检索结果-内蒙古大学图书馆

arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Variant interpretation using population databases: lessons from gnomAD

arXiv

引用

arXiv 2021年

作者： Gudmundsson, Sanna Singer-Berk, Moriel Watts, Nicholas A. Phu, William Goodrich, Julia K. Solomonson, Matthew Rehm, Heidi L. MacArthur, Daniel G. O'Donnell-Luria, Anne Abreu, Maria Aguilar Salinas, Carlos A. Ahmad, Tariq Albert, Christine M. Alföldi, Jessica Ardissino, Diego Armean, Irina M. Atzmon, Gil Banks, Eric Barnard, John Baxter, Samantha M. Beaugerie, Laurent Benjamin, Emelia J. Benjamin, David Bergelson, Louis Boehnke, Michael Bonnycastle, Lori L. Bottinger, Erwin P. Bowden, Donald W. Bown, Matthew J. Brant, Steven Calvo, Sarah E. Campos, Hannia Chambers, John C. Chan, Juliana C. Chao, Katherine R. Chapman, Sinéad Chasman, Daniel Chisholm, Rex Cho, Judy Chowdhury, Rajiv Chung, Mina K. Chung, Wendy Cibulskis, Kristian Cohen, Bruce Collins, Ryan L. Connolly, Kristen M. Correa, Adolfo Covarrubias, Miguel Cummings, Beryl Dabelea, Dana Daly, Mark J. Danesh, John Darbar, Dawood Denny, Joshua Donnelly, Stacey Duggirala, Ravindranath Dupuis, Josée Ellinor, Patrick T. Elosua, Roberto Emery, James England, Eleina Erdmann, Jeanette Esko, Tõnu Evangelista, Emily Farjoun, Yossi Fatkin, Diane Ferriera, Steven Florez, Jose Francioli, Laurent Franke, Andre Färkkilä, Martti Gabriel, Stacey Garimella, Kiran Gauthier, Laura D. Gentry, Jeff Getz, Gad Glahn, David C. Glaser, Benjamin Glatt, Stephen J. Goldstein, David Gonzalez, Clicerio Goodrich, Julia Groop, Leif Gudmundsson, Sanna Gupta, Namrata Haessly, Andrea Haiman, Christopher Hall, Ira Hanis, Craig Harms, Matthew Hiltunen, Mikko Holi, Matti M. Hultman, Christina M. Jalas, Chaim Jeandet, Thibault Kallela, Mikko Kaplan, Diane Kaprio, Jaakko Karczewski, Konrad J. Kathiresan, Sekar Kenny, Eimear Kim, Bong-Jo Kim, Young Jin Kirov, George Kooner, Jaspal Koskinen, Seppo Krumholz, Harlan M. Kugathasan, Subra Kwak, Soo Heon Laakso, Markku Lake, Nicole Langsford, Trevyn Laricchia, Kristen M. Lehtimäki, Terho Lek, Monkol Lipscomb, Emily Llanwarne, Christopher Loos, Ruth J.F. Lubitz, Steven A. Luna, Teresa Tusie Ma, Ronald C.W. MacArthur, Daniel G. Marcus, Gregory M. Marrugat, Ja University of Miami Miller School of Medicine Gastroenterology Miami United States Unidad de Investigacion de Enfermedades Metabolicas Instituto Nacional de Ciencias Medicas y Nutricion Mexico City Mexico Peninsula College of Medicine and Dentistry Exeter United Kingdom Division of Preventive Medicine Brigham and Women's Hospital BostonMA United States Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Analytic and Translational Genetics Unit Massachusetts General Hospital BostonMA United States Department of Cardiology University Hospital Parma Italy European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge United Kingdom Department of Biology Faculty of Natural Sciences University of Haifa Haifa Israel Department of Medicine and Genetics Albert Einstein College of Medicine BronxNY United States Data Science Platform Broad Institute of MIT and Harvard CambridgeMA United States Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic ClevelandOH United States Sorbonne Université APHP Gastroenterology Department Saint Antoine Hospital Paris France NHLBI Boston University's Framingham Heart Study FraminghamMA United States Department of Medicine Boston University School of Medicine BostonMA United States Department of Epidemiology Boston University School of Public Health BostonMA United States Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann ArborMS United States National Human Genome Research Institute National Institutes of Health BethesdaMD United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Biochemistry Wake Forest School of Medicine Winston-SalemNC

Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease-gene relationships. The Genome Aggregation database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://***/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per-base expression levels, constraint scores, and variant co-occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease. © 2021, CC BY.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Erratum to 'Relation Between Mitral Valve Prolapse and Erectile Dysfunction (from a Nationwide Case-Control Study)': The American Journal of Cardiology 2019 Nov 15;124(10):1590-1593./ doi: 10.1016/***.2019.08.010

引用

The American journal of cardiology 2021年 142卷 161页

作者： Shiu-Dong Chung Ju-Chi Liu Tsai-Ning Lou Ben-Chang Shia Herng-Ching Lin Li-Ting Kao Division of Urology Department of Surgery Far Eastern Memorial Hospital Taipei Taiwan Department of Computer Science and Engineering and Graduate Program in Biomedical Informatics College of Informatics Yuan-Ze University Chung-Li Taiwan Sleep Research Center Taipei Medical University Hospital Taipei Taiwan. Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Cardiovascular Medicine Department of Medicine Medical University Hospital-Shuang Ho Hospital TaipeiTaiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. Big Data Research Center Taipei Medical University Taipei Taiwan. Sleep Research Center Taipei Medical University Hospital Taipei Taiwan School of Health Care Administration Taipei Medical University Taipei Taiwan Graduate Institute of Life Sciences National Defense Medical Center Taipei Taiwan Department of Pharmacy Practice Tri-Service General Hospital Taipei Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan.. Electronic address: kaoliting@***.

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

引用

Nature genetics 2024年第10期56卷 2281页

作者： Qingbo S Wang Takanori Hasegawa Ho Namkoong Ryunosuke Saiki Ryuya Edahiro Kyuto Sonehara Hiromu Tanaka Shuhei Azekawa Shotaro Chubachi Yugo Takahashi Saori Sakaue Shinichi Namba Kenichi Yamamoto Yuichi Shiraishi Kenichi Chiba Hiroko Tanaka Hideki Makishima Yasuhito Nannya Zicong Zhang Rika Tsujikawa Ryuji Koike Tomomi Takano Makoto Ishii Akinori Kimura Fumitaka Inoue Takanori Kanai Koichi Fukunaga Seishi Ogawa Seiya Imoto Satoru Miyano Yukinori Okada Department of Genome Informatics Graduate School of Medicine University of Tokyo Tokyo Japan. qingbow@m.u-tokyo.ac.jp. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. qingbow@m.u-tokyo.ac.jp. M&D Data Science Center Tokyo Medical and Dental University Tokyo Japan. Department of Infectious Diseases Keio University School of Medicine Tokyo Japan. hounamugun@keio.jp. Department of Pathology and Tumor Biology Kyoto University Kyoto Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Department of Respiratory Medicine and Clinical Immunology Osaka University Graduate School of Medicine Suita Japan. Department of Genome Informatics Graduate School of Medicine University of Tokyo Tokyo Japan. Division of Pulmonary Medicine Department of Medicine Keio University School of Medicine Tokyo Japan. Faculty of Medicine Osaka University Suita Japan. Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA. Divisions of Genetics and Rheumatology Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Laboratory of Children's Health and Genetics Division of Health Science Osaka University Graduate School of Medicine Suita Japan. Department of Pediatrics Osaka University Graduate School of Medicine Suita Japan. Division of Genome Analysis Platform Development National Cancer Center Research Institute Tokyo Japan. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Health Science Research and Development Center (HeRD) Tokyo Medical and Dental University Tokyo Japan. Laboratory of Veterinary Infectious Disease Department of Veterinary Medicine Kitasato University Tokyo Japan. Department of Respiratory Medicine Nagoya University Graduate School of Medicine Nagoya Japan. In

