id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2105.08626 | Andy Liaw | Robert P. Sheridan, Andy Liaw, Matthew Tudor | Light Gradient Boosting Machine as a Regression Method for Quantitative
Structure-Activity Relationships | 32 pages, 4 figures | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | In the pharmaceutical industry, where it is common to generate many QSAR
models with large numbers of molecules and descriptors, the best QSAR methods
are those that can generate the most accurate predictions but that are also
insensitive to hyperparameters and are computationally efficient. Here we
compare Light Gradient Boosting Machine (LightGBM) to random forest,
single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30
in-house data sets. While any boosting algorithm has many adjustable
hyperparameters, we can define a set of standard hyperparameters at which
LightGBM makes predictions about as accurate as single-task deep neural nets,
but is a factor of 1000-fold faster than random forest and ~4-fold faster than
XGBoost in terms of total computational time for the largest models. Another
very useful feature of LightGBM is that it includes a native method for
estimating prediction intervals.
| [
{
"created": "Wed, 28 Apr 2021 20:19:44 GMT",
"version": "v1"
}
] | 2021-05-19 | [
[
"Sheridan",
"Robert P.",
""
],
[
"Liaw",
"Andy",
""
],
[
"Tudor",
"Matthew",
""
]
] | In the pharmaceutical industry, where it is common to generate many QSAR models with large numbers of molecules and descriptors, the best QSAR methods are those that can generate the most accurate predictions but that are also insensitive to hyperparameters and are computationally efficient. Here we compare Light Gradient Boosting Machine (LightGBM) to random forest, single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30 in-house data sets. While any boosting algorithm has many adjustable hyperparameters, we can define a set of standard hyperparameters at which LightGBM makes predictions about as accurate as single-task deep neural nets, but is a factor of 1000-fold faster than random forest and ~4-fold faster than XGBoost in terms of total computational time for the largest models. Another very useful feature of LightGBM is that it includes a native method for estimating prediction intervals. |
1306.5709 | Adam Marblestone | Adam H. Marblestone, Bradley M. Zamft, Yael G. Maguire, Mikhail G.
Shapiro, Thaddeus R. Cybulski, Joshua I. Glaser, Dario Amodei, P. Benjamin
Stranges, Reza Kalhor, David A. Dalrymple, Dongjin Seo, Elad Alon, Michel M.
Maharbiz, Jose M. Carmena, Jan M. Rabaey, Edward S. Boyden, George M. Church
and Konrad P. Kording | Physical Principles for Scalable Neural Recording | null | null | 10.3389/fncom.2013.00137 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simultaneously measuring the activities of all neurons in a mammalian brain
at millisecond resolution is a challenge beyond the limits of existing
techniques in neuroscience. Entirely new approaches may be required, motivating
an analysis of the fundamental physical constraints on the problem. We outline
the physical principles governing brain activity mapping using optical,
electrical,magnetic resonance, and molecular modalities of neural recording.
Focusing on the mouse brain, we analyze the scalability of each method,
concentrating on the limitations imposed by spatiotemporal resolution, energy
dissipation, and volume displacement. We also study the physics of powering and
communicating with microscale devices embedded in brain tissue.
| [
{
"created": "Mon, 24 Jun 2013 19:04:50 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jun 2013 19:08:53 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Jul 2013 19:53:59 GMT",
"version": "v3"
},
{
"created": "Wed, 3 Jul 2013 15:10:29 GMT",
"version": "v4"
},
{
"cre... | 2020-02-04 | [
[
"Marblestone",
"Adam H.",
""
],
[
"Zamft",
"Bradley M.",
""
],
[
"Maguire",
"Yael G.",
""
],
[
"Shapiro",
"Mikhail G.",
""
],
[
"Cybulski",
"Thaddeus R.",
""
],
[
"Glaser",
"Joshua I.",
""
],
[
"Amodei",
"Dario... | Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical,magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. We also study the physics of powering and communicating with microscale devices embedded in brain tissue. |
2107.11740 | Weihua Deng Professor | Chongcan Li, Yong Cong, and Weihua Deng | Identifying the fragment structure of the organic compounds by deeply
learning the original NMR data | 12 pages, 8 figures | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We preprocess the raw NMR spectrum and extract key characteristic features by
using two different methodologies, called equidistant sampling and peak
sampling for subsequent substructure pattern recognition; meanwhile may provide
the alternative strategy to address the imbalance issue of the NMR dataset
frequently encountered in dataset collection of statistical modeling and
establish two conventional SVM and KNN models to assess the capability of two
feature selection, respectively. Our results in this study show that the models
using the selected features of peak sampling outperform the ones using the
other. Then we build the Recurrent Neural Network (RNN) model trained by Data B
collected from peak sampling. Furthermore, we illustrate the easier
optimization of hyper parameters and the better generalization ability of the
RNN deep learning model by comparison with traditional machine learning SVM and
KNN models in detail.
| [
{
"created": "Sun, 25 Jul 2021 06:45:46 GMT",
"version": "v1"
}
] | 2021-07-27 | [
[
"Li",
"Chongcan",
""
],
[
"Cong",
"Yong",
""
],
[
"Deng",
"Weihua",
""
]
] | We preprocess the raw NMR spectrum and extract key characteristic features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition; meanwhile may provide the alternative strategy to address the imbalance issue of the NMR dataset frequently encountered in dataset collection of statistical modeling and establish two conventional SVM and KNN models to assess the capability of two feature selection, respectively. Our results in this study show that the models using the selected features of peak sampling outperform the ones using the other. Then we build the Recurrent Neural Network (RNN) model trained by Data B collected from peak sampling. Furthermore, we illustrate the easier optimization of hyper parameters and the better generalization ability of the RNN deep learning model by comparison with traditional machine learning SVM and KNN models in detail. |
1912.12248 | Chandre Dharma-wardana | M. W. C. Dharma-wardana (NRC Canada) | Discussion on a "Dynamic model to conceptualize multiple environmental
pathways to the epidemic of Chronic Kidney Disease of unknown etiology
(CKDu)" | two figures | null | null | null | q-bio.QM nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jayasinghe et al. [Science of the Total Environment, 705 (2020) 135766]
propose a 'dynamical' model of Chronic Kidney Disease of Unknown etiology
(CKDu) wherein CKDu arises as an emergent property of a complex system where
they claim that weak multiple environmental factors contribute. They criticize
the usual approaches as being "reductionist". We use their model as a basis of
a discussion on the possibility of treating CKDu as an emergent property
resulting from the interaction of multiple weak environmental factors with the
organism. The model does not reveal anything beyond what is already known from
simple considerations of well-known feed-back loops, but has the merit of
re-stating those issues in a different format. If a proper weighting of the
possible environmental factors is included, most proposed environmental factors
drop out and what Jayasinghe et al. call the "reductionist" approach naturally
arises due to the weight of evidence. The theory that the consumption of water
containing fluoride and magnesium ions as found in water from regolith aquifers
drawn via house-hold wells is found to clearly hold within this model when
proper weighting is included. However, we show by examples that such models can
be easily misused, leading to completely misleading conclusions. A response
formalism useful in the theory of complex systems and emergent modes is
presented in the context of the current problem. In addition to there being a
lack of adequate data to fully implement such a theory, it is seen that such
elaborations are unnecessary and misleading in the present context.
| [
{
"created": "Fri, 27 Dec 2019 17:18:09 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jan 2020 15:54:40 GMT",
"version": "v2"
}
] | 2020-01-07 | [
[
"Dharma-wardana",
"M. W. C.",
"",
"NRC Canada"
]
] | Jayasinghe et al. [Science of the Total Environment, 705 (2020) 135766] propose a 'dynamical' model of Chronic Kidney Disease of Unknown etiology (CKDu) wherein CKDu arises as an emergent property of a complex system where they claim that weak multiple environmental factors contribute. They criticize the usual approaches as being "reductionist". We use their model as a basis of a discussion on the possibility of treating CKDu as an emergent property resulting from the interaction of multiple weak environmental factors with the organism. The model does not reveal anything beyond what is already known from simple considerations of well-known feed-back loops, but has the merit of re-stating those issues in a different format. If a proper weighting of the possible environmental factors is included, most proposed environmental factors drop out and what Jayasinghe et al. call the "reductionist" approach naturally arises due to the weight of evidence. The theory that the consumption of water containing fluoride and magnesium ions as found in water from regolith aquifers drawn via house-hold wells is found to clearly hold within this model when proper weighting is included. However, we show by examples that such models can be easily misused, leading to completely misleading conclusions. A response formalism useful in the theory of complex systems and emergent modes is presented in the context of the current problem. In addition to there being a lack of adequate data to fully implement such a theory, it is seen that such elaborations are unnecessary and misleading in the present context. |
1907.03612 | Michael Cole | Takuya Ito, Luke Hearne, Ravi Mill, Carrisa Cocuzza, Michael W. Cole | Discovering the Computational Relevance of Brain Network Organization | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Understanding neurocognitive computations will require not just localizing
cognitive information distributed throughout the brain but also determining how
that information got there. We review recent advances in linking empirical and
simulated brain network organization with cognitive information processing.
Building on these advances, we offer a new framework for understanding the role
of connectivity in cognition - network coding (encoding/decoding) models. These
models utilize connectivity to specify the transfer of information via neural
activity flow processes, successfully predicting the formation of cognitive
representations in empirical neural data. The success of these models supports
the possibility that localized neural functions mechanistically emerge (are
computed) from distributed activity flow processes that are specified primarily
by connectivity patterns.
| [
{
"created": "Mon, 8 Jul 2019 13:41:45 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Oct 2019 14:31:20 GMT",
"version": "v2"
}
] | 2019-10-22 | [
[
"Ito",
"Takuya",
""
],
[
"Hearne",
"Luke",
""
],
[
"Mill",
"Ravi",
""
],
[
"Cocuzza",
"Carrisa",
""
],
[
"Cole",
"Michael W.",
""
]
] | Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition - network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns. |
2201.11868 | Sarah McIntyre | Anne Margarette S. Maallo (1), Basil Duvernoy (1), H{\aa}kan Olausson
(1), Sarah McIntyre (1) ((1) Center for Social and Affective Neuroscience,
Link\"oping University, Sweden) | Naturalistic stimuli in touch research | 16 pages, 1 figure, commentary/review paper. Keywords: touch,
haptics, sensory systems, naturalistic stimuli, social touch | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Neural mechanisms of touch are typically studied in laboratory settings using
robotic or other types of well-controlled devices. Such stimuli are very
different from highly complex naturalistic human-to-human touch interactions.
The lack of scientifically useful naturalistic stimuli hampers progress,
particularly in social touch research. Vision science, on the other hand, has
benefitted from inventions such as virtual reality systems that have provided
researchers with precision control of naturalistic stimuli. In the field of
touch research, producing and manipulating stimuli is particularly challenging
due to the complexity of skin mechanics. Here we review the history of touch
neuroscience focusing on the contrast between strictly controlled and
naturalistic stimuli and compare with vision science. We discuss new methods
that may overcome the obstacles with precision-controlled tactile stimuli, and
recent successes in naturalistic texture production. In social touch research,
precise tracking and measurement of naturalistic human-to-human touch
interactions offers exciting new possibilities.
| [
{
"created": "Wed, 26 Jan 2022 14:01:51 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Apr 2022 16:47:35 GMT",
"version": "v2"
}
] | 2022-04-21 | [
[
"Maallo",
"Anne Margarette S.",
""
],
[
"Duvernoy",
"Basil",
""
],
[
"Olausson",
"Håkan",
""
],
[
"McIntyre",
"Sarah",
""
]
] | Neural mechanisms of touch are typically studied in laboratory settings using robotic or other types of well-controlled devices. Such stimuli are very different from highly complex naturalistic human-to-human touch interactions. The lack of scientifically useful naturalistic stimuli hampers progress, particularly in social touch research. Vision science, on the other hand, has benefitted from inventions such as virtual reality systems that have provided researchers with precision control of naturalistic stimuli. In the field of touch research, producing and manipulating stimuli is particularly challenging due to the complexity of skin mechanics. Here we review the history of touch neuroscience focusing on the contrast between strictly controlled and naturalistic stimuli and compare with vision science. We discuss new methods that may overcome the obstacles with precision-controlled tactile stimuli, and recent successes in naturalistic texture production. In social touch research, precise tracking and measurement of naturalistic human-to-human touch interactions offers exciting new possibilities. |
2306.00041 | Wenting Ye | Wenting Ye, Chen Li, Yang Xie, Wen Zhang, Hong-Yu Zhang, Bowen Wang,
Debo Cheng, Zaiwen Feng | Causal Intervention for Measuring Confidence in Drug-Target Interaction
Prediction | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying and discovering drug-target interactions(DTIs) are vital steps in
drug discovery and development. They play a crucial role in assisting
scientists in finding new drugs and accelerating the drug development process.
Recently, knowledge graph and knowledge graph embedding (KGE) models have made
rapid advancements and demonstrated impressive performance in drug discovery.
However, such models lack authenticity and accuracy in drug target
identification, leading to an increased misjudgment rate and reduced drug
development efficiency. To address these issues, we focus on the problem of
drug-target interactions, with knowledge mapping as the core technology.
Specifically, a causal intervention-based confidence measure is employed to
assess the triplet score to improve the accuracy of the drug-target interaction
prediction model. Experimental results demonstrate that the developed
confidence measurement method based on causal intervention can significantly
enhance the accuracy of DTI link prediction, particularly for high-precision
models. The predicted results are more valuable in guiding the design and
development of subsequent drug development experiments, thereby significantly
improving the efficiency of drug development.
| [
{
"created": "Wed, 31 May 2023 13:13:45 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Nov 2023 13:36:53 GMT",
"version": "v2"
}
] | 2023-11-15 | [
[
"Ye",
"Wenting",
""
],
[
"Li",
"Chen",
""
],
[
"Xie",
"Yang",
""
],
[
"Zhang",
"Wen",
""
],
[
"Zhang",
"Hong-Yu",
""
],
[
"Wang",
"Bowen",
""
],
[
"Cheng",
"Debo",
""
],
[
"Feng",
"Zaiwen",
... | Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive performance in drug discovery. However, such models lack authenticity and accuracy in drug target identification, leading to an increased misjudgment rate and reduced drug development efficiency. To address these issues, we focus on the problem of drug-target interactions, with knowledge mapping as the core technology. Specifically, a causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model. Experimental results demonstrate that the developed confidence measurement method based on causal intervention can significantly enhance the accuracy of DTI link prediction, particularly for high-precision models. The predicted results are more valuable in guiding the design and development of subsequent drug development experiments, thereby significantly improving the efficiency of drug development. |
2303.09084 | Trung Phan | Kien T. Pham, Duc M. Nguyen, Duy V. Tran, Vi D. Ao, Huy D. Tran, Tuan
K. Do and Trung V. Phan | Stress-Induced Mutagenesis Can Further Boost Population Success in
Static Ecology | null | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | We have developed a mathematical model that captures stress-induced
mutagenesis, a fundamental aspect of pathogenic and neoplastic evolutionary
dynamics, on the fitness landscape with multiple relevant genetic traits as a
high-dimensional Euclidean space. In this framework, stress-induced mutagenesis
manifests as a heterogeneous diffusion process. We show how increasing
mutations, and thus reducing exploitation, in a static ecology with fixed
carrying capacity and maximum growth rates, can paradoxically boost population
size. Remarkably, this unexpected biophysical phenomenon applies universally to
any number of traits.
| [
{
"created": "Thu, 16 Mar 2023 05:04:23 GMT",
"version": "v1"
}
] | 2023-03-17 | [
[
"Pham",
"Kien T.",
""
],
[
"Nguyen",
"Duc M.",
""
],
[
"Tran",
"Duy V.",
""
],
[
"Ao",
"Vi D.",
""
],
[
"Tran",
"Huy D.",
""
],
[
"Do",
"Tuan K.",
""
],
[
"Phan",
"Trung V.",
""
]
] | We have developed a mathematical model that captures stress-induced mutagenesis, a fundamental aspect of pathogenic and neoplastic evolutionary dynamics, on the fitness landscape with multiple relevant genetic traits as a high-dimensional Euclidean space. In this framework, stress-induced mutagenesis manifests as a heterogeneous diffusion process. We show how increasing mutations, and thus reducing exploitation, in a static ecology with fixed carrying capacity and maximum growth rates, can paradoxically boost population size. Remarkably, this unexpected biophysical phenomenon applies universally to any number of traits. |
q-bio/0312013 | Cyrill Muratov | Cyrill B. Muratov, Eric Vanden-Eijnden, Weinan E | Noise-driven transition to quasi-deterministic limit cycle dynamics in
excitable systems | 4 pages, 3 figures submitted to PRL | null | null | null | q-bio.QM q-bio.NC | null | The effect of small-amplitude noise on excitable systems with large
time-scale separation is analyzed. It is found that small random perturbations
of the fast excitatory variable result in the onset of a quasi-deterministic
limit cycle behavior, absent without noise. The limit cycle is established at a
critical value of the amplitude of the noise, and its period is nontrivially
determined by the relationship between the noise amplitude and the time scale
ratio. It is argued that this effect might provide a mechanism by which the
function of biological systems operating in noisy environments can be robustly
controlled by the level of the noise.
| [
{
"created": "Wed, 10 Dec 2003 00:44:18 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Muratov",
"Cyrill B.",
""
],
[
"Vanden-Eijnden",
"Eric",
""
],
[
"E",
"Weinan",
""
]
] | The effect of small-amplitude noise on excitable systems with large time-scale separation is analyzed. It is found that small random perturbations of the fast excitatory variable result in the onset of a quasi-deterministic limit cycle behavior, absent without noise. The limit cycle is established at a critical value of the amplitude of the noise, and its period is nontrivially determined by the relationship between the noise amplitude and the time scale ratio. It is argued that this effect might provide a mechanism by which the function of biological systems operating in noisy environments can be robustly controlled by the level of the noise. |
1312.6336 | Sen Pei | Sen Pei, Shaoting Tang, Shu Yan, Shijin Jiang, Xiao Zhang, Zhiming
Zheng | How to enhance the dynamic range of excitatory-inhibitory excitable
networks | 7 pages, 9 figures | Physical Review E 86 (2), 021909, 2012 | 10.1103/PhysRevE.86.021909 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the collective dynamics of excitatory-inhibitory excitable
networks in response to external stimuli. How to enhance dynamic range, which
represents the ability of networks to encode external stimuli, is crucial to
many applications. We regard the system as a two-layer network (E-Layer and
I-Layer) and explore the criticality and dynamic range on diverse networks.
Interestingly, we find that phase transition occurs when the dominant
eigenvalue of E-layer's weighted adjacency matrix is exactly one, which is only
determined by the topology of E-Layer. Meanwhile, it is shown that dynamic
range is maximized at critical state. Based on theoretical analysis, we propose
an inhibitory factor for each excitatory node. We suggest that if nodes with
high inhibitory factors are cut out from I-Layer, dynamic range could be
further enhanced. However, because of the sparseness of networks and passive
function of inhibitory nodes, the improvement is relatively small compared
tooriginal dynamic range. Even so, this provides a strategy to enhance dynamic
range.
| [
{
"created": "Sun, 22 Dec 2013 03:05:19 GMT",
"version": "v1"
}
] | 2013-12-24 | [
[
"Pei",
"Sen",
""
],
[
"Tang",
"Shaoting",
""
],
[
"Yan",
"Shu",
""
],
[
"Jiang",
"Shijin",
""
],
[
"Zhang",
"Xiao",
""
],
[
"Zheng",
"Zhiming",
""
]
] | We investigate the collective dynamics of excitatory-inhibitory excitable networks in response to external stimuli. How to enhance dynamic range, which represents the ability of networks to encode external stimuli, is crucial to many applications. We regard the system as a two-layer network (E-Layer and I-Layer) and explore the criticality and dynamic range on diverse networks. Interestingly, we find that phase transition occurs when the dominant eigenvalue of E-layer's weighted adjacency matrix is exactly one, which is only determined by the topology of E-Layer. Meanwhile, it is shown that dynamic range is maximized at critical state. Based on theoretical analysis, we propose an inhibitory factor for each excitatory node. We suggest that if nodes with high inhibitory factors are cut out from I-Layer, dynamic range could be further enhanced. However, because of the sparseness of networks and passive function of inhibitory nodes, the improvement is relatively small compared tooriginal dynamic range. Even so, this provides a strategy to enhance dynamic range. |
0801.3344 | Nicolas Clauvelin | N. Clauvelin, B. Audoly and S. Neukirch | Mechanical response of plectonemic DNA: an analytical solution | 14 pages, 4 figures | null | 10.1021/ma702713x | null | q-bio.BM | null | We consider an elastic rod model for twisted DNA in the plectonemic regime.
The molecule is treated as an impenetrable tube with an effective, adjustable
radius. The model is solved analytically and we derive formulas for the contact
pressure, twisting moment and geometrical parameters of the supercoiled region.
We apply our model to magnetic tweezer experiments of a DNA molecule subjected
to a tensile force and a torque, and extract mechanical and geometrical
quantities from the linear part of the experimental response curve. These
reconstructed values are derived in a self-contained manner, and are found to
be consistent with those available in the literature.
| [
{
"created": "Tue, 22 Jan 2008 11:36:36 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Clauvelin",
"N.",
""
],
[
"Audoly",
"B.",
""
],
[
"Neukirch",
"S.",
""
]
] | We consider an elastic rod model for twisted DNA in the plectonemic regime. The molecule is treated as an impenetrable tube with an effective, adjustable radius. The model is solved analytically and we derive formulas for the contact pressure, twisting moment and geometrical parameters of the supercoiled region. We apply our model to magnetic tweezer experiments of a DNA molecule subjected to a tensile force and a torque, and extract mechanical and geometrical quantities from the linear part of the experimental response curve. These reconstructed values are derived in a self-contained manner, and are found to be consistent with those available in the literature. |
1506.07299 | Guido Tiana | Guido Tiana | The effect of disorder in the contact probability of elongated
conformations of biopolymers | null | Phys. Rev. E 92, 010702 (2015) | 10.1103/PhysRevE.92.010702 | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biopolymers are characterized by heterogeneous interactions, and usually
perform their biological tasks forming contacts within domains of limited size.
Combining polymer theory with a replica approach, we study the scaling
properties of the probability of contact formation in random heteropolymers as
a function of their linear distance. It is found that close or above the
theta--point, it is possible to define a contact probability which is typical
(i.e. "self-averaging") for different realizations of the heterogeneous
interactions, and which displays an exponential cut--off, dependent on
temperature and on the interaction range. In many cases this cut--off is
comparable with the typical sizes of domains in biopolymers. While it is well
known that disorder causes interesting effects at low temperature, the behavior
elucidated in the present study is an example of a non--trivial effect at high
temperature.
| [
{
"created": "Wed, 24 Jun 2015 09:50:05 GMT",
"version": "v1"
}
] | 2015-08-05 | [
[
"Tiana",
"Guido",
""
]
] | Biopolymers are characterized by heterogeneous interactions, and usually perform their biological tasks forming contacts within domains of limited size. Combining polymer theory with a replica approach, we study the scaling properties of the probability of contact formation in random heteropolymers as a function of their linear distance. It is found that close or above the theta--point, it is possible to define a contact probability which is typical (i.e. "self-averaging") for different realizations of the heterogeneous interactions, and which displays an exponential cut--off, dependent on temperature and on the interaction range. In many cases this cut--off is comparable with the typical sizes of domains in biopolymers. While it is well known that disorder causes interesting effects at low temperature, the behavior elucidated in the present study is an example of a non--trivial effect at high temperature. |
2303.16429 | Tiago Lubiana | Tiago Lubiana, Rafael Lopes, Pedro Medeiros, Juan Carlo Silva, Andre
Nicolau Aquime Goncalves, Vinicius Maracaja-Coutinho, and Helder I Nakaya | Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in
Computational Biology | 14 pages, 1 figure | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-sa/4.0/ | The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the
scientific community. ChatGPT is a general-purpose chatbot powered by large
language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous
fields, including computational biology. In this article, we offer ten tips
based on our experience with ChatGPT to assist computational biologists in
optimizing their workflows. We have collected relevant prompts and reviewed the
nascent literature in the field, compiling tips we project to remain pertinent
for future ChatGPT and LLM iterations, ranging from code refactoring to
scientific writing to prompt engineering. We hope our work will help
bioinformaticians to complement their workflows while staying aware of the
various implications of using this technology. Additionally, to track new and
creative applications for bioinformatics tools such as ChatGPT, we have
established a GitHub repository at
https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical
adherence to ChatGPT and other LLMs will increase the efficiency of
computational biologists, ultimately advancing the pace of scientific discovery
in the life sciences.
| [
{
"created": "Wed, 29 Mar 2023 03:24:42 GMT",
"version": "v1"
}
] | 2023-03-30 | [
[
"Lubiana",
"Tiago",
""
],
[
"Lopes",
"Rafael",
""
],
[
"Medeiros",
"Pedro",
""
],
[
"Silva",
"Juan Carlo",
""
],
[
"Goncalves",
"Andre Nicolau Aquime",
""
],
[
"Maracaja-Coutinho",
"Vinicius",
""
],
[
"Nakaya",
... | The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist computational biologists in optimizing their workflows. We have collected relevant prompts and reviewed the nascent literature in the field, compiling tips we project to remain pertinent for future ChatGPT and LLM iterations, ranging from code refactoring to scientific writing to prompt engineering. We hope our work will help bioinformaticians to complement their workflows while staying aware of the various implications of using this technology. Additionally, to track new and creative applications for bioinformatics tools such as ChatGPT, we have established a GitHub repository at https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical adherence to ChatGPT and other LLMs will increase the efficiency of computational biologists, ultimately advancing the pace of scientific discovery in the life sciences. |
q-bio/0703066 | Supratim Sengupta | Supratim Sengupta, Xiaoguang Yang, Paul G. Higgs | The Mechanisms of Codon Reassignments in Mitochondrial Genetic Codes | 53 pages (45 pages, including 4 figures + 8 pages of supplementary
information). To appear in J.Mol.Evol | J. Mol. Evol. 64 (2007) 662-688 | null | null | q-bio.PE q-bio.GN | null | Many cases of non-standard genetic codes are known in mitochondrial genomes.
We carry out analysis of phylogeny and codon usage of organisms for which the
complete mitochondrial genome is available, and we determine the most likely
mechanism for codon reassignment in each case. Reassignment events can be
classified according to the gain-loss framework. The gain represents the
appearance of a new tRNA for the reassigned codon or the change of an existing
tRNA such that it gains the ability to pair with the codon. The loss represents
the deletion of a tRNA or the change in a tRNA so that it no longer translates
the codon. One possible mechanism is Codon Disappearance, where the codon
disappears from the genome prior to the gain and loss events. In the
alternative mechanisms the codon does not disappear. In the Unassigned Codon
mechanism, the loss occurs first, whereas in the Ambiguous Intermediate
mechanism, the gain occurs first. Codon usage analysis gives clear evidence of
cases where the codon disappeared at the point of the reassignment and also
cases where it did not disappear. Codon disappearance is the probable
explanation for stop to sense reassignments and a small number of reassignments
of sense codons. However, the majority of sense to sense reassignments cannot
be explained by codon disappearance. In the latter cases, by analysis of the
presence or absence of tRNAs in the genome and of the changes in tRNA
sequences, it is sometimes possible to distinguish between the Unassigned Codon
and Ambiguous Intermediate mechanisms. We emphasize that not all reassignments
follow the same scenario and that it is necessary to consider the details of
each case carefully.
| [
{
"created": "Fri, 30 Mar 2007 10:09:57 GMT",
"version": "v1"
}
] | 2007-07-17 | [
[
"Sengupta",
"Supratim",
""
],
[
"Yang",
"Xiaoguang",
""
],
[
"Higgs",
"Paul G.",
""
]
] | Many cases of non-standard genetic codes are known in mitochondrial genomes. We carry out analysis of phylogeny and codon usage of organisms for which the complete mitochondrial genome is available, and we determine the most likely mechanism for codon reassignment in each case. Reassignment events can be classified according to the gain-loss framework. The gain represents the appearance of a new tRNA for the reassigned codon or the change of an existing tRNA such that it gains the ability to pair with the codon. The loss represents the deletion of a tRNA or the change in a tRNA so that it no longer translates the codon. One possible mechanism is Codon Disappearance, where the codon disappears from the genome prior to the gain and loss events. In the alternative mechanisms the codon does not disappear. In the Unassigned Codon mechanism, the loss occurs first, whereas in the Ambiguous Intermediate mechanism, the gain occurs first. Codon usage analysis gives clear evidence of cases where the codon disappeared at the point of the reassignment and also cases where it did not disappear. Codon disappearance is the probable explanation for stop to sense reassignments and a small number of reassignments of sense codons. However, the majority of sense to sense reassignments cannot be explained by codon disappearance. In the latter cases, by analysis of the presence or absence of tRNAs in the genome and of the changes in tRNA sequences, it is sometimes possible to distinguish between the Unassigned Codon and Ambiguous Intermediate mechanisms. We emphasize that not all reassignments follow the same scenario and that it is necessary to consider the details of each case carefully. |
1705.01436 | Sebastian James | Sebastian James, Olivia A. Bell, Muhammed A. M. Nazli, Rachel E.
