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Lessons learned from the IMMREP23 TCR-epitope prediction challenge

Immunoinformatics 2024年 16卷

作者： Morten Nielsen Anne Eugster Mathias Fynbo Jensen Manisha Goel Andreas Tiffeau-Mayer Aurelien Pelissier Sebastiaan Valkiers María Rodríguez Martínez Barthélémy Meynard-Piganeeau Victor Greiff Thierry Mora Aleksandra M. Walczak Giancarlo Croce Dana L Moreno David Gfeller Pieter Meysman Justin Barton Department of Health Technology Technical University of Denmark Lyngby DK-2800 Denmark Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Division of Infection and Immunity University College London United Kingdom Institute of Computational Life Sciences ZHAW 8400 Winterthur Switzerland Department of Oncology UNIL CHUV Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland Biomedical Informatics and Data Science Yale School of Medicine 06510 New Haven United States Computational and Quantitative Biology LCQB UMR 7238 Institut de Biologie Paris Seine CNRS Sorbonne Universite Paris 75005 France Department of Immunology University of Oslo and Oslo University Hospital Oslo Norway Laboratoire de Physique de l'Ecole Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Cité F-75005 Paris France University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London United Kingdom

Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions. The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets. To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions. The benchmark confirms that current methods achieve reasonable performance in the "seen" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved. Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.

关键词： TCR specificity Benchmark Prediction Machine learning

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Greedy can beat pure dynamic programming

arXiv

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arXiv 2018年

作者： Jukna, Stasys Seiwert, Hannes Institut für Informatik Goethe Universität Frankfurt Am Main Germany Affiliated with the Institute of Data Science and Digital Technologies Faculty of Mathematics Informatics of Vilnius University Lithuania

Many dynamic programming algorithms for discrete 0-1 optimization problems are "pure" in that their recursion equations only use min/max and addition operations, and do not depend on actual input weights. The well-known greedy algorithm of Kruskal solves the minimum weight spanning tree problem on n-vertex graphs using only O(n2logn) operations. We prove that any pure DP algorithm for this problem must perform 2ω(n) operations. Since the greedy algorithm can also badly fail on some optimization problems, easily solvable by pure DP algorithms, our result shows that the computational powers of these two types of algorithms are incomparable. Copyright © 2018, The Authors. All rights reserved.

关键词： Tropics

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130 Low Gene Copy Numbers (GCN) of complement C4 and C4A deficiency are highly significant genetic risk factors for idiopathic inflammatory myopathies and its major subgroups

引用

Immunobiology 2023年第5期228卷 152581-152581页

作者： Zhou, Danlei King, Emily Rothwell, Simon Kryštůfková, Olga Notarnicola, Antonella Coss, Samantha Aziz, Rabheh Miller, Katherine Dang, Amanda Yu, Richard Drew, Joanne Lundstrom, Emeli Pachman, Lauren Mamyrova, Gulnara Curiel, Rodolfo De Paepe, Boel De Bleecker, Jan Payton, Antony Ollier, William O'Hanlon, Terrance Targoff, Ira Flegel, Willy Sivaraman, Vidya Obele, Edward Akoghlanian, Shoghik Driest, Kyla Spencer, Charles Ling Wu, Yee Nagaraja, Haikady Ardoin, Stacy Chinoy, Hector Rider, Lisa Miller, Frederick Lundberg, Ingrid Padyukov, Leonid Vencovský, Jiří Lamb, Janine Yu, Chack-Yung Abigail Wexner Research Institute Nationwide Children’s Hospital Columbus Ohio USA National Institute for Health Research Manchester Biomedical Research Centre Manchester United Kingdom Institute of Rheumatology and Department of Rheumatology Charles University Prague Czech Republic Division of Rheumatology Department of Medicine Solna Karolinska Institutet Stockholm Sweden Division of Allergy/Immunology and Rheumatology University at Buffalo Jacobs School of Medicine and Biomedical Sciences Buffalo NY USA Division of Rheumatology Nationwide Children’s Hospital Columbus Ohio USA Department of Pediatrics Northwestern University Feinberg School of Medicine Chicago IL USA Division of Rheumatology Department of Medicine George Washington University School of Medicine and Health Sciences Washington DC USA Department of Neurology Ghent University Hospital Ghent Belgium Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Faculty of Science and Engineering Manchester Metropolitan University Manchester United Kingdom Environmental Autoimmunity Group Clinical Research Branch National Institute of Environmental Health Sciences Bethesda MD USA

