“highly significant”

A one-way ANOVA confirmed that the differences among residue classes are highly significant ( F (3, 14,983) = 682.6, p < 2.2 × 10 –16 ), supporting the hypothesis that CDR loops correspond to weakly coupled, energetically uncoupled regions when compared to the structural core of nanobodies.

Pearson’s Chi-square test was used to analyze whether certain isolated sources had a higher proportion of a specific lineage, revealing highly significant differences ( P < 2.2 × 10 −16 ).

The results of the statistical linear regression between RSSI and the logarithm of the node-gateway distance (logD) are robust and highly significant (R2 = 0.808, p -value < 2.2 × 10 −16 , residual standard error = 10.68 dBm).

Given that we observe 117 mutations in hypomethylated CGIs, but only expect 50.0 (117*(1/2.34*)) with the genome-wide autosomal mutation rate, this represents a highly significant enrichment (Poisson test, p = 2.2 × 10 −16 ).

Although correlation is less than observed for the H3K4me3 replicates, many H3K4me3-positive loci are bound by TBP and the correlation is highly significant (r = 0.40, p<2.2 * 10 -16 ).

Analysis of variance showed that the genotypic (G) and the year (Y) effects for fruit weight were highly significant for each population ( P values < 2.2e-16).

Transcripts that lose m6A during differentiation show highly significant increase in TE in glioblastoma samples irrespective of subtype (GSC1: p<2.2e-16, GSC2: p<3.4e-11, GSC3: p<2.2e-16) (Fig B in S1 Text ).

andersonii (tested via independent samples Wilcoxon rank-sum test), while its F is value was also significantly lower ( P < 0.05).Furthermore, verification of Rarefied Allelic Richness (Ar) via the Kruskal-Wallis rank sum test revealed a highly significant difference between the two species (chi-squared = 1770.3, df = 1, P < 2.2e-16), confirming that the genetic diversity of B. andersonii is robustly higher than that of B. tianguii even after standardizing sample sizes ( Figure 3 ). 3.3.

Furthermore, we saw a slight but highly significant correlation (Pearson R 2 = 0.308452; P <2.2e -16 ) between a transcript sequence’s similarity to rRNA and the magnitude of the difference in coverage between the no selection and rRNA-depleted libraries (Additional file 11 : Figure S9 and Additional file 12 ).

We observed a highly significant effect of humidity (GLMM: p < 2.2e−16, x 2 = 266.93, df = 4; Table 2 B; Fig. 3 ) on T p of adult flies.

Almost half of these genes (47%, 1,633/3,450) are present within our set of single-copy orthologs, which represents a highly significant enrichment (Fisher’s exact test P < 2.2 × 10 − 16 ).

We detected a highly significant positive correlation between F ST and absolute values of α (HV: r = 0.24, p < 2.2e-16; MR: r = 0.24, p < 2.2e-16; Figure 3 ), indicative of relationships between genetic differentiation, divergent selection, and reproductive isolation.

We found a highly significant positive correlation of number of hits per target site with motif score (Spearman's correlation, ρ = 0.154; P < 2.2 × 10 −16 ) and weak positive correlation with palindrome score (Spearman's correlation, ρ = 0.029; P = 0.003).

In addition, 82.5% of DMR-associated transposons are annotated as containing a transposable element gene, a highly significant enrichment compared with all annotated transposons (Fisher’s exact test, P <2.2e−16).

Our results demonstrate that subtype indicators are highly significant in predicting survival ( p -value < 2.2 × 10 −16 ).

Cross‐correlation analysis revealed a highly significant relationship between the number of D calls from the manual validation and automatic classifier (Pearson's correlation coefficient = .991, p < 2.2 × 10 −16 ; Figure A1 ).

The power of per-gene testing was constrained by sample size, but results were highly significant (Fisher Exact p-value < 2.2e-16) when variants were combined across all genes [Table S8].

Additionally, a Kruskal-Wallis test revealed a highly significant difference in ANI across the three comparison types (χ2 = 6,332.9, df = 2, P < 2.2e – 16).

As expected, we observed a highly significant difference between RefSeq mRNAs (NM) and RefSeq non-coding RNAs (NR) for both the CPC and the RNA-code score (CPC t = 56.4326 [2.239631;2.400904], p-value<2.2×10 −16 , RNAcode t = 39.6171 [34.44711;38.03595], p-value<2.2×10 16 ).

The differences in TSSL and SYL values between the OsSYL3 AA and OsSYL3 CC genotypes were highly significant (Welch’s t ‐test, P = 2.20 × 10 –16 ) (Figure 2d ).

Pearson correlation analysis of these whole‐sample average expression profiles revealed highly significant correlations between replicates (Peach: r = 0.94, p < 2.2e‐16; Nectarine: r = 0.91, p < 2.2e‐16), with LOESS regression confirming the linear relationship (Figure S15A ).

For all three species comparisons, a linear regression of %ID and log 2 hybridization ratio showed a strong and highly significant correlation ( D. sechellia : Multiple R 2 = 0.3257, P < 2.2e-16; D. simulans : Multiple R 2 = 0.2920, P < 2.2e-16; D. yakuba : Multiple R 2 = 0.4083, P < 2.2e-16) (Figure 2 ), with the data for D. yakuba showing the strongest correlation.

Analysis of variance based on the reparametrized polynomial-interaction model with MPR effects showed a highly significant impact of partial yields on test-day yields ( P <2.2e-16).

The Spearman’s rank correlation coefficient of 0.74 between all d(fl, endo, g) and d(com, endo, g) scores is highly significant ( P = 2.2e −16 ), reflecting resemblance in genome size and signal peptide content of free-living bacteria and commensals.

Besides mean values, the paired t tests of the IASs were highly significant between IASs from different SDs ( p < 2.2 × 10 − 16 ).