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P.677 Serotonin transporter gene polymorphisms and confidence in adult relationships affect the number of followed people on Instagram

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European Neuropsychopharmacology 2020年 40卷 S385-S386页

作者： Bonassi, A. Cataldo, I. Gabrieli, G. Foo, J. Nee Lepri, B. Esposito, G. University of Trento Department of Psychology and Cognitive Science Rovereto Italy Nanyang Technological University Psychology Program- School of Social Sciences Singapore Singapore Genome Institute of Singapore Human Genetics Singapore Singapore Fondazione Bruno Kessler Mobile and Social Computing Lab Trento Italy

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Global cross-cultural validation of a brief measure for identifying potential suicide risk in 42 countries

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Public Health 2024年 229卷 13-23页

作者： Gewirtz-Meydan, A. Koós, M. Nagy, L. Kraus, S.W. Demetrovics, Z. Potenza, M.N. Ballester-Arnal, R. Batthyány, D. Bergeron, S. Billieux, J. Burkauskas, J. Cárdenas-López, G. Carvalho, J. Castro-Calvo, J. Chen, L. Ciocca, G. Corazza, O. Csako, R. Fernandez, D.P. Fujiwara, H. Fernandez, E.F. Fuss, J. Gabrhelík, R. Gjoneska, B. Gola, M. Grubbs, J.B. Hashim, H.T. Islam, M.S. Ismail, M. Jiménez-Martínez, M.C. Jurin, T. Kalina, O. Klein, V. Költő, A. Lee, S.-K. Lewczuk, K. Lin, C.-Y. Lochner, C. López-Alvarado, S. Lukavská, K. Mayta-Tristán, P. Miller, D.J. Orosová, O. Orosz, G. Ponce, F.P. Quintana, G.R. Quintero Garzola, G.C. Ramos-Diaz, J. Rigaud, K. Rousseau, A. De Tubino Scanavino, M. Schulmeyer, M.K. Sharan, P. Shibata, M. Shoib, S. Sigre-Leirós, V. Sniewski, L. Spasovski, O. Steibliene, V. Stein, D.J. Strong, C. Ünsal, B.C. Vaillancourt-Morel, M.-P. Van Hout, M.C. Bőthe, B. School of Social Work Faculty of Social Welfare and Health Sciences University of Haifa Israel Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen Essen Germany Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceutical and Biological Sciences University of Hertfordshire United Kingdom Department of Psychology and Cognitive Science University of Trento Italy Department of Psychology and Neuroscience Auckland University of Technology Auckland New Z

Objectives: This study aimed to examine the psychometric properties of the P4 suicide screener in a multinational sample. The primary goal was to evaluate the reliability and validity of the scale and investigate its convergent validity by analyzing its correlation with depression, anxiety, and substance use. Study design: The study design is a cross-sectional self-report study conducted across 42 countries. Methods: A cross-sectional, self-report study was conducted in 42 countries, with a total of 82,243 participants included in the final data set. Results: The study provides an overview of suicide ideation rates across 42 countries and confirms the structural validity of the P4 screener. The findings indicated that sexual and gender minority individuals exhibited higher rates of suicidal ideation. The P4 screener showed adequate reliability, convergence, and discriminant validity, and a cutoff score of 1 is recommended to identify individuals at risk of suicidal behavior. Conclusions: The study supports the reliability and validity of the P4 suicide screener across 42 diverse countries, highlighting the importance of using a cross-cultural suicide risk assessment to standardize the identification of high-risk individuals and tailoring culturally sensitive suicide prevention strategies. © 2024 The Royal Society for Public Health

关键词： Assessment Cross-cultural Suicide Validation

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Author Correction: A policy-driven framework for conserving the best of Earth's remaining moist tropical forests

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Nature ecology & evolution 2024年第12期8卷 2336页

作者： Andrew J Hansen Patrick Burns Jamison Ervin Scott J Goetz Matthew Hansen Oscar Venter James E M Watson Patrick A Jantz Anne L S Virnig Kevin Barnett Rajeev Pillay Scott Atkinson Christina Supples Susana Rodríguez-Buritica Dolors Armenteras Department of Ecology Montana State University Bozeman MT USA. hansen@montana.edu. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA. United Nations Development Programme New York NY USA. Department of Geographical Sciences University of Maryland College Park MD USA. Natural Resources and Environmental Studies Institute University of Northern British Columbia Prince George British Columbia Canada. Centre for Biodiversity and Conservation Science The University of Queensland Brisbane Queensland Australia. School of Earth and Environmental Sciences The University of Queensland Brisbane Queensland Australia. Global Conservation Program Wildlife Conservation Society New York NY USA. Department of Ecology Montana State University Bozeman MT USA. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Bogotá Colombia. Departamento de Biología Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia.

