INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89444, 2019, 'plos', 'Learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization', 'Majarian, Timothy D|Murphy, Robert F|Lakdawala, Seema S', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States', 5, 'Animals|Computational Biology|Dogs|Genome, Viral|In Situ Hybridization, Fluorescence|Influenza A virus|Madin Darby Canine Kidney Cells|Models, Genetic|RNA, Viral|Virion|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006199', '30689627', 'Within influenza virus infected cells, viral genomic RNA are selectively packed into progeny virions, which predominantly contain a single copy of 8 viral RNA segments. Intersegmental RNA-RNA interactions are thought to mediate selective packaging of each viral ribonucleoprotein complex (vRNP). Clear evidence of a specific interaction network culminating in the full genomic set has yet to be identified. Using multi-color fluorescence in situ hybridization to visualize four vRNP segments within a single cell, we developed image-based models of vRNP-vRNP spatial dependence. These models were used to construct likely sequences of vRNP associations resulting in the full genomic set. Our results support the notion that selective packaging occurs during cytoplasmic transport and identifies the formation of multiple distinct vRNP sub-complexes that likely form as intermediate steps toward full genomic inclusion into a progeny virion. The methods employed demonstrate a statistically driven, model based approach applicable to other interaction and assembly problems.'),
(89445, 2019, 'plos', 'Mechanistic framework predicts drug-class specific utility of antiretrovirals for HIV prophylaxis', 'Duwal, Sulav|Dickinson, Laura|Khoo, Saye|von Kleist, Max', 'Department of Mathematics & Computer Science, Freie Universität Berlin, Germany.|Institute of Translational Medicine, University of Liverpool, United Kingdom.|Institute of Translational Medicine, University of Liverpool, United Kingdom.|Department of Mathematics & Computer Science, Freie Universität Berlin, Germany.', 'Germany|United Kingdom|United Kingdom|Germany', 1, 'Anti-Retroviral Agents|Computational Biology|HIV Infections|HIV-1|Humans|Models, Statistical|Pre-Exposure Prophylaxis', 'Journal Article', '10.1371/journal.pcbi.1006740', '30699105', 'Currently, there is no effective vaccine to halt HIV transmission. However, pre-exposure prophylaxis (PrEP) with the drug combination Truvada can substantially decrease HIV transmission in individuals at risk. Despite its benefits, Truvada-based PrEP is expensive and needs to be taken once-daily, which often leads to inadequate adherence and incomplete protection. These deficits may be overcome by next-generation PrEP regimen, including currently investigated long-acting formulations, or patent-expired drugs. However, poor translatability of animal- and ex vivo/in vitro experiments, and the necessity to conduct long-term (several years) human trials involving considerable sample sizes (N>1000 individuals) are major obstacles to rationalize drug-candidate selection. We developed a prophylaxis modelling tool that mechanistically considers the mode-of-action of all available drugs. We used the tool to screen antivirals for their prophylactic utility and identify lower bound effective concentrations that can guide dose selection in PrEP trials. While in vitro measurable drug potency usually guides PrEP trial design, we found that it may over-predict PrEP potency for all drug classes except reverse transcriptase inhibitors. While most drugs displayed graded concentration-prophylaxis profiles, protease inhibitors tended to switch between none- and complete protection. While several treatment-approved drugs could be ruled out as PrEP candidates based on lack-of-prophylactic efficacy, darunavir, efavirenz, nevirapine, etravirine and rilpivirine could more potently prevent infection than existing PrEP regimen (Truvada). Notably, some drugs from this candidate set are patent-expired and currently neglected for PrEP repurposing. A next step is to further trim this candidate set by ruling out compounds with ominous safety profiles, to assess different administration schemes in silico and to test the remaining candidates in human trials.'),
(89446, 2019, 'plos', 'Prediction of ultra-high-order antibiotic combinations based on pairwise interactions', 'Katzir, Itay|Cokol, Murat|Aldridge, Bree B|Alon, Uri', 'Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Axcella Health, Cambridge, MA.|Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA.|Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|-|-|Israel', 9, 'Anti-Bacterial Agents|Computational Biology|Drug Combinations|Escherichia coli|Microbial Sensitivity Tests|Models, Biological|Models, Statistical|Mycobacterium tuberculosis', 'Journal Article', '10.1371/journal.pcbi.1006774', '30699106', 'Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.'),
(89447, 2019, 'plos', 'Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures', 'Clark, Jordan J|Benson, Mark L|Smith, Richard D|Carlson, Heather A', 'Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America.', 'United States|United States|United States|United States', 11, 'Crystallography, X-Ray|Databases, Protein|Ligands|Pliability|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1371/journal.pcbi.1006705', '30699115', 'Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 xc3x85. Analysis of xcfx871 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the xcfx871 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, xcfx871 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.'),
(89448, 2019, 'plos', 'Social dynamics modeling of chrono-nutrition', 'Di Stefano, Alessandro|Scatà, Marialisa|Vijayakumar, Supreeta|Angione, Claudio|La Corte, Aurelio|Liò, Pietro', 'Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), CNIT (National Inter-University Consortium for Telecommunications) Catania, Italy.|Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), CNIT (National Inter-University Consortium for Telecommunications) Catania, Italy.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.|Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), CNIT (National Inter-University Consortium for Telecommunications) Catania, Italy.|Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.', 'Italy|Italy|United Kingdom|United Kingdom|Italy|United Kingdom', 0, 'Bacteria|Biomass|Cluster Analysis|Computational Biology|Feeding Behavior|Gastrointestinal Microbiome|Humans|Metabolome|Models, Biological|Shift Work Schedule|Social Behavior|Time Factors', 'Journal Article', '10.1371/journal.pcbi.1006714', '30699206', 'Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the gut-human behavior axis and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and chrono-nutrition play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions.'),
(89449, 2019, 'plos', 'Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator', 'Mombaerts, Laurent|Carignano, Alberto|Robertson, Fiona C|Hearn, Timothy J|Junyang, Jin|Hayden, David|Rutterford, Zoe|Hotta, Carlos T|Hubbard, Katherine E|Maria, Marti Ruiz C|Yuan, Ye|Hannah, Matthew A|Goncalves, Jorge|Webb, Alex A R', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|BASF Agricultural Solutions Belgium NV, Gent, Belgium.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.', 'Luxembourg|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Arabidopsis Proteins|Circadian Rhythm|Gene Expression Profiling|Gene Expression Regulation, Plant|Models, Biological|Niacinamide|Systems Biology|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1006674', '30703082', 'The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems.'),
(89450, 2019, 'plos', 'Predicting change: Approximate inference under explicit representation of temporal structure in changing environments', 'Markovixc4x87, Dimitrije|Reiter, Andrea M F|Kiebel, Stefan J', 'Department of Psychology, Technische Universitxc3xa4t Dresden, Dresden, Germany.|Department of Psychology, Technische Universitxc3xa4t Dresden, Dresden, Germany.|Department of Psychology, Technische Universitxc3xa4t Dresden, Dresden, Germany.', 'Germany|Germany|Germany', 0, 'Anticipation, Psychological|Brain|Computational Biology|Databases, Factual|Decision Making|Humans|Magnetic Resonance Imaging|Models, Psychological|Models, Statistical|Reversal Learning', 'Journal Article', '10.1371/journal.pcbi.1006707', '30703108', 'In our daily lives timing of our actions plays an essential role when we navigate the complex everyday environment. It is an open question though how the representations of the temporal structure of the world influence our behavior. Here we propose a probabilistic model with an explicit representation of state durations which may provide novel insights in how the brain predicts upcoming changes. We illustrate several properties of the behavioral model using a standard reversal learning design and compare its task performance to standard reinforcement learning models. Furthermore, using experimental data, we demonstrate how the model can be applied to identify participants beliefs about the latent temporal task structure. We found that roughly one quarter of participants seem to have learned the latent temporal structure and used it to anticipate changes, whereas the remaining participants behavior did not show signs of anticipatory responses, suggesting a lack of precise temporal expectations. We expect that the introduced behavioral model will allow, in future studies, for a systematic investigation of how participants learn the underlying temporal structure of task environments and how these representations shape behavior.'),
(89451, 2019, 'plos', 'RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion', 'Koch, Sabine|Kohrs, Fabian|Lahmann, Patrick|Bissinger, Thomas|Wendschuh, Stefan|Benndorf, Dirk|Reichl, Udo|Klamt, Steffen', 'Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany.|Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Anaerobiosis|Biofuels|Bioreactors|Biotechnology|Computational Biology|Metabolic Networks and Pathways|Methane|Microbiota|Models, Biological|Proteomics', 'Journal Article', '10.1371/journal.pcbi.1006759', '30707687', 'Constraint-based modeling (CBM) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology, biomedicine, and various biotechnological processes. While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models, CBM of communities with balanced growth is more complicated, not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models. Moreover, the solution space of these community models often contains biologically unrealistic solutions, which, even with model linearization and under application of certain objective functions, cannot easily be excluded. Here we present RedCom, a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks. By discarding (single-species) net conversions that violate a minimality criterion in the exchange fluxes, it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts (instead of biomass) to fulfill the requirements of other species. We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants. Compared to full (bilinear and linearized) community models, we found that the reduced community models obtained with RedCom are not only much smaller but allow, also in the largest model with nine species, extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates. Furthermore, the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures. For an enrichment culture for growth on ethanol, we also used metaproteomic data to further constrain the solution space of the community models. Both model and proteomic data indicated a dominance of acetoclastic methanogens (Methanosarcinales) and Desulfovibrionales being the least abundant group in this microbial community.'),
(89452, 2019, 'plos', 'Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited', 'Osthus, Dave|Daughton, Ashlynn R|Priedhorsky, Reid', 'Los Alamos National Laboratory, Los Alamos, New Mexico, USA.|Los Alamos National Laboratory, Los Alamos, New Mexico, USA.|Los Alamos National Laboratory, Los Alamos, New Mexico, USA.', 'Mexico|Mexico|Mexico', 8, 'Disease Outbreaks|Forecasting|Humans|Influenza, Human|Internet|Public Health|Seasons|United States', 'Journal Article', '10.1371/journal.pcbi.1006599', '30707689', 'The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when perfect nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.'),
(89453, 2019, 'plos', 'Social evolution under demographic stochasticity', 'McLeod, David V|Day, Troy', 'Institute for Integrative Biology, ETH Zürich, Zürich, Switzerland.|Department of Mathematics and Statistics, Department of Biology Queens University, Kingston, ON, Canada.', 'Switzerland|Canada', 0, 'Altruism|Biological Evolution|Birth Rate|Computer Simulation|Demography|Ecosystem|Environment|Models, Biological|Mortality|Population Dynamics|Selection, Genetic|Social Behavior|Sociological Factors|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1006739', '30716064', 'How social traits such as altruism and spite evolve remains an open question in evolutionary biology. One factor thought to be potentially important is demographic stochasticity. Here we provide a general theoretical analysis of the role of demographic stochasticity in social evolution. We show that the evolutionary impact of stochasticity depends on how the social action alters the recipients life cycle. If the action alters the recipients death rate, then demographic stochasticity always favours altruism and disfavours spite. On the other hand, if the action alters the recipients birth rate, then stochasticity can either favour or disfavour both altruism and spite depending on the ratio of the rate of population turnover to the population size. Finally, we also show that this ratio is critical to determining if demographic stochasticity can reverse the direction of selection upon social traits. Our analysis thus provides a general understanding of the role of demographic stochasticity in social evolution.'),
(89454, 2019, 'plos', 'Mapping DNA sequence to transcription factor binding energy in vivo', 'Barnes, Stephanie L|Belliveau, Nathan M|Ireland, William T|Kinney, Justin B|Phillips, Rob', 'Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.|Department of Physics, California Institute of Technology, Pasadena, California, United States of America.|Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.', 'United States|United States|United States|United States|United States', 7, 'Binding Sites|Chromosome Mapping|Computational Biology|DNA|Gene Expression Regulation|Gene Regulatory Networks|Models, Molecular|Promoter Regions, Genetic|Protein Binding|Sequence Analysis, DNA|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1371/journal.pcbi.1006226', '30716072', 'Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding sites DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factors sequence specificity.'),
(89455, 2019, 'plos', 'Characteristics of measles epidemics in China (1951-2004) and implications for elimination: A case study of three key locations', 'Yang, Wan|Li, Juan|Shaman, Jeffrey', 'Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States.|Beijing Center for Disease Control and Prevention, Beijing, China.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States.', 'United States|China|United States', 6, 'Adolescent|Adult|Aged|Child|Child, Preschool|China|Epidemics|Humans|Incidence|Infant|Infant, Newborn|Measles|Measles Vaccine|Middle Aged|Vaccination|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1006806', '30716080', 'Measles is a highly infectious, severe viral disease. The disease is targeted for global eradication, however, this result has proven challenging. In China, where countrywide vaccination coverage for the last decade has been above 95% (the threshold for measles elimination), measles continues to cause large epidemics. To diagnose factors contributing to the persistency of measles, here we develop a model-inference system to infer measles transmission dynamics in China. The model-inference system uses demographic and vaccination data for each year as model inputs to directly account for changing population dynamics (including births, deaths, migrations, and vaccination). In addition, it simultaneously estimates unobserved model variables and parameters based on incidence data. When fitted to yearly incidence data for the entire population, it is able to accurately estimate independent, out-of-sample age-specific incidence. Using this validated model-inference system, we are thus able to estimate epidemiological and demographical characteristics key to measles transmission during 1951-2004 for three key locations in China, including its capital Beijing. These characteristics include age-specific population susceptibility and incidence rates, the basic reproductive number (R0), reporting rate, population mixing intensity, and amplitude of seasonality. Key differences among the three sites reveal population and epidemiological characteristics crucial for understanding the current persistence of measles epidemics in China. We also discuss the implications our findings have for future elimination strategies.'),
(89456, 2019, 'plos', 'DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network', 'Pu, Limeng|Govindaraj, Rajiv Gandhi|Lemoine, Jeffrey Mitchell|Wu, Hsiao-Chun|Brylinski, Michal', 'Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America.|Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America.', 'United States|United States|United States|United States|United States', 10, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Deep Learning|Ligands|Models, Molecular|Neural Networks (Computer)|Protein Binding|Proteins', 'Journal Article', '10.1371/journal.pcbi.1006718', '30716081', 'Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.'),
(89457, 2019, 'plos', 'Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks', 'Metwally, Ahmed A|Yu, Philip S|Reiman, Derek|Dai, Yang|Finn, Patricia W|Perkins, David L', 'Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.|Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, United States of America.|Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.|Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.|Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States of America.|Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.', 'United States|United States|United States|United States|United States|United States', 1, 'Classification|Food Hypersensitivity|Forecasting|Humans|Longitudinal Studies|Machine Learning|Memory, Long-Term|Memory, Short-Term|Microbiota|Neural Networks (Computer)|Support Vector Machine', 'Journal Article', '10.1371/journal.pcbi.1006693', '30716085', 'Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an increase in predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Support Vector Machine, Random Forest, and LASSO regression. We further evaluated whether the training of LSTM networks benefits from reduced representations of microbial features. We considered sparse autoencoder for extraction of potential latent representations in addition to standard feature selection procedures based on Minimum Redundancy Maximum Relevance (mRMR) and variance prior to the training of LSTM networks. The comprehensive evaluation reveals that LSTM networks with the mRMR selected features achieve significantly better performance compared to the other tested machine learning models.'),
(89458, 2019, 'plos', 'Noise-resistant developmental reproducibility in vertebrate somite formation', 'Naoki, Honda|Akiyama, Ryutaro|Sari, Dini Wahyu Kartika|Ishii, Shin|Bessho, Yasumasa|Matsui, Takaaki', 'Laboratory of Theoretical Biology, Research Center for Dynamic Living Systems, Graduate School of Biostudies, Kyoto University, Yoshidakonoecho, Sakyo, Kyoto, Japan.|Gene Regulation Research, Division of Biological Science, Nara Institute of Science and Technology, Takayama, Nara, Japan.|Gene Regulation Research, Division of Biological Science, Nara Institute of Science and Technology, Takayama, Nara, Japan.|Graduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyo, Kyoto, Japan.|Gene Regulation Research, Division of Biological Science, Nara Institute of Science and Technology, Takayama, Nara, Japan.|Gene Regulation Research, Division of Biological Science, Nara Institute of Science and Technology, Takayama, Nara, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 1, 'Animals|Artifacts|Body Patterning|Circadian Rhythm Signaling Peptides and Proteins|Developmental Biology|Embryo, Mammalian|Embryonic Development|Gene Expression Regulation, Developmental|MAP Kinase Signaling System|Mesoderm|Models, Molecular|Reproducibility of Results|Somites|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1006579', '30716091', 'The reproducibility of embryonic development is remarkable, although molecular processes are intrinsically stochastic at the single-cell level. How the multicellular system resists the inevitable noise to acquire developmental reproducibility constitutes a fundamental question in developmental biology. Toward this end, we focused on vertebrate somitogenesis as a representative system, because somites are repeatedly reproduced within a single embryo whereas such reproducibility is lost in segmentation clock gene-deficient embryos. However, the effect of noise on developmental reproducibility has not been fully investigated, because of the technical difficulty in manipulating the noise intensity in experiments. In this study, we developed a computational model of ERK-mediated somitogenesis, in which bistable ERK activity is regulated by an FGF gradient, cell-cell communication, and the segmentation clock, subject to the intrinsic noise. The model simulation generated our previous in vivo observation that the ERK activity was distributed in a step-like gradient in the presomitic mesoderm, and its boundary was posteriorly shifted by the clock in a stepwise manner, leading to regular somite formation. Here, we showed that this somite regularity was robustly maintained against the noise. Removing the clock from the model predicted that the stepwise shift of the ERK activity occurs at irregular timing with irregular distance owing to the noise, resulting in somite size variation. This model prediction was recently confirmed by live imaging of ERK activity in zebrafish embryos. Through theoretical analysis, we presented a mechanism by which the clock reduces the inherent somite irregularity observed in clock-deficient embryos. Therefore, this study indicates a novel role of the segmentation clock in noise-resistant developmental reproducibility.'),
(89459, 2019, 'plos', 'Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities', 'Marsland, Robert|Cui, Wenping|Goldford, Joshua|Sanchez, Alvaro|Korolev, Kirill|Mehta, Pankaj', 'Department of Physics, Boston University, Boston, MA, USA.|Department of Physics, Boston University, Boston, MA, USA.|Bioinformatics Program, Boston University, Boston, MA, USA.|Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.|Department of Physics, Boston University, Boston, MA, USA.|Department of Physics, Boston University, Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States', 17, 'Computational Biology|Databases, Factual|Ecosystem|Energy Metabolism|Microbial Consortia|Models, Biological|Stochastic Processes|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1006793', '30721227', 'A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a transition as a function of available energy fluxes from a resource-limited regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is the overlap between species consumption preferences. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experimental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.'),
(89460, 2019, 'plos', 'Apollo: Democratizing genome annotation', 'Dunn, Nathan A|Unni, Deepak R|Diesh, Colin|Munoz-Torres, Monica|Harris, Nomi L|Yao, Eric|Rasche, Helena|Holmes, Ian H|Elsik, Christine G|Lewis, Suzanna E', 'Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Deptartment of Bioengineering, University of California, Berkeley, California, United States of America.|Translational and Integrative Sciences Lab, Oregon State University, Corvallis, Oregon, United States of America.|Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Deptartment of Bioengineering, University of California, Berkeley, California, United States of America.|Bioinformatics Group, University of Freiburg, Freiburg, Germany.|Deptartment of Bioengineering, University of California, Berkeley, California, United States of America.|Animal Sciences Division, University of Missouri, Columbia, Missouri, United States of America.|Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.', 'United States|United States|United States|United States|United States|United States|Germany|United S', 0, 'Chromosome Mapping|Computational Biology|Database Management Systems|Genome|Genomics|Information Storage and Retrieval|Internet|Molecular Sequence Annotation|Software|User-Computer Interface', 'Journal Article', '10.1371/journal.pcbi.1006790', '30726205', 'Genome annotation is the process of identifying the location and function of a genomes encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, we present Apollo, an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform. Some of Apollos newer user interface features include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible.'),
(89461, 2019, 'plos', 'A data-driven interactome of synergistic genes improves network-based cancer outcome prediction', 'Allahyar, Amin|Ubels, Joske|de Ridder, Jeroen', 'Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.|Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.|Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 4, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Forecasting|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Prognosis', 'Journal Article', '10.1371/journal.pcbi.1006657', '30726216', 'Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.'),
(89462, 2019, 'plos', 'Population dynamics and entrainment of basal ganglia pacemakers are shaped by their dendritic arbors', 'Tiroshi, Lior|Goldberg, Joshua A', 'Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Faculty of Medicine, Jerusalem, Israel.|Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Faculty of Medicine, Jerusalem, Israel.', 'Canada|Canada', 4, 'Action Potentials|Animals|Basal Ganglia|Corpus Striatum|Dendrites|Electric Stimulation|Female|Male|Mice|Mice, Transgenic|Neural Inhibition|Neural Pathways|Neuronal Plasticity|Neurons|Pacemaker, Artificial', 'Journal Article', '10.1371/journal.pcbi.1006782', '30730886', 'The theory of phase oscillators is an essential tool for understanding population dynamics of pacemaking neurons. GABAergic pacemakers in the substantia nigra pars reticulata (SNr), a main basal ganglia (BG) output nucleus, receive inputs from the direct and indirect pathways at distal and proximal regions of their dendritic arbors, respectively. We combine theory, optogenetic stimulation and electrophysiological experiments in acute brain slices to ask how dendritic properties impact the propensity of the various inputs, arriving at different locations along the dendrite, to recruit or entrain SNr pacemakers. By combining cable theory with sinusoidally-modulated optogenetic activation of either proximal somatodendritic regions or the entire somatodendritic arbor of SNr neurons, we construct an analytical model that accurately fits the empirically measured somatic current response to inputs arising from illuminating the soma and various portions of the dendritic field. We show that the extent of the dendritic tree that is illuminated generates measurable and systematic differences in the pacemakers phase response curve (PRC), causing a shift in its peak. Finally, we show that the divergent PRCs correctly predict differences in two major features of the collective dynamics of SNr neurons: the fidelity of population responses to sudden step-like changes in inputs, and the phase latency at which SNr neurons are entrained by rhythmic stimulation, which can occur in the BG under both physiological and pathophysiological conditions. Our novel method generates measurable and physiologically meaningful spatial effects, and provides the first empirical demonstration of how the collective responses of SNr pacemakers are determined by the transmission properties of their dendrites. SNr dendrites may serve to delay distal striatal inputs so that they impinge on the spike initiation zone simultaneously with pallidal and subthalamic inputs in order to guarantee a fair competition between the influence of the monosynaptic direct- and polysynaptic indirect pathways.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89463, 2019, 'plos', 'A local measure of symmetry and orientation for individual spikes of grid cells', 'Weber, Simon N|Sprekeler, Henning', 'Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Germany.|Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Germany.', 'Germany|Germany', 2, 'Action Potentials|Algorithms|Animals|Computational Biology|Grid Cells|Mammals|Models, Neurological', 'Journal Article', '10.1371/journal.pcbi.1006804', '30730888', 'Grid cells have attracted broad attention because of their highly symmetric hexagonal firing patterns. Recently, research has shifted its focus from the global symmetry of grid cell activity to local distortions both in space and time, such as drifts in orientation, local defects of the hexagonal symmetry, and the decay and reappearance of grid patterns after changes in lighting condition. Here, we introduce a method that allows to visualize and quantify such local distortions, by assigning both a local grid score and a local orientation to each individual spike of a neuronal recording. The score is inspired by a standard measure from crystallography, which has been introduced to quantify local order in crystals. By averaging over spikes recorded within arbitrary regions or time periods, we can quantify local variations in symmetry and orientation of firing patterns in both space and time.'),
(89464, 2019, 'plos', 'Information integration in large brain networks', 'Toker, Daniel|Sommer, Friedrich T', 'Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, United States of America.|Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, United States of America.', 'United States|United States', 7, 'Animals|Cerebral Cortex|Computational Biology|Information Theory|Macaca|Models, Neurological|Nerve Net', 'Journal Article', '10.1371/journal.pcbi.1006807', '30730907', 'An outstanding problem in neuroscience is to understand how information is integrated across the many modules of the brain. While classic information-theoretic measures have transformed our understanding of feedforward information processing in the brains sensory periphery, comparable measures for information flow in the massively recurrent networks of the rest of the brain have been lacking. To address this, recent work in information theory has produced a sound measure of network-wide integrated information, which can be estimated from time-series data. But, a computational hurdle has stymied attempts to measure large-scale information integration in real brains. Specifically, the measurement of integrated information involves a combinatorial search for the informational weakest link of a network, a process whose computation time explodes super-exponentially with network size. Here, we show that spectral clustering, applied on the correlation matrix of time-series data, provides an approximate but robust solution to the search for the informational weakest link of large networks. This reduces the computation time for integrated information in large systems from longer than the lifespan of the universe to just minutes. We evaluate this solution in brain-like systems of coupled oscillators as well as in high-density electrocortigraphy data from two macaque monkeys, and show that the informational weakest link of the monkey cortex splits posterior sensory areas from anterior association areas. Finally, we use our solution to provide evidence in support of the long-standing hypothesis that information integration is maximized by networks with a high global efficiency, and that modular network structures promote the segregation of information.'),
(89465, 2019, 'plos', 'BioJava 5: A community driven open-source bioinformatics library', 'Lafita, Aleix|Bliven, Spencer|Prlixc4x87, Andreas|Guzenko, Dmytro|Rose, Peter W|Bradley, Anthony|Pavan, Paolo|Myers-Turnbull, Douglas|Valasatava, Yana|Heuer, Michael|Larson, Matt|Burley, Stephen K|Duarte, Jose M', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|Zurich University of Applied Sciences (ZHAW), Zurich CH-8021, Switzerland.|San Diego Supercomputer Center, UCSD, San Diego, CA 92093, USA.|Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.|Structural Bioinformatics Laboratory, San Diego Supercomputer Center, UCSD, San Diego, CA 92093, USA.|Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, UK.|Genomnia srl, Bresso, Milan, Italy.|Institute for Neurodegenerative Diseases, University of California, San Francisco, CA 94143, USA.|Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.|RISELab, University of California Berkeley, Berkeley, CA, USA.|DNASTAR, Madison, WI, USA.|Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.|Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.', 'United Kingdom|Switzerland|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Italy|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Access to Information|Algorithms|Computational Biology|Gene Library|Genome|Genomics|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1371/journal.pcbi.1006791', '30735498', 'BioJava is an open-source project that provides a Java library for processing biological data. The project aims to simplify bioinformatic analyses by implementing parsers, data structures, and algorithms for common tasks in genomics, structural biology, ontologies, phylogenetics, and more. Since 2012, we have released two major versions of the library (4 and 5) that include many new features to tackle challenges with increasingly complex macromolecular structure data. BioJava requires Java 8 or higher and is freely available under the LGPL 2.1 license. The project is hosted on GitHub at https://github.com/biojava/biojava. More information and documentation can be found online on the BioJava website (http://www.biojava.org) and tutorial (https://github.com/biojava/biojava-tutorial). All inquiries should be directed to the GitHub page or the BioJava mailing list (http://lists.open-bio.org/mailman/listinfo/biojava-l).'),
