. In plot A we can observe that the percentile lines begin very close together, showing a high certainty of the population value. The percentile lines then "fan out" as time progresses, indicating the increasing uncertainty. The lines reach a wide spread by 2030. At this point, 50 percent of all trajectories are contained within a range of 1.5 billion, 90 percent within 3.76 billion. Beyond 2030 the percentile lines remain approximately the same width apart. The percentile lines retain a similar trajectory to that of the standard run. For the other variables we see that the percentile lines also follow similar paths to that of the standard run.
Fraction remaining of non-renewable resources, and industrial output per capita undergo an increase in uncertainty in the early decades, followed by a convergence in later years. The increase in certainty comes from the fall of non-renewable resources and collapse in industrial output towards their limit of zero. The Sene ca effect is clearly visible in the industrial output per capita variable. 4 The variables food per capita, crude birth rate, and crude death rate have a more consistent level of uncertainty as indicated by the consistent spacing between percentile lines.
It is interesting to note the average and median of the population remains well below the standard run. This suggests that pop ulation is more likely to stay at a level lower than that of the standard run.
Real world data will be discussed in section Real world comparison.
Standard deviation
To fully understand how the uncertainty of the output varies with time, the standard deviations (SDs) of the PDFs at each time interval were calculated. Figure 9 (p. 282) shows the SDs of the population (a), fraction remaining of non-renewable resources (b), and industrial output per capita (c). We can see that the population's SD gradually increases to a maximum of approximately 1.5 billion. This is a large value considering that the maximum population of the standard run is approximately 7 billion. It begins to decrease after 2070 as most trajectories fall to lower values.
The SD of the fraction remaining of non-renewable resource quickly climbs to a maximum of 0.18 by 2020. This is a large SD given the variable's bounds of 0 to 1. After 2020, the SD begins to fall as trajectories converge as seen in figure 8 b. Industrial output per capita likewise has a very sharp increase in SD. It peaks at around 110 dollars per person in the year 2000, a significant value compared to the 300 dollars per person of the standard run at the same point in time. Figure 10 (p. 282) shows the SD of each variable normalised by its 1970 standard run value. The normalised SDs of the variables food per capita, fraction remaining of non-renewable resources, and crude birth rate remain reasonably steady for the years sim-> 3 The supplementary material is available at www.oekom. de/publikationen/zeitschriften/gaia/supplementary-material/c-157. 4 The "Seneca effect" is a description proposed by Bardi (2017) for sudden and unexpected collapse. The term is named after the Roman philosopher Seneca who wrote "Increases are of sluggish growth, but the way to ruin is rapid" (Lucius Annaeus Seneca, Moral Letters to Lucilius, XCI, 6). ing of non-renewable resources data were taken from the British Petroleum Energy Outlook (BP 2018). An exponential function was fitted to the data to estimate missing data (i. e., pre-1970 records). An upper (150 zettajoule) and lower (60 zettajoule) estimate for total fuel reserves was used to mimic Turner's (2012) estimates. Food per capita data were taken from calories/capita/day data from the Food and Agricultural Organisation (FAO 2018). This too was scaled to align with the 1970 standard run value. The real world data for industrial output per capita and food per capital align very well. However, industrial output per capita is now (in the last few years) beginning to exceed the standard run values. Real world birth rates, and even more so death rates, have been lower than predicted.These two deviations between predicted and realised trajectories, however, partially neutralise each other, resulting in the real world population now being slightly higher (approximately eleven percent) than predicted. The fraction of non-renewable resources remaining in the real world is well above that predicted by the standard run. In the W3 model, the dwindling resource pool is what prompts the down turn of the industrial output. As the real world data have not reached such a level by now, the W3 model would suggest that the decline in industrial output is yet to come, as evident in the real world data of GDP per capita, or IOPC.
It should be noted that data agreement is not conclusive of model validity. Many different models could produce similar alignment. An ordinary exponential function (simply calibrated to fit real world data) would also align nicely with population and industrial out-ulated, reaching maxima of 0.25, 0.20, and 0.16 respectively. Crude death rate normalised SD is steady at approximately 0.2 for the first five decades, and then experiences a sharp spike reaching 0.41 in the year 2040. This period corresponds to the beginning of the population decline in the model.
We can see from this that the severity of the decline varies strongly between simulation runs. The normalised SD of industrial output per capita quickly rises to a high of 0.54 around the turn of the millennium. It then proceeds to decline to a comparable level to that of food per capita, fraction remaining of non-renewable resources, and crude birth rate. The normalised SD of population continually rises to a maximum of 0.43 and then declines slowly. Normalised persistent pollution normalised SD reaches a high of around 3.6 during the years 2030 to 2060. This is the most uncertain variable as the modelling of pollution is very difficult, and due to the large increase in pollution from 1970 levels. Figure 11 (p. 283) zooms into the comparison of the standard run with real world data from 1970 to 2017 as indicated in figure 8. The data for population (POP), deaths (CDR), and births (CBR) were obtained from the United Nations' (UN) Department of Economic and Social Affairs' (DESA) Population Division (UN DESA Population Division 2017). Industrial output per capita (IOPC) data were approximated using global GDP data and the population data. GDP data were taken from the UN DESA Statistics Division (2018). This was scaled to align with the 1970 standard run value, allowing for easy comparison of trends. Fraction remain- It is evident that the model as such is sensitive to input error; however, the trends, that is, the shapes of the trajectories, are less so. Thus, while the exact numbers produced by the model may be of little practical use, the behaviours exhibited by the model would still be of interest. RESEARCH put per capita development to date. However, it would offer little insight into the actual mechanics to the phenomenon behind the data. The only way to begin to validate the standard run simulation is to actually observe a collapse (by which point it is too late to avoid it) in the real world. This is a very hard test to satisfy as the data and variables used to calibrate the model have most likely changed as our society has progressed, for example, concerning efficiencies in material usage or the introduction of unconventional fossil fuels. Thus the collapse would most likely be dif ferent to that shown in the standard run.
