检索结果-内蒙古大学图书馆

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

引用

NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

来源：评论

学校读者我要写书评

暂无评论

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

引用

Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

来源：评论

学校读者我要写书评

暂无评论

Surgical data science-from concepts toward clinical translation

arXiv

引用

arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic

引用

Nature human behaviour 2022年第9期6卷 1318-1319页

作者： Ke Wang Amit Goldenberg Charles A Dorison Jeremy K Miller Andero Uusberg Jennifer S Lerner James J Gross Bamikole Bamikole Agesin Márcia Bernardo Olatz Campos Luis Eudave Karolina Grzech Daphna Hausman Ozery Emily A Jackson Elkin Oswaldo Luis Garcia Shira Meir Drexler Anita Penić Jurković Kafeel Rana John Paul Wilson Maria Antoniadi Kermeka Desai Zoi Gialitaki Elizaveta Kushnir Khaoula Nadif Olalla Niño Bravo Rafia Nauman Marlies Oosterlinck Myrto Pantazi Natalia Pilecka Anna Szabelska I M M van Steenkiste Katarzyna Filip Andreea Ioana Bozdoc Gabriela Mariana Marcu Elena Agadullina Matúš Adamkovič Marta Roczniewska Cecilia Reyna Angelos P Kassianos Minja Westerlund Lina Ahlgren Sara Pöntinen Gabriel Agboola Adetula Pinar Dursun Azuka Ikechukwu Arinze Nwadiogo Chisom Arinze Chisom Esther Ogbonnaya Izuchukwu L G Ndukaihe Ilker Dalgar Handan Akkas Paulo Manuel Macapagal Savannah Lewis Irem Metin-Orta Francesco Foroni Megan Willis Anabela Caetano Santos Aviv Mokady Niv Reggev Merve A Kurfali Martin R Vasilev Nora L Nock Michal Parzuchowski Mauricio F Espinoza Barría Marek Vranka Markéta Braun Kohlová Ivan Ropovik Mikayel Harutyunyan Chunhui Wang Elvin Yao Maja Becker Efisio Manunta Gwenael Kaminski Jordane Boudesseul Dafne Marko Kortnee Evans David M G Lewis Andrej Findor Anais Thibault Landry John Jamir Benzon Aruta Manuel S Ortiz Zahir Vally Ekaterina Pronizius Martin Voracek Claus Lamm Maurice Grinberg Ranran Li Jaroslava Varella Valentova Giovanna Mioni Nicola Cellini Sau-Chin Chen Janis Zickfeld Karis Moon Habiba Azab Neil Levy Alper Karababa Jennifer L Beaudry Leanne Boucher W Matthew Collins Anna Louise Todsen Kevin van Schie Jáchym Vintr Jozef Bavolar Lada Kaliska Valerija Križanić Lara Samojlenko Razieh Pourafshari Sandra J Geiger Julia Beitner Lara Warmelink Robert M Ross Ian D Stephen Thomas J Hostler Soufian Azouaghe Randy McCarthy Anna Szala Caterina Grano Claudio Singh Solorzano Gulnaz Anjum William Jimenez-Leal Maria Bradford Laura Calderón Pérez Julio E Cruz Vásquez Oscar J Galindo-Caballero Juan Cami Harvard Kennedy School Harvard University Cambridge MA USA. Harvard Business School Harvard University Cambridge MA USA. Kellogg School of Management Northwestern University Evanston IL USA. Department of Psychology Willamette University Salem OR USA. millerj@willamette.edu. Institute of Psychology University of Tartu Tartu Estonia. Harvard Kennedy School and Department of Psychology Harvard University Cambridge MA USA. Department of Psychology Stanford University Stanford CA USA. Adekunle Ajasin University Akungba Akoko Nigeria. Faculdade de Psicologia e Ciências da Educação Universidade do Porto Porto Portugal. University of Deusto Barakaldo Spain. University of Navarra Pamplona Spain. University of Valencia Valencia Spain. Stockholm University Stockholm Sweden. California State University Northridge Los Angeles CA USA. Indiana University of Pennsylvania Indiana PA USA. Universidad de Navarra Pamplona Spain. Department of Cognitive Psychology Institute of Cognitive Neuroscience Ruhr University Bochum Bochum Germany. Kindergarten Kustošija Zagreb Croatia. GC University Lahore Lahore Pakistan. Montclair State University Jersey City NJ USA. European University of Cyprus Nicosia Cyprus. Indiana University of Pennsylvania Akron PA USA. Leiden University Culemborg The Netherlands. Stockholm Sweden. Poggio Imperiale Italy. Barakaldo Spain. Islamabad Pakistan. Gent Belgium. Oxford Internet Institute University of Oxford Oxford UK. Warsaw Poland. Queen's University Belfast Belfast Ireland. Universiteit Leiden Leiden The Netherlands. Institute of Psychology Jagiellonian University Krakow Poland. Department of Psychology Lucian Blaga University of Sibiu Sibiu Romania. Carol Davila University of Medicine and Pharmacy Bucharest Romania. National Research University Higher School of Economics Moscow Russia. Institute of Psychology Faculty of Arts University of Presov Presov Slovakia. Institute of Social Sciences CSPS Sl

