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Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

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Physical Review D 2021年第2期104卷 022004-022004页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic R. L. Byer L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Università di Napoli “Federico II” Complesso Universitario

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density ΩGW≤5.8×10−9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; ΩGW(f)≤3.4×10−9 at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and ΩGW(f)≤3.9×10−10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2/3, and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z≳2

关键词： Gravitational wave detection

NWChem: Past, present, and future

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arXiv 2020年

作者： Aprà, E. Bylaska, E.J. de Jong, W.A. Govind, N. Kowalski, K. Straatsma, T.P. Valiev, M. van Dam, H.J.J. Alexeev, Y. Anchell, J. Anisimov, V. Aquino, F.W. Atta-Fynn, R. Autschbach, J. Bauman, N.P. Becca, J.C. Bernholdt, D.E. Bhaskaran-Nair, K. Bogatko, S. Borowski, P. Boschen, J. Brabec, J. Bruner, A. Cauët, E. Chen, Y. Chuev, G.N. Cramer, C.J. Daily, J. Deegan, M.J.O. Dunning, T.H. Dupuis, M. Dyall, K.G. Fann, G.I. Fischer, S.A. Fonari, A. Früchtl, H. Gagliardi, L. Garza, J. Gawande, N. Ghosh, S. Glaesemann, K. Götz, A.W. Hammond, J. Helms, V. Hermes, E.D. Hirao, K. Hirata, S. Jacquelin, M. Jensen, L. Johnson, B.G. Jónsson, H. Kendall, R.A. Klemm, M. Kobayashi, R. Konkov, V. Krishnamoorthy, S. Krishnan, M. Lin, Z. Lins, R.D. Littlefield, R.J. Logsdail, A.J. Lopata, K. Ma, W. Marenich, A.V. Martin del Campo, J. Mejia-Rodriguez, D. Moore, J.E. Mullin, J.M. Nakajima, T. Nascimento, D.R. Nichols, J.A. Nichols, P.J. Nieplocha, J. Otero-de-la-Roza, A. Palmer, B. Panyala, A. Pirojsirikul, T. Peng, B. Peverati, R. Pittner, J. Pollack, L. Richard, R.M. Sadayappan, P. Schatz, G.C. Shelton, W.A. Silverstein, D.W. Smith, D.M.A. Soares, T.A. Song, D. Swart, M. Taylor, H.L. Thomas, G.S. Tipparaju, V. Truhlar, D.G. Tsemekhman, K. van Voorhis, T. Vázquez-Mayagoitia, Á. Verma, P. Villa, O. Vishnu, A. Vogiatzis, K.D. Wang, D. Weare, J.H. Williamson, M.J. Windus, T.L. Woliński, K. Wong, A.T. Wu, Q. Yang, C. Yu, Q. Zacharias, M. Zhang, Z. Zhao, Y. Harrison, R.J. Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Brookhaven National Laboratory UptonNY11973 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Intel Corporation Santa ClaraCA95054 United States QSimulate CambridgeMA02139 United States Department of Physics University of Texas at Arlington ArlingtonTX76019 United States Department of Chemistry University at Buffalo State University of New York BuffaloNY14260 United States Department of Chemistry Pennsylvania State University University ParkPA16802 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Washington University St. LouisMO63130 United States 4G Clinical WellesleyMA02481 United States Faculty of Chemistry Maria Curie-Sklodowska University in Lublin Lublin20-031 Poland Department of Chemistry Iowa State University AmesIA50011 United States J. Heyrovský Institute of Physical Chemistry Academy of Sciences of the Czech Republic Prague 818223 Czech Republic Department of Chemistry and Physics University of Tennessee at Martin MartinTN38238 United States Université libre de Bruxelles BrusselsB-1050 Belgium Facebook Menlo ParkCA94025 United States Institute of Theoretical and Experimental Biophysics Russian Academy of Science Pushchino Moscow142290 Russia Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota MinneapolisMN55455 United States SKAO Jodrell Bank Observatory MacclesfieldSK11 9DL United Kingdom Department of Chemistry University of Washington SeattleWA98195 United States Dirac Solutions PortlandOR97229 United States Chemistry Division U. S. Naval Research Laboratory WashingtonDC20375 United States School of Chemistry and Biochemis

Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the last few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach and outlook. Copyright © 2020, The Authors. All rights reserved.