Real world comparison
Meadows and his team understood this issue very well. The W3 model was also used to examine other transitions into the future. The model has mechanisms that allow for modifications in parameters to be made at a particular point in time. This was designed to enable the simulation of events like government poli cy changes or technological advancements. The standard run can only represent a society trapped in 1970's behaviour and technol ogy.
An issue regarding the model is the exclusion of an explicit renewable resource sector. Because of this the model is nearly guaranteed to produce a "collapse". While the addition of a renewable energy or resource pool would allow for a greater chance of a sustainable future (one in which variables plateau), it would potentially still result in a "collapse". For example, the energy study of Dale et al. (2012), which investigated the futures of non-renewable and renewable energy production, showed a sharp downturn in total energy production after the year 2060, despite strong growth in the renewable energy sector.
Another issue is that energy and material resources are lumped together in a single variable. This limits the complexity of the possible scenarios. It is clear that many intricacies of the world have been consolidated into single variables in the W3 model. This causes many issues regarding model usability and structural accuracy and could cause larger errors than parameter uncertainty.
Other investigations have been made into the relationship of the W3 scenarios and real world data. Examples include Turner (2012) and Pasqualino (2015).
Knowledge gained
After performing the uncertainty analysis, our knowledge of the W3 model has greatly improved. While past local SA studies allowed sensitive parameters to be identified and then hypothetical scenarios tested, this uncertainty analysis identifies more accurately the model's sensitivity to input errors.
It is evident that the model as such is sensitive to input error; however the trends, that is, the shapes of the trajectories, are less so. Thus, while the exact numbers produced by the model may be of little practical use, the behaviours exhibited by the model would still be of interest.
Limitations
The results presented in this paper demonstrate the error sensitivity in the standard run scenario. The results are only relevant for a situation in which the parameters stay fixed to their initial values, that is, as they were in the first half of the 20 th century. An addition to this study would be to allow "stabilising policies" to be implemented at a certain year in the simulation. The year in which the policies are implemented would be given a PDF to illustrate the uncertainty of when society would make a significant change to its behaviour. This addition could improve our understanding of uncertainties in the W3 model. This study only investigates uncertainty caused by model input errors. The question of the accuracy of the model itself is still open for debate. While the model captures many economic and ecological relationships in our world, it still omits or aggregates most aspects.
The removal of trajectories which drastically diverge from historic data, for example, the lowest trajectory in figure 5, would improve the accuracy of the results. However, as they contribute a low proportion of all simulation runs, their effect on the results is negligible, thus it was deemed unnecessary to remove them for this study. > FIGURE 8: Graphs of percentiles, mean and standard run over time for A pop ulation (POP)(people), B fraction remain ing of non-renewable resourc es (NRFR)(unit less), C industrial output per capita (IOPC) (1970 $US), D food per capita (FPC) (vegetable kg), E crude birth rate (CBR)(births per 1000 people), and F crude death rate (CDR)(deaths per 1000 people). Percentiles (5, 25, 50, 75, 95)(dotted lines), mean (dashed line), standard run (solid line), and real world data (red line). There are two red lines in plot B, as an upper and lower estimate for total fuel reserves.
Conclusion
The results show that the unpredictability of the model is noteworthy. The variables population(POP), industrial output per capita (IOPC), fraction remaining of non-renewable resources (NRFR), and normalised persistent pollution (PPOLX) produced normalised standard deviations of up to 0.43, 0.54, 0.20, and 3.6 respectively. The general trends of the variables are predictable, with more than 95 percent of simulation runs producing nearly identically shaped trend lines. This is demonstrated by the similar shape of each percentile line, and by the fact that these lines do not drastically diverge as time progresses. It is evident that trajectories can be found that indicate a favour able future for humanity, that is, no sudden downturn in popula tion or access to resources, without policy intervention for the time period modelled. However, these trajectories reside in areas of low probability, and thus should not be considered as likely outcomes.
For the authors of The Limits to Growth, a study such as this one might have proved useful in explaining the model's behaviour to its critics and demonstrating its main purpose, that is, allowing the study of possible trends, not producing precise predictions.
Author contributions: A.H. performed research and wrote the paper; B.S. and M.R. provided research direction and contributed to the writing. This research has been conducted with the support of the Australian Government Research Training Program Scholarship. Bestellung an<EMAIL_ADDRESS>Leseproben, Informationen zur Zeitschrift und Abobedingungen: www.oekologisches-wirtschaften.de | 7,273 | 2019-10-18T00:00:00.000 | [
"Economics"
] |
Interrupting separateness , disrupting comfort : An autoethnographic account of lived religion , ubuntu and spatial justice
As a member of a research community that is exploring the African social value of ubuntu, part of my personal journey has been to move away trying to dissect a reified ubuntu to learn to practice ubuntu as embodied action. One of the ways I have done this arises from my location in the Eastern Cape. Distances between cities, towns and villages in South Africa’s largest province can be great. On any journey I make, I see people standing alongside the road, holding signs indicating their desired destination. As part of unlearning my innate selfishness and desire for comfort, and discovering ubuntu, I regularly pick up people alongside the road. They are poor, black and often from rural communities. This act has been a space for discovery, discomfort and profound challenge.
