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Call to Action: data, Diversity, and STEM Education

Change 2018年第2期50卷 40-47页

作者： Jones, Jason Williams, Aaron Whitaker, Shanta Yingling, Stephanie Inkelas, Karen Gates, John Jason Jones is the Assistant Director of the Office of Diversity and Engagement at the University of Virginia School of Engineering and Applied Science. Throughout his years at UVA he has worked in the Contemplative Sciences Center Curry School of Education Women’s Center and the Office of the Dean of Students. Aaron Williams is the Senior Data Research Scientist in the Office of Diversity and Engagement at the University of Virginia School of Engineering and Applied Science. Before joining UVA Williams worked as a principal data analyst at a Fortune 100 financial technology company building models and reporting dashboards from enterprise scale data warehouses. Shanta Whitaker is a health policy consultant at Logistics Management Institute (LMI) a management consulting firm based in Tysons VA. Before switching to health policy Dr. Whitaker was the Associate Director of the Health Professions Advising Program at Yale University. Stephanie Yingling is a government consultant at Logistics Management Institute (LMI) a management consulting firm based in Tysons VA. An Air Force veteran she is also a PhD candidate pursuing her research in the field of Education focusing areas of Organizational Leadership and Science Technology Engineering and Mathematics (STEM) Inclusion. Karen Kurotsuchi Inkelas is an Associate Professor in the Higher Education Program Curry School of Education at the University of Virginia. She also serves as Research Director for the Crafting Success for Underrepresented Scientists and Engineers Initiative as well as a Research Director of Undergraduate Initiatives for the Contemplative Sciences Center. John Gates is the Associate Dean and Chief Diversity Officer in the University of Virginia School of Engineering and Applied Science. He is also the Principal and Chief Strategist for Criticality Management Consulting.

To meet projected labor demands in science and engineering, the workforce of the future must be educated today in increasing numbers. Indeed, science, technology, engineering, and mathematics (STEM) underpin the government's ability to defend the nation and to assure the vitality of the economy. STEM jobs are the fastest growing occupational category and, by 2020, 65% of all jobs in the U.S. will require a post-secondary degree with STEM literacy skills (Carnevale, Smith, & Strohl, 2013). However, according to the U.S. Department of Education, less than 25 percent of college students pursuing bachelor's degrees will be specializing in STEM fields (National Center for Education Statistics, 2012). In addition, the current STEM workforce is predominantly male and White or Asian, even as women and racial and ethnic minority groups are projected to comprise greater percentages of the U.S. population in the coming decades. To meet the growing global demand for a STEM workforce, every segment of the U.S. population will need to be engaged in a successful STEM education. A first critical step will be for academia, government, and nongovernmental agencies to understand the challenges diverse students face in their quest to join the STEM workforce. The goal of the project described in this article is to close the achievement gap of underrepresented minority and underserved students (URMS) and to contribute to addressing the STEM crisis facing the nation. In addition to the longitudinal, five phase research project outlined in this article, UVA engineering's Office of Diversity and Education (ODE) will work to assemble a comprehensive clearinghouse of survey and records data on factors related to STEM education from sources such as admissions, student affairs, student health, academic records and campus-wide surveys in institutional research offices, and UVA's own research and work.

关键词： STEM Education Achievement Gap Disproportionate Representation Minority Group Students Equal Education Higher Education Mixed Methods Research Longitudinal Studies Racial Differences Gender Differences Socioeconomic Status First Generation College Students At Risk Students Information Management Grade Point AverageVirginia

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics

arXiv

引用

arXiv 2017年

作者： Jayasumana, Anura P. Paffenroth, Randy Ramasamy, Sridhar Department of Electrical & Computer Engineering Colorado State University Fort CollinsCO80525 Department of Mathematical Sciences Department of Computer Science Data Science Program Worcester Polytechnic Institute WorcesterMA01609

For many important network types (e.g., sensor networks in complex harsh environments and social networks) physical coordinate systems (e.g., Cartesian), and physical distances (e.g., Euclidean), are either difficult to discern or inappropriate. Accordingly, Topology Preserving Maps (TPMs) derived from a Virtual-Coordinate (VC) system representing the distance to a small set of anchors is an attractive alternative to physical coordinates for many network algorithms. Herein, we present an approach, based on the theory of low-rank matrix completion, to recover geometric properties of a network with only partial information about the VCs of nodes. In particular, our approach is a combination of geodesic recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. Distortion evaluated using the change of distance among node pairs shows that even with up to 40% to 60% of random coordinates missing, accurate TPMs can be obtained. TPM generation can now also be based on different context appropriate VC systems or measurements as long as they characterize each node with distances to a small set of random nodes (instead of a global set of anchors). The proposed method is a significant generalization that allows the topology to be extracted from a random set of graph geodesics, making it applicable in contexts such as social networks where VC generation may not be *** Codes 15A83 Copyright © 2017, The Authors. All rights reserved.

