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IEEE International Conference on Cognitive Informatics

作者： Mukesh Kumar Rohil Rohan Kumar Rohil Divyesakshi Rohil Anurag Runthala Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani India Department of Mathematics Birla Institute of Technology and Science Pilani Pilani India Department of Chemistry Birla Institute of Technology and Science Pilani Pilani India Dept. of Data Protection and Governance Engineering Veritas Technologies LLC Mountain View USA

ISBN: (纸本)9781538633618

The task of providing Natural Language Interface (NLI) to any domain specific knowledge base is much demanding despite (potentially) favorable factors like low volume of vocabulary, unambiguous and precise meaning of words, less number of relations among the entities, etc. The simplification of this task has been proposed and presented in this research. The authors have made a successful effort to develop an NLI system to answer the user's simple queries (in English) about the properties of chemical elements and their grouping in the Periodic Table. Adding to the ease, the user is not required to know anything about the structure of the knowledge base of the elements, since the software is implemented (using Logic Programming constructs) in Prolog wherein program and data are treated indistinguishably. Firstly, the system accepts a query and subsequently, it can analyze and understand the query, if the query contains all words within the domain specific vocabulary. Finally, it efficiently searches the knowledge base to answer the query, by reducing search space using artificial intelligence techniques (like symbolic manipulation). If the query is not understood by the system, it reports to the user the words not available in the knowledge base and the particular relations among the entities which could not be set. The knowledge base (~150 KB) contains the properties of chemical elements, their arrangement in Periodic Table and the inter-relationships among these properties. In a nutshell, the research suggests that to develop an NLI to a domain specific knowledge base, it is better to develop a parser capable of handling the entities and their interrelationships as understood in the domain; hence, only little is to be coded for the various grammars, languages, transition networks, etc.

关键词： Natural language processing Knowledge based systems Handheld computers Cognitive informatics Cognitive systems

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Author Correction: CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

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Nature communications 2025年第1期16卷 1569页

作者： Chunge Zhong Wen-Jie Jiang Yingjia Yao Zexu Li You Li Shengnan Wang Xiaofeng Wang Wenjuan Zhu Siqi Wu Jing Wang Shuangshuang Fan Shixin Ma Yeshu Liu Han Zhang Wenchang Zhao Lu Zhao Yi Feng Zihan Li Ruifang Guo Li Yu Fengyun Pei Jun Hu Xingzhi Feng Zihuan Yang Zhengjia Yang Xueying Yang Yue Hou Danni Zhang Dake Xu Ren Sheng Yihao Li Lijun Liu Hua-Jun Wu Jun Huang Teng Fei Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health Sciences Northeastern University Shenyang 110819 China. National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 China. Foshan Graduate School of Innovation Northeastern University Foshan 528311 China. Peking University Third Hospital Beijing 100191 China. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Clinical Research Center the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Institute of Gastroenterology Guangzhou China. Department of Cardiothoracic Surgery Jinqiu Hospital of Liaoning Province Shenyang China. Department of Thoracic Surgery The Fourth Affiliated Hospital of China Medical University Shenyang China. Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education) School of Materials Science and Engineering Northeastern University Shenyang China. BeiGene Institute BeiGene (Shanghai) Research & Development Co. Ltd 200131 Shanghai China. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Peking University Cancer Hospital & Institute Beijing 100142 China. hjwu@***. Department of Biomedical Informatics School of Basic Medical Sciences Peking University Health Science Center Beijing 100191 China. hjwu@***. Center for Precision Medicine Multi-Omics Research Institute of Advanced Clinical Medicine Peking University Beijing 100191 China. hjwu@***. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. huan

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Risk prediction models for patients who leave without being seen 67

Risk prediction models for patients who leave without being ...

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67th Annual Conference and Expo of the institute of Industrial Engineers 2017

作者： Sun, Muyang Kang, Hyojung Data Science Institute University of Virginia CharlottesvilleVA22904 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States

ISBN: (纸本)9780983762461

In the U.S., approximately two percent of patients who visit an emergency department (ED) leave the system without being seen by care providers. Leaving without being seen (LWBS) is a serious issue in the ED because appropriate medical treatment may be delayed, and this delay can lead to poor patient health outcomes. Previous studies have examined various factors associated with LWBS in the ED. However, these studies tend to focus on developing descriptive models that estimate a probability of LWBS patients without considering patients' willingness to wait to see a care provider in the ED. The objective of this paper is to develop a new risk index that incorporates patients' ED length of stay (LOS). We compared the performance of this new index with the risk computed through a machine-learning method that does not include the time component. The result showed that the newly-introduced, time-sensitive risk index performs better at identifying LWBS patients compared to other prediction models considered in the study. This new index may help EDs identify patients who tend to leave the system after staying in the ED for a short amount of time and may help intervene promptly before these patients leave the system.