来源：评论

学校读者我要写书评

暂无评论

The science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

引用

JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

来源：评论

学校读者我要写书评

暂无评论

Constraints on black-hole charges with the 2017 EHT observations of M87*

引用

Physical Review D 2021年第10期103卷 104047-104047页

作者： Prashant Kocherlakota Luciano Rezzolla Heino Falcke Christian M. Fromm Michael Kramer Yosuke Mizuno Antonios Nathanail Héctor Olivares Ziri Younsi Kazunori Akiyama Antxon Alberdi Walter Alef Juan Carlos Algaba Richard Anantua Keiichi Asada Rebecca Azulay Anne-Kathrin Baczko David Ball Mislav Baloković John Barrett Bradford A. Benson Dan Bintley Lindy Blackburn Raymond Blundell Wilfred Boland Katherine L. Bouman Geoffrey C. Bower Hope Boyce Michael Bremer Christiaan D. Brinkerink Roger Brissenden Silke Britzen Avery E. Broderick Dominique Broguiere Thomas Bronzwaer Do-Young Byun John E. Carlstrom Andrew Chael Chi-kwan Chan Shami Chatterjee Koushik Chatterjee Ming-Tang Chen Yongjun Chen (陈永军) Paul M. Chesler Ilje Cho Pierre Christian John E. Conway James M. Cordes Thomas M. Crawford Geoffrey B. Crew Alejandro Cruz-Osorio Yuzhu Cui Jordy Davelaar Mariafelicia De Laurentis Roger Deane Jessica Dempsey Gregory Desvignes Sheperd S. Doeleman Ralph P. Eatough Joseph Farah Vincent L. Fish Ed Fomalont Raquel Fraga-Encinas Per Friberg H. Alyson Ford Antonio Fuentes Peter Galison Charles F. Gammie Roberto García Olivier Gentaz Boris Georgiev Ciriaco Goddi Roman Gold José L. Gómez Arturo I. Gómez-Ruiz Minfeng Gu (顾敏峰) Mark Gurwell Kazuhiro Hada Daryl Haggard Michael H. Hecht Ronald Hesper Luis C. Ho (何子山) Paul Ho Mareki Honma Chih-Wei L. Huang Lei Huang (黄磊) David H. Hughes Shiro Ikeda Makoto Inoue Sara Issaoun David J. James Buell T. Jannuzi Michael Janssen Britton Jeter Wu Jiang (江悟) Alejandra Jimenez-Rosales Michael D. Johnson Svetlana Jorstad Taehyun Jung Mansour Karami Ramesh Karuppusamy Tomohisa Kawashima Garrett K. Keating Mark Kettenis Dong-Jin Kim Jae-Young Kim Jongsoo Kim Junhan Kim Motoki Kino Jun Yi Koay Yutaro Kofuji Patrick M. Koch Shoko Koyama Carsten Kramer Thomas P. Krichbaum Cheng-Yu Kuo Tod R. Lauer Sang-Sung Lee Aviad Levis Yan-Rong Li (李彦荣) Zhiyuan Li (李志远) Michael Lindqvist Rocco Lico Greg Lindahl Jun Liu (刘俊) Kuo Liu Elisabetta Liuzzo Wen-Ping Lo Andrei P. Lobanov Laurent Loinard Colin Lonsdale Ru-Sen Lu Institut für Theoretische Physik Goethe-Universität Max-von-Laue-Strasse 1 60438 Frankfurt Germany Frankfurt Institute for Advanced Studies Ruth-Moufang-Strasse 1 60438 Frankfurt Germany School of Mathematics Trinity College Dublin 2 Ireland Department of Astrophysics Institute for Mathematics Astrophysics and Particle Physics (IMAPP) Radboud University P.O. Box 9010 6500 GL Nijmegen Netherlands Black Hole Initiative at Harvard University 20 Garden Street Cambridge Massachusetts 02138 USA Center for Astrophysics—Harvard & Smithsonian 60 Garden Street Cambridge Massachusetts 02138 USA Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 D-53121 Bonn Germany Tsung-Dao Lee Institute and School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 D-60438 Frankfurt am Main Germany Department of Physics National and Kapodistrian University of Athens Panepistimiopolis GR 15783 Zografos Greece Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking Surrey RH5 6NT United Kingdom Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road Westford Massachusetts 01886 USA National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Department of Physics Faculty of Science University of Malaya 50603 Kuala Lumpur Malaysia Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New York New York 10010 USA Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd Taipei 10617 Taiwan R.O.C. Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 E-46100 Burjassot València Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 E-46980 Paterna

