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Small P values may not yield robust findings:an example using REST-meta-PD

science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Wildlife-microbiome interactions and disease: exploring opportunities for disease mitigation across ecological scales

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Drug Discovery Today: Disease Models 2018年 28卷 105-115页

作者： Williams, Candace L. Caraballo-Rodríguez, Andrés Mauricio Allaband, Celeste Zarrinpar, Amir Knight, Rob Gauglitz, Julia M. Institute for Conservation Research San Diego Zoo Global United States Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California La Jolla San Diego 92307 CA United States Biomedical Sciences Graduate Program University of California La Jolla San Diego 92307 CA United States Department of Medicine University of California La Jolla San Diego 92307 CA United States Veterans Affairs San Diego Healthcare System San Diego California University of California La Jolla San Diego 92307 CA United States Department of Pediatrics University of California La Jolla San Diego 92037 CA United States Department of Bioengineering University of California La Jolla San Diego 92093 CA United States Department of Computer Science and Engineering University of California La Jolla San Diego 92093 CA United States Center for Microbiome Innovation University of California La Jolla San Diego 92307 CA United States

Captive wildlife are a unique set of animals, whose diverse host–microbe symbioses are underexplored. Compared to their wild counterparts they are particularly susceptible to a variety of diseases, many of which have explicit or purported links to the microbiome. In this perspective, we will examine how the microbiome influences gastrointestinal disorders, metabolic dysregulation, reproduction, and disease susceptibility in captive wildlife. Investigation of wildlife, and specifically captive wildlife, affords a unique opportunity to gain understanding of the broad diversity of the associated microbiota and learn from nature's molecular and microbial responses to disease. Studies like these could lead to the discovery of new interventions, ranging from dietary changes to the use of microbes or their natural products as treatment. Intervention strategies can lead to the discovery of medically relevant small molecules and the development of a novel platform for N-of-1 targeted medical investigations. © 2019 Elsevier Ltd

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Analysis of user readiness toward ICT usage at small medium enterprise in south tangerang

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Journal of Physics: Conference Series 2018年第1期1007卷 012042-012042页

作者： Napitupulu, D Syafrullah, M Rahim, R Abdullah, D Setiawan, MI Research Center for Quality System & Testing Technology Indonesian Institute of Sciences Tangerang Selatan Banten Indonesia Program of Master of Computer Science Universitas Budi Luhur Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia

Utilization of Information and Communication Technology (ICT) is still relatively low in the level of SMEs due to various limitations ranging from access to capital, till the marketing network. ICT is present to provide the ability for SMEs in improving the benefits and competitive advantage of the organization. This study aims to determine the level of readiness of SMEs in utilizing technology, especially ICT. The methodology used is a survey to see the technology readiness of 107 SMEs in South Tangerang selected by purposive sampling. The approach used is TRI (Technology Readiness Index) which is the individual perception of technology based on four criteria that is optimism, innovativeness, discomfort and insecurity. The results showed that the optimism and innovativeness variables significantly positively influence the technology readiness while the variables of discomfort and insecurity also significantly positively influence the readiness of ICT.

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Corrigendum: A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018 (2021 Environ. Res. Lett. 16 073005)

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Environmental Research Letters 2022年第4期17卷 049502-049502页

作者： William F Lamb Thomas Wiedmann Julia Pongratz Robbie Andrew Monica Crippa Jos G J Olivier Dominik Wiedenhofer Giulio Mattioli Alaa Al Khourdajie Jo House Shonali Pachauri Maria Figueroa Yamina Saheb Raphael Slade Klaus Hubacek Laixiang Sun Suzana Kahn Ribeiro Smail Khennas Stephane de la Rue du Can Lazarus Chapungu Steven J Davis Igor Bashmakov Hancheng Dai Shobhakar Dhakal Xianchun Tan Yong Geng Baihe Gu Jan Minx Mercator Research Institute on Global Commons and Climate Change Torgauer Straße 12–15 4 EUREF Campus #19 10829 Berlin Germany School of Earth and Environment University of Leeds LS2 9JT Leeds United Kingdom Sustainability Assessment Program School of Civil and Environmental Engineering UNSW Sydney Sydney Australia Max Planck Institute for Meteorology Bundesstrasse 53 20146 Hamburg Germany Department of Geography Ludwig-Maximilians-Universität Munich Luisenstrasse 37 80333 Munich Germany CICERO Center for International Climate Research Oslo Norway European Commission Joint Research Centre Ispra VA Italy PBL Netherlands Environmental Assessment Agency Den Haag The Netherlands Institute of Social Ecology University of Natural Resources and Life Sciences Schottenfeldgasse 29 1070 Vienna Austria Department of Transport Planning TU Dortmund University August-Schmidt-Straße 10 44227 Dortmund Germany Centre for Environmental Policy Imperial College London London United Kingdom School of Geographical Sciences University of Bristol University Road BS8 1SS Bristol United Kingdom International Institute for Applied Systems Analysis (IIASA) Schlossplatz 1 2361 Laxenburg Austria Department of Management Society and Communication Copenhagen Business School Copenhagen Denmark Openexp 17 Bd Lefebvre 75015 Paris France Integrated Research for Energy Environment and Society University of Groningen 9747AG Groningen The Netherlands Department of Geographical Sciences University of Maryland College Park MD 20742 United States of America School of Finance and Management SOAS University of London WC1H 0XG London United Kingdom Institute of Blue and Green Development Weihai Institute of Interdisciplinary Research Shandong University Weihai 264209 People’s Republic of China Federal University of Rio de Janeiro (Universidade Federal do Rio de Janeiro—UFRJ) Transport Engineering Programme (Programa de Engenharia de Transportes—PET) COPPE-UFRJ Rio de Janeiro Brazil Energy

