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Out on the Land: Income, Subsistence Activities, and Food Sharing Networks in Nain, Labrador

Journal of Anthropology 2013年第1期2013卷

作者： Kirk Dombrowski Emily Channell Bilal Khan Joshua Moses Evan Misshula Department of Anthropology John Jay College City University of New York 524 West 59th Street New York NY 10019 USAcuny.edu Doctoral Program in Anthropology CUNY Graduate Center 365 5th Avenue New York NY 10016 USAcuny.edu Department of Mathematics and Computer Science John Jay College City University of New York 524 West 59th Street New York NY 10019 USAcuny.edu Culture and Mental Health Research Unit 4333 Chemin de la Cote Ste-Catherine Montreal QC Canada H3T 1E4 Doctoral Program in Criminal Justice CUNY Graduate Center 365 5th Avenue New York NY 10016 USAcuny.edu

In recent Inuit ethnography, a major concern has been how and to what extent contemporary Inuit participate in and depend on subsistence activities, particularly in the context of increasing wage employment and growing participation in the cash economy. This paper provides an analysis of these activities in the predominately Inuit community of Nain, Labrador. Using social network data and demographic information collected between January and June 2010, we examine the interconnections between subsistence activities—obtaining “country food” through activities such as hunting, fishing, and collecting—with access to the means of obtaining subsistence resources (such as snow mobiles, cabins, and boats), employment status, and income. Our data indicate that individuals with higher employment status and income tend to be more central to the network of subsistence food sharing, but not because they have greater access to hunting tools or equipment (they do not). We conclude that those individuals who play the most central role in the network are those who are financially able to do so, regardless of access to hunting tools/means.

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MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

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Bioinformatics Advances 2023年第1期3卷

作者： Tsai, Dong-Ming Chang, Ching-Yao Lin, Shih-Ming Kuo, Tien-Chueh Wang, San-Yuan Chen, Guan-Yuan Kuo, Ching-Hua Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106216 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei 100225 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 106216 Taiwan Master Program for Clinical Pharmacogenomics and Pharmacoproteomics School of Pharmacy Taipei Medical University Taipei 110301 Taiwan Forensic Medicine College of Medicine National Taiwan University Taipei 100225 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei 100225 Taiwan

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AN EVALUATION OF THE SEAKEEPING QUALITIES OF NAVAL COMBATANTS

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Naval Engineers Journal 1978年第1期90卷 23-40页

作者： OLSON, STEPHEN R. USN The Author received his BS degree in Naval Science upon graduation from the U.S. Naval Academy in 1967 and his MS degree in Operations Research from the U. S. Naval Postgraduate School Monterey Calij. in 1975. He is a qualified Suflace Warfare Oflcer and has sewed as the Electronics Maintenance Ofleer and CIC Oflcer in the USS Shelton (DO-790) and as Operations Officer in the USS John R. Craig (DD-885). He was a member of the Precommissioning Crew and Chief Engineer in the USS Flint (AE-32) from commissioning through her maiden deployment to the Western Pacific. While attending Postgraduate School he spent jbur months with the CVN Program Manager's Office (PMS-392) and the Patrol Frigate Pmgram Manager's Office (PMS-399). His graduate thesis was entitled “A Computer Model to Assess Financing Provisions of Naval FPIF Shipbuilding Contracts.” The seakeeping analysis reported hereaper was undertaken while the Author was assigned as a Systems Analyst at the Center jbr Naval Analyses. Currently he is assigned as a Special Project OfJicer jbr the Naval Personnel Program Support Activity. LCdr. Olson's military decorations include the Navy Commendation Medal and the Navy Achievement Medal with 2 Stars in lieu of second award.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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SHIPBOARD MAIN BOILER AND FEED PUMP CONTROL-SYSTEM ONLINE ALIGNMENT VERIFICATION

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NAVAL ENGINEERS JOURNAL 1982年第6期94卷 39-46页

作者： BANHAM, JW ADAM, DJ James W. Banham:holds positions both as Director of the Machinery Automation Systems Department of the Naval Ship Systems Engineering Station and as Assistant Chairman of the Mechanical and Industrial Engineering Department of Drexel University's Evening College where he holds the rank of Adjunct Associate Professor. He is the author of a text on Numerical Methods Applications in Engineering. In addition to numerous technical papers he is also the author of the ISA film on Boiler Feedwater Control Systems. A registered professional engineer in the state of Pennsylvania Mr. Banham is co-author of a forthcoming handbook on preparing for the Professional Engineering Examination in mechanical engineering. Mr. Banham holds a bachelor of science degree in mechanical engineering from the Pennsylvania State University his graduate studies were taken at the University of Pennsylvania. He is currently a member of the American Society for Engineering Education and the Instrument Society of America. He served as a member of the ISA Education Committee from 1964 through 1973 and as a member of the ISA Power Plant Dynamics Committee since 1969. His service on the latter committee includes terms as Executive Secretary Vice-Chairman and Chairman. Among other honors Mr. Banham was the winner of the Naval Ship Engineering Center's first Technical Achievement Award (1963) Technical Publication Award (1974) and Equal Employment Opportunity Award (1978). He was also the recipient of Drexel University's Laura S. Campbell Award for Excellence in Teaching (1978). He has taught undergraduate courses in classical control theory numerical methods computer programming systems design and analysis and instrumentation. He also teaches systems theory and computer science in an EIT Review and heat transfer in a PE Review conducted by the Drexel University Department of Continuing Professional Education. Mr. David J. Adam:is a Project Engineer in the Naval Sea Systems Command (PMS301) Steam Propulsion Plant Improvement Program where he i

