To explore the nature of semantic deficit in Alzheimer's disease patients (AD patients) we compared two tasks that are known to be very different with respect to the type of attentional demand and conscious effort they require: lexical decision (automatic) in a semantic priming paradigm and semantic relatedness judgements (intentional). In order to minimise post-lexical facilitation we devised a semantic priming experiment that met an automatic condition as much as possible, and we selected patients without severe word recognition deficits. AD patients showed reduced accuracy in the semantic relatedness judgements as compared to controls. Some effect of priming was found, but this was weaker than in normals. AD patients also differed from controls on targets preceded by a nonlinguistic prime (neutral condition) where their reaction times were slower as compared to neutral condition.
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http://dx.doi.org/10.1080/01688639608408994 | DOI Listing |
Sci Rep
January 2025
School of Electronic and Information Engineering, Changsha Institute of Technology, Changsha, 410200, China.
In order to solve the limitations of flipped classroom in personalized teaching and interactive effect improvement, this paper designs a new model of flipped classroom in colleges and universities based on Virtual Reality (VR) by combining the algorithm of Contrastive Language-Image Pre-Training (CLIP). Through cross-modal data fusion, the model deeply combines students' operation behavior with teaching content, and improves teaching effect through intelligent feedback mechanism. The test data shows that the similarity between video and image modes reaches 0.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Electrical and Computer Engineering Department, The University of Alabama, Tuscaloosa, AL 35487, USA.
Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Medical Device and Healthcare, Dongguk University, Seoul 04620, Republic of Korea.
Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method.
View Article and Find Full Text PDFMed Image Anal
January 2025
ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France.
Instance segmentation of surgical instruments is a long-standing research problem, crucial for the development of many applications for computer-assisted surgery. This problem is commonly tackled via fully-supervised training of deep learning models, requiring expensive pixel-level annotations to train. In this work, we develop a framework for instance segmentation not relying on spatial annotations for training.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections.
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