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arXiv 2018年

作者： Gottesman, Omer Johansson, Fredrik Meier, Joshua Dent, Jack Lee, Donghun Srinivasan, Srivatsan Zhang, Linying Ding, Yi Wihl, David Peng, Xuefeng Yao, Jiayu Lage, Isaac Mosch, Christopher Lehman, Li-Wei H. Komorowski, Matthieu Faisal, Aldo Celi, Leo Anthony Sontag, David Doshi-Velez, Finale Paulson School of Engineering and Applied Sciences Harvard University Institute for Medical Engineering and Science MIT T.H. Chan School of Public Health Harvard University Department of Statistics Harvard University Laboratory for Computational Physiology Harvard-MIT Health Sciences & Technology MIT Department of Surgery and Cancer Faculty of Medicine Imperial College London Department of Bioengineering Imperial College London Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center MIT Critical Data

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Author Correction: Genetic effects on the timing of parturition and links to fetal birth weight

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Nature genetics 2023年第7期55卷 1250页

作者： Pol Solé-Navais Christopher Flatley Valgerdur Steinthorsdottir Marc Vaudel Julius Juodakis Jing Chen Triin Laisk Abigail L LaBella David Westergaard Jonas Bacelis Ben Brumpton Line Skotte Maria C Borges Øyvind Helgeland Anubha Mahajan Matthias Wielscher Frederick Lin Catherine Briggs Carol A Wang Gunn-Helen Moen Robin N Beaumont Jonathan P Bradfield Abin Abraham Gudmar Thorleifsson Maiken E Gabrielsen Sisse R Ostrowski Dominika Modzelewska Ellen A Nohr Elina Hypponen Amit Srivastava Octavious Talbot Catherine Allard Scott M Williams Ramkumar Menon Beverley M Shields Gardar Sveinbjornsson Huan Xu Mads Melbye William Lowe Luigi Bouchard Emily Oken Ole B Pedersen Daniel F Gudbjartsson Christian Erikstrup Erik Sørensen Rolv T Lie Kari Teramo Mikko Hallman Thorhildur Juliusdottir Hakon Hakonarson Henrik Ullum Andrew T Hattersley Line Sletner Mario Merialdi Sheryl L Rifas-Shiman Thora Steingrimsdottir Denise Scholtens Christine Power Jane West Mette Nyegaard John A Capra Anne H Skogholt Per Magnus Ole A Andreassen Unnur Thorsteinsdottir Struan F A Grant Elisabeth Qvigstad Craig E Pennell Marie-France Hivert Geoffrey M Hayes Marjo-Riitta Jarvelin Mark I McCarthy Deborah A Lawlor Henriette S Nielsen Reedik Mägi Antonis Rokas Kristian Hveem Kari Stefansson Bjarke Feenstra Pål Njolstad Louis J Muglia Rachel M Freathy Stefan Johansson Ge Zhang Bo Jacobsson Department of Obstetrics and Gynaecology Sahlgrenska Academy Institute of Clinical Science University of Gothenburg Gothenburg Sweden. pol.sole.navais@gu.se. Department of Obstetrics and Gynaecology Sahlgrenska Academy Institute of Clinical Science University of Gothenburg Gothenburg Sweden. deCODE genetics/Amgen Reykjavik Iceland. Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH USA. Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH USA. Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia. Department of Biological Sciences Vanderbilt University Nashville TN USA. Novo Nordisk Foundation Center for Protein Research University of Copenhagen Copenhagen Denmark. Department of Obstetrics and Gynaecology Copenhagen University Hospital Hvidovre Hvidovre Denmark. Methods and Analysis Statistics Denmark Copenhagen Denmark. K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and Nursing Norwegian University of Science and Technology Trondheim Norway. Department of Epidemiology Research Statens Serum Institut Copenhagen Denmark. MRC Integrative Epidemiology Unit University of Bristol Bristol UK. Population Health Sciences Bristol Medical School University of Bristol Bristol UK. Department of Genetics and Bioinformatics Health Data and Digitalization Norwegian Institute of Public Health Oslo Norway. Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK. Genentech South San Francisco CA USA. Department of Epidemiology and Biostatistics MRC-PHE Centre for Environment and Health School of Public Health Imperial College London London UK. Department of Dermatology Medical University of Vienna Vienna Austria. Northwestern University Feinberg School of Medicine Chicago IL USA. Department of Population Medicine Harvard M

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Revisiting simple neural networks for learning representations of knowledge graphs

arXiv

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arXiv 2017年

作者： Ravishankar, Srinivas Chandrahas Talukdar, Partha Pratim Department of Computer Science R.V. College of Engineering Bangalore India Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge graph

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First-passage and first-hitting times of Lévy flights and Lévy walks

arXiv

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arXiv 2019年

作者： Palyulin, Vladimir V. Blackburn, George Lomholt, Michael A. Watkins, Nicholas W. Metzler, Ralf Klages, Rainer Chechkin, Aleksei V. Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow121205 Russia Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 DresdenD-01187 Germany £ Centre for Fusion Space and Astrophysics University of Warwick Coventry United Kingdom MEMPHYS - Centre for Biomembrane Physics Department of Physics Chemistry and Pharmacy University of Southern Denmark Odense M5230 Denmark Institute for Physics & Astronomy University of Potsdam Potsdam-GolmD-14476 Germany Centre for the Analysis of Time Series London School of Economics and Political Sciences London United Kingdom Faculty of Science Technology Engineering and Mathematics Open University Milton Keynes United Kingdom Institut für Theoretische Physik Technische Universität Berlin Hardenbergstraße 36 Berlin10623 Germany Queen Mary University of London School of Mathematical Sciences Mile End Road LondonE1 4NS United Kingdom Akhiezer Institute for Theoretical Physics National Science Centre "Kharkov Institute of Physics and Technology" Kharkov61108 Ukraine

For both Lévy flight and Lévy walk search processes we analyse the full distribution of first-passage and first-hitting (or first-arrival) times. These are, respectively, the times when the particle moves across a point at some given distance from its initial position for the first time, or when it lands at a given point for the first time. For Lévy motions with their propensity for long relocation events and thus the possibility to jump across a given point in space without actually hitting it ("leapovers"), these two definitions lead to significantly different results. We study the first-passage and first-hitting time distributions as functions of the Lévy stable index, highlighting the different behaviour for the cases when the first absolute moment of the jump length distribution is finite or infinite. In particular we examine the limits of short and long times. Our results will find their application in the mathematical modelling of random search processes as well as computer algorithms. Copyright © 2019, The Authors. All rights reserved.

