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Quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by myotube and nuclei segmentation using deep learning.
Bioinformatics
January 2025
Department of Robotics & Mechatronics Engineering, DGIST, Daegu, 42988, South Korea.
Motivation: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle development. However, human experimenters may often derive unreliable results owing to the unusual shape of the myotube, which causes significant measurement variability.
View Article and Find Full Text PDFDeep Learning-Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach.
Brain Behav
January 2025
Department of Biomedical Engineering, Meybod University, Meybod, Iran.
Purpose: A debilitating and poorly understood symptom of Parkinson's disease (PD) is freezing of gait (FoG), which increases the risk of falling. Clinical evaluations of FoG, relying on patients' subjective reports and manual examinations by specialists, are unreliable, and most detection methods are influenced by subject-specific factors.
Method: To address this, we developed a novel algorithm for detecting FoG events based on movement signals.
Leveraging anatomical constraints with uncertainty for pneumothorax segmentation.
Health Care Sci
December 2024
Centre for Quantitative Medicine, Duke-NUS Medical School Singapore.
Background: Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space-the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as "lung + space." While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach.
View Article and Find Full Text PDFImproving dependability with low power fault detection model for skinny-hash.
PLoS One
December 2024
School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
The increasing popularity and prevalence of Internet of Things (IoT) applications have led to the widespread use of IoT devices. These devices gather information from their environment and send it across a network. IoT devices are unreliable due to their susceptibility to defect that arise intentionally or spontaneously.
View Article and Find Full Text PDFAn Investigation of Extended-Dimension Embedded CKF-SLAM Based on the Akaike Information Criterion.
Sensors (Basel)
December 2024
College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Simultaneous localization and mapping (SLAM) faces significant challenges due to high computational costs, low accuracy, and instability, which are particularly problematic because SLAM systems often operate in real-time environments where timely and precise state estimation is crucial. High computational costs can lead to delays, low accuracy can result in incorrect mapping and localization, and instability can make the entire system unreliable, especially in dynamic or complex environments. As the state-space dimension increases, the filtering error of the standard cubature Kalman filter (CKF) grows, leading to difficulties in multiplicative noise propagation and instability in state estimation results.
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