检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

来源：评论

学校读者我要写书评

暂无评论

GenoML: Automated machine learning for genomics

arXiv

引用

arXiv 2021年

作者： Makarious, Mary B. Leonard, Hampton L. Vitale, Dan Iwaki, Hirotaka Saffo, David Sargent, Lana Dadu, Anant Castaño, Eduardo Salmerón Carter, John F. Maleknia, Melina Botia, Juan A. Blauwendraat, Cornelis Campbell, Roy H. Hashemi, Sayed Hadi Singleton, Andrew B. Nalls, Mike A. Faghri, Faraz Laboratory of Neurogenetics National Institute on Aging National Institutes of Health BethesdaMD United States Department of Clinical and Movement Neurosciences UCL Queen Square Institute of Neurology London United Kingdom UCL Movement Disorders Centre University College London London United Kingdom Center for Alzheimer’s and Related Dementias National Institutes of Health BethesdaMD United States Data Tecnica International LLC Glen EchoMD United States Tübingen Germany Khoury College of Computer Sciences Northeastern University BostonMA United States School of Nursing Virginia Commonwealth University RichmondVA United States Geriatric Pharmacotherapy Program School of Pharmacy Virginia Commonwealth University VA United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States Departamento de Ingeniería de la Información y las Comunicaciones Universidad de Murcia Spain ModelOp ChicagoIL United States Georgia Institute of Technology AtlantaGA United States Department of Molecular Neuroscience UCL Queen Square Institute of Neurology London United Kingdom

GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain expertise to clean, pre-process, harmonize and perform quality control of the data. Furthermore, tuning, validation, and interpretation involve taking into account the biology and possibly the limitations of the underlying data collection, protocols, and technology. GenoML’s mission is to bring machine learning for genomics and clinical data to non-experts by developing an easy-to-use tool that automates the full development, evaluation, and deployment process. Emphasis is put on open science to make workflows easily accessible, replicable, and transferable within the scientific community. Source code and documentation is available at https://***. Copyright © 2021, The Authors. All rights reserved.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Democratising Knowledge Representation with BioCypher

arXiv

引用

arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

A New Census of the 0.2 < z < 3.0 Universe, Part II: The Star-Forming Sequence

arXiv

引用

arXiv 2021年

作者： Leja, Joel Speagle, Joshua S. Ting, Yuan-Sen Johnson, Benjamin D. Conroy, Charlie Whitaker, Katherine E. Nelson, Erica J. van Dokkum, Pieter Franx, Marijn Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Statistical Sciences University of Toronto TorontoONM5S 3G3 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Research School of Astronomy & Astrophysics Australian National University Cotter Rd. Weston ACT2611 Australia Institute for Advanced Study PrincetonNJ08540 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08540 United States Observatories of the Carnegie Institution of Washington 813 Santa Barbara Street PasadenaCA91101 United States Research School of Computer Science Australian National University ActonACT2601 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Department of Astronomy University of Massachusetts Amherst 710 N Pleasant Street AmherstMA01003 United States Copenhagen Denmark Astrophysical & Planetary Sciences 391 UCB 2000 Colorado Ave Duane Physics Building Rm. E226 BoulderCO80309 United States Department of Astronomy Yale University New HavenCT06511 United States Leiden Observatory Leiden University LeidenNL-2300 RA Netherlands

We use the panchromatic SED-fitting code Prospector to measure the galaxy logM∗-logSFR relationship (the ‘star-forming sequence’) across 0.2 ∗-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization 0.2 − 0.5 dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing ∼ 0.2 − 0.5 dex offset with observations at 0.5 ∗, log SFR, z) is made available online a) to facilitate straightforward comparisons with future work. Copyright © 2021, The Authors. All rights reserved.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

Cholesterol allosteric modulation of the oxytocin receptor

引用

Biophysical Journal 2025年

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

关键词：

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

Small P values may not yield robust findings:an example using REST-meta-PD

引用

science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

来源：评论

学校读者我要写书评

暂无评论

A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst

arXiv

引用

arXiv 2023年

作者： Yang, Yu-Han Troja, Eleonora O’Connor, Brendan Fryer, Chris L. Im, Myungshin Durbak, Joe Paek, Gregory S.H. Ricci, Roberto De Bom, Clécio R. Gillanders, James H. Castro-Tirado, Alberto J. Peng, Zong-Kai Dichiara, Simone Ryan, Geoffrey van Eerten, Hendrik Dai, Zi-Gao Chang, Seo-Won Choi, Hyeonho De, Kishalay Hu, Youdong Kilpatrick, Charles D. Kutyrev, Alexander Jeong, Mankeun Lee, Chung-Uk Makler, Martin Navarete, Felipe Pérez-García, Ignacio Department of Physics University of Rome "Tor Vergata" via della Ricerca Scientifica 1 RomeI-00133 Italy INAF - Istituto Nazionale di Astrofisica Rome00133 Italy Department of Physics The George Washington University 725 21st Street NW WashingtonDC20052 United States Department of Astronomy University of Maryland College ParkMD20742-4111 United States Astrophysics Science Division NASA Goddard Space Flight Center 8800 Greenbelt Rd GreenbeltMD20771 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States The University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The University of New Mexico AlbuquerqueNM87131 United States The George Washington University WashingtonDC20052 United States SNU Astronomy Research Center Astronomy Program Dept. of Physics & Astronomy Seoul National University Seoul08826 Korea Republic of Istituto Nazionale di Ricerca Metrologica TorinoI-10135 Italy INAF - Istituto di Radioastronomia via Gobetti 101 BolognaI-40129 Italy Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de JaneiroCEP 22290-180 Brazil Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas lote J2 quadra J RJ ItaguaíCEP 23810-000 Brazil Glorieta de la Astronomía s/n Granada18080 Spain Unidad Asociada al CSIC Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenierías Industriales Arquitecto Francisco Peñalosa 6 Málaga29071 Spain Institute for Frontier in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China Department of Astronomy and Astrophysics The Pennsylvania State University 525 Davey Lab University ParkPA16802 United States Perimeter Institute for Theoretical Phy

Kilonovae are a rare class of astrophysical transients powered by the radioactive decay of nuclei heavier than iron, synthesized in the merger of two compact objects 1–4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2, 5. On timescales of weeks to months, its behavior is predicted to differ depending on the ejecta composition and merger remnant6–9. However, late-time observations of known kilonovae are either missing or limited10, 11. Here we report observations of a luminous red transient with a quasi-thermal spectrum, following an unusual gamma-ray burst of long duration. We classify this thermal emission as a kilonova and track its evolution up to two months after the burst. At these late times, the recession of the photospheric radius and the rapidly-decaying bolometric luminosity (Lbol ∝ t−2.7±0.4) support the recombination of lanthanide-rich ejecta as they cool. Copyright © 2023, The Authors. All rights reserved.

关键词： Rare earth elements

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

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

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：