Perineuronal nets (PNNs) are extracellular matrix structures that form around some types of neurons at the end of critical periods, limiting neuronal plasticity. In the adult brain, PNNs play a crucial role in the regulation of learning and cognitive processes. Neuropeptide Y (NPY) is involved in the regulation of many physiological functions, including learning and memory abilities, via activation of Y1 receptors (Y1Rs). Here we demonstrated that the conditional depletion of the gene encoding the Y1R for NPY in adult forebrain excitatory neurons (Npy1r mutant mice), induces a significant slowdown in spatial learning, which is associated with a robust intensification of PNN expression and an increase in the number of c-Fos expressing cells in the cornus ammonis 1 (CA1) of the dorsal hippocampus. Importantly, the enzymatic digestion of PNNs in CA1 normalizes c-Fos activity and completely rescues learning abilities of Npy1r mice. These data highlight a previously unknown functional link between NPY-Y1R transmission and PNNs, which may play a role in the control of dorsal hippocampal excitability and related cognitive functions.
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http://dx.doi.org/10.1016/j.neuropharm.2020.108425 | DOI Listing |
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India.
Drought is one of the most detrimental natural calamities to the economy. Despite its significant consequences, the evolution from meteorological to agricultural and hydrological droughts still needs to be explored. A thorough investigation was carried out in India's eastern hills and plateau region to determine the extent of drought's impact through indices.
View Article and Find Full Text PDFMed Phys
January 2025
School of Computer Science and Engineering, Beihang University, Beijing, China.
Background: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.
View Article and Find Full Text PDFThe spatial arrangement of cells plays a pivotal role in shaping tissue functions in various biological systems and diseased microenvironments. However, it is still under-investigated of the topological coordinating rules among different cell types as tissue spatial patterns. Here, we introduce the Triangulation cellular community motif Neural Network (TrimNN), a bottom-up approach to estimate the prevalence of sizeable conservative cell organization patterns as Cellular Community (CC) motifs in spatial transcriptomics and proteomics.
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