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

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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

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

data-driven brain network models predict individual variability in behavior

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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

SN 2018agk: A prototypical Type Ia Supernova with a smooth power-law rise in Kepler (K2)

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arXiv 2021年

作者： Wang, Qinan Rest, Armin Zenati, Yossef Ridden-Harper, Ryan Dimitriadis, Georgios Narayan, Gautham Villar, V. Ashley Magee, Mark R. Foley, Ryan J. Shaya, Edward J. Garnavich, Peter Wang, Lifan Hu, Lei Bódi, Attila Armstrong, Patrick Auchettl, Katie Barclay, Thomas Barentsen, Geert Bognár, Zsófia Brimacombe, Joseph Bulger, Joanna Burke, Jamison Challis, Peter Chambers, Kenneth Coulter, David A. Csörnyei, Géza Cseh, Borbála Deckers, Maxime Dotson, Jessie L. Galbany, Lluís González-Gaitán, Santiago Gromadzki, Mariusz Gully-Santiago, Michael Hanyecz, Ottó Hedges, Christina Hiramatsu, Daichi Hosseinzadeh, Griffin Howell, D. Andrew Howell, Steve B. Huber, Mark E. Jha, Saurabh W. Jones, David O. Könyves-Tóth, Réka Kalup, Csilla Kilpatrick, Charles D. Kriskovics, Levente Li, Wenxiong Lowe, Thomas B. Margheim, Steven McCully, Curtis Mitra, Ayan Muñoz, Jose A. Nicholl, Matt Nordin, Jakob Pál, András Pan, Yen-Chen Piro, Anthony L. Rest, Sofia Rino-Silvestre, João Rojas-Bravo, César Sárneczky, Krisztián Siebert, Matthew R. Smartt, Stephen J. Smith, Ken Sódor, Ádám Stritzinger, Maximilian D. Szabó, Róbert Szakáts, Róbert Tucker, Brad E. Vinkó, József Wang, Xiaofeng Wheeler, J. Craig Young, David R. Zenteno, Alfredo Zhang, Kaicheng Zsidi, Gabriella Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States Space Telescope Science Institute BaltimoreMD21218 United States Israel Excellence Fellowship Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States School of Physics Trinity College Dublin The University of Dublin Dublin 2 Ireland Department of Astronomy University of Illinois 1002 W. Green St. Urbana-ChampaignIL61801 United States Center for Astrophysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Astronomy Department University of Maryland College ParkMD20742 United States University of Notre Dame Notre DameIN46556 United States Mitchell Institute for Fundamental Physics Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Nanjing210033 China Konkoly Observatory CSFK Konkoly-Thege M. ut 15-17 Budapest1121 Hungary MTA CSFK Lendület Near-Field Cosmology Research Group Konkoly Thege Miklós út 15-17 Budapest1121 Hungary Mt Stromlo Observatory The Research School of Astronomy and Astrophysics Australian National University ACT2601 Australia School of Physics The University of Melbourne VIC3010 Australia University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Bay Area Environmental Research Institute P.O. Box 25 Moffett Field CA94035 United States Coral Towers Observatory Cairns Australia Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96

We present the 30-min cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1-day cadence DECam observations within the first ~5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly-evolving early colors in a narrow range (g - i ≈ -0.20±0.20 mag) within the first ~ 10 days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc's evolution is similar to normal SNe Ia without excess in g - i. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical non-degenerate companion undergoing Roche-lobe overflow at viewing angles smaller than 45°. © 2021, CC BY.

关键词： Supernovae

IDTxl: The information dynamics toolkit xl: A python package for the efficient analysis of multivariate information dynamics in networks

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arXiv 2018年

作者： Wollstadt, Patricia Lizier, Joseph T. Vicente, Raul Finn, Conor Martinez-Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael MEG Unit Brain Imaging Center Goethe-University Frankfurt Fankfurt am Main Germany Centre for Complex Systems Faculty of Engineering and IT University of Sydney SydneyNSW Australia Computational Neuroscience Lab Institute of Computer Science Tartu Estonia Data61 CSIRO EppingNSW Australia Communications and Signal Theory and Telematics Engineering University of Valladolid Valladolid Spain Computational Neurodynamics Group Department of Computing Imperial College London London United Kingdom Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Campus Institute for Dynamics of Biological Networks Georg-August Universität Göttingen Germany

We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://***/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006;MacKay, 2003;Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. Information theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable. TE has become popular in many scientific disciplines to infer dependencies and whole networks from data. Notable application domains include neuroscience (Wibral, Vicente, & Lindner, 2014) and dynamical systems analysis (Lizier, Prokopenko, & Zomaya, 2014) (see Bossomaier, Barnett, Harré, & Lizier (2016) for an introduction to TE and a comprehensive discussion of its application). In the majority of the applications, TE is used in a bivariate fashion, where information transfer is quantified between all sourcetarget pairs. In a multivariate setting, however, such a bivariate analysis may infer spurious or redundant interactions, where multiple sources provide the same information about the target. Conversely, bivariate analysis may also miss synergistic

