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Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children

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NeuroToxicology 2025年 108卷 218-230页

作者： England-Mason, Gillian MacEachern, Sarah J. Amador, Kimberly Soomro, Munawar Hussain Reardon, Anthony J.F. MacDonald, Amy M. Kinniburgh, David W. Letourneau, Nicole Giesbrecht, Gerald F. Martin, Jonathan W. Forkert, Nils D. Dewey, Deborah Department of Pediatrics Cumming School of Medicine University of Calgary Calgary AB Canada Alberta Children's Hospital Research Institute University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada Department of Psychology Faculty of Arts University of Calgary Calgary AB United States Biomedical Engineering Graduate Program University of Calgary Calgary AB Canada Department of Radiology Cumming School of Medicine University of Calgary Calgary AB Canada Department of Laboratory Medicine and Pathology University of Alberta Edmonton AB Canada Alberta Centre for Toxicology University of Calgary Calgary AB Canada Faculty of Nursing University of Calgary Calgary AB Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary Calgary AB Canada Department of Psychiatry Cumming School of Medicine University of Calgary Calgary AB Canada Science for Life Laboratory Department of Environmental Science Stockholm University Stockholm Sweden Department of Clinical Neurosciences University of Calgary Calgary AB Canada

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approaches may facilitate more comprehensive examinations of the contributions of chemical exposures, sociodemographic factors, and maternal mental health to child neurodevelopment. A machine learning pipeline that utilized feature selection and ranking was applied to investigate which common prenatal chemical exposures and sociodemographic factors best predict neurodevelopmental outcomes in young children. Data from 406 maternal-child pairs enrolled in the APrON study were used. Maternal concentrations of 32 environmental chemical exposures (i.e., phthalates, bisphenols, per- and polyfluoroalkyl substances (PFAS), metals, trace elements) measured during pregnancy and 11 sociodemographic factors, as well as measures of maternal mental health and urinary creatinine were entered into the machine learning pipeline. The pipeline, which consisted of a RReliefF variable selection algorithm and support vector machine regression model, was used to identify and rank the best subset of variables predictive of cognitive, language, and motor development outcomes on the Bayley Scales of Infant Development-Third Edition (Bayley-III) at 2 years of age. Bayley-III cognitive scores were best predicted using 29 variables, resulting in a correlation coefficient of r = 0.27 (R²=0.07). For language outcomes, 45 variables led to the best result (r = 0.30;R²=0.09), whereas for motor outcomes 33 variables led to the best result (r = 0.28, R²=0.09). Environmental chemicals, sociodemographic factors, and maternal mental health were found to be highly ranked predictors of cognitive, language, and motor development in young children. Our findings demonstrate the potential of machine learning approaches to identify and dete

关键词： APrON Developmental neurotoxicology Epidemiology Infant and child neurotoxicity studies Neurobehavioural Testing

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Modeling supports the implementation of urban greening as a response to the challenges of sustainable spatial planning

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Journal of Environmental Management 2025年 386卷 125648页

作者： Kim, Eun Sub Hyun, Jung Hee Kim, Na Yeon Heo, Su Jeong Kim, Da Seul Lee, Dong Kun Low-Carbon and Climate Impact Research Centre School of Energy and Environment City University of Hong Kong Tat Chee Ave Hong Kong Kowloon Tong China Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Forest Disaster & Environment Department National Institute of Forest Science Seoul Korea Republic of Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of

Urban greening is critical for sustainable urban development, climate change mitigation, and biodiversity conservation. However, the effectiveness of urban greening varies depending on the specific goals (e.g., enhancing biodiversity, reducing urban heat, or both) and their spatial implementation. To address the spatial variability in the effectiveness of greening, we propose a spatial decision support model based on the non-dominated sorting genetic algorithm-II (NSGA-II). This model aims to optimize urban greening locations to maximize biomass density, mitigate urban heat stress, and improve landscape connectivity. Applied to Suwon City, South Korea, the model's effectiveness was evaluated against a business-as-usual (BAU) scenario across four scenarios: connectivity-based, biomass density-based, heat stress-based, and an integrated-based scenario. The integrated approach, balancing trade-offs between ecological benefits and implementation costs, outperformed the BAU scenario by 8.84 %. Despite highlighting a weaker correlation with heat stress mitigation, this outcome indicates significant improvements in biomass density and landscape connectivity. Our findings underscore the necessity of an integrated planning approach to urban greening, as it can contribute toward attaining urban development goals. Additionally, by proposing an app-based model for policymakers, our outputs should enable the reconciliation of multiple environmental objectives in urban landscapes. © 2025 Elsevier Ltd

