Clinical Relevance: Measuring the impact of spatial attention on signal detection in damaged parts of the visual field can be a useful tool for eye care practitioners.
Background: Studies on letter perception have shown that glaucoma exacerbates difficulties to detect a target within flankers (crowding) in parafoveal vision. A target can be missed because it is not seen or because attention was not focused at that location. This prospective study evaluates the contribution of spatial pre-cueing on target detection.
Method: Fifteen patients and 15 age-matched controls were presented with letters displayed for 200 ms. Participants were asked to identify the orientation of the target letter T in two conditions: an isolated letter (uncrowded condition) and a letter with two flankers (crowded condition). The spacing between target and flankers was manipulated. The stimuli were randomly displayed at the fovea and at the parafovea at 5° left or right of fixation. A spatial cue preceded the stimuli in 50% of the trials. When present, the cue always signalled the correct location of the target.
Results: Pre-cueing the spatial location of the target significantly improved performance for both foveal and parafoveal presentations in patients but not in controls who were at ceiling level. Unlike controls, patients exhibited an effect of crowding at the fovea with a higher accuracy for the isolated target than for the target flanked by two letters with no spacing between the elements.
Conclusion: Higher susceptibility to central crowding supports data showing abnormal foveal vision in glaucoma. Exogenous orienting of attention facilitates perception in parts of the visual field with reduced sensitivity.
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http://dx.doi.org/10.1080/08164622.2023.2182185 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Functional Materials and Electrochemistry Lab, Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.
The rational design and synthesis of bifunctionally active and durable oxygen electrocatalysts have garnered significant attention for electrochemical energy conversion and storage. Intermetallic nanostructures are particularly promising for these applications due to their unique catalytic properties and exceptional durability. In this study, we present a fascinating synthetic approach for the direct synthesis of a bifunctional oxygen electrocatalyst based on nitrogen-doped carbon-encapsulated ordered PdFe (o-PdFe@NC) intermetallic, using a cyano-bridged bimetallic single-source precursor tailored for aqueous rechargeable zinc-air batteries (ZABs).
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January 2025
Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, P. R. China.
The propensity of zinc (Zn) to form irregular electrodeposits at liquid-solid interfaces emerges as a fundamental barrier to high-energy, rechargeable batteries that use zinc anodes. So far, tremendous efforts are devoted to tailoring interfaces, while atomic-scale reaction mechanisms and the related nanoscale strain at the electrochemical interface receive less attention. Here, the underlying atomic-scale reaction mechanisms and the associated nanoscale strain at the electrochemical alloy interface are investigate, using gold-zinc alloy protective layer as a model system.
View Article and Find Full Text PDFLaryngoscope
January 2025
Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.
View Article and Find Full Text PDFFront Comput Neurosci
December 2024
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
Background: Automatic sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. While previous research has achieved high classification performance, most current sleep staging networks have only been validated in healthy populations, ignoring the impact of Obstructive Sleep Apnea (OSA) on sleep stage classification. In addition, it remains challenging to effectively improve the fine-grained detection of polysomnography (PSG) and capture multi-scale transitions between sleep stages.
View Article and Find Full Text PDFFront Aging Neurosci
December 2024
Arizona State University-Banner Neurodegenerative Disease Research Center at the Biodesign Institute, Arizona State University, Tempe, AZ, United States.
Background: The 3xTg-AD transgenic mouse model of Alzheimer's disease (AD) is an important tool to investigate the relationship between development of pathological amyloid-β (Aβ) and tau, neuroinflammation, and cognitive impairments. Traditional behavioral tasks assessing aspects of learning and memory, such as mazes requiring spatial navigation, unfortunately suffer from several shortcomings, including the stress of human handling and not probing species-typical behavior. The automated IntelliCage system was developed to circumvent such issues by testing mice in a social environment while measuring multiple aspects of cognition.
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