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Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

Genome biology 2021年第1期22卷 246页

作者： Kim-Anh Lê Cao Al J Abadi Emily F Davis-Marcisak Lauren Hsu Arshi Arora Alexis Coullomb Atul Deshpande Yuzhou Feng Pratheepa Jeganathan Melanie Loth Chen Meng Wancen Mu Vera Pancaldi Kris Sankaran Dario Righelli Amrit Singh Joshua S Sodicoff Genevieve L Stein-O'Brien Ayshwarya Subramanian Joshua D Welch Yue You Ricard Argelaguet Vincent J Carey Ruben Dries Casey S Greene Susan Holmes Michael I Love Matthew E Ritchie Guo-Cheng Yuan Aedin C Culhane Elana Fertig Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. kimanh.lecao@nimelb.edu.au. Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. McKusick-Nathans Institute of the Department of Genetic Medicine Johns Hopkins School of Medicine Baltimore MD USA. Data Science Dana-Farber Cancer Institute Boston MA USA. Biostatistics Harvard TH Chan School of Public Health Boston MA USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. Centre de Recherches en Cancérologie de Toulouse (INSERM) Université Paul Sabatier III Toulouse France. Cancer Convergence Institute and Division of Quantitative Sciences Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA. Department of Mathematics and Statistics McMaster University Hamilton Canada. Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) School of Life Sciences Technical University of Munich Munich Germany. Department of Biostatistics UNC Chapel Hill NC USA. Barcelona Supercomputing Center Barcelona Spain. Department of Statistics University of Wisconsin Madison WI USA. Department of Statistical Sciences University of Padova Padova PD Italy. Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada. PROOF Centre of Excellence Vancouver BC Canada. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Neuroscience Johns Hopkins University Baltimore MD USA. Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore MD USA. Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering University of Michiga

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer"

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EBioMedicine 2023年 94卷 104709页

作者： Timo Hess Carlo Maj Jan Gehlen Oleg Borisov Stephan L Haas Ines Gockel Michael Vieth Guillaume Piessen Hakan Alakus Yogesh Vashist Carina Pereira Michael Knapp Vitalia Schüller Alexander Quaas Heike I Grabsch Jessica Trautmann Ewa Malecka-Wojciesko Anna Mokrowiecka Jan Speller Andreas Mayr Julia Schröder Axel M Hillmer Dominik Heider Florian Lordick Ángeles Pérez-Aísa Rafael Campo Jesús Espinel Fernando Geijo Concha Thomson Luis Bujanda Federico Sopeña Ángel Lanas María Pellisé Claudia Pauligk Thorsten Oliver Goetze Carolin Zelck Julian Reingruber Emadeldin Hassanin Peter Elbe Sandra Alsabeah Mats Lindblad Magnus Nilsson Nicole Kreuser René Thieme Francesca Tavano Roberta Pastorino Dario Arzani Roberto Persiani Jin-On Jung Henrik Nienhüser Katja Ott Ralf R Schumann Oliver Kumpf Susen Burock Volker Arndt Anna Jakubowska Małgorzta Ławniczak Victor Moreno Vicente Martín Manolis Kogevinas Marina Pollán Justyna Dąbrowska Antonio Salas Olivier Cussenot Anne Boland-Auge Delphine Daian Jean-Francois Deleuze Erika Salvi Maris Teder-Laving Gianluca Tomasello Margherita Ratti Chiara Senti Valli De Re Agostino Steffan Arnulf H Hölscher Katharina Messerle Christiane Josephine Bruns Armands Sīviņš Inga Bogdanova Jurgita Skieceviciene Justina Arstikyte Markus Moehler Hauke Lang Peter P Grimminger Martin Kruschewski Nikolaos Vassos Claus Schildberg Philipp Lingohr Karsten Ridwelski Hans Lippert Nadine Fricker Peter Krawitz Per Hoffmann Markus M Nöthen Lothar Veits Jakob R Izbicki Adrianna Mostowska Federico Martinón-Torres Daniele Cusi Rolf Adolfsson Geraldine Cancel-Tassin Aksana Höblinger Ernst Rodermann Monika Ludwig Gisela Keller Andres Metspalu Hermann Brenner Joerg Heller Markus Neef Michael Schepke Franz Ludwig Dumoulin Lutz Hamann Renato Cannizzaro Michele Ghidini Dominik Plaßmann Michael Geppert Peter Malfertheiner Olivier Glehen Tomasz Skoczylas Marek Majewski Jan Lubiński Orazio Palmieri Stefania Boccia Anna Latiano Nuria Aragones Thomas Schmidt Mário Dinis-Ribeiro Rui Medeiros Salah-Eddin Al-Batran Mārcis Leja Juozas Institute of Human Genetics University of Marburg Marburg Germany Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany. Institute for Genomic Statistics and Bioinformatics Medical Faculty University of Bonn Bonn Germany. Institute of Human Genetics University of Marburg Marburg Germany. Department of Upper GI Diseases Karolinska University Hospital and Unit of Gastroenterology and Rheumatology Karolinska Institutet Stockholm Sweden. Department of Visceral Transplant Thoracic and Vascular Surgery University Hospital Leipzig Leipzig Germany. Institute for Pathology Friedrich-Alexander-University Erlangen-Nuernberg Klinikum Bayreuth Bayreuth Germany. Department of Digestive and Oncological Surgery Claude Huriez Hospital CHU Lille Lille France. Department of General Visceral Cancer and Transplant Surgery University of Cologne Cologne Germany. Department of General Visceral and Thoracic Surgery University Medical Center Hamburg-Eppendorf University of Hamburg Hamburg Germany Centre for Surgical Oncology Medias Klinikum Burghausen Burghausen Germany. Molecular Oncology and Viral Pathology Group IPO Porto Research Center (CI-IPOP) Portuguese Institute of Oncology of Porto (IPO Porto) 4200-072 Porto Portugal Porto Comprehensive Cancer Center & RISE @ CI-IPO University of Porto 4200-450 Porto Portugal. Institute of Medical Biometrics Informatics and Epidemiology (IMBIE) Medical Faculty University of Bonn Bonn Germany. Institute of Pathology University Hospital Cologne Medical Faculty University of Cologne Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht the Netherlands Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom. Department of Digestive Tract Diseases Medical University of Lodz Lodz Poland. Department of Mathematics and Computer Science Unive

