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The Automatic Learning for the Rapid Classification of Events (ALeRCE) alert broker

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arXiv 2020年

作者： Förster, Francisco Cabrera-Vives, G. Castillo-Navarrete, E. Estévez, P.A. Sánchez-Sáez, P. Arredondo, J. Bauer, F.E. Carrasco-Davis, R. Catelan, M. Elorrieta, F. Eyheramendy, S. Huijse, P. Pignata, G. Reyes, E. Reyes, I. Rodríguez-Mancini, D. Ruz-Mieres, D. Valenzuela, C. Álvarez-Maldonado, I. Astorga, N. Borissova, J. Clocchiatti, A. de Cicco, D. Donoso-Oliva, C. Graham, M.J. Kurtev, R. Mahabal, A. Maureira, J.C. Molina-Ferreiro, R. Moya, A. Palma, W. Prez-Carrasco, M. Protopapas, P. Romero, M. Sabatini-Gacitua, L. Snchez, A. San Martín, J. Sepúlveda-Cobo, C. Vera, E. Vergara, J.R. Center for Mathematical Modeling University of Chile AFB170001 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile Department of Computer Science University of Concepcin Chile Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Department of Engineering and Science Universidad Adolfo Ibaez Av. Diagonal Las Torres 2700 Santiago Chile Centro de Astroingeniería Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Department of Mathematics and Computer Science Universidad de Santiago de Chile Av. Libertador Bernardo OHiggins 3663 Estación Central Santiago Chile Instituto de Informática Universidad Austral de Chile General Lagos 2086 Valdivia Chile Departamento de Ciencias Físicas Universidad Andres Bello Avda. Republica 252 Santiago Chile Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Instituto de Física y Astronomía Universidad de Valparaíso Av. Gran Bretaña 1111 Playa Ancha Casilla5030 Chile Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Institute for Applied Computational Science Harvard University CambridgeMA United States Departmento de Informática y Computación Universidad Tecnológica Metropolitana Santiago Chile

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self–consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean–led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow–up facilities. ALeRCE uses a pipeline which includes the real–time ingestion, aggregation, cross–matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp–based classifier, designed for rapid classification, and a light–curve–based classifier, which uses the multi–band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see https://***). Since we began operating our real–time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real–time processing of 9.7×107 alerts, the stamp classification of 1.9 × 107 objects, the light curve classification of 8.5 × 105 objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi–stream ecosystem dominated by LSST. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Probabilistic enhancement of the Failure Forecast Method using a stochastic differential equation and application to volcanic eruption forecasts

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arXiv 2018年

作者： Bevilacqua, Andrea Pitman, E. Bruce Patra, Abani Neri, Augusto Bursik, Marcus Voight, Barry Department of Earth Sciences University at Buffalo BuffaloNY United States Department of Materials Design and Innovation University at Buffalo NY United States Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY United States Computational Data Science and Engineering University at Buffalo BuffaloNY United States Sezione di Pisa Istituto Nazionale di Geosica e Vulcanologia Pisa Italy Department of Geosciences Pennsylvania State University University ParkPA United States

We introduce a doubly stochastic method for performing material failure theory based forecasts of volcanic eruptions. The method enhances the well known Failure Forecast Method equation, introducing a new formulation similar to the Hull-White model in financial mathematics. In particular, we incorporate a stochastic noise term in the original equation, and systematically characterize the uncertainty. The model is a stochastic differential equation with mean reverting paths, where the traditional ordinary differential equation defines the mean solution. Our implementation allows the model to make excursions from the classical solutions, by including uncertainty in the estimation. The doubly stochastic formulation is particularly powerful, in that it provides a complete posterior probability distribution, allowing users to determine a worst case scenario with a specified level of confidence. We apply the new method on historical datasets of precursory signals, across a wide range of possible values of convexity in the solutions and amounts of scattering in the observations. The results show the increased forecasting skill of the doubly stochastic formulation of the equations if compared to statistical regression. Copyright © 2018, The Authors. All rights reserved.

