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arXiv 2017年

作者： Dittrich, Fabio De Oliveira, Luiz E. S. Britto, Alceu S. Koerich, Alessandro L. Laboratoire d'Imagerie Vision et Intelligence Artificielle Ecole de Technologie Superieure 1100 Notre-Dame West MontrealQCH3A 1E3 Canada Federal University of Paraná Department of Informatics Centro Politécnico Curitiba PR81531-970 Brazil Pontifical Catholic University of Paraná Postgraduate Program in Computer Science R. Imaculada Conceiç aõ 1155 Curitiba PR80215-901 Brazil Ecole de Technologie Supérieure Department of Software and IT Engineering 1100 Notre-Dame West MontrealQCH3A 1E3 Canada

This paper presents two novel approaches for people counting in crowded and open environments that combine the information gathered by multiple views. Multiple camera are used to expand the field of view as well as to mitigate the problem of occlusion that commonly affects the performance of counting methods using single cameras. The first approach is regarded as a direct approach and it attempts to segment and count each individual in the crowd. For such an aim, two head detectors trained with head images are employed: One based on support vector machines and another based on Adaboost perceptron. The second approach, regarded as an indirect approach employs learning algorithms and statistical analysis on the whole crowd to achieve counting. For such an aim, corner points are extracted from groups of people in a foreground image and computed by a learning algorithm which estimates the number of people in the scene. Both approaches count the number of people on the scene and not only on a given image or video frame of the scene. The experimental results obtained on the benchmark PETS2009 video dataset show that proposed indirect method surpasses other methods with improvements of up to 46.7% and provides accurate counting results for the crowded scenes. On the other hand, the direct method shows high error rates due to the fact that the latter has much more complex problems to solve, such as segmentation of heads. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

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Connections among university faculty engaged in the first two years of engineering and their impact on faculty attitudes and practice 123

Connections among university faculty engaged in the first tw...

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123rd ASEE Annual Conference and Exposition

作者： Middleton, James A. Krause, Stephen J. Judson, Eugene Culbertson, Robert J. Ross, Lydia Hjelmstad, Keith D. Park, Yong-Seok Collofello, James Smith, Bethany B. Department of Mechanical and Aerospace Engineering United States Center for Research on Education in Science Mathematics Engineering and Technology Arizona State University United States Materials Science Program Fulton School of Engineering Arizona State University United States Arizona State University Mary Lou Fulton Teachers College United States Department of Physics United States Arizona State University Department of Civil Engineering School of Sustainable Engineering and the Built Environment United States Arizona State University Department of Computer Science and Engineering School of Computing Informatics and Decision Systems Engineering Ira A. Fulton Schools of Engineering United States

This research paper examined the connectedness of STEM faculty to others both within and across academic departments who might be potential resources for diffusion of Learner-centered practices, and the impact of participants' social networks on their Learner-centered beliefs and practices. The research question under investigation was: To what extent is the degree of social connectedness among faculty within and among STEM departments related to the degree of implementation of Learner-centered practices? Participants were recruited from Physics, Chemistry, Mathematics departments, and from departments in the College of Engineering. The sample began with 21 randomly-selected faculty from each department engaged in the STEM instruction of first and second year engineering students. The second level consisted of the colleagues identified by the first level as people they utilize as resources for improving their instruction. The connectedness of faculty in the network was examined using network analysis, focusing on both number of connections, and depth of connections. Attitude and Observation Protocol measures were used as outcomes. Results show that faculty classified as Learner-Centered had deeper and more extensive social networks. Results are discussed in terms of the need for intra- and inter-departmental faculty professional development experiences that both introduce and encourage faculty to trial new tools and techniques, but that also run for the longterm, supporting collaborative organizations of faculty working together to transform early engineering experiences. © American Society for Engineering Education, 2016.

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Initial BIBFRAME 2.0 Modeling for the Library Information Spotlight “Opera Planet”

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Journal of Library Metadata 2016年第3-4期16卷 202-227页

作者： Xu, Amanda Singh, Abhishek Ramamurthy, Anusha Xu, Changhui Abdelraouf, Mostafa M. Ding, Ying University of Iowa Libraries Iowa City IA United States School of Informatics and Computing Indiana University Bloomington IN United States Computer Science University of Iowa Iowa City IA United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City IA United States

The library linked data environment promises to meet libraries' needs for agility in content delivery and user engagement on the Web. This project chose BIBFRAME 2.0 to demonstrate the purpose by covering the initial BIBFRAME 2.0 modeling and conversion of the sample bibliographic data in Opera Planet, a collection of opera librettos, scores, videos, sound recordings, streaming media, and so forth. The project also developed a proof-of-concept demo site for linked library data application for content enhancement and visualization using various tools, particularly the MARC to BIBFRAME Transformation Service, RDF Translator, AngularJS 2.0, and external open linked data. © 2016, Published with license by Taylor & Francis.