关键词： High level languages

Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

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Genome biology 2021年第1期22卷 246页

作者： Kim-Anh Lê Cao Al J Abadi Emily F Davis-Marcisak Lauren Hsu Arshi Arora Alexis Coullomb Atul Deshpande Yuzhou Feng Pratheepa Jeganathan Melanie Loth Chen Meng Wancen Mu Vera Pancaldi Kris Sankaran Dario Righelli Amrit Singh Joshua S Sodicoff Genevieve L Stein-O'Brien Ayshwarya Subramanian Joshua D Welch Yue You Ricard Argelaguet Vincent J Carey Ruben Dries Casey S Greene Susan Holmes Michael I Love Matthew E Ritchie Guo-Cheng Yuan Aedin C Culhane Elana Fertig Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. kimanh.lecao@nimelb.edu.au. Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. McKusick-Nathans Institute of the Department of Genetic Medicine Johns Hopkins School of Medicine Baltimore MD USA. Data Science Dana-Farber Cancer Institute Boston MA USA. Biostatistics Harvard TH Chan School of Public Health Boston MA USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. Centre de Recherches en Cancérologie de Toulouse (INSERM) Université Paul Sabatier III Toulouse France. Cancer Convergence Institute and Division of Quantitative Sciences Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA. Department of Mathematics and Statistics McMaster University Hamilton Canada. Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) School of Life Sciences Technical University of Munich Munich Germany. Department of Biostatistics UNC Chapel Hill NC USA. Barcelona Supercomputing Center Barcelona Spain. Department of Statistics University of Wisconsin Madison WI USA. Department of Statistical Sciences University of Padova Padova PD Italy. Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada. PROOF Centre of Excellence Vancouver BC Canada. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Neuroscience Johns Hopkins University Baltimore MD USA. Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore MD USA. Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering University of Michiga

Euclid Quick data Release (Q1). The Strong Lensing Discovery Engine C - Finding lenses with machine learning

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arXiv 2025年

作者： Lines, N.E.P. Collett, T.E. Walmsley, M. Rojas, K. Li, T. Leuzzi, L. Manjón-García, A. Vincken, S.H. Wilde, J. Holloway, P. Verma, A. Metcalf, R.B. Andika, I.T. Melo, A. Melchior, M. Domínguez Sánchez, H. Díaz-Sánchez, A. Acevedo Barroso, J.A. Clément, B. Krawczyk, C. Pearce-Casey, R. Serjeant, S. Courbin, F. Despali, G. Gavazzi, R. Schuldt, S. Degaudenzi, H. Ecker, L.R. Enzi, W.J.R. Finner, K. Galan, A. Giocoli, C. Hogg, N.B. Jahnke, K. Kruk, S. Mahler, G. More, A. Nagam, B.C. Pearson, J. Sainz de Murieta, A. Scarlata, C. Sluse, D. Sonnenfeld, A. Spiniello, C. Thai, T.T. Tortora, C. Ulivi, L. Weisenbach, L. Zumalacarregui, M. Aghanim, N. Altieri, B. Amara, A. Andreon, S. Auricchio, N. Aussel, H. Baccigalupi, C. Baldi, M. Balestra, A. Bardelli, S. Battaglia, P. Bender, R. Bernardeau, F. Biviano, A. Bonchi, A. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Cañas-Herrera, G. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Chambers, K.C. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Costille, A. Courtois, H.M. Cropper, M. Da Silva, A. De Lucia, G. Di Giorgio, A.M. Dolding, C. Dole, H. Dubath, F. Duncan, C.A.J. Dupac, X. Escoffier, S. Fabricius, M. Farina, M. Farinelli, R. Faustini, F. Ferriol, S. Finelli, F. Fotopoulou, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. George, K. Gillard, W. Gillis, B. Gómez-Alvarez, P. Gracia-Carpio, J. Granett, B.R. Grazian, A. Grupp, F. Guzzo, L. Gwyn, S. Haugan, S.V.H. Holmes, W. Hook, I.M. Hormuth, F. Hornstrup, A. Hudelot, P. Jhabvala, M. Keihänen, E. Kermiche, S. Kiessling, A. Kubik, B. Kümmel, M. Kunz, M. Kurki-Suonio, H. Le Boulc’h, Q. Le Brun, A.M.C. Le Mignant, D. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Mainetti, G. Maino, D. Maiorano, E. Mansutti, O. Marcin, S. Marggraf, O. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom University of Applied Sciences and Arts of Northwestern Switzerland School of Engineering Windisch5210 Switzerland Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Departamento Física Aplicada Universidad Politécnica de Cartagena Campus Muralla del Mar Murcia Cartagena30202 Spain Martí i Franquès 1 Barcelona08028 Spain Department of Physics Oxford University Keble Road OxfordOX1 3RH United Kingdom Technical University of Munich TUM School of Natural Sciences Physics Department James-Franck-Str. 1 Garching85748 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Str. 1 Garching85748 Germany Instituto de Física de Cantabria Edificio Juan Jordá Avenida de los Castros Santander39005 Spain Observatoire de Sauverny Versoix1290 Switzerland Lausanne1015 Switzerland School of Physical Sciences The Open University Milton KeynesMK7 6AA United Kingdom Passeig de Lluís Companys 23 Barcelona08010 Spain INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Aix-Marseille Université CNRS CNES LAM Marseille France Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Sw

Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed previously. With 0′′ . 16 resolution covering a third of the sky, the Euclid telescope will revolutionise strong lens finding, with 170 000 lenses forecasted to be discovered amongst its 1.5 billion galaxies. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of Euclid data (Q1), covering 63 deg2. The models are validated with citizen scientists and expert visual inspection. We focus on the best performing network: a fine-tuned version of the Zoobot pretrained model, originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that Zoobot was tasked to find strong lenses within, the top 1000 ranked objects contained 122 grade A lenses (almost certain lenses), and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection discovered 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top 20 000 by Zoobot. When extrapolated to the full Euclid survey, the highest ranked one million images will contain 75 000 grade A or B strong gravitational lenses. © 2025, CC BY.

关键词： Cosmology

AccuRA: Accurate alignment of short reads on scalable reconfigurable accelerators 16

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AccuRA: Accurate alignment of short reads on scalable reconf...

16th International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2016

作者： Natarajan, Santhi Kumar, N Krishna Pal, Debnath Nandy, S.K. Centre for Nano Science and Engineering Indian Institute of Science Bangalore India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

ISBN: (纸本)9781509030767

Classified as a big data problem, Short Read Mapping (SRM) within the Next Generation Sequencing (NGS) pipeline presents profound technical and computing challenges. Existing solutions handle the high volume of data leveraging heuristics, to claim notable performance standards. The results from SRM leave a huge impact across fields, including medical diagnostics and drug discovery. In this context, we need precise, affordable, reliable and actionable results from SRM, to support any application, with uncompromised accuracy and performance. Here, we present AccuRA, a massively parallel, scalable, high performance reconfigurable accelerator for accurate alignment of short reads. Supplemented with multithreaded firmware architecture, AccuRA precisely aligns short reads, at a fine-grained single nucleotide resolution, and offers full coverage of the genome. AccuRA's parallel dynamic programming kernels seamlessly perform traceback process in hardware simultaneously along with forward scan, thus achieving SRM in the minimum possible and deterministic time. The AccuRA prototype, hosting eight kernel units on a single reconfigurable device, aligns short reads with an alignment performance of 20.48 Giga Cell Updates Per Second (GCUPs). AccuRA also scales well at multiple levels of design granularity, while successfully aligning genomes of various sizes, ranging from small archeal, bacterial, fungal genomes, to the large mammalian human genome. © 2016 IEEE.

关键词： Firmware

Structure-based Fitness Prediction for the Variable-structure DANNA Neuromorphic Architecture

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Structure-based Fitness Prediction for the Variable-structur...

International Joint Conference on Neural Networks

作者： Aleksander Klibisz Grant Bruer James S. Plank Catherine D. Schuman Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee USA 37996 Computational Data Analytics Oak Ridge National Laboratory Oak Ridge Tennessee USA 37831

ISBN: (纸本)9781509061839

In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), where thousands of networks are generated and then evaluated by determining fitness scores on specific tasks. Fitness scores guide the generation of new networks until a target fitness is achieved. One source of performance overhead during EO is the simulation of the task on each network to determing its fitness. To mitigate this source of overhead, we formulate the Static Fitness Prediction Task (SFPT), for predicting a network's fitness without direct simulation. Our hypothesis is that we can use SFPT to predict a network's fitness sufficiently accurately to reject a significant portion of networks during EO without having to simulate them, thereby making the EO more efficient. We propose a data-driven approach to the SFPT on the neuromorphic model DANNA [1]. Our approach transforms networks into directed graphs and extracts structural features to train an ancillary model for predicting the fitness of new networks. We analyze the extracted features and evaluate several predictive models to predict the fitness of networks for five tasks. Our results demonstrate a predictive capacity in these features and models. Our primary contribution is to demonstrate the utility of graph-level features extracted from variable-structure networks to predict network fitness and circumvent expensive simulations.

关键词： Predictive models Feature extraction computational modeling Neurons Training Reservoirs Genetic algorithms