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Infectious Disease Modelling 2018年第1期3卷 1-12页

作者： Mauricio Santillana Ashleigh Tuite Tahmina Nasserie Paul Fine David Champredon Leonid Chindelevitch Jonathan Dushoff David Fisman Computation Health Informatics Program Boston Children's HospitalBostonMAUSA Department of Pediatrics Harvard Medical SchoolBontonMAUSA Dalla Lana School of Public Health University of TorontoTorontoOntarioCanada BlueDot TorontoOntarioCanada Department of Infectious Disease Epidemiology London School of Hygiene and Tropical MedicineLondonUK Agent-Based Modelling Laboratory York UniversityTorontoOntarioCanada School of Computing Science Simon Fraser UniversityBurnabyBritish ColumbiaCanada Department of Theoretical Biology McMaster UniversityHamiltonOntarioCanada Department of Medicine Faculty of MedicineUniversity of TorontoTorontoOntarioCanada

Mathematical models are often regarded as recent innovations in the description and analysis of infectious disease outbreaks and epidemics,but simple mathematical expressions have been in use for projection of epidemic trajectories for more than a *** recently introduced a single equation model(the incidence decay with exponential adjustment,or IDEA model)that can be used for short-term epidemiological *** the mid-19th century,*** Farr made the observation that epidemic events rise and fall in a roughly symmetrical pattern that can be approximated by a bell-shaped *** noticed that this time-evolution behavior could be captured by a single mathematical formula(“Farr's law”)that could be used for epidemic *** show here that the IDEA model follows Farr's law,and show that for intuitive assumptions,Farr's Law can be derived from the IDEA ***,we show that both mathematical approaches,Farr's Law and the IDEA model,resemble solutions of a susceptible-infectious-removed(SIR)compartmental differential-equation model in an asymptotic limit,where the changes of disease transmission respond to control measures,and not only to the depletion of susceptible *** suggests that the concept of the reproduction number eR 0T was implicitly captured in Farr's(pre-microbial era)work,and also suggests that control of epidemics,whether via behavior change or intervention,is as integral to the natural history of epidemics as is the dynamics of disease transmission.

关键词： equation exponential decay

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Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems

SSRN

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SSRN 2020年

作者： Williams, Brooke A. Venter, Oscar Allan, James R. Atkinson, Scott C. Rehbein, Jose A. Ward, Michelle S. Marco, Moreno Di Grantham, Hedley S. Ervin, Jamison Goetz, Scott Hansen, Andrew J. Jantz, Patrick Pillay, Rajeev Rodríguez-Buriticá, Susana Supples, Christina Virnig, Anne L.S. Watson, James E.M. School of Earth and Environmental Sciences University of Queensland St LuciaQLD Australia Centre for Biodiversity and Conservation Science School of Biological Sciences University of Queensland St LuciaQLD4072 Australia University of Northern British Columbia Prince GeorgeBritish Colombia Canada University of Amsterdam P.O. Box 94240 Amsterdam1090 GE Netherlands 304 East 45 Street Floor 15 New YorkNY10017 United States World Bank WashingtonDC20433 United States Dept. of Biology and Biotechnology Sapienza University of Rome viale dell’Università 32 Rome00185 Italy Wildlife Conservation Society Global Conservation Program New York United States Northern Arizona University Flagstaff AZ86011-5693 United States Ecology Department Montana State University BozemanMT59717 United States School of Informatics Computing and Cyber Systems Northern Arizona University Natural Resources and Environmental Studies Institute University of Northern British Columbia Prince GeorgeBCV2N 4Z9 Canada

Our ability to map humanity’s influence across Earth has evolved, thanks to powerful computing, a network of earth observing satellites, and new bottom-up census and crowd-sourced data. Here, we provide the latest temporally inter-comparable maps of the terrestrial Human Footprint, and assessment of change in human pressure at global, biome, and ecoregional scales. In 2013, 42% of terrestrial Earth could be considered relatively free of anthropogenic disturbance, and 25% could be classed as ‘wilderness’ (the least degraded end of the human footprint spectrum). Between 2000 and 2013, 1.9 million km 2 - an area the size of Mexico - of land relatively free of human disturbance became highly modified. The majority of this occurred within tropical and subtropical grasslands, savannah, and shrubland ecosystems, but the rainforests of Southeast Asia also underwent rapid modification . Our results show that humanity’s footprint is eroding Earth’s last intact ecosystems, and greater efforts are urgently needed to retain them. © 2020, The Authors. All rights reserved.