Introduction
As a member of a research community that is exploring the African social value of ubuntu, part of my personal journey has been to move away trying to dissect a reified ubuntu to learn to practice ubuntu as embodied action.One of the ways I have done this arises from my location in the Eastern Cape.Distances between cities, towns and villages in South Africa's largest province can be great.On any journey I make, I see people standing alongside the road, holding signs indicating their desired destination.As part of unlearning my innate selfishness and desire for comfort, and discovering ubuntu, I regularly pick up people alongside the road.They are poor, black and often from rural communities.This act has been a space for discovery, discomfort and profound challenge.
In this article, I use a fictionalised encounter between myself and a passenger to juxtapose narratives on ubuntu and spatial justice drawn from academic literature.The intersection of these narratives creates a space for reflection on my experience as a white South African, and particularly my past experience as a pastor leading a South African faith community as they simultaneously (and paradoxically) pursued and evaded justice.The shared journey in my car functions as a metaphor for the dialectical intersection of the temporal, social and spatial in the South African context, and as a microcosm of its social spaces that both reflect and create the social stratifications that perpetuate injustice.Ubuntu as action disrupts my privileged space but also highlights the inherent limitations of such disruptions and points to the need for more fundamental reconfigurations of space.Richardson (2000), Clough (2002) and others have demonstrated how fictionalised narrative can be a legitimate and powerful way to present research findings.Although I have had many such encounters, the encounter described here is fictional and draws from many experiences, as well as from my imagination.The narration of the encounter may therefore be termed 'ethnographic fiction' (Richardson 2000:11).Richardson describes such evocative representations as a radical departure from social scientific naturalisms, through which, 'We find ourselves attending to feelings, ambiguities, temporal sequences, blurred experiences, and so on; we struggle to find a textual place for ourselves, our doubts and our uncertainties'.This article uses a fictionalised encounter as the basis for an autoethnographic exploration of the intersections between the South African social value of ubuntu and the notion of spatial justice.Ubuntu describes the interconnectedness of human lives.It asserts that a person is only a person through other people, a recognition that calls for deep respect, empathy and kindness.Ubuntu is expressed in selfless generosity and sharing.The spatial turn in the social sciences and humanities has resulted in a concern with the relationship between space and justice.It recognises that space is not simply an empty container in which people live and act, but is something that is constructed by social relations -and simultaneously constitutive of them.While this recognition gives rise to spatial perspectives on justice, what constitutes spatial, justice, as distinct from other notions of justice, and how such justice is to be achieved are contested.Building on the work of legal scholar, Andreas Philippopoulos-Mihalopoulos, on spatial justice, I argue that the notion of ubuntu is able to shape our understanding of spatial justice, and when practised, it is able to disrupt space and challenge dominant spatial configurations.
Interrupting separateness, disrupting comfort: An autoethnographic account of lived religion, ubuntu and spatial justice
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Autoethnography recognises the way in which research is influenced by personal experience, and rather than suppress this, it seeks to use such experience to enrich the research.The notion of evocative autoethnography (Ellis & Bochner 2000, 2006) is useful for clarifying my use of this narrative.Through it, I hope to stimulate deeper, empathetic understanding of both the sphere in which this research took place and my own experience as a researcher.Ellis (2004:46) has proposed that personal narratives, as a form of autoethnography, can contribute to a deeper understanding of the self as it intersects with the lives of participants or with a cultural context.It does this by inviting readers into the author's world, which becomes a resource for them to reflect on and understand their own lives.
My use of fictionalised narrative as evocative autoethnography is motivated by two factors.Firstly, I wish to diminish the gap between myself as the researcher and what is being researched in order to reflect my simultaneous involvement in the research process (Meerwald 2013:45).Secondly, and perhaps more importantly, I wish to reflect my preference for a narrative approach to research, exploring the contextually situated stories of people.Rather than explore reified notions of ubuntu, spatial justice and lived religion, I prefer to explore their confluence in a story.The story connects them to each other and to a concrete context.The fictional encounter in my car draws on empirical research conducted in East London, South Africa, in 2015.Twenty people, from urban and rural areas, were asked to tell stories of their experience about the presence or absence of ubuntu in the communities where they lived. 1I have made many such journeys, but rather than describing one of them, a fictionalised representation of the journey allows me to draw on multiple experiences and the emotions and thoughts precipitated by these encounters.
Research approach
I follow Van Huyssteen's postfoundational approach to research, which is suspicious of foundationalism's claims of objectivity and representational knowledge, and also of non-foundationalism's assertion of complete relativity.This approach seeks to reflect the balance between 'the way our beliefs are anchored in interpreted experience, and the broader networks of beliefs in which our rationally compelling experiences are already embedded' (Van Huyssteen 2006:22).This postfoundational approach is also attractive because of the possibilities it offers for interdisciplinary research.All forms of human rationality are the same because the same interpretive processes are evident across the spectrum of disciplines (Van Huyssteen 1999:44-45).