关键词： Risk assessment

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Polarized x-rays constrain the disk-jet geometry in the black hole x-ray binary Cygnus X-1

arXiv

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arXiv 2022年

作者： Krawczynski, Henric Muleri, Fabio Dovčiak, Michal Veledina, Alexandra Cavero, Nicole Rodriguez Svoboda, Jiri Ingram, Adam Matt, Giorgio Garcia, Javier A. Loktev, Vladislav Negro, Michela Poutanen, Juri Kitaguchi, Takao Podgorný, Jakub Rankin, John Zhang, Wenda Berdyugin, Andrei Berdyugina, Svetlana V. Bianchi, Stefano Blinov, Dmitry Capitanio, Fiamma Di Lalla, Niccolò Draghis, Paul Fabiani, Sergio Kagitani, Masato Kravtsov, Vadim Kiehlmann, Sebastian Latronico, Luca Lutovinov, Alexander A. Mandarakas, Nikos Marin, Frédéric Marinucci, Andrea Miller, Jon M. Mizuno, Tsunefumi Molkov, Sergey V. Omodei, Nicola Petrucci, Pierre-Olivier Ratheesh, Ajay Sakanoi, Takeshi Semena, Andrei N. Skalidis, Raphael Soffitta, Paolo Tennant, Allyn F. Thalhammer, Phillipp Tombesi, Francesco Weisskopf, Martin C. Wilms, Joern Zhang, Sixuan Agudo, Iván Antonelli, Lucio A. Bachetti, Matteo Baldini, Luca Baumgartner, Wayne H. Bellazzini, Ronaldo Bongiorno, Stephen D. Bonino, Raffaella Brez, Alessandro Bucciantini, Niccolò Castellano, Simone Cavazzuti, Elisabetta Ciprini, Stefano Costa, Enrico De Rosa, Alessandra Del Monte, Ettore Di Gesu, Laura Di Marco, Alessandro Donnarumma, Immacolata Doroshenko, Victor Ehlert, Steven R. Enoto, Teruaki Evangelista, Yuri Ferrazzoli, Riccardo Gunji, Shuichi Hayashida, Kiyoshi Heyl, Jeremy Iwakiri, Wataru Jorstad, Svetlana G. Karas, Vladimir Kolodziejczak, Jeffery J. Monaca, Fabio La Liodakis, Ioannis Maldera, Simone Manfreda, Alberto Marscher, Alan P. Marshall, Herman L. Mitsuishi, Ikuyuki Ng, Chi-Yung O’Dell, Stephen L. Oppedisano, Chiara Papitto, Alessandro Pavlov, George G. Peirson, Abel L. Perri, Matteo Pesce-Rollins, Melissa Pilia, Maura Possenti, Andrea Puccetti, Simonetta Ramsey, Brian D. Romani, Roger W. Sgrò, Carmelo Slane, Patrick Spandre, Gloria Tamagawa, Toru Tavecchio, Fabrizio Taverna, Roberto Tawara, Yuzuru Thomas, Nicholas E. Trois, Alessio Tsygankov, Sergey Turolla, Roberto Vink, Jacco Wu, Kinwah Xie, Fei Zane, Silvia Physics Department McDonnell Center for the Space Sciences Washington University in St. Louis St. LouisMO63130 United States Istituto di Astrofisica e Planetologia Spaziali Istituto Nazionale di Astrofisica Roma00133 Italy Astronomical Institute The Czech Academy of Sciences Praha 414100 Czech Republic Department of Physics and Astronomy University of Turku 20014 Finland Royal Institute of Technology Stockholm University StockholmSE-106 91 Sweden Space Research Institute The Russian Academy of Sciences Moscow117997 Russia School of Mathematics Statistics and Physics Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Dipartimento di Matematica e Fisica Università degli Studi Roma Tre Roma00146 Italy California Institute of Technology PasadenaCA91125 United States University of Maryland Baltimore County BaltimoreMD21250 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Cluster for Pioneering Research 2-1 Hirosawa Saitama Wako351-0198 Japan Université de Strasbourg Centre national de la recherche scientifique Observatoire Astronomique de Strasbourg Unité Mixte de Recherche 7550 Strasbourg67000 France Astronomical Institute Charles University PragueCZ-18000 Czech Republic National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Leibniz-Institut für Sonnenphysik FreiburgD-79104 Germany IRSOL Istituto Ricerche Solari Aldo e Cele Daccò Faculty of Informatics Università della Svizzera italiana Via Patocci 57 Locarno Switzerland Euler Institute Faculty of Informatics Università della Svizzera italiana Via la Santa 1 Lugano6962 Switzerland Institute of Astrophysics Foundation for Research and Technology-Hellas HeraklionGR-71110 Greece Department of Physics Univ. of Crete HeraklionGR-70013 Greece Department of Physics Kavli Institute for Particle Astroph