关键词： Biotic

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Applying an Agile science Roadmap to Integrate and Evaluate Ethical Frameworks Throughout the Lifecycle and Use of Artificial Intelligence Tools in the Intensive Care Unit

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Critical Care Nursing Clinics of North America 2025年第2期37卷 347-363页

作者： Lindroth, Heidi Sahajwani, Juhi Hudson, Mark Heier, Laura Gonzalez, Andrew A. Bhattacharyya, Anirban Zheng, Zhi Boustani, Malaz Herasevich, Vitaly McGowan, Michelle Barry, Barbara Division of Nursing Research Department of Nursing Mayo Clinic Rochester MN United States Center for Aging Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Center for Health Innovation and Implementation Science School of Medicine Indiana University Indianapolis IN United States Department of Critical Care Medicine Mayo Clinic Jacksonville FL United States School of Psychology and Counselling The Open University Milton Keynes United Kingdom Department of Graduate Nursing Viterbo University La Crosse WI United States Department of Neurosurgery Mayo Clinic Rochester MN United States Center for Health Services Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Surgical Outcomes and Quality Improvement Center Department of Surgery School of Medicine Indiana University Indianapolis IN United States Department of Electrical Engineering University of Notre Dame Notre Dame IN United States Department of Anesthesiology and Perioperative Medicine Mayo Clinic Rochester MN United States Biomedical Ethics Departments of Quantitative Health Sciences and Artificial Intelligence and Informatics Biomedical Ethics Research Program Mayo Clinic Rochester MN United States Kern Center Mayo Clinic Rochester MN United States

This article summarizes existing ethical frameworks for healthcare artificial intelligence (AI) and ambient sensing technology, such as computer vision, and examines their application to improve patient outcomes in the intensive care unit (ICU). Integrating ethical considerations such as privacy, fairness, and autonomy into the lifecycle of an AI tool is necessary to fully harvest AI's potential to deliver safe, high-quality, personalized, and low-cost healthcare services that provide positive experiences for patients, families, and clinicians while improving health outcomes. Drawing from agile science, the article proposes a practical roadmap for clinician and researcher use to identify, integrate, and monitor ethical considerations throughout the lifecycle of an AI tool with the intention to improve patient care. A use case illustrates the application of the agile science-informed roadmap that details the development of a passive digital marker for delirium severity.

关键词： Agile science AI tools Artificial intelligence Computer vision Delirium severity Ethical frameworks for health care AI ICU Passive digital marker

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Unconventional assemblies of bisacylhydrazones:The role of water for circularly polarized luminescence

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Aggregate 2022年第5期3卷 185-193页

作者： Hye Jin Cho Dong Yeun Jeong Hwihyun Moon Taewoo Kim You Kyoung Chung Yeongdong Lee Zonghoon Lee Joonsuk Huh Youngmin You Changsik Song Department of Chemistry Sungkyunkwan UniversitySuwonGyeonggi 16419Republic of Korea Division of Chemical Engineering and Materials Science Graduate Program in System Health Science and EngineeringEwha Womans UniversitySeoul 03760Republic of Korea Center for Multidimensional Carbon Materials Institute for Basic Science(IBS)Department of Materials Science and EngineeringUlsan National Institute of Science and Technology(UNIST)Ulsan 44919Republic of Korea

