Studies with Deaf and blind individuals demonstrate that linguistic and sensory experiences during sensitive periods have potent effects on neurocognitive basis of language. Native users of sign and spoken languages recruit similar fronto-temporal systems during language processing. By contrast, delays in sign language access impact proficiency and the neural basis of language. Analogously, early but not late-onset blindness modifies the neural basis of language. People born blind recruit 'visual' areas during language processing, show reduced left-lateralization of language and enhanced performance on some language tasks. Sensitive period plasticity in and outside fronto-temporal language systems shapes the neural basis of language.
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http://dx.doi.org/10.1016/j.cobeha.2020.10.011 | DOI Listing |
J Ethnopharmacol
January 2025
School of Basic Medical Sciences, Guangdong Pharmaceutical University, Guangzhou, China. Electronic address:
Ethnopharmacological Relevance: As digestive health issues rise and interest in natural therapies grows, traditional herbs like Cassia Seed are gaining attention for their antioxidant, laxative, and digestive benefits.
Aim Of The Study: This study aimed to optimize the fermentation conditions of Cassia seed using microbial technology to enhance the content of anthraquinone compounds, thereby augmenting its pharmacological effects, particularly in promoting intestinal peristalsis and alleviating constipation.
Materials And Methods: Fermentation of Cassia Seed was conducted under controlled microbial conditions.
Zhonghua Yi Xue Za Zhi
January 2025
Department of Otorhinolaryngology and Head and Neck Surgery, Air Force Medical Center, Beijing100142, China.
To simplify the Chinese version of the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ), develop the simplified Chinese version of VIMSSQ, and evaluate its performance. A cross-sectional study was conducted between May and July 2023. The Chinese version of the VIMSSQ was distributed to 783 university students at North China University of Science and Technology.
View Article and Find Full Text PDFNeuroimage
January 2025
School of information science and technology, Northwest University, Xi'an, China. Electronic address:
Macroscale neuroimaging results have revealed significant differences in the structural and functional connectivity patterns of gyri and sulci in the primate cerebral cortex. Despite these findings, understanding these differences at the molecular level has remained challenging. This study leverages a comprehensive dataset of whole-brain in situ hybridization (ISH) data from marmosets, with updates continuing through 2024, to systematically analyze cortical folding patterns.
View Article and Find Full Text PDFBr J Hosp Med (Lond)
January 2025
Speech and Language Rehabilitation Department, Beijing Rehabilitation Hospital Affiliated with Capital Medical University, Beijing, China.
The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients.
View Article and Find Full Text PDFMolecules
January 2025
Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.
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