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Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses

npj Genomic Medicine 2024年第1期9卷 1-24页

作者： Demidov, German Yaldiz, Burcu Garcia-Pelaez, José de Boer, Elke Schuermans, Nika Van de Vondel, Liedewei Paramonov, Ida Johansson, Lennart F. Musacchia, Francesco Benetti, Elisa Bullich, Gemma Sablauskas, Karolis Beltran, Sergi Gilissen, Christian Hoischen, Alexander Ossowski, Stephan de Voer, Richarda Lohmann, Katja Oliveira, Carla Topf, Ana Vissers, Lisenka E. L. M. Zguro, Kristina Zara, Federico Zaganas, Ioannis Yépez, Vicente A. Wirth, Brunhilde Webb, David Verdin, Hannah Van Nieuwenhove, Erika van Montfrans, Joris van Heurck, Roxane van der Veken, Lars Valle, Laura Uva, Paolo Udd, Bjarne Trimouille, Aurelien Thompson, Rachel Tartaglia, Marco Sznajer, Yves Striano, Pasquale Steinke-Lange, Verena Spilioti, Martha Scudieri, Paolo Schröck, Evelin Schon, Katherine Scala, Marcello Savarese, Marco Santen, Gijs W. E. Rump, Andreas Ruivenkamp, Claudia A. L. Roos, Andreas Rooryck, Caroline Riva, Antonella Renieri, Alessandra Ratnaike, Thiloka Radio, Francesca Clementina Privitera, Flavia De la Paz, Manuel Posada Poppe, Bruce Polavarapu, Kiran Platzer, Konrad Peterlin, Borut Pérez-Dueñas, Belén Pauly, Martje Parrini, Elena Olimpio, Catarina Reghan, Mary O. de Benito, Daniel Natera Osorio, Andrés Nascimento Munell, Francina Münchau, Alexander Moortgat, Stéphanie Monestier, Olivier Molnar, Maria Judit Meunier, Colombine Mertes, Christian Mei, Davide Maystadt, Isabelle May, Patrick Maver, Ales Mathioudakis, Lambros Masclaux, Frédéric Mary, Sandrine Delgado, Beatriz Martínez Marcé-Grau, Anna Malaichamy, Sivasankar Macaya, Alfons Luknárová, Rebeka Martín, Estrella López Lochmüller, Hanns Leitão, Elsa Lederer, Damien Leavis, Helen Laner, Andreas Lacombe, Didier Krass, Leon Korff, Christian Kokosali, Evgenia Karakaya, Mert Karadurmus, Deniz Kapaki, Elisabeth Johari, Mridul Iacomino, Michele Huibers, Manon Horvath, Rita Holinski-Feder, Elke Hoffer, Mariette J. V. Herzog, Rebecca Hemelsoet, Dimitri Bouveret, Eva Hammar Haimel, Matthias Hackman, Peter Guerrini, Renzo Grimbacher, Bodo Greally, Marie Ghani, Hamidah Gagneur, Julien Institute of Medical Genetics and Applied Genomics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Tübingen Germany Department of Human Genetics Radboud University Medical Center Nijmegen Netherlands Department of Clinical Genetics Maastricht University Medical Center Maastricht Netherlands i3S - Instituto de Investigação e Inovação em Saúde Rua Alfredo Allen 208 Porto 4200-135 Portugal IPATIMUP - Institute of Molecular Pathology and Immunology University of Porto Porto Portugal Faculty of Medicine University of Porto Porto Portugal Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Clinical Genetics Erasmus MC University Medical Center Rotterdam Netherlands Center for Medical Genetics Ghent University Hospital Ghent Belgium Translational Neurosciences Faculty of Medicine and Health Sciences University of Antwerp Antwerp Belgium Laboratory of Neuromuscular Pathology Institute Born-Bunge University of Antwerp Antwerp Belgium Centro Nacional de Análisis Genómico (CNAG) C/Baldiri Reixac 4 Barcelona 08028 Spain Universitat de Barcelona (UB) Barcelona Spain University of Groningen University Medical Center Groningen Department of Genetics Groningen Netherlands Center for Human Technologies Italian Institute of Technology (IIT) Genova Italy Telethon Institute for Genetics and Medicine Pozzuoli (Napoli) 80078 Italy Department of Medical Biotechnologies Med Biotech Hub and Competence Center University of Siena Siena 53100 Italy Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Departament de Genètica Microbiologia i Estadística Facultat de Biologia Universitat de Barcelona (UB) Barcelona Spain Radboud Institute for Molecular Life Sciences Nijmegen Netherlands Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI) Radboud University Medical Center Nijmegen N

