We examine the nature of motor representations evoked during comprehension of written sentences describing hand actions. We distinguish between two kinds of hand actions: a functional action, applied when using the object for its intended purpose, and a volumetric action, applied when picking up or holding the object. In Experiment 1, initial activation of both action representations was followed by selection of the functional action, regardless of sentence context. Experiment 2 showed that when the sentence was followed by a picture of the object, clear context-specific effects on evoked action representations were obtained. Experiment 3 established that when a picture of an object was presented alone, the time course of both functional and volumetric actions was the same. These results provide evidence that representations of object-related hand actions are evoked as part of sentence processing. In addition, we discuss the conditions that elicit context-specific evocation of motor representations.
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Sci Rep
December 2024
College of Electronic Engineering, National University of Defense Technology, Hefei, 230000, China.
Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS's transmission efficiency.
View Article and Find Full Text PDFMalays J Pathol
December 2024
Universiti Sains Malaysia, School of Dental Sciences, Health Campus, Kubang Kerian, Kelantan, Malaysia.
Introduction: Oral cancer is considered the sixth most common form of cancer worldwide. It causes significant morbidity and mortality, especially in low socioeconomic status groups. However, Cancer chemoprevention encompasses the use of specific compounds to suppress the growth of tumours or inhibit carcinogenesis.
View Article and Find Full Text PDFSci Rep
December 2024
School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China.
In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.
View Article and Find Full Text PDFGeriatrics (Basel)
December 2024
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Background: Hand dexterity is affected by normal aging and neuroinflammatory processes in the brain. Understanding the relationship between hand dexterity and brain structure in neurotypical older adults may be informative about prodromal pathological processes, thus providing an opportunity for earlier diagnosis and intervention to improve functional outcomes.
Methods: this study investigates the associations between hand dexterity and brain measures in neurotypical older adults (≥65 years) using the Nine-Hole Peg Test (9HPT) and magnetic resonance imaging (MRI).
Biomimetics (Basel)
December 2024
School of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance.
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