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Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle

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Scientific Reports 2024年第1期14卷 1-12页

作者： Lee, Tsair-Fwu Chiu, Chien-Liang Liu, Yen-Hsien Chang, Chu-Ho Shao, Jen-Chung Guo, Shih-Sian Liao, Yi-Lun Chen, Chia-Hui Tseng, Chin-Dar Chao, Pei-Ju Lee, Shen-Hao Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Medical Physics and Informatics Laboratory of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung 80708 Taiwan PhD Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Radiation Oncology Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine Linkou Taiwan

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer–Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices. © The Author(s) 2024.

关键词： Abnormal body temperature probability (ABTP) Dairy cattle Least absolute shrinkage and selection operator (LASSO) Temperature-humidity index (THI)

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A Framework for Interpretability in Machine Learning for Medical imaging

arXiv

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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning

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Initial Results of a Prototype AWSM-PET Device for Augmented Whole-body PET imaging

Initial Results of a Prototype AWSM-PET Device for Augmented...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Y. Chen M. Hambdi S. Komarov R. A. Mintzer S. Cho D. Pocci A. Thomas J. Corbeil J. Breuer M. Judenhofer N. Wu J. Frye F. Dehdashti J. A. O’Sullivan R. Laforest S. B. Siegel Y.-C. Tai Imaging Science PhD Program Washington University in St.Louis St. Louis Missouri United States of America Mallinckrodt Institute of Radiology Washington University in St. Louis St. Louis Missouri United States of America Molecular Imaging Siemens Medical Solutions USA Inc Knoxville Tennessee United States of America Department of Chemistry Washington University in St. Louis St. Louis Missouri United States of America Department of Biostatistics Washington University in St. Louis St. Louis United States of America Department of Electrical and Systems Engineering Washington University in St. Louis Missouri St. Louis United States of America

The Augmented Whole-body Scanning via Magnifying PET (AWSM-PET) is a technology designed to enhance the diagnostic accuracy of whole-body (WB) PET/CT imaging for the detection of small lesions. We recently constructed the first prototype AWSM-PET device that consists of two panels of high-resolution time-of-flight (TOF) PET detectors (using ~1mm pitched LSO crystals), integrated with the standard readout electronics and event processing architecture of the Siemens Biograph Vision scanner. The system exhibits excellent spatial, energy and timing resolution, with an average coincidence resolving time of ~183 ps FWHM and an average energy resolution of ~11% FWHM at 511 keV. Custom firmware and software have been developed to encode the large number of detector elements and to enable coincidence detection between the AWSM-PET detectors and the Biograph Vision *** tests of the AWSM-PET device show the image resolution of the Biograph Vision scanner is improved from the original >3mm to ~2mm FWHM. A GPU-based list-mode image reconstruction framework has been expanded to incorporate the point-spread function (PSF) of the native scanner and the AWSM-PET detectors to optimize the spatial resolution and improve image *** step-and-shoot and continuous-bed motion imaging protocols are supported for the AWSM-PET operation and are being evaluated for the reconstructed image resolution and the uniformity of the overall system sensitivity across a long axial FOV. Extensive phantom imaging studies are ongoing to evaluate the system's performance. Initial human imaging studies will be presented in the upcoming 2023 Medical imaging Conference.

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Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection

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Scientific Reports 2023年第1期13卷 1-12页

作者： Lin, Chih-Hsueh Hsu, Ping-I Tseng, Chin-Dar Chao, Pei-Ju Wu, I-Ting Ghose, Supratip Shih, Chih-An Lee, Shen-Hao Ren, Jia-Hong Shie, Chang-Bih Lee, Tsair-Fwu Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Medical Physics and Informatics Laboratory of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Division of Gastroenterology Department of Medicine An Nan Hospital China Medical University Tainan Taiwan Department of Education and Research An Nan Hospital China Medical University Tainan Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine Antai Medical Care Corporation Antai Tian-Sheng Memorial Hospital Pingtung County Donggan Taiwan Department of Nursing Meiho University Pingtung County Neipu Taiwan Department of Radiation Oncology Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine Linkou Taiwan Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung 80708 Taiwan PhD Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists’ impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice. © 2023, Springer Nature Limited.

