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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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Reading race: AI recognizes patient's racial identity in medical images

arXiv

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arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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Proton acceleration in thermonuclear nova explosions revealed by gamma rays (Apr, 10.1038/s41550-022-01640-z, 2022)

引用

NATURE ASTRONOMY 2022年第6期6卷 760-760页

作者： Acciari, V. A. Ansoldi, S. Antonelli, L. A. Engels, A. Arbet Artero, M. Asano, K. Baack, D. Babic, A. Baquero, A. de Almeida, U. Barres Barrio, J. A. Batkovic, I. Gonzalez, J. Becerra Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bokenkamp, H. Bonnoli, G. Bosnjak, Z. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S. M. Colombo, E. Contreras, J. L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Mendez, C. Delgado Depaoli, D. Di Pierro, F. Di Venere, L. Espineira, E. Do Souto Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Elsaesser, D. Ramazani, V. Fallah Alonso, L. Farina Fattorini, A. Fonseca, M. V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. Lopez, R. J. Garcia Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, J. G. Green, D. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hutten, M. Inada, T. Ishio, K. Iwamura, Y. Martinez, I. Jimenez Jormanainen, J. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. Lopez-Coto, R. Lopez-Moya, M. Lopez-Oramas, A. Loporchio, S. de Oliveira Fraga, B. Machado Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martinez, M. Aguilar, A. Mas Mazin, D. Menchiari, S. Mender, S. Micanovic, S. Miceli, D. Miener, T. Miranda, J. M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Rosillo, M. Nievas Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J. M. Pavletic, L. Penil, P. Persic, M. Pihet, M. Moroni, P. G. Prada Prandini, E. Priyadarshi, C. Puljak, I. Rhode, W. Ribo, M. Rico, Universidad de La Laguna Instituto de Astrofísica de Canarias and Departamento de Astrofísica La Laguna Spain (GRID:grid.10041.34) (ISNI:***) Università di Udine and INFN Trieste Udine Italy (GRID:grid.5390.f) (ISNI:0000 0001 2113 062X) International Center for Relativistic Astrophysics (ICRA) Rome Italy (GRID:grid.5390.f) National Institute for Astrophysics (INAF) Rome Italy (GRID:grid.4293.c) (ISNI:0000 0004 1792 8585) Max-Planck-Institut für Physik Munich Germany (GRID:grid.435824.c) (ISNI:0000 0001 2375 0603) The Barcelona Institute of Science and Technology (BIST) Institut de Física d’Altes Energies (IFAE) Barcelona Spain (GRID:grid.473715.3) (ISNI:0000 0004 6475 7299) Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X) Technische Universität Dortmund Dortmund Germany (GRID:grid.5675.1) (ISNI:0000 0001 0416 9637) Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb Croatia (GRID:grid.4808.4) (ISNI:0000 0001 0657 4636) Universidad Complutense de Madrid IPARCOS Institute and EMFTEL Department Madrid Spain (GRID:grid.4795.f) (ISNI:0000 0001 2157 7667) Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134) Università di Padova and INFN Padua Italy (GRID:grid.5608.b) (ISNI:0000 0004 1757 3470) University of Lodz Department of Astrophysics Faculty of Physics and Applied Informatics Lodz Poland (GRID:grid.10789.37) (ISNI:0000 0000 9730 2769) Università di Siena and INFN Pisa Siena Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641) Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany (GRID:grid.7683.a) (ISNI:0000 0004 0492 0453) ETH Zürich Zurich Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780) Instituto de Astrofísica de Andalucía-CSIC Granada Spain (GRID:grid.450285.e) (ISNI:0000 0004 1793 7043) Università di Pisa and INFN Pisa Pisa Italy (GRID:grid.5395.

Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surface of the white dwarf3, brightening the white dwarf to ~105 solar luminosities and triggering ejection of the accumulated matter. Novae provide the extreme conditions required to accelerate particles, electrons or protons, to high energies. Here we present the detection of gamma rays by the MAGIC telescopes from the 2021 outburst of RS Ophiuchi, a recurrent nova with a red giant companion, which allowed us to accurately characterize the emission from a nova in the 60 GeV to 250 GeV energy range. The theoretical interpretation of the combined Fermi LAT and MAGIC data suggests that protons are accelerated to hundreds of gigaelectronvolts in the nova shock. Such protons should create bubbles of enhanced cosmic ray density, of the order of 10 pc, from the recurrent *** of the 2021 outburst of the nova RS Oph in very-high-energy gamma rays by the MAGIC telescopes is reported. Investigation of the gamma-ray emission provides evidence for acceleration of protons within the nova shock, which then propagate outwards to create bubbles of enhanced cosmic ray density.

