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AB0384 THE EFFECT OF A LIFESTYLE PROGRAM ON PATIENTS AT RISK FOR RHEUMATOID ARTHRITIS:THE “PLANTS FOR JOINTS” PILOT RANDOMIZED CLINICAL TRIAL

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Annals of the Rheumatic Diseases 2022年 81卷 1319-1320页

作者： C. Wagenaar W. Walrabenstein M. van der Leeden F. Turkstra J. Twisk M. Boers H. van Middendorp P. Weijs D. van Schaardenburg Amsterdam Rheumatology Center Reade Rheumatology Amsterdam Netherlands Amsterdam Rheumatology Center Amsterdam University Medical Centers Universiteit van Amsterdam Rheumatology Amsterdam Netherlands Amsterdam Rheumatology Center Reade Rehabilitation Medicine Amsterdam Netherlands Department of Rehabilitation Medicine Amsterdam University Medical Centers Vrije Universiteit Rehabilitation Medicine Amsterdam Netherlands Amsterdam Movement Sciences Research Institute Rehabilitation Medicine Amsterdam Netherlands Amsterdam University Medical Centers Vrije Universiteit Department of Epidemiology and Data Science Amsterdam Netherlands Institute of Psychology Health Medical & Neuropsychology Unit Leiden University Social and Behavioral Sciences Leiden Netherlands Department of Nutrition and Dietetics Center of Expertise Urban Vitality Amsterdam University of Applied Sciences Department of Nutrition and Dietetics Amsterdam Netherlands

Background An unhealthy lifestyle increases the risk of developing rheumatoid arthritis (RA). Interventions including plant-based diets, physical activity, and stress management have shown benefits for RA patients but have not yet been evaluated in a program for patients at risk for RA. Objectives To investigate preliminary effectiveness of a multidisciplinary lifestyle program on RA risk in patients at risk for RA, in comparison to usual care. Methods In the “Plants for Joints” (PFJ) pilot RCT, patients with anti-citrullinated protein antibody (ACPA) positive arthralgia were randomized to the PFJ or control group. Both groups received usual care while the PFJ group additionally followed a 16-week lifestyle program based on a whole food plant-based diet, physical activity, and stress management. 1 The primary outcome was risk of developing RA as quantified by the RA risk score (including family history, symmetry, stiffness, pain level, RF/ACPA level). 2 The maximum score (13 points) was given to participants who developed RA. Secondary outcomes included self-reported pain intensity, inflammatory, anthropometric, and metabolic markers, and serum levels of ACPA and rheumatoid factor (RF). A linear mixed model for between group analysis was used, adjusted for baseline values. Results 14 out of 17 included patients completed the study (all female, mean age 47 years). Three patients ( n = 2 PFJ group, n = 1 control group) were diagnosed with RA after mean 9.6 weeks and remained in the study. After 16 weeks there was no significant difference in RA risk score between PFJ and control groups ( Figure 1 ). Compared to the control group, the PFJ group had significantly lower fat mass and LDL-cholesterol after 16 weeks ( Table 1 ). After the intervention the PFJ group had an average weight loss of 4.9 kg, of which 2.7 kg was fat mass. Table 1 . Results reported as mean (SD) when normally distributed and median [Q1 – Q3] when skewed. P -value <0.05 = significant. RA = rheumatoi