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Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals

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NATURE MEDICINE 2023年第12期29卷 3184-3192页

作者： Zhou, Hang Kember, Rachel. L. Deak, Joseph. D. Xu, Heng Tolkumo, Sylvanus Yuan, Kai Lind, Penelope A. Farajzadeh, Lella Wang, Lu Hatoum, Alexander. S. Johnson, Jessica Lee, Hyunjoo Mallard, Travis. T. Xu, Jiayi Johnston, Keira J. A. Johnson, Emma C. Nielsen, Trine Tollerup Galimberti, Marco Dao, Cecilia Levey, Daniel F. Overstreet, Cassie Byrne, Enda. M. Gillespie, Nathan. A. Gordon, Scott Sanchez-Roige, Sandra Madden, Paamela A. F. Heath, Andrew C. Medland, Sarah E. Martin, Nicholas G. Ge, Tian Smoller, Jordan. W. Hougaard, David. M. Borglum, Anders D. Demontis, Ditte Krystal, John. H. Gaziano, J. Michael Edenberg, Howard J. Agarwal, Arpana Justice, Amy. C. Stein, Murray B. Kranzler, Henry. R. Gelernter, Joel Department of Psychiatry Yale School of Medicine New Haven CT USA Veterans Affairs Connecticut Healthcare System West Haven CT USA Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT USA Crescenz Veterans Affairs Medical Center Philadelphia PA USA Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Stanley Center for Psychiatric Research The Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Department of Medicine Massachusetts General Hospital Boston MA USA Psychiatric Genetics QIMR Berghofer Medical Research Institute Brisbane Queensland Australia School of Biomedical Sciences Queensland University of Technology Brisbane Queensland Australia Faculty of Medicine University of Queensland Brisbane Queensland Australia Department of Biomedicine – Human Genetics Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Aarhus Denmark Center for Genomics and Personalized Medicine Aarhus Denmark Department of Psychological and Brain Sciences Washington University in St. Louis Saint Louis MO USA Pamela Sklar Division of Psychiatric Genomics Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Psychiatric and Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Psychiatry Washington University School of Medicine Saint Louis MO USA Child Health Research Centre The University of Queensland Brisbane Queensland Australia Institute for Psychiatric and Behavioral Genetics Department of Psychiatry Virginia Commonwealth University Richmond VA USA Genetic Epidemiology QIMR Berghofer Med

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147;African, N = 122,571;Latin American, N = 38,962;East Asian, N = 13,551;and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer. A multi-ancestry genome-wide association study of problematic alcohol use in one million individuals identified 110 risk variants and shows that multi-ancestry polygenic scores improve risk prediction compared with single-ancestry scores

关键词： Addiction Genome-wide association studies

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Modern applications of machine learning in quantum sciences

arXiv

引用

arXiv 2022年

作者： Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Plodzien, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okula, Robert Muñoz–Gil, Gorka Vargas–Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michal Lewenstein, Maciej Dauphin, Alexandre Faculty of Physics University of Warsaw Poland ICFO - Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Center for Computational Quantum Physics Flatiron Institute New York United States Department of Physics University of Basel Switzerland Institute for Theoretical Physics Heinrich Heine University Düsseldorf Germany Institute for Quantum Inspired and Quantum Optimization Hamburg University of Technology Germany Université de Paris CNRS Laboratoire Matériaux et Phénomènes Quantiques France Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Applied Physics Aalto University Espoo Finland Institute of Physics Albert-Ludwig University of Freiburg Germany International Centre for Theory of Quantum Technologies University of Gdańsk Poland Department of Algorithms and System Modeling Faculty of Electronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Poland Institute for Theoretical Physics University of Innsbruck Austria Department of Chemistry University of Toronto Canada Vector Institute for Artificial Intelligence MaRS Centre Toronto Canada Department of Chemistry and Chemical Biology McMaster University Hamilton Canada Barcelona Supercomputing Center Spain LIACS Leiden University Netherlands CMAP École Polytechnique France Switzerland Menten AI Inc. Palo AltoCA United States Niels Bohr Institute Copenhagen Denmark CPHT CNRS École Polytechnique Institut Polytechnique de Paris PalaiseauF-91128 France Beijing National Lab for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Songshan Lake Materials Laboratory Dongguan China Perimeter Institute for Theoretical Physics Waterloo Canada Kavli Institute of Nanoscience Delft University of Technology DelftNL-2600 GA Netherlands Department of