关键词： computational chemistry

Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

Sequence diversity analyses of an improved rhesus macaque genome enhance its biomedical utility

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science 2020年第6523期370卷 000-000页

作者： Warren, Wesley C. Harris, R. Alan Haukness, Marina Fiddes, Ian T. Murali, Shwetha C. Fernandes, Jason Dishuck, Philip C. Storer, Jessica M. Raveendran, Muthuswamy Hillier, LaDeana W. Porubsky, David Mao, Yafei Gordon, David Vollger, Mitchell R. Lewis, Alexandra P. Munson, Katherine M. DeVogelaere, Elizabeth Armstrong, Joel Diekhans, Mark Walker, Jerilyn A. Tomlinson, Chad Graves-Lindsay, Tina A. Kremitzki, Milinn Salama, Sofie R. Audano, Peter A. Escalona, Merly Maurer, Nicholas W. Antonacci, Francesca Mercuri, Ludovica Maggiolini, Flavia A. M. Catacchio, Claudia Rita Underwood, Jason G. O'Connor, David H. Sanders, Ashley D. Korbel, Jan O. Ferguson, Betsy Kubisch, H. Michael Picker, Louis Kalin, Ned H. Rosene, Douglas Levine, Jon Abbott, David H. Gray, Stanton B. Sanchez, Mar M. Kovacs-Balint, Zsofia A. Kemnitz, Joseph W. Thomasy, Sara M. Roberts, Jeffrey A. Kinnally, Erin L. Capitanio, John P. Skene, J. H. Pate Platt, Michael Cole, Shelley A. Green, Richard E. Ventura, Mario Wiseman, Roger W. Paten, Benedict Batzer, Mark A. Rogers, Jeffrey Eichler, Evan E. Department of Animal Sciences Bond Life Sciences Center University of Missouri Columbia MO 65211 USA. Department of Surgery School of Medicine University of Missouri Columbia MO 65211 USA. Institute of Data Science and Informatics University of Missouri Columbia MO 65211 USA. Human Genome Sequencing Center Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Computational Genomics Laboratory University of California-Santa Cruz Santa Cruz CA 95064 USA. Inscripta Inc. Boulder CO 80301 USA. Department of Genome Sciences University of Washington Seattle WA 98195 USA. Howard Hughes Medical Institute University of Washington Seattle WA 98195 USA. Department of Biomolecular Engineering University of California-Santa Cruz Santa Cruz CA 95064 USA. Department of Biological Sciences Louisiana State University Baton Rouge LA 70803 USA. Institue for Systems Biology Seattle WA 98109 USA. McDonnell Genome Institute Washington University St. Louis MO 63108 USA. Department of Biology University of Bari ’Aldo Moro’ 70125 Bari Italy. Pacific Biosciences of California Seattle WA 94025 USA. Department of Pathology and Laboratory Medicine Wisconsin National Primate Research Center University of Wisconsin-Madison Madison WI 53711 USA. European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany. Division of Genetics Oregon National Primate Research Center Oregon Health and Science University Beaverton OR 97006 USA. Tulane National Primate Research Center Covington LA 70433 USA. Oregon National Primate Research Center and Vaccine and Gene Therapy Institute Oregon Health Sciences University Beaverton OR 97006 USA. Department of Psychiatry University of Wisconsin School of Medicine and Public Health Madison WI 53719 USA. Department of Anatomy and Neurobiology Boston University School of Medicine Boston MA 02118 USA. Department of Neuroscience University of Wisconsin Madison WI 53175 USA. Wisconsin N

The rhesus macaque (Macaca mulatta) is the most widely studied nonhuman primate (NHP) in biomedical research. We present an updated reference genome assembly (Mmul_10, contig N50 = 46 Mbp) that increases the sequence contiguity 120-fold and annotate it using 6.5 million full-length transcripts, thus improving our understanding of gene content, isoform diversity, and repeat organization. With the improved assembly of segmental duplications, we discovered new lineage-specific genes and expanded gene families that are potentially informative in studies of evolution and disease susceptibility. Whole-genome sequencing (WGS) data from 853 rhesus macaques identified 85.7 million single-nucleotide variants (SNVs) and 10.5 million indel variants, including potentially damaging variants in genes associated with human autism and developmental delay, providing a framework for developing noninvasive NHP models of human disease.