Pearce, Jonathan Spencer, Katie Tyrrell, Phillip J. Paine, Timothy J. Heaton,
Sean Anderson, Mauro Da Lio, Kevin Gurney | Target-distractor Synchrony Affects Performance in a Novel Motor Task
for Studying Action Selection | 28 pages, 12 figures, journal article | null | 10.1371/journal.pone.0176945 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of action selection in humans can present challenges of task design
since our actions are usually defined by many degrees of freedom and therefore
occupy a large action-space. While saccadic eye-movement offers a more
constrained paradigm for investigating action selection, the study of
reach-and-grasp in upper limbs has often been defined by more complex
scenarios, not easily interpretable in terms of such selection. Here we present
a novel motor behaviour task which addresses this by limiting the action space
to a single degree of freedom in which subjects have to track (using a stylus)
a vertical coloured target line displayed on a tablet computer, whilst ignoring
a similarly oriented distractor line in a different colour. We ran this task
with 55 subjects and showed that, in agreement with previous studies, the
presence of the distractor generally increases the movement latency and
directional error rate. Further, we used two distractor conditions according to
whether the distractor's location changes asynchronously or synchronously with
the location of the target. We found that the asynchronous distractor yielded
poorer performance than its synchronous counterpart, with significantly higher
movement latencies and higher error rates. We interpret these results in an
action selection framework with two actions (move left or right) and competing
'action requests' offered by the target and distractor. As such, the results
provide insights into action selection performance in humans and supply data
for directly constraining future computational models therein.
| [
{
"created": "Wed, 3 May 2017 14:00:38 GMT",
"version": "v1"
}
] | 2017-05-04 | [
[
"James",
"Sebastian",
""
],
[
"Bell",
"Olivia A.",
""
],
[
"Nazli",
"Muhammed A. M.",
""
],
[
"Pearce",
"Rachel E.",
""
],
[
"Spencer",
"Jonathan",
""
],
[
"Tyrrell",
"Katie",
""
],
[
"Paine",
"Phillip J.",
... | The study of action selection in humans can present challenges of task design since our actions are usually defined by many degrees of freedom and therefore occupy a large action-space. While saccadic eye-movement offers a more constrained paradigm for investigating action selection, the study of reach-and-grasp in upper limbs has often been defined by more complex scenarios, not easily interpretable in terms of such selection. Here we present a novel motor behaviour task which addresses this by limiting the action space to a single degree of freedom in which subjects have to track (using a stylus) a vertical coloured target line displayed on a tablet computer, whilst ignoring a similarly oriented distractor line in a different colour. We ran this task with 55 subjects and showed that, in agreement with previous studies, the presence of the distractor generally increases the movement latency and directional error rate. Further, we used two distractor conditions according to whether the distractor's location changes asynchronously or synchronously with the location of the target. We found that the asynchronous distractor yielded poorer performance than its synchronous counterpart, with significantly higher movement latencies and higher error rates. We interpret these results in an action selection framework with two actions (move left or right) and competing 'action requests' offered by the target and distractor. As such, the results provide insights into action selection performance in humans and supply data for directly constraining future computational models therein. |
q-bio/0612023 | Cecile Caretta | C. Caretta Cartozo, D. Garlaschelli, C. Ricotta, M. Barthelemy, G.
Caldarelli | Quantifying the taxonomic diversity in real species communities | 12 pages, 4 figures | J. Phys. A: Math. Theor. 41, 224012 (2008) | 10.1088/1751-8113/41/22/224012 | null | q-bio.PE nlin.AO physics.data-an | null | We analyze several florae (collections of plant species populating specific
areas) in different geographic and climatic regions. For every list of species
we produce a taxonomic classification tree and we consider its statistical
properties. We find that regardless of the geographical location, the climate
and the environment all species collections have universal statistical
properties that we show to be also robust in time. We then compare observed
data sets with simulated communities obtained by randomly sampling a large pool
of species from all over the world. We find differences in the behavior of the
statistical properties of the corresponding taxonomic trees. Our results
suggest that it is possible to distinguish quantitatively real species
assemblages from random collections and thus demonstrate the existence of
correlations between species.
| [
{
"created": "Wed, 13 Dec 2006 13:56:28 GMT",
"version": "v1"
}
] | 2008-06-13 | [
[
"Cartozo",
"C. Caretta",
""
],
[
"Garlaschelli",
"D.",
""
],
[
"Ricotta",
"C.",
""
],
[
"Barthelemy",
"M.",
""
],
[
"Caldarelli",
"G.",
""
]
] | We analyze several florae (collections of plant species populating specific areas) in different geographic and climatic regions. For every list of species we produce a taxonomic classification tree and we consider its statistical properties. We find that regardless of the geographical location, the climate and the environment all species collections have universal statistical properties that we show to be also robust in time. We then compare observed data sets with simulated communities obtained by randomly sampling a large pool of species from all over the world. We find differences in the behavior of the statistical properties of the corresponding taxonomic trees. Our results suggest that it is possible to distinguish quantitatively real species assemblages from random collections and thus demonstrate the existence of correlations between species. |
2311.03411 | Chenwei Zhang | Chenwei Zhang, Jordan Lovrod, Boyan Beronov, Khanh Dao Duc, Anne
Condon | ViDa: Visualizing DNA hybridization trajectories with
biophysics-informed deep graph embeddings | Accepted to Machine Learning in Computational Biology as Oral
presentation and PMLR acceptance | null | null | null | q-bio.QM cs.AI cs.HC cs.LG q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Visualization tools can help synthetic biologists and molecular programmers
understand the complex reactive pathways of nucleic acid reactions, which can
be designed for many potential applications and can be modelled using a
continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization
approach for DNA reaction trajectories that uses a 2D embedding of the
secondary structure state space underlying the CTMC model. To this end, we
integrate a scattering transform of the secondary structure adjacency, a
variational autoencoder, and a nonlinear dimensionality reduction method. We
augment the training loss with domain-specific supervised terms that capture
both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA
hybridization reactions. Our results demonstrate that the domain-specific
features lead to significant quality improvements over the state-of-the-art in
DNA state space visualization, successfully separating different folding
pathways and thus providing useful insights into dominant reaction mechanisms.
| [
{
"created": "Mon, 6 Nov 2023 05:27:29 GMT",
"version": "v1"
}
] | 2023-11-08 | [
[
"Zhang",
"Chenwei",
""
],
[
"Lovrod",
"Jordan",
""
],
[
"Beronov",
"Boyan",
""
],
[
"Duc",
"Khanh Dao",
""
],
[
"Condon",
"Anne",
""
]
] | Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DNA reaction trajectories that uses a 2D embedding of the secondary structure state space underlying the CTMC model. To this end, we integrate a scattering transform of the secondary structure adjacency, a variational autoencoder, and a nonlinear dimensionality reduction method. We augment the training loss with domain-specific supervised terms that capture both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA hybridization reactions. Our results demonstrate that the domain-specific features lead to significant quality improvements over the state-of-the-art in DNA state space visualization, successfully separating different folding pathways and thus providing useful insights into dominant reaction mechanisms. |
1209.3829 | Jack Cowan | J D Cowan, J Neuman, and W van Drongelen | Self-organized criticality in a network of interacting neurons | 17 pages, 4 figures, submitted to Journal of Statistical Mechanics | null | 10.1088/1742-5468/2013/04/P04030 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper contains an analysis of a simple neural network that exhibits
self-organized criticality. Such criticality follows from the combination of a
simple neural network with an excitatory feedback loop that generates
bistability, in combination with an anti-Hebbian synapse in its input pathway.
Using the methods of statistical field theory, we show how one can formulate
the stochastic dynamics of such a network as the action of a path integral,
which we then investigate using renormalization group methods. The results
indicate that the network exhibits hysteresis in switching back and forward
between its two stable states, each of which loses its stability at a
saddle-node bifurcation. The renormalization group analysis shows that the
fluctuations in the neighborhood of such bifurcations have the signature of
directed percolation. Thus the network states undergo the neural analog of a
phase transition in the universality class of directed percolation. The network
replicates precisely the behavior of the original sand-pile model of Bak, Tang
& Wiesenfeld.
| [
{
"created": "Tue, 18 Sep 2012 02:25:50 GMT",
"version": "v1"
}
] | 2015-06-11 | [
[
"Cowan",
"J D",
""
],
[
"Neuman",
"J",
""
],
[
"van Drongelen",
"W",
""
]
] | This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway. Using the methods of statistical field theory, we show how one can formulate the stochastic dynamics of such a network as the action of a path integral, which we then investigate using renormalization group methods. The results indicate that the network exhibits hysteresis in switching back and forward between its two stable states, each of which loses its stability at a saddle-node bifurcation. The renormalization group analysis shows that the fluctuations in the neighborhood of such bifurcations have the signature of directed percolation. Thus the network states undergo the neural analog of a phase transition in the universality class of directed percolation. The network replicates precisely the behavior of the original sand-pile model of Bak, Tang & Wiesenfeld. |
2208.02552 | Amir Jahangiri | Amir Jahangiri, Xiao Han, Dmitry Lesovoy, Tatiana Agback, Peter
Agback, Adnane Achour, Vladislav Orekhov | NMR spectrum reconstruction as a pattern recognition problem | null | null | 10.1016/j.jmr.2022.107342 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A new deep neural network based on the WaveNet architecture (WNN) is
presented, which is designed to grasp specific patterns in the NMR spectra.
When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits
from pattern recognition of the corresponding point spread function (PSF)
pattern produced by each spectral peak resulting in the highest quality and
robust reconstruction of the NUS spectra as demonstrated in simulations and
exemplified in this work on 2D 1H-15N correlation spectra of three
representative globular proteins with different sizes: Ubiquitin (8.6 kDa),
Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also
demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13 HMQC
spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS
schedule, which so far was not fully exploited, can be used for designing new
powerful NMR processing techniques that surpass the existing algorithmic
methods.
| [
{
"created": "Thu, 4 Aug 2022 09:49:51 GMT",
"version": "v1"
}
] | 2022-12-05 | [
[
"Jahangiri",
"Amir",
""
],
[
"Han",
"Xiao",
""
],
[
"Lesovoy",
"Dmitry",
""
],
[
"Agback",
"Tatiana",
""
],
[
"Agback",
"Peter",
""
],
[
"Achour",
"Adnane",
""
],
[
"Orekhov",
"Vladislav",
""
]
] | A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of the corresponding point spread function (PSF) pattern produced by each spectral peak resulting in the highest quality and robust reconstruction of the NUS spectra as demonstrated in simulations and exemplified in this work on 2D 1H-15N correlation spectra of three representative globular proteins with different sizes: Ubiquitin (8.6 kDa), Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13 HMQC spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS schedule, which so far was not fully exploited, can be used for designing new powerful NMR processing techniques that surpass the existing algorithmic methods. |
1810.05077 | Abdullah Alchihabi | Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim, Sharlene D.
Newman, Fatos T. Yarman Vural | On the Brain Networks of Complex Problem Solving | null | null | null | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complex problem solving is a high level cognitive process which has been
thoroughly studied over the last decade. The Tower of London (TOL) is a task
that has been widely used to study problem-solving. In this study, we aim to
explore the underlying cognitive network dynamics among anatomical regions of
complex problem solving and its sub-phases, namely planning and execution. A
new brain network construction model establishing dynamic functional brain
networks using fMRI is proposed. The first step of the model is a preprocessing
pipeline that manages to decrease the spatial redundancy while increasing the
temporal resolution of the fMRI recordings. Then, dynamic brain networks are
estimated using artificial neural networks. The network properties of the
estimated brain networks are studied in order to identify regions of interest,
such as hubs and subgroups of densely connected brain regions. The major
similarities and dissimilarities of the network structure of planning and
execution phases are highlighted. Our findings show the hubs and clusters of
densely interconnected regions during both subtasks. It is observed that there
are more hubs during the planning phase compared to the execution phase, and
the clusters are more strongly connected during planning compared to execution.
| [
{
"created": "Wed, 10 Oct 2018 09:22:21 GMT",
"version": "v1"
}
] | 2018-10-12 | [
[
"Alchihabi",
"Abdullah",
""
],
[
"Ekmekci",
"Omer",
""
],
[
"Kivilcim",
"Baran B.",
""
],
[
"Newman",
"Sharlene D.",
""
],
[
"Vural",
"Fatos T. Yarman",
""
]
] | Complex problem solving is a high level cognitive process which has been thoroughly studied over the last decade. The Tower of London (TOL) is a task that has been widely used to study problem-solving. In this study, we aim to explore the underlying cognitive network dynamics among anatomical regions of complex problem solving and its sub-phases, namely planning and execution. A new brain network construction model establishing dynamic functional brain networks using fMRI is proposed. The first step of the model is a preprocessing pipeline that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using artificial neural networks. The network properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The major similarities and dissimilarities of the network structure of planning and execution phases are highlighted. Our findings show the hubs and clusters of densely interconnected regions during both subtasks. It is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution. |
2305.03257 | Tom Bertalan | Tianqi Cui, Tom S. Bertalan, Nelson Ndahiro, Pratik Khare, Michael
Betenbaugh, Costas Maranas, Ioannis G. Kevrekidis | Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell
Bioreactors | null | null | null | null | q-bio.QM cs.LG math.DS | http://creativecommons.org/licenses/by/4.0/ | Fed-batch culture is an established operation mode for the production of
biologics using mammalian cell cultures. Quantitative modeling integrates both
kinetics for some key reaction steps and optimization-driven metabolic flux
allocation, using flux balance analysis; this is known to lead to certain
mathematical inconsistencies. Here, we propose a physically-informed
data-driven hybrid model (a "gray box") to learn models of the dynamical
evolution of Chinese Hamster Ovary (CHO) cell bioreactors from process data.
The approach incorporates physical laws (e.g. mass balances) as well as kinetic
expressions for metabolic fluxes. Machine learning (ML) is then used to (a)
directly learn evolution equations (black-box modelling); (b) recover unknown
physical parameters ("white-box" parameter fitting) or -- importantly -- (c)
learn partially unknown kinetic expressions (gray-box modelling). We encode the
convex optimization step of the overdetermined metabolic biophysical system as
a differentiable, feed-forward layer into our architectures, connecting partial
physical knowledge with data-driven machine learning.
| [
{
"created": "Fri, 5 May 2023 03:09:33 GMT",
"version": "v1"
}
] | 2023-05-08 | [
[
"Cui",
"Tianqi",
""
],
[
"Bertalan",
"Tom S.",
""
],
[
"Ndahiro",
"Nelson",
""
],
[
"Khare",
"Pratik",
""
],
[
"Betenbaugh",
"Michael",
""
],
[
"Maranas",
"Costas",
""
],
[
"Kevrekidis",
"Ioannis G.",
""
... | Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies. Here, we propose a physically-informed data-driven hybrid model (a "gray box") to learn models of the dynamical evolution of Chinese Hamster Ovary (CHO) cell bioreactors from process data. The approach incorporates physical laws (e.g. mass balances) as well as kinetic expressions for metabolic fluxes. Machine learning (ML) is then used to (a) directly learn evolution equations (black-box modelling); (b) recover unknown physical parameters ("white-box" parameter fitting) or -- importantly -- (c) learn partially unknown kinetic expressions (gray-box modelling). We encode the convex optimization step of the overdetermined metabolic biophysical system as a differentiable, feed-forward layer into our architectures, connecting partial physical knowledge with data-driven machine learning. |
1205.0665 | Cencini Massimo Dr. | Massimo Cencini, Simone Pigolotti and Miguel A. Mu\~noz | What ecological factors shape species-area curves in neutral models? | 20 pages, 5 figures, merged with supplementary information (Accepted
on PLoS ONE) | PLoS ONE 7(6): e38232 (2012) | 10.1371/journal.pone.0038232 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding factors that shape biodiversity and species coexistence across
scales is of utmost importance in ecology, both theoretically and for
conservation policies. Species-area relationships (SARs), measuring how the
number of observed species increases upon enlarging the sampled area,
constitute a convenient tool for quantifying the spatial structure of
biodiversity. While general features of species-area curves are quite universal
across ecosystems, some quantitative aspects can change significantly. Several
attempts have been made to link these variations to ecological forces. Within
the framework of spatially explicit neutral models, here we scrutinize the
effect of varying the local population size (i.e. the number of individuals per
site) and the level of habitat saturation (allowing for empty sites). We
conclude that species-area curves become shallower when the local population
size increases, while habitat saturation, unless strongly violated, plays a
marginal role. Our findings provide a plausible explanation of why SARs for
microorganisms are flatter than those for larger organisms.
| [
{
"created": "Thu, 3 May 2012 09:55:49 GMT",
"version": "v1"
}
] | 2012-06-06 | [
[
"Cencini",
"Massimo",
""
],
[
"Pigolotti",
"Simone",
""
],
[
"Muñoz",
"Miguel A.",
""
]
] | Understanding factors that shape biodiversity and species coexistence across scales is of utmost importance in ecology, both theoretically and for conservation policies. Species-area relationships (SARs), measuring how the number of observed species increases upon enlarging the sampled area, constitute a convenient tool for quantifying the spatial structure of biodiversity. While general features of species-area curves are quite universal across ecosystems, some quantitative aspects can change significantly. Several attempts have been made to link these variations to ecological forces. Within the framework of spatially explicit neutral models, here we scrutinize the effect of varying the local population size (i.e. the number of individuals per site) and the level of habitat saturation (allowing for empty sites). We conclude that species-area curves become shallower when the local population size increases, while habitat saturation, unless strongly violated, plays a marginal role. Our findings provide a plausible explanation of why SARs for microorganisms are flatter than those for larger organisms. |
1708.08407 | Jinbo Xu | Sheng Wang, Zhen Li, Yizhou Yu and Jinbo Xu | Folding membrane proteins by deep transfer learning | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational elucidation of membrane protein (MP) structures is challenging
partially due to lack of sufficient solved structures for homology modeling.
Here we describe a high-throughput deep transfer learning method that first
predicts MP contacts by learning from non-membrane proteins (non-MPs) and then
predicting three-dimensional structure models using the predicted contacts as
distance restraints. Tested on 510 non-redundant MPs, our method has contact
prediction accuracy at least 0.18 better than existing methods, predicts
correct folds for 218 MPs (TMscore at least 0.6), and generates
three-dimensional models with RMSD less than 4 Angstrom and 5 Angstrom for 57
and 108 MPs, respectively. A rigorous blind test in the continuous automated
model evaluation (CAMEO) project shows that our method predicted
high-resolution three-dimensional models for two recent test MPs of 210
residues with RMSD close to 2 Angstrom. We estimated that our method could
predict correct folds for between 1,345 and 1,871 reviewed human multi-pass MPs
including a few hundred new folds, which shall facilitate the discovery of
drugs targeting at membrane proteins.
| [
{
"created": "Mon, 28 Aug 2017 16:38:52 GMT",
"version": "v1"
}
] | 2017-08-29 | [
[
"Wang",
"Sheng",
""
],
[
"Li",
"Zhen",
""
],
[
"Yu",
"Yizhou",
""
],
[
"Xu",
"Jinbo",
""
]
] | Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-membrane proteins (non-MPs) and then predicting three-dimensional structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs (TMscore at least 0.6), and generates three-dimensional models with RMSD less than 4 Angstrom and 5 Angstrom for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation (CAMEO) project shows that our method predicted high-resolution three-dimensional models for two recent test MPs of 210 residues with RMSD close to 2 Angstrom. We estimated that our method could predict correct folds for between 1,345 and 1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at membrane proteins. |
2004.01665 | Fidel Santamaria | Horacio G. Rotstein and Fidel Santamaria | Present and future frameworks of theoretical neuroscience: outcomes of a
community discussion | Workshop outcomes, 9 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We organized a workshop on the "Present and Future Frameworks of Theoretical
Neuroscience", with the support of the National Science Foundation. The
objective was to identify the challenges and strategies that this field will
need to tackle in order to incorporate vast and multi-scale streams of
experimental data from the technologies developed by the BRAIN initiative. The
participants, divided in workgroups, identified five key areas that, while not
exhaustive, cover multiple aspects of current challenges needed to be
developed: Dynamics-statistics; multi-scale integration; coding; brain-body
integration; and structure of neuroscience theories. While each area is
different, there were coincidences on finding theoretical paths to incorporate
biophysics, energetics, and ethology with more abstract coding and
computational approaches. Each workgroup has continued to work after the
meeting to develop the ideas seeded there, which are started to being
published. Here, we provide a perspective of the discussions of each workgroup
that point to building on the present foundations of theoretical neuroscience
and extend them by incorporating multi-scale information with the objective of
providing mechanistic insights into the nervous system.
| [
{
"created": "Fri, 3 Apr 2020 16:38:31 GMT",
"version": "v1"
}
] | 2020-04-06 | [
[
"Rotstein",
"Horacio G.",
""
],
[
"Santamaria",
"Fidel",
""
]
] | We organized a workshop on the "Present and Future Frameworks of Theoretical Neuroscience", with the support of the National Science Foundation. The objective was to identify the challenges and strategies that this field will need to tackle in order to incorporate vast and multi-scale streams of experimental data from the technologies developed by the BRAIN initiative. The participants, divided in workgroups, identified five key areas that, while not exhaustive, cover multiple aspects of current challenges needed to be developed: Dynamics-statistics; multi-scale integration; coding; brain-body integration; and structure of neuroscience theories. While each area is different, there were coincidences on finding theoretical paths to incorporate biophysics, energetics, and ethology with more abstract coding and computational approaches. Each workgroup has continued to work after the meeting to develop the ideas seeded there, which are started to being published. Here, we provide a perspective of the discussions of each workgroup that point to building on the present foundations of theoretical neuroscience and extend them by incorporating multi-scale information with the objective of providing mechanistic insights into the nervous system. |
2201.05262 | Adam Svahn | Adam J. Svahn, Sheryl L. Chang, Rebecca J. Rockett, Oliver M. Cliff,
Qinning Wang, Alicia Arnott, Marc Ramsperger, Tania C. Sorrell, Vitali
Sintchenko, Mikhail Prokopenko | Genome-wide networks reveal emergence of epidemic strains of Salmonella
Enteritidis | null | International Journal of Infectious Diseases, Volume 117 (2022),
65 - 73 | 10.1016/j.ijid.2022.01.056 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: To enhance monitoring of high-burden foodborne pathogens, there
is opportunity to combine pangenome data with network analysis.
Methods: Salmonella enterica subspecies Enterica serovar Enteritidis isolates
were referred to the New South Wales (NSW) Enteric Reference Laboratory between
August 2015 and December 2019 (1033 isolates in total), inclusive of a
confirmed outbreak. All isolates underwent whole genome sequencing. Distances
between genomes were quantified by in silico MLVA as well as core SNPs, which
informed construction of undirected networks. Prevalence-centrality spaces were
generated from the undirected networks. Components on the undirected SNP
network were considered alongside a phylogenetic tree representation.
Results: Outbreak isolates were identifiable as distinct components on the
MLVA and SNP networks. The MLVA network based centrality/prevalence space did
not delineate the outbreak, whereas the outbreak was clearly delineated in the
SNP network based centrality/prevalence space. Components on the undirected SNP
network showed a high concordance to the SNP clusters based on phylogenetic
analysis.
Conclusions: Bacterial whole genome data in network based analysis can
improve the resolution of population analysis. High concordance of network
components and SNP clusters is promising for rapid population analyses of
foodborne Salmonella spp. due to the low overhead of network analysis.
| [
{
"created": "Fri, 14 Jan 2022 00:44:51 GMT",
"version": "v1"
},
{
"created": "Mon, 31 Jan 2022 00:13:53 GMT",
"version": "v2"
}
] | 2022-03-08 | [
[
"Svahn",
"Adam J.",
""
],
[
"Chang",
"Sheryl L.",
""
],
[
"Rockett",
"Rebecca J.",
""
],
[
"Cliff",
"Oliver M.",
""
],
[
"Wang",
"Qinning",
""
],
[
"Arnott",
"Alicia",
""
],
[
"Ramsperger",
"Marc",
""
],
... | Objectives: To enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. Methods: Salmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico MLVA as well as core SNPs, which informed construction of undirected networks. Prevalence-centrality spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. Results: Outbreak isolates were identifiable as distinct components on the MLVA and SNP networks. The MLVA network based centrality/prevalence space did not delineate the outbreak, whereas the outbreak was clearly delineated in the SNP network based centrality/prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. Conclusions: Bacterial whole genome data in network based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. due to the low overhead of network analysis. |
0912.4502 | Steven Kelk | Leo van Iersel and Steven Kelk | A short note on the tractability of constructing phylogenetic networks
from clusters | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In [2] it was proven that the Cass algorithm is a polynomial-time algorithm
for constructing level<=2 networks from clusters. Here we demonstrate, for each
k>=0, a polynomial-time algorithm for constructing level-k phylogenetic
networks from clusters. Unlike Cass the algorithm scheme given here is only of
theoretical interest. It does, however, strengthen the hope that efficient
polynomial-time algorithms (and perhaps fixed parameter tractable algorithms)
exist for this problem.
| [
{
"created": "Tue, 22 Dec 2009 20:29:29 GMT",
"version": "v1"
}
] | 2009-12-23 | [
[
"van Iersel",
"Leo",
""
],
[
"Kelk",
"Steven",
""
]
] | In [2] it was proven that the Cass algorithm is a polynomial-time algorithm for constructing level<=2 networks from clusters. Here we demonstrate, for each k>=0, a polynomial-time algorithm for constructing level-k phylogenetic networks from clusters. Unlike Cass the algorithm scheme given here is only of theoretical interest. It does, however, strengthen the hope that efficient polynomial-time algorithms (and perhaps fixed parameter tractable algorithms) exist for this problem. |
2401.04745 | Hadi Mahmodi | Hadi Mahmodi, Christopher G. Poulton, Mathew N. Lesley, Glenn Oldham,
Hui Xin Ong, Steven J. Langford, Irina V. Kabakova | Principal Component Analysis in Application to Brillouin Microscopy Data | null | null | null | null | q-bio.QM physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brillouin microscopy has recently emerged as a new bio-imaging modality that
provides information on the micromechanical properties of biological materials,
cells and tissues. The data collected in a typical Brillouin microscopy
experiment represents the high-dimensional set of spectral information. Its
analysis requires non-trivial approaches due to subtlety in spectral variations
as well as spatial and spectral overlaps of measured features. This article
offers a guide to the application of Principal Component Analysis (PCA) for
processing Brillouin imaging data. Being unsupervised multivariate analysis,
PCA is well-suited to tackle processing of complex Brillouin spectra from
heterogeneous biological samples with minimal a priori information
requirements. We point out the importance of data pre-processing steps in order
to improve outcomes of PCA. We also present a strategy where PCA combined with
k-means clustering method can provide a working solution to data reconstruction
and deeper insights into sample composition, structure and mechanics.
| [
{
"created": "Tue, 9 Jan 2024 09:58:01 GMT",
"version": "v1"
}
] | 2024-01-11 | [
[
"Mahmodi",
"Hadi",
""
],
[
"Poulton",
"Christopher G.",
""
],
[
"Lesley",
"Mathew N.",
""
],
[
"Oldham",
"Glenn",
""
],
[
"Ong",
"Hui Xin",
""
],
[
"Langford",
"Steven J.",
""
],
[
"Kabakova",
"Irina V.",
"... | Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the micromechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics. |
2004.14533 | Krishna Dasaratha | Krishna Dasaratha | Virus Dynamics with Behavioral Responses | null | null | null | null | q-bio.PE econ.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by epidemics such as COVID-19, we study the spread of a contagious
disease when behavior responds to the disease's prevalence. We extend the SIR
epidemiological model to include endogenous meeting rates. Individuals benefit
from economic activity, but activity involves interactions with potentially
infected individuals. The main focus is a theoretical analysis of contagion
dynamics and behavioral responses to changes in risk. We obtain a simple
condition for when public-health interventions or variants of a disease will
have paradoxical effects on infection rates due to risk compensation.
