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https://www.readbyqxmd.com/read/29149201/the-influence-of-filtering-and-downsampling-on-the-estimation-of-transfer-entropy
#1
Immo Weber, Esther Florin, Michael von Papen, Lars Timmermann
Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator...
2017: PloS One
https://www.readbyqxmd.com/read/29149191/resting-state-fmri-in-sleeping-infants-more-closely-resembles-adult-sleep-than-adult-wakefulness
#2
Anish Mitra, Abraham Z Snyder, Enzo Tagliazucchi, Helmut Laufs, Jed Elison, Robert W Emerson, Mark D Shen, Jason J Wolff, Kelly N Botteron, Stephen Dager, Annette M Estes, Alan Evans, Guido Gerig, Heather C Hazlett, Sarah J Paterson, Robert T Schultz, Martin A Styner, Lonnie Zwaigenbaum, Bradley L Schlaggar, Joseph Piven, John R Pruett, Marcus Raichle
Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs...
2017: PloS One
https://www.readbyqxmd.com/read/29146466/causal-mapping-of-emotion-networks-in-the-human-brain-framework-and-initial-findings
#3
Julien Dubois, Hiroyuki Oya, J Michael Tyszka, Matthew Howard, Frederick Eberhardt, Ralph Adolphs
Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala...
November 13, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/29145845/network-pharmacological-mechanisms-of-vernonia-anthelmintica-l-in-the-treatment-of-vitiligo-isorhamnetin-induction-of-melanogenesis-via-up-regulation-of-melanin-biosynthetic-genes
#4
Ji Ye Wang, Hong Chen, Yin Yin Wang, Xiao Qin Wang, Han Ying Chen, Mei Zhang, Yun Tang, Bo Zhang
BACKGROUND: Vitiligo is a long-term skin disease characterized by the loss of pigment in the skin. The current therapeutic approaches are limited. Although the anti-vitiligo mechanisms of Vernonia anthelmintica (L.) remain ambiguous, the herb has been broadly used in Uyghur hospitals to treat vitiligo. The overall objective of the present study aims to identify the potential lead compounds from Vernonia anthelmintica (L.) in the treatment of vitiligo via an oral route as well as the melanogenic mechanisms in the systematic approaches in silico of admetSAR and substructure-drug-target network-based inference (SDTNBI)...
November 16, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/29145805/anat-2-0-reconstructing-functional-protein-subnetworks
#5
Yomtov Almozlino, Nir Atias, Dana Silverbush, Roded Sharan
BACKGROUND: ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. RESULTS: Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets...
November 16, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29145803/unraveling-the-evolution-and-coevolution-of-small-regulatory-rnas-and-coding-genes-in-listeria
#6
Franck Cerutti, Ludovic Mallet, Anaïs Painset, Claire Hoede, Annick Moisan, Christophe Bécavin, Mélodie Duval, Olivier Dussurget, Pascale Cossart, Christine Gaspin, Hélène Chiapello
BACKGROUND: Small regulatory RNAs (sRNAs) are widely found in bacteria and play key roles in many important physiological and adaptation processes. Studying their evolution and screening for events of coevolution with other genomic features is a powerful way to better understand their origin and assess a common functional or adaptive relationship between them. However, evolution and coevolution of sRNAs with coding genes have been sparsely investigated in bacterial pathogens. RESULTS: We designed a robust and generic phylogenomics approach that detects correlated evolution between sRNAs and protein-coding genes using their observed and inferred patterns of presence-absence in a set of annotated genomes...
November 16, 2017: BMC Genomics
https://www.readbyqxmd.com/read/29145449/estimation-of-the-proteomic-cancer-co-expression-sub-networks-by-using-association-estimators
#7
Cihat Erdoğan, Zeyneb Kurt, Banu Diri
In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study...
2017: PloS One
https://www.readbyqxmd.com/read/29142227/population-genetic-structure-of-the-land-snail-camaena-cicatricosa-stylommatophora-camaenidae-in-china-inferred-from-mitochondrial-genes-and-its2-sequences
#8
Weichuan Zhou, Haifang Yang, Hongli Ding, Shanping Yang, Junhong Lin, Pei Wang
The phylogeographic structure of the land snail Camaena cicatricosa was analyzed in this study based on mitochondrial gene (COI and 16srRNA, mt DNA) and internal transcribed spacer (ITS2) sequences in 347 individuals. This snail is the vector of the zoonotic food-borne parasite Angiostrongylus cantonensis and one of the main harmful snails distributed exclusively in China. The results revealed significant fixation indices of genetic differentiation and high gene flow between most populations except several populations...
November 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29140991/a-bayesian-method-for-detecting-pairwise-associations-in-compositional-data
#9
Emma Schwager, Himel Mallick, Steffen Ventz, Curtis Huttenhower
Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix...
November 15, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29139548/prospects-from-systems-serology-research
#10
REVIEW
Kelly B Arnold, Amy W Chung
Antibodies are highly functional glycoproteins capable of providing immune protection through multiple mechanisms, including direct pathogen neutralization and the engagement of their Fc-portions with surrounding effector immune cells and immune components that induce anti-pathogenic responses. Small modifications to multiple antibody biophysical features induced by vaccines and other therapeutic regimens can significantly alter functional immune outcomes, though it is difficult to predict which combinations confer protective immunity...
