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Author Correction: Spatial communication systems across languages reflect universal action constraints

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Nature human behaviour 2024年第1期8卷 181页

作者： Kenny R Coventry Harmen B Gudde Holger Diessel Jacqueline Collier Pedro Guijarro-Fuentes Mila Vulchanova Valentin Vulchanov Emanuela Todisco Maria Reile Merlijn Breunesse Helen Plado Juergen Bohnemeyer Raed Bsili Michela Caldano Rositsa Dekova Katharine Donelson Diana Forker Yesol Park Lekhnath Sharma Pathak David Peeters Gabriella Pizzuto Baris Serhan Linda Apse Florian Hesse Linh Hoang Phuong Hoang Yoko Igari Keerthana Kapiley Tamar Haupt-Khutsishvili Sara Kolding Katri Priiki Ieva Mačiukaitytė Vaisnavi Mohite Tiina Nahkola Sum Yi Tsoi Stefan Williams Shunei Yasuda Angelo Cangelosi Jon Andoni Duñabeitia Ramesh Kumar Mishra Roberta Rocca Jurģis Šķilters Mikkel Wallentin Eglė Žilinskaitė-Šinkūnienė Ozlem Durmaz Incel School of Psychology University of East Anglia Norwich UK. k.coventry@uea.ac.uk. School of Psychology University of East Anglia Norwich UK. Helmholtz Institute Department of Experimental Psychology Utrecht University Utrecht the Netherlands. Hanse-Wissenschaftskolleg Delmenhorst Germany. Department of English Friedrich-Schiller-Universität Jena Jena Germany. Department of Spanish Modern and Classic Philology University of the Balearic Islands Palma Spain. Language Acquisition and Language Processing Lab Department of Language and Literature Norwegian University of Science and Technology Trondheim Norway. Department of Spanish Language Linguistics and Literature Theory University of Seville Seville Spain. Institute of Estonian and General Linguistics University of Tartu Tartu Estonia. Centre for the Arts in Society Leiden University Leiden the Netherlands. Võro Institute Võru Estonia. Department of Linguistics University at Buffalo Buffalo NY USA. Danieli Telerobot Srl Genoa Italy. Istituto Italiano di Tecnologia (IIT) Genoa Italy. Paisii Hilendarski University of Plovdiv Plovdiv Bulgaria. Department of English University of Nevada Reno NV USA. Department of Slavonic Languages and Caucasus Studies Friedrich-Schiller-Universität Jena Jena Germany. Cognitive Science Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland. Cognitive Science and Psycholinguistics Lab Central Department of Linguistics Tribhuvan University Kathmandu Nepal. Centre for Neural and Cognitive Sciences School of Medical Sciences University of Hyderabad Hyderabad India. Department of Communication and Cognition TiCC Tilburg University Tilburg the Netherlands. Max Planck Institute for Psycholinguistics Nijmegen the Netherlands. Department of Chemistry University of Liverpool Liverpool UK. Department of Computer Science University of Manchester Manchester UK. Laboratory for Perceptual and Cognitive Systems Faculty of Computing University o

Deep neural networks with weighted averaged overnight airflow features for sleep apnea-hypopnea severity classification

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arXiv 2018年

作者： Lakhan, Payongkit Ditthapron, Apiwat Banluesombatkul, Nannapas Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46%, 85.39% and 92.69% in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70%. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

Fast stochastic ordinal embedding with variance reduction and adaptive step size

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

Universal joint feature extraction for P300 EEG classification using multi-task autoencoder

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arXiv 2018年

作者： Ditthapron, Apiwat Banluesombatkul, Nannapas Ketrat, Sombat Chuangsuwanich, Ekapol Wilaiprasitporn, Theerawit Computer Department Worcester Polytechnic Institute WorcesterMA United States Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand

The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the Event-Related Potential Encoder Network (ERPENet);a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets. ERPENet incorporates Convolutional neural Networks (CNNs) and Long Short-Term Memory (LSTM), in an autoencoder setup, which tries to simultaneously compress the input EEG signal and extract related P300 features into a latent vector. Here, we can infer the process for generating the latent vector as universal joint feature extraction. The network also includes a classification part for attended and unattended events classification as an auxiliary task. We experimented on six different P300 datasets. The results show that the latent vector exhibits better compression capability than the previous state-of-the-art semi-supervised autoencoder model. For attended and unattended events classification, pre-trained weights are adopted as initial weights and tested on unseen P300 datasets to evaluate the adaptability of the model, which shortens the training process as compared to using random Xavier weight initialization. At the compression rate of 6.84, the classification accuracy outperforms conventional P300 classification models: XdawnLDA, DeepConvNet, and EEGNet achieving 79.37% - 88.52% classification accuracy depending on the dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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