A black hole x-ray binary (XRB) system forms when gas is stripped from a normal star and accretes onto a black hole, which heats the gas sufficiently to emit x-rays. We report a polarimetric observation of the XRB Cygnus X-1 using the Imaging x-ray Polarimetry Explorer. The electric field position angle aligns with the outflowing jet, indicating that the jet is launched from the inner x-ray emitting region. The polarization degree is 4.01 ± 0.20% at 2 to 8 kiloelectronvolts, implying that the accretion disk is viewed closer to edge-on than the binary orbit. The observations reveal that hot x-ray emitting plasma is spatially extended in a plane perpendicular to the jet axis, not parallel to the jet. Copyright © 2022, The Authors. All rights reserved.

关键词： Stars

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Author Correction: Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

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Nature biotechnology 2023年第7期41卷 1026页

作者： Rosa Lundbye Allesøe Agnete Troen Lundgaard Ricardo Hernández Medina Alejandro Aguayo-Orozco Joachim Johansen Jakob Nybo Nissen Caroline Brorsson Gianluca Mazzoni Lili Niu Jorge Hernansanz Biel Cristina Leal Rodríguez Valentas Brasas Henry Webel Michael Eriksen Benros Anders Gorm Pedersen Piotr Jaroslaw Chmura Ulrik Plesner Jacobsen Andrea Mari Robert Koivula Anubha Mahajan Ana Vinuela Juan Fernandez Tajes Sapna Sharma Mark Haid Mun-Gwan Hong Petra B Musholt Federico De Masi Josef Vogt Helle Krogh Pedersen Valborg Gudmundsdottir Angus Jones Gwen Kennedy Jimmy Bell E Louise Thomas Gary Frost Henrik Thomsen Elizaveta Hansen Tue Haldor Hansen Henrik Vestergaard Mirthe Muilwijk Marieke T Blom Leen M 't Hart Francois Pattou Violeta Raverdy Soren Brage Tarja Kokkola Alison Heggie Donna McEvoy Miranda Mourby Jane Kaye Andrew Hattersley Timothy McDonald Martin Ridderstråle Mark Walker Ian Forgie Giuseppe N Giordano Imre Pavo Hartmut Ruetten Oluf Pedersen Torben Hansen Emmanouil Dermitzakis Paul W Franks Jochen M Schwenk Jerzy Adamski Mark I McCarthy Ewan Pearson Karina Banasik Simon Rasmussen Søren Brunak Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. Department of Health Technology Technical University of Denmark Kongens Lyngby Denmark. Copenhagen Research Centre for Mental Health Mental Health Centre Copenhagen Copenhagen University Hospital Copenhagen Denmark. Department of Immunology and Microbiology Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. C.N.R. Institute of Neuroscience Padova Italy. Wellcome Centre for Human Genetics University of Oxford Oxford UK. Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Research Unit of Molecular Epidemiology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Bavaria Germany. Institute of Epidemiology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Bavaria Germany. Chair of Food Chemistry and Molecular and Sensory Science Technical University of Munich Freising Germany. Metabolomics and Proteomics Core Helmholtz Zentrum Muenchen German Research Center for Environmental Health Neuherberg Germany. Affinity Proteomics Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH Royal Institute of Technology Solna Sweden. Research and Development Global Development Translational Medicine and Clinical Pharmacology Sanofi-Aventis Deutschland Frankfurt Germany. Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. University of Exeter Medical School Exeter UK. The Immunoassay Biomarker Core Laboratory School of Medicine University of Dundee Dundee UK. Research Centre for Optimal Health Department of Life Sciences University of Westminster London UK. Section

