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The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
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http://dx.doi.org/10.1126/science.adh1938 | DOI Listing |
IEEE J Biomed Health Inform
September 2025
The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference.
View Article and Find Full Text PDFJ Synchrotron Radiat
November 2025
State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People's Republic of China.
This study develops an integrated X-ray absorption spectroscopy (XAS) photoemission electron microscopy (PEEM) platform on beamline BL09U at the Shanghai Synchrotron Radiation Facility (SSRF), enabling nanoscale characterization of complex materials through energy-resolved imaging and local-area XAS. By using the wide range of energy tunability, full access to different polarizations and PEEM's surface sensitivity, we have established a gap-monochromator control system under the EPICS framework to synchronize the elliptically polarized undulator (EPU) gap and monochromator energy dynamically, optimizing photon flux stability for absorption fine structure analysis. Combining X-ray magnetic circular dichroism (XMCD) and X-ray magnetic linear dichroism (XMLD) with PEEM and local-area XAS, this platform achieves concurrent mapping of electronic structures and magnetic domains in ferromagnetic nano-patterns, as demonstrated through our studies of NiFe Permalloy using this system.
View Article and Find Full Text PDFNatl Sci Rev
September 2025
The Centre of Nanoscale Science and Technology and Key Laboratory of Functional Polymer Materials, Institute of Polymer Chemistry, Renewable Energy Conversion and Storage Center (RECAST), College of Chemistry, Nankai University, Tianjin 300071, China.
Contactless human-machine interfaces (C-HMIs) are revolutionizing artificial intelligence (AI)-driven domains, yet face application limitations due to narrow sensing ranges, environmental fragility, and structural rigidity. To address these obstacles, we developed a flexible photonic C-HMI (Flex-PCI) using flexible visible-blind near-infrared organic photodetectors. In addition to its unprecedented performance across key metrics, including broad detection range (0.
View Article and Find Full Text PDFJ Pathol Inform
November 2025
Pathology and Laboratory Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA.
Evaluation of tumor infiltrating lymphocytes as recommended by current guidelines is largely based on stromal regions within the tumor. In the context of epithelial malignancies, the epithelial region and the epithelial-stromal interface are not assessed, because of technical difficulties in manually discerning lymphocytes when admixed with epithelial tumor cells. The inability to quantify immune cells in epithelial-associated areas may negatively impact evaluation of patient response to immune checkpoint therapies.
View Article and Find Full Text PDFMed Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
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