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arXiv 2024年

作者： Evans, Matthew L. Bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar B. Beltrán, Daniel Blokhin, Evgeny Boland, Tara M. Balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez Eckert, Hagen Eimre, Kristjan Montero, María Elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro Baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu Bonella, Sara Botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis Edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. © 2024, CC BY.

关键词： Electronic data interchange

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Author Correction: Nano-achiral complex composites for extreme polarization optics

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Nature 2024年第8022期631卷 E19页

作者： Jun Lu Wenbing Wu Felippe Mariano Colombari Ali Jawaid Bryan Seymour Kody Whisnant Xiaoyang Zhong Wonjin Choi Nikolaos Chalmpes Joerg Lahann Richard A Vaia André Farias de Moura Dhriti Nepal Nicholas A Kotov Department of Chemical Engineering University of Michigan Ann Arbor MI USA. Biointerfaces Institute University of Michigan Ann Arbor MI USA. Center for Complex Particle Systems (COMPASS) University of Michigan Ann Arbor MI USA. Brazilian Biorenewables National Laboratory Brazilian Center for Research in Energy and Materials Campinas Brazil. Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson AFB Dayton OH USA. UES Inc. Dayton OH USA. ARCTOS Technology Solutions Beavercreek OH USA. Department of Materials Science and Engineering Cornell University Ithaca NY USA. Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson AFB Dayton OH USA. richard.vaia@us.af.mil. Department of Chemistry Federal University of São Carlos São Carlos Brazil. moura@ufscar.br. Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson AFB Dayton OH USA. dhriti.nepal.1@afrl.af.mil. Department of Chemical Engineering University of Michigan Ann Arbor MI USA. kotov@umich.edu. Biointerfaces Institute University of Michigan Ann Arbor MI USA. kotov@umich.edu. Center for Complex Particle Systems (COMPASS) University of Michigan Ann Arbor MI USA. kotov@umich.edu. Department of Materials Science and Engineering University of Michigan Ann Arbor MI USA. kotov@umich.edu.

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Erratum: Stochastic spin flips in polariton condensates: nonlinear tuning from GHz to sub-Hz (2018 New J. Phys. 20 075008)

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New Journal of Physics 2018年第8期20卷

作者： Yago del Valle-Inclan Redondo Hamid Ohadi Yuri G Rubo Orr Beer Andrew J Ramsay Symeon I Tsintzos Zacharias Hatzopoulos Pavlos G Savvidis Jeremy J Baumberg NanoPhotonics Centre Department of Physics Cavendish Laboratory University of Cambridge Cambridge CB3 0HE United Kingdom Author to whom any correspondence should be addressed. SUPA School of Physics and Astronomy University of St Andrews St Andrews KY16 9SS United Kingdom Instituto de Energías Renovables Universidad Nacional Autónoma de México Temixco Morelos 62580 Mexico Center for Theoretical Physics of Complex Systems Institute for Basic Science (IBS) Daejeon 34051 Republic of Korea Hitachi Cambridge Laboratory Hitachi Europe Ltd Cambridge CB3 0HE United Kingdom Microelectronics Research Group IESL-FORTH Institute of Electronic Structure and Laser 71110 Heraklion Crete Greece Department of Materials Science and Technology University of Crete 71110 Heraklion Crete Greece SOLAB St. Petersburg State University 198504 St. Petersburg Russia

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PARALLEL AND DISTRIBUTED TLM COMPUTATION WITH SIGNAL-PROCESSING FOR ELECTROMAGNETIC-FIELD MODELING