关键词： Volcanoes

Strikingly high fraction of fast rotators in Magellanic Cloud star clusters

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arXiv 2025年

作者： Ettorre, Greta Mazzi, Alessandro Girardi, Léo Marigo, Paola Pastorelli, Giada Goudfrooij, Paul Williams, Benjamin F. Bellini, Andrea Bressan, Alessandro Chen, Yang Correnti, Matteo Costa, Guglielmo Dalcanton, Julianne J. Facchini, Pietro Fouesneau, Morgan Nguyen, Chi Thanh Volpato, Guglielmo Dipartimento di Fisica e Astronomia "Galileo Galilei" Università di Padova Vicolo dell’Osservatorio 3 PadovaI-35122 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna Via Gobetti 93/2 BolognaI-40129 Italy INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Gobetti 93/3 Bologna40129 Italy INAF - Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Space Telescope Science Institute 3700 San Martin drive BaltimoreMD21218 United States Department of Astronomy University of Washington Box 351580 SeattleWA98195 United States SISSA International School for Advanced Studies Trieste Italy School of Physics and optoelectronic engineering Anhui University Hefei230601 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China INAF - Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio Catone Rome00078 Italy ASI - Space Science Data Center Via del Politecnico RomeI-00133 Italy Université de Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Astronomisches Rechen-Institut Zentrum für Astronomie der Universität Heidelberg Mönchhofstr. 12-14 HeidelbergD-69120 Germany Königstuhl 17 Heidelberg69117 Germany INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 Trieste34143 Italy

There has been growing evidence that the rich star clusters in the Magellanic Clouds contain significant fractions of rapidly rotating stars. In this work, we aim to constrain these fractions by studying the colour-magnitude diagrams of four star clusters, selected among those with the most striking signatures of fast rotators. Using isochrones derived from parsec v2.0 stellar tracks, we generate distinct stellar populations, each covering a limited interval of initial rotation rates ωi, referred to as ‘Partial Models’ (PMs). Using optimization algorithms and Monte Carlo Markov Chains, PMs are combined to create the final best-fitting model. In our analysis, we adopt two key assumptions: a uniform age and an isotropic distribution of stellar spin axes within each cluster. The solutions are allowed to explore the entire range of ωi, and different values of age, metallicity, distance and foreground extinction. We find that the rotational velocity distributions in all four clusters reveal a high fraction of stars with ωi close to the break-up value, in all cases. Specifically, the fraction of stars with ωi > 0.7 exceeds 80% in the clusters NGC 419 of the Small Magellanic Cloud (SMC) and NGC 1831 and NGC 1866 of the Large Magellanic Cloud (LMC). For NGC 2203 of the LMC, this fraction is smaller, although it still exceeds 50%, confirming that also this cluster is mainly populated by fast-rotating stars. Copyright © 2025, The Authors. All rights reserved.

关键词： Monte Carlo methods

Corrigendum to “Single-walled carbon nanotube membranes for optical applications in the extreme ultraviolet rang” [155, (2019) 734–739]

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Carbon 2020年 156卷 576-576页

作者： V.M. Gubarev V.Y. Yakovlev M.G. Sertsu O.F. Yakushev V.M. Krivtsun Yu G. Gladush I.A. Ostanin A. Sokolov F. Schäfers V.V. Medvedev A.G. Nasibulin Moscow Institute of Physics and Technology (State University) Institutskiy pereulok str. 9 Dolgoprudny Moscow Region 141701 Russia Skolkovo Institute of Science and Technology Nobel str. 3 Moscow 121205 Russia Helmholtz Zentrum Berlin (BESSY-II) Albert-Einstein-Strasse 15 D-12489 Berlin Germany P. N. Lebedev Physical Institute of the Russian Academy of Science Moscow Russia Institute for Spectroscopy of the Russian Academy of Science Moscow Troitsk Russia Faculty of Physics Higher School of Economics Old Basmannya 21/4 Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Multi-Scale Mechanics (MSM) Faculty of Engineering Technology MESA+ University of Twente Enschede the Netherlands Department of Applied Physics Aalto University 15100 FI-00076 Aalto Finland

Dynamic load balancing strategies for graph applications on GPUs

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arXiv 2017年

作者： Raval, Ananya Nasre, Rupesh Kumar, Vivek Vasudevan, R. Vadhiyar, Sathish Pingali, Keshav Department of Computational and Data Sciences Indian Institute of Science Bangalore Department of Computer Science and Engineering Indian Institute of Technology Madras Institute for Computational Engineering and Sciences University of Texas at Austin United States Department of Computer Science University of Texas at Austin United States

Acceleration of graph applications on GPUs has found large interest due to the ubiquitous use of graph processing in various domains. The inherent irregularity in graph applications leads to several challenges for parallelization. A key challenge, which we address in this paper, is that of load-imbalance. If the work-assignment to threads uses node-based graph partitioning, it can result in skewed task-distribution, leading to poor load-balance. In contrast, if the work-assignment uses edge-based graph partitioning, the load-balancing is better, but the memory requirement is relatively higher. This makes it unsuitable for large graphs. In this work, we propose three techniques for improved load-balancing of graph applications on GPUs. Each technique brings in unique advantages, and a user may have to employ a specific technique based on the requirement. Using Breadth First Search and Single Source Shortest Paths as our processing kernels, we illustrate the effectiveness of each of the proposed techniques in comparison to the existing node-based and edge-based mechanisms. Copyright © 2017, The Authors. All rights reserved.