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Layerwise perturbation-based adversarial training for hard drive health degree prediction

arXiv

引用

arXiv 2018年

作者： Zhang, Jianguo Wang, Ji He, Lifang Li, Zhao Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL United States College of Systems Engineering National University of Defense Technology Changsha Hunan China Weill Cornell Department of Healthcare Policy & Research Cornell University NY United States Alibaba Group Hangzhou Zhejiang China Shanghai Institute for Advanced Communication and Data Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China

With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance. Given the highly imbalanced SMART datasets, it is a nontrivial work to predict the health degree precisely. The proposed model would encounter overfitting and biased fitting problems if it is trained by the traditional methods. In order to resolve this problem, we propose two strategies to better utilize imbalanced data and improve performance. Firstly, we design a layerwise perturbation-based adversarial training method which can add perturbations to any layers of a neural network to improve the generalization of the network. Secondly, we extend the training method to the semi-supervised settings. Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model. Our extensive experiments on two real-world hard drive datasets demonstrate the superiority of the proposed schemes for both supervised and semi-supervised classification. The model trained by the proposed method can correctly predict the hard drive health status 5 and 15 days in advance. Finally, we verify the generality of the proposed training method in other similar anomaly detection tasks where the dataset is imbalanced. The results argue that the proposed methods are applicable to other domains. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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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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Expanding the horizon of automated metamaterials discovery via quantum annealing

arXiv

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arXiv 2019年

作者： Kitai, Koki Guo, Jiang Ju, Shenghong Tanaka, Shu Tsuda, Koji Shiomi, Junichiro Tamura, Ryo Graduate School of Frontier Sciences University of Tokyo Chiba277-8568 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Green Computing Systems Research Organization Waseda University Tokyo162-0042 Japan JST PRESTO Saitama332-0012 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Ibaraki305-0044 Japan

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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Contesting the BACPAP consortium's consensus - Authors' reply

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The Lancet. Rheumatology 2024年第7期6卷 e419-e420页

作者： Eva Kosek Jo Nijs Alessandro Chiarotto Chad Cook Lieven A Danneels César Fernández-de-Las-Peñas Paul W Hodges Bart Koes Adriaan Louw Raymond Ostelo Gwendolyne G M Scholten-Peeters Michele Sterling Othman Alkassabi Hana Alsobayel Darren Beales Paraskevi Bilika Jacqui R Clark Liesbet De Baets Christophe Demoulin Rutger M J de Zoete Ömer Elma Annelie Gutke Rikard Hanafi Sabina Hotz Boendermaker Eva Huysmans Eleni Kapreli Mari Lundberg Anneleen Malfliet Ney Meziat Filho Felipe J J Reis Lennard Voogt Kory Zimney Rob Smeets Bart Morlion Kurt de Vlam Steven Z George Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden Department of Surgical Sciences Uppsala University Uppsala Sweden. Pain in Motion Research Group Department of Physiotherapy Human Physiology and Anatomy Faculty of Physical Education and Physiotherapy Vrije Universiteit Brussel Brussels Belgium Chronic Pain Rehabilitation Department of Physical Medicine and Physiotherapy University Hospital Brussels Brussels Belgium Department of Health and Rehabilitation Unit of Physiotherapy Institute of Neuroscience and Physiology Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Electronic address: jo.nijs@vub.be. Department of General Practice Erasmus University Medical Center University Medical Center Rotterdam Rotterdam Netherlands Department of Health Sciences Vrije Universiteit Amsterdam Amsterdam Netherlands. Department of Orthopaedic Surgery Duke Clinical Research Institute and Department of Population Health Sciences Duke University Durham NC USA. Department of Rehabilitation Sciences Ghent University Ghent Belgium. Department of Physical Therapy Occupational Therapy Physical Medicine and Rehabilitation Universidad Rey Juan Carlos Madrid Spain. School of Health and Rehabilitation Sciences University of Queensland Brisbane QLD Australia. Research Unit of General Practice Department of Public Health and Center for Muscle and Joint Health University of Southern Denmark Odense Denmark. Department of Pain Science Evidence in Motion Story City IA USA. Department of Health Sciences Vrije Universiteit Amsterdam Amsterdam Netherlands Amsterdam Movement Sciences and Department of Epidemiology and Data Science Amsterdam University Medical Center Vrije Universiteit Amsterdam Amsterdam Netherlands. Faculty of Behavioural and Movement Sciences Vrije Universiteit Amsterdam Amsterdam Netherlands. RECOVER Injury Research Centre University of Queensland Brisbane QLD Australia. Physiotrio Riyadh Saudi Arabia College of Applied

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