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Congress on Evolutionary Computation

作者： Rodrigo Lisbôa Pereira Marco A. Florenzano Mollinetti Mário Tasso Ribeiro Serra Neto Adilson de Almeida Neto Daniel Leal Souza Edson Koiti Yasojima Otávio Noura Teixeira Roberto Célio Limão de Oliveira Federal Rural University of Amazon (UFRA) Federal University of Pará (UFPA) Pará Brazil Systems Optimization Laboratory Tsukuba University Tsukuba Ibaraki Japan University Centre of the State of Pará (CESUPA) Belém Pará Brazil Electrical Engineering Post Graduation Program Federal University of Pará (UFPA) Belém Pará Brazil Electrical Engineering Post Graduation Program Federal University of Pará (UFPA) Pará Brazil Computer Engineering Post Graduation Program Federal University of Pará (UFPA) Pará Brazil Laboratory of Bionspired Computing (LCBIO) Federal University of Pará (UFPA) Belém Pará Brazil

This article presents a parameter study on the applied game theory in Genetic Algorithm (GA), performing an analysis of the game Prisoner's Dilemma applied in the solution of four constrained engineering problems. Simulations were applied in four different variations of the game, in order to find the best configuration for the analyzed problems. Then, after obtaining the best configuration of the game, we performed an analysis of the incidence of Alpha and Beta weights, present in GA fitness with Social Interaction, using the game that best suited each problem, employing sixteen different weight configurations. At end, the results obtained authenticated the influence of the Social Interaction process in the essence of GA.

关键词： Genetic algorithms Games Game theory Optimization Biological cells Sociology Statistics

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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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Global priority areas for ecosystem restoration (vol 586, pg 724, 2022)

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NATURE 2022年第7926期609卷 E7-E7页

作者： Strassburg, Bernardo B. N. Iribarrem, Alvaro Beyer, Hawthorne L. Cordeiro, Carlos Leandro Crouzeilles, Renato Jakovac, Catarina C. Braga Junqueira, Andre Lacerda, Eduardo Latawiec, Agnieszka E. Balmford, Andrew Brooks, Thomas M. Butchart, Stuart H. M. Chazdon, Robin L. Erb, Karl-Heinz Brancalion, Pedro Buchanan, Graeme Cooper, David Diaz, Sandra Donald, Paul F. Kapos, Valerie Leclere, David Miles, Lera Obersteiner, Michael Plutzar, Christoph de M. Scaramuzza, Carlos Alberto Scarano, Fabio R. Visconti, Piero Rio Conservation and Sustainability Science Centre Department of Geography and the Environment Pontifical Catholic University Rio de Janeiro Brazil International Institute for Sustainability Rio de Janeiro Brazil Programa de Pós Graduacão em Ecologia Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Botanical Garden Research Institute of Rio de Janeiro Rio de Janeiro Brazil Forest Ecology and Management Group Wageningen University Wageningen The Netherlands Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Barcelona Spain Department of Geography Fluminense Federal University Niterói Brazil Department of Production Engineering Logistics and Applied Computer Science Faculty of Production and Power Engineering University of Agriculture in Kraków Kraków Poland School of Environmental Sciences University of East Anglia Norwich UK Department of Ecology and Evolutionary Biology University of Connecticut Storrs CT USA World Resources Institute Global Restoration Initiative Washington DC USA Tropical Forests and People Research Centre University of the Sunshine Coast Sunshine Coast Queensland Australia School of Biological Sciences University of Queensland St Lucia Queensland Australia Department of Zoology University of Cambridge Cambridge UK BirdLife International Cambridge UK RSPB Centre for Conservation Science Royal Society for the Protection of Birds Edinburgh UK International Union for Conservation of Nature (IUCN) Gland Switzerland World Agroforestry Center (ICRAF) University of the Philippines Los Baños The Philippines Institute for Marine and Antarctic Studies University of Tasmania Hobart Tasmania Australia Institute of Social Ecology University of Natural Resources and Life Sciences Vienna Vienna Austria Division of Conservation Biology Vegetation Ecology and Landscape Ecology University of Vienna Vienna Austria Department of Forest Sciences ‘Luiz de Queiroz’ College of Agriculture University of São Pau

