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Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data

Applied Network Science 2018年第1期3卷 46页

作者： Fotouhi, Babak Momeni, Naghmeh Riolo, Maria A. Buckeridge, David L. Program for Evolutionary Dynamics Harvard University Cambridge United States Sloan School of Management Massachusetts Institute of Technology Cambridge United States Center for the Study of Complex Systems University of Michigan Ann ArborMI United States Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Canada

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods. © 2018, The Author(s).

关键词： Epidemiology

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Author Correction: Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics

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Nature communications 2022年第1期13卷 1404页

作者： Victoria Romeo-Aznar Laís Picinini Freitas Oswaldo Gonçalves Cruz Aaron A King Mercedes Pascual Department of Ecology and Evolution University of Chicago Chicago IL USA. Departamento de Ecología Genética y Evolución and Instituto IEGEBA (CONICET-UBA) Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Ciudad Universitaria Pabellón 2 C1428EHA Buenos Aires Argentina. Mansueto Institute for Urban Innovation The University of Chicago Chicago IL USA. Postgraduate Program of Epidemiology in Public Health - Escola Nacional de Saúde Pública Sergio Arouca - Fundação Oswaldo Cruz Rio de Janeiro Brazil. Programa de Computação Científica - Fundação Oswaldo Cruz Rio de Janeiro Brazil. Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor MI USA. Center for the Study of Complex Systems University of Michigan Ann Arbor MI USA. The Santa Fe Institute Santa Fe NM USA. Department of Ecology and Evolution University of Chicago Chicago IL USA. pascual@uchicago.edu. The Santa Fe Institute Santa Fe NM USA. pascual@uchicago.edu.

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Exoplanet Biosignatures: Future Directions

arXiv

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arXiv 2017年

作者： Walker, Sara I. Bains, William Cronin, Leroy DasSarma, Shiladitya Danielache, Sebastian Domagal-Goldman, Shawn Kacar, Betul Kiang, Nancy Y. Lenardic, Adrian Reinhard, Christopher T. Moore, William Schwieterman, Edward W. Shkolnik, Evgenya L. Smith, Harrison B. School of Earth and Space Exploration Arizona State University TempeAZ United States Beyond Center for Fundamental Concepts in Science Arizona State University TempeAZ United States ASU-Santa Fe Institute Center for Biosocial Complex Systems Arizona State University TempeAZ United States Blue Marble Space Institute of Science SeattleWA United States MIT 77 Mass Ave CambridgeMA02139 United States School of Chemistry University of Glasgow GlasgowG12 8QQ United Kingdom Department of Microbiology and Immunology Institute of Marine and Environmental Technology University of Maryland School of Medicine BaltimoreMD United States Department of Materials and Life Science Faculty of Science and Technology Sophia University Tokyo Japan Earth Life Institute Tokyo Institute of Technology Tokyo Japan NASA Goddard Space Flight Center GreenbeltMD United States NASA Astrobiology Institute Virtual Planetary Laboratory Team University of Washington SeattleWA United States Organismic and Evolutionary Biology Harvard University CambridgeMA United States NASA Astrobiology Institute Reliving the Past Team University of Montana MissoulaMT United States University of Arizona Department of Molecular and Cell Biology and Astronomy Tucson AZ NASA Goddard Institute for Space Studies New YorkNY United States Department of Earth Science Rice University HoustonTX United States School of Earth and Atmospheric Sciences Georgia Institute of Technology AtlantaGA United States NASA Astrobiology Institute Alternative Earths Team University of California RiversideCA United States Department of Atmospheric and Planetary Sciences Hampton University HamptonVA United States National Institute of Aerospace HamptonVA United States Department of Earth Sciences University of California RiversideCA United States NASA Postdoctoral Program Universities Space Research Association ColumbiaMD United States School of Earth and Space Exploration Arizona State University PO Box

