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Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

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The Lancet Respiratory Medicine 2020年第6期8卷 585-596页

作者： Soriano, Joan B Kendrick, Parkes J Paulson, Katherine R Gupta, Vinay Abrams, Elissa M Adedoyin, Rufus Adesoji Adhikari, Tara Ballav Advani, Shailesh M Agrawal, Anurag Ahmadian, Elham Alahdab, Fares Aljunid, Syed Mohamed Altirkawi, Khalid A Alvis-Guzman, Nelson Anber, Nahla Hamed Andrei, Catalina Liliana Anjomshoa, Mina Ansari, Fereshteh Antó, Josep M Arabloo, Jalal Athari, Seyyede Masoume Athari, Seyyed Shamsadin Awoke, Nefsu Badawi, Alaa Banoub, Joseph Adel Mattar Bennett, Derrick A Bensenor, Isabela M Berfield, Kathleen S Sachiko Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Brauer, Michael Bukhman, Gene Butt, Zahid A Cámera, Luis Alberto Car, Josip Carrero, Juan J Carvalho, Felix Castañeda-Orjuela, Carlos A Choi, Jee-Young Jasmine Christopher, Devasahayam J Cohen, Aaron J Dandona, Lalit Dandona, Rakhi Dang, Anh Kim Daryani, Ahmad de Courten, Barbora Demeke, Feleke Mekonnen Demoz, Gebre Teklemariam De Neve, Jan-Walter Desai, Rupak Dharmaratne, Samath Dhamminda Diaz, Daniel Douiri, Abdel Driscoll, Tim Robert Duken, Eyasu Ejeta Eftekhari, Aziz Elkout, Hajer Endries, Aman Yesuf Fadhil, Ibtihal Faro, Andre Farzadfar, Farshad Fernandes, Eduarda Filip, Irina Fischer, Florian Foroutan, Masoud Garcia-Gordillo, M.A. Gebre, Abadi Kahsu Gebremedhin, Ketema Bizuwork Gebremeskel, Gebreamlak Gebremedhn Gezae, Kebede Embaye Ghoshal, Aloke Gopal Gill, Paramjit Singh Gillum, Richard F Goudarzi, Houman Guo, Yuming Gupta, Rajeev Hailu, Gessessew Bugssa Hasanzadeh, Amir Hassen, Hamid Yimam Hay, Simon I Hoang, Chi Linh Hole, Michael K Horita, Nobuyuki Hosgood, H Dean Hostiuc, Mihaela Househ, Mowafa Ilesanmi, Olayinka Stephen Ilic, Milena D Irvani, Seyed Sina Naghibi Islam, Sheikh Mohammed Shariful Jakovljevic, Mihajlo Jamal, Amr A Jha, Ravi Prakash Jonas, Jost B Kabir, Zubair Kasaeian, Amir Kasahun, Gebremicheal Gebreslassie Kassa, Getachew Mullu Kefale, Adane Teshome Kengne, Andre Pascal Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khubchandani, Jagdish Kim, Young-Eun Kim, Yun Jin Kisa, Hospital Universitario de La Princesa (Princess University Hospital) Madrid Spain Autonomous University of Madrid Madrid Spain Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES) (Center for Biomedical Research in Respiratory Diseases Network) Madrid Spain Institute for Health Metrics and Evaluation Seattle WA United States Department of Surgery University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States University of Washington Seattle WA United States Section of Allergy and Clinical Immunology University of Manitoba Winnipeg MB Canada Ophthalmology Department University of Manitoba Winnipeg MB Canada Department of Paediatrics University of British Columbia Vancouver BC Canada School of Population and Public Health University of British Columbia Vancouver BC Canada Department of Medical Rehabilitation Obafemi Awolowo University Ile-Ife Nigeria Department of Public Health Aarhus University Aarhus Denmark Nepal Health Frontiers University of Southern Denmark Kathmandu Nepal Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC United States Institute of Genomics and Integrative Biology Council of Scientific & Industrial Research Delhi India Internal Medicine Baylor College of Medicine Houston TX United States Research Center for Chronic Kidney Disease Tabriz University of Medical Sciences Tabriz Iran Research Center for Evidence Based Medicine Tabriz University of Medical Sciences Tabriz Iran Joint Ukraine-Azerbaijan International Research and Education Center of Nanobiotechnology and Functional Nanosystems Baku Azerbaijan Mayo Evidence-based Practice Center Mayo Clinic Foundat

Background: Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. Findings: In 2017, 544·9 million people (95% uncertainty interval [UI] 506·9–584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8–7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–4 044 819) in 2017, an increase of 18·0% since 1990, while

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Ensemble Transform Sensitivity Method for Adaptive Observations

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Advances in Atmospheric sciences 2016年第1期33卷 10-20页

