On the contrary, the differences between sucrose and water acceptance were highly significant inside the hive (z ratio = 3.751, p = 0.001).
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In the best-supported model (ΔAICc < 2), PC1 (bamboo structure) and PC2 (tree structure) exerted highly significant positive effects on habitat utilization (PC1: β = 4.023, p = 0.001; PC2: β = 3.321, p = 0.002).
The mean age difference between SA determined using G-P method (from American upper class population) and the CA of this South Indian population was 0.02 years for girls, which was not significant ( P > 0.05) and 0.23 years for males and was found highly significant ( P < 0.001).
3.4 Population genomic analyses For each RAD dataset, all pairwise comparisons of genetic differentiation ( F ST ) between clusters identified in STRUCTURE and AWCLUST were highly significant ( p ‐value <0.001).
The differences between the SARIFA-positive invasion front adipocyte areas were highly significant ( p < 0.001) (Fig. 5 ).
The constant term (intercept) has a coefficient of 226.788, with a standard error of 13.364 and a highly significant t-value of 16.969 ( p < 0.001), indicating that it contributes significantly to the model.
The primary marker of kidney health, ACR, demonstrated a highly significant reduction from 108.44 to 65.79 ( P < 0.001).
The dataset was highly suitable for dimension reduction, as indicated by a Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy of 0.860 and a highly significant Bartlett’s test of sphericity ( P < 0.001).
Three p -value ranges were used to determine the differences between the values: p < 0.05 (statistically significant), p < 0.01 (highly significant) and p < 0.001 (very highly significant).
After cleaning the phone surfaces with a 70% isopropanol solution, the baseline contamination rate of 90.4% was reduced to 14.0%, showing highly significant differences ( p < 0.001).
This interaction was not significant in Block 1 and 2, p values ≥ 0.543, became highly significant in Block 3 and 4, p values < 0.001, but was no longer significant in Block 5, χ 2 (1) = 1.878, p = 0.171.
The main effect of the grouping factor was statistically significant ( F = 5.667, p < 0.05), and the main effect of the time factor was highly significant ( F = 67.998, p < 0.001).
PLD is highly significant ( p < 0.001) for dispersal in the model.
Highly significant ( P < 0.001) positive correlations were detected between LCBD and a complement of physicochemical variables, including potassium ( r = 0.53), TN ( r = 0.50), DIC ( r = 0.45), TP ( r = 0.44), dissolved organic carbon (DOC; r = 0.42), magnesium ( r = 0.40), sodium ( r = 0.38), sulfate ( r = 0.37), chlorophyll a ( r = 0.33), lake colour ( r = 0.22), and pH ( r = 0.22).
We found highly significant V G and V E ( P -value <0.001) for almost all the traits ( Fig. 2B ; Supplementary Table S6 ).
There was a highly significant correlation ( r 2 0·87; P < 0·001) between the experimentally determined faecal Ca excretion and the faecal Ca excretion predicted by the equation of Kienzle et al .
Similar associations were also detected upon fitting the model with biological age acceleration categories from quartile 1 to quartile 4, yielding a significant trend ( P < 0.001).
Results IL-33 expression showed a positive trend among controls (1/26 = 3.8%), aMCI (9/35 = 25.7%), and AD (17/39 = 43.6%) (trend analysis: P < 0.001).
Differences between absolute moral values and nonmoral values were again highly significant across tradeoff modalities ( p < 0.001, see Table S3 ).
Statistical analysis using Pearson Chi-Square tests revealed a highly significant association between p16INK4A expression and severity levels in the negative group (p<0.001), suggesting varying expression patterns across severity levels.
This trend reached statistical significance only when comparing the earliest implantation group ( ≤ 1 year) with the group implanted between 3 and 8 years ( p < 0.04) and was highly significant when compared to the latest implantation group (14–43 years; p < 0.001).
genus) is a highly significant predictor (F (4,38) = 9.01, p < 0.001) of maxilla shape, accounting for 45.2% of the shape variation.
To identify CpGs with highly significant tumor subtype-specific changes, stringent cut-offs ( P < 0.001 and Δb >0.20) were set for methylation-level changes relative to controls for each tumor subtype.
Statistical analysis confirmed that this reduction was highly significant [ t = 14.85, df = 199, paired t -test, P < 0.001; Table 1 ].
There was a highly significant correlation between MOS and PSNR (r = 0.69, P < 0.001).