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SWATH—THE VSTOL AIRCRAFT CARRIER FOR THE POST‐1990's

Naval Engineers Journal 1977年第1期89卷 47-54页

作者： CHILDERS, RADM.K.C. GLOECKLER, FREDERICK M. STEVENS, ROBERT M. USN (RET.) RAdm. K.C. Childers USN (Ret.):graduated from the U.S. Naval Academy in 1939. and later completed his graduate studies at California Institute of Technology from which he received his MS and AE degrees. He was a fighter pilot in the aircraft carriers USS Ranger and USS Essex during World War II and an instructor at the Guided Missile School. Ft. Bliss Texas from 1947 until 1949 at which time he came to Washington. D.C. as an Assistant Division Director Ships Installation Division Bureau of Aeronautics. In addition his active duty career included assignments as Naval Air Systems Command Representative Atlantic Assistant Commander for Material Acquisition Naval Air Systems Command and Deputy Project Manager for the FlllB/Phoenix Program. Bureau of Naval Weapons. During the first five years of the Polaris Program he was responsible for all testing at the Atlantic Missile Range. He also served as Commander of the Naval Missile Center where he directed the test and evaluation of Airborne Weapon Systems and had been on an earlier assignment the Missile Test Officer. His military decorations include the Silver Star the Legion of Merit two Air Medals the Navy Commendation Medal and a Presidential Unit Citation. Currently he is employed as the Manager of the Analysis and Evaluation Department at CERBERONICS. Inc. Falls Church. Va. Mr. Frederick M. Gloeckler: currently a Consultant to CERBERONICS Inc. graduated from New York University from which he received his BS degree. He began his career with the Department of the Navy in 1938. and culminated it with his retirement in 1972 at which time he was engaged in VSTOL aircraft analysis and was the Director Advanced Systems Division Naval Air Systems Command (and its predecessor organizations). During this period he made major contributions to the Fleet Ballistic Missile Program the F-14 A-7 and S-3 Aircraft Programs and the Phoenix Condor and Harpoon Missile Programs. In 1951 Mr. Gloeckler organized‘ and directed the Systems Engineering Divis

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THE NO FRAME CONCEPT - ITS IMPACT ON SHIPYARD COST

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NAVAL ENGINEERS JOURNAL 1984年第3期96卷 218-232页

作者： NAPPI, NS WALZ, RW WIERNICKI, CJ Natale S. Nappi:graduated from City College of New York in 1954 with a B.S. degree in civil engineering and received his M.S. in civil engineering from the Polytechnic Institute of Brooklyn in 1959. He began his professional career in 1954 at the New York Naval Shipyard as a naval architect (structures) performing detail structural design and fabrication studies for CVAs LPDs DDs and CGs and eventually became a supervisory naval architect (structures). From 1965 to 1973 he was a member of the staff of the Computer-Aided Design Division at the David Taylor Naval Ship Research and Development Center (DTNSRDC). As such he was involved in the development of the computer structural design tool the SSDP (in association with Frank M. Lev) and automated detail design programs (CASDOS). His current position is Senior Naval Architect Consultant in the structural integrity group of the Ship Structures Division Structures Department DTNSRDC. Mr. Nappi is the author and co-author of numerous technical papers and reports covering a wide spectrum of topics such as automated structural design process design for producibility and survivability material weight and cost trade-off studies and structural weight determination for high performance ships (i.e. SES SWATH HYSWAS). He has lectured on the subjects of design for survivability and ship structures at the Naval Post Graduate School and MIT. He is a member of ASNE ASCE U.S. Naval Institute Sigma Xi and is a registered Professional Engineer in the State of New York. Mr. Nappi was a member of the NAVSEA working commitee for the computer supported design planning effort and is currently a member of the DTNSRDC ASSET Advisory Committee. Ronald W. Walz:graduated in 1974 from Pennsylvania State University with a B.S. degree in civil engineering. He began his professional career in 1974 at the David Taylor Naval Ship Research and Development Center as a structural engineer in the structural design concepts group of the Ship Structures Division Structures Department.