关键词： Biodiversity

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GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

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Nature genetics 2023年第9期55卷 1471-1482页

作者： Remi Stevelink Dorothee Kasteleijn-Nolst Trenite Bobby P. C. Koeleman Dick Lindhout Anja C. M. Sonsma Ciarán Campbell Gianpiero L. Cavalleri Norman Delanty Hany El-Naggar Austin Lacey Peter Widdess-Walsh Colin P. Doherty Siwei Chen Claire Churchhouse Mark J. Daly Yen-Chen A. Feng Daniel P. Howrigan Mitja I. Kurki Benjamin M. Neale Aarno Palotie Wei Zhou Namrata Gupta Henrike O. Heyne Bassel Abou-Khalil Kevin F. Haas R. S. Taneja Oluyomi M. Adesoji Michael Nothnagel Peter Nürnberg Thomas Sander Zaid Afawi Ilan Blatt Amos D. Korczyn Elisabetta Amadori Ganna Balagura Antonella Riva Marcello Scala Paolo Scudieri Pasquale Striano Federico Zara Michele Iacomino Francesca Madia Alison Anderson Patrick Kwan Terence J. O’Brien Slavé Petrovski K. Meng Tan Marian Todaro Joseph Anderson Danielle M. Andrade Quratulain Zulfiqar Ali Grazia Annesi Pauls Auce Andreja Avbersek Simona Balestrini Vera Braatz Claudia B. Catarino Krishna Chinthapalli Jennifer Jamnadas-Khoda Dalia Kasperavičiūte Costin Leu Josemir W. Sander Sanjay M. Sisodiya Sara Zagaglia Melanie Bahlo Karen L. Oliver Mark D. Baker Beata Fonferko-Shadrach William O. Pickrell Carmen Barba Claudia Bianchini Viola Doccini Renzo Guerrini Davide Mei Martino Montomoli Elena Parrini Annalisa Vetro Karen Barboza Fabrice Bartolomei Thomas Bast Bernhard J. Steinhoff Larry Baum Stacey S. Cherny Pak Sham Tobias Baumgartner Christian E. Elger Kerstin Hallmann Wolfram S. Kunz Rainer Surges Randi von Wrede Gábor Zsurka Betül Baykan Nerses Bebek Yeşim Kesim Albert J. Becker Susanne Schoch Felicitas Becker Caitlin A. Bennett Samuel F. Berkovic Stephanie L. Leech Brigid M. Regan Ingrid E. Scheffer Bianca Berghuis Ahmad Beydoun Wassim Nasreddine Francesca Bisulli Lidia Di Vito Lorenzo Ferri Tania Giangregorio Laura Licchetta Raffaella Minardi Barbara Mostacci Carlotta Stipa Paolo Tinuper Lorenzo Muccioli Federica Pondrelli Dheeraj R. Bobbili Emadeldin Hassanin Roland Krause Zied Landoulsi Patrick May Ingo Borggraefe Christian Bosselmann Christian Hengsbach Josua Kegele Stephen Lauxmann Department of Genetics University Medical Center Utrecht Utrecht The Netherlands Stichting Epilepsie Instellingen Nederland (SEIN) Heemstede The Netherlands School of Pharmacy and Biomolecular Sciences The Royal College of Surgeons in Ireland Dublin Ireland The FutureNeuro Research Centre Dublin Ireland Department of Neurology Beaumont Hospital Dublin Ireland Department of Neurology St. James’s Hospital Dublin Ireland Analytic and Translational Genetics Unit Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and M.I.T Cambridge MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Psychiatric & Neurodevelopmental Genetics Unit Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston MA USA Division of Biostatistics Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Hasso Plattner Institute Digital Health Center University of Potsdam Potsdam Germany Hasso Plattner Institute Mount Sinai School of Medicine New York City NY USA Department of Neurology Vanderbilt University Medical Center Nashville TN USA Cologne Center for Genomics (CCG) University of Cologne Faculty of Medicine and University Hospital Cologne Cologne Germany University Hospital Cologne Cologne Germany Department of Neurology Charité Universitaetsmedizin Berlin Campus Virchow-Clinic Berlin Germany Tel-Aviv University Sackler Faculty of Medicine Ramat Aviv Israel Department of Neurology Sheba Medical Center Ramat Gan Israel Department of Neurosciences Rehabilitation Ophthalmology Genetics Maternal and Child Health University of Genova Genova Italy IRCCS Istituto Giannina Gaslini Genova Italy Department of Medicine University of Melbour

