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Findings from the Tushirikiane-4-MH (Supporting Each Other for Mental Health) Mobile Health-Supported Virtual Reality Randomized controlled Trial Among Urban Refugee Youth in Kampala, Uganda

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Global Mental Health 2025年 12卷 e12-e12页

作者： Logie, Carmen H. Okumu, Moses Admassu, Zerihun MacKenzie, Frannie Gittings, Lesley Kortenaar, Jean-Luc Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada United Nations University Institute for Water Environment & Health Hamilton ON Canada Centre for Gender and Sexuality Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign United States Uganda Christian University Mukono Uganda School of Health Studies Western University London ON Canada Centre for Social Science Research University of Cape Town South Africa Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development (YARID) Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada

Background: Virtual reality (VR) for mental health promotion remains understudied in low income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. Methods: This randomized controlled trial assessed VR alone (Arm 1), VR followed by group problem management plus (GPM+) (Arm 2), and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16-25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental wellbeing, and adaptive coping. Analyses were conducted at three timepoints (baseline, 8-weeks, 16-weeks) using generalized estimating equations (GEE). Results: Among participants (n=335, mean age: 20.77, standard deviation: 3.01;cisgender women: n=158, cisgender men: n=173, transgender women: n=4) we found no depression reductions for Arms 1 or 2 at 16-weeks compared with Arm 3. At 16-weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self compassion was significantly higher in Arms 1 and Arm 2 compared with Arm 3. Discussion: VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression. © The Author(s), 2025. Published by Cambridge University Press.

关键词： depression refugees self-compassion Uganda virtual reality youth

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

引用

arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Principal Component Analysis on Graph-Hessian

Principal Component Analysis on Graph-Hessian

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yichen Pan Yicong Zhou Weifeng Liu Liqiang Nie College of Control Science and Engineering China University of Petroleum (East China) Qingdao China Faculty of Science and Technology University of Macau Macau China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Principal Component Analysis (PCA) is a widely used linear dimensionality reduction method, which assumes that the data are drawn from a low-dimensional affine subspace of a high-dimensional space. However, it only uses the feature information of the samples. By exploiting structural information of data and embedding it into the PCA framework, the local positional relationship between samples in the original space can be preserved, so that the performance of downstream tasks based on PCA can be improved. In this paper, we introduce Hessian regularization into PCA and propose a new model called Graph-Hessian Principal Component Analysis (GHPCA). Hessian can correctly use the intrinsic local geometry of the data manifold. It is better able to maintain the neighborhood relationship between data in high-dimensional space. Compared with other Laplacian-based models, our model can obtain more abundant structural information after dimensionality reduction, and it can better restore low-dimensional structures. By comparing with several methods of PCA, GLPCA, RPCA and RPCAG, through the K-means clustering experiments on USPS handwritten digital dataset, YALE face dataset and COIL20 object image dataset, it is proved that our models are superior to other principal component analysis models in clustering tasks.

关键词： Principal component analysis Sparse matrices Robustness Matrix decomposition Analytical models Manifolds Task analysis

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The study of the stability and bifurcation of 6D fractional-order gene regulatory network system with delay

The study of the stability and bifurcation of 6D fractional-...

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第30届中国过程控制会议（CPCC 2019）

作者： Zhe Zhang Jing Zhang Yingjie Zhang Can Ding FanyongCheng Feng Liu College of Electrical and Information Engineering Hunan University College of Computer Science and Electronic Engineering Hunan University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University School of Automation China University of Geosciences

This paper studies the stability of the six-dimensional Fractional gene regulatory networks(GRNs) with three variables and the bifurcation of second-order system *** paper presents the basic interaction model figure of three variables GRNs,which contains the relationship between the three ***, in order to improve the accuracy of the study, in analysis of ***, a numerical simulation example is provided to verify the effectiveness and the advantage of the proposed stability and bifurcation criterion.