Our understanding of strong gravity near supermassive compact objects has recently improved thanks to the measurements made by the Event Horizon Telescope (EHT). We use here the M87* shadow size to infer constraints on the physical charges of a large variety of nonrotating or rotating black holes. For example, we show that the quality of the measurements is already sufficient to rule out that M87* is a highly charged dilaton black hole. Similarly, when considering black holes with two physical and independent charges, we are able to exclude considerable regions of the space of parameters for the doubly-charged dilaton and the Sen black holes.

关键词： Alternative gravity theories Experimental studies of gravity General relativity Radio, microwave, & sub-mm astronomy Accretion disk & black-hole plasma Classical black holes Galaxies Electromagnetic radiation astronomy

来源：评论

学校读者我要写书评

暂无评论

Fermi and Swift Observations of Grb 190114C: Tracing The Evolution of High-Energy Emission From Prompt to Afterglow

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Burns, E. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Costantin, D. Cutini, S. D’Ammando, F. DeKlotz, M. de la Torre Luque, P. de Palma, F. Desai, A. Lalla, N. Di Venere, L. Di Dirirsa, F. Fana Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Gill, R. Giordano, F. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Lemoine-Goumard, M. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Malyshev, D. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Parkinson, P.M. Saz Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Takahashi, M.N. Tak, D. Thayer, J.B. Thompson, D.J. Torres, D.F. Troja, E. Valverde, J. van Klaveren, B. Wood, K. Yassine, M. Zaharijas, G. Bhat, P.N. Briggs, M.S. Cleveland, W. Giles, M. Goldstein, A. Hui, M. Malacaria, C. Preece, R. Roberts, O. Veres, P. von Kienlin, A. Cenko, S.B. O’Brien, P. Beardmore, A.P. Lien, A. Osborne, J.P. Tohuvavohu, A. D’Elia, V. Da, A. Perri, M. Gropp, J. Klingler, N. Capalbi, M. Tagliaferri, G. Stamatikos, M. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Kanazawa Ishikawa920-1192 Department of Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany NASA Goddard Space Flight Center GreenbeltMD20771 United States NASA Postdoctoral Program Fellow United States In

We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory. The prompt gamma-ray emission was detected by the Fermi Gamma-ray Burst Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope (UVOT). The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal shock to external shock dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We con

