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Physical Review Letters 2018年第24期121卷 248003-248003页

作者： Arman Boromand Alexandra Signoriello Fangfu Ye Corey S. O’Hern Mark D. Shattuck Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of the City University of New York New York New York 10031 USA

We introduce the deformable particle (DP) model for cells, foams, emulsions, and other soft particulate materials, which adds to the benefits and eliminates deficiencies of existing models. The DP model combines the ability to model individual soft particles with the shape-energy function of the vertex model, and adds arbitrary particle deformations. We focus on 2D deformable polygons with a shape-energy function that is minimized for area a0 and perimeter p0 and repulsive interparticle forces. We study the onset of jamming versus particle asphericity, A=p02/4πa0, and find that the packing fraction grows with A until reaching A*=1.16 of the underlying Voronoi cells at confluence. We find that DP packings above and below A* are solidlike, which helps explain the solid-to-fluid transition at A* in the vertex model as a transition from tension- to compression-dominated regimes.

关键词： Jamming Monolayer films Molecular dynamics

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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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Cover Image, Volume 86, Issue 2

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Proteins: Structure, Function, and bioinformatics 2018年第2期86卷

作者： Guang Song Department of Computer Science Iowa State University Ames Iowa 50011 Program of Bioinformatics and Computational Biology Iowa State University Ames Iowa 50011 L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames Iowa State University Ames Iowa 50011

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

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Nature 2025年第8046期637卷 E22页

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

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Author Correction: PGBD5 promotes site-specific oncogenic mutations in human tumors

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Nature genetics 2020年第11期52卷 1265页

作者： Anton G Henssen Richard Koche Jiali Zhuang Eileen Jiang Casie Reed Amy Eisenberg Eric Still Ian C MacArthur Elias Rodríguez-Fos Santiago Gonzalez Montserrat Puiggròs Andrew N Blackford Christopher E Mason Elisa de Stanchina Mithat Gönen Anne-Katrin Emde Minita Shah Kanika Arora Catherine Reeves Nicholas D Socci Elizabeth Perlman Cristina R Antonescu Charles W M Roberts Hanno Steen Elizabeth Mullen Stephen P Jackson David Torrents Zhiping Weng Scott A Armstrong Alex Kentsis Molecular Pharmacology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany. Cancer Biology & Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Program in Bioinformatics and Integrative Biology Department of Biochemistry and Molecular Pharmacology University of Massachusetts Medical School Worcester MA USA. Joint BSC-CRG-IRB Research Program in Computational Biology Barcelona Supercomputing Center (BSC-CNS) Barcelona Spain. The Wellcome Trust/Cancer Research UK Gurdon Institute University of Cambridge Cambridge UK. Institute for Computational Biomedicine Weill Cornell Medical College New York NY USA. Antitumor Assessment Core Facility Memorial Sloan Kettering Cancer Center New York NY USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. New York Genome Center New York NY USA. Bioinformatics Core Memorial Sloan Kettering Cancer Center New York NY USA. Northwestern University Feinberg School of Medicine Ann & Robert H. Lurie Children's Hospital of Chicago Chicago IL USA. Department of Pathology Memorial Sloan Kettering Cancer Center New York NY USA. Department of Oncology St. Jude Children's Research Hospital Memphis TN USA. Department of Pathology Boston Children's Hospital Boston MA USA. Department of Pediatric Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Biochemistry University of Cambridge Cambridge UK. The Wellcome Trust Sanger Institute Hinxton Cambridge UK. Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. Weill Cornell Medical College Cornell University New York NY USA. Department of Pediatrics Memorial Sloan Kettering Cancer Center New York NY USA. Molecular Pharmacology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Evolution at the edge of expanding populations

arXiv

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arXiv 2017年

作者： Deforet, Maxime Carmona-Fontaine, Carlos Korolev, Kirill S. Xavier, Joao B. Sorbonne Université Centre National de la Recherche Rcientifique Laboratoire Jean Perrin LJP Paris75005 France Center for Genomics and Systems Biology Department of Biology New York University New York CityNY10003 United States Department of Physics Graduate Program in Bioinformatics Boston University BostonMA02215 Program in Computational Biology Memorial Sloan-Kettering Cancer Center New York CityNY10065 United States

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this 'survival of the fastest?' We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hyperswarmer mutant which disperses ∼ 100% faster but pays a growth cost of ∼ 10% to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate (v) results from an interplay of growth (r) and dispersal (D) and is independent of the carrying capacity: v = 2 √rD. A mutant can take over the edge only if its expansion rate (v2) exceeds the expansion rate of the established species' (v1);this simple condition (v2> v1) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations. Copyright © 2017, The Authors. All rights reserved.

