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7th Conference on medicine Meets Virtual Reality, MMVR 1999

作者： Voß, Gerrit Hahn, James K. Müller, Wolfgang Lindeman, Rob Fraunhofer Institute for Computer Graphics Dep. Visualization and Virtual Reality Rundeturmstr. 6 64283 Darmstadt Germany Laboratory for Advanced Computer Applications in Medicine Institute for Computer Graphics George Washington University Washington DC 20052 United States

ISBN: (纸本)9051994451

In the context of medicine cutting is one of the most important operations. Many surgical tasks start with an incision, allowing the surgeon to access the region of interest, using either conventional or minimal invasive surgery techniques. The work presented herein should be seen in the context of surgical training and preoperative planning using computer assisted medical simulation systems. The main result of this work is the proposed cutting algorithm based on predefined templates. To integrate this approach in the existing surgical training simulator and to handle additional tasks like soft tissue deformation, needed by the medical environment, a generic simulation framework was developed. This framework is able to utilize given multiprocessor- and network-environments to achieve the desired realtime interactivity.

关键词： Image segmentation

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Training environment for inferior vena caval filter placement

Training environment for inferior vena caval filter placemen...

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6th Conferenc on medicine Meets Virtual Reality, MMVR 1998

作者： Hahn, James K. Kaufman, Roger Winick, Adam B. Carleton, Thurston Park, Youngser Lindeman, Rob Oh, Kwang-Man Ai-Ghreimil, Nadia Walsh, Raymond J. Loew, Murray Gerber, Joanne Sankar, Siddarth Laboratory for Advanced Computer Applications in Medicine Institute for Computer Graphics George Washington University Washington DC 20052 United States Department of Electrical Engineering and Computer Science George Washington University Washington DC 20052 United States Department of Radiology George Washington University Washington DC 20052 United States Department of Civil and Mechanical Engineering George Washington University Washington DC 20052 United States Department of Anatomy and Cell Biology George Washington University Washington DC 20052 United States Institute for Medical Imaging and Image Analysis George Washington University Washington DC 20052 United States

ISBN: (纸本)9051993862

This paper describes a Virtual Environment system designed to aid in training interventional radiologists in inferior vena cava filter placement. It is being developed as part of a VE simulator for a number of surgical and interventional radiology procedures at the laboratory for advanced computer applications in medicine at the George Washington University. In this procedure a filter is placed in the inferior vena cava to prevent blood clots from the lower portion of the body from reaching the lungs and causing a pulmonary embolus. The simulation is designed to provide both tutorial and testing modes for the filter placement procedure.

关键词： Virtual reality

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Intratumor Graph Neural Network of Tumor-Associated Collagen Signatures from Multiphoton Microscopy Empowers Prognosis of 995 Breast Cancer Patients

Intratumor Graph Neural Network of Tumor-Associated Collagen...

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Microscopy Histopathology and Analytics, Microscopy 2022

作者： Qiu, Lida Kang, Deyong Wang, Chuan Guo, Wenhui Fu, Fangmeng Wu, Qingxiang Xi, Gangqin He, Jiajia Zheng, Liqin Zhang, Qingyuan Liao, Xiaoxia Li, Lianhuang Chen, Jianxin Tu, Haohua Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou350007 China College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Department of Pathology Fujian Medical University Union Hospital Fuzhou350001 China Department of Breast Surgery Fujian Medical University Union Hospital Fuzhou350001 China Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin150081 China National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana Urbana-Champaign United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We perform cancer prognosis based on 8 collagen signatures obtained by sampling a histological section with multiphoton microscopy. The model with intratumor graph neural network (IGNN) significantly outperforms that ... 详细信息

ISBN: (纸本)9781557528209

关键词： Collagen

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DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

arXiv

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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XXIV World Allergy Congress 2015 Seoul, Korea. 14-17 October 2015 Abstracts

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WORLD ALLERGY ORGANIZATION JOURNAL 2016年第1期9卷 1-206页

