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Investigation of Study Procedures to Estimate Sensitivity and Reliability of a Virtual Physical Assessment Developed for Workplace Concussions: Method-Comparison Feasibility Study

JMIR Neurotechnology

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JMIR Neurotechnology 2024年 3卷

作者： Keely Barnes Heidi Sveistrup Mark Bayley Mary Egan Martin Bilodeau Michel Rathbone Monica Taljaard Motahareh Karimijashni Shawn Marshall School of Rehabilitation Sciences Faculty of Health Sciences University of Ottawa 200 Lees Ave. Ottawa ON Canada Bruyère Research Institute Ottawa ON Canada Acute Care Program Ottawa Hospital Research Institute Ottawa ON Canada School of Human Kinetics Faculty of Health Sciences University of Ottawa Ottawa ON Canada Systems and Computer Engineering Technology Carleton University Ottawa ON Canada Kite Research Institute Toronto Rehabilitation Institute University Health Network Toronto ON Canada Division of Physical Medicine and Rehabilitation Temerty Faculty of Medicine University of Toronto Toronto ON Canada Department of Medicine Division of Neurology Faculty of Health Sciences McMaster University Hamilton ON Canada School of Epidemiology and Public Health University of Ottawa Ottawa ON Canada Department of Medicine University of Ottawa Ottawa ON Canada

Background Remote approaches to workplace concussion assessment have demonstrated value to end users. The feasibility of administering physical concussion assessment measures in a remote context has been minimally explored, and there is limited information on important psychometric properties of physical assessment measures used in remote contexts. Objective The objectives of this feasibility study were to determine recruitment capability for a future larger-scale study aimed at determining sensitivity and reliability of the remote assessment, time required to complete study assessments, and acceptability of remote assessment to people with brain injuries and clinicians; document preliminary results of the sensitivity of the remote assessment when compared to the in-person assessment; and estimate the preliminary interrater and intrarater reliability of the remote assessments to inform procedures of a future larger-scale study that is adequately powered to reliably estimate these parameters of interest. Methods People living with acquired brain injury attended 2 assessments (1 in-person and 1 remote) in a randomized order. The measures administered in these assessments included the finger-to-nose test; balance testing; and the Vestibular/Ocular Motor Screening (VOMS) tool, including documentation of change in symptoms and distance for near point convergence, saccades, cervical spine range of motion, and evaluation of effort. Both assessments occurred at the Ottawa Hospital Rehabilitation Center. After the assessments, a clinician different from the person who completed the original assessments then viewed and documented findings independently on the recordings of the remote assessment. The same second clinician viewed the recording again approximately 1 month following the initial observation. Results The rate of recruitment was 61% (20/33) of people approached, with a total of 20 patient-participants included in the feasibility study. A total of 3 clinicians partic

关键词： brain injury virtual assessment remote evaluation concussion adult clinician review in-person comparison sensitivity reliability acceptability survey feasibility study psychometric properties vestibular/ocular motor screening VOMS workplace clinician hospital rehabilitation center brain neurology neuroscience neurotechnology technology digital intervention digital health psychometrics physical assessment clinical assessment workplace safety mobile phone

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Conventional and adaptive strategies applied to DC motor control using microcontroller PIC18F4520 7

Conventional and adaptive strategies applied to DC motor con...

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7th Brazilian Electrical systems Symposium, SBSE 2018

作者： Taveiros, Filipe Emanuel Vieira Barros, Luciano Sales Barros, Camila Mara Vital Electrical Engineering and Computing Graduate Program of Federal University of Rio Grande Do Norte Natal Brazil Electrical Engineering Department of Federal University of Rio Grande Do Norte Natal Brazil Computer Systems Department of Federal University of Paraíba João Pessoa Brazil

ISBN: (纸本)9781538633632

In this paper strategies to control the speed of a generic DC motor system are discussed. They range from the conventional, like PID and state-feedback, to reference model adaptive algorithms such as MRAC, VS-MRAC and DMARC. The goal of this study is to determine the best control strategy for high-performance applications regarding to stability, robustness and optimal controller effort. In theory and sometimes in practice, all these control strategies proof themselves reliable, however, in heavy-duty set-point references the application requires high performance of the controller under disturbances, unmodeled dynamics and variable speed reference conditions. The use of appropriate control strategies can highly contribute to this purpose. To support the conclusions, the control strategies have been embedded in a microcontroller PIC18F4520 and applied to a test motor in order to obtain experimental results. © 2018 IEEE.