关键词： Bacteria

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A clonal expression biomarker associates with lung cancer mortality (vol 25, pg 1540, 2019)

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NATURE MEDICINE 2020年第7期26卷 1148-1148页

作者： Biswas, Dhruva Birkbak, Nicolai J. Rosenthal, Rachel Hiley, Crispin T. Lim, Emilia L. Papp, Krisztian Boeing, Stefan Krzystanek, Marcin Djureinovic, Dijana La Fleur, Linnea Greco, Maria Doeme, Balazs Fillinger, Janos Brunnstrom, Hans Wu, Yin Moore, David A. Skrzypski, Marcin Abbosh, Christopher Litchfield, Kevin Al Bakir, Maise Watkins, Thomas B. K. Veeriah, Selvaraju Wilson, Gareth A. Jamal-Hanjani, Mariam Moldvay, Judit Botling, Johan Chinnaiyan, Arul M. Micke, Patrick Hackshaw, Allan Bartek, Jiri Csabai, Istvan Szallasi, Zoltan Herrero, Javier McGranahan, Nicholas Swanton, Charles Cancer Research UK Lung Cancer Centre of Excellence University College London Cancer Institute Paul O’Gorman Building London UK Bill Lyons Informatics Centre University College London Cancer Institute Paul O’Gorman Building London UK Cancer Evolution and Genome Instability Laboratory The Francis Crick Institute London UK Department of Molecular Medicine Aarhus University Aarhus Denmark Bioinformatics Research Centre Aarhus University Aarhus Denmark Department of Physics of Complex Systems ELTE Eötvös Loránd University Budapest Hungary Bioinformatics and Biostatistics The Francis Crick Institute London UK Danish Cancer Society Research Center Copenhagen Denmark Department of Immunology Genetics and Pathology Uppsala University Uppsala Sweden Genomics Equipment Park The Francis Crick Institute London UK Department of Tumor Biology National Korányi Institute of Pulmonology Semmelweis University Budapest Hungary Division of Thoracic Surgery Comprehensive Cancer Center Medical University of Vienna Vienna Austria Department of Thoracic Surgery National Institute of Oncology Semmelweis University Budapest Hungary Department of Pathology National Korányi Institute of Pulmonology Semmelweis University Budapest Hungary Department of Pathology National Institute of Oncology Budapest Hungary Lund University Laboratory Medicine Region Skåne Department of Clinical Sciences Lund Pathology Lund Sweden Department of Pathology UCL Cancer Institute London UK Department of Oncology and Radiotherapy Medical University of Gdansk Gdansk Poland SE-NAP Brain Metastasis Research Group 2nd Department of Pathology Semmelweis University Budapest Hungary Michigan Center for Translational Pathology University of Michigan Ann Arbor USA Department of Pathology University of Michigan Ann Arbor USA Rogel Cancer Center University of Michigan Ann Arbor USA Department of Urology University of Michigan Ann Arbor USA Howard Hughes Medical Institute University of Michigan An

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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Nature communications 2020年第1期11卷 6232页

作者： Matthew H Bailey William U Meyerson Lewis Jonathan Dursi Liang-Bo Wang Guanlan Dong Wen-Wei Liang Amila Weerasinghe Shantao Li Yize Li Sean Kelso Gordon Saksena Kyle Ellrott Michael C Wendl David A Wheeler Gad Getz Jared T Simpson Mark B Gerstein Li Ding The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. Alvin J. Siteman Cancer Center Washington University School of Medicine St. Louis MO 63108 USA. Yale School of Medicine Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto ON M5G 0A3 Canada. The Hospital for Sick Children Toronto ON M5G 1X8 Canada. Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Biomedical Engineering Oregon Health and Science University Portland OR 97239 USA. Department of Mathematics Washington University in St. Louis St. Louis MO 63130 USA. Department of Genetics Washington University School of Medicine St.Louis MO 63110 USA. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Harvard Medical School Boston MA 02115 USA. Center for Cancer Research Massachusetts General Hospital Boston MA 02114 USA. Department of Pathology Massachusetts General Hospital Boston MA 02114 USA. Department of Computer Science University of Toronto Toronto ON M5S Canada. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Computer Science Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. lding@wustl.edu. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. lding@wustl.edu. Alvin J. Siteman Cancer Center Wash

Correction to this paper has been published: https://***/10.1038/s41467-020-20128-w.

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Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

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Microbiome 2018年第1期6卷 212页

作者： Justin D Silverman Heather K Durand Rachael J Bloom Sayan Mukherjee Lawrence A David Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. Medical Scientist Training Program Duke University Durham NC 27708 USA. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC 27708 USA. Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. lawrence.david@duke.edu. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. lawrence.david@duke.edu. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. lawrence.david@duke.edu. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. lawrence.david@duke.edu.

AbstractFollowing publication of the original article [1], the authors noticed an error in the presentation of equations in the PDF version.

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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL biology 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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