检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Safanelli, José Lucas Sanderman, Jonathan Bloom, Dellena Todd-Brown, Katherine Parente, Leandro L. Hengl, Tomislav Adam, Sean Albinet, Franck Ben-Dor, Eyal Boot, Claudia M. Bridson, James H. Chabrillat, Sabine Deiss, Leonardo Demattê, José A.M. Demyan, M. Scott Dercon, Gerd Doetterl, Sebastian van Egmond, Fenny Ferguson, Rich Garrett, Loretta G. Haddix, Michelle L. Haefele, Stephan M. Heilin, Maria Hernandez-Allica, Javier Huang, Jingyi Jastrow, Julie D. Karyotis, Konstantinos Machmuller, Megan Malefetsane, Khesuoe Margenot, Andrew Matamala, Roser Miesel, Jessica R. Mouazen, Abdul M. Nagel, Penelope Patel, Sunita Qaswar, Muhammad Ramakhanna, Selebalo Resch, Christian Robertson, A.H. Jean Roudier, Pierre Sabetizade, Marmar Shabtai, Itamar Sherif, Faisal Sinha, Nishant Six, Johan Summerauer, Laura Thomas, Cathy L. Toloza, Arsenio Tomczyk, Beata Tsakiridis, Nikolaos L. van Wesemael, Bas Woodings, Finnleigh Zalidis, George Żelazny, Wiktor R. Woodwell Climate Research Center FalmouthMA United States Department of Environmental Engineering Sciences University of Florida GainesvilleFL United States OpenGeoHub Foundation Wageningen Netherlands TU Bergakademie Freiberg Freiberg Germany Soil and Water Management and Crop Nutrition Laboratory Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture International Atomic Energy Agency Austria Remote Sensing Laboratory Department of Geography and Human Environment Porter School of Environment and Earth Science Tel Aviv University Tel Aviv69978 Israel Department of Chemistry Colorado State University Fort CollinsCO United States Scion Rotorua3046 New Zealand GFZ German research center for geosciences Potsdam Germany The Ohio State University WoosterOH United States Luiz de Queiroz College of Agriculture University of São Paulo SP Piracicaba Brazil The Ohio State University ColumbusOH United States Department of Environmental Systems Science ETH Zurich Zurich8092 Switzerland ISRIC World Soil Information Wageningen Netherlands USDA-NRCS Kellogg Soil Survey Laboratory LincolnNE United States Department of Soil and Crop Sciences Colorado State University Fort CollinsCO United States Sustainable Soils and Crops Rothamsted Research United Kingdom University of Wisconsin-Madison MadisonWI United States Environmental Science Division Argonne National Laboratory LemontIL United States Laboratory of Remote Sensing Spectroscopy GIS Department of Agriculture Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Agricultural Research Maseru Lesotho University of Illinois Urbana-Champaign UrbanaIL United States Michigan State University Department of Plant Soil and Microbial Sciences East LansingMI United States Department of Environment Ghent University Coupure Links 653 Gent9000 Belgium Persistence Data Mining Inc. La Jolla CA United States The James Hutton Institute Carigiebuckler AberdeenAB15 8QH Uni

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spect

关键词： Libraries

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The effectiveness of backward contact tracing in networks

arXiv

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arXiv 2020年

作者： Kojaku, Sadamori Hébert-Dufresne, Laurent Mones, Enys Lehmann, Sune Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States DTU Compute Technical University of Denmark Kgs. Lyngby 2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen K1353 Denmark Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

