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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models

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Monthly Notices of the Royal Astronomical Society: Letters 2024年第1期527卷 L173-L178页

作者： Legin, Ronan Ho, Matthew Lemos, Pablo Perreault-Levasseur, Laurence Ho, Shirley Hezaveh, Yashar Wandelt, Benjamin Department of Physics Universite de Montreal Montreal H2V 0B3 Canada Mila - Quebec Artificial Intelligence Institute Montreal H2S 3H1 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montreal H2V 0B3 Canada Sorbonne Universite CNRS UMR 7095 Institut d'Astrophysique de Paris 98 bis bd Arago Paris 75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York 10010 NY United States Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 ON Canada

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods. © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

关键词： early Universe large-scale structure of Universe methods: statistical

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Social stress drives the multi-wave dynamics of COVID-19 outbreaks

arXiv

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arXiv 2021年

作者： Kastalskiy, Innokentiy A. Pankratova, Evgeniya V. Mirkes, Evgeny M. Kazantsev, Victor B. Gorban, Alexander N. Department of Neurotechnology Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia 46 Ulyanov St. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University 14 Nevsky St. Kaliningrad236016 Russia Laboratory of Perspective Methods for Analysis of Multidimensional Data Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Applied Mathematics Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Mathematics University of Leicester University Rd LeicesterLE1 7RH United Kingdom Neuroscience and Cognitive Technology Laboratory Innopolis University 1 Universitetskaya St. Innopolis420500 Russia Laboratory of Neuromodeling Samara State Medical University 18 Gagarin St. Samara443079 Russia

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic sociocultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. These constants characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time, and can further help in the development of management strategies specific to a particular society. © 2021, CC BY.

关键词： Epidemiology

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Dynamics, Statistics, and Predictability of Rossby Waves, Heat Waves, and Spatially Compounding Extreme Events

Dynamics, Statistics, and Predictability of Rossby Waves, He...

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作者： Lembo, Valerio Bordoni, Simona Bevacqua, Emanuele Domeisen, Daniela I.V. Franzke, Christian L.E. Galfi, Vera M. Garfinkel, Chaim I. Grams, Christian M. Hochman, Assaf Jha, Roshan Kornhuber, Kai Kwasniok, Frank Lucarini, Valerio Messori, Gabriele Pappert, Duncan Perez-Fernandez, Iago Riboldi, Jacopo Russo, Emmanuele Shaw, Tiffany A. Strigunova, Iana Strnad, Felix Yiou, Pascal Zagar, Nedjeljka Bologna Italy University of Trento Trento Italy Department of Compound Environmental Risks Helmholtz Centre for Environmental Research – UFZ Leipzig Germany University of Lausanne Lausanne Switzerland Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Center for Climate Physics Institute for Basic Science Busan Korea Republic of Pusan National University Busan Korea Republic of Institute for Environmental Studies Vrije Universiteit Amsterdam Amsterdam Netherlands Fredy and Nadine Herrmann Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel Federal Office of Meteorology and Climatology MeteoSwiss Zurich-Airport Switzerland IDP in Climate Studies Indian Institute of Technology Bombay Mumbai India International Institute of Applied Systems Analysis Laxenburg Austria Potsdam Institute for Climate impact Research Potsdam Germany Lamont-Doherty Earth Observatory Columbia University New YorkNY United States Department of Mathematics and Statistics University of Exeter Exeter United Kingdom School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Earth Sciences Uppsala University Uppsala Sweden Uppsala University Uppsala Sweden Department of Meteorology Bolin Centre for Climate Research Stockholm University Stockholm Sweden Institute of Geography University of Bern Bern Switzerland University of Bern Bern Switzerland Departamento de Ciencias de la atmósfera y Física de los Océanos Facultad de Ciencias Universidad de la República Montevideo Uruguay Department of the Geophysical Sciences The University of Chicago ChicagoIL United States Machine Learning in Climate Science University of Tübingen Tübingen Germany Laboratoire des Sciences du Climat et de’Environnement UMR8212 CEA-CNRS-UVSQ IPSL & Université Paris-Saclay Gif-sur-Yvette France Meteorological Institute CEN Universität Hamburg Hamburg Germany