Transversal rationality (Schrag 1992:148;Van Huyssteen 2006:20) able to bring different perspectives, each form of rationality contributing to a deeper understanding.The convergence of disciplines and narratives that is facilitated by transversality 'points to a sense of transition, lying across, extending over, intersecting, meeting and conveying without becoming identical' (Van Huyssteen 2007:19).A helpful metaphor might be each discipline or narrative as a beam of light, which is able to illuminate the area of research in a unique manner.
My preference for Van Huyssteen's postfoundational approach is also informed by possibilities offered by transversal rationality to limit the impact of epistemological racism.Transversal rationality creates a space where the rationalities of different cultures, as well as different academic disciplines, can intersect and create knowledge.Van Huyssteen (2006:16) argues that transversal rationality eliminates the tendency to unify different kinds of knowledge.A particular form of knowledge cannot be viewed as superior to other forms of knowledge.A narrative approach to research is congruent with a postfoundational epistemology because it begins with the contextually situated stories of people (Müller 2009:204).
Lived religion Ganzevoort (2009:1) proposes that practical theology be described as the hermeneutics of lived religion.In contrast to the focus of other theological disciplines, which focus on the texts that constitute religious traditions, or on the concepts and ideas that define the parameters of a religion, practical theology is concerned with 'the transcending patterns of action and meaning embedded in and contributing to the relation with the sacred' (Ganzevoort 2009:3).He defines religion, which includes categories such as spirituality and faith, as 'the transcending patterns of action and meaning embedded in and contributing to the relation with the sacred'.
As the hermeneutics of lived religion, practical theology is therefore concerned with the social construction of meaning and the processes of interpretation by which people make sense of life in general, but particularly life in relation to the sacred.This is similar to what Ammerman (2007:5) describes as 'everyday religion', which gives priority to the experiences of those who are not religious experts and their activities outside of the boundaries of organised religion.
As a practical theologian, I am interested in how people live out their faith in particular social contexts.I attempt to describe and critically reflect on praxis, whether this takes place within the structures of organised religion or in broader social and cultural contexts.The goal of this reflection is the transformation of praxis.In this article, I reflect on my own experience, particularly as it is disrupted and challenged by the story of a South African with a vastly different life experience to my own, by the ubuntu narrative evoked by this encounter.
Ubuntu's disruption of space
I enjoy driving long distances.This is fortunate because my work, whether as a researcher or as a management consultant, often involves long road trips.Over the years, I have found ways to make the hours I spend behind the wheel of my car enjoyable.I am able to access the large collection of music on my smartphone via the Bluetooth on my car's sound system.Familiarity with various routes means I know where to buy good coffee (I confess to being something of a coffee snob).
And then there is my car; I love my car.It is a BMW X3, bought second hand, but still a beautiful machine.It is powerful, safe and comfortable.It insulates me from much of the harshness of road travel, along with my carefully chosen music, coffee and a variety of snacks.
It was still dark when I left East London, heading to Mthathadark, cold and wet.My first stop was at a boutique coffee franchise at an all-night filling station.I ordered a double cappuccino; a large, freshly baked apple and cinnamon muffin; and added a bottle of still mineral water before paying and leaving.As I turn onto the N2 freeway, there is the familiar site of someone standing by the road, waiting for the offer of a lift.I am simultaneously moved to stop by the thought of people waiting in the rain, and tempted not to stop by thoughts of wet car upholstery and inconvenience and even the risk of being a victim of crime.I stop and lower the window to speak to the man standing next to the vehicle.
I ask him where he is going.He tells me he is going to Idutywa, a small town between Butterworth and Mthatha.I open the door and signal for him to get in.He settles into the seat nervously.It is unusual for a white South African to stop and offer a lift in this way, almost unheard of.He looks uneasy.Grateful, but uneasy.We travel without speaking for a few minutes.The music that was so relaxing a few seconds before I stopped now feels like an intruder.I cannot imagine that Bongani is a fan of alternative rock music.I am not sure whether to simply turn it down, and allow it to fill what might otherwise be an uncomfortable silence, or turn it off.I turn the music off.
I introduce myself and ask him for his name.It is Bongani.I ask him if he lives in East London.He tells me that he is returning to a village near Idutywa after an unsuccessful attempt to find a job in East London.He tells me that there are no jobs where he lives.There is only poverty.He lives with his mother and four younger siblings.He finished matric the previous year, and now feels the pressure of providing for his family.
While he is talking, I glance at the coffee nestling in its holder in the centre console and think whether I should still drink it.
It seems rude to do so, because there is only one cup and I cannot offer him any.I think about the muffin.I take it out, break it into two and offer him one of the halves.He accepts, gratefully.I tell him that I am trying to learn about ubuntu; I ask if he will tell me about it.He smiles: Ubuntu is something that is part of our culture.It does not matter if you are poor.We are all poor, but even with that poverty you will see ubuntu.
Even if what they have is a little, people will share their food with someone in the community who does not have anything.
If you take our weddings, everyone in the community is invited.Everyone can come and eat meat -even that drunk guy.Ubuntu is about treating all people with respect; it is about courtesy and compassion.