Discovering and isolating infected individuals is a cornerstone of epidemic control1-7. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control8-15. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing-tracing to whom disease spreads, "backward" tracing-tracing from whom disease spreads-is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that-in terms of the number of prevented transmissions per isolation-case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Perspectives for self-driving labs in synthetic biology

arXiv

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

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Clustering by orthogonal NMF model and non-convex penalty optimization

arXiv

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arXiv 2019年

作者： Wang, Shuai Chang, Tsung-Hui Cui, Ying Pang, Jong-Shi The Shenzhen Research Institute of Big Data School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States The Department of Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found a promising clustering model and can outperform the classical K-means. However, solving the ONMF model is a challenging optimization problem because the coupling of the orthogonality and non-negativity constraints introduces a mixed combinatorial aspect into the problem due to the determination of the correct status of the variables (positive or zero). Most of the existing methods directly deal with the orthogonality constraint in its original form via various optimization techniques, but are not scalable for large-scale problems. In this paper, we propose a new ONMF based clustering formulation that equivalently transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable. One smooth penalty formulation and one non-smooth penalty formulation are respectively studied. We build theoretical conditions for the penalized problems to provide feasible stationary solutions to the ONMF based clustering problem, as well as proposing efficient algorithms for solving the penalized problems of the two NCP methods. Experimental results based on both synthetic and real datasets are presented to show that the proposed NCP methods are computationally time efficient, and either match or outperform the existing K-means and ONMF based methods in terms of the clustering performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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Roadmap for Optical Tweezers

arXiv

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arXiv 2022年

作者： Volpe, Giovanni Maragò, Onofrio M. Rubinzstein-Dunlop, Halina Pesce, Giuseppe Stilgoe, Alexander B. Volpe, Giorgio Tkachenko, Georgiy Truong, Viet Giang Chormaic, Síle Nic Kalantarifard, Fatemeh Elahi, Parviz Käll, Mikael Callegari, Agnese Marqués, Manuel I. Neves, Antonio A.R. Moreira, Wendel L. Fontes, Adriana Cesar, Carlos L. Saija, Rosalba Saidi, Abir Beck, Paul Eismann, Jörg S. Banzer, Peter Fernandes, Thales F.D. Pedaci, Francesco Bowen, Warwick P. Vaippully, Rahul Lokesh, Muruga Roy, Basudev Thalhammer, Gregor Ritsch-Marte, Monika García, Laura Pérez Arzola, Alejandro V. Castillo, Isaac Pérez Argun, Aykut Muenker, Till M. Vos, Bart E. Betz, Timo Cristiani, Ilaria Minzioni, Paolo Reece, Peter J. Wang, Fan McGloin, David Ndukaife, Justus C. Quidant, Romain Roberts, Reece P. Laplane, Cyril Volz, Thomas Gordon, Reuven Hanstorp, Dag Marmolejo, Javier Tello Bruce, Graham D. Dholakia, Kishan Li, Tongcang Brzobohatý, Oto Simpson, Stephen H. Zemánek, Pavel Ritort, Felix Roichman, Yael Bobkova, Valeriia Wittkowski, Raphael Denz, Cornelia Kumar, G.V. Pavan Foti, Antonino Donato, Maria Grazia Gucciardi, Pietro G. Gardini, L. Bianchi, G. Kashchuk, A. Capitanio, M. Paterson, Lynn Jones, P.H. Berg-Sørensen, Kirstine Barooji, Younes F. Oddershede, Lene B. Pouladian, Pegah Preece, Daryl Adiels, Caroline Beck De Luca, Anna Chiara Magazzù, A. Ciriza, D. Bronte Iatì, M.A. Swartzlander, Grover A. Department of Physics University of Gothenburg Gothenburg41296 Sweden CNR IPCF Istituto per i Processi Chimico-Fisici Messina Italy ARC CoE for Engineered Quantum Systems School of Mathematics and Physics The University of Queensland Australia Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom Dipartimento di Fisica Università degli studi di Napoli Federico II Italy Okinawa Institute of Science and Technology Graduate University Japan Department of Health Technology Technical University of Denmark Lyngby Denmark Physics Department Boğaziçi University Istanbul Turkey Chalmers University of Technology Gothenburg Sweden Departamento de Física de Materiales IFIMAC Instituto Nicolás Cabrera Universidad Autónoma de Madrid Spain Santo André São Paulo Brazil Tratamento de dados geofísicos Tecnologia e processamento de geologia e geofísica Exploração Petrobras - Petróleo Brasileiro S.A. Rio de Janeiro Rio de Janeiro Brazil Pernambuco Recife Brazil Ceará Fortaleza Brazil Campinas São Paulo Brazil Dipartimento di Scienze Matematiche e Informatiche Scienze Fisiche e Scienze della Terra Università di Messina Italy Institute of Optics Information and Photonics University Erlangen-Nuremberg Staudtstr. 7/B2 ErlangenD-91058 Germany Max Planck Institute for the Science of Light Staudtstr. 2 ErlangenD-91058 Germany Institute of Physics University of Graz NAWI Graz Universitätsplatz 5 Graz8010 Austria Centre de Biologie Structurale CNRS INSERM Univ.Montpellier France Indian Institute of Technology Madras India Institute for Biomedical Physics Medical University of Innsbruck Austria Instituto de Física Universidad Nacional Autónoma de México Ciudad de MéxicoC. P. 04510 Mexico Departamento de Física Universidad Autónoma Metropolitana-Iztapalapa San Rafael Atlixco 186 Ciudad de México09340 Mexico Third Institute of Physics - Biophysics Georg August University Göttingen Germany Department of Electrical