作者： Yu ZHANG Yuanfu XIE Hongli WANG Dehui CHEN Zoltan TOTH Nanjing University of Information Science and Technology Nanjing 210044 Chinese Academy of Meteorological Science Beijing 100081 Global Systems Division Earth System Research Laboratory NOAA Boulder CO 80305 USA Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins CO 80523 USA National Meteorological Centr Beijing 100081

The Ensemble Transform（ET） method has been shown to be useful in providing guidance for adaptive observation *** predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble *** this paper,a new ET-based sensitivity（ETS） method,which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction,is proposed to specify regions for possible adaptive *** is a first order approximation of the ET;it requires just one calculation of a transformation matrix,increasing computational efficiency（60%-80%reduction in computational cost）.An explicit mathematical formulation of the ETS gradient is derived and *** the ET and ETS methods are applied to the Hurricane Irene（2011） case and a heavy rainfall case for *** numerical results imply that the sensitive areas estimated by the ETS and ET are ***,ETS is much more efficient,particularly when the resolution is higher and the number of ensemble members is larger.

关键词： adaptive observation high impact weather ensemble transform

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Efficient parallel tree reductions on distributed memory environments

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Scalable Computing 2017年第1期18卷 1-15页

作者： Kakehi, Kazuhiko Matsuzakl, Kiminori Emoto, Kento Academic Co-Innovation Division UTokyo Innovation Platform Co. Ltd. 3-40-10 Hongo Bunkyo-ku Tokyo113-0033 Japan School of Information Kochi University of Technology 185 Tosayamadacho-Miyanokuchi Kami Kochi782-8502 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan

A new approach for fast parallel reductions on trees over distributed memory environments is presented. The start point of our approach is to employ the serialized representation of trees. Along this data representation with high memory locality and ease of initial data representation, we developed an parallelized algorithm which shares the essence with the parallel algorithm for parentheses matching problems. Our algorithm not only is proven to be theoretically efficient, but also has a fast implementation in a BSP style. © 2017. SCPE.

关键词： Trees (mathematics)

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An Inductive method to select simulation points

An Inductive method to select simulation points

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作者： Choi, MinSeong Fukuda, Takashi Goshima, Masahiro Sakai, Shuichi Graduate School of Information Science and Technology University of Tokyo Tokyo113-8656 Japan Tokyo Software and Systems Development Laboratory IBM Japan Tokyo103-8510 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

The time taken for processor simulation can be drastically reduced by selecting simulation points, which are dynamic sections obtained from the simulation result of processors. The overall behavior of the program can be estimated by simulating only these sections. The existing methods to select simulation points, such as SimPoint, used for selecting simulation points are deductive and based on the idea that dynamic sections executing the same static section of the program are of the same phase. However, there are counterexamples for this idea. This paper proposes an inductive method, which selects simulation points from the results obtained by pre-simulating several processors with distinctive microarchitectures, based on assumption that sections in which all the distinctive processors have similar istructions per cycle (IPC) values are of the same phase. We evaluated the first 100G instructions of SPEC 2006 programs. Our method achieved an IPC estimation error of approximately 0.1% by simulating approximately 0.05% of the 100G instructions. © Copyright 2016 The institute of Electronics, information and Communication Engineers.

关键词： Program processors

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Quantifying local predictability of the Lorenz system using the nonlinear local Lyapunov exponent

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Atmospheric and Oceanic science Letters 2017年第5期10卷 372-378页

作者： HUAI Xiao-Wei LI Jian-Ping DING Rui-Qiang FENG Jie LIU De-Qiang State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing China College of Earth Science University of Chinese Academy of Sciences Beijing China College of Global Change and Earth System Science (GCESS) Beijing Normal University Beijing China Joint Center for Global Change Studies Beijing Normal University Beijing China College of Atmospheric Sciences Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China GIobal Systems Division Earth System Research Laboratory/Oceanic and Atmospheric Research/National Oceanic and Atmospheric Administration Boulder CO USA Fujian Meteorological Observatory Fuzhou China

The nonlinear local Lyapunov exponent（NLLE） can be used as a quantification of the local predictability limit of chaotic systems. In this study, the phase-spatial structure of the local predictability limit over the Lorenz-63 system is investigated. It is found that the inner and outer rims of each regime of the attractor have a high probability of a longer than average local predictability limit, while the center part is the opposite. However, the distribution of the local predictability limit is nonuniformly organized, with adjacent points sometimes showing quite distinct error *** source of local predictability is linked to the local dynamics, which is related to the region in the phase space and the duration on the current regime.