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Global, regional, and national burden of neck pain, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

引用

The Lancet Rheumatology 2024年第3期6卷 e142-e155页

作者： Wu, Ai-Min Cross, Marita Elliott, James M. Culbreth, Garland T. Cousin, Ewerton Haile, Lydia M. Steinmetz, Jaimie D. Hagins, Hailey Kopec, Jacek A. Brooks, Peter M. Woolf, Anthony D. Kopansky-Giles, Deborah R. Walton, David M. Treleaven, Julia M. Dreinhoefer, Karsten E. Betteridge, Neil Abbasifard, Mitra Abbasi-Kangevari, Zeinab Addo, Isaac Yeboah Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Aithala, Janardhana P. Alhalaiqa, Fadwa Alhalaiqa Naji Alimohamadi, Yousef Amiri, Sohrab Amu, Hubert Antony, Benny Arabloo, Jalal Aravkin, Aleksandr Y. Asghari-Jafarabadi, Mohammad Atomsa, Gamechu Hunde Azadnajafabad, Sina Azzam, Ahmed Y. Baghdadi, Soroush Balogun, Saliu A. Balta, Asaminew Birhanu Banach, Maciej Banakar, Morteza Barrow, Amadou Bashiri, Azadeh Bekele, Alehegn Bensenor, Isabela M. Bhardwaj, Pankaj Bhat, Ajay Nagesh Bilchut, Awraris Hailu Briggs, Andrew M. Buchbinder, Rachelle Cao, Chao Chaurasia, Akhilanand Chirinos-Caceres, Jesus Lorenzo Christensen, Steffan Wittrup McPhee Coberly, Kaleb Dadras, Omid Dai, Xiaochen de Luca, Katie Dehghan, Azizallah Dong, Huan-Ji Ekholuenetale, Michael Elhadi, Muhammed Eshetu, Habitu Birhan Eskandarieh, Sharareh Etaee, Farshid Fagbamigbe, Adeniyi Francis Fares, Jawad Fatehizadeh, Ali Feizkhah, Alireza Ferreira, Manuela L. Ferreira, Nuno Fischer, Florian Franklin, Richard Charles Ganesan, Balasankar Gebremichael, Mathewos Alemu Gerema, Urge Gholami, Ali Ghozy, Sherief Gill, Tiffany K. Golechha, Mahaveer Goleij, Pouya Golinelli, Davide Graham, Simon Matthew Haj-Mirzaian, Arvin Harlianto, Netanja I. Hartvigsen, Jan Hasanian, Mohammad Hassen, Mohammed Bheser Hay, Simon I. Hebert, Jeffrey J. Heidari, Golnaz Hoveidaei, Amir Human Hsiao, Alexander Kevin Ibitoye, Segun Emmanuel Iwu, Chidozie C.D. Jacob, Louis Janodia, Manthan Dilipkumar Jin, Yingzhao Jonas, Jost B. Joshua, Charity Ehimwenma Kandel, Himal Khader, Yousef Saleh Khajuria, Himanshu Khan, Ejaz Ahmad Khan, Moien A.B. Khatatbeh, Moawiah Mohammad Khateri, Sorour Khayat Kashani, Hamid Reza Khonji, Mohammad Saeid Department of Orthopaedics The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China Faculty of Medicine and Health University of Sydney Sydney NSW Australia Global Alliance for Musculoskeletal Health Sydney NSW Australia Kolling Institute of Medical Research University of Sydney Northern Sydney Local Health District St Leonards NSW Australia Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States School of Population and Public Health University of British Columbia Vancouver BC Canada Arthritis Research Canada Richmond BC Canada US Department of Veterans Affairs (VA) Birmingham AL United States Centre for Health Policy University of Melbourne Melbourne VIC Australia Bone and Joint Research Group Royal Cornwall Hospital Truro United Kingdom Department of Family and Community Medicine University of Toronto Toronto ON Canada Department of Research and Innovation Canadian Memorial Chiropractic College Toronto ON Canada Department of Physical Therapy University of Sydney Sydney NSW Australia School of Physical Therapy Western University London ON Canada School of Health and Rehabilitation Sciences The University of Queensland Brisbane Australia Center of Musculoskeletal Surgery Charité Universitätsmedizin Berlin (Charité Medical University Berlin) Berlin Germany London United Kingdom Department of Internal Medicine Rafsanjan University of Medical Sciences Rafsanjan Iran Clinical Research Development Unit Rafsanjan University of Medical Sciences Rafsanjan Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Tehran University of Medical Sciences Tehran Iran Centre for Social Research in Health University of New South Wales Sydney NSW