作者： [Anonymous] Korea University College of Medicine Guro Hospital Seoul South Korea College of Medicine Korea University Seoul South Korea Korea University Guro Hospital Seoul South Korea Seoul National University Boramae Hospital Seoul South Korea Division of Clinical Immunology Faculty of Medicine University of Debrecen Debrecen Hungary Meiji Pharmaceutical University Tokyo Japan Suri Medical Foundation Hospital Uttar Pradesh State India Shahid Beheshti University Tehran Iran Airlangga University Teaching Hospital Surabaya Indonesia Tokyo Metropolitan Police Hospital Tokyo Japan Shinichiro KogaSamsung Changwon Hospital Seoul South Korea Sapporo Tokushukai Hospital Sapporo Japan Department of Dermatology Yonsei University College of Medicine Seoul South Korea Brain Korea 21 PLUS Project for Medical Science Yonsei University College of Medicine Seoul South Korea Sureyyapasa Chest Diseases and Chest Surgery Training Hospital Istanbul Turkey Kangnam Sacred Heart Hospital Seoul South Korea Sakai City Medical Center Sakai Japan Osaka University Suita Japan Yonsei University College of Medicine Seoul South Korea Osaka-Minami Medical Center Kawachinagano Japan Inha University College of Medicine Incheon South Korea Inha University Hospital Incheon South Korea Center for Rhinology and Allergology Wiesbaden Wiesbaden Germany Leti Pharma Gmbh Witten Germany Postgraduate Institute of Medical Education and Research Chandigarh India West Bengal Technical University Kolkata India Institute of Microbiology Academy of Sciences of the Czech Republic Prague Czech Republic Seoul National University Hospital Seoul South Korea Ent and Allergy Centre(INDIA) Panchkula India University of Santo Tomas Hospital Manila Philippines University of Santo Tomas Faculty of Medicine Manila Philippines Kyung Hee University Hospital at Gang-Dong Seoul South Korea Sangji University Wonju South Korea Division of Allergy and Immunology Department of Internal Medicine Yonsei University Colle

A1 Pirfenidone inhibits TGF-b1-induced extracellular matrix production in nasal polyp-derived fibroblasts Jae-Min Shin, Heung-Man Lee, Il-Ho Park A2 The efficacy of a 2-week course of oral steroid in the treatment of chronic spontaneous urticaria refractory to antihistamines Hyun-Sun Yoon, Gyeong Yul Park A3 The altered distribution of follicular t helper cells may predict a more pronounced clinical course of primary sjögren’s syndrome Margit Zeher A4 Betamethasone suppresses Th2 cell development induced by langerhans cell like dendritic cells Katsuhiko Matsui, Saki Tamai, Reiko Ikeda A5 An evaluation of variousallergens in cases of allergic bronchial asthma at lucknow and neighbouring districts by intradermal skintest Drsushil Suri, Dranu Suri A6 Evaluation ferqency of ADHD in childhood asthma Marzieh Heidarzadeh Arani A7 Steven johnson syndrome caused by typhoid fever in a child Azwin Lubis, Anang Endaryanto A8 Chronic Bronchitis with Radio Contrast Media Hypersensitivity: A Case with Hypothesized GINA Step 1 Asthma Shinichiro Koga A9 The association between asthma and depression in Korean adult : An analysis of the fifth korea national health and nutrition examination survey (2010-2012) Lee Ju Suk A10 Management of allergic disease exacerbations in pregnancy Yasunobu Tsuzuki A11 Subcutaneous immunotherapy mouse model for atopic dermatitis Seo Hyeong Kim, Jung U Shin, Ji Yeon Noh, Shan Jin, Shan Jin, Hemin Lee, Jungsoo Lee, Chang Ook Park, Kwang Hoon Lee, Kwang Hoon Lee A12 Atopic disease and/or atopy are risk factors for local anesthetic allergy in patients with history of hypersensitivity reactions to drugs? Fatma Merve Tepetam A13 Food hypersensitivity in patients with atopic dermatitis in Korea Chun Wook Park, Jee Hee Son, Soo Ick Cho, Yong Se Cho, Yun Sun Byun, Yoon Seok Yang, Bo Young Chung, Hye One Kim, Hee Jin Cho A14 Anaphylaxis caused by an ant (Brachyponera chinensis) in Japan Yoshinori Katada, Toshio Tanaka, Akihiko Nakabayashi, Koji Nishida, Kenichi A

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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection Human behaviour

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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