Recent studies indicate that medial-temporal lobe (MTL) damage, either from focal lesions or neurodegenerative disease (e.g., semantic dementia), impairs perception as well as long-term declarative memory. Notably, however, these two patient groups show different performance for meaningful versus unfamiliar stimuli. In amnesics with nonprogressive MTL lesions, the use of meaningful stimuli, compared with unfamiliar items, boosted discrimination performance. In semantic dementia, a condition characterized by progressive deterioration of conceptual knowledge in the context of anterolateral temporal lobe damage, performance for meaningful stimuli was equivalent to that for unfamiliar items. To further investigate these findings, we scanned healthy volunteers while they performed odd-one-out discriminations involving familiar (i.e., meaningful/famous) and unfamiliar (i.e., novel) objects and faces and a baseline task of size oddity. Outside the scanner, volunteers' recognition memory was assessed. We found above baseline activity in the perirhinal cortex and hippocampus for all object and face discriminations and above baseline activity in the temporal pole for all face discriminations. The comparison of meaningful, relative to novel, faces and objects, revealed increased activity in the perirhinal cortex and hippocampus. In the temporal pole, we also found activity related to meaningfulness for faces but not for objects. Importantly, these meaningfulness effects were evident even for discriminations that were not subsequently well remembered, suggesting that the difference between meaningful and novel stimuli reflects perceptual or conceptual processes rather than solely incidental encoding into long-term memory. The results provide further evidence that the MTL is recruited during complex perceptual discrimination and additionally suggest that these structures are recruited in semantic processing of objects and faces.
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Sensors (Basel)
December 2024
Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
CAS Key Laboratory of Organic Solids, Beijing National Laboratory for Molecular Sciences (BNLMS), CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
In the face of advancements in microrobotics, intelligent control and precision medicine, artificial muscle actuation systems must meet demands for precise control, high stability, environmental adaptability and high integration miniaturization. Carbon materials, being lightweight, strong and highly conductive and flexible, show great potential for artificial muscles. Inspired by the butterfly's proboscis, we have developed a carbon-based artificial muscle, hydrogen-substituted graphdiyne muscle (HsGDY-M), fabricated efficiently using an emerging hydrogen-substituted graphdiyne (HsGDY) film with an asymmetrical surface structure.
View Article and Find Full Text PDFSci Rep
January 2025
Chongqing Vocational Institute of Tourism, Chongqing, China.
To enhance enterprises' interactive exploration capabilities for unstructured chart data, this paper proposes a multimodal chart question-answering method. Facing the challenge of recognizing curved and irregular text in charts, we introduce Gaussian heatmap encoding technology to achieve character-level precise text annotation. Additionally, we combine a key point detection algorithm to extract numerical information from the charts and convert it into structured table data.
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Information Technology (IT) and Engineering, Melbourne Institute of Technology, Melbourne, VIC, Australia.
Introduction: Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation.
View Article and Find Full Text PDFSci Rep
January 2025
Shandong Yankuang Intelligent Manufacturing Co., Jining, 272000, China.
The hydraulic column is a core component in the coal mine support system, however, the real-time monitoring of the hydraulic column during the service process of the hydraulic support faces challenges. To address these issues, a high-precision stress mapping method of hydraulic column is proposed. The hydraulic column loss function was constructed to guide the data-driven model training, and the cylinder stress mechanism model was established by using the elastic-plastic theory of thick-walled cylinder.
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