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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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Correction to: Analysis of the Regularization Parameters of Primal–Dual Interior Method for Convex Objectives Applied to 1H Low Field Nuclear Magnetic Resonance Data Processing

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Applied Magnetic Resonance 2018年第1期50卷 521-521页

作者： Campisi-Pinto, Salvatore Levi, Ofer Benson, Diamanta Cohen, Meir Resende, Maysa Teixeira Saunders, Michael Linder, Charles Wiesman, Zeev Phyto-Lipid Biotechnology Laboratory Department of Biotechnology Engineering Faculty of Engineering Sciences Ben-Gurion University of the Negev Beer-Sheva Israel Department of Mathematics and Computer Science Program of Industrial Engineering and Management The Open University Ra’anana Israel Department of Management Science and Engineering (MS&E) Stanford University Stanford USA

In the original publication, the acknowledgment section was not published and it is given below now.

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Micro-Nano Fabrication: Fabrication of Sub-Micrometer-Sized MoS2Thin-Film Transistor by Phase Mode AFM Lithography (Small 49/2018)

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Small 2018年第49期14卷

作者： Lianqing Liu Jialin Shi Meng Li Peng Yu Tie Yang Guangyong Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China University of Chinese Academy of Science Beijing 100049 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15213 USA

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HOPE: Enabling efficient service orchestration in software-defined data centers 16

HOPE: Enabling efficient service orchestration in software-d...

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30th International Conference on Supercomputing, ICS 2016

作者： Hu, Yang Li, Chao Liu, Longjun Li, Tao Department of Electrical and Computer Engineering University of Florida United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781450343619

The functional scope of today's software-defined data centers (SDDC) has expanded to such an extent that servers face a growing amount of critical background operational tasks like load monitoring, logging, migration, and duplication, etc. These ancillary operations, which we refer to as management operations, often nibble the stringent data center power envelope and exert a tremendous amount of pressure on front-end user tasks. However, existing power capping, peak shaving, and time shifting mechanisms mainly focus on managing data center power demand at the "macro level" - they do not distinguish ancillary background services from user tasks, and therefore often incur significant performance degradation and energy overhead. In this study we explore "micro-level" power management in SDDC: tuning a specific set of critical loads for the sake of overall system efficiency and performance. Specifically, we look at management operations that can often lead to resource contention and energy overhead in an IaaS SDDC. We assess the feasibility of this new power management paradigm by characterizing the resource and power impact of various management operations. We propose HOPE, a new system optimization framework for eliminating the potential efficiency bottleneck caused by the management operations in the SDDC. HOPE is implemented on a customized OpenStack cloud environment with heavily instrumented power infrastructure. We thoroughly validate HOPE models and optimization efficacy under various user workload scenarios. Our deployment experiences show that the proposed technique allows SDDC to reduce energy consumption by 19%, reduce management operation execution time by 25.4%, and in the meantime improve workload performance by 30%. © 2016 ACM.

关键词： Power management

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Organ-on-a-Chip: Ex vivo Dynamics of Human Glioblastoma Cells in a Microvasculature-on-a-Chip System Correlates with Tumor Heterogeneity and Subtypes (Adv. Sci. 8/2019)

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Advanced science 2019年第8期6卷 1970046-1970046页

作者： Yang Xiao Dongjoo Kim Burak Dura Kerou Zhang Runchen Yan Huamin Li Edward Han Joshua Ip Pan Zou Jun Liu Ann Tai Chen Alexander O. Vortmeyer Jiangbing Zhou Rong Fan Department of Biomedical Engineering Yale University New Haven CT 06520 USA School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 USA Applied Math Program Yale University New Haven CT 06520 USA Department of Neurosurgery Yale School of Medicine New Haven CT 06520 USA Department of Pathology Indiana University Health Pathology Laboratory Indianapolis IN 46202 USA Yale Comprehensive Cancer Center New Haven CT 06520 USA

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data

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Journal of Physics: Conference Series 2019年第1期1361卷

作者： Dahlan Abdullah H Hartono Erianto Ongko Cut Ita Erliana Defi Irwansyah Akbar Iskandar Ayu Esteka Sari Abdussakir Yuniningsih Nurintan Asyiah Siregar Muhammad Syahrul Kahar Raditya Faisal Waliulu Ainna Amalia Nuning Kurniasih Muhammad Arifin Syafiul Muzid Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang MalangIndonesia Department of Economic and Business Universitas Pembangunan Nasional 'Veteran' Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting.