I ask Bongani if ubuntu could be an answer for how we live together in South Africa.He seems unsure: Most white people do not have ubuntu.They only live for themselves.If you are black and poor it seems like they do not even see you.They do not want to know about your struggles.
I am aware that my small gesture of offering Bongani a lift, as an expression of ubuntu, does not really scratch the surface when it comes to the struggles he faces, living in rural poverty.It might be an act of ubuntu, but as an isolated act it leaves too much unchanged.I will continue to live in affluence and comfort.His struggle will continue.
We reach the intersection with the gravel road that leads to the village where Bongani lives.It is still raining, so I ask him how far down the road his village is.He tells me it is about 15 minutes' drive.I glance at my watch.I have time before my first meeting, so I offer to take him there.The road is bad.It is rutted and sections have been partially washed away.In the wet weather, it requires careful navigation and the journey takes longer than expected.As I drive I notice that there are no shops in his community, apart from a tiny spaza shop that adjoins one of the houses.Bongani tells me that they have to travel to Idutywa when they need to buy food.A return journey in a taxi costs R40, which is a substantial encroachment on the money that is available for life's necessities.
I drop him off at his home, a simple, one-roomed dwelling on the side of a grassy hill, with a corrugated iron roof that has rocks on it to secure it against wind and storm.An outhouse stands some 15 m from the house; on the other side of the house, there is a small cultivated patch of earth where maize and spinach are growing.
I think of Bongani's mother and siblings inside the house.I think about hunger, deprivation and isolation.I think about a world, an existence from which I am isolated.I am forced to think about what else I have that I should share with Bongani.I am simultaneously aware that sharing or giving without meaningful relationship risks being patronising; it could even be a strategy to ease my privilege-induced guilt rather than a solution to the injustice that exists within our relationship.
Transversal rationality and interdisciplinary conversation
The notion of transversal rationality proposed by Schrag and Van Huysteen will form the basis for an interdisciplinary dialogue in this article.The perspectives of ubuntu and spatial justice are offered here because they have the potential to illuminate my encounter with Bongani.As a form of transversal rationality, these narratives can be placed over and alongside my own story, and the points of intersection can be explored.In what I have described as 'transversal narrativity' (Eliastam 2015), I have previously proposed that new meanings are able to emerge at such intersections, meanings that are able to disrupt dominant stories and make it possible for new stories to emerge.
This echoes Müller's (2011:4) metaphor of an ecotone for postfoundationalist practical theology.An ecotone is a transition zone between adjacent but different communities of plants or animals, where different communities meet and integrate.This gives rise to a wider variety of species found in this transitional zone, in what is called the 'edge effect'.For Müller, the practical theologian's ecotone is the delicate public space created through interdisciplinary dialogue.It is a space where practical theology can explore a number of diverse narratives, allowing multiple habitats to be visited and re-visited.In the fictional encounter that follows, my car becomes an ecotone in which different stories intersect: my personal story, the story of my passenger as well as broader social discourses.
As a form of transversal rationality, narratives on ubuntu and spatial justice are drawn from literature to illuminate the encounter in my car, and the stark contrast between Bongani's world and my world that it highlighted.
Ubuntu
Cornell and Van Marle argue that ubuntu is simultaneously ontology, epistemology and ethical value system.As such, it transcends major distinctions in Western philosophy.They write: Ubuntu is a philosophy on how human beings are intertwined in a world of ethical relations from the moment they are born.Fundamentally, this inscription is part of our finitude.We are born into a language, a kinship group, a tribe, a nation, and a family.We come into a world obligated to others, and those others are obligated to us.We are mutually obligated to support each other on our respective paths to becoming unique and singular persons.(Cornell & Van Marle 2015:2) Ubuntu resonates with universal values of human worth and dignity.It has been translated in different ways: as 'humanity' (Shutte 2001:2); 'African humanness' (Broodryk 2002:13); 'humanism or humaneness' (Mnyaka & Motlhabi 2009:63); or 'the process of becoming an ethical human being' (Mkhize 2008:35).
For Mkhize (2008:43), ubuntu, 'incorporates ideas of social justice, righteousness, care, empathy for others and respect'.Mnyaka and Motlhabi (2009:74) argue that ubuntu, 'is inclusive … it is best realised in deeds of kindness, compassion, caring, sharing, solidarity and sacrifice'.Makhudu (1993:40) proposes that, 'every facet of African life is shaped to embrace ubuntu as a process and philosophy which reflects the African heritage, traditions, culture, customs, beliefs, value system and the extended family structures'.Chikanda (1990) regards ubuntu as African humanism.It encompasses sensitivity to other people's needs, charity, sympathy, care, respect, consideration and kindness.
Bishop Desmond Tutu (1999:34-35) writes that the significance of ubuntu is that, '"a person is a person through other people."It is not "I think therefore I am."It says rather: "I am human because I belong."I participate, I share'.The notion of ubuntu points to the interconnectedness of human beings, with the implication that people should treat each other as though we are all members of an extended family (Gish 2004:122).Tutu lists the spiritual attributes of ubuntu: generosity, compassion, hospitality, caring and sharing.
People with ubuntu are compassionate and gentle.They do not take advantage of others.They use their strength for the benefit of the weak.If someone lacked ubuntu, they lacked something essential to being fully human.Tutu (1999:35) argues that this sense of shared humanity means that a person's humanity is diminished when others are humiliated or oppressed.