In this book, we provide a comprehensive introduction to the most recentadvances in the application of machine learning methods in quantum sciences. Wecover the use of deep learning and kernel methods in supervised, unsupervised,and reinforcement learning algorithms for phase classification, representationof many-body quantum states, quantum feedback control, and quantum circuitsoptimization. Moreover, we introduce and discuss more specialized topics suchas differentiable programming, generative models, statistical approach tomachine learning, and quantum machine learning. © 2022, CC BY.

关键词： Deep learning

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Searching for Beyond the Standard Model physics using the improved description of 100Mo 2vββ decay spectral shape with CUPID-Mo

arXiv

引用

arXiv 2024年

作者： Augier, C. Barabash, A.S. Bellini, F. Benato, G. Beretta, M. Bergé, L. Billard, J. Borovlev, Yu A. Cardani, L. Casali, N. Cazes, A. Celi, Emanuela Chapellier, M. Chiesa, D. Dafinei, I. Danevich, F.A. De Jesus, M. Dixon, T. Dumoulin, L. Eitel, K. Ferri, F. Fujikawa, B.K. Gascon, J. Gironi, L. Giuliani, A. Grigorieva, V.D. Gros, M. Helis, D.L. Huang, H.Z. Huang, R. Imbert, L. Juillard, A. Khalife, H. Kleifges, M. Kobychev, V.V. Kolomensky, Yu G. Konovalov, S.I. Kotila, J. Loaiza, P. Ma, L. Makarov, E.P. de Marcillac, P. Mariam, R. Marini, L. Marnieros, S. Navick, X.F. Nones, C. Norman, E.B. Olivieri, E. Ouellet, J.L. Pagnanini, L. Pattavina, L. Paul, B. Pavan, M. Peng, H. Pessina, G. Pirro, S. Poda, D.V. Polischuk, O.G. Pozzi, S. Previtali, E. Redon, Th Rojas, A. Rozov, S. Sanglard, V. Scarpaci, J.A. Schmidt, B. Shen, Y. Shlegel, V.N. Šimkovic, F. Singh, V. Tomei, C. Tretyak, V.I. Umatov, V.I. Vagneron, L. Velázquez, M. Ware, B. Welliver, B. Winslow, L. Xue, M. Yakushev, E. Zarytskyy, M. Zolotarova, A.S. Univ Lyon Université Lyon 1 CNRS IN2P3 IP2I-Lyon VilleurbanneF-69622 France National Research Centre Kurchatov Institute Kurchatov Complex of Theoretical and Experimental Physics Moscow117218 Russia Dipartimento di Fisica Sapienza Università di Roma P.le Aldo Moro 2 RomeI-00185 Italy INFN Sezione di Roma P.le Aldo Moro 2 RomeI-00185 Italy Gran Sasso Science Institute L’AquilaI-67100 Italy AssergiI-67100 Italy Department of Physics University of California BerkeleyCA94720 United States Université Paris-Saclay CNRS IN2P3 IJCLab Orsay91405 France Nikolaev Institute of Inorganic Chemistry Novosibirsk630090 Russia Dipartimento di Fisica Università di Milano-Bicocca MilanoI-20126 Italy INFN Sezione di Milano-Bicocca MilanoI-20126 Italy Institute for Nuclear Research of National Academy of Sciences of Ukraine Kyiv03028 Ukraine INFN Sezione di Roma Tor Vergata Via della Ricerca Scientifica 1 RomeI-00133 Italy Institute for Astroparticle Physics Karlsruhe Institute of Technology Karlsruhe76021 Germany IRFU CEA Université Paris-Saclay Gif-sur-YvetteF-91191 France Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Fudan University Shanghai200433 China Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe76021 Germany 409 Atomistilor Street Bucharest-Magurele077125 Romania Finnish Institute for Educational Research University of Jyväskylä P.O. Box 35 JyväskyläFI-40014 Finland Center for Theoretical Physics Sloane Physics Laboratory Yale University New HavenCT06520-8120 United States Massachusetts Institute of Technology CambridgeMA02139 United States Physik Department Technische Universität München GarchingD-85748 Germany Department of Modern Physics University of Science and Technology of China Hefei230027 China Université Grenoble Alpes CNRS Grenoble INP LPSC/LSM-IN2P3 Modane73500 France Laboratory of Nuclear Problems JINR Moscow