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Verbalization: Narration of autonomous robot experience 25

Verbalization: Narration of autonomous robot experience

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Rosenthal, Stephanie Selvaraj, Sai P. Veloso, Manuela Software Engineering Institute United States Robotics Institute United States Computer Science Department Carnegie Mellon University Pittsburgh United States

Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.

关键词： Mobile robots

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Author Correction: RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

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Nature communications 2021年第1期12卷 3128页

作者： Alessandro Bonetti Federico Agostini Ana Maria Suzuki Kosuke Hashimoto Giovanni Pascarella Juliette Gimenez Leonie Roos Alex J Nash Marco Ghilotti Christopher J F Cameron Matthew Valentine Yulia A Medvedeva Shuhei Noguchi Eneritz Agirre Kaori Kashi Joachim Luginbühl Riccardo Cazzoli Saumya Agrawal Nicholas M Luscombe Mathieu Blanchette Takeya Kasukawa Michiel de Hoon Erik Arner Boris Lenhard Charles Plessy Gonçalo Castelo-Branco Valerio Orlando Piero Carninci RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. alessandro.bonetti@riken.jp. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. alessandro.bonetti@riken.jp. The Francis Crick Institute 1 Midland Road London NW1 1AT UK. RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. Department of Medicine (H7) Karolinska Institutet Stockholm 141 86 Sweden. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. Faculty of Medicine Imperial College London Institute of Clinical Sciences London W12 0NN UK. MRC London Institute of Medical Sciences London W12 0NN UK. School of Computer Science McGill University Montréal QC Canada. Department of Biochemistry and Goodman Cancer Research Centre McGill University Montréal QC Canada. Institute of Bioengineering Research Centre of Biotechnology Russian Academy of Science 117312 Moscow Russia. Department of Computational Biology Vavilov Institute of General Genetics Russian Academy of Science 119991 Moscow Russia. Department of Biological and Medical Physics Moscow Institute of Physics and Technology 141701 Dolgoprudny Moscow Region Russia. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy. UCL Genetics Institute University College London London WC1E 6BT UK. Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha Onna-son Kunigami-gun Okinawa 904-0495 Japan. Sars International Centre for Marine Molecular Biology University of Bergen 5008 Bergen Norway. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. valerio.orlando@kaust.edu.sa. KAUST Environmental Epigenetics Program King Abdullah University of Science and Technology (KAUS

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Ambient-pressure CVD of graphene on low-index Ni surfaces using methane: A combined experimental and first-principles study

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Physical Review Materials 2018年第7期2卷 073404-073404页

作者： Daniela L. Mafra Jimena A. Olmos-Asar Fabio R. Negreiros Alfonso Reina Ki Kang Kim Mildred S. Dresselhaus Jing Kong Gary J. Mankey Paulo T. Araujo Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Centro de Ciências Naturais e Humanas Universidade Federal do ABC Santo André 09210-580 SP Brazil INFIQC CONICET Departamento de Química Teórcia y Computacional Facultad de Ciencias Químicas Universidad Nacional de Córdoba X5000HUA Argentina Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Energy and Materials Engineering Dongguk University-Seoul Seoul 04620 Republic of Korea Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy The University of Alabama Tuscaloosa Alabama 35487 USA Center for Materials for Information Technology (MINT Center) The University of Alabama Tuscaloosa Alabama 35401 USA Natural Sciences Institute Graduate Program in Physics - Federal University of Para Belem PA 66075-110 Brazil

The growth of large area single-layer graphene (1-LG) is studied using ambient pressure chemical vapor deposition on single-crystal Ni(111), Ni(110), and Ni(100). By varying both the furnace temperature in the range of 800–1100 °C and the gas flow through the growth chamber, uniform, high-quality 1-LG is obtained for Ni(111) and Ni(110) single crystals and for Ni(100) thin films. Surprisingly, only multilayer graphene growth could be obtained for single-crystal Ni(100). The experimental results are analyzed to determine the optimum combination of temperature and gas flow. Characterization with optical microscopy, Raman spectroscopy, and optical transmission support our findings. Density-functional theory calculations are performed to determine the energy barriers for diffusion, segregation, and adsorption, and model the kinetic pathways for formation of different carbon structures on the low-index surfaces of Ni.

关键词： Growth Synthesis 2-dimensional systems Carbon-based materials Atomic force microscopy Chemical vapor deposition Density functional calculations Density functional theory Raman spectroscopy

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