Behavioral responses are most likely to undermine public-health interventions
near the peak of severe diseases.
| [
{
"created": "Thu, 30 Apr 2020 01:16:31 GMT",
"version": "v1"
},
{
"created": "Tue, 12 May 2020 03:03:15 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Jun 2020 13:58:23 GMT",
"version": "v3"
},
{
"created": "Thu, 3 Feb 2022 18:58:51 GMT",
"version": "v4"
},
{
"cr... | 2023-09-25 | [
[
"Dasaratha",
"Krishna",
""
]
] | Motivated by epidemics such as COVID-19, we study the spread of a contagious disease when behavior responds to the disease's prevalence. We extend the SIR epidemiological model to include endogenous meeting rates. Individuals benefit from economic activity, but activity involves interactions with potentially infected individuals. The main focus is a theoretical analysis of contagion dynamics and behavioral responses to changes in risk. We obtain a simple condition for when public-health interventions or variants of a disease will have paradoxical effects on infection rates due to risk compensation. Behavioral responses are most likely to undermine public-health interventions near the peak of severe diseases. |
1806.07365 | Trang-Anh Estelle Nghiem | Trang-Anh Nghiem, Jean-Marc Lina, Matteo di Volo, Cristiano Capone,
Alan C. Evans, Alain Destexhe, and Jennifer S. Goldman | State equation from the spectral structure of human brain activity | 6 pages, 4 figures | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural electromagnetic (EM) signals recorded non-invasively from individual
human subjects vary in complexity and magnitude. Nonetheless, variation in
neural activity has been difficult to quantify and interpret, due to complex,
broad-band features in the frequency domain. Studying signals recorded with
magnetoencephalography (MEG) from healthy young adult subjects while in resting
and active states, a systematic framework inspired by thermodynamics is applied
to neural EM signals. Despite considerable inter-subject variation in terms of
spectral entropy and energy across time epochs, data support the existence of a
robust and linear relationship defining an effective state equation, with
higher energy and lower entropy in the resting state compared to active,
consistently across subjects. Mechanisms underlying the emergence of
relationships between empirically measured effective state functions are
further investigated using a model network of coupled oscillators, suggesting
an interplay between noise and coupling strength can account for coherent
variation of empirically observed quantities. Taken together, the results show
macroscopic neural observables follow a robust, non-trivial conservation rule
for energy modulation and information generation.
| [
{
"created": "Tue, 19 Jun 2018 17:49:40 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Jul 2018 14:45:06 GMT",
"version": "v2"
}
] | 2018-07-04 | [
[
"Nghiem",
"Trang-Anh",
""
],
[
"Lina",
"Jean-Marc",
""
],
[
"di Volo",
"Matteo",
""
],
[
"Capone",
"Cristiano",
""
],
[
"Evans",
"Alan C.",
""
],
[
"Destexhe",
"Alain",
""
],
[
"Goldman",
"Jennifer S.",
""
... | Neural electromagnetic (EM) signals recorded non-invasively from individual human subjects vary in complexity and magnitude. Nonetheless, variation in neural activity has been difficult to quantify and interpret, due to complex, broad-band features in the frequency domain. Studying signals recorded with magnetoencephalography (MEG) from healthy young adult subjects while in resting and active states, a systematic framework inspired by thermodynamics is applied to neural EM signals. Despite considerable inter-subject variation in terms of spectral entropy and energy across time epochs, data support the existence of a robust and linear relationship defining an effective state equation, with higher energy and lower entropy in the resting state compared to active, consistently across subjects. Mechanisms underlying the emergence of relationships between empirically measured effective state functions are further investigated using a model network of coupled oscillators, suggesting an interplay between noise and coupling strength can account for coherent variation of empirically observed quantities. Taken together, the results show macroscopic neural observables follow a robust, non-trivial conservation rule for energy modulation and information generation. |
1601.07970 | Seung Ki Baek | Seung Ki Baek, Hyeong-Chai Jeong, Christian Hilbe, and Martin A. Nowak | Comparing reactive and memory-one strategies of direct reciprocity | 18 pages, 7 figures | Sci. Rep. 6, 25676 (2016) | 10.1038/srep25676 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Direct reciprocity is a mechanism for the evolution of cooperation based on
repeated interactions. When individuals meet repeatedly, they can use
conditional strategies to enforce cooperative outcomes that would not be
feasible in one-shot social dilemmas. Direct reciprocity requires that
individuals keep track of their past interactions and find the right response.
However, there are natural bounds on strategic complexity: Humans find it
difficult to remember past interactions accurately, especially over long
timespans. Given these limitations, it is natural to ask how complex strategies
need to be for cooperation to evolve. Here, we study stochastic evolutionary
game dynamics in finite populations to systematically compare the evolutionary
performance of reactive strategies, which only respond to the co-player's
previous move, and memory-one strategies, which take into account the own and
the co-player's previous move. In both cases, we compare deterministic strategy
and stochastic strategy spaces. For reactive strategies and small costs, we
find that stochasticity benefits cooperation, because it allows for
generous-tit-for-tat. For memory one strategies and small costs, we find that
stochasticity does not increase the propensity for cooperation, because the
deterministic rule of win-stay, lose-shift works best. For memory one
strategies and large costs, however, stochasticity can augment cooperation.
| [
{
"created": "Fri, 29 Jan 2016 02:57:37 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Mar 2016 05:17:49 GMT",
"version": "v2"
},
{
"created": "Mon, 23 May 2016 05:49:42 GMT",
"version": "v3"
}
] | 2016-05-24 | [
[
"Baek",
"Seung Ki",
""
],
[
"Jeong",
"Hyeong-Chai",
""
],
[
"Hilbe",
"Christian",
""
],
[
"Nowak",
"Martin A.",
""
]
] | Direct reciprocity is a mechanism for the evolution of cooperation based on repeated interactions. When individuals meet repeatedly, they can use conditional strategies to enforce cooperative outcomes that would not be feasible in one-shot social dilemmas. Direct reciprocity requires that individuals keep track of their past interactions and find the right response. However, there are natural bounds on strategic complexity: Humans find it difficult to remember past interactions accurately, especially over long timespans. Given these limitations, it is natural to ask how complex strategies need to be for cooperation to evolve. Here, we study stochastic evolutionary game dynamics in finite populations to systematically compare the evolutionary performance of reactive strategies, which only respond to the co-player's previous move, and memory-one strategies, which take into account the own and the co-player's previous move. In both cases, we compare deterministic strategy and stochastic strategy spaces. For reactive strategies and small costs, we find that stochasticity benefits cooperation, because it allows for generous-tit-for-tat. For memory one strategies and small costs, we find that stochasticity does not increase the propensity for cooperation, because the deterministic rule of win-stay, lose-shift works best. For memory one strategies and large costs, however, stochasticity can augment cooperation. |
1708.09273 | Dhananjay Suresh | Angela B. Javurek, Dhananjay Suresh, William G. Spollen, Marcia L.
Hart, Sarah A. Hansen, Mark R. Ellersieck, Nathan J. Bivens, Scott A. Givan,
Anandhi Upendran, Raghuraman Kannan, Cheryl S. Rosenfeld | Gut Dysbiosis and Neurobehavioral Alterations in Rats Exposed to Silver
Nanoparticles | 14 figures, 15 pages | Scientific Reports 7, Article number: 2822 (2017) | 10.1038/s41598-017-02880-0 | null | q-bio.TO q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Due to their antimicrobial properties, silver nanoparticles (AgNPs) are being
used in non-edible and edible consumer products. It is not clear though if
exposure to these chemicals can exert toxic effects on the host and gut
microbiome. Conflicting studies have been reported on whether AgNPs result in
gut dysbiosis and other changes within the host. We sought to examine whether
exposure of Sprague-Dawley male rats for two weeks to different shapes of
AgNPs, cube (AgNC) and sphere (AgNS) affects gut microbiota, select behaviors,
and induces histopathological changes in the gastrointestinal system and brain.
In the elevated plus maze (EPM), AgNS-exposed rats showed greater number of
entries into closed arms and center compared to controls and those exposed to
AgNC. AgNS and AgNC treated groups had select reductions in gut microbiota
relative to controls. Clostridium spp., Bacteroides uniformis,
Christensenellaceae, and Coprococcus eutactus were decreased in AgNC exposed
group, whereas, Oscillospira spp., Dehalobacterium spp., Peptococcaeceae,
Corynebacterium spp., Aggregatibacter pneumotropica were reduced in AgNS
exposed group. Bacterial reductions correlated with select behavioral changes
measured in the EPM. No significant histopathological changes were evident in
the gastrointestinal system or brain. Findings suggest short-term exposure to
AgNS or AgNC can lead to behavioral and gut microbiome changes.
| [
{
"created": "Thu, 24 Aug 2017 21:47:42 GMT",
"version": "v1"
}
] | 2017-08-31 | [
[
"Javurek",
"Angela B.",
""
],
[
"Suresh",
"Dhananjay",
""
],
[
"Spollen",
"William G.",
""
],
[
"Hart",
"Marcia L.",
""
],
[
"Hansen",
"Sarah A.",
""
],
[
"Ellersieck",
"Mark R.",
""
],
[
"Bivens",
"Nathan J.",... | Due to their antimicrobial properties, silver nanoparticles (AgNPs) are being used in non-edible and edible consumer products. It is not clear though if exposure to these chemicals can exert toxic effects on the host and gut microbiome. Conflicting studies have been reported on whether AgNPs result in gut dysbiosis and other changes within the host. We sought to examine whether exposure of Sprague-Dawley male rats for two weeks to different shapes of AgNPs, cube (AgNC) and sphere (AgNS) affects gut microbiota, select behaviors, and induces histopathological changes in the gastrointestinal system and brain. In the elevated plus maze (EPM), AgNS-exposed rats showed greater number of entries into closed arms and center compared to controls and those exposed to AgNC. AgNS and AgNC treated groups had select reductions in gut microbiota relative to controls. Clostridium spp., Bacteroides uniformis, Christensenellaceae, and Coprococcus eutactus were decreased in AgNC exposed group, whereas, Oscillospira spp., Dehalobacterium spp., Peptococcaeceae, Corynebacterium spp., Aggregatibacter pneumotropica were reduced in AgNS exposed group. Bacterial reductions correlated with select behavioral changes measured in the EPM. No significant histopathological changes were evident in the gastrointestinal system or brain. Findings suggest short-term exposure to AgNS or AgNC can lead to behavioral and gut microbiome changes. |
1311.1555 | Xiaohua Zhou | Xiaohua Zhou and Shengli Zhang | Manipulate the coiling and uncoiling movements of Lepidoptera proboscis
by its conformation optimizing | 7 pages, 6 figures | null | null | null | q-bio.TO cond-mat.mtrl-sci cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many kinds of adult Lepidoptera insects possess a long proboscis which is
used to suck liquids and has the coiling and uncoiling movements. Although
experiments revealed qualitatively that the coiling movement is governed by the
hydraulic mechanism and the uncoiling movement is due to the musculature and
the elasticity, it needs a quantitative investigation to reveal how insects
achieve these behaviors accurately. Here a quasi-one-dimensional (Q1D)
curvature elastica model is proposed to reveal the mechanism of these
behaviors. We find that the functions of internal stipes muscle and basal
galeal muscle which locate at the bottom of proboscis are to adjust the initial
states in the coiling and uncoiling processes, respectively. The function of
internal galeal muscle which exists along proboscis is to adjust the line
tension. The knee bend shape is due to the local maximal spontaneous curvature
and is an advantage for nectar-feeding butterfly. When there is no knee bend,
the proboscis of fruit-piercing butterfly is easy to achieve the piercing
movement which induced by the increase of internal hydraulic pressure. All of
the results are in good agreement with experiential observation. Our study
provides a revelatory method to investigate the mechanical behaviors of other
1D biologic structures, such as proboscis of marine snail and elephant. Our
method and results are also significant in designing the bionic devices.
| [
{
"created": "Thu, 7 Nov 2013 01:07:45 GMT",
"version": "v1"
}
] | 2013-11-08 | [
[
"Zhou",
"Xiaohua",
""
],
[
"Zhang",
"Shengli",
""
]
] | Many kinds of adult Lepidoptera insects possess a long proboscis which is used to suck liquids and has the coiling and uncoiling movements. Although experiments revealed qualitatively that the coiling movement is governed by the hydraulic mechanism and the uncoiling movement is due to the musculature and the elasticity, it needs a quantitative investigation to reveal how insects achieve these behaviors accurately. Here a quasi-one-dimensional (Q1D) curvature elastica model is proposed to reveal the mechanism of these behaviors. We find that the functions of internal stipes muscle and basal galeal muscle which locate at the bottom of proboscis are to adjust the initial states in the coiling and uncoiling processes, respectively. The function of internal galeal muscle which exists along proboscis is to adjust the line tension. The knee bend shape is due to the local maximal spontaneous curvature and is an advantage for nectar-feeding butterfly. When there is no knee bend, the proboscis of fruit-piercing butterfly is easy to achieve the piercing movement which induced by the increase of internal hydraulic pressure. All of the results are in good agreement with experiential observation. Our study provides a revelatory method to investigate the mechanical behaviors of other 1D biologic structures, such as proboscis of marine snail and elephant. Our method and results are also significant in designing the bionic devices. |
1802.01612 | Debayan Chakraborty | Debayan Chakraborty, Naoto Hori, and Dave Thirumalai | Sequence-dependent Three Interaction Site (TIS) Model for Single and
Double-stranded DNA | null | null | 10.1021/acs.jctc.8b00091 | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a robust coarse-grained model for single and double stranded DNA
by representing each nucleotide by three interaction sites (TIS) located at the
centers of mass of sugar, phosphate, and base. The resulting TIS model includes
base-stacking, hydrogen bond, and electrostatic interactions as well as
bond-stretching and bond angle potentials that account for the polymeric nature
of DNA. The choices of force constants for stretching and the bending
potentials were guided by a Boltzmann inversion procedure using a large
representative set of DNA structures extracted from the Protein Data Bank. Some
of the parameters in the stacking interactions were calculated using a learning
procedure, which ensured that the experimentally measured melting temperatures
of dimers are faithfully reproduced. Without any further adjustments, the
calculations based on the TIS model reproduces the experimentally measured salt
and sequence dependence of the size of single stranded DNA (ssDNA), as well as
the persistence lengths of poly(dA) and poly(dT) chains. Interestingly, upon
application of mechanical force the extension of poly(dA) exhibits a plateau,
which we trace to the formation of stacked helical domains. In contrast, the
force-extension curve (FEC) of poly(dT) is entropic in origin, and could be
described by a standard polymer model. We also show that the persistence length
of double stranded DNA is consistent with the prediction based on the worm-like
chain. The persistence length, which decreases with increasing salt
concentration, is in accord with the Odijk-Skolnick-Fixman theory intended for
stiff polyelectrolyte chains near the rod limit. The range of applications,
which did not require adjusting any parameter after the initial construction
based solely on PDB structures and melting profiles of dimers, attests to the
transferability and robustness of the TIS model for ssDNA and dsDNA.
| [
{
"created": "Mon, 5 Feb 2018 19:22:43 GMT",
"version": "v1"
}
] | 2018-08-01 | [
[
"Chakraborty",
"Debayan",
""
],
[
"Hori",
"Naoto",
""
],
[
"Thirumalai",
"Dave",
""
]
] | We develop a robust coarse-grained model for single and double stranded DNA by representing each nucleotide by three interaction sites (TIS) located at the centers of mass of sugar, phosphate, and base. The resulting TIS model includes base-stacking, hydrogen bond, and electrostatic interactions as well as bond-stretching and bond angle potentials that account for the polymeric nature of DNA. The choices of force constants for stretching and the bending potentials were guided by a Boltzmann inversion procedure using a large representative set of DNA structures extracted from the Protein Data Bank. Some of the parameters in the stacking interactions were calculated using a learning procedure, which ensured that the experimentally measured melting temperatures of dimers are faithfully reproduced. Without any further adjustments, the calculations based on the TIS model reproduces the experimentally measured salt and sequence dependence of the size of single stranded DNA (ssDNA), as well as the persistence lengths of poly(dA) and poly(dT) chains. Interestingly, upon application of mechanical force the extension of poly(dA) exhibits a plateau, which we trace to the formation of stacked helical domains. In contrast, the force-extension curve (FEC) of poly(dT) is entropic in origin, and could be described by a standard polymer model. We also show that the persistence length of double stranded DNA is consistent with the prediction based on the worm-like chain. The persistence length, which decreases with increasing salt concentration, is in accord with the Odijk-Skolnick-Fixman theory intended for stiff polyelectrolyte chains near the rod limit. The range of applications, which did not require adjusting any parameter after the initial construction based solely on PDB structures and melting profiles of dimers, attests to the transferability and robustness of the TIS model for ssDNA and dsDNA. |
2111.13785 | Xiaoyu Zhang | Xiaoyu Zhang and Yike Guo | OmiTrans: generative adversarial networks based omics-to-omics
translation framework | 9 pages, 9 figures | BIBM 2022 Regular Paper | null | null | q-bio.GN cs.AI cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development of high-throughput experimental technologies,
different types of omics (e.g., genomics, epigenomics, transcriptomics,
proteomics, and metabolomics) data can be produced from clinical samples. The
correlations between different omics types attracts a lot of research interest,
whereas the stduy on genome-wide omcis data translation (i.e, generation and
prediction of one type of omics data from another type of omics data) is almost
blank. Generative adversarial networks and the variants are one of the most
state-of-the-art deep learning technologies, which have shown great success in
image-to-image translation, text-to-image translation, etc. Here we proposed
OmiTrans, a deep learning framework adopted the idea of generative adversarial
networks to achieve omics-to-omics translation with promising results. OmiTrans
was able to faithfully reconstruct gene expression profiles from DNA
methylation data with high accuracy and great model generalisation, as
demonstrated in the experiments.
| [
{
"created": "Sat, 27 Nov 2021 00:45:10 GMT",
"version": "v1"
}
] | 2022-11-18 | [
[
"Zhang",
"Xiaoyu",
""
],
[
"Guo",
"Yike",
""
]
] | With the rapid development of high-throughput experimental technologies, different types of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples. The correlations between different omics types attracts a lot of research interest, whereas the stduy on genome-wide omcis data translation (i.e, generation and prediction of one type of omics data from another type of omics data) is almost blank. Generative adversarial networks and the variants are one of the most state-of-the-art deep learning technologies, which have shown great success in image-to-image translation, text-to-image translation, etc. Here we proposed OmiTrans, a deep learning framework adopted the idea of generative adversarial networks to achieve omics-to-omics translation with promising results. OmiTrans was able to faithfully reconstruct gene expression profiles from DNA methylation data with high accuracy and great model generalisation, as demonstrated in the experiments. |
2110.09642 | Mohamed Mehdaoui | Mohamed Mehdaoui | A review of commonly used compartmental models in epidemiology | null | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | In order to model an epidemic, different approaches can be adopted. Mainly,
the deterministic approach and the stochastic one. Recently, a large amount of
literature has been published using the two approaches. The aim of this paper
is to illustrate the usual framework used for commonly adopted compartmental
models in epidemiology and introduce variant analytic and numerical tools that
interfere on each one of those models, as well as the general related types of
existing, ongoing and future possible contributions.
| [
{
"created": "Mon, 18 Oct 2021 22:43:53 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Oct 2021 19:46:05 GMT",
"version": "v2"
},
{
"created": "Thu, 7 Jul 2022 09:43:38 GMT",
"version": "v3"
},
{
"created": "Thu, 26 Jan 2023 20:49:15 GMT",
"version": "v4"
}
] | 2023-01-30 | [
[
"Mehdaoui",
"Mohamed",
""
]
] | In order to model an epidemic, different approaches can be adopted. Mainly, the deterministic approach and the stochastic one. Recently, a large amount of literature has been published using the two approaches. The aim of this paper is to illustrate the usual framework used for commonly adopted compartmental models in epidemiology and introduce variant analytic and numerical tools that interfere on each one of those models, as well as the general related types of existing, ongoing and future possible contributions. |
2006.04566 | Ad\'an Myers y Guti\'errez | Po-E Li, Ad\'an Myers y Guti\'errez, Karen Davenport, Mark Flynn, Bin
Hu, Chien-Chi Lo, Elais Player Jackson, Migun Shakya, Yan Xu, Jason Gans, and
Patrick S. G. Chain | A Public Website for the Automated Assessment and Validation of
SARS-CoV-2 Diagnostic PCR Assays | Application Note. Main: 2 pages, 1 figure. Supplementary: 6 pages, 8
figures, 1 table. Total: 8 pages, 9 figures, 1 table. Application url:
https://covid19.edgebioinformatics.org/#/assayValidation Contact: Jason Gans
(jgans@lanl.gov) and Patrick Chain (pchain@lanl.gov) Submitted to:
Bioinformatics | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Summary: Polymerase chain reaction-based assays are the current gold standard
for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we
need to constantly assess whether existing PCR-based assays will continue to
detect all known viral strains. To enable the continuous monitoring of
SARS-CoV-2 assays, we have developed a web-based assay validation algorithm
that checks existing PCR-based assays against the ever-expanding genome
databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics.
The assay screening results are displayed as a heatmap, showing the number of
mismatches between each detection and each SARS-CoV-2 genome sequence. Using a
mismatch threshold to define detection failure, assay performance is summarized
with the true positive rate (recall) to simplify assay comparisons.
Availability: https://covid19.edgebioinformatics.org/#/assayValidation.
Contact: Jason Gans (jgans@lanl.gov) and Patrick Chain (pchain@lanl.gov)
| [
{
"created": "Mon, 8 Jun 2020 13:17:07 GMT",
"version": "v1"
}
] | 2020-06-09 | [
[
"Li",
"Po-E",
""
],
[
"Gutiérrez",
"Adán Myers y",
""
],
[
"Davenport",
"Karen",
""
],
[
"Flynn",
"Mark",
""
],
[
"Hu",
"Bin",
""
],
[
"Lo",
"Chien-Chi",
""
],
[
"Jackson",
"Elais Player",
""
],
[
"... | Summary: Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true positive rate (recall) to simplify assay comparisons. Availability: https://covid19.edgebioinformatics.org/#/assayValidation. Contact: Jason Gans (jgans@lanl.gov) and Patrick Chain (pchain@lanl.gov) |
1407.0320 | Shilpa Nadimpalli | Shilpa Nadimpalli, Anton V. Persikov and Mona Singh | Pervasive variation of transcription factor orthologs contributes to
regulatory network evolution | 29 pages, 5 figures, 5 supplemental figures, 3 supplemental tables | PLOS Genetics 11(3): e1005011. 2015 | 10.1371/journal.pgen.1005011 | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differences in transcriptional regulatory networks underlie much of the
phenotypic variation observed across organisms. Changes to cis-regulatory
elements are widely believed to be the predominant means by which regulatory
networks evolve, yet examples of regulatory network divergence due to
transcription factor (TF) variation have also been observed. To systematically
ascertain the extent to which TFs contribute to regulatory divergence, we
analyzed the evolution of the largest class of metazoan TFs, Cys2-His2 zinc
finger (C2H2-ZF) TFs, across 12 Drosophila species spanning ~45 million years
of evolution. Remarkably, we uncovered that a significant fraction of all
C2H2-ZF 1-to-1 orthologs in flies exhibit variations that can affect their
DNA-binding specificities. In addition to loss and recruitment of C2H2-ZF
domains, we found diverging DNA-contacting residues in ~47% of domains shared
between D. melanogaster and the other fly species. These diverging
DNA-contacting residues, found in ~66% of the D. melanogaster C2H2-ZF genes in
our analysis and corresponding to ~24% of all annotated D. melanogaster TFs,
show evidence of functional constraint: they tend to be conserved across
phylogenetic clades and evolve slower than other diverging residues. These same
variations were rarely found as polymorphisms within a population of D.
melanogaster flies, indicating their rapid fixation. The predicted
specificities of these dynamic domains gradually change across phylogenetic
distances, suggesting stepwise evolutionary trajectories for TF divergence.
Further, whereas proteins with conserved C2H2-ZF domains are enriched in
developmental functions, those with varying domains exhibit no functional
enrichments. Our work suggests that a subset of highly dynamic and largely
unstudied TFs are a likely source of regulatory variation in Drosophila and
other metazoans.
| [
{
"created": "Tue, 1 Jul 2014 16:59:57 GMT",
"version": "v1"
}
] | 2017-04-28 | [
[
"Nadimpalli",
"Shilpa",
""
],
[
"Persikov",
"Anton V.",
""
],
[
"Singh",
"Mona",
""
]
] | Differences in transcriptional regulatory networks underlie much of the phenotypic variation observed across organisms. Changes to cis-regulatory elements are widely believed to be the predominant means by which regulatory networks evolve, yet examples of regulatory network divergence due to transcription factor (TF) variation have also been observed. To systematically ascertain the extent to which TFs contribute to regulatory divergence, we analyzed the evolution of the largest class of metazoan TFs, Cys2-His2 zinc finger (C2H2-ZF) TFs, across 12 Drosophila species spanning ~45 million years of evolution. Remarkably, we uncovered that a significant fraction of all C2H2-ZF 1-to-1 orthologs in flies exhibit variations that can affect their DNA-binding specificities. In addition to loss and recruitment of C2H2-ZF domains, we found diverging DNA-contacting residues in ~47% of domains shared between D. melanogaster and the other fly species. These diverging DNA-contacting residues, found in ~66% of the D. melanogaster C2H2-ZF genes in our analysis and corresponding to ~24% of all annotated D. melanogaster TFs, show evidence of functional constraint: they tend to be conserved across phylogenetic clades and evolve slower than other diverging residues. These same variations were rarely found as polymorphisms within a population of D. melanogaster flies, indicating their rapid fixation. The predicted specificities of these dynamic domains gradually change across phylogenetic distances, suggesting stepwise evolutionary trajectories for TF divergence. Further, whereas proteins with conserved C2H2-ZF domains are enriched in developmental functions, those with varying domains exhibit no functional enrichments. Our work suggests that a subset of highly dynamic and largely unstudied TFs are a likely source of regulatory variation in Drosophila and other metazoans. |
1802.00502 | Frank Stollmeier | Frank Stollmeier and Jan Nagler | Unfair and Anomalous Evolutionary Dynamics from Fluctuating Payoffs | 6 pages, 8 pages supplement | Physical Review Letters 120, 058101 (2018) | 10.1103/PhysRevLett.120.058101 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolution occurs in populations of reproducing individuals. Reproduction
depends on the payoff a strategy receives. The payoff depends on the
environment that may change over time, on intrinsic uncertainties, and on other
sources of randomness. These temporal variations in the payoffs can affect
which traits evolve. Understanding evolutionary game dynamics that are affected
by varying payoffs remains difficult. Here we study the impact of arbitrary
amplitudes and covariances of temporally varying payoffs on the dynamics. The
evolutionary dynamics may be "unfair", meaning that, on average, two coexisting
strategies may persistently receive different payoffs. This mechanism can
induce an anomalous coexistence of cooperators and defectors in the Prisoner's
Dilemma, and an unexpected selection reversal in the Hawk-Dove game.
| [
{
"created": "Thu, 1 Feb 2018 21:56:47 GMT",
"version": "v1"
}
] | 2018-02-05 | [
[
"Stollmeier",
"Frank",
""
],
[
"Nagler",
"Jan",
""
]
] | Evolution occurs in populations of reproducing individuals. Reproduction depends on the payoff a strategy receives. The payoff depends on the environment that may change over time, on intrinsic uncertainties, and on other sources of randomness. These temporal variations in the payoffs can affect which traits evolve. Understanding evolutionary game dynamics that are affected by varying payoffs remains difficult. Here we study the impact of arbitrary amplitudes and covariances of temporally varying payoffs on the dynamics. The evolutionary dynamics may be "unfair", meaning that, on average, two coexisting strategies may persistently receive different payoffs. This mechanism can induce an anomalous coexistence of cooperators and defectors in the Prisoner's Dilemma, and an unexpected selection reversal in the Hawk-Dove game. |
2408.00770 | Paul Linton | Paul Linton | Linton Stereo Illusion | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new illusion that challenges our understanding of stereo vision.
