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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

Data science in science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: proceedings

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BMC Proceedings 2017年第9期11卷 1-6页

作者： L. Leon Dent Sammed N. Mandape Siddharth Pratap Jianan Dong Jamaine Davis Jennifer A. Gaddy Kofi Amoah Steve Damo Dana R. Marshall Jacob Jones Toni Brandt Gilberto Diaz Qingguo Wang Todd Gary Ashwini Yenamandra Marina Z. Ghattas Marwa Elrakaiby Ramy K. Aziz Hamdallah H. Zedan Moamen Elmassry Marwa ElRakaiby Mariam Lotfy Jarrad Marcel Rania A. Khattab Maha M. Abdelfattah Jack A. Gilbert Pouya Dini Shavahn C. Loux Kirsten E. Scoggin Alejandro Esteller-Vico Edward L. Squires Mats H. T. Troedsson Barry A. Ball Peter Daels Kalpani De Silva Ernest Bailey Joel C. Stephens Theodore S. Kalbfleisch Christine E. Dolin Lauren G. Poole Gavin E. Arteel Michael L. Merchant Daniel W. Wilkey Michelle T. Barati Eric C. Rouchka Richard M. Higashi Teresa W-M Fan Hunter Moseley Andrew N Lane Department of Surgery Meharry Medical College Nashville TN 37208 USA Bioinformatics Core Meharry Medical College Nashville TN 37208 USA Department of Biochemistry and Cancer Biology Meharry Medical College Nashville TN 37208 USA Department of Medicine Vanderbilt University Medical Center Nashville TN 37232 USA Department of Life and Physical Sciences Fisk University Nashville TN 37208 USA Department of Pathology Anatomy and Cell Biology Meharry Medical College Nashville TN 37208 USA College of Computing and Technology Lipscomb University 1 University Park Drive Nashville TN 37204 USA Vanderbilt University Medical Center Nashville TN 37232 USA Office of Research Middle Tennessee State University 1301 East Main St Murfreesboro TN 37132 USA Department of Microbiology and Immunology Faculty of Pharmacy Cairo University Cairo Egypt Department of Biological Sciences Texas Tech University Lubbock TX 79409 USA Clinical Operations Department Ray-Clinical Research Organization Giza Egypt Genomic and Systems Biology Bioscience Division Argonne National Laboratory Argonne IL 60439 USA Research Institute of Ophthalmology Giza Egypt Department of Surgery University of Chicago Chicago IL 60637 USA Microbial Ecology Group Argonne National Laboratory Argonne IL 60439 USA Department of Veterinary Science University of Kentucky Lexington KY 40536 USA Department of Reproduction Faculty of Veterinary Medicine University of Gent Merelbeke Belgium Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville KY 40292 USA Department of Veterinary Science University of Kentucky Lexington KY 40506 USA Genomics GPS Guilford CT 06437 USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville KY 40292 USA Department of Pharmacology & Toxicology University of Louisville Louisville KY 40202 USA Department of Medicine University of Louisville

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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CENTML CONTROL SYSTEM TRAINING THROUGH STA TIC AND DYNAMIC SIMULATION

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Naval Engineers Journal 1980年第2期92卷 196-206页

作者： HALL, EDWIN E. MOSS, DONALD G. NORRIS, CLIFFORD S. PETERSON, HAROLD D. Mr. Edwin E. Hall received his Bachelor's degree in Electronics from Oklahoma City University. He also is a certified College-Level Instructor in the state of Florida and has done graduate work in Computer Science at the University of Florida. As the Technical Publications and Training Manager Simulation and Control Systems Department. General Electric Company Daytona Beach Fla. he currently is responsible for the costing. planning performance scheduling and timely completion of the Department's Technical Manuals and Technical Training Programs. These Training and Manual contracts cover Ship Systems Programs. Simulation Programs. and Communications Programs for the Armed Services and commercial customers. Mr. Hall has over twenty-years experience as a Technical Writer and Instructor and for the past seventeen years has been at the General Electric Company's Daytona Beach Facility. His experience ranges from teaching Basic Electronics and Radar Circuitry as a civilian instructor for the U.S. Army to writing Manuals Proposals Reports Specifications and Brochures for General Electric's product lines. Mr. Donald G. MOSS is a graduate of Kansas State University from which he received both his B.S. degree in Business Administration and his M.S. degree in Electrical Engineering. He is presently a Senior Systems Engineer for Control Systems in the Simulation and Control Systems Department at the General Electric Company's Daytona Beach Facility. He has been employed by General Electric for 23 years — the first seven as an Engineer and Program Manager on the Fire Control Systems for the Fleet Ballistics Missile Program the next six managing the design of control and checkout equipment on the APOLLO Program and the last ten years working on control equipment for the machinery plants of new Navy ships. Over the span of years at General Electric he has worked on propulsion control for the DD-963 propulsion electric plant and auxiliary control for the FFG-7: propulsion boiler burner electric auxiliary and car