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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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The importance of socio-technical resources for software ecosystems management

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Journal of Innovation in Digital Ecosystems 2016年第2期3卷 98-113页

作者： Thaiana Lima Rodrigo Pereira dos Santos Jonice Oliveira Cláudia Werner System Engineering and Computer Science Department PESC/COPPE/UFRJ–Federal University of Rio de Janeiro Zip Code 21945-970–Rio de Janeiro Brazil

Software Ecosystem (SECO) is often understood as a set of actors interacting among themselves and manipulating artifacts with the support of a common technology platform. Usually, SECO approaches can be designed as an environment whose component repository is gathering stakeholders as well as software products and components. By manipulating software artifacts, a technical network emerges from interactions made over the component repository in order to reuse artifacts, improving code quality, downloading, selling, buying etc. Although technical repositories are essential to store SECO’s artifacts, the interaction among actors in an emerging social network is a key factor to strengthen the SECO’s through increasing actor’s participation, e.g., developing new software, reporting bugs, and communicating with suppliers. In the SECO context, both the internal and external actors keep the platform’s components updated and documented, and even support requirements and suggestions for new releases and bug fixes. However, those repositories often lack resources to support actors’ relationships and consequently to improve the reuse processes by stimulating actors’ interactions, information exchange and better understanding on how artifacts are manipulated by actors. In this paper, we focused on investigating SECO as component repositories that include socio-technical resources. As such, we present a survey that allowed us to identify the relevance of each resource for a SECO based on component repositories, initially focused on the Brazilian scenario. This paper also describes the analysis of the data collected in that survey. Information of other SECO elements extracted from the data is also presented, e.g., the participants’ profile and how they behave within a SECO. As an evolution of our research, a study for evaluating the availability and the use of such resources on top of two platforms was also conducted with experts in collaborative development in order to analyze th

关键词： Software ecosystems Socio-technical resources Component repositories Software reuse

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Supporting negotiation and socialization for component markets in software ecosystems context

Supporting negotiation and socialization for component marke...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Rodrigo Santos Cláudia Werner Lucas Tostes Thaiana Lima Gabriel Barbosa System Engineering and Computer Science Department COPPE/UFRJ-Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781509016341

Organizations open up their software platforms and reusable assets to others, including partners and third-party developers around the world, creating software ecosystems (SECOs). It can contribute to minimize nontechnical barriers of software reuse in industry because it explores potential benefits from the relations among companies and stakeholders. An inhibitor is the complexity in defining value for reusable assets in a scenario where producers try to meet customers' expectations, and vice-versa. This paper presents a value-based mechanism to support component negotiation and socialization processes in a reuse repository in the SECO context as an extension of the Brechó-EcoSys environment. Social resources were integrated into the mechanism in order to aid component negotiation. An evaluation of the negotiation mechanism was performed, based on an analysis of its elements and functions against critical factors in the negotiation within a SECO, identified in a previous systematic literature review. In addition, an analysis of the social resources supporting the negotiation mechanism was performed against popular sociotechnical elements for SECOs, identified in a previous survey with experts in the field.

关键词： Stakeholders Ecosystems Context Documentation Software reusability Software engineering

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A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom

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Scientific Data 2017年第1期4卷 170090-170096页

作者： Trevisan-Silva, Dilza Bednaski, Aline V. Fischer, Juliana S.G. Veiga, Silvio S. Bandeira, Nuno Guthals, Adrian Marchini, Fabricio K. Leprevost, Felipe V. Barbosa, Valmir C. Senff-Ribeiro, Andrea Carvalho, Paulo C. Department of Cell Biology Federal University of Paraná Curitiba 81531-980 Brazil Computational Mass Spectrometry and Proteomics Group Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Center for Computational Mass Spectrometry University of California San Diego 92093-0404 United States Functional Genomics Laboratory Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Mass Spectrometry Facility RPT02H Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Rio de Janeiro 21941-914 Brazil Laboratory of Toxinology Fiocruz Rio de Janeiro 21040-900 Brazil

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

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On Social Network Firewall Selection

On Social Network Firewall Selection

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International Conference on Computing, Networking and Communications

作者： Anisha Mazumder Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University

ISBN: (纸本)9781467385800

It is common knowledge that the decision of an individual regarding adoption of a product or technology is, more often than not, heavily influenced by their friends and acquaintances. In real world, there are different competing products and innovations that try to garner as many loyal followers as possible. Over the past few years, there has been a significant interest in the research community to study social network problems with a flavor of competition. Such problems often focus on identification of a set of people in a given social network by the competing players in order to achieve some goal. In this paper, we introduce the weighted Segregating Vertex Set (wSVS) problem, in which we are given a weighted undirected graph with a subset of nodes identified as the seedset of the first player and the goal for the second player is to identify a subset of nodes (firewall) of minimum cumulative weight, such that the total weight of the nodes reachable by the first player is strictly less than the total weight of the nodes not reachable by the first player. Thus, the second player tries to contain the reach of the first player within the social network community. This problem is also relevant for containment of disease in epidemiology, containment of forest fire and several other domains. We prove that this problem is NP-complete and provide an optimal solution through the use of Mixed Integer Linear programming. We also provide a heuristic solution for the wSVS problem and show its efficacy through detailed experimentation. Our heuristic solution delivers near optimal solution in lesser time compared to that needed to find the optimal solution.

关键词： Social network services Sociology Statistics Companies Particle separators Diseases Social computing

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