(89466, 2019, 'plos', 'Correlation Structure in Micro-ECoG Recordings is Described by Spatially Coherent Components', 'Rogers, Nicholas|Hermiz, John|Ganji, Mehran|Kaestner, Erik|Kxc4xb1lxc4xb1xc3xa7, Kxc4xb1vxc4xb1lcxc4xb1m|Hossain, Lorraine|Thunemann, Martin|Cleary, Daniel R|Carter, Bob S|Barba, David|Devor, Anna|Halgren, Eric|Dayeh, Shadi A|Gilja, Vikash', 'Physics, University of California San Diego, La Jolla, CA, United States of America.|Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America.|Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America.|Department of Neurosciences, University of California San Diego, La Jolla, CA, United States of America.|Department of Neurosciences, University of California San Diego, La Jolla, CA, United States of America.|Materials Science and Engineering, NanoEngineering University of California San Diego, La Jolla, CA, United States of America.|Department of Radiology, University of California San Diego, La Jolla, CA, United States of America.|Department of Neurosurgery, University of California San Diego, La Jolla, CA, United States of America.|Department of Neurosurgery, University of California San Diego, La Jolla, CA, United States of America.|Department of Neurosurgery, University of California San Diego, La Jolla, CA, United States of America.|Department of Radiology, University of California San Diego, La Jolla, CA, United States of America.|Department of Radiology, University of California San Diego, La Jolla, CA, United States of America.|Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America.|Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 5, 'Animals|Brain-Computer Interfaces|Cerebral Cortex|Electric Conductivity|Electrocorticography|Electrodes, Implanted|Electroencephalography|Electrophysiological Phenomena|Humans|Mice|Microelectrodes|Polymers|Records', 'Journal Article', '10.1371/journal.pcbi.1006769', '30742605', 'Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.'),
(89467, 2019, 'plos', 'Network-guided prediction of aromatase inhibitor response in breast cancer', 'Ruffalo, Matthew|Thomas, Roby|Chen, Jian|Lee, Adrian V|Oesterreich, Steffi|Bar-Joseph, Ziv', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Womens Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.|Womens Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.|Womens Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.|Womens Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Aromatase Inhibitors|Breast Neoplasms|Cell Line, Tumor|Computational Biology|Drug Resistance, Neoplasm|Female|Gene Expression Regulation, Neoplastic|Genetic Testing|Humans|Neural Networks (Computer)|Receptors, Estrogen', 'Journal Article', '10.1371/journal.pcbi.1006730', '30742607', 'Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.'),
(89468, 2019, 'plos', 'Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15', 'Funk, Sebastian|Camacho, Anton|Kucharski, Adam J|Lowe, Rachel|Eggo, Rosalind M|Edmunds, W John', 'Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.|Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.|Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.|Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.|Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.|Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Decision Making|Disease Outbreaks|Epidemics|Epidemiologic Methods|Forecasting|Hemorrhagic Fever, Ebola|Humans|Models, Statistical|Models, Theoretical|Sierra Leone|Time', 'Journal Article', '10.1371/journal.pcbi.1006785', '30742608', 'Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and bias of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time for Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but model predictions were increasingly unreliable at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making based on predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.'),
(89469, 2019, 'plos', 'A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex', 'Zhang, Qingtian|Hu, Xiaolin|Hong, Bo|Zhang, Bo', 'Department of Computer Science and Technology, Tsinghua University, Beijing, China.|Department of Computer Science and Technology, Tsinghua University, Beijing, China.|School of Medicine, Tsinghua University, Beijing, China.|Department of Computer Science and Technology, Tsinghua University, Beijing, China.', 'China|China|China|China', 2, 'Acoustic Stimulation|Acoustics|Animals|Auditory Cortex|Auditory Pathways|Auditory Perception|Computational Biology|Computer Simulation|Humans|Mesencephalon|Neurons|Phonetics|Speech', 'Journal Article', '10.1371/journal.pcbi.1006766', '30742609', 'The auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised deep learning model consisting of alternating sparse coding and max pooling layers on cochleogram-filtered human speech. Evaluation of the response properties revealed that computing units in lower layers exhibited spectro-temporal receptive fields (STRFs) similar to those of inferior colliculus neurons measured in physiological experiments, including properties such as sound onset and termination, checkerboard pattern, and spectral motion. Units in upper layers tended to be tuned to phonetic features such as plosivity and nasality, resembling the results of field recording in human auditory cortex. Variation of the sparseness level of the units in each higher layer revealed a positive correlation between the sparseness level and the strength of phonetic feature encoding. The activities of the units in the top layer, but not other layers, correlated with the dynamics of the first two formants (F1, F2) of all phonemes, indicating the encoding of phoneme dynamics in these units. These results suggest that the principles of sparse coding and max pooling may be universal in the human auditory pathway.'),
(89470, 2019, 'plos', 'How good are pathogenicity predictors in detecting benign variants?', 'Niroula, Abhishek|Vihinen, Mauno', 'Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, Lund, Sweden.|Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, Lund, Sweden.', 'Sweden|Sweden', 10, 'Amino Acid Substitution|Computational Biology|Databases, Genetic|Exome|Forecasting|Gene Frequency|Genetic Variation|Humans|Sensitivity and Specificity|Sequence Analysis, DNA|Virulence', 'Journal Article', '10.1371/journal.pcbi.1006481', '30742610', 'Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency xe2x89xa51% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.'),
(89471, 2019, 'plos', 'Cell geometry determines symmetric and asymmetric division plane selection in Arabidopsis early embryos', 'Moukhtar, Julien|Trubuil, Alain|Belcram, Katia|Legland, David|Khadir, Zhor|Urbain, Aurélie|Palauqui, Jean-Christophe|Andrey, Philippe', 'Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.|MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.|MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.|Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000 Versailles, France.', 'France|France|France|France|France|France|France|France', 2, 'Arabidopsis|Arabidopsis Proteins|Cell Division|Cell Lineage|Cell Nucleus|Cell Shape|Computer Simulation|Models, Statistical', 'Journal Article', '10.1371/journal.pcbi.1006771', '30742612', 'Plant tissue architecture and organ morphogenesis rely on the proper orientation of cell divisions. Previous attempts to predict division planes from cell geometry in plants mostly focused on 2D symmetric divisions. Using the stereotyped division patterns of Arabidopsis thaliana early embryogenesis, we investigated geometrical principles underlying plane selection in symmetric and in asymmetric divisions within complex 3D cell shapes. Introducing a 3D computational model of cell division, we show that area minimization constrained on passing through the cell centroid predicts observed divisions. Our results suggest that the positioning of division planes ensues from cell geometry and gives rise to spatially organized cell types with stereotyped shapes, thus underlining the role of self-organization in the developing architecture of the embryo. Our data further suggested the rule could be interpreted as surface minimization constrained by the nucleus position, which was validated using live imaging of cell divisions in the stomatal cell lineage.'),
(89472, 2019, 'plos', 'Maps of variability in cell lineage trees', 'Hicks, Damien G|Speed, Terence P|Yassin, Mohammed|Russell, Sarah M', 'Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.|Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia.|Peter MacCallum Cancer Centre, Parkville, Victoria 3052, Australia.|Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.', 'Australia|Australia|Australia|Australia', 3, 'Animals|Body Patterning|Caenorhabditis elegans|Cell Differentiation|Cell Lineage|Computational Biology|Gene Expression Regulation, Developmental|Genetic Variation|Models, Biological|Phenotype', 'Journal Article', '10.1371/journal.pcbi.1006745', '30753182', 'New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.'),
(89473, 2019, 'plos', 'Convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes', 'Chen, Yangchun Frank|Xia, Yu', 'Department of Bioengineering, McGill University, Montreal, Quebec, Canada.|Department of Bioengineering, McGill University, Montreal, Quebec, Canada.', 'Canada|Canada', 2, 'Computational Biology|Host-Pathogen Interactions|Humans|Mutation|Phenotype|Protein Domains|Protein Interaction Maps|Proteins|Viral Proteins|Virus Diseases|Viruses', 'Journal Article', '10.1371/journal.pcbi.1006762', '30759076', 'An important goal of systems medicine is to study disease in the context of genetic and environmental perturbations to the human interactome network. For diseases with both genetic and infectious contributors, a key postulate is that similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This postulate has so far only been tested for a few viral species at the level of whole proteins. Here, we expand the scope of viral species examined, and test this postulate more rigorously at the higher resolution of protein domains. Focusing on diseases with both genetic and viral contributors, we found significant convergent perturbation of the human domain-resolved interactome by endogenous genetic mutations and exogenous viral proteins inducing similar disease phenotypes. Pan-cancer, pan-oncovirus analysis further revealed that domains of human oncoproteins either physically targeted or structurally mimicked by oncoviruses are enriched for cancer driver rather than passenger mutations, suggesting convergent targeting of cancer driver pathways by diverse oncoviruses. Our study provides a framework for high-resolution, network-based comparison of various disease factors, both genetic and environmental, in terms of their impacts on the human interactome.'),
(89474, 2019, 'plos', 'Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling', 'Shahar, Nitzan|Hauser, Tobias U|Moutoussis, Michael|Moran, Rani|Keramati, Mehdi|NSPN consortium|Dolan, Raymond J', 'Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.|Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 12, 'Adolescent|Adult|Animals|Computational Biology|Decision Making|Female|Humans|Male|Models, Psychological|Models, Statistical|Reaction Time|Reproducibility of Results|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1006803', '30759077', 'A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.'),
(89475, 2019, 'plos', 'Identifying the mechanism for superdiffusivity in mouse fibroblast motility', 'Passucci, Giuseppe|Brasch, Megan E|Henderson, James H|Zaburdaev, Vasily|Manning, M Lisa', 'Physics Department, Syracuse University, Syracuse, New York, United States of America.|Biomedical and Chemical Engineering Department, Syracuse University, Syracuse, New York, United States of America.|Biomedical and Chemical Engineering Department, Syracuse University, Syracuse, New York, United States of America.|Institute of Supercomputing Technologies, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.|Physics Department, Syracuse University, Syracuse, New York, United States of America.', 'United States|United States|United States|-|United States', 3, 'Animals|Cell Culture Techniques|Cell Movement|Computational Biology|Diffusion|Fibroblasts|Mice|Models, Biological|Movement|Probability', 'Journal Article', '10.1371/journal.pcbi.1006732', '30763309', 'We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t1/2 in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Lévy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not Lévy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.'),
(89476, 2019, 'plos', 'IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis', 'Monier, Brandon|McDermaid, Adam|Wang, Cankun|Zhao, Jing|Miller, Allison|Fennell, Anne|Ma, Qin', 'Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States of America.|Department of Agronomy, Horticulture, and Plant Science, BioSNTR, South Dakota State University, Brookings, SD, United States of America.|Department of Agronomy, Horticulture, and Plant Science, BioSNTR, South Dakota State University, Brookings, SD, United States of America.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States of America.|Department of Biology, Saint Louis University, St. Louis, MO, United States of America.|Department of Agronomy, Horticulture, and Plant Science, BioSNTR, South Dakota State University, Brookings, SD, United States of America.|Department of Agronomy, Horticulture, and Plant Science, BioSNTR, South Dakota State University, Brookings, SD, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 6, 'Cells, Cultured|Cluster Analysis|Computational Biology|Databases, Factual|Gene Expression Profiling|Humans|RNA|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1371/journal.pcbi.1006792', '30763315', 'Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBIs Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.'),
(89477, 2019, 'plos', 'Bioinformatics calls the school: Use of smartphones to introduce Python for bioinformatics in high schools', 'Velez Rueda, Ana Julia|Benítez, Guillermo I|Marchetti, Julia|Hasenahuer, Marcia Anahí|Fornasari, María Silvina|Palopoli, Nicolas|Parisi, Gustavo', 'Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 0, 'Computational Biology|Curriculum|Education|Humans|Learning|Problem-Based Learning|Schools|Smartphone|Software|Students', 'Journal Article', '10.1371/journal.pcbi.1006473', '30763318', 'The dynamic nature of technological developments invites us to rethink the learning spaces. In this context, science education can be enriched by the contribution of new computational resources, making the educational process more up-to-date, challenging, and attractive. Bioinformatics is a key interdisciplinary field, contributing to the understanding of biological processes that is often underrated in secondary schools. As a useful resource in learning activities, bioinformatics could help in engaging students to integrate multiple fields of knowledge (logical-mathematical, biological, computational, etc.) and generate an enriched and long-lasting learning environment. Here, we report our recent project in which high school students learned basic concepts of programming applied to solving biological problems. The students were taught the Python syntax, and they coded simple tools to answer biological questions using resources at hand. Notably, these were built mostly on the students own smartphones, which proved to be capable, readily available, and relevant complementary tools for teaching. This project resulted in an empowering and inclusive experience that challenged differences in social background and technological accessibility.'),
(89478, 2019, 'plos', 'Efficient neural decoding of self-location with a deep recurrent network', 'Tampuu, Ardi|Matiisen, Tambet|Ólafsdóttir, H Freyja|Barry, Caswell|Vicente, Raul', 'Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia.|Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia.|Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia.', 'Estonia|Estonia|United Kingdom|United Kingdom|Estonia', 4, 'Action Potentials|Animals|Bayes Theorem|Forecasting|Hippocampus|Machine Learning|Male|Memory|Models, Neurological|Neural Networks (Computer)|Neurons|Place Cells|Rats|Rats, Inbred Strains|Spatial Processing', 'Journal Article', '10.1371/journal.pcbi.1006822', '30768590', 'Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animals location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings. RNNs are biologically plausible models of neural circuits that learn to incorporate relevant temporal context without the need to make complicated assumptions about the use of prior information to predict the current state. When decoding animal position from spike counts in 1D and 2D-environments, we show that the RNN consistently outperforms a standard Bayesian approach with either flat priors or with memory. In addition, we also conducted a set of sensitivity analysis on the RNN decoder to determine which neurons and sections of firing fields were the most influential. We found that the application of RNNs to neural data allowed flexible integration of temporal context, yielding improved accuracy relative to the more commonly used Bayesian approaches and opens new avenues for exploration of the neural code.'),
(89479, 2019, 'plos', 'Verbalizing phylogenomic conflict: Representation of node congruence across competing reconstructions of the neoavian explosion', 'Franz, Nico M|Musher, Lukas J|Brown, Joseph W|Yu, Shizhuo|Ludäscher, Bertram', 'School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America.|Richard Gilder Graduate School and Department of Ornithology, American Museum of Natural History, New York, New York, United States of America.|Department of Animal and Plant Sciences, University of Sheffield, Sheffield, United Kingdom.|Department of Computer Science, University of California at Davis, Davis, California, United States of America.|School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.', 'United States|United States|United Kingdom|United States|United States', 0, 'Animals|Birds|Computational Biology|Computer Simulation|Genomics|Humans|Language|Phylogeny', 'Journal Article', '10.1371/journal.pcbi.1006493', '30768597', 'Phylogenomic research is accelerating the publication of landmark studies that aim to resolve deep divergences of major organismal groups. Meanwhile, systems for identifying and integrating the products of phylogenomic inference-such as newly supported clade concepts-have not kept pace. However, the ability to verbalize node concept congruence and conflict across multiple, in effect simultaneously endorsed phylogenomic hypotheses, is a prerequisite for building synthetic data environments for biological systematics and other domains impacted by these conflicting inferences. Here we develop a novel solution to the conflict verbalization challenge, based on a logic representation and reasoning approach that utilizes the language of Region Connection Calculus (RCC-5) to produce consistent alignments of node concepts endorsed by incongruent phylogenomic studies. The approach employs clade concept labels to individuate concepts used by each source, even if these carry identical names. Indirect RCC-5 modeling of intensional (property-based) node concept definitions, facilitated by the local relaxation of coverage constraints, allows parent concepts to attain congruence in spite of their differentially sampled children. To demonstrate the feasibility of this approach, we align two recent phylogenomic reconstructions of higher-level avian groups that entail strong conflict in the neoavian explosion region. According to our representations, this conflict is constituted by 26 instances of input whole concept overlap. These instances are further resolvable in the output labeling schemes and visualizations as split concepts, which provide the labels and relations needed to build truly synthetic phylogenomic data environments. Because the RCC-5 alignments fundamentally reflect the trained, logic-enabled judgments of systematic experts, future designs for such environments need to promote a culture where experts routinely assess the intensionalities of node concepts published by our peers-even and especially when we are not in agreement with each other.'),
(89480, 2019, 'plos', 'Identifying individual risk rare variants using protein structure guided local tests (POINT)', 'Marceau West, Rachel|Lu, Wenbin|Rotroff, Daniel M|Kuenemann, Melaine A|Chang, Sheng-Mao|Wu, Michael C|Wagner, Michael J|Buse, John B|Motsinger-Reif, Alison A|Fourches, Denis|Tzeng, Jung-Ying', 'Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.|Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.|Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Center for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, North Carolina, United States of America.|Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America.|Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.|Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.', 'United States|United States|United States|United States|-|United States|United States|United States|', 2, 'Angiopoietin-like 4 Protein|Cholesterol Ester Transfer Proteins|Computational Biology|Computer Simulation|Genetic Association Studies|Genetic Predisposition to Disease|Genetic Variation|Humans|Models, Genetic|Proprotein Convertase 9|Protein Structure, Tertiary|Risk Factors|Sequence Analysis, DNA', 'Journal Article', '10.1371/journal.pcbi.1006722', '30779729', 'Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89481, 2019, 'plos', 'A numerical approach for a discrete Markov model for progressing drug resistance of cancer', 'Maeda, Masayuki|Yamashita, Hideaki', 'Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan.|Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan.', 'Japan|Japan', 0, 'Biochemical Phenomena|Computer Simulation|Drug Resistance, Neoplasm|Humans|Markov Chains|Models, Genetic|Models, Statistical|Models, Theoretical|Mutation|Neoplasms|Probability', 'Journal Article', '10.1371/journal.pcbi.1006770', '30779730', 'The presence of treatment-resistant cells is an important factor that limits the efficacy of cancer therapy, and the prospect of resistance is considered the major cause of the treatment strategy. Several recent studies have employed mathematical models to elucidate the dynamics of generating resistant cancer cells and attempted to predict the probability of emerging resistant cells. The purpose of this paper is to present numerical approach to compute the number of resistant cells and the emerging probability of resistance. Stochastic model was designed and developed a method to approximately but efficiently compute the number of resistant cells and the probability of resistance. To model the progression of cancer, a discrete-state, two-dimensional Markov process whose states are the total number of cells and the number of resistant cells was employed. Then exact analysis and approximate aggregation approaches were proposed to calculate the number of resistant cells and the probability of resistance when the cell population reaches detection size. To confirm the accuracy of computed results of approximation, relative errors between exact analysis and approximation were computed. The numerical values of our approximation method were very close to those of exact analysis calculated in the range of small detection size M = 500, 100, and 1500. Then computer simulation was performed to confirm the accuracy of computed results of approximation when the detection size was M = 10000,30000,50000,100000 and 1000000. All the numerical results of approximation fell between the upper level and the lower level of 95% confidential intervals and our method took less time to compute over a broad range of cell size. The effects of parameter change on emerging probabilities of resistance were also investigated by computed values using approximation method. The results showed that the number of divisions until the cell population reached the detection size is important for emerging the probability of resistance. The next step of numerical approach is to compute the emerging probabilities of resistance under drug administration and with multiple mutation. Another effective approximation would be necessary for the analysis of the latter case.'),
(89482, 2019, 'plos', 'Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions', 'Rüdiger, Daniel|Kupke, Sascha Young|Laske, Tanja|Zmora, Pawel|Reichl, Udo', 'Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 6, 'Animals|Apoptosis|Dogs|Host-Pathogen Interactions|Humans|Influenza A virus|Madin Darby Canine Kidney Cells|Models, Biological|Orthomyxoviridae Infections|Systems Biology|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006819', '30779733', 'Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use.'),
(89483, 2019, 'plos', 'Multi-modality in gene regulatory networks with slow promoter kinetics', 'Ali Al-Radhawi, M|Del Vecchio, Domitilla|Sontag, Eduardo D', 'Departments of Electrical and Computer Engineering and of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Departments of Electrical and Computer Engineering and of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.', 'United States|United States|United States', 8, 'Cell Differentiation|Computational Biology|Computer Simulation|Gene Expression Regulation|Gene Regulatory Networks|Kinetics|Models, Biological|Models, Genetic|Phenotype|Poisson Distribution|Probability|Promoter Regions, Genetic|Protein Binding|Stochastic Processes|Transcription, Genetic', 'Journal Article', '10.1371/journal.pcbi.1006784', '30779734', 'Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.'),
(89484, 2019, 'plos', 'Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis', 'Zhou, Hongyu|Dong, Zheng|Verkhivker, Gennady|Zoltowski, Brian D|Tao, Peng', 'Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, United States of America.|Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, United States of America.|Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America.|Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, United States of America.|Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Allosteric Regulation|Allosteric Site|Computational Biology|Fungal Proteins|Machine Learning|Markov Chains|Molecular Dynamics Simulation|Optogenetics|Protein Conformation', 'Journal Article', '10.1371/journal.pcbi.1006801', '30779735', 'The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an atomistic allosteric mechanism leading to the discovery of the unexpected importance of Axcexb1/Axcexb2 and previously overlooked Excexb1/Fxcexb1 loops in the conformational change. This approach could be applicable to other allosteric proteins in general to provide interpretable atomistic representations of their otherwise elusive allosteric mechanisms.'),
(89485, 2019, 'plos', 'Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes', 'Grytten, Ivar|Rand, Knut D|Nederbragt, Alexander J|Storvik, Geir O|Glad, Ingrid K|Sandve, Geir K', 'Department of informatics, University of Oslo, Oslo, Norway.|Department of Mathematics, University of Oslo, Oslo, Norway.|Department of informatics, University of Oslo, Oslo, Norway.|Department of Mathematics, University of Oslo, Oslo, Norway.|Department of Mathematics, University of Oslo, Oslo, Norway.|Department of informatics, University of Oslo, Oslo, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 1, 'Algorithms|Arabidopsis|Chromatin Immunoprecipitation|Genome|Genomics|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1006731', '30779737', 'Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.'),
(89486, 2019, 'plos', 'A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications', 'Peng, He|Zeng, Xiangxiang|Zhou, Yadi|Zhang, Defu|Nussinov, Ruth|Cheng, Feixiong', 'Department of Computer Science, Xiamen University, Xiamen, Fujian, China.|Department of Computer Science, Xiamen University, Xiamen, Fujian, China.|Department of Chemistry and Biochemistry, Ohio University, Athens, OH, United States of America.|Department of Computer Science, Xiamen University, Xiamen, Fujian, China.|Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States of America.|Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States of America.', 'China|China|United States|China|United States|United States', 0, 'Algorithms|Base Sequence|Cluster Analysis|Data Analysis|Gene Expression Profiling|Gene Expression Regulation|Genome|High-Throughput Nucleotide Sequencing|RNA, Small Cytoplasmic|ROC Curve|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1371/journal.pcbi.1006772', '30779739', 'Recent advances in next-generation sequencing and computational technologies have enabled routine analysis of large-scale single-cell ribonucleic acid sequencing (scRNA-seq) data. However, scRNA-seq technologies have suffered from several technical challenges, including low mean expression levels in most genes and higher frequencies of missing data than bulk population sequencing technologies. Identifying functional gene sets and their regulatory networks that link specific cell types to human diseases and therapeutics from scRNA-seq profiles are daunting tasks. In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. Gene subnetworks that represent specific gene co-expression patterns are inferred from the components of a decomposed matrix of scRNA-seq profiles. We showed that single-cell gene subnetworks identified by COAC from multiple time points within cell phases can be used for cell type identification with high accuracy (83%). In addition, COAC-inferred subnetworks from melanoma patients scRNA-seq profiles are highly correlated with survival rate from The Cancer Genome Atlas (TCGA). Moreover, the localized gene subnetworks identified by COAC from individual patients scRNA-seq data can be used as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating characteristic curves ranges from 0.728 to 0.783) in cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. In summary, COAC offers a powerful tool to identify potential network-based diagnostic and pharmacogenomics biomarkers from large-scale scRNA-seq profiles. COAC is freely available at https://github.com/ChengF-Lab/COAC.'),
(89487, 2019, 'plos', 'A saturated reaction in repressor synthesis creates a daytime dead zone in circadian clocks', 'Uriu, Koichiro|Tei, Hajime', 'Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Japan.|Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Japan.', 'Japan|Japan', 0, 'Animals|Circadian Clocks|Circadian Rhythm|Drosophila|Feedback, Physiological|Light|Models, Biological|Models, Theoretical|RNA, Messenger|Repressor Proteins', 'Journal Article', '10.1371/journal.pcbi.1006787', '30779745', 'Negative feedback loops (NFLs) for circadian clocks include light-responsive reactions that allow the clocks to shift their phase depending on the timing of light signals. Phase response curves (PRCs) for light signals in various organisms include a time interval called a dead zone where light signals cause no phase shift during daytime. Although the importance of the dead zone for robust light entrainment is known, how the dead zone arises from the biochemical reactions in an NFL underlying circadian gene expression rhythms remains unclear. In addition, the observation that the light-responsive reactions in the NFL vary between organisms raises the question as to whether the mechanism for dead zone formation is common or distinct between different organisms. Here we reveal by mathematical modeling that the saturation of a biochemical reaction in repressor synthesis in an NFL is a common mechanism of daytime dead zone generation. If light signals increase the degradation of a repressor protein, as in Drosophila, the saturation of repressor mRNA transcription nullifies the effect of light signals, generating a dead zone. In contrast, if light signals induce the transcription of repressor mRNA, as in mammals, the saturation of repressor translation can generate a dead zone by cancelling the influence of excess amount of mRNA induced by light signals. Each of these saturated reactions is located next to the light-responsive reaction in the NFL, suggesting a design principle for daytime dead zone generation.'),
(89488, 2019, 'plos', 'Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas', 'van IJzendoorn, David G P|Szuhai, Karoly|Briaire-de Bruijn, Inge H|Kostine, Marie|Kuijjer, Marieke L|Bovée, Judith V M G', 'Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Norway|Netherlands', 9, 'Biomarkers, Tumor|Computational Biology|Databases, Genetic|Drug Discovery|Female|Gene Expression Profiling|Humans|Kaplan-Meier Estimate|Machine Learning|Male|Middle Aged|Prognosis|Sarcoma|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1006826', '30785874', 'Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities. Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas. Gene expression data was used from the Cancer Genome Atlas, the Genotype-Tissue Expression project and the French Sarcoma Group. We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network. The three groups corresponded to subtypes that are morphologically overlapping. Using a random forest algorithm, we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST, and that we validated using qRT-PCR in an independent series. Next, we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm. The prognostic genes were further validated in expression data from the French Sarcoma Group. One of these, HMMR, was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval. Furthermore, reconstruction of regulatory networks combined with data from the Connectivity Map showed, amongst others, that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes. A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition. In this study we identified novel diagnostic markers, prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets. Thus, machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities.'),
(89489, 2019, 'plos', 'Independent working memory resources for egocentric and allocentric spatial information', 'Aagten-Murphy, David|Bays, Paul M', 'Department of Psychology, University of Cambridge, Cambridge, United Kingdom.|Department of Psychology, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom', 0, 'Adult|Attention|Computer Simulation|Cues|Female|Humans|Male|Memory, Short-Term|Mental Recall|Psychomotor Performance|Space Perception|Spatial Analysis|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1006563', '30789899', 'Visuospatial working memory enables us to maintain access to visual information for processing even when a stimulus is no longer present, due to occlusion, our own movements, or transience of the stimulus. Here we show that, when localizing remembered stimuli, the precision of spatial recall does not rely solely on memory for individual stimuli, but additionally depends on the relative distances between stimuli and visual landmarks in the surroundings. Across three separate experiments, we consistently observed a spatially selective improvement in the precision of recall for items located near a persistent landmark. While the results did not require that the landmark be visible throughout the memory delay period, it was essential that it was visible both during encoding and response. We present a simple model that can accurately capture human performance by considering relative (allocentric) spatial information as an independent localization estimate which degrades with distance and is optimally integrated with egocentric spatial information. Critically, allocentric information was encoded without cost to egocentric estimation, demonstrating independent storage of the two sources of information. Finally, when egocentric and allocentric estimates were put in conflict, the model successfully predicted the resulting localization errors. We suggest that the relative distance between stimuli represents an additional, independent spatial cue for memory recall. This cue information is likely to be critical for spatial localization in natural settings which contain an abundance of visual landmarks.'),
