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International Conference for Smart Health, ICSH 2015

作者： Afghah, Fatemeh Razi, Abolfazl Reza Soroushmehr, S.M. Molaei, Somayeh Ghanbari, Hamid Najarian, Kayvan Electrical Engineering and Computer Science Department Northern Arizona University FlagstaffAZ86011 United States Department of Emergency Medicine University of Michigan Ann ArborMI48109 United States Michigan Center for Integrative Research in Critical Care MCIRCC University of Michigan Ann ArborMI48109 United States Internal Medicine University of Michigan Ann ArborMI48109 United States Computational Medicine and Bioinformatics Department University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9783319291741

False alarm is one of the main concerns in intensive care units which could result in care disruption, sleep deprivation, insensitivity of care–givers to alarms and so on. Many approaches such as improving the quality of physiological signals by filtering and developing more accurate sensors have been proposed in the last two decades to suppress the rate of false alarm. Moreover, some multi–parameter/feature methods have been developed to classify the alarms more accurately. One of the main problems facing these methods is that they neglect those features that individually have low impact on the accuracy. In this paper, we propose a model based on coalition game that considers the inter–features mutual information which results in gaining the accuracy of the classification. simulation results on a database produced by four hospitals shows the superior performance of the proposed method compared to other existing methods. © Springer International Publishing Switzerland 2016.

关键词： Intensive care units

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Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)

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Ecological Processes 2022年第1期11卷 222-237页

作者： Cuijuan Liao Yizhao Chen Jingmeng Wang Yishuang Liang Yansong Huang Zhongyi Lin Xingjie Lu Yuanyuan Huang Feng Tao Danica Lombardozzi Almut Arneth Daniel SGoll Atul Jain Stephen Sitch Yanluan Lin Wei Xue Xiaomeng Huang Yiqi Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijingChina Joint Innovation Center for Modern Forestry Studies College of Biology and the EnvironmentNanjing Forestry UniversityNanjingChina Department of Geography University of CambridgeCambridgeUK School of Life Science Nanjing UniversityNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Guangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesSchool of Atmospheric SciencesSun Yat-Sen UniversityGuangzhouChina CSIRO Oceans and Atmosphere Aspendale 3195Australia Terrestrial Sciences Section Climate and Global DynamicsNational Center for Atmospheric ResearchBoulderCO 80305USA Karlsruhe Institute of Technology Institute of Meteorology and Climate Research/Atmospheric Environmental ResearchGarmisch-PartenkirchenGermany UniversitéParis Saclay CEA-CNRS-UVSQLSCE/IPSLGif sur YvetteFrance Department of Atmospheric Sciences University of IllinoisUrbanaILUSA College of Life and Environmental Sciences University of ExeterExeterUK Department of Computer Science and Technology Tsinghua UniversityBeijingChina Center for Ecosystem Science and Society Department of Biological SciencesNorthern Arizona UniversityFlagstaffAZUSA

Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C *** the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)*** the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)*** comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly ***:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer *** analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth ***:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process *** analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model *** stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the en

关键词： Soil organic carbon Inter-model comparison Uncertainty analysis Carbon-nitrogen coupling Vertical resolved soil biogeochemistry structure

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Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

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Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

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Emergence of Natural and Robust Bipedal Walking by Learning to Control Up to Ninety Muscles from Biologically Plausible Objectives

SSRN

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SSRN 2023年

作者： Schumacher, Pierre Geijtenbeek, Thomas Caggiano, Vittorio Kumar, Vikash Schmitt, Syn Martius, Georg Haeufle, Daniel F.B. Max-Planck Institute for Intelligent Systems Tübingen Germany Hertie Institute for Clinical Brain Research Center for Integrative Neuroscience Tübingen Germany Goatstream Netherlands Meta AI New York United States Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany Institute of Computer Engineering Heidelberg University Germany

Humans show unparalleled ability when maneuvering diverse terrains. While motor control researchers have studied these skills in simulation for a long time, most control approaches are only able to generate single motions. Recently, reinforcement learning~(RL) has shown great promise in the development of robust controllers capable of generating complex behaviors. The application to highly complex biomechanical models was, however, only possible under extensive use of motion capture data, limiting the abilities of the resulting agents. In this paper, we demonstrate that the combination of a recent RL algorithm with a biologically plausible reward function is capable of learning controllers for 4 different musculoskeletal human models and achieves bipedal motion with up to 90 muscles without demonstrations. Our controllers are robust enough to generalize to diverse and unseen terrains, while only a single objective function is needed for training. We validate our findings on 4 models in 2 different simulators and 2 different tasks, walking and running. We see this as a first step for the motor control, biomechanics, and rehabilitation communities to generate highly complex human movements with RL, without needing to collect large amounts of experimental data or limiting themselves to simple models that are not representative of the diverse skills that human beings exhibit every day. © 2023, The Authors. All rights reserved.

关键词： Muscle

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Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models

arXiv

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arXiv 2023年

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multiobjective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning (RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Climate warming restructures seasonal dynamics of grassland soil microbial communities

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mLife 2022年第3期1卷 245-256页

作者： Xue Guo Mengting Yuan Jiesi Lei Zhou Shi Xishu Zhou Jiabao Li Ye Deng Yunfeng Yang Liyou Wu Yiqi Luo James MTiedje Jizhong Zhou State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Institute for Environmental Genomics and Department of Microbiology and Plant Biology University of OklahomaNormanOklahomaUSA Department of Environmental Science Policyand ManagementUniversity of CaliforniaBerkeleyCaliforniaUSA Gladstone Institutes and Chan‐Zuckerberg Biohub San FranciscoCaliforniaUSA School of Minerals Processing and Bioengineering Central South UniversityChangshaChina Key Laboratory of Environmental and Applied Microbiology Chinese Academy of Sciences and Environmental Microbiology&Key Laboratory of Sichuan ProvinceChengdu Institute of BiologyChinese Academy of SciencesChengduChina Key Laboratory of Environmental Biotechnology Research Center for Eco‐Environmental SciencesChinese Academy of SciencesBeijingChina Department of Biological Sciences Center for Ecosystem Science and SocietyNorthern Arizona UniversityFlagstaffArizonaUSA Center for Microbial Ecology Michigan State UniversityEast LansingMichiganUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA Earth and Environmental Sciences Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA.

Soil microbial community's responses to climate warming alter the global carbon *** temperate ecosystems,soil microbial communities function along seasonal ***,little is known about how the responses of soil microbial communities to warming vary when the season *** this study,we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie *** results showed that warming significantly(p=0.001)shifted community structure,such that the differences of microbial communities between warming and control plots increased nonlinearly(R^(2)=0.578,p=0.021)from spring to ***,warming significantly(p<0.050)increased microbial network complexity and robustness,especially during the colder seasons,despite large variations in network size and complexity in different *** addition,the relative importance of stochastic processes in shaping the microbial community decreased by warming in fall and winter but not in spring and *** study indicates that climate warming restructures the seasonal dynamics of soil microbial community in a temperate *** seasonality of microbial responses to warming may enlarge over time and could have significant impacts on the terrestrial carbon cycle.

关键词： climate warming microbial community microbial network seasonal dynamics

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons

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Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]

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Chaos (Woodbury, N.Y.) 2017年第6期27卷 069901页

作者： Benjamin Letham Portia A Letham Cynthia Rudin Edward P Browne Operations Research Center Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA. Department of Chemical Engineering Arizona State University Tempe Arizona 85281 USA. Department of Computer Science and Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA.

The bound in Eq. (6) of Ref. 1 has a 2 that should be a 4. The correct bound is thus

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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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