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The individual study area-based models also improved with the addition of habitat variables, with some models explaining a higher percentage of the variance in chlorophyll.
Finally, through analysis of dMyc's targets and comparing them to the predicted targets of miR-308, we propose models explaining a concerted gene regulation program by dMyc and miR-308.
The A models always provided a better description of the data than their respective parent CA or IA models, while there were no significant improvements by using models explaining a dose level dependent antagonism (DL).
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Additionally, our models explain a novel concept for partial agonism, termed dynamic ligand binding.
First, though all models explained a significant amount of variability in effects (as shown by QM), all model fit tests (QE results) were very large and statistically significant.
Both science models explained a significant amount of effect-size heterogeneity, as respectively indicated by QM 3) = 18.9, p <.05 and QM 2) = 13.8, p <.05.
Both models explained a significant amount of heterogeneity in the math gaps, as indicated by QM 3) = 16.3, p <.05 and QM 2) = 8.0, p <.05, respectively.
The final regression models explained a high degree of variance in predicting isokinetic shoulder flexion (R2 = 0.59) and extension (R2 = 0.67) with a subset of four and five inputs, respectively.
Moreover, two models that often describe spatial abundance occupancy relationships well, the He Gaston and negative binomial models, explained a high amount of the variation in our temporal data.
The models explained a substantial portion of this variation using relatively easily collected and intuitive predictors.
LUR models explained a large fraction of the spatial variation of the two OP metrics.
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