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arXiv 2023年

作者： Fitzmaurice, Evan Stefánsson, Guðmundur Kavanagh, Robert D. Mahadevan, Suvrath Cañas, Caleb I. Winn, Joshua N. Robertson, Paul Ninan, Joe P. Albrecht, Simon Callingham, J.R. Cochran, William D. Delamer, Megan Ford, Eric B. Kanodia, Shubham Lin, Andrea S.J. Marcussen, Marcus L. Pope, Benjamin J.S. Ramsey, Lawrence W. Roy, Arpita Vedantham, Harish Wright, Jason T. Department of Astronomy & Astrophysics Pennsylvania State University University Park PA16802 United States Center for Exoplanets and Habitable Worlds Pennsylvania State University University Park PA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University Park PA16802 United States Institute for Computational and Data Sciences Scholar United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C8000 Denmark Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands McDonald Observatory Department of Astronomy The University of Texas at Austin 2515 Speedway AustinTX78712 United States Center for Planetary Systems Habitability The University of Texas at Austin 2515 Speedway AustinTX78712 United States Center for Astrostatistics 525 Davey Laboratory The Pennsylvania State University University Park PA16802 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW DC Washington20015 United States School of Mathematics & Physics University of Queensland St LuciaQLD4072 Australia Centre for Astrophysics University of Southern Queensland West Street ToowoombaQLD4350 Australia Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States Kapteyn Ast

The LHS 1610 system consists of a nearby (d = 9.7 pc) M5 dwarf hosting a candidate brown dwarf companion in a 10.6 day, eccentric (e ∼ 0.37) orbit. We confirm this brown dwarf designation and estimate its mass (49.5−+43.35 MJup) and inclination (114.5◦+7−1040) by combining discovery radial velocities (RVs) from TRES and new RVs from the Habitable-zone Planet Finder with the available Gaia astrometric two-body solution. We highlight a discrepancy between the measurement of the eccentricity from the Gaia two-body solution (e = 0.52±0.03) and the RV only solution (e = 0.3702±0.0003). We discuss possible reasons for this discrepancy, which can be further probed when the Gaia astrometric time series become available as part of Gaia DR4. As a nearby mid M star hosting a massive short-period companion with a well-characterized orbit, LHS 1610 b is a promising target to look for evidence of sub-Alfvénic interactions and/or auroral emission at optical and radio wavelengths. LHS 1610 has a flare rate (0.28±0.07 flares/day) on the higher-end for its rotation period (84±8 days), similar to other mid M dwarf systems such as Proxima Cen and YZ Ceti that have recent radio detections compatible with star-planet interactions. While available TESS photometry is insufficient to determine an orbital phase-dependence of the flares, our complete orbital characterization of this system makes it attractive to probe star-companion interactions with additional photometric and radio observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Giant stars

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PRECISION MEDICINE: CROSSING THE BIOMEDICAL SCALES WITH AI

The Journal of Precision Medicine: Health and Disease

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The Journal of Precision Medicine: Health and Disease 2025年

作者： Sharat Israni Gary D. Bader Sergio E. Baranzini John A. Capra Marina Sirota Christina V. Theodoris Chun Jimmie Ye Bakar Comp. Health Sci. Institute (BCHSI) UCSF San Francisco CA USA NIH Data Scholar 2022-24 The Donnelly Centre Temerty Faculty of Medicine University of Toronto Toronto Canada Weill Institute for Neurosciences Department of Neurology UCSF San Francisco CA USA Division of Bioinformatics Department of Epidemiology and Biostatistics & Associate Director BCHSI UCSF San Francisco CA USA Bakar Computational Health Sciences Institute Department of Pediatrics UCSF San Francisco CA USA Gladstone Institute of Cardiovascular Disease San Francisco CA USA Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Dept. of Pediatrics Institute for Human Genetics Cardiovascular Research Institute UCSF San Francisco CA USA Gladstone-UCSF Institute of Genomic Immunology San Francisco CA USA Depts. of Epidemiology & Biostatistics of Rheumatology and of Bioengr. & Therapeutic Sciences UCSF San Francisco CA USA BCHSI Institute for Human Genetics and Parker Institute for Cancer Immunotherapy UCSF San Francisco CA USA Chan Zuckerberg Biohub San Francisco CA USA Arc Institute Palo Alto CA USA

