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Forecasting Exchange Rate Value at Risk using Deep Belief Network Ensemble based Approach

Procedia Computer science 2018年 139卷 25-32页

作者： Kaijian He Lei Ji Geoffrey K.F. Tso Bangzhu Zhu Yingchao Zou Hunan Engineering Research Center for Industrial Big Data and Intelligent Decision Making Hunan University of Science and Technology Xiangtan 411201 China School of Business Hunan University of Science and Technology Xiangtan 411201 China Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Business School Nanjing University of Information Science & Technology Nanjing 210044 China College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China

In this paper, we propose a new Value at Risk estimate based on the Deep Belief Network ensemble model with Empirical Mode Decomposition (EMD) technique. It attempts to capture the multi-scale data features with the EMD-DBN ensemble model and predict the risk movement more accurately. Individual data components are extracted using EMD model while individual forecasts can be calculated at different scales using ARMA-GARCH model. The DBN model is introduced to search for the optimal nonlinear ensemble weights to combine the individual forecasts at different scales into the ensembled exchange rate VaR forecasts. Empirical studies using major exchange rates confirm that the proposed model demonstrates the superior performance compared to the benchmark models.

关键词： Exchange rate forecasting Empirical Mode Decomposition Deep Belief Network Value at Risk JEL: F31 C45 C53

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Taking the pulse of COVID-19: A spatiotemporal perspective

arXiv

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arXiv 2020年

作者： Yang, Chaowei Sha, Dexuan Liu, Qian Li, Yun Lan, Hai Guan, Weihe Wendy Hu, Tao Li, Zhenlong Zhang, Zhiran Thompson, John Hoot Wang, Zifu Wong, David Ruan, Shiyang Yu, Manzhu Richardson, Douglas Zhang, Luyao Hou, Ruizhi Zhou, You Zhong, Cheng Tian, Yifei Beaini, Fayez Carte, Kyla Flynn, Colin Liu, Wei Pfoser, Dieter Bao, Shuming Li, Mei Zhang, Haoyuan Liu, Chunbo Jiang, Jie Du, Shihong Zhao, Liang Lu, Mingyue Li, Lin Zhou, Huan NSF Spatiotemporal Innovation Center George Mason Univ. FairfaxVA22030 United States Department of Geography and GeoInformation Science George Mason Univ. FairfaxVA22030 United States Department of Geographical Sciences University of Maryland College ParkMD20742 United States Center for Geographic Analysis Harvard University CambridgeMA02138 United States Geoinformation and Big Data Research Lab Department of Geography University of South Carolina ColumbiaSC29208 United States Chinese Academy of Surveying and Mapping Beijing China Cloud Compute and Storage Operations Information Technology Services George Mason University FairfaxVA22030 United States Department of Geography Pennsylvania State University State CollegePA16801 United States School of Business Management East China Normal University Zhongshanbei Road Shanghai3663 China School of Mathematical Sciences East China Normal University Zhongshanbei Road Shanghai3663 China Department of Psychology George Mason University FairfaxVA22030 United States Department of Computer Science George Mason University FairfaxVA22030 United States Department of Information Technology George Mason University FairfaxVA22030 United States Department of Biology George Mason University FairfaxVA22030 United States College of Land Science and Technology China Agricultural University Beijing100083 China China Data Institute Ann ArborMI United States Institute of Remote Sensing and GIS Peking University Beijing100871 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing100044 China School of Geographical Sciences Nanjing University of Information Science & Technology 219 Ningliu Road Nanjing Jiangsu210044 China School of Resource and Environmental Sciences Wuhan University 129 Luoyu Rd Wuhan Hubei430079 China School of Geodesy a

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts;in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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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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Exploiting Minipage-Level Mapping to Improve Write Efficiency of NAND Flash

Exploiting Minipage-Level Mapping to Improve Write Efficienc...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Hao Lv You Zhou Fei Wu Weijun Xiao Xubin He Zhonghai Lu Changsheng Xie Shenzhen research institute of Huazhong University of Science and Technology Shenzhen China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Ministry of Education Key Laboratory of Data Storage System Wuhan China Department of Electrical and Computer Engineering Virginia Commonwealth University Richmond USA College of Science and Technology Temple University Philadelphia USA School of Information and Communication Technology KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781538683682

Pushing NAND flash memory to higher density, manufacturers are aggressively enlarging the flash page size. However, the sizes of I/O requests in a wide range of scenarios do not grow accordingly. Since a page is the unit of flash read/write operations, traditional flash translation layers (FTLs) maintain the page mapping regularity. Hence, small random write requests become common, leading to extensive partial logical page writes. This write inefficiency significantly degrades the performance and increases the write amplification of flash storage. In this paper, we first propose a configurable mapping layer, called minipage, whose size is set to match I/O request sizes. The minipage-level mapping provides better flexibility in handling small writes at the cost of sequential read performance degradation and a larger mapping table. Then, we propose a new FTL, called PM-FTL, that exploits the minipage-level mapping to improve write efficiency and utilizes the page-level mapping to reduce the costs caused by the minipage-level mapping. Finally, trace-driven simulation results show that compared to traditional FTLs, PM-FTL reduces the write amplification and flash storage response time by an average of 33.4% and 19.1%, up to 57.7% and 34%, respectively, under 16KB flash pages and 4KB minipages.

