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Showing 1,861 - 1,880 results of 21,342 for search '(( significant decrease decrease ) OR ( significantly change decrease ))', query time: 0.47s Refine Results
  1. 1861
    Table 6_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx

    Table 6_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  2. 1862
    Table 5_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx

    Table 5_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  3. 1863
    Table 7_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx

    Table 7_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  4. 1864
    Table 9_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx

    Table 9_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  5. 1865
    Image 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif

    Image 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  6. 1866
    Image 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif

    Image 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  7. 1867
    Image 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif

    Image 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  8. 1868
    Image 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif

    Image 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

    Published 2025
    “…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”
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  9. 1869
    Spatial patterns of developmental changes in nodal entropy using different statistical models.

    Spatial patterns of developmental changes in nodal entropy using different statistical models. by Tianyuan Lei (19683861)

    Published 2024
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  10. 1870
    The Throttle Effect in Metal–Organic Frameworks for Distinguishing Water Isotopes

    The Throttle Effect in Metal–Organic Frameworks for Distinguishing Water Isotopes by Xiao Xiao (99147)

    Published 2024
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  11. 1871
    The Throttle Effect in Metal–Organic Frameworks for Distinguishing Water Isotopes

    The Throttle Effect in Metal–Organic Frameworks for Distinguishing Water Isotopes by Xiao Xiao (99147)

    Published 2024
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  12. 1872
    Regression situation of each cross-section.

    Regression situation of each cross-section. by Puzhen An (21169189)

    Published 2025
    “…Furthermore, as the coarse particle content rises, the strata loss rate tends to decrease gradually. The final settlement curve, calculated using the method that considers changes in coarse particle content, is closer to the measured values. …”
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  13. 1873
    Statistical Table of Formation Loss Rate <i>V1.</i>

    Statistical Table of Formation Loss Rate <i>V1.</i> by Puzhen An (21169189)

    Published 2025
    “…Furthermore, as the coarse particle content rises, the strata loss rate tends to decrease gradually. The final settlement curve, calculated using the method that considers changes in coarse particle content, is closer to the measured values. …”
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  14. 1874
    Fitting Results for Each Operating Condition.

    Fitting Results for Each Operating Condition. by Puzhen An (21169189)

    Published 2025
    “…Furthermore, as the coarse particle content rises, the strata loss rate tends to decrease gradually. The final settlement curve, calculated using the method that considers changes in coarse particle content, is closer to the measured values. …”
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  15. 1875
    Effects of SGLT-2is during the time course.

    Effects of SGLT-2is during the time course. by Ningning Wang (560944)

    Published 2025
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  16. 1876
    MaxEnt-generated wheat suitability maps (Esri ArcGIS Pro 2024) with classification predicted for different period of time (the wheat distribution is given between brackets).

    MaxEnt-generated wheat suitability maps (Esri ArcGIS Pro 2024) with classification predicted for different period of time (the wheat distribution is given between brackets). by Khalifa M. Al-Kindi (3719854)

    Published 2025
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  17. 1877
    The variables selected for the model predicting the geographical distribution of wheat.

    The variables selected for the model predicting the geographical distribution of wheat. by Khalifa M. Al-Kindi (3719854)

    Published 2025
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  18. 1878
    The association between wheat distribution and bioclimatic factors for 1970–2020.

    The association between wheat distribution and bioclimatic factors for 1970–2020. by Khalifa M. Al-Kindi (3719854)

    Published 2025
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  19. 1879
    Wheat distribution and bioclimatic factors for 2021–2040. Panels a–e show the associations between wheat distribution and key climatic parameters: temperature seasonality, average temperature for the coldest quarter, precipitation in the wettest month, precipitation seasonality, and precipitation in the coldest quarter, respectively.

    Wheat distribution and bioclimatic factors for 2021–2040. Panels a–e show the associations between wheat distribution and key climatic parameters: temperature seasonality, average... by Khalifa M. Al-Kindi (3719854)

    Published 2025
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  20. 1880
    Schematic representation of the sequential steps in the analytical process.

    Schematic representation of the sequential steps in the analytical process. by Khalifa M. Al-Kindi (3719854)

    Published 2025
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