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The eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries

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Journal of Psychiatric Research 2023年第1期165卷 16-27页

作者： Lee, Chih-Ting Lin, Chung-Ying Koós, Mónika Nagy, Léna Kraus, Shane W. Demetrovics, Zsolt Potenza, Marc N. Ballester-Arnal, Rafael Batthyány, Dominik Bergeron, Sophie Billieux, Joël Burkauskas, Julius Cárdenas-López, Georgina Carvalho, Joana Castro-Calvo, Jesús Chen, Lijun Ciocca, Giacomo Corazza, Ornella Csako, Rita I. Fernandez, David P. Fernandez, Elaine F. Fujiwara, Hironobu Fuss, Johannes Gabrhelík, Roman Gewirtz-Meydan, Ateret Gjoneska, Biljana Gola, Mateusz Grubbs, Joshua B. Hashim, Hashim T. Islam, Md. Saiful Ismail, Mustafa Jiménez-Martínez, Martha Jurin, Tanja Kalina, Ondrej Klein, Verena Költő, András Lee, Sang-Kyu Lewczuk, Karol Lochner, Christine López-Alvarado, Silvia Lukavská, Kateřina Mayta-Tristán, Percy Milea, Ionut Miller, Dan J. Orosová, Oľga Orosz, Gábor Ponce, Fernando P. Quintana, Gonzalo R. Garzola, Gabriel C.Quintero Ramos-Diaz, Jano Rigaud, Kévin Rousseau, Ann Scanavino, Marco De Tubino Schulmeyer, Marion K. Sharan, Pratap Shibata, Mami Shoib, Sheikh Sigre-Leirós, Vera Sniewski, Luke Spasovski, Ognen Steibliene, Vesta Stein, Dan J. Strizek, Julian Ünsal, Berk C. Vaillancourt-Morel, Marie-Pier Van Hout, Marie Claire Bőthe, Beáta Department of Family Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan Biostatistics Consulting Center National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro CPUP: Center for Psychology at Porto University Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceuti

The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) is an instrument to screen substance-use-related health risks. However, little is known whether the ASSIST could be further shortened while remaining psychometrically sound across different countries, languages, gender identities, and sexual-orientation-based groups. The study aimed to validate a shortened 11-item ASSIST (ASSIST-11). Using the International Sex Survey data, 82,243 participants (M _age = 32.39 years) across 42 countries and 26 languages completed questions from the ASSIST-11 regarding gender identity, sexual orientation, and other information. Confirmatory factor analysis (CFA) and multigroup CFA (MGCFA) evaluated the ASSIST-11's structure and tested measurement invariance across groups. Cronbach's α and McDonald's ω were used to examine the internal consistency. Cohen's d and independent t-tests were used to examine known-group validity. The ASSIST-11 was unidimensional across countries, languages, age groups, gender identities (i.e., men, women, and gender-diverse individuals), and sexual orientations (i.e., heterosexual and sexual minority individuals). Cronbach's α was 0.63 and McDonald's ω was 0.68 for the ASSIST-11. Known-group validity was supported by Cohen's d (range between 0.23 and 0.40) with significant differences (p-values<0.001). The ASSIST-11 is a modified instrument with a unidimensional factor structure across different languages, age groups, countries, gender identities, and sexual orientations. The low internal consistency of the ASSIST-11 might be acceptable as it assesses a broad concept (i.e., use of several different substances). Healthcare providers and researchers may use the ASSIST-11 to quickly assess substance-use information from general populations and evaluate the need to follow up with more detailed questions about substance use. © 2023 Elsevier Ltd

关键词： Addictive behaviors Cross-cultural study Factor structure Measurement invariance Psychometrics

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Adaptive Noise Cancelation Using Fuzzy Brain Emotional Learning Network

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and computing 2018年 650卷 171-182页

作者： Zhou, Qianqian Lin, Chih-Min Chao, Fei Fujian Provincial Key Laboratory of the Brain-like Computing Cognitive Science Department School of Informatics Xiamen University XiamenFujian China Department of Electrical Engineering Yuan Ze University Chung-LiTao-Yuan320 Taiwan

ISBN: (纸本)9783319669380

This paper proposes a fuzzy brain emotional learning network for adaptive noise cancelation. The proposed network is based on brain emotional learning algorithm which is developed according to the emotional learning process of mammalian and the fuzzy inference is added for better ability to handle uncertainties. Parameters in the network are modified online by the derived adaption laws. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, in order to demonstrate the performance of the proposed filter, it is applied in a signal processing application where different source signals and noise signals are used. A comparison between the proposed method, Least mean square algorithm and a fuzzy cerebellar model articulation controller filter shows that the proposed method can converge faster even when the source signal is corrupted severely. © 2018, Springer International Publishing AG.