A Temporally and Spatially Explicit, Mechanistic Estimation of Ragweed Pollen Concentrations Across Europe

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SSRN

SSRN 2023年

作者： Makra, László Matyasovszky, István Tusnády, Gábor Ziska, Lewis H. Hess, Jeremy J. Nyúl, László G. Chapman, Daniel S. Coviello, Luca Gobbi, Andrea Jurman, Giuseppe Furlanello, Cesare Brunato, Mauro Damialis, Athanasios Charalampopoulos, Athanasios Müller-Schärer, Heinz Schneider, Norbert Szabó, Bence Sümeghy, Zoltán Páldy, Anna Magyar, Donát Bergmann, Karl-Christian Deák, Áron József Mikó, Edit Thibaudon, Michel Oliver, Gilles Albertini, Roberto Bonini, Maira Šikoparija, Branko Radišić, Predrag Mitrović Josipović, Mirjana Gehrig, Regula Severova, Elena Rodinkova, Victoria Palamarchuk, Olena Stjepanović, Barbara Ianovici, Nicoleta Berger, Uwe Kofol Seliger, Andreja Rybníček, Ondřej Myszkowska, Dorota Dąbrowska-Zapart, Katarzyna Majkowska-Wojciechowska, Barbara Weryszko-Chmielewska, Elzbieta Grewling, Lukasz Rapiejko, Piotr Malkiewicz, Malgorzata Šaulienė, Ingrida Shalaboda, Valentina Prykhodo, Olexander Maleeva, Anna Ščevková, Jana Bullock, James M. Institute of Economics and Rural Development Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Department of Meteorology Eötvös Loránd University P.O.B. 32 Budapest1518 Hungary Alfréd Rényi Institute of Mathematics P.O.B 127 Budapest1364 Hungary Mailman School of Public Health Columbia University New YorkNY10032 United States Department of Global Health University of Washington SeattleWA98105 United States Department of Image Processing and Computer Graphics University of Szeged P.O.B. 652 Szeged6701 Hungary Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Scotland StirlingFK9 4LA United Kingdom University of Trento Enogis s.r.l. Trento Italy Bruno Kessler Foundation Trento Italy HK3 Lab Orobix Life Srl Rovereto Bergamo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Terrestrial Ecology and Climate Change Department of Ecology School of Biology Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Departement of Biology Unit of Ecology and Evolution University of Fribourg FribourgCH-1700 Switzerland National Institute of Environmental Health Albert Flórián út 2-6 Budapest1097 Hungary Institute of Allergology Charité Hindenburgdamm 30 Berlin12203 Germany Institute of Animal Science and Wildlife Management Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Réseau National de Surveillance Aérobiologique 11 chemin de la Creuzille Le Plat du Pin Brussieu696905 France Laboratory of Hygiene and Aerobiology Department of Medicine and Surgery University of Parma U.O. Medicina Interna di Continuità Azienda Ospedaliero-Universitaria di Parma Via Gramsci 14 Parma43126 Italy Hygiene and Public Health Service via Spagliardi 19 Parabiago Milan20015 Italy BioSensе Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Dr

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://***/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. © 2023, The Authors. All rights reserved.

关键词： Climate change

Common Limitations of Image processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

External exposome and incident asthma across the life course in 14 European cohorts: a prospective analysis within the EXPANSE project

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The Lancet Regional Health - Europe 2025年