Understanding the precise molecular arrangement of chiral supramolecular polymers is essential not only to comprehend complex superstructures like proteins and DNA but also for the development of next-generation optoelectronic materials,including materials displaying high-performance circularly polarized luminescence(CPL).Herein,we report the first chiral supramolecular polymer systems based on hydrazone–pyridinium conjugates comprising alkyl chains of different lengths,which afforded control of the apparent supramolecular *** supramolecular chirality is governed basically by the remote chiral centers of alkyl chains,helicity inversion was achieved by controlling the conditions under which the hydrazone building blocks underwent aggregation(i.e.,solvent compositions or temperature).More importantly,the addition of water to the system led to aggregationinduced hydrazone deprotonation,which resulted in a completely different selfassembly *** water molecules played an essential role,forming the assembly’s channel-like backbone,around which hydrazone molecules gathered as a result of hydrogen bonding *** co-assembly of an achiral hydrazone luminophore with the given supramolecular polymer system allowed the fabrication of a novel CPL-active hydrazone-based material exhibiting a high maximum value for the photoluminescence dissymmetry factor of -2.6×10^(-2).

关键词： circularly polarized luminescence(CPL) helicity control hydrazone self-assembly structural water supramolecular polymerization

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Accessing the viscera: Technologies for interoception research

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Current Opinion in Neurobiology 2025年 93卷 103050-103050页

作者： Pang, Karen K.L. Mondal, Rajib Sahasrabudhe, Atharva Anikeeva, Polina Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States K. Lisa Yang Brain-Body Center Massachusetts Institute of Technology United States McGovern Institute for Brain Research Massachusetts Institute of Technology United States Research Laboratory of Electronics Massachusetts Institute of Technology United States MIT-Harvard Graduate Program in Health Sciences and Technology United States Department of Materials Science and Engineering Massachusetts Institute of Technology United States

Interoception, or the perception and regulation of body signals by the central nervous system, is critical for maintaining homeostasis and coordination of behaviors. Deciphering the mechanisms of interoception requires identifying pathways and decoding of diverse signals across the brain-body axis. These studies are enabled by tools to modulate and record physiological processes in the brain and visceral organs. While numerous advanced neurotechnologies are well-established in the brain, these techniques often offer limited utility for other organs, such as the gastrointestinal tract, heart, liver, or bladder. In this review, we highlight recent advances in technologies for recording and modulation of visceral organ physiology in small animals in vivo, with a focus on implantable bioelectronic organ interfaces that can be deployed in behaving animals. We discuss how such interfaces are made possible through innovations in materials and electronics and outline unmet technological challenges in interoception research. © 2025 Elsevier Ltd

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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Psychosocial Risk Assessment by Fine Kinney and ANFIS Method: A Case Study in a Metal Processing Plant

Psychosocial Risk Assessment by Fine Kinney and ANFIS Method...

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AHFE Virtual Conferences on Safety Management and Human Error, Reliability, Resilience, and Performance, 2020

作者： Baç, Nalan Ekmekci, Ismail Graduate School of Science and Engineering Occupational Health and Safety Program Istanbul Commerce University Istanbul Turkey

ISBN: (纸本)9783030509453

Occupational health and safety practices continue to improve, but accidents and occupational health issues continue to cause problems for management and employees. Although psychosocial risks and hazards have substantial adverse effects, they are often overlooked in most risk assessment practices. The objective of this work is to assess the employee psychosocial risks by a case study using the COPSOQII questionnaire at a metal processing plant. Inputs from COPSOQII questionnaire were used to assess psychosocial hazards in Fine Kinney analysis. The Fine-Kinney method contains fuzzy decisions. This uncertainty is resolved by using the ANFIS (Adaptive Neuro-Fuzzy Inference system) module. Results of the ANFIS model indicate that when actual risks and those predicted by the model are compared, the model could predict the risk scores with high accuracy. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Risk assessment

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Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease

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Scientific Reports 2025年第1期15卷 1-14页

作者： Perron, Jarrad Scramstad, Carly Ko, Ji Hyun Graduate Program in Biomedical Engineering Price Faculty of Engineering University of Manitoba 75 Chancellor’s Circle Winnipeg R3T 5V6 MB Canada PrairieNeuro Research Centre Kleysen Institute for Advanced Medicine Health Sciences Centre 710 William Avenue Winnipeg R3E 3J7 MB Canada Section of Neurology Department of Internal Medicine Rady Faculty of Health Sciences University of Manitoba Winnipeg MB Canada Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences University of Manitoba 130-745 Bannatyne Avenue Winnipeg R3E 0J9 MB Canada