关键词： angularjs2 BIBFRAME 2.0 model and vocabulary linked open data MARC21 metadata conversion visualization

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A learning model for intelligent agents applied to poultry farming 17

A learning model for intelligent agents applied to poultry f...

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17th International Conference on Enterprise Information Systems, ICEIS 2015

作者： Ribeiro, Richardson Teixeira, Marcelo Wirth, André L. Borges, André P. Enembreck, Fabrício Department of Informatics Federal University of Technology-Paraná Pato Branco Brazil Graduate Program in Computer Science Pontificial Catholical University-Paraná Curitiba Brazil

ISBN: (纸本)9789897580970

This paper proposes a learning model for taking-decision problems using intelligent agents technologies combined with instance-based machine learning techniques. Our learning model is applied to a real case to support the daily decisions of a poultry farmer. The agent of the system is used to generate action policies, in order to control a set of factors in the daily activities, such as food-meat conversion, amount of food to be consumed, time to rest, weight gain, comfort temperature, water and energy to be consumed, etc. The perception of the agent is ensured by a set of sensors scattered by the physical structure of the poultry. The principal role of the agent is to perform a set of actions in a way to consider aspects such as productivity and profitability without compromising bird welfare. Experimental results have shown that, for the decision-taking process in poultry farming, our model is sound, advantageous and can substantially improve the agent actions in comparison with equivalent decision when taken by a human specialist.

关键词： Decision making

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Determinants of participants' follow-up and characterization of representativeness in flu near you, a participatory disease surveillance system

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JMIR Public Health and Surveillance 2017年第2期3卷 e18页

作者： Baltrusaitis, Kristin Santillana, Mauricio Crawley, Adam W. Chunara, Rumi Smolinski, Mark Brownstein, John S. Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Biostatistics Boston University School of Public Health Boston MA United States Harvard Medical School Boston MA United States Harvard School of Engineering and Applied Sciences Cambridge MA United States Skoll Global Threats Fund San Francisco CA United States Global Institute of Public Health New York University New York NY United States Computer Science and Engineering New York University New York NY United States

Background: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. Objective: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY's high-participation users, and summarize results from a user survey of a cohort of FNY participants. Methods: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY's 2015 and 2016 end-of-season user surveys were calculated. Results: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first

关键词： Community-based participatory research Crowdsourcing Digital disease detection Human Influenza Public health informatics Public health surveillance

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DOMMINO 2.0: Integrating structurally resolved protein-, RNA-, and DNA-mediated Macromolecular interactions

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Database 2016年第2016期2016卷 bav114页

作者： Kuang, Xingyan Dhroso, Andi Han, Jing Ginger Shyu, Chi-Ren Korkin, Dmitry Informatics Institute University of Missouri Columbia MO United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA United States Department of Electrical and Computer Engineering United States Department of Computer Science University of Missouri Columbia MO United States Ohio State University Comprehensive Cancer Center Columbus OH United States

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N-termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary intera

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Binary classifier calibration using a Bayesian non-parametric approach

Binary classifier calibration using a Bayesian non-parametri...

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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Naeini, Mahdi Pakdaman Cooper, Gregory F. Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh United States Computer Science Department University of Pittsburgh United States

ISBN: (纸本)9781510811522

Learning probabilistic predictive models that are well calibrated is critical for many prediction and decisionmaking tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods. Copyright © SIAM.

关键词： Calibration

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Iran in transition (vol 393, pg 1984, 2019)

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LANCET 2019年第10186期393卷 2124-2124页

作者： Danaei, G. Farzadfar, F. Kelishadi, R. [a]Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA [b]Scientific Association for Public Health in Iran Boston MA USA Scientific Association for Public Health in Iran Boston MA USA [c]Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran [d]Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran [e]Diabetes Research Center Tehran University of Medical Sciences Tehran Iran Diabetes Research Center Tehran University of Medical Sciences Tehran Iran [f]Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran [g]Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran [h]Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran [i]School of Public Health Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran [j]Faculty of Medicine Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Te

Summary Being the second-largest country in the Middle East, Iran has a long history of civilisation during which several dynasties have been overthrown and established and health-related structures have been reorganised. Iran has had the replacement of traditional practices with modern medical treatments, emergence of multiple pioneer scientists and physicians with great contributions to the advancement of science, environmental and ecological changes in addition to large-scale natural disasters, epidemics of multiple communicable diseases, and the shift towards non-communicable diseases in recent decades. Given the lessons learnt from political instabilities in the past centuries and the approaches undertaken to overcome health challenges at the time, Iran has emerged as it is today. Iran is now a country with a population exceeding 80 million, mainly inhabiting urban regions, and has an increasing burden of non-communicable diseases, including cardiovascular diseases, hypertension, diabetes, malignancies, mental disorders, substance abuse, and road injuries.

