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Constraints on Cosmic Strings Using Data from the Third Advanced LIGO–Virgo Observing Run

Physical Review Letters 2021年第24期126卷 241102-241102页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Auclair P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. D LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S.Angelo I-80126 Napoli Italy OzGrav School of Physics and Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illi

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO–Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10−15. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

关键词： Cosmic strings & domain walls Gravitational waves

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Search for Gravitational Waves Associated with Fast Radio Bursts Detected by CHIME/FRB during the LIGO-Virgo Observing Run O3a

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Astrophysical Journal 2023年第2期955卷 155-155页

作者： Abbott, R. Abbott, T.D. Acernese, F. Ackley, K. Adams, C. Adhikari, N. Adhikari, R.X. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Agatsuma, K. Aggarwal, N. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Allocca, A. Altin, P.A. Amato, A. Anand, C. Anand, S. Ananyeva, A. Anderson, S.B. Anderson, W.G. Ando, M. Andrade, T. Andres, N. Andrić, T. Angelova, S.V. Ansoldi, S. Antelis, J.M. Antier, S. Appert, S. Arai, Koya Arai, Y. Araki, S. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Aronson, S.M. Arun, K.G. Asada, H. Asali, Y. Ashton, G. Aso, Y. Assiduo, M. Aston, S.M. Astone, P. Aubin, F. Austin, C. Babak, S. Badaracco, F. Bader, M.K.M. Badger, C. Bae, S. Bae, Y. Baer, A.M. Bagnasco, S. Bai, Y. Baiotti, L. Baird, J. Bajpai, R. Ball, M. Ballardin, G. Ballmer, S.W. Balsamo, A. Baltus, G. Banagiri, S. Bankar, D. Barayoga, J.C. Barbieri, C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartlett, J. Barton, M.A. Bartos, I. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Bejger, M. Belahcene, I. Benedetto, V. Beniwal, D. Bennett, T.F. Bentley, J.D. BenYaala, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bhandare, R. Bhardwaj, U. Bhattacharjee, D. Bhaumik, S. Bilenko, I.A. Billingsley, G. Bini, S. Birney, R. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boer, M. Bogaert, G. Boldrini, M. Bonavena, L.D. Bondu, F. Bonilla, E. Bonnand, R. Booker, P. Boom, B.A. Bork, R. Boschi, V. Bose, N. Bose, S. Bossilkov, V. Boudart, V. Bouffanais, Y. Boumerdassi, A. Bozzi, A. Bradaschia, C. Brady, P.R. Bramley, A. Branch, A. Branchesi, M. Brau, J.E. Breschi, M. Briant, T. Briggs, J.H. Brillet, A. Brinkmann, M. Brockill, P. Brooks, A.F. Brooks, J. Brown, D.D. Brunett, S. Bruno, G. Bruntz, R. Bryant, J. Buchanan, J. B LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Louisiana State University Baton Rouge 70803 LA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia LIGO Livingston Observatory Livingston 70754 LA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston 60208 IL United States Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Università di Napoli “Federico II Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière Villeurbanne F-69622

We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTC-2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 10⁵¹-10⁵⁷ erg for models with central GW frequencies in the range 70-3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB. © 2023. The Author(s). Published by the American Astronomical Society.

关键词： Gravitational wave astronomy Radio transient sources

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A novel self-learning optimal control approach for decentralized guaranteed cost control of a class of complex nonlinear systems

A novel self-learning optimal control approach for decentral...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Ding Wang Hongwen Ma Pengfei Yan Derong Liu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

In this paper, a novel self-learning optimal control approach is established to design the decentralized guaranteed cost control of a class of complex nonlinear systems under uncertain environment. By expressing the interconnected sub-systems as a whole system, establishing an appropriate bounded function, and defining a modified cost function, the decentralized guaranteed cost control problem is transformed into an optimal control problem. Then, the online policy iteration algorithm is employed to solve iteratively the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system. A critic neural network is constructed to obtain the optimal control approximately. At last, a simulation example is provided to verify the effectiveness of the present control approach.

关键词： Cost function Optimal control Uncertainty Nonlinear systems Feedback control Mathematical model

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Multiple data-based ADP structures to solve the infinite horizon optimal control problem

Multiple data-based ADP structures to solve the infinite hor...

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International Conference on Information Science and Technology (ICIST)

作者： Ruizhuo Song Qinglai Wei School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

This paper proposes the multiple actor-critic structures to obtain the optimal control via input-output data. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance index functions may be define for disparate categories. Neural networks are used to approximate the critic network and action network, respectively. It is proven that the model error asymptotically converges to zero and the closed unknown system is uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.

关键词： Programming Gold Optical computing

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Distributed Adaptive Output Regulation controllers of Heterogeneous Linear Multi-agent systems with Directed Graphs

Distributed Adaptive Output Regulation Controllers of Hetero...