关键词： Flash memories Random access memory Metadata Electronic mail Time factors Memory management Market research

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

Summary Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood *** We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor *** The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy *** By co-analysing geospatial trends in d

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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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Thalamo-cortical structural co-variation networks are related to familial risk for schizophrenia in the context of lower nuclei volume estimates in patients: an ENIGMA study

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Biological psychiatry 2025年

作者： Annalisa Lella Linda A Antonucci Roberta Passiatore Loredana Bellantuono Pierluigi Selvaggi Teresa Popolizio Guido Di Sciascio Alessandro Saponaro Patrizia Ricci Mario Altamura Giuseppe Blasi Antonio Rampino Chris Vriend Vince D Calhoun Kelly Rootes-Murdy Aaron L Goldman Inmaculada Baeza Josefina Castro-Fornieles Gisela Sugranyes Elena De la Serna Edith Pomarol-Clotet Mar Fatjó-Vilas Raymond Salvador Andriana Karuk Paola Fuentes-Claramonte David C Glahn Amanda L Rodrigue John Blangero Lei Wang Taeyoung Lee Karolin E Einenkel Saskia Hamers Oliver Gruber Adrian Preda Young-Chul Chung Soyolsaikhan Odkhuu Corentin Vallée Paola Dazzan Machteld Marcelis Stijn Michielse Katharina Brosch Frederike Stein Igor Nenadić Benjamin Straube Florian Thomas-Odenthal Tilo Kircher Sean Carruthers Susan L Rossell Phillip J Sumner Tamsyn E Van Rheenen Caroline Demro Ian S Ramsay Scott R Sponheim Rebekka Lencer Susanne Meinert Tim Hahn Udo Dannlowski Dominik Grotegerd Mariateresa Ciccarelli Felice Iasevoli Giuseppe Pontillo Godfrey D Pearlson Derin Cobia Fabrizio Piras Nerisa Banaj Daniela Vecchio Marjolein E A Barendse Neeltje E M van Haren Hang Joon Jo Kang Sim Yann Quidé Melissa J Green Rachael Slate Giacomo Cecere Wolfgang Omlor Stephanie Homan Philipp Homan Sophia I Thomopoulos Jessica A Turner Theo G M van Erp Paul M Thompson Alessandro Bertolino Giulio Pergola Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy. Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy Lieber Institute for Brain Development Johns Hopkins Medical Campus Baltimore MD USA. Istituto Nazionale di Fisica Nucleare Bari Italy. Azienda Ospedaliero-Universitaria Consorziale Policlinico Bari Italy. Neuroradiology Unit Scientific Institute for Research Hospitalization and Health Care Casa Sollievo della Sofferenza 71013 San Giovanni Rotondo Foggia Italy. Department of Mental Health ASL Bari Bari Italy. Department of Mental Health ASL Brindisi Brindisi Italy. Department of Mental Health ASL Barletta Andria Trani BAT Italy. Department of Clinical and Experimental Medicine University of Foggia Italy. Amsterdam UMC Vrije Universiteit Amsterdam Department of Psychiatry and Department of Anatomy and Neuroscience de Boelelaan 1117 Amsterdam the Netherlands Compulsivity Impulsivity and Attention Amsterdam Neuroscience de Boelelaan 1117 Amsterdam the Netherlands. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University Atlanta GA USA. Department of Child and Adolescent Psychiatry and Psychology 2021 SGR 01319 Institute of Neuroscience Hospital Clínic Barcelona Barcelona University of Barcelona Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) CIBERSAM Spain. FIDMAG Germanes Hospitalàries Research Foundation CIBERSAM ISCIII Barcelona Spain. Department of Psychiatry and Behavioral Sciences Boston Children's Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Human Genetics and South Texas Diabetes and Obesity Institute School of

BACKGROUND:Structural brain differences in the thalamus and the cortex have been widely reported in schizophrenia (SCZ) relative to neurotypical controls (NC). Most prior studies examined the thalamus as a whole, single region-of-interest. Additionally, findings in individuals at familial high-risk for schizophrenia (FHR) remain inconclusive. Here, we investigated whether local and network-wide thalamic-related structural alterations vary as a function of familial risk for schizophrenia. METHODS:Structural MRI scans were obtained from 5,197 participants (3,409 NC, 257 FHR, 1,531 SCZ) across 32 cross-sectional samples within the ENIGMA Consortium. Random-effects meta-analyses, and network analyses were conducted on (i) local thalamic alterations (volume estimates of seven thalamic subdivisions), and (ii) network-wide thalamic alterations (thickness and surface-related thalamo-cortical/cortico-cortical co-variation patterns) across groups (NC, FHR, SCZ). RESULTS:Individuals with SCZ showed significantly lower gray matter volume estimates in the anterior, pulvinar, medial, posterior, and ventral thalamic subdivisions compared to NC (q<0.05). FHR did not differ from NC. At the network-wide level, thalamo-cortical co-variations discriminated FHR from NC (q<0.05), with FHR showing intermediate co-variation between SCZ and NC. Cortico-cortical co-variation patterns revealed that SCZ and FHR shared similarly disconnected clustering configurations, distinct from NC (q<0.05). CONCLUSIONS:Results revealed lower thalamic volume estimates in SCZ but not in FHR, hence yielding no evidence of a familial risk trait, whereas thalamo-cortical and cortico-cortical co-variation estimates were associated with familial risk for SCZ These findings suggest that, once the thalamus is parsed into subdivisions, network-wide thalamo-cortical features may identify trait-dependent, neurobiological correlates of genetic risk for SCZ.

关键词： Familial high risk for schizophrenia meta-analyses morphometric measures structural neuroimaging thalamic subdivisions thalamo-cortical networks

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Global climate

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Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

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Author Correction: Federated learning enables big data for rare cancer boundary detection

引用

Nature communications 2023年第1期14卷 436页

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

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