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Genetic landscape in undiagnosed patients with syndromic hearing loss revealed by whole exome sequencing and phenotype similarity search.
Hum Genet
January 2025
Division of Hearing and Balance Research, National Institute of Sensory Organs, NHO Tokyo Medical Center, 2-5-1 Higashigaoka, Meguro-Ku, Tokyo, 152-8902, Japan.
There are hundreds of rare syndromic diseases involving hearing loss, many of which are not targeted for clinical genetic testing. We systematically explored the genetic causes of undiagnosed syndromic hearing loss using a combination of whole exome sequencing (WES) and a phenotype similarity search system called PubCaseFinder. Fifty-five families with syndromic hearing loss of unknown cause were analyzed using WES after prescreening of several deafness genes depending on patient clinical features.
View Article and Find Full Text PDFEnhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection.
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFUnifying topological structure and self-attention mechanism for node classification in directed networks.
Sci Rep
January 2025
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.
View Article and Find Full Text PDFLeveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery.
Sci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
View Article and Find Full Text PDFEnhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation.
Sci Rep
January 2025
Electronics and Communication Engineering Dept. Faculty of Engineering, Horus University, New Damietta, Egypt.
Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance.
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