作者： Yu, Zhebin Kress, Sara Blay, Natalia Gregor, Petr Kukk, Hanna-Maria Leskien, Miriam Majewska, Renata Oosterwegel, Max J. Szabó, Daniel ten Have, Margreet Klánová, Jana Mikeš, Ondřej Bergström, Anna Bussalleu, Alonso de Cid, Rafael Dalecka, Andrea Dadvand, Payam van Dorsselaer, Saskia Fischer, Krista de Hoogh, Kees Koppelman, Gerard H. Kronberg, Jaanika Metspalu, Andres Milani, Lili Esko, Tõnu Metspalu, Mait Lakerveld, Jeroen Ljungman, Petter Merid, Simon Kebede Macek, Pawel Manczuk, Marta Merritt, Anne-Sophie Pac, Agnieszka Palta, Priit Pershagen, Göran Peters, Annette Pikhart, Hynek Saucy, Apolline Schikowski, Tamara Shen, Youchen Standl, Marie Tonne, Cathryn Vermeulen, Roel Vlaanderen, Jelle Vonk, Judith M. Wolf, Kathrin Ek, Carl Henrik Gruzieva, Olena Gehring, Ulrike Melén, Erik Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden IUF – Leibniz Research Institute for Environmental Medicine Düsseldorf Germany Genomes for Life-GCAT Lab CORE Program Germans Trias I Pujol Research Institute (IGTP) Badalona Spain Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP) Badalona Spain RECETOX Faculty of Science Masaryk University Brno Czech Republic Institute of Genomics University of Tartu Estonia Institute of Mathematics and Statistics University of Tartu Estonia Institute of Epidemiology Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg Germany Institute for Medical Information Processing Biometry and Epidemiology Medical Faculty Ludwig-Maximilians-Universität München Munich Germany Chair of Epidemiology and Preventive Medicine Jagiellonian University Medical College Kraków Poland Institute for Risk Assessment Sciences Utrecht University Utrecht Netherlands Netherlands Institute of Mental Health and Addiction Utrecht Netherlands Centre for Occupational and Environmental Medicine Region Stockholm Stockholm Sweden Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland ISGlobal Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CIBER Epidemiología y Salud Pública Spain University of Groningen University Medical Center Groningen Beatrix Children's Hospital Department of Pediatric Pulmonology and Pediatric Allergology Groningen Netherlands University of Groningen University Medical Center Groningen Groningen Research Institute for Asthma and COPD (GRIAC) Groningen Netherlands Department of Epidemiology and Data Science Amsterdam UMC Location Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Cardiology Danderyd Hospital Stockholm Sweden Department of Clinical Sciences and Education Södersjukhuset Karolinska Institutet Stockholm Sweden Collegium Medicum Jan Koc

Background: The joint impact of exposure to multiple urban environmental factors on asthma remains unclear. Methods: We analysed data from 14 European cohorts to assess the impact of the urban exposome on asthma incidence across the life course. We linked three external exposome domains (air pollution, built environment, ambient temperature) to the participants’ home addresses at baseline. We performed k-means clustering within each domain and assessed associations of clusters with asthma adjusting for potentially relevant covariates in cohort-specific analyses, with subsequent separate meta-analyses for birth and adult cohorts. An environmental risk score using a coefficient-weighted sum approach was used to assess the impact of combining the three domains. Findings: A total of 7428 incident asthma cases were identified among 349,037 participants (from birth up to age 70+). Overall, we observed higher risks of asthma for clusters characterized by high particulate matter and nitrogen dioxide exposure in adults (OR_meta = 1.13, 95%CI:1.01–1.25), and clusters characterized by high built-up area and low levels of greenness in both children and adults (OR_meta = 1.36, 95%CI: 1.14–1.64 for birth cohorts and OR_meta = 1.15, 95%CI: 1.03–1.28 for adult cohorts, respectively). The joint exposure using the environment risk score combining the three domains was consistently associated with higher risks of incident asthma (OR_meta = 1.13, 95%CI: 1.07–1.20 for birth cohorts, OR_meta = 1.15, 95%CI: 1.10–1.20 for adult cohorts per 20% increase). On average 11.6% of the incident asthma cases could be attributed to environmental risk score above cohort-specific median levels. Interpretation: Multiple environmental exposures jointly contribute to incident asthma risk across the life course. Urban planning accounting for these factors may help mitigate asthma development. Funding: This study was funded by the European Union's Horizon 2

关键词： Asthma Cohort Exposome Life course

Affective EEG-Based Person Identification Using the Deep Learning Approach

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arXiv 2018年

作者： Wilaiprasitporn, Theerawit Ditthapron, Apiwat Matchaparn, Karis Tongbuasirilai, Tanaboon Banluesombatkul, Nannapas Chuangsuwanich, Ekapol Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Engineering Rayong Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States Computer Engineering Department King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Science and Technology Linköping University Sweden Computer Engineering Department Chulalongkorn University Bangkok Thailand

Electroencephalography (EEG) is another mode forperforming Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEG-based PI using a deep learning approach. We proposed a cascade of deep learning using a combination of Convolutional neural Networks (CNNs) and Recurrent neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent Unit (CNN-GRU). The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90–100% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100% mean CRR comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time. Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography