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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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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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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Spectral evidence for unidirectional charge density wave in detwinned BaNi2As2

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Physical Review B 2023年第8期108卷 L081104-L081104页

作者： Yucheng Guo Mason Klemm Ji Seop Oh Yaofeng Xie Bing-Hua Lei Luca Moreschini Cheng Chen Ziqin Yue Sergey Gorovikov Tor Pedersen Matteo Michiardi Sergey Zhdanovich Andrea Damascelli Jonathan Denlinger Makoto Hashimoto Donghui Lu Chris Jozwiak Aaron Bostwick Eli Rotenberg Sung-Kwan Mo Rob G. Moore Junichiro Kono Robert J. Birgeneau David J. Singh Pengcheng Dai Ming Yi Department of Physics and Astronomy Rice University Houston Texas 77005 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California Berkeley California 94720 USA Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of Oxford Oxford OX1 3PU United Kingdom Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Canadian Light Source Inc. 44 Innovation Boulevard Saskatoon SK S7N 2V3 Canada Department of Physics and Astronomy University of British Columbia Vancouver British Columbia V6T 1Z1 Canada Quantum Matter Institute University of British Columbia Vancouver British Columbia V6T 1Z4 Canada Max Planck Institute for Chemical Physics of Solids Nöthnitzer Straße 40 01187 Dresden Germany Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA Department of Chemistry University of Missouri Columbia Missouri 65211 USA

In the iron-based superconductors, unconventional superconductivity emerges in proximity to intertwined electronic orders consisting of an electronic nematic order and a spin density wave (SDW). Recently, BaNi2As2, like its well-known iron-based analog BaFe2As2, has been discovered to host a symmetry-breaking structural transition but coupled to a unidirectional charge density wave (CDW) instead of SDW, providing a novel platform to study intertwined orders. Here, through a systematic angle-resolved photoemission spectroscopy study combined with a detwinning B1g uniaxial strain, we identify distinct spectral evidence of band evolution due to the structural transition as well as CDW-induced band folding. In contrast to the nematicity and spin density wave in BaFe2As2, the structural and CDW order parameters in BaNi2As2 are observed to be strongly coupled and do not separate in the presence of uniaxial strain. Furthermore, no nematic band splitting is resolved above the structural transition. Our measurements point to a likely lattice origin of the CDW order in BaNi2As2.

关键词： Charge density waves Nematic phase transition Iron-based superconductors Angle-resolved photoemission spectroscopy

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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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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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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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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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Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

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Nature communications 2025年第1期16卷 4900页

作者： Dhammika Leshan Wannigama Mohan Amarasiri Phatthranit Phattharapornjaroen Cameron Hurst Charin Modchang John Jefferson V Besa Kazuhiko Miyanaga Longzhu Cui Stefan Fernandez Angkana T Huang Puey Ounjai W K C P Werawatte Ali Hosseini Rad S M Porames Vatanaprasan Dylan John Jay Thammakorn Saethang Sirirat Luk-In Phitsanuruk Kanthawee Wanwara Thuptimdang Ratana Tacharoenmuang Bernadina Cynthia S P H Spencer Vitharana Natharin Ngamwongsatit Hitoshi Ishikawa Takashi Furukawa Yangzhong Wang Andrew C Singer Naveen Kumar Devanga Ragupathi Tanittha Chatsuwan Kazunari Sei Asuka Nanbo Asada Leelahavanichkul Talerngsak Kanjanabuch Hiroshi Hamamoto Paul G Higgins Daisuke Sano Anthony Kicic José O Valdebenito Jonas Bonnedahl Sam Trowsdale Parichart Hongsing Aisha Khatib Kenji Shibuya Shuichi Abe Department of Infectious Diseases Faculty of Medicine Yamagata University Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Yamagata Prefectural University of Health Sciences Kamiyanagi Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. The Lygodium Ceylon Health and Environmental Policy Research Center Colombo Sri Lanka. leshanwannigama@med.id.yamagata-u.ac.jp. Biofilms and Antimicrobial Resistance Consortium of ODA receiving countries The University of Sheffield Sheffield UK. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Sendai Miyagi Japan. Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand. HRH Princess Chulabhorn Disaster and Emergency Medicine Center Chulabhorn Royal Academy Bangkok Thailand. Department of Clinical Epidemiology Faculty of Medicine Thammasat University Rangsit Thailand. Center of Excellence in Applied Epidemiology Thammasat University Rangsit Thailand. Mater Research Institute University of Queensland Brisbane QLD Australia. Biophysics Group Department of Physics Faculty of Science Mahidol University Bangkok Thailand. Centre of Excellence in Mathematics MHESI Bangkok Thailand. Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok Thailand. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Co

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

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