The illusion consists of a small circle (at 40cm) in front of a large circle
(at 50cm), with constant angular sizes throughout. We move the large circle
forward by 10cm (to 40cm) and back again (to 50cm). What distance should we
move the small circle forward and back, so the circles look like they are
moving rigidly in depth together? Constant physical distance (10cm) or constant
disparity (6.7cm)? Observers choose constant disparity. This leads us to four
conclusions: First, perceived stereo depth appears to reflect retinal
disparities, not 3D geometry. Second, doubling disparity appears to double
perceived depth, suggesting that perceived stereo depth is proportional to
disparity. Third, changes in vergence appear to have no effect on perceived
depth. Fourth, stereo 'depth constancy' appears to be a cognitive (not
perceptual) phenomenon, reflecting our experience of a world distorted in
perceived stereo depth. Finally, when angular size is not held constant, the
illusion is no longer noticeable. However, the perceived stereo depth remains
the same in both conditions, suggesting that this looming cue only affects our
judgment, but not our visual experience, of motion in depth.
| [
{
"created": "Mon, 15 Jul 2024 19:00:27 GMT",
"version": "v1"
}
] | 2024-08-05 | [
[
"Linton",
"Paul",
""
]
] | We present a new illusion that challenges our understanding of stereo vision. The illusion consists of a small circle (at 40cm) in front of a large circle (at 50cm), with constant angular sizes throughout. We move the large circle forward by 10cm (to 40cm) and back again (to 50cm). What distance should we move the small circle forward and back, so the circles look like they are moving rigidly in depth together? Constant physical distance (10cm) or constant disparity (6.7cm)? Observers choose constant disparity. This leads us to four conclusions: First, perceived stereo depth appears to reflect retinal disparities, not 3D geometry. Second, doubling disparity appears to double perceived depth, suggesting that perceived stereo depth is proportional to disparity. Third, changes in vergence appear to have no effect on perceived depth. Fourth, stereo 'depth constancy' appears to be a cognitive (not perceptual) phenomenon, reflecting our experience of a world distorted in perceived stereo depth. Finally, when angular size is not held constant, the illusion is no longer noticeable. However, the perceived stereo depth remains the same in both conditions, suggesting that this looming cue only affects our judgment, but not our visual experience, of motion in depth. |
2311.17067 | Bradly Alicea | Bradly Alicea | Hypergraphs Demonstrate Anastomoses During Divergent Integration | 21 pages, 8 figures | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complex networks can be used to analyze structures and systems in the embryo.
Not only can we characterize growth and the emergence of form, but also
differentiation. The process of differentiation from precursor cell populations
to distinct functional tissues is of particular interest. These phenomena can
be captured using a hypergraph consisting of nodes represented by cell type
categories and arranged as a directed cyclic graph (lineage hypergraph) and a
complex network (spatial hypergraph). The lineage hypergraph models the
developmental process as an n-ary tree, which can model two or more descendent
categories per division event. A lineage tree based on the mosaic development
of the nematode C. elegans (2-ary tree), is used to capture this process. Each
round of divisions produces a new set of categories that allow for exchange of
cells between types. An example from single-cell morphogenesis based on the
cyanobacterial species Nostoc punctiforme (multiple discontinuous 2-ary tree)
is also used to demonstrate the flexibility of this method. This model allows
for new structures to emerge (such as a connectome) while also demonstrating
how precursor categories are maintained for purposes such as dedifferentiation
or other forms of cell fate plasticity. To understand this process of divergent
integration, we analyze the directed hypergraph and categorical models, in
addition to considering the role of network fistulas (spaces that conjoin two
functional modules) and spatial restriction.
| [
{
"created": "Fri, 24 Nov 2023 02:55:42 GMT",
"version": "v1"
}
] | 2023-11-30 | [
[
"Alicea",
"Bradly",
""
]
] | Complex networks can be used to analyze structures and systems in the embryo. Not only can we characterize growth and the emergence of form, but also differentiation. The process of differentiation from precursor cell populations to distinct functional tissues is of particular interest. These phenomena can be captured using a hypergraph consisting of nodes represented by cell type categories and arranged as a directed cyclic graph (lineage hypergraph) and a complex network (spatial hypergraph). The lineage hypergraph models the developmental process as an n-ary tree, which can model two or more descendent categories per division event. A lineage tree based on the mosaic development of the nematode C. elegans (2-ary tree), is used to capture this process. Each round of divisions produces a new set of categories that allow for exchange of cells between types. An example from single-cell morphogenesis based on the cyanobacterial species Nostoc punctiforme (multiple discontinuous 2-ary tree) is also used to demonstrate the flexibility of this method. This model allows for new structures to emerge (such as a connectome) while also demonstrating how precursor categories are maintained for purposes such as dedifferentiation or other forms of cell fate plasticity. To understand this process of divergent integration, we analyze the directed hypergraph and categorical models, in addition to considering the role of network fistulas (spaces that conjoin two functional modules) and spatial restriction. |
1905.06038 | Alberto P\'erez-Cervera | Alberto P\'erez-Cervera, Tere M. Seara and Gemma Huguet | Phase-locked states in oscillating neural networks and their role in
neural communication | null | null | 10.1016/j.cnsns.2019.104992 | null | q-bio.NC math.DS nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of communication through coherence (CTC) proposes that brain
oscillations reflect changes in the excitability of neurons, and therefore the
successful communication between two oscillating neural populations depends not
only on the strength of the signal emitted but also on the relative phases
between them. More precisely, effective communication occurs when the emitting
and receiving populations are properly phase locked so the inputs sent by the
emitting population arrive at the phases of maximal excitability of the
receiving population. To study this setting, we consider a population rate
model consisting of excitatory and inhibitory cells modelling the receiving
population, and we perturb it with a time-dependent periodic function modelling
the input from the emitting population. We consider the stroboscopic map for
this system and compute numerically the fixed and periodic points of this map
and their bifurcations as the amplitude and the frequency of the perturbation
are varied. From the bifurcation diagram, we identify the phase-locked states
as well as different regions of bistability. We explore carefully the dynamics
emphasizing its implications for the CTC theory. In particular, we study how
the input gain depends on the timing between the input and the inhibitory
action of the receiving population. Our results show that naturally an optimal
phase locking for CTC emerges, and provide a mechanism by which the receiving
population can implement selective communication. Moreover, the presence of
bistable regions, suggests a mechanism by which different communication regimes
between brain areas can be established without changing the structure of the
network
| [
{
"created": "Wed, 15 May 2019 09:05:23 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jul 2019 10:51:10 GMT",
"version": "v2"
},
{
"created": "Wed, 11 Sep 2019 09:16:25 GMT",
"version": "v3"
}
] | 2019-09-12 | [
[
"Pérez-Cervera",
"Alberto",
""
],
[
"Seara",
"Tere M.",
""
],
[
"Huguet",
"Gemma",
""
]
] | The theory of communication through coherence (CTC) proposes that brain oscillations reflect changes in the excitability of neurons, and therefore the successful communication between two oscillating neural populations depends not only on the strength of the signal emitted but also on the relative phases between them. More precisely, effective communication occurs when the emitting and receiving populations are properly phase locked so the inputs sent by the emitting population arrive at the phases of maximal excitability of the receiving population. To study this setting, we consider a population rate model consisting of excitatory and inhibitory cells modelling the receiving population, and we perturb it with a time-dependent periodic function modelling the input from the emitting population. We consider the stroboscopic map for this system and compute numerically the fixed and periodic points of this map and their bifurcations as the amplitude and the frequency of the perturbation are varied. From the bifurcation diagram, we identify the phase-locked states as well as different regions of bistability. We explore carefully the dynamics emphasizing its implications for the CTC theory. In particular, we study how the input gain depends on the timing between the input and the inhibitory action of the receiving population. Our results show that naturally an optimal phase locking for CTC emerges, and provide a mechanism by which the receiving population can implement selective communication. Moreover, the presence of bistable regions, suggests a mechanism by which different communication regimes between brain areas can be established without changing the structure of the network |
1610.06886 | Adrianna Loback | Adrianna R. Loback, Jason S. Prentice, Mark L. Ioffe, Michael J. Berry
II | Noise-Robust Modes of the Retinal Population Code have the Geometry of
"Ridges" and Correspond with Neuronal Communities | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An appealing new principle for neural population codes is that correlations
among neurons organize neural activity patterns into a discrete set of
clusters, which can each be viewed as a noise-robust population "codeword".
Previous studies assumed that these codewords corresponded geometrically with
local peaks in the probability landscape of neural population responses. Here,
we analyze multiple datasets of the responses of ~150 retinal ganglion cells
and show that local probability peaks are absent under broad, non-repeated
stimulus ensembles, which are characteristic of natural behavior. However, we
find that neural activity still forms noise-robust clusters in this regime,
albeit clusters with a different geometry. We start by defining a soft local
maximum, which is a local probability maximum when constrained to a fixed spike
count. Next, we show that soft local maxima are robustly present, and can
moreover be linked across different spike count levels in the probability
landscape to form a "ridge". We found that these ridges are comprised of
combinations of spiking and silence in the neural population such that all of
the spiking neurons are members of the same neuronal community, a notion from
network theory. We argue that a neuronal community shares many of the
properties of Donald Hebb's classic cell assembly, and show that a simple,
biologically plausible decoding algorithm can recognize the presence of a
specific neuronal community.
| [
{
"created": "Fri, 21 Oct 2016 18:25:36 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Mar 2017 15:56:11 GMT",
"version": "v2"
}
] | 2017-03-14 | [
[
"Loback",
"Adrianna R.",
""
],
[
"Prentice",
"Jason S.",
""
],
[
"Ioffe",
"Mark L.",
""
],
[
"Berry",
"Michael J.",
"II"
]
] | An appealing new principle for neural population codes is that correlations among neurons organize neural activity patterns into a discrete set of clusters, which can each be viewed as a noise-robust population "codeword". Previous studies assumed that these codewords corresponded geometrically with local peaks in the probability landscape of neural population responses. Here, we analyze multiple datasets of the responses of ~150 retinal ganglion cells and show that local probability peaks are absent under broad, non-repeated stimulus ensembles, which are characteristic of natural behavior. However, we find that neural activity still forms noise-robust clusters in this regime, albeit clusters with a different geometry. We start by defining a soft local maximum, which is a local probability maximum when constrained to a fixed spike count. Next, we show that soft local maxima are robustly present, and can moreover be linked across different spike count levels in the probability landscape to form a "ridge". We found that these ridges are comprised of combinations of spiking and silence in the neural population such that all of the spiking neurons are members of the same neuronal community, a notion from network theory. We argue that a neuronal community shares many of the properties of Donald Hebb's classic cell assembly, and show that a simple, biologically plausible decoding algorithm can recognize the presence of a specific neuronal community. |
0810.1024 | Peter Hinow | Peter Hinow, Philip Gerlee, Lisa J. McCawley, Vito Quaranta, Madalina
Ciobanu, Shizhen Wang, Jason M. Graham, Bruce P. Ayati, Jonathan Claridge,
Kristin R. Swanson, Mary Loveless, Alexander R. A. Anderson | A Spatial Model of Tumor-Host Interaction: Application of Chemotherapy | revised version, 25 pages, 9 figures, minor misprints corrected | Math. Biosci. Eng. 6(3):521-545, 2009 | null | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider chemotherapy in a spatial model of tumor growth.
The model, which is of reaction-diffusion type, takes into account the complex
interactions between the tumor and surrounding stromal cells by including
densities of endothelial cells and the extra-cellular matrix. When no treatment
is applied the model reproduces the typical dynamics of early tumor growth. The
initially avascular tumor reaches a diffusion limited size of the order of
millimeters and initiates angiogenesis through the release of vascular
endothelial growth factor (VEGF) secreted by hypoxic cells in the core of the
tumor. This stimulates endothelial cells to migrate towards the tumor and
establishes a nutrient supply sufficient for sustained invasion. To this model
we apply cytostatic treatment in the form of a VEGF-inhibitor, which reduces
the proliferation and chemotaxis of endothelial cells. This treatment has the
capability to reduce tumor mass, but more importantly, we were able to
determine that inhibition of endothelial cell proliferation is the more
important of the two cellular functions targeted by the drug. Further, we
considered the application of a cytotoxic drug that targets proliferating tumor
cells. The drug was treated as a diffusible substance entering the tissue from
the blood vessels. Our results show that depending on the characteristics of
the drug it can either reduce the tumor mass significantly or in fact
accelerate the growth rate of the tumor. This result seems to be due to
complicated interplay between the stromal and tumor cell types and highlights
the importance of considering chemotherapy in a spatial context.
| [
{
"created": "Mon, 6 Oct 2008 17:38:30 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Feb 2009 16:36:36 GMT",
"version": "v2"
},
{
"created": "Thu, 9 Apr 2009 16:30:23 GMT",
"version": "v3"
}
] | 2010-03-10 | [
[
"Hinow",
"Peter",
""
],
[
"Gerlee",
"Philip",
""
],
[
"McCawley",
"Lisa J.",
""
],
[
"Quaranta",
"Vito",
""
],
[
"Ciobanu",
"Madalina",
""
],
[
"Wang",
"Shizhen",
""
],
[
"Graham",
"Jason M.",
""
],
[
... | In this paper we consider chemotherapy in a spatial model of tumor growth. The model, which is of reaction-diffusion type, takes into account the complex interactions between the tumor and surrounding stromal cells by including densities of endothelial cells and the extra-cellular matrix. When no treatment is applied the model reproduces the typical dynamics of early tumor growth. The initially avascular tumor reaches a diffusion limited size of the order of millimeters and initiates angiogenesis through the release of vascular endothelial growth factor (VEGF) secreted by hypoxic cells in the core of the tumor. This stimulates endothelial cells to migrate towards the tumor and establishes a nutrient supply sufficient for sustained invasion. To this model we apply cytostatic treatment in the form of a VEGF-inhibitor, which reduces the proliferation and chemotaxis of endothelial cells. This treatment has the capability to reduce tumor mass, but more importantly, we were able to determine that inhibition of endothelial cell proliferation is the more important of the two cellular functions targeted by the drug. Further, we considered the application of a cytotoxic drug that targets proliferating tumor cells. The drug was treated as a diffusible substance entering the tissue from the blood vessels. Our results show that depending on the characteristics of the drug it can either reduce the tumor mass significantly or in fact accelerate the growth rate of the tumor. This result seems to be due to complicated interplay between the stromal and tumor cell types and highlights the importance of considering chemotherapy in a spatial context. |
1512.02826 | Kazuhiro Takemoto | Kazuhiro Takemoto | Habitat variability does not generally promote metabolic network
modularity in flies and mammals | 21 pages, 4 figures | Biosystems 139, 46-54 (2016) | 10.1016/j.biosystems.2015.12.004 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of species habitat range is an important topic over a wide
range of research fields. In higher organisms, habitat range evolution is
generally associated with genetic events such as gene duplication. However, the
specific factors that determine habitat variability remain unclear at higher
levels of biological organization (e.g., biochemical networks). One widely
accepted hypothesis developed from both theoretical and empirical analyses is
that habitat variability promotes network modularity; however, this
relationship has not yet been directly tested in higher organisms. Therefore, I
investigated the relationship between habitat variability and metabolic network
modularity using compound and enzymatic networks in flies and mammals. Contrary
to expectation, there was no clear positive correlation between habitat
variability and network modularity. As an exception, the network modularity
increased with habitat variability in the enzymatic networks of flies. However,
the observed association was likely an artifact, and the frequency of gene
duplication appears to be the main factor contributing to network modularity.
These findings raise the question of whether or not there is a general
mechanism for habitat range expansion at a higher level (i.e., above the gene
scale). This study suggests that the currently widely accepted hypothesis for
habitat variability should be reconsidered.
| [
{
"created": "Wed, 9 Dec 2015 12:03:50 GMT",
"version": "v1"
}
] | 2016-01-11 | [
[
"Takemoto",
"Kazuhiro",
""
]
] | The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered. |
1304.0542 | Joshua Vogelstein | David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian,
Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B.
Dunson, Lawrence Carin | Multichannel Electrophysiological Spike Sorting via Joint Dictionary
Learning & Mixture Modeling | 14 pages, 9 figures | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a construction for joint feature learning and clustering of
multichannel extracellular electrophysiological data across multiple recording
periods for action potential detection and discrimination ("spike sorting").
Our construction improves over the previous state-of-the art principally in
four ways. First, via sharing information across channels, we can better
distinguish between single-unit spikes and artifacts. Second, our proposed
"focused mixture model" (FMM) elegantly deals with units appearing,
disappearing, or reappearing over multiple recording days, an important
consideration for any chronic experiment. Third, by jointly learning features
and clusters, we improve performance over previous attempts that proceeded via
a two-stage ("frequentist") learning process. Fourth, by directly modeling
spike rate, we improve detection of sparsely spiking neurons. Moreover, our
Bayesian construction seamlessly handles missing data. We present
state-of-the-art performance without requiring manually tuning of many
hyper-parameters on both a public dataset with partial ground truth and a new
experimental dataset.
| [
{
"created": "Tue, 2 Apr 2013 06:31:17 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Aug 2013 02:50:15 GMT",
"version": "v2"
}
] | 2013-08-06 | [
[
"Carlson",
"David E.",
""
],
[
"Vogelstein",
"Joshua T.",
""
],
[
"Wu",
"Qisong",
""
],
[
"Lian",
"Wenzhao",
""
],
[
"Zhou",
"Mingyuan",
""
],
[
"Stoetzner",
"Colin R.",
""
],
[
"Kipke",
"Daryl",
""
],
... | We propose a construction for joint feature learning and clustering of multichannel extracellular electrophysiological data across multiple recording periods for action potential detection and discrimination ("spike sorting"). Our construction improves over the previous state-of-the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) elegantly deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage ("frequentist") learning process. Fourth, by directly modeling spike rate, we improve detection of sparsely spiking neurons. Moreover, our Bayesian construction seamlessly handles missing data. We present state-of-the-art performance without requiring manually tuning of many hyper-parameters on both a public dataset with partial ground truth and a new experimental dataset. |
2106.13148 | Mark Leake | Xin Jin, Ji-Eun Lee, Charley Schaefer, Xinwei Luo, Adam J. M. Wollman,
Alex L. Payne-Dwyer, Tian Tian, Xiaowei Zhang, Xiao Chen, Yingxing Li, Tom C.
B. McLeish, Mark C. Leake, Fan Bai | Membraneless organelles formed by liquid-liquid phase separation
increase bacterial fitness | null | Sci Adv. 2021 Oct 22;7(43):eabh2929 | 10.1126/sciadv.abh2929 | null | q-bio.BM cond-mat.soft physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Liquid-liquid phase separation is emerging as a crucial phenomenon in several
fundamental cell processes. A range of eukaryotic systems exhibit liquid
condensates. However, their function in bacteria, which in general lack
membrane-bound compartments, remains less clear. Here, we used high-resolution
optical microscopy to observe single bacterial aggresomes, nanostructured
intracellular assemblies of proteins, to undercover their role in cell stress.
We find that proteins inside aggresomes are mobile and undergo dynamic
turnover, consistent with a liquid state. Our observations are in quantitative
agreement with phase-separated liquid droplet formation driven by interacting
proteins under thermal equilibrium that nucleate following diffusive collisions
in the cytoplasm. We have discovered aggresomes in multiple species of
bacteria, and show that these emergent, metastable liquid-structured protein
assemblies increase bacterial fitness by enabling cells to tolerate
environmental stresses.
| [
{
"created": "Thu, 24 Jun 2021 16:24:15 GMT",
"version": "v1"
}
] | 2023-01-25 | [
[
"Jin",
"Xin",
""
],
[
"Lee",
"Ji-Eun",
""
],
[
"Schaefer",
"Charley",
""
],
[
"Luo",
"Xinwei",
""
],
[
"Wollman",
"Adam J. M.",
""
],
[
"Payne-Dwyer",
"Alex L.",
""
],
[
"Tian",
"Tian",
""
],
[
"Zha... | Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses. |
2007.02032 | Chris Antonopoulos Dr | Ian Cooper, Argha Mondal, Chris G. Antonopoulos | Dynamic tracking with model-based forecasting for the spread of the
COVID-19 pandemic | 17 pages, 13 figures | null | 10.1016/j.chaos.2020.110298 | null | q-bio.PE nlin.CD physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a susceptible-infected-removed (SIR) model has been used to
track the evolution of the spread of the COVID-19 virus in four countries of
interest. In particular, the epidemic model, that depends on some basic
characteristics, has been applied to model the time evolution of the disease in
Italy, India, South Korea and Iran. The economic, social and health
consequences of the spread of the virus have been cataclysmic. Hence, it is
essential that available mathematical models can be developed and used for the
comparison to be made between published data sets and model predictions. The
predictions estimated from the SIR model here, can be used in both the
qualitative and quantitative analysis of the spread. It gives an insight into
the spread of the virus that the published data alone cannot do by updating
them and the model on a daily basis. For example, it is possible to detect the
early onset of a spike in infections or the development of a second wave using
our modeling approach. We considered data from March to June, 2020, when
different communities are severely affected. We demonstrate predictions
depending on the model's parameters related to the spread of COVID-19 until
September 2020. By comparing the published data and model results, we conclude
that in this way, it may be possible to better reflect the success or failure
of the adequate measures implemented by governments and individuals to mitigate
and control the current pandemic.
| [
{
"created": "Sat, 4 Jul 2020 07:42:32 GMT",
"version": "v1"
}
] | 2020-10-28 | [
[
"Cooper",
"Ian",
""
],
[
"Mondal",
"Argha",
""
],
[
"Antonopoulos",
"Chris G.",
""
]
] | In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of the COVID-19 virus in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics, has been applied to model the time evolution of the disease in Italy, India, South Korea and Iran. The economic, social and health consequences of the spread of the virus have been cataclysmic. Hence, it is essential that available mathematical models can be developed and used for the comparison to be made between published data sets and model predictions. The predictions estimated from the SIR model here, can be used in both the qualitative and quantitative analysis of the spread. It gives an insight into the spread of the virus that the published data alone cannot do by updating them and the model on a daily basis. For example, it is possible to detect the early onset of a spike in infections or the development of a second wave using our modeling approach. We considered data from March to June, 2020, when different communities are severely affected. We demonstrate predictions depending on the model's parameters related to the spread of COVID-19 until September 2020. By comparing the published data and model results, we conclude that in this way, it may be possible to better reflect the success or failure of the adequate measures implemented by governments and individuals to mitigate and control the current pandemic. |
2305.04128 | Zilong Wang | Zilong Wang, Thomas R. Shultz, Ardvan S. Nobandegani | A Computational Model of Children's Learning and Use of Probabilities
Across Different Ages | 10 figures, 2 tables, 8 pages. Abstract submitted to Cognitive
Science Society 2023 Conference | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent empirical work has shown that human children are adept at learning and
reasoning with probabilities. Here, we model a recent experiment investigating
the development of school-age children's non-symbolic probability reasoning
ability using the Neural Probability Learner and Sampler (NPLS) system. We
demonstrate that NPLS can accurately simulate children's probability judgments
at different ages, tasks and difficulty levels to discriminate two
probabilistic choices through accurate probability learning and sampling. An
extension of NPLS using a skewed heuristic distribution can also model
children's tendency to wrongly select the outcome with more favorable items but
less likely to draw the favorable ones when the probabilistic choices are
similar. We discuss the roles of two model parameters that can be adjusted to
simulate the probability matching versus probability maximization phenomena in
children, and why frequency biases children's probabilistic judgments.
| [
{
"created": "Sat, 6 May 2023 20:13:47 GMT",
"version": "v1"
}
] | 2023-05-09 | [
[
"Wang",
"Zilong",
""
],
[
"Shultz",
"Thomas R.",
""
],
[
"Nobandegani",
"Ardvan S.",
""
]
] | Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate children's probability judgments at different ages, tasks and difficulty levels to discriminate two probabilistic choices through accurate probability learning and sampling. An extension of NPLS using a skewed heuristic distribution can also model children's tendency to wrongly select the outcome with more favorable items but less likely to draw the favorable ones when the probabilistic choices are similar. We discuss the roles of two model parameters that can be adjusted to simulate the probability matching versus probability maximization phenomena in children, and why frequency biases children's probabilistic judgments. |
1301.4640 | Francesc Rossell\'o | Gabriel Cardona, Arnau Mir, Francesc Rossello, Lucia Rotger, David
Sanchez | Cophenetic metrics for phylogenetic trees, after Sokal and Rohlf | The "authors' cut" of a paper published in BMC Bioinformatics 14:3
(2013). 46 pages | BMC Bioinformatics 14:3 (2013) | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic tree comparison metrics are an important tool in the study of
evolution, and hence the definition of such metrics is an interesting problem
in phylogenetics. In a paper in Taxon fifty years ago, Sokal and Rohlf proposed
to measure quantitatively the difference between a pair of phylogenetic trees
by first encoding them by means of their half-matrices of cophenetic values,
and then comparing these matrices. This idea has been used several times since
then to define dissimilarity measures between phylogenetic trees but, to our
knowledge, no proper metric on weighted phylogenetic trees with nested taxa
based on this idea has been formally defined and studied yet. Actually, the
cophenetic values of pairs of different taxa alone are not enough to single out
phylogenetic trees with weighted arcs or nested taxa. In this paper we define a
family of cophenetic metrics that compare phylogenetic trees on a same set of
taxa by encoding them by means of their vectors of cophenetic values of pairs
of taxa and depths of single taxa, and then computing the $L^p$ norm of the
difference of the corresponding vectors. Then, we study, either analytically or
numerically, some of their basic properties: neighbors, diameter, distribution,
and their rank correlation with each other and with other metrics.
| [
{
"created": "Sun, 20 Jan 2013 09:07:58 GMT",
"version": "v1"
}
] | 2013-01-22 | [
[
"Cardona",
"Gabriel",
""
],
[
"Mir",
"Arnau",
""
],
[
"Rossello",
"Francesc",
""
],
[
"Rotger",
"Lucia",
""
],
[
"Sanchez",
"David",
""
]
] | Phylogenetic tree comparison metrics are an important tool in the study of evolution, and hence the definition of such metrics is an interesting problem in phylogenetics. In a paper in Taxon fifty years ago, Sokal and Rohlf proposed to measure quantitatively the difference between a pair of phylogenetic trees by first encoding them by means of their half-matrices of cophenetic values, and then comparing these matrices. This idea has been used several times since then to define dissimilarity measures between phylogenetic trees but, to our knowledge, no proper metric on weighted phylogenetic trees with nested taxa based on this idea has been formally defined and studied yet. Actually, the cophenetic values of pairs of different taxa alone are not enough to single out phylogenetic trees with weighted arcs or nested taxa. In this paper we define a family of cophenetic metrics that compare phylogenetic trees on a same set of taxa by encoding them by means of their vectors of cophenetic values of pairs of taxa and depths of single taxa, and then computing the $L^p$ norm of the difference of the corresponding vectors. Then, we study, either analytically or numerically, some of their basic properties: neighbors, diameter, distribution, and their rank correlation with each other and with other metrics. |
1909.02949 | Angelyn Lao | Honeylou F. Farinas, Eduardo R. Mendoza, and Angelyn R. Lao | Species subsets and embedded networks of S-systems | null | Farinas, H. F., Mendoza, E. R., & Lao, A. R. (2020). Structural
Properties of an S-system Model of Mycobacterium tuberculosis Gene
Regulation. Philippine Journal of Science, 149(3), 539-555 | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Magombedze and Mulder (2013) studied the gene regulatory system of
\textit{Mycobacterium Tuberculosis} (\textit{Mtb}) by partitioning this into
three subsystems based on putative gene function and role in dormancy/latency
development. Each subsystem, in the form of $S$-system, is represented by an
embedded chemical reaction network (CRN), defined by a species subset and a
reaction subset induced by the set of digraph vertices of the subsystem. Based
on the network decomposition theory initiated by Feinberg in 1987, we have
introduced the concept of incidence-independent and developed the theory of
$\mathscr{C}$- and $\mathscr{C}^*$-decompositions including their structure
theorems in terms of linkage classes. With the $S$-system CRN $\mathscr{N}$ of
Magombedze and Mulder's \textit{Mtb} model, its reaction set partition induced
decomposition of subnetworks that are not CRNs of $S$-system but constitute
independent decomposition of $\mathscr{N}$. We have also constructed a new
$S$-system CRN $\mathscr{N}^*$ for which the embedded networks are
$\mathscr{C}^*$-decomposition. We have shown that subnetworks of $\mathscr{N}$
and the embedded networks (subnetworks of $\mathscr{N}^*$) are digraph
homomorphisms. Lastly, we attempted to explore modularity in the context of
CRN.
| [
{
"created": "Mon, 2 Sep 2019 04:23:48 GMT",
"version": "v1"
}
] | 2021-10-27 | [
[
"Farinas",
"Honeylou F.",
""
],
[
"Mendoza",
"Eduardo R.",
""
],
[
"Lao",
"Angelyn R.",
""
]
] | Magombedze and Mulder (2013) studied the gene regulatory system of \textit{Mycobacterium Tuberculosis} (\textit{Mtb}) by partitioning this into three subsystems based on putative gene function and role in dormancy/latency development. Each subsystem, in the form of $S$-system, is represented by an embedded chemical reaction network (CRN), defined by a species subset and a reaction subset induced by the set of digraph vertices of the subsystem. Based on the network decomposition theory initiated by Feinberg in 1987, we have introduced the concept of incidence-independent and developed the theory of $\mathscr{C}$- and $\mathscr{C}^*$-decompositions including their structure theorems in terms of linkage classes. With the $S$-system CRN $\mathscr{N}$ of Magombedze and Mulder's \textit{Mtb} model, its reaction set partition induced decomposition of subnetworks that are not CRNs of $S$-system but constitute independent decomposition of $\mathscr{N}$. We have also constructed a new $S$-system CRN $\mathscr{N}^*$ for which the embedded networks are $\mathscr{C}^*$-decomposition. We have shown that subnetworks of $\mathscr{N}$ and the embedded networks (subnetworks of $\mathscr{N}^*$) are digraph homomorphisms. Lastly, we attempted to explore modularity in the context of CRN. |
1002.4599 | Jan Hasenauer | J. Hasenauer, S. Waldherr, M. Doszczak, P. Scheurich, and F. Allgower | Density-based modeling and identification of biochemical networks in
cell populations | 17 pages, 6 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many biological processes heterogeneity within cell populations is an
important issue. In this work we consider populations where the behavior of
every single cell can be described by a system of ordinary differential
equations. Heterogeneity among individual cells is accounted for by differences
in parameter values and initial conditions. Hereby, parameter values and
initial conditions are subject to a distribution function which is part of the
model specification. Based on the single cell model and the considered
parameter distribution, a partial differential equation model describing the
distribution of cells in the state and in the output space is derived.
