Showing 41 - 60 results of 8,894 for search '(( significant challenge posed ) OR ( significant ((i.e decrease) OR (teer decrease)) ))', query time: 0.85s Refine Results
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    Concreteness and emotional valence of episodic future thinking (EFT) independently affect the dynamics of intertemporal decisions by Cinzia Calluso (5662420)

    Published 2019
    “…<div><p>During intertemporal decisions, the value of future rewards decreases as a function of the delay of its receipt (temporal discounting, TD). …”
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    Y-27632 collaborated with BA to attenuate the increase in the integrity and decrease in the permeability of epithelial barrier injury induced by LPS in Caco2 monolayers. by Luqiong Liu (11537092)

    Published 2024
    “…<p>(<b>A)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on days 1–22. (<b>B)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on day 22. …”
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    The effect of HA digestion and HA replenishment alone or with CS on barrier function measured by TEER. by Charlotte J. van Ginkel (20790466)

    Published 2025
    “…PS decreased TEER. (C) Different treatments (treatment groups n = 8)with HA and/or CS did not affect TEER recovery after PS treatment, full recovery was seen in all groups after 24 hours. …”
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    TGMF-Pose: Text-guided multi-view 3D pose estimation and fusion network for online sports instruction by Xiaohong Qi (22523377)

    Published 2025
    “…It consists of three core components that enhance the fine-grained representation of semantic information and the accuracy of depth estimation: (1) The joint feature embedding module models the distances and angles between keypoints in 2D pose estimation. Guided by text-based prompts, it captures subtle geometric differences in limb movements across various sports. (2) The multi-view generator effectively addresses the challenge of limb occlusion by estimating the complete 3D pose of the central frame through querying keypoint features from nearby available frames, guided by textual prompts and geometric constraints. (3) The multi-view fusion module aggregates information from all views to refine features and achieve accurate pose depth estimation.…”
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