Dynamic Simulation is defined as the hardware and software required to present to the student operator visual and audible cues and responses that are the same as those encountered when operating the Control Consoles aborad ship. This type of simulation is considered essential to the development of operator skills for critical tasks. Less critical tasks can be practiced using static simulation devices. This paper discusses the utilization of these types of training devices to train Navy crews. An overview of the Machinery Control systems of the FFG 7 and AO 177 Class ships is discussed to provide an understanding of state‐of‐the‐art equipment development. The training programs for these two ship Classes are then reviewed to show how simulation devices are currently used by the Training Community, and this is followed by a brief discussion of the additional benefits that also are derived. © 1980 by the American Society of Naval Engineers, Inc.

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PB-09Alpha-synuclein Binds Unconventional Microtubules That Have a Unique Function

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Microscopy 2017年第SUPPL_1期66卷 i37-i37页

作者： Toba, Shiori Jin, Mingyue Yamada, Masami Matsumoto, Sakiko Yasunaga, Takuo Fukunaga, Yuko Miyazawa, Atsuo Kojima, Hiroaki Arai, Yoshiyuki Nagai, Takeharu Hirotsune, Shinji Osaka City Univ. Grad. Sch. of Medicine Present address: Dept. of Emergency Medical Technology Hirosaki University of Health and Welfare Junior College Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan JST-SENTAN JST-CREST Grad. Sch. of Life Science Univ. of Hyogo RSC-Univ. of Hyogo Leading Program Center RIKEN SPring-8 Center Advanced ICT Research Inst. National Inst. of Information and Communications Technology and Inst. of Scientific and Industrial Research Osaka Univ. Japan.

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ARCTIC TRAFFICABILITY program - A REVIEW

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NAVAL ENGINEERS JOURNAL 1984年第3期96卷 169-175页

作者： VOELKER, R GLEN, IF SEIBOLD, F BAYLY, I Richard Voelker:is Vice President of ARCTEC Incorporated a firm specializing in cold regions technology. He has been responsible for the management of thePolarClass Traffic-ability Program since its inception and annually participates in the field data collection in the Arctic. His prior experience includes positions with the U.S. Coast Guard in the icebreaker design project the Military Sealift Command and at Newport News Shipbuilding. He is a graduate of N. Y.S. Maritime College and has a MS degree from the University of Michigan. I.F. Glen:received his professional degrees in naval architecture from the Royal Naval Engineering College Manadon Plymouth and RN College Greenwich London entering the Royal Corps of Naval Constructors in 1967. After serving as a Constructor Lieutenant in the Royal Navy's Far East Fleet for a short period he joined the Polaris submarine project team in Bath England in 1968. In 1971 he was seconded to the Canadian Department of National Defense in Ottawa as a Constructor Lieutenant Commander under NATO exchange arrangements where he had responsibilities initially for conventional submarines and latterly for computer aided conceptual design. He ventured to Bath England in 1974 and joined Forward Design Group. In 1975 he took a position as a civilian engineer in the Canadian Defense Department and was Head of Hull Systems Engineering from 1977 to 1979. He joined ARCTEC CANADA LIMITED in 1980 and in addition to managing ice model testing projects and full scale trials has specialized in structural response of ships to ice impact. He headed ARCTEC's Kanata Laboratory from 1981 to 1983 when he was promoted to president. Frederick Seibold:is a research program manager with the Maritime Administration's Office of Advanced Ship Development and Technology. He is responsible for the marine science program which includes research in the areas of ship powering structures and propeller performance and Arctic technology. Mr. Seibold has been employed by Mar Ad since 1961 having hel

This paper describes a multiyear program to make an operational assessment on the feasibility of a year-round Arctic marine transportation system to serve Alaska. Specifically, the three objectives were to: collect meteorological and ice data along potential marine routes; instrument the hull and propulsion machinery to improve design critera for ice-worthy ships; and demonstrate that ships can operate in midwinter Alaskan Arctic ice conditions. The U.S. Coast Guard's Polar class icebreakers were used to make the operational assessment by annually extending the route northward and by operating throughout the winter season. This paper reviews some of the operational and technical achievements to date, as well as plans for future Arctic deployments.

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Correction: Liquid-capped encoded microcapsules for multiplex assays

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Lab on a chip 2017年第4期17卷 738页

作者： Younghoon Song Yunjin Jeong Taehong Kwon Daewon Lee Dong Yoon Oh Tae-Joon Park Junhoi Kim Jiyun Kim Sunghoon Kwon Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea. Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea. Department of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea and Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea and Seoul National University Hospital Biomedical Research Institute Seoul National University Hospital Seoul 151-742 Republic of Korea and Quantamatrix Inc. Seoul 151-742 Republic of Korea.

Correction for 'Liquid-capped encoded microcapsules for multiplex assays' by Younghoon Song et al., Lab Chip, 2017, DOI: .

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