“highly significant”

The correlations between K xgI and 1/HOMA-IR and between K xgI and HOMA2 were positive and highly significant both in the whole sample (r = 0.565, P < 0.001 and r = 0.581, P < 0.001 respectively) and in the reduced sub-sample (r = 0.572, P < 0.001 and r = 0.558, P < 0.001 and respectively).

While in the latter these excess costs are still highly significant (€ 2,406.33, p <.001), they are largely reduced in those with lower SES (€ 538.62, p =.170; see also Figure 1.II.(b) (Fig. 1) ).

Data were considered statistically significant at P value ≤0.01 and highly significant at P < 0.001.

When adverse events were evaluated for the worst toxicity encountered during treatment, there was little difference between the groups, but when they were assessed per cycle, alternating treatment was associated with fewer severe (grade 3 or 4) adverse effects ( P = 0.013), and the difference was highly significant for cycles when gemcitabine was administered in group D/G ( P < 0.001).

Regression analysis showed a highly significant relationship between the two methods ( R 2 = 0.78, P < .001).

This between-species difference is highly significant (p<0.001; t-test; N = 8, ambient temperature: 22–24°C).

Nine of the loci showed significant effects in this independent data set (p < 0.05), five of which were highly significant (p < 0.001).

Specifically, the DOI x CRF x M100907 interaction was highly significant, F(1,41) = 10.78, 15.04, p < 0.001, and the follow up tests confirmed that neither CRF nor DOI alone affected the frequency of open arm entries.

The correlation between the amount of foam applied and time until hands felt dry was highly significant (p < 0.001; Pearson's correlation coefficient: 0.724; 95% confidence interval: 0.52-0.93).

Presentation time again increased linearly with the number of targets that needed to be encoded into WM in both the pop-out and non pop-out conditions (linear fits were R 2 = .976 for pop-out and R 2 = .987 for non pop-out); these changes were highly significant, F (4, 36) = 66.6, p < .001, η2 = .88 (see Figure 7a ).

The main factor spatial scrambling revealed a highly significant effect, F (1, 3) = 310.1, p < .001, i.e., mean recognition rates for spatially normal stimuli were higher than for spatially scrambled stimuli (98.4% and 50.8%, respectively).

Bartlett’s test of sphericity was highly significant (χ 2 = 514.4, df = 210, p < .001), indicating some relationships existed between variables, which makes the correlation matrix suitable for factor analysis.

This difference was highly significant at p<0.001 (1-way analysis of variance [ANOVA], F = 38.824, done on logarithmic RNA concentrations).

For the combined data from both cohorts, the correlation ( r = 0.27) was highly significant ( p < 0.001).

Differences between forests in the duration of suppressions and the percentage of time in suppressions were highly significant (Mann-Whitney tests, P < 0.001).

stercoralis infections was 32.9% in Bandamaji and 12.8% in Dole, which revealed a highly significant difference (χ 2 = 15.3, p<0.001).

The difference between the ap-Gal4 and ap-Gal4/+; UAS-myc-fry-GFP flies was highly significant (p < 0.001, Mann-Whitney Rank Sum test).

There was a highly significant elevation of sICAM-1, hsCRP and sE-selectin (p < 0.001) in both the infected group, and culture positive subgroup, compared with the not infected.

In all HIFs except HIF332, the comparison of plants that were fixed for each parental allele (Col or Bur) at the QTL region revealed a highly significant difference in shoot growth ( P <0.001).

The effect of the MPEP injections in nicotine-treated rats on responding on the active lever was more highly significant than that measured in the saline-treated rats (nicotine × MPEP F (2,20) = 10.3, P < 0.001).

1B ), using the non-parametric Mann-Whitney test: in both cases the difference is highly significant ( P<0.001 ).

This change was highly significant ( P < 0.001; Table 2 ).

glaucoma severity and BCVA grades was found to be highly significant by Kendall's tau-b test ( P < 0.001).

The mean age of the alcohol users was 35.16 ± 10.2 compared to the opioid users, whose mean age was 26.09 ± 5.65, and this difference was found to be, statistically, highly significant ( P < 0.001).

From Table 2 it can be seen that, the maximum number of girls, that is, 237 out of 698 girls (33.95%) experienced dysmenorrhea every month, and 118 (16.90%) experienced it in most of the months, and it was statistically highly significant ( P <0.001).