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Tract Integrity Predicts Neurological Outcomes in Neonatal Hypoxic-Ischemic Encephalopathy

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Pediatrics & Neonatology 2025年

作者： Pei-Ling Tsai Yu-Chun Lo Yi-Shan Tsai Yea-Shwu Hwang Ting-Chun Lin Yao-Wen Liang Chen-En Huang Fu-Chun Hsu Yi-Fang Tu You-Yin Chen Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan PhD Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Medical Imaging National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Occupational Therapy College of Medicine National Cheng Kung University Tainan Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei Taiwan College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Pediatrics National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Institute of Clinical Medicine College of Medicine National Cheng Kung University Tainan Taiwan Medical Device Innovation and Translation Center National Yang Ming Chiao Tung University Taipei Taiwan

Objective Early prognostication in infants with neonatal hypoxic-ischemic encephalopathy (HIE) is crucial for follow-up management. Alternations of microstructural integrity of neural fiber tracts in large-scale neural networks might predict neurological outcomes. Methods All consecutively-admitted patients with neonatal HIE from 2016 to 2020 were enrolled. Diffusion tensor imaging (DTI) tractography was performed at 4–7 days of age. Neurodevelopmental outcomes at 24 months of age were assessed with Bayley Scales of Infant Development, 3rd edition. Results A total of 62 HIE infants were enrolled, and 53 of these underwent DTI. Fractional anisotropy (FA) and mean diffusivity (MD) were measured to indicate the microstructural integrity of tracts, including the arcuate fasciculus, uncinate fasciculus, inferior fronto-occipital fasciculus, corticospinal tract (CST), and optic radiations (OR). Lower FA values of target tracts were associated with lower developmental scores at 24 months of age, particularly in the CST and OR (all P ≤ 0.003). The prognostic accuracy of FA values in CST surpassed that in the region of thalamus/basal ganglia (area under curves 0.839, 95% CI: 0.728–0.950 vs. 0.755, 95% CI: 0.583–0.928). Furthermore, the correlation between FA values in target tracts and developmental scores was most significant in infants with HIE stage III and this was not affected by therapeutic hypothermia. Conclusion Tract integrity in the CST and OR detected by DTI provides valuable insights for early prognostication in infants with HIE, regardless of therapeutic hypothermia.

关键词： neonatal hypoxic-ischemic encephalopathy diffusion tensor imaging tractography corticospinal tract optic radiations neurodevelopmental outcome

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MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis

arXiv

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arXiv 2023年

作者： Zulueta-Coarasa, Teresa Jug, Florian Mathur, Aastha Moore, Josh Muñoz-Barrutia, Arrate Anita, Liviu Babalola, Kola Bankhead, Pete Gilloteaux, Perrine Gogoberidze, Nodar Jones, Martin Kleywegt, Gerard J. Korir, Paul Kreshuk, Anna Yoldaş, Aybüke Küpcü Marconato, Luca Narayan, Kedar Norlin, Nils Oezdemir, Bugra Riesterer, Jessica Rzepka, Norman Sarkans, Ugis Serrano, Beatriz Tischer, Christian Uhlmann, Virginie Ulman, Vladimír Hartley, Matthew European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Human Technopole V.le Rita Levi-Montalcini 1 Milan20017 Italy Heidelberg Meyerhofstraße 1 Heidelberg69117 Germany German BioImaging Gesellschaft für Mikroskopie und Bildanalyse e.V. Konstanz Germany Universidad Carlos III de Madrid Leganés Madrid Spain Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain Edinburgh Pathology Centre for Genomic and Experimental Medicine CRUK Scotland Centre University of Edinburgh Edinburgh United Kingdom Nantes Université CHU Nantes CNRS Inserm BioCore US16 SFR Bonamy NantesF 44000 France Imaging Platform Broad Institute Cambridge United States Francis Crick Institute London United Kingdom Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg69117 Germany Genome Biology Unit Heidelberg Germany Heidelberg Germany Collaboration for joint PhD degree between EMBL and Heidelberg University Faculty of Biosciences Germany Center for Molecular Microscopy Center for Cancer Research National Cancer Institute NIH BethesdaMD United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD United States Lund University 221 00 Sweden Knight Cancer Institute Oregon Health & Science University PortlandOR United States Scalable Minds GmbH Potsdam Germany European Molecular Biology Laboratory Heidelberg Germany IT4Innovations VSB – Technical University of Ostrava Ostrava-Poruba Czech Republic

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data. © 2023, CC BY.