The current experiments searching for neutrinoless double-β (0βvβ) decay also collect large statistics of Standard Model allowed two-neutrino double-β (2vββ) decay events. These can be used to search for Beyond Standard Model (BSM) physics via 2vββ decay spectral distortions. 100Mo has a natural advantage due to its relatively short half-life, allowing higher β2vβ decay statistics at equal exposures compared to the other isotopes. We demonstrate the potential of the dual read-out bolometric technique exploiting a 100Mo exposure of 1.47 kg × y, acquired in the CUPID-Mo experiment at the Modane underground laboratory (France). We set limits on 0vββ decays with the emission of one or more Majorons, on 2vββ decay with Lorentz violation, and 2vββ decay with a sterile neutrino emission. In this analysis, we investigate the systematic uncertainty induced by modeling the 2vββ decay spectral shape parameterized through an improved model, an effect never considered before. This work motivates searches for BSM processes in the upcoming CUPID experiment, which will collect the largest amount of 2vββ decay events among the next-generation experiments. © 2024, CC BY.

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CSF1R + myeloid-monocytic cells drive CAR-T cell resistance in aggressive B cell lymphoma

引用

Cancer Cell 2025年

作者： David Stahl Philipp Gödel Hyatt Balke-Want Rahil Gholamipoorfard Paul Segbers Luis Tetenborg Mirjam Koker Janina Dörr Lisa Gregor Daniel Bachurski France Rose Adrian G. Simon Zinaida Good Josefine Jakob Björn Häupl Marieke Nill Ruth Flümann Tobias Riet Dinah Lange Stuart J. Blakemore Roland T. Ullrich Department I of Internal Medicine Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf University of Cologne Faculty of Medicine and University Hospital Cologne Cologne Germany Center for Molecular Medicine Cologne (CMMC) University of Cologne Cologne Germany Else Kröner Forschungskolleg Clonal Evolution in Cancer University Hospital Cologne Cologne Germany Cancer Research Center Cologne Essen (CCCE) Faculty of Medicine and University Hospital Cologne University of Cologne Cologne Germany Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD) Faculty of Medicine and University Hospital of Cologne Cologne Germany Stanford Center for Cancer Cell Therapy Stanford Cancer Institute Stanford University Stanford CA USA Department of Translational Genomics Faculty of Medicine and University Hospital Cologne University of Cologne Cologne Germany Division of Clinical Pharmacology Department of Medicine IV LMU University Hospital Member of the German Center for Lung Research (DZL) LMU Munich Munich Germany CECAD Center of Excellence on Cellular Stress Responses in Aging-Associated Diseases University of Cologne Cologne Germany University of Cologne Institute for Biomedical Informatics Faculty of Medicine and University Hospital Cologne Cologne Germany Institute of Pathology University Hospital Cologne Faculty of Medicine University of Cologne Cologne Germany Department of Biomedical Data Science Stanford University School of Medicine Stanford CA USA Parker Institute for Cancer Immunotherapy Stanford University School of Medicine Stanford CA USA Department of Medicine Hematology and Oncology University Hospital Goethe University Frankfurt Frankfurt am Main Germany Frankfurt Cancer Institute (FCI) Frankfurt am Main Germany German Cancer Consortium (DKTK) partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Frankfurt am Main Germany German Cancer Research Center (DKFZ) Heidelberg Germany Max P

