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significant species » significant declines (Expand Search), significant spatial (Expand Search), significant genes (Expand Search)
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Showing 81 - 100 results of 9,006 for search '(( significant species decrease ) OR ( significant ((greatest decrease) OR (visits decreased)) ))', query time: 0.40s Refine Results
  1. 81
    Time course of optical coherence tomography and fundus findings in a representative non-recurrent eye.

    Time course of optical coherence tomography and fundus findings in a representative non-recurrent eye. by Yasuyuki Sotani (20114144)

    Published 2025
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  2. 82
    Time course of central retinal thickness (CRT) in recurrence and non-recurrence groups.

    Time course of central retinal thickness (CRT) in recurrence and non-recurrence groups. by Yasuyuki Sotani (20114144)

    Published 2025
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  3. 83
    Time course of best-corrected visual acuity (BCVA) in all eyes.

    Time course of best-corrected visual acuity (BCVA) in all eyes. by Yasuyuki Sotani (20114144)

    Published 2025
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  4. 84
    Time course of central retinal thickness (CRT) in all eyes.

    Time course of central retinal thickness (CRT) in all eyes. by Yasuyuki Sotani (20114144)

    Published 2025
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  5. 85
    Annual treatment frequencies in all eyes.

    Annual treatment frequencies in all eyes. by Yasuyuki Sotani (20114144)

    Published 2025
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  6. 86
    Annual treatment frequencies in recurrence and non-recurrence groups.

    Annual treatment frequencies in recurrence and non-recurrence groups. by Yasuyuki Sotani (20114144)

    Published 2025
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  7. 87
    Time course of best-corrected visual acuity (BCVA) in recurrence and non-recurrence groups.

    Time course of best-corrected visual acuity (BCVA) in recurrence and non-recurrence groups. by Yasuyuki Sotani (20114144)

    Published 2025
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  8. 88
    Preoperative background characteristics of the patients.

    Preoperative background characteristics of the patients. by Yasuyuki Sotani (20114144)

    Published 2025
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  9. 89
    Rotenone exposure decreases GSH level in porcine oocytes.

    Rotenone exposure decreases GSH level in porcine oocytes. by Geun Heo (14185618)

    Published 2022
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  10. 90
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), including national parks, national monuments, and national forests included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with a diameter of 2.5 microns.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), i... by Kira Minehart (21029571)

    Published 2025
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  11. 91
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), including city parks, county parks, and regional parks included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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  12. 92
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), including state parks and state recreation areas included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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  13. 93
    Predicted and observed wine sales for individual states.

    Predicted and observed wine sales for individual states. by Yingjie Hu (1579000)

    Published 2021
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  14. 94
    Feature importance of the numeric input variables in the optimized RF models for the estimation of spirits, wine, and beer sales.

    Feature importance of the numeric input variables in the optimized RF models for the estimation of spirits, wine, and beer sales. by Yingjie Hu (1579000)

    Published 2021
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  15. 95
    The architecture of the DNN model for estimating alcohol sales.

    The architecture of the DNN model for estimating alcohol sales. by Yingjie Hu (1579000)

    Published 2021
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  16. 96
    Fig 7 -

    Fig 7 - by Yingjie Hu (1579000)

    Published 2021
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  17. 97
    Four categories of input features for the machine learning models.

    Four categories of input features for the machine learning models. by Yingjie Hu (1579000)

    Published 2021
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  18. 98
    VIF values of the variables in three MLR models.

    VIF values of the variables in three MLR models. by Yingjie Hu (1579000)

    Published 2021
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  19. 99
    Fig 15 -

    Fig 15 - by Yingjie Hu (1579000)

    Published 2021
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  20. 100
    Fig 5 -

    Fig 5 - by Yingjie Hu (1579000)

    Published 2021
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