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Correction to: CpG‑creating mutations are costly in many human viruses

Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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63P Ki67 values and MammaPrint (MP) genomic risk in early breast cancer: Correlation by ductal vs lobular histology

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ESMO Open 2023年第1期8卷 101287-101287页

作者： E. Zamora L. Sanz Gomez L. Joval C. Ortiz Velez M.A. Arumi de Dios M. Borrell Puy D.A. Gomez Puerto I.I.L. Leao M.A. Rezqallah Aron V. Navarro Garces G. Villacampa C. Saura Manich P. Gomez Pardo V. Peg Cámara M. Bellet-Ezquerra Medical Oncology Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Dept. Hospital Universitario Vall d'Hebron Barcelona Spain Oncology Data Science Group (ODysSey) VHIO Vall d'Hebrón Institute of Oncology Barcelona Spain Breast Cancer and Melanoma Dept. Vall d'Hebron University Hospital Barcelona Spain Oncology Department Universitat de Barcelona - Facultat de Medicina Barcelona Spain Dept. Medical Oncology Hospital Universitario Vall d'Hebron Barcelona Spain Medical Oncology Department Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Medical Oncology Department CHVNG/E - Centro Hospitalar de Vila Nova de Gaia/Espinho - Unidade 1 - EPE-SNS Vila Nova De Gaia Portugal Oncología Médica Dept. Vall d'Hebron University Hospital Barcelona Spain Oncology Data Science Group (ODysSey) Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Statistics Department SOLTI - Grupo Espanol de Estudio Barcelona Spain Head Breast Cancer Program Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Dept. Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Dept. of Pathology Vall d'Hebron University Hospital Barcelona Spain Oncology Dept. Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain

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Ten quick tips for deep learning in biology

arXiv

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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

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OS12.7.A Characterization of intra-tumoral heterogeneity and differential immune activation during malignant progression of meningiomas on single cell level

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Neuro-Oncology 2021年第SUPPLEMENT_2期23卷 ii15-ii16页

作者： Blume, C Dogan, H Schweizer, L Wick, W Weller, M Mann, M Kalamarides, M von Deimling, A Schlesner, M Sahm, F CCU Neuropathology German Cancer Research Centre (DKFZ) Heidelberg Germany Dept. of Neuropathology University Hospital Heidelberg Heidelberg Germany Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany German Cancer Research Centre (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Department of Neurology Clinical Neuroscience Center University Hospital and University of Zürich Zürich Switzerland NNF Center for Protein Research Faculty of Health Sciences University of Copenhagen Copenhagen Denmark Sorbonne Université and Department of Neurosurgery Pitié Salpêtrière Hospital Paris France Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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Corrigendum to "Sex differences on multikinase inhibitors toxicity in patients with advanced gastroenteropancreatic neuroendocrine tumours" [Eur J cancer 188 (2023) 39-48]

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European journal of cancer (Oxford, England : 1990) 2024年 203卷 114061页

作者： Jorge Hernando Maria Roca-Herrera Alejandro García-Álvarez Eric Raymond Philippe Ruszniewski Matthew H Kulke Enrique Grande Rocío García Carbonero Daniel Castellano Ramón Salazar Toni Ibrahim Alex Teule Vicente Alonso Nicola Fazio Juan W Valle Salvatore Tafuto Ana Carmona Victor Navarro Jaume Capdevila Gastrointestinal and Endocrine Tumor Unit Medical Oncology Department Vall d'Hebron University Hospital Vall d'Hebron Institute of Oncology Barcelona Spain. Electronic address: jhernando@***. Gastrointestinal and Endocrine Tumor Unit Medical Oncology Department Vall d'Hebron University Hospital Vall d'Hebron Institute of Oncology Barcelona Spain. Department of Oncology Paris Saint-Joseph Hospital 185 rue Raymond Losserand 75014 Paris France. Université Paris Cité and Dept of Pancreatology-Digestive Oncology Beaujon Hospital Clichy France. Hematology/Oncology Unit Boston Medical Center USA. Medical Oncology Department MD Anderson Cancer Center Madrid Madrid Spain. Oncology Department Hospital Universitario 12 de Octubre Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12) Universidad Complutense de Madrid (UCM) CNIO CIBERONC Madrid Spain. Oncología Médica Hospital Universitario 12 de Octubre Madrid Spain. Medical Oncology Department - Institut Català d'Oncologia Oncobell Program-IDIBELL University of Barcelona CIBERONC Barcelona Spain. Osteoncology Bone and Soft Tissue Sarcomas and Innovative Therapies Unit IRCCS Istituto Ortopedico Rizzoli Bologna Italy. Department of Medical Oncology Hereditary Cancer Program Program in Molecular Mechanisms and Experimental Therapy in Oncology (Oncobell) IDIBELL Catalan Institute of Oncology l'Hospitalet del Llobregat Spain. Oncology Department Hospital Universitario Miguel Servet Instituto Investigación Sanitaria Aragón CIBERONC Zaragoza Spain. Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors European Institute of Oncology Milan Italy. Division of Cancer Sciences University of Manchester & Department of Medical Oncology The Christie NHS Foundation Trust Manchester UK. Sarcoma and Rare Tumors Unit Istituto Nazionale Tumori I.R.C.C.S. Fondazione "G.Pascale" Naples Italy. Oncology Data Science Group (ODysSey) Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain.