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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Manifold learning for organizing unstructured sets of process observations

arXiv

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arXiv 2018年

作者： Dietrich, Felix Kooshkbaghi, Mahdi Bollt, Erik M. Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699-5815 United States

Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In this paper, we use manifold learning to organize unstructured ensembles of observations ("trials") of a system's response surface. We have no control over where every trial starts;and during each trial operating conditions are varied by turning "agnostic" knobs, which change system parameters in a systematic but unknown way. As one (or more) knobs "turn" we record (possibly partial) observations of the system response. We demonstrate how such partial and disorganized observation ensembles can be integrated into coherent response surfaces whose dimension and parametrization can be systematically recovered in a data-driven fashion. The approach can be justified through the Whitney and Takens embedding theorems, allowing reconstruction of manifolds/attractors through different types of observations. We demonstrate our approach by organizing unstructured observations of response surfaces, including the reconstruction of a cusp bifurcation surface for Hydrogen combustion in a Continuous Stirred Tank Reactor. Finally, we demonstrate how this observation-based reconstruction naturally leads to informative transport maps between input parameter space and output/state variable *** codes 37M20 Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Designing an Institutional Research Management System Framework for Higher Education

Designing an Institutional Research Management System Framew...

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International Conference on Information Management and Technology (ICIMTech)

作者： Noerlina Siti Elda Hiererra Bahtiar Saleh Abbas Brilly Andro Makalew Desi Maya Kristin Tirta Nugraha Mursitama David Information Systems Department Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Bina Nusantara University Jakarta Indonesia Mobile Application & Technology Program Bina Nusantara University Jakarta Indonesia International Relations Department Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

The following topics are dealt with: Internet; organisational aspects; data analysis; educational institutions; information systems; knowledge management; statistical analysis; further education; computer aided instru... 详细信息

关键词： Manuals Education Proposals Information systems Requirements engineering System analysis and design Conferences

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Predictable and precise template-free CRISPR editing of pathogenic variants (vol 563, pg 646, 2018)

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NATURE 2019年第7746期567卷 E1-E2页

作者： Shen, Max W. Arbab, Mandana Hsu, Jonathan Y. Worstell, Daniel Culbertson, Sannie J. Krabbe, Olga Cassa, Christopher A. Liu, David R. Gifford, David K. Sherwood, Richard I. Computational and Systems Biology Program Massachusetts Institute of Technology Cambridge USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA Merkin Institute of Transformative Technologies in Healthcare Broad Institute of Harvard and MIT Cambridge USA Department of Chemistry and Chemical Biology Harvard University Cambridge USA Howard Hughes Medical Institute Harvard University Cambridge USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge USA Molecular Pathology Unit Center for Cancer Research and Center for Computational and Integrative Biology Massachusetts General Hospital Charlestown USA Division of Genetics Department of Medicine Brigham and Women’s Hospital and Harvard Medical School Boston USA Hubrecht Institute for Developmental Biology and Stem Cell Research Royal Netherlands Academy of Arts and Sciences (KNAW) Utrecht The Netherlands Broad Institute of MIT and Harvard Cambridge USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

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Applying the MVC EA-IRPM to reporting-guidelines in medicine: A strategy that is a web system 4

Applying the MVC EA-IRPM to reporting-guidelines in medicine...

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4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

作者： Letouze, Patrick De Souza, J.I.M. Silva, Valeria Martins Climaco, Glaubos Chagas, F.B. Ishihara, Joao Y. Graduate Program of Computational Modeling of Systems Universidade Federal Do Tocantins Quadra 109 Norte Avenida NS-15 Palmas77001-090 Brazil Graduate Program of Systems' Engineering and Computation - PESC Universidade Federal Do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics at the Universidade Federal Do Maranh/ Ao - UFMA Brazil Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041794

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered;(2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible;(3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller Evolutionary Acquisition Interdisciplinary Research Project Management (MVC EA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines. © 2017 IEEE.

