bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differential Community Detection in Paired Biological Networks
Authors: Mall Raghvendra , Ullah Ehsan , Kunjia Khalid , Bensmail Halima 
Published: June 2017

AbstractMotivationBiological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures.MethodsThe task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast two-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. We then apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities.ResultsOur proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different power-law exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding network-inferred bio-markers/pathways associated with a trait of interest.ConclusionThe proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available athttps://sites.google.com/site/raghvendramallmlresearcher/codes.
Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies
Authors: Retkute Renata , Townsend Alexandra J. , Murchie Erik H. , Jensen Oliver E. , Preston Simon P. 
Published: June 2017

AbstractBackground and AimsDiurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterise light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resultant data are complex and high dimensional. This necessitates development of more economical models for summarising the data and for simulating realistic light patterns over the course of a day.MethodsHigh-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 minute) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from.Key ResultsFor a wide range of contrasting 3D canopies, the stochastic model is able to summarise, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data.ConclusionsThe model provides a way to summarise highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.
Network Connectivity Determines Cortical Thinning In Early Parkinson’s Disease Progression
Authors: Yau Y. , Zeighami Y. , Baker T. , Larcher K. , Vainik U. , Dadar M. , Fonov V. , Hagmann P. , Griffa A. , Mišić B. , Collins D.L. , Dagher A. 
Published: June 2017

ABSTRACTParkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor and cognitive deficits. The neurodegenerative process is thought to move stereotypically from the brainstem up to the cerebral cortex, possibly reflecting the spread of toxic alpha-synuclein molecules. Using a large, longitudinal, multi-center database ofde novoPD patients, we tested whether focal reductions in cortical thickness could be explained by disease spread from a subcortical “disease reservoir” along the brain’s connectome. PD patients (n=105) and matched controls (n=57) underwent T1-MRI at entry and one year later. Over this period, PD patients demonstrated significantly greater loss of cortical thickness than healthy controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical regions with greater connectivity (measured functionally or structurally) to a “disease reservoir” evaluated via MRI at baseline demonstrated greater atrophy one year later. The atrophy pattern in the ventral frontal lobes resembled one described in certain cases of Alzheimer’s disease. Moreover, a multiple linear regression model suggested that cortical thinning was associated with impaired cognitive function at follow-up. Our findings suggest that disease propagation to the cortex in PD follows neural connectivity, and that disease spread to the cortex may herald the onset of cognitive impairment.
PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia
Authors: Chen Kathleen M. , Tan Jie , Way Gregory P. , Doing Georgia , Hogan Deborah A. , Greene Casey S. 
Published: June 2017

AbstractBackgroundInvestigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.ResultsWe developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance asfunctionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data.ConclusionsThe PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.
ComM is a hexameric helicase that promotes branch migration during natural transformation in diverse Gram-negative species
Authors: Nero Thomas M. , Dalia Triana N. , Wang Joseph Che-Yen , Kysela David T. , Bochman Matthew L. , Dalia Ankur B. 
Published: June 2017

ABSTRACTAcquisition of foreign DNA by natural transformation is an important mechanism of adaptation and evolution in diverse microbial species. Here, we characterize the mechanism of ComM, a broadly conserved AAA+ protein previously implicated in homologous recombination of transforming DNA (tDNA) in naturally competent Gram-negative bacterial species.In vivo, we found that ComM was required for efficient comigration of linked genetic markers inVibrio choleraeandAcinetobacter baylyi, which is consistent with a role in branch migration. Also, ComM was particularly important for integration of tDNA with increased sequence heterology, suggesting that its activity promotes the acquisition of novel DNA sequences.In vitro, we showed that purified ComM binds ssDNA, oligomerizes into a hexameric ring, and has bidirectional helicase and branch migration activity. Based on these data, we propose a model for tDNA integration during natural transformation. This study provides mechanistic insight into the enigmatic steps involved in tDNA integration and uncovers the function of a protein required for this conserved mechanism of horizontal gene transfer.
A Bayesian computational approach to explore the optimal duration of a cell proliferation assay
Authors: Browning Alexander P , McCue Scott W , Simpson Matthew J 
Published: June 2017