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 1995年第3-4期8卷 169-185页

作者： SO, PPM ESWARAPPA, C HOEFER, WJR NSERC/MPR Teltech Research Chair in RF Engineering Department of Electrical and Computer Engineering University of Victoria Victoria British Columbia V8W 3P6 Canada Poman So received his B.Sc. degree in computer science and physics from the University of Toronto Toronto Onatario Canada in 1985. He obtained his B.A. Sc. and M.A.Sc. degrees in electrical engineering (summa cum laude) from the University of Ottawa Ottawa Ontario Canada in 1987 and 1989 respectively. Mr So was a research engineer in the University of Ottawa from January 1989 to October 1991 his research interests included CAD techniques of microwave circuits and numerical methods for electromagnetic wave modelling. He specialized in the development of electromagnetic engineering CAD software and successfully implemented a number of electromagnetic wave simulators based on the two-dimensional and three-dimensional transmission-line matrix methods. From August 1990 to February 1991 he accompanied Professor W. J. R. Hoefer to Rome Italy and Sophia Antiopolas France. During that time he implemented a parallel version of the 3D-TLM simulator for the CM-2 Connection Machine using C-Star and X Windows Library. He is a research engineer and a Ph.D. student at the University of Victoria Victoria British Columbia Canada. He ported the 2D- and 3D-TLM simulators to DECmpp 1200 massively parallel computer using MPL C and C++. He also combined OSA90/hope a commercially available microwave CAD program with a 2D-TLM simulation module for geometry optimization using the Datapipe technique of OSA90/hope. Recently he has developed a distributed client-server computing technique for the TLM method this technique could accelerate the TLM simulation by more than an order of magnitude. Channabasappa Eswarappa received the M.Tech. degree in electrical engineering from the Indian Institute of Technology Kanpur India in 1983 and Ph. D. degree from the University of Ottawa Canada in 1990. He worked as an Assistant Executive Engineer and later as

This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributed computing in the UNIX environment, and the combination of TLM analysis with Prony's method as well as with autoregressive moving average (ARMA) digital signal processing for electromagnetic field modelling. By combining these advanced computation techniques, typical electromagnetic field modelling of microwave structures by TLM analysis can be accelerated by a few orders of magnitude.

关键词： Mathematical models

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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

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Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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What it takes to solve the Origin(s) of Life: An integrated review of techniques

arXiv

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arXiv 2023年

作者： Asche, Silke Bautista, Carla Boulesteix, David Champagne-Ruel, Alexandre Mathis, Cole Markovitch, Omer Peng, Zhen Adams, Alyssa Dass, Avinash Vicholous Buch, Arnaud Camprubi, Eloi Colizzi, Enrico Sandro Dromiack, Hannah Erastova, Valentina Garcia, Amanda Grimaud, Ghjuvan Halpern, Aaron Harrison, Stuart A. Jordan, Seán F. Jia, Tony Z. Kahana, Amit Kolchinsky, Artemy Moron-Garcia, Odin Mizuuchi, Ryo Nan, Jingbo Orlova, Yuliia Pearce, Ben K.D. Paschek, Klaus Preiner, Martina Pinna, Silvana Rodríguez-Román, Eduardo Schwander, Loraine Sharma, Siddhant Smith, Harrison B. Vieira, Andrey Xavier, Joana C. School of Chemistry University of Glasgow Glasgow United Kingdom Université Laval QC Canada Département de Biologie Faculté des Sciences et de Génie Université Laval QC Canada Université Laval QC Canada Laboratoire Génie des Procédés et Matériaux CentraleSupélec Gif-sur-Yvette France Université de Montréal Montréal Canada Beyond Center for Fundamental Concepts in Science Arizona State University TempeAZ United States Blue Marble Space Institute of Science SeattleWA United States Department of Bacteriology University of Wisconsin-Madison MadisonWI United States Department of Geoscience University of Wisconsin-Madison MadisonWI United States Cross Labs Kyoto Japan Origins Institute Department of Physics and Astronomy McMaster University Hamilton Canada School of Integrated Biological and Chemical Sciences University of Texas Rio Grande Valley EdinburgTX United States Sainsbury Laboratory University of Cambridge United Kingdom Department of Physics Arizona State University TempeAZ United States School of Chemistry University of Edinburgh Joseph Black Building Edinburgh United Kingdom APC Microbiome Ireland University College Cork Co. Cork Ireland Food Biosciences Department Teagasc Food Research Centre Moorepark Co. Cork Fermoy Ireland Department of Genetics Evolution and Environment University College London London United Kingdom Department of Life Sciences Atlantic Technological University ATU Sligo Sligo Ireland Earth-Life Science Institute Tokyo Institute of Technology Meguro-ku Tokyo Japan ICREA-Complex Systems Lab Universitat Pompeu Fabra Barcelona Spain Almería Spain Department of Electrical Engineering and Bioscience Faculty of Science and Engineering Waseda University Shinjuku Tokyo Japan Southern University of Science and Technology Department of Ocean Science and Engineering Shenzhen China University of Amsterdam Swammerdam Institute of Life Sciences Amsterdam Netherlands Johns Hopkins University BaltimoreMD Uni

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly - from analytical chemistry to mathematical models - and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem. Copyright © 2023, The Authors. All rights reserved.