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Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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NeoN: Neuromorphic control for autonomous robotic navigation

NeoN: Neuromorphic control for autonomous robotic navigation

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IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

作者： J. Parker Mitchell Grant Bruer Mark E. Dean James S. Plank Garrett S. Rose Catherine D. Schuman Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN Computational Data Analytics Oak Ridge National Laboratory Oak Ridge TN

In this paper we describe the use of a new neuromorphic computing framework to implement the navigation system for a roaming, obstacle avoidance robot. Using a Dynamic Adaptive Neural Network Array (DANNA) structure, our TENNLab (Laboratory of Tennesseans Exploring Neural Networks) hardware/software co-design framework and evolutionary optimization (EO) as the training algorithm, we create, train, implement, and test a spiking neural network autonomous robot control system using an array of neuromorphic computing elements built on an FPGA. The simplicity and flexibility of the DANNA neuromorphic computing elements allow for sufficient scale and connectivity on a Xilinx Kintex-7 FPGA to support sensory input and motor control for a mobile robot to navigate a dynamically changing environment. We further describe how more complex capabilities can be added using the same platform, e.g. object identification and tracking.

关键词： Neurons Robot sensing systems Neuromorphics Biological neural networks Computer architecture

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Signed iterative random forests to identify enhancer-associated transcription factor binding

arXiv

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arXiv 2018年

作者： Kumbier, Karl Basu, Sumanta Frise, Erwin Celniker, Susan E. Brown, James B. Yu, Bin University of California San Francisco Department of Pharmaceutical Chemistry San Francisco94158 United States Cornell University Department of Statistics and Data Science Ithaca14853 United States Lawrence Berkeley National Biological Systems and Engineering Division Berkeley94720 United States Lawrence Berkeley National Laboratory Division of Environmental Genomics and Systems Biology Berkeley94720 United States University of California Berkeley Department of Statistics Berkeley94720 United States University of California Berkeley Department of Electrical Engineering and Computer Science Berkeley94720 United States University of California Berkeley Center for Computational Biology Berkeley94720 United States

Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable machine learning method: signed iterative random forests (siRF), which we use to infer regulatory interactions among transcription factors and functional binding signatures surrounding enhancer elements in Drosophila melanogaster. Copyright © 2018, The Authors. All rights reserved.

关键词： Transcription factors

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Computer-aided whole-cell design: Taking a holistic approach by integrating synthetic with systems biology

arXiv

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arXiv 2020年

作者： Marucci, Lucia Barberis, Matteo Karr, Jonathan Ray, Oliver Race, Paul R. de Souza Andrade, Miguel Grierson, Claire Hoffmann, Stefan Andreas Landon, Sophie Rech, Elibio Rees-Garbutt, Joshua Seabrook, Richard Shaw, William Woods, Christopher Department of Engineering Mathematics University of Bristol BristolBS81UB United Kingdom School of Cellular and Molecular Medicine University of Bristol BS81TD Bristol United Kingdom University of Bristol BS81TQ Bristol United Kingdom Systems Biology School of Biosciences and Medicine Faculty of Health and Medical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Centre for Mathematical and Computational Biology CMCB University of Surrey Guildford SurreyGU2 7XH United Kingdom Synthetic Systems Biology and Nuclear Organization Swammerdam Institute for Life Sciences University of Amsterdam Amsterdam1098 XH Netherlands Icahn Institute for Data Science and Genomic Technology Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai 1255 5th Avenue Suite C2 New York10029 United States Department of Computer Science University of Bristol BristolBS81UB United Kingdom School of Biochemistry University of Bristol BristolBS8 1TD United Kingdom Brazilian Agriculture Research Corporation National Institute of Science and Technology - Synthetic Biology BrasíliaDF70770-917 Brazil Universidade de Brasília DF Brazil School of Biological Sciences University of Bristol BristolBS8 1TQ United Kingdom Manchester Institute of Biotechnology University of Manchester 131 Princess Street ManchesterM1 7DN United Kingdom University of Bristol BristolBS8 1UH United Kingdom Department of Bioengineering Imperial College London LondonSW7 2AZ United Kingdom School of Chemistry University of Bristol BristolBS8 1TS United Kingdom

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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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data-driven brain network models predict individual variability in behavior

arXiv

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arXiv 2018年

作者： Bansal, Kanika Medaglia, John D. Bassett, Danielle S. Vettel, Jean M. Muldoon, Sarah F. Department of Mathematics University at Buffalo BuffaloNY United States U.S. Army Research Laboratory Aberdeen Proving GroundMD United States Department of Biomedical Engineering Columbia University New YorkNY United States Department of Psychology Drexel University PhiladelphiaPA United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biomedical Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA United States Department of Psychological and Brain Sciences University of California Santa BarbaraCA United States Computational and Data-Enabled Science and Engineering Program University at Buffalo-SUNY BuffaloNY United States

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to predict individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive complexity, and to pave the way for the development of personalized therapeutics. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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