Background: Neck pain is a highly prevalent condition that leads to considerable pain, disability, and economic cost. We present the most current estimates of neck pain prevalence and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) by age, sex, and location, with forecasted prevalence to 2050. Methods: Systematic reviews identified population-representative surveys used to estimate the prevalence of and YLDs from neck pain in 204 countries and territories, spanning from 1990 to 2020, with additional data from opportunistic review. Medical claims data from Taiwan (province of China) were also included. Input data were pooled using DisMod-MR 2.1, a Bayesian meta-regression tool. Prevalence was forecast to 2050 using a mixed-effects model using Socio-demographic Index as a predictor and multiplying by projected population estimates. We present 95% UIs for every metric based on the 2·5th and 97·5th percentiles of 100 draws of the posterior distribution. Findings: Globally, in 2020, neck pain affected 203 million (95% uncertainty interval [UI] 163–253) people. The global age-standardised prevalence rate of neck pain was estimated to be 2450 (1960–3040) per 100 000 population and global age-standardised YLD rate was estimated to be 244 (165–346) per 100 000. The age-standardised prevalence rate remained stable between 1990 and 2020 (percentage change 0·2% [–1·3 to 1·7]). Globally, females had a higher age-standardised prevalence rate (2890 [2330–3620] per 100 000) than males (2000 [1600–2480] per 100 000), with the prevalence peaking between 45 years and 74 years in male and female sexes. By 2050, the estimated global number of neck pain cases is projected to be 269 million (219–322), with an increase of 32·5% (23·9–42·3) from 2020 to 2050. Decomposition analysis of the projections showed population growth was the primary contributing factor, followed by population ageing. Interpretation: Although age-standardi

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τ-FPL: Tolerance-constrained learning in linear time

arXiv

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arXiv 2018年

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research - China Shanghai China College of Computing Georgia Institute of Technology AtlantaGA United States

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, τ -False Positive Learning (τ - FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ -FPL over existing approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Mathematical transformations

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Beyond Low Earth Orbit: Biological research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Retracted: Implementation Method CUSUM to Determine the Accident Prone Areas in Web Based

Retracted: Implementation Method CUSUM to Determine the Acci...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Dinata, Rozzi Kesuma Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Riasti, Berliana Kusuma Iskandar, Akbar Sari, Ifit Novita Nurmawati, N. Nanuru, Ricardo Freedom Putri, Lusi Dwi Manurung, Rosida Tiurma Sriadhi, S. Saleh, A.A. Astari, T. Khaddafi, Muammar Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics Universitas Sebelas Maret Surakarta Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Departement Of: Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Department of Civil Engineering Universitas Lancang Kuning Pekanbaru Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Elementary School Teacher Education STKIP Citra Bangsa Aceh Utara Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

At the Unit Laka Lantas Polres Lhokseumawe determining accident-prone areas on roads in Lhokseumawe still using manual systems. Determination of accident-prone areas are less effective because the police Unit Laka Lantas Polres Lhokseumawe just using the data of the last year and had to repeatedly calculate manually accident-prone areas based on the number of human casualties. In this study the system implementation CUSUM method (Cummulative Summary) to determine the accident-prone areas designed using the web-based programming language PHP. In this system there are three processes to determine areas prone to accidents, the accident rate calculations based on the weighted severity, blacksite calculations using the Z-Score method for determining areas prone to accident blackspots and calculations using the CUSUM method to determine the critical points of an accident. The data used are secondary data obtained from the Police Unit Laka Lantas Polres Lhokseumawe years 2009-2013. © Published under licence by IOP Publishing Ltd.

关键词： Accidents

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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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