The spatial turn
The spatial turn, that has impacted various disciplines, saw the old geographical notion of 'place' problematised as 'space'.Henri Lefebvre argued that 'Physical space has no "reality" without the energy that is deployed within it' (Lefebvre 1992:13).Space is constituted by social relations rather than its physical characteristics, and is therefore, '… is not a thing but rather a set of relations between things (objects and products)' (1992:83).Space is not simply a container for people, buildings, things and activities; it is both constituted by social relations and constitutive of them.Human existence is embedded in social, temporal and spatial dimensions which are dialectically related and which constitute each other.Lupton's description of this way of viewing space is helpful: … space cannot be thought of as fixed or absolute, but as socially produced: a social construct not a physical entity.Space cannot exist independently of human activity, since its meaning is produced by the social relations of people within and outside it, through the ways that they use it and imagine it.Space also produces particular forms of activity and sets of relations by configuring the identities and understandings of people who occupy it.In this sense, places cannot be thought of only in physical and locational terms as a backdrop to human activity, nor only as containers in which people are gathered and in which they interact.(Lupton 2009:112) Harvey echoes Lefebvre in arguing that space consists of relationships between things.Harvey (1996) views space as relational; space does not exist prior to the things that make it up, as if it were a container that is waiting to be filled with things.Instead, space is the relationship among those things.Instead of focusing on the manner in which things are distributed on a map, Lefebvre and Harvey explore the processes that shape spaces, paying particular attention to social relations.
According to Harvey's Marxist analysis, space reflects commodity production with the consequence that conflict over space mirrors class conflict.We live in a world where market forces collaborate with the state to preserve the advantages of a minority, which gives rise to the unequal and unjust distribution of resources.Harvey (1993:310) argued that space reflects the 'prevailing ideology of ruling groups' and is 'fashioned by the dynamics of market forces'.Massey (2005) described space as a product of social interrelations and embedded practices that was framed by a number of histories.Space is produced at an ideological level as well as the material level.Beebe, Davis and Geadle (2012) observe that: Space was dynamic, constructed, and contested.It was where issues of sexuality, race, class, and gender -among a myriad of other power and/or knowledge struggles -were sited, created, and fought out.(p.524) Creswell (2004:29) points out that space is 'not simply an outcome of social processes … it was, once established, a tool in the creation, maintenance and transformation of relations of domination, oppression and exploitation'.
Spatial justice
Following from the assertions of Lefebvre, Harvey and others that all social processes are spatially produced, it is evident that relations of justice are also spatially produced.'Guiding the exploration [of spatial justice] from the start is the idea that justice, however it might be defined, has a consequential geography, a spatial expression that is more than just a background reflection or set of physical attributes to be descriptively mapped' (Soja 2010:1).The manner in which the spatial world is organised shapes social relationships.Since space is the medium in which humans live, it is where inclusion or exclusion finds material expression.
Soja emphasises that the search for spatial justice cannot replace the search for social, economic or environmental justice.Rather, the spatial justice perspective is able to bring greater clarity and understanding to these concepts and provide insights into the extension of justice in the social and political arena.'In the view taken here, everything that is social (justice included) is simultaneously and inherently spatial, just as everything spatial, at least with regard to the human world, is simultaneously and inherently socialized' (Soja 2010:5-6).The significant contribution of spatial justice is the manner in which it highlights the instrumentality of space in producing social relations characterised by (in) justice.By highlighting the role of space in producing justice and injustice, a spatial justice perspective is able to illuminate social relations and point to changes that will bring about greater justice in society.Williams (2013) explains: Spatial justice is an analytical framework that makes space, understood as a physical, social, and mental production, a central category for understanding justice.Theorizing spatial justice involves both understanding how spatial relationships produce social relations and developing normative frameworks for evaluating those social relationships.(p.4) Philippopoulos-Mihalopoulos (2010), Williams (2013) and Ansaloni and Tedeschi (2015), among others, wrestle with the question of what such a normative framework should look like.Approaches to spatial justice tend to focus on ethical and moral issues in the planning process.Unequal treatment is highlighted, and following Rawls (1971) approaches to justice tend to be distributive and either focus on radical social change and redistribution of resources, or on criteria that could help planners create and implement policies that favour the least advantaged in society.Purcell (2002:101-102) describes the former as an attempt to 'restructure the power relations that underlie the production of urban space, fundamentally shifting control away from capital and the state and toward urban inhabitants'.In contrast, planning scholars such as Fainstein (2009Fainstein ( , 2010) ) and Campbell (2006) argue that justice is an evaluative criterion, which involves universal norms that transcend the particular that must be applied in policy-making in order to achieve a just city through a fair distribution of benefits.
Ansaloni and Tedeschi (2015:2) express concerns about the course taken by contemporary debate over spatial justice.They argue that it is based on meta-narratives and try to identify moral issues and the universal values that should be applied to them.They demonstrate the difficulties inherent in trying to identify the best practices or fairest solutions, and question whether these even exist, let alone whether they can be applied in a particular context.