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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.

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Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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Nature biotechnology 2020年第4期38卷 504页

作者： Christian Lieven Moritz E Beber Brett G Olivier Frank T Bergmann Meric Ataman Parizad Babaei Jennifer A Bartell Lars M Blank Siddharth Chauhan Kevin Correia Christian Diener Andreas Dräger Birgitta E Ebert Janaka N Edirisinghe José P Faria Adam M Feist Georgios Fengos Ronan M T Fleming Beatriz García-Jiménez Vassily Hatzimanikatis Wout van Helvoirt Christopher S Henry Henning Hermjakob Markus J Herrgård Ali Kaafarani Hyun Uk Kim Zachary King Steffen Klamt Edda Klipp Jasper J Koehorst Matthias König Meiyappan Lakshmanan Dong-Yup Lee Sang Yup Lee Sunjae Lee Nathan E Lewis Filipe Liu Hongwu Ma Daniel Machado Radhakrishnan Mahadevan Paulo Maia Adil Mardinoglu Gregory L Medlock Jonathan M Monk Jens Nielsen Lars Keld Nielsen Juan Nogales Intawat Nookaew Bernhard O Palsson Jason A Papin Kiran R Patil Mark Poolman Nathan D Price Osbaldo Resendis-Antonio Anne Richelle Isabel Rocha Benjamín J Sánchez Peter J Schaap Rahuman S Malik Sheriff Saeed Shoaie Nikolaus Sonnenschein Bas Teusink Paulo Vilaça Jon Olav Vik Judith A H Wodke Joana C Xavier Qianqian Yuan Maksim Zakhartsev Cheng Zhang Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark. Systems Biology Lab Amsterdam Institute of Molecular and Life Sciences (AIMMS) Vrije Universiteit Amsterdam Amsterdam the Netherlands. BioQUANT/COS Heidelberg University Heidelberg Germany. Ecole Polytechnique Fédérale de Lausanne Laboratory of Computational Systems Biotechnology Lausanne Switzerland. iAMB-Institute of Applied Microbiology ABBt-Aachen Biology and Biotechnology RWTH Aachen University Aachen Germany. Department of Bioengineering University of California La Jolla CA USA. Department of Chemical Engineering and Applied Chemistry University of Toronto Toronto Ontario Canada. Human Systems Biology Laboratory Instituto Nacional de Medicina Genomica & Coordinación de la Investigación Científica-Red de Apoyo a la Investigación UNAM Mexico City Mexico. Institute for Systems Biology Seattle WA USA. Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens Institute for Biomedical Informatics (IBMI) Tübingen Germany. Department of Computer Science University of Tübingen Tübingen Germany. German Center for Infection Research (DZIF) partner site Tübingen Tübingen Germany. Australian Institute for Bioengineering and Nanotechnology The University of Queensland Brisbane Queensland Australia. Argonne National Laboratory Lemont IL USA. Analytical Biosciences Division of Systems Biomedicine and Pharmacology Leiden Academic Centre for Drug Research Leiden University Leiden the Netherlands. Department of Systems Biology Centro Nacional de Biotecnología Consejo Superior de Investigaciones Científicas (CNB-CSIC) Madrid Spain. Centro de Biotecnología y Genómica de Plantas (CBGP UPM-INIA) Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Madrid Spain. Department of Animal and Aquacultural Sciences Faculty of Biosciences Norwegian University of Life Sciences Oslo Norway

