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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Building a quantum engineering undergraduate program

arXiv

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arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： engineering education

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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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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computer AIDS FOR SHIP DESIGN, INTEGRATION AND CONTROL

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 73-87页

作者： CARLSON, CM JOHNSON, RA HELMING, FW Mr. Craig M. Carlson received his B.S. degree in Naval Architecture and Marine Engineering from the University of Michigan in 1970 and began his career with the Department of the Navy at the Naval Ship Engineering Center (NAVSEC). In 1972. he returned to the University of Michigan under the NAVSEC Long Term Training Program and received his M.S. degree in Naval Architecture and Marine Engineering. After returning to the Ship Arrangements Branch at NAVSEC. he was assigned as Task Leader for General Arrangements for the PGG PCG PHM. and MCM ship designs and was awarded Outstanding Performance Awards in 1974 and 1975. In addition he was Manager of the Arrangement Subsystem of the Navy's Computer-Aided Ship Design and Construction Program (CASDAC). In October 1979. he became Manager of the CASDAC Hull Design System. Currently. he also is enrolled in the M.S. of Computer Science Program at Johns Hopkins University. Mr. Carlson previously has presented technical papers at ASNE Day 1974 and 1978 as well as at the 1979 DOD Manufacturing Technology Advisory Group Conference. Besides ASNE. which he joined in 1972. he is a member of SNAME. ASE. and the U.S. Naval Institute. Mr. Robert A. Johnson is a Naval Architect in Surface Combatants Design (SEA 03D3). Ship Design Integration Directorate Naval Sea Systems Command. He received an Associate in Engineering degree in Drafting and Design Technology in 1959. his B.S. degree in Aerospace Engineering in 1965. and his M.S. degree in Engineering Mechanics in 1970. all from the Pennsylvania State University. In 1973. he was selected for the NA VSEC Hull Division s Long Term Training Program at the University of Michigan subsequently receiving his M.S.E. degree in Naval Architecture in 1974. Mr. Johnson began his career with the Ordnance Research Laboratory at Pennsylvania State University in 1959 where he worked. on the design of hydroelastic submarine models and conducted research in the area of flow induced structural vibrations. In 1967 he joined HRB-Singer at State Colle

This paper presents an integrated approach to computer-Aided Ship Design for U.S. Navy preliminary and contract design. An integrated Hull Design System (HDS), currently under development by the Hull Group of the Naval Sea systems Command (NAVSEA 32). is the vehicle for the discussion. This paper is directed toward practicing ship design professionals and the managers of the ship design process. Primary emphasis of this paper, and of the development effort currently under way, is on aiding ship design professionals in their work. Focus is on integration and management control of the extremely complex set of processes which make up naval ship design. The terminology of the Ship Designer and Design Manager is used. The reader needs no familiarity with the technologies of computer science.

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Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in Bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data

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Microbiology spectrum 2023年第3期11卷 e0347922页

作者： Chia-Ru Chung Hsin-Yao Wang Chun-Han Yao Li-Ching Wu Jang-Jih Lu Jorng-Tzong Horng Tzong-Yi Lee Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan. Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung University Taoyuan Taiwan. Department of Biomedical Sciences and Engineering National Central University Taoyuan Taiwan. College of Medicine Chang Gung University Taoyuan Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan Taiwan. Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan. Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan.

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including a

关键词： MALDI-TOF MS antimicrobial resistance cephalosporin cephalosporins fluoroquinolones machine learning matrix-assisted laser desorption ionization–time of flight mass spectrometry penicillin penicillins

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FUEL-CELL POWER-PLANTS FOR SURFACE FLEET APPLICATIONS

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NAVAL ENGINEERS JOURNAL 1994年第3期106卷 59-76页

作者： GOUBAULT, P GREENBERG, M HEIDENREICH, T WOERNER, J Philippe Goubault:graduated in 1983 from the “Ecole Nationale Superieure de Techniques Avancees” in Paris with a major in naval architecture. After one year of military service with the French navy he worked as naval architect and program director for the French navy between 1984 and 1988. He was in charge of the development of AGNES200 Surface Effect Ship design which completed its sea trials in 1992. He also was responsible for the construction of five ships (four hydrographic vessels and one experimental MCM vessel) which entered service between 1988 and 1991. He has been involved in a number of projects and studies for the U.S. Navy U.S. Coast Guard and other foreign and domestic customers. At Band Lavis & Associates Inc. Mr. Goubault has expanded the computer tools used to conduct parametric analysis of advanced hullforms and has developed cost-effectiveness assessment tools and methodologies for both commercial and military ships. Mr. Goubault is a member of ASNE. Marc Greenberg:is employed as a cost analyst at the cost and economic analysis branch systems assessment and engineering division Naval Surface Warfare Center. He provides cost estimates and analyses of Navy ship and submarine technologies and has assisted in the development of parametric cost models since 1991. Employed as an electronics engineer by the U.S. Army Information Systems Command from 1989 to 1991 he provided support in simulation design and construction of high frequency and microwave communication systems. Mr. Greenberg received his BS degree in ceramic science and engineering from the Pennsylvania State University May 1987. He is a member of MORS. Todd Heidenreich:is employed in the design analysis and tools branch systems assessment and engineering division of the Carde-rock Division Naval Surface Warfare Center. He is involved as a project naval architect in the conceptual design of future surface ship designs future technology impact assessments and the assessment of current domestic and foreign surface ship desig

This paper describes the results of a study undertaken to determine the impact of fuel cell technology on the design and effectiveness of future naval surface combatants. The study involved the collection of data to characterize four different fuel cell technologies: proton exchange membrane, molten carbonate, phosphoric acid, and solid oxide fuel cells. This information was used to expand current computer models to develop specific fuel cell plants that met the power requirements for several applications on a nominal 5000 Lton destroyer and a nominal 2000 Lton corvette. Each of the fuel cell technologies was incorporated into several applications aboard the destroyer and the corvette. These applications included combinations of centralized and distributed ship service power, and propulsion power. In addition, the impact of fuel cell technology was determined for a ship service power backfit option aboard a DDG-51 class destroyer. The results of the impact on the ship designs were analyzed and a military effectiveness assessment was conducted to address such issues as the impact of fuel cells on mobility, survivability, affordability, and on the environment. The paper identifies which aspects of the fuel cell technologies have the greatest impact upon the ship designs and their operational costs. Recommendations are given for future technology development efforts required to make fuel cells suitable for Navy service.

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