Semantic dementia (SD) is a syndrome of progressive loss of semantic knowledge for objects and people. International criteria propose that SD be included in the frontotemporal lobar degeneration syndromes, with progressive non-fluent aphasia and frontotemporal dementia (FTD). However, several related syndromes have been defined that clinically and conceptually share both similarities and differences with SD: fluent progressive aphasia, progressive prosopagnosia, temporal variant of FTD. In order to establish a French consensus for the diagnosis and modalities of evaluation and follow-up of SD, a working group, composed of neurologists, neuropsychologists and speech-therapists, was established by the Groupe de réflexion sur les évaluations cognitives (GRECO). New criteria were elaborated, based on clinical, neuropsychological, and imaging data. They define typical and atypical forms of SD. A diagnosis of typical SD relies on an isolated and progressive loss of semantic knowledge, attested by a deficit of word comprehension and a deficit of objects and/or people identification, with imaging showing temporal atrophy and/or hypometabolism. SD is atypical if the deficit of semantic knowledge is present only within a single modality (verbal versus visual), or if non-semantic deficits (mild and not present at onset) and/or neurological signs, are associated with the semantic loss.
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http://dx.doi.org/10.1016/j.neurol.2008.02.031 | DOI Listing |
Front Big Data
January 2025
AI Institute, University of South Carolina, Columbia, SC, United States.
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.
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January 2025
Zhengzhou University of Light Industry, Zhengzhou, 450001, China.
Visual-language models (VLMs) excel in cross-modal reasoning by synthesizing visual and linguistic features. Recent VLMs use prompt learning for fine-tuning, allowing adaptation to various downstream tasks. TCP applies class-aware prompt tuning to improve VLMs generalization, yet its reliance on fixed text templates as prior knowledge can limit adaptability to fine-grained category distinctions.
View Article and Find Full Text PDFFront Nutr
January 2025
Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia.
Introduction: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.
Methods: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge.
Bioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
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