来源：评论

学校读者我要写书评

暂无评论

Discovery and therapeutic implications of bioactive dihydroxylated phenolic acids in patients with severe heart disease and conditions associated with inflammation and hypoxia

引用

Pharmacological research 2022年 185卷 106458页

作者： Yajun Bai Pu Jia Ye Zhao Lingjian Yang Xiaoxiao Wang Xue Wang Jing Wang Ni'er Zhong Huaxiang Deng Linxiang Du Jiacheng Fang Yanbo Xue Yongyong Chen Shuomo Gao Ying Feng Yi Yan Tianzheng Xiong Jinbin Liu Ying Sun Jing Xie Xirui He Xuexia An Pei Liu Jinjin Xu Fanggang Qin Xue Meng Qian Yin Qiuxiang Yang Rong Gao Xiaokang Gao Kai Luo Qiannan Li Xing Wang Jing Liang Puye Yang Yajun Zhang Sha Liao Shixiang Wang Xinfeng Zhao Chaoni Xiao Jie Yu Qinshe Liu Rui Wang Ning Peng Xiaowen Wang Jianbo Guo Xia Li Haijing Liu Yan Bai Zijian Li Youyi Zhang Yefei Nan Qunzheng Zhang Xunli Zhang Jin'e Lei Erna Alberts Angélique de Man Hye Kyong Kim Su-Jung Hsu Yu Sheng Jia Joerg Riener Jianbin Zheng Wanbin Zhang Xiaopu Zheng Yujie Cai Mei Wang Tai-Ping Fan Xiaohui Zheng Northwest University Xi'an 710069 China. First Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an 710061 China. School of Biotechnology Jiangnan University Wuxi 214122 China. First Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an 710061 China Institute of Vascular Medicine Peking University Third Hospital and Key Laboratory of Molecular Cardiovascular Sciences Ministry of Education Beijing 100083 China. Northwest University Xi'an 710069 China Department of Infectious Diseases Xi'an North Hospital Xi'an 710043 China. Department of Pharmacology University of Cambridge Cambridge CB2 1PD UK. Shaanxi Provincial People's Hospital Xi'an 710068 China. Shaanxi Institute for Food and Drug Control Xi'an 710065 China. Department of Pharmacology University of Cambridge Cambridge CB2 1PD UK School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an 710055 China. Institute of Vascular Medicine Peking University Third Hospital and Key Laboratory of Molecular Cardiovascular Sciences Ministry of Education Beijing 100083 China. Electronic address: zhangyy@***. College of Chemistry and Chemical Engineering Xi'an Shiyou University Xi'an 710065 China. Faculty of Engineering and the Environment University of Southampton Southampton SO17 1BJ UK. Department of Intensive Care Medicine Research VUmc Intensive Care (REVIVE) Amsterdam Cardiovascular Sciences (ACS) Amsterdam Infection and Immunity Institute (AI&amp II) Amsterdam University Medical Centers Location VUmc De Boelelaan 1117 1081 HV Amsterdam the Netherlands. Plant Science and Natural Products Institute of Biology Leiden Leiden University Sylviusweg 72 2333BE Leiden the Netherlands. Leiden University-European Center for Chinese Medicine and Natural Compounds Institute of Biology Leiden University Sylviusweg 72 2333BE Leiden the Netherlands. Agilent Technologies Hewlett-Packard-Strasse 8 76337 Waldbronn Germany. School of Chemistry a