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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An Object Oriented Parallel Finite Element Scheme for Computations of PDEs: Design and Implementation

An Object Oriented Parallel Finite Element Scheme for Comput...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Sashikumaar Ganesan Volker John Gunar Matthies Raviteja Meesala Abdus Shamim Ulrich Wilbrandt Department of Computational and Data Science Indian Institute of Science Bangalore India Department of Mathematics and Computer Science Free University of Berlin Berlin Germany Department of Mathematics Technical University Dresden Dresden Germany

ISBN: (纸本)9781509057740

Parallel finite element algorithms based on object-oriented concepts are presented. Moreover, the design and implementation of a data structure proposed are utilized in realizing a parallel geometric multigrid method. The ParFEMapper and the ParFECommunicator are the key components of the data structure in the proposed parallel scheme. These classes are constructed based on the type of finite elements (continuous or nonconforming or discontinuous) used. The proposed solver is compared with the open source direct solvers, MUMPS and PasTiX. Further, the performance of the parallel multigrid solver is analyzed up to 1080 processors. The solver shows a very good speedup up to 960 processors and the problem size has to be increased in order to maintain the good speedup when the number of processors are increased further. As a result, the parallel solver is able to handle large scale problems on massively parallel supercomputers. The proposed parallel finite element algorithms and multigrid solver are implemented in our in-house package ParMooN.

关键词： Program processors Finite element analysis Supercomputers data structures Iterative methods Multigrid methods Numerical models

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Network Coding-Aware IEEE 802.11 MAC Protocol Using Batch Transmissions and Multiple Reverse Direction Exchanges

Network Coding-Aware IEEE 802.11 MAC Protocol Using Batch Tr...

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Ad-Hoc and Sensor networks Symposium

作者： Raul Palacios-Trujillo Biniam Hailu Dabi Jesus Alonso-Zarate Fabrizio Granelli Frank H. P. Fitzek Nelson L. S. da Fonseca Institute of Computing University of Campinas Department of Information Engineering and Computer Science University of Trento Centre Tecnologic de Telecomunicacions de Catalunya (CTTC) Communication Networks Group Technical University of Dresden

ISBN: (纸本)9781479966653

It has been shown in the literature that Network Coding (NC) can boost the performance of wireless networks. However, to really obtain the potential gain of NC, efficient Medium Access Control (MAC) protocols that operate with awareness of the NC functions are necessary. In this paper, we propose a novel NC-aware MAC protocol for IEEE 802.11 wireless networks that combines k-batch transmissions and multiple receiver-initiated reverse direction exchanges involving NC data to boost the overall network performance. The proposed protocol allows any node to transmit a burst of data packets in a single channel access invocation. Then, an intermediate node can transmit an NC data packet when receiving a valid data packet from a source node, without contending for channel access. Both analytical and simulation results presented in this paper show the high throughput and energy efficiency of the proposed protocol with gains ranging from 33% to 298% when compared to existing mechanisms based on the IEEE 802.11 Standard.

关键词： Wireless networks MAC protocol data packets Methotrexate-Actinomycin-Chlorambucil Regimen Numerical controls Transmissions reversing Line of nodes

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Solar energetic particle events observed by the PAMELA mission