关键词： Sensor networks

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Publisher Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2023年第1期55卷 166页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Paul Jongjoon Choi Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

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Characterization of Markarian 421 during its most violent year: Multiwavelength variability and correlations

arXiv

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arXiv 2025年

作者： Abe, K. Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Engels, A. Arbet Arcaro, C. Asano, K. Baack, D. Babić, A. de Almeida, U. Barres Barrio, J.A. Batković, I. Bautista, A. Baxter, J. González, J. Becerra Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Ammando, F. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. de Menezes, R. Delfino, M. Delgado, J. Mendez, C. Delgado Di Pierro, F. Di Tria, R. Di Venere, L. Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. López, R.J. García Garczarczyk, M. Gasparyan, S. Gaug, M. Paiva, J.G. Giesbrecht Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Llorente, J. Herrera Hrupec, D. Imazawa, R. Ishio, K. Martínez, I. Jiménez Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. González, M. Molero Molina, E. Mondal, H.A. Moralejo, A. Morcuende, D. Nakamori, T. Nanci, C. Neustroev, V. Nickel, L. Nigro, C. Nikolić, L. Nilsson, K. Nishijima, K. Ekoume, T. Njoh Noda, K. Nozaki, S. Okumura, A. Otero-Santos, J. Paiano, S. Japanese MAGIC Group Department of Physics Tokai University Kanagawa Hiratsuka259-1292 Japan The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Università di Siena INFN Pisa SienaI-53100 Italy Bellaterra BarcelonaE-08193 Spain Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain RomeI-00136 Italy Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik GarchingD-85748 Germany Università di Padova INFN PadovaI-35131 Italy Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia URCA RJ Rio de Janeiro22290-180 Brazil IPARCOS Institute EMFTEL Department Universidad Com-plutense de Madrid MadridE-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna TenerifeE-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka51000 Croatia Universität Würzburg WürzburgD-97074 Germany Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan ZeuthenD-15738 Germany Armenian MAGIC Group: ICRANet-Armenia Yerevan0019 Armenia Split21000 Croatia Croatian MAGIC Group J

Aims. Mrk 421 was in its most active state around early 2010, which led to the highest TeV gamma-ray flux ever recorded from any active galactic nuclei (AGN). We aim to characterize the multiwavelength behavior during this exceptional year for Mrk 421, and evaluate whether it is consistent with the picture derived with data from other less exceptional years. Methods. We investigated the period from November 5, 2009, (MJD 55140) until July 3, 2010, (MJD 55380) with extensive coverage from very-high-energy (VHE;E > 100 GeV) gamma rays to radio with MAGIC, VERITAS, Fermi-LAT, RXTE, Swift, GASP-WEBT, VLBA, and a variety of additional optical and radio telescopes. We characterized the variability by deriving fractional variabilities as well as power spectral densities (PSDs). In addition, we investigated images of the jet taken with VLBA and the correlation behavior among different energy bands. Results. Mrk 421 was in widely different states of activity throughout the campaign, ranging from a low-emission state to its highest VHE flux ever recorded. We find the strongest variability in X-rays and VHE gamma rays, and PSDs compatible with power-law functions with indices around 1.5. We observe strong correlations between X-rays and VHE gamma rays at zero time lag with varying characteristics depending on the exact energy band. We also report a marginally significant (∼ 3σ) positive correlation between high-energy (HE;E > 100 MeV) gamma rays and the ultraviolet band. We detected marginally significant (∼ 3σ) correlations between the HE and VHE gamma rays, and between HE gamma rays and the X-ray, that disappear when the large flare in February 2010 is excluded from the correlation study, hence indicating the exceptionality of this flaring event in comparison with the rest of the campaign. The 2010 violent activity of Mrk421 also yielded the first ejection of features in the VLBA images of the jet of Mrk 421. Yet the large uncertainties in the ejection times of these unprece

关键词： Active Galactic Nuclei

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Erratum to 'Relation Between Mitral Valve Prolapse and Erectile Dysfunction (from a Nationwide Case-Control Study)': The American Journal of Cardiology 2019 Nov 15;124(10):1590-1593./ doi: 10.1016/***.2019.08.010

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The American journal of cardiology 2021年 142卷 161页

作者： Shiu-Dong Chung Ju-Chi Liu Tsai-Ning Lou Ben-Chang Shia Herng-Ching Lin Li-Ting Kao Division of Urology Department of Surgery Far Eastern Memorial Hospital Taipei Taiwan Department of Computer Science and Engineering and Graduate Program in Biomedical Informatics College of Informatics Yuan-Ze University Chung-Li Taiwan Sleep Research Center Taipei Medical University Hospital Taipei Taiwan. Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Cardiovascular Medicine Department of Medicine Medical University Hospital-Shuang Ho Hospital TaipeiTaiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. Big Data Research Center Taipei Medical University Taipei Taiwan. Sleep Research Center Taipei Medical University Hospital Taipei Taiwan School of Health Care Administration Taipei Medical University Taipei Taiwan Graduate Institute of Life Sciences National Defense Medical Center Taipei Taiwan Department of Pharmacy Practice Tri-Service General Hospital Taipei Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan.. Electronic address: kaoliting@***.

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Feedforward architectures driven by inhibitory interactions

arXiv

引用

arXiv 2017年

作者： Billeh, Yazan N. Schaub, Michael T. Computation and Neural Systems Program California Institute of Technology Université Catholique de Louvain Massachusetts Institute of Technology Institute for Data Systems and Society Massachusetts Institute of Technology Department of Engineering Science University of Oxford

Directed information transmission is paramount for many social, physical, and biological sys- tems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neu- rons play only a stabilizing role for the network dy- namics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the di- versity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network- architectural basis than often assumed. Copyright © 2017, The Authors. All rights reserved.

关键词： Brain

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The effect of social support and resource support on emotional exhaustion, insomnia, and suicidal ideation among allied health trainees and post-graduate year doctors in Taiwan

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BMC psychology 2024年第1期12卷 322页

作者： Po-Ching Huang Chung-Ying Lin Ru-Yi Huang Jung-Sheng Chen Mark D Griffiths Carol Strong Hsiao-Wen Wang Chiung-Yu Chen Nai-Ying Ko Shyh-Jou Shieh School of Physical Therapy Graduate Institute of Rehabilitation Science College of Medicine Chang Gung University 259 Wen-Hua 1st Rd. Taoyuan 333323 Taiwan. Biostatistics Consulting Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. cylin36933@***. Department of Public Health College of Medicine National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. cylin36933@***. Department of Occupational Therapy College of Medicine National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. cylin36933@***. Institute of Allied Health Sciences College of Medicine National Cheng Kung University 1 University Rd. Tainan 701401 Taiwan. cylin36933@***. Division of Family Medicine Taipei Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and School of Medicine Tzu Chi University No. 289 Jianguo Rd. Xindian Dist. New Taipei 23142 Taiwan. ruyi.star@***. Data Science Degree Program National Taiwan University and Academia Sinica No. 1 Sec. 4 Roosevelt Rd. Taipei 10617 Taiwan. ruyi.star@***. Department of Medical Research E-Da Hospital I-Shou University No.1 Yida Rd. Yanchao Dist Kaohsiung 824005 Taiwan. nicky071214@***. International Gaming Research Unit Psychology Department Nottingham Trent University 50 Shakespeare St Nottingham NG1 4FQ UK. Department of Public Health College of Medicine National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. Department of Hydraulic and Ocean Engineering College of Engineering National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. Department of Internal Medicine College of Medicine National Cheng Kung University Hospital National Cheng Kung University 1 University Rd. East Dist Tainan 701401 Taiwan. Department of Internal Medicine College of Medicine National Cheng Kung University 1 University Rd. East Dist Tainan

BACKGROUND:COVID-19-related stigmatization refers to COVID-19-related judgements by others that devalue the individual. Such stigmatization towards healthcare workers may cause psychological burden and negative consequences. Such stigmatization may have particularly overwhelmed allied health trainees (AHTs) and post-graduate year doctors (PGYDs) because they just started their medical career. Social support and resource support have been reported to benefit psychological health and reduce stigmatization. Therefore, the present study used a cross-sectional study design to investigate the association between perceived stigma, self-stigma, psychological distress, and negative outcomes (including emotional exhaustion, insomnia and suicidal ideation) among AHTs and PGYDs in Taiwan. METHODS:An online survey distributed between July and December, 2022 received 522 responses. Variables were assessed using the 21-item Depression, Anxiety and Stress Scale, Insomnia Severity Index and a series of self-designed questions to assess social support, resource support, perceived stigma, self-stigma, emotional exhaustion, and suicidal ideation. RESULTS:Structural equation modeling showed that perceived stigma was associated with self-stigma (standardized coefficient [β] = 0.428, p < 0.001), and self-stigma was associated with psychological distress (β = 0.197, p < 0.001), as well as being associated with emotional exhaustion, insomnia, and suicidal ideation (β = 0.349, 0.556 and 0.212, all p-values < 0.001). While social support and resource support were negatively associated with perceived stigma (β= - 0.175 and - 0.152, p < 0.01), additional associations were found between social support and emotional exhaustion (β= - 0.093, p < 0.001), as well as between resource support and insomnia (β= - 0.120, p < 0.001). CONCLUSIONS:The results showed that COVID-19 related stigmatization was correlated to the detrimental consequences of emotional exhaustion, insomnia and suicidal ideation. Cle

关键词： COVID-19 Insomnia Stigma Suicidality Support

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