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LtU-ILI: AN ALL-IN-ONE FRAMEWORK FOR IMPLICIT INFERENCE IN ASTROPHYSICS AND COSMOLOGY

arXiv

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arXiv 2024年

作者： Ho, Matthew Bartlett, Deaglan J. Chartier, Nicolas Cuesta-Lazaro, Carolina Ding, Simon Lapel, Axel Lemos, Pablo Lovell, Christopher C. Makinen, T. Lucas Modi, Chirag Pandya, Viraj Pandey, Shivam Perez, Lucia A. Wandelt, Benjamin Bryan, Greg L. UMR 7095 98 bis bd Arago ParisF-75014 France Center for the Gravitational-Wave Universe Astronomy Program Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom & Astrophysics Group Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States 4 place Jussieu ParisF-75252 Cedex 5 France

This paper presents the learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry;inferring cosmology from matter power spectra and halo point clouds;characterizing progenitors in gravitational wave signals;capturing physical dust parameters from galaxy colors and luminosities;and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://***/maho3/ltu-ili. © 2024, CC BY.

关键词： Cosmology

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Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project

arXiv

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arXiv 2022年

作者： Llera, Alberto Brammer, M. Oakley, B. Tillmann, J. Zabihi, M. Mei, T. Charman, T. Ecker, C. Dell'Acqua, F. Banaschewski, T. Moessnang, C. Baron-Cohen, S. Holt, R. Durston, S. Murphy, D. Loth, E. Buitelaar, J.K. Floris, D.L. Beckmann, C.F. Donders Institute for Brain Cognition and Behaviour Centre for Cognitive Neuroimaging Nijmegen Netherlands Department of Cognitive Neuroscience Radboud University Medical Centre Nijmegen Netherlands LIS Data Solutions Machine Learning Group Santander Spain Methods of Plasticity Research Department of Psychology University of Zurich Switzerland Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Roche Pharma Research and Early Development Neuroscience and Rare Diseases Roche Innovation Center Basel F. Hoffmann-La Roche Ltd. Basel Switzerland Sackler Institute for Translational Neurodevelopment Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Karakter Child and Adolescent Psychiatry University Centre Nijmegen 6525 GC Netherlands Department of Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim University of Heidelberg Germany Department of Applied Psychology SRH University Heidelberg Germany Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Frankfurt am Main Goethe University Frankfurt Germany Central Institute of Mental Health University of Heidelberg Mannheim Germany Autism Research Centre Department of Psychiatry University of Cambridge United Kingdom Department of Psychiatry Brain Center Rudolf Magnus University Medical Center Utrecht Netherlands University of Oxford OxfordOX3 9DU United Kingdom

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, e.g. [1]-[3], as well as in psychiatric cohorts, e.g. [4]-[7]. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to explore relationships between multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N=764) and deeply characterised (i.e. range of clinical and cognitive instruments administered) sample of N=453 autistic individuals and N=311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies, mean and median imputation, as well as a Round Robin regression approach involving four independent multivariate regression models including a linear model, Bayesian Ridge regression [8], as well as several non-linear models, Decision Trees [9], Extra Trees [10] and K-Neighbours regression [11]. We evaluate the models using the traditional mean square error towards removed available data, and consider in addition the KL divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This allows the selection of a unique model to impute missing data for the LEAP project and deliver a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future. Copyright © 2022, The Authors. All rights reserved.

关键词： Diseases

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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SEX DIFFERENCES IN WHITE MATTER HYPERTENSITIES ARE MODIFIED BY MENOPAUSE: THE RHINELAND STUDY

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Cerebral Circulation: Cognition and Behavior 2024年 6卷 100110-100110页

