Rhythmic abilities are impaired in developmental coordination disorder (DCD) but learning deficit of procedural skills implying temporal sequence is still unclear. Current contradictory results suggest that procedural learning deficits in DCD highly depend on learning conditions. The present study proposes to test the role of sensory modality of stimulations (visual or auditory) on synchronization, learning, and retention of temporal verbal sequences in children with and without DCD. We postulated a deficit in learning particularly with auditory stimulations, in association with atypical cortical thickness of three regions of interesting: sensorimotor, frontal and parietal regions. Thirty children with and without DCD (a) performed a synchronization task to a regular temporal sequence and (b) practiced and recalled a novel non-regular temporal sequences with auditory and visual modalities. They also had a magnetic resonance imaging to measure their cortical thickness. Results suggested that children with DCD presented a general deficit in synchronization of a regular temporal verbal sequence irrespective of the sensory modality, but a specific deficit in learning and retention of auditory non-regular verbal temporal sequence. Stability of audio-verbal synchronization during practice correlated with cortical thickness of the sensorimotor cortex. For the first time, our results suggest that synchronization deficits in DCD are not limited to manual tasks. This deficit persists despite repeated exposition and practice of an auditory temporal sequence, which suggests a possible alteration in audio-verbal coupling in DCD. On the contrary, control of temporal parameters with visual stimuli seems to be less affected, which opens perspectives for clinical practice.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/desc.13009 | DOI Listing |
Viruses
November 2024
Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Tobacco curly shoot virus (TbCSV), a begomovirus, causes significant economic losses in tobacco and tomato crops across East, Southeast, and South Asia. Despite its agricultural importance, the evolutionary dynamics and emergence process of TbCSV remain poorly understood. This study analyzed the phylodynamics of TbCSV by examining its nucleotide sequences of the coat protein (CP) gene collected between 2000 and 2022.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computing Sciences, University of East Anglia (UEA), Norwich, NR4 7TJ, UK.
Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK.
Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!