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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Shared abnormality of white matter integrity in schizophrenia and bipolar disorder: A comparative voxel-based meta-analysis

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Schizophrenia Research 2017年 185卷 41-50页

作者： Dong, Debo Wang, Yulin Chang, Xuebin Jiang, Yuchao Klugah-Brown, Benjamin Luo, Cheng Yao, Dezhong Key Laboratory for NeuroInformation of Ministry of Education High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Faculty of Psychological and Educational Sciences Department of Experimental and Applied Psychology Research Group of Biological Psychology Vrije Universiteit Brussel Brussels 1040 Belgium Faculty of Psychology and Educational Sciences Department of Data Analysis Ghent University Henri Dunantlaan 2 Ghent B-9000 Belgium

Patients with schizophrenia and bipolar disorder (BD) shared a significant overlap in genetic susceptibility, pharmacological treatment responses, neuropsychological deficits, and epidemiological features. However, it remains unknown whether these clinical overlaps are mediated by shared or disorder-specific abnormalities of white matter integrity. In this voxel-based meta-analytic comparison of whole-brain white matter integrity, we aimed to identify the shared or disorder-specific structural abnormalities between schizophrenia and BD. A comprehensive literature search was conducted up to February 2016 to identify studies that compared between patients and healthy controls (HC) by using whole-brain diffusion approach (schizophrenia: 24 datasets with 754 patients vs. 775 HC;BD: 23 datasets with 705 patients vs. 679 HC). Voxel-wise meta-analyses were conducted and restricted to unified template using seed-based d-Mapping. Abnormal white matter integrity was calculated within each condition and a direct comparison of effect size was performed of alterations between two conditions. Two regions with significant reductions of fractional anisotropy (FA) characterized abnormal water diffusion in both disorders: the genu of the corpus callosum (CC) and posterior cingulum fibers. There was no significant difference found between the two disorders. Our results highlighted shared impairments of FA at genu of the CC and left posterior cingulum fibers, which suggests that, phenotypic overlap between schizophrenia and BD could be related to common brain circuit dysfunction. © 2017 Elsevier B.V.

关键词： Bipolar Diffusion tensor imaging Fractional anisotropy Schizophrenia Structural connectivity White matter

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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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Robust Gait Recognition under Unconstrained Environments Using Hybrid Descriptions

Robust Gait Recognition under Unconstrained Environments Usi...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Lingxiang Yao Worapan Kusakunniran Qiang Wu Jian Zhang Zhenmin Tang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand School of Electrical and Data Engineering University of Technology Sydney Sydney Australia

Gait is one of the key biometric features that has been widely applied for human identification. Appearance-based features and motion-based features are the two mainly used presentations in the gait recognition. However, appearance-based features are sensitive to the body shape changes and silhouette extraction from real-world images and videos also remains a challenge. As for motion features, due to the difficulty in extracting the underlying models from gait sequences, the localization of human joints lacks of high reliability and strong robustness. This paper proposes a new approach which utilizes Two-Point Gait (TPG) as the motion feature to remedy the deficiency of the appearance feature based on Gait Energy Image (GEI), in order to increase the robustness of gait recognition under the unconstrained environments with view changes and cloth changes. Another contribution of this paper is that this is the first time that TPG has been applied for view change and cloth change issues since it was proposed. The extensive experiments show that the proposed method is more invariant to the view change and cloth change, and can significantly improve the robustness of gait recognition.

关键词： Feature extraction Gait recognition Robustness Legged locomotion Matrix decomposition Videos Shape

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Publisher Correction: Base editors for simultaneous introduction of C-to-T and A-to-G mutations

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Nature biotechnology 2020年第7期38卷 901页

作者： Rina C Sakata Soh Ishiguro Hideto Mori Mamoru Tanaka Kenji Tatsuno Hiroki Ueda Shogo Yamamoto Motoaki Seki Nanami Masuyama Keiji Nishida Hiroshi Nishimasu Kazuharu Arakawa Akihiko Kondo Osamu Nureki Masaru Tomita Hiroyuki Aburatani Nozomu Yachie Synthetic Biology Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan. College of Arts and Sciences University of Tokyo Tokyo Japan. Institute for Advanced Biosciences Keio University Tsuruoka Japan. Graduate School of Media and Governance Keio University Fujisawa Japan. Genome Science Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan. Biological Data Science Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan. Engineering Biology Research Center Kobe University Kobe Japan. Graduate School of Science Technology and Innovation Kobe University Kobe Japan. Department of Biological Sciences School of Science University of Tokyo Tokyo Japan. Faculty of Environment and Information Studies Keio University Fujisawa Japan. Department of Chemical Science and Engineering Graduate School of Engineering Kobe University Kobe Japan. Synthetic Biology Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan. yachie@synbiol.rcast.u-tokyo.ac.jp. College of Arts and Sciences University of Tokyo Tokyo Japan. yachie@synbiol.rcast.u-tokyo.ac.jp. Institute for Advanced Biosciences Keio University Tsuruoka Japan. yachie@synbiol.rcast.u-tokyo.ac.jp. Graduate School of Media and Governance Keio University Fujisawa Japan. yachie@synbiol.rcast.u-tokyo.ac.jp. Department of Biological Sciences School of Science University of Tokyo Tokyo Japan. yachie@synbiol.rcast.u-tokyo.ac.jp. PRESTO Japan Science and Technology Agency (JST) Tokyo Japan. yachie@synbiol.rcast.u-tokyo.ac.jp.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Deep neural networks improve radiologists' performance in breast cancer screening