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Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology

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Biological psychiatry. Cognitive neuroscience and neuroimaging 2025年

作者： Ana I Silva Ida E Sønderby George Kirov Abdel Abdellaoui Ingrid Agartz David Ames Nicola J Armstrong Eric Artiges Tobias Banaschewski Anne S Bassett Carrie E Bearden John Blangero Rune Boen Dorret I Boomsma Robin Bülow Nancy J Butcher Vince Calhoun Linda E Campbell Eva W C Chow Simone Ciufolini Michael C Craig Benedicto Crespo-Farroco Adam C Cunningham Shareefa Dalvie Eileen Daly Paola Dazzan Eco J C de Geus Greig I de Zubicaray Joanne L Doherty Gary Donohoe Mark Drakesmith Thomas Espeseth Vincent Frouin Hugh Garavan David C Glahn Naomi J Goodrich-Hunsaker Penny A Gowland Hans J Grabe Antoine Grigis Maria Gudbrandsen Boris A Gutman Jan Haavik Asta K Håberg Jeremy Hall Andreas Heinz Sarah Hohmann Jouke-Jan Hottenga Sébastien Jacquemont Neda Jahanshad Rachel K Jonas Derek K Jones Erik G Jönsson Sanne Koops Kuldeep Kumar Stephanie Le Hellard Herve Lemaitre Jingyu Liu Astri J Lundervold Jean-Luc Martinot Karen A Mather Donna M McDonald-McGinn Katie L McMahon Allan F McRae Sarah E Medland Clara A Moreau Kieran C Murphy Declan Murphy Robin M Murray Frauke Nees Michael J Owen Marie-Laure Paillère Martinot Diimitri Papadopoulos Orfanos Tomas Paus Luise Poustka Tiago Reis Marques David R Roalf Perminder S Sachdev Freda Scheffler J Eric Schmitt Gunter Schumann Vidar M Steen Dan J Stein Lachlan T Strike Alexander Teumer Anbupalam Thalamuthu Sophia I Thomopoulos Diana Tordesillas-Gutiérrez Julian N Trollor Anne Uhlmann Ariana Vajdi Dennis van 't Ent Therese van Amelsvoort Marianne B M van den Bree Dennis van der Meer Javier Vázquez-Bourgon Julio E Villalón-Reina Uwe Völker Henry Völzke Jacob A S Vorstman Lars T Westlye Nigel Williams Katharina Wittfeld Margaret J Wright Paul M Thompson Ole A Andreassen David E J Linden School for Mental Health and Neuroscience Department of Psychiatry and Neuropsychology Faculty of Health Medicine and Life Sciences Maastricht University Maastricht The Netherlands Neuroscience and Mental Health Innovation Institute Cardiff University Cardiff United Kingdom Center for Magnetic Resonance Research Department of Radiology University of Minnesota Minneapolis MN USA. Electronic address: silvaai@um.edu. Centre for Precision Psychiatry Institute of Clinical Medicine University of Oslo Oslo Norway Department of Medical Genetics Oslo University Hospital Oslo Norway KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo Oslo Norway. Division of Psychological Medicine and Clinical Neurosciences Cardiff University Cardiff United Kingdom Centre for Neuropsychiatric Genetics and Genomics School of Medicine Cardiff University Cardiff United Kingdom. Department of Psychiatry Amsterdam UMC University of Amsterdam Amsterdam the Netherlands. Norwegian Centre for Mental Disorders Research (NORMENT) Institute of Clinical Medicine University of Oslo Oslo Norway Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet & Stockholm Health Care Services Stockholm Region Stockholm Sweden Department of Psychiatric Research Diakonhjemmet Hospital Oslo Norway. University of Melbourne Academic Unit for Psychiatry of Old Age Kew Victoria Australia. Mathematics and Statistics Curtin University Perth WA Australia. Institut National de la Santé et de la Recherche Médicale INSERM U1299 "Trajectoires développementales et psychiatrie" Université Paris-Saclay Ecole Normale supérieure Paris-Saclay CNRS Centre Borelli Gif-sur-Yvette France Psychiatry Department EPS Barthélémy Durand Etampes Île-de-France France. Department of Child and Adolescent Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim University of Heidelberg Mannheim Germany. Clinical Genetics Research Program Centre for

BACKGROUND:Copy number variants (CNVs) increase risk for neurodevelopmental conditions. The neurobiological mechanisms linking these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals carrying CNVs are associated with their degree of penetrance. METHODS:We investigated if increased CNV-penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA-CNV consortium. In the main analyses, we included 9,268 individuals (aged 7 to 90 years, 54% females), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 non-carriers. CNV-penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk to develop either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder and congenital malformations). RESULTS:For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. CONCLUSIONS:Our findings link global and regional cortical morphometric features with CNV-penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

关键词： autism neuroimaging psychiatric disorders schizophrenia structural imaging

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