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2017 ASABE Annual International Meeting

作者： Karunarathne, Sampath A. Chung, Matthias Ogejo, Jactone A. Biological Systems Engineering Virginia Tech BlacksburgVA24061 United States Department of Mathematics Virginia Tech BlacksburgVA24061 United States Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA24061 United States

The biogeochemical processes responsible for production and emission of ammonia from stored liquid dairy manure are governed by environmental factors (e.g. manure temperature, moisture) and manure characteristics (e.g. total ammoniacal nitrogen concentration, pH). These environmental factors and manure characteristics vary spatially as a result of spatially heterogeneous physical, chemical, and biological properties of manure. Existing process-based models used for estimating ammonia emission do not consider these spatial variations leading to inaccurate estimations. In this study, a one-dimensional compartmental process-based model was developed to (i) estimate spatial variation of temperature and substrate concentration (ii) estimate spatial variations and rates of biogeochemical processes, and (iii) estimate production and emission of ammonia from a liquid manure storage tank. Model performance was evaluated using experimental data obtained from National Air Emissions Monitoring Study. A sensitivity analysis was performed and air temperature, manure pH, wind speed, and manure total ammoniacal nitrogen concentration were identified as the most sensitive model inputs. The model was used to estimate ammonia emission from a liquid dairy manure storage of a dairy farm located in Rockingham County in Virginia. Ammonia emission was estimated under different management and weather scenarios: two different manure storage periods in cold season from November to April and in warm season from May to October using historical weather data of the Rockingham County. Results suggest greater ammonia emission and manure nitrogen loss for the manure storage period in warm season compared to the storage period in cold season.

关键词： Liquids

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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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Preface

OpenAccess Series in Informatics

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OpenAccess Series in Informatics 2019年 70卷 IX-X页

作者： Eskevich, Maria de Melo, Gerard Fäth, Christian McCrae, John P. Buitelaar, Paul Chiarcos, Christian Klimek, Bettina Dojchinovski, Milan CLARIN ERIC Utrecht Netherlands Department of Computer Science Rutgers University New BrunswickNJ United States Applied Computational Linguistics Goethe University Frankfurt Germany Insight Centre for Data Analytics National University of Ireland Galway Ireland Agile Knowledge Engineering and Semantic Web University of Leipzig Germany

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Evaluating reinforcement learning algorithms in observational health settings

arXiv

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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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Sequence diversity analyses of an improved rhesus macaque genome enhance its biomedical utility

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science 2020年第6523期370卷 000-000页

作者： Warren, Wesley C. Harris, R. Alan Haukness, Marina Fiddes, Ian T. Murali, Shwetha C. Fernandes, Jason Dishuck, Philip C. Storer, Jessica M. Raveendran, Muthuswamy Hillier, LaDeana W. Porubsky, David Mao, Yafei Gordon, David Vollger, Mitchell R. Lewis, Alexandra P. Munson, Katherine M. DeVogelaere, Elizabeth Armstrong, Joel Diekhans, Mark Walker, Jerilyn A. Tomlinson, Chad Graves-Lindsay, Tina A. Kremitzki, Milinn Salama, Sofie R. Audano, Peter A. Escalona, Merly Maurer, Nicholas W. Antonacci, Francesca Mercuri, Ludovica Maggiolini, Flavia A. M. Catacchio, Claudia Rita Underwood, Jason G. O'Connor, David H. Sanders, Ashley D. Korbel, Jan O. Ferguson, Betsy Kubisch, H. Michael Picker, Louis Kalin, Ned H. Rosene, Douglas Levine, Jon Abbott, David H. Gray, Stanton B. Sanchez, Mar M. Kovacs-Balint, Zsofia A. Kemnitz, Joseph W. Thomasy, Sara M. Roberts, Jeffrey A. Kinnally, Erin L. Capitanio, John P. Skene, J. H. Pate Platt, Michael Cole, Shelley A. Green, Richard E. Ventura, Mario Wiseman, Roger W. Paten, Benedict Batzer, Mark A. Rogers, Jeffrey Eichler, Evan E. Department of Animal Sciences Bond Life Sciences Center University of Missouri Columbia MO 65211 USA. Department of Surgery School of Medicine University of Missouri Columbia MO 65211 USA. Institute of Data Science and Informatics University of Missouri Columbia MO 65211 USA. Human Genome Sequencing Center Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Computational Genomics Laboratory University of California-Santa Cruz Santa Cruz CA 95064 USA. Inscripta Inc. Boulder CO 80301 USA. Department of Genome Sciences University of Washington Seattle WA 98195 USA. Howard Hughes Medical Institute University of Washington Seattle WA 98195 USA. Department of Biomolecular Engineering University of California-Santa Cruz Santa Cruz CA 95064 USA. Department of Biological Sciences Louisiana State University Baton Rouge LA 70803 USA. Institue for Systems Biology Seattle WA 98109 USA. McDonnell Genome Institute Washington University St. Louis MO 63108 USA. Department of Biology University of Bari ’Aldo Moro’ 70125 Bari Italy. Pacific Biosciences of California Seattle WA 94025 USA. Department of Pathology and Laboratory Medicine Wisconsin National Primate Research Center University of Wisconsin-Madison Madison WI 53711 USA. European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany. Division of Genetics Oregon National Primate Research Center Oregon Health and Science University Beaverton OR 97006 USA. Tulane National Primate Research Center Covington LA 70433 USA. Oregon National Primate Research Center and Vaccine and Gene Therapy Institute Oregon Health Sciences University Beaverton OR 97006 USA. Department of Psychiatry University of Wisconsin School of Medicine and Public Health Madison WI 53719 USA. Department of Anatomy and Neurobiology Boston University School of Medicine Boston MA 02118 USA. Department of Neuroscience University of Wisconsin Madison WI 53175 USA. Wisconsin N

The rhesus macaque (Macaca mulatta) is the most widely studied nonhuman primate (NHP) in biomedical research. We present an updated reference genome assembly (Mmul_10, contig N50 = 46 Mbp) that increases the sequence contiguity 120-fold and annotate it using 6.5 million full-length transcripts, thus improving our understanding of gene content, isoform diversity, and repeat organization. With the improved assembly of segmental duplications, we discovered new lineage-specific genes and expanded gene families that are potentially informative in studies of evolution and disease susceptibility. Whole-genome sequencing (WGS) data from 853 rhesus macaques identified 85.7 million single-nucleotide variants (SNVs) and 10.5 million indel variants, including potentially damaging variants in genes associated with human autism and developmental delay, providing a framework for developing noninvasive NHP models of human disease.

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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

引用

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

引用

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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