关键词： Fuzzy inference

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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An egocentric perspective on active vision and visual object learning in toddlers 7

An egocentric perspective on active vision and visual object...

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7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017

作者： Bambach, Sven Crandall, David J. Smith, Linda B. Yu, Chen School of Informatics and Computing Indiana University BloomingtonIN47405 United States Cognitive Science Program Indiana University BloomingtonIN47405 United States Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

ISBN: (纸本)9781538637159

Toddlers quickly learn to recognize thousands of everyday objects despite the seemingly suboptimal training conditions of a visually cluttered world. One reason for this success may be that toddlers do not just passively perceive visual information, but actively explore and manipulate objects around them. The work in this paper is based on the idea that active viewing and exploration creates 'clean' egocentric scenes that serve as high-quality training data for the visual system. We tested this idea by collecting first-person video data of free toy play between toddler-parent pairs. We use the raw frames from this data, weakly annotated with toy object labels, to train state-of-the-art machine learning models for object recognition (Convolutional Neural Networks, or CNNs). We run several training simulations, varying quantity and quality of the training data. Our results show that scenes captured by parents and toddlers have different properties, and that toddler scenes lead to models that learn more robust visual representations of the toy objects in them. © 2017 IEEE.

关键词： Toys

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Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge

arXiv

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arXiv 2020年

作者： Schirmer, Markus D. Venkataraman, Archana Rekik, Islem Kim, Minjeong Mostofsky, Stewart Beth Nebel, Mary Rosch, Keri Seymour, Karen Crocetti, Deana Irzan, Hassna Hütel, Michael Ourselin, Sebastien Marlow, Neil Melbourne, Andrew Levchenko, Egor Zhou, Shuo Kunda, Mwiza Lu, Haiping Dvornek, Nicha C. Zhuang, Juntang Pinto, Gideon Samal, Sandip Bernal-Rusiel, Jorge L. Pienaar, Rudolph Chung, Ai Wern Massachusetts General Hospital Harvard Medical School Boston United States German Center for Neurodegenerative Diseases Bonn Germany Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States BASIRA lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom Department of Computer Science University of North Carolina at Greensboro United States Center for Neurodevelopmental and Imaging Research Kennedy Krieger Institute Baltimore United States Department of Neurology Johns Hopkins School of Medicine United States Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine Baltimore United States Department of Neuropsychology Kennedy Krieger Institute Baltimore United States Department of Medical Physics and Biomedical Engineering University College London United Kingdom School of Biomedical Engineering and Imaging Sciences Kings College London United Kingdom Institute for Womens Health University College London United Kingdom Institute of Cognitive Neuroscience Higher School of Economics Moscow Russia Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Department of Radiology & Biomedical Imaging Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Teradyte LLC Coral GablesFL United States Fetal-Neonatal Neuroimaging and Developmental Science Center Division of Newborn Medicine Boston Children’s Hospital Harvard Medical School BostonMA United States

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort;and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthews correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method fo

关键词： Diseases

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Tracking COVID-19 using online search

arXiv

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arXiv 2020年

作者： Lampos, Vasileios Majumder, Maimuna S. Yom-Tov, Elad Edelstein, Michael Moura, Simon Hamada, Yohhei Rangaka, Molebogeng X. McKendry, Rachel A. Cox, Ingemar J. Department of Computer Science University College London London United Kingdom Computational Health Informatics Program Boston Children's Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States Microsoft Research Herzeliya Israel National Infection Service Public Health England London United Kingdom Institute for Global Health University College London London United Kingdom Division of Epidemiology and Biostatistics University of Cape Town Cape Town South Africa London Centre for Nanotechnology University College London London United Kingdom Division of Medicine University College London London United Kingdom Centre for Communication and Computing University of Copenhagen Copenhagen Denmark

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest -as opposed to infections- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches. © 2020, CC BY.

关键词： Time series

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