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

The FAIR Guiding Principles for scientific data management and stewardship

引用

Scientific data 2016年第1期3卷 160018页

作者： Mark D Wilkinson Michel Dumontier I Jsbrand Jan Aalbersberg Gabrielle Appleton Myles Axton Arie Baak Niklas Blomberg Jan-Willem Boiten Luiz Bonino da Silva Santos Philip E Bourne Jildau Bouwman Anthony J Brookes Tim Clark Mercè Crosas Ingrid Dillo Olivier Dumon Scott Edmunds Chris T Evelo Richard Finkers Alejandra Gonzalez-Beltran Alasdair J G Gray Paul Groth Carole Goble Jeffrey S Grethe Jaap Heringa Peter A C 't Hoen Rob Hooft Tobias Kuhn Ruben Kok Joost Kok Scott J Lusher Maryann E Martone Albert Mons Abel L Packer Bengt Persson Philippe Rocca-Serra Marco Roos Rene van Schaik Susanna-Assunta Sansone Erik Schultes Thierry Sengstag Ted Slater George Strawn Morris A Swertz Mark Thompson Johan van der Lei Erik van Mulligen Jan Velterop Andra Waagmeester Peter Wittenburg Katherine Wolstencroft Jun Zhao Barend Mons Center for Plant Biotechnology and Genomics Universidad Politécnica de Madrid Madrid 28223 Spain. Stanford University Stanford 94305-5411 USA. Nature Genetics New York 10004-1562 USA. Euretos and Phortos Consultants Rotterdam 2741 CA The Netherlands. ELIXIR Wellcome Genome Campus Hinxton CB10 1SA UK. Lygature Eindhoven 5656 AG The Netherlands. Vrije Universiteit Amsterdam Dutch Techcenter for Life Sciences Amsterdam 1081 HV The Netherlands. Office of the Director National Institutes of Health Rockville 20892 USA. TNO Zeist 3700 AJ The Netherlands. Department of Genetics University of Leicester Leicester LE1 7RH UK. Harvard Medical School Boston Massachusetts MA 02115 USA. Harvard University Cambridge Massachusetts MA 02138 USA. Data Archiving and Networked Services (DANS) The Hague 2593 HW The Netherlands. GigaScience Beijing Genomics Institute Shenzhen 518083 China. Department of Bioinformatics Maastricht University Maastricht 6200 MD The Netherlands. Wageningen UR Plant Breeding Wageningen 6708 PB The Netherlands. Oxford e-Research Center University of Oxford Oxford OX1 3QG UK. Heriot-Watt University Edinburgh EH14 4AS UK. School of Computer Science University of Manchester Manchester M13 9PL UK. Center for Research in Biological Systems School of Medicine University of California San Diego La Jolla California 92093-0446 USA. Dutch Techcenter for the Life Sciences Utrecht 3501 DE The Netherlands. Department of Human Genetics Leiden University Medical Center Dutch Techcenter for the Life Sciences Leiden 2300 RC The Netherlands. Dutch TechCenter for Life Sciences and ELIXIR-NL Utrecht 3501 DE The Netherlands. VU University Amsterdam Amsterdam 1081 HV The Netherlands. Leiden Center of Data Science Leiden University Leiden 2300 RA The Netherlands. Netherlands eScience Center Amsterdam 1098 XG The Netherlands. National Center for Microscopy and Imaging Research UCSD San Diego 92103 USA. Phortos Consultants San Diego 92011 USA. SciELO/FAPESP Pr

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

arXiv

引用

arXiv 2021年

作者： Morgan, Robert Nord, B. Bechtol, K. González, S.J. Buckley-Geer, E. Möller, A. Park, J.W. Kim, A.G. Birrer, S. Aguena, M. Annis, J. Bocquet, S. Brooks, D. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. da Costa, L.N. Davis, T.M. De Vicente, J. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Gaztanaga, E. Giannini, G. Gruen, D. Gruendl, R.A. Gutierrez, G. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Maia, M.A.G. Miquel, R. Palmese, A. Paz-Chinchón, F. Pereira, M.E.S. Pieres, A. Plazas Malagón, A.A. Reil, K. Roodman, A. Sanchez, E. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Legacy Survey of Space and Time 933 North Cherry Avenue TucsonAZ85721 United States Centre for Astrophysics & Supercomputing Swinburne University of Technology VIC3122 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Physics Department William Jewell College LibertyMO64068 United States Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway Instituto de Fisica Teorica UAM/CSIC

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories – no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova – within high-fidelity simulations of three cosmic survey data sets – the Dark Energy Survey (DES), Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

来源：评论

学校读者我要写书评

暂无评论

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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：