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Showing 701 - 720 results of 21,342 for search '(( significant spatial decrease ) OR ( significant decrease decrease ))', query time: 0.35s Refine Results
  1. 701
    Prolonged starvation leads to a delay in cell cycle re-entry and decrease in H3K27ac in Vasa2+/Piwi1+ cells.

    Prolonged starvation leads to a delay in cell cycle re-entry and decrease in H3K27ac in Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

    Published 2025
    “…During starvation (T<sub>5ds</sub>, T<sub>20ds</sub>), MFI levels of H3K27ac progressively decreased <b>(F)</b> while levels H3K27me3 did not change significantly <b>(G)</b>. …”
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  2. 702
    A prepubertal CD decreases fat mass accumulation and prevents lean mass loss in adulthood despite a chronic HFD in Sprague-Dawley male rats.

    A prepubertal CD decreases fat mass accumulation and prevents lean mass loss in adulthood despite a chronic HFD in Sprague-Dawley male rats. by Ana Aguilar-Lozano (20614619)

    Published 2025
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  3. 703
    Table_2_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.xlsx

    Table_2_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.xlsx by Kristin R. Anderson (5478734)

    Published 2024
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  4. 704
    Image_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.jpg

    Image_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.jpg by Kristin R. Anderson (5478734)

    Published 2024
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  5. 705
    Table_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.pdf

    Table_1_A novel anxiety-associated SNP identified in LYNX2 (LYPD1) is associated with decreased protein binding to nicotinic acetylcholine receptors.pdf by Kristin R. Anderson (5478734)

    Published 2024
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  6. 706
    Table 1_Centella asiatica enhances diabetic wound healing by decreasing macrophage-driven inflammation via the AKT/MAPK/NF-κB pathway.xlsx

    Table 1_Centella asiatica enhances diabetic wound healing by decreasing macrophage-driven inflammation via the AKT/MAPK/NF-κB pathway.xlsx by Fugang Xiao (22152127)

    Published 2025
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  7. 707
    Enrichment analysis of genes from the significant MS module.

    Enrichment analysis of genes from the significant MS module. by Lai Jiang (2401513)

    Published 2025
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  8. 708
    RTA408 significantly reduces RGCs loss in glaucoma.

    RTA408 significantly reduces RGCs loss in glaucoma. by Hongmei Qian (538647)

    Published 2024
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  9. 709
    <b> </b> Spatial memory assessment in Morris water maze test. 5A: Effect of different treatments on time spent in the target zone. Alcl3 significantly decreased the time spent in the target zone with respect to the saline-treated group (p < 0.01). Two injections of exosomes prevented the impairment effect of Alcl3 on spatial memory compared to the Alcl3-treated group (p < 0.05). Two or five injections of exosomes in healthy rats impaired spatial memory. 5B: The swimming trajectory of each group in the probe test. No.2 was the quadrant area. Each value represents mean ± SEM, n = 6, **p < 0.01 and ***p < .001 vs. saline group and <sup>+</sup>p < 0.05 vs. Alcl3 group. <i>Abbreviation</i>s: Sal, Saline; Exo [2], Two injections of exosomes; Exo [5], Five injections of exosomes.

    <b> </b> Spatial memory assessment in Morris water maze test. 5A: Effect of different treatments on time spent in the target zone. Alcl3 significantly decreased the time spent in the target zone with respect to the saline-treated group (p < 0.01).... by Atefeh Sadeghi (21253051)

    Published 2025
    “…Alcl3 significantly decreased the time spent in the target zone with respect to the saline-treated group (p < 0.01). …”
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  10. 710
    Water footprint at regional levels in China.

    Water footprint at regional levels in China. by Jianqin Ma (20328414)

    Published 2025
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  11. 711
    Characteristics of water footprint by sector at the regional level in China.

    Characteristics of water footprint by sector at the regional level in China. by Jianqin Ma (20328414)

    Published 2025
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  12. 712
    The distribution of water footprint levels in China. a, 2005. b, 2010. c, 2015. d, 2022.

    The distribution of water footprint levels in China. a, 2005. b, 2010. c, 2015. d, 2022. by Jianqin Ma (20328414)

    Published 2025
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  13. 713
    Synergy and trade-offs of sectoral water use at the provincial level.

    Synergy and trade-offs of sectoral water use at the provincial level. by Jianqin Ma (20328414)

    Published 2025
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  14. 714
    Synergy and trade-offs of sectoral water use at the regional level in China.

    Synergy and trade-offs of sectoral water use at the regional level in China. by Jianqin Ma (20328414)

    Published 2025
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  15. 715
    Decoupling index of water footprint and GDP at the regional level in China.

    Decoupling index of water footprint and GDP at the regional level in China. by Jianqin Ma (20328414)

    Published 2025
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  16. 716
    Supporting information.

    Supporting information. by Jianqin Ma (20328414)

    Published 2025
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  17. 717
    Decoupling index of water footprint and GDP at the provincial level.

    Decoupling index of water footprint and GDP at the provincial level. by Jianqin Ma (20328414)

    Published 2025
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  18. 718
    Second-order partial correlation analysis of sectoral water use in China from 2005 to 2022.

    Second-order partial correlation analysis of sectoral water use in China from 2005 to 2022. by Jianqin Ma (20328414)

    Published 2025
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  19. 719
    Characteristics of water footprint by sector at the provincial level.

    Characteristics of water footprint by sector at the provincial level. by Jianqin Ma (20328414)

    Published 2025
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  20. 720
    Correlations between significantly different microbiota and metabolites.

    Correlations between significantly different microbiota and metabolites. by Bhuripit Saraphol (20855573)

    Published 2025
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