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第三十四届中国控制会议

作者： Zhongkui Li Michael Z.Q.Chen Zhengtao Ding State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Aerospace Engineering College of Engineering Peking University Department of Mechanical Engineering The University of Hong Kong The Control Systems Centre School of Electrical and Electronic Engineering University of ManchesterSackville Street Building

ISBN: (纸本)9781467374439

This paper considers the cooperative output regulation problem for linear multi-agent systems with a directed communication graph, heterogeneous linear agent dynamics, and an exosystem whose output is available to only a subset of agents. By using a distributed adaptive observer for the agents to estimate the exogenous signal, a distributed adaptive controller is designed. Compared with the existing works, one main contribution of this paper is that the proposed control schemes can be designed and implemented by each agent in a fully distributed fashion for general directed graphs.

关键词： Multi-agent system,output regulation,cooperative control,adaptive control.

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An Adaptive Method based on QRCP Decomposition for Single Sample Problem 33

An Adaptive Method based on QRCP Decomposition for Single Sa...

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第三十三届中国控制会议

作者： HU Changhui LU Xiaobo DU Yijun School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of EducationSoutheast University

ISBN: (纸本)9781479947249

In this paper,an adaptive approximation image reconstruction method based on orthogonal triangular with column pivoting(QRCP) decomposition algorithm is proposed for single sample problem in face *** using QRCP the single sample and its transpose are decomposed to two sets of basis *** an adaptive approximation image reconstruction method is proposed to reconstruct two approximation images from the two basis image sets *** single training sample and its two approximation images of each object form a new training set,which can make the fisher linear discriminant analysis(FLDA) be applied to single sample problem in face *** performance of the proposed method is verified on Yale,FERET,and ORL face *** experimental results indicate that the proposed method is efficient and outperforms some existing methods which are proposed to overcome the single sample problem.

关键词： Face recognition,single sample problem,orthogonal triangular decomposition with column pivoting,adaptive approximation image reconstruction method

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GROUP CONSENSUS FOR MULTIPLE NETWORKED EULER-LAGRANGE systems WITH PARAMETRIC UNCERTAINTIES

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Journal of systems Science & complexity 2014年第4期27卷 632-649页

作者： HU Hongxiang ZHANG Zhe YU Li YU Wenwu XIE Guangming Department of Mathematics Hangzhou Dianzi University Zhejiang Provincial United Key Laboratory of Embedded Systems Department of AutomationZhejiang University of Technology Department of Mathematics Southeast University School of Electrical and Computer Engineering RMIT University Faculty of Engineering King Abdulaziz University Center for Systems and Control State Key Laboratory of Turbulence and Complex SystemsCollege of EngineeringPeking University

In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain *** the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus *** is shown that the group consensus is reachable even when the system contains the uncertain ***,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is *** results are finally provided to validate the effectiveness of the theoretical analysis.

关键词： Euler-Lagrange system group consensus multi-agent systems switching topologies.

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Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 248-256页

作者： Zhen Zhang Dongbin Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Department of Electric Engineering College of Automation EngineeringQingdao University

In this paper,we propose a clique-based sparse reinforcement learning(RL) algorithm for solving cooperative *** aim is to accelerate the learning speed of the original sparse RL algorithm and to make it applicable for tasks decomposed in a more general ***,a transition function is estimated and used to update the Q-value function,which greatly reduces the learning ***,it is more reasonable to divide agents into cliques,each of which is only responsible for a specific *** this way,the global Q-value function is decomposed into the sum of several simpler local Q-value *** decomposition is expressed by a factor graph and exploited by the general maxplus algorithm to obtain the greedy joint *** results show that the proposed approach outperforms others with better performance.

关键词： Sensors Games Learning (artificial intelligence) Approximation algorithms Algorithm design and analysis Heuristic algorithms Sparse matrices

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Online critic-identifier-actor algorithm for optimal control of nonlinear systems

Online critic-identifier-actor algorithm for optimal control...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Hanquan Lin Qinglai Wei Derong Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

In this paper, a novel critic-identifier-actor optimal control scheme is designed for discrete-time affine nonlinear systems with uncertainties. A neural identifier is established to learn the unknown control coefficient matrix for affine nonlinear system working together with an actor-critic-based scheme to solve the optimal control in online and forward-in-time manner without value or policy iterations. A critic network learns approximate value function at each step. Another actor network attempts to improve the current policy based on the approximate value function. The weights of all neural networks (NNs) are updated at each sampling instant. Lyapunov theory is utilized to prove the stability of the closed-loop system. A simulation example is provided to illustrate the effectiveness of the developed method.

关键词： Optimal control Artificial neural networks Nonlinear systems Discrete-time systems Mathematical model Estimation error

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

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Nature 2023年第7965期618卷 E19-E20页

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

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