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The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions.

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The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD.

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Background: The arterial stiffening is attributed to the intrinsic structural stiffening and/or load-dependent stiffening by increased blood pressure (BP). The respective lifetime alterations and major determinants of the two components with normal aging are not clear.

Methods: A total of 3053 healthy adults (1922 women) aged 18-79 years were enrolled.

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This study investigates the gross morphological and morphometric characteristics of thoracic and lumbar intervertebral discs (IVDs) in guinea pigs, utilising micro-CT imaging and anatomical dissection. The findings reveal 13 thoracic and six lumbar IVDs were identified, with thoracic discs transitioning from rounded forms at T1-T3 to triangular and heart-shaped structures at T4-T13, while lumbar IVDs exhibited a consistently flattened heart shape. Morphometric analysis revealed statistically significant differences, with lumbar IVDs being larger in lateral and dorsoventral width, disc area, annulus fibrosus (AF) area and nucleus pulposus (NP) area, and ventral height compared to thoracic discs.

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CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.

Med Image Comput Comput Assist Interv

October 2024

Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.

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