Philippopoulos-Mihalopoulos (2010:187) points out that the majority of literature reduces the concept of spatial justice into an alternative version of social, distributive or regional justice.Philippopoulos-Mihalopoulos asks 'If spatial justice is simply a just distribution of resources in a given region, one is left wondering whether any justice can possibly afford not being "spatial" in this narrow sense.On the contrary, if the peculiar characteristics of space are to be taken into account, a concept of justice will have to be rethought on a much more fundamental level than that'.He argues that the notion of space in spatial justice needs to transcend the regional.The juxtaposition of ubuntu and spatial justice opens up possibilities for a rethinking of justice, which contributes to Philippopoulos-Mihalopoulos' proposal.
In search of spatial justice
My encounter with Bongani highlights the configuration of space in the Eastern Cape that perpetuates the social injustice.
Where each of our lives reflects the anomalies of apartheid urban planning that reserved the cities and suburbs for white people and pushed black South Africans to the periphery, particularly with the creation of Bantustan 'homelands'.
There has been a gradual influx of affluent black South Africans into the suburbs in post-apartheid South Africa, but the majority, like Bongani, remain on the margins.This existence on the margins deprives them from resources, from access to opportunities and from meaningful participation in the economy.
Like Bongani, many black South Africans live in poverty, cut off from social services, economic infrastructure and the opportunity to improve their lives.Their spatial positioning makes any form of self-actualisation almost impossible.Rather, it seems inevitable that they will exist in enduring poverty, without hope of substantial change, without hope of justice.This captures what, for Mendieta (2010:446), is at the heart of social exclusion, it is 'to be deprived access to the space in which we can be properly human'.
In contrast, my location in space makes it possible for me to live in relative comfort, access resources such as quality education and healthcare, find employment, be mobile and so on.My location in space is also a source of social capital because it connects me to a valuable invisible network of institutions and relationships that are based on a shared collective identity and shared values.These give me access to information, opportunities and influence.
An act of ubuntu opens my life to the presence of the Other.
It brings an awareness of our location in space, and of how injustice has been inscribed on it.Ubuntu can never be a one-off act though, and emerging ubuntu invites further expressions of solidarity and sharing.
Ubuntu, justice and space
There is a conflict over space.Ansaloni and Tedeschi (2015:1) go as far as asserting that reality is 'the relentless encounters of bodies (assemblages) whose fight for space determines unique temporary agreements (spatial justice) as a result of power exchanges (affects) among these bodies'.
For Philippopoulos-Mihalopoulos (2010:198), this conflict points to the 'impossibility' (in the Derridean sense) of spatial justice, because all claims are enmeshed in a net of monadic positions that can each only be occupied by one body at any particular moment in time.'The demand for spatial justice unfolds a monadology of the particular body, an irreplaceability of position and an impossibility of sharing the same space at the same time' (Philippopoulos-Mihalopoulos 2010:198).This shapes his understanding of spatial justice: Spatial justice has to be thought in terms of embodiment and spatiality, on the one hand firmly located in the particularity of one's body right here, and on the other, within the folds of a universal impossibility of simultaneous emplacement.Simply put, spatial justice is the strife to conciliate the arguably justified demands of both ego and alter to be simultaneously at precisely the same space, to occupy precisely the same corporeal trace in space at precisely the same time.Thought in this way, spatial justice is a strife for and also an argument to abandon the ubiquitous quest for identity, and look instead for a relationality that connects void rather populated spaces.Indeed, this is the radical call of spatial justice: the demand for a plural, emplaced oneness … (Philippopoulos-Mihalopoulos 2010:199) The notion of a form of justice based on relationality, and a plural, emplaced oneness echoes ubuntu language.Philippopoulos-Mihalopoulos recognises that building spatial justice requires a 'radical ethical gesture' in order to transcend the perpetual conflict inherent in social relationships.He explains: This is perhaps the crux of the concept of spatial justice -and indeed the answer to the kind of justice that spatiality dictates: that the only way in which its demands can be met is through a withdrawal, through the departure of the one who occupies the contested space, and the simultaneous conceding of priority to the other's claim.(Philippopoulos-Mihalopoulos 2010:200) Philippopoulos- Mihalopoulos (2010:199) points out that 'it may be relatively easy to care for the ones "over there", but what about the ones who want to be "right here", right where we stand?'.To answer this question, Philippopoulos-Mihalopoulos touches on the notion of kenotic withdrawal (Lévinas 1969;Weil 1992) to describe this concession.Kenosis is a sacrificial emptying, modelled on the example of Jesus that is described in the work of Meister Eckhart and Jacob Böhme.In order to constitute justice, Philippopoulos-Mihalopoulos argues that this withdrawal and concession must exist in a permanent state of oscillation, in which the one for whom I withdraw does the same for others.
If my reading of Philippopoulos-Mihalopoulos is correct, ubuntu is uniquely able to answer the call he makes for a radical ethical gesture.Ubuntu is relational, and gives material expression to the plural, emplaced oneness that spatial justice demands.It is a withdrawal from contested space that gives priority to the other, and this is expressed in a multitude of reciprocal relationships.
A relational, ubuntu spatial justice is able to address issues of power, domination and exploitation.It challenges the abuse of power in spatial injustice (Marcuse 2010:90-92) because it denounces power over in favour of power with -a sharing of power.Ubuntu teaches us that the first step towards spatial justice is relational.Like Philippopoulos-Mihalopoulos' notion of spatial justice, ubuntu is a process of constant becoming.It offers a relational ethic for spatial justice that is unique in its ability to resolve issues of inclusion and/or exclusion, power and oppression.
