Showing 2,301 - 2,320 results of 3,940 for search 'significant ((((((gap decrease) OR (teer decrease))) OR (greatest decrease))) OR (mean decrease))', query time: 0.66s Refine Results
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    GWAS analysis using Cluster 2 data (shown in S1 Fig). by Ke Li (106849)

    Published 2024
    “…(C) The corresponding synonymous (triangles) amino acid changes that associate with increased or decreased viral copies. Data shown as means with 95% confidence intervals.…”
  7. 2307
  8. 2308

    GWAS analysis using only Cluster 1 data (shown in S1 Fig). by Ke Li (106849)

    Published 2024
    “…(C) The corresponding synonymous (triangles) amino acid changes that associate with increased or decreased viral copies. Data shown as means with 95% confidence intervals.…”
  9. 2309

    GWAS analysis using Cluster 3 data (shown in S1 Fig). by Ke Li (106849)

    Published 2024
    “…(C) The corresponding non-synonymous (circles) amino acid changes that associate with increased or decreased viral copies. Data shown as means with 95% confidence intervals.…”
  10. 2310

    GWAS analysis using Cluster 4 data (shown in S1 Fig). by Ke Li (106849)

    Published 2024
    “…(C) The corresponding synonymous (triangles) and non-synonymous (circles) amino acid changes that associate with increased or decreased viral copies. Data shown as means with 95% confidence intervals.…”
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  12. 2312
  13. 2313
  14. 2314

    GWAS analysis identifies several single nucleotide polymorphisms (SNPs) that are associated with the changes in viral copies. by Ke Li (106849)

    Published 2024
    “…(C) The corresponding synonymous (triangles) and non-synonymous (circles) amino acid changes that associate with increased or decreased viral copies. Data shown as means with 95% confidence intervals. …”
  15. 2315

    GWAS analysis identifies several single nucleotide polymorphisms (SNPs) that are associated with the changes in viral copies. by Ke Li (106849)

    Published 2024
    “…(C) The corresponding synonymous (triangles) and non-synonymous (circles) amino acid changes that associate with increased or decreased viral copies. Data is shown as means with 95% confidence intervals. …”
  16. 2316

    Modulation of performance during learning. by Daniel Ramandi (10047543)

    Published 2025
    “…(B) A longitudinal comparison of the average exponential fit weight between WT and zQ175 mice reveals that while WT mice show a decreasing trend, indicating a shift towards longer duration (“timed”) trials, zQ175 mice maintain a consistently high exponential weight, demonstrating no significant change over time (b; RM two-way ANOVA, genotype p = 0.0106 F(1, 22) = 7.795, days p < 0.0001 F(5.262, 115.4) = 7.616, interaction p < 0.0001 F(57, 1250) = 2.834). …”
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    Domains of NoCs. by Gizachew Tadele Tiruneh (8407686)

    Published 2025
    “…The <i>"Improve Primary Health Care Service Delivery (IPHCSD)"</i> project, implemented by JSI and Amref Health Africa since April 2022, seeks to address these gaps through a Networks of Care (NoCs) approach. …”
  19. 2319

    Arrangement of PHC facilities in a woreda NoCs. by Gizachew Tadele Tiruneh (8407686)

    Published 2025
    “…The <i>"Improve Primary Health Care Service Delivery (IPHCSD)"</i> project, implemented by JSI and Amref Health Africa since April 2022, seeks to address these gaps through a Networks of Care (NoCs) approach. …”
  20. 2320

    Validation and predictive accuracy of the cerebrovascular model, by Hadi Esfandi (21387211)

    Published 2025
    “…<p>(a-f) dependency of blood flow in capillaries on the cortical depth and ABNP values: (a) mean and standard deviation of blood flow in Cap1 segments for various ABNP values, (b) layer-specific average blood flow in capillaries across the entire monophasic flat autoregulation range, (c) indicates that the superficial layers have a more nonuniform hemodynamic distribution in capillaries, (d) mean capillary blood flow across different vascular layers at physiological ABNP values (50-70 mmHg), (e) WT changes in superficial PA segments are more significant than in deeper segments across the autoregulation range. …”