November 15, 2017: Immunology
https://www.readbyqxmd.com/read/29139050/modeling-mesoscopic-cortical-dynamics-using-a-mean-field-model-of-conductance-based-networks-of-adaptive-exponential-integrate-and-fire-neurons
#11
Yann Zerlaut, Sandrine Chemla, Frederic Chavane, Alain Destexhe
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons...
November 15, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29133956/network-inference-from-glycoproteomics-data-reveals-new-reactions-in-the-igg-glycosylation-pathway
#12
Elisa Benedetti, Maja Pučić-Baković, Toma Keser, Annika Wahl, Antti Hassinen, Jeong-Yeh Yang, Lin Liu, Irena Trbojević-Akmačić, Genadij Razdorov, Jerko Štambuk, Lucija Klarić, Ivo Ugrina, Maurice H J Selman, Manfred Wuhrer, Igor Rudan, Ozren Polasek, Caroline Hayward, Harald Grallert, Konstantin Strauch, Annette Peters, Thomas Meitinger, Christian Gieger, Marija Vilaj, Geert-Jan Boons, Kelley W Moremen, Tatiana Ovchinnikova, Nicolai Bovin, Sakari Kellokumpu, Fabian J Theis, Gordan Lauc, Jan Krumsiek
Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach...
November 14, 2017: Nature Communications
https://www.readbyqxmd.com/read/29131816/automated-visualization-of-rule-based-models
#13
John Arul Prakash Sekar, Jose-Juan Tapia, James R Faeder
Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size...
November 13, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29128975/transcutaneous-vagus-nerve-stimulation-tvns-modulates-flow-experience
#14
Lorenza S Colzato, Gina Wolters, Corinna Peifer
Flow has been defined as a pleasant psychological state that people experience when completely absorbed in an activity. Previous correlative evidence showed that the vagal tone (as indexed by heart rate variability) is a reliable marker of flow. So far, it has not yet been demonstrated that the vagus nerve plays a causal role in flow. To explore this we used transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that increases activation of the locus coeruleus (LC) and norepinephrine release...
November 11, 2017: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
https://www.readbyqxmd.com/read/29125126/improving-grn-re-construction-by-mining-hidden-regulatory-signals
#15
Ming Shi, Weiming Shen, Yanwen Chong, Hong-Qiang Wang
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators...
December 2017: IET Systems Biology
https://www.readbyqxmd.com/read/29122012/drug-target-ontology-to-classify-and-integrate-drug-discovery-data
#16
Yu Lin, Saurabh Mehta, Hande Küçük-McGinty, John Paul Turner, Dusica Vidovic, Michele Forlin, Amar Koleti, Dac-Trung Nguyen, Lars Juhl Jensen, Rajarshi Guha, Stephen L Mathias, Oleg Ursu, Vasileios Stathias, Jianbin Duan, Nooshin Nabizadeh, Caty Chung, Christopher Mader, Ubbo Visser, Jeremy J Yang, Cristian G Bologa, Tudor I Oprea, Stephan C Schürer
BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome...
November 9, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/29120828/the-efficacy-of-respondent-driven-sampling-for-the-health-assessment-of-minority-populations
#17
Grazyna Badowski, Lilnabeth P Somera, Brayan Simsiman, Hye-Ryeon Lee, Kevin Cassel, Alisha Yamanaka, JunHao Ren
BACKGROUND: Respondent driven sampling (RDS) is a relatively new network sampling technique typically employed for hard-to-reach populations. Like snowball sampling, initial respondents or "seeds" recruit additional respondents from their network of friends. Under certain assumptions, the method promises to produce a sample independent from the biases that may have been introduced by the non-random choice of "seeds." We conducted a survey on health communication in Guam's general population using the RDS method, the first survey that has utilized this methodology in Guam...
October 2017: Cancer Epidemiology
https://www.readbyqxmd.com/read/29118696/a-theory-of-how-columns-in-the-neocortex-enable-learning-the-structure-of-the-world
#18
Jeff Hawkins, Subutai Ahmad, Yuwei Cui
Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/29118406/single-cell-co-expression-subnetwork-analysis
#19
Thomas E Bartlett, Sören Müller, Aaron Diaz
Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models...
November 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29117534/building-predictive-models-of-genetic-circuits-using-the-principle-of-maximum-caliber
#20
Taylor Firman, Gábor Balázsi, Kingshuk Ghosh
Learning the underlying details of a gene network is a major challenge in cellular and synthetic biology. We address this challenge by building a chemical kinetic model that utilizes information encoded in the stochastic protein expression trajectories typically measured in experiments. The applicability of the proposed method is demonstrated in an auto-activating genetic circuit, a common motif in natural and synthetic gene networks. Our approach is based on the principle of maximum caliber (MaxCal)-a dynamical analog of the principle of maximum entropy-and builds a minimal model using only three constraints: 1) protein synthesis, 2) protein degradation, and 3) positive feedback...
November 7, 2017: Biophysical Journal
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