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Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition.
Front Public Health
December 2024
Wastewater Technology Research, Wastewater Disposal, German Environment Agency, Berlin, Germany.
Introduction: Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex.
View Article and Find Full Text PDFDeep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Protomix: a Python package for H-NMR metabolomics data preprocessing.
Bioinform Adv
November 2024
Laboratory of Molecular Science and Engineering, Åbo Akademi University, Henrikinkatu 2, Turku 20500, Finland.
Motivation: NMR-based metabolomics is a field driven by technological advancements, necessitating the use of advanced preprocessing tools. Despite this need, there is a remarkable scarcity of comprehensive and user-friendly preprocessing tools in Python. To bridge this gap, we have developed Protomix-a Python package designed for metabolomics research.
View Article and Find Full Text PDFDeep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage.
Front Neurol
December 2024
CLAIM - Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
Introduction: Radiological scores used to assess the extent of subarachnoid hemorrhage are limited by intrarater and interrater variability and do not utilize all available information from the imaging. Image segmentation enables precise identification and delineation of objects or regions of interest and offers the potential for automatization of score assessments using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aneurysmal subarachnoid hemorrhage outcome prediction.
View Article and Find Full Text PDFThe application of multi-scale simulation in advanced electronic packaging.
Fundam Res
November 2024
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems from environmental conditions. The design of electronic packaging is highly complex and requires the consideration of multi-physics phenomena, such as thermal transport, electromagnetic fields, and mechanical stress. This review presents a comprehensive overview of the multiphysics coupling of electric, magnetic, thermal, mechanical, and fluid fields, which are crucial for assessing the performance and reliability of electronic devices.
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