Despite the improvement, approximately 60% of patients with relapsed or refractory (r/r) aggressive B cell lymphoma (B-NHL) do not achieve durable benefit from CAR-T cell therapy. To elucidate factors associated with CAR-T therapy resistance, we conducted high-dimensional analyses of pre- and post-CAR-T cell specimens. In patients with non-durable response, we identified a prognostically relevant lymphoma-associated myeloid-monocytic (LAMM) gene signature. In-depth profiling revealed a distinct CSF1R + CD14 + CD68 + LAMM cell population in both human and murine B-NHL that inhibits CAR-T cell function and correlates with poor outcome. Cell-cell inference analysis uncovered that LAMM cells impair CAR-T cell function through a direct LAMM-T cell interaction via the PGE 2 -EP2/EP4 axis. In an autochthonous lymphoma mouse model, combined anti-CD19 CAR-T cell therapy with CSF1R blockade exhibited synergistic effects and improved survival. These findings provide strong rationale for combining anti-CD19 CAR-T cells with CSF1R inhibitors in treating r/r aggressive B-NHL patients.

关键词： CAR-T cell aggressive B-NHL CSF1R lymphoma tumor microenvironment myeloid cells

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PLUMED Tutorials: a collaborative, community-driven learning ecosystem

arXiv

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arXiv 2024年

作者： Tribello, Gareth A. Bonomi, Massimiliano Bussi, Giovanni Camilloni, Carlo Armstrong, Blake I. Arsiccio, Andrea Aureli, Simone Ballabio, Federico Bernetti, Mattia Bonati, Luigi Brookes, Samuel G.H. Faidon Brotzakis, Z. Capelli, Riccardo Ceriotti, Michele Chan, Kam-Tung Cossio, Pilar Dasetty, Siva Donadio, Davide Ensing, Bernd Ferguson, Andrew L. Fraux, Guillaume Gale, Julian D. Gervasio, Francesco Luigi Giorgino, Toni Herringer, Nicholas S.M. Hocky, Glen M. Hoff, Samuel E. Invernizzi, Michele Languin-Cattoën, Olivier Leone, Vanessa Limongelli, Vittorio Lopez-Acevedo, Olga Marinelli, Fabrizio Martinez, Pedro Febrer Masetti, Matteo Mehdi, Shams Michaelides, Angelos Murtada, Mhd Hussein Parrinello, Michele Piaggi, Pablo M. Pietropaolo, Adriana Pietrucci, Fabio Pipolo, Silvio Pritchard, Claire Raiteri, Paolo Raniolo, Stefano Rapetti, Daniele Rizzi, Valerio Rydzewski, Jakub Salvalaglio, Matteo Schran, Christoph Seal, Aniruddha Zadeh, Armin Shayesteh Silva, Tomás F.D. Spiwok, Vojtěch Stirnemann, Guillaume Sucerquia, Daniel Tiwary, Pratyush Valsson, Omar Vendruscolo, Michele Voth, Gregory A. White, Andrew D. Wu, Jiangbo Centre for Quantum Materials and Technology School of Mathematics and Physics Queen’s University Belfast United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Via Bonomea 265 Trieste34136 Italy Department of Biosciences University of Milano Milano Italy School of Molecular and Life Sciences Curtin University GPO Box U1987 PerthWA6845 Australia Coriolis Pharma Fraunhoferstrasse 18b Martinsried82152 Germany School of Pharmaceutical Sciences University of Geneva Rue Michel-Servet 1 GenevaCH-1206 Switzerland Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva GenevaCH-1206 Switzerland Swiss Bioinformatics Institute University of Geneva GenevaCH-1206 Switzerland Via Celoria 26 Milan20133 Italy Department of Biomolecular Sciences University of Urbino "Carlo Bo" Piazza Rinascimento 6 Urbino61029 Italy Department of Pharmacy and Biotechnology Alma Mater Studiorum University of Bologna Via Belmeloro 6 Bologna40126 Italy Atomistic Simulations Italian Institute of Technology Genova Italy Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Institute for Bioinnovation Biomedical Sciences Research Center "Alexander Fleming" Vari16672 Greece Laboratory of Computational Science and Modeling Institut des Matériaux École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Chemistry University of California Davis DavisCA95616 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States AI4Science Lab Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Computational Chemistry Group Van’t Hoff Institute for Molecular Sciences Unive

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce "PLUMED Tutorials", a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. © 2024, CC BY.