For the estimation of the parameter distribution within the model, we
consider experimental data as obtained from flow cytometric analysis. From
these noise-corrupted data a density-based statistical data model is derived.
Using this data model the parameter distribution within the cell population is
computed using convex optimization techniques.
To evaluate the proposed method, a model for the caspase activation cascade
is considered. It is shown that for known noise properties the unknown
parameter distributions in this model are well estimated by the proposed
method.
| [
{
"created": "Wed, 24 Feb 2010 18:16:22 GMT",
"version": "v1"
}
] | 2010-02-25 | [
[
"Hasenauer",
"J.",
""
],
[
"Waldherr",
"S.",
""
],
[
"Doszczak",
"M.",
""
],
[
"Scheurich",
"P.",
""
],
[
"Allgower",
"F.",
""
]
] | In many biological processes heterogeneity within cell populations is an important issue. In this work we consider populations where the behavior of every single cell can be described by a system of ordinary differential equations. Heterogeneity among individual cells is accounted for by differences in parameter values and initial conditions. Hereby, parameter values and initial conditions are subject to a distribution function which is part of the model specification. Based on the single cell model and the considered parameter distribution, a partial differential equation model describing the distribution of cells in the state and in the output space is derived. For the estimation of the parameter distribution within the model, we consider experimental data as obtained from flow cytometric analysis. From these noise-corrupted data a density-based statistical data model is derived. Using this data model the parameter distribution within the cell population is computed using convex optimization techniques. To evaluate the proposed method, a model for the caspase activation cascade is considered. It is shown that for known noise properties the unknown parameter distributions in this model are well estimated by the proposed method. |
1309.0599 | Suman Kumar Banik | Arnab Bandyopadhyay, Soumi Biswas, Alok Kumar Maity and Suman K Banik | Analysis of DevR regulated genes in Mycobacterium tuberculosis | Title changed in this submission. 33 pages, 16 figures, 2 tables | null | null | null | q-bio.MN physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The DevRS two component system of Mycobacterium tuberculosis is responsible
for its dormancy in host and becomes operative under hypoxic condition. It is
experimentally known that phosphorylated DevR controls the expression of
several downstream genes in a complex manner. In the present work we propose a
theoretical model to show role of binding sites in DevR mediated gene
expression. Individual and collective role of binding sites in regulating DevR
mediated gene expression has been shown via modeling. Objective of the present
work is two fold. First, to describe qualitatively the temporal dynamics of
wild type genes and their known mutants. Based on these results we propose that
DevR controlled gene expression follows a specific pattern which is efficient
in describing other DevR mediated gene expression. Second, to analyze behavior
of the system from information theoretical point of view. Using the tools of
information theory we have calculated molecular efficiency of the system and
have shown that it is close to the maximum limit of isothermal efficiency.
| [
{
"created": "Tue, 3 Sep 2013 07:30:36 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Jan 2014 11:41:16 GMT",
"version": "v2"
}
] | 2014-01-30 | [
[
"Bandyopadhyay",
"Arnab",
""
],
[
"Biswas",
"Soumi",
""
],
[
"Maity",
"Alok Kumar",
""
],
[
"Banik",
"Suman K",
""
]
] | The DevRS two component system of Mycobacterium tuberculosis is responsible for its dormancy in host and becomes operative under hypoxic condition. It is experimentally known that phosphorylated DevR controls the expression of several downstream genes in a complex manner. In the present work we propose a theoretical model to show role of binding sites in DevR mediated gene expression. Individual and collective role of binding sites in regulating DevR mediated gene expression has been shown via modeling. Objective of the present work is two fold. First, to describe qualitatively the temporal dynamics of wild type genes and their known mutants. Based on these results we propose that DevR controlled gene expression follows a specific pattern which is efficient in describing other DevR mediated gene expression. Second, to analyze behavior of the system from information theoretical point of view. Using the tools of information theory we have calculated molecular efficiency of the system and have shown that it is close to the maximum limit of isothermal efficiency. |
1610.02258 | Weiliang Chen | Weiliang Chen, Erik De Schutter | Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion
Simulation with High Performance Computers | null | null | null | null | q-bio.QM cs.CE physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic, spatial reaction-diffusion simulations have been widely used in
systems biology and computational neuroscience. However, the increasing scale
and complexity of simulated models and morphologies have exceeded the capacity
of any serial implementation. This led to development of parallel solutions
that benefit from the boost in performance of modern large-scale
supercomputers. In this paper, we describe an MPI-based, parallel
Operator-Splitting implementation for stochastic spatial reaction-diffusion
simulations with irregular tetrahedral meshes. The performance of our
implementation is first examined and analyzed with simulations of a simple
model. We then demonstrate its usage in real-world research by simulating the
reaction-diffusion components of a published calcium burst model in both
Purkinje neuron sub-branch and full dendrite morphologies. Simulation results
indicate that our implementation is capable of achieving super-linear speedup
for balanced loading simulations with reasonable molecule density and mesh
quality. In the best scenario a parallel simulation with 2000 processes
achieves more than 3600 times of speedup relative to its serial SSA counterpart
and more than 20 times of speedup relative to parallel simulation with 100
processes. While simulation performance is affected by unbalanced loading, a
substantial speedup can still be observed without any special treatment.
| [
{
"created": "Fri, 7 Oct 2016 12:52:07 GMT",
"version": "v1"
}
] | 2016-10-10 | [
[
"Chen",
"Weiliang",
""
],
[
"De Schutter",
"Erik",
""
]
] | Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of simulated models and morphologies have exceeded the capacity of any serial implementation. This led to development of parallel solutions that benefit from the boost in performance of modern large-scale supercomputers. In this paper, we describe an MPI-based, parallel Operator-Splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its usage in real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario a parallel simulation with 2000 processes achieves more than 3600 times of speedup relative to its serial SSA counterpart and more than 20 times of speedup relative to parallel simulation with 100 processes. While simulation performance is affected by unbalanced loading, a substantial speedup can still be observed without any special treatment. |
1409.3899 | Momiao Xiong | Junhai Jiang, Nan Lin, Shicheng Guo, Jinyun Chen and Momiao Xiong | Methods for Joint Imaging and RNA-seq Data Analysis | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emerging integrative analysis of genomic and anatomical imaging data which
has not been well developed, provides invaluable information for the holistic
discovery of the genomic structure of disease and has the potential to open a
new avenue for discovering novel disease susceptibility genes which cannot be
identified if they are analyzed separately. A key issue to the success of
imaging and genomic data analysis is how to reduce their dimensions. Most
previous methods for imaging information extraction and RNA-seq data reduction
do not explore imaging spatial information and often ignore gene expression
variation at genomic positional level. To overcome these limitations, we extend
functional principle component analysis from one dimension to two dimension
(2DFPCA) for representing imaging data and develop a multiple functional linear
model (MFLM) in which functional principal scores of images are taken as
multiple quantitative traits and RNA-seq profile across a gene is taken as a
function predictor for assessing the association of gene expression with
images. The developed method has been applied to image and RNA-seq data of
ovarian cancer and KIRC studies. We identified 24 and 84 genes whose
expressions were associated with imaging variations in ovarian cancer and KIRC
studies, respectively. Our results showed that many significantly associated
genes with images were not differentially expressed, but revealed their
morphological and metabolic functions. The results also demonstrated that the
peaks of the estimated regression coefficient function in the MFLM often
allowed the discovery of splicing sites and multiple isoform of gene
expressions.
| [
{
"created": "Sat, 13 Sep 2014 02:05:29 GMT",
"version": "v1"
}
] | 2014-09-16 | [
[
"Jiang",
"Junhai",
""
],
[
"Lin",
"Nan",
""
],
[
"Guo",
"Shicheng",
""
],
[
"Chen",
"Jinyun",
""
],
[
"Xiong",
"Momiao",
""
]
] | Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimension (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and KIRC studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoform of gene expressions. |
2407.10700 | Fatemeh Mohammadi | Sean Dewar, Georg Grasegger, Kaie Kubjas, Fatemeh Mohammadi, and
Anthony Nixon | Single-cell 3D genome reconstruction in the haploid setting using
rigidity theory | null | null | null | null | q-bio.GN math.CO math.MG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article considers the problem of 3-dimensional genome reconstruction for
single-cell data, and the uniqueness of such reconstructions in the setting of
haploid organisms. We consider multiple graph models as representations of this
problem, and use techniques from graph rigidity theory to determine
identifiability. Biologically, our models come from Hi-C data, microscopy data,
and combinations thereof. Mathematically, we use unit ball and sphere packing
models, as well as models consisting of distance and inequality constraints. In
each setting, we describe and/or derive new results on realisability and
uniqueness. We then propose a 3D reconstruction method based on semidefinite
programming and apply it to synthetic and real data sets using our models.
| [
{
"created": "Mon, 15 Jul 2024 13:16:04 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Dewar",
"Sean",
""
],
[
"Grasegger",
"Georg",
""
],
[
"Kubjas",
"Kaie",
""
],
[
"Mohammadi",
"Fatemeh",
""
],
[
"Nixon",
"Anthony",
""
]
] | This article considers the problem of 3-dimensional genome reconstruction for single-cell data, and the uniqueness of such reconstructions in the setting of haploid organisms. We consider multiple graph models as representations of this problem, and use techniques from graph rigidity theory to determine identifiability. Biologically, our models come from Hi-C data, microscopy data, and combinations thereof. Mathematically, we use unit ball and sphere packing models, as well as models consisting of distance and inequality constraints. In each setting, we describe and/or derive new results on realisability and uniqueness. We then propose a 3D reconstruction method based on semidefinite programming and apply it to synthetic and real data sets using our models. |
2406.10184 | Martin Guillemaud | Martin Guillemaud, Louis Cousyn, Vincent Navarro and Mario Chavez | Hyperbolic embedding of brain networks as a tool for epileptic seizures
forecasting | null | null | null | null | q-bio.NC physics.data-an | http://creativecommons.org/licenses/by/4.0/ | The evidence indicates that intracranial EEG connectivity, as estimated from
daily resting state recordings from epileptic patients, may be capable of
identifying preictal states. In this study, we employed hyperbolic embedding of
brain networks to capture non-trivial patterns that discriminate between
connectivity networks from days with (preictal) and without (interictal)
seizure. A statistical model was constructed by combining hyperbolic geometry
and machine learning tools, which allowed for the estimation of the probability
of an upcoming seizure. The results demonstrated that representing brain
networks in a hyperbolic space enabled an accurate discrimination (85%) between
interictal (no-seizure) and preictal (seizure within the next 24 hours) states.
The proposed method also demonstrated excellent prediction performances, with
an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier
skill score of 0.12 and 0.37, respectively). In conclusion, our findings
indicate that representations of brain connectivity in a latent geometry space
can reveal a daily and reliable signature of the upcoming seizure(s), thus
providing a promising biomarker for seizure forecasting.
| [
{
"created": "Fri, 14 Jun 2024 17:09:23 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Jun 2024 20:47:08 GMT",
"version": "v2"
}
] | 2024-06-21 | [
[
"Guillemaud",
"Martin",
""
],
[
"Cousyn",
"Louis",
""
],
[
"Navarro",
"Vincent",
""
],
[
"Chavez",
"Mario",
""
]
] | The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture non-trivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no-seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting. |
1911.02364 | Adam Safron | Adam Safron | Rapid Anxiety Reduction (RAR): A unified theory of humor | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here I propose a novel theory in which humor is the feeling of Rapid Anxiety
Reduction (RAR). According to RAR, humor can be expressed in a simple formula:
-d(A)/dt. RAR has strong correspondences with False Alarm Theory, Benign
Violation Theory, and Cognitive Debugging Theory, all of which represent either
special cases or partial descriptions at alternative levels of analysis. Some
evidence for RAR includes physiological similarities between hyperventilation
and laughter and the fact that smiles often indicate negative affect in
non-human primates (e.g. fear grimaces where teeth are exposed as a kind of
inhibited threat display). In accordance with Benign Violation Theory, if humor
reliably indicates both a) anxiety induction, b) anxiety reduction, and c) the
time-course over which anxiety is reduced, then the intersection of these
conditions productively constrains inference spaces over latent mental states
with respect to the values and capacities of the persons experiencing humor. In
this way, humor is a powerful cypher for understanding persons in both
individual and social contexts, with far-reaching implications. Finally, if
humor can be expressed in such a simple formula with clear ties to
phenomenology, and yet this discovery regarding such an essential part of the
human experience has remained undiscovered for this long, then this is an
extremely surprising state of affairs worthy of further investigation. Towards
this end, I propose an analogy can be found with consciousness studies, where
in addition to the "Hard problem" of trying to explain humor, we would do well
to consider a "Meta-Problem" of why humor seems so difficult to explain, and
why relatively simple explanations may have eluded us for this long. (Please
note: RAR was conceived in 2008, and last majorly updated in 2012.)
| [
{
"created": "Sat, 2 Nov 2019 21:56:22 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Nov 2019 03:51:16 GMT",
"version": "v2"
}
] | 2019-11-11 | [
[
"Safron",
"Adam",
""
]
] | Here I propose a novel theory in which humor is the feeling of Rapid Anxiety Reduction (RAR). According to RAR, humor can be expressed in a simple formula: -d(A)/dt. RAR has strong correspondences with False Alarm Theory, Benign Violation Theory, and Cognitive Debugging Theory, all of which represent either special cases or partial descriptions at alternative levels of analysis. Some evidence for RAR includes physiological similarities between hyperventilation and laughter and the fact that smiles often indicate negative affect in non-human primates (e.g. fear grimaces where teeth are exposed as a kind of inhibited threat display). In accordance with Benign Violation Theory, if humor reliably indicates both a) anxiety induction, b) anxiety reduction, and c) the time-course over which anxiety is reduced, then the intersection of these conditions productively constrains inference spaces over latent mental states with respect to the values and capacities of the persons experiencing humor. In this way, humor is a powerful cypher for understanding persons in both individual and social contexts, with far-reaching implications. Finally, if humor can be expressed in such a simple formula with clear ties to phenomenology, and yet this discovery regarding such an essential part of the human experience has remained undiscovered for this long, then this is an extremely surprising state of affairs worthy of further investigation. Towards this end, I propose an analogy can be found with consciousness studies, where in addition to the "Hard problem" of trying to explain humor, we would do well to consider a "Meta-Problem" of why humor seems so difficult to explain, and why relatively simple explanations may have eluded us for this long. (Please note: RAR was conceived in 2008, and last majorly updated in 2012.) |
2312.17480 | Wensha Zhang | Wensha Zhang, Lam Si Tung Ho, Toby Kenney | Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck
model | null | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 1. Abrupt environmental changes can lead to evolutionary shifts in not only
mean (optimal value), but also variance of descendants in trait evolution.
There are some methods to detect shifts in optimal value but few studies
consider shifts in variance. 2. We use a multi-optima and multi-variance OU
process model to describe the trait evolution process with shifts in both
optimal value and variance and provide analysis of how the covariance between
species changes when shifts in variance occur along the path. 3. We propose a
new method to detect the shifts in both variance and optimal values based on
minimizing the loss function with L1 penalty. We implement our method in a new
R package, ShiVa (Detection of evolutionary shifts in variance). 4. We conduct
simulations to compare our method with the two methods considering only shifts
in optimal values (l1ou; PhylogeneticEM). Our method shows strength in
predictive ability and includes far fewer false positive shifts in optimal
value compared to other methods when shifts in variance actually exist. When
there are only shifts in optimal value, our method performs similarly to other
methods. We applied our method to the cordylid data, ShiVa outperformed l1ou
and phyloEM, exhibiting the highest log-likelihood and lowest BIC.
| [
{
"created": "Fri, 29 Dec 2023 05:44:41 GMT",
"version": "v1"
}
] | 2024-01-01 | [
[
"Zhang",
"Wensha",
""
],
[
"Ho",
"Lam Si Tung",
""
],
[
"Kenney",
"Toby",
""
]
] | 1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in variance. 2. We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance and provide analysis of how the covariance between species changes when shifts in variance occur along the path. 3. We propose a new method to detect the shifts in both variance and optimal values based on minimizing the loss function with L1 penalty. We implement our method in a new R package, ShiVa (Detection of evolutionary shifts in variance). 4. We conduct simulations to compare our method with the two methods considering only shifts in optimal values (l1ou; PhylogeneticEM). Our method shows strength in predictive ability and includes far fewer false positive shifts in optimal value compared to other methods when shifts in variance actually exist. When there are only shifts in optimal value, our method performs similarly to other methods. We applied our method to the cordylid data, ShiVa outperformed l1ou and phyloEM, exhibiting the highest log-likelihood and lowest BIC. |
2110.01192 | Deeptajyoti Sen Dr. | Deeptajyoti Sen and Sudeshna Sinha | Influence of Allee Effect on Extreme Events in Coupled Three Species
Systems | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | We consider the dynamics of two coupled three-species population patches,
incorporating the Allee Effect, focussing on the onset of extreme events in the
coupled system. First we show that the interplay between coupling and the Allee
effect may change the nature of the dynamics, with regular periodic dynamics
becoming chaotic in a range of Allee parameters and coupling strengths.
Further, the growth in the vegetation population displays an explosive blow-up
beyond a critical value of coupling strength and Allee parameter. Most
interestingly, we observe that beyond a threshold of coupling strength and
Allee parameter, the population densities of all three species exhibit non-zero
probability of yielding extreme events. The emergence of extreme events in the
predator populations in the patches is the most prevalent, and the probability
of obtaining large deviations in the predator populations is not affected
significantly by either the coupling strength or the Allee effect. In the
absence of the Allee effect the prey population in the coupled system exhibits
no extreme events for low coupling strengths, but yields a sharp increase in
extreme events after a critical strength of coupling. The vegetation population
in the patches display a small finite probability of extreme events for strong
enough coupling, only in the presence of Allee effect. Lastly we consider the
influence of additive noise on the continued prevalence of extreme events. Very
significantly, we find that noise suppresses the unbounded vegetation growth
that was induced by a combination of Allee effect and coupling. Further, we
demonstrate that noise mitigates extreme events in all three populations, and
beyond a noise level we do not observe any extreme events in the system at all.
This finding has important bearing on the potential observability of extreme
events in natural and laboratory systems.
| [
{
"created": "Mon, 4 Oct 2021 05:13:02 GMT",
"version": "v1"
}
] | 2021-10-05 | [
[
"Sen",
"Deeptajyoti",
""
],
[
"Sinha",
"Sudeshna",
""
]
] | We consider the dynamics of two coupled three-species population patches, incorporating the Allee Effect, focussing on the onset of extreme events in the coupled system. First we show that the interplay between coupling and the Allee effect may change the nature of the dynamics, with regular periodic dynamics becoming chaotic in a range of Allee parameters and coupling strengths. Further, the growth in the vegetation population displays an explosive blow-up beyond a critical value of coupling strength and Allee parameter. Most interestingly, we observe that beyond a threshold of coupling strength and Allee parameter, the population densities of all three species exhibit non-zero probability of yielding extreme events. The emergence of extreme events in the predator populations in the patches is the most prevalent, and the probability of obtaining large deviations in the predator populations is not affected significantly by either the coupling strength or the Allee effect. In the absence of the Allee effect the prey population in the coupled system exhibits no extreme events for low coupling strengths, but yields a sharp increase in extreme events after a critical strength of coupling. The vegetation population in the patches display a small finite probability of extreme events for strong enough coupling, only in the presence of Allee effect. Lastly we consider the influence of additive noise on the continued prevalence of extreme events. Very significantly, we find that noise suppresses the unbounded vegetation growth that was induced by a combination of Allee effect and coupling. Further, we demonstrate that noise mitigates extreme events in all three populations, and beyond a noise level we do not observe any extreme events in the system at all. This finding has important bearing on the potential observability of extreme events in natural and laboratory systems. |
0904.3534 | Thierry Rabilloud | Thierry Rabilloud (BBSI) | Solubilization of Proteins in 2DE: An Outline | null | Methods in molecular biology (Clifton, N.J.) 519 (2009) 19-30 | 10.1007/978-1-59745-281-6_2 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein solubilization for two-dimensional electrophoresis (2DE) has to break
molecular interactions to separate the biological contents of the material of
interest into isolated and intact polypeptides. This must be carried out in
conditions compatible with the first dimension of 2DE, namely isoelectric
focusing. In addition, the extraction process must enable easy removal of any
nonprotein component interfering with the isoelectric focusing. The constraints
brought in this process by the peculiar features of isoelectric focusing are
discussed, as well as their consequences in terms of possible solutions and
limits for the solubilization process.
| [
{
"created": "Wed, 22 Apr 2009 19:25:35 GMT",
"version": "v1"
}
] | 2009-04-23 | [
[
"Rabilloud",
"Thierry",
"",
"BBSI"
]
] | Protein solubilization for two-dimensional electrophoresis (2DE) has to break molecular interactions to separate the biological contents of the material of interest into isolated and intact polypeptides. This must be carried out in conditions compatible with the first dimension of 2DE, namely isoelectric focusing. In addition, the extraction process must enable easy removal of any nonprotein component interfering with the isoelectric focusing. The constraints brought in this process by the peculiar features of isoelectric focusing are discussed, as well as their consequences in terms of possible solutions and limits for the solubilization process. |
1712.01931 | Moumita Bhattacharya | M. Bhattacharya, C. Jurkovitz and H. Shatkay | Assessing Chronic Kidney Disease from Office Visit Records Using
Hierarchical Meta-Classification of an Imbalanced Dataset | 8 pages, 5 figures, 4 tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting
13% of the US population. The disease is often a silent condition, making its
diagnosis challenging. Identifying CKD stages from standard office visit
records can help in early detection of the disease and lead to timely
intervention. The dataset we use is highly imbalanced. We propose a
hierarchical meta-classification method, aiming to stratify CKD by severity
levels, employing simple quantitative non-text features gathered from office
visit records, while addressing data imbalance. Our method effectively
stratifies CKD severity levels obtaining high average sensitivity, precision
and F-measure (~93%). We also conduct experiments in which the dimensionality
of the data is significantly reduced to include only the most salient features.
Our results show that the good performance of our system is retained even when
using the reduced feature sets, as well as under much reduced training sets,
indicating that our method is stable and generalizable.
| [
{
"created": "Fri, 17 Nov 2017 21:12:24 GMT",
"version": "v1"
}
] | 2017-12-07 | [
[
"Bhattacharya",
"M.",
""
],
[
"Jurkovitz",
"C.",
""
],
[
"Shatkay",
"H.",
""
]
] | Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting 13% of the US population. The disease is often a silent condition, making its diagnosis challenging. Identifying CKD stages from standard office visit records can help in early detection of the disease and lead to timely intervention. The dataset we use is highly imbalanced. We propose a hierarchical meta-classification method, aiming to stratify CKD by severity levels, employing simple quantitative non-text features gathered from office visit records, while addressing data imbalance. Our method effectively stratifies CKD severity levels obtaining high average sensitivity, precision and F-measure (~93%). We also conduct experiments in which the dimensionality of the data is significantly reduced to include only the most salient features. Our results show that the good performance of our system is retained even when using the reduced feature sets, as well as under much reduced training sets, indicating that our method is stable and generalizable. |
1205.3337 | Nuno Crokidakis | Nuno Crokidakis, Silvio M. Duarte Queiros | Probing into the effectiveness of self-isolation policies in epidemic
control | 15 pages, 6 figures, to appear in JSTAT | J. Stat. Mech. P06003 (2012) | 10.1088/1742-5468/2012/06/P06003 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we inspect the reliability of controlling and quelling an
epidemic disease mimicked by a Susceptible-Infected-Susceptible (SIS) model
defined on a complex network by means of current and implementable quarantine
and isolation policies. Specifically, we consider that each individual in the
network is originally linked to two types of individuals: members of the same
household and acquaintances. The topology of this network evolves taking into
account a probability $q$ that aims at representing the quarantine or isolation
process in which the connection with acquaintances is disrupted according to
standard policies of control of epidemics. Within current policies of
self-isolation and standard infection rates, our results show that the
propagation is either only controllable for hypothetical rates of compliance or
uncontrollable at all.
| [
{
"created": "Tue, 15 May 2012 12:08:15 GMT",
"version": "v1"
}
] | 2012-06-12 | [
[
"Crokidakis",
"Nuno",
""
],
[
"Queiros",
"Silvio M. Duarte",
""
]
] | In this work, we inspect the reliability of controlling and quelling an epidemic disease mimicked by a Susceptible-Infected-Susceptible (SIS) model defined on a complex network by means of current and implementable quarantine and isolation policies. Specifically, we consider that each individual in the network is originally linked to two types of individuals: members of the same household and acquaintances. The topology of this network evolves taking into account a probability $q$ that aims at representing the quarantine or isolation process in which the connection with acquaintances is disrupted according to standard policies of control of epidemics. Within current policies of self-isolation and standard infection rates, our results show that the propagation is either only controllable for hypothetical rates of compliance or uncontrollable at all. |
1501.00682 | Jonathan Mason | Jonathan Mason | Quasi-Conscious Multivariate Systems | 33 pages (double spacing), 11 figures, 15 Tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conscious experience is awash with underlying relationships. Moreover, for
various brain regions such as the visual cortex, the system is biased toward
some states. Representing this bias using a probability distribution shows that
the system can define expected quantities. The mathematical theory in the
present paper links these facts by using expected float entropy (efe), which is
a measure of the expected amount of information needed, to specify the state of
the system, beyond what is already known about the system from relationships
that appear as parameters. Under the requirement that the relationship
parameters minimise efe, the brain defines relationships. It is proposed that
when a brain state is interpreted in the context of these relationships the
brain state acquires meaning in the form of the relational content of the
associated experience. For a given set, the theory represents relationships
using weighted relations which assign continuous weights, from 0 to 1, to the
elements of the Cartesian product of that set. The relationship parameters
include weighted relations on the nodes of the system and on their set of
states. Examples obtained using Monte-Carlo methods (where relationship
parameters are chosen uniformly at random) suggest that efe distributions with
long left tails are most important.