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

arXiv

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arXiv 2023年

作者： Despotovic, Vladimir Kim, Sang-Yoon Hau, Ann-Christin Kakoichankava, Aliaksandra Klamminger, Gilbert Georg Borgmann, Felix Bruno Kleine Frauenknecht, Katrin B.M. Mittelbronn, Michel Nazarov, Petr V. Bioinformatics Platform Data Integration and Analysis Unit Luxembourg Institute of Health Strassen Luxembourg Dr. Senckenberg Institute of Neurooncology University Hospital Frankfurt Frankfurt am Main Germany Edinger Institute Institute of Neurology Goethe University Frankfurt am Main Germany Frankfurt Cancer Institute Goethe University Frankfurt am Main Germany University Cancer Center Frankfurt Frankfurt am Main Germany University Hospital Goethe University Frankfurt am Main Germany Laboratoire national de santé National Center of Pathology Dudelange Luxembourg Multi-Omics Data Science group Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre of Neuropathology Dudelange Luxembourg Klinik für Frauenheilkunde Geburtshilfe und Reproduktionsmedizin Saarland University Homburg Germany Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg Department of Life Sciences and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist’s efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas;thus, providing insights to pathologists concerning the most informative parts of the WSI. © 2023, CC BY-NC-ND.

关键词： Tumors

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How to design the biofeedback training for the rehabilitation of PTSD patients?

How to design the biofeedback training for the rehabilitatio...

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Cognitive sciences, Genomics and Bioinformatics (CSGB)

作者： Olga Bazanova Alexander Zakharov Irina Larkova Tatiana Maryanovskaya Andrey Melnikov Sargylana Ermolaeva Ekaterina Nikolenko Igor Shirolapov Laboratory of Computer systems of Biofeedback The Research Institute of Molecular Biology and Biophysics Dolgoprudny Russia Educational and Scientific Center for Humanities and Social Science The Moscow Institute of Physics and Technology Novosibirsk Russia Research Institute of Neurosciences Samara State Medical University Samara Russia Laboratory of psychophysiological support Lugansk Republican Center for Emergency Medical Care and Disaster Medicine Lugansk Russia Mechanic-Mathematic Department Novosibirsk National Research State University Novosibirsk Russia Department of Physiology The Russian University of Sport «GTSOLIFK» Moscow Russia Master's Degree in Applied Data Analysis in the Medical Field The Moscow Institute of Physics and Technology Dolgoprudny Russia Department of Clinical Neuroscience Behavior and Neurotechnology Federal State Budgetary Scientific Institution “Scientific Research Institute of Neurosciences and Medicine” Novosibirsk Russia

Rationale for this review is the pressing medical and social problem of the posttraumatic stress disorder (PTSD) treatment. It is known that biofeedback technology is one of the most effective methods of treatment and rehabilitation for emotional profile disorders. In order to develop biofeedback aimed to PTSD rehabilitation, we conducted a literature search on this problem. However, it appears that at the moment there is not enough unambiguous data on this type of biofeedback design. The pathogenesis of PTSD is closely related to impaired efficiency of sensorimotor integration (SMI). The effectiveness of therapy for psychosomatic disorders in patients with PTSD can be increased by restoring normal sensorimotor integration. The review examines various autonomic, electrophysiological and postural markers of destroyed sensorimotor integration (SMI) in individuals with PTSD. We have found that the most informative indicators of SMI in norm include: an increase in EEG power in the individually determined high-frequency alpha subrange, a decrease in the speed of body sway and energy demands to maintain a vertical posture, and a decrease in EMG activity of muscles not involved in cognitive or psychomotor performance. We intend to use these indicators in diagnostic purposes and to develop neurofeedback technology for SMI recovery in patients with PTSD.

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COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

