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Singing is a universal human attribute. Previous studies suggest that the ability to produce words through singing can be preserved in poststroke aphasia (PSA) and that this is mainly subserved by the spared parts of the left-lateralized language network. However, it remains unclear to what extent the production of rhythmic-melodic acoustic patterns in singing remains preserved in aphasia and which neural networks and hemisphere(s) are involved in this. In this cross-sectional study, we set out to investigate the structural neural networks underpinning singing production abilities by combining a whole-brain white matter correlational tractography approach together with a comprehensive appraisal of pitch, melodic contour, and rhythm production accuracy during both spontaneous and cued singing in a sample of 45 patients with PSA. Our results indicate that PSA patients have poorer singing accuracy (pitch, melodic contour, and rhythm) than matched healthy controls (N = 33). The network associated with singing accuracy in aphasia was identified in the left hemisphere-dominant dual stream network involved in auditory-motor integration of speech, but also extends to multiple other associative and projection pathways, also in the right hemisphere. The results provide insight into alternative communication methods and therapeutic approaches, leveraging music's inherent structure to aid in language recovery and rehabilitation.
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http://dx.doi.org/10.1111/nyas.15357 | DOI Listing |
Bioinformatics
September 2025
Centre National de Recherche en Génomique Humaine, Institut François Jacob CEA Université Paris-Saclay.
Motivation: Graph Neural Network (GNN) models have emerged in many fields and notably for biological networks constituted by genes or proteins and their interactions. The majority of enrichment study methods apply over-representation analysis and gene/protein set scores according to the existing overlap between pathways. Such methods neglect knowledges coming from the interactions between the gene/protein sets.
View Article and Find Full Text PDFComput Assist Surg (Abingdon)
December 2025
Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
To develop a DeepSurv model for predicting survival in pancreatic adenocarcinoma patients, evaluating the benefit of surgical versus non-surgical treatment across different stages, including stage IV subcategories. Clinical data were extracted from the SEER database (2000-2020). Patients were randomly divided into a model-building group and an experimental group.
View Article and Find Full Text PDFChaos
September 2025
A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, Ulyanova Street 46, Nizhny Novgorod 603950, Russia.
The Kuramoto model, a paradigmatic framework for studying synchronization, exhibits a transition to collective oscillations only above a critical coupling strength in the thermodynamic limit. However, real-world systems are finite, and their dynamics can deviate significantly from mean-field predictions. Here, we investigate finite-size effects in the Kuramoto model below the critical coupling, where the theory in the thermodynamic limit predicts complete asynchrony.
View Article and Find Full Text PDFBrief Bioinform
August 2025
State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
The systematic identification of human-virus protein-protein interactions (PPIs) is a critical step toward elucidating the underlying mechanisms of viral infection, directly informing the development of targeted interventions against existing and emerging viral threats. In this work, we presented DeepGNHV, an end-to-end framework that integrated a pretrained protein language model with structural features derived from AlphaFold2 and leveraged graph attention networks to predict human-virus PPIs. In comparison to other state-of-the-art approaches, DeepGNHV exhibited superior predictive performance, especially when applied to viral proteins absent from the training process, indicating its strong generalization capability for detecting newly emerging virus-related PPIs.
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang Road, Shushan District, Hefei, Anhui 230036, China.
Protein-nucleic acid binding sites play a crucial role in biological processes such as gene expression, signal transduction, replication, and transcription. In recent years, with the development of artificial intelligence, protein language models, graph neural networks, and transformer architectures have been adopted to develop both structure-based and sequence-based predictive models. Structure-based methods benefit from the spatial relationship between residues and have shown promising performance.
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