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical tweezers

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology

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Nature Medicine 2025年 1-12页

作者： Wu, Yue Ehlert, Ben Metwally, Ahmed A. Perelman, Dalia Park, Heyjun Brooks, Andrew Wallace Abbasi, Fahim Michael, Basil Celli, Alessandra Bejikian, Caroline Ayhan, Ekrem Lu, Yingzhou Lancaster, Samuel M. Hornburg, Daniel Ramirez, Lucia Bogumil, David Pollock, Sarah Wong, Frank Bradley, Denver Gutjahr, Georg Rangan, Ekanath Srihari Wang, Tao McGuire, Lettie Venkat Rangan, P. Ræder, Helge Shipony, Zohar Lipson, Doron McLaughlin, Tracey Snyder, Michael P. Department of Genetics Stanford University Stanford CA United States Stanford Cardiovascular Institute Stanford University Stanford CA United States Department of Biomedical Data Science Stanford University Stanford CA United States Systems and Biomedical Engineering Department Cairo University Giza Egypt Google LLC Mountain View CA United States Department of Medicine Stanford University Stanford CA United States Department of International Health Johns Hopkins Bloomberg School of Public Health Baltimore MD United States Stanford Diabetes Research Centre Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Metabolic Health Center Stanford University Stanford CA United States Ultima Genomics Newark CA United States Amrita School of Medicine Amrita Vishwa Vidyapeetham (University) Kochi India Amrita Vishwa Vidyapeetham (University) Kochi India Department of Clinical Science University of Bergen Bergen Norway Department of Pediatrics Haukeland University Hospital Bergen Norway

Elevated postprandial glycemic responses (PPGRs) are associated with type 2 diabetes and cardiovascular disease. PPGRs to the same foods have been shown to vary between individuals, but systematic characterization of the underlying physiologic and molecular basis is lacking. We measured PPGRs using continuous glucose monitoring in 55 well-phenotyped participants challenged with seven different standard carbohydrate meals administered in replicate. We also examined whether preloading a rice meal with fiber, protein or fat (‘mitigators’) altered PPGRs. We performed gold-standard metabolic tests and multi-omics profiling to examine the physiologic and molecular basis for interindividual PPGR differences. Overall, rice was the most glucose-elevating carbohydrate meal, but there was considerable interindividual variability. Individuals with the highest PPGR to potatoes (potato-spikers) were more insulin resistant and had lower beta cell function, whereas grape-spikers were more insulin sensitive. Rice-spikers were more likely to be Asian individuals, and bread-spikers had higher blood pressure. Mitigators were less effective in reducing PPGRs in insulin-resistant as compared to insulin-sensitive participants. Multi-omics signatures of PPGR and metabolic phenotypes were discovered, including insulin-resistance-associated triglycerides, hypertension-associated metabolites and PPGR-associated microbiome pathways. These results demonstrate interindividual variability in PPGRs to carbohydrate meals and mitigators and their association with metabolic and molecular profiles. © The Author(s) 2025.