A proposed cost effective alternative to current U.S. Navy structurally configured hulls is presented in this paper. This proposed design for producibility concept involves the elimination of structural stanchions and transverse web frames. The potential impact of this “no frame” concept on structural design, weight and construction and material costs for naval surface frigates and destroyers is reflected in 1) reduced costs for the installation of distributive systems and 2) a reduced number and complexity of structural details providing a more reliable and less costly structure. This study was performed in three parts: 1) Determine the most feasible length between bulkheads without frames; 2) Using this length perform detail weight studies and construction and material costs analysis comparison on a 72-foot long hull module, with and without frames, for a FFG-7, and 3) Estimate the saving in man hours of labor on the installation of distributive systems and shipfitting for an FFG-7. For the feasible length studies on the “no frame” structural configuration, thirty-seven strength, weight and vertical center of gravity studies were performed on two ship classes; twenty-two on the FFG-7 class and fifteen on the DD-963 class. The detailed weight studies and construction and material cost analyses were conducted for FFG-7 “no frame” and “as built” modules. Results indicating the “no frame” concept module was 6.8% heavier and 14.8% less costly than the “as built” module. For the impact of an FFG-7 “no frame” structurally configured hull on the cost of labor required for the installation of distributive systems and for other functional work such as ship fitting, welding, and electrical, this study indicated a reduction of 169,206 labor hours per ship, representing 7.12% of the total required man hours to fabricate an FFG-7 class ship. With the employment of the “no frame” concept, certain areas of significant concern and potential risk were addressed. These include: 1) t

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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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Corrigendum to: Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

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Cerebral cortex (New York, N.Y. : 1991) 2021年第8期31卷 3974页

作者： Ru Kong Jingwei Li Csaba Orban Mert R Sabuncu Hesheng Liu Alexander Schaefer Nanbo Sun Xi-Nian Zuo Avram J Holmes Simon B Eickhoff B T Thomas Yeo Department of Electrical and Computer Engineering ASTAR-NUS Clinical Imaging Research Centre Singapore Institute for Neurotechnology and Memory Networks Program National University of Singapore Singapore. School of Electrical and Computer Engineering Cornell University Ithaca NY USA. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. CAS Key Laboratory of Behavioral Sciences and Research Center for Lifespan Development of Brain and Mind (CLIMB) Institute of Psychology Beijing China. University of Chinese Academy of Sciences Beijing China. Department of Psychology Yale University New Haven CT USA. Institute for Systems Neuroscience Medical Faculty Heinrich-Heine University Düsseldorf Düsseldorf Germany. Institute of Neuroscience and Medicine Brain & Behaviour (INM-7) Research Center Jülich Jülich Germany. Centre for Cognitive Neuroscience DukeNUS Medical School Singapore. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore.

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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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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THE UNITED STATES NAVY'S “DESIGN WORK STUDY” APPROACH TO THE DEVELOPMENT OF SHIPBOARD CONTROL systems

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Naval Engineers Journal 1976年第6期88卷 62-74页