We report the results of a comprehensive copy number variant (CNV) reanalysis of 9171 exome sequencing datasets from 5757 families affected by a rare disease (RD). The data reanalysed was extremely heterogeneous, having been generated using 28 different enrichment kits by 42 different research groups across Europe partnering in the Solve-RD project. Each research group had previously undertaken their own analysis of the data but failed to identify disease-causing variants. We applied three CNV calling algorithms to maximise sensitivity, and rare CNVs overlapping genes of interest, provided by four partner European Reference Networks, were taken forward for interpretation by clinical experts. This reanalysis has resulted in a molecular diagnosis being provided to 51 families in this sample, with ClinCNV performing the best of the three algorithms. We also identified partially explanatory pathogenic CNVs in a further 34 individuals. This work illustrates the value of reanalysing ES cold cases for CNVs. © The Author(s) 2024.

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Launching the VASCO Citizen science Project

arXiv

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arXiv 2020年

作者： Villarroel, Beatriz Pelckmans, Kristiaan Solano, Enrique Laaksoharju, Mikael Souza, Abel Dom, Onyeuwaoma Nnaemeka Laggoune, Khaoula Mimouni, Jamal Guergouri, Hichem Mattsson, Lars García, Aurora Lago Soodla, Johan Castillo, Diego Shultz, Matthew E. Aworka, Rubby Comerón, Sébastien Geier, Stefan Marcy, Geoffrey W. Gupta, Alok C. Bergstedt, Josefine Bär, Rudolf E. Buelens, Bart Enriquez, Emilio Mellon, Christopher K. Prieto, Almudena Wamalwa, Dismas Simiyu de Souza, Rafael S. Ward, Martin J. Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-106 91 Sweden Instituto de Astrofísica de Canarias Avda Vía Láctea s/n Tenerife La LagunaE-38205 Spain Department of Information Technology Uppsala University Box 337 UppsalaSE-751 05 Sweden P.O. Box 78 Villanueva de la CañadaE-28691 Spain Autonomous Distributed Systems Lab Department of Computing Science Umeå Universitet Umeå901 87 Sweden Center for Basic Space Science National Space Research and Development Agency P.O. Box 2022 Nsukka Enugu410002 Nigeria Sirius Astronomy Association Sirius Astronomy Association BP 18 Cité du 20 Aout Constantine25000 Algeria Department of Physics University of Constantine 1 LPMPS CERIST Constantine25000 Algeria Science of Matter Division Research Unit in Scientific Mediation CERIST Constantine25016 Algeria IES Tartessos Barriada Hiconsa s/n Camas Sevilla41900 Spain Department of Physics & Astronomy University of Delaware 217 Sharp Lab NewarkDE19716 United States Ghana Space Science and Technology Institute Atomic-Haatso Road Kwabenya Box LG 80 Accra233 Ghana University of Ghana Summerhill Estates Ltd Rd Santeo P.O. Box LG 25 Legon Accra Ghana Departamento de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38206 Spain Cuesta de San José s/n La Palma Breña Baja38712 Spain Space Laser Awareness 3883 Petaluma Hill Rd Santa RosaCA95404 United States Manora Peak Nainital263 001 India Freelance Uppsala752 17 Sweden Institute for Particle Physics and Astrophysics ETH Zürich Wolfgang-Pauli-Strasse 27 ZürichCH-8093 Switzerland Mol2400 Belgium Department of Astrophysics IMAPP Radboud University Nijmegen Nijmegen6525 AJ Netherlands Galileo project affiliate Meru Physics Department University of Science and Technology Meru P.O. Box Kenya972-60200 Kenya Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 80

The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a time interval of 70 years, searching for unusual and exotic transients. We present herein the VASCO Citizen science Project, which can identify unusual candidates driven by three different approaches: hypothesis, exploratory, and machine learning, which is particularly useful for SETI searches. To address the big data challenge, VASCO combines three methods: the Virtual Observatory, user-aided machine learning, and visual inspection through citizen science. Here we demonstrate the citizen science project and its improved candidate selection process, and we give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon, and Nigeria. At the moment of writing, the citizen science project has carefully examined 15,593 candidate image pairs in the data (ca. 10% of the candidates), and has so far identified 798 objects classified as "vanished". The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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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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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. 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 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 California The University of Alabama in Huntsville Huntsville Alabama IBS Center for Climate Physics Busan South Korea German Aerospace Center (DLR) Oberpfaffenhofen Wessling Germany Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder and Physical Sciences Division NOAA/Earth System Research Labo