关键词： Fractional-order system Gene regulatory network system Bifurcation analysis Stability analysis

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Identification of Cancer Development Related Pathways Based on Co-Expression Analyses

Identification of Cancer Development Related Pathways Based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Zhihang Wang Chaoyi Yin Peishuo Sun Ying Xu Huiyan Sun School of Artificial Intelligence Jilin University Changchun Innovation Group of Marine Engineering Materials and Corrosion Control Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China Computational Systems Biology Lab Institute of Bioinformatics University of Georgia Athens USA School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.

关键词： Cancer Correlation Indexes Biological processes Physiology Immune system Genetic expression

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Front Cover: Micrometer-Sized Liposome-Based Systems: A Hierarchical Breakdown (ChemSystemsChem 3/2025)

ChemSystemsChem

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ChemSystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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Finite-sample analysis of decentralized temporal-difference learning with linear function Approximation

arXiv

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arXiv 2019年

作者： Sun, Jun Wang, Gang Giannakis, Georgios B. Yang, Qinmin Yang, Zaiyue College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an ‘additional’ projection step to control the ‘gradient’ bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type—in the sense that they hold under the most practical assumptions —which is made possible by means of a novel multi-step Lyapunov analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Sampling

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Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning

TechRxiv

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TechRxiv 2021年

作者： Fan, Rui Bowd, Christopher Christopher, Mark Brye, Nicole Proudfoot, James A. Rezapour, Jasmin Belghith, Akram Goldbaum, Michael H. Chuter, Benton Girkin, Christopher A. Fazio, Massimo A. Liebmann, Jeffrey M. Weinreb, Robert N. Gordon, Mae O. Kass, Michael A. Kriegman, David Zangwill, Linda M. Hamilton Glaucoma Center Viterbi Family Department of Ophthalmology Shiley Eye Institute University of California La Jolla San DiegoCA United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Ophthalmology Universitätsmedizin der Johannes Gutenberg-Universität Mainz Rheinland-Pfalz Mainz Germany Department of Ophthalmology School of Medicine The University of Alabama at Birmingham BirminghamAL United States Department of Biomedical Engineering School of Engineering The University of Alabama at Birmingham BirminghamAL United States Bernard and Shirlee Brown Glaucoma Research Laboratory Edward S. Harkness Eye Institute Columbia University Medical Center New YorkNY United States Department of Ophthalmology and Visual Sciences Washington University School of Medicine Washington University St. LouisMO United States

Importance: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of endpoints in clinical trials. Objective: To investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG). Study Design: Diagnostic/prognostic Study Setting and Participants: 1,636 OHTS participants from 22 sites with a mean follow-up (range) of 10.7 (0.0, 14.3) years. 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. Three independent test sets were used to evaluate the generalizability of the model. Main Outcome and Measures: Areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities were calculated to compare model performance. Evaluation of false-positive rates was used to determine whether the DL model detected POAG before the Endpoint Committee POAG determination. Results: The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their follow-up. The diagnostic accuracy of the DL model developed on the optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. Conclusion and Relevance: The model’s high diagnostic accuracy using OHTS photographs suggests that DL has the potential to standardize and automate POAG determination for cl

关键词： Ophthalmology

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Recent Advances in Image Dehazing

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 410-436页

作者： Wencheng Wang Xiaohui Yuan College of Information and Control Engineering Weifang University IEEE Department of Computer Science and Engineering University of North Texas

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision *** image dehazing is an important issue and has been widely researched in the field of computer *** role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to *** paper reviews the main techniques of image dehazing that have been developed over the past ***,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based *** methods are analyzed and corresponding sub-categories are introduced according to principles and *** quality evaluation methods are then described,sorted and discussed in ***,research progress is summarized and future research directions are suggested.

关键词： Atmospheric scattering model image dehazing image enhancement quality assessment

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Nonlinear Virtual Inertia control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

arXiv

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arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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