作者： PLATO, ARTIS I. GAMBREL, WILLIAM DAVID Artis I. Plato:is Head of the Design Work Study/ Shipboard Manning/Human Factors Engineering Section Systems Engineering and Analysis Branch Naval Ship Engineering Center (NAVSEC). He graduated from the City College of New York in 1956 receiving his Bachelor of Mechanical Engineering degree. Following this he started work at the New York Naval Shipyard in the Internal Combustion Engine and Cargo Elevator Section. During 1957 and 1958 he was called up for active duty with the U.S. Army Corps of Engineers and served in Europe with a Construction Engineer Battalion. After release from active duty he returned to the shipyard where he remained until 1961 when he transferred to the Naval Supply Research and Development Facility Bayonne New Jersey. Initially he was in charge of an Engineering Support Test Group and the drafting services for the whole Facility. Later he became a Project Engineer in the Food Services Facilities Branch with duties that included planning and designing new afloat and ashore messing facilities for the Navy. In 1966 he transferred to NAVSEC as a Project Engineer in the Design Work Study Section and in this capacity worked on selected projects and manning problems for new construction and also developed a computer program (Manpower Determination Model) that makes accurate crew predictions for feasibility studies. In 1969 he became Head of the Section. He has been active in the U.S. Army Reserve since his release from active duty and his duties have included command of an Engineer Company various Staff positions and his present assignment as Operations Officer for a Civil Affairs Group. He has completed the U. S. A rmy Corps of Engineers Career Course and the Civil Affairs Career Course and is presently enrolled in the U.S. Army Command and General Staff College non-resident course. Additionally he completed graduate studies at American University Washington D.C in 1972 receiving his MSTM degree in Technology of Management and is a member of ASE ASME CAA U. S. Naval Instit

The purpose of this paper is to discuss a system analysis technique called “Design Work Study”, that is used by the U.S. Navy for the development of improved ship control systems. The Design Work Study approach is one which brings about the most efficient and cost effective man‐machine combination for the ship control functions. Adherence to this process insures that the advantages of automation are balanced against incurred operational and maintenance expense. At present, U.S. Navy doctrine requires that certain ship operating stations, such as steering and propulsion control, be manned at specified ship conditions of readiness. Therefore, if personnel are needed to fulfill these combat environment obligations, excessive automation may only result in unnecessary additional acquisition costs and increased personnel skill requirements. The Design Work Study process can be applied to establish the required fine balance between the degree of built‐in automation and the shipboard manpower levels. In order to explain the techniques that are used for this process, the Authors will present a brief summary of the Design Work Study approach for optimizing shipboard control systems, including an explanation of the formal procedural tools that are applicable. In addition, a practical case study of ship control system design for one of the latest classes of Navy destroyers will be presented. This example will illustrate the resultant savings and benefits that can be realized by a judicious application of this technique. © 1976 American Society of Naval Engineers

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Publisher Correction: Alpha-synuclein facilitates to form short unconventional microtubules that have a unique function in the axonal transport

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Scientific reports 2018年第1期8卷 8019页

作者： Shiori Toba Mingyue Jin Masami Yamada Kanako Kumamoto Sakiko Matsumoto Takuo Yasunaga Yuko Fukunaga Atsuo Miyazawa Sakiko Fujita Kyoko Itoh Shinji Fushiki Hiroaki Kojima Hideki Wanibuchi Yoshiyuki Arai Takeharu Nagai Shinji Hirotsune Department of Genetic Disease Research Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8585 Japan. Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Kawazu 680-4 Iizuka Fukuoka 820-850 Japan. JST-SENTAN 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan. JST-CREST 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan. Graduate School of Life Science University of Hyogo 3-2-1 Kouto Kamigori-cho Ako-gun Hyogo 678-1297 Japan. RSC-University of Hyogo Leading Program Center RIKEN SPring-8 Center 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5148 Japan. Graduate School of Materials Science Nara Institute of Science and Technology 8916-5 Takayama Ikoma Nara 630-0101 Japan. Department of Pathology and Applied Neurobiology Kyoto Prefectural University of Medicine Graduate School of Medical Sciences Kajii-cho Kawaramachi-Hirokoji Kamigyo-ku Kyoto 602-8566 Japan. Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Kobe 651-2492 Japan. Department of Pathology Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8586 Japan. Department of Biomolecular Science and Engineering Institute of Scientific and Industrial Research Osaka University Mihoga-oka 8-1 Osaka 567-0047 Japan. Department of Genetic Disease Research Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8585 Japan. shinjih@med.osaka-cu.ac.jp.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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