检索结果-内蒙古大学图书馆

Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Ming-Hsiang Su Chung-Hsien Wu Kun-Yi Huang Qian-Bei Hong Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan

ISBN: (纸本)9781538653128

This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

关键词： Emotion recognition Databases Logic gates Feature extraction Semantics Speech recognition computer science

来源：评论

学校读者我要写书评

暂无评论

Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial Networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

来源：评论

学校读者我要写书评

暂无评论

Ten quick tips for deep learning in biology

arXiv

引用

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Retracted: Implementation Method CUSUM to Determine the Accident Prone Areas in Web Based

Retracted: Implementation Method CUSUM to Determine the Acci...

引用

2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information systems, and Information Technology, WMA-Mathcomtech 2018

作者： Dinata, Rozzi Kesuma Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Riasti, Berliana Kusuma Iskandar, Akbar Sari, Ifit Novita Nurmawati, N. Nanuru, Ricardo Freedom Putri, Lusi Dwi Manurung, Rosida Tiurma Sriadhi, S. Saleh, A.A. Astari, T. Khaddafi, Muammar Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics Universitas Sebelas Maret Surakarta Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Departement Of: Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Department of Civil Engineering Universitas Lancang Kuning Pekanbaru Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Elementary School Teacher Education STKIP Citra Bangsa Aceh Utara Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

At the Unit Laka Lantas Polres Lhokseumawe determining accident-prone areas on roads in Lhokseumawe still using manual systems. Determination of accident-prone areas are less effective because the police Unit Laka Lantas Polres Lhokseumawe just using the data of the last year and had to repeatedly calculate manually accident-prone areas based on the number of human casualties. In this study the system implementation CUSUM method (Cummulative Summary) to determine the accident-prone areas designed using the web-based programming language PHP. In this system there are three processes to determine areas prone to accidents, the accident rate calculations based on the weighted severity, blacksite calculations using the Z-Score method for determining areas prone to accident blackspots and calculations using the CUSUM method to determine the critical points of an accident. The data used are secondary data obtained from the Police Unit Laka Lantas Polres Lhokseumawe years 2009-2013. © Published under licence by IOP Publishing Ltd.

关键词： Accidents

来源：评论

学校读者我要写书评

暂无评论

Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

引用

arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

FINDING CONTINUITY AND DISCONTINUITY IN FISH SCHOOLS VIA INTEGRATED INFORMATION THEORY

arXiv

引用

arXiv 2018年

作者： Niizato, Takayuki Sakamoto, Kotaro Mototake, Yoh-ichi Murakami, Hisashi Tomaru, Takenori Hoshika, Tomotaro Fukushima, Toshiki Faculty of Engineering Information and Systems University of Tsukuba Japan Human Biology Program University of Tsukuba Japan Graduate School of Frontier Sciences University of Tokyo Japan Research Center for Advanced Science and Technology University of Tokyo Japan Department of Computer Science and Engineering Toyohashi University of Technology Japan

Collective behaviour is known to be the result of diverse dynamics and is sometimes likened to a living system. Although many studies have revealed the dynamics of various collective behaviours, their main focus was on the information process inside the collective, not on the whole system itself. For example, the qualitative difference between two elements and three elements as a system has rarely been investigated. Tononi et al. have proposed Integrated Information Theory (IIT) to measure the degree of consciousness Φ. IIT postulates that the amount of information loss caused by certain partitions is equivalent to the degree of information integration in the system. This measure is not only useful for estimating the degree of consciousness but can also be applied to more general network systems. Here we applied IIT (in particular, IIT 3.0 using PyPhi) to analyse real fish schools (Plecoglossus altivelis). Our hypothesis in this study is a very simple one: a living system evolves to raise its Φ value. If we accept this hypothesis, IIT reveals the existence of continuous and discontinuous properties as group size varies. For example, leadership in the fish school emerged for a school size of four or above;but not below three. Furthermore, this transition was not observed by measuring mutual information or in a simple Boids model. This result suggests that integrated information Φ can reveal some inherent properties which cannot be observed using other measures. We also discuss how the fish recognition of the figure-ground relation, that is, what determines the relevant ON and OFF states, may reveal various optimal paths for obtaining the functional evolution of collective behaviour. Copyright © 2018, The Authors. All rights reserved.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

引用

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

来源：评论

学校读者我要写书评

暂无评论

Modelling and reducing fuel flow pulsation of a fuel-metering system during pump mode switching in a turbofan engine

引用

IOP Conference Series: Earth and Environmental science 2019年第5期240卷

作者： S Masuda F Shimizu M Fuchiwaki K Tanaka Control Systems Engineering Department Research & Engineering Division Aero Engines Space & Defense Business Area IHI Corporation 229 Tonogaya Mizuho-machi Nishitama-gun Tokyo 190-1297 Japan Department of Computer Science and Systems Engineering Doctoral Program Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan Department of Mechanical Information Science and Technology School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan

In this research, the transient characteristics of a new type fuel metering system for aircraft turbofan engines using two types of pumps, a gear pump and a centrifugal pump, were studied by modelling and simulation. This new fuel metering system is capable of hydraulically unloading the operating state of each pump by the pump mode switching mechanism within the permissible range of the pump rotation speed and the fuel flow rate and it is possible to reduce the fuel temperature rise due to the pressure loss in the circuit in the operation region of the entire fuel system. In addition to this, by using a small centrifugal pump that can realize high discharge flow rate, it is possible to reduce weight compared to the conventional fuel system. However, when the pump mode is switched, unexpected fluctuation in the fuel flow rate can occur when a large change in the inlet pressure of the system occurs, the pressure control unit cannot follow the change, and the transient phenomenon cannot be suppressed. At this time, the pressure waveform generated at the time of changing the pump mode includes a high frequency component such that the pressure control unit, which plays a role of keeping the metering valve differential pressure constant, cannot follow up. Therefore, in this paper, we focused on the frequency of the pressure change waveform when the pump mode was changed and studied the model of the fuel system inlet pressure in the gear pump mode. Based on that model, we propose a method to reduce the frequency characteristics of the pressure change occurring at the time of pump mode change.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

引用

Cell 2025年第12期188卷 3167-3183.e18页

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：