(89490, 2019, 'plos', 'Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models', 'Funnell, Tyler|Zhang, Allen W|Grewal, Diljot|McKinney, Steven|Bashashati, Ali|Wang, Yi Kan|Shah, Sohrab P', 'Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.|Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.|Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.|Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.|Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.|Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.|Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.', 'United States|Canada|United States|Canada|Canada|Canada|United States', 5, 'Computational Biology|Genetic Variation|Genome|Humans|Machine Learning|Models, Statistical|Mutation|Neoplasms|Point Mutation|Prognosis|Sequence Analysis, DNA|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1006799', '30794536', 'Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.'),
(89491, 2019, 'plos', 'Structure and variability of delay activity in premotor cortex', 'Even-Chen, Nir|Sheffer, Blue|Vyas, Saurabh|Ryu, Stephen I|Shenoy, Krishna V', 'Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.|Department of Computer Science, Stanford University, Stanford, CA, United States of America.|Department of Bioengineering, Stanford University, Stanford, CA, United States of America.|Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, United States of America.|Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.', 'United States|United States|United States|United States|United States', 2, 'Animals|Electrodes, Implanted|Electromyography|Macaca mulatta|Motor Cortex|Movement|Psychomotor Performance|Reaction Time', 'Journal Article', '10.1371/journal.pcbi.1006808', '30794541', 'Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an initial condition which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs.'),
(89492, 2019, 'plos', 'Electrical synapses regulate both subthreshold integration and population activity of principal cells in response to transient inputs within canonical feedforward circuits', 'Pham, Tuan|Haas, Julie S', 'Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America.|Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America.', 'United States|United States', 0, 'Brain|Computational Biology|Computer Simulation|Connexins|Electrical Synapses|Gap Junctions|Interneurons|Ion Channels|Neural Inhibition|Neurons|Neurotransmitter Agents|Synapses', 'Journal Article', '10.1371/journal.pcbi.1006440', '30802238', 'As information about the world traverses the brain, the signals exchanged between neurons are passed and modulated by synapses, or specialized contacts between neurons. While neurotransmitter-based synapses tend to exert either excitatory or inhibitory pulses of influence on the postsynaptic neuron, electrical synapses, composed of plaques of gap junction channels, continuously transmit signals that can either excite or inhibit a coupled neighbor. A growing body of evidence indicates that electrical synapses, similar to their chemical counterparts, are modified in strength during physiological neuronal activity. The synchronizing role of electrical synapses in neuronal oscillations has been well established, but their impact on transient signal processing in the brain is much less understood. Here we constructed computational models based on the canonical feedforward neuronal circuit and included electrical synapses between inhibitory interneurons. We provided discrete closely-timed inputs to the circuits, and characterize the influence of electrical synapse strength on both subthreshold summation and spike trains in the output neuron. Our simulations highlight the diverse and powerful roles that electrical synapses play even in simple circuits. Because these canonical circuits are represented widely throughout the brain, we expect that these are general principles for the influence of electrical synapses on transient signal processing across the brain.'),
(89493, 2019, 'plos', 'Controlling seizure propagation in large-scale brain networks', 'Olmi, Simona|Petkoski, Spase|Guye, Maxime|Bartolomei, Fabrice|Jirsa, Viktor', 'Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, 2004 route des Lucioles-Boîte Postale 93 06902 Sophia Antipolis, Cedex, France.|Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMR_S 1106, 13005, Marseille, France.|Faculté de Médecine de la Timone, centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, Aix-Marseille Université, 13005, Marseille, France.|Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France.|Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMR_S 1106, 13005, Marseille, France.', 'France|France|France|France|France', 0, 'Adult|Brain|Brain Mapping|Computer Simulation|Electrodes, Implanted|Electroencephalography|Epilepsies, Partial|Female|Humans|Imaging, Three-Dimensional|Magnetic Resonance Imaging|Male|Middle Aged|Nerve Net|Seizures|Signal Processing, Computer-Assisted', 'Journal Article', '10.1371/journal.pcbi.1006805', '30802239', 'Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patients virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern, (ii) seizure propagation is characterized by a systematic sequence of brain states, (iii) propagation can be controlled by an optimal intervention on the connectivity matrix, (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.'),
(89494, 2019, 'plos', 'A computational model of epithelial solute and water transport along a human nephron', 'Layton, Anita T|Layton, Harold E', 'Department of Mathematics, Duke University, Durham, North Carolina, United States of America.|Department of Mathematics, Duke University, Durham, North Carolina, United States of America.', 'United States|United States', 0, 'Biological Transport|Computational Biology|Computer Simulation|Epithelial Cells|Glomerular Filtration Rate|Humans|Kidney|Models, Biological|Nephrons|Solute Carrier Proteins', 'Journal Article', '10.1371/journal.pcbi.1006108', '30802242', 'We have developed the first computational model of solute and water transport from Bowman space to the papillary tip of the nephron of a human kidney. The nephron is represented as a tubule lined by a layer of epithelial cells, with apical and basolateral transporters that vary according to cell type. The model is formulated for steady state, and consists of a large system of coupled ordinary differential equations and algebraic equations. Model solution describes luminal fluid flow, hydrostatic pressure, luminal fluid solute concentrations, cytosolic solute concentrations, epithelial membrane potential, and transcellular and paracellular fluxes. We found that if we assume that the transporter density and permeabilities are taken to be the same between the human and rat nephrons (with the exception of a glucose transporter along the proximal tubule and the H+-pump along the collecting duct), the model yields segmental deliveries and urinary excretion of volume and key solutes that are consistent with human data. The model predicted that the human nephron exhibits glomerulotubular balance, such that proximal tubular Na+ reabsorption varies proportionally to the single-nephron glomerular filtration rate. To simulate the action of a novel diabetic treatment, we inhibited the Na+-glucose cotransporter 2 (SGLT2) along the proximal convoluted tubule. Simulation results predicted that the segments Na+ reabsorption decreased significantly, resulting in natriuresis and osmotic diuresis.'),
(89495, 2019, 'plos', '16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses', 'Woloszynek, Stephen|Zhao, Zhengqiao|Chen, Jian|Rosen, Gail L', 'Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, United States of America.|Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, United States of America.|Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, New York, United States of America.|Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, United States of America.', 'United States|United States|United States|United States', 3, 'Algorithms|Cluster Analysis|Computational Biology|High-Throughput Nucleotide Sequencing|Microbiota|RNA, Ribosomal, 16S|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1006721', '30807567', 'Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding (embedding) each sequence into a dense, low-dimensional, numeric vector space. Here, we use Skip-Gram word2vec to embed k-mers, obtained from 16S rRNA amplicon surveys, and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples. We demonstrate that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis. We show that sequence embeddings preserve relevant information about the sequencing data such as k-mer context, sequence taxonomy, and sample class. Specifically, the sequence embedding space resolved differences among phyla, as well as differences among genera within the same family. Distances between sequence embeddings had similar qualities to distances between alignment identities, and embedding multiple sequences can be thought of as generating a consensus sequence. In addition, embeddings are versatile features that can be used for many downstream tasks, such as taxonomic and sample classification. Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters. Lastly, the k-mer embedding space captured distinct k-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites. Together, our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data. Moreover, because the embeddings are trained in an unsupervised manner, unlabeled data can be embedded and used to bolster supervised machine learning tasks.'),
(89496, 2019, 'plos', 'Phylogenies from dynamic networks', 'Metzig, Cornelia|Ratmann, Oliver|Bezemer, Daniela|Colijn, Caroline', 'Dept of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.|Dept of Mathematics, Imperial College London, London, United Kingdom.|Stichting HIV Monitoring, Amsterdam, the Netherlands.|Dept of Mathematics, Simon Fraser University, Burnaby, Canada.', 'United Kingdom|United Kingdom|Netherlands|Canada', 4, 'Algorithms|Computational Biology|Computer Simulation|Disease Outbreaks|Disease Transmission, Infectious|HIV Infections|HIV-1|Humans|Models, Biological|Phylogeny|Principal Component Analysis|Supervised Machine Learning', 'Journal Article', '10.1371/journal.pcbi.1006761', '30807578', 'The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically. However, this relationship is not explicitly known and is usually studied in simulations, often with the simplifying assumption that the contact network is static in time, though human contact networks are dynamic. We simulate pathogen phylogenetic trees on dynamic Erdxc5x91s-Renyi random networks and on two dynamic networks with skewed degree distribution, of which one is additionally clustered. We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies. Our tree features include the number of small substructures (cherries, pitchforks) in the trees, measures of tree imbalance (Sackin index, Colless index), features derived from network science (diameter, closeness), as well as features using the internal branch lengths from the tip to the root. Using principal component analysis we find that the network dynamics influence the shapes of phylogenies, as does the network type. We also compare dynamic and time-integrated static networks. We find, in particular, that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks. We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate (using supervised learning methods). We find that both mis-specification of the underlying network and its parameters (mean degree, turnover rate) have a strong adverse effect on the ability to estimate the transmission parameter. We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks, infection rates and turnover rates. Our results point to the importance of correctly estimating and modelling contact networks with dynamics when using phylodynamic tools to estimate epidemiological parameters.'),
(89497, 2019, 'plos', 'Correction: Epistasis in genomic and survival data of cancer patients', 'Matlak, Dariusz|Szczurek, Ewa', 'Dept of Mathematics, Simon Fraser University, Burnaby, Canada.|Dept of Mathematics, Simon Fraser University, Burnaby, Canada.', 'Canada|Canada', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006887', '30811379', '[This corrects the article DOI: 10.1371/journal.pcbi.1005626.].'),
(89498, 2019, 'plos', 'Script of Scripts: A pragmatic workflow system for daily computational research', 'Wang, Gao|Peng, Bo', 'Department of Human Genetics, The University of Chicago, Chicago, IL, United States of America.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.', 'United States|United States', 0, 'Computational Biology|Computing Methodologies|Reproducibility of Results|Research|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1371/journal.pcbi.1006843', '30811390', 'Computationally intensive disciplines such as computational biology often require use of a variety of tools implemented in different scripting languages and analysis of large data sets using high-performance computing systems. Although scientific workflow systems can powerfully organize and execute large-scale data-analysis processes, creating and maintaining such workflows usually comes with nontrivial learning curves and engineering overhead, making them cumbersome to use for everyday data exploration and prototyping. To bridge the gap between interactive analysis and workflow systems, we developed Script of Scripts (SoS), an interactive data-analysis platform and workflow system with a strong emphasis on readability, practicality, and reproducibility in daily computational research. For exploratory analysis, SoS has a multilanguage scripting format that centralizes otherwise-scattered scripts and creates dynamic reports for publication and sharing. As a workflow engine, SoS provides an intuitive syntax for creating workflows in process-oriented, outcome-oriented, and mixed styles, as well as a unified interface for executing and managing tasks on a variety of computing platforms with automatic synchronization of files among isolated file systems. As illustrated herein by real-world examples, SoS is both an interactive analysis tool and pipeline platform suitable for different stages of method development and data-analysis projects. In particular, SoS can be easily adopted in existing data analysis routines to substantially improve organization, readability, and cross-platform computation management of research projects.'),
(89499, 2019, 'plos', 'Maximum entropy and population heterogeneity in continuous cell cultures', 'Fernandez-de-Cossio-Diaz, Jorge|Mulet, Roberto', 'Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Physics Faculty, Cuba.|Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Physics Faculty, Cuba.', 'Cuba|Cuba', 3, 'Animals|Batch Cell Culture Techniques|Bioreactors|CHO Cells|Cell Culture Techniques|Cell Survival|Cricetulus|Entropy|Metabolic Networks and Pathways|Models, Theoretical|Nonlinear Dynamics', 'Journal Article', '10.1371/journal.pcbi.1006823', '30811392', 'Continuous cultures of mammalian cells are complex systems displaying hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis, as well as sharp transitions between different metabolic states. In this context mathematical models may suggest control strategies to steer the system towards desired states. Although even clonal populations are known to exhibit cell-to-cell variability, most of the currently studied models assume that the population is homogeneous. To overcome this limitation, we use the maximum entropy principle to model the phenotypic distribution of cells in a chemostat as a function of the dilution rate. We consider the coupling between cell metabolism and extracellular variables describing the state of the bioreactor and take into account the impact of toxic byproduct accumulation on cell viability. We present a formal solution for the stationary state of the chemostat and show how to apply it in two examples. First, a simplified model of cell metabolism where the exact solution is tractable, and then a genome-scale metabolic network of the Chinese hamster ovary (CHO) cell line. Along the way we discuss several consequences of heterogeneity, such as: qualitative changes in the dynamical landscape of the system, increasing concentrations of byproducts that vanish in the homogeneous case, and larger population sizes.'),
(89500, 2019, 'plos', 'Development and validation of influenza forecasting for 64 temperate and tropical countries', 'Kramer, Sarah C|Shaman, Jeffrey', 'Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.', 'United States|United States', 4, 'Computer Simulation|Disease Outbreaks|Epidemics|Forecasting|Humans|Incidence|Influenza, Human|Models, Statistical|Retrospective Studies', 'Journal Article', '10.1371/journal.pcbi.1006742', '30811396', 'Accurate forecasts of influenza incidence can be used to inform medical and public health decision-making and response efforts. However, forecasting systems are uncommon in most countries, with a few notable exceptions. Here we use publicly available data from the World Health Organization to generate retrospective forecasts of influenza peak timing and peak intensity for 64 countries, including 18 tropical and subtropical countries. We find that accurate and well-calibrated forecasts can be generated for countries in temperate regions, with peak timing and intensity accuracy exceeding 50% at four and two weeks prior to the predicted epidemic peak, respectively. Forecasts are significantly less accurate in the tropics and subtropics for both peak timing and intensity. This work indicates that, in temperate regions around the world, forecasts can be generated with sufficient lead time to prepare for upcoming outbreak peak incidence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89501, 2019, 'plos', 'CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis', 'Ayati, Marzieh|Wiredja, Danica|Schlatzer, Daniela|Maxwell, Sean|Li, Ming|Koyutürk, Mehmet|Chance, Mark R', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.', '-|-|-|-|-|-|-', 3, 'Bayes Theorem|Binding Sites|Computational Biology|Databases, Protein|Humans|Mass Spectrometry|Phosphorylation|Phosphotransferases|Protein Binding|Protein Interaction Mapping|Protein Kinases|Proteome|Sequence Analysis, Protein|Software|Substrate Specificity', 'Journal Article', '10.1371/journal.pcbi.1006678', '30811403', 'We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.'),
(89502, 2019, 'plos', 'Predictability in process-based ensemble forecast of influenza', 'Pei, Sen|Cane, Mark A|Shaman, Jeffrey', 'Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America.|Lamont-Doherty Earth Observatory, Columbia University, New York, NY, United States of America.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America.', 'United States|United States|United States', 0, 'Computer Simulation|Disease Outbreaks|Forecasting|Humans|Incidence|Influenza, Human|Models, Statistical|Models, Theoretical|Retrospective Studies|Seasons', 'Journal Article', '10.1371/journal.pcbi.1006783', '30817754', 'Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.'),
(89503, 2019, 'plos', 'Changes in morphogen kinetics and pollen grain size are potential mechanisms of aberrant pollen aperture patterning in previously observed and novel mutants of Arabidopsis thaliana', 'Plourde, Shayne M|Amom, Prativa|Tan, Michelle|Dawes, Adriana T|Dobritsa, Anna A', 'Department of Mathematics, The Ohio State University, Columbus, Ohio, United States of America.|Department of Molecular Genetics, The Ohio State University, Columbus, Ohio, United States of America.|Department of Molecular Genetics, The Ohio State University, Columbus, Ohio, United States of America.|Department of Mathematics, The Ohio State University, Columbus, Ohio, United States of America.|Department of Molecular Genetics, The Ohio State University, Columbus, Ohio, United States of America.', 'United States|United States|United States|United States|United States', 4, 'Arabidopsis|Computational Biology|Computer Simulation|Kinetics|Models, Biological|Morphogenesis|Mutation|Pollen', 'Journal Article', '10.1371/journal.pcbi.1006800', '30817762', 'Pollen provides an excellent system to study pattern formation at the single-cell level. Pollen surface is covered by the pollen wall exine, whose deposition is excluded from certain surface areas, the apertures, which vary between the species in their numbers, positions, and morphology. What determines aperture patterns is not understood. Arabidopsis thaliana normally develops three apertures, equally spaced along the pollen equator. However, Arabidopsis mutants whose pollen has higher ploidy and larger volume develop four or more apertures. To explore possible mechanisms responsible for aperture patterning, we developed a mathematical model based on the Gierer-Meinhardt system of equations. This model was able to recapitulate aperture patterns observed in the wild-type and higher-ploidy pollen. We then used this model to further explore geometric and kinetic factors that may influence aperture patterns and found that pollen size, as well as certain kinetic parameters, like diffusion and decay of morphogens, could play a role in formation of aperture patterns. In conjunction with mathematical modeling, we also performed a forward genetic screen in Arabidopsis and discovered two mutants with aperture patterns that had not been previously observed in this species but were predicted by our model. The macaron mutant develops a single ring-like aperture, matching the unusual ring-like pattern produced by the model. The doughnut mutant forms two pore-like apertures at the poles of the pollen grain. Further tests on these novel mutants, motivated by the modeling results, suggested the existence of an area of inhibition around apertures that prevents formation of additional apertures in their vicinity. This work demonstrates the ability of the theoretical model to help focus experimental efforts and to provide fundamental insights into an important biological process.'),
(89504, 2019, 'plos', 'Integration of single-cell RNA-seq data into population models to characterize cancer metabolism', 'Damiani, Chiara|Maspero, Davide|Di Filippo, Marzia|Colombo, Riccardo|Pescini, Dario|Graudenzi, Alex|Westerhoff, Hans Victor|Alberghina, Lilia|Vanoni, Marco|Mauri, Giancarlo', 'Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy.|Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy.|SYSBIO Centre of Systems Biology, 20126, Milan, Italy.|Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy.|SYSBIO Centre of Systems Biology, 20126, Milan, Italy.|Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy.|Dept. of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands.|SYSBIO Centre of Systems Biology, 20126, Milan, Italy.|SYSBIO Centre of Systems Biology, 20126, Milan, Italy.|Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Netherlands|Italy|Italy|Italy', 0, 'Adenocarcinoma of Lung|Algorithms|Breast Neoplasms|Computational Biology|Computer Simulation|Female|Gene Expression Profiling|Genetics, Population|Humans|Male|Metabolic Networks and Pathways|Neoplasms|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1006733', '30818329', 'Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes, 2) allows to identify clusters of cells with different growth rates within the population, 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.'),
(89505, 2019, 'plos', 'ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics', 'Hoffmann, Moritz|Fröhner, Christoph|Noé, Frank', 'Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.|Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Simulation|Diffusion|Kinetics|Models, Biological|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1371/journal.pcbi.1006830', '30818351', 'Interacting-particle reaction dynamics (iPRD) combines the simulation of dynamical trajectories of interacting particles as in molecular dynamics (MD) simulations with reaction kinetics, in which particles appear, disappear, or change their type and interactions based on a set of reaction rules. This combination facilitates the simulation of reaction kinetics in crowded environments, involving complex molecular geometries such as polymers, and employing complex reaction mechanisms such as breaking and fusion of polymers. iPRD simulations are ideal to simulate the detailed spatiotemporal reaction mechanism in complex and dense environments, such as in signalling processes at cellular membranes, or in nano- to microscale chemical reactors. Here we introduce the iPRD software ReaDDy 2, which provides a Python interface in which the simulation environment, particle interactions and reaction rules can be conveniently defined and the simulation can be run, stored and analyzed. A C++ interface is available to enable deeper and more flexible interactions with the framework. The main computational work of ReaDDy 2 is done in hardware-specific simulation kernels. While the version introduced here provides single- and multi-threading CPU kernels, the architecture is ready to implement GPU and multi-node kernels. We demonstrate the efficiency and validity of ReaDDy 2 using several benchmark examples. ReaDDy 2 is available at the https://readdy.github.io/ website.'),
(89506, 2019, 'plos', 'Learning the payoffs and costs of actions', 'Möller, Moritz|Bogacz, Rafal', 'MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.|MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom', 0, 'Animals|Avoidance Learning|Basal Ganglia|Choice Behavior|Computational Biology|Computer Simulation|Dopamine|Humans|Learning|Models, Neurological|Motivation|Neural Pathways|Reinforcement (Psychology)|Reward', 'Journal Article', '10.1371/journal.pcbi.1006285', '30818357', 'A set of sub-cortical nuclei called basal ganglia is critical for learning the values of actions. The basal ganglia include two pathways, which have been associated with approach and avoid behavior respectively and are differentially modulated by dopamine projections from the midbrain. Inspired by the influential opponent actor learning model, we demonstrate that, under certain circumstances, these pathways may represent learned estimates of the positive and negative consequences (payoffs and costs) of individual actions. In the model, the level of dopamine activity encodes the motivational state and controls to what extent payoffs and costs enter the overall evaluation of actions. We show that a set of previously proposed plasticity rules is suitable to extract payoffs and costs from a prediction error signal if they occur at different moments in time. For those plasticity rules, successful learning requires differential effects of positive and negative outcome prediction errors on the two pathways and a weak decay of synaptic weights over trials. We also confirm through simulations that the model reproduces drug-induced changes of willingness to work, as observed in classical experiments with the D2-antagonist haloperidol.'),
(89507, 2019, 'plos', 'Modeling and MEG evidence of early consonance processing in auditory cortex', 'Tabas, Alejandro|Andermann, Martin|Schuberth, Valeria|Riedel, Helmut|Balaguer-Ballester, Emili|Rupp, André', 'Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.|Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.|Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.|Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.|Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom.|Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|United Kingdom|Germany', 3, 'Acoustic Stimulation|Adult|Auditory Cortex|Auditory Perception|Brain Mapping|Computer Simulation|Evoked Potentials, Auditory|Female|Humans|Magnetoencephalography|Male|Nerve Net|Neurons|Pitch Perception', 'Journal Article', '10.1371/journal.pcbi.1006820', '30818358', 'Pitch is a fundamental attribute of auditory perception. The interaction of concurrent pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In this work, we propose that human auditory cortex (AC) processes pitch and consonance through a common neural network mechanism operating at early cortical levels. First, we developed a new model of neural ensembles incorporating realistic neuronal and synaptic parameters to assess pitch processing mechanisms at early stages of AC. Next, we designed a magnetoencephalography (MEG) experiment to measure the neuromagnetic activity evoked by dyads with varying degrees of consonance or dissonance. MEG results show that dissonant dyads evoke a pitch onset response (POR) with a latency up to 36 ms longer than consonant dyads. Additionally, we used the model to predict the processing time of concurrent pitches, here, consonant pitch combinations were decoded faster than dissonant combinations, in line with the experimental observations. Specifically, we found a striking match between the predicted and the observed latency of the POR as elicited by the dyads. These novel results suggest that consonance processing starts early in human auditory cortex and may share the network mechanisms that are responsible for (single) pitch processing.'),
(89508, 2019, 'plos', 'Spatial clustering and common regulatory elements correlate with coordinated gene expression', 'Zhang, Jingyu|Chen, Hengyu|Li, Ruoyan|Taft, David A|Yao, Guang|Bai, Fan|Xing, Jianhua', 'Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.|Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.|Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, United States of America.|Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.', 'China|China|China|United States|United States|China|United States', 0, 'Animals|Cell Differentiation|Cell Line|Cluster Analysis|Embryonic Development|Gene Expression Profiling|Gene-Environment Interaction|Humans|Mice|Regulatory Sequences, Nucleic Acid|Sequence Analysis, RNA|Signal Transduction|Transforming Growth Factor beta', 'Journal Article', '10.1371/journal.pcbi.1006786', '30822341', 'Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-xcexb2 treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.'),
(89509, 2019, 'plos', 'The genetic basis for adaptation of model-designed syntrophic co-cultures', 'Lloyd, Colton J|King, Zachary A|Sandberg, Troy E|Hefner, Ying|Olson, Connor A|Phaneuf, Patrick V|OBrien, Edward J|Sanders, Jon G|Salido, Rodolfo A|Sanders, Karenina|Brennan, Caitriona|Humphrey, Gregory|Knight, Rob|Feist, Adam M', 'Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, United States of America.|Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 4, 'Adaptation, Physiological|Algorithms|Biological Evolution|Coculture Techniques|Escherichia coli|Genes, Bacterial|Models, Biological|Mutation', 'Journal Article', '10.1371/journal.pcbi.1006213', '30822347', 'Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities.'),
(89510, 2019, 'plos', 'Genesis of the xcexb1xcexb2 T-cell receptor', 'Dupic, Thomas|Marcou, Quentin|Walczak, Aleksandra M|Mora, Thierry', 'Laboratoire de physique thxc3xa9orique et hautes xc3xa9nergies, CNRS and Sorbonne Universitxc3xa9, 4 Place Jussieu, 75005 Paris, France.|Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.|Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.|Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.', 'France|France|France|France', 13, 'Chromosomes, Human|Humans|Probability|Receptors, Antigen, T-Cell, alpha-beta|Recombination, Genetic', 'Journal Article', '10.1371/journal.pcbi.1006874', '30830899', 'The T-cell (TCR) repertoire relies on the diversity of receptors composed of two chains, called xcexb1 and xcexb2, to recognize pathogens. Using results of high throughput sequencing and computational chain-pairing experiments of human TCR repertoires, we quantitively characterize the xcexb1xcexb2 generation process. We estimate the probabilities of a rescue recombination of the xcexb2 chain on the second chromosome upon failure or success on the first chromosome. Unlike xcexb2 chains, xcexb1 chains recombine simultaneously on both chromosomes, resulting in correlated statistics of the two genes which we predict using a mechanistic model. We find that xe2x88xbc35% of cells express both xcexb1 chains. Altogether, our statistical analysis gives a complete quantitative mechanistic picture that results in the observed correlations in the generative process. We learn that the probability to generate any TCRxcexb1xcexb2 is lower than 10(-12) and estimate the generation diversity and sharing properties of the xcexb1xcexb2 TCR repertoire.'),
(89511, 2019, 'plos', 'Correction: In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability', 'Vavourakis, Vasileios|Stylianopoulos, Triantafyllos|Wijeratne, Peter A', 'Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.|Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.|Laboratoire de physique de lENS, CNRS, Sorbonne Universitxc3xa9, and xc3x89cole normale supxc3xa9rieure (PSL), 24 rue Lhomond, 75005 Paris, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006880', '30830900', '[This corrects the article DOI: 10.1371/journal.pcbi.1006460.].'),
(89512, 2019, 'plos', 'The gradient of the reinforcement landscape influences sensorimotor learning', 'Cashaback, Joshua G A|Lao, Christopher K|Palidis, Dimitrios J|Coltman, Susan K|McGregor, Heather R|Gribble, Paul L', 'Human Performance Laboratory, University of Calgary, Calgary, Alberta, Canada.|Department of Physiology and Pharmacology, Western University, London, Ontario, Canada.|Graduate Program in Neuroscience, Western University, London, Ontario, Canada.|Graduate Program in Neuroscience, Western University, London, Ontario, Canada.|Graduate Program in Neuroscience, Western University, London, Ontario, Canada.|Department of Physiology and Pharmacology, Western University, London, Ontario, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Hand|Humans|Learning|Motor Skills|Probability|Reinforcement (Psychology)|Task Performance and Analysis', 'Journal Article', '10.1371/journal.pcbi.1006839', '30830902', 'Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action-the reinforcement gradient-to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning.'),
