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第29届中国数据库学术会议

作者： Rong Chuitian 荣垂田 Xu Tianren 徐天任 Du Xiaoyong 杜小勇 Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry o 教育部数据工程与知识工程重点实验室(中国人民大学) 北京 100872 中国人民大学信息学院北京 100872 School of Information Renmin University of China Beijing 100872 Key Laboratory of Education Inform 中国人民大学信息学院北京 100872 民族教育信息化教育部重点实验室(云南师范大学) 昆明 650500

集合相似连接(set similarity join)是指在给定的数据集中，按照基于集合间覆盖关系的相似度计算方法来衡量数据之间的相似度、并找出所有相似度不小于给定阈值的数据对的操作。集合相似连接作为一种新的基本操作在很多领域中有重要应... 详细信息

集合相似连接(set similarity join)是指在给定的数据集中，按照基于集合间覆盖关系的相似度计算方法来衡量数据之间的相似度、并找出所有相似度不小于给定阈值的数据对的操作。集合相似连接作为一种新的基本操作在很多领域中有重要应用。随着社会网络、移动应用以及在线服务的发展，使得数据收集的效率和规模得到了很大的提高，同时给相似连接操作带来新的挑战。根据集合相似的必要条件，提出了相似集合之间的差异度。利用差异度和鸽巢原理，提出了一种新颖的基于数据划分的集合相似连接计算方法，该方法对集合进行自适应的均衡划分，并利用基于划分块的过滤方法来提高过滤的效率。为了进一步提高过滤的效果和相似连接的效率，利用划分块的位置信息提出了增强的过滤方法。针对提出的方法，在不同的环境下进行了实验，实验结果表明，该方法与已有的方法相比可以有效地提高相似连接的效率。

关键词：数据库集合相似连接均衡划分法阈值

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Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

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Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Ruiying Du Zhen Wang Xiong Luo Haiyan Zhao Ping Luan Jipeng Ouyang Song Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School Beijing University of Science and Technology Shunde Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Investigation Writing - original draft Writing - review & editing The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Weiping Wang Zhen Wang and Haiyan Zhao. Contribution: Conceptualization Formal analysis Contribution: Formal analysis Supervision Department of Neurology Peking University Third Hospital Beijing China Contribution: Data curation Formal analysis Guangdong Second People's Hospital Guangzhou Guangdong China Department of Neurology Shunde Hospital Southern Medical University Shunde Guangdong China Contribution: Conceptualization Data curation Southeast Asia Project Manager Department of China Petroleum Pipeline Engineering Co. Ltd. Langfang Hebei China Contribution: Software Supervision

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node-centric network model, which only calculates correlations between brain regions. Considering the interaction of low-order correlations between pairs of brain regions, we use an edge-centric network model to study high-order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high-amplitude co-fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co-fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high-order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI.

关键词： co-fluctuation edge-centric functional flexibility overlapping community spatiotemporal

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

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The Computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

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Tunable schiff-based networks with different bonding sites for enhanced photocatalytic activity under visible-light irradiation: The effects of steric hindrance

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Environmental research 2024年 246卷 118200页

作者： Han Wang Piao Xu Eydhah Almatrafi Ziwei Wang Chengyun Zhou Ting Xiong Hong Qin Yangzhuo He Hailan Yang Zhuotong Zeng Guangming Zeng School of Metallurgy and Environment Central South University Changsha 410083 China Department of Dermatology Second Xiangya Hospital Central South University Changsha 410011 China College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China. Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha 410205 China. Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. Electronic address: zgming@***.

Organic polymers hold great potential in photocatalysis considering their low cost, structural tailorability, and well-controlled degree of conjugation for efficient electron transfer. Among the polymers, Schiff base networks (SNWs) with high nitrogen content have been noticed. Herein, a series of SNWs is synthesized based on the melamine units and dialdehydes with different bonding sites. The chemical and structural variation caused by steric hindrance as well as the related photoelectric properties of the SNW samples are investigated, along with the application exploration on photocatalytic degradation and energy production. The results demonstrate that only SNW-o based on o-phthalaldehyde responds to visible light, which extends to over 550 nm. SNW-o shows the highest tetracycline degradation rate of 0.02516 min, under 60-min visible light irradiation. Moreover, the HO production of SNW-o is 2.14 times higher than that of g-CN. The enhanced photocatalytic activity could be ascribed to the enlarged visible light adsorption and intramolecular electron transfer. This study indicates the possibility to regulate the optical and electrical properties of organic photocatalysts on a molecular level, providing an effective strategy for rational supramolecular engineering to the applications of organic materials in photocatalysis.

关键词： H(2)O(2) production Photocatalytic degradation Schiff-based networks Steric hindrance Structure tunability

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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NaCdSb: An Orthorhombic Zintl Phase with Exceptional Intrinsic Thermoelectric Performance

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Angewandte Chemie 2022年第3期135卷

作者： Kai Guo Yuting Zhang Song Yuan Qinghang Tang Chen Lin Pengfei Luo Jiong Yang Shusheng Pan Li-Dong Zhao Guofeng Cheng Jiye Zhang Jun Luo School of Physics and Materials Science Guangzhou University Guangzhou 510006 China Research Center for Advanced Information Materials (CAIM) Huangpu Research & Graduate School of Guangzhou University Sino-Singapore Guangzhou Knowledge City Huangpu District Guangzhou 510555 China Contribution: Conceptualization (lead) Formal analysis (equal) Writing - original draft (lead) School of Materials Science and Engineering Shanghai University 99 Shangda Road Shanghai 200444 China Contribution: Data curation (equal) Investigation (equal) Validation (lead) Contribution: Data curation (lead) Formal analysis (equal) Materials Genome Institute Shanghai University 99 Shangda Road Shanghai 200444 China Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and School of Physics and Technology Wuhan University Wuhan 430072 China Contribution: Investigation (equal) Contribution: Investigation (equal) Methodology (supporting) Contribution: Investigation (equal) Writing - review & editing (supporting) School of Materials Science and Engineering Beihang University Beijing 100191 China Contribution: Methodology (equal) Writing - review & editing (equal) Analysis & Testing Center for Inorganic Materials Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai 200050 China Contribution: Formal analysis (equal) Investigation (supporting) Contribution: Data curation (equal) Writing - review & editing (supporting) Contribution: Supervision (lead) Writing - review & editing (equal)

Many Zintl phases are promising thermoelectric materials owning to their features like narrow band gaps, multiband behaviors, ideal charge transport tunnels, and loosely bound cations. Herein we show a new Zintl phase NaCdSb with exceptional intrinsic thermoelectric performance. Pristine NaCdSb exhibits semiconductor behaviors with an experimental hole concentration of 2.9×10 18 cm −3 and a calculated band gap of 0.5 eV. As the temperature increases, the hole concentration rises gradually and approaches its optimal one, leading to a high power factor of 11.56 μW cm −1 K −2 at 673 K. The ultralow thermal conductivity is derived from the small phonon group velocity and short phonon lifetime, ascribed to the structural anharmonicity of Cd−Sb bonds. As a consequence, a maximum zT of 1.3 at 673 K has been achieved without any doping optimization or structural modification, demonstrating that NaCdSb is a remarkable thermoelectric compound with great potential for performance improvement.

关键词： Electron-Crystal NaCdSb Phonon-Glass Thermoelectric Properties Zintl Phase

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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