Ubuntu leads to a disruption of space.It brings me into contact with the Other, whose relative disadvantage calls for a concrete response from me.The response is not based on a metanarrative, or on an ethic abstracted from some moral code.The response is based on our shared humanity, our fundamental interconnectedness in this world.To the extent that the humanity of the Other is diminished, my humanity withers.This recognition precipitates a reconfiguration of our relationship, which in turn brings about a shift in spatial arrangements between us, albeit a small one.
The challenge for me was that the first ubuntu-inspired movement towards the Other both disrupted spatial configurations and called for further disruption.Incipient relationship called for further movement, for letting go of my position of privilege in favour of greater sharing and more significant reconfiguration of the social relationship between us.My personal discomfort arose from my experience that practicing ubuntu, as a white person in post-apartheid South Africa, was a bit like pulling on a loose thread in a jersey.The act was likely to be the start of a process, a process that could lead to the unravelling and disintegration of much of my privilege and comfort.But, from an ubuntu perspective, this erosion of my privilege over and power over is intrinsic to my becoming fully human.
Ubuntu, spatial justice and lived religion
For Bergmann (2007:353), theology is already and always spatial.This is particularly true of practical theology as the study of lived religion in particular localities.An explicit spatial justice perspective exposes the intersection between lived religion and social relations, and interrogates religious practices as these occur in the spaces produced at various geographic locations and places.My life and story intersect with Bongani's in the place I call a car.Knott highlights the significance such an encounter has for an understanding of lived religion: If what we mean by place is that nexus in space in which social relations occur, which may be material or metaphorical and which is necessarily interconnected (with places) and full of power, then (a space) has the potential to contain and express religion (religion being those social relations given meaning by a certain type of ideology, set of traditions, values ritual practices.(Knott 2005:134) Obadia (2015:206) argues that spaces and places can be viewed as 'empirical locations where cultural and social processes occur, and where the sense of community, identity and belonging, and religious experiences, are framed'.Religion is always located.The sites, places and spaces within which lived religion occurs are produced by social processes and forces.Lived religion simultaneously shapes space and is shaped by the spatial configurations in which it exists.Space, in particular through its local manifestations, tells a story about religion within that space.Its topography mirrors the contours of religious belief and practice far more accurately than catechisms or statements of faith.
It is difficult to disagree with Obadia's (2015:206) contention that religion is currently aligned with what Derrida describes as the 'tele-techno-media-scientist, capitalistic, political and economic' facets of global society (Derrida 1998:65).Instead of exercising agency towards justice, religion in general (and Christianity in particular) may be complicit in perpetuating injustice, both at a structural and local level.
My experience as the leader of a congregation in Cape Town's Southern suburbs reflects some of this unholy contradiction.In 2002, I was appointed as senior pastor of a congregation located on the urban edge between affluent, predominantly white, suburbia and the sprawling poverty of those spaces on the periphery of the city that apartheid planners had demarcated for black people.At that time, the membership of the church was almost entirely white, and its liturgy reflected an affinity for the soft rock sub-culture of the West Coast of the United States rather than its location in Africa.
A pastoral emphasis on Biblical imperatives to pursue reconciliation and justice led in tentative attempts to reach out to less advantaged people in the communities around us.However, the presence of homeless people and refugees in Sunday services disrupted suburban religious comfort.There were complaints from some members about the odour of unwashed bodies, about 'disruption' in the children's church from the presence of other cultures and about people begging after the Sunday morning service.It was enough to let 'them' into 'our' space.The demands for the reconfiguration of that space that their presence now demanded were too much to bear.This was particularly evident in the arena of finances, where the diversion of church finances towards justice and restitution initiatives resulted in conflict.
Just as the ubuntu act of opening my car door to Bongani called for further concrete expression of ubuntu, opening the doors of our congregational meeting place to the least advantaged in our community called for deeper, sacrificial withdrawal for our spatial position of privilege.Such sacrifice is not easy.I remember the unembarrassed remonstration given to me, at a church leadership meeting, that if I 'had my way there would not be churches where white people felt comfortable'.Inviting the presence of the Other disrupted the prevailing ordering of space and exposed the subterranean ideological forces that shaped religious practice for many members of that congregation.
This suggests the need for a transformation of religious practice that moves people towards a relational justice that is sacrificial and dynamic.Ubuntu simultaneously disrupts spatial configurations and creates an in-between space, which could facilitate such a transformation.The spatial configuration of the world in which Bongani and I live is such that it risks making him invisible to me.Ubuntu's recognition of shared humanity, combined with respect, compassion and care is expressed in sacrificial withdrawal from the space that I occupy, so that another can occupy it -and then also give way to another.Ubuntu gives rise to a spatial justice that moves beyond redistribution and self-actualisation and becomes deeply relational and sacrificial.As such, it offers potential for the shaping of a theology of spatial justice.Graham (2011:267) points out that, 'How we find ourselves is ultimately about being placed in relationship -both spatial and cosmological -to a range of "Others" across time, culture, and species, but also to a divine horizon'.
describes the possibility of conversation between various disciplines or narratives, in which areas of shared interest can be explored without needing to assimilate the perspective of one discipline into another.Multiple voices are 1.The statements recorded in inverted commas in my fictionalised encounter with Bongani are actual statements about ubuntu drawn from these interviews. | 8,794.8 | 2016-11-25T00:00:00.000 | [
"Philosophy"
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