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Quantum computing at the frontiers of biological sciences

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

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Genome biology 2019年第1期20卷 72页

作者： Alexa B R McIntyre Rachid Ounit Ebrahim Afshinnekoo Robert J Prill Elizabeth Hénaff Noah Alexander Samuel S Minot David Danko Jonathan Foox Sofia Ahsanuddin Scott Tighe Nur A Hasan Poorani Subramanian Kelly Moffat Shawn Levy Stefano Lonardi Nick Greenfield Rita R Colwell Gail L Rosen Christopher E Mason Tri-Institutional Program in Computational Biology and Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10021 USA. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York NY 10021 USA. Department of Computer Science and Engineering University of California Riverside CA 92521 USA. School of Medicine New York Medical College Valhalla NY 10595 USA. Accelerated Discovery Lab IBM Almaden Research Center San Jose CA 95120 USA. One Codex Reference Genomics San Francisco CA 94103 USA. University of Vermont Burlington VT 05405 USA. CosmosID Inc Rockville MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland Institute for Advanced Computer Studies (UMIACS) College Park MD 20742 USA. HudsonAlpha Institute for Biotechnology Huntsville AL 35806 USA. Johns Hopkins University Bloomberg School of Public Health Baltimore MD USA. Department of Electrical and Computer Engineering Drexel University Philadelphia PA 19104 USA. gail.l.rosen@***. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10021 USA. chm2042@med.cornell.edu. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York NY 10021 USA. chm2042@med.cornell.edu. The Feil Family Brain and Mind Research Institute New York NY 10065 USA. chm2042@med.cornell.edu.

Following publication of the original article [1], the authors would like to highlight the following two corrections.

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

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 ofBRCA1andBRCA2from 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 11,207BRCA1andBRCA2variants, with 6909 being coding, covering 23.4% ofBRCA1andBRCA2VUS 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 forBRCA1and 93.3% sensitivity and 86.6% specificity forBRCA2. 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.

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Hypostatic jammed packings of frictionless nonspherical particles

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arXiv 2017年

作者： VanderWerf, Kyle Jin, Weiwei Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Mechanics and Engineering Science Peking University Beijing100871 China Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naïve constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and -polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a master-like form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of "quartic" modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable. C

关键词： Degrees of freedom (mechanics)

Stability of Inverse Transport Equation in Diffusion Scaling And Fokker-Planck Limit

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arXiv 2017年

作者： Chen, Ke LI, Qin Wang, Li Mathematics Department University of Wisconsin-Madison 480 Lincoln Dr. MadisonWI53705 United States Mathematics Department and Wisconsin Institute of Discovery University of Wisconsin-Madison 480 Lincoln Dr. MadisonWI53705 United States Department of Mathematics Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 244 Mathematics Building BuffaloNY14206 United States

We consider the inverse problem of reconstructing the scattering and absorption coefficients using boundary measurements for a time dependent radiative transfer equation (RTE). As the measurement is mostly polluted by errors, both experimental and computational, an important question is to quantify how the error is amplified in the process of reconstruction. In the forward setting, the solution to the RTE behaves differently in different regimes, and the stability of the inverse problem vary accordingly. In particular, we consider two scalings in this paper. The first one concerns with a diffusive scaling whose macroscopic limit is a diffusion equation. In this case, we showed, following the similar approach as in [Chen, Li and Wang, arXiv:1703.00097], that the stability degrades when the limit is taken. The second one considers a highly forward peaked scattering, wherein the scattering operator is approximated by a Fokker-Planck operator as a limit. In this case, we showed that a fully recover of the scattering coefficient is less possible in the limit, whereas obtaining a rescaled version of the scattering coefficient becomes more practice friendly. Copyright © 2017, The Authors. All rights reserved.

关键词： Inverse problems