“highly significant”

These distinctions are highly significant ( P = 7.07 × 10 −17 between TN and FN + FP, P = 2.07 × 10 −19 between TP and FN + FP).

All NB histopathology types (64 out of 64) were correctly assigned according to the 7-GeneSig, and the inter-rate reliability of assignments was highly significant (Kappa measure of agreement p = 7.489E-17, Table 2 ).

The increased dynamic range was highly significant across units (paired t test, P = 7.5e-17, t = -8.6, df = 442).

For each overlap, the hypergeometric test was applied, and all overlaps were found to be highly significant ( Supplemental Figure 7B ; hypergeometric test P values from proximal to distal bins, DMRs: P ≈ 7.7 × 10 –17 , P ≈ 2.5 × 10 –33 , P ≈ 1.8 × 10 –16 , P ≈ 5.1 × 10 –13 ; DARs: P ≈ 4.1 × 10 –47 , P ≈ 1.3 × 10 –64 , P ≈ 1.2 × 10 –33 , P ≈ 7.9 × 10 –36 ).

The results for a panel of the 12 genes were in good agreement with the LongSAGE data (Table 2 ) and there was a highly significant correlation (r = 0.79, p = 8.52E-17) between the two techniques.

By contrast, the global expression pattern showed a highly significant association with post conception age (Kruskal Wallis test, p = 8.592 ×10 -17 , Fig. 1b ) demonstrating a more prominent contribution of the developmental stage to the observed changes in gene expression than differences attributed to regional variations.

The respective amino acid sequence (ERKA) was found to be highly significant and positively associated with IA-2A positivity (OR 2.15, P = 8.83 * 10 −17 ).

We also observed in the ventricle a spatial, very highly significant overlap for regions where both epigenetic marks decreased ( p < 9x10 −17 ).

Changes in mean activation magnitude in both regions between unscaled and scaled t-values are of the order of 10% of unscaled activation magnitude and are highly significant (a t-test over all voxels yields a p-value of p < 10 − 16 ).

The correlation between the distance of GO groups in the 0.001 cutoff co-evolution network (that is, their evolutionary distance) and their distance in the corresponding GO ontology network (that is, their functional distance) is highly significant: 0.38 for cellular component, 0.16 for biological process and 0.43 for molecular function (all three with P -values <10 -16 ; a similar trend is observed using the 0.01 cutoff network).

STRING analysis reveals a highly significant clustering ( p = 1 × 10 −16 ) of over 90% of the proteins identified ( Figure 7 ), with the existence of sub-clusters related to ER protein quality control, mitochondria membrane protein translocation, nuclear import, and stress granules.

Moreover, the Pearson correlation between association strength of shared SNPs across the two tissues was highly significant ( P <10 −16 ), highlighting the conservative genetic architecture of C18∶1, C18∶3, C20∶0 and C20∶1 in the two tissues.

Protein-protein interaction (PPI) analysis using STRING ( Szklarczyk et al., 2019 ) revealed that the hub proteins, including MSN, EZR, C1qa, a key component of the complement cascade, and GFAP, belong to a PPI network with highly significant interactions (p-value = 1.0e-16) ( Figure 4 B).

The expected number of edges is 393 and has a PPI enrichment p -value was highly significant ( p < 10 −16 ).

This detrimental effect of antibiotics was highly significant among the phyla Bacteroidota and Firmicutes_A (p < 1e–16, OR 0.7) and observed at various host ages ( Figure S2 E).

DAPs Are Involved in Wound Repair and Blood Coagulation Are Associated with Hydration Protein–protein interaction analyses of DAPs using STRING [ 19 ] resulted in a highly significant clustering (PPI enrichment value of p < 1.0 × 10 −16 ) consisting of 34 nodes and 126 edges ( Figure 2 ).

Using STRING analyses, we identified a highly significant protein–protein interaction (PPI) enrichment ( P = 10 −16 ) only for the downregulated genes of H2A.Z.1‐depleted cells.

These results show that all four topological measures provide highly significant ( p -value < 1e-16) enrichment in pathogen targets, with betweenness and clustering coefficient displaying highest enrichment, thereby demonstrating the added value of incorporating PPI data into inferred networks for a generalizable approach to identify target regulatory nodes within networks.

The differences between benthic and pelagic species in both measures were highly significant (P ~ 10 -16 ).

PPI enrichment analysis was highly significant (P < 1.0e-16; Fig 5B ), indicating that the observed interaction density was greater than expected by chance and suggesting coordinated involvement of these genes in bladder cancer–related pathways.

The C > A mutations observed were associated with highly significant sequence-selectivity, being marked by an excess at CpCpT (NGCII082, odds ratio (OR) = 3.2, P -value < 10 -16 , χ 2 test) or TpCpA sites (NGCII092, OR = 1.7, P -value < 10 -16 , χ 2 test) and extensions of these motifs (Materials and methods; Note 6 and Figure S6 in Additional file 1 and Table S14 in Additional file 6 ).

The difference between the two distributions was highly significant ( p < 10 −16 , two-sided Wilcoxon rank-sum test).

The interaction network between identified proteins in the osteocyte secretome reveals a highly significant degree of protein‐protein interaction ( P < 10 ‐16 ) as illustrated in Figure S1 .

By comparing the joint mt / apico haplotype frequencies ( Supplementary Table 3 ), we found that the dependence between mt and apico was highly significant ( χ 2 =64,921, d.f.=39,566, P <10 −16 ), providing strong evidence of co-inheritance of the two organelles.

This was again underpinned by the ANOVA analysis, reporting a highly significant influence of the flyer ( p < 10 − 16 ), but only a weak influence of the base ( p ≈ 0.086 ).