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer’s disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients with mild cognitive impairment (MCI) may remain stable for several years even with positive AD biomarkers. Using fluorodeoxyglucose PET and biomarker data from 594 ADNI patients, a neural network ensemble was trained to forecast cognition from MCI diagnostic baseline. Training data comprised PET studies of patients with biological AD. The ensemble discriminated between progressive and stable prodromal subjects (MCI with positive amyloid and tau) at baseline with 88.6% area-under-curve, 88.6% (39/44) accuracy, 73.7% (14/19) sensitivity and 100% (25/25) specificity in the test set. It also correctly classified all other test subjects (healthy or AD continuum subjects across the cognitive spectrum) with 86.4% accuracy (206/239), 77.4% sensitivity (33/42) and 88.23% (165/197) specificity. By identifying patients with prodromal AD who will not progress to dementia, our model could significantly reduce overall societal burden and cost if implemented as a screening tool. The model’s high positive predictive value in the prodromal test set makes it a practical means for selecting candidates for anti-amyloid therapy and trials. © The Author(s) 2025.

关键词： Alzheimer’s disease Deep learning FDG PET MCI Neuroimaging Prodromal

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Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes

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ACS Sensors 2025年 2025 May 15页

作者： Um, Hyun-Kyung Noh, Eunseo Yoo, Chaehwa Lee, Hyang Woon Kang, Je-Won Lee, Byoung Hoon Lee, Jung-Rok Department of Mechanical and Biomedical Engineering Ewha Womans University Seoul 03760 South Korea Graduate Program in Smart Factory Ewha Womans University Seoul 03760 South Korea Department of Chemical Engineering and Materials Science Ewha Womans University Seoul 03760 South Korea Graduate Program in System Health Science and Engineering Ewha Womans University Seoul 03760 South Korea School of Electrical Engineering Chungbuk National University Cheongju 28644 South Korea Department of Neurology and Medical Science School of Medicine Ewha Womans University Seoul 03760 South Korea Ewha Medical Research Institute Ewha Womans University Seoul 03760 South Korea Department of Electronic and Electrical Engineering Ewha Womans University Seoul 03760 South Korea

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for diagnosing sleep disorders;however, its discomfort and inconvenience limit its accessibility. To address these issues, a wearable device (WD) integrated with stretchable transparent electrodes (STEs) is developed in this study for multisignal sleep monitoring and artificial intelligence (AI)-driven sleep staging. Utilizing conductive and flexible STEs, the WD records multiple biological signals (electroencephalogram [EEG], electrooculogram [EOG], electromyogram [EMG], photoplethysmography, and motion data) with high precision and low noise, comparable to PSG (<4 μV_RMS). It achieves a 73.2% accuracy and a macro F1 score of 0.72 in sleep staging using an AI model trained on multisignal inputs. Notably, accuracy marginally improves when using only the EEG, EOG, and EMG channels, which may simplify future device designs. This WD offers a compact, multisignal solution for at-home sleep monitoring, with the potential for use as an evaluation tool for personalized sleep therapies. © 2025 American Chemical Society.

关键词： biopotential signals deep-learning algorithm electroencephalography sleep stage analysis stretchable transparent electrodes wearable devices

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Materials and techniques for achieving clinical excellence with indirect composite restorations.

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Dental clinics of North America 2001年第1期45卷 71-81页

作者： Brucia, J.J. Post-graduate Comprehensive Restorative Program Baylor College of Dentistry Texas A&amp M University System Health Science Center at Dallas Dallas Texas United States

Indirect resin restoration technology continues to grow at an unbelievable rate. This article has outlined some of the materials available within this category. There are others that the author has not had the opportunity to work with. At this time, the author believes the primary indication for the use of this class of material is esthetic inlays and onlays as well as a well-planned conservative [figure: see text] fixed partial denture in a low stress area. If unclear about the best material to use in a given situation, the dentist should consult the laboratory technician to discuss the technician's experience and confidence with the different systems. Many times the physical properties of the material are not as important as the ability of the technician and his or her experience with the different handling properties of the available systems when examining the short-term and long-term success of the restoration.

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