关键词： Program processors

High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification

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Nature Communications 2025年第1期16卷 1-18页

作者： Zanti, Maria O’Mahony, Denise G. Parsons, Michael T. Dorling, Leila Dennis, Joe Boddicker, Nicholas J. Chen, Wenan Hu, Chunling Naven, Marc Yiangou, Kristia Ahearn, Thomas U. Ambrosone, Christine B. Andrulis, Irene L. Antoniou, Antonis C. Auer, Paul L. Baynes, Caroline Bodelon, Clara Bogdanova, Natalia V. Bojesen, Stig E. Bolla, Manjeet K. Brantley, Kristen D. Camp, Nicola J. Campbell, Archie Castelao, Jose E. Cessna, Melissa H. Chang-Claude, Jenny Chen, Fei Chenevix-Trench, Georgia Kristensen, Vessela N. Conroy, Don M. Czene, Kamila De Nicolo, Arcangela Domchek, Susan M. Dörk, Thilo Dunning, Alison M. Eliassen, A. Heather Evans, D. Gareth Fasching, Peter A. Figueroa, Jonine D. Flyger, Henrik Gago-Dominguez, Manuela García-Closas, Montserrat Glendon, Gord González-Neira, Anna Grassmann, Felix Hadjisavvas, Andreas Haiman, Christopher A. Hamann, Ute Hart, Steven N. Hartman, Mikael B. A. Ho, Weang-Kee Hodge, James M. Hoppe, Reiner Howell, Sacha J. Jakubowska, Anna Khusnutdinova, Elza K. Ko, Yon-Dschun Kraft, Peter Kristensen, Vessela N. Lacey, James V. Li, Jingmei Lim, Geok Hoon Lindström, Sara Lophatananon, Artitaya Luccarini, Craig Mannermaa, Arto Martinez, Maria Elena Mavroudis, Dimitrios Milne, Roger L. Muir, Kenneth Nathanson, Katherine L. Nuñez-Torres, Rocio Obi, Nadia Olson, Janet E. Palmer, Julie R. Panayiotidis, Mihalis I. Patel, Alpa V. Pharoah, Paul D. P. Polley, Eric C. Rashid, Muhammad U. Ruddy, Kathryn J. Saloustros, Emmanouil Sawyer, Elinor J. Schmidt, Marjanka K. Southey, Melissa C. Tan, Veronique Kiak-Mien Teo, Soo Hwang Teras, Lauren R. Torres, Diana Trentham-Dietz, Amy Truong, Thérèse Vachon, Celine M. Wang, Qin Weitzel, Jeffrey N. Yadav, Siddhartha Yao, Song Zirpoli, Gary R. Cline, Melissa S. Devilee, Peter Tavtigian, Sean V. Goldgar, David E. Couch, Fergus J. Easton, Douglas F. Spurdle, Amanda B. Michailidou, Kyriaki Biostatistics Unit The Cyprus Institute of Neurology & Genetics Nicosia Cyprus Department of Medical Genetics Oslo University Hospital Oslo Norway Population Health Program QIMR Berghofer Medical Research Institute Brisbane QLD Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Department of Quantitative Health Sciences Division of Computational Biology Mayo Clinic Rochester MN United States Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN United States Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department of Health and Human Services Bethesda MD United States Department of Cancer Prevention and Control Roswell Park Comprehensive Cancer Institute Buffalo NY United States Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Toronto Ontario Canada Department of Molecular Genetics University of Toronto Toronto Ontario Canada Division of Biostatistics Data Science Institute and Cancer Center Medical College of Wisconsin Milwaukee WI United States Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge Cambridge United Kingdom Department of Population Science American Cancer Society Atlanta GA United States Radiation Oncology Research Unit Hannover Medical School Hannover Germany Copenhagen General Population Study Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Department of Clinical Biochemistry Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Medical Oncology Dana-Farber Cancer Institute Boston MA United States Department of Internal Medicine and Huntsman Cancer Institute University of Utah Salt Lake City UT United States Centre for Genomic a

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants;exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants;these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification. © The Author(s) 2025.