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Sensors 2024年第5期24卷 1557页

作者： Bougourzi, Fares Distante, Cosimo Dornaika, Fadi Taleb-Ahmed, Abdelmalik Hadid, Abdenour Chaudhary, Suman Yang, Wanting Qiang, Yan Anwar, Talha Breaban, Mihaela Elena Hsu, Chih-Chung Tai, Shen-Chieh Chen, Shao-Ning Tricarico, Davide Chaudhry, Hafiza Ayesha Hoor Fiandrotti, Attilio Grangetto, Marco Spatafora, Maria Ausilia Napoli Ortis, Alessandro Battiato, Sebastiano Institute of Applied Sciences and Intelligent Systems National Research Council of Italy Lecce73100 Italy Laboratoire LISSI University Paris-Est Creteil Paris Vitry sur Seine94400 France Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Manuel Lardizabal 1 San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48011 Spain UMR 8520 Universite Polytechnique Hauts-de-France Université de Lille CNRS Valenciennes59313 France Sorbonne Center for Artificial Intelligence Sorbonne University of Abu Dhabi P.O. Box 38044 Abu Dhabi United Arab Emirates College of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China School of Computing National University of Computer and Emerging Sciences Islamabad44000 Pakistan Faculty of Computer Science Alexandru Ioan Cuza University Iasi700506 Romania Institute of Data Science National Cheng Kung University No. 1 University Rd. East Dist. Tainan City701 Taiwan Dipartimento di Informatica Universita degli Studi di Torino Corso Svizzera 185 Torino10149 Italy Department of Mathematics and Computer Science University of Catania Catania95125 Italy

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. © 2024 by the authors.

关键词： COVID-19

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability

arXiv

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arXiv 2020年

作者： Ozbulak, Utku Peck, Jonathan de Neve, Wesley Goossens, Bart Saeys, Yvan van Messem, Arnout Department of Electronics and Information Systems Ghent University Ghent Belgium Center for Biotech Data Science Ghent University Global Campus Incheon Korea Republic of Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Data Mining and Modeling for Biomedicine VIB Inflammation Research Center Ghent Belgium Department of Telecommunications and Information Processing Ghent University - imec Ghent Belgium

Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In this study, we show that effective regional perturbations can be generated without resorting to complex methods. We develop a very simple regional adversarial perturbation attack method using cross-entropy sign, one of the most commonly used losses in adversarial machine learning. Our experiments on ImageNet with multiple models reveal that, on average, 76% of the generated adversarial examples maintain model-to-model transferability when the perturbation is applied to local image regions. Depending on the selected region, these localized adversarial examples require significantly less Lp norm distortion (for p ∈ {0, 2, ∞}) compared to their non-local counterparts. These localized attacks therefore have the potential to undermine defenses that claim robustness under the aforementioned norms. Copyright © 2020, The Authors. All rights reserved.

关键词： Perturbation techniques

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Multidomain Model for Optic Nerve Potassium Clearance: Roles of Glial Cells and Perivascular Spaces

arXiv

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arXiv 2024年

作者： Xiao, Shanfeng Huang, Huaxiong Eisenberg, Robert Song, Zilong Xu, Shixin School of Mathematical Sciences Soochow University 1 Shi-Zi Street Jiangsu Province Suzhou215006 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519088 China Laboratory of Mathematics and Complex Systems MOE Beijing China Normal University Beijing100875 China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States Math and Statistics Department Utah State University Old Main Hill LoganUT84322 United States Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Kunshan China

The accumulation of potassium in the extracellular space surrounding nerve cells is a fundamental aspect of biophysics that has garnered significant attention in recent research. This phenomenon holds implications for various neurological conditions, including spreading depression, migraine, certain types of epilepsy, and potentially, learning processes. A quantitative analysis is essential for understanding the dynamics of potassium clearance following a series of action potentials. This clearance process involves multiple structures along the nerve, including glia, the extracellular space, axons, and the perivascular space, necessitating a spatially distributed systems approach akin to the cable equations of nerve physiology. In this study, we propose a multi-domain model for the optic nerve to investigate potassium accumulation and clearance dynamics. The model accounts for the convection, diffusion, and electrical migration of fluid and ions, revealing the significant roles of glia and the perivascular space in potassium buffering. Specifically, our findings suggest that potassium clearance primarily occurs through convective flow within the syncytia of glia, driven by osmotic pressure differences. Additionally, the perivascular space serves as a crucial pathway for potassium buffering and fluid circulation, further contributing to the overall clearance process. Importantly, our model’s adaptability allows for its application to diverse structures with distinct channel and transporter distributions across the six compartments, extending its utility beyond the optic nerve. © 2024, CC BY-SA.

关键词： Neurons

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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