Joint attention behaviors involve sharing attention with others to an object or event by means of eye-gazing or pointing, which form the common basis for communication. There are two types of these behaviors: responding to joint attention (RJA) and initiating joint attention (IJA). RJA is the ability to follow the gaze of others, suggesting reception of a social signal from others; IJA is the ability to voluntarily direct the attention of others, to share the experience of an object or event, suggesting transmission of a social signal to others. Infants experience these roles (as signal receiver and signal transmitter) throughout the first year of life and learn social cognitive skills. Recent neuroimaging studies indicate that joint attention is supported by widely distributed neural systems with nodes in the dorsomedial prefrontal cortex, the orbitofrontal cortex and insula, the anterior and posterior cingulate cortex, the superior temporal cortex, the precuneus and parietal cortex, and the amygdala and striatum.
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http://dx.doi.org/10.11477/mf.1416201392 | DOI Listing |
ACS Nano
January 2025
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore.
Metal nanoclusters (NCs), owing to their atomic precision and unique molecule-like properties, have gained widespread attention for applications ranging from catalysis to bioimaging. In recent years, proteins, with their hierarchical structures and diverse functionalities, have emerged as good candidates for functionalizing metal NCs, rendering metal NC-protein conjugates with combined and even synergistically enhanced properties featured by both components. In this Perspective, we explore key questions regarding why proteins serve as complementary partners for metal NCs, the methodologies available for conjugating proteins with metal NCs, and the characterization techniques necessary to elucidate the structures and interactions within this emerging bionano system.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Biomedical and Robotics Engineering, Incheon National University, Incheon, Korea.
Gait disturbance is one of the most common symptoms in patients with Parkinson's disease (PD) that is closely associated with poor clinical outcomes. Recently, video-based human pose estimation (HPE) technology has attracted attention as a cheaper and simpler method for performing gait analysis than marker-based 3D motion capture systems. However, it remains unclear whether video-based HPE is a feasible method for measuring temporospatial and kinematic gait parameters in patients with PD and how this function varies with camera position.
View Article and Find Full Text PDFVet Sci
January 2025
Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR 523808, China.
Measuring limb joint angles is crucial for understanding horse conformation, performance, injury diagnosis, and prevention. While Thoroughbred horses have been extensively studied, local Pakistani breeds (e.g.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi 243-0297, Kanagawa, Japan.
Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories.
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