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A Mini Review of Trends towards Automated and Non-Invasive Techniques for Early Detection of Lung Cancer: From Radiomics through Proteogenomics to Breathomics

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Journal of Physics: Conference Series 2019年第3期1378卷

作者： Funmilayo S. Moninuola Emmanuel Adetiba Oluwadamilola I. Oshin Anthony A. Atayero Ademola Adeyeye Department of Electrical and Information Engineering Covenant University Nigeria HRA Institute for Systems Science Durban University of Technology Durban P.O. Box 1334 Durban South Africa Division of Oncology Department of Surgery Afe Babalola University Ado-Ekiti Nigeria

Carcinoma of the Lung is one of the most common cancers in the world and the leading cause of tumor-related deaths. Less than 15% of patients survive 5 years post diagnosis due to its relatively poor prognosis. This has been ascribed to lack of effective diagnostic methods for early detection. Different medical imaging techniques such as chest radiography, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are used in routine clinical practice for tumor detection. These techniques are medically unsatisfactory and inconvenient for patients due to poor diagnostic accuracy. Endobronchial biopsies are the gold standard for diagnosis but have the inherent risk of full or partial invasive procedures. Thus, diagnostic technology that uses data mining algorithms with medical image analysis, generally known as radiomics emerged. Radiomics extracts complex information from conventional radiographic images and quantitatively correlates image features with diagnostic and therapeutic outcomes. In spite of the benefits, radiomics is prone to high false positives and there is no established standard for acquisition of parameters. Further efforts towards outcome improvement led to the proteomic and genomic (proteogenomic) approach to lung cancer detection. Although proteogenomic has a diagnostic edge over traditional techniques, variations in bio-specimen and heterogeneity of lung cancer still possess a major challenge. Recent findings have established that changes normally occur in the gene or protein due to tumor growth in the lungs and this often leads to peroxidation of cell membrane that releases Volatile Organic Compounds (VOCs) through the breath of Lung Cancer patients. The comprehensive analysis of breath VOCs, which is tagged Breathomics in the literature, unveils opportunities for noninvasive biomarker discovery towards early detection. Breathomics has therefore become the current pace-setter in medical diagnostics research because of its non-invasiveness and

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Physical parameters of the multi-planet systems HD 106315 and GJ 9827

arXiv

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arXiv 2020年

作者： Kosiarek, Molly R. Berardo, David A. Crossfield, Ian J.M. Laguna, Cesar Akana Murphy, Joseph M. Howell, Steve B. Henry, Gregory W. Isaacson, Howard Weiss, Lauren M. Petigura, Erik A. Fulton, Benjamin Behmard, Aida Hirsch, Lea A. Teske, Johanna Burt, Jennifer A. Mills, Sean M. Chontos, Ashley Mocnik, Teo Howard, Andrew W. Werner, Michael Livingston, John H. Krick, Jessica Beichman, Charles Gorjian, Varoujan Kreidberg, Laura Morley, Caroline Christiansen, Jessie L. Morales, Farisa Y. Scott, Nicholas J. Crane, Jeffrey D. Rosenthal, Lee J. Grunblatt, Samuel K. Rubenzahl, Ryan A. Dalba, Paul A. Giacalone, Steven Villanueva, Chiara Dane Liu, Qingtian Dai, Fei Hill, Michelle L. Rice, Malena Kane, Stephen R. Mayo, Andrew W. Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics & Astronomy University of Kansas 1082 Malott 1251 Wescoe Hall Dr. LawrenceKS66045 United States Department of Physics University of California Santa CruzCA95064 United States NASA Ames Research Center Moffett FieldCA94035 United States Center of Excellence in Information Systems Tennessee State University NashvilleTN37209 United States 501 Campbell Hall University of California at Berkeley BerkeleyCA94720 United States Centre for Astrophysics University of Southern Queensland ToowoombaQLD Australia Institute for Astronomy University of Hawai‘i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States NASA Exoplanet Science Institute Caltech-IPAC MC 314-6 1200 E California Blvd PasadenaCA91125 United States Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA91125 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University StanfordCA United States Earth & Planets Laboratory Carnegie Institution of Washington 5241 Broad Branch Road N.W. WashingtonDC20015 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Institute for Astronomy University of Hawaii HonoluluHI96822 United States Gemini Observatory Northern Operations Center 670 N. A’ohoku Place HiloHI96720 United States Department of Astronomy University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Caltech/IPAC 1200 E. California Blvd PasadenaCA91125 United States IPAC/NASA

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引用

作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Warsaw Poland Electric Power University Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Science and Technology Str. Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology VNU University of Engineering and Technology Xuan Thuy Hanoi Viet Nam

We introduce comparisons w.r.t. information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. This notion is useful for concept learning in description logics when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons w.r.t. information in paraconsistent description logics. As consequences, we obtain also invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2017 - IOS Press and the authors. All rights reserved.