关键词： Metadata

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Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

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Psychiatry Research 2024年 342卷 115972-115972页

作者： Solmi, Marco Thompson, Trevor Estradé, Andrés Agorastos, Agorastos Radua, Joaquim Cortese, Samuele Dragioti, Elena Vancampfort, Davy Thygesen, Lau Caspar Aschauer, Harald Schlögelhofer, Monika Aschauer, Elena Schneeberger, Andres Huber, Christian G. Hasler, Gregor Conus, Philippe Cuénod, Kim Q. Do von Känel, Roland Arrondo, Gonzalo Fusar-Poli, Paolo Gorwood, Philip Llorca, Pierre-Michel Krebs, Marie-Odile Scanferla, Elisabetta Kishimoto, Taishiro Rabbani, Golam Skonieczna-Żydecka, Karolina Brambilla, Paolo Favaro, Angela Takamiya, Akihiro Zoccante, Leonardo Colizzi, Marco Bourgin, Julie Kamiński, Karol Moghadasin, Maryam Seedat, Soraya Matthews, Evan Wells, John Vassilopoulou, Emilia Gadelha, Ary Su, Kuan-Pin Kwon, Jun Soo Kim, Minah Lee, Tae Young Papsuev, Oleg Manková, Denisa Boscutti, Andrea Gerunda, Cristiano Saccon, Diego Righi, Elena Monaco, Francesco Croatto, Giovanni Cereda, Guido Demurtas, Jacopo Brondino, Natascia Veronese, Nicola Enrico, Paolo Politi, Pierluigi Ciappolino, Valentina Pfennig, Andrea Bechdolf, Andreas Meyer-Lindenberg, Andreas Kahl, Kai G. Domschke, Katharina Bauer, Michael Koutsouleris, Nikolaos Winter, Sibylle Borgwardt, Stefan Bitter, Istvan Balazs, Judit Czobor, Pál Unoka, Zsolt Mavridis, Dimitris Tsamakis, Konstantinos Bozikas, Vasilios P. Tunvirachaisakul, Chavit Maes, Michael Rungnirundorn, Teerayuth Supasitthumrong, Thitiporn Haque, Ariful Brunoni, Andre R. Costardi, Carlos Gustavo Schuch, Felipe Barreto Polanczyk, Guilherme Luiz, Jhoanne Merlyn Fonseca, Lais Aparicio, Luana V. Valvassori, Samira S. Nordentoft, Merete Vendsborg, Per Hoffmann, Sofie Have Sehli, Jihed Sartorius, Norman Heuss, Sabina Guinart, Daniel Hamilton, Jane Kane, John Rubio, Jose Sand, Michael Koyanagi, Ai Solanes, Aleix Andreu-Bernabeu, Alvaro Cáceres, Antonia San José Arango, Celso Díaz-Caneja, Covadonga M. Hidalgo-Mazzei, Diego Vieta, Eduard Gonzalez-Peñas, Javier Fortea, Lydia Parellada, Mara Fullana, Miquel A. Verdolini, Norma Andrlíková, Eva Janků, Karolina Millan, Mark J. Honciuc, Mihaela Moniuszko-Mal University of Ottawa Department of Psychiatry ON Canada The Ottawa Hospital Department of Mental Health ON Canada Ottawa Hospital Research Institute (OHRI) Neurosciences Program Ottawa Ontario Canada Early Psychosis: Interventions and Clinical-detection (EPIC) Lab Department of Psychosis Studies Institute of Psychiatry Psychology & Neuroscience King's College London London United Kingdom Centre for Innovation in Mental Health-Developmental Lab School of Psychology University of Southampton and NHS Trust Southampton United Kingdom Charité Universitätsmedizin Berlin Department of Child and Adolescent Psychiatry Berlin Germany Centre for Chronic Illness and Ageing University of Greenwich London United Kingdom University of Southampton Centre for Innovation in Mental Health Southampton United Kingdom Universidad Católica Department of Psychology Montevideo Uruguay Aristotle University of Thessaloniki II. Dept. of Psychiatry Division of Neurosciences Medical School Faculty of Health Sciences Greece Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Imaging of Mood- and Anxiety-Related Disorders (IMARD) University of Barcelona CIBERSAM Instituto de Salud Carlos III Barcelona Spain Karolinska Institutet Centre for Psychiatric Research and Education Department of Clinical Neuroscience Stockholm Sweden Linköping University Pain and Rehabilitation Centre and Department of Health Medicine and Caring Sciences Linköping Sweden University of Ioannina Research Laboratory Psychology of Patients Families & Health Professionals Department of Nursing School of Health Sciences Ioannina Greece Katholieke Universiteit Leuven (KU Leuven) Department of Rehabilitation Sciences Leuven Belgium National Institute of Public Health University of Southern Denmark Denmark BioPsyC - Biopsychosocial Corporation Non-profit association for Research Funding Ltd. Vienna Austria University of California San Diego California United States University of Basel Universitä

International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ², penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies. © 2024 The Authors

关键词： Covid-19 Mental health P-factor Pandemic Psychiatry Psychopathology Survey Well-being WHO-5

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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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