(89513, 2019, 'plos', 'Computational geometry for modeling neural populations: From visualization to simulation', 'de Kamps, Marc|Lepperød, Mikkel|Lai, Yi Ming', 'Institute for Artificial and Biological Intelligence, University of Leeds, Leeds, West Yorkshire, United Kingdom.|Institute of Basic Medical Sciences, and Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.|Institute for Artificial and Biological Intelligence, University of Leeds, Leeds, West Yorkshire, United Kingdom.', 'United Kingdom|Norway|United Kingdom', 1, 'Action Potentials|Computer Simulation|Models, Neurological|Neurons|Stochastic Processes|Synapses', 'Journal Article', '10.1371/journal.pcbi.1006729', '30830903', 'The importance of a mesoscopic description level of the brain has now been well established. Rate based models are widely used, but have limitations. Recently, several extremely efficient population-level methods have been proposed that go beyond the characterization of a population in terms of a single variable. Here, we present a method for simulating neural populations based on two dimensional (2D) point spiking neuron models that defines the state of the population in terms of a density function over the neural state space. Our method differs in that we do not make the diffusion approximation, nor do we reduce the state space to a single dimension (1D). We do not hard code the neural model, but read in a grid describing its state space in the relevant simulation region. Novel models can be studied without even recompiling the code. The method is highly modular: variations of the deterministic neural dynamics and the stochastic process can be investigated independently. Currently, there is a trend to reduce complex high dimensional neuron models to 2D ones as they offer a rich dynamical repertoire that is not available in 1D, such as limit cycles. We will demonstrate that our method is ideally suited to investigate noise in such systems, replicating results obtained in the diffusion limit and generalizing them to a regime of large jumps. The joint probability density function is much more informative than 1D marginals, and we will argue that the study of 2D systems subject to noise is important complementary to 1D systems.'),
(89514, 2019, 'plos', 'Soma-axon coupling configurations that enhance neuronal coincidence detection', 'Goldwyn, Joshua H|Remme, Michiel W H|Rinzel, John', 'Department of Mathematics and Statistics, Swarthmore College, Swarthmore, Pennsylvania, United States of America.|Institute for Theoretical Biology, Humboldt University Berlin, Berlin, Germany.|Center for Neural Science, New York University, New York, New York, United States of America.', 'United States|Germany|United States', 3, 'Action Potentials|Animals|Axons|Ion Channel Gating|Neurons|Sodium Channels|Superior Olivary Complex|Synapses', 'Journal Article', '10.1371/journal.pcbi.1006476', '30830905', 'Coincidence detector neurons transmit timing information by responding preferentially to concurrent synaptic inputs. Principal cells of the medial superior olive (MSO) in the mammalian auditory brainstem are superb coincidence detectors. They encode sound source location with high temporal precision, distinguishing submillisecond timing differences among inputs. We investigate computationally how dynamic coupling between the input region (soma and dendrite) and the spike-generating output region (axon and axon initial segment) can enhance coincidence detection in MSO neurons. To do this, we formulate a two-compartment neuron model and characterize extensively coincidence detection sensitivity throughout a parameter space of coupling configurations. We focus on the interaction between coupling configuration and two currents that provide dynamic, voltage-gated, negative feedback in subthreshold voltage range: sodium current with rapid inactivation and low-threshold potassium current, IKLT. These currents reduce synaptic summation and can prevent spike generation unless inputs arrive with near simultaneity. We show that strong soma-to-axon coupling promotes the negative feedback effects of sodium inactivation and is, therefore, advantageous for coincidence detection. Furthermore, the feedforward combination of strong soma-to-axon coupling and weak axon-to-soma coupling enables spikes to be generated efficiently (few sodium channels needed) and with rapid recovery that enhances high-frequency coincidence detection. These observations detail the functional benefit of the strongly feedforward configuration that has been observed in physiological studies of MSO neurons. We find that IKLT further enhances coincidence detection sensitivity, but with effects that depend on coupling configuration. For instance, in models with weak soma-to-axon and weak axon-to-soma coupling, IKLT in the axon enhances coincidence detection more effectively than IKLT in the soma. By using a minimal model of soma-to-axon coupling, we connect structure, dynamics, and computation. Although we consider the particular case of MSO coincidence detectors, our method for creating and exploring a parameter space of two-compartment models can be applied to other neurons.'),
(89515, 2019, 'plos', 'Determination of effective synaptic conductances using somatic voltage clamp', 'Li, Songting|Liu, Nan|Yao, Li|Zhang, Xiaohui|Zhou, Douglas|Cai, David', 'School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.|State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.|State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.|State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.|School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.|School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|China|China', 0, 'Animals|Computer Simulation|Dendrites|Hippocampus|Membrane Potentials|Models, Neurological|Neurons|Patch-Clamp Techniques|Rats, Sprague-Dawley|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1006871', '30835719', 'The interplay between excitatory and inhibitory neurons imparts rich functions of the brain. To understand the synaptic mechanisms underlying neuronal computations, a fundamental approach is to study the dynamics of excitatory and inhibitory synaptic inputs of each neuron. The traditional method of determining input conductance, which has been applied for decades, employs the synaptic current-voltage (I-V) relation obtained via voltage clamp. Due to the space clamp effect, the measured conductance is different from the local conductance on the dendrites. Therefore, the interpretation of the measured conductance remains to be clarified. Using theoretical analysis, electrophysiological experiments, and realistic neuron simulations, here we demonstrate that there does not exist a transform between the local conductance and the conductance measured by the traditional method, due to the neglect of a nonlinear interaction between the clamp current and the synaptic current in the traditional method. Consequently, the conductance determined by the traditional method may not correlate with the local conductance on the dendrites, and its value could be unphysically negative as observed in experiment. To circumvent the challenge of the space clamp effect and elucidate synaptic impact on neuronal information processing, we propose the concept of effective conductance which is proportional to the local conductance on the dendrite and reflects directly the functional influence of synaptic inputs on somatic membrane potential dynamics, and we further develop a framework to determine the effective conductance accurately. Our work suggests re-examination of previous studies involving conductance measurement and provides a reliable approach to assess synaptic influence on neuronal computation.'),
(89516, 2019, 'plos', 'In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics', 'Berg, David R|Offord, Chetan P|Kemler, Iris|Ennis, Matthew K|Chang, Lawrence|Paulik, George|Bajzer, Zeljko|Neuhauser, Claudia|Dingli, David', 'Department of Information Technology, Mayo Clinic, Rochester, Minnesota.|Molecular Medicine, Mayo Clinic, Rochester, Minnesota.|Molecular Medicine, Mayo Clinic, Rochester, Minnesota.|Molecular Medicine, Mayo Clinic, Rochester, Minnesota.|Molecular Medicine, Mayo Clinic, Rochester, Minnesota.|International Business Machines, Rochester, Minnesota.|Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota.|Department of Mathematics, University of Houston, Houston, Texas.|Molecular Medicine, Mayo Clinic, Rochester, Minnesota.', '-|-|-|-|-|-|-|-|-', 0, 'Cell Line, Tumor|Computer Simulation|Humans|In Vitro Techniques|Lentivirus|Measles virus|Models, Biological|Neoplasms|Oncolytic Virotherapy|Spheroids, Cellular|Tumor Microenvironment|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006773', '30835721', 'Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells. The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response. Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo. This indicates that the spatial dimension may have a major influence on the dynamics of virus spread. We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models. We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy. The parameter range leading to tumor eradication is small and even harder to achieve in 3D. The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium. The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy. Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy.'),
(89517, 2019, 'plos', 'New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx', 'Mounir, Mohamed|Lucchetta, Marta|Silva, Tiago C|Olsen, Catharina|Bontempi, Gianluca|Chen, Xi|Noushmehr, Houtan|Colaprico, Antonio|Papaleo, Elena', 'Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.|Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.|Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.|Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.|Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.|Sylvester Comprehensive Cancer Center, Miami, Florida, United States of America.|Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.|Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.|Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.', 'Denmark|Denmark|Brazil|Belgium|Belgium|United States|Brazil|Belgium|Denmark', 0, 'Carcinogenesis|Datasets as Topic|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasms', 'Journal Article', '10.1371/journal.pcbi.1006701', '30835723', 'The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.'),
(89518, 2019, 'plos', 'Evolutionary model for the unequal segregation of high copy plasmids', 'Münch, Karin|Münch, Richard|Biedendieck, Rebekka|Jahn, Dieter|Müller, Johannes', 'Institute of Microbiology, Technische Universität Braunschweig, Spielmannstr. 7, D-38106 Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Spielmannstr. 7, D-38106 Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Spielmannstr. 7, D-38106 Braunschweig, Germany.|Institute of Microbiology, Technische Universität Braunschweig, Spielmannstr. 7, D-38106 Braunschweig, Germany.|Centre for Mathematical Sciences, Technische Universität München, Boltzmannstr. 3, D-85747 Garching/Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany', 2, 'Biological Evolution|Drug Resistance, Microbial|Models, Biological|Plasmids', 'Journal Article', '10.1371/journal.pcbi.1006724', '30835726', 'Plasmids are extrachromosomal DNA elements of microorganisms encoding beneficial genetic information. They were thought to be equally distributed to daughter cells during cell division. Here we use mathematical modeling to investigate the evolutionary stability of plasmid segregation for high-copy plasmids-plasmids that are present in up to several hundred copies per cell-carrying antibiotic resistance genes. Evolutionary stable strategies (ESS) are determined by numerical analysis of a plasmid-load structured population model. The theory predicts that the evolutionary stable segregation strategy of a cell depends on the plasmid copy number: For low and medium plasmid load, both daughters receive in average an equal share of plasmids, while in case of high plasmid load, one daughter obtains distinctively and systematically more plasmids. These findings are in good agreement with recent experimental results. We discuss the interpretation and practical consequences.'),
(89519, 2019, 'plos', 'Bacterial surface colonization, preferential attachment and fitness under periodic stress', 'Grinberg, Maor|Orevi, Tomer|Kashtan, Nadav', 'The department of Plant Pathology and Microbiology, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.|The department of Plant Pathology and Microbiology, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.|The department of Plant Pathology and Microbiology, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.', 'Israel|Israel|Israel', 0, 'Bacteria|Bacterial Adhesion|Desiccation|Stress, Physiological|Surface Properties', 'Journal Article', '10.1371/journal.pcbi.1006815', '30835727', 'Early bacterial surface colonization is not a random process wherein cells arbitrarily attach to surfaces and grow, but rather, attachment events, movement and cellular interactions induce non-random spatial organization. We have only begun to understand how the apparent self-organization affects the fitness of the population. A key factor contributing to fitness is the tradeoff between solitary-planktonic and aggregated surface-attached biofilm lifestyles. Though planktonic cells typically grow faster, bacteria in aggregates are more resistant to stress such as desiccation, antibiotics and predation. Here we ask if and to what extent informed surface-attachments improve fitness during early surface colonization under periodic stress conditions. We use an individual-based modeling approach to simulate foraging planktonic cells colonizing a surface under alternating wet-dry cycles. Such cycles are common in the largest terrestrial microbial habitats-soil, roots, and leaf surfaces-that are not constantly saturated with water and experience daily periods of desiccation stress. We compared different surface-attachment strategies, and analyzed the emerging spatio-temporal dynamics of surface colonization and population yield as a measure of fitness. We demonstrate that a simple strategy of preferential attachment (PA), biased to dense sites, carries a large fitness advantage over any random attachment across a broad range of environmental conditions-particularly under periodic stress.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89520, 2019, 'plos', 'Goal-related feedback guides motor exploration and redundancy resolution in human motor skill acquisition', 'Rohde, Marieke|Narioka, Kenichi|Steil, Jochen J|Klein, Lina K|Ernst, Marc O', 'Institute for Innovation and Technology (IIT) Berlin, Germany.|Center of Excellence: Cognitive Interaction Technology (CITEC), University of Bielefeld, Bielefeld, Germany.|Center of Excellence: Cognitive Interaction Technology (CITEC), University of Bielefeld, Bielefeld, Germany.|Center of Excellence: Cognitive Interaction Technology (CITEC), University of Bielefeld, Bielefeld, Germany.|Center of Excellence: Cognitive Interaction Technology (CITEC), University of Bielefeld, Bielefeld, Germany.', 'Germany|Germany|Germany|Germany|Germany', 2, 'Adult|Biomechanical Phenomena|Feedback|Goals|Humans|Motor Skills|Robotics', 'Journal Article', '10.1371/journal.pcbi.1006676', '30835770', 'The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood, such as the mastery of tools, musical instruments or sports. How novel sensorimotor skills are learned from scratch is yet largely unknown. In particular, the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements (motor redundancy). How humans learn to resolve motor redundancy and by which principles they explore high-dimensional motor spaces has hardly been investigated. To study this question, we trained human participants in an unfamiliar and redundant visually-guided manual control task. We qualitatively compare the experimental results with simulation results from a population of artificial agents that learned the same task by Goal Babbling, which is an inverse-model learning approach for robotics. In Goal Babbling, goal-related feedback guides motor exploration and thereby enables robots to learn an inverse model directly from scratch, without having to learn a forward model first. In the human experiment, we tested whether different initial conditions (starting positions of the hand) influence the acquisition of motor synergies, which we identified by Principal Component Analysis in the motor space. The results show that the human participants solutions are spatially biased towards the different starting positions in motor space and are marked by a gradual co-learning of synergies and task success, similar to the dynamics of motor learning by Goal Babbling. However, there are also differences between human learning and the Goal Babbling simulations, as humans tend to predominantly use Degrees of Freedom that do not have a large effect on the hand position, whereas in Goal Babbling, Degrees of Freedom with a large effect on hand position are used predominantly. We conclude that humans use goal-related feedback to constrain motor exploration and resolve motor redundancy when learning a new sensorimotor mapping, but in a manner that differs from the current implementation of Goal Babbling due to different constraints on motor exploration.'),
(89521, 2019, 'plos', 'Tuft dendrites of pyramidal neurons operate as feedback-modulated functional subunits', 'Eberhardt, Florian|Herz, Andreas V M|Häusler, Stefan', 'Bernstein Center for Computational Neuroscience Munich, Germany.|Bernstein Center for Computational Neuroscience Munich, Germany.|Bernstein Center for Computational Neuroscience Munich, Germany.', 'Germany|Germany|Germany', 3, 'Action Potentials|Animals|CA1 Region, Hippocampal|Dendrites|Feedback|Models, Biological|Pyramidal Cells|Synapses', 'Journal Article', '10.1371/journal.pcbi.1006757', '30840615', 'Dendrites of pyramidal cells exhibit complex morphologies and contain a variety of ionic conductances, which generate non-trivial integrative properties. Basal and proximal apical dendrites have been shown to function as independent computational subunits within a two-layer feedforward processing scheme. The outputs of the subunits are linearly summed and passed through a final non-linearity. It is an open question whether this mathematical abstraction can be applied to apical tuft dendrites as well. Using a detailed compartmental model of CA1 pyramidal neurons and a novel theoretical framework based on iso-response methods, we first show that somatic sub-threshold responses to brief synaptic inputs cannot be described by a two-layer feedforward model. Then, we relax the core assumption of subunit independence and introduce non-linear feedback from the output layer to the subunit inputs. We find that additive feedback alone explains the somatic responses to synaptic inputs to most of the branches in the apical tuft. Individual dendritic branches bidirectionally modulate the thresholds of their input-output curves without significantly changing the gains. In contrast to these findings for precisely timed inputs, we show that neuronal computations based on firing rates can be accurately described by purely feedforward two-layer models. Our findings support the view that dendrites of pyramidal neurons possess non-linear analog processing capabilities that critically depend on the location of synaptic inputs. The iso-response framework proposed in this computational study is highly efficient and could be directly applied to biological neurons.'),
(89522, 2019, 'plos', 'Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy', 'Kheifetz, Yuri|Scholz, Markus', 'Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.|Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.', 'Germany|Germany', 2, 'Antineoplastic Agents|Blood Platelets|Humans|Lymphoma, Non-Hodgkin|Megakaryocytes|Models, Biological|Polyethylene Glycols|Stem Cells|Thrombocytopenia|Thrombopoiesis|Thrombopoietin|Time Factors', 'Journal Article', '10.1371/journal.pcbi.1006775', '30840616', 'Thrombocytopenia is a major side-effect of cytotoxic cancer therapies. The aim of precision medicine is to develop therapy modifications accounting for the individuals risk.'),
(89523, 2019, 'plos', 'Humans use multi-objective control to regulate lateral foot placement when walking', 'Dingwell, Jonathan B|Cusumano, Joseph P', 'Department of Kinesiology, Pennsylvania State University, University Park, State College, Pennsylvania, United States of America.|Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, State College, Pennsylvania, United States of America.', 'United States|United States', 0, 'Adult|Aged|Biomechanical Phenomena|Foot|Humans|Middle Aged|Movement|Psychomotor Performance|Walking', 'Journal Article', '10.1371/journal.pcbi.1006850', '30840620', 'A fundamental question in human motor neuroscience is to determine how the nervous system generates goal-directed movements despite inherent physiological noise and redundancy. Walking exhibits considerable variability and equifinality of task solutions. Existing models of bipedal walking do not yet achieve both continuous dynamic balance control and the equifinality of foot placement humans exhibit. Appropriate computational models are critical to disambiguate the numerous possibilities of how to regulate stepping movements to achieve different walking goals. Here, we extend a theoretical and computational Goal Equivalent Manifold (GEM) framework to generate predictive models, each posing a different experimentally testable hypothesis. These models regulate stepping movements to achieve any of three hypothesized goals, either alone or in combination: maintain lateral position, maintain lateral speed or heading, and/or maintain step width. We compared model predictions against human experimental data. Uni-objective control models demonstrated clear redundancy between stepping variables, but could not replicate human stepping dynamics. Most multi-objective control models that balanced maintaining two of the three hypothesized goals also failed to replicate human stepping dynamics. However, multi-objective models that strongly prioritized regulating step width over lateral position did successfully replicate all of the relevant step-to-step dynamics observed in humans. Independent analyses confirmed this control was consistent with linear error correction and replicated step-to-step dynamics of individual foot placements. Thus, the regulation of lateral stepping movements is inherently multi-objective and balances task-specific trade-offs between competing task goals. To determine how people walk in their environment requires understanding both walking biomechanics and how the nervous system regulates movements from step-to-step. Analogous to mechanical templates of locomotor biomechanics, our models serve as control templates for how humans regulate stepping movements from each step to the next. These control templates are symbiotic with well-established mechanical templates, providing complimentary insights into walking regulation.'),
(89524, 2019, 'plos', 'A stochastic simulation of skeletal muscle calcium transients in a structurally realistic sarcomere model using MCell', 'Holash, Robert John|MacIntosh, Brian R', 'Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.|Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.', 'Canada|Canada', 1, 'Adenosine Triphosphate|Animals|Calcium|Models, Biological|Monte Carlo Method|Muscle, Skeletal|Sarcomeres|Stochastic Processes|Troponin C', 'Journal Article', '10.1371/journal.pcbi.1006712', '30845143', 'Skeletal muscle contraction is initiated when an action potential triggers the release of Ca2+ into the sarcomere in a process referred to as excitation-contraction coupling. The speed and scale of this process makes direct observation very challenging and invasive. To determine how the concentration of Ca2+ changes within the myofibril during a single activation, several simulation models have been developed. These models follow a common pattern, divide the half sarcomere into a series of compartments, then use ordinary differential equations to solve reactions occurring within and between the compartments. To further develop this type of simulation, we have created a realistic structural model of a skeletal muscle myofibrillar half-sarcomere using MCell software that incorporates the myofilament lattice structure. Using this simulation model, we were successful in reproducing the averaged calcium transient during a single activation consistent with both the experimental and previous simulation results. In addition, our simulation demonstrated that the inclusion of the myofilament lattice within our model produced an asymmetric distribution of Ca2+, with more Ca2+ accumulating near the Z-disk and less Ca2+ reaching the m-line. This asymmetric distribution of Ca2+ is also apparent when we examine how the Ca2+ are bound to the troponin-C proteins along the actin filaments. Our simulation model also allowed us to produce advanced visualizations of this process, including two simulation animations, allowing us to view Ca2+ release, diffusion, binding and uptake within the myofibrillar half-sarcomere.'),
(89525, 2019, 'plos', 'Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen', 'Liu, Joanne K|Lloyd, Colton|Al-Bassam, Mahmoud M|Ebrahim, Ali|Kim, Ji-Nu|Olson, Connor|Aksenov, Alexander|Dorrestein, Pieter|Zengler, Karsten', 'Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, California, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.|Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, California, United States of America.|Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, California, United States of America.|Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 8, 'Biocatalysis|Carbon|Clostridium|Energy Metabolism|Fermentation|Gene Expression Regulation, Bacterial|Genes, Bacterial|Metals|Models, Biological|Proteins|Proteome|Reproducibility of Results', 'Journal Article', '10.1371/journal.pcbi.1006848', '30845144', 'The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.'),
(89526, 2019, 'plos', 'A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions', 'Engebretsen, Solveig|Engø-Monsen, Kenth|Frigessi, Arnoldo|Freiesleben de Blasio, Birgitte', 'Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.|Telenor Research, Telenor Group, Fornebu, Norway.|Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.|Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.', 'Norway|Norway|Norway|Norway', 1, 'Algorithms|Cluster Analysis|Communicable Disease Control|Communicable Diseases|Disease Outbreaks|Humans|Models, Theoretical|Travel|Urbanization|Vaccination', 'Journal Article', '10.1371/journal.pcbi.1006879', '30845153', 'The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies-uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation.'),
(89527, 2019, 'plos', 'Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations', 'Anandakrishnan, Ramu|Varghese, Robin T|Kinney, Nicholas A|Garner, Harold R', 'Gibbs Cancer Center and Research Institute, Spartanburg, SC, United States of America.|Gibbs Cancer Center and Research Institute, Spartanburg, SC, United States of America.|Gibbs Cancer Center and Research Institute, Spartanburg, SC, United States of America.|Gibbs Cancer Center and Research Institute, Spartanburg, SC, United States of America.', 'United States|United States|United States|United States', 4, 'Age of Onset|Carcinogenesis|Humans|Models, Genetic|Mutation|Mutation Rate|Neoplasms|Probability', 'Journal Article', '10.1371/journal.pcbi.1006881', '30845172', 'Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.'),
(89528, 2019, 'plos', 'A new clustering and nomenclature for beta turns derived from high-resolution protein structures', 'Shapovalov, Maxim|Vucetic, Slobodan|Dunbrack, Roland L', 'Fox Chase Cancer Center, Philadelphia PA, United States of America.|Department of Computer and Information Sciences, Temple University, Philadelphia PA, United States of America.|Fox Chase Cancer Center, Philadelphia PA, United States of America.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Cluster Analysis|Protein Conformation|Proteins|Reproducibility of Results|Terminology as Topic', 'Journal Article', '10.1371/journal.pcbi.1006844', '30845191', 'Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I, II, II, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (xe2x89xa41.2 xc3x85). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., A and D for alpha regions on the left side of the Ramachandran map and a and d for equivalent regions on the right-hand side, classical Type I turns are AD turns and Type I turns are ad). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.'),
(89529, 2019, 'plos', 'Predicting the mechanism and rate of H-NS binding to AT-rich DNA', 'Riccardi, Enrico|van Mastbergen, Eva C|Navarre, William Wiley|Vreede, Jocelyne', 'Department of Chemistry, NTNU, Trondheim, Norway.|Vant Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.|Vant Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.', 'Norway|Netherlands|Canada|Netherlands', 4, 'Adenine|Bacterial Proteins|DNA|DNA-Binding Proteins|Hydrogen Bonding|Kinetics|Molecular Dynamics Simulation|Protein Binding|Thymine', 'Journal Article', '10.1371/journal.pcbi.1006845', '30845209', 'Bacteria contain several nucleoid-associated proteins that organize their genomic DNA into the nucleoid by bending, wrapping or bridging DNA. The Histone-like Nucleoid Structuring protein H-NS found in many Gram-negative bacteria is a DNA bridging protein and can structure DNA by binding to two separate DNA duplexes or to adjacent sites on the same duplex, depending on external conditions. Several nucleotide sequences have been identified to which H-NS binds with high affinity, indicating H-NS prefers AT-rich DNA. To date, highly detailed structural information of the H-NS DNA complex remains elusive. Molecular simulation can complement experiments by modelling structures and their time evolution in atomistic detail. In this paper we report an exploration of the different binding modes of H-NS to a high affinity nucleotide sequence and an estimate of the associated rate constant. By means of molecular dynamics simulations, we identified three types of binding for H-NS to AT-rich DNA. To further sample the transitions between these binding modes, we performed Replica Exchange Transition Interface Sampling, providing predictions of the mechanism and rate constant of H-NS binding to DNA. H-NS interacts with the DNA through a conserved QGR motif, aided by a conserved arginine at position 93. The QGR motif interacts first with phosphate groups, followed by the formation of hydrogen bonds between acceptors in the DNA minor groove and the sidechains of either Q112 or R114. After R114 inserts into the minor groove, the rest of the QGR motif follows. Full insertion of the QGR motif in the minor groove is stable over several tens of nanoseconds, and involves hydrogen bonds between the bases and both backbone and sidechains of the QGR motif. The rate constant for the process of H-NS binding to AT-rich DNA resulting in full insertion of the QGR motif is in the order of 10(6) M-1s-1, which is rate limiting compared to the non-specific association of H-NS to the DNA backbone at a rate of 10(8) M-1s-1.'),
(89530, 2019, 'plos', 'An enriched network motif family regulates multistep cell fate transitions with restricted reversibility', 'Ye, Yujie|Kang, Xin|Bailey, Jordan|Li, Chunhe|Hong, Tian', 'Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of America.|Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.|Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of America.|Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.|Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, Tennessee, United States of America.', 'United States|China|United States|China|United States', 0, 'Cell Differentiation|Cell Lineage|Gene Regulatory Networks|Models, Biological|Stem Cells|Stochastic Processes|T-Lymphocytes|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1006855', '30845219', 'Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.'),
(89531, 2019, 'plos', 'How adaptive plasticity evolves when selected against', 'Rago, Alfredo|Kouvaris, Kostas|Uller, Tobias|Watson, Richard', 'Department of Biology, Lund University, Lund, Sweden.|Institute for Life Sciences/Electronics and Computer Science, Southampton University, Southampton, United Kingdom.|Department of Biology, Lund University, Lund, Sweden.|Institute for Life Sciences/Electronics and Computer Science, Southampton University, Southampton, United Kingdom.', 'Sweden|United Kingdom|Sweden|United Kingdom', 1, 'Adaptation, Physiological|Biological Evolution|Models, Theoretical|Mutation|Selection, Genetic', 'Journal Article', '10.1371/journal.pcbi.1006260', '30849069', 'Adaptive plasticity allows organisms to cope with environmental change, thereby increasing the populations long-term fitness. However, individual selection can only compare the fitness of individuals within each generation: if the environment changes more slowly than the generation time (i.e., a coarse-grained environment) a population will not experience selection for plasticity even if it is adaptive in the long-term. How does adaptive plasticity then evolve? One explanation is that, if competing alleles conferring different degrees of plasticity persist across multiple environments, natural selection between genetic lineages could select for adaptive plasticity (lineage selection). We show that adaptive plasticity can evolve even in the absence of such lineage selection. Instead, we propose that adaptive plasticity in coarse-grained environments evolves as a by-product of inefficient short-term natural selection: populations that rapidly evolve their phenotypes in response to selective pressures follow short-term optima, with the result that they have reduced long-term fitness across environments. Conversely, populations that accumulate limited genetic change within each environment evolve long-term adaptive plasticity even when plasticity incurs short-term costs. These results remain qualitatively similar regardless of whether we decrease the efficiency of natural selection by increasing the rate of environmental change or decreasing mutation rate, demonstrating that both factors act via the same mechanism. We demonstrate how this mechanism can be understood through the concept of learning rate. Our work shows how plastic responses that are costly in the short term, yet adaptive in the long term, can evolve as a by-product of inefficient short-term selection, without selection for plasticity at either the individual or lineage level.'),
