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https://www.readbyqxmd.com/read/28720428/inhibitors-of-connexin-and-pannexin-channels-as-potential-therapeutics
#1
REVIEW
Joost Willebrords, Michaël Maes, Sara Crespo Yanguas, Mathieu Vinken
While gap junctions support the exchange of a number of molecules between neighboring cells, connexin hemichannels provide communication between the cytosol and the extracellular environment of an individual cell. The latter equally holds true for channels composed of pannexin proteins, which display an architecture reminiscent of connexin hemichannels. In physiological conditions, gap junctions are usually open, while connexin hemichannels and, to a lesser extent, pannexin channels are typically closed, yet they can be activated by a number of pathological triggers...
July 15, 2017: Pharmacology & Therapeutics
https://www.readbyqxmd.com/read/28719614/beat-id-towards-a-computationally-low-cost-single-heartbeat-biometric-identity-check-system-based-on-electrocardiogram-wave-morphology
#2
Joana S Paiva, Duarte Dias, João P S Cunha
In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT)...
2017: PloS One
https://www.readbyqxmd.com/read/28715330/anti-impulse-noise-edge-detection-via-anisotropic-morphological-directional-derivatives
#3
Peng-Lang Shui, Fu-Ping Wang
Traditional differential-based edge detection suffers from abrupt degradation in performance when images are corrupted by impulse noises. The morphological operators such as the median filters and weighted median filters possess the intrinsic ability to counteract impulse noise. In this paper, by combining the biwindow configuration with weighted median filters, anisotropic morphological directional derivatives (AMDD) robust to impulse noise are proposed to measure the local grayscale variation around a pixel...
July 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28712994/recognition-of-white-matter-bundles-using-local-and-global-streamline-based-registration-and-clustering
#4
REVIEW
Eleftherios Garyfallidis, Marc-Alexandre Côté, Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent Petit, David Fortin, Stephen Cunanne, Maxime Descoteaux
Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive knowledge of white matter anatomy. This virtual dissection often requires several inclusion and exclusion regions-of-interest that make it a process that is very hard to reproduce across experts. Having automated tools that can extract white matter bundles for tract-based studies of large numbers of people is of great interest for neuroscience and neurosurgical planning. The purpose of our proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform...
July 13, 2017: NeuroImage
https://www.readbyqxmd.com/read/28710702/bayesian-inference-for-biomarker-discovery-in-proteomics-an-analytic-solution
#5
Noura Dridi, Audrey Giremus, Jean-Francois Giovannelli, Caroline Truntzer, Melita Hadzagic, Jean-Philippe Charrier, Laurent Gerfault, Patrick Ducoroy, Bruno Lacroix, Pierre Grangeat, Pascal Roy
This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought...
December 2017: EURASIP Journal on Bioinformatics & Systems Biology
https://www.readbyqxmd.com/read/28709883/pyrolysis-characteristics-and-kinetics-of-microalgae-via-thermogravimetric-analysis-tga-a-state-of-the-art-review
#6
REVIEW
Quang-Vu Bach, Wei-Hsin Chen
Pyrolysis is a promising route for biofuels production from microalgae at moderate temperatures (400-600°C) in an inert atmosphere. Depending on the operating conditions, pyrolysis can produce biochar and/or bio-oil. In practice, knowledge for thermal decomposition characteristics and kinetics of microalgae during pyrolysis is essential for pyrolyzer design and pyrolysis optimization. Recently, the pyrolysis kinetics of microalgae has become a crucial topic and received increasing interest from researchers...
June 19, 2017: Bioresource Technology
https://www.readbyqxmd.com/read/28708561/gpf-gmm-inspired-feature-preserving-point-set-filtering
#7
Xuequan Lu, Shihao Wu, Honghua Chen, Sai-Kit Yeung, Wenzhi Chen, Matthias Zwicker
Point set filtering, which aims at reconstructing noise-free point sets from their corresponding noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of point set filtering is to preserve geometric features of the underlying geometry while at the same time removing the noise. State-of-the-art point set filtering methods still struggle with this issue: some are not designed to recover sharp features, and others cannot well preserve geometric features, especially fine-scale features...
July 11, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28708556/deep-learning-on-sparse-manifolds-for-faster-object-segmentation
#8
Jacinto C Nascimento, Gustavo Carneiro
We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708555/going-deeper-with-contextual-cnn-for-hyperspectral-image-classification
#9
Hyungtae Lee, Heesung Kwon
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708554/distance-metric-learning-via-iterated-support-vector-machines
#10
Wangmeng Zuo, Faqiang Wang, David Zhang, Liang Lin, Yuchi Huang, Deyu Meng, Lei Zhang
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs)...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708550/unsupervised-myocardial-segmentation-for-cardiac-bold
#11
Ilkay Oksuz, Anirban Mukhopadhyay, Rohan Dharmakumar, Sotirios A Tsaftaris
A fully automated 2D+time myocardial segmentation framework is proposed for Cardiac Magnetic Resonance (CMR) Blood-Oxygen-Level-Dependent (BOLD) datasets. Ischemia detection with CINE BOLD CMR relies on spatiotemporal patterns in myocardial intensity but these patterns also trouble supervised segmentation methods, the de-facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace...
