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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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Silicone breast implant modification review: Overcoming capsular contracture

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Biomaterials Research 2018年第1期22卷 319-327页

作者： Shin, Byung Ho Kim, Byung Hwi Kim, Sujin Lee, Kangwon Choy, Young Bin Heo, Chan Yeong Seoul National University College of Medicine Department of Biomedical Engineering Seoul 03080 South Korea Seoul National University Department of Transdisciplinary Studies Graduate School of Convergence Science and Technology Seoul 08826 South Korea Seoul National University Interdisciplinary Program for Bioengineering College of Engineering Seoul 08826 South Korea Seoul National University Department of Plastic and Reconstructive Surgery College of Medicine Seoul 03080 South Korea Seoul National University Bundang Hospital Department of Plastic and Reconstructive Surgery Seongnam 13620 South Korea Seoul National University Institute of Medical and Biological Engineering Medical Research Center Seoul 03080 South Korea Advanced Institutes of Convergence Technology Suwon Gyeonggi-do 16229 South Korea

Background: Silicone implants are biomaterials that are frequently used in the medical industry due to their physiological inertness and low toxicity. However, capsular contracture remains a concern in long-term transplantation. To date, several studies have been conducted to overcome this problem. This review summarizes and explores these trends. Main body: First, we examined the overall foreign body response from initial inflammation to fibrosis capsule formation in detail and introduced various studies to overcome capsular contracture. Secondly, we introduced that the main research approaches are to inhibit fibrosis with anti-inflammatory drugs or antibiotics, to control the topography of the surface of silicone implants, and to administer plasma treatment. Each study examined aspects of the various mechanisms by which capsular contracture could occur, and addressed the effects of inhibiting fibrosis. Conclusion: This review introduces various silicone surface modification methods to date and examines their limitations. This review will help identify new directions in inhibiting the fibrosis of silicone implants. © 2018 The Author(s).

关键词： Capsular contracture Fibrosis Foreign body reaction Inflammation Modification Silicone implant

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Author Correction: Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

Nature cancer

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Nature cancer 2023年 2023 May 19页

作者： Do Young Hyeon Dowoon Nam Youngmin Han Duk Ki Kim Gibeom Kim Daeun Kim Jingi Bae Seunghoon Back Dong-Gi Mun Inamul Hasan Madar Hangyeore Lee Su-Jin Kim Hokeun Kim Sangyeop Hyun Chang Rok Kim Seon Ah Choi Yong Ryoul Kim Juhee Jeong Suwan Jeon Yeon Woong Choo Kyung Bun Lee Wooil Kwon Seunghyuk Choi Taewan Goo Taesung Park Young-Ah Suh Hongbeom Kim Ja-Lok Ku Min-Sik Kim Eunok Paek Daechan Park Keehoon Jung Sung Hee Baek Jin-Young Jang Daehee Hwang Sang-Won Lee School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Chemistry and Center for Proteogenome Research Korea University Seoul Republic of Korea. Department of Surgery and Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Seoul Republic of Korea. Creative Research Initiatives Center for Epigenetic Code and Diseases Seoul National University Seoul Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. Department of Pathology Seoul National University College of Medicine Seoul Korea. Department of Computer Science Hanyang University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Department of Statistics Seoul National University Seoul Republic of Korea. Korean Cell Line Bank Laboratory of Cell Biology Cancer Research Institute Seoul National University College of Medicine Seoul Korea. Department of New Biology Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. dpark@ajou.ac.kr. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. keehoon.jung@snu.ac.kr. Institute of Allergy and Clinical Immunology Seoul National University Medical Research Center Seoul Republic of Korea. keehoon.jung@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of

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Housing and husbandry factors affecting zebrafish novel tank test responses: a global multi-laboratory study

Lab animal

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Lab animal 2025年第6期54卷 2025 May 26页

