The results revealed that treatment had a highly significant effect on isoflavonoid levels ( p < 0.0001), indicating that treatment (control vs. infected) significantly influences isoflavonoid concentrations ( Supplementary Table S2 ).
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Two-way ANOVA revealed highly significant genotype effects for TaExpA6 (p < 0.0001), with Bonferroni’s multiple comparisons test showing consistent and significant changes in mRNA levels at 10, 14, and 21 DAA (adjusted p-value < 0.05, Figure 6E ).
The highly significant slopes ( P < 0.0001) between the linear relationships ranged from 0.7 to 1.1, with the weakest relationships apparent for Q. robur (average slope of 0.8 for all methods), likely caused the relatively low amount of detect vessels and its variance between methods.
The regressions implicating altitude and latitude of provenances were highly significant ( P < 0.0001), resulting in R 2 ranging between 0.21 and 0.51 (Table 2 ).
The association between CT repeatability and the wind parameters (average wind speed, maximum wind speed and wind direction) were generally poor and not significant, with the exception of 2016 ArduCrop where correlations were highly significant ( P < 0.0001) and ranged from 0.24 to 0.28.
Analysis of variance showed highly significant variation due to year-by-genotype interaction effects ( p < 0.0001) in both HRP and CGP ( Table 2 ).
Positive effects of favorable alleles have additive effects The study of the correlation between the number of QTLs with NPQTL and DI revealed that in five datasets, both showed a highly significant negative correlation (r = −0.48–0.71, p < 0.0001), with the mean dataset exhibiting the strongest correlation (r = −0.71; p < 0.0001) ( Supplementary Figure S3 ).
The results of ANOVA analysis showed highly significant ( P<0.0001 ) variances of genotype ( G ), environment ( E ) and genotype × environment ( G × E ) interaction for the SOC ( Table 1 ).
Mean performances and analysis variance The pooled ANOVA indicated highly significant differences for restorer lines, with a significant effect of environments and G × E interaction (GEI) ( p < 0.0001) for all of the studied traits, indicating substantial phenotypic variation among the 240 rice restorer lines across the three test environments for all of the traits studied.
3 Results 3.1 Effects of climate, geographic site, land-use system, and their interactions on tree diversity Among the factors tested, land-use system had a highly significant effect ( P < 0.0001) on tree species richness, abundance, evenness, and Shannon diversity indices ( Table 3 ).
As shown in Supplementary Table 1 , there were extremely significant differences in phenotype between different environmental conditions ( P < 0.0001).
A highly significant variation ( P < 0.0001) among the genotypes for both WSC accumulation and remobilization per GDD was found in all environments.
Interaction of any two of the three tested variables and the combined three variables was highly significant ( P < 0.0001, Mixed models).
The analysis of variance for stem rust reaction showed highly significant differences ( P < 0.0001) among accessions included in the whole collection, the durum sub-sample and Q2 group.
The Mantel test between Dim1-Dim2 matrices was highly significant (r=0.817, p<0.0001), revealing that the external phenotype of the landrace is very recognizable and easy to differentiate from other varieties.
The multi-locus model for GWAS resulted in the identification of 15 highly significant associations ( p < 0.0001) for culm strength traits.
Firstly, using geographic origin as a trait, a highly significant association was found ( AI = 8.8954, P < 0.0001; PS = 69.3392, P < 0.0001) ( Table 2 ).
This correlation is highly significant for the 24 non-agricultural strains ( r 2 = 0.47, P = 0.0002) while it was not significant for the 31 agricultural strains ( r 2 = 0.03, P = 0.33), suggesting that the relationship between aggressiveness and sclerotial production differ among these two populations of strains.
Precisely, PAL was more correlated with the non-reducing sugars ( R 2 =0.9767, p =0.0002) than reducing and total soluble sugars, while anthocyanin exhibited a highly significant correlation with non-reducing sugars ( R 2 =0.969, p =0.0004) and total soluble sugars ( R 2 =0.9686, p =0.0004) compared to the reducing sugars.
Indeed, ABA3 expression kinetics obtained after infection by prtE and outC are not significantly or weakly significantly different from that obtained after buffer inoculation ( p -value = 0.0830 and 0.0411, respectively) in contrast to the highly significant differences between the WT strain ( p -value = 0.0004) or the hrcC mutant ( p -value = 0.0006) and the buffer ( Figure 3 and Supplementary Table S2 ).
Results Correlation Between Physiological and Thermal Variables The correlation between physiological (GFS, starch, frost hardiness and water content) and thermal (minimum, average and maximum temperature averaged over the last 1–30 days before harvest) variables were all highly significant ( P < 0.0005; Table S1 ).
Results obtained from PLS-DA ( Figure 2A ) revealed that metabolic features formed distinct clusters (15 cells in each group), and this difference was highly significant ( p = 5 × 10 -4 ; permutation test in Metaboanalyst 3.0).
Considering that the measured plastidial proteome contains approximately 1,500 proteins ( Zybailov et al., 2008 ), such overlap is 8.5 times more than expected by chance and highly significant (Fisher exact test, p -value = 0.0006).
The differences in FLA value between alleles OsFLA6 AA and OsFLA6 GG were highly significant (Welch's t -test; P = 6.06E−04) ( Figure 3E ).
Under R → RB → R and the reverse sequence, RB → R → RB, fitting the g s data to a linear mixed-effects model ( Tables S8 and S9 ) showed a highly significant effect of light (R-RB-R, p=0.0007;RB-R-RB, p=0.0060), but the main effect of species (R-RB-R, p=0.3455; RB-R-RB, p=0.8859) and its interaction with light (R-RB-R, p=0.2055; RB-R-RB, p=0.7486) were not significantly different.