关键词： Nonlinear local Lyapunov exponent local predictability limit chaotic system Lorenz system

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Transferability of European-derived Alzheimer’s disease polygenic risk scores across multiancestry populations

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Nature Genetics 2025年 1-13页

作者： Aude Nicolas Benjamin Grenier-Boley Céline Bellenguez Philippe Amouyel Jean-Charles Lambert Richard Sherva Rui Zhang Mark Logue Lindsay Farrer Yoontae Kim Kun Ho Lee Jungsoo Gim Masataka Kikuchi Jigyasha Timsina Carlos Cruchaga Itziar de Rojas Mercè Boada Pablo García-González Raquel Puerta Agustín Ruiz Oriol Dols-Icardo Fermin Moreno Jordi Pérez-Tur María J. Bullido Pablo Mir Luis M. Real Jose María García-Alberca Eloy Rodriguez-Rodriguez Sonia Moreno-Grau Pablo Garcia-Gonzalez Carla Abdelnour Daniel Alcolea Montserrat Alegret Rafael Blesa Dolores Buiza-Rueda Miguel Calero Laura Cervera-Carles Jordi Clarimon Ana Espinosa Juan Fortea Ana Frank-García Jose Maria García-Alberca Isabel Hernández Carmen Lage Alberto Lleó Adolfo López-de Munain Marta Marquié Angel Martín Montes Miguel Medina Adelina Orellana Gemma Ortega Alberto Rábano Maitée Rosende-Roca Ángela Sanabria Pascual Sánchez-Juan Lluís Tárraga Sergi Valero Miren Zulaica María Carolina Dalmasso Xiaopu Zhou Han Cao Nancy Y. Ip Amy K. Y. Fu Fanny C. F. Ip Yann Le Guen Michael D. Greicius Carlos E. Arboleda-Bustos Jenny Ortega-Rojas Ana Gabriela Concha Mera Maria F. Mahecha Rodrigo Pardo Gonzalo Arboleda Humberto Arboleda Maria Aparecida Camargos Bicalho Edgar Nunes de Moraes Marco Túlio Gualberto Cintra Luiz Armando de Marco Bernardo de Mattos Viana Marco Aurélio Romano Silva Maëlenn Guerchet Pierre-Marie Preux Pascal Mbelesso Sven van der Lee Yolande A. L. Pijnenburg Henne Holstege Philip Scheltens Marc Hulsman Niccolo Tesí Wiesje van der Flier Danielle Posthuma Monica Goss Claudia L. Satizabal Bernard Fongang Sudha Seshadri Atahualpa Castillo Julie Williams Rebecca Sims Fahri Küçükali Jasper Van Dongen Kristel Sleegers Qiong Yang Oliver Peters Josef Priller Anja Schneider Frank Jessen Martin Dichgans Dan Rujescu Norbert Scherbaum Jürgen Deckert Steffi Riedel-Heller Lucrezia Hausner Laura Molina-Porcel Emrah Düzel Timo Grimmer Jens Wiltfang Jonathan Vogelgsang Stefanie Heilmann-Heimbach Per Hoffmann Markus M. Möthen Susanne Moebus Thomas Tegos Tsolaki Anthou U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement Université Lille Inserm CHU Lille Institut Pasteur de Lille Lille France Institut du Cerveau - Paris Brain Institute - ICM Sorbonne Université CNRS APHP Hôpital de La Pitié Salpêtrière Inserm Paris France Behavioral Sciences Division National Center for PTSD VA Boston Healthcare System Boston MA USA Department of Medicine (Biomedical Genetics) Boston University Boston MA USA Department of Biostatistics Boston University School of Public Health Boston MA USA Department of Psychiatry Boston University Chobanian and Avedisian School of Medicine Boston MA USA Biomedical Genetics/Department of Medicine Boston University Chobanian and Avedisian School of Medicine Boston MA USA Department of Epidemiology Boston University Boston MA USA Department of Neurology Boston University Boston MA USA Department of Ophthalmology Boston University Boston MA USA BK FOUR Department of Integrative Biological Sciences Chosun University Gwangju Republic of Korea Department of Biomedical Science Chosun University Gwangju Republic of Korea Gwangju Alzheimer’s and Related Dementia (GARD) Cohort Research Center Chosun University Gwangju Republic of Korea Graduate School of Frontier Science University of Tokyo Tokyo Japan Department of Psychiatry Washington University School of Medicine St. Louis MO USA NeuroGenomics and Informatics Center Washington University School of Medicine St. Louis MO USA ACE Alzheimer Center Barcelona Universitat Internacional de Catalunya Barcelona Spain CIBERNED Network Center for Biomedical Research in Neurodegenerative Diseases National Institute of Health Carlos III Madrid Spain Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases University of Texas Health Sciences Center San Antonio TX USA Genetics of Neurodegenerative Diseases Unit IIB Sant Pau Barcelona Spain Department of Neurology Hospital Universitario Donostia S

A polygenic score (PGS) for Alzheimer’s disease (AD) was derived recently from data on genome-wide significant loci in European ancestry populations. We applied this PGS to populations in 17 European countries and observed a consistent association with the AD risk, age at onset and cerebrospinal fluid levels of AD biomarkers, independently of apolipoprotein E locus (APOE). This PGS was also associated with the AD risk in many other populations of diverse ancestries. A cross-ancestry polygenic risk score improved the association with the AD risk in most of the multiancestry populations tested when theAPOEregion was included. Finally, we found that the PGS/polygenic risk score captured AD-specific information because the association weakened as the diagnosis was broadened. In conclusion, a simple PGS captures the AD-specific genetic information that is common to populations of different ancestries, although studies of more diverse populations are still needed to better characterize the genetics of AD.

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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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CALET results after three years on the International Space Station

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Journal of Physics: Conference Series 2020年第1期1468卷

作者： Y Asaoka O Adriani Y Akaike K Asano M G Bagliesi E Berti G Bigongiari W R Binns S Bonechi M Bongi A Bruno J H Buckley N Cannady G Castellini C Checchia M L Cherry G Collazuol V Di Felice K Ebisawa H Fuke T G Guzik T Hams K Hibino M Ichimura K Ioka W Ishizaki M H Israel K Kasahara J Kataoka R Kataoka Y Katayose C Kato N Kawanaka Y Kawakubo K Kohri H S Krawczynski J F Krizmanic J Link P Maestro P S Marrocchesi A M Messineo J W Mitchell S Miyake A A Moiseev M Mori N Mori H M Motz K Munakata S Nakahira J Nishimura G A de Nolfo S Okuno N Opsina J F Ormes S Ozawa L Pacini F Palma V Pal'shin P Papini B F Rauch S B Ricciarini K Sakai T Sakamoto M Sasaki Y Shimizu A Shiomi R Sparvoli P Spillantini F Stolzi S Sugita J E Suh A Sulaj I Takahashi M Takita T Tamura T Terasawa S Torii Y Tsunesada Y Uchihori E Vannuccini J P Wefel K Yamaoka S Yanagita A Yoshida K Yoshida WISE Waseda University 3-4-1 Okubo Shinjuku Tokyo 169-8555 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Department of Physics University of Florence Via Sansone 1 - 50019 Sesto Fiorentino Italy INFN Sezione di Florence Via Sansone 1 - 50019 Sesto Fiorentino Italy Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA ICRR The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 - 56127 Pisa Italy Department of Physics and McDonnell Center for the Space Sciences Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Heliospheric Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA IFAC CNR Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA University of Rome 'Tor Vergata' Via della Ricerca Scientifica 1 00133 Rome Italy INFN Sezione di Rome 'Tor Vergata' Via della Ricerca Scientifica 1 00133 Rome Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan CRESST and Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori 036-8561 Japa

The CALET (CALorimetric Electron Telescope) space experiment, which is currently conducting direct cosmic-ray observations onboard the International Space Station (ISS), is an all-calorimetric instrument optimized for cosmic-ray electron measurements with capability to measure hadrons and gamma-rays. Since the start of observation in October 2015, smooth and continuous operations have taken place. In this paper, we will give a brief summary of the CALET observations ranging from charged cosmic rays, gamma-rays, to space weather, while focusing on the energy spectra of electrons and protons.

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Formalization of resilience for constraint-based dynamic systems

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Journal of Reliable Intelligent Environments 2016年第1期2卷 17-35页

作者： Schwind, Nicolas Magnin, Morgan Inoue, Katsumi Okimoto, Tenda Sato, Taisuke Minami, Kazuhiro Maruyama, Hiroshi Principles of Informatics Research Division National Institute of Informatics Tokyo Japan Transdisciplinary Research Integration Center The Research Organization of Information and Systems Tokyo Japan Institut de Recherche en Communications et Cybernétique de Nantes LUNAM Université École Centrale de Nantes IRCCyN UMR CNRS 6597 Nantes France Tokyo Japan Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology Tokyo Japan Graduate School of Maritime Science Kobe University Kobe Japan Tokyo Japan Department of Statistical Modeling The Institute of Statistical Mathematics The Research Organization of Information and Systems Tokyo Japan Tokyo Japan

Many researchers in different fields are interested in building resilient systems that can absorb shocks and recover from damages caused by unexpected large-scale events. Existing approaches mainly focus on the evaluation of the resilience of systems from a qualitative point of view, or pay particular attention to some domain-dependent aspects of the resilience. In this paper, we introduce a very general, abstract computational model rich enough to represent a large class of constraint-based dynamic systems. Taking our inspiration from the literature, we propose a simple parameterized property which captures the main features of resilience independently from a particular application domain, and we show how to assess the resilience of a constraint-based dynamic system through this new resilience property. © 2015, Springer International Publishing Switzerland.

关键词： Constraint theory

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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