作者： Courtney Hillman Barbara D Fontana Tamara G Amstislavskaya Maria A Gorbunova Stefani Altenhofen Karissa Barthelson Leonardo M Bastos João V Borba Carla D Bonan Caroline H Brennan Amaury Farías-Cea Austin Cooper Jamie Corcoran Eduardo R Dondossola Luis M Martinez-Duran Matheus Gallas-Lopes David S Galstyan Ella O Garcia Ewan Gerken Robert Hindges Justin W Kenney Maxim A Kleshchev Tatiana O Kolesnikova Adele Leggieri Sergey L Khatsko Michael Lardelli Guilherme Lodetti Giulia Lombardelli Ana C Luchiari Stefani M Portela Violeta Medan Lirane M Moutinho Evgeny V Nekhoroshev Barbara D Petersen Maureen L Petrunich-Rutherford Angelo Piato Maurizio Porfiri Emily Read Cássio M Resmim Eduardo P Rico Denis B Rosemberg Murilo S de Abreu Catia A Salazar Thailana Stahlhofer-Buss Júlia R Teixeira Ana M Valentim Alexander V Zhdanov Patricio Iturriaga-Vásquez Xian Wang Ryan Y Wong Allan V Kalueff Matthew O Parker Surrey Sleep Research Centre Department of Clinical and Experimental Medicine School of Biosciences University of Surrey Surrey UK. Laboratory of Experimental Neuropsychobiology Department of Biochemistry and Molecular Biology Natural and Exact Sciences Center Federal University of Santa Maria Santa Maria Brazil. Department of Biological Sciences Wayne State University Detroit MI USA. Laboratory of Experimental Models of Neuropsychiatric Disorders Scientific Research Institute of Neurosciences and Medicine Novosibirsk State University Novosibirsk Russia. Ural Federal University Yekaterinburg Russia. Laboratório de Neuroquímica e Psicofarmacologia Escola de Ciências da Saúde e da Vida Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil. Alzheimer's Disease Genetics Laboratory School of Biological Sciences Faculty of Sciences Engineering and Technology The University of Adelaide Adelaide South Australia Australia. Childhood Dementia Research Group College of Medicine and Public Health Flinders Health and Medical Research Institute Flinders University Adelaide South Australia Australia. Laboratório de Psicofarmacologia e Comportamento Departamento de Farmacologia Instituto de Ciências Básicas da Saúde Universidade Federal do Rio Grande do Sul Porto Alegre Brazil. School of Biological and Behavioural Sciences Queen Mary University of London London UK. Molecular Pharmacology and Medicinal Chemistry Lab Facultad de Ingeniería y Ciencias Universidad de la Frontera Temuco Chile. Department of Psychology University of Nebraska at Omaha Omaha NE USA. Translational Psychiatry Laboratory Graduate Program in Health Sciences University of Southern Santa Catarina Criciúma Brazil. St. Petersburg State University St. Petersburg Russia. Department of Psychology Indiana University Northwest Gary IN USA. Centre for Developmental Neurobiology and MRC Centre for Neurodevelopmental Disorders King's College London London UK. Neurobiology Program Sir

The reproducibility crisis in bioscience, characterized by inconsistent study results, impedes our understanding of biological processes. Global collaborative studies offer a unique solution to this problem. Here, we present a global collaboration using the zebrafish (Danio rerio) novel tank test, a popular behavioral assay for anxiety-like responses. We analyzed data from 20 laboratories worldwide, focusing on housing conditions and experimental setups. Our study included 488 adult zebrafish, tested for 5 min, focusing on a variety of variables. Key findings show that female zebrafish exhibit more anxiety-like behavior than males, highlighting sex as a critical variable. Housing conditions, including higher stocking densities and specific feed types, also influenced anxiety levels. Optimal conditions (5 fish/L) and nutritionally rich feeds (for example, rotifers) mitigated anxiety-like behaviors. Environmental stressors, such as noise and transportation, significantly impacted behavior. We recommend standardizing testing protocols to account for sex differences, optimal stocking densities, nutritionally rich feeds and minimizing stressors to improve the reliability of zebrafish behavioral studies.

