Excerpts

Omitting the aberrant plot pair one, there was a highly significant negative effect of the icing treatment on flowering in 2014 (Wald statistic = 35.6, P < 0.001).

However, their combined application was highly significant ( P = 0.001) .

11 Further, pooled analysis of the TNT and IDEAL trials showed a trend for decreased CV event risk with lowering of triglycerides ( P < 0.001), although this was attenuated by adjustment for HDL-C and the ratio of apo B/apo A–I. 104 In T2DM patients, the FIELD and ACCORD Lipid studies showed that marked atherogenic dyslipidaemia (triglycerides ≥2.3 mmol/L or 204 mg/dL

The 1.0 μM roxarsone plus siVEGF or siFlk1 groups had clearly significantly decreased EC migration compared to the 1.0 μM roxarsone group ( P < 0.001).

Reported pain was frequent across the years, affecting 70% of patients with an increasing trend ( p < 0.001).

The regression coefficients were highly significant for samples A1, A3 and A5 ( P < 0.001), with R2 values of 0.982, 0.959, and 0.905, respectively.

PHMB, ET and BA decreased viability by 47.7%, 46% and 41.3% on average, also showing a highly significant difference from the control ( p < 0.001).

Both Wilk’s Λ and Pillai’s trace were highly significant ( p < 0.001) for each of the single IVs (treatment, exposure time and cultivar), for the two-way interactions (treatment ∗ exposure time, treatment ∗ cultivar, exposure time ∗ cultivar) and the three-way interaction treatment ∗ exposure time ∗ cultivar.

For the High Self-Regulators profile, IA was a strong and highly significant predictor (OR = 5.948, 95% CI [3.152, 11.235], p < 0.001), indicating that each one-unit increase in IA was associated with nearly a six-fold increase in the odds of belonging to the High Self-Regulators profile relative to the Moderate Self-Regulators profile.

There is a highly significant ( p < 0.001) change reported from pre-training to post-training across the capacities.

A highly significant change was seen in the levels of motivation toward contemplation and action stage after 2 weeks of inpatient stay ( Z = 5.745, P < 0.001).

The results indicated a positive trend in the AAPCs for all three indices, with the ASIR (AAPC = 63.4, 95% CI: 62.1–64.6%, p < 0.001), ASMR (AAPC = 74.5, 95% CI: 60.3–88.6%, p < 0.001), and ASDR (AAPC = 32.2, 95% CI: 26.2–38.3%, p < 0.001) indices demonstrating increasing trends ( Tables 5 , 6 ).

Although some of these fixed effects were quite significant ( P < 0.001), their use here was primarily to control for unobserved spatial variations.

Sperm morphology The percentage of normal sperm morphology in animals treated with DXM alone also showed a highly significant difference (p<0.001) when compared to the control group.

Milk Yield and Composition—Milk microbiology There were highly significant differences ( p < 0.001) in milk yield at the farm and animal level and also in the gross composition of milk during the milk collection period ( Table 3 and Figure 4 ).

However, correlation between the sNfL levels and the final I-RODS ( r = −0.36; p = 0.1) did not reach statistical significance. sNfL levels correlated with NF155 titers at baseline (n = 36; r = 0.43, p = 0.001) and at every time point available (n = 105; r = 0.34, p < 0.001).

The difference was highly significant ( p < 0.001) using the one-way ANOVA test ( Table 3B ).

Similarly, when greater numbers of whitefly (80) were used, a highly significant preference ( p < 0.001) for the commercial tomato species was observed (Fig. 3a ).

A Kruskal-Wallis test evaluating the effect of age group on IC volume showed a highly significant effect of the age-group on the mean IC-volume (p<0.001).

Repeated-measures ANOVA demonstrated a highly significant change in Cobb angle over time ( F = 101.18, p < 0.001) with a very large effect size (η 2 = 0.87), indicating that time, i.e., the course of brace treatment accounted for 87% of the variance in Cobb angle.

The correlations between depression and anxiety (0.75), depression and stress (0.83), and anxiety and stress (0.81) all indicate very strong positive correlations, with all tests being highly significant ( P < 0.001).

Statistical analysis via ANOