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We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of the U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. The 3-D consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from the U.K. Biobank. It is then tested on the 756 different cases from the U.K. Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, and RVSC with 16 cases). Results comparable or even better than the state of the art in terms of distance measures are achieved. They also emphasize the assets of our method, namely, enhanced spatial consistency (currently neither considered nor achieved by the state of the art), and the generalization ability to unseen cases even from other databases.
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Like other integral membrane proteins, the activity of the Sarco/Endoplasmic Reticulum Ca(2+)-ATPase (SERCA) is regulated by the membrane environment. Cholesterol is present in the endoplasmic reticulum membrane at low levels, and it has the potential to affect SERCA activity both through direct, specific interaction with the protein or through indirect interaction through changes of the overall membrane properties. There are experimental data arguing for both modes of action for a cholesterol-mediated regulation of SERCA. In the current study, coarse-grained molecular dynamics simulations are used to address how a mixed lipid-cholesterol membrane interacts with SERCA. Candidates for direct regulatory sites with specific cholesterol binding modes are extracted from the simulations. The binding pocket for thapsigargin, a nanomolar inhibitor of SERCA, has been suggested as a cholesterol binding site. However, the thapsigargin binding pocket displayed very little cholesterol occupation in the simulations. Neither did atomistic simulations of cholesterol in the thapsigargin binding pocket support any specific interaction. The current study points to a non-specific effect of cholesterol on SERCA activity, and offers an alternative interpretation of the experimental results used to argue for a specific effect.
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The impact of high siltation and accumulation of organic and waste material in the intertidal of the dammed Ba Lai River in Vietnam as part of the Mekong estuarine system was investigated by means of marine free-living nematodes. Nutrients content (nitrate, ammonium, total phosphorus, total nitrogen), total suspended solids, total organic carbon, coliform, bacteria E. coli, pH, dissolved oxygen, total dissolved solids, methane and hydrogen sulfide concentration, and the nematode communities were characterized in sediment at selected stations along the river above and below the dam. Our results found elevated methane concentrations at the upstream side of the dam while hydrogen sulfide concentrations found to be highest in the downstream side of the dam. Furthermore, methane and hydrogen sulfide concentrations were correlated to nematode community characteristics such as trophic composition densities and genera composition. There was a clear difference between the communities above and below the dam. The discontinuous nematode community distribution indicated that the Ba Lai River is impacted by dam construction. Potentially the high deposition and eutrophication could turn the area into a methane-rich area related to predicted impact on nematodes.
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In this paper, a novel approach to the problem of elasticity reconstruction is introduced. In this approach, the solution of the wave equation is expanded as a sum of waves travelling in different directions sharing a common wave number. In particular, the solutions for the scalar and vector potentials which are related to the dilatational and shear components of the displacement respectively are expanded as sums of travelling waves. This solution is then used as a model and fitted to the measured displacements. The value of the shear wave number which yields the best fit is then used to find the elasticity at each spatial point. The main advantage of this method over direct inversion methods is that, instead of taking the derivatives of noisy measurement data, the derivatives are taken on the analytical model. This improves the results of the inversion. The dilatational and shear components of the displacement can also be computed as a byproduct of the method, without taking any derivatives. Experimental results show the effectiveness of this technique in magnetic resonance elastography. Comparisons are made with other state-of-the-art techniques.
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Earth-abundant tungsten carbide exhibits potential hydrogen evolution reaction (HER) catalytic activity owing to its Pt-like d-band electronic structure, which, unfortunately, suffers from the relatively strong tungsten-hydrogen binding, deteriorating its HER performance. Herein, a catalyst design concept of incorporating late transition metal into early transition metal carbide is proposed for regulating the metal-H bonding strength and largely enhancing the HER performance, which is employed to synthesize CoW bi-metallic carbide Co6 W6 C by a "disassembly-assembly" approach in a confined environment. Such synthesized Co6 W6 C nanocatalyst features the optimal Gibbs free energy of *H intermediate and dissociation barrier energy of H2 O molecules as well by taking advantage of the electron complementary effect between Co and W species, which endows the electrocatalyst with excellent HER performance in both alkaline and seawater/alkaline electrolytes featuring especially low overpotentials, elevated current densities, and much-enhanced operation durability in comparison to commercial Pt/C catalyst. Moreover, a proof-of-concept Mg/seawater battery equipped with Co6 W6 C-2-600 as cathode offers a peak power density of 9.1 mW cm-2 and an open-circuit voltage of ≈1.71 V, concurrently realizing hydrogen production and electricity output.
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Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient.
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Purpose The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification. Design/methodology/approach Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set. Findings The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods. Practical implications - The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification. Originality/value The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors' knowledge, such an approach was not present in the literature before.
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Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean +/- standard deviation of 0.66mm +/- 0.14, which is closer to the ground truth value 0.67mm +/- 0.15 as compared to the state-of-the-art techniques. (C) 2018 Elsevier Ltd. All rights reserved.
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The art, science, and technology of mammography have developed steadily over the past 35 y. Mammography is a central tool for diagnosis of symptoms of breast cancer. In addition, periodic screening of asymptomatic women in certain age groups has been clearly demonstrated to contribute to reduction of mortality from breast cancer. Technical improvements have allowed the examination to be carried out at substantially lower radiation dose than was necessary to obtain a good image in the 1970's, while at the same time providing greatly improved contrast, spatial resolution, dynamic range and tissue coverage. Digital mammography overcomes many of the technical limitations inherent in screen-film mammography and has been shown to offer increased accuracy for women under 50 and those with dense breasts. The radiation risk associated with mammography cannot be ignored, however, modern analysis suggests that it is very low, especially compared to the benefits from the exam. Nevertheless, imaging should be conducted with careful attention to efficient use of the radiation. New techniques, currently under development and evaluation, promise to add further to the value of mammography.
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CeF3 nanophosphors have been extensively investigated in recent years for lighting and numerous bio-applications. Downconversion emissions in CeF3:Eu(3+)/Tb(3+) phosphors were studied with the objective of attaining a white light emitting composition, by means of a simple co-precipitation method. The material was characterized by X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FT-IR) and Photoluminescence (PL). Uniformly distributed nanoparticles were obtained with an average particle size range of 8-10 nm. Various studies were undertook utilizing different doping concentrations and respective fluorescence studies were carried out to optimize dopant concentrations while achieving maximum luminescence intensity. From PL results, it was observed that the efficient energy transfers from the donor to the acceptor ions. Different concentrations of Tb(3+), Eu(3+) were doped in order to achieve a white light emitting phosphor for UV-based Light Emitting Diodes (LEDs). The nanoparticles showed characteristic emission of respective dopants (Eu(3+), Tb(3+)) when excited at the 4f → 5d transition of Ce(3+). The chromaticity coordinates for CeF3 doped with Eu(3+) and Tb(3+) were calculated and an emission very close to white light was observed.
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The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.
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The abundance of image-level labels and the lack of large scale detailed annotations (e.g. bounding boxes, segmentation masks) promotes the development of weakly supervised learning (WSL) models. In this work, we propose a novel framework for WSL of deep convolutional neural networks dedicated to learn localized features from global image-level annotations. The core of the approach is a new latent structured output model equipped with a pooling function which explicitly models negative evidence, e.g. a cow detector should strongly penalize the prediction of the bedroom class. We show that our model can be trained end-to-end for different visual recognition tasks: multi-class and multi-label classification, and also structured average precision (AP) ranking. Extensive experiments highlight the relevance of the proposed method: our model outperforms state-of-the art results on six datasets. We also show that our framework can be used to improve the performance of state-of-the-art deep models for large scale image classification on ImageNet. Finally, we evaluate our model for weakly supervised tasks: in particular, a direct adaptation for weakly supervised segmentation provides a very competitive model.
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Trichodina centrostrigeata Basson, Van As et Paperna, 1983 from Oreochromis mossambicus (Peters) and O. niloticus (Linnaeus) from different host populations from Argentina, Mexico and South Africa was reviewed. Although T. centrostrigeata has a distinct denticle structure that makes morphological taxonomic inferences uncomplicated, variation of the denticles within and among individuals and populations were still observed. While traditional taxonomy of mobilines is heavily reliant on morphometrics, and recently even more so on molecular analysis, this paper proposes the use of geometric morphometry, specifically elliptical Fourier analysis, to address morphological conflicts that arise when comparing different populations. By applying this technique, combined with traditional taxonomy, it was found that T. centrostrigeata in this study can be grouped into two separate morphotypes, the first (type a) from aquaculture farms in Argentina and Mexico and the second (type b) from a natural habitat in Glen Alpine Dam, South Africa. This study supports the validity of geometric morphometry as an additional technique to distinguish not only between species but also evolutionary plasticity of the same species from different localities and habitats.
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SYF2, also known as p29/NTC31/CBPIN, encodes a nuclear protein that interacts with Cyclin D-type binding-protein 1. SYF2 has been reported to be involved in pre-mRNA splicing and cell cycle regulation. In the present study, we observed that SYF2 was obviously upregulated in HCC tumor tissues and cell lines, and its level was positively correlated with the tumor grade and Ki-67 expression, as well as poor prognosis of HCC. In vitro, using serum starvation-refeeding experiment, our results suggested that SYF2 was upregulated in proliferating HCC cells, and was positive correlated with the expression of PCNA and Cyclin D1. In addition, depletion of SYF2 decreased PCNA and Cyclin D1 levels. Accordingly, interference of SYF2 resulted in cells cycle arrest at G1/S phase in Huh7 HCC cells. Furthermore, we found that SYF2 might interact with Cyclin D1 and could confer doxorubicin resistance in HCC cells. These findings revealed that SYF2 might play a regulatory role in the proliferation of HCC cells. In summary, SYF2 may be a novel prognostic marker and serve as a potential therapeutic target in HCC.
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Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.
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This paper designed and implemented a low-cost surveillance and information system for museums with two new coding schemes using visible light communication. For security, we use a key in place of the clock in the Manchester coding. For the enhanced data transfer, we use the unbalanced time duration for data one and zero. The prototype system was built with two low-cost microcontrollers, an LED, and linear light sensors. With 1-m distance in a dark environment, the maximum data transfer rate was roughly 1.93 kbits/s with a 10 Phi white LED. Under the dark-to-modest ambient brightness of up to 120 lux, the white LEDs were performed well in terms of the transmission distance, compared with LEDs with different colors. The experiments with flashlights reveal that our system can easily detect the prohibited use of flashlight. Experiments with the mobile phones' flashlight also revealed that photographing with the flashlight would have a worse impact on arts than the continuous illumination. We also measured the distance between arts to avoid inevitable interference between illumination sources in neighbor.
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Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher-student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.
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Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model.
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People working as a team can achieve more than when working alone due to a team's ability to parallelize the completion of tasks. In collaborative search tasks, this necessitates the formation of effective division of labor strategies to minimize redundancies in search. For such strategies to be developed, team members need to perceive the task's relevant components and how they evolve over time, as well as an understanding of what others will do so that they can structure their own behavior to contribute to the team's goal. This study explored whether the capacity for team members to coordinate effectively can be related to how participants structure their search behaviors in an online multiplayer collaborative search task. Our results demonstrated that the structure of search behavior, quantified using detrended fluctuation analysis, was sensitive to contextual factors that limit a participant's ability to gather information. Further, increases in the persistence of movement fluctuations during search behavior were found as teams developed more effective coordinative strategies and were associated with better task performance.
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Chronic pain in the head and face region has a predicted prevalence of 20% in Europe, and is more common in women than men. The etiology of temporomandibular disorder (TMD) is multifactorial, and high levels of psychological stress amplify its symptoms. We were interested in how headache reported in RDC/TMD is associated with stress. Individuals with an average age of 18 years were included in the study. They were all volunteer participants in a research project. Clinical evaluation of each subject was performed using the RDC/TMD dual-axis diagnostic system. All participants filled out the PSS-10 questionnaire. A total of 138 individuals participated in the study, of which 107 were female. Headache was reported by 83 participants (59.4%), with females presenting higher scores on the PSS-10; this was statistically significant. A comparison between PSS-10 questionnaire results and headache level shows insignificant differences. However, the higher the stress level in the participant, the higher the headache score. Females are more susceptible to perceived stress, which can have an effect on TMD.
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Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include 'No Referable Diabetic Macular Edema Grade (DME)' and 'Referable DME' while five categories consist of 'Proliferative diabetic retinopathy', 'Severe', 'Moderate', 'Mild', and 'No diabetic retinopathy'. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.
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Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.
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This paper discusses a dendrochronological approach to studying works of art associated with Shintoism, an indigenous religion of Japan. Chronological studies of Shinto artwork are, by comparison, lagging behind the studies on artwork associated with the other primary religion of Japan, Buddhism. This author believes that a scientific approach, such as dendrochronology, could play an effective part in narrowing this gap. In this experiment, we conducted a series of nondestructive imaging of wooden Shinto sculptures, utilizing a micro-focus X-ray Computed Tomography (CT) system, and performed tree-ring width measurements using digital image measurement software to obtain dendrochronological information. In terms of scanning operations, one of two methods was used according to the size of the object under review. The larger object, a statue of a guardian lion-dog (Komainu), was dendrochronologically dated to 1581, and the smaller deity statues were dated 1178. The dendrochronological data gained through this experiment will be an extremely valuable resource for future studies on Shinto artwork in Japan. (C) 2016 Elsevier GmbH. All rights reserved.
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Nonrigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation burden and increase the registration accuracy has become an intensive area of research. In this paper, we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of end-inhale to end-exhale lung CT scan pairs, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art sampling based techniques, particularly for registration of images with large deformations.
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This work presents the design and test of a switched-cap 3D DC/DC converter able to work up to 60V. The switches and the control circuits are integrated single-chip in a high-voltage (HV) MOS technology, and the passive devices are stacked on top of the chip. As an innovation versus the state-of-the-art, the work first presents the design of integrated passive devices, based on through silicon vias (TSV) MOS-compatible technology, which are suitable for switching converter applications up to 60V. Then, the implementation and experimental characterization of the switched-cap 3D DC/DC is proposed, with the silicon TSV capacitors stacked on top of the 0.35 mu m HV-MOS die. Compared with the state-of-the-art, the proposed 3D DC/DC converter is a compact circuit, able to directly regulate a wide input voltage range (from 6V to 60V) to a 5V, 2W output. Hence, it is suitable to supply low-power loads, such as control units and/or sensors, directly from the 48V power line available in hybrid vehicles or telecom and networking systems.
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Homicide prediction is a challenging task due to the spatio-temporal sparsity of these crime events. In this paper we report the results of using several approaches to mitigate this sparsity condition in machine learning models specially tailored towards modeling homicides events. Since spatial resolution is a direct determinant of sparsity, we focus on the performance of these models across different resolutions of interest to police authorities. We use a simple count model as benchmark and propose some enhancements of it directed towards improving prediction performance. We then compare the results to more complex models motivated by manifold learning and graph signal processing methods. We found that the simple benchmark models are as good as state of the art models for low resolution, but, as resolution increases, the performance of machine learning models outperform the benchmark. These results provide a rationality for the use of state of the art machine learning models for homicide prediction at the high resolution of interest for the deployment of police resources.
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In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets. (C) 2020 Elsevier Ltd. All rights reserved.
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The human sodium-glucose co-transporter 2 (hSGLT2) is a transporter responsible for reabsorption of glucose in the proximal convoluted tubule of the kidney. hSGLT2 inhibitors, including luseogliflozin, have been developed as drugs for type 2 diabetes mellitus. Only luseogliflozin contains a thiosugar ring in its chemical structure, while other hSGLT2 inhibitors contain glucose rings. Consequently, we focused on the binding interactions of hSGLT2 with sugars and thiosugars. We first revealed that the binding affinities of thiosugars are stronger than those of sugars through molecular dynamics simulations of Vibrio parahaemolyticus, sodium-galactose co-transporter, and human hSGLT2. We then demonstrated that Na(+) dissociates from the protein to the cytoplasmic solution more slowly in the thiosugar system than in the sugar system. These differences between sugars and thiosugars are discussed on the basis of the different binding modes due to the atom at the 5-position of the sugar and thiosugar rings. Finally, as a result of Na(+) dissociation, we suggest that the dissociation of thiosugars is slower than that of sugars.
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This paper reviews the state of the art in sensors and automated inspection devices for enhanced sewer inspection. Efficiency, safety, environmental, and legislative concerns have made inspection and assessment of communal sewers a central issue to water and sewerage companies. Nowadays, the standard sewer inspection system is based on a wheeled platform on which a closed circuit television (CCTV) camera is mounted. One of the disadvantages of camera inspection systems is that they can only detect a small proportion of all possible damage in a sewer. The inspection outcome of such systems relies not only on the quality of the acquired images, but also on the off-line recognition and classification conducted by human operators. In consequence, CCTV-based platforms are frequently not effective. Infrared, microwave, optical, and ultrasonic-based sensors have been proposed to complement the existing CCTV-based approach and to improve inspection results. New inspection devices employing multiple sensors and being capable of carrying out remote sewer inspection tasks are under research.
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In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian compressive sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms. (C) 2011 Elsevier B.V. All rights reserved.
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The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast semantic segmentation of LiDAR point cloud, which is the point-wise classification of the point cloud within the sensor framerate, has attracted attention in recognition of the driving environment. Although the voxel and fusion-based semantic segmentation models are the state-of-the-art model in point cloud semantic segmentation recently, their real-time performance suffer from high computational load due to high voxel resolution. In this paper, we propose the fast voxel-based semantic segmentation model using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model is designed to outperform at both high and low voxel resolution using point convolution-based feature extraction. Moreover, the proposed model accelerates the feature propagation using 3D sparse convolution after the feature extraction. The experimental results demonstrate that the proposed model outperforms the state-of-the-art real-time models in semantic segmentation of SemanticKITTI and
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In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word problem-solving task. In automatic mathematical word problem-solving task, a machine learning model should deduce an answer to a given problem by acquiring implied numeric information. To build a robust model that can sufficiently utilize numeric information to solve various mathematical word problems, such a model should address two challenges: (1) filling in missing world knowledge required to solve the given mathematical word problem, and (2) understanding the implied relationship between numbers and variables. To address these two challenges, we propose template-based multitask generation (TM-generation). To address challenge (1), we utilize the state-of-the-art language models called ELECTRA. To address challenge (2), we propose an operator identification layer that models the relationship between numbers and variables. Our experimental results show that using the MAWPS and Math23k datasets, state-of-the-art performance was achieved: 85.2% in MAWPS and 85.3% in Math23k.
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Reinforcement learning has been widely-used for applications in planning. control. and decision making. Rather than using instructive feedback as in supervised learning. reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster. is able to escape from local minima. and has excellent disturbance rejection capability. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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Highly bloating ceramsite has been successfully prepared from forthcoming industrial wastes, the white mud (WM), which is the residue from H2SO4-treated coal fly ash obtained from our previous industrial art. In this art the oil shale (OS) and black cotton soil (BCS) were used as foaming and fluxing agents, respectively. The products were demonstrated to reach two types of ceramsite defined by GB/T17431.2-2010. The best dosages were found at the mass ratio of WM:BCS:OS = 7:7:6. The optimal sintering temperature is 1230 degrees C, and a two-stage sintering strategy was chosen to prepare better qualified ceramsite. The total porosity reaches 52.55%, which affords a low bulk density of 318 kg/m(3). The formation mechanism was indicated to be bloated by OS, and pores were formed by the sealing effect of vitrified BCS. The WM works as scaffolds to provide mechanical strength. The ternary diagram of SiO2-Al2O3-alkaline/alkaline earth was rectified concerning the composition area in preparing super-light-weight ceramsite.
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Constructed wetland (CW) is an effective ecological technology to treat water pollution and has the significant advantages of high impact resistance, simple construction process, and low maintenance cost. However, under extreme conditions such as low temperature, high salt concentration, and multiple types of pollutants, some bottlenecks exist, including the difficulty in improving operating efficiency and the low pollutant removal rate. Microbial electrochemical technology is an emerging clean energy technology and has the similar structure and pollutant removal mechanism to CW. Microbial electrochemistry combined with CW can improve the overall removal effect of pollutants in wetlands. This review summarizes characterization methods of microbial electrochemistry-enhanced constructed wetland systems, construction methods of different composite systems, mechanisms of single and composite systems, and removal effects of composite systems on different pollutants in water bodies. Based on the shortcomings of existing studies, the potential breakthroughs in microbial electrochemistry-enhanced constructed wetlands are proposed for developing the optimization solution of constructed wetlands.
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Nowadays, sensor/access-point coverage is an essential problem for wireless communication and sensor systems, which will significantly impact the quality of access, monitoring, and surveillance. Indoor sensor/access-point placement still remains very challenging as the regions of interest (ROIs) or underlying geometries may be in an arbitrary polygonal shape. In this work, we would like to study how to place a sensor/access-point to maximize the coverage area within an arbitrary polygonal ROI. Our novel approach is based on finding the maximum-area clique over the visibility graph corresponding to the indoor geometry. According to many examples, our proposed optimal sensor/access-point scheme can lead to larger coverage efficiencies than the existing solution to the art gallery problem (AGP) and the conventional Delaunay triangulation method. Our new scheme is of great practical value as it can be applied for not only both convex and nonconvex simply-connected polygons but also both convex and nonconvex multiply-connected polygons with internal holes.
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In this study, a reduced-reference image-quality-assessment (IQA) method for screen content images, named as feature-quality-index (FQI) is proposed. The proposed method is based on the fact that the human visual system is more sensitive towards change in features than intensity or structure. Reduced features from the reference and distorted images are first extracted. In order to find the preserved features in the distorted image, a feature matching process with a reduced number of distance calculations is proposed, namely reduced-distance method. To reflect the importance of the matched features and their distance, the inner product between the normalised scale and distance vector is obtained. Extensive comparisons are performed on two available benchmark databases namely SIQAD and QACS, with eight reduced-reference, and nine full-reference state-of-the-art IQA techniques to demonstrate the consistency, accuracy, and robustness of the proposed FQI. The subjective evaluation of mean opinion score shows that FQI outperforms the current state-of-the-art IQA techniques.
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This meta-analysis aimed to summarize the evidence regarding attitudes of Iranian nurses and related factors towards end-of-life (EOL) care. PubMed, Web of Science, Scopus, Magiran, Iranmedex, Scientific Information Database, and Google Scholar search engine were searched using Persian and English appropriate keywords from the earliest records up to September 11, 2020. A total of 849 nurses were included in six studies. After a meta-analysis of the mean score of nurses' attitudes, the pooled mean was 80.07 out of 120 (Q(5)=4.32, I-squared=0.00%; 95%CI: 73.53-86.60; p < 0.001). Marital status, ward type, education level, a history of participating in EOL care workshops, personal study of EOL care, experience of caring for a dying family member or close people, natural and approach acceptance, fear of death, and professional autonomy had a significant positive relationship with nurses' attitudes towards EOL care. Therefore, further large-scale studies considering potential confounding variables are needed to confirm our findings.
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The present study further investigated the effects of fluctuated low-temperature combined with high-humidity thawing (FLHT) on the gelling properties of pork myofibrillar proteins (MPs). Results showed that compared with refrigerator thawing (RT) and low-temperature combined with high-humidity thawing (LHT), FLHT effectively reduced the protein aggregation and maintained the relative stability by decreasing the variation in the turbidity and absolute ζ-potential value. The rheological results confirmed its improved elastic gel network. Meanwhile, FLHT samples exhibited markedly higher WHC with lower cooking loss (P < 0.05). The whiteness and strength of MPs gel were significantly higher in the FLHT group (P < 0.05). Moreover, there was no difference in textural properties between FLHT samples and fresh meat (FS) (P > 0.05), due to its homogeneous and compact microstructure. Therefore, FLHT plays an essential role in holding a superior gel quality and a compact structure, thereby evidencing its potential application in meat thawing.
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Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images. ZSL for medical images has remained largely unexplored. We design a novel strategy for generalized zero-shot diagnosis of chest radiographs. In doing so, we leverage the potential of multi-view semantic embedding, a useful yet less-explored direction for ZSL. Our design also incorporates a self-training phase to tackle the problem of noisy labels alongside improving the performance for classes not seen during training. Through rigorous experiments, we show that our model trained on one dataset can produce consistent performance across test datasets from different sources including those with very different quality. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for generalized zero-shot chest x-ray diagnosis.
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Afidopyropen, a novel insecticide, is highly effective against piercing insects such as the tea leafhopper. The residual levels of afidopyropen and M440I007 in tea cultivation, processing, and brewing were studied. During tea cultivation, afidopyropen dissipated faster in fresh tea shoots in the rainy season (T1/2 of 1.2-2.5 d) than that in the dry season (T1/2 of 3.1-4.4 d); afidopyropen was metabolized into M440I007, the level of which peaked in 1 d, and degraded rapidly (over 90 %) afterward 3 d. The green tea processing steps had little effect on decreasing the afidopyropen residue (PF of 0.90-1.18). Low infusion rates of afidopyropen (16.7 %-17.7 %) and M440I007 (4.1 %-6.2 %) were observed from dry green tea to infusion; furthermore, the risk of ingesting afidopyropen from drinking tea was low, with the risk quotient values < 0.0001. This study can offer guidance on the rational application of afidopyropen in tea plants.
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In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions.
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Zinc bioavailability with the presence of other elements in wheat grains might be affected by fertilizers. A long-term field experiment was conducted to examine effects of N fertilizer on Zn bioavailability in wheat grain tissues, with changes in the concentrations, distribution, and speciation of Zn as well as P and sulfur S via synchrotron-based technology. Results showed that addition of N fertilizer was associated with changes in Zn concentrations and distributions in grain tissues, especially in the crease region and endosperm. Simultaneously, N addition enhanced Zn-S colocalization in the crease region and endosperm and lowered the P/Zn ratio and Zn-P colocalization. Addition of N fertilizer with P increased Zn-cysteine (9.2%) and decreased Zn-phytate (47.3%) in the crease region, leading to potentially higher grain Zn bioavailability. Thus, addition of N fertilizer improved concentrations and bioavailability of Zn, by coordinating the relationships among Zn, P and S within wheat grains.
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We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin.
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A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms.. After brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.
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I examine a speculative diagnosis made by Sigmund Freud regarding his patient's mother in his landmark 1905 paper describing a hysterical illness. Freud considered the impact of Dora's mother's mental state on her daughter, wondering whether the mother might suffer from a 'housewife's psychosis'. Here was an emphasis on the social structures of the times and differences between the parents in terms of sexual freedom and societal limitations placed on women. Freud's description drew attention to Dora's anxieties in relation to her parents, in particular the state of their sexual relationship and the apparently sanctioned entry of another couple, Frau and Herr K, into the parental relationship. In particular, the role of syphilis in the aetiology of sexual disturbances was considered, affecting men and their sexual partners, specifically their wives, who faced lifelong risks of morbidity, inadequate treatment and psychic disturbances at this time in 19th century Vienna.
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This article indicates that work standardization is an effective tool for the improvement of occupational safety in any process performed by people. Work standardization has an impact on improving productivity, quality and engaging employees in improving current working methods. The article shows that in the construction industry there are problems concerning the selection of operations for work standardization due to the specificity of the operations performed there. As a result, work standardization is not a common methodology used in the construction industry, which may be one of the reasons for the greater number of accidents and near misses when compared to the manufacturing industry. The article presents the author's safety-complication-frequency (SCF) model for the selection of operations for work standardization, which is dedicated to the construction industry. The SCF model enables operations with the highest priority in terms of implementation for work standardization to be selected.
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Immunotherapy against amyloid-beta (Aβ) is a promising option for the treatment of Alzheimer's disease (AD). Aβ exists as various species, including monomers, oligomers, protofibrils, and insoluble fibrils in plaques. Oligomers and protofibrils have been shown to be toxic, and removal of these aggregates might represent an effective treatment for AD. We have characterized the binding properties of lecanemab, aducanumab, and gantenerumab to different Aβ species with inhibition ELISA, immunodepletion, and surface plasmon resonance. All three antibodies bound monomers with low affinity. However, lecanemab and aducanumab had very weak binding to monomers, and gantenerumab somewhat stronger binding. Lecanemab was distinctive as it had tenfold stronger binding to protofibrils compared to fibrils. Aducanumab and gantenerumab preferred binding to fibrils over protofibrils. Our results show different binding profiles of lecanemab, aducanumab, and gantenerumab that may explain clinical results observed for these antibodies regarding both efficacy and side effects.
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Mucins are large, highly glycosylated transmembrane and secreted proteins that line and protect epithelial surfaces. However, the details of mucin biosynthesis and packaging in vivo are largely unknown. Here, we demonstrate that multiple distinct mucins undergo intragranular restructuring during secretory granule maturation in vivo, forming unique structures that are spatially segregated within the same granule. We further identify temporally-regulated genes that influence mucin restructuring, including those controlling pH (Vha16-1), Ca2+ ions (fwe) and Cl- ions (Clic and ClC-c). Finally, we show that altered mucin glycosylation influences the dimensions of these structures, thereby affecting secretory granule morphology. This study elucidates key steps and factors involved in intragranular, rather than intergranular segregation of mucins through regulated restructuring events during secretory granule maturation. Understanding how multiple distinct mucins are efficiently packaged into and secreted from secretory granules may provide insight into diseases resulting from defects in mucin secretion.
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Ieoh Ming Pei is a modern world-renowned architect with a large number of excellent architectural works worldwide. His architectural design inherits the traditional architectural concept and integrates the Chinese and Western architectural ideas, which does not stick to stereotype and conventions, and dare to innovate, producing a significant and profound influence on China and even the world's architectural industry. This study introduced the background of Ieoh Ming Pei (hereinafter referred to as Pei or I.M. Pei )'s architectural design concept, analyzed Pei's concept from the perspectives of "tradition combined with modernity" and "art integrated with technology", discussed the influence of Pei's design concept on modem landscape architecture design in terms of "art law" and "connotation emotion", and expounded the function of Pei's concept on landscape architecture design, which is of great importance for landscape architecture design and has a place in the powerful world.
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Nucleic acids play a pivotal role in life processes. The endeavours to shed light on the essential properties of these intriguing building blocks led us to the synthesis of different analogues and the investigation of their properties. First various peptide nucleic acid monomers and oligomers have been synthesized, using an Fmoc/acyl protecting group strategy, and their properties studied. The serendipitous discovery of a side reaction of coupling agents led us to the elaboration of a peptide sequencing method. The capricious behaviour of guanine derivatives spurred the determination of their substitution pattern using 13 C, 15 N NMR, and mass spectrometric methods. The properties of guanines initiated the logical transition to the study of supramolecular systems composed of purine analogues. Thus, xanthine and uracil derivatives have been obtained and their supramolecular self-assembly properties scrutinized in gas, solid, and liquid states and at solid-liquid interfaces.
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The state-of-the-art trackers using deep learning technology have no special strategy to capture the geometric deformation of the target. Based on that the affine manifold can better capture the target shape change and that the higher level of Convolutional Neural Network (CNN) can better describe semantic information of objects, we propose a new tracking algorithm combining affine transformation with convolutional features to track targets with dramatic deformation. First, the affine transformation is applied to predict possible locations of a target, then a correlative filter is designed to compute the appearance confidence score for determining the final target location. Furthermore, a standard discriminative correlation filter is used to develop the effect of convolutional features, which is more efficient than other methods used for CNN Networks. Comprehensive experiments demonstrate the outstanding performance of our tracking algorithm compared to the state-of-the-art techniques in the public benchmarks.
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Although mainstream partial nitrification-anammox (PN-A) is a highly efficient and sustainable wastewater treatment process, it is difficult to achieve and stabilize due to the competition among functional bacteria. In this study, achieving one-stage mainstream anammox via regulating bacteria community structure was studied in a lab-scale biological aerated filter (BAF). The results showed that high free ammonia with 89.57 mg/L, nitrite nitrogen (NO2--N) competition between anammox bacteria (AnAOB) and nitrite oxidizing bacteria (NOB), and backwash regulated the bacteria community structure. After backwash, Candidatus Kuenenia became the dominant bacteria and the relative abundance increased to 5.56 %. In BAF, one-stage mainstream anammox with total nitrogen (TN) being lower than 15 mg/L in the effluent was achieved using lag-time of bacteria activity recovery caused by alternating operation of high and low ammonia nitrogen (NH4+-N), which have great potential applied in municipal wastewater treatment plants (MWWTPs).
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With the rapid development of computer and network technology, the application of mobile app has been involved in all walks of life, especially the traditional art teaching mode. Therefore, it is of great significance to design an app with art learning and education as the main content based on the concept of young people's cognitive psychology and art training. This paper first analyzes the current situation and development trend of art education app at home and abroad, then analyzes and studies the design and application needs of education app at home, clarifies the needs of students and teachers, analyzes the visual and psychological characteristics of teenagers and their impact on interface and interaction design, and finally designs an art education app that meets the domestic needs. The results show that the app can enhance the interest of young people in art learning.
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This paper proposes a highly efficient bitmap-based approach for discovery of so-called sentinels. Sentinels represent schema level relationships between changes over time in certain measures in a multidimensional data cube. Sentinels are actionable and notify users based on previous observations, for example, that revenue might drop within two months if an increase in customer problems combined with a decrease in website traffic is observed. We significantly extend prior art by representing the sentinel mining problem by bitmap operations, using bitmapped encoding of so-called indication streams. We present a very efficient algorithm, SentBit, that is 2-3 orders of magnitude faster than the state of the art, and utilizes CPU specific instructions and the multicore architectures available on modern processors. The SentBit algorithm scales efficiently to very large data sets, which is verified by extensive experiments on both real and synthetic data.
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Naïve T-cell activation requires signals from both the T-cell receptor (TCR) and the costimulatory molecule CD28. A central mediator of the TCR and CD28 signals is the scaffold protein CARMA1, which functions by forming a complex with partner proteins, Bcl10 and MALT1. A well-known function of the CARMA1 signaling complex is to mediate activation of IκB kinase (IKK) and its target transcription factor NF-κB, thereby promoting T-cell activation and survival. Recent evidence suggests that CARMA1 also mediates TCR/CD28-stimulated activation of the IKK-related kinase TBK1, which plays a role in regulating the homeostasis and migration of T cells. Moreover, the CARMA1 complex connects the TCR/CD28 signals to the activation of mTORC1, a metabolic kinase regulating various aspects of T-cell functions. This review will discuss the mechanism underlying the activation of the CARMA1-dependent signaling pathways and their roles in regulating T-cell functions.
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In this work, the development of exo-olefin compounds (R-CH2 C(=CH2 )Z) as chain-transfer agents for the sulfur-free reversible addition-fragmentation chain transfer (RAFT) radical polymerization of methacrylates in homogeneous solution is described. A series of exo-olefin compounds with a methyl methacrylate (MMA) dimer structure as the R group and a substituted α-methylstyrene unit as the -CH2 C(=CH2 )Z (Z: Ph-Y) group were synthesized and used for the radical polymerization of MMA in toluene and PhC(CF3 )2 OH. These compounds underwent transfer of the CH2 C(=CH2 )Z group via addition-fragmentation of the propagating methacryloyl radical. More electron-donating (Y) substituents, such as methoxy and dimethylamino groups, produced polymers with narrower molecular weight distributions. A continuous monomer addition method further improved molecular weight control and enabled the synthesis of colorless, sulfur-free, multiblock copolymers of methacrylates in homogeneous solutions.
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This paper presents a SAR converter based mixed-signal multiplier for the feature extraction of neural signals using quadratic operators. After a thorough analysis of design principles and circuit-level aspects, the proposed architecture is explored for the implementation of two quadratic operators often used for the characterization of neural activity, the moving average energy (MAE) operator and the nonlinear energy operator (NEO). Programmable chips for both operators have been implemented in a HV-180 nm CMOS process. Experimental results confirm their suitability for energy computation and action potential detection and the accomplished areaxpower performance is compared to prior art. The MAE and NEO prototypes, at a sampling rate of 30kS/s, consume 116 nW and 178 nW, respectively, and digitize both the input neural signal and the operator outcome, with no need for digital multipliers.
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At present. with the continuous improvement of the economic level of our society, people's quality of life has also been significantly improved. In recent years. our society has vigorously advocated the concept of eco-environmental protection and strived to create an environment-friendly indoor environment in the field of architecture. Therefore, ecological concept should be integrated into indoor environment design to provide people with a green and healthy living environment. Therefore, the next research will focus on the concept of ecology and its application in indoor environmental art design. In this paper, the basic principles of environment, art and ecology arc interrelated, and the basic basis and conception of ecological philosophy in environmental art design are deeply explored, providing reference for the artificial transformation of natural and modern environmental art design.
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In this work, two kinds of imine-type covalent organic framework (COF) nanoparticles are demonstrated as efficient photocatalytic initiators to trigger the free-radical polymerization of acrylamide (AM) to prepare polyacrylamide (PAM) hydrogels under visible light irradiation, without any assistance from the co-initiator. Simultaneously, the COF nanoparticles bearing vinyl side groups (COF-V) promote covalent cross-linking of the polymer chains, which significantly reinforces the mechanical properties of the nanocomposite hydrogel. The obtained PAM/COF-V hydrogel is highly stretchable with an extraordinary elongation up to 3300% strain. On the other hand, the COF nanoparticles modified with methoxy moieties (COF-OMe) endow the resulting PAM/COF-OMe hydrogel with a promising fluorescence feature. In addition, this strategy provides a visible-light-regulated photocatalytic polymerization approach with a simplified recipe to fabricate COF-based nanocomposite hydrogels or resins with diverse functions.
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Accurate detection of bone marrow involvement in patients with lymphoma is of crucial importance because of the prognostic and therapeutic consequences. Bone marrow trephine biopsy (BMB) is currently regarded as the method of choice for the evaluation of the bone marrow in lymphoma, but it is invasive, has a risk of complications, and lacks sufficient sensitivity due to the possibility of sampling errors. Bone marrow imaging, if accurate, may (partially) replace BMBand/or may improve the sensitivity of BMB by guiding the biopsy to the location that appears to be involved by lymphoma at imaging. In this scientific communication, general concepts of bone marrow imaging, state-of-the-art imaging modalities, and future imaging strategies for the assessment of the bone marrow in lymphoma will be reviewed and discussed.
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By applying the potassium salts of cyclopentadienyl-phosphine ligands LK to CoCl2 , the corresponding cobalt chlorides (1, LCoII Cl) were prepared. By reducing complexes 1 with KHBEt3 under a N2 atmosphere, bridging end-on complexes, LCoI -N2 -CoI L (2 a and 2 b), were successfully obtained. 15 N2 -labeled [15 N2 ]-2 a was prepared under 15 N2 /14 N2 exchange in THF solution. LCoI -N2 -CoI L complex 2 a could react with P4 molecules to release N2 and generate a Co-P4 -Co moiety 4. Further reduction of complex 2 b led to cleavage of a P-C bond in the cyclopentadienyl-phosphine ligand to provide novel μ-PCy2 -bridged Co0 -N2 complex 5. DFT calculations confirmed the experimental observations.
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The active power-decoupling (APD) method is an effective solution to handle the inherent double-line frequency ripple power in single-phase power systems. It removes the bulky passive devices and facilitates the improvement of the system power density and even the reliability. This article provides a comprehensive review of the prior-art and state-of-the-art control strategies in APD. They are categorized into four groups according to the basic control ideas of "power balance," "harmonic suppression," "volt-second balance/charge balance," and "virtual impedance." And the specific control strategies under each control idea are discussed and compared. The pros and cons of each control idea are also presented . Finally, this article draws a sketch of the global trends in APD control.
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Campbell and colleagues propose a robust and rigorous strategic model to address and reduce Relationship Violence and Sexual Misconduct (RVSM) at Michigan State University, which significantly advances the field of RVSM prevention and education, particularly for survivors belonging to marginalized populations. While prior efforts have addressed RVSM on college and university campuses, Campbell and colleagues' model is groundbreaking in its ability to reduce RVSM against lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual/agender (LGBTQIA+) students of color, by its principles of intersectional and trauma-informed action. This commentary highlights the contributions of Campbell et al.'s model and provides recommendations for enhancing programming and postassault services by addressing the totality of LGBTQIA+ survivors of color's identities.
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We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE's complexity low. We compare the proposed online KDE to the state-of-the-art approaches on examples of estimating stationary and non-stationary distributions, and on examples of classification. The results show that the online KDE outperforms or achieves a comparable performance to the state-of-the-art and produces models with a significantly lower complexity while allowing online adaptation. (C) 2011 Elsevier Ltd. All rights reserved.
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We sequenced the complete plastid genome (plastome) for Piper kadsura, a woody vine endemic to East Asia. This species is part of the largest genus within Piperaceae and its genome is almost identical to its congener P. cenocladum. The plastome for P. kadsura comprises 131 genes, including four unique rRNAs, 30 tRNAs, and 79 protein-coding genes. It retains ycf1 as an intact open reading frame. Our phylogenetic analysis demonstrated the monophyly of the Piper genus. The additional plastome sequence found in this evolutionarily and economically important genus will be a valuable, fundamental tool for future studies of phylogenetic relationships among basal angiosperms, and will provide a useful resource for molecular breeding programs.
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Articular cartilage defects remain the most common and challenging joint disease. Cartilage lacks the self-healing capacity after injury due to its avascularity. Recently, stem cell-based therapy has been applied for cartilage regeneration. However, the critical target for stem cells during chondrogenesis remains unclear. We first reported that LDL receptor-related protein 3 (LRP3) expression was markedly increased during chondrogenesis in stem cells. Furthermore, LRP3 was an effective chondrogenic stimulator, as confirmed by knockdown and overexpression experiments and RNA sequencing. In addition, inhibition of LRP3 suppressed proliferation and induced apoptosis. Therefore, our study first defined a new chondrogenic stimulator, LRP3, with detailed clarification, which provided a novel target for stem cell-based cartilage regeneration.
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The electromagnetic (EM)-simulator-based tuning process for rapid microwave design can combine EM accuracy with circuit-design speed. Our own approach is based on the intuitive engineering idea of space mapping. In this article, we explain the art of microwave design optimization through tuning space mapping procedures. We list various appropriate types of models (called surrogates). We demonstrate the implementation of these surrogates through a simple bandstop filter. We provide application examples using commercial simulation software. Our purpose is to help microwave engineers understand the tuning space mapping methodology and to inspire new implementations and applications. (C) 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 639651, 2012.
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Deep convolutional neural networks (CNNs) are the state-of-the-art methods in the domain of classification of remote sensing (RS) data. However, traditional CNN models suffer from huge computational costs in learning land-use and land-cover features, particularly in large-scale RS problems. To address this issue, we propose a reliable mono- and dual-regulated contractive-expansive-contractive (MRCEC/DRCEC) CNN for scene based multispectral (MS) image classification. The proposed technique increases the accuracy of learning and minimizes the loss in the feature maps by incorporating the CEC approach in the classification. Extensive experiments conducted on the Sentinel-2 EuroSATallbands dataset pointed out that the proposed model outperforms the state-of-the-art models such as EfficientNet-B0, RESNet-50, and EfficientNet-B7.
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This paper presents a 30 Gb/s/link 2.2 Tb/s/mm(2) inductive-coupling link for a high-speed DRAM interface. The data rate per layout area is the highest among DRAM interfaces reported up to now. The proposed interface employs a high-speed injection-locking CDR technique that utilizes the derivative property of inductive coupling. Compared to conventional injection-locking CDR based on an XOR edge detector, the proposed technique doubles the operation speed and increases the data rate to 30 Gb/s/link. As a result, the data rate per layout area is increased to 2.2 Tb/s/mm(2), which is 2X that of the state-of-the-art inductive-coupling link, and 22X that of the state-of-the-art wired link.
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Today's population is expanding quickly, and there is a growing desire for aesthetics. Smiles and other friendly facial expressions communicate joy and assurance. They are the essential elements of nonverbal communication and play a significant part in establishing a person's first impression. The altered passive eruption, which results in the excessive gingival display (EGD) when the gingival edge is situated incisal to the cervical convexity of the crown, is one of the factors affecting aesthetics. It has an impact on the patient's appearance and grin. The management of EGD becomes crucial. The following case study covers the control of EGD with a crown lengthening operation.
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Splenic artery pseudoaneurysm is the most common of all the visceral artery pseudoaneurysms. Presentation is often variable and the condition demands immediate diagnosis and management because pseudoaneurysm rupture increases morbidity and mortality. It is associated with pancreatitis and other conditions like abdominal trauma, chronic pancreatitis, pseudocyst of the pancreas, liver transplantation, and, rarely, peptic ulcer disease. We present a case of a giant splenic artery pseudoaneurysm measuring 14x8 cm. Proximal and distal control of the vessels could not be achieved during the procedure because of local adhesions and inflammation and it was necessary to cross clamp the supraceliac aorta to control bleeding.
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Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.
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Imaging mass cytometry (IMC) is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. A unique high-dimensional perspective comprehensively and accurately depicts the complex interaction of phenotype, signalling pathway and tumour microenvironment and is widely used in solid tumours. However, the application scenarios of IMC in basic medicine and clinical research in solid tumours lack systematic introduction and classification. This paper reviews the application of IMC in depicting the panorama of the tumour microenvironment, revealing tumour spatial heterogeneity, clarifying tumour pharmacological mechanisms, assisting in new drug development, and dynamically evaluating the efficacy of immunotherapy in solid tumours.
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A limited array of reactive intermediates have enabled a wealth of discoveries in N-heterocyclic carbene organocatalysis. In this study, the viability of α,β-unsaturated sulfonyl azoliums as double electrophiles in new reactions is examined. Specifically, the (3+3) annulation of such species with the trimethylsilyl enol ethers of various 1,3-dicarbonyl compounds has been developed. This reaction provides access to a range of novel unsaturated δ-sultones (18 examples) in good yields (40-88 %) under mild reaction conditions. Mechanistic studies and the development of an enantioselective variant (55 % yield, 73:27 e.r.) support the intermediacy of an α,β-unsaturated sulfonyl azolium species.
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Rational design of hydrogen-bonded organic frameworks (HOFs) with multiple functionalities is highly sought after but challenging. Herein, we report a multifunctional HOF (HOF-FJU-2) built from 4,4',4'',4'''-(9H-carbazole-1,3,6,8-tetrayl)tetrabenzaldehyde molecule with tetrabenzaldeyde for their H bonding interactions and carbazole N-H site for its specific recognition of small molecules. The Lewis acid N-H sites allow HOF-FJU-2 facilely separate acetone from its mixture with another solvent like methanol with smaller pKa value. The donor (D)-π-acceptor (A) aromatic nature of the organic building molecule endows this HOF with solvent dependent luminescent/chromic properties, so the column acetone/methanol separation on HOF-FJU-2 can be readily visualized.
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The methanol extract of the seeds of Khaya ivorensis afforded two new mexicanolide limonoids, ivorensines A and B (1 and 2), together with one known compound, ruageanin D (3). The structures of the isolated compounds were established based on 1 D and 2 D (1H-1H COSY, HMQC, and HMBC) NMR spectroscopy, in addition to high resolution mass spectrometry. The isolated limonoids were tested in vitro for antimicrobial potentials against 5 pathogenic microorganisms. As a result, compounds 1-3 exhibited antimicrobial activity against the tested Gram negative bacteria at the minimum inhibitory concentration values less than 50 μg/ml.
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This survey presents a brief discussion of different aspects of digital image watermarking. Included in the present discussion are these general concepts: major characteristics of digital watermark, novel and recent applications of watermarking, different kinds of watermarking techniques and common watermark embedding and extraction process. In addition, recent state-of-art watermarking techniques, potential issues and available solutions are discussed in brief. Further, the performance summary of the various state-of-art watermarking techniques is presented in tabular format. This survey contribution will be useful for the researchers to implement efficient watermarking techniques for secure e-governance applications.
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John Squire did not only produce leading works in the muscle field, he also significantly contributed to the vascular permeability field by ultrastructural analysis of the endothelial glycocalyx. Presented here is a review of his involvement in the field by his main collaborator C.C. Michel and his last postdoctoral researcher KP Arkill. We end on a reinterpretation of his work that arguably links to our current understanding of endothelial glycocalyx structure and composition predicting 6 glycosaminoglycans fibres per syndecan core protein, only achieved in the endothelium by dimerization.
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Ovarian fibroma is the most common benign pure stromal tumor. It has no specific clinical manifestation, most of which are pelvic or adnexal masses. 10-15% of cases with hydrothorax or ascites, after tumor resection, hydrothorax and ascites disappear, known as Meigs Syndrome. The elevated level of CA125 in a few patients was easily misdiagnosed as ovarian malignant tumor. A case of bilateral Ovarian fibroma associated with Meigs Syndrome is reported and the literature is reviewed in order to improve the understanding of the changes and avoid misdiagnosis.
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Rapid development in cloud computing has made an impact on the manufacturing industry. Consequently, cloud manufacturing has been proposed and has become a hot topic in the past 3 years. Many technologies such as service-oriented architecture, resource virtualisation, service ontology and modelling, service composition and management, and product data integration have been used to build the architecture of cloud manufacturing platforms. In this article, the authors survey the state of the art in the area of cloud manufacturing, identify recent research directions, and discuss potential research opportunities.
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What is the state-of-the-art in sketch recognition and what are some important future research directions in matching sketches with digital face images? This opinion paper focuses on answering these questions through proposing three important steps that need to move the field forward: (i) create a large, real world forensic sketch database, (ii) develop fundamental understanding of human cognition of processing sketches, and (iii) develop improved algorithms for matching sketches with mugshot photos. (C) 2016 Elsevier B.V. All rights reserved.
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State-of-the-art voice codecs have been developed to extend the input bandwidth to enhance quality while maintaining interoperability with a legacy codec. Most of them employ a modified discrete cosine transform (MDCT) for coding their extended band We propose a source filter model-based coding algorithm of MDCT spectral coefficients, apply it to the ITU-T G.711.1 super wideband (SWB) extension codec, and subjectively test it to validate the model. A subjective test shows abetter quality over the standardized SWB codec.
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In view of the shortage of research on the seismic performance of medical imaging equipment, this paper investigates and summarizes the seismic regulatory requirements and seismic tests of medical imaging equipment, and focuses on the parameter selection, detection steps, result evaluation and detection equipment requirements of seismic detection of medical imaging equipment. The seismic test data of medical imaging equipment with various installation modes are analyzed, and the seismic performance of medical imaging equipment is analyzed and summarized.
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Graves' dermopathy is one of the extra-thyroidal manifestations of Graves' disease (GD) and is characterized by the accumulation of glycosaminoglycans in the reticular dermis. In the majority of cases, pretibial myxedema is self-limiting but, in some cases, it can lead to structural and functional damage. Topical steroids with occlusive dressing remain the conventional treatment, but intralesional steroids have shown promising results. We hereby present a case of pretibial myxedema treated successfully with intralesional triamcinolone acetate.
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With the evolution of the computing capabilities of supercomputers, there are growing and diversified demands for numerical modeling of natural phenomena and engineering problems. Fast and robust numerical algorithms are essential as a basis for scientific applications. NEC has been developing the ASL/SX Series mathematical libraries to facilitate the state-of-art numerical algorithms in building scientific and engineering application programs. This paper gives an outline of the features and performance of the ASL/SX Series.
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We propose a gradient-based global feature and its application to seam carving. We focus on areas, rather than points and lines, to be assigned as important elements for expressing the rough location of salient objects in an image. The proposed feature is calculated with a low computational load based on gray-scale intensity. The superior performance of the proposed gradient-based global feature, as compared to state-of-the-art salient features for seam carving, is demonstrated experimentally.
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On the basis of the previous examinations of the historical windows of the main building of the Pushkin State Museum of Fine Arts by the authors [1] using a certified software package, the multi-variant analysis of the methods of increasing efficiency of the existing translucent structures was conducted. The recommendations for restoration of the historical translucent structures which are the parts of this state-protected cultural heritage object were developed.
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This paper reports on state-of-the-art power-frequency-normalized phase noise of 21 dB, obtained from a CMOS-integrated LC oscillator with a measured phase noise of -112 dBc/Hz at 100 kHz from the 5.3-GHz center frequency and with power consumption of 15 mW. The excellent performance (if the oscillator is attributed to the careful selection of the oscillator-circuit parameters, which determined using the genetic algorithm. (C) 2004 Wiley Periodicals, Inc.
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We present the characterization of three cryogenic sapphire oscillators (CSOs) using the three-cornered-hat method. Easily implemented with commercial components and instruments, this method reveals itself very useful to analyze the fractional frequency stability limitations of these state-of-the-art ultrastable oscillators. The best unit presents a fractional frequency stability better than 5 x 10(-16) at 1 s and below 2 x 10(-16) for tau < 5000 s.
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In this paper the review of the state-of-art of electric power system reliability analyses methodology as well as the new trends are given. The definition and the relationships between electric power system reliability and electric energy security and quality of electricity supply are presented. The available models, methods and computer tools are discussed. (Today and tomorrow of analyses' methodology of electric power system reliability).
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We demonstrate a novel all-optical static RAM cell that exploits wavelength diversity in the incoming optical streams towards reducing the number of active elements. The circuit requires only three semiconductor optical amplifiers-cross gain modulation gates for successful read/write operation, yielding a 25% reduction in power consumption compared to state-of-the-art configurations. Proof-of-concept experimental verification is presented at 8 Mb/s using fiber-interconnected off-the-shelf bulk components.
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In this article we give efficient solutions to the construction of structured dictionaries for sparse representations. We study circulant and Toeplitz structures and give fast algorithms based on least squares solutions. We take advantage of explicit circulant structures and we apply the resulting algorithms to shift-invariant learning scenarios. Synthetic experiments and comparisons with state-of-the-art methods show the superiority of the proposed methods.
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This paper presents a tutorial overview of Continuous-Time Delta-Sigma Modulators (CTDSM); their operating principles to understand what is important intuitively and architectures to achieve higher conversion efficiency and to operate low supply voltage, design methods against loop stability problem, tuning methods of the bandwidth and so on. A survey of cutting-edge CMOS implementations is described.
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This paper reviews the state-of-the-art of hydrostatic leveling and tilt measurement, with an emphasis on the Pellissier H5, and the National Radio Astronomy Observatory NPH6. Details of the NPH6 design are described, experimental results are discussed, suggestions are made for further enhancements, and additional potential applications are offered. (c) 2004 Elsevier Inc. All rights reserved.
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A novel 1-D sampling method is proposed to calculate 2-D diamond-shaped discrete frequency distributions, which are often treated in the harmonic balance method. The proposed method fills up the entire 2-D discrete frequency plane with diamond-shaped rhombic tiles and makes the number of sampling points equal to that of the necessary frequency components.
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A new European Doctoral Program on Metamaterials has been initiated by the European Union (EU) Network of Excellence METAMORPHOSE. So far, twenty European academic institutions have established a consortium that operates a geographically distributed doctoral school in the emerging and multidisciplinary field of metamaterials.
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To commemorate the SPIE Medical Imaging 50th anniversary, this article provides a brief review of the Picture Archiving and Communication Systems (PACS) and Informatics conferences. Important topics and advances, contributing researchers from both academia and industry, and key papers are noted.
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And the combined event is going to put us in a very good position to deploy those, deploy that all for now through our more than 600 sales resources around the U.S. So we think it's a great opportunity.
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And what we’ve said in the past and we’ll continue to do is, we want to be more focus where there are distressed retailers or we can buy the real estate underneath where there is a cap rate arbitrage.
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