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P06-08: AOP-BOT: An AI assistance for AOP development

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Toxicology Letters 2023年 384卷 S105-S105页

作者： S. Kumar N. ZaidKilani S. Khalid L. Slater A. Karakoltzidis N. Papaioannou P. Nymark K.E. Tollefsen H.A. Rashwan K. Audouze V. Kumar Fundació Institut d'Investigació Sanitària Pere Virgili Department of chemical Engineering Tarragona Spain Environmental Analysis and Management Group Department of chemical Engineering Tarragona Spain Centre for Health Data Science University of Birmingham Institute of Cancer and Genomics Birmingham UK University of Birmingham Centre for Environmental Research & Justice (CERJ) Birmingham UK Aristotle University of Thessaloniki Environmental Engineering LaboratoryDepartment of Chemical Engineering Thessaloniki Greece Center for Interdisciplinary Research and Innovation HERACLES Research Center on the Exposome and Health Thessaloniki Greece Karolinska Institute Institute of Environmental Medicine Stockholm Sweden Norwegian Institute for Water Research (NIVA) Økernveien Norway Norwegian University of Life Sciences (NMBU) Faculty of Environmental Sciences and Natural Resource Management (MINA) Elizabeth Stephansens v. 15 1430 Ås Norway Norwegian University of Life Sciences (NMBU) Centre for Environmental Radioactivity Elizabeth Stephansens v. 15 1430 Ås Norway University of Paris Department of Systems Biology and Bioinformatics (INSERM) Paris France

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Correction: Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients

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Leukemia 2023年第2期37卷 503页

作者： Hosuk Ryou Korsuk Sirinukunwattana Alan Aberdeen Gillian Grindstaff Bernadette J Stolz Helen Byrne Heather A Harrington Nikolaos Sousos Anna L Godfrey Claire N Harrison Bethan Psaila Adam J Mead Gabrielle Rees Gareth D H Turner Jens Rittscher Daniel Royston Nuffield Division of Clinical Laboratory Sciences Radcliffe Department of Medicine University of Oxford Oxford UK. Institute of Biomedical Engineering (IBME) Department of Engineering Science University of Oxford Oxford UK. Big Data Institute/Li Ka Shing Centre for Health Information and Discovery University of Oxford Oxford UK. Ground Truth Labs Oxford UK. Oxford NIHR Biomedical Research Centre Oxford University Hospitals NHS Foundation Trust Oxford UK. Department of Mathematics University of California Los Angeles CA USA. Mathematical Institute University of Oxford Oxford UK. Laboratory for Topology and Neuroscience École Polytechnique Fédérale de Lausanne Lausanne Switzerland. Ludwig Institute for Cancer Research University of Oxford Oxford UK. Wellcome Centre for Human Genetics University of Oxford Oxford UK. Department of Haematology Oxford University Hospitals NHS Foundation Trust Oxford UK. MRC Weatherall Institute of Molecular Medicine Radcliffe Department of Medicine University of Oxford Oxford UK. Haematopathology & Oncology Diagnostics Service Cambridge University Hospitals NHS Foundation Trust Cambridge UK. Department of Haematology Guy's and St Thomas' NHS Foundation Trust London UK. Department of Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK. Nuffield Division of Clinical Laboratory Sciences Radcliffe Department of Medicine University of Oxford Oxford UK. daniel.royston@ndcls.ox.ac.uk. Department of Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK. daniel.royston@ndcls.ox.ac.uk.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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