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OPTIMADE, an API for exchanging materials data

arXiv

引用

arXiv 2021年

作者： Andersen, Casper W. Armiento, Rickard Blokhin, Evgeny Conduit, Gareth J. Dwaraknath, Shyam Evans, Matthew L. Fekete, Ádám Gopakumar, Abhijith Gražulis, Saulius Merkys, Andrius Mohamed, Fawzi Oses, Corey Pizzi, Giovanni Rignanese, Gian-Marco Scheidgen, Markus Talirz, Leopold Toher, Cormac Winston, Donald Aversa, Rossella Choudhary, Kamal Colinet, Pauline Curtarolo, Stefano Di Stefano, Davide Draxl, Claudia Er, Suleyman Esters, Marco Fornari, Marco Giantomassi, Matteo Govoni, Marco Hautier, Geoffroy Hegde, Vinay Horton, Matthew K. Huck, Patrick Huhs, Georg Hummelshøj, Jens Kariryaa, Ankit Kozinsky, Boris Kumbhar, Snehal Liu, Mohan Marzari, Nicola Morris, Andrew J. Mostofi, Arash A. Persson, Kristin A. Petretto, Guido Purcell, Thomas Ricci, Francesco Rose, Frisco Scheffler, Matthias Speckhard, Daniel Uhrin, Martin Vaitkus, Antanas Villars, Pierre Waroquiers, David Wolverton, Chris Wu, Michael Yang, Xiaoyu École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Materials Design and Informatics Unit Department of Physics Chemistry and Biology Linköping University Sweden Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Theory of Condensed Matter Group Cavendish Laboratory 19 J.J. Thomson Avenue CambridgeCB3 0HE United Kingdom Lawrence Berkeley Labs BerkeleyCA United States Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Department of Physics King's College London London United Kingdom Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Institute of Biotechnology Life Science Center Vilnius University Saulétekio av. 7 VilniusLT-10257 Lithuania Institute of Computer Science Faculty of Mathematics and Informatics Vilnius University Naugarduko g. 24 VilniusLT-03225 Lithuania Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 Berlin14195 Germany Center for Autonomous Materials Design Duke University DurhamNC27708 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Humboldt-Universität zu Berlin Institut für Physik and IRIS Adlershof Berlin12489 Germany École Polytechnique Fédérale de Lausanne Sion1951 Switzerland Trieste34136 Italy Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Ansys 300 Rustat House 62 Clifton Rd CambridgeCB1 7EG United Kingdom De Zaale 20 Eindhoven5612 AJ Netherlands Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Argonne National Laboratory LemontIL60439 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Los AltosCA940

The Open databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. © 2021, CC BY.

关键词： Specifications

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The Reply

引用

The American journal of medicine 2020年第6期133卷 e330-e332页

作者： Justas Simonavicius Roma Puronaite Hans-Peter Brunner-La Rocca Department of Cardiology Maastricht University Medical Centre the Netherlands Centre of Cardiology and Angiology Vilnius University Hospital Santaros Klinikos Lithuania Clinic of Internal Diseases Family Medicine and Oncology Faculty of Medicine Vilnius University Lithuania. Department of Information Systems Centre of Informatics and Development Vilnius University Hospital Santaros Klinikos Lithuania Clinic of Cardiac and Vascular diseases Faculty of Medicine Vilnius University Lithuania Institute of Data Science and Digital Technologies Faculty of Mathematics and Informatics Vilnius University Lithuania. Department of Cardiology Maastricht University Medical Centre the Netherlands.

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