Best fitting models and significant effects are shown in bold.
According to the best fitting and most significant polynomial equation, pressure and time were the most important factors determining the inactivation extent.
According to the best fitting and most significant polynomial equation, pressure and time were the factors determining the Enterococcus inactivation, while temperature was not statistically significant.
Neither of conventional DWI and DTI metrics (axial, radial, and mean ADC by mono-exponential fitting, and FA) showed significant differences between the groups.
The best fitting model obtained (lowest AIC and significant ΔAIC ≥ 2.5) included the factor locus only, whereas year and the interaction between year and locus were not significant (ΔAIC ≤ 1.7).
They used single SNP log-rank tests, 3-SNP haplotype analyses and Cox multiple regression with stepwise selection on a subset of the genotypes (due to the problems of SNP collinearity and over-fitting), and observed no significant genetic association.
Although the differences between unimodal and bimodal fitting are not significant in terms of goodness of fit, the differences in process predictions are considerable with the bimodal soil simulating water content measurements much better than unimodal soil.
However, a significant improvement in overall fitting and reduction in objective function value (OFV) (dOFV = -125.81) was observed by including this pathway after long-term infusion.
Linear fitting revealed significant correlation in 18 features in the experiment group, as follows (full names in Table 1): First order: Kurtosis GLCM: Contrast, Correlation, Energy, Homogeneity GLRLM: SRE, LRE, GLN, RLN, RP, LRHGE GLSZM: SZE, LZE, GLN, ZSN, ZP, SZHGE, LZHGE.
Global fitting of significant vectors was performed with Savuka.
