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Assessing the SNP genotype (additive inheritance model) as dependent variable and the insulin secretion parameter as independent variable with inclusion of the aforementioned confounders in the multiple regression models yielded very similar rankings (Tables S3 and S4).
However, both models yielded very similar estimates of the population genomic parameters.
Different substitution models yielded very similar results and GTR model with multiple hit corrections was eventually used in this study.
The two-, three-, and four-factor models yielded very similar findings to those of the one-factor model (Table 3).
Residual confounding by other measured factors might be of minor importance in this study, as our age/sex-adjusted models and multivariate models yielded very similar results.
For C or G HPTs, second through fifth order Markov models yielded very similar observed/expected curves, and for A or T HPTs, first and second order Markov models yielded very similar observed/expected curves, but higher order Markov models may exhibit overfitting.
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Indeed, both stiffness models yield very acceptable results for a static analysis.
The primary interest (circumcision's effect) is reflected by β1, and the two models yield very similar results.
Despite these differences in strategies, in general the different models yield very similar predictive abilities, which suggests that for within-breed or within-line selection, the strategy that the model uses has generally limited impact on the results.
The resulting model yielded very excellent agreement, with a root mean square deviation calculated over all the heavy atoms below 2,5 Å and root mean square fluctuations of the side chains of 2,1 Å (s.d. 0,7 Å) (see Figure S1), i.e., values comparable to standard MD simulations using well resolved structures.
This model yields very satisfactory results for removing image noise while preserving edges, see [7, 37].
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