关键词： Data description

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0193 Sleepiness and Slow Rolling Eye Movements are Increased During Autonomous Versus Manual DrivingGet access

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Sleep 2019年第SUPPLEMENT_1期42卷 A79–A79页

作者： Wong, Lily R Kuriyagawa, Yukiyo Gowda, Nikhil Cravalho, Patrick Wilaiprasitporn, Theerawit Garcia, Javier Flynn-Evans, Erin E San Jose State University Research Foundation Moffett Field CA USA NASA Ames Research Center Fatigue Countermeasures Laboratory Human Systems Integration Division Moffett Field CA USA Nihon University College of Industrial Technology Chiba Japan Vidyasirimedhi Institute of Science & Technology School of Information Science and Technology Bio-inspired Robotics and Neural Engineering Lab Rayong Thailand U.S. Army Research Laboratory Aberdeen Proving Ground MD USA

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Antarctica and the Southern Ocean

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Bulletin of the American Meteorological Society 2020年第8期101卷 S287-S320页

作者： Blunden, Jessica Arndt, Derek S. Abrahamsen, E. Povl Barreira, Sandra Bitz, Cecilia M. Butler, Amy Clem, Kyle R. Colwell, Steve Coy, Lawrence De Laat, Jos Du Plessis, Marcel D. Fogt, Ryan L. Fricker, Helen Amanda Fyfe, John Gardner, Alex S. Gille, Sarah T. Gorte, Tessa Gregor, L. Hobbs, Will Johnson, Bryan Keenan, Eric Keller, Linda M. Kramarova, Natalya A. Lazzara, Matthew A. Lenaerts, Jan T. M. Lieser, Jan L. Liu, Hongxing Long, Craig S. MacLennan, Michelle Massom, Robert A. Massonnet, François Mazloff, Matthew R. Mikolajczyk, David Narayanan, A. Nash, Eric R. Newman, Paul A. Petropavlovskikh, Irina Pitts, Michael Queste, Bastien Y. Reid, Phillip Roquet, F. Santee, Michelle L. Scambos, Ted A. Stammerjohn, Sharon Strahan, Susan Swart, Sebastiann Wang, Lei Andersen, Andrea Griffin, Jessicca Hammer, Gregory Love-Brotak, S. Elizabeth Misch, Deborah J. Riddle, Deborah B. Veasey, Sara W. Polar Oceans Group British Antarctic Survey Cambridge United Kingdom Argentine Naval Hydrographic Service Buenos Aires Argentina Atmospheric Sciences Department University of Washington SeattleWA United States NOAA/ESRL Chemical Sciences Division BoulderCO United States School of Geography Environment and Earth Sciences Victoria University of Wellington Wellington New Zealand British Antarctic Survey Cambridge United Kingdom Science Systems and Applications Inc. NASA Goddard Space Flight Center GreenbeltMD United States DeBilt Netherlands Department of Marine Science University of Gothenburg Gothenburg Sweden Southern Ocean Carbon and Climate Observatory CSIR South Africa Department of Geography Ohio University AthensOH United States Scripps Institution of Oceanography University of California-San Diego San DiegoCA United States Canadian Centre for Climate Modelling and Analysis University of Victoria VictoriaBC Canada Jet Propulsion Laboratory California Insitute of Technology PasadenaCA United States Department of Atmospheric and Oceanic Sciences University of Colorado-Boulder BoulderCO United States ETH Zurich Switzerland University of Tasmania HobartTAS Australia NOAA/OAR Earth System Research Laboratory Global Monitoring Division University of Colorado BoulderCO United States Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison MadisonWI United States NASA Goddard Space Flight Center GreenbeltMD United States Department of Physical Sciences School of Arts and Sciences Madison Area Technical College Space Science and Engineering Center University of Wisconsin-Madison MadisonWI United States Bureau of Meteorology HobartTAS Australia Institute for Marine and Antarctic Studies University of Tasmania HobartTAS Australia Department of Geography University of Cincinnati CincinnatiOH United States NOAA/NWS National Centers for Environmental Prediction College ParkMD United States HobartTAS Austral

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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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