AbstractCell proliferation assays are routinely used to explore how a low density monolayer of cells grows with time. For a typical cell line with a doubling time of 12 hours (or longer), a standard cell proliferation assay conducted over 24 hours provides excellent information about the low-density exponential growth rate, but limited information about crowding effects that occur at higher densities. To explore how we can best detect and quantify crowding effects, we present a suite of in silico proliferation assays where cells proliferate according to a generalised logistic growth model. Using approximate Bayesian computation we show that data from a standard cell proliferation assay cannot reliably distinguish between classical logistic growth and more general non-logistic growth models. We then explore, and quantify, the trade-off between increasing the duration of the experiment and the associated decrease in uncertainty in the crowding mechanism.
The cavity method for community ecology
Authors: Barbier Matthieu , Arnoldi Jean-François 
Published: June 2017

AbstractThis article is addressed to researchers and students in theoretical ecology, as an introduction to “disordered systems” approaches from statistical physics, and how they can help understand large ecological communities. We discuss the relevance of these approaches, and how they fit within the broader landscape of models in community ecology. We focus on a remarkably simple technique, the cavity method, which allows to derive the equilibrium properties of Lotka-Volterra systems. We present its predictions, the new intuitions it suggests, and its technical underpinnings. We also discuss a number of new results concerning possible extensions, including different functional responses and community structures.
HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection
Authors: Stansfield John C. , Dozmorov Mikhail G. 
Published: June 2017

AbstractChanges in spatial chromatin interactions are now emerging as a unifying mechanism or-chestrating regulation of gene expression. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biasesbetween two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. We developed a simple and effective methodHiCcomparefor the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). The method identifies region-specific chromatin interaction changes complementary to changes due to large-scale genomic rearrangements, such as copy number variants (CNVs).HiCcompareoutperforms methods for normalizing individual Hi-C datasets in detectinga prioriknown chromatin interaction differences in simulated and real-life settings while detecting biologically relevant changes.HiCcompareis freely available as a Bioconductor R packagehttps://bioconductor.org/packages/HiCcompare/.Author SummaryAdvances in chromosome conformation capture sequencing technologies (Hi-C) have sparked interest in studying the 3-dimensional (3D) chromatin interaction structure of the human genome. The 3D structure of the genome is now considered as a primary regulator of gene expression. Changes to the 3D chromatin interactions are now emerging as a hallmark of cancer and other complex diseases. With the growing availability of Hi-C data generated under different conditions (e.g. tumor-normal, cell-type-specific), methods are needed to compare them. However, biases in Hi-C data hinder their comparative analysis. To account for biases, several normalization techniques have been developed for removing biases in individual Hi-C datasets, but very few were designed to account for between-datasets biases. We developed a new method and R packageHiCcomparefor the joint normalization of multiple Hi-C datasets and differential chromatin interaction detection. Our results show the superiority of our joint normalization methods compared to methods for normalizing individual datasets in detecting true chromatin interaction changes.HiCcompareenables further research into discovering the dynamics of 3D genomic changes.
An integrative method to unravel the host-parasite interactome: an orthology-based approach
Authors: Cuesta-Astroz Yesid , Santos Alberto , Oliveira Guilherme , Jensen Lars J. 
Published: June 2017

AbstractThe study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require clear understanding of the molecular crosstalk between parasites and their hosts. As yet, experimental large–scale identification of host–parasite molecular interactions remains challenging and the use of in silico predictions becomes then necessary. Here, we propose a computational integrative approach to predict host–parasite protein–protein interaction (PPI) networks resulting from the infection of human by 12 different parasites. We used an orthology-based method to transfer high–confidence intra–species interactions obtained from the STRING database to the corresponding inter–species protein pairs in the host–parasite system. To reduce the number of spurious predictions, our approach uses either the parasites predicted secretome and membrane proteins or only the secretome depending on whether they are uni- or multicellular respectively. Besides, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasites growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association to known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.Author SummaryA protein-protein interaction (PPI) network is a collection of interactions between proteins from one or more organisms. Host–parasite PPIs are key to understanding the biology of different parasitic diseases, since predicting PPIs enable to know more about the parasite invasion, infection and persist. Our understanding of PPIs between host and parasites is still very limited, as no many systematic experimental studies have so far been performed. Efficacy of treatments for parasitic diseases is limited and in many cases parasites evolve resistance. Thus, there is an urgent need to develop novel drugs or vaccines for these neglected diseases, and thus interest in the functions and interactions of proteins associated with parasitism processes. Here we developed an in silico method to shed light on the interactome in twelve human parasites by combining an orthology based strategy and integrating, domain–domain interaction data, sub-cellular localization and to give a spatial context, we only took in to account those human tissues that support the parasite’s tropism. Here we show that is possible to identify relevant interactions across different parasites and their human host and that these interactions are well supported based on the biology of the parasites.
Neuronal adaptation and optimal coding in economic decisions
Authors: Rustichini Aldo , Conen Katherine E , Cai Xinying , Padoa-Schioppa Camillo 
Published: June 2017

