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An essential aspect of human cognition is supported by a rich reservoir of abstract concepts without tangible external referents (e.g., "honor", "relationship", "direction"). While decades of research showed that the neural organization of conceptual knowledge referring to concrete words respects domains of evolutionary salience and sensorimotor attributes, the organization principles of abstract word meanings are poorly understood. Here, we provide neuropsychological evidence for a domain (sociality) and attribute (emotion) structure in abstract word processing. Testing 34 brain-damaged patients on a word-semantic judgment task, we observed double dissociations between social and nonsocial words and a single dissociation of sparing of emotional (relative to non-emotional) words. The lesion profiles of patients with specific dissociations suggest potential neural correlates positively or negatively associated with each dimension. These results unravel a general domain-attribute architecture of word meanings and highlight the roles of the social domain and the emotional attribute in the non-object semantic space.
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http://dx.doi.org/10.1038/s41598-021-02824-9 | DOI Listing |
Neural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
View Article and Find Full Text PDFComput Biol Med
August 2025
The First People Hospital of Foshan, Foshan City CN, China. Electronic address:
Brain Tumor Segmentation (BTS) is crucial for accurate diagnosis and treatment planning, but existing CNN and Transformer-based methods often struggle with feature fusion and limited training data. While recent large-scale vision models like Segment Anything Model (SAM) and CLIP offer potential, SAM is trained on natural images, lacking medical domain knowledge, and its decoder struggles with accurate tumor segmentation. To address these challenges, we propose the Medical SAM-Clip Grafting Network (MSCG), which introduces a novel SC-grafting module.
View Article and Find Full Text PDFActa Psychol (Amst)
September 2025
Management Department, Faculty of Economics, Administrative, and Social Sciences, Alanya University, 07400, Alanya, Antalya, Turkiye. Electronic address:
Online communities such as Reddit offer neurodivergent individuals a unique space to express emotions, seek psychosocial support, and negotiate identity outside conventional social constraints. Understanding how these communities articulate and structure emotional discourse is essential for inclusive technology design. This study employed a hybrid natural language processing (NLP) framework that integrates lexicon-based sentiment analysis (VADER) with transformer-based topic modeling (BERTopic).
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The personalization of cancer treatment through drug combinations is critical for improving healthcare outcomes, increasing effectiveness, and reducing side effects. Computational methods have become increasingly important to prioritize synergistic drug pairs because of the vast search space of possible chemicals. However, existing approaches typically rely solely on global molecular structures, neglecting information exchange between different modality representations and interactions between molecular and fine-grained fragments, leading to limited understanding of drug synergy mechanisms for personalized treatment.
View Article and Find Full Text PDFMed Phys
August 2025
Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
Background: Accurate retinal vessel segmentation from Optical Coherence Tomography Angiography (OCTA) images is vital in ophthalmic medicine, particularly for the early diagnosis and monitoring of diseases, such as diabetic retinopathy and hypertensive retinopathy. The retinal vascular system exhibits complex characteristics, including branching, crossing, and continuity, which are crucial for precise segmentation and subsequent medical analysis. However, traditional pixel-wise vessel segmentation methods focus on learning how to effectively divide each pixel into different categories, relying mainly on local features, such as intensity and texture, and often neglecting the intrinsic structural properties of vessels.
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