Showing 2,361 - 2,380 results of 5,273 for search 'significantly ((((((linear decrease) OR (mean decrease))) OR (nn decrease))) OR (greater decrease))', query time: 0.47s Refine Results
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    Effect of Viscosol on the expression of inflammatory cytokines, chemokines as well as cell death pathways. by Summan Thahiem (20988319)

    Published 2025
    “…The results shown are represented as mean ±  SD. Significant differences compared to control (****<i>p</i> < 0.0001, ***<i>p</i> < 0.0001, **<i>p</i> < 0.01 and * <i>p</i> <  0.05; two-wayANOVA and Tukey’s test).…”
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    The TOR inhibitors Rapamycin and AZD-8055 strongly reduce RPS6 phosphorylation and cell proliferation in Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

    Published 2025
    “…<i>n</i> = 2–4 biological replicates per condition, with 15 individuals per replicate. Significance levels for Student <i>t</i> test are indicated for adjusted <i>p</i> values: *<i>p</i> < 0.05, ***<i>p</i> < 0.001, ***<i>p</i> < 0.0001. d: day(s), n.s.: non-significant. …”
  12. 2372

    Flowchart of participants in this study. by Yubo Teng (21246745)

    Published 2025
    “…Participants were categorized into quartiles based on UACR levels. Multivariate linear regression models were used to evaluate the association between UACR and cognitive scores. …”
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    Major hyperparameters of RF-SVR. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”
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    Pseudo code for coupling model execution process. by Jintao Li (448681)

    Published 2024
    “…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”