AbstractDuring economic decisions, neurons in orbitofrontal cortex (OFC) encode the values of offered goods. Importantly, their responses adapt to the range of values available in any given context. Prima facie, range adaptation seems to provide an efficient representation. However, uncorrected adaptation in the encoding of offer values would induce arbitrary choice biases. Thus a fundamental and open question is whether range adaptation is behaviorally advantageous. Here we present a theory of optimal coding for economic decisions. In a nutshell, the representation of offer values is optimal if it ensures maximal expected payoff. In this framework, we examine the activity ofoffer valuecells in non-human primates. We show that their firing rates are quasi-linear functions of the offered values, even when optimal tuning functions would be highly non-linear. Most importantly, we demonstrate that for linear tuning functions range adaptation maximizes the expected payoff, even if the effects of adaptation are corrected to avoid choice biases. Thus value coding in OFC is functionally rigid (linear tuning) but parametrically plastic (range adaptation with optimal gain). Importantly, the benefit of range adaptation outweighs the cost of functional rigidity. While generally suboptimal, linear tuning may facilitate transitive choices.
Glucose limitation inLactococcusshapes a single-peaked fitness landscape exposing membrane occupancy as a constraint
Authors: Price Claire E. , dos Santos Filipe Branco , Hesseling Anne , Uusitalo Jaakko J. , Bachmann Herwig , Benavente Vera , Goel Anisha , Berkhout Jan , Bruggeman Frank J. , Marrink Siewert-Jan , Montalban-Lopez Manolo , Jong Anne de , Kok Jan , Molenaar Douwe , Poolman Bert , Teusink Bas , Kuipers Oscar P. 
Published: June 2017

ABSTRACTA central theme in biology is to understand the molecular basis of fitness: which strategies succeed under which conditions; how are they mechanistically implemented; and which constraints shape trade-offs between alternative strategies. We approached these questions with parallel bacterial evolution experiments in chemostats. Chemostats provide a constant environment with a defined resource limitation (glucose), in which the growth rate can be controlled. UsingLactococcus lactis, we found a single mutation in a global regulator of carbon metabolism, CcpA, to confer predictable fitness improvements across multiple growth rates.In silicoprotein structural analysis complemented with biochemical and phenotypic assays, show that the mutation reprograms the CcpA regulon, specifically targeting transporters. This supports that membrane occupancy, rather than biosynthetic capacity, is the dominant constraint for the observed fitness enhancement. It also demonstrates that cells can modulate a pleiotropic regulator to work around limiting constraints.
More than Drylands but Less of Dry Forests
Authors: Cruz Marcelino de la , Quintana-Ascencio Pedro F. , Cayuela Luis , Espinosa Carlos I. , Escudero Adrián 
Published: June 2017

AbstractThe recent Global Drylands Assessment of forests is based on both an incomplete delimitation of dry forests distribution and on an old and incorrect delimitation of drylands. Its sampling design includes a large proportion of plots located in humid ecosystems and ignores critical areas for the conservation of dry forests. Therefore its results and conclusions are unreliable.
Centriole triplet microtubules are required for stable centriole formation and inheritance in human cells
Authors: Wang Jennifer T. , Kong Dong , Hoerner Christian R. , Loncarek Jadranka , Stearns Tim 
Published: June 2017

SummaryCentrioles are composed of long-lived microtubules arranged in nine triplets. In unicellular eukaryotes, loss of the noncanonical tubulins, delta-tubulin and epsilon tubulin, result in loss of the triplet microtubule structure. However, the contribution of triplet microtubules to mammalian centriole formation and stability is unknown. Here, we report the first characterization of delta-tubulin and epsilon-tubulinnullhuman cells. Centrioles in cells lacking either delta-tubulin or epsilon-tubulin lack triplet microtubules and fail to undergo centriole maturation. These aberrant centrioles are formedde novoeach cell cycle, but are unstable and do not persist to the next cell cycle, leading to a futile cycle of centriole formation and disintegration. Disintegration can be suppressed by paclitaxel treatment. Delta-tubulin and epsilon-tubulin physically interact, indicating that these tubulins act together to maintain triplet microtubules and that these are necessary for inheritance of centrioles from one cell cycle to the next.