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Npj imaging 2024年第1期2卷 56页

作者： Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E Black Alan R Moody April Khademi Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada. Institute for Biomedical Engineering Science Tech (iBEST) a Partnership between St. Michael's Hospital and Toronto Metropolitan University Toronto ON Canada. Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada. Department of Medical Imaging University of Toronto Toronto ON Canada. Institute of Medical Science University of Toronto Toronto ON Canada. Horvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto ON Canada. Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto ON Canada. L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto ON Canada. Vector Institute for Artificial Intelligence Toronto ON Canada.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

关键词： Brain imaging Image processing

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Author Correction: Cellulose Mediated Transferrin Nanocages for Enumeration of Circulating Tumor Cells for Head and Neck Cancer

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Scientific reports 2020年第1期10卷 16607页

作者： Raj Shankar Hazra Narendra Kale Gourishankar Aland Burhanuddin Qayyumi Dipankar Mitra Long Jiang Dilpreet Bajwa Jayant Khandare Pankaj Chaturvedi Mohiuddin Quadir Department of Mechanical Engineering Materials and Nanotechnology Program North Dakota State University Fargo ND 58108 USA. School of Pharmacy Maharashtra Institute of Technology-WPU Pune India. Actorius Innovations and Research (AIR) Pvt. Ltd Pune India. Department of Medical Oncology Tata Memorial Hospital Mumbai 400012 Maharashtra India. Department of Electrical and Computer Engineering North Dakota State University Fargo ND 58108 USA. Department of Mechanical and Industrial Engineering Montana State University Bozeman MT 59717-3800 USA. School of Pharmacy Maharashtra Institute of Technology-WPU Pune India. jayant.khandare@mippune.edu.in. Actorius Innovations and Research (AIR) Pvt. Ltd Pune India. jayant.khandare@mippune.edu.in. Department of Coatings and Polymeric Materials North Dakota State University Fargo ND 58108 USA. mohiuddin.quadir@ndsu.edu.

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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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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MODELING AND CONTROL OF ELEVATORS BY STATECHARTS

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Asian Journal of Control 2008年第2期6卷

作者： Yi-Sheng Huang Sheng-Luen Chung Mu-Der Jeng Department of Aeronautical Engineering Chung Cheng Institute of Technology National Defense University Tashi Taoyuan 335 Taiwan R.O.C. Department of Electrical Engineering National Taiwan University of Science and Technology Taipei 106 Taiwan R.O.C. Department of Electrical Engineering National Taiwan Ocean University Kellung 202 Taiwan R.O.C. MuDer Jeng received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute Troy NY in 1992. Since August 1992 Dr. Jeng has been with National Taiwan Ocean University Keelung Taiwan where he is currently a full Professor at the Department of Electrical Engineering. His current research interests include Petri nets discrete event systems computer integrated manufacturing semiconductor factory automation embedded systems. Dr. Jeng is the author/co-author of over 120 book chapters journal papers and conference papers. Dr. Jeng received the Franklin V. Taylor Outstanding Paper Award from the IEEE Systems Man and Cybernetics Society in 1993. He was granted the Research Award by the National Science Council of Taiwan annually from 1994 to 2000. He is an Associate Editor for IEEE Transactions on Systems Man and Cybernetics-Part A IEEE Transactions on Robotics and Automation IEEE Transactions on Robotics and serves on the Editorial Board of International Journal of Computer Integrated Manufacturing. He has been a Guest Editor for eight leading journals. Dr. Jeng is the Chair of the Technical Committee on Discrete Event Systems of the IEEE SMC Society and the Founding Chair of the Technical Committee on Semiconductor Factory Automation of the IEEE Robotics and Automation Society. He served as the Exhibitions Chair of 2003 IEEE International Conference on Robotics and Automation and the Special Sessions Chair of 2004 IEEE International Conference on Networking Sensing and Control. He serves as a Program Co-Chair of 2005 IEEE International Conference on Networking Sensing and Control and the Organization Commit

Statechart has been utilized as a visual formalism for the modeling of complex and interactive systems for its illuminating features on describing properties of causality, concurrency, and synchronization. This paper presents the application of satechart to the modeling, design and implementation of an elevator system, whose system behavior involves aggregating complexity of state descriptions, and imposition of underlying control policy. Based on the operational flow of an elevator, we derive the associated statechart model by looking into the inherent hierarchical structure of the elevator. The advantage of the proposed approach is the clear presentation of system behavior in terms of conditions and events that cause the transitions in system dynamics. Implementation of the controlled elevator based on the modeled statechart is also presented.

关键词： Statechart interactive systems elevator

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

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Nature 2024年第8021期631卷 E13页

作者： Sean L Metzger Kaylo T Littlejohn Alexander B Silva David A Moses Margaret P Seaton Ran Wang Maximilian E Dougherty Jessie R Liu Peter Wu Michael A Berger Inga Zhuravleva Adelyn Tu-Chan Karunesh Ganguly Gopala K Anumanchipalli Edward F Chang Department of Neurological Surgery University of California San Francisco San Francisco CA USA. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Speech Graphics Ltd Edinburgh UK. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Neurological Surgery University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. edward.chang@ucsf.edu.