| [
{
"created": "Sun, 4 Jan 2015 15:05:53 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jun 2015 21:29:54 GMT",
"version": "v2"
},
{
"created": "Sun, 9 Aug 2015 22:10:19 GMT",
"version": "v3"
}
] | 2015-08-11 | [
[
"Mason",
"Jonathan",
""
]
] | Conscious experience is awash with underlying relationships. Moreover, for various brain regions such as the visual cortex, the system is biased toward some states. Representing this bias using a probability distribution shows that the system can define expected quantities. The mathematical theory in the present paper links these facts by using expected float entropy (efe), which is a measure of the expected amount of information needed, to specify the state of the system, beyond what is already known about the system from relationships that appear as parameters. Under the requirement that the relationship parameters minimise efe, the brain defines relationships. It is proposed that when a brain state is interpreted in the context of these relationships the brain state acquires meaning in the form of the relational content of the associated experience. For a given set, the theory represents relationships using weighted relations which assign continuous weights, from 0 to 1, to the elements of the Cartesian product of that set. The relationship parameters include weighted relations on the nodes of the system and on their set of states. Examples obtained using Monte-Carlo methods (where relationship parameters are chosen uniformly at random) suggest that efe distributions with long left tails are most important. |
1201.5344 | Martin Depken | Martin Depken, Juan M. R. Parrondo, Stephan W. Gril | Irregular transcription dynamics for rapid production of high-fidelity
transcripts | 10 pages, 6 figures | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both genomic stability and sustenance of day-to-day life rely on efficient
and accurate readout of the genetic code. Single-molecule experiments show that
transcription and replication are highly stochastic and irregular processes,
with the polymerases frequently pausing and even reversing direction. While
such behavior is recognized as stemming from a sophisticated proofreading
mechanism during replication, the origin and functional significance of
irregular transcription dynamics remain controversial. Here, we theoretically
examine the implications of RNA polymerase backtracking and transcript cleavage
on transcription rates and fidelity. We illustrate how an extended state space
for backtracking provides entropic fidelity enhancements that, together with
additional fidelity checkpoints, can account for physiological error rates. To
explore the competing demands of transcription fidelity, nucleotide
triphosphate (NTP) consumption and transcription speed in a physiologically
relevant setting, we establish an analytically framework for evaluating
transcriptional performance at the level of extended sequences. Using this
framework, we reveal a mechanism by which moderately irregular transcription
results in astronomical gains in the rate at which extended high-fidelity
transcripts can be produced under physiological conditions.
| [
{
"created": "Wed, 25 Jan 2012 19:03:33 GMT",
"version": "v1"
}
] | 2012-01-26 | [
[
"Depken",
"Martin",
""
],
[
"Parrondo",
"Juan M. R.",
""
],
[
"Gril",
"Stephan W.",
""
]
] | Both genomic stability and sustenance of day-to-day life rely on efficient and accurate readout of the genetic code. Single-molecule experiments show that transcription and replication are highly stochastic and irregular processes, with the polymerases frequently pausing and even reversing direction. While such behavior is recognized as stemming from a sophisticated proofreading mechanism during replication, the origin and functional significance of irregular transcription dynamics remain controversial. Here, we theoretically examine the implications of RNA polymerase backtracking and transcript cleavage on transcription rates and fidelity. We illustrate how an extended state space for backtracking provides entropic fidelity enhancements that, together with additional fidelity checkpoints, can account for physiological error rates. To explore the competing demands of transcription fidelity, nucleotide triphosphate (NTP) consumption and transcription speed in a physiologically relevant setting, we establish an analytically framework for evaluating transcriptional performance at the level of extended sequences. Using this framework, we reveal a mechanism by which moderately irregular transcription results in astronomical gains in the rate at which extended high-fidelity transcripts can be produced under physiological conditions. |
1805.07316 | Konstantin Blyuss | F. Fatehi Chenar, Y.N. Kyrychko, K.B. Blyuss | Effects of viral and cytokine delays on dynamics of autoimmunity | 25 pages, 8 figures | Mathematics 6, 66 (2018) | 10.3390/math6050066 | null | q-bio.QM nlin.CD q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major contribution to the onset and development of autoimmune disease is
known to come from infections. An important practical problem is identifying
the precise mechanism by which the breakdown of immune tolerance as a result of
immune response to infection leads to autoimmunity. In this paper, we develop a
mathematical model of immune response to a viral infection, which includes T
cells with different activation thresholds, regulatory T cells (Tregs), and~a
cytokine mediating immune dynamics. Particular emphasis is made on the role of
time delays associated with the processes of infection and mounting the immune
response. Stability analysis of various steady states of the model allows us to
identify parameter regions associated with different types of immune behaviour,
such as, normal clearance of infection, chronic infection, and autoimmune
dynamics. Numerical simulations are used to illustrate different dynamical
regimes, and to identify basins of attraction of different dynamical states. An
important result of the analysis is that not only the parameters of the system,
but also the initial level of infection and the initial state of the immune
system determine the progress and outcome of the dynamics.
| [
{
"created": "Fri, 18 May 2018 16:30:33 GMT",
"version": "v1"
}
] | 2018-05-21 | [
[
"Chenar",
"F. Fatehi",
""
],
[
"Kyrychko",
"Y. N.",
""
],
[
"Blyuss",
"K. B.",
""
]
] | A major contribution to the onset and development of autoimmune disease is known to come from infections. An important practical problem is identifying the precise mechanism by which the breakdown of immune tolerance as a result of immune response to infection leads to autoimmunity. In this paper, we develop a mathematical model of immune response to a viral infection, which includes T cells with different activation thresholds, regulatory T cells (Tregs), and~a cytokine mediating immune dynamics. Particular emphasis is made on the role of time delays associated with the processes of infection and mounting the immune response. Stability analysis of various steady states of the model allows us to identify parameter regions associated with different types of immune behaviour, such as, normal clearance of infection, chronic infection, and autoimmune dynamics. Numerical simulations are used to illustrate different dynamical regimes, and to identify basins of attraction of different dynamical states. An important result of the analysis is that not only the parameters of the system, but also the initial level of infection and the initial state of the immune system determine the progress and outcome of the dynamics. |
1808.01971 | Ji\v{r}\'i Jan\'a\v{c}ek | Ji\v{r}\'i Jan\'a\v{c}ek and Daniel Jir\'ak | Volume tensor of pheasant brain compartments estimated by Fakir probe | The paper was submitted to Image Analysis & Stereology | Image Analysis and Stereology 38 (3), 2019, 255-260 and 261-267 | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The volume tensor provides robust estimate of object shape and orientation in
space. The tensor is estimated from 3D data set by the Fakir probe, an
interactive method using intersections of the objects boundary with a virtual
lines. The method thus can be applied to objects that cannot be segmented
automatically. Marking the intersections instead of segmenting the whole object
reduces the workload required for obtaining sufficiently precise results. We
present theoretical results on the variance of estimate of integrals by
systematic sampling that enable calculation of the shape estimate precision. To
demonstrate the ability of Fakir technique, we measure the changes in shape and
orientation of pheasant brain compartments during development.
| [
{
"created": "Fri, 3 Aug 2018 08:18:13 GMT",
"version": "v1"
}
] | 2019-12-18 | [
[
"Janáček",
"Jiří",
""
],
[
"Jirák",
"Daniel",
""
]
] | The volume tensor provides robust estimate of object shape and orientation in space. The tensor is estimated from 3D data set by the Fakir probe, an interactive method using intersections of the objects boundary with a virtual lines. The method thus can be applied to objects that cannot be segmented automatically. Marking the intersections instead of segmenting the whole object reduces the workload required for obtaining sufficiently precise results. We present theoretical results on the variance of estimate of integrals by systematic sampling that enable calculation of the shape estimate precision. To demonstrate the ability of Fakir technique, we measure the changes in shape and orientation of pheasant brain compartments during development. |
0807.4279 | Tijana Ivancevic | Tijana T. Ivancevic, Lakhmi C. Jain, John Pattison and Alex Hariz | Preterm Birth Analysis Using Nonlinear Methods (a preliminary study) | 21 pages, 5 figures, Latex | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this report we review modern nonlinearity methods that can be used in the
preterm birth analysis. The nonlinear analysis of uterine contraction signals
can provide information regarding physiological changes during the menstrual
cycle and pregnancy. This information can be used both for the preterm birth
prediction and the preterm labor control.
Keywords: preterm birth, complex data analysis, nonlinear methods
| [
{
"created": "Sun, 27 Jul 2008 07:43:14 GMT",
"version": "v1"
}
] | 2008-07-29 | [
[
"Ivancevic",
"Tijana T.",
""
],
[
"Jain",
"Lakhmi C.",
""
],
[
"Pattison",
"John",
""
],
[
"Hariz",
"Alex",
""
]
] | In this report we review modern nonlinearity methods that can be used in the preterm birth analysis. The nonlinear analysis of uterine contraction signals can provide information regarding physiological changes during the menstrual cycle and pregnancy. This information can be used both for the preterm birth prediction and the preterm labor control. Keywords: preterm birth, complex data analysis, nonlinear methods |
1910.07440 | Thomas Booth | Thomas Booth, Matthew Williams, Aysha Luis, Jorge Cardoso, Ashkan
Keyoumars, Haris Shuaib | Machine learning and glioma imaging biomarkers | null | null | 10.1016/j.crad.2019.07.001 | null | q-bio.QM cs.LG eess.IV stat.ML | http://creativecommons.org/licenses/by/4.0/ | Aim: To review how machine learning (ML) is applied to imaging biomarkers in
neuro-oncology, in particular for diagnosis, prognosis, and treatment response
monitoring.
Materials and Methods: The PubMed and MEDLINE databases were searched for
articles published before September 2018 using relevant search terms. The
search strategy focused on articles applying ML to high-grade glioma biomarkers
for treatment response monitoring, prognosis, and prediction.
Results: Magnetic resonance imaging (MRI) is typically used throughout the
patient pathway because routine structural imaging provides detailed anatomical
and pathological information and advanced techniques provide additional
physiological detail. Using carefully chosen image features, ML is frequently
used to allow accurate classification in a variety of scenarios. Rather than
being chosen by human selection, ML also enables image features to be
identified by an algorithm. Much research is applied to determining molecular
profiles, histological tumour grade, and prognosis using MRI images acquired at
the time that patients first present with a brain tumour. Differentiating a
treatment response from a post-treatment-related effect using imaging is
clinically important and also an area of active study (described here in one of
two Special Issue publications dedicated to the application of ML in glioma
imaging).
Conclusion: Although pioneering, most of the evidence is of a low level,
having been obtained retrospectively and in single centres. Studies applying ML
to build neuro-oncology monitoring biomarker models have yet to show an overall
advantage over those using traditional statistical methods. Development and
validation of ML models applied to neuro-oncology require large, well-annotated
datasets, and therefore multidisciplinary and multi-centre collaborations are
necessary.
| [
{
"created": "Wed, 28 Aug 2019 12:44:30 GMT",
"version": "v1"
}
] | 2019-10-17 | [
[
"Booth",
"Thomas",
""
],
[
"Williams",
"Matthew",
""
],
[
"Luis",
"Aysha",
""
],
[
"Cardoso",
"Jorge",
""
],
[
"Keyoumars",
"Ashkan",
""
],
[
"Shuaib",
"Haris",
""
]
] | Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. Results: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). Conclusion: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary. |
1804.03430 | Tsvi Tlusty | Tsvi Tlusty | The self-referring DNA and protein: a remark on physical and geometrical
aspects | 17 pages, 9 figures; title updated and typos corrected | Philos Trans A 2016 Mar 13; 374(2063). pii: 20150070 | 10.1098/rsta.2015.0070 | null | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | All known life forms are based upon a hierarchy of interwoven feedback loops,
operating over a cascade of space, time and energy scales. Among the most basic
loops are those connecting DNA and proteins. For example, in genetic networks,
DNA genes are expressed as proteins, which may bind near the same genes and
thereby control their own expression. In this molecular type of self-reference,
information is mapped from the DNA sequence to the protein and back to DNA.
There is a variety of dynamic DNA-protein self-reference loops, and the purpose
of this remark is to discuss certain geometrical and physical aspects related
to the back and forth mapping between DNA and proteins. The discussion raises
basic questions regarding the nature of DNA and proteins as self-referring
matter, which are examined in a simple toy model.
| [
{
"created": "Tue, 10 Apr 2018 10:12:58 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Apr 2018 07:11:34 GMT",
"version": "v2"
}
] | 2018-04-13 | [
[
"Tlusty",
"Tsvi",
""
]
] | All known life forms are based upon a hierarchy of interwoven feedback loops, operating over a cascade of space, time and energy scales. Among the most basic loops are those connecting DNA and proteins. For example, in genetic networks, DNA genes are expressed as proteins, which may bind near the same genes and thereby control their own expression. In this molecular type of self-reference, information is mapped from the DNA sequence to the protein and back to DNA. There is a variety of dynamic DNA-protein self-reference loops, and the purpose of this remark is to discuss certain geometrical and physical aspects related to the back and forth mapping between DNA and proteins. The discussion raises basic questions regarding the nature of DNA and proteins as self-referring matter, which are examined in a simple toy model. |
q-bio/0703064 | Brigitte Gaillard | S. Bourgeon (DEPE-Iphc), T. Raclot (DEPE-Iphc) | Triiodothyronine suppresses humoral immunity but not T-cell-mediated
immune response in incubating female eiders (Somateria mollissima) | null | Gen. Comp. Endocrinol. 151 (2007) 188-194 | 10.1016/j.ygcen.2007.01.020 | null | q-bio.PE | null | Immunity is believed to share limited resources with other physiological
functions and this may partly account for the fitness costs of reproduction.
Previous studies have shown that the acquired immunity of female common eiders
(Somateria mollissima) is suppressed during the incubation fast. To save
energy, triiodothyronine (T3) is adaptively decreased during fasting in most
bird species, despite T3 levels are maintained throughout incubation in female
eiders. However, the relationship between thyroid hormones and the immune
system is not fully understood. The current study aimed to determine the
endocrine mechanisms that underlie immunosuppression in incubating female
eiders. ...
| [
{
"created": "Thu, 29 Mar 2007 12:20:26 GMT",
"version": "v1"
}
] | 2009-07-24 | [
[
"Bourgeon",
"S.",
"",
"DEPE-Iphc"
],
[
"Raclot",
"T.",
"",
"DEPE-Iphc"
]
] | Immunity is believed to share limited resources with other physiological functions and this may partly account for the fitness costs of reproduction. Previous studies have shown that the acquired immunity of female common eiders (Somateria mollissima) is suppressed during the incubation fast. To save energy, triiodothyronine (T3) is adaptively decreased during fasting in most bird species, despite T3 levels are maintained throughout incubation in female eiders. However, the relationship between thyroid hormones and the immune system is not fully understood. The current study aimed to determine the endocrine mechanisms that underlie immunosuppression in incubating female eiders. ... |
2003.05647 | Mitsuo Kawato | Mitsuo Kawato, Shogo Ohmae, Huu Hoang, Terry Sanger | 50 years since the Marr, Ito, and Albus models of the cerebellum | P.4, 5, 8, 10, 14, 16, 18, 22, and some references added | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fifty years have passed since David Marr, Masao Ito, and James Albus proposed
seminal models of cerebellar functions. These models share the essential
concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes,
guided by climbing-fiber activities during sensorimotor learning. However, they
differ in several important respects, including holistic versus complementary
roles of the cerebellum, pattern recognition versus control as computational
objectives, potentiation versus depression of synaptic plasticity, teaching
signals versus error signals transmitted by climbing-fibers, sparse expansion
coding by granule cells, and cerebellar internal models. In this review, we
evaluate the different features of the three models based on recent
computational and experimental studies. While acknowledging that the three
models have greatly advanced our understanding of cerebellar control mechanisms
in eye movements and classical conditioning, we propose a new direction for
computational frameworks of the cerebellum. That is, hierarchical reinforcement
learning with multiple internal models.
| [
{
"created": "Thu, 12 Mar 2020 07:38:30 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Mar 2020 09:14:37 GMT",
"version": "v2"
},
{
"created": "Wed, 25 Mar 2020 00:23:31 GMT",
"version": "v3"
},
{
"created": "Mon, 15 Jun 2020 08:21:36 GMT",
"version": "v4"
}
] | 2020-06-16 | [
[
"Kawato",
"Mitsuo",
""
],
[
"Ohmae",
"Shogo",
""
],
[
"Hoang",
"Huu",
""
],
[
"Sanger",
"Terry",
""
]
] | Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate the different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum. That is, hierarchical reinforcement learning with multiple internal models. |
1707.06881 | Denis Horv\'ath | Denis Horv\'ath, Branislav Brutovsky | A New Conceptual Framework for the Therapy by Optimized Multidimensional
Pulses of Therapeutic Activity. The case of Multiple Myeloma Model | 37 pages, 11 figures, 8 tables | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed simulation methodology to assess eventual therapeutic efficiency
of exogenous multiparametric changes in a four-component cellular system
described by the system of ordinary differential equations. The method is
numerically implemented to simulate the temporal behavior of a cellular system
of multiple myeloma cells. The problem is conceived as an inverse optimization
task where the alternative temporal changes of selected parameters of the
ordinary differential equations represent candidate solutions and the objective
function quantifies the goals of the therapy. The system under study consists
of two main cellular components, tumor cells and their cellular environment,
respectively. The subset of model parameters closely related to the environment
is substituted by exogenous time dependencies - therapeutic pulses combining
continuous functions and discrete parameters subordinated thereafter to the
optimization. Synergistic interaction of temporal parametric changes has been
observed and quantified whereby two or more dynamic parameters show effects
that absent if either parameter is stimulated alone. We expect that the
theoretical insight into unstable tumor growth provided by the sensitivity and
optimization studies could, eventually, help in designing combination
therapies.
| [
{
"created": "Fri, 21 Jul 2017 13:02:43 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Sep 2017 09:31:10 GMT",
"version": "v2"
},
{
"created": "Thu, 25 Jan 2018 17:52:50 GMT",
"version": "v3"
},
{
"created": "Sat, 2 Jun 2018 07:56:19 GMT",
"version": "v4"
}
] | 2018-06-05 | [
[
"Horváth",
"Denis",
""
],
[
"Brutovsky",
"Branislav",
""
]
] | We developed simulation methodology to assess eventual therapeutic efficiency of exogenous multiparametric changes in a four-component cellular system described by the system of ordinary differential equations. The method is numerically implemented to simulate the temporal behavior of a cellular system of multiple myeloma cells. The problem is conceived as an inverse optimization task where the alternative temporal changes of selected parameters of the ordinary differential equations represent candidate solutions and the objective function quantifies the goals of the therapy. The system under study consists of two main cellular components, tumor cells and their cellular environment, respectively. The subset of model parameters closely related to the environment is substituted by exogenous time dependencies - therapeutic pulses combining continuous functions and discrete parameters subordinated thereafter to the optimization. Synergistic interaction of temporal parametric changes has been observed and quantified whereby two or more dynamic parameters show effects that absent if either parameter is stimulated alone. We expect that the theoretical insight into unstable tumor growth provided by the sensitivity and optimization studies could, eventually, help in designing combination therapies. |
1512.00397 | Ehsaneddin Asgari | Ehsaneddin Asgari, Kiavash Garakani and Mohammad R.K Mofrad | A New Approach for Scalable Analysis of Microbial Communities | null | null | null | null | q-bio.GN cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microbial communities play important roles in the function and maintenance of
various biosystems, ranging from human body to the environment. Current methods
for analysis of microbial communities are typically based on taxonomic
phylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencing
data. In typical characterizations of microbial communities, studies deal with
billions of micobial sequences, aligning them to a phylogenetic tree. We
introduce a new approach for the efficient analysis of microbial communities.
Our new reference-free analysis tech- nique is based on n-gram sequence
analysis of 16S rRNA data and reduces the processing data size dramatically (by
105 fold), without requiring taxonomic alignment. The proposed approach is
applied to characterize phenotypic microbial community differ- ences in
different settings. Specifically, we applied this approach in classification of
microbial com- munities across different body sites, characterization of oral
microbiomes associated with healthy and diseased individuals, and
classification of microbial communities longitudinally during the develop- ment
of infants. Different dimensionality reduction methods are introduced that
offer a more scalable analysis framework, while minimizing the loss in
classification accuracies. Among dimensionality re- duction techniques, we
propose a continuous vector representation for microbial communities, which can
widely be used for deep learning applications in microbial informatics.
| [
{
"created": "Tue, 1 Dec 2015 19:27:13 GMT",
"version": "v1"
}
] | 2015-12-02 | [
[
"Asgari",
"Ehsaneddin",
""
],
[
"Garakani",
"Kiavash",
""
],
[
"Mofrad",
"Mohammad R. K",
""
]
] | Microbial communities play important roles in the function and maintenance of various biosystems, ranging from human body to the environment. Current methods for analysis of microbial communities are typically based on taxonomic phylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencing data. In typical characterizations of microbial communities, studies deal with billions of micobial sequences, aligning them to a phylogenetic tree. We introduce a new approach for the efficient analysis of microbial communities. Our new reference-free analysis tech- nique is based on n-gram sequence analysis of 16S rRNA data and reduces the processing data size dramatically (by 105 fold), without requiring taxonomic alignment. The proposed approach is applied to characterize phenotypic microbial community differ- ences in different settings. Specifically, we applied this approach in classification of microbial com- munities across different body sites, characterization of oral microbiomes associated with healthy and diseased individuals, and classification of microbial communities longitudinally during the develop- ment of infants. Different dimensionality reduction methods are introduced that offer a more scalable analysis framework, while minimizing the loss in classification accuracies. Among dimensionality re- duction techniques, we propose a continuous vector representation for microbial communities, which can widely be used for deep learning applications in microbial informatics. |
1611.00294 | Tilo Schwalger | Tilo Schwalger, Moritz Deger and Wulfram Gerstner | Towards a theory of cortical columns: From spiking neurons to
interacting neural populations of finite size | Simulation code available from
https://github.com/schwalger/mesopopdyn_gif | PLoS Comput. Biol., 13(4):e1005507, 2017 | 10.1371/journal.pcbi.1005507 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural population equations such as neural mass or field models are widely
used to study brain activity on a large scale. However, the relation of these
models to the properties of single neurons is unclear. Here we derive an
equation for several interacting populations at the mesoscopic scale starting
from a microscopic model of randomly connected generalized integrate-and-fire
neuron models. Each population consists of 50 -- 2000 neurons of the same type
but different populations account for different neuron types. The stochastic
population equations that we find reveal how spike-history effects in
single-neuron dynamics such as refractoriness and adaptation interact with
finite-size fluctuations on the population level. Efficient integration of the
stochastic mesoscopic equations reproduces the statistical behavior of the
population activities obtained from microscopic simulations of a full spiking
neural network model. The theory describes nonlinear emergent dynamics like
finite-size-induced stochastic transitions in multistable networks and
synchronization in balanced networks of excitatory and inhibitory neurons. The
mesoscopic equations are employed to rapidly simulate a model of a local
cortical microcircuit consisting of eight neuron types. Our theory establishes
a general framework for modeling finite-size neural population dynamics based
on single cell and synapse parameters and offers an efficient approach to
analyzing cortical circuits and computations.
| [
{
"created": "Tue, 1 Nov 2016 16:56:09 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Nov 2016 18:48:07 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Apr 2017 08:41:24 GMT",
"version": "v3"
}
] | 2017-04-24 | [
[
"Schwalger",
"Tilo",
""
],
[
"Deger",
"Moritz",
""
],
[
"Gerstner",
"Wulfram",
""
]
] | Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50 -- 2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics like finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly simulate a model of a local cortical microcircuit consisting of eight neuron types. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. |
1302.6294 | Carlos Pe\~na | Carlos Pe\~na and Marianne Espeland | Diversity dynamics in Nymphalidae butterflies: Effect of phylogenetic
uncertainty on diversification rate shift estimates | 23 pages, 7 figures, 2 tables and 12 supplementary material files.
Both authors contributed equally to this work | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The family Nymphalidae is the largest family within the true butterflies and
has been used to develop hypotheses explaining evolutionary interactions
between plants and insects. Theories of insect and hostplant dynamics predict
accelerated diversification in some scenarios. We investigated whether
phylogenetic uncertainty affects a commonly used method (MEDUSA, modelling
evolutionary diversity using stepwise AIC) for estimating shifts in
diversification rates in lineages of the family Nymphalidae, by extending the
method to run across a random sample of phylogenetic trees from the posterior
distribution of a Bayesian run. We found that phylogenetic uncertainty greatly
affects diversification rate estimates. Different trees from the posterior
distribution can give diversification rates ranging from high values to almost
zero for the same clade, and for some clades both significant rate increase and
decrease were estimated. Only three out of 13 significant shifts found on the
maximum credibility tree were consistent across more than 95% of the trees from
the posterior: (i) accelerated diversification for Solanaceae feeders in the
tribe Ithomiini; (ii) accelerated diversification in the genus Charaxes, and
(iii) deceleration in the Danaina. By using the binary speciation and
extinction model (BISSE), we found that a hostplant shift to Solanaceae or a
codistributed character is responsible for the increase in diversification rate
in Ithomiini, and the result is congruent with the diffuse cospeciation
hypothesis. A shift to Apocynaceae is not responsible for the slowdown of
diversification in Danaina. Our results show that taking phylogenetic
uncertainty into account when estimating diversification rate shifts is of
great importance, and relying on the maximum credibility tree alone potentially
can give erroneous results.
| [
{
"created": "Tue, 26 Feb 2013 02:04:23 GMT",
"version": "v1"
}
] | 2013-02-27 | [
[
"Peña",
"Carlos",
""
],
[
"Espeland",
"Marianne",
""
]
] | The family Nymphalidae is the largest family within the true butterflies and has been used to develop hypotheses explaining evolutionary interactions between plants and insects. Theories of insect and hostplant dynamics predict accelerated diversification in some scenarios. We investigated whether phylogenetic uncertainty affects a commonly used method (MEDUSA, modelling evolutionary diversity using stepwise AIC) for estimating shifts in diversification rates in lineages of the family Nymphalidae, by extending the method to run across a random sample of phylogenetic trees from the posterior distribution of a Bayesian run. We found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees from the posterior distribution can give diversification rates ranging from high values to almost zero for the same clade, and for some clades both significant rate increase and decrease were estimated. Only three out of 13 significant shifts found on the maximum credibility tree were consistent across more than 95% of the trees from the posterior: (i) accelerated diversification for Solanaceae feeders in the tribe Ithomiini; (ii) accelerated diversification in the genus Charaxes, and (iii) deceleration in the Danaina. By using the binary speciation and extinction model (BISSE), we found that a hostplant shift to Solanaceae or a codistributed character is responsible for the increase in diversification rate in Ithomiini, and the result is congruent with the diffuse cospeciation hypothesis. A shift to Apocynaceae is not responsible for the slowdown of diversification in Danaina. Our results show that taking phylogenetic uncertainty into account when estimating diversification rate shifts is of great importance, and relying on the maximum credibility tree alone potentially can give erroneous results. |
1202.4214 | Chengcheng Ji | Chengcheng Ji, Liang Wu, Wenchan Zhao, Sishuo Wang, Jianhao Lv | Echinoderms have bilateral tendencies | null | PLoS ONE 7(1): e28978 (2012) | 10.1371/journal.pone.0028978 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Echinoderms take many forms of symmetry. Pentameral symmetry is the major
form and the other forms are derived from it. However, the ancestors of
echinoderms, which originated from Cambrian period, were believed to be
bilaterians. Echinoderm larvae are bilateral during their early development.