arXiv

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arXiv 2019年

作者： Wu, Nan Phang, Jason Park, Jungkyu Shen, Yiqiu Huang, Zhe Zorin, Masha Jastrzebski, Stanislaw Févry, Thibault Katsnelson, Joe Kim, Eric Wolfson, Stacey Parikh, Ujas Gaddam, Sushma Lin, Leng Leng Young Ho, Kara Weinstein, Joshua D. Reig, Beatriu Gao, Yiming Toth, Hildegard Pysarenko, Kristine Lewin, Alana Lee, Jiyon Airola, Krystal Mema, Eralda Chung, Stephanie Hwang, Esther Samreen, Naziya Kim, S. Gene Heacock, Laura Moy, Linda Cho, Kyunghyun Geras, Krzysztof J. Department of Radiology New York University School of Medicine 660 First Ave New YorkNY10016 United States Center for Data Science New York University 60 5th Ave New YorkNY10011 United States Courant Institute of Mathematical Sciences New York University 251 Mercer St New YorkNY10012 United States Perlmutter Cancer Center NYU Langone Health 160 E 34th St New YorkNY10016 United States Center for Advanced Imaging Innovation and Research NYU Langone Health 660 First Ave New YorkNY10016 United States Department of Information Technology NYU Langone Health 360 Park Ave South New YorkNY10010 United States CIFAR Global Scholar Department of Computer Science and Technology University of Cambridge William Gates Building 15 JJ Thomson Ave CambridgeCB3 0FD United Kingdom Faculty of Mathematics and Information Technologies Jagiellonian University Lojasiewicza 6 Kraków30-348 Poland SUNY Downstate College of Medicine 450 Clarkson Ave New YorkNY11203 United States

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population. We attribute the high accuracy of our model to a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and find our model to be as accurate as experienced radiologists when presented with the same data. Finally, we show that a hybrid model, averaging probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To better understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, model design, training procedure, errors, and properties of its internal representations. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Corrections Notice to: Evolved Algorithm and Vibration Stability for Nonlinear Disturbed Security Systems

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Sound & Vibration 2019年第6期53卷 309-309页

作者： Tim Chen Wray Marriott Ann Nicholson Tim Chen Mars Kmieckowiak Jcy Chen Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam Department of Data Science Monash UniversityMelbourneVictoriaAustralia Dyson School of Design Engineering Imperial College LondonSouth Kensington Campus London SW72AZUK Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Faculty of Mathematics and Natural Sciences Albertus-Magnus-Platz50923 KölnGerman Industrial Technology Research Institute 31057 HsinchuTaiwanDepartment of Artificial IntelligenceUniversity of MarylandMaryland 20742USA

The authors would like to make the following corrections to our published paper[1].The first author’s name is corrected from“Tcw Chen1”to“Tim Chen1”.The first affiliation address is corrected from“1 Department of Electrical and Computer Engineering,North South University,Dhaka-1229,Bagladesh.”to“1 faculty of information technology,Ton Duc Thang University,Ho Chi Minh City,Vietnam.”.

关键词： correction corrected Vietnam

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Visualizing the Energy of Scattered data by Using Cubic Timmer Triangular Patches

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Journal of Physics: Conference Series 2019年第1期1366卷

作者： F A M Ali S A A Karim S C Dass V Skala A Saaban M K Hasan I Hashim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Darul Ridzuan Malaysia Fundamental and Applied Sciences Department and Centre for Smart Grid Energy Research (CSMER) Institute of Autonomous System Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia School of Computer Science and Engineering University of West Bohemia Plzen School of Quantitative Sciences UUMCAS Universiti Utara Malaysia Kedah Malaysia Centre for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia Centre for Modelling & Data Science Faculty of Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

This paper discusses the application of the new cubic Timmer triangular patches constructed by Ali et al. [1] to interpolate the irregularly scattered data with C 1 continuity. In order to apply the cubic Timmer triangular patches for scattered data interpolation, the data is first triangulated by using the Delaunay algorithm, and then the sufficient condition for C 1 continuity is derived along the adjacent triangles. Two methods will be used to calculate the cubic Timmer ordinates on each triangle. The convex combination between three local schemes Ti , i = 1,2,3 will be used to produce the C 1 surface everywhere. The proposed scheme will be tested to visualize one energy data set with irregular shape properties. Numerical and graphical results are presented by using MATLAB. Comparisons between the proposed scheme and existing schemes such as cubic Ball and cubic Bézier triangular patches are also carried out. The results indicate that the surface produced by cubic Timmer triangular patch is better than surface produced using cubic Ball and cubic Bezier triangular patches with overall coefficient of determination R2 value obtained to be larger than 0.9234.

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