(89532, 2019, 'plos', 'Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines', 'Van Liedekerke, Paul|Neitsch, Johannes|Johann, Tim|Alessandri, Kevin|Nassoy, Pierre|Drasdo, Dirk', 'INRIA de Paris and Sorbonne Universités UPMC Univ paris 6, LJLL Team Mamba, France.|IZBI, University of Leipzig, Germany.|IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.|Institut dOptique Graduate School, Talence, France.|Institut dOptique Graduate School, Talence, France.|INRIA de Paris and Sorbonne Universités UPMC Univ paris 6, LJLL Team Mamba, France.', 'France|Germany|Germany|France|France|France', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006273', '30849070', 'Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite experimental response curves look largely different. We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized by measurable biophysical and cell-biological parameters. Cell cycle progression in our model is controlled by volumetric strain, the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility. After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule, the model adequately predicts the growth curve in i) soft and rigid capsules, ii) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution, and iii) for other cell types with different growth kinetics from the growth kinetics in absence of external stress. Our model simulation results suggest a generic, even quantitatively same, growth response of cell populations upon externally applied mechanical stress, as it can be quantitatively predicted using the same growth progression function.'),
(89533, 2019, 'plos', 'Methods for computing the maximum performance of computational models of fMRI responses', 'Lage-Castellanos, Agustin|Valente, Giancarlo|Formisano, Elia|De Martino, Federico', 'Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.|Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.|Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.|Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Brain|Computer Simulation|Humans|Magnetic Resonance Imaging|Monte Carlo Method|Reproducibility of Results', 'Journal Article', '10.1371/journal.pcbi.1006397', '30849071', 'Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models. The accuracy of such prediction is used as an indicator of how well the model describes the computations underlying the brain function that is being considered. However, the prediction accuracy is bounded by the proportion of the variance of the brain response which is related to the measurement noise and not to the stimuli (or cognitive functions). This bound to the performance of a computational model has been referred to as the noise ceiling. In previous fMRI applications two methods have been proposed to estimate the noise ceiling based on either a split-half procedure or Monte Carlo simulations. These methods make different assumptions over the nature of the effects underlying the data, and, importantly, their relation has not been clarified yet. Here, we derive an analytical form for the noise ceiling that does not require computationally expensive simulations or a splitting procedure that reduce the amount of data. The validity of this analytical definition is proved in simulations, we show that the analytical solution results in the same estimate of the noise ceiling as the Monte Carlo method. Considering different simulated noise structure, we evaluate different estimators of the variance of the responses and their impact on the estimation of the noise ceiling. We furthermore evaluate the interplay between regularization (often used to estimate model fits to the data when the number of computational features in the model is large) and model complexity on the performance with respect to the noise ceiling. Our results indicate that when considering the variance of the responses across runs, computing the noise ceiling analytically results in similar estimates as the split half estimator and approaches the true noise ceiling under a variety of simulated noise scenarios. Finally, the methods are tested on real fMRI data acquired at 7 Tesla.'),
(89534, 2019, 'plos', 'A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows', 'Yang, Pei-Chi|Purawat, Shweta|Ieong, Pek U|Jeng, Mao-Tsuen|DeMarco, Kevin R|Vorobyov, Igor|McCulloch, Andrew D|Altintas, Ilkay|Amaro, Rommie E|Clancy, Colleen E', 'Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California Davis, Davis, California, United States of America.|San Diego Supercomputer Center (SDSC), University of California, San Diego, La Jolla, California, United States of America.|Department of Chemistry and Biochemistry, National Biomedical Computation Resource, Drug Design Data Resource (D3R), University of California San Diego, La Jolla, California, United States of America.|Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California Davis, Davis, California, United States of America.|Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California Davis, Davis, California, United States of America.|Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California Davis, Davis, California, United States of America.|Departments of Bioengineering and Medicine, University of California, San Diego, La Jolla, California, United States of America.|San Diego Supercomputer Center (SDSC), University of California, San Diego, La Jolla, California, United States of America.|Department of Chemistry and Biochemistry, National Biomedical Computation Resource, Drug Design Data Resource (D3R), University of California San Diego, La Jolla, California, United States of America.|Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California Davis, Davis, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 0, 'Computer Simulation|Heart|Humans|Models, Cardiovascular|Proof of Concept Study|Reproducibility of Results|Workflow', 'Journal Article', '10.1371/journal.pcbi.1006856', '30849072', 'Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing. While there are good examples if the use of scientific workflows in bioinformatics, medical informatics, biomedical imaging and data analysis, there are fewer examples in multi-scale computational modeling in general and cardiac electrophysiology in particular. Cardiac electrophysiology simulation is a mature area of multi-scale computational biology that serves as an excellent use case for developing and testing new scientific workflows. In this article, we develop, describe and test a computational workflow that serves as a proof of concept of a platform for the robust integration and implementation of a reusable and reproducible multi-scale cardiac cell and tissue model that is expandable, modular and portable. The workflow described leverages Python and Kepler-Python actor for plotting and pre/post-processing. During all stages of the workflow design, we rely on freely available open-source tools, to make our workflow freely usable by scientists.'),
(89535, 2019, 'plos', 'OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling', 'Shen, Fangzhou|Sun, Renliang|Yao, Jie|Li, Jian|Liu, Qian|Price, Nathan D|Liu, Chenguang|Wang, Zhuo', 'Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.|Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|Institute for Systems Biology, Seattle, Washington, United States of America.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|China|United States|China|China', 0, 'Algorithms|Butylene Glycols|Computer Simulation|Ethanol|Genes, Fungal|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological|Mutation|Saccharomyces cerevisiae', 'Journal Article', '10.1371/journal.pcbi.1006835', '30849073', 'The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.'),
(89536, 2019, 'plos', 'Effectiveness of Ultra-Low Volume insecticide spraying to prevent dengue in a non-endemic metropolitan area of Brazil', 'Marini, Giovanni|Guzzetta, Giorgio|Marques Toledo, Cecilia A|Teixeira, Mauro|Rosà, Roberto|Merler, Stefano', 'Dipartimento di Biodiversità ed Ecologia Molecolare, Centro Ricerca e Innovazione, Fondazione Edmund Mach, San Michele allAdige, Trento, Italy.|Epilab-JRU, FEM-FBK Joint Research Unit, Province of Trento, Italy.|Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.|Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.|Dipartimento di Biodiversità ed Ecologia Molecolare, Centro Ricerca e Innovazione, Fondazione Edmund Mach, San Michele allAdige, Trento, Italy.|Epilab-JRU, FEM-FBK Joint Research Unit, Province of Trento, Italy.', 'Italy|Italy|Brazil|Brazil|Italy|Italy', 5, 'Animals|Brazil|Cluster Analysis|Dengue|Humans|Incidence|Insecticides|Mosquito Control|Mosquito Vectors', 'Journal Article', '10.1371/journal.pcbi.1006831', '30849074', 'Management of vector population is a commonly used method for mitigating transmission of mosquito-borne infections, but quantitative information on its practical public health impact is scarce. We study the effectiveness of Ultra-Low Volume (ULV) insecticide spraying in public spaces for preventing secondary dengue virus (DENV) cases in Porto Alegre, a non-endemic metropolitan area in Brazil. We developed a stochastic transmission model based on detailed entomological, epidemiological and population data, accounting for the geographical distribution of mosquitoes and humans in the study area and spatial transmission dynamics. The model was calibrated against the distribution of DENV cluster sizes previously estimated from the same geographical setting. We estimated a ULV-induced mortality of 40% for mosquitoes and found that the implemented control protocol avoided about 24% of symptomatic cases occurred in the area throughout the 2015-2016 epidemic season. Increasing the radius of treatment or the mortality of mosquitoes by treating gardens and/or indoor premises would greatly improve the result of control, but trade-offs with respect to increased efforts need to be carefully analyzed. We found a moderate effectiveness for ULV-spraying in public areas, mainly due to the limited ability of this strategy in effectively controlling the vector population. These results can be used to support the design of control strategies in low-incidence, non-endemic settings.'),
(89537, 2019, 'plos', 'A dynamic power-law sexual network model of gonorrhoea outbreaks', 'Whittles, Lilith K|White, Peter J|Didelot, Xavier', 'Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.|Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.|School of Life Sciences, University of Warwick, Coventry, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Disease Outbreaks|Gonorrhea|Humans|Models, Theoretical|Sexual Behavior', 'Journal Article', '10.1371/journal.pcbi.1006748', '30849080', 'Human networks of sexual contacts are dynamic by nature, with partnerships forming and breaking continuously over time. Sexual behaviours are also highly heterogeneous, so that the number of partners reported by individuals over a given period of time is typically distributed as a power-law. Both the dynamism and heterogeneity of sexual partnerships are likely to have an effect in the patterns of spread of sexually transmitted diseases. To represent these two fundamental properties of sexual networks, we developed a stochastic process of dynamic partnership formation and dissolution, which results in power-law numbers of partners over time. Model parameters can be set to produce realistic conditions in terms of the exponent of the power-law distribution, of the number of individuals without relationships and of the average duration of relationships. Using an outbreak of antibiotic resistant gonorrhoea amongst men have sex with men as a case study, we show that our realistic dynamic network exhibits different properties compared to the frequently used static networks or homogeneous mixing models. We also consider an approximation to our dynamic network model in terms of a much simpler branching process. We estimate the parameters of the generation time distribution and offspring distribution which can be used for example in the context of outbreak reconstruction based on genomic data. Finally, we investigate the impact of a range of interventions against gonorrhoea, including increased condom use, more frequent screening and immunisation, concluding that the latter shows great promise to reduce the burden of gonorrhoea, even if the vaccine was only partially effective or applied to only a random subset of the population.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89538, 2019, 'plos', 'A spectrum of routing strategies for brain networks', 'Avena-Koenigsberger, Andrea|Yan, Xiaoran|Kolchinsky, Artemy|van den Heuvel, Martijn P|Hagmann, Patric|Sporns, Olaf', 'Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America.|IU Network Institute, Indiana University, Bloomington, IN, United States of America.|Santa Fe Institute, Santa Fe, NM, United States of America.|Connectome Lab, Complex Trait Genetics, Department of Neuroscience, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam.|Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.|Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America.', 'India|India|United States|-|Switzerland|India', 9, 'Brain Mapping|Cohort Studies|Communication|Connectome|Humans|Models, Neurological|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1006833', '30849087', 'Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally cheap but inefficient. We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bias on the networks communication cost. We identify routing strategies that approach a (highly efficient) shortest-path communication process with a relatively small global information bias on the systems dynamics. Moreover, we show that the cost of routing messages from and to hub nodes varies as a function of the global information bias driving the systems dynamics. Finally, we implement the model to identify individual subject differences from a communication dynamics point of view. The present framework departs from the classical shortest paths vs. diffusion dichotomy, unifying both models under a single family of dynamical processes that differ by the extent to which global information about the network topology influences the routing patterns of neural signals traversing the network.'),
(89539, 2019, 'plos', 'The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates', 'de Groot, Daan H|van Boxtel, Coco|Planqué, Robert|Bruggeman, Frank J|Teusink, Bas', 'Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Catalysis|Enzymes|Kinetics|Metabolic Flux Analysis|Metabolic Networks and Pathways|Proteins', 'Journal Article', '10.1371/journal.pcbi.1006858', '30856167', 'Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized-in a static environment under mass-conservation and enzyme expression constraints-we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.'),
(89540, 2019, 'plos', 'Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer', 'Chapman, Margaret P|Risom, Tyler|Aswani, Anil J|Langer, Ellen M|Sears, Rosalie C|Tomlin, Claire J', 'Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, United States of America.|Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, United States of America.|Department of Industrial Engineering and Operations Research, University of California Berkeley, Berkeley, California, United States of America.|Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, United States of America.|Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, United States of America.|Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, United States of America.', 'United States|United States|United States|United States|United States|United States', 0, 'Antineoplastic Agents|Biomarkers, Tumor|Cell Death|Cell Differentiation|Cell Division|Cell Line, Tumor|Dimethyl Sulfoxide|Epithelial-Mesenchymal Transition|Female|Humans|Imidazoles|Models, Biological|Pyridones|Pyrimidinones|Quinolines|Triple Negative Breast Neoplasms', 'Journal Article', '10.1371/journal.pcbi.1006840', '30856168', 'Drug resistance in breast cancer cell populations has been shown to arise through phenotypic transition of cancer cells to a drug-tolerant state, for example through epithelial-to-mesenchymal transition or transition to a cancer stem cell state. However, many breast tumors are a heterogeneous mixture of cell types with numerous epigenetic states in addition to stem-like and mesenchymal phenotypes, and the dynamic behavior of this heterogeneous mixture in response to drug treatment is not well-understood. Recently, we showed that plasticity between differentiation states, as identified with intracellular markers such as cytokeratins, is linked to resistance to specific targeted therapeutics. Understanding the dynamics of differentiation-state transitions in this context could facilitate the development of more effective treatments for cancers that exhibit phenotypic heterogeneity and plasticity. In this work, we develop computational models of a drug-treated, phenotypically heterogeneous triple-negative breast cancer (TNBC) cell line to elucidate the feasibility of differentiation-state transition as a mechanism for therapeutic escape in this tumor subtype. Specifically, we use modeling to predict the changes in differentiation-state transitions that underlie specific therapy-induced changes in differentiation-state marker expression that we recently observed in the HCC1143 cell line. We report several statistically significant therapy-induced changes in transition rates between basal, luminal, mesenchymal, and non-basal/non-luminal/non-mesenchymal differentiation states in HCC1143 cell populations. Moreover, we validate model predictions on cell division and cell death empirically, and we test our models on an independent data set. Overall, we demonstrate that changes in differentiation-state transition rates induced by targeted therapy can provoke distinct differentiation-state aggregations of drug-resistant cells, which may be fundamental to the design of improved therapeutic regimens for cancers with phenotypic heterogeneity.'),
(89541, 2019, 'plos', 'Pan-cancer association of a centrosome amplification gene expression signature with genomic alterations and clinical outcome', 'de Almeida, Bernardo P|Vieira, André F|Paredes, Joana|Bettencourt-Dias, Mónica|Barbosa-Morais, Nuno L', 'Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.|i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.|i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 8, 'Atlases as Topic|Breast Neoplasms|Centrosome|Chromosome Aberrations|Female|Gene Expression Profiling|Genomic Instability|Humans|Mutation|Prognosis|Transcriptome|Treatment Outcome|Up-Regulation', 'Journal Article', '10.1371/journal.pcbi.1006832', '30856170', 'Centrosome amplification (CA) is a common feature of human tumours and a promising target for cancer therapy. However, CAs pan-cancer prevalence, molecular role in tumourigenesis and therapeutic value in the clinical setting are still largely unexplored. Here, we used a transcriptomic signature (CA20) to characterise the landscape of CA-associated gene expression in 9,721 tumours from The Cancer Genome Atlas (TCGA). CA20 is upregulated in cancer and associated with distinct clinical and molecular features of breast cancer, consistently with our experimental CA quantification in patient samples. Moreover, we show that CA20 upregulation is positively associated with genomic instability, alteration of specific chromosomal arms and C>T mutations, and we propose novel molecular players associated with CA in cancer. Finally, high CA20 is associated with poor prognosis and, by integrating drug sensitivity with drug perturbation profiles in cell lines, we identify candidate compounds for selectively targeting cancer cells exhibiting transcriptomic evidence for CA.'),
(89542, 2019, 'plos', 'Myopic control of neural dynamics', 'Hocker, David|Park, Il Memming', 'Department of Neurobiology and Behavior Stony Brook University, Stony Brook, New York, United States of America.|Department of Neurobiology and Behavior Stony Brook University, Stony Brook, New York, United States of America.', 'United States|United States', 0, 'Algorithms|Decision Making|Humans|Nervous System Diseases|Neural Networks (Computer)|Nonlinear Dynamics', 'Journal Article', '10.1371/journal.pcbi.1006854', '30856171', 'Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience, however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment input to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This myopic controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this controls utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic controls utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.'),
(89543, 2019, 'plos', 'Latent goal models for dynamic strategic interaction', 'Iqbal, Shariq N|Yin, Lun|Drucker, Caroline B|Kuang, Qian|Gariépy, Jean-François|Platt, Michael L|Pearson, John M', 'Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.|Duke Institute for Brain Sciences, Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, 'Animals|Computational Biology|Decision Making|Goals|Macaca mulatta|Male|Models, Neurological|Reward|Video Games', 'Journal Article', '10.1371/journal.pcbi.1006895', '30856172', 'Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience. However, most previous studies have used experimental paradigms in which choices are discrete (and few), play is static, and optimal solutions are known. Yet in natural environments, interactions between agents typically involve continuous action spaces, ongoing dynamics, and no known optimal solution. Here, we seek to bridge this divide by using a penalty shot task in which pairs of monkeys competed against each other in a competitive, real-time video game. We modeled monkeys strategies as driven by stochastically evolving goals, onscreen positions that served as set points for a control model that produced observed joystick movements. We fit this goal-based dynamical system model using approximate Bayesian inference methods, using neural networks to parameterize players goals as a dynamic mixture of Gaussian components. Our model is conceptually simple, constructed of interpretable components, and capable of generating synthetic data that capture the complexity of real player dynamics. We further characterized players strategies using the number of change points on each trial. We found that this complexity varied more across sessions than within sessions, and that more complex strategies benefited offensive players but not defensive players. Together, our experimental paradigm and model offer a powerful combination of tools for the study of realistic social dynamics in the laboratory setting.'),
(89544, 2019, 'plos', 'A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory', 'Athanasiadou, Rodoniki|Neymotin, Benjamin|Brandt, Nathan|Wang, Wei|Christiaen, Lionel|Gresham, David|Tranchina, Daniel', 'Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America.|Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America.|Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America.|Center for Developmental Genetics, Department of Biology, New York University, New York, New York, United States of America.|Center for Developmental Genetics, Department of Biology, New York University, New York, New York, United States of America.|Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America.|Department of Biology, New York University, New York, New York, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Calibration|Ciona|Gene Expression|Genes, Fungal|High-Throughput Nucleotide Sequencing|Likelihood Functions|Models, Statistical|RNA, Fungal|Saccharomyces cerevisiae|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1006794', '30856174', 'A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.'),
(89545, 2019, 'plos', 'Synaptic plasticity onto inhibitory neurons as a mechanism for ocular dominance plasticity', 'Bono, Jacopo|Clopath, Claudia', 'Department of Bioengineering, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.', 'United Kingdom|United Kingdom', 1, 'Animals|Dominance, Ocular|Evoked Potentials, Visual|Models, Neurological|Neural Inhibition|Neuronal Plasticity|Neurons|Presynaptic Terminals', 'Journal Article', '10.1371/journal.pcbi.1006834', '30860990', 'Ocular dominance plasticity is a well-documented phenomenon allowing us to study properties of cortical maturation. Understanding this maturation might be an important step towards unravelling how cortical circuits function. However, it is still not fully understood which mechanisms are responsible for the opening and closing of the critical period for ocular dominance and how changes in cortical responsiveness arise after visual deprivation. In this article, we present a theory of ocular dominance plasticity. Following recent experimental work, we propose a framework where a reduction in inhibition is necessary for ocular dominance plasticity in both juvenile and adult animals. In this framework, two ingredients are crucial to observe ocular dominance shifts: a sufficient level of inhibition as well as excitatory-to-inhibitory synaptic plasticity. In our model, the former is responsible for the opening of the critical period, while the latter limits the plasticity in adult animals. Finally, we also provide a possible explanation for the variability in ocular dominance shifts observed in individual neurons and for the counter-intuitive shifts towards the closed eye.'),
(89546, 2019, 'plos', 'Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation', 'Luque, Niceto R|Naveros, Francisco|Carrillo, Richard R|Ros, Eduardo|Arleo, Angelo', 'Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.|Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain.|Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain.|Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain.|Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.', 'France|Spain|Spain|Spain|France', 3, 'Action Potentials|Adaptation, Physiological|Animals|Eye Movements|Humans|Learning|Long-Term Potentiation|Purkinje Cells|Reflex, Vestibulo-Ocular|Synapses', 'Journal Article', '10.1371/journal.pcbi.1006298', '30860991', 'Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.'),
(89547, 2019, 'plos', 'Optimizing the depth and the direction of prospective planning using information values', 'Sezener, Can Eren|Dezfouli, Amir|Keramati, Mehdi', 'Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.|Data61, CSIRO, Australia.|Gatsby Computational Neuroscience Unit, Sainsbury Wellcome Centre, University College London, London, UK.', 'Germany|Australia|United Kingdom', 1, 'Algorithms|Animals|Choice Behavior|Humans|Planning Techniques|Prospective Studies|Reward', 'Journal Article', '10.1371/journal.pcbi.1006827', '30861001', 'Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future. Expanding deep trees, however, is computationally taxing. Therefore, machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future. Two outstanding questions in this scheme are in which directions the search tree should be expanded?, and when should the expansion stop?. Here we propose a principled solution to these questions based on a speed/accuracy tradeoff: deeper expansion in the appropriate directions leads to more accurate planning, but at the cost of slower decision-making. Our simulation results show how this algorithm expands the search tree effectively and efficiently in a grid-world environment. We further show that our algorithm can explain several behavioral patterns in animals and humans, namely the effect of time-pressure on the depth of planning, the effect of reward magnitudes on the direction of planning, and the gradual shift from goal-directed to habitual behavior over the course of training. The algorithm also provides several predictions testable in animal/human experiments.'),
(89548, 2019, 'plos', 'A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus', 'Moon, Sifat A|Cohnstaedt, Lee W|McVey, D Scott|Scoglio, Caterina M', 'Department of Electrical & Computer Engineering, Kansas State University, Manhattan, Kansas, United States of America.|Arthropod-Borne Animal Diseases Research Unit, Center for Grain and Animal Health Research, USDA ARS, Manhattan, Kansas, United States of America.|Arthropod-Borne Animal Diseases Research Unit, Center for Grain and Animal Health Research, USDA ARS, Manhattan, Kansas, United States of America.|Department of Electrical & Computer Engineering, Kansas State University, Manhattan, Kansas, United States of America.', 'United States|United States|United States|United States', 3, 'Animals|Birds|Culicidae|Humans|Models, Theoretical|Monte Carlo Method|Mosquito Vectors|United States|West Nile Fever|West Nile virus|Zoonoses', 'Journal Article', '10.1371/journal.pcbi.1006875', '30865618', 'West Nile virus (WNV)-a mosquito-borne arbovirus-entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential-short-range dispersal, 2) power-law-long-range dispersal in all directions, and 3) power-law biased by flyway direction -long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.'),
(89549, 2019, 'plos', 'Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence', 'Ngô, Manh Cuong|Heide-Jørgensen, Mads Peter|Ditlevsen, Susanne', 'Greenland Institute of Natural Resources, Nuuk, Greenland.|Greenland Institute of Natural Resources, Nuuk, Greenland.|Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.', 'Greenland|Greenland|Denmark', 0, 'Animals|Diving|Feeding Behavior|Male|Markov Chains|Models, Theoretical|Whales', 'Journal Article', '10.1371/journal.pcbi.1006425', '30870414', 'Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours.'),
(89550, 2019, 'plos', 'The logic of ionic homeostasis: Cations are for voltage, but not for volume', 'Dmitriev, Andrey V|Dmitriev, Alexander A|Linsenmeier, Robert A', 'Biomedical Engineering Department, Northwestern University, Evanston, Illinois, United States of America.|Independent Researcher, Evanston, Illinois, United States of America.|Biomedical Engineering Department, Northwestern University, Evanston, Illinois, United States of America.', 'United States|United States|United States', 2, 'Computer Simulation|Electric Conductivity|Energy Metabolism|Homeostasis|Ion Transport|Membrane Potentials|Osmolar Concentration|Potassium|Sodium', 'Journal Article', '10.1371/journal.pcbi.1006894', '30870418', 'Neuronal activity is associated with transmembrane ionic redistribution, which can lead to an osmotic imbalance. Accordingly, activity-dependent changes of the membrane potential are sometimes accompanied by changes in intracellular and/or extracellular volume. Experimental data that include distributions of ions and volume during neuronal activity are rare and rather inconsistent partly due to the technical difficulty of performing such measurements. However, progress in understanding the interrelations among ions, voltage and volume has been achieved recently by computational modelling, particularly charge-difference modelling. In this work a charge-difference computational model was used for further understanding of the specific roles for cations and anions. Our simulations show that without anion conductances the transmembrane movements of cations are always osmotically balanced, regardless of the stoichiometry of the pump or the ratio of Na+ and K+ conductances. Yet any changes in cation conductance or pump activity are associated with changes of the membrane potential, even when a hypothetically electroneutral pump is used in calculations and K+ and Na+ conductances are equal. On the other hand, when a Cl- conductance is present, the only way to keep the Cl-equilibrium potential in accordance with the changed membrane potential is to adjust cell volume. Importantly, this voltage-evoked Cl--dependent volume change does not affect intracellular cation concentrations or the amount of energy that is necessary to support the system. Taking other factors into consideration (i.e. the presence of internal impermeant poly-anions, the activity of cation-Cl- cotransporters, and the buildup of intra- and extracellular osmolytes, both charged and electroneutral) adds complexity, but does not change the main principles.'),
(89551, 2019, 'plos', 'Boolean model of growth signaling, cell cycle and apoptosis predicts the molecular mechanism of aberrant cell cycle progression driven by hyperactive PI3K', 'Sizek, Herbert|Hamel, Andrew|Deritei, Dávid|Campbell, Sarah|Ravasz Regan, Erzsébet', 'Biochemistry and Molecular Biology, The College of Wooster, Wooster, OH, United States of America.|Biochemistry and Molecular Biology, The College of Wooster, Wooster, OH, United States of America.|Department of Physics, Pennsylvania State University, State College, PA, United States of America.|Biochemistry and Molecular Biology, The College of Wooster, Wooster, OH, United States of America.|Biochemistry and Molecular Biology, The College of Wooster, Wooster, OH, United States of America.', 'United States|United States|United States|United States|United States', 1, 'Apoptosis|Cell Cycle|Computer Simulation|DNA Replication|Enzyme Activation|Humans|Intercellular Signaling Peptides and Proteins|Phosphatidylinositol 3-Kinases|Replication Origin|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1006402', '30875364', 'The PI3K/AKT signaling pathway plays a role in most cellular functions linked to cancer progression, including cell growth, proliferation, cell survival, tissue invasion and angiogenesis. It is generally recognized that hyperactive PI3K/AKT1 are oncogenic due to their boost to cell survival, cell cycle entry and growth-promoting metabolism. That said, the dynamics of PI3K and AKT1 during cell cycle progression are highly nonlinear. In addition to negative feedback that curtails their activity, protein expression of PI3K subunits has been shown to oscillate in dividing cells. The low-PI3K/low-AKT1 phase of these oscillations is required for cytokinesis, indicating that oncogenic PI3K may directly contribute to genome duplication. To explore this, we construct a Boolean model of growth factor signaling that can reproduce PI3K oscillations and link them to cell cycle progression and apoptosis. The resulting modular model reproduces hyperactive PI3K-driven cytokinesis failure and genome duplication and predicts the molecular drivers responsible for these failures by linking hyperactive PI3K to mis-regulation of Polo-like kinase 1 (Plk1) expression late in G2. To do this, our model captures the role of Plk1 in cell cycle progression and accurately reproduces multiple effects of its loss: G2 arrest, mitotic catastrophe, chromosome mis-segregation / aneuploidy due to premature anaphase, and cytokinesis failure leading to genome duplication, depending on the timing of Plk1 inhibition along the cell cycle. Finally, we offer testable predictions on the molecular drivers of PI3K oscillations, the timing of these oscillations with respect to division, and the role of altered Plk1 and FoxO activity in genome-level defects caused by hyperactive PI3K. Our model is an important starting point for the predictive modeling of cell fate decisions that include AKT1-driven senescence, as well as the non-intuitive effects of drugs that interfere with mitosis.'),
(89552, 2019, 'plos', 'Self-organization of conducting pathways explains electrical wave propagation in cardiac tissues with high fraction of non-conducting cells', 'Kudryashova, Nina|Nizamieva, Aygul|Tsvelaya, Valeriya|Panfilov, Alexander V|Agladze, Konstantin I', 'Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.|Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.|Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.|Department of Physics and Astronomy, Ghent University, Ghent, Belgium.|Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.', '-|-|-|Belgium|-', 4, 'Animals|Animals, Newborn|Arrhythmias, Cardiac|Computer Simulation|Heart|Heart Conduction System|Models, Cardiovascular|Rats', 'Journal Article', '10.1371/journal.pcbi.1006597', '30883540', 'Cardiac fibrosis occurs in many forms of heart disease and is considered to be one of the main arrhythmogenic factors. Regions with a high density of fibroblasts are likely to cause blocks of wave propagation that give rise to dangerous cardiac arrhythmias. Therefore, studies of the wave propagation through these regions are very important, yet the precise mechanisms leading to arrhythmia formation in fibrotic cardiac tissue remain poorly understood. Particularly, it is not clear how wave propagation is organized at the cellular level, as experiments show that the regions with a high percentage of fibroblasts (65-75%) are still conducting electrical signals, whereas geometric analysis of randomly distributed conducting and non-conducting cells predicts connectivity loss at 40% at the most (percolation threshold). To address this question, we used a joint in vitro-in silico approach, which combined experiments in neonatal rat cardiac monolayers with morphological and electrophysiological computer simulations. We have shown that the main reason for sustainable wave propagation in highly fibrotic samples is the formation of a branching network of cardiomyocytes. We have successfully reproduced the morphology of conductive pathways in computer modelling, assuming that cardiomyocytes align their cytoskeletons to fuse into cardiac syncytium. The electrophysiological properties of the monolayers, such as conduction velocity, conduction blocks and wave fractionation, were reproduced as well. In a virtual cardiac tissue, we have also examined the wave propagation at the subcellular level, detected wavebreaks formation and its relation to the structure of fibrosis and, thus, analysed the processes leading to the onset of arrhythmias.'),
(89553, 2019, 'plos', 'Building a mechanistic mathematical model of hepatitis C virus entry', 'Kalemera, Mphatso|Mincheva, Dilyana|Grove, Joe|Illingworth, Christopher J R', 'Institute of Immunity and Transplantation, Division of Infection and Immunity, University College London, United Kingdom.|Department of Genetics, University of Cambridge, Cambridge, United Kingdom.|Institute of Immunity and Transplantation, Division of Infection and Immunity, University College London, United Kingdom.|Department of Genetics, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Cell Line, Tumor|Computational Biology|Hepacivirus|Hepatitis C|Host-Pathogen Interactions|Humans|Models, Molecular|Receptors, Virus|Virus Internalization', 'Journal Article', '10.1371/journal.pcbi.1006905', '30883541', 'The mechanism by which hepatitis C virus (HCV) gains entry into cells is a complex one, involving a broad range of host proteins. Entry is a critical phase of the viral lifecycle, and a potential target for therapeutic or vaccine-mediated intervention. However, the mechanics of HCV entry remain poorly understood. Here we describe a novel computational model of viral entry, encompassing the relationship between HCV and the key host receptors CD81 and SR-B1. We conduct experiments to thoroughly quantify the influence of an increase or decrease in receptor availability upon the extent of viral entry. We use these data to build and parameterise a mathematical model, which we then validate by further experiments. Our results are consistent with sequential HCV-receptor interactions, whereby initial interaction between the HCV E2 glycoprotein and SR-B1 facilitates the accumulation CD81 receptors, leading to viral entry. However, we also demonstrate that a small minority of viruses can achieve entry in the absence of SR-B1. Our model estimates the impact of the different obstacles that viruses must surmount to achieve entry, among virus particles attaching to the cell surface, around one third of viruses accumulate sufficient CD81 receptors, of which 4-8% then complete the subsequent steps to achieve productive infection. Furthermore, we make estimates of receptor stoichiometry, in excess of 10 receptors are likely to be required to achieve viral entry. Our model provides a tool to investigate the entry characteristics of HCV variants and outlines a framework for future quantitative studies of the multi-receptor dynamics of HCV entry.'),
(89554, 2019, 'plos', 'Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission', 'Valdano, Eugenio|Manrubia, Susanna|Gómez, Sergio|Arenas, Alex', 'Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.|National Centre for Biotechnology (CSIC), Madrid, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.', 'Spain|Spain|Spain|Spain', 1, 'Animals|Biological Evolution|Host-Pathogen Interactions|Humans|Models, Theoretical|Prevalence|Symbiosis|Virus Diseases|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006876', '30883545', 'Multipartite viruses replicate through a puzzling evolutionary strategy. Their genome is segmented into two or more parts, and encapsidated in separate particles that appear to propagate independently. Completing the replication cycle, however, requires the full genome, so that a systemic infection of a host requires the concurrent presence of several particles. This represents an apparent evolutionary drawback of multipartitism, while its advantages remain unclear. A transition from monopartite to multipartite viral forms has been described in vitro under conditions of high multiplicity of infection, suggesting that cooperation between defective mutants is a plausible evolutionary pathway towards multipartitism. However, it is unknown how the putative advantages that multipartitism might enjoy at the microscopic level affect its epidemiology, or if an explicit advantange is needed to explain its ecological persistence. In order to disentangle which mechanisms might contribute to the rise and fixation of multipartitism, we here investigate the interaction between viral spreading dynamics and host population structure. We set up a compartmental model of the spread of a virus in its different forms and explore its epidemiology using both analytical and numerical techniques. We uncover that the impact of host contact structure on spreading dynamics entails a rich phenomenology of ecological relationships that includes cooperation, competition, and commensality. Furthermore, we find out that multipartitism might rise to fixation even in the absence of explicit microscopic advantages. Multipartitism allows the virus to colonize environments that could not be invaded by the monopartite form, while homogeneous contacts between hosts facilitate its spread. We conjecture that these features might have led to an increase in the diversity and prevalence of multipartite viral forms concomitantly with the expansion of agricultural practices.'),
(89555, 2019, 'plos', 'Stochastic neural field model of stimulus-dependent variability in cortical neurons', 'Bressloff, Paul C', 'Department of Mathematics, University of Utah, Salt Lake City, Utah, USA.', 'United States', 0, 'Animals|Cerebral Cortex|Models, Neurological|Neurons|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1006755', '30883546', 'We use stochastic neural field theory to analyze the stimulus-dependent tuning of neural variability in ring attractor networks. We apply perturbation methods to show how the neural field equations can be reduced to a pair of stochastic nonlinear phase equations describing the stochastic wandering of spontaneously formed tuning curves or bump solutions. These equations are analyzed using a modified version of the bivariate von Mises distribution, which is well-known in the theory of circular statistics. We first consider a single ring network and derive a simple mathematical expression that accounts for the experimentally observed bimodal (or M-shaped) tuning of neural variability. We then explore the effects of inter-network coupling on stimulus-dependent variability in a pair of ring networks. These could represent populations of cells in two different layers of a cortical hypercolumn linked via vertical synaptic connections, or two different cortical hypercolumns linked by horizontal patchy connections within the same layer. We find that neural variability can be suppressed or facilitated, depending on whether the inter-network coupling is excitatory or inhibitory, and on the relative strengths and biases of the external stimuli to the two networks. These results are consistent with the general observation that increasing the mean firing rate via external stimuli or modulating drives tends to reduce neural variability.'),
(89556, 2019, 'plos', 'Modeling second-order boundary perception: A machine learning approach', 'DiMattina, Christopher|Baker, Curtis L', 'Computational Perception Laboratory, Department of Psychology, Florida Gulf Coast University, Fort Myers, Florida, United States of America.|McGill Vision Research Unit, Department of Ophthalmology, McGill University, Montreal, Quebec, Canada.', 'United States|Canada', 1, 'Contrast Sensitivity|Humans|Machine Learning|Models, Theoretical|Photic Stimulation|Psychophysics|Visual Perception', 'Journal Article', '10.1371/journal.pcbi.1006829', '30883556', 'Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89557, 2019, 'plos', 'An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections', 'Perkins, T Alex|Reiner, Robert C|España, Guido|Ten Bosch, Quirine A|Verma, Amit|Liebman, Kelly A|Paz-Soldan, Valerie A|Elder, John P|Morrison, Amy C|Stoddard, Steven T|Kitron, Uriel|Vazquez-Prokopec, Gonzalo M|Scott, Thomas W|Smith, David L', 'Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America.|Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.|Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America.|Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA.|Department of Entomology and Nematology, University of California, Davis, CA, United States of America.|Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America.|Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America.|Department of Entomology and Nematology, University of California, Davis, CA, United States of America.|Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America.|Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.|Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.|Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.|Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.', 'United States|United States|United States|United States|-|United States|United States|United States|', 2, 'Calibration|Child|Computer Simulation|Dengue|Humans|Peru|Systems Analysis|Uncertainty|Viral Vaccines', 'Journal Article', '10.1371/journal.pcbi.1006710', '30893294', 'Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.'),
(89558, 2019, 'plos', 'Identification of pathways associated with chemosensitivity through network embedding', 'Wang, Sheng|Huang, Edward|Cairns, Junmei|Peng, Jian|Wang, Liewei|Sinha, Saurabh', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Drug Resistance, Neoplasm|Drug Screening Assays, Antitumor|Gene Expression Profiling|Gene Regulatory Networks|Humans', 'Journal Article', '10.1371/journal.pcbi.1006864', '30893303', 'Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.'),
(89559, 2019, 'plos', 'ZnT2 is an electroneutral proton-coupled vesicular antiporter displaying an apparent stoichiometry of two protons per zinc ion', 'Golan, Yarden|Alhadeff, Raphael|Warshel, Arieh|Assaraf, Yehuda G', 'The Fred Wyszkowski Cancer Research Laboratory, Department of Biology, Technion-Israel Institute of Technology, Haifa, Israel.|Department of Chemistry, University of Southern California, Los Angeles, California.|Department of Chemistry, University of Southern California, Los Angeles, California.|The Fred Wyszkowski Cancer Research Laboratory, Department of Biology, Technion-Israel Institute of Technology, Haifa, Israel.', 'Israel|-|-|Israel', 6, 'Acids|Alkalies|Cation Transport Proteins|Chelating Agents|Dose-Response Relationship, Drug|Humans|Hydrogen-Ion Concentration|MCF-7 Cells|Macrolides|Models, Molecular|Protons|Vacuolar Proton-Translocating ATPases|Zinc', 'Journal Article', '10.1371/journal.pcbi.1006882', '30893306', 'Zinc is a vital trace element crucial for the proper function of some 3,000 cellular proteins. Specifically, zinc is essential for key physiological processes including nucleic acid metabolism, regulation of gene expression, signal transduction, cell division, immune- and nervous system functions, wound healing, and apoptosis. Consequently, impairment of zinc homeostasis disrupts key cellular functions resulting in various human pathologies. Mammalian zinc transport proceeds via two transporter families ZnT and ZIP. However, the detailed mechanism of action of ZnT2, which is responsible for vesicular zinc accumulation and zinc secretion into breast milk during lactation, is currently unknown. Moreover, although the putative coupling of zinc transport to the proton gradient in acidic vesicles has been suggested, it has not been conclusively established. Herein we modeled the mechanism of action of ZnT2 and demonstrated both computationally and experimentally, using functional zinc transport assays, that ZnT2 is indeed a proton-coupled zinc antiporter. Bafilomycin A1, a specific inhibitor of vacuolar-type proton ATPase (V-ATPase) which alkalizes acidic vesicles, abolished ZnT2-dependent zinc transport into intracellular vesicles. Moreover, using LysoTracker Red and Lyso-pHluorin, we further showed that upon transient ZnT2 overexpression in intracellular vesicles and addition of exogenous zinc, the vesicular pH underwent alkalization, presumably due to a proton-zinc antiport, this phenomenon was reversed in the presence of TPEN, a specific zinc chelator. Finally, based on computational energy calculations, we propose that ZnT2 functions as an antiporter with a stoichiometry of 2H+/Zn2+ ion. Hence, ZnT2 is a proton motive force-driven, electroneutral vesicular zinc exchanger, concentrating zinc in acidic vesicles on the expense of proton extrusion to the cytoplasm.'),
(89560, 2019, 'plos', 'ChIPulate: A comprehensive ChIP-seq simulation pipeline', 'Datta, Vishaka|Hannenhalli, Sridhar|Siddharthan, Rahul', 'Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bengaluru, Karnataka, India.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.|The Institute of Mathematical Sciences/HBNI, Taramani, Chennai, India.', 'India|United States|India', 0, 'Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|DNA|DNA-Binding Proteins|Escherichia coli|Escherichia coli Proteins|High-Throughput Nucleotide Sequencing|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1006921', '30897079', 'ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate.'),
(89561, 2019, 'plos', 'Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks', 'Beiran, Manuel|Ostojic, Srdjan', 'Group for Neural Theory, Laboratoire de Neurosciences Cognitives Computationnelles, Département dÉtudes Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France.|Group for Neural Theory, Laboratoire de Neurosciences Cognitives Computationnelles, Département dÉtudes Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France.', 'France|France', 1, 'Animals|Computational Biology|Models, Neurological|Nerve Net|Neurons|Synapses|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1006893', '30897092', 'Neural activity in awake behaving animals exhibits a vast range of timescales that can be several fold larger than the membrane time constant of individual neurons. Two types of mechanisms have been proposed to explain this conundrum. One possibility is that large timescales are generated by a network mechanism based on positive feedback, but this hypothesis requires fine-tuning of the strength or structure of the synaptic connections. A second possibility is that large timescales in the neural dynamics are inherited from large timescales of underlying biophysical processes, two prominent candidates being intrinsic adaptive ionic currents and synaptic transmission. How the timescales of adaptation or synaptic transmission influence the timescale of the network dynamics has however not been fully explored. To address this question, here we analyze large networks of randomly connected excitatory and inhibitory units with additional degrees of freedom that correspond to adaptation or synaptic filtering. We determine the fixed points of the systems, their stability to perturbations and the corresponding dynamical timescales. Furthermore, we apply dynamical mean field theory to study the temporal statistics of the activity in the fluctuating regime, and examine how the adaptation and synaptic timescales transfer from individual units to the whole population. Our overarching finding is that synaptic filtering and adaptation in single neurons have very different effects at the network level. Unexpectedly, the macroscopic network dynamics do not inherit the large timescale present in adaptive currents. In contrast, the timescales of network activity increase proportionally to the time constant of the synaptic filter. Altogether, our study demonstrates that the timescales of different biophysical processes have different effects on the network level, so that the slow processes within individual neurons do not necessarily induce slow activity in large recurrent neural networks.'),
(89562, 2019, 'plos', 'Rapid interpretation of small-angle X-ray scattering data', 'Weiel, Marie|Reinartz, Ines|Schug, Alexander', 'Department of Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Department of Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany.|Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.', 'Germany|Germany|Germany', 4, 'Computational Biology|Molecular Dynamics Simulation|Protein Conformation|Proteins|Scattering, Small Angle|X-Ray Diffraction', 'Journal Article', '10.1371/journal.pcbi.1006900', '30901335', 'The fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecules dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our methods performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time.'),
(89563, 2019, 'plos', 'Scalable nonlinear programming framework for parameter estimation in dynamic biological system models', 'Shin, Sungho|Venturelli, Ophelia S|Zavala, Victor M', 'Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.', 'United States|United States|United States', 0, 'Bacteria|Gastrointestinal Microbiome|Humans|Models, Biological|Nonlinear Dynamics|Software|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1006828', '30908479', 'We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stiff differential equations as well as many experimental data sets and parameters. The proposed framework uses cutting-edge modeling and solution tools which are computationally efficient, robust, and easy-to-use. Specifically, our framework uses a time discretization approach that: i) avoids repetitive simulations of the dynamic model, ii) enables fully algebraic model implementations and computation of derivatives, and iii) enables the use of computationally efficient nonlinear interior point solvers that exploit sparse and structured linear algebra techniques. We demonstrate these capabilities by solving estimation problems for synthetic human gut microbiome community models. We show that an instance with 156 parameters, 144 differential equations, and 1,704 experimental data points can be solved in less than 3 minutes using our proposed framework (while an off-the-shelf simulation-based solution framework requires over 7 hours). We also create large instances to show that the proposed framework is scalable and can solve problems with up to 2,352 parameters, 2,304 differential equations, and 20,352 data points in less than 15 minutes. The proposed framework is flexible and easy-to-use, can be broadly applied to dynamic models of biological systems, and enables the implementation of sophisticated estimation techniques to quantify parameter uncertainty, to diagnose observability/uniqueness issues, to perform model selection, and to handle outliers.'),
(89564, 2019, 'plos', 'Searching algorithm for Type IV effector proteins (S4TE) 20: Improved tools for Type IV effector prediction, analysis and comparison in proteobacteria', 'Noroy, Christophe|Lefrançois, Thierry|Meyer, Damien F', 'CIRAD, UMR ASTRE, Petit-Bourg, Guadeloupe, France.|ASTRE, Univ Montpellier, CIRAD, INRA, Montpellier, France.|CIRAD, UMR ASTRE, Petit-Bourg, Guadeloupe, France.', 'France|France|France', 6, 'Algorithms|DNA, Bacterial|Genome, Bacterial|Genomics|Proteobacteria|Sequence Alignment|Sequence Analysis, DNA|Type IV Secretion Systems|User-Computer Interface', 'Journal Article', '10.1371/journal.pcbi.1006847', '30908487', 'Bacterial pathogens have evolved numerous strategies to corrupt, hijack or mimic cellular processes in order to survive and proliferate. Among those strategies, Type IV effectors (T4Es) are proteins secreted by pathogenic bacteria to manipulate host cell processes during infection. They are delivered into eukaryotic cells in an ATP-dependent manner via the type IV secretion system, a specialized multiprotein complex. T4Es contain a wide spectrum of features including eukaryotic-like domains, localization signals or a C-terminal translocation signal. A combination of these features enables prediction of T4Es in a given bacterial genome. In this study, we developed a web-based comprehensive suite of tools with a user-friendly graphical interface. This version 2.0 of S4TE (Searching Algorithm for Type IV Effector Proteins, http://sate.cirad.fr) enables accurate prediction and comparison of T4Es. Search parameters and threshold can be customized by the user to work with any genome sequence, whether publicly available or not. Applications range from characterizing effector features and identifying potential T4Es to analyzing the effectors based on the genome G+C composition and local gene density. S4TE 2.0 allows the comparison of putative T4E repertoires of up to four bacterial strains at the same time. The software identifies T4E orthologs among strains and provides a Venn diagram and lists of genes for each intersection. New interactive features offer the best visualization of the location of candidate T4Es and hyperlinks to NCBI and Pfam databases. S4TE 2.0 is designed to evolve rapidly with the publication of new experimentally validated T4Es, which will reinforce the predictive power of the algorithm. The computational methodology can be used to identify a wide spectrum of candidate bacterial effectors that lack sequence conservation but have similar amino acid characteristics. This approach will provide very valuable information about bacterial host-specificity and virulence factors and help identify host targets for the development of new anti-bacterial molecules.'),
(89565, 2019, 'plos', 'Evaluating reproducibility of AI algorithms in digital pathology with DAPPER', 'Bizzego, Andrea|Bussola, Nicole|Chierici, Marco|Maggio, Valerio|Francescatto, Margherita|Cima, Luca|Cristoforetti, Marco|Jurman, Giuseppe|Furlanello, Cesare', 'Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Pathology Unit, Santa Chiara Hospital, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Algorithms|Artificial Intelligence|Histological Techniques|Humans|Image Interpretation, Computer-Assisted|Reproducibility of Results|Software', 'Journal Article', '10.1371/journal.pcbi.1006269', '30917113', 'Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDAs MAQC project, designed to analyze causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test three different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with four datasets (5, 10, 20 or 30 classes), for a total of 53, 000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for diagnostic tests (e.g., random labels) to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1, 060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning pipeline and the Histological Imaging-Newsy Tiles (HINT) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for digital pathology.'),
(89566, 2019, 'plos', 'LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities', 'Wang, Lei|You, Zhu-Hong|Chen, Xing|Li, Yang-Ming|Dong, Ya-Nan|Li, Li-Ping|Zheng, Kai', 'Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China.|Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|Department of Electrical Computer and Telecommunications Engineering Technology, Rochester Institute of Technology, Rochester, United States of America.|Xiangya School of Public Health, Central South University, Changsha, China.|Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China.|Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China.', 'China|China|China|United States|China|China|China', 3, 'Algorithms|Area Under Curve|Computational Biology|Genetic Predisposition to Disease|Humans|Logistic Models|MicroRNAs|Neoplasms|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1006865', '30917115', 'Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.'),
(89567, 2019, 'plos', 'Conformational ensemble of native xcexb1-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations', 'Brodie, Nicholas I|Popov, Konstantin I|Petrotchenko, Evgeniy V|Dokholyan, Nikolay V|Borchers, Christoph H', 'University of Victoria -Genome British Columbia Proteomics Centre, Vancouver Island Technology Park, Victoria, British Columbia, Canada.|Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, United States of America.|University of Victoria -Genome British Columbia Proteomics Centre, Vancouver Island Technology Park, Victoria, British Columbia, Canada.|Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, United States of America.|University of Victoria -Genome British Columbia Proteomics Centre, Vancouver Island Technology Park, Victoria, British Columbia, Canada.', 'Canada|United States|Canada|United States|Canada', 9, 'Humans|Molecular Dynamics Simulation|Protein Conformation|Proteomics|Recombinant Proteins|alpha-Synuclein', 'Journal Article', '10.1371/journal.pcbi.1006859', '30917118', 'Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction. Here, we apply a recently-developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations (CL-DMD) for the determination of conformational ensemble of the intrinsically disordered protein xcexb1-synuclein in the solution. The predicted structures were in agreement with hydrogen-deuterium exchange, circular dichroism, surface modification, and long-distance crosslinking data. We found that xcexb1-synuclein is present in solution as an ensemble of rather compact globular conformations with distinct topology and inter-residue contacts, which is well-represented by movements of the large loops and formation of few transient secondary structure elements. Non-amyloid component and C-terminal regions were consistently found to contain xcexb2-structure elements and hairpins.'),
(89568, 2019, 'plos', 'Ten principles for machine-actionable data management plans', 'Miksa, Tomasz|Simms, Stephanie|Mietchen, Daniel|Jones, Sarah', 'SBA Research & TU Wien, Vienna, Austria.|California Digital Library, University of California, Oakland, United States of America.|Data Science Institute, University of Virginia, Charlottesville, United States of America.|Digital Curation Centre, Glasgow, United Kingdom.', 'Austria|United States|United States|United Kingdom', 0, 'Automation|Data Interpretation, Statistical|Documentation', 'Journal Article', '10.1371/journal.pcbi.1006750', '30921316', 'Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.'),
(89569, 2019, 'plos', 'Agent-based modeling of morphogenetic systems: Advantages and challenges', 'Glen, Chad M|Kemp, Melissa L|Voit, Eberhard O', 'The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.', 'Georgia|Georgia|Georgia', 15, 'Apoptosis|Cell Proliferation|Models, Biological|Morphogenesis|Systems Analysis', 'Journal Article', '10.1371/journal.pcbi.1006577', '30921323', 'The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction-diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.'),
(89570, 2019, 'plos', 'Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers', 'Nussinov, Ruth|Jang, Hyunbum|Tsai, Chung-Jung|Cheng, Feixiong', 'Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America.|Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America.|Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America.|Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.', 'United States|United States|United States|United States', 21, 'Computer Simulation|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasm Proteins|Neoplasms|Precision Medicine|Protein Conformation|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1006658', '30921324', 'At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumors genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses-all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation, therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.'),
(89571, 2019, 'plos', 'A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K In human populations', 'Li, Weiling|Lin, Lin|Malhotra, Raunaq|Yang, Lei|Acharya, Raj|Poss, Mary', 'The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.|Department of Statistics, The Pennsylvania State University, University Park, PA, United States of America.|The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.|Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.|The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.|Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.', 'United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Continental Population Groups|Endogenous Retroviruses|Genetics, Population|Genome, Human|Genome, Viral|Genomics|Humans|Molecular Epidemiology|Proviruses|Software', 'Journal Article', '10.1371/journal.pcbi.1006564', '30921327', 'Human Endogenous Retrovirus type K (HERV-K) is the only HERV known to be insertionally polymorphic, not all individuals have a retrovirus at a specific genomic location. It is possible that HERV-Ks contribute to human disease because people differ in both number and genomic location of these retroviruses. Indeed viral transcripts, proteins, and antibody against HERV-K are detected in cancers, auto-immune, and neurodegenerative diseases. However, attempts to link a polymorphic HERV-K with any disease have been frustrated in part because population prevalence of HERV-K provirus at each polymorphic site is lacking and it is challenging to identify closely related elements such as HERV-K from short read sequence data. We present an integrated and computationally robust approach that uses whole genome short read data to determine the occupation status at all sites reported to contain a HERV-K provirus. Our method estimates the proportion of fixed length genomic sequence (k-mers) from whole genome sequence data matching a reference set of k-mers unique to each HERV-K locus and applies mixture model-based clustering of these values to account for low depth sequence data. Our analysis of 1000 Genomes Project Data (KGP) reveals numerous differences among the five KGP super-populations in the prevalence of individual and co-occurring HERV-K proviruses, we provide a visualization tool to easily depict the proportion of the KGP populations with any combination of polymorphic HERV-K provirus. Further, because HERV-K is insertionally polymorphic, the genome burden of known polymorphic HERV-K is variable in humans, this burden is lowest in East Asian (EAS) individuals. Our study identifies population-specific sequence variation for HERV-K proviruses at several loci. We expect these resources will advance research on HERV-K contributions to human diseases.'),
(89572, 2019, 'plos', 'Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas', 'Sheppard, Lawrence W|Defriez, Emma J|Reid, Philip C|Reuman, Daniel C', 'Department of Ecology and Evolutionary Biology and Kansas Biological Survey, University of Kansas, Lawrence, Kansas, USA.|Department of Life Sciences, Imperial College London, Ascot, United Kingdom.|Marine Institute, Plymouth University, Drake Circus, Plymouth, United Kingdom.|Department of Ecology and Evolutionary Biology and Kansas Biological Survey, University of Kansas, Lawrence, Kansas, USA.', 'United States|United Kingdom|United Kingdom|United States', 5, 'Chlorophyll|Climate|Ecosystem|Oceans and Seas|Phytoplankton', 'Journal Article', '10.1371/journal.pcbi.1006744', '30921328', 'Large-scale spatial synchrony is ubiquitous in ecology. We examined 56 years of data representing chlorophyll density in 26 areas in British seas monitored by the Continuous Plankton Recorder survey. We used wavelet methods to disaggregate synchronous fluctuations by timescale and determine that drivers of synchrony include both biotic and abiotic variables. We tested these drivers for statistical significance by comparison with spatially synchronous surrogate data. Identification of causes of synchrony is distinct from, and goes beyond, determining drivers of local population dynamics. We generated timescale-specific models, accounting for 61% of long-timescale (> 4yrs) synchrony in a chlorophyll density index, but only 3% of observed short-timescale (< 4yrs) synchrony. Thus synchrony and its causes are timescale-specific. The dominant source of long-timescale chlorophyll synchrony was closely related to sea surface temperature, through a climatic Moran effect, though likely via complex oceanographic mechanisms. The top-down action of Calanus finmarchicus predation enhances this environmental synchronising mechanism and interacts with it non-additively to produce more long-timescale synchrony than top-down and climatic drivers would produce independently. Our principal result is therefore a demonstration of interaction effects between Moran drivers of synchrony, a new mechanism for synchrony that may influence many ecosystems at large spatial scales.'),
(89573, 2019, 'plos', 'Bayes-optimal estimation of overlap between populations of fixed size', 'Larremore, Daniel B', 'Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, United States of America.', 'United States', 0, 'Bayes Theorem|Plasmodium falciparum|Population Density|Reproducibility of Results|Stochastic Processes|Uncertainty', 'Journal Article', '10.1371/journal.pcbi.1006898', '30925165', 'Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects-species, taxonomical units, or gene variants, depending on the context-can be directly counted. In practice, however, only a fraction of each populations objects are likely to be sampled due to stochastic data collection or sequencing techniques. Although methods exists for quantifying population overlap under subsampled conditions, their bias is well documented and the uncertainty of their estimates cannot be quantified. Here we derive and validate a method to rigorously estimate the population overlap from incomplete samples when the total number of objects, species, or genes in each population is known, a special case of the more general xcexb2-diversity problem that is particularly relevant in the ecology and genomic epidemiology of malaria. By solving a Bayesian inference problem, this method takes into account the rates of subsampling and produces unbiased and Bayes-optimal estimates of overlap. In addition, it provides a natural framework for computing the uncertainty of its estimates, and can be used prospectively in study planning by quantifying the tradeoff between sampling effort and uncertainty.'),