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Characterization of temporarily-captured minimoon 2020 CD3 by Keck time-resolved spectrophotometry

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arXiv 2020年

作者： Bolin, Bryce T. Fremling, Christoffer Holt, Timothy R. Hankins, Matthew J. Ahumada, Tomás Anand, Shreya Bhalerao, Varun Burdge, Kevin B. Copperwheat, Chris M. Coughlin, Michael Deshmukh, Kunal P. De, Kishalay Kasliwal, Mansi M. Morbidelli, Alessandro Purdum, Josiah N. Quimby, Robert Bodewits, Dennis Chang, Chan-Kao Ip, Wing-Huen Hsu, Chen-Yen Laher, Russ R. Lin, Zhong-Yi Lisse, Carey M. Masci, Frank J. Ngeow, Chow-Choong Tan, Hanjie Zhai, Chengxing Burruss, Rick Dekany, Richard Delacroix, Alexandre Duev, Dmitry A. Graham, Matthew Hale, David Kulkarni, Shrinivas R. Kupfer, Thomas Mahabal, Ashish Mróz, Przemyslaw J. Neill, James D. Riddle, Reed Rodriguez, Hector Smith, Roger M. Soumagnac, Maayane T. Walters, Richard Yan, Lin Zolkower, Jeffry Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States IPAC Mail Code 100-22 Caltech 1200 E. California Blvd. PasadenaCA91125 United States University of Southern Queensland Computational Engineering and Science Research Centre QLD Australia Southwest Research Institute Department of Space Studies BoulderCO80302 United States Department of Astronomy University of Maryland College ParkMD20742 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India Astrophysics Research Institute Liverpool John Moores University 146 Brownlow Hill LiverpoolL3 5RF United Kingdom School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Bombay Powai Mumbai400076 India Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange Boulevard de l’Observatoire CS 34229 Nice cedex 406304 France Department of Astronomy San Diego State University 5500 Campanile Dr San DiegoCA92182 United States University of Tokyo Institutes for Advanced Study University of Tokyo KashiwaChiba277-8583 Japan Physics Department Leach Science Center Auburn University AuburnAL36832 United States Institute of Astronomy National Central University 32001 Taiwan Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA

We present rotationally-averaged visible spectrophotometry of minimoon 2020 CD3, the second asteroid known to become temporarily captured by the Earth-Moon system’s gravitational field. The spectrophotometry was taken with Keck I/LRIS between wavelengths 434 nm and 912 nm in B, g, V, R, I and RG850 filters as it was leaving the Earth-Moon system on 2020 March 23 UTC. The broad-band spectrophotometry of 2020 CD3 most closely resembles the spectra of V-type asteroids and some Lunar rock samples with a reddish slope of ∼18%/100 nm between 434 nm and 761 nm corresponding to colors of g-r = 0.62±0.08, r-i = 0.21 ± 0.06 and an absorption band at ∼900 nm corresponding to i-z = -0.54±0.10 (DeMeo et al. 2009;Isaacson et al. 2011). Combining our measured 31.9±0.1 absolute magnitude with an albedo of 0.35 typical for V-type asteroids (DeMeo & Carry 2013), we determine 2020 CD3’s size to be ∼1.0±0.1 m making it the first minimoon and one of the smallest asteroids to be spectrally characterized. We use our time-series photometry to detect periodic lightcurve variations with a −4 false alarm probability corresponding to a lightcurve period of ∼573 s and a lightcurve amplitude of ∼1 mag implying 2020 CD3 possesses a b/a axial ratio of ∼2.5. In addition, we extend the observational arc of 2020 CD3 to 37 days between 2020 February 15 UTC and 2020 March 23 UTC. From the improved orbital solution for 2020 CD3, we estimate its likely duration of its capture to be ∼2 y, and we measure the non-gravitation perturbation on its orbit due to radiation pressure with an area-to-mass ratio of 6.9±2.4×10−4 m2/kg implying a density of 2.1±0.7 g/cm3, broadly compatible with the densities of other meter-scale asteroids (e.g., Micheli et al. 2012;Mommert et al. 2014) and Lunar rock (∼2.4 g/cm3, Kiefer et al. 2012). We searched for pre-discovery observations of 2020 CD3 in the ZTF archive as far back as 2018 October (Masci et al. 2019), but were unable to locate any positive detections. Copyright © 2

关键词： Asteroids