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arXiv 2017年

作者： Venkitaraman, Arun Zachariah, Dave Department of Information Science and Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Information Technology Division of Systems and Control Uppsala University UppsalaSE- 752 37 Sweden

We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross-validation or parameter tuning by building upon a hyperparameter-free framework. Our approach does not require the graph to be undirected and also accommodates varying noise levels across different nodes. Experiments using real-world datasets show that the proposed method offers significant performance gains in prediction, in comparison with the graphs frequently associated with these datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Optimal Innovation-Based Deception Attack on Remote State Estimation

Optimal Innovation-Based Deception Attack on Remote State Es...

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American control Conference

作者： Shuang Wu Ziyang Guo Dawei Shi Karl Henrik Johansson Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 China ACCESS Linnaeus Centre School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509045839

The security issue in cyber-physical systems has attracted growing interests in the last decades. This paper considers how false data injection attack can degrade the estimation quality of a remote state estimation system. In this system, smart sensors measure a dynamic process and send preprocessed data through a communication network to a remote estimator to estimate the process. It is assumed that there are malicious attackers in the communication network, who are able to obtain and falsify all the data sent by the sensors. It is common that the remote estimator is equipped with a residue-based detector to detect potential attacks. We propose a class of deception attack and analyze its feasibility. We show that the proposed attack enables the attacker to inject false data into the remote estimator without being detected. We derive a criterion to judge the optimality of performance of this type of attack in the sense of maximizing the estimation error covariance. Furthermore, we find that a simple linear attack strategy, which flips the sign of intercepted signal, satisfies the optimality criterion. We present numerical examples to illustrate our theoretical results.

关键词： Cyber-physical systems (CPS) deception attack remote state estimation

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Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

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IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 113-120页

作者： Fei-Yue Wang Jun Jason Zhang Xinhu Zheng Xiao Wang Yong Yuan Xiaoxiao Dai Jie Zhang Liuqing Yang IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology the Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver the Department of Computer Science and Engineering University of Minnesota the Qingdao Academy of Intelligent Industries (QAII) Qingdao Shandong China and SKL-MCCS CASIA the Department of Electrical and Computer Engineering Colorado State University

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of Automation.

关键词： Religious buildings

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Hearing loss prevalence and years lived with disability, 1990-2019: Findings from the Global Burden of Disease Study 2019

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The Lancet 2021年第10278期397卷 996-1009页

作者： Haile, Lydia M. Kamenov, Kaloyan Briant, Paul Svitil Orji, Aislyn U. Steinmetz, Jaimie D. Abdoli, Amir Abdollahi, Mohammad Abu-Gharbieh, Eman Afshin, Ashkan Ahmed, Haroon Rashid, Tarik Ahmed Akalu, Yonas Alahdab, Fares Alanezi, Fahad Mashhour Alanzi, Turki M. Al Hamad, Hanadi Ali, Liaqat Alipour, Vahid Al-Raddadi, Rajaa M. Amu, Hubert Arabloo, Jalal Arab-Zozani, Morteza Arulappan, Judie Ashbaugh, Charlie Atnafu, Desta Debalkie Babar, Zaheer-Ud-Din Baig, Atif Amin Banik, Palash Chandra Bärnighausen, Till Winfried Barrow, Amadou Bender, Rose G. Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bibi, Sadia Bijani, Ali Burkart, Katrin Cederroth, Christopher R. Charan, Jaykaran Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Dagnew, Amare Belachew Dagnew, Baye Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Desalew, Assefa Dhamnetiya, Deepak Dhimal, Mandira Lamichhane Dhimal, Meghnath Doyle, Kerrie E. Duncan, Bruce B. Ekholuenetale, Michael Filip, Irina Fischer, Florian Franklin, Richard Charles Gaidhane, Abhay Motiramji Gaidhane, Shilpa Gallus, Silvano Ghamari, Farhad Ghashghaee, Ahmad Ghozali, Ghozali Gilani, Syed Amir Glǎvan, Ionela-Roxana Golechha, Mahaveer Goulart, Barbara Niegia Garcia Gupta, Veer Bala Gupta, Vivek Kumar Hamidi, Samer Hammond, Billy Randall Hay, Simon I. Hayat, Khezar Heidari, Golnaz Hoffman, Howard J. Hopf, Kathleen Pillsbury Hosseinzadeh, Mehdi Househ, Mowafa Hussain, Rabia Hwang, Bing-Fang Iavicoli, Ivo Ibitoye, Segun Emmanuel Ilesanmi, Olayinka Stephen Irvani, Seyed Sina Naghibi Islam, Sheikh Mohammed Shariful Iwagami, Masao Jacob, Louis Jayapal, Sathish Kumar Jha, Ravi Prakash Jonas, Jost B. Kalhor, Rohollah Al-Salihi, Nawzad Kameran Kandel, Himal Kasa, Ayele Semachew Kayode, Gbenga A. Khalilov, Rovshan Khan, Ejaz Ahmad Khatib, Mahalaqua Nazli Kosen, Soewarta Koyanagi, Ai Kumar, G. Anil Landires, Ivan Lasrado, Savita Lim, Stephen S. Liu, Xuefeng Lobo, Stany W. Lugo, Alessandra Makki, Alaa Mendoza, Walter Mersha, Amanual Getnet Mihretie, Kebadnew Mulatu Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Noncommunicable Diseases World Health Organization (WHO) Geneva Switzerland Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran University of Medical Sciences Tehran Iran School of Pharmacy Tehran University of Medical Sciences Tehran Iran Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Department of Biosciences COMSATS Institute of Information Technology Islamabad Pakistan Department of Computer Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Medical Physiology University of Gondar Gondar Ethiopia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Health Information Management and Technology Department Dammam Saudi Arabia Geriatric and Long Term Care Department Hamad Medical Corporation Doha Qatar Rumailah Hospital Hamad Medical Corporation Doha Qatar Department of Biological Sciences National University of Medical Sciences (NUMS) Rawalpindi Pakistan Health Management and Economics Research Center Tehran Iran Department of Community Medicine King Abdulaziz University Jeddah Saudi Arabia Department of Population and Behavioural Sciences University of Health and Allied Sciences Ho Ghana Social Determinants of Health Research Center Birjand University of Medical Sciences Birjand Iran Department of Maternal and Child Health Sultan Qaboos University Muscat Oman Department of Health System and Health Economics Bahir Dar University Bahir Dar Ethiopia Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Unit of Biochemistry Universiti

Background: Hearing loss affects access to spoken language, which can affect cognition and development, and can negatively affect social wellbeing. We present updated estimates from the Global Burden of Disease (GBD) study on the prevalence of hearing loss in 2019, as well as the condition's associated disability. Methods: We did systematic reviews of population-representative surveys on hearing loss prevalence from 1990 to 2019. We fitted nested meta-regression models for severity-specific prevalence, accounting for hearing aid coverage, cause, and the presence of tinnitus. We also forecasted the prevalence of hearing loss until 2050. Findings: An estimated 1·57 billion (95% uncertainty interval 1·51–1·64) people globally had hearing loss in 2019, accounting for one in five people (20·3% [19·5–21·1]). Of these, 403·3 million (357·3–449·5) people had hearing loss that was moderate or higher in severity after adjusting for hearing aid use, and 430·4 million (381·7–479·6) without adjustment. The largest number of people with moderate-to-complete hearing loss resided in the Western Pacific region (127·1 million people [112·3–142·6]). Of all people with a hearing impairment, 62·1% (60·2–63·9) were older than 50 years. The Healthcare Access and Quality (HAQ) Index explained 65·8% of the variation in national age-standardised rates of years lived with disability, because countries with a low HAQ Index had higher rates of years lived with disability. By 2050, a projected 2·45 billion (2·35–2·56) people will have hearing loss, a 56·1% (47·3–65·2) increase from 2019, despite stable age-standardised prevalence. Interpretation: As populations age, the number of people with hearing loss will increase. Interventions such as childhood screening, hearing aids, effective management of otitis media and meningitis, and cochlear implants have the potential to ameliorate this burden. Because the burden of moderate-to-complete hearing loss is concentrated in countries with low health-ca

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Learning policies for Markov decision processes from data

arXiv

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arXiv 2017年

作者： Hanawal, Manjesh K. Liu, Hao Zhu, Henghui Paschalidis, Ioannis Ch. Industrial Engineering and Operations Research IIT-Bombay PowaiMH 400076 India Center for Information and Systems Engineering Boston University BostonMA02215 United States College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang310027 China Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are known a priori, however, only an unknown subset of them could be relevant. The policy parameters that correspond to an observed target policy are recovered using `1-regularized logistic regression that best fits the observed state-action samples. We establish bounds on the difference between the average reward of the estimated and the original policy (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order O(√Ε), it suffices to use training sample size on the order of Ω(log n · poly(1/Ε)), where n is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.90C40 (Primary) 90C39, 93E35, 60J10 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Markov processes

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li 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 Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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