“highly significant”

Each Spearman correlation was highly significant ( p < 0.0001), except for the relationships of sleep with pain ( p = 0.0008), with general activity ( p = 0.0004), with walking ability ( p = 0.0068), and with relations with others ( p = 0.0029).

There was also a highly significant correlation between the two methods in the hourly captures over the 12-h collection period (Pearson's r = 0.83; p < 0.0001).

However, over the course of 6 months post-second dose, 111/113 (98.5%) LTCH staff exhibited highly significant decays ( P < 0.0001) in anti-spike and anti-RBD IgG antibody levels with approximate half-lives of 63 (31–73) days and 57 (28–71) days, respectively, ( Fig. 5A through F ).

This response rate to peptide 4 was highly significant, with p < 0.0001.

The results indicate that comparisons evidenced highly significant correlations between values computed by the lumped model and simulated mechanical model for both networks with p < 0.0001.

K S and the block size show a strong negative correlation, and Pearson's correlation coefficient is r = −0.288, which is highly significant ( p <0.0001, permutation test).

The difference between the two cell types was highly significant (P < 0.0001) (Fig. 5 c).

The frequency of G-allele of TNF-α was present in an increased number in control (94%) than in CCS (60%), which was highly significant ( p < 0.0001).

This difference was highly significant compared to all other cancer types and defined as a bona fide hotspot for CLL ( p < 0.0001, chi-square test).

Our findings uncovered a highly significant effect of MS ( p < 0.0001).

In all areas that were analyzed (CA1, CA3, hilus, and dentate gyrus), there was a highly significant main effect for group ( p < 0.0001 for all areas) but not for treatment (CA3: p = 0.2708; CA1: p = 0.6038; dentate gyrus: p = 0.6610; hilus: p = 0.8269).

Cadmium The Kruskal–Wallis test revealed highly significant differences (Kruskal–Wallis χ 2 = 27.00, p < 0.0001) between medians.

Substitution saturation is marginally significant for the SKL1 family (p = 0.07) and highly significant for the SK family (p<0.0001).

Similarly, the percentages of antigen-experienced CD4 + T cells subsets that could be identified as specific CD4 T cell subsets in AIT-treated asthmatic mice showed highly significant increases in T H 1 ( p < 0.0001) and T H 17 ( p < 0.01) cells, while decreases were noted for antigen-experienced CD4 + T H 2 ( p < 0.0001) and Treg cells ( p < 0.0001) compared to the asthmatic mice ( Figure S5-c ).

When the same cohort was stratified by CAB, the results showed a higher difference in DMFS (10.84%) between the low‐ and high‐risk categories with a highly significant P ‐value of <0.0001 (Figure 2 A) suggesting that CAB is a better prognostic predictor in comparison to node status.

Moreover, a highly significant correlation was observed (r = 0.669, p < 0.0001) between pure tone average (PTA) thresholds at implant activation and those at the last follow-up, as analyzed using Pearson’s correlation coefficient.

Allelic association tests yielded 93 highly significant SNPs (p < 10 -4 , corrected for permutation tests).

The multivariate logistic regression model was highly significant ( p < 0.0001, R2 = 0.54) and showed that all these factors were independent in predicting mortality of sepsis.

In the vaccine arm, the IFN-γ secreting T cells increased in 14/15 pts and in case of pt #013 they were already high in the PRE sample and remained unchanged in the POST1 sample The difference between IFN-γ secreting T cells in PRE and POST1 samples was statistically highly significant ( p = 0.0001).

When the two time curves were compared over the period 0–16 h, differences determined by two-way ANOVA were highly significant (p<0.0001); Figure 2B shows the significances of the differences in cell death determined after 2 h of observation using multiple comparison two-way ANOVA (p<0.0001: control/Vac; p<0.0001: Vac/Vac+ATP).

These effects were highly significant after adjustment for multiple comparisons (p<0.0001, Table S2 ).

The Pearson correlation between the floristic ordination and the constrained ordination was 0.893 on Axis 1 and 0.786 on Axis 2; both the correlations and the size of the eigenvalues were highly significant (p < 0.0001 by Monte Carlo permutation tests).

Variance analyses showed that the effects of population, photoperiod treatment and population × photoperiod treatment interaction on days to bolting, days to flowering, rosette leaf number, and flower stem length were highly significant ( P ≤ 0.0001; Supplementary Table S2 ).

The difference with control cells was highly significant (one-tailed Fisher’s exact test, p<0.0001).

In many tissues the PHI test for recombination was highly significant ( p <0.0001).