关键词： Three term control systems

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3D Tortuosity: a Morphological Characterization of the Central Sulcus to Differentiate Patients with Alzheimer's Disease and Controls

3D Tortuosity: a Morphological Characterization of the Centr...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Maria Julieta Mateos Jorge Márquez Ernesto Bribiesca Universidad Nacional Autónoma de México Graduate Program in Computer Science and Engineering Ciudad Universitaria Coyoacán CDMX México Instituto de Ciencias Aplicadas y Tecnología Universidad Nacional Autónoma de México Circuito Exterior S/N Ciudad Universitaria Mexico City Universidad Nacional Autónoma de México Institute of Research in Applied Mathematics and Systems (IIMAS) Circuito Escolar 3000 Ciudad Universitaria Coyoacán CDMX

Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring great interest, as this relates to cognitive function. The sulci are a depression in the cerebral cortex and represent ≈ 70% of the cortical surface. The sulcal width, depth and cortical thickness are the most frequent morphological descriptors applied to the sulci. In this work, the 3D tortuosity is proposed as new descriptor to capture information about the twist and turns of the sulci. 1 The 3D tortuosity of the central sulcus of both hemispheres was measured using the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution MRI of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. As it is known AD causes significant gray matter loss which cause morphological changes in the cortical structure. It is expected for the tortuosity to capture these changes and serve as a biomarker to differentiate between populations. The result of the Wilcoxon tests show that the tortuosity values of the central sulci are significantly larger for the AD patients (p < 0.05) for the left hemisphere, which is consistent with the hypothesis. The evaluation of the proposed tortuosity measurement of the Central Sulcus as a potential biomarker was performed and the results indicate its effectiveness to extract additional anatomical information and discriminate between AD patients and Controls.

关键词： Brain Three-dimensional displays Shape Shape measurement Sociology Surface morphology Skeleton

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Development of Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine) acceptor material for organic solar cells applications

Development of Heterocyclic-C61-butyric Acid Methyl Ester/ P...

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IEEE Conference on Nanotechnology

作者： Muath Bani Salim Reza Nekovei Tamer Elwaie Jeyakumar Ramanujam Sustainable Energy Systems Engineering Program Texas A&M University–Kingsville Kingsville TX Electrical Engineering and Computer Science Texas A&M University–Kingsville Kingsville TX Pharmaceutical Chemistry Department Cairo University Cairo Egypt Physics of Energy Harvesting Division CSIR-National Physical Laboratory Delhi India

This paper presents a novel modification on the Phenyl-C61-butyric acid methyl ester (PCBM) for better performance as an acceptor material by introducing a Heterocyclic ring (Pyrazine) in the place of the Phenyl group of the PCBM to form a new acceptor material, Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine). The electronic properties for the new material are obtained theoretically through the use of the Density Functional Theory (DFT) method with the help of the Gaussian 16 program. The results show that the HCBM-Pyrazine can create more voltage if used with any donor material. Besides that, it has a considerably higher electron affinity, making it a more efficient electron acceptor than the original PCBM. Furthermore, the results show that both molecules have closer absorption spectrums and reorganization energies.

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Work in Progress – Establishing a Master program in Cyber Physical systems: Basic Findings and Future Perspectives

Work in Progress – Establishing a Master Program in Cyber P...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Isam Ishaq Rashid Jayousi Salaheddin Odeh Ezzaldeen Edwan Asmaa Shaheen Mohamed Elnaggar Ahmed Elagha Samy Salamah Christian Weber Hasan Abu Rasheed Raimund Kirner Martina Doolan Hamidreza Ahmadian Roman Obermaisser Dhiah el Diehn I. Abou-Tair Ala Khalifeh Sahel Alouneh Zaid Alhalhouli Khalid Alemerien Faiez Gargouri Bassem Bouaziz Nadia Aloui Mohammad Saleh Computer Engineering and Science Departments Al-Quds University Jerusalem Palestine Engineering Program and Computer Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Institute of Knowledge Based Systems & Knowledge Management University of Siegen Siegen Germany University of Siegen Siegen Germany School of Engineering and Computer Science University of Hertfordshire Hatfield UK Chair of Embedded Systems University of Siegen Siegen Germany German Jordanian University Amman Jordan School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan Computer and Information Technology Department Tafila Technical University Tafila Jordan Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia Higher Institute of Applied Sciences and Technology of Mateur University of Carthage Mateure Tunisia KTH Royal Institute of Technology Stockholm Sweden

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.

关键词： computer science Internet of Things Industries Knowledge engineering Embedded systems Europe Collaboration

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

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Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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IoT-Based Architecture for Data Analytics of Arboviruses in Smart Cities

IoT-Based Architecture for Data Analytics of Arboviruses in ...

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IEEE Symposium on computers and Communications

作者： Priscylla da S. Tavares Emanuel B. Rodrigues Group of Computer Networks Software Engineering and Systems (GREat) Graduate Program in Computer Science (MDCC) Federal University of Ceará (UFC) Fortaleza Brazil

With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.

关键词： Internet of Things Data analytics Health care Smart city

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Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

arXiv

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arXiv 2021年

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Department of Radiology School of Medicine University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States College of Computer National University of Defense Technology Changsha410073 China Department of Bioengineering Swanson School of Engineering University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Department of Radiology University of Pittsburgh University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh 4401 Penn Ave PittsburghPA15224 United States Magee-Womens Hospital University of Pittsburgh Medical Center 300 Halket St PittsburghPA15213 United States Department of Biomedical Informatics University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Intelligent Systems Program University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States

Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model’s performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional "middle" classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. © 2021, CC BY.

关键词： Image classification

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