Precision Medicine, targeted for each person, has presented a major challenge in scaling up to wide use. Reflecting the true behavior of the human body, it requires inquiry across the biomedical scales, from the genome all the way up to the environmental factors impinging on that person. In computation terms, this is a problem of unprecedented complexity. With AI showing its potential, the thoughtful application of advanced machine intelligence, knowledge networks, machine reasoning power and new computational paradigms—guided by human oversight at every stage—can help unlock major advances in treatment and prevention.

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Exploring depression treatment response by using polygenic risk scoring across diverse populations

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The American Journal of Human Genetics 2025年

作者： Sandra Lapinska Aditya Pimplaskar Zhuozheng Shi Yi Ding Clara Frydman-Gani Kangcheng Hou Vidhya Venkateswaran Kristin Boulier Loes M. Olde Loohuis Bogdan Pasaniuc Graduate Group in Genomics and Computational Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA USA Division of Population Sciences Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Data and Technology Advancement Scholar National Institutes of Health Office of Data Science Strategy National Institute of Dental and Craniofacial Research Bethesda MD USA Department of Medicine Division of Cardiology University of California Los Angeles Los Angeles CA USA Center for Neurobehavioral Genetics Semel Institute for Neuroscience and Human Behavior David Geffen School of Medicine at UCLA Los Angeles CA USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Institute for Precision Health University of California Los Angeles Los Angeles CA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Institute of Biomedical Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Treatment-resistant depression (TRD), usually defined as limited or no response to at least two antidepressants, occurs in approximately one-third of individuals diagnosed with major depressive disorder (MDD). Studies of individuals of European ancestry highlight a genetic overlap between TRD and MDD. We analyzed two large and diverse biobanks, the UCLA ATLAS Community Health Study (ATLAS) and the All of Us Research Program (AoU), to test for associations between a polygenic score for major depression (MDD-PGS) and TRD. Compared to treatment responders, TRD individuals have higher MDD-PGS across all ancestries. MDD-PGS was significantly associated with response to selective serotonin reuptake inhibitors in individuals of European and Hispanic/Latin American genetic ancestries in both biobanks. In AoU, a decreased MDD-PGS was observed in response to tricyclics or serotonin modulators in individuals of European American ancestry and in response to serotonin and norepinephrine reuptake inhibitors in individuals of African American ancestry. ATLAS found that MDD-PGS showed lower odds of responding to atypical agents than did TRD in MDD-affected individuals belonging to the Hispanic/Latin American group, MDD-PGS was associated with atypical agents. Overall, by leveraging larger sample sizes from two diverse biobanks, we provide new insights into antidepressant response and treatment specificity for MDD in individuals of diverse genetic ancestries.

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Real-time prediction of COVID-19 related mortality using electronic health records

arXiv

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arXiv 2020年

作者： Schwab, Patrick Mehrjou, Arash Parbhoo, Sonali Celi, Leo Anthony Hetzel, Jürgen Hofer, Markus Schölkopf, Bernhard Bauer, Stefan F. Hoffmann-La Roche Ltd Basel Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany ETH Zurich Switzerland John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge United States Department of Medicine Beth Israel Deaconess Medical Center Harvard Medical School Boston United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Harvard-MIT Health Sciences & Technology Cambridge United States Department of Medical Oncology and Pneumology University Hospital of Tübingen Germany Department of Pneumology Kantonsspital Winterthur Switzerland CIFAR Azrieli Global Scholar