Our initial studies detected elevated levels of 3,4-dihydroxyphenyllactic acid (DHPLA) in urine samples of patients with severe heart disease when compared with healthy subjects. Given the reported anti-inflammatory properties of DHPLA and related dihydroxylated phenolic acids (DPAs), we embarked on an exploratory multi-centre investigation in patients with no urinary tract infections to establish the possible pathophysiological significance and therapeutic implications of these findings. Chinese and Caucasian patients being treated for severe heart disease or those conditions associated with inflammation (WBC ≥ 10 ×10/L or hsCRP ≥ 3.0 mg/L) and/or hypoxia (PaO ≤ 75 mmHg) were enrolled; their urine samples were analyzed by HPLC, HPLC-MS, GC-MS and biotransformation assays. DHPLA was detected in urine samples of patients, but undetectable in healthy volunteers. Dynamic monitoring of inpatients undergoing treatment showed their DHPLA levels declined in proportion to their clinical improvement. In DHPLA-positive patients' fecal samples, Proteus vulgaris and P. mirabilis were more abundant than healthy volunteers. In culture, these gut bacteria were capable of reversible interconversion between DOPA and DHPLA. Furthermore, porcine and rodent organs were able to metabolize DOPA to DHPLA and related phenolic acids. The elevated levels of DHPLA in these patients suggest bioactive DPAs are generated de novo as part of a human's defense mechanism against disease. Because DHPLA isolated from Radix Salvia miltiorrhizae has a multitude of pharmacological activities, these data underpin the scientific basis of this medicinal plant's ethnopharmacological applications as well as highlighting the therapeutic potential of endogenous, natural or synthetic DPAs and their derivatives in humans.

关键词： 3,4-dihydroxyphenylalanine (DOPA) 3,4-dihydroxyphenyllactic acid (DHPLA) Dihydroxylated phenolic acids Human microbiota and mammalian tissues Inflammation and/or hypoxia Isopropyl 3-(3,4-dihydroxyphenyl)- 2-hydroxypropanoate (IDHP)

来源：评论

学校读者我要写书评

暂无评论

Model-protected multi-task learning

arXiv

引用

arXiv 2018年

作者： Liang, Jian Liu, Ziqi Zhou, Jiayu Jiang, Xiaoqian Zhang, Changshui Wang, Fei Department of Automation Tsinghua University State Key Laboratory of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Beijing100084 China Department of Computer Science Xi'an Jiaotong University Xi'an710049 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Biomedical Informatics University of California San Diego San DiegoCA92093 United States Department of Healthcare Policy and Research Weill Cornell Medical College New York CityNY10065 United States

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can "leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

引用

NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

来源：评论

学校读者我要写书评

暂无评论

Mössbauer spectrometry study of biological tissues

Mössbauer spectrometry study of biological tissues

引用

作者： Bonková, I. Miglierini, M. Bujdoš, M. Kopáni, M. Comenius University in Bratislava Faculty of Natural Sciences Institute of Laboratory Research on Geomaterials Ilkovicova 6 Bratislava842 15 Slovakia Slovak University of Technology in Bratislava Faculty of Electrical Engineering and Information Technology Institute of Nuclear and Physical Engineering Ilkovicova 3 Bratislava812 19 Slovakia Comenius University in Bratislava Faculty of Medicine Department of Medical Physics Biophysics Informatics and Telemedicine Sasinkova 4 Bratislava811 08 Slovakia

Magnetic microstructure of iron contained in selected biological tissues is characterized and mutually compared. We have studied three types of biological samples prepared from human brain, human and horse spleen. Original samples were lyophilized (dried in a vacuum) thus providing powder forms. As a principal method of study, 57Fe Mössbauer spectrometry in transmission mode was used. The Mössbauer spectrometry experiments were performed at room (≈ 300 K) and at liquid helium (4.2 K) temperature. At room temperature Mössbauer spectra show doublet-like features. Such behaviour indicates possible presence of nanoparticles with fluctuating magnetic moments that acquire arbitrary positions. On the other hand, low temperature Mössbauer spectrometry measurements demonstrate significant contribution of sextets that confirmed occurrence of blocked magnetic moments of iron-containing particles. Different relative contributions of magnetic components in the low temperature spectra for the three inspected biological tissues suggest differences in the blocking temperatures of the magnetic nanoparticles present in them.

关键词： Magnetic moments

来源：评论

学校读者我要写书评

暂无评论

The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

引用

arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A network approach to quantifying radiotherapy effect on cancer: radiosensitive gene group centrality

arXiv

引用

arXiv 2018年

作者： Yao, Yu-Xiang Bing, Zhi-Tong Huang, Liang Huang, Zi-Gang Lai, Ying-Cheng School of Physical Science and Technology Lanzhou University Lanzhou730000 China Evidence Based Medicine Center School of Basic Medical Science of Lanzhou University Lanzhou730000 China Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou730000 China Department of Computational Physics Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Institute of Health and Rehabilitation Science School of Life Science and Technology Xi’an Jiaotong University Xi’an710049 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends tobe more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：