During embryonic development of starfish and sea urchins, the position and the
developmental sequence of each arm are fixed, implying an auxological
anterior/posterior axis. Starfish also possess the Hox gene cluster, which
controls symmetrical development. Overall, echinoderms are thought to have a
bilateral developmental mechanism and process. In this article, we focused on
adult starfish behaviors to corroborate its bilateral tendency. We weighed
their central disk and each arm to measure the position of the center of
gravity. We then studied their turning-over behavior, crawling behavior and
fleeing behavior statistically to obtain the center of frequency of each
behavior. By joining the center of gravity and each center of frequency, we
obtained three behavioral symmetric planes. These behavioral bilateral
tendencies might be related to the A/P axis during the embryonic development of
the starfish. It is very likely that the adult starfish is, to some extent,
bilaterian because it displays some bilateral propensity and has a definite
behavioral symmetric plane. The remainder of bilateral symmetry may have
benefited echinoderms during their evolution from the Cambrian period to the
present.
| [
{
"created": "Mon, 20 Feb 2012 03:32:08 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Ji",
"Chengcheng",
""
],
[
"Wu",
"Liang",
""
],
[
"Zhao",
"Wenchan",
""
],
[
"Wang",
"Sishuo",
""
],
[
"Lv",
"Jianhao",
""
]
] | Echinoderms take many forms of symmetry. Pentameral symmetry is the major form and the other forms are derived from it. However, the ancestors of echinoderms, which originated from Cambrian period, were believed to be bilaterians. Echinoderm larvae are bilateral during their early development. During embryonic development of starfish and sea urchins, the position and the developmental sequence of each arm are fixed, implying an auxological anterior/posterior axis. Starfish also possess the Hox gene cluster, which controls symmetrical development. Overall, echinoderms are thought to have a bilateral developmental mechanism and process. In this article, we focused on adult starfish behaviors to corroborate its bilateral tendency. We weighed their central disk and each arm to measure the position of the center of gravity. We then studied their turning-over behavior, crawling behavior and fleeing behavior statistically to obtain the center of frequency of each behavior. By joining the center of gravity and each center of frequency, we obtained three behavioral symmetric planes. These behavioral bilateral tendencies might be related to the A/P axis during the embryonic development of the starfish. It is very likely that the adult starfish is, to some extent, bilaterian because it displays some bilateral propensity and has a definite behavioral symmetric plane. The remainder of bilateral symmetry may have benefited echinoderms during their evolution from the Cambrian period to the present. |
1011.4902 | Andrey Shapkin | A. G. Shapkin, M. V. Taborov, Yu. G. Shapkin | Recording and Reproduction of Pattern Memory Trace in EEG by Direct
Electrical Stimulation of Brain Cortex | Article: 9 pages, 3 figures | Bulletin of ESCC SB RAMS, 2011, No.4(80), part 1, p. 289-294 (in
Russian) | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study demonstrates the capability of external signal recording into
memory and the reproduction of memory trace of this pattern in EEG by direct AC
electrical stimulation of rat cerebral cortex. Additionally, we examine shifts
of the DC potential level related to these phenomena. We show that in the
course of memory trace reproduction, consecutive phases of engram activation
and relaxation are registered and accompanied by corresponding negative and
positive DC shifts. The observed electrophysiological changes may reflect
consecutive activation and inhibition phases of neural ensembles participating
in engram formation.
| [
{
"created": "Mon, 22 Nov 2010 18:35:22 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2011 08:55:00 GMT",
"version": "v2"
}
] | 2011-11-23 | [
[
"Shapkin",
"A. G.",
""
],
[
"Taborov",
"M. V.",
""
],
[
"Shapkin",
"Yu. G.",
""
]
] | This study demonstrates the capability of external signal recording into memory and the reproduction of memory trace of this pattern in EEG by direct AC electrical stimulation of rat cerebral cortex. Additionally, we examine shifts of the DC potential level related to these phenomena. We show that in the course of memory trace reproduction, consecutive phases of engram activation and relaxation are registered and accompanied by corresponding negative and positive DC shifts. The observed electrophysiological changes may reflect consecutive activation and inhibition phases of neural ensembles participating in engram formation. |
1304.7214 | George Bass Ph.D. | George E. Bass and James E. Chenevey | Stimulation of Enzyme Reaction Rates by Crystalline Substrate
Irradiation: Dependence on Identity of Irradiated Substance | arXiv admin note: text overlap with arXiv:0706.1748 | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study reported here concerns a phenomenon, discovered and extensively
investigated by Sorin Comorosan, wherein enzyme initial reaction rates are
enhanced as a consequence of incorporation of solutions derived from previously
irradiated crystalline material into the reaction medium. Effective irradiation
times conform to a sharply oscillatory pattern. In most reports, the irradiated
crystalline material has been the substrate for the enzyme reaction to be
studied, but there have been exceptions. The experiments presented here serve
to confirm and extend this latter aspect of the phenomenon. It is found that
the initial reaction rates for the lactic acid dehydrogenase (LDH) conversion
of pyruvate to lactate can be stimulated by irradiation of crystalline deposits
of sodium chloride, sodium bromide, potassium chloride and diatomaceous earth.
Similarly, stimulation of the LDH conversion of lactate to pyruvate is
demonstrated for irradiated sodium chloride. There appears to be no required
chemical feature of the irradiated material other than crystalline state.
| [
{
"created": "Fri, 26 Apr 2013 16:14:53 GMT",
"version": "v1"
}
] | 2013-04-29 | [
[
"Bass",
"George E.",
""
],
[
"Chenevey",
"James E.",
""
]
] | The study reported here concerns a phenomenon, discovered and extensively investigated by Sorin Comorosan, wherein enzyme initial reaction rates are enhanced as a consequence of incorporation of solutions derived from previously irradiated crystalline material into the reaction medium. Effective irradiation times conform to a sharply oscillatory pattern. In most reports, the irradiated crystalline material has been the substrate for the enzyme reaction to be studied, but there have been exceptions. The experiments presented here serve to confirm and extend this latter aspect of the phenomenon. It is found that the initial reaction rates for the lactic acid dehydrogenase (LDH) conversion of pyruvate to lactate can be stimulated by irradiation of crystalline deposits of sodium chloride, sodium bromide, potassium chloride and diatomaceous earth. Similarly, stimulation of the LDH conversion of lactate to pyruvate is demonstrated for irradiated sodium chloride. There appears to be no required chemical feature of the irradiated material other than crystalline state. |
1306.1439 | Christophe Guyeux | Jacques M. Bahi, Christophe Guyeux, Jean-Marc Nicod, Laurent Philippe | Protein structure prediction software generate two different sets of
conformations. Or the study of unfolded self-avoiding walks | Under submission | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-avoiding walks (SAW) are the source of very difficult problems in
probabilities and enumerative combinatorics. They are also of great interest as
they are, for instance, the basis of protein structure prediction in
bioinformatics. Authors of this article have previously shown that, depending
on the prediction algorithm, the sets of obtained conformations differ: all the
self-avoiding walks can be reached using stretching-based algorithms whereas
only the folded SAWs can be attained with methods that iteratively fold the
straight line. A first study of (un)folded self-avoiding walks is presented in
this article. The contribution is majorly a survey of what is currently known
about these sets. In particular we provide clear definitions of various subsets
of self-avoiding walks related to pivot moves (folded or unfoldable SAWs, etc.)
and the first results we have obtained, theoretically or computationally, on
these sets. A list of open questions is provided too, and the consequences on
the protein structure prediction problem is finally investigated.
| [
{
"created": "Thu, 6 Jun 2013 15:34:00 GMT",
"version": "v1"
}
] | 2013-06-07 | [
[
"Bahi",
"Jacques M.",
""
],
[
"Guyeux",
"Christophe",
""
],
[
"Nicod",
"Jean-Marc",
""
],
[
"Philippe",
"Laurent",
""
]
] | Self-avoiding walks (SAW) are the source of very difficult problems in probabilities and enumerative combinatorics. They are also of great interest as they are, for instance, the basis of protein structure prediction in bioinformatics. Authors of this article have previously shown that, depending on the prediction algorithm, the sets of obtained conformations differ: all the self-avoiding walks can be reached using stretching-based algorithms whereas only the folded SAWs can be attained with methods that iteratively fold the straight line. A first study of (un)folded self-avoiding walks is presented in this article. The contribution is majorly a survey of what is currently known about these sets. In particular we provide clear definitions of various subsets of self-avoiding walks related to pivot moves (folded or unfoldable SAWs, etc.) and the first results we have obtained, theoretically or computationally, on these sets. A list of open questions is provided too, and the consequences on the protein structure prediction problem is finally investigated. |
2004.07334 | Nils Bertschinger | Nils Bertschinger | Visual explanation of country specific differences in Covid-19 dynamics | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This report provides a visual examination of Covid-19 case and death data. In
particular, it shows that country specific differences can too a large extend
be explained by two easily interpreted parameters. Namely, the delay between
reported cases and deaths and the fraction of cases observed. Furthermore, this
allows to lower bound the actual total number of people already infected.
| [
{
"created": "Wed, 15 Apr 2020 20:46:51 GMT",
"version": "v1"
}
] | 2020-04-17 | [
[
"Bertschinger",
"Nils",
""
]
] | This report provides a visual examination of Covid-19 case and death data. In particular, it shows that country specific differences can too a large extend be explained by two easily interpreted parameters. Namely, the delay between reported cases and deaths and the fraction of cases observed. Furthermore, this allows to lower bound the actual total number of people already infected. |
2003.12954 | Eric Jones | Eric W. Jones, Parker Shankin-Clarke, and Jean M. Carlson | Navigation and control of outcomes in a generalized Lotka-Volterra model
of the microbiome | 24 pages, 10 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The generalized Lotka-Volterra (gLV) equations model the microbiome as a
collection of interacting ecological species. Here we use a particular
experimentally-derived gLV model of C. difficile infection (CDI) as a case
study to generate methods that are applicable to generic gLV models. We examine
how to transition gLV systems between multiple steady states through the
application of direct control protocols, which alter the state of the system
via the instantaneous addition or subtraction of microbial species. Then, the
geometry of the basins of attraction of point attractors is compressed into an
attractor network, which decomposes a multistable high-dimensional landscape
into web of bistable subsystems. This attractor network is used to identify
efficient (total intervention volume minimizing) protocols that drive the
system from one basin to another. In some cases, the most efficient control
protocol is circuitous and will take the system through intermediate steady
states with sequential interventions. Clinically, the efficient control of the
microbiome has pertinent applications for bacteriotherapies, which seek to
remedy microbiome-affiliated diseases by directly altering the composition of
the gut microbiome.
| [
{
"created": "Sun, 29 Mar 2020 06:27:16 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Jul 2020 03:13:19 GMT",
"version": "v2"
}
] | 2020-07-24 | [
[
"Jones",
"Eric W.",
""
],
[
"Shankin-Clarke",
"Parker",
""
],
[
"Carlson",
"Jean M.",
""
]
] | The generalized Lotka-Volterra (gLV) equations model the microbiome as a collection of interacting ecological species. Here we use a particular experimentally-derived gLV model of C. difficile infection (CDI) as a case study to generate methods that are applicable to generic gLV models. We examine how to transition gLV systems between multiple steady states through the application of direct control protocols, which alter the state of the system via the instantaneous addition or subtraction of microbial species. Then, the geometry of the basins of attraction of point attractors is compressed into an attractor network, which decomposes a multistable high-dimensional landscape into web of bistable subsystems. This attractor network is used to identify efficient (total intervention volume minimizing) protocols that drive the system from one basin to another. In some cases, the most efficient control protocol is circuitous and will take the system through intermediate steady states with sequential interventions. Clinically, the efficient control of the microbiome has pertinent applications for bacteriotherapies, which seek to remedy microbiome-affiliated diseases by directly altering the composition of the gut microbiome. |
1011.0825 | Etienne Joly | Etienne Joly (IPBS) | The existence of species rests on a metastable equilibrium between
inbreeding and outbreeding. An essay on the close relationship between
speciation, inbreeding and recessive mutations | 52 pages | Biology Direct 6 (2011) 62 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Speciation corresponds to the progressive establishment of
reproductive barriers between groups of individuals derived from an ancestral
stock. Since Darwin did not believe that reproductive barriers could be
selected for, he proposed that most events of speciation would occur through a
process of separation and divergence, and this point of view is still shared by
most evolutionary biologists today. Results: I do, however, contend that, if so
much speciation occurs, the most likely explanation is that there must be
conditions where reproductive barriers can be directly selected for. In other
words, situations where it is advantageous for individuals to reproduce
preferentially within a small group and reduce their breeding with the rest of
the ancestral population. This leads me to propose a model whereby new species
arise not by populations splitting into separate branches, but by small
inbreeding groups "budding" from an ancestral stock. This would be driven by
several advantages of inbreeding, and mainly by advantageous recessive
phenotypes, which could only be retained in the context of inbreeding.
Reproductive barriers would thus not arise as secondary consequences of
divergent evolution in populations isolated from one another, but under the
direct selective pressure of ancestral stocks. Many documented cases of
speciation in natural populations appear to fit the model proposed, with more
speciation occurring in populations with high inbreeding coefficients, and many
recessive characters identified as central to the phenomenon of speciation,
with these recessive mutations expected to be surrounded by patterns of limited
genomic diversity. Conclusions: Whilst adaptive evolution would correspond to
gains of function that would, most of the time, be dominant, this type of
speciation by budding would thus be driven by mutations resulting in the
advantageous loss of certain functions since recessive mutations very often
correspond to the inactivation of a gene. A very important further advantage of
inbreeding is that it reduces the accumulation of recessive mutations in
genomes. A consequence of the model proposed is that the existence of species
would correspond to a metastable equilibrium between inbreeding and
outbreeding, with excessive inbreeding promoting speciation, and excessive
outbreeding resulting in irreversible accumulation of recessive mutations that
could ultimately only lead to extinction.
| [
{
"created": "Wed, 3 Nov 2010 08:49:21 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Nov 2010 13:00:27 GMT",
"version": "v2"
},
{
"created": "Sat, 13 Aug 2011 06:46:22 GMT",
"version": "v3"
},
{
"created": "Fri, 9 Dec 2011 13:15:46 GMT",
"version": "v4"
}
] | 2011-12-12 | [
[
"Joly",
"Etienne",
"",
"IPBS"
]
] | Background: Speciation corresponds to the progressive establishment of reproductive barriers between groups of individuals derived from an ancestral stock. Since Darwin did not believe that reproductive barriers could be selected for, he proposed that most events of speciation would occur through a process of separation and divergence, and this point of view is still shared by most evolutionary biologists today. Results: I do, however, contend that, if so much speciation occurs, the most likely explanation is that there must be conditions where reproductive barriers can be directly selected for. In other words, situations where it is advantageous for individuals to reproduce preferentially within a small group and reduce their breeding with the rest of the ancestral population. This leads me to propose a model whereby new species arise not by populations splitting into separate branches, but by small inbreeding groups "budding" from an ancestral stock. This would be driven by several advantages of inbreeding, and mainly by advantageous recessive phenotypes, which could only be retained in the context of inbreeding. Reproductive barriers would thus not arise as secondary consequences of divergent evolution in populations isolated from one another, but under the direct selective pressure of ancestral stocks. Many documented cases of speciation in natural populations appear to fit the model proposed, with more speciation occurring in populations with high inbreeding coefficients, and many recessive characters identified as central to the phenomenon of speciation, with these recessive mutations expected to be surrounded by patterns of limited genomic diversity. Conclusions: Whilst adaptive evolution would correspond to gains of function that would, most of the time, be dominant, this type of speciation by budding would thus be driven by mutations resulting in the advantageous loss of certain functions since recessive mutations very often correspond to the inactivation of a gene. A very important further advantage of inbreeding is that it reduces the accumulation of recessive mutations in genomes. A consequence of the model proposed is that the existence of species would correspond to a metastable equilibrium between inbreeding and outbreeding, with excessive inbreeding promoting speciation, and excessive outbreeding resulting in irreversible accumulation of recessive mutations that could ultimately only lead to extinction. |
2006.13334 | Ian Leifer | Ian Leifer, Flaviano Morone, Saulo D. S. Reis, Jose S. Andrade Jr.,
Mariano Sigman, Hernan A. Makse | Circuits with broken fibration symmetries perform core logic
computations in biological networks | null | PLoS Comput Biol 2020,16(6): e1007776 | 10.1371/journal.pcbi.1007776 | null | q-bio.GN math.GR physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that logic computational circuits in gene regulatory networks arise
from a fibration symmetry breaking in the network structure. From this idea we
implement a constructive procedure that reveals a hierarchy of genetic
circuits, ubiquitous across species, that are surprising analogues to the
emblematic circuits of solid-state electronics: starting from the transistor
and progressing to ring oscillators, current-mirror circuits to toggle switches
and flip-flops. These canonical variants serve fundamental operations of
synchronization and clocks (in their symmetric states) and memory storage (in
their broken symmetry states). These conclusions introduce a theoretically
principled strategy to search for computational building blocks in biological
networks, and present a systematic route to design synthetic biological
circuits.
| [
{
"created": "Tue, 23 Jun 2020 21:10:28 GMT",
"version": "v1"
}
] | 2020-06-25 | [
[
"Leifer",
"Ian",
""
],
[
"Morone",
"Flaviano",
""
],
[
"Reis",
"Saulo D. S.",
""
],
[
"Andrade",
"Jose S.",
"Jr."
],
[
"Sigman",
"Mariano",
""
],
[
"Makse",
"Hernan A.",
""
]
] | We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits. |
2303.06340 | Shi-Ju Ran | Yu-Jia An, Sheng-Chen Bai, Lin Cheng, Xiao-Guang Li, Cheng-en Wang,
Xiao-Dong Han, Gang Su, Shi-Ju Ran, Cong Wang | Intelligent diagnostic scheme for lung cancer screening with Raman
spectra data by tensor network machine learning | 10 pages, 7 figures | null | null | null | q-bio.QM cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) has brought tremendous impacts on biomedical
sciences from academic researches to clinical applications, such as in
biomarkers' detection and diagnosis, optimization of treatment, and
identification of new therapeutic targets in drug discovery. However, the
contemporary AI technologies, particularly deep machine learning (ML), severely
suffer from non-interpretability, which might uncontrollably lead to incorrect
predictions. Interpretability is particularly crucial to ML for clinical
diagnosis as the consumers must gain necessary sense of security and trust from
firm grounds or convincing interpretations. In this work, we propose a
tensor-network (TN)-ML method to reliably predict lung cancer patients and
their stages via screening Raman spectra data of Volatile organic compounds
(VOCs) in exhaled breath, which are generally suitable as biomarkers and are
considered to be an ideal way for non-invasive lung cancer screening. The
prediction of TN-ML is based on the mutual distances of the breath samples
mapped to the quantum Hilbert space. Thanks to the quantum probabilistic
interpretation, the certainty of the predictions can be quantitatively
characterized. The accuracy of the samples with high certainty is almost
100$\%$. The incorrectly-classified samples exhibit obviously lower certainty,
and thus can be decipherably identified as anomalies, which will be handled by
human experts to guarantee high reliability. Our work sheds light on shifting
the ``AI for biomedical sciences'' from the conventional non-interpretable ML
schemes to the interpretable human-ML interactive approaches, for the purpose
of high accuracy and reliability.
| [
{
"created": "Sat, 11 Mar 2023 07:57:37 GMT",
"version": "v1"
}
] | 2023-03-14 | [
[
"An",
"Yu-Jia",
""
],
[
"Bai",
"Sheng-Chen",
""
],
[
"Cheng",
"Lin",
""
],
[
"Li",
"Xiao-Guang",
""
],
[
"Wang",
"Cheng-en",
""
],
[
"Han",
"Xiao-Dong",
""
],
[
"Su",
"Gang",
""
],
[
"Ran",
"Shi... | Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability. |
1303.0805 | Richard A Neher | Fabio Zanini and Richard A. Neher | Deleterious synonymous mutations hitchhike to high frequency in HIV-1
env evolution | null | null | 10.1128/JVI.01529-13 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intrapatient HIV-1 evolution is dominated by selection on the protein level
in the arms race with the adaptive immune system. When cytotoxic CD8+ T-cells
or neutralizing antibodies target a new epitope, the virus often escapes via
nonsynonymous mutations that impair recognition. Synonymous mutations do not
affect this interplay and are often assumed to be neutral. We analyze
longitudinal intrapatient data from the C2-V5 part of the envelope gene (env)
and observe that synonymous derived alleles rarely fix even though they often
reach high frequencies in the viral population. We find that synonymous
mutations that disrupt base pairs in RNA stems flanking the variable loops of
gp120 are more likely to be lost than other synonymous changes, hinting at a
direct fitness effect of these stem-loop structures in the HIV-1 RNA.
Computational modeling indicates that these synonymous mutations have a
(Malthusian) selection coefficient of the order of -0.002 and that they are
brought up to high frequency by hitchhiking on neighboring beneficial
nonsynonymous alleles. The patterns of fixation of nonsynonymous mutations
estimated from the longitudinal data and comparisons with computer models
suggest that escape mutations in C2-V5 are only transiently beneficial, either
because the immune system is catching up or because of competition between
equivalent escapes.
| [
{
"created": "Mon, 4 Mar 2013 19:41:41 GMT",
"version": "v1"
}
] | 2014-03-25 | [
[
"Zanini",
"Fabio",
""
],
[
"Neher",
"Richard A.",
""
]
] | Intrapatient HIV-1 evolution is dominated by selection on the protein level in the arms race with the adaptive immune system. When cytotoxic CD8+ T-cells or neutralizing antibodies target a new epitope, the virus often escapes via nonsynonymous mutations that impair recognition. Synonymous mutations do not affect this interplay and are often assumed to be neutral. We analyze longitudinal intrapatient data from the C2-V5 part of the envelope gene (env) and observe that synonymous derived alleles rarely fix even though they often reach high frequencies in the viral population. We find that synonymous mutations that disrupt base pairs in RNA stems flanking the variable loops of gp120 are more likely to be lost than other synonymous changes, hinting at a direct fitness effect of these stem-loop structures in the HIV-1 RNA. Computational modeling indicates that these synonymous mutations have a (Malthusian) selection coefficient of the order of -0.002 and that they are brought up to high frequency by hitchhiking on neighboring beneficial nonsynonymous alleles. The patterns of fixation of nonsynonymous mutations estimated from the longitudinal data and comparisons with computer models suggest that escape mutations in C2-V5 are only transiently beneficial, either because the immune system is catching up or because of competition between equivalent escapes. |
2106.02085 | Kelly Iarosz | Antonio M Batista, Silvio L T Souza, Kelly C Iarosz, Alexandre C L
Almeida, Jos\'e D Szezech, Enrique C Gabrick, Michele Mugnaine, Gefferson L
dos Santos, Iber\^e L Caldas | Simulation of deterministic compartmental models for infectious diseases
dynamics | null | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Infectious diseases are caused by pathogenic microorganisms and can spread
through different ways. Mathematical models and computational simulation have
been used extensively to investigate the transmission and spread of infectious
diseases. In other words, mathematical model simulation can be used to analyse
the dynamics of infectious diseases, aiming to understand the effects and how
to control the spread. In general, these models are based on compartments,
where each compartment contains individuals with the same characteristics, such
as susceptible, exposed, infected, and recovered. In this paper, we cast
further light on some classical epidemic models, reporting possible outcomes
from numerical simulation. Furthermore, we provide routines in a repository for
simulations.
| [
{
"created": "Thu, 3 Jun 2021 19:02:18 GMT",
"version": "v1"
}
] | 2021-06-07 | [
[
"Batista",
"Antonio M",
""
],
[
"Souza",
"Silvio L T",
""
],
[
"Iarosz",
"Kelly C",
""
],
[
"Almeida",
"Alexandre C L",
""
],
[
"Szezech",
"José D",
""
],
[
"Gabrick",
"Enrique C",
""
],
[
"Mugnaine",
"Michele"... | Infectious diseases are caused by pathogenic microorganisms and can spread through different ways. Mathematical models and computational simulation have been used extensively to investigate the transmission and spread of infectious diseases. In other words, mathematical model simulation can be used to analyse the dynamics of infectious diseases, aiming to understand the effects and how to control the spread. In general, these models are based on compartments, where each compartment contains individuals with the same characteristics, such as susceptible, exposed, infected, and recovered. In this paper, we cast further light on some classical epidemic models, reporting possible outcomes from numerical simulation. Furthermore, we provide routines in a repository for simulations. |
2011.10657 | Mohammad Ali Moni | Sakifa Aktar, Md. Martuza Ahamad, Md. Rashed-Al-Mahfuz, AKM Azad,
Shahadat Uddin, A H M Kamal, Salem A. Alyami, Ping-I Lin, Sheikh Mohammed
Shariful Islam, Julian M.W. Quinn, Valsamma Eapen, and Mohammad Ali Moni | Predicting Patient COVID-19 Disease Severity by means of Statistical and
Machine Learning Analysis of Blood Cell Transcriptome Data | null | JMIR Med Inform 2021;9(4):e25884, PMID: 33779565 | 10.2196/25884 | JMIR ms#25884 | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Introduction: For COVID-19 patients accurate prediction of disease severity
and mortality risk would greatly improve care delivery and resource allocation.
There are many patient-related factors, such as pre-existing comorbidities that
affect disease severity. Since rapid automated profiling of peripheral blood
samples is widely available, we investigated how such data from the peripheral
blood of COVID-19 patients might be used to predict clinical outcomes.
Methods: We thus investigated such clinical datasets from COVID-19 patients
with known outcomes by combining statistical comparison and correlation methods
with machine learning algorithms; the latter included decision tree, random
forest, variants of gradient boosting machine, support vector machine,
K-nearest neighbour and deep learning methods.
Results: Our work revealed several clinical parameters measurable in blood
samples, which discriminated between healthy people and COVID-19 positive
patients and showed predictive value for later severity of COVID-19 symptoms.
We thus developed a number of analytic methods that showed accuracy and
precision for disease severity and mortality outcome predictions that were
above 90%.
Conclusions: In sum, we developed methodologies to analyse patient routine
clinical data which enables more accurate prediction of COVID-19 patient
outcomes. This type of approaches could, by employing standard hospital
laboratory analyses of patient blood, be utilised to identify, COVID-19
patients at high risk of mortality and so enable their treatment to be
optimised.
| [
{
"created": "Thu, 19 Nov 2020 10:32:46 GMT",
"version": "v1"
}
] | 2021-04-20 | [
[
"Aktar",
"Sakifa",
""
],
[
"Ahamad",
"Md. Martuza",
""
],
[
"Rashed-Al-Mahfuz",
"Md.",
""
],
[
"Azad",
"AKM",
""
],
[
"Uddin",
"Shahadat",
""
],
[
"Kamal",
"A H M",
""
],
[
"Alyami",
"Salem A.",
""
],
[... | Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity. Since rapid automated profiling of peripheral blood samples is widely available, we investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes. Methods: We thus investigated such clinical datasets from COVID-19 patients with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, K-nearest neighbour and deep learning methods. Results: Our work revealed several clinical parameters measurable in blood samples, which discriminated between healthy people and COVID-19 positive patients and showed predictive value for later severity of COVID-19 symptoms. We thus developed a number of analytic methods that showed accuracy and precision for disease severity and mortality outcome predictions that were above 90%. Conclusions: In sum, we developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approaches could, by employing standard hospital laboratory analyses of patient blood, be utilised to identify, COVID-19 patients at high risk of mortality and so enable their treatment to be optimised. |
2207.13141 | Chen Li | Yifeng Zhang, Qihan Xuan, Qiyuan Fu, Chen Li | Simulation of snakes using vertical body bending to traverse terrain
with large height variation | null | null | null | null | q-bio.QM physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Snake moves across various terrains by bending its elongated body. Recent
studies discovered that snakes can use vertical bending to traverse terrain of
large height variation, such as horizontally oriented cylinders, a wedge
(Jurestovsky, Usher, Astley, 2021, J. Exp. Biol.), and uneven terrain (Fu & Li,
2020, Roy. Soc. Open Sci.; Fu, Astley, Li, 2022 Bioinspiration & Biomimetics).
Here, to understand how vertical bending generates propulsion, we developed a
dynamic simulation of a snake traversing a wedge (height = 0.05 body length,
slope = 27 degrees) and a half cylindrical obstacle (height = 0.1 body length).
By propagating down the body an internal torque profile with a maximum around
the obstacle, the simulated snake moved forward as observed in the animal.
Remarkably, even when frictional drag is low (snake-terrain kinetic friction
coefficient of 0.20), the body must push against the wedge with a pressure 5
times that from body weight to generate sufficient forward propulsion to move
forward. This indicated that snakes are highly capable of bending vertically to
push against the environment to generate propulsion. Testing different
controllers revealed that contact force feedback further helps generate and
maintain propulsion effectively under unknown terrain perturbations.
| [
{
"created": "Tue, 26 Jul 2022 18:41:45 GMT",
"version": "v1"
}
] | 2022-07-28 | [
[
"Zhang",
"Yifeng",
""
],
[
"Xuan",
"Qihan",
""
],
[
"Fu",
"Qiyuan",
""
],
[
"Li",
"Chen",
""
]
] | Snake moves across various terrains by bending its elongated body. Recent studies discovered that snakes can use vertical bending to traverse terrain of large height variation, such as horizontally oriented cylinders, a wedge (Jurestovsky, Usher, Astley, 2021, J. Exp. Biol.), and uneven terrain (Fu & Li, 2020, Roy. Soc. Open Sci.; Fu, Astley, Li, 2022 Bioinspiration & Biomimetics). Here, to understand how vertical bending generates propulsion, we developed a dynamic simulation of a snake traversing a wedge (height = 0.05 body length, slope = 27 degrees) and a half cylindrical obstacle (height = 0.1 body length). By propagating down the body an internal torque profile with a maximum around the obstacle, the simulated snake moved forward as observed in the animal. Remarkably, even when frictional drag is low (snake-terrain kinetic friction coefficient of 0.20), the body must push against the wedge with a pressure 5 times that from body weight to generate sufficient forward propulsion to move forward. This indicated that snakes are highly capable of bending vertically to push against the environment to generate propulsion. Testing different controllers revealed that contact force feedback further helps generate and maintain propulsion effectively under unknown terrain perturbations. |
1611.09212 | Riccardo Franco | Riccardo Franco | Towards a new quantum cognition model | null | null | null | null | q-bio.NC cs.AI quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents a new quantum-like model for cognition explicitly based
on knowledge. It is shown that this model, called QKT (quantum knowledge-based
theory), is able to coherently describe some experimental results that are
problematic for the prior quantum-like decision models. In particular, I
consider the experimental results relevant to the post-decision cognitive
dissonance, the problems relevant to the question order effect and response
replicability, and those relevant to the grand-reciprocity equations. A new set
of postulates is proposed, which evidence the different meaning given to the
projectors and to the quantum states. In the final part, I show that the use of
quantum gates can help to better describe and understand the evolution of
quantum-like models.
| [
{
"created": "Wed, 23 Nov 2016 23:17:10 GMT",
"version": "v1"
}
] | 2016-11-29 | [
[
"Franco",
"Riccardo",
""
]
] | This article presents a new quantum-like model for cognition explicitly based on knowledge. It is shown that this model, called QKT (quantum knowledge-based theory), is able to coherently describe some experimental results that are problematic for the prior quantum-like decision models. In particular, I consider the experimental results relevant to the post-decision cognitive dissonance, the problems relevant to the question order effect and response replicability, and those relevant to the grand-reciprocity equations. A new set of postulates is proposed, which evidence the different meaning given to the projectors and to the quantum states. In the final part, I show that the use of quantum gates can help to better describe and understand the evolution of quantum-like models. |
1906.02241 | Yun Zhao | Yun Zhao, Elmer Guzman, Morgane Audouard, Zhuowei Cheng, PaulK.