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we present the COVID-19 Early Warning System (CovEWS), a clinical risk scoring system for assessing COVID-19 related mortality risk. CovEWS provides continuous real-time risk scores for individual patients with clinically meaningful predictive performance up to 192 hours (8 days) in advance, and is automatically derived from patients' electronic health records (EHRs) using machine learning. We trained and evaluated CovEWS using de-identified data from a cohort of 66 430 COVID-19 positive patients seen at over 69 healthcare institutions in the United States (US), Australia, Malaysia and India amounting to an aggregated total of over 2 863 years of patient observation time. On an external test cohort of 5 005 patients, CovEWS predicts COVID-19 related mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at a sensitivity greater than 95% between respectively 1 and 192 hours prior to observed mortality events - significantly outperforming existing generic and COVID-19 specific clinical risk scores. CovEWS could enable clinicians to intervene at an earlier stage, and may therefore help in preventing or mitigating COVID-19 related mortality. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Sustainable computational science: The ReScience initiative

arXiv

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arXiv 2017年

作者： Rougier, Nicolas P. Hinsen, Konrad Alexandre, Frédéric Arildsen, Thomas Barba, Lorena Benureau, Fabien C.Y. Titus Brown, C. de Buyl, Pierre Caglayan, Ozan Davison, Andrew P. Delsuc, Marc André Detorakis, Georgios Diem, Alexandra K. Drix, Damien Enel, Pierre Girard, Benoît Guest, Olivia Hall, Matt G. Henriques, Rafael Neto Hinaut, Xavier Jaron, Kamil S. Khamassi, Mehdi Klein, Almar Manninen, Tiina Marchesi, Pietro McGlinn, Dan Metzner, Christoph Petchey, Owen L. Plesser, Hans Ekkehard Poisot, Timothée Ram, Karthik Ram, Yoav Roesch, Etienne Rossant, Cyrille Rostami, Vahid Shifman, Aaron Stimberg, Marcel Stachelek, Joseph Stollmeier, Frank Vaggi, Federico Viejo, Guillaume Vitay, Julien Vostinar, Anya Yurchak, Roman Zito, Tiziano INRIA Bordeaux Sud-Ouest Talence France Institut des Maladies Neurodégénératives Université de Bordeaux CNRS UMR 5293 Bordeaux France LaBRI Université de Bordeaux Bordeaux INP CNRS UMR 5800 Talence France Centre de Biophysique Moléculaire CNRS UPR4301 Orléans France Synchrotron SOLEIL Division Expériences Gif sur Yvette France Department of Electronic Systems Technical Faculty of IT and Design Aalborg University Denmark Department of Mechanical and Aerospace Engineering George Washington University WashingtonDC United States Department of Population Health and Reproduction University of California Davis DavisCA United States Instituut voor Theoretische Fysica KU Leuven Belgium Le Mans France Unité de Neurosciences Information et Complexité CNRS FRE 3693 Gif sur Yvette France Institut de Génétique et de Biologie Moléculaire et Cellulaire INSERM U964 CNRS UMR 7104 Université de Strasbourg Illkirch France Department of Cognitive Sciences University of California Irvine United States Computational Engineering and Design Faculty of Engineering and the Environment University of Southampton United Kingdom Department of Computer Science Humboldt-Universität zu Berlin Friedman Brain Institute Icahn School of Medicine Mount SinaiNY United States Institut des Systèmes Intelligent et de Robotique Sorbonne Universités UPMC Univ Paris 06 CNRS Paris France Experimental Psychology University College London United Kingdom UCL Great Ormond St Institute of Child Health University College London London United Kingdom MRC Cognition and Brain Sciences Unit Cambridge United Kingdom Department of Ecology and Evolution University of Lausanne Lausanne Switzerland Independent scholar Enschede Netherlands BioMediTech Institute and Faculty of Biomedical Sciences and Engineering Tampere University of Technology Tampere Finland Swammerdam Institute for Life Sciences Center for Neuroscience Faculty of Science University of Amsterdam Amsterdam Netherlands Depart

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests. Copyright © 2017, The Authors. All rights reserved.

关键词： Open source software

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