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Residue detection in the large intestine from colonoscopy video using the support vector machine method

Residue detection in the large intestine from colonoscopy vi...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： M. Cho H. J. Kong J. H. Kim B. Jeon K. S. Hong S. Kim Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Republic of Korea Department of Biomedical Engineering Chungnam National University College of Medicine Daejeon Korea Department of Gastroenterology Seoul National University Boramae Medical Center Seoul Korea Department of Gastroenterology Mediplex Sejong Hospital Incheon Korea Institute of Medical and Biological Engineering Medical Research Center Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781538670804;9788993215168

As colonoscopy is the gold standard screening test for colorectal cancer, studies of applying image processing to colonoscopy video are increasing. When performing colonoscopy or applying image processing to colonoscopy video, the presence of a residue in the large intestine is a negative affecting factor. In this reason, the colonoscopist evaluates and records the colon cleanliness after performing colonoscopy. However, these assessments can be influenced by the subjectivity of the colonoscopist. In order to quantify the frequency of residues in the large intestine, we applied image processing and machine learning techniques to the digital images obtained from colonoscopy. Support vector machine technique was applied to distinguish informative frames from colonoscopy videos, and to classify residue frames and the others. The sensitivity, specificity, and accuracy were evaluated for five patients.

关键词： Colonoscopy Support vector machines Intestines Image processing Endoscopes Cancer Colon

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

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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

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Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro Xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh Xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora X Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

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Guidelines and definitions for research on epithelial-mesenchymal transition (vol 21, pg 341, 2020)

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NATURE REVIEWS MOLECULAR CELL BIOLOGY 2021年第12期22卷 834-834页

作者： Yang, Jing Antin, Parker Berx, Geert Blanpain, Cedric Brabletz, Thomas Bronner, Marianne Campbell, Kyra Cano, Amparo Casanova, Jordi Christofori, Gerhard Dedhar, Shoukat Derynck, Rik Ford, Heide L. Fuxe, Jonas Garcia de Herreros, Antonio Goodall, Gregory J. Hadjantonakis, Anna-Katerina Huang, Ruby Y. J. Kalcheim, Chaya Kalluri, Raghu Kang, Yibin Khew-Goodall, Yeesim Levine, Herbert Liu, Jinsong Longmore, Gregory D. Mani, Sendurai A. Massague, Joan Mayor, Roberto McClay, David Mostov, Keith E. Newgreen, Donald F. Nieto, M. Angela Puisieux, Alain Runyan, Raymond Savagner, Pierre Stanger, Ben Stemmler, Marc P. Takahashi, Yoshiko Takeichi, Masatoshi Theveneau, Eric Thiery, Jean Paul Thompson, Erik W. Weinberg, Robert A. Williams, Elizabeth D. Xing, Jianhua Zhou, Binhua P. Sheng, Guojun Departments of Pharmacology and Pediatrics Moores Cancer Center University of California San Diego La Jolla CA USA Department of Cellular and Molecular Medicine University of Arizona Tucson AZ USA Molecular and Cellular Oncology Lab Department of Biomedical Molecular Biology Ghent University Cancer Research Institute Ghent (CRIG) VIB Center for Inflammation Research Ghent Belgium Laboratory of Stem Cells and Cancer Université Libre de Bruxelles Bruxelles Belgium Department of Experimental Medicine 1 Nikolaus-Fiebiger-Center for Molecular Medicine Friedrich-Alexander-University Erlangen-Nürnberg Erlangen Germany Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA Department of Biomedical Science and Bateson Centre University of Sheffield Sheffield UK Departamento de Bioquímica Universidad Autónoma de Madrid (UAM) Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (CSIC-UAM) IdiPAZ & Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Madrid Spain Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology/Institut de Biologia Molecular de Barcelona (IBMB-CSIC) Barcelona Spain Department of Biomedicine University of Basel Basel Switzerland Department of Biochemistry and Molecular Biology University of British Columbia and British Columbia Cancer Research Centre Vancouver BC Canada Departments of Cell and Tissue Biology and Anatomy University of California at San Francisco San Francisco CA USA Department of Pharmacology University of Colorado Anschutz Medical Campus Aurora CO USA Department of Laboratory Medicine (LABMED) Division of Pathology Karolinska University Hospital and Department of Microbiology Tumor and Cell Biology (MTC) Karolinska Institutet Stockholm Sweden Programa de Recerca en Càncer Institut Hospital del Mar d’Investigacions Mèdiques (IMIM) and Departament de Ciències Experimentals i de la Salut Universitat Pompeu Fabra Barcelona Spain C

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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