Hansma, Kenneth S. Kosik, and Linda Petzold | A Deep Learning Framework for Classification of in vitro Multi-Electrode
Array Recordings | 14 pages, in ICDM 2019 | null | null | null | q-bio.NC cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-Electrode Arrays (MEAs) have been widely used to record neuronal
activities, which could be used in the diagnosis of gene defects and drug
effects. In this paper, we address the problem of classifying in vitro MEA
recordings of mouse and human neuronal cultures from different genotypes, where
there is no easy way to directly utilize raw sequences as inputs to train an
end-to-end classification model. While carefully extracting some features by
hand could partially solve the problem, this approach suffers from obvious
drawbacks such as difficulty of generalizing. We propose a deep learning
framework to address this challenge. Our approach correctly classifies neuronal
culture data prepared from two different genotypes -- a mouse Knockout of the
delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from
Williams syndrome. By splitting the long recordings into short slices for
training, and applying Consensus Prediction during testing, our deep learning
approach improves the prediction accuracy by 16.69% compared with feature based
Logistic Regression for mouse MEA recordings. We further achieve an accuracy of
95.91% using Consensus Prediction in one subset of mouse MEA recording data,
which were all recorded at six days in vitro. As high-density MEA recordings
become more widely available, this approach could be generalized for
classification of neurons carrying different mutations and classification of
drug responses.
| [
{
"created": "Wed, 5 Jun 2019 18:36:31 GMT",
"version": "v1"
}
] | 2019-06-07 | [
[
"Zhao",
"Yun",
""
],
[
"Guzman",
"Elmer",
""
],
[
"Audouard",
"Morgane",
""
],
[
"Cheng",
"Zhuowei",
""
],
[
"Hansma",
"PaulK.",
""
],
[
"Kosik",
"Kenneth S.",
""
],
[
"Petzold",
"Linda",
""
]
] | Multi-Electrode Arrays (MEAs) have been widely used to record neuronal activities, which could be used in the diagnosis of gene defects and drug effects. In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal cultures from different genotypes, where there is no easy way to directly utilize raw sequences as inputs to train an end-to-end classification model. While carefully extracting some features by hand could partially solve the problem, this approach suffers from obvious drawbacks such as difficulty of generalizing. We propose a deep learning framework to address this challenge. Our approach correctly classifies neuronal culture data prepared from two different genotypes -- a mouse Knockout of the delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from Williams syndrome. By splitting the long recordings into short slices for training, and applying Consensus Prediction during testing, our deep learning approach improves the prediction accuracy by 16.69% compared with feature based Logistic Regression for mouse MEA recordings. We further achieve an accuracy of 95.91% using Consensus Prediction in one subset of mouse MEA recording data, which were all recorded at six days in vitro. As high-density MEA recordings become more widely available, this approach could be generalized for classification of neurons carrying different mutations and classification of drug responses. |
1708.09665 | Peter Czuppon | Peter Czuppon and Arne Traulsen | Fixation probabilities in populations under demographic fluctuations | 31 pages, 7 figures | Journal of Mathematical Biology, 2018 | 10.1007/s00285-018-1251-9 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We study the fixation probability of a mutant type when introduced into a
resident population. As opposed to the usual assumption of constant pop-
ulation size, we allow for stochastically varying population sizes. This is
implemented by a stochastic competitive Lotka-Volterra model. The compe- tition
coefficients are interpreted in terms of inverse payoffs emerging from an
evolutionary game. Since our study focuses on the impact of the competition
values, we assume the same birth and death rates for both types. In this gen-
eral framework, we derive an approximate formula for the fixation probability
{\phi} of the mutant type under weak selection. The qualitative behavior of
{\phi} when compared to the neutral scenario is governed by the invasion
dynamics of an initially rare type. Higher payoffs when competing with the
resident type yield higher values of {\phi}. Additionally, we investigate the
influence of the remaining parameters and find an explicit dependence of {\phi}
on the mixed equilibrium value of the corresponding deterministic system (given
that the parameter values allow for its existence).
| [
{
"created": "Thu, 31 Aug 2017 11:13:20 GMT",
"version": "v1"
}
] | 2018-06-08 | [
[
"Czuppon",
"Peter",
""
],
[
"Traulsen",
"Arne",
""
]
] | We study the fixation probability of a mutant type when introduced into a resident population. As opposed to the usual assumption of constant pop- ulation size, we allow for stochastically varying population sizes. This is implemented by a stochastic competitive Lotka-Volterra model. The compe- tition coefficients are interpreted in terms of inverse payoffs emerging from an evolutionary game. Since our study focuses on the impact of the competition values, we assume the same birth and death rates for both types. In this gen- eral framework, we derive an approximate formula for the fixation probability {\phi} of the mutant type under weak selection. The qualitative behavior of {\phi} when compared to the neutral scenario is governed by the invasion dynamics of an initially rare type. Higher payoffs when competing with the resident type yield higher values of {\phi}. Additionally, we investigate the influence of the remaining parameters and find an explicit dependence of {\phi} on the mixed equilibrium value of the corresponding deterministic system (given that the parameter values allow for its existence). |
1708.01792 | Abhishek Deshpande | Abhishek Deshpande and Thomas E. Ouldridge | High rates of fuel consumption are not required by insulating motifs to
suppress retroactivity in biochemical circuits | 26 pages, 19 figures, To appear in Engineering Biology | null | null | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Retroactivity arises when the coupling of a molecular network $\mathcal{U}$
to a downstream network $\mathcal{D}$ results in signal propagation back from
$\mathcal{D}$ to $\mathcal{U}$. The phenomenon represents a breakdown in
modularity of biochemical circuits and hampers the rational design of complex
functional networks. Considering simple models of signal-transduction
architectures, we demonstrate the strong dependence of retroactivity on the
properties of the upstream system, and explore the cost and efficacy of
fuel-consuming insulating motifs that can mitigate retroactive effects. We find
that simple insulating motifs can suppress retroactivity at a low fuel cost by
coupling only weakly to the upstream system $\mathcal{U}$. However, this design
approach reduces the signalling network's robustness to perturbations from leak
reactions, and potentially compromises its ability to respond to
rapidly-varying signals.
| [
{
"created": "Sat, 5 Aug 2017 17:24:45 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Aug 2017 12:39:27 GMT",
"version": "v2"
},
{
"created": "Fri, 11 Aug 2017 10:50:49 GMT",
"version": "v3"
},
{
"created": "Tue, 7 Nov 2017 00:07:35 GMT",
"version": "v4"
}
] | 2017-11-08 | [
[
"Deshpande",
"Abhishek",
""
],
[
"Ouldridge",
"Thomas E.",
""
]
] | Retroactivity arises when the coupling of a molecular network $\mathcal{U}$ to a downstream network $\mathcal{D}$ results in signal propagation back from $\mathcal{D}$ to $\mathcal{U}$. The phenomenon represents a breakdown in modularity of biochemical circuits and hampers the rational design of complex functional networks. Considering simple models of signal-transduction architectures, we demonstrate the strong dependence of retroactivity on the properties of the upstream system, and explore the cost and efficacy of fuel-consuming insulating motifs that can mitigate retroactive effects. We find that simple insulating motifs can suppress retroactivity at a low fuel cost by coupling only weakly to the upstream system $\mathcal{U}$. However, this design approach reduces the signalling network's robustness to perturbations from leak reactions, and potentially compromises its ability to respond to rapidly-varying signals. |
1410.6455 | Yong Kong | Yong Kong | Btrim: A fast, lightweight adapter and quality trimming program for
next-generation sequencing technologies | 8 pages, 1 figure | Genomics, 98, 152-153 (2001) | 10.1016/j.ygeno.2011.05.009 | null | q-bio.GN cs.CE cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Btrim is a fast and lightweight software to trim adapters and low quality
regions in reads from ultra high-throughput next-generation sequencing
machines. It also can reliably identify barcodes and assign the reads to the
original samples. Based on a modified Myers's bit-vector dynamic programming
algorithm, Btrim can handle indels in adapters and barcodes. It removes low
quality regions and trims off adapters at both or either end of the reads. A
typical trimming of 30M reads with two sets of adapter pairs can be done in
about a minute with a small memory footprint. Btrim is a versatile stand-alone
tool that can be used as the first step in virtually all next-generation
sequence analysis pipelines. The program is available at
\url{http://graphics.med.yale.edu/trim/}.
| [
{
"created": "Thu, 23 Oct 2014 18:56:10 GMT",
"version": "v1"
}
] | 2024-05-28 | [
[
"Kong",
"Yong",
""
]
] | Btrim is a fast and lightweight software to trim adapters and low quality regions in reads from ultra high-throughput next-generation sequencing machines. It also can reliably identify barcodes and assign the reads to the original samples. Based on a modified Myers's bit-vector dynamic programming algorithm, Btrim can handle indels in adapters and barcodes. It removes low quality regions and trims off adapters at both or either end of the reads. A typical trimming of 30M reads with two sets of adapter pairs can be done in about a minute with a small memory footprint. Btrim is a versatile stand-alone tool that can be used as the first step in virtually all next-generation sequence analysis pipelines. The program is available at \url{http://graphics.med.yale.edu/trim/}. |
0710.5665 | Peter Hinow | Shizhen Emily Wang, Peter Hinow, Nicole Bryce, Alissa M. Weaver,
Lourdes Estrada, Carlos L. Arteaga, Glenn F. Webb | A mathematical model quantifies proliferation and motility effects of
TGF--$\beta$ on cancer cells | 15 pages, 4 figures; to appear in Computational and Mathematical
Methods in Medicine | Comput. Math. Methods Med. 10:71-83, 2009 | null | null | q-bio.QM | null | Transforming growth factor (TGF) $\beta$ is known to have properties of both
a tumor suppressor and a tumor promoter. While it inhibits cell proliferation,
it also increases cell motility and decreases cell--cell adhesion. Coupling
mathematical modeling and experiments, we investigate the growth and motility
of oncogene--expressing human mammary epithelial cells under exposure to
TGF--$\beta$. We use a version of the well--known Fisher--Kolmogorov equation,
and prescribe a procedure for its parametrization. We quantify the simultaneous
effects of TGF--$\beta$ to increase the tendency of individual cells and cell
clusters to move randomly and to decrease overall population growth. We
demonstrate that in experiments with TGF--$\beta$ treated cells \textit{in
vitro}, TGF--$\beta$ increases cell motility by a factor of 2 and decreases
cell proliferation by a factor of 1/2 in comparison with untreated cells.
| [
{
"created": "Tue, 30 Oct 2007 14:52:40 GMT",
"version": "v1"
},
{
"created": "Sat, 9 Feb 2008 17:13:15 GMT",
"version": "v2"
},
{
"created": "Thu, 1 May 2008 15:40:23 GMT",
"version": "v3"
}
] | 2009-03-27 | [
[
"Wang",
"Shizhen Emily",
""
],
[
"Hinow",
"Peter",
""
],
[
"Bryce",
"Nicole",
""
],
[
"Weaver",
"Alissa M.",
""
],
[
"Estrada",
"Lourdes",
""
],
[
"Arteaga",
"Carlos L.",
""
],
[
"Webb",
"Glenn F.",
""
]
... | Transforming growth factor (TGF) $\beta$ is known to have properties of both a tumor suppressor and a tumor promoter. While it inhibits cell proliferation, it also increases cell motility and decreases cell--cell adhesion. Coupling mathematical modeling and experiments, we investigate the growth and motility of oncogene--expressing human mammary epithelial cells under exposure to TGF--$\beta$. We use a version of the well--known Fisher--Kolmogorov equation, and prescribe a procedure for its parametrization. We quantify the simultaneous effects of TGF--$\beta$ to increase the tendency of individual cells and cell clusters to move randomly and to decrease overall population growth. We demonstrate that in experiments with TGF--$\beta$ treated cells \textit{in vitro}, TGF--$\beta$ increases cell motility by a factor of 2 and decreases cell proliferation by a factor of 1/2 in comparison with untreated cells. |
2311.00269 | Dhaker Kroumi | Dhaker Kroumi and Sabin Lessard | Evolutionary game with stochastic payoffs in a finite island model | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In this paper, we consider a two-player two-strategy game with random payoffs
in a population subdivided into $d$ demes, each containing $N$ individuals at
the beginning of any given generation and experiencing local extinction and
recolonization with some fixed probability $m$ after reproduction and selection
among offspring. Within each deme, offspring engage in random pairwise
interactions, and the payoffs are assumed to have means and variances
proportional to the inverse of the population size. By verifying the conditions
given in Ethier and Nagylaki (1980) to approximate Markov chains with two time
scales, we establish that the discrete-time evolutionary dynamics with $Nd$
generations as unit of time converges to a continuous-time diffusion as
$d\rightarrow\infty$. The infinitesimal mean and variance of this diffusion are
expressed in terms of the population-scaled means and variances of the payoffs
besides identity-by-descent measures between offspring in the same deme in a
neutral population. We show that the probability for a strategy to fix in the
population starting from an initial frequency $(Nd)^{-1}$ generally increases
as the payoffs to that strategy exhibit less variability or the payoffs to the
other strategy more variability. As a result, differences in variability can
make this fixation probability for cooperation larger than the corresponding
one for defection. As the deme-scaled extinction rate $\nu=mN$ decreases for
$N$ large enough and $m$ small enough, creating a higher level of identity
among offspring within demes, the differences between the population-scaled
variances of the payoffs for interacting offspring of different types increases
this effect to a greater extent than the differences for interacting offspring
of the same type.
| [
{
"created": "Wed, 1 Nov 2023 03:36:12 GMT",
"version": "v1"
}
] | 2023-11-02 | [
[
"Kroumi",
"Dhaker",
""
],
[
"Lessard",
"Sabin",
""
]
] | In this paper, we consider a two-player two-strategy game with random payoffs in a population subdivided into $d$ demes, each containing $N$ individuals at the beginning of any given generation and experiencing local extinction and recolonization with some fixed probability $m$ after reproduction and selection among offspring. Within each deme, offspring engage in random pairwise interactions, and the payoffs are assumed to have means and variances proportional to the inverse of the population size. By verifying the conditions given in Ethier and Nagylaki (1980) to approximate Markov chains with two time scales, we establish that the discrete-time evolutionary dynamics with $Nd$ generations as unit of time converges to a continuous-time diffusion as $d\rightarrow\infty$. The infinitesimal mean and variance of this diffusion are expressed in terms of the population-scaled means and variances of the payoffs besides identity-by-descent measures between offspring in the same deme in a neutral population. We show that the probability for a strategy to fix in the population starting from an initial frequency $(Nd)^{-1}$ generally increases as the payoffs to that strategy exhibit less variability or the payoffs to the other strategy more variability. As a result, differences in variability can make this fixation probability for cooperation larger than the corresponding one for defection. As the deme-scaled extinction rate $\nu=mN$ decreases for $N$ large enough and $m$ small enough, creating a higher level of identity among offspring within demes, the differences between the population-scaled variances of the payoffs for interacting offspring of different types increases this effect to a greater extent than the differences for interacting offspring of the same type. |
1211.7167 | Ali R. Mohazab | Ali R. Mohazab and Steven S. Plotkin | Polymer uncrossing and knotting in protein folding, and their role in
minimal folding pathways | null | null | 10.1371/journal.pone.0053642 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/3.0/ | We introduce a method for calculating the extent to which chain non-crossing
is important in the most efficient, optimal trajectories or pathways for a
protein to fold. This involves recording all unphysical crossing events of a
ghost chain, and calculating the minimal uncrossing cost that would have been
required to avoid such events. A depth-first tree search algorithm is applied
to find minimal transformations to fold $\alpha$, $\beta$, $\alpha/\beta$, and
knotted proteins. In all cases, the extra uncrossing/non-crossing distance is a
small fraction of the total distance travelled by a ghost chain. Different
structural classes may be distinguished by the amount of extra uncrossing
distance, and the effectiveness of such discrimination is compared with other
order parameters. It was seen that non-crossing distance over chain length
provided the best discrimination between structural and kinetic classes. The
scaling of non-crossing distance with chain length implies an inevitable
crossover to entanglement-dominated folding mechanisms for sufficiently long
chains. We further quantify the minimal folding pathways by collecting the
sequence of uncrossing moves, which generally involve leg, loop, and elbow-like
uncrossing moves, and rendering the collection of these moves over the unfolded
ensemble as a multiple-transformation "alignment". The consensus minimal
pathway is constructed and shown schematically for representative cases of an
$\alpha$, $\beta$, and knotted protein. An overlap parameter is defined between
pathways; we find that $\alpha$ proteins have minimal overlap indicating
diverse folding pathways, knotted proteins are highly constrained to follow a
dominant pathway, and $\beta$ proteins are somewhere in between. Thus we have
shown how topological chain constraints can induce dominant pathway mechanisms
in protein folding.
| [
{
"created": "Fri, 30 Nov 2012 07:15:22 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Mohazab",
"Ali R.",
""
],
[
"Plotkin",
"Steven S.",
""
]
] | We introduce a method for calculating the extent to which chain non-crossing is important in the most efficient, optimal trajectories or pathways for a protein to fold. This involves recording all unphysical crossing events of a ghost chain, and calculating the minimal uncrossing cost that would have been required to avoid such events. A depth-first tree search algorithm is applied to find minimal transformations to fold $\alpha$, $\beta$, $\alpha/\beta$, and knotted proteins. In all cases, the extra uncrossing/non-crossing distance is a small fraction of the total distance travelled by a ghost chain. Different structural classes may be distinguished by the amount of extra uncrossing distance, and the effectiveness of such discrimination is compared with other order parameters. It was seen that non-crossing distance over chain length provided the best discrimination between structural and kinetic classes. The scaling of non-crossing distance with chain length implies an inevitable crossover to entanglement-dominated folding mechanisms for sufficiently long chains. We further quantify the minimal folding pathways by collecting the sequence of uncrossing moves, which generally involve leg, loop, and elbow-like uncrossing moves, and rendering the collection of these moves over the unfolded ensemble as a multiple-transformation "alignment". The consensus minimal pathway is constructed and shown schematically for representative cases of an $\alpha$, $\beta$, and knotted protein. An overlap parameter is defined between pathways; we find that $\alpha$ proteins have minimal overlap indicating diverse folding pathways, knotted proteins are highly constrained to follow a dominant pathway, and $\beta$ proteins are somewhere in between. Thus we have shown how topological chain constraints can induce dominant pathway mechanisms in protein folding. |
2405.18343 | Emilio Mendiola | Emilio A. Mendiola, Raza Rana Mehdi, Dipan J. Shah, Reza Avazmohammadi | On in-silico estimation of left ventricular end-diastolic pressure from
cardiac strains | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Left ventricular diastolic dysfunction (LVDD) is a group of diseases that
adversely affect the passive phase of the cardiac cycle and can lead to heart
failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable
prognostic measure in LVDD patients, traditional invasive methods of measuring
LVEDP present risks and limitations, highlighting the need for alternative
approaches. This paper investigates the possibility of measuring LVEDP
non-invasively using inverse in-silico modeling. We propose the adoption of
patient-specific cardiac modeling and simulation to estimate LVEDP and
myocardial stiffness from cardiac strains. We have developed a high-fidelity
patient-specific computational model of the left ventricle. Through an inverse
modeling approach, myocardial stiffness and LVEDP were accurately estimated
from cardiac strains that can be acquired from in vivo imaging, indicating the
feasibility of computational modeling to augment current approaches in the
measurement of ventricular pressure. Integration of such computational
platforms into clinical practice holds promise for early detection and
comprehensive assessment of LVDD with reduced risk for patients.
| [
{
"created": "Tue, 28 May 2024 16:41:21 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Mendiola",
"Emilio A.",
""
],
[
"Mehdi",
"Raza Rana",
""
],
[
"Shah",
"Dipan J.",
""
],
[
"Avazmohammadi",
"Reza",
""
]
] | Left ventricular diastolic dysfunction (LVDD) is a group of diseases that adversely affect the passive phase of the cardiac cycle and can lead to heart failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable prognostic measure in LVDD patients, traditional invasive methods of measuring LVEDP present risks and limitations, highlighting the need for alternative approaches. This paper investigates the possibility of measuring LVEDP non-invasively using inverse in-silico modeling. We propose the adoption of patient-specific cardiac modeling and simulation to estimate LVEDP and myocardial stiffness from cardiac strains. We have developed a high-fidelity patient-specific computational model of the left ventricle. Through an inverse modeling approach, myocardial stiffness and LVEDP were accurately estimated from cardiac strains that can be acquired from in vivo imaging, indicating the feasibility of computational modeling to augment current approaches in the measurement of ventricular pressure. Integration of such computational platforms into clinical practice holds promise for early detection and comprehensive assessment of LVDD with reduced risk for patients. |
1410.5123 | Giovanni Punzi | Maria Michela Del Viva, Giovanni Punzi | The brain as a trigger system | Presented by M. Del Viva at the Conference "Technology and
Instrumentation in Particle Physics 2014" (TIPP 2014), June 2-6, 2014,
Amsterdam, The Netherlands | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are significant analogies between the issues related to real-time event
selection in HEP, and the issues faced by the human visual system. In fact, the
visual system needs to extract rapidly the most important elements of the
external world from a large flux of information, for survival purposes. A rapid
and reliable detection of visual stimuli is essential for triggering autonomic
responses to emotive stimuli, for initiating adaptive behaviors and for
orienting towards potentially interesting/ dangerous stimuli. The speed of
visual processing can be as fast as 20 ms, about only 20 times the duration of
the elementary information exchanges by the action potential. The limitations
to the brain capacity to process visual information, imposed by intrinsic
energetic costs of neuronal activity, and ecological limits to the size of the
skull, require a strong data reduction at an early stage, by creating a compact
summary of relevant information, the so called "primal sketch", to be handled
by further levels of processing. This is quite similar to the problem of
experimental HEP of providing fast data reduction at a reasonable monetary
cost, and with a practical device size. As a result of a joint effort of HEP
physicists and practicing vision scientists, we recently proposed that not only
the problems are similar, but the solutions adopted in the two cases also have
strong similarities, and their parallel study can actually shed light on each
other. Modeling the visual system as a trigger processor leads to a deeper
understanding, and even very specific predictions of its functionality.
Conversely, the insights gained from this new approach to vision, can lead to
new ideas for enhancing the capabilities of artificial vision systems, and HEP
trigger systems as well.
| [
{
"created": "Sun, 19 Oct 2014 23:03:57 GMT",
"version": "v1"
}
] | 2014-10-21 | [
[
"Del Viva",
"Maria Michela",
""
],
[
"Punzi",
"Giovanni",
""
]
] | There are significant analogies between the issues related to real-time event selection in HEP, and the issues faced by the human visual system. In fact, the visual system needs to extract rapidly the most important elements of the external world from a large flux of information, for survival purposes. A rapid and reliable detection of visual stimuli is essential for triggering autonomic responses to emotive stimuli, for initiating adaptive behaviors and for orienting towards potentially interesting/ dangerous stimuli. The speed of visual processing can be as fast as 20 ms, about only 20 times the duration of the elementary information exchanges by the action potential. The limitations to the brain capacity to process visual information, imposed by intrinsic energetic costs of neuronal activity, and ecological limits to the size of the skull, require a strong data reduction at an early stage, by creating a compact summary of relevant information, the so called "primal sketch", to be handled by further levels of processing. This is quite similar to the problem of experimental HEP of providing fast data reduction at a reasonable monetary cost, and with a practical device size. As a result of a joint effort of HEP physicists and practicing vision scientists, we recently proposed that not only the problems are similar, but the solutions adopted in the two cases also have strong similarities, and their parallel study can actually shed light on each other. Modeling the visual system as a trigger processor leads to a deeper understanding, and even very specific predictions of its functionality. Conversely, the insights gained from this new approach to vision, can lead to new ideas for enhancing the capabilities of artificial vision systems, and HEP trigger systems as well. |
1411.6772 | Erwan Bigan | Erwan Bigan, St\'ephane Douady and Jean-Marc Steyaert | On necessary and sufficient conditions for proto-cell stationary growth | Fifth International Workshop on Static Analysis and Systems Biology
(SASB 2014), Munich, Sept 10, 2014. To be published in Electronic Notes in
Theoretical Computer Science | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a generic proto-cell model consisting of any conservative
chemical reaction network embedded within a membrane. The membrane results from
the self-assembly of one of the chemical species (membrane precursor) and is
semi-permeable to some other chemical species (nutrients) diffusing from an
outside growth medium into the proto-cell. Inside the proto-cell, nutrients are
metabolized into all other chemical species including the membrane precursor,
and the membrane grows in area and the proto-cell in volume. Investigating the
conditions under which such a proto-cell may reach stationary growth, we prove
that a simple necessary condition is that each moiety be fed with some nutrient
flux; and that a sufficient condition for the existence of a stationary growth
regime is that every siphon containing any species participating in the
membrane precursor incorporation kinetics also contains the support of a moiety
that is fed with some nutrient flux. These necessary and sufficient conditions
hold regardless of chemical reaction kinetics, membrane parameters or nutrient
flux diffusion characteristics.
| [
{
"created": "Tue, 25 Nov 2014 09:01:17 GMT",
"version": "v1"
}
] | 2014-11-26 | [
[
"Bigan",
"Erwan",
""
],
[
"Douady",
"Stéphane",
""
],
[
"Steyaert",
"Jean-Marc",
""
]
] | We consider a generic proto-cell model consisting of any conservative chemical reaction network embedded within a membrane. The membrane results from the self-assembly of one of the chemical species (membrane precursor) and is semi-permeable to some other chemical species (nutrients) diffusing from an outside growth medium into the proto-cell. Inside the proto-cell, nutrients are metabolized into all other chemical species including the membrane precursor, and the membrane grows in area and the proto-cell in volume. Investigating the conditions under which such a proto-cell may reach stationary growth, we prove that a simple necessary condition is that each moiety be fed with some nutrient flux; and that a sufficient condition for the existence of a stationary growth regime is that every siphon containing any species participating in the membrane precursor incorporation kinetics also contains the support of a moiety that is fed with some nutrient flux. These necessary and sufficient conditions hold regardless of chemical reaction kinetics, membrane parameters or nutrient flux diffusion characteristics. |
2006.05034 | Erica Graham | E. J. Graham, N. Elhadad, D. Albers | Reduced model for female endocrine dynamics: Validation and functional
variations | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A normally functioning menstrual cycle requires significant crosstalk between
hormones originating in ovarian and brain tissues. Reproductive hormone
dysregulation may cause abnormal function and sometimes infertility. The
inherent complexity in this endocrine system is a challenge to identifying
mechanisms of cycle disruption, particularly given the large number of unknown
parameters in existing mathematical models. We develop a new endocrine model to
limit model complexity and use simulated distributions of unknown parameters
for model analysis. By employing a comprehensive model evaluation, we identify
a collection of mechanisms that differentiate normal and abnormal phenotypes.
We also discover an intermediate phenotype--displaying relatively normal
hormone levels and cycle dynamics--that is grouped statistically with the
irregular phenotype. Results provide insight into how clinical symptoms
associated with ovulatory disruption may not be detected through hormone
measurements alone.
| [
{
"created": "Tue, 9 Jun 2020 03:34:38 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Aug 2022 14:46:53 GMT",
"version": "v2"
}
] | 2022-08-30 | [
[
"Graham",
"E. J.",
""
],
[
"Elhadad",
"N.",
""
],
[
"Albers",
"D.",
""
]
] | A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype--displaying relatively normal hormone levels and cycle dynamics--that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.