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Assessment of Simplified Surveillance for Congenital Rubella Syndrome in Sudan, 2014-2017.
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Global Immunization Division, United States Centers for Disease Control and Prevention, Atlanta, GA 30329, USA.
Background/objectives: Congenital rubella syndrome (CRS) is a constellation of serious multi-organ birth defects following rubella virus infection during early pregnancy. Countries in which rubella vaccination has not yet been introduced can have a high burden of this disease. Data on CRS burden and epidemiology are needed to guide the introduction of a rubella vaccine and monitor progress for rubella elimination, but the multi-system nature of CRS manifestations and required specialized testing creates a challenge for conducting CRS surveillance in developing settings such as Sudan.
View Article and Find Full Text PDFWearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review.
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REMIT (Research on Economics, Management and Information Technologies), IJP (Instituto Jurídico Portucalense), Universidade Portucalense, Rua Dr. António Bernardino de Almeida, 541-619, 4200-072 Porto, Portugal.
Some previous studies have focused on using physiological signals to detect stress in individuals with ASD through wearable devices, yet few have focused on how to design such solutions. Wearable technology may be a valuable tool to aid parents and caregivers in monitoring the emotional states of individuals with ASD who are at high risk of experiencing very stressful situations. However, effective wearable devices for individuals with ASD may need to differ from solutions for those without ASD.
View Article and Find Full Text PDFDCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection.
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School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).
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Department of Psychiatry, Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents.
View Article and Find Full Text PDFFused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption.
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School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China.
Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features.
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