Exoplanet science promises a continued rapid accumulation of new observations in the near future, energizing a drive to understand and interpret the forthcoming wealth of data to identify signs of life beyond our Solar System. The large statistics of exoplanet samples, combined with the ambiguity of our understanding of universal properties of life and its signatures, necessitate a quantitative framework for biosignature assessment Here, we introduce a Bayesian framework for guiding future directions in life detection, which permits the possibility of generalizing our search strategy beyond biosignatures of known life. The Bayesian methodology provides a language to define quantitatively the conditional probabilities and confidence levels of future life detection and, importantly, may constrain the prior probability of life with or without positive detection. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from stellar and planetary context, the contingencies of evolutionary history and the universalities of physics and chemistry. We discuss how the Bayesian framework can guide our search strategies, including determining observational wavelengths or deciding between targeted searches or larger, lower resolution surveys. Our goal is to provide a quantitative framework not entrained to specific definitions of life or its signatures, which integrates the diverse disciplinary perspectives necessary to confidently detect alien life. Copyright © 2017, The Authors. All rights reserved.

关键词： Extrasolar planets

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Expectation-driven migration promotes cooperation by group interactions

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Physical Review E 2012年第6期85卷 066104-066104页

作者： Te Wu Feng Fu Yanling Zhang Long Wang Center for Systems and Control State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China Program for Evolutionary Dynamics Harvard University Cambridge Massachusetts 02138 USA

“Voting with feet” describes the prominent social phenomenon that people tend to move away from deteriorating neighborhoods and search for and join prosperous groups. To quantify the role this kind of expectation-driven migration plays in the evolution of cooperation, here we study a simple yet effective model of cooperation based on spatial public goods games. The population structure is characterized by a square lattice with some nodes being left empty. Individuals have expectations toward their current habitats. Dissatisfied players, whose expectation is not met after interacting with all directly connected neighbors, tend to abstain from the groups of low quality by moving away and explore the physical niches of avail. How fast interaction happens relatively to selection is regulated by the time-scale ratio of game interaction to natural selection. Under strong selection, simulation results show that cooperation is greatly improved for either low, moderate, or high expectations compared to whenever the expectation-driven migration is absent. Further explorations reveal that neither too high nor too low but rather a combination of moderate expectations and rapid interaction establishes cooperation for a moderate public goods enhancement factor. There exists an optimal interval of expectation level most favoring the evolution of cooperation as the required time-scale ratio is minimized.

关键词： GROUP theory PUBLIC goods MATHEMATICAL models NATURAL selection SIMULATION methods & models SPATIAL analysis (Statistics)

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Inertia in strategy switching transforms the strategy evolution

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Physical Review E 2011年第6期84卷 066103-066103页

作者： Yanling Zhang Feng Fu Te Wu Guangming Xie Long Wang Center for Systems and Control State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China Program for Evolutionary Dynamics Harvard University Cambridge Massachusetts 02138 USA

A recent experimental study [Traulsen et al., Proc. Natl. Acad. Sci. 107, 2962 (2010)] shows that human strategy updating involves both direct payoff comparison and the cost of switching strategy, which is equivalent to inertia. However, it remains largely unclear how such a predisposed inertia affects 2×2 games in a well-mixed population of finite size. To address this issue, the “inertia bonus” (strategy switching cost) is added to the learner payoff in the Fermi process. We find how inertia quantitatively shapes the stationary distribution and that stochastic stability under inertia exhibits three regimes, with each covering seven regions in the plane spanned by two inertia parameters. We also obtain the extended “1/3” rule with inertia and the speed criterion with inertia; these two findings hold for a population above two. We illustrate the above results in the framework of the Prisoner's Dilemma game. As inertia varies, two intriguing stationary distributions emerge: the probability of coexistence state is maximized, or those of two full states are simultaneously peaked. Our results may provide useful insights into how the inertia of changing status quo acts on the strategy evolution and, in particular, the evolution of cooperation.