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

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A large-scale ENIGMA multisite replication study of brain age in depression

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Neuroimage: Reports 2022年第4期2卷 100149-100149页

作者： Han, Laura K.M. Dinga, Richard Leenings, Ramona Hahn, Tim Cole, James H. Aftanas, Lyubomir I. Amod, Alyssa R. Besteher, Bianca Colle, Romain Corruble, Emmanuelle Couvy-Duchesne, Baptiste Danilenko, Konstantin V. Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Goya-Maldonado, Roberto Groenewold, Nynke A. Hamilton, Paul Ichikawa, Naho Ipser, Jonathan C. Itai, Eri Koopowitz, Sheri-Michelle Li, Meng Okada, Go Okamoto, Yasumasa Churikova, Olga S. Osipov, Evgeny A. Penninx, Brenda W.J.H. Pomarol-Clotet, Edith Rodríguez-Cano, Elena Sacchet, Matthew D. Shinzato, Hotaka Sim, Kang Stein, Dan J. Uyar-Demir, Aslihan Veltman, Dick J. Schmaal, Lianne Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia Orygen Parkville VIC Australia Department of Cognitive Science & Artificial Intelligence Tilburg University Tilburg Netherlands Institute for Translational Psychiatry University of Münster Münster Germany Faculty of Mathematics and Computer Science University of Münster Münster Germany Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London United Kingdom Scientific Research Institute of Neuroscience and Medicine Novosibirsk Russian Federation Novosibirsk State University Novosibirsk Russian Federation Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town South Africa Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Service Hospitalo-Universitaire de Psychiatrie de Bicêtre Mood Center Paris Saclay Assistance Publique-Hôpitaux de Paris Hôpitaux Universitaires Paris-Saclay Hôpital de Bicêtre Le Kremlin Bicêtre F-94275 France MOODS Team INSERM 1018 CESP (Centre de Recherche en Epidémiologie et Santé des Populations) Université Paris-Saclay Faculté de Médecine Paris-Saclay Le Kremlin Bicêtre F-94275 France Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de La Pitié Salpêtrière Paris F-75013 France Institute for Molecular Bioscience The University of Queensland St Lucia 4072 QLD Australia FIDMAG Hermanas Hospitalarias Research Foundation Barcelona Spain CIBERSAM Barcelona Spain SoCAT Lab Department of Psychiatry School of Medicine Ege University Izmir Turkey Department of Psychology Stanford University Stanford CA United States Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab) University Medical Center Göttingen Göttingen Germany Center for Social and Affective Neuroscience Department o

Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases;+1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (***/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R² = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-s

关键词： Biological aging Brain age Depression ENIGMA consortium Replication study

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Very-high-energy gamma-ray emission from GRB 201216C detected by MAGIC 37

Very-high-energy gamma-ray emission from GRB 201216C detecte...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Suda, Yusuke Nava, Lara Noda, Koji Bošnjak, Željka Asano, Katsuaki Longo, Francesco Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, J.G. Green, D. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. López-Coto, R. López-Moya, M. López-Oramas, A. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Street number Merate Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia University of Trieste Department of Physics via Valerio 2 Trieste Italy INFN Sezione di Trieste via Valerio 2 Trieste Italy Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Università di Udine INFN Trieste UdineI-33100 Italy RomeI-00136 Italy ETH Zürich ZürichCH-8093 Switzerland Bellaterra BarcelonaE-08193 Spain The University of Tokyo Chiba Kashiwa277-8582 Japan Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil Università di Padova INFN PadovaI-35131 Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Università di Siena INFN Pisa SienaI-53100 Italy ZeuthenD-15738 Germany Max-Planck-Institut für Physik MünchenD-80805 Germany Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University o

Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe. Many aspects of GRB physics are still under debate, such as the origin of their gamma-ray emission above the GeV energy range. In 2019, MAGIC detected TeV gamma rays from the long GRB 190114C, whose emission can be well explained by synchrotron-self Compton emission by relativistic electrons. However, it is still unclear whether such a process is common in GRBs, given the reduced number of GRBs detected until now at the very high energies (VHE). GRB 201216C is a long GRB and is the second one detected by MAGIC in this energy range. After receiving the alert provided by Swift-BAT, MAGIC automatically slewed to the GRB position, starting observations 56 seconds after the GRB onset. In the offline analyses of the collected data, we confirmed the detection of gamma-ray emission with a significance above 5 sigma. Following measurements from optical facilities, the redshift of this GRB was estimated to be z = 1.1. This makes GRB 201216C the most distant object ever detected by ground-based gamma-ray telescopes. In this contribution we will show the analysis results of the MAGIC data, also in comparison with past detected GRBs in the same energy range. Finally, accounting for available multi-wavelength observations, we will comment on the possible origin of the VHE emission detected by MAGIC. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Germanium compounds

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Optimal modularity and memory capacity of neural reservoirs

arXiv

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arXiv 2017年

作者： Rodriguez, Nathaniel Izquierdo, Eduardo Ahn, Yong-Yeol School of Informatics and Computing Indiana University BloomingtonIN United States Cognitive Science Program Indiana University BloomingtonIN United States Indiana University Network Science Institute BloomingtonIN United States

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization. Copyright © 2017, The Authors. All rights reserved.

关键词： Network architecture

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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Formal Ontology Generation by deep machine learning 16

Formal Ontology Generation by deep machine learning

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16th IEEE International Conference on cognitive informatics and cognitive computing, ICCI*CC 2017

作者： Wang, Yingxu Valipour, Mehrdad Zatarain, Omar D. Gavrilova, Marina L. Hussain, Amir Howard, Newton Patel, Shushma International Institute of Cognitive Informatics and Cognitive Computing Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive CalgaryALT2N1N4 Canada Dept. of Computer Science University of Stirlling United Kingdom Oxford Neurocomputation Lab University of Oxford United Kingdom School of Engineering South Bank University London United Kingdom

ISBN: (纸本)9781538607701

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the latest advances in cognitive knowledge learning and formal semantic analyses, an Algorithm of Formal Ontology Generation (AFOG) is developed. The methodology of AFOG enables autonomous generation of quantitative ontologies in knowledge engineering and semantic comprehension via deep machine learning. A set of experiments demonstrates applications of AFOG in cognitive computing, semantic computing, machine learning and computational intelligence. © 2017 IEEE.

关键词： Knowledge representation

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Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Nature Mental Health

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Nature Mental Health 2024年 1120页

作者： Cynthia H. Y. Fu Danilo Arnone Roland Zahn Allan H. Young Mathilde Antoniades Guray Erus Jose A. Garcia Yong Fan Haochang Shou Ashish Singh Christos Davatzikos Stephen R. Arnott Stephen C. Strother Taolin Chen Kun Qin Qiyong Gong Ki Sueng Choi Helen S. Mayberg Cherise Chin Fatt Madhukar H. Trivedi Benicio N. Frey Vibe G. Frokjaer Melanie Ganz Gitte M. Knudsen Beata R. Godlewska Catherine J. Harmer Stefanie Hassel Keith Ho Susan Rotzinger Sidney H. Kennedy Andrew M. McIntosh Aleks Stolicyn Heather C. Whalley Matthew D. Sacchet Jonathan Savitz Teresa A. Victor Martin P. Paulus Irina Strigo Duygu Tosun Dongtao Wei Jiang Qiu Toby Wise Ian M. Anderson J. F. William Deakin Rebecca Elliott W. Edward Craighead Boadie W. Dunlop Ian H. Gotlib Chao-Gan Yan School of Psychology University of East London London UK Centre for Affective Disorders Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King’s College London London UK South London and Maudsley NHS Foundation Trust Bethlem Royal Hospital London UK Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA USA Rotman Research Institute Baycrest Centre Toronto Ontario Canada Department of Medical Biophysics University of Toronto Toronto Ontario Canada Huaxi MR Research Center Department of Radiology West China Hospital Sichuan University Chengdu China Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China Department of Radiology Taihe Hospital Hubei University of Medicine Shiyan China Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Center for Depression Research and Clinical Care University of Texas Southwestern Medical Center Dallas TX USA Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton Ontario Canada Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic St Joseph’s Healthcare Hamilton Hamilton Ontario Canada Neurobiology Research Unit University Hospital Rigshospitalet Copenhagen Denmark Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Psychiatry Psychiatric Centre Copenhagen Copenhagen Denmark Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Psychiatry University of Oxford Oxford UK Oxford Health NHS Foundation Trust Warneford Hospital Oxford UK Mathison Centre for Me

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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