(89574, 2019, 'plos', 'Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data', 'Campbell, Finlay|Cori, Anne|Ferguson, Neil|Jombart, Thibaut', 'MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom.|MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom.|MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom.|MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Bayes Theorem|Communicable Diseases|Computational Biology|Contact Tracing|Disease Outbreaks|Genome, Viral|Humans|Models, Biological|SARS Virus|Severe Acute Respiratory Syndrome|Singapore|Software', 'Journal Article', '10.1371/journal.pcbi.1006930', '30925168', 'There exists significant interest in developing statistical and computational tools for inferring who infected whom in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89575, 2019, 'plos', 'Evolutionary multiplayer games on graphs with edge diversity', 'Su, Qi|Zhou, Lei|Wang, Long', 'Center for Systems and Control, College of Engineering, Peking University, Beijing, China.|Center for Systems and Control, College of Engineering, Peking University, Beijing, China.|Center for Systems and Control, College of Engineering, Peking University, Beijing, China.', 'China|China|China', 3, 'Computational Biology|Computer Simulation|Cooperative Behavior|Game Theory|Humans|Interpersonal Relations|Software', 'Journal Article', '10.1371/journal.pcbi.1006947', '30933968', 'Evolutionary game dynamics in structured populations has been extensively explored in past decades. However, most previous studies assume that payoffs of individuals are fully determined by the strategic behaviors of interacting parties, and social ties between them only serve as the indicator of the existence of interactions. This assumption neglects important information carried by inter-personal social ties such as genetic similarity, geographic proximity, and social closeness, which may crucially affect the outcome of interactions. To model these situations, we present a framework of evolutionary multiplayer games on graphs with edge diversity, where different types of edges describe diverse social ties. Strategic behaviors together with social ties determine the resulting payoffs of interactants. Under weak selection, we provide a general formula to predict the success of one behavior over the other. We apply this formula to various examples which cannot be dealt with using previous models, including the division of labor and relationship- or edge-dependent games. We find that labor division can promote collective cooperation markedly. The evolutionary process based on relationship-dependent games can be approximated by interactions under a transformed and unified game. Our work stresses the importance of social ties and provides effective methods to reduce the calculating complexity in analyzing the evolution of realistic systems.'),
(89576, 2019, 'plos', 'Inferring rates of metastatic dissemination using stochastic network models', 'Gerlee, Philip|Johansson, Mia', 'Mathematical Sciences, Chalmers University of Technology, Sweden.|Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.', 'Sweden|Sweden', 0, 'Computational Biology|Female|Humans|Kinetics|Lymphatic Metastasis|Male|Models, Biological|Neoplasm Invasiveness|Neoplasm Metastasis|Neoplasm Seeding|Neoplastic Cells, Circulating|Ovarian Neoplasms|Stochastic Processes|Tongue Neoplasms', 'Journal Article', '10.1371/journal.pcbi.1006868', '30933969', 'The formation of metastases is driven by the ability of cancer cells to disseminate from the site of the primary tumour to target organs. The process of dissemination is constrained by anatomical features such as the flow of blood and lymph in the circulatory system. We exploit this fact in a stochastic network model of metastasis formation, in which only anatomically feasible routes of dissemination are considered. By fitting this model to two different clinical datasets (tongue & ovarian cancer) we show that incidence data can be modelled using a small number of biologically meaningful parameters. The fitted models reveal site specific relative rates of dissemination and also allow for patient-specific predictions of metastatic involvement based on primary tumour location and stage. Applied to other data sets this type of model could yield insight about seed-soil effects, and could also be used in a clinical setting to provide personalised predictions about the extent of metastatic spread.'),
(89577, 2019, 'plos', 'Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs', 'Liang, Cheng|Yu, Shengpeng|Luo, Jiawei', 'School of Information Science and Engineering, Shandong Normal University, Jinan, China.|School of Information Science and Engineering, Shandong Normal University, Jinan, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.', 'China|China|China', 14, 'Algorithms|Biomarkers, Tumor|Computational Biology|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Machine Learning|MicroRNAs|Models, Genetic|Models, Statistical|Thyroid Neoplasms', 'Evaluation Studies', '10.1371/journal.pcbi.1006931', '30933970', 'Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.'),
(89578, 2019, 'plos', 'Ten simple rules for organizing a webinar series', 'Fadlelmola, Faisal M|Panji, Sumir|Ahmed, Azza E|Ghouila, Amel|Akurugu, Wisdom A|Domelevo Entfellner, Jean-Baka|Souiai, Oussema|Mulder, Nicola|H3ABioNet Research working group as members of the H3Africa Consortium', 'Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.|Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Laboratory of Transmission, Control and Immunobiology of Infections (LTCII), Institut Pasteur de Tunis, Tunis-Belvédère, Tunisia.|Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana.|South African MRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville 7535, Cape Town, South Africa.|Laboratory of BioInformatics Biomathematics and bioStatistics, Institut Pasteur de SalTunis, Tunis, Tunisia.|Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.|Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.', 'Sudan|South Africa|Sudan|Tunisia|Ghana|South Africa|Tunisia|South Africa|South Africa', 9, 'Communication|Computational Biology|Computer Communication Networks|Humans|Internet', 'Editorial', '10.1371/journal.pcbi.1006671', '30933972', '-'),
(89579, 2019, 'plos', 'Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features', 'Magaret, Craig A|Benkeser, David C|Williamson, Brian D|Borate, Bhavesh R|Carpp, Lindsay N|Georgiev, Ivelin S|Setliff, Ian|Dingens, Adam S|Simon, Noah|Carone, Marco|Simpkins, Christopher|Montefiori, David|Alter, Galit|Yu, Wen-Han|Juraska, Michal|Edlefsen, Paul T|Karuna, Shelly|Mgodi, Nyaradzo M|Edugupanti, Srilatha|Gilbert, Peter B', 'Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.|Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.|Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.|Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.|Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Duke University School of Medicine, Duke University, Durham, North Carolina, United States of America.|Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America.|Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|University of Zimbabwe College of Health Sciences Clinical Trials Research Centre, Harare, Zimbabwe.|Department of Medicine, Division of Infectious Diseases, Emory University, Atlanta, Georgia, United States of America.|Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.', 'United States|Georgia|United States|United States|United States|United States|United States|United S', 3, 'Amino Acid Sequence|Antibodies, Monoclonal|Antibodies, Neutralizing|Binding Sites|CD4 Antigens|Computer Simulation|Forecasting|Glycosylation|HIV Antibodies|HIV Envelope Protein gp160|HIV Infections|HIV-1|Humans|Protein Binding', 'Journal Article', '10.1371/journal.pcbi.1006952', '30933973', 'The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts, and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites, the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.'),
(89580, 2019, 'plos', 'Structure, stability and specificity of the binding of ssDNA and ssRNA with proteins', 'Pal, Arumay|Levy, Yaakov', 'Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel.|Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel', 2, 'Animals|Base Sequence|Computational Biology|DNA, Single-Stranded|DNA-Binding Proteins|Humans|Models, Molecular|Nucleic Acid Conformation|Protein Binding|Protein Conformation|Protein Folding|Protein Interaction Domains and Motifs|RNA|RNA-Binding Proteins|Static Electricity|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1006768', '30933978', 'Recognition of single-stranded DNA (ssDNA) or single-stranded RNA (ssRNA) is important for many fundamental cellular functions. A variety of single-stranded DNA-binding proteins (ssDBPs) and single-stranded RNA-binding proteins (ssRBPs) have evolved that bind ssDNA and ssRNA, respectively, with varying degree of affinities and specificities to form complexes. Structural studies of these complexes provide key insights into their recognition mechanism. However, computational modeling of the specific recognition process and to predict the structure of the complex is challenging, primarily due to the heterogeneity of their binding energy landscape and the greater flexibility of ssDNA or ssRNA compared with double-stranded nucleic acids. Consequently, considerably fewer computational studies have explored interactions between proteins and single-stranded nucleic acids compared with protein interactions with double-stranded nucleic acids. Here, we report a newly developed energy-based coarse-grained model to predict the structure of ssDNA-ssDBP and ssRNA-ssRBP complexes and to assess their sequence-specific interactions and stabilities. We tuned two factors that can modulate specific recognition: base-aromatic stacking strength and the flexibility of the single-stranded nucleic acid. The model was successfully applied to predict the binding conformations of 12 distinct ssDBP and ssRBP structures with their cognate ssDNA and ssRNA partners having various sequences. Estimated binding energies agreed well with the corresponding experimental binding affinities. Bound conformations from the simulation showed a funnel-shaped binding energy distribution where the native-like conformations corresponded to the energy minima. The various ssDNA-protein and ssRNA-protein complexes differed in the balance of electrostatic and aromatic energies. The lower affinity of the ssRNA-ssRBP complexes compared with the ssDNA-ssDBP complexes stems from lower flexibility of ssRNA compared to ssDNA, which results in higher rate constants for the dissociation of the complex (koff) for complexes involving the former.'),
(89581, 2019, 'plos', 'Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE)', 'Meng, Hailong|Yaari, Gur|Bolen, Christopher R|Avey, Stefan|Kleinstein, Steven H', 'Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America.|Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.|Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.|Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America.', 'United States|Israel|United States|United States|United States', 1, 'Computational Biology|Gene Expression|Gene Expression Profiling|Humans|Influenza, Human|Probability|Software|Vaccination', 'Journal Article', '10.1371/journal.pcbi.1006899', '30939133', 'Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies.'),
(89582, 2019, 'plos', 'Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits', 'Gu, Yifan|Qi, Yang|Gong, Pulin', 'School of Physics, University of Sydney, New South Wales, Australia.|School of Physics, University of Sydney, New South Wales, Australia.|School of Physics, University of Sydney, New South Wales, Australia.', 'Australia|Australia|Australia', 3, 'Action Potentials|Animals|Cerebral Cortex|Computational Biology|Computer Simulation|Humans|Models, Neurological|Nerve Net|Neural Pathways|Neurons|Spatio-Temporal Analysis', 'Journal Article', '10.1371/journal.pcbi.1006902', '30939135', 'Experimental studies have begun revealing essential properties of the structural connectivity and the spatiotemporal activity dynamics of cortical circuits. To integrate these properties from anatomy and physiology, and to elucidate the links between them, we develop a novel cortical circuit model that captures a range of realistic features of synaptic connectivity. We show that the model accounts for the emergence of higher-order connectivity structures, including highly connected hub neurons that form an interconnected rich-club. The circuit model exhibits a rich repertoire of dynamical activity states, ranging from asynchronous to localized and global propagating wave states. We find that around the transition between asynchronous and localized propagating wave states, our model quantitatively reproduces a variety of major empirical findings regarding neural spatiotemporal dynamics, which otherwise remain disjointed in existing studies. These dynamics include diverse coupling (correlation) between spiking activity of individual neurons and the population, dynamical wave patterns with variable speeds and precise temporal structures of neural spikes. We further illustrate how these neural dynamics are related to the connectivity properties by analysing structural contributions to variable spiking dynamics and by showing that the rich-club structure is related to the diverse population coupling. These findings establish an integrated account of structural connectivity and activity dynamics of local cortical circuits, and provide new insights into understanding their working mechanisms.'),
(89583, 2019, 'plos', 'A quick guide for student-driven community genome annotation', 'Hosmani, Prashant S|Shippy, Teresa|Miller, Sherry|Benoit, Joshua B|Munoz-Torres, Monica|Flores-Gonzalez, Mirella|Mueller, Lukas A|Wiersma-Koch, Helen|DElia, Tom|Brown, Susan J|Saha, Surya', 'Boyce Thompson Institute, Ithaca, New York.|Division of Biology, Kansas State University, Manhattan, Kansas.|Division of Biology, Kansas State University, Manhattan, Kansas.|Department of Biological Sciences, University of Cincinnati, Cincinnati, Ohio.|Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology, Berkeley, California.|Boyce Thompson Institute, Ithaca, New York.|Boyce Thompson Institute, Ithaca, New York.|Indian River State College, Fort Pierce, Florida.|Indian River State College, Fort Pierce, Florida.|Division of Biology, Kansas State University, Manhattan, Kansas.|Boyce Thompson Institute, Ithaca, New York.', '-|-|-|-|-|-|-|India|India|-|-', 4, 'Computational Biology|Databases, Genetic|Genomics|Guidelines as Topic|Humans|Models, Genetic|Molecular Sequence Annotation|Students', 'Journal Article', '10.1371/journal.pcbi.1006682', '30943207', 'High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the course of many years. The majority of gene models present in biological databases today have been identified in draft genome assemblies using automated annotation pipelines that are frequently based on orthologs from distantly related model organisms and usually have minor or major errors. Manual curation is time consuming and often requires substantial expertise, but is instrumental in improving gene model structure and identification. Manual annotation may seem to be a daunting and cost-prohibitive task for small research communities but involving undergraduates in community genome annotation consortiums can be mutually beneficial for both education and improved genomic resources. We outline a workflow for efficient manual annotation driven by a team of primarily undergraduate annotators. This model can be scaled to large teams and includes quality control processes through incremental evaluation. Moreover, it gives students an opportunity to increase their understanding of genome biology and to participate in scientific research in collaboration with peers and senior researchers at multiple institutions.'),
(89584, 2019, 'plos', 'Genotype-phenotype relations of the von Hippel-Lindau tumor suppressor inferred from a large-scale analysis of disease mutations and interactors', 'Minervini, Giovanni|Quaglia, Federica|Tabaro, Francesco|Tosatto, Silvio C E', 'Department of Biomedical Sciences, University of Padova, Padova, Italy.|Department of Biomedical Sciences, University of Padova, Padova, Italy.|Department of Biomedical Sciences, University of Padova, Padova, Italy.|Department of Biomedical Sciences, University of Padova, Padova, Italy.', 'Italy|Italy|Italy|Italy', 6, 'Carcinoma, Renal Cell|Computational Biology|Genes, Tumor Suppressor|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Kidney Neoplasms|Molecular Docking Simulation|Mutation|Polycythemia|Protein Interaction Domains and Motifs|Protein Interaction Maps|Protein Processing, Post-Translational|Von Hippel-Lindau Tumor Suppressor Protein|von Hippel-Lindau Disease', 'Journal Article', '10.1371/journal.pcbi.1006478', '30943211', 'Familiar cancers represent a privileged point of view for studying the complex cellular events inducing tumor transformation. Von Hippel-Lindau syndrome, a familiar predisposition to develop cancer is a clear example. Here, we present our efforts to decipher the role of von Hippel-Lindau tumor suppressor protein (pVHL) in cancer insurgence. We collected high quality information about both pVHL mutations and interactors to investigate the association between patient phenotypes, mutated protein surface and impaired interactions. Our data suggest that different phenotypes correlate with localized perturbations of the pVHL structure, with specific cell functions associated to different protein surfaces. We propose five different pVHL interfaces to be selectively involved in modulating proteins regulating gene expression, protein homeostasis as well as to address extracellular matrix (ECM) and ciliogenesis associated functions. These data were used to drive molecular docking of pVHL with its interactors and guide Petri net simulations of the most promising alterations. We predict that disruption of pVHL association with certain interactors can trigger tumor transformation, inducing metabolism imbalance and ECM remodeling. Collectively taken, our findings provide novel insights into VHL-associated tumorigenesis. This highly integrated in silico approach may help elucidate novel treatment paradigms for VHL disease.'),
(89585, 2019, 'plos', 'Conservation of conformational dynamics across prokaryotic actins', 'Ng, Natalie|Shi, Handuo|Colavin, Alexandre|Huang, Kerwyn Casey', 'Department of Bioengineering, Stanford University, Stanford, CA, United States of America.|Department of Bioengineering, Stanford University, Stanford, CA, United States of America.|Biophysics Program, Stanford University, Stanford, CA, United States of America.|Department of Bioengineering, Stanford University, Stanford, CA, United States of America.', 'United States|United States|United States|United States', 1, 'Actins|Bacterial Proteins|Computational Biology|Crystallography, X-Ray|Cytoskeletal Proteins|Escherichia coli Proteins|Models, Molecular|Molecular Dynamics Simulation|Protein Conformation|Protein Interaction Domains and Motifs|Pyrobaculum|Structural Homology, Protein', 'Journal Article', '10.1371/journal.pcbi.1006683', '30951524', 'The actin family of cytoskeletal proteins is essential to the physiology of virtually all archaea, bacteria, and eukaryotes. While X-ray crystallography and electron microscopy have revealed structural homologies among actin-family proteins, these techniques cannot probe molecular-scale conformational dynamics. Here, we use all-atom molecular dynamic simulations to reveal conserved dynamical behaviors in four prokaryotic actin homologs: MreB, FtsA, ParM, and crenactin. We demonstrate that the majority of the conformational dynamics of prokaryotic actins can be explained by treating the four subdomains as rigid bodies. MreB, ParM, and FtsA monomers exhibited nucleotide-dependent dihedral and opening angles, while crenactin monomer dynamics were nucleotide-independent. We further show that the opening angle of ParM is sensitive to a specific interaction between subdomains. Steered molecular dynamics simulations of MreB, FtsA, and crenactin dimers revealed that changes in subunit dihedral angle lead to intersubunit bending or twist, suggesting a conserved mechanism for regulating filament structure. Taken together, our results provide molecular-scale insights into the nucleotide and polymerization dependencies of the structure of prokaryotic actins, suggesting mechanisms for how these structural features are linked to their diverse functions.'),
(89586, 2019, 'plos', 'Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: Continuously varying exponents mimic M/EEG results', 'Dalla Porta, Leonardo|Copelli, Mauro', 'Departamento de Física, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil.|Departamento de Física, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil.', 'Brazil|Brazil', 8, 'Action Potentials|Alpha Rhythm|Brain|Computational Biology|Electroencephalography|Humans|Likelihood Functions|Magnetoencephalography|Models, Neurological|Nerve Net|Neurons|Stochastic Processes|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1006924', '30951525', 'We revisit the CROS (CRitical OScillations) model which was recently proposed as an attempt to reproduce both scale-invariant neuronal avalanches and long-range temporal correlations. With excitatory and inhibitory stochastic neurons locally connected in a two-dimensional disordered network, the model exhibits a transition where alpha-band oscillations emerge. Precisely at the transition, the fluctuations of the network activity have nontrivial detrended fluctuation analysis (DFA) exponents, and avalanches (defined as supra-threshold activity) have power law distributions of size and duration. We show that, differently from previous results, the exponents governing the distributions of avalanche size and duration are not necessarily those of the mean-field directed percolation universality class (3/2 and 2, respectively). Instead, in a narrow region of parameter space, avalanche exponents obtained via a maximum-likelihood estimator vary continuously and follow a linear relation, in good agreement with results obtained from M/EEG data. In that region, moreover, the values of avalanche and DFA exponents display a spread with positive correlations, reproducing human MEG results.'),
(89587, 2019, 'plos', 'Model diagnostics and refinement for phylodynamic models', 'Lau, Max S Y|Grenfell, Bryan T|Worby, Colin J|Gibson, Gavin J', 'Department of Ecology and Evolutionary Biology, Princeton University, New Jersey, USA.|Department of Ecology and Evolutionary Biology, Princeton University, New Jersey, USA.|Broad Institute, Cambridge, MA 02142, USA.|Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK.', 'Jersey|Jersey|United Kingdom|United Kingdom', 0, 'Animals|Computational Biology|Computer Simulation|Disease Outbreaks|Humans|Models, Statistical|Molecular Epidemiology|Phylogeny|Viruses', 'Journal Article', '10.1371/journal.pcbi.1006955', '30951528', 'Phylodynamic modelling, which studies the joint dynamics of epidemiological and evolutionary processes, has made significant progress in recent years due to increasingly available genomic data and advances in statistical modelling. These advances have greatly improved our understanding of transmission dynamics of many important pathogens. Nevertheless, there remains a lack of effective, targetted diagnostic tools for systematically detecting model mis-specification. Development of such tools is essential for model criticism, refinement, and calibration. The idea of utilising latent residuals for model assessment has already been exploited in general spatio-temporal epidemiological settings. Specifically, by proposing appropriately designed non-centered, re-parameterizations of a given epidemiological process, one can construct latent residuals with known sampling distributions which can be used to quantify evidence of model mis-specification. In this paper, we extend this idea to formulate a novel model-diagnostic framework for phylodynamic models. Using simulated examples, we show that our framework may effectively detect a particular form of mis-specification in a phylodynamic model, particularly in the event of superspreading. We also exemplify our approach by applying the framework to a dataset describing a local foot-and-mouth (FMD) outbreak in the UK, eliciting strong evidence against the assumption of no within-host-diversity in the outbreak. We further demonstrate that our framework can facilitate model calibration in real-life scenarios, by proposing a within-host-diversity model which appears to offer a better fit to data than one that assumes no within-host-diversity of FMD virus.'),
(89588, 2019, 'plos', 'Multiple factors influence the morphology of the bipolar electrogram: An in silico modeling study', 'Hwang, Minki|Kim, Jaehyuk|Lim, Byounghyun|Song, Jun-Seop|Joung, Boyoung|Shim, Eun Bo|Pak, Hui-Nam', 'Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.|Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.|Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.|Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.|Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.|Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, Kangwon-do, Republic of Korea.|Division of Cardiology, Yonsei University Health System, Seoul, Republic of Korea.', '-|-|-|-|-|-|-', 2, 'Atrial Fibrillation|Catheter Ablation|Computational Biology|Computer Simulation|Electrodes|Electrophysiologic Techniques, Cardiac|Electrophysiological Phenomena|Heart Conduction System|Humans|Models, Cardiovascular|Reproducibility of Results|User-Computer Interface', 'Journal Article', '10.1371/journal.pcbi.1006765', '30951529', 'Although bipolar electrograms (Bi-egms) are commonly used for catheter mapping and ablation of cardiac arrhythmias, the accuracy and reproducibility of Bi-egms have not been evaluated. We aimed to clarify the influence of the catheter orientation (CO), catheter contact angle (CA), local conduction velocity (CV), scar size, and catheter type on the Bi-egm morphology using an in silico 3-dimensional realistic model of atrial fibrillation. We constructed a 3-dimensional, realistic, in silico left atrial model with activation wave propagation including bipolar catheter models. Bi-egms were obtained by computing the extracellular potentials from the distal and proximal electrodes. The amplitude and width were measured on virtual Bi-egms obtained under different conditions created by changing the CO according to the wave direction, catheter-atrial wall CA, local CV, size of the non-conductive area, and catheter type. Bipolar voltages were also compared between virtual and clinically acquired Bi-egms. Bi-egm amplitudes were lower for a perpendicular than parallel CO relative to the wave direction (p<0.001), lower for a 90° than 0° CA (p<0.001), and lower for a CV of 0.13m/s than 0.48m/s (p<0.001). Larger sized non-conductive areas were associated with a decreased bipolar amplitude (p<0.001) and increased bipolar width (p<0.001). Among three commercially available catheters (Orion, Pentaray, and Thermocool), those with more narrowly spaced and smaller electrodes produced higher voltages on the virtual Bi-egms (p<0.001). Multiple factors including the CO, CA, CV, and catheter design significantly influence the Bi-egm morphology. Universal voltage cut-off values may not be appropriate for bipolar voltage-guided substrate mapping.'),
(89589, 2019, 'plos', 'BEAST 25: An advanced software platform for Bayesian evolutionary analysis', 'Bouckaert, Remco|Vaughan, Timothy G|Barido-Sottani, Joëlle|Duchêne, Sebastián|Fourment, Mathieu|Gavryushkina, Alexandra|Heled, Joseph|Jones, Graham|Kühnert, Denise|De Maio, Nicola|Matschiner, Michael|Mendes, Fábio K|Müller, Nicola F|Ogilvie, Huw A|du Plessis, Louis|Popinga, Alex|Rambaut, Andrew|Rasmussen, David|Siveroni, Igor|Suchard, Marc A|Wu, Chieh-Hsi|Xie, Dong|Zhang, Chi|Stadler, Tanja|Drummond, Alexei J', 'Centre of Computational Evolution, University of Auckland, Auckland, New Zealand.|ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland.|ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland.|Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Victoria, Australia.|ithree institute, University of Technology Sydney, Sydney, Australia.|Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand.|Independent researcher, Auckland, New Zealand.|Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 Göteborg, Sweden.|Max Planck Institute for the Science of Human History, Jena, Germany.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridgeshire, UK.|Department of Environmental Sciences, University of Basel, 4051 Basel, Switzerland.|Centre of Computational Evolution, University of Auckland, Auckland, New Zealand.|ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland.|Department of Computer Science, Rice University, Houston, TX 77005-1892, USA.|Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.|Centre of Computational Evolution, University of Auckland, Auckland, New Zealand.|Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK.|Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA.|Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, W2 1PG, UK.|Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.|Department of Statistics, University of Oxford, OX1 3LB, UK.|Centre of Computational Evolution, University of Auckland, Auckland, New Zealand.|Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China.|ETH Zürich, Department of Biosystems Science and Engineering, 4058 Basel, Switzerland.|Centre of Computational Evolution, University of Auckland, Auckland, New Zealand.', 'New Zealand|Switzerland|Switzerland|Australia|Australia|New Zealand|New Zealand|Sweden|Germany|United Kingdom|Swi', 163, 'Animals|Bayes Theorem|Biological Evolution|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Markov Chains|Models, Genetic|Monte Carlo Method|Phylogeny|Software', 'Journal Article', '10.1371/journal.pcbi.1006650', '30958812', 'Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.'),
(89590, 2019, 'plos', 'Dynamic filopodial forces induce accumulation, damage, and plastic remodeling of 3D extracellular matrices', 'Malandrino, Andrea|Trepat, Xavier|Kamm, Roger D|Mak, Michael', 'Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Institute for Bioengineering of Catalonia, Barcelona, Spain.|Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Yale University, Biomedical Engineering Department, New Haven, Connecticut, United States of America.', 'United States|Spain|United States|United States', 14, 'Biomechanical Phenomena|Biopolymers|Cell Line|Cellular Microenvironment|Computational Biology|Computer Simulation|Extracellular Matrix|Human Umbilical Vein Endothelial Cells|Humans|Imaging, Three-Dimensional|Kinetics|Models, Biological|Pseudopodia', 'Journal Article', '10.1371/journal.pcbi.1006684', '30958816', 'The mechanical properties of the extracellular matrix (ECM)-a complex, 3D, fibrillar scaffold of cells in physiological environments-modulate cell behavior and can drive tissue morphogenesis, regeneration, and disease progression. For simplicity, it is often convenient to assume these properties to be time-invariant. In living systems, however, cells dynamically remodel the ECM and create time-dependent local microenvironments. Here, we show how cell-generated contractile forces produce substantial irreversible changes to the density and architecture of physiologically relevant ECMs-collagen I and fibrin-in a matter of minutes. We measure the 3D deformation profiles of the ECM surrounding cancer and endothelial cells during stages when force generation is active or inactive. We further correlate these ECM measurements to both discrete fiber simulations that incorporate fiber crosslink unbinding kinetics and continuum-scale simulations that account for viscoplastic and damage features. Our findings further confirm that plasticity, as a mechanical law to capture remodeling in these networks, is fundamentally tied to material damage via force-driven unbinding of fiber crosslinks. These results characterize in a multiscale manner the dynamic nature of the mechanical environment of physiologically mimicking cell-in-gel systems.'),
(89591, 2019, 'plos', 'Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms', 'Kalimeri, Kyriaki|Delfino, Matteo|Cattuto, Ciro|Perrotta, Daniela|Colizza, Vittoria|Guerrisi, Caroline|Turbelin, Clement|Duggan, Jim|Edmunds, John|Obi, Chinelo|Pebody, Richard|Franco, Ana O|Moreno, Yamir|Meloni, Sandro|Koppeschaar, Carl|Kjelsø, Charlotte|Mexia, Ricardo|Paolotti, Daniela', 'ISI Foundation, Turin, Italy.|ISI Foundation, Turin, Italy.|ISI Foundation, Turin, Italy.|ISI Foundation, Turin, Italy.|INSERM, Sorbonne Université, Institut Pierre Louis dEpidémiologie et de Santé Publique, IPLESP, Paris, France.|Sorbonne Université, INSERM, Institut Pierre Louis dEpidémiologie et de Santé Publique, IPLESP, Paris, France.|Sorbonne Université, INSERM, Institut Pierre Louis dEpidémiologie et de Santé Publique, IPLESP, Paris, France.|School of Computer Science, National University of Ireland Galway, Galway, Ireland.|Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.|Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom.|Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|ISI Foundation, Turin, Italy.|IFISC, Institute for Cross-Disciplinary Physics and Complex Systems (CSIC-UIB), Palma de Mallorca, Spain.|Science in Action BV, Amsterdam, The Netherlands.|Statens Serum Institut, Copenhagen, Denmark.|Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisbon, Portugal.|ISI Foundation, Turin, Italy.', 'Italy|Italy|Italy|Italy|France|France|France|Ireland|United Kingdom|United Kingdom|United Kingdom|Po', 3, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Epidemics|Europe|Humans|Incidence|Influenza, Human|Internet|Models, Statistical|Seasons|Self Report|Sentinel Surveillance|Syndrome|Unsupervised Machine Learning', 'Journal Article', '10.1371/journal.pcbi.1006173', '30958817', 'Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.'),