关键词： INERTIA (Mechanics) PRISONER'S dilemma game PROBABILITY theory STOCHASTIC analysis PHYSICS research QUANTITATIVE research

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Effects of heterogeneous wealth distribution on public cooperation with collective risk

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Physical Review E 2010年第1期82卷 016102-016102页

作者： Jing Wang Feng Fu Long Wang Center for Systems and Control State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China Program for Evolutionary Dynamics Harvard University One Brattle Square Cambridge Massachusetts 02138 USA

The distribution of wealth among individuals in real society can be well described by the Pareto principle or “80-20 rule.” How does such heterogeneity in initial wealth distribution affect the emergence of public cooperation, when individuals, the rich and the poor, engage in a collective-risk enterprise, not to gain a profit but to avoid a potential loss? Here we address this issue by studying a simple but effective model based on threshold public goods games. We analyze the evolutionary dynamics for two distinct scenarios, respectively: one with fair sharers versus defectors and the other with altruists versus defectors. For both scenarios, particularly, we in detail study the dynamics of the population with dichotomic initial wealth—the rich versus the poor. Moreover, we demonstrate the possible steady compositions of the population and provide the conditions for stability of these steady states. We prove that in a population with heterogeneous wealth distribution, richer individuals are more likely to cooperate than poorer ones. Participants with lower initial wealth may choose to cooperate only if all players richer than them are cooperators. The emergence of pubic cooperation largely relies on rich individuals. Furthermore, whenever the wealth gap between the rich and the poor is sufficiently large, cooperation of a few rich individuals can substantially elevate the overall level of social cooperation, which is in line with the well-known Pareto principle. Our work may offer an insight into the emergence of cooperative behavior in real social situations where heterogeneous distribution of wealth among individual is omnipresent.

关键词： Pareto principle

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Individual-based simulation of sexual selection: A quantitative genetic approach

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Procedia Computer Science 2010年第1期1卷 2003-2011页

作者： D. van Dijk P.M.A. Sloot J.C. Tay M.C. Schut Computational Science University of Amsterdam Sciencepark 107 1098 XG Amsterdam The Netherlands Evolutionary and Complex Systems Program School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Department of Artificial Intelligence Vrije Universiteit Amsterdam 1081 HV Amsterdam The Netherlands

Sexual selection has been mathematically modeled using quantitative genetics as well as population genetics. Two-locus simulation models have been used to study the evolution of male display and female preference. We present an individual-based simulation model of sexual selection in a quantitative genetic context. We show that under certain conditions Fisherian self-reinforcing sexual selection takes effect, predicted by Lande’s analytic model of female choice. We also show that the dynamics involved in the co-evolution of male display and female preference is much more complex than mathematics would predict. We therefore argue that the study of sexual selection through individual-based simulation could give new and more realistic insight into a world dominated by overly simplified equations.

关键词： Sexual selection Individual-based modeling Agent-based modeling

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Partner selections in public goods games with constant group size

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Physical Review E 2009年第2期80卷 026121-026121页

作者： Te Wu Feng Fu Long Wang Center for Systems and Control State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China Program for Evolutionary Dynamics Harvard University Cambridge Massachusetts 02138 USA

Most of previous studies concerning the public goods game assume either participation is unconditional or the number of actual participants in a competitive group changes over time. How the fixed group size, prescribed by social institutions, affects the evolution of cooperation is still unclear. We propose a model where individuals with heterogeneous social ties might well engage in differing numbers of public goods games, yet with each public goods game being constant size during the course of evolution. To do this, we assume that each focal individual unidirectionally selects a constant number of interaction partners from his immediate neighbors with probabilities proportional to the degrees or the reputations of these neighbors, corresponding to degree-based partner selection or reputation-based partner selection, respectively. Because of the stochasticity the group formation is dynamical. In both selection regimes, monotonical dependence of the stationary density of cooperators on the group size was found, the latter over the whole range but the former over a restricted range of the renormalized enhancement factor. Moreover, the reputation-based regime can substantially improve cooperation. To interpret these differences, the microscopic characteristics of individuals are probed. We later extend the degree-based partner selection to general cases where focal individuals have preferences toward their neighbors of varying social ties to form groups. As a comparison, we as well investigate the situation where individuals locating on the degree regular graphs choose their coplayers at random. Our results may give some insights into better understanding the widespread teamwork and cooperation in the real world.

关键词： Condensed matter physics

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