Among the highly significant markers (with P < 0.001) in the QTLRs, 48.1% were generated by markers with MAF < 0.15 ( S1 Table ).
Excerpts
This was highly significant ( P <0.001) for the interaction between trial 4 (4×MIC) and time for L . monocytogenes , S . aureus and S .
The difference between the two groups was highly significant (2-tailed P value = 0.001).
On the other hand, there was a highly significant improvement in PVF in the BCP group (p < 0.001), an effect that was significant compared to the PLA group (p = 0.012) or the unaffected limb of the same group (p < 0.001).
Kaplan-Meier curves demonstrated that models discriminated well and log-rank test were all highly significant (all p<0.001), except upper urinary tract model (p = 0.366).
However, threshold shift was highly significant for all frequencies (p<0.001).
Differences were considered significant when p < .05(*), and very significant when p ≤ 0.01(**), and extremely significant when p ≤ 0,001(***).
Our tabulated prediction accuracy (averaged over 39 subjects completing the experiment) over the full stimulus set (see Table 1 ) was highly significant for both valence (p < .001; signed rank; α = .05; h 0 : μ = .5) and arousal (p < .001; signed rank; α = .05; h 0 : μ = .5).
The multivariate regression of the shape component on size, estimated by the logarithm of the centroid size, was highly significant (F = 16.5, p <0.001, dl = 105).
majoris is known to decrease with age [42] , and age had a highly significant effect also in the present analysis (GLM (bird age) F 1,301 = 19.1, p<0.001, Figure 1 ).
We found highly significant (p < 0.001) and positive correlations between connectedness with nature of respondents and the degree to which they expressed feelings that the competition: motivated conservation in general; motivated electricity conservation; motivated water conservation; and empowered conservation actions. 3.3.5.
All classification results were highly significant (p<0.001 by permutation testing).
The correct paragraph is: When applying these intensity distributions from the “training sample” (N = 1282) to the “holdout sample” (N = 641), occupational and total PAEE displayed an increasing trend across occupational groups (Figure 2), with the highest values in heavy manual workers (p<0.001).
Despite these correlations, three of the variables were highly significant in the full model (P < 0.001), and excluding the two that were not (slope and mean temperature; P > 0.7) did not substantially change their coefficients or standard errors.
The human group demonstrated a highly significant advantage in procedure time (p = 0.001).
Effect of Bd infection load on thermal tolerance limits The linear model used to analyse differences in CTmax of tadpoles was highly significant (R 2 = 0.63, F 3,76 = 41.359, p < 0.001).
Comparisons between the different treatment groups were analyzed via one-way ANOVA and the least significant difference (LSD), and differences were considered significant at p < 0.05 * and highly significant at p < 0.001.
A formal trend test treating loneliness as an ordinal variable was highly significant (p < 0.001).
Both correlations were highly significant (p < 0.001).
The statistical difference was extremely significant after deworming (chi-square −112.907, P<0.001).
A one-way related-means analysis of variance (ANOVA) revealed a highly significant effect of masker condition [F(2,10) = 58.07, p < 0.001].
Acoustic startle response measured in another cohort of mice (9 controls and 10 mutants aged 9 weeks) showed an altered curve: there was a highly significant interaction between genotype and startle pulse intensity (F (7,11) = 4.153, p<0.001), and Bonferroni posthoc tests revealed a significantly lower response of mutants at maximum dB (p<0.01, Fig. 2F ).
In this context, all correlations except for Chinese and Russian are strong and highly significant (at p < .001 or better).
Fisher’s exact test based on the concatenated Cytb and CR datasets obtained a highly significant ( P <0.001) relationship between genetic variation sites and lineage.
These categorical data were best fit by a highly significant (p<0.001) power law regression of the form: bulk density = a * percent organic matter (−exp b).