作者： Lohner, Valerie Pehlivan, Gokhan Sanroma-Guëll, Gerard Miloschewski, Anne Schirmer, Markus D. Stöcker, Tony Reuter, Martin Breteler, Monique M.B. Population Health Sciences German Center for Neurodegenerative diseases (DZNE) Bonn Germany Statistics and Machine Learning German Center for Neurodegenerative Diseases (DZNE) Bonn Germany J. Philip Kistler Stroke Research Center Massachusetts General Hospital Harvard Medical School Boston Clinic for Neuroradiology University Hospital Bonn Germany MR Physics German Center for Neurodegenerative Diseases (DZNE) Bonn Germany Department of Physics and Astronomy University of Bonn Bonn Germany Image Analysis German Center for Neurodegenerative Diseases (DZNE) Bonn Germany A.A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston Massachusetts Department of Radiology Harvard Medical School Boston Massachusetts USA Institute for Medical Biometry Informatics and Epidemiology (IMBIE) Faculty of Medicine University of Bonn Germany

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Complementary App-Based Yoga Home Exercise Therapy for Patients With Axial Spondyloarthritis: Usability Study

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JMIR Formative research 2024年 8卷 e57185页

作者： Grube, Lara Petit, Pascal Vuillerme, Nicolas Nitschke, Marlies Nwosu, Obioma Bertrand Knitza, Johannes Krusche, Martin Seifer, Ann-Kristin Eskofier, Bjoern Schett, Georg Morf, Harriet Department of Internal Medicine 3- Rheumatology & Immunology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Deutsches Zentrum Immuntherapie Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany AGEIS Université Grenoble Alpes Grenoble France Institut Universitaire de France Paris France LabCom Telecom4Health Orange Labs & Université Grenoble Alpes CNRS Inria Grenoble France Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute for Digital Medicine University Hospital of Giessen and Marburg Philipps-University Marburg Marburg Germany III. Department of Medicine University Medical Center Hamburg-Eppendorf Hamburg Germany Translational Digital Health Group Institute of AI for Health German Research Center for Environmental Health Helmholtz Zentrum München Neuherberg Germany

Background: Axial spondyloarthritis (AS) is a chronic inflammatory rheumatic disease characterized by potentially disabling inflammation of the spine and adjacent joints. Regular exercise is a cornerstone of treatment. However, patients with AS currently have little support. YogiTherapy (MaD Lab) is an app developed to support patients with AS by providing instructions for yoga-based home exercise therapy. Objective: This study aimed to evaluate the usability and acceptance of the newly designed YogiTherapy app for patients with AS. Methods: Patients completed the User Version of the Mobile Application Rating Scale (uMARS) and net promoter score (NPS) questionnaires after the app introduction. Wilcoxon Mann-Whitney rank sum test, chi-square test for count data, and correlation analysis were conducted to examine the usability of the app, acceptance, and patient characteristics. Results: A total of 65 patients with AS (33, 51% female;age: mean 43.3, SD 13.6 years) were included in the study from May 2022 to June 2023. Subsequently, the data were analyzed. Usability was rated moderate, with a mean uMARS of 3.35 (SD 0.47) points on a scale from 0 to 5. The highest-rated uMARS dimension was information (mean 3.88, SD 0.63), followed by functionality (mean 3.84, SD 0.87). Females reported a significantly higher uMARS total score than males (mean 3.47, SD 0.48 vs mean 3.23, SD 0.45;P=.03, Vargha and Delaney A [VDA] 0.66, 95% CI 0.53-0.77). The mean average of the NPS was 6.23 (SD 2.64) points (on a scale from 0 to 10), based on 43% (26/65 nonpromoters, 42% (25/65) indifferent, and 15% (9/65) promoters. A total of 7% (5/65) of those surveyed did not answer the question. When applying the NPS formula, the result is –26%. The NPS showed a positive correlation with the usage of mobile apps (r=0.39;P=.02). uMARS functionality was significantly higher rated by patients younger than 41 years (mean 4.17, SD 0.55 vs mean 3.54, SD 1;P<.001;VDA 0.69, 95% CI 0.56-0.80). Patients consi

关键词： adjacent joints ankylosing spondylitis app apps axial spondylarthritis chronic correlation analysis DHA digital health application digital therapeutics DTx eHealth exercise home exercise joint joints patient patient acceptance patients physical exercise rheumatic disease self-assessment spine spondyloarthritis therapy usability usability study Yoga YogiTherapy

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