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Meeting abstracts from the first European Emergency Medical Services congress (EMS2016) Abstracts

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SCANDINAVIAN JOURNAL OF TRAUMA RESUSCITATION & EMERGENCY MEDICINE 2017年第SUPPL 1期25卷 16-16页

作者： [Anonymous] Health Services Research Division of Research Singapore General Hospital Singapore Singapore London School of Medicine London UK Unit for Pre-hospital Emergency Care - Clinical Support Team Singapore General Hospital Singapore Singapore Centre for Quantitative Medicine Duke-NUS Graduate Medical School Singapore Singapore Department of Emergency Medicine KK Women’s and Children’s Hospital Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Health Services and Systems Research Duke-NUS Graduate Medical School Singapore Singapore Prehospital Center Region Zeeland Denmark Slagelse Hospital Slagelse Denmark Karolinska Institutet Department of Medicine Solna Center for Resuscitation Science Stockholm Sweden The Royal institute of technology (KTH) School of Architecture and the Built Environment Department of Urban Planning and Environment Division of geoinformatics Stockholm Sweden Karolinska Institutet Department of Clinical Science Danderyd University Hospital Stockholm Sweden Emergency Medical Services Copenhagen University of Copenhagen Copenhagen Denmark Unit of Quality and Patient Safety Copenhagen Denmark Falck Emergency Copenhagen Denmark National Institute of Public Health University of Southern Denmark Copenhagen Denmark Department of Anaesthesiology and Intensive Care Hvidovre Hospital University of Copenhagen Copenhagen Denmark Department of Anaesthesia Centre of Head and Orthopaedics Rigshospitalet University of Copenhagen Copenhagen Denmark The Institute of Health Science and Technology Aalborg University Aalborg Denmark Copenhagen Academy for Medical Education and Simulation Copenhagen Denmark Department of Emergency Medicine Odense University Hospital Odense Denmark Department of Emergency Medicine Hospital of South West Jutland Esbjerg Denmark SATS Students’ Society for Anesthesiology & Traumatology Copenhagen Denmark Copenhagen Academy for Medical Education and Simulation Herlev D

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Improving the Selectivity and Stability of Supported Cobalt Catalysts via Static Bi-Doping and Dynamic Trace CO2Co-Feeding During Propane Dehydrogenation

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Angewandte Chemie 2024年第3期137卷

作者： Yongbin Yao Jingnan Wang Qiang Liu Can Yu Zhan Gao Fangli Yuan Prof. Xi Wang Department of Physics School of Physical Science and Engineering Beijing Jiaotong University Beijing 100044 P. R. China Tangshan Research Institute of Beijing Jiaotong University Tangshan City Hebei Beijing 100044 P. R. China Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou 350108 P. R. China School of Chemical Engineering and Technology Tianjin University Tianjin 300072 P. R. China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing 100049 China State Key Laboratory of Multi-phase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 PR China

Simultaneously enhancing selectivity and stability on supported propane dehydrogenation (PDH) catalysts remains a formidable challenge. Here, we report a combined static and dynamic strategy to address these issues synergistically. Firstly, we demonstrate a feasible sol-gel method for preparing atomically-dispersed Bi-decorated metal nanoparticle catalysts (MBi/Al 2 O 3 , M=Fe, Co, Ni, and Zn). In PDH testing, the total selectivity of by-products (CH 4 and C 2 H 6 ) significantly decreases to 4 % for CoBi catalysts due to the static Bi-doping, compared with 16 % for Co-supported catalysts. Secondly, to enhance catalytic stability, we introduce a dynamic trace CO 2 co-feeding route. 10CoBi/Al 2 O 3 catalysts exhibit superior durability against coke formation for 330 hours in PDH under a 40 % C 3 H 8 atmosphere followed by pure C 3 H 8 conditions at 600 °C while maintaining propylene selectivity at 96 %. Notably, introducing trace CO 2 leads to a remarkable 6-fold decrease in the deactivation rate constant ( k d ). Multiple characterizations and density functional theory calculations reveal that charge transfer from atomically-distributed Bi to Co nanoparticles benefits lowering the energy of C 3 H 6 adsorption thereby suppressing by-products. Furthermore, the dynamic co-feeding of trace CO 2 facilitates coke removal, suppressing catalyst deactivation. The static Bi-doping and dynamic trace CO 2 co-feeding strategy contributes simultaneously to increased selectivity and stability on supported PDH catalysts.

关键词： Static Bi-doping Dynamic trace CO2 co-feeding Cobalt Heterogeneous catalysis Propane dehydrogenation

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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