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第Sup1期18卷 1-79页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Bra

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Correction to: Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

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European radiology 2021年第10期31卷 8068页

作者： Francesco Ria Wanyi Fu Jocelyn Hoye W Paul Segars Anuj J Kapadia Ehsan Samei Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Medical Physics Graduate Program Departments of Radiology Physics Biomedical Engineering and Electrical and Computer Engineering Duke University 2424 Erwin Road Suite 302 Durham NC 27710 USA.

A Correction to this paper has been published: https://***/10.1007/s00330-021-07903-z

关键词： Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry a shareable link is not currently available for this article.Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative

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The correlation calibration of PAPER-64 data

arXiv

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arXiv 2021年

作者： Gogo, Tamirat G. Ma, Yin-Zhe Kittiwisit, Piyanat Sievers, Jonathan L. Parsons, Aaron R. Pober, Jonathan C. Jacobs, Daniel C. Cheng, Carina Kolopanis, Matthew Liu, Adrian Kohn, Saul A. Aguirre, James E. Ali, Zaki S. Bernardi, Gianni Bradley, Richard F. DeBoer, David R. Dexter, Matthew R. Dillon, Joshua S. Klima, Pat MacMahon, David H.E. Moore, David F. Nunhokee, Chuneeta D. Walbrugh, William P. Walker, Andre School of Chemistry and Physics University of KwaZulu-Natal Westville Campus Private Bag X54001 Durban4000 South Africa University of KwaZulu-Natal Durban4000 South Africa Department of Physics McGill University 3600 University Street MontrealQCH3A 2T8 Canada Department of Astronomy UC Berkeley BerkeleyCA94720 United States Radio Astronomy Laboratory UC Berkeley BerkeleyCA94720 United States Department of Physics Brown University ProvidenceRI02912 United States School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Department of Physics and McGill Space Institute McGill University MontrealQC Canada Gravity & the Extreme Universe Program Canadian Institute for Advanced Research 661 University Ave. Suite 505 TorontoONM5G 1M1 Canada Department of Physics and Astronomy U. Penn. PhiladelphiaPA United States INAF Istituto di Radioastronomia via Gobetti 101 Bologna40129 Italy Department of Physics and Electronics Rhodes University PO Box 94 Grahamstown6140 South Africa South African Radio Astronomy Observatory Black River Park 2 Fir Street Observatory Cape Town7925 South Africa Department of Electrical and Computer Engineering U. Virginia CharlottesvilleVA United States National Radio Astronomy Obs. CharlottesvilleVA United States Department of Astronomy U. Virginia CharlottesvilleVA United States

Observation of redshifted 21-cm signal from the Epoch of Reionization (EoR) is challenging due to contamination from the bright foreground sources that exceed the signal by several orders of magnitude. The removal of this very high foreground relies on accurate calibration to keep the intrinsic property of the foreground with frequency. Commonly employed calibration techniques for these experiments are the sky model-based and the redundant baseline-based calibration approaches. However, the sky model-based and redundant baseline-based calibration methods could suffer from sky-modeling error and array redundancy imperfection issues, respectively. In this work, we introduce the hybrid correlation calibration (CorrCal) scheme, which aims to bridge the gap between redundant and sky-based calibration by relaxing redundancy of the array and including sky information into the calibration formalisms. We demonstrate the slight improvement of power spectra, about −6% deviation at the bin right on the horizon limit of the foreground wedge-like structure, relative to the power spectra before the implementation of CorrCal to the data from the Precision Array for Probing the Epoch of Reionization (PAPER) experiment, which was otherwise calibrated using redundant baseline calibration. This small improvement of the foreground power spectra around the wedge limit could be suggestive of reduced spectral structure in the data after CorrCal calibration, which lays the foundation for future improvement of the calibration algorithm and implementation method. © 2021, CC BY.

关键词： Calibration

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Correction to: Pharmacological Rescue With SR8278, A Circadian Nuclear Receptor REV-ERBα Antagonist As A Therapy for Mood Disorders In Parkinson's Disease

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Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 2022年第4期19卷 1432页

作者： Jeongah Kim Inah Park Sangwon Jang Mijung Choi Doyeon Kim Woong Sun Youngshik Choe Ji-Woong Choi Cheil Moon Sung Ho Park Han Kyoung Choe Kyungjin Kim Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. Department of Anatomy College of Medicine Korea University Seoul Korea. Program in Neurosciences & Mental Health The Hospital for Sick Children Toronto ON Canada. Korea Brain Research Institute (KBRI) Daegu Korea. Department of Electrical Engineering and Computer Science DGIST Daegu Korea. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. Department of Biological Sciences Ulsan National Institute of Science and Technology (UNIST) Ulsan 44919 Korea. Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. kyungjin@dgist.ac.kr. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. kyungjin@dgist.ac.kr.

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