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Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents (vol 49, pg 275, 2023)

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1399-1403页

作者： Hassan, Lamiece Sawyer, Chelsea Peek, Niels Lovell, Karina Carvalho, Andre F. Solmi, Marco Tilston, George Sperrin, Matthew Firth, Joseph Division of Psychology and Mental Health The University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK Centre for Health Informatics Division of Informatics Imaging and Data Sciences The University of Manchester M13 9PL UK NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester UK Manchester Academic Health Science Centre National Institute for Health Research Manchester Biomedical Research Centre The University of Manchester Manchester M13 9PL UK Division of Nursing Midwifery and Social Work University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre School of Medicine Barwon Health Deakin University Geelong Victoria Australia Psychiatry Department University of Ottawa Ottawa ON Canada The Ottawa Hospital University of Ottawa Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute (OHRI) University of Ottawa Ottawa ON Canada Greater Manchester Mental Health NHS Foundation Trust Manchester Academic Health Science Centre Manchester M13 9PL UK

Background and hypothesis Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk post-vaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. Study Design Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD) and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios;RRs) between people with SMI (N=190,188) and age-sex matched controls (N=760,752), adjusted for sociodemographic factors, pre-existing comorbidities and vaccination *** Results Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.14, CI 2.66-3.71) and/or BD (RR 3.17, CI 2.15-4.67). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.53, CI 1.24-1.88) and BD (RR 2.28, CI 1.49-3.49), but not recurrent MDD (RR 0.92, CI 0.78-1.09). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. Conclusions People with SMI, notably schizophrenia and bipolar disorder, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritised people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Analysis and Design of University Teaching Equipment Management Information System

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Journal of Cyber Security 2021年第3期3卷 177-185页

作者： Yu Xue Xiang Jiao Chishe Wang Huanlun Chen Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent years,our university recruitment of students scale is increasing year by year,the size of the corresponding teaching equipment is also growing,therefore to develop a teaching equipment management information system is necessary,not only can help universities to effective use of the existing teaching resources,also can update scrap equipment,related equipment maintenance,and build a good learning environment to students and to the improvement of the teaching quality of colleges and universities play a reliable safeguard *** paper first introduces some common development tools,and then analyzes the user functional requirements and data requirements of the system,and analyzes the feasibility of the system development from many aspects,finally based on B/S mode,using Java language,JSP technology and MySQL database design and implementation of a teaching equipment management information *** main functional modules of the system include equipment basic information management,equipment loan and return information management,equipment maintenance information management,equipment scrap information management,the interface of each functional module is shown in the paper.

关键词： Information systems teaching equipment management system analysis

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Design and Implementation of Enterprise Recruitment Mini program

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Journal of Cyber Security 2021年第3期3卷 125-132页

作者： Yu Xue Nan Wei Junyang Han Chishe Wang Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job *** front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using *** Spring Boot+Spring MVC framework,implemented in Java *** is managed using MySql *** function of this company’s recruitment applet is similar to the ordinary traditional native recruitment *** achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.

关键词： Mini-program recruitment WeChat developer tools MyEclipse

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Spin-tunable thermoelectric performance in monolayer chromium pnictides

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Physical Review Materials 2022年第6期6卷 064010-064010页

作者： Melania S. Muntini Edi Suprayoga Sasfan A. Wella Iim Fatimah Lila Yuwana Tosawat Seetawan Adam B. Cahaya Ahmad R. T. Nugraha Eddwi H. Hasdeo Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Research Center for Quantum Physics National Research and Innovation Agency Tangerang Selatan 15314 Indonesia Center of Excellence on Alternative Energy Research and Development Institution Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Program of Physics Faculty of Science and Technology Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Department of Physics Faculty of Mathematics and Natural Sciences Universitas Indonesia Depok 16424 Indonesia Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg

Historically, finding two-dimensional (2D) magnets is well known to be a difficult task due to the instability against thermal spin fluctuations. Metals are also normally considered poor thermoelectric (TE) materials. Combining intrinsic magnetism in two dimensions with conducting properties, one may expect to get the worst for thermoelectrics. However, we will show this is not always the case. Here, we investigate the spin-dependent TE properties of monolayer chromium pnictides (CrX, where X=P, As, Sb, and Bi) using first-principles calculations of electron- and phonon-energy dispersion, along with the Boltzmann transport formalism under an energy-dependent relaxation time approximation. All the CrX monolayers are dynamically stable and they also exhibit half metallicity with ferromagnetic ordering. Using the spin-valve setup with an antiparallel spin configuration, the half metallicity and ferromagnetism in monolayer CrX enable the manipulation of spin degrees of freedom to tune the TE figure of merit (ZT). At an optimized chemical potential and operating temperature of 500 K, the maximum ZT values (≈0.22, 0.12, and 0.09) with the antiparallel spin-valve setup in CrAs, CrSb, and CrBi improve up to almost twice the original values (ZT≈0.12, 0.08, and 0.05) without the spin-valve configuration. Only in CrP, which is the lightest species and less spin polarized among CrX, the maximum ZT (≈0.34) without the spin-valve configuration is larger than that (≈0.19) with the spin-valve one. We also find that, at 500 K, all the CrX monolayers possess exceptional TE power factors of about 0.02–0.08 W/m K2, which could be one of the best values among 2D conductors.

关键词： First-principles calculations Thermopower Monolayer films Electronic structure

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Improved diabetes disease prediction IWFO model using machine learning algorithms

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Gunavathi V. Sivakumar B. Senthil Kumar S. Vijayalakshmi Associate professor School of Science Department of Data Science Christ University Pune India Associate Professor & Program Leader School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Assistant Professor Department of Computer Science Sree Narayana Guru College Coimbatore India

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level.

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ADAM-1: AI and Bioinformatics for Alzheimer’s Detection and Microbiome-Clinical data Integrations

arXiv

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arXiv 2025年

作者： Huang, Ziyuan Sekhon, Vishaldeep Kaur Guo, Ouyang Newman, Mark Sadeghian, Roozbeh Vaida, Maria L. Jo, Cynthia Ward, Doyle Bucci, Vanni Haran, John P. Department of Microbiology UMass Chan Medical School WorcesterMA01655 United States Department of Geriatric Medicine and Gerontology Johns Hopkins University BaltimoreMD21218 United States Data Sciences Harrisburg University of Science and Technology HarrisburgPA17101 United States DevIS Innovation Labs DevIS LLC ArlingtonVA22201 United States Department of Emergency Medicine UMass Chan Medical School WorcesterMA01655 United States Program in Microbiome Dynamics UMass Chan Medical School WorcesterMA01655 United States

The Alzheimer’s Disease Analysis Model Generation 1 (ADAM-1) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer’s disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden its scalability and applicability for Alzheimer’s research and *** Codes 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Classification (of information)

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine 3

Maintaining the Quality and Aroma of Coffee with Fuzzy Logic...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Samodro, Bayu Mahesworo, Bharuno Suparyanto, Teddy Surya Atmaja, Don Bosco Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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