arXiv

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arXiv 2018年

作者： Bruno, A. Bazilevskaya, G.A. Boezio, M. Christian, E.R. de Nolfo, G.A. Martucci, M. Merge, M. Mikhailov, V.V. Munini, R. Richardson, I.G. Ryan, J.M. Stochaj, S. Adriani, O. Barbarino, G.C. Bellotti, R. Bogomolov, E.A. Bongi, M. Bonvicini, V. Bottai, S. Cafagna, F. Campana, D. Carlson, P. Casolino, M. Castellini, G. de Santis, C. Di Felice, V. Galper, A.M. Karelin, A.V. Koldashov, S.V. Koldobskiy, S. Krutkov, S.Y. Kvashnin, A.N. Leonov, A. Malakhov, V. Marcelli, L. Mayorov, A.G. Menn, W. Mocchiutti, E. Monaco, A. Mori, N. Osteria, G. Panico, B. Papini, P. Pearce, M. Picozza, P. Ricci, M. Ricciarini, S.B. Simon, M. Sparvoli, R. Spillantini, P. Stozhkov, Y.I. Vacchi, A. Vannuccini, E. Vasilyev, G.I. Voronov, S.A. Yurkin, Y.T. Zampa, G. Zampa, N. Heliophysics Division NASA Goddard Space Flight Center GreenbeltMD United States INFN Sezione di Bari BariI-70126 Italy Lebedev Physical Institute MoscowRU-119991 Russia INFN Sezione di Trieste TriesteI-34149 Italy Department of Physics University of Rome "Tor Vergata" RomeI-00133 Italy INFN Laboratori Nazionali di Frascati Via Enrico Fermi 40 FrascatiI-00044 Italy INFN Sezione di Rome "Tor Vergata" RomeI-00133 Italy National Research Nuclear University MEPhI MoscowRU-115409 Russia Department of Astronomy University of Maryland College ParkMD United States Space Science Center University of New Hampshire DurhamNH United States Electrical and Computer Engineering New Mexico State University Las CrucesNM United States Department of Physics and Astronomy University of Florence Sesto FiorentinoFlorenceI-50019 Italy INFN Sezione di Florence Sesto FiorentinoFlorenceI-50019 Italy Department of Physics University of Naples "Federico II" NaplesI-80126 Italy INFN Sezione di Naples NaplesI-80126 Italy Department of Physics University of Bari BariI-70126 Italy Ioffe Physical Technical Institute St. PetersburgRU-194021 Russia KTH Department of Physics Oskar Klein Centre for Cosmoparticle RIKEN Advanced Science Institute Wako-shi Saitama Japan IFAC Sesto FiorentinoFlorenceI-50019 Italy Science Data Center Via del Politecnico snc RomeI-00133 Italy Department of Physics Universität Siegen SiegenD-57068 Germany KTH Department of Physics Oskar Klein Centre for Cosmoparticle Physics AlbaNova University Centre StockholmSE-10691 Sweden IAPS/INAF RomeI-00133 Italy Department of Mathematics Computer Science and Physics University of Udine UdineI-33100 Italy

Despite the significant progress achieved in recent years, the physical mechanisms underlying the origin of solar energetic particles (SEPs) are still a matter of debate. The complex nature of both particle acceleration and transport poses challenges to developing a universal picture of SEP events that encompasses both the low-energy (from tens of keV to a few hundreds of MeV) observations made by space-based instruments and the GeV particles detected by the worldwide network of neutron monitors in ground-level enhancements (GLEs). The high-precision data collected by the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) satellite experiment offer a unique opportunity to study the SEP fluxes between ∼80 MeV and a few GeV, significantly improving the characterization of the most energetic events. In particular, PAMELA can measure for the first time with good accuracy the spectral features at moderate and high energies, providing important constraints for current SEP models. In addition, the PAMELA observations allow the relationship between low and high-energy particles to be investigated, enabling a clearer view of the SEP origin. No qualitative distinction between the spectral shapes of GLE, sub-GLE and non-GLE events is observed, suggesting that GLEs are not a separate class, but are the subset of a continuous distribution of SEP events that are more intense at high energies. While the spectral forms found are to be consistent with diffusive shock acceleration theory, which predicts spectral rollovers at high energies that are attributed to particles escaping the shock region during acceleration, further work is required to explore the relative influences of acceleration and transport processes on SEP spectra. Copyright © 2018, The Authors. All rights reserved.

关键词： Astrophysics

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Utilizing Cat Swarm Optimization in Allocating the Sink Node in the Wireless Sensor Network Environment 3

Utilizing Cat Swarm Optimization in Allocating the Sink Node...

引用

3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016

作者： Tsai, Pei-Wei Kong, Lingping Vaclav, Snasel Pan, Jeng-Shyang Istanda, Vaci Hu, Zhi-Yong College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Computer Science VSB Technical University of Ostrava Ostrava Czech Republic Council of Indigenous Peoples Executive Yuan New Taipei City Taiwan

ISBN: (纸本)9781509010936

A general wireless sensor network (WSN) is a system composed of a large number of sensor nodes. In the WSN system, all sensor nodes are responsible for sensing the interested events and transmitting the related information back to the sink node. The sink node acts like the brain of the whole WSN system for processing the collected data and giving control commands. The position of the sink node directly affects the data transmission efficiency and the distance from the terminal sensor node to the sink node. Thus, choosing the correct place to place the sink node in the whole network is a delicate work. In this paper, we propose a sink node placement method by proposing a newly designed object function and utilizing Cat Swarm Optimization (CSO) to find out the near best location for placing the sink node. The simulation results indicate that our proposed method presents good performance compared with the conventional Particle Swarm Optimization (PSO) algorithm in reducing the overall energy consumption and prolongs the lifetime of network in an efficient way. © 2016 IEEE.

关键词： Sensor nodes

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