Showing 1 - 20 results of 76,286 for search '(( significant temporal scales ) OR ( significant ((gap decrease) OR (a decrease)) ))', query time: 0.99s Refine Results
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    Temporal scale or landmark dependent significant correlations. by Bastien Perroy (14134841)

    Published 2025
    “…<p>Temporal scale or landmark dependent significant correlations.…”
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    Regenerating axons are increased by ATRA and decreased by clodronate. by Valeria De La Rosa-Reyes (6658613)

    Published 2021
    “…<p>(A—E) Confocal images of immunohistochemical staining of growing axons with anti-GAP43 antibody (green), in longitudinal sections of the optic nerve at 2 weeks after axotomy. …”
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    Overexpression of Gαq decreases cell number and cell size. by Dharsan K. Soundarrajan (11632145)

    Published 2021
    “…Overexpression of Gαq in the pouch results in a decrease in total wing area, cell number and cell size. 10 samples were analyzed per condition. …”
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    Syndecan ectodomain fragments decrease endothelial resistance via rho kinase. by Melanie Jannaway (6703577)

    Published 2019
    “…In the presence of RWJ56110, rhS3ED fragments mediated a significant decrease in TER response (n = 3, p<0.0001). …”
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    NgR1 KO mice exhibited an increase in excitatory synapses and a decrease in inhibitory synapses, indicating an imbalance of synaptic transmission. by Jinwei Zhang (462455)

    Published 2025
    “…The inhibitory synaptic density of NgR1 mice showed a significant decrease when compared to WT mice (***P <  0.001). …”
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    Post hoc t-test comparisons of FuzzyEn across temporal scales in groups with significant ANCOVA results. by Ayumu Ueno (20899814)

    Published 2025
    “…<p>As the post hoc <i>t</i>-test, The average of FuzzyEn dependency on the temporal scale in the groups (TD, ADHD, and drug-naïve ADHD) were compared in the groups that were significant in the ANCOVA. …”
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    Multi-scale temporal attention component. by Kang Xu (708915)

    Published 2025
    “…<div><p>Accurate traffic flow prediction is vital for intelligent transportation systems but presents significant challenges. Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal scales, which restricts effective future flow prediction; (2) reliance on predefined graph structures in graph neural networks, making it challenging to accurately model the spatial relationships in complex road networks; and (3) end-to-end training, which often results in unclear optimization directions for model parameters, thereby limiting improvements in predictive performance. …”