(89592, 2019, 'plos', 'The intrinsic dimension of protein sequence evolution', 'Facco, Elena|Pagnani, Andrea|Russo, Elena Tea|Laio, Alessandro', 'SISSA, Trieste, Italy.|DISAT, Politecnico di Torino, Torino, Italy.|SISSA, Trieste, Italy.|SISSA, Trieste, Italy.', 'Italy|Italy|Italy|Italy', 1, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Evolution, Molecular|Models, Molecular|Mutation|Phylogeny|Protein Conformation|Protein Folding|Proteins|Sequence Homology, Amino Acid|Structural Homology, Protein', 'Journal Article', '10.1371/journal.pcbi.1006767', '30958823', 'It is well known that, in order to preserve its structure and function, a protein cannot change its sequence at random, but only by mutations occurring preferentially at specific locations. We here investigate quantitatively the amount of variability that is allowed in protein sequence evolution, by computing the intrinsic dimension (ID) of the sequences belonging to a selection of protein families. The ID is a measure of the number of independent directions that evolution can take starting from a given sequence. We find that the ID is practically constant for sequences belonging to the same family, and moreover it is very similar in different families, with values ranging between 6 and 12. These values are significantly smaller than the raw number of amino acids, confirming the importance of correlations between mutations in different sites. However, we demonstrate that correlations are not sufficient to explain the small value of the ID we observe in protein families. Indeed, we show that the ID of a set of protein sequences generated by maximum entropy models, an approach in which correlations are accounted for, is typically significantly larger than the value observed in natural protein families. We further prove that a critical factor to reproduce the natural ID is to take into consideration the phylogeny of sequences.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89593, 2019, 'plos', 'Contextual influence on confidence judgments in human reinforcement learning', 'Lebreton, Maël|Bacily, Karin|Palminteri, Stefano|Engelmann, Jan B', 'CREED, Amsterdam School of Economics (ASE), Universiteit van Amsterdam, Amsterdam, the Netherlands.|CREED, Amsterdam School of Economics (ASE), Universiteit van Amsterdam, Amsterdam, the Netherlands.|Human Reinforcement Learning team, Université de Recherche Paris Sciences et Lettres, Paris, France.|CREED, Amsterdam School of Economics (ASE), Universiteit van Amsterdam, Amsterdam, the Netherlands.', 'Netherlands|Netherlands|France|Netherlands', 3, 'Adult|Choice Behavior|Decision Making|Female|Humans|Judgment|Learning|Male|Reinforcement (Psychology)|Self Concept|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1006973', '30958826', 'The ability to correctly estimate the probability of ones choices being correct is fundamental to optimally re-evaluate previous choices or to arbitrate between different decision strategies. Experimental evidence nonetheless suggests that this metacognitive process-confidence judgment- is susceptible to numerous biases. Here, we investigate the effect of outcome valence (gains or losses) on confidence while participants learned stimulus-outcome associations by trial-and-error. In two experiments, participants were more confident in their choices when learning to seek gains compared to avoiding losses, despite equal difficulty and performance between those two contexts. Computational modelling revealed that this bias is driven by the context-value, a dynamically updated estimate of the average expected-value of choice options, necessary to explain equal performance in the gain and loss domain. The biasing effect of context-value on confidence, revealed here for the first time in a reinforcement-learning context, is therefore domain-general, with likely important functional consequences. We show that one such consequence emerges in volatile environments, where the (in)flexibility of individuals learning strategies differs when outcomes are framed as gains or losses. Despite apparent similar behavior- profound asymmetries might therefore exist between learning to avoid losses and learning to seek gains.'),
(89594, 2019, 'plos', 'Recentrifuge: Robust comparative analysis and contamination removal for metagenomics', 'Martí, Jose Manuel', 'Institute for Integrative Systems Biology (I2SysBio), Valencia, Spain.', 'Spain', 8, 'Algorithms|Big Data|Computational Biology|DNA Contamination|High-Throughput Nucleotide Sequencing|Humans|Internet|Metagenome|Metagenomics|Microbiota|Sequence Analysis, DNA|Software|Whole Genome Sequencing', 'Comparative Study', '10.1371/journal.pcbi.1006967', '30958827', 'Metagenomic sequencing is becoming widespread in biomedical and environmental research, and the pace is increasing even more thanks to nanopore sequencing. With a rising number of samples and data per sample, the challenge of efficiently comparing results within a specimen and between specimens arises. Reagents, laboratory, and host related contaminants complicate such analysis. Contamination is particularly critical in low microbial biomass body sites and environments, where it can comprise most of a sample if not all. Recentrifuge implements a robust method for the removal of negative-control and crossover taxa from the rest of samples. With Recentrifuge, researchers can analyze results from taxonomic classifiers using interactive charts with emphasis on the confidence level of the classifications. In addition to contamination-subtracted samples, Recentrifuge provides shared and exclusive taxa per sample, thus enabling robust contamination removal and comparative analysis in environmental and clinical metagenomics. Regarding the first area, Recentrifuges novel approach has already demonstrated its benefits showing that microbiomes of Arctic and Antarctic solar panels display similar taxonomic profiles. In the clinical field, to confirm Recentrifuges ability to analyze complex metagenomes, we challenged it with data coming from a metagenomic investigation of RNA in plasma that suffered from critical contamination to the point of preventing any positive conclusion. Recentrifuge provided results that yielded new biological insight into the problem, supporting the growing evidence of a blood microbiota even in healthy individuals, mostly translocated from the gut, the oral cavity, and the genitourinary tract. We also developed a synthetic dataset carefully designed to rate the robust contamination removal algorithm, which demonstrated a significant improvement in specificity while retaining a high sensitivity even in the presence of cross-contaminants. Recentrifuges official website is www.recentrifuge.org. The data and source code are anonymously and freely available on GitHub and PyPI. The computing code is licensed under the AGPLv3. The Recentrifuge Wiki is the most extensive and continually-updated source of documentation for Recentrifuge, covering installation, use cases, testing, and other useful topics.'),
(89595, 2019, 'plos', 'Stytra: An open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments', 'Štih, Vilim|Petrucco, Luigi|Kist, Andreas M|Portugues, Ruben', 'Research Group of Sensorimotor Control, Max Planck Institute of Neurobiology, Martinsried, Germany.|Research Group of Sensorimotor Control, Max Planck Institute of Neurobiology, Martinsried, Germany.|Research Group of Sensorimotor Control, Max Planck Institute of Neurobiology, Martinsried, Germany.|Research Group of Sensorimotor Control, Max Planck Institute of Neurobiology, Martinsried, Germany.', 'Germany|Germany|Germany|Germany', 6, 'Animals|Behavior, Animal|Computational Biology|Computer Systems|Equipment Design|Larva|Metadata|Models, Neurological|Neural Networks (Computer)|Photic Stimulation|Programming Languages|Restraint, Physical|Software|Video Recording|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1006699', '30958870', 'We present Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.'),
(89596, 2019, 'plos', 'Confidence resets reveal hierarchical adaptive learning in humans', 'Heilbron, Micha|Meyniel, Florent', 'Cognitive Neuroimaging Unit / NeuroSpin center / Institute for Life Sciences Frédéric Joliot / Fundamental Research Division / Commissariat à lEnergie Atomique et aux énergies alternatives, INSERM, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, France.|Cognitive Neuroimaging Unit / NeuroSpin center / Institute for Life Sciences Frédéric Joliot / Fundamental Research Division / Commissariat à lEnergie Atomique et aux énergies alternatives, INSERM, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, France.', 'France|France', 5, 'Adult|Brain|Choice Behavior|Cognition|Deep Learning|Female|Humans|Learning|Male|Uncertainty|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1006972', '30964861', 'Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cognition. On the other hand, non-hierarchical (flat) models remain influential and can learn efficiently, even in uncertain and changing environments. Here, we show that previously proposed hallmarks of hierarchical learning, which relied on reports of learned quantities or choices in simple experiments, are insufficient to categorically distinguish hierarchical from flat models. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results support the hierarchical learning framework, and demonstrate how confidence can be a useful metric in learning theory.'),
(89597, 2019, 'plos', 'Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning', 'Conti, Simone|Karplus, Martin', 'Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.|Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.', 'United States|United States', 3, 'AIDS Vaccines|Amino Acid Sequence|Antibodies, Neutralizing|CD4 Antigens|CD4 Lymphocyte Count|Computational Biology|Epitopes|HIV Antibodies|HIV Infections|HIV-1|Humans|Inhibitory Concentration 50|Machine Learning', 'Journal Article', '10.1371/journal.pcbi.1006954', '30970017', 'HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses in the Seaman panel that are neutralized by the antibody. Experimentally the neutralization ability is measured as the half maximal inhibitory concentration of the antibody (IC50). To avoid such time-consuming experimental measurements, we developed a computational approach to estimate the IC50 and use it to determine the antibody breadth. Given that no direct method exists for calculating IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these are used to train a Multi-Layer Perceptron (an Artificial Neural Network) to predict the value of the IC50 (by regression), or if the antibody binds or not to the virus (by classification). The neural networks are trained by use of experimental IC50 values collected in the CATNAP database. The computed breadths obtained by regression and classification are reported and the importance of having some related information in the data set for obtaining accurate predictions is analyzed. This approach is expected to prove useful for the design of HIV bnAbs, where the computation of the potency must be accompanied by a computation of the breadth, and for evaluating the efficiency of potential vaccination schemes developed through modeling and simulation.'),
(89598, 2019, 'plos', 'Physical constraints on accuracy and persistence during breast cancer cell chemotaxis', 'Varennes, Julien|Moon, Hye-Ran|Saha, Soutick|Mugler, Andrew|Han, Bumsoo', 'Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana, United States of America.|School of Mechanical Engineering, Purdue University, West Lafayette Indiana, United States of America.|Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana, United States of America.|Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana, United States of America.|School of Mechanical Engineering, Purdue University, West Lafayette Indiana, United States of America.', 'India|India|India|India|India', 1, 'Breast Neoplasms|Cell Line, Tumor|Cell Movement|Chemotaxis|Computer Simulation|Female|Humans|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1006961', '30970018', 'Directed cell motion in response to an external chemical gradient occurs in many biological phenomena such as wound healing, angiogenesis, and cancer metastasis. Chemotaxis is often characterized by the accuracy, persistence, and speed of cell motion, but whether any of these quantities is physically constrained by the others is poorly understood. Using a combination of theory, simulations, and 3D chemotaxis assays on single metastatic breast cancer cells, we investigate the links among these different aspects of chemotactic performance. In particular, we observe in both experiments and simulations that the chemotactic accuracy, but not the persistence or speed, increases with the gradient strength. We use a random walk model to explain this result and to propose that cells chemotactic accuracy and persistence are mutually constrained. Our results suggest that key aspects of chemotactic performance are inherently limited regardless of how favorable the environmental conditions are.'),
(89599, 2019, 'plos', 'Ten simple rules towards healthier research labs', 'Maestre, Fernando T', 'Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Móstoles, Spain.', 'Spain', 5, 'Computational Biology|Humans|Laboratories|Laboratory Personnel|Occupational Stress|Research Personnel|Workplace', 'Editorial', '10.1371/journal.pcbi.1006914', '30973866', 'The negative effects of extremely competitive academic and research environments on the performance and health of researchers are well known and common worldwide. The prevalence of these effects, particularly among early career researchers, calls for a more humane and people-centered way of working within research labs. Although there is growing concern about the urgent need for a better life-work balance when doing science, there are not many examples about how this could be achieved in practice. In this article, I introduce 10 simple rules to make the working environment of research labs more nurturing, collaborative, and people-centered. These rules are directed towards existing and future principal investigators (PIs) but will be of interest to anyone working in a research lab and/or dealing with how to improve working conditions for scientists.'),
(89600, 2019, 'plos', 'Network motifs and their origins', 'Stone, Lewi|Simberloff, Daniel|Artzy-Randrup, Yael', 'Biomathematics Unit, School of Zoology, Faculty of Life Sciences, Tel Aviv University, Israel.|Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America.|Department of Theoretical and Computational Ecology, IBED, University of Amsterdam, Amsterdam, the Netherlands.', 'Israel|United States|Netherlands', 0, 'Computational Biology|Ecosystem|Gene Regulatory Networks|Humans|Models, Biological|Neural Networks (Computer)|Social Networking|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1006749', '30973867', 'Modern network science is a new and exciting research field that has transformed the study of complex systems over the last 2 decades. Of particular interest is the identification of small network motifs that might be embedded in a larger network and that indicate the presence of evolutionary design principles or have an overly influential role on system-wide dynamics. Motifs are patterns of interconnections, or subgraphs, that appear in an observed network significantly more often than in compatible randomized networks. The concept of network motifs was introduced into Systems Biology by Milo, Alon and colleagues in 2002, quickly revolutionized the field, and it has had a huge impact in wider scientific domains ever since. Here, we argue that the same concept and tools for the detection of motifs were well known in the ecological literature decades into the last century, a fact that is generally not recognized. We review the early history of network motifs, their evolution in the mathematics literature, and their recent rediscoveries.'),
(89601, 2019, 'plos', 'Reconciling high-throughput gene essentiality data with metabolic network reconstructions', 'Blazier, Anna S|Papin, Jason A', 'Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.|Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.', 'United States|United States', 4, 'Computational Biology|Culture Media|DNA Transposable Elements|Databases, Genetic|Genes, Essential|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Metabolic Networks and Pathways|Models, Genetic|Mutagenesis|Pseudomonas aeruginosa', 'Journal Article', '10.1371/journal.pcbi.1006507', '30973869', 'The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches, however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.'),
(89602, 2019, 'plos', 'Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder', 'Al-Shaer, Abrar E|Flentke, George R|Berres, Mark E|Garic, Ana|Smith, Susan M', 'Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, North Carolina, United States of America.|Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, North Carolina, United States of America.|Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.|Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.|Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, North Carolina, United States of America.', 'United States|United States|United States|United States|United States', 2, 'Animals|Big Data|Chick Embryo|Cluster Analysis|Computational Biology|Databases, Nucleic Acid|Disease Models, Animal|Ethanol|Exons|Female|Fetal Alcohol Spectrum Disorders|Gene Expression Profiling|Humans|MicroRNAs|Pregnancy|Principal Component Analysis|Unsupervised Machine Learning', 'Journal Article', '10.1371/journal.pcbi.1006937', '30973878', 'Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize genes based on an adjusted p-value, which is not always biologically relevant. We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. This was performed on an RNA-Seq paired-end dataset derived from alcohol-exposed neural fold-stage chick crania, wherein alcohol causes facial deficits recapitulating those of FASD. A principal component analysis along with k-means clustering was utilized to extract exons that deviated from baseline expression. This identified 6857 differentially expressed exons representing 1251 geneIDs, 391 of these genes were identified in a prior gene-level analysis of this dataset. It also identified exons encoding 23 microRNAs (miRNAs) having significantly differential expression profiles in response to alcohol. We developed an RDAVID pipeline to identify KEGG pathways represented by these exons, and separately identified predicted KEGG pathways targeted by these miRNAs. Several of these (ribosome biogenesis, oxidative phosphorylation) were identified in our prior gene-level analysis. Other pathways are crucial to facial morphogenesis and represent both novel (focal adhesion, FoxO signaling, insulin signaling) and known (Wnt signaling) alcohol targets. Importantly, there was substantial overlap between the exomes themselves and the predicted miRNA targets, suggesting these miRNAs contribute to the gene-level expression changes. Our novel application of unsupervised machine learning in conjunction with statistical analyses facilitated the discovery of signaling pathways and miRNAs that inform mechanisms underlying FASD.'),
(89603, 2019, 'plos', 'Model-based analysis of influenza A virus replication in genetically engineered cell lines elucidates the impact of host cell factors on key kinetic parameters of virus growth', 'Laske, Tanja|Bachmann, Mandy|Dostert, Melanie|Karlas, Alexander|Wirth, Dagmar|Frensing, Timo|Meyer, Thomas F|Hauser, Hansjörg|Reichl, Udo', 'Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Department of Molecular Biology, Max Planck Institute for Infection Biology, Berlin, Germany.|Research Group Model Systems for Infection and Immunity, Helmholtz Center for Infection Research, Braunschweig, Germany.|Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Department of Molecular Biology, Max Planck Institute for Infection Biology, Berlin, Germany.|Department of Gene Regulation and Differentiation, Helmholtz Center for Infection Research, Braunschweig, Germany.|Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'A549 Cells|Active Transport, Cell Nucleus|Animals|Computational Biology|Computer Simulation|Dogs|Genetic Engineering|Genome, Viral|Host Microbial Interactions|Humans|Influenza A virus|Influenza Vaccines|Kinetics|Madin Darby Canine Kidney Cells|Models, Biological|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006944', '30973879', 'The best measure to limit spread of contagious diseases caused by influenza A viruses (IAVs) is annual vaccination. The growing global demand for low-cost vaccines requires the establishment of high-yield production processes. One possible option to address this challenge is the engineering of novel vaccine producer cell lines by manipulating gene expression of host cell factors relevant for virus replication. To support detailed characterization of engineered cell lines, we fitted an ordinary differential equation (ODE)-based model of intracellular IAV replication previously established by our group to experimental data obtained from infection studies in human A549 cells. Model predictions indicate that steps of viral RNA synthesis, their regulation and particle assembly and virus budding are promising targets for cell line engineering. The importance of these steps was confirmed in four of five single gene overexpression cell lines (SGOs) that showed small, but reproducible changes in early dynamics of RNA synthesis and virus release. Model-based analysis suggests, however, that overexpression of the selected host cell factors negatively influences specific RNA synthesis rates. Still, virus yield was rescued by an increase in the virus release rate. Based on parameter estimations obtained for SGOs, we predicted that there is a potential benefit associated with overexpressing multiple host cell genes in one cell line, which was validated experimentally. Overall, this model-based study on IAV replication in engineered cell lines provides a step forward in the dynamic and quantitative characterization of IAV-host cell interactions. Furthermore, it suggests targets for gene editing and indicates that overexpression of multiple host cell factors may be beneficial for the design of novel producer cell lines.'),
(89604, 2019, 'plos', 'Ten quick tips for creating an effective lesson', 'Wilson, Greg', 'RStudio, Toronto, Ontario, Canada.', 'Canada', 0, 'Computational Biology|Humans|Learning|Linguistics|Models, Educational|Models, Psychological|Motivation|Peer Group|Photic Stimulation|Students|Teaching', 'Journal Article', '10.1371/journal.pcbi.1006915', '30973880', 'We present 10 tips for building effective lessons that are grounded in empirical research on pedagogy and cognitive psychology and that we have found to be practically useful in both classroom and free-range settings.'),
(89605, 2019, 'plos', 'Microstimulation in a spiking neural network model of the midbrain superior colliculus', 'Kasap, Bahadir|van Opstal, A John', 'Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.|Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.', 'Netherlands|Netherlands', 1, 'Action Potentials|Animals|Computational Biology|Electric Stimulation|Haplorhini|Humans|Models, Neurological|Nerve Net|Neural Networks (Computer)|Neurons|Saccades|Superior Colliculi', 'Journal Article', '10.1371/journal.pcbi.1006522', '30978180', 'The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the site of stimulation produces a normometric saccade vector with little effect of the stimulation parameters. Moreover, electrically evoked saccades (E-saccades) have kinematic properties that strongly resemble natural, visual-evoked saccades (V-saccades). These findings support models in which the saccade vector is determined by a center-of-gravity computation of activated neurons, while its trajectory and kinematics arise from downstream feedback circuits in the brainstem. Recent single-unit recordings, however, have indicated that the SC population also specifies instantaneous kinematics. These results support an alternative model, in which the desired saccade trajectory, including its kinematics, follows from instantaneous summation of movement effects of all SC spike trains. But how to reconcile this model with microstimulation results? Although it is thought that microstimulation activates a large population of SC neurons, the mechanism through which it arises is unknown. We developed a spiking neural network model of the SC, in which microstimulation directly activates a relatively small set of neurons around the electrode tip, which subsequently sets up a large population response through lateral synaptic interactions. We show that through this mechanism the population drives an E-saccade with near-normal kinematics that are largely independent of the stimulation parameters. Only at very low stimulus intensities the network recruits a population with low firing rates, resulting in abnormally slow saccades.'),
(89606, 2019, 'plos', 'A global map of the protein shape universe', 'Han, Xusi|Sit, Atilla|Christoffer, Charles|Chen, Siyang|Kihara, Daisuke', 'Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.|Department of Mathematics & Statistics, Eastern Kentucky University, Richmond, Kentucky, United States of America.|Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.|Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.|Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.', 'India|United States|India|India|India', 4, 'Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Protein Structural Elements|Proteins|Software', 'Journal Article', '10.1371/journal.pcbi.1006969', '30978181', 'Proteins are involved in almost all functions in a living cell, and functions of proteins are realized by their tertiary structures. Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures. In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy, here we map and classify three-dimensional (3D) surface shapes of proteins into a similarity space. Surface shapes of proteins were represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search. In addition to single chains of proteins, we have also analyzed the shape space occupied by protein complexes. From the mapping, we have obtained various new insights into the relationship between shapes, main-chain folds, and complex formation. The unique view obtained from shape mapping opens up new ways to understand design principles, functions, and evolution of proteins.'),
(89607, 2019, 'plos', 'Assessing intra-lab precision and inter-lab repeatability of outgrowth assays of HIV-1 latent reservoir size', 'Rosenbloom, Daniel I S|Bacchetti, Peter|Stone, Mars|Deng, Xutao|Bosch, Ronald J|Richman, Douglas D|Siliciano, Janet D|Mellors, John W|Deeks, Steven G|Ptak, Roger G|Hoh, Rebecca|Keating, Sheila M|Dimapasoc, Melanie|Massanella, Marta|Lai, Jun|Sobolewski, Michele D|Kulpa, Deanna A|Busch, Michael P|Reservoir Assay Validation and Evaluation Network (RAVEN) Study Group', 'Department of Systems Biology, Columbia University College of Physicians and Surgeons, New York, New York, United States of America.|Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.|Center for Biostatistics in AIDS Research, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.|University of California San Diego, La Jolla, California, United States of America.|Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.|Division of HIV, Infectious Diseases and Global Medicine, University of California San Francisco, San Francisco, California, United States of America.|Southern Research, Frederick, Maryland, United States of America.|Division of HIV, Infectious Diseases and Global Medicine, University of California San Francisco, San Francisco, California, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.|University of California San Diego, La Jolla, California, United States of America.|Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.|Southern Research, Frederick, Maryland, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.|Blood Systems Research Institute, San Francisco, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 8, 'Anti-HIV Agents|Bayes Theorem|CD4-Positive T-Lymphocytes|Computational Biology|Computer Simulation|HIV Infections|HIV-1|Humans|Leukocytes, Mononuclear|Likelihood Functions|Markov Chains|Monte Carlo Method|Reproducibility of Results|Viral Load|Virus Latency|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1006849', '30978183', 'Quantitative viral outgrowth assays (QVOA) use limiting dilutions of CD4+ T cells to measure the size of the latent HIV-1 reservoir, a major obstacle to curing HIV-1. Efforts to reduce the reservoir require assays that can reliably quantify its size in blood and tissues. Although QVOA is regarded as a gold standard for reservoir measurement, little is known about its accuracy and precision or about how cell storage conditions or laboratory-specific practices affect results. Owing to this lack of knowledge, confidence intervals around reservoir size estimates-as well as judgments of the ability of therapeutic interventions to alter the size of the replication-competent but transcriptionally inactive latent reservoir-rely on theoretical statistical assumptions about dilution assays. To address this gap, we have carried out a Bayesian statistical analysis of QVOA reliability on 75 split samples of peripheral blood mononuclear cells (PBMC) from 5 antiretroviral therapy (ART)-suppressed participants, measured using four different QVOAs at separate labs, estimating assay precision and the effect of frozen cell storage on estimated reservoir size. We found that typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. Systematic assay differences comprised a 24-fold range between the assays with highest and lowest scales, likely reflecting differences in viral outgrowth readout and input cell stimulation protocols. We also found that controlled-rate freezing and storage of samples did not cause substantial differences in QVOA compared to use of fresh cells (95% probability of < 2-fold change), supporting continued use of frozen storage to allow transport and batched analysis of samples. Finally, we simulated an early-phase clinical trial to demonstrate that batched analysis of pre- and post-therapy samples may increase power to detect a three-fold reservoir reduction by 15 to 24 percentage points.'),
(89608, 2019, 'plos', 'The relative contribution of color and material in object selection', 'Radonjixc4x87, Ana|Cottaris, Nicolas P|Brainard, David H', 'Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006950', '30978187', 'Object perception is inherently multidimensional: information about color, material, texture and shape all guide how we interact with objects. We developed a paradigm that quantifies how two object properties (color and material) combine in object selection. On each experimental trial, observers viewed three blob-shaped objects-the target and two tests-and selected the test that was more similar to the target. Across trials, the target object was fixed, while the tests varied in color (across 7 levels) and material (also 7 levels, yielding 49 possible stimuli). We used an adaptive trial selection procedure (Quest+) to present, on each trial, the stimulus test pair that is most informative of underlying processes that drive selection. We present a novel computational model that allows us to describe observers selection data in terms of (1) the underlying perceptual stimulus representation and (2) a color-material weight, which quantifies the relative importance of color vs. material in selection. We document large individual differences in the color-material weight across the 12 observers we tested. Furthermore, our analyses reveal limits on how precisely selection data simultaneously constrain perceptual representations and the color-material weight. These limits should guide future efforts towards understanding the multidimensional nature of object perception.'),
(89609, 2019, 'plos', 'GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity', 'Flagel, Lex E|Blackman, Benjamin K|Fishman, Lila|Monnahan, Patrick J|Sweigart, Andrea|Kelly, John K', 'Bayer Crop Science, Chesterfield, MO, United States of America.|Department of Plant and Microbial Biology, University of California-Berkeley, Berkeley, CA, United States of America.|Division of Biological Sciences, University of Montana, Missoula, MT, United States of America.|Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America.|Department of Genetics, University of Georgia, Athens, GA, United States of America.|Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States of America.', 'United States|United States|United States|United States|Georgia|United States', 3, 'Algorithms|Animals|Biological Evolution|Chromosome Mapping|Computational Biology|Drosophila|Genetic Variation|Genetics, Population|Genome|Genomics|Hybridization, Genetic|Markov Chains|Mimulus|Phenotype|Quantitative Trait Loci', 'Journal Article', '10.1371/journal.pcbi.1006949', '30986215', 'Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.'),
(89610, 2019, 'plos', 'Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions', 'Richelle, Anne|Chiang, Austin W T|Kuo, Chih-Chung|Lewis, Nathan E', 'Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America.|Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America.|Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America.|Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America.', 'United States|United States|United States|United States', 6, 'Algorithms|Animals|Cell Line, Tumor|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Genomics|High-Throughput Screening Assays|Humans|Metabolic Networks and Pathways|Models, Biological|Neoplasms|Principal Component Analysis', 'Journal Article', '10.1371/journal.pcbi.1006867', '30986217', 'Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.'),
(89611, 2019, 'plos', 'Competing evolutionary paths in growing populations with applications to multidrug resistance', 'Nicholson, Michael D|Antal, Tibor', 'School of Physics and Astronomy, University of Edinburgh, Edinburgh, Scotland.|School of Mathematics, University of Edinburgh, Edinburgh, Scotland.', '-|-', 2, 'Bacteria|Bacterial Infections|Biological Evolution|Computational Biology|Drug Resistance, Multiple|Drug Resistance, Multiple, Bacterial|Drug Resistance, Neoplasm|Evolution, Molecular|Humans|Models, Biological|Mutation|Neoplasms|Phenotype|Probability|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1006866', '30986219', 'Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represent cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.'),
(89612, 2019, 'plos', 'Passenger mutations accurately classify human tumors', 'Salvadores, Marina|Mas-Ponte, David|Supek, Fran', 'Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain.', 'Spain|Spain|Spain', 5, 'Algorithms|Computational Biology|DNA, Neoplasm|Exome|Genomics|Humans|Machine Learning|Mutation|Neoplasms|Software|Whole Exome Sequencing|Whole Genome Sequencing', 'Journal Article', '10.1371/journal.pcbi.1006953', '30986244', 'Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations-mutated oncogenes or tumor suppressors, pathways and hotspots-classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.');