关键词： Clinical research

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A molecular single-cell lung atlas of lethal COVID-19 (vol 595, pg 114, 2021)

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NATURE 2021年第7882期598卷 E2-E2页

作者： Melms, Johannes C. Biermann, Jana Huang, Huachao Wang, Yiping Nair, Ajay Tagore, Somnath Katsyv, Igor Rendeiro, Andre F. Amin, Amit Dipak Schapiro, Denis Frangieh, Chris J. Luoma, Adrienne M. Filliol, Aveline Fang, Yinshan Ravichandran, Hiranmayi Clausi, Mariano G. Alba, George A. Rogava, Meri Chen, Sean W. Ho, Patricia Montoro, Daniel T. Kornberg, Adam E. Han, Arnold S. Bakhoum, Mathieu F. Anandasabapathy, Niroshana Suarez-Farinas, Mayte Bakhoum, Samuel F. Bram, Yaron Borczuk, Alain Guo, Xinzheng V. Lefkowitch, Jay H. Marboe, Charles Lagana, Stephen M. Del Portillo, Armando Tsai, Emily J. Zorn, Emmanuel Markowitz, Glen S. Schwabe, Robert F. Schwartz, Robert E. Elemento, Olivier Saqi, Anjali Hibshoosh, Hanina Que, Jianwen Izar, Benjamin Department of Medicine Division of Hematology/Oncology Columbia University Irving Medical Center New York NY USA Columbia Center for Translational Immunology Columbia University Irving Medical Center New York NY USA Herbert Irving Comprehensive Cancer Center Columbia University Irving Medical Center New York NY USA Program for Mathematical Genomics Columbia University New York NY USA Columbia Center for Human Development Columbia University Irving Medical Center New York NY USA Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA Department of Medicine Columbia University Irving Medical Center New York NY USA Institute of Human Nutrition Columbia University New York NY USA Department of Systems Biology Columbia University Irving Medical Center New York NY USA Department of Pathology and Cell Biology Columbia University Irving Medical Center New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Caryl and Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medical College New York NY USA Department of Dermatology Weill Cornell Medical College New York NY USA Meyer Cancer Center Weill Cornell Medical College New York NY USA Laboratory of Systems Pharmacology Harvard Medical School Boston MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Cancer Immunology and Virology Dana-Farber Cancer Center Boston MA USA Human Immune Monitoring Core Columbia University Irving Medical Center New York NY USA Department of Medicine Division of Pulmonary and Critical Care Massachusetts General Hospital Boston MA USA Cell Circu

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Citizen science and the United Nations Sustainable Development Goals (vol 2, pg 922, 2019)

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NATURE SUSTAINABILITY 2019年第11期2卷 1063-1063页

作者： Fritz, Steffen See, Linda Carlson, Tyler Haklay, Mordechai (Muki) Oliver, Jessie L. Fraisl, Dilek Mondardini, Rosy Brocklehurst, Martin Shanley, Lea A. Schade, Sven Wehn, Uta Abrate, Tommaso Anstee, Janet Arnold, Stephan Billot, Matthew Campbell, Jillian Espey, Jessica Gold, Margaret Hager, Gerid He, Shan Hepburn, Libby Hsu, Angel Long, Deborah Maso, Joan McCallum, Ian Muniafu, Maina Moorthy, Inian Obersteiner, Michael Parker, Alison J. Weisspflug, Maike West, Sarah Ecosystem Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria School of Resource and Environmental Management Simon Fraser University Burnaby Canada University College London London UK School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Australian Citizen Science Association c/o Faculty of Science Partner Engagement and Outreach University of Sydney Sydney Australia University of Natural Resources and Life Sciences Vienna Austria Competence Center Citizen Science ETH Zurich and University of Zurich Zurich Switzerland Citizen Science Global Partnership Washington DC USA European Citizen Science Association Museum für Naturkunde Berlin Berlin Germany Nelson Institute University of Wisconsin at Madison Madison USA European Commission Joint Research Center Ispra Italy IHE Delft Institute for Water Education Delft The Netherlands World Meteorological Organization Geneva Switzerland Commonwealth Scientific and Industrial Research Organisation Canberra Australia Federal Statistical Office – Destatis Wiesbaden Germany UN Environment Programme Nairobi Kenya United Nations Sustainable Development Solutions Network New York USA CitizenScience.Asia Hong Kong China Yale-NUS College Singapore Singapore University of Dundee Dundee UK Scottish Environment LINK Perth UK Grumets Research Group CREAF Universitat Autònoma de Barcelona Barcelona Spain African Citizen Science Association Nairobi Kenya Woodrow Wilson Center Washington DC USA Museum für Naturkunde Berlin Berlin Germany Stockholm Environment Institute University of York York UK

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

关键词： Planning

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