Showing 2,481 - 2,500 results of 18,297 for search 'significant ((((gap decrease) OR (((a decrease) OR (nn decrease))))) OR (mean decrease))', query time: 0.43s Refine Results
  1. 2481

    All-Atom Simulations Reveal the Effect of Membrane Composition on the Signaling of the NKG2A/CD94/HLA‑E Immune Receptor Complex by Martin Ljubič (15680161)

    Published 2024
    “…The decreased membrane thickness in the DPLC model caused a significant transmembrane domain tilt, altering the linker protrusion angle and potentially disrupting the hydrogen bonding network in the extracellular domain. …”
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    S1 File - by Michael Gulledge (20577135)

    Published 2025
    “…Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions. …”
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  9. 2489

    Absolute β convergence results. by Ke Liu (121889)

    Published 2025
    “…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”
  10. 2490

    Conditional β convergence results. by Ke Liu (121889)

    Published 2025
    “…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”
  11. 2491

    Markov transition probability matrix (k = 4). by Ke Liu (121889)

    Published 2025
    “…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”
  12. 2492

    Markov transition probability matrix (k = 4). by Ke Liu (121889)

    Published 2025
    “…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”
  13. 2493

    Regression results. by Ke Liu (121889)

    Published 2025
    “…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”
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  15. 2495

    SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”
  16. 2496

    Screening process diagram. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”
  17. 2497

    SHAP waterfall plot. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”
  18. 2498

    SHAP decision plot. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”
  19. 2499

    LASSO regression visualization plot. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”
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    SHAP dependence plots. by Wentao Yang (205781)

    Published 2025
    “…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”