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Transcriptome analysis and gene networks in a rare pediatric tumor

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Biotechnology Research and Innovation 2019年第2期3卷 252-264页

作者： Michel L. Leite Elio F. Vanin Stephen Iannaccone Nicolau B. da Cunha Sérgio de Alencar Fabricio F. Costa Laboratory of Data Science Genomic Sciences and Biotechnology Program UCB Brasilia SGAN 916 Modulo B Bloco C 70.790-160 Brasília DF Brazil Cancer Biology and Epigenomics Program USA Developmental Biology Ann & Robert H Lurie Children’s Hospital of Chicago Research Center and Northwestern University’s Feinberg School of Medicine 2430 N. Halsted St. Box 220 Chicago IL USA The Polsky Center for Entrepreneurship and Innovation University of Chicago 1452 E 53rd St Chicago IL USA MATTER Chicago 222 W. Merchandise Mart Plaza Suite 12th Floor Chicago IL 60.654 USA

Ependymoma, a rare pediatric tumor originating from ependymal cells located in the lining of ventricular surfaces in the brain, presents great challenges in treatment despite advances in neurosurgical techniques. In order to identify new molecular biomarkers that could improve clinical management and outcomes, we have used RNA-seq to profile the Whole Transcriptome of three ependymoma samples and one normal control brain tissue, producing a total of 2.5 Gigabases of sequencing data. Different protein-coding gene databases were interrogated for known annotated genes to calculate RPKM and Fold Changes (FGs) for each clustered gene. Using this approach, we were able to identify Differentially Expressed Genes (DEGs) in ependymomas. KEGG pathways and Gene Ontology (GO) categories enriched in the DEGs were analyzed using Enrichr, and protein interaction networks were then built using MetaCore™. Thirty differentially expressed protein-coding genes associated with neurogenesis were identified by TaqMan Real-Time PCR, 17 of these showing statistically significant differential expression. Based on these results, we have identified IGF-2 as highly over-expressed in ependymomas and that this is due to loss of DNA methylation in its promoter region. In conclusion, we believe that some of these genes, specially IGF-2 , may be of clinical importance, opening new avenues for disease management and new therapies.

关键词： Ependymomas Transcriptome analysis Expression Big data analytics Epigenetic regulation Potential biomarkers.

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Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

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1549P Molecular alterations (MA) with potential therapeutic implications in KRAS wild-type (WT) pancreatic cancer patients

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Annals of Oncology 2020年 31卷 S948-S948页

作者： D.A. Acosta Eyzaguirre H. Verdaguer E. Buxò A. Garcia-Alvarez E. Sardo J. Hernando M. Diez Garcia M. Guardiola F.J. Ros Montañá I. Baraibar Argota N. Saoudi D.H. Marmolejo Castaneda S. Aguilar A. Sierra A. Vivancos R. Dienstmann T. Macarulla Mercadé Medical Oncology Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Sanatorio Allende Cordoba Argentina Oncology Data Science Vall d'Hebron University Hospital and Vall d´Hebron Institute of Oncology (VHIO) Barcelona Spain Prescreening Program VHIO Barcelona Spain Cancer Genomics Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Characterizing weibo social media posts from wuhan, china during the early stages of the COVID-19 pandemic: Qualitative content analysis

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JMIR Public Health and Surveillance 2020年第4期6卷 e24125页

作者： Xu, Qing Shen, Ziyi Shah, Neal Cuomo, Raphael Cai, Mingxiang Brown, Matthew Li, Jiawei Mackey, Tim Department of Healthcare Research and Policy University of California San Diego - Extension San Diego CA United States Global Health Policy and Data Institute San Diego CA United States S-3 Research LLC San Diego CA United States Masters Program in Computer Science Jacobs School of Engineering University of California San Diego San Diego CA United States Department of Anesthesiology School of Medicine University of California San Diego San Diego CA United States US Embassy National Cancer Institute National Institutes of Health Beijing China Department of Anesthesiology Division of Infectious Diseases and Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. Objective: This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. Methods: We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. Results: We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. Conclusions: Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxie

关键词： Attitude Behavior China Content analysis COVID-19 data mining Infodemic Infodemiology Infoveillance Knowledge Social media Weibo

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