July 12, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28707628/brainframe-a-node-level-heterogeneous-accelerator-platform-for-neuron-simulations
#12
Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harry Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris de Zeeuw, Christos Strydis
OBJECTIVE: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements...
July 14, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28705497/quicksilver-fast-predictive-image-registration-a-deep-learning-approach
#13
Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer
This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization...
July 10, 2017: NeuroImage
https://www.readbyqxmd.com/read/28701814/modelling-the-impacts-of-pests-and-diseases-on-agricultural-systems
#14
M Donatelli, R D Magarey, S Bregaglio, L Willocquet, J P M Whish, S Savary
The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems...
July 2017: Agricultural Systems
https://www.readbyqxmd.com/read/28699566/entity-recognition-from-clinical-texts-via-recurrent-neural-network
#15
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, Hua Xu
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28696395/a-fine-grained-and-privacy-preserving-query-scheme-for-fog-computing-enhanced-location-based-service
#16
Xue Yang, Fan Yin, Xiaohu Tang
Location-based services (LBS), as one of the most popular location-awareness applications, has been further developed to achieve low-latency with the assistance of fog computing. However, privacy issues remain a research challenge in the context of fog computing. Therefore, in this paper, we present a fine-grained and privacy-preserving query scheme for fog computing-enhanced location-based services, hereafter referred to as FGPQ. In particular, mobile users can obtain the fine-grained searching result satisfying not only the given spatial range but also the searching content...
July 11, 2017: Sensors
https://www.readbyqxmd.com/read/28696049/increased-nutritional-value-in-food-crops
#17
Nieves Goicoechea, M Carmen Antolín
Modern agriculture and horticulture must combine two objectives that seem to be almost mutually exclusive: to satisfy the nutritional needs of an increasing human population and to minimize the negative impact on the environment. These two objectives are included in the Goal 2 of the 2030 Agenda for Sustainable Development of the United Nations: 'End hunger, achieve food security and improved nutrition and promote sustainable agriculture'. Enhancing the nutritional levels of vegetables would improve nutrient intake without requiring an increase in consumption...
July 11, 2017: Microbial Biotechnology
https://www.readbyqxmd.com/read/28695205/revisiting-ancestral-polyploidy-in-plants
#18
Colin Ruprecht, Rolf Lohaus, Kevin Vanneste, Marek Mutwil, Zoran Nikoloski, Yves Van de Peer, Staffan Persson
Whole-genome duplications (WGDs) or polyploidy events have been studied extensively in plants. In a now widely cited paper, Jiao et al. presented evidence for two ancient, ancestral plant WGDs predating the origin of flowering and seed plants, respectively. This finding was based primarily on a bimodal age distribution of gene duplication events obtained from molecular dating of almost 800 phylogenetic gene trees. We reanalyzed the phylogenomic data of Jiao et al. and found that the strong bimodality of the age distribution may be the result of technical and methodological issues and may hence not be a "true" signal of two WGD events...
July 2017: Science Advances
https://www.readbyqxmd.com/read/28693508/unsuccessful-tb-treatment-outcomes-with-a-focus-on-hiv-co-infected-cases-a-cross-sectional-retrospective-record-review-in-a-high-burdened-province-of-south-africa
#19
M C Engelbrecht, N G Kigozi, P Chikobvu, S Botha, H C J van Rensburg
BACKGROUND: South Africa did not meet the MDG targets to reduce TB prevalence and mortality by 50% by 2015, and the TB cure rate remains below the WHO target of 85%. TB incidence in the country is largely fuelled by the HIV epidemic, and co-infected patients are more likely to have unsuccessful TB treatment outcomes. This paper analyses the demographic and clinical characteristics of new TB patients with unsuccessful treatment outcomes, as well as factors associated with unsuccessful treatment outcomes for HIV co-infected patients...
July 10, 2017: BMC Health Services Research
https://www.readbyqxmd.com/read/28693478/reliable-biomarker-discovery-from-metagenomic-data-via-reglrsd-algorithm
#20
Mustafa Alshawaqfeh, Ahmad Bashaireh, Erchin Serpedin, Jan Suchodolski
BACKGROUND: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms...
July 10, 2017: BMC Bioinformatics
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