One of the most serious problems encountered in Naval steam plants following World War II was the unreliable performance of boiler and main feedpump pneumatic control systems. In addition to control component and system design deficiencies, these control systems suffered from inadequate methods to measure and adjust system alignment. This paper describes the development of a set of procedures for on-line alignment verification (OLV) of pneumatic main boiler and feedpump control systems. The procedures are designed for use by N avy control system technicians and, in addition to on-line alignment verification, provide guidance for troubleshooting and for performing system alignment. Procedure static checks measure steady state steaming performance and OLV procedure dynamic checks measure the ability of the boiler and control systems to respond to load changes. The paper describes typical control system characteristics that influence OLV procedure content and the supporting analysis that was used to establish alignment criteria ranges that satisfy both steady state and transient performance requirements. Also described is the alignment criteria tolerance analysis along with the steps involved in a typical OLV check procedure development. Descriptions of the various OLV checks, troubleshooting procedures and alignment procedures are provided. Typical shipboard implementation requirements are described and experience to date with the procedures is provided along with a status report on OLV procedure implementations.

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HFLTS-TOPSIS With Pseudo-distance in Determining The Best Lecturers

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： Dahlan Abdullah H Hartono Kundharu Saddhono Nuning Kurniasih Akbar Iskandar Dian Rianita Purwarno Rianti Setyawasih Erwinsyah Satria Tato Nuryanto Lilik Herawati Sujinah I Ketut Sudarsana Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Faculty of Literature Universitas Islam Sumatera Utara Medan Indonesia Department of Management Faculty of Economic Universitas Islam 45 Bekasi Indonesia Faculty of Teacher Training and Education Universitas Bung Hatta Padang Indonesia FITK IAIN Syekh Nurjati Cirebon Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Cirebon Indonesia Faculty of Education University Muhammadiyah of Surabaya Surabaya Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

Decision making is sometimes faced with a situation where assessment is given in the form of linguistic variables, which have a certain range of values so that decision making cannot be given in the form of numerical values. One approach commonly used is Hesitant Fuzzy Linguistic Term Sets (HFLTS) which is a multi-criteria and multi-alternative decision making method. In TOPSIS which has the concept of distance measurement, basically the chosen one is the best alternative is an alternative that has the closest distance to a positive solution and the farthest distance with a negative solution. The incorporation of HFLTS-TOPSIS has the advantage that decision making can be done in the form of variable linguistics and also a situation where there are benefit criteria and cost criteria. This study will use the HFLTS-TOPSIS approach that has been modified in the distance calculation section so that distance calculation is based on the difference of preference degree between the two HFLTS. The results of research conducted by a number of researchers show that using pseudo-distance will provide advantages compared to the standard HLFTS-TOPSIS. Pseudo-distance HFLTS-TOPSIS will be applied in the selection of the best lecturers. The criteria used in this study are: Student Satisfaction, Research, Number of late, and number of complaints from students.

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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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MOOCs – international information and education phenomenon?

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Bulletin of the Association for Information science and Technology 2014年第5期40卷

作者： Lee Wilson Anatoliy Gruzd Graduate Student School of Information Management in the master of library and information studies program at Dalhousie University. He is currently working as a research assistant at the Dalhousie Social Media Lab working on a SSHRC funded Insight grant project entitled “Learning Analytics for the Social Media Age” Associate Professor School of Information Management director of the Social Media Lab at the Faculty of Management and cross-appointed to the Faculty of Computer Science at Dalhousie University Canada. He is also a co-editor of a new multidisciplinary journal on Big Data and Society to be published by Sage and a member of the Institute for Big Data Analytics at Dalhousie University

EDITOR'S SUMMARY Since the 1990s massive open online courses (MOOCs) have offered web-based learning on a large scale and with open access. The leading MOOC providers in 2014 – Udemy, Coursera and edX – vary in detail but share the goal of facilitating learning for unlimited audiences at no cost or minimal charge, overcoming socioeconomic hurdles and opening education to all. The potential is strong, and data shows promising registration figures from India and economically developing countries. Yet MOOCs fall short of their goal of widespread and readily accessed education, impeded by technology challenges, lack of basic education and predominance of English as the language of instruction. Maintaining a high standard of educational quality is challenging, and attrition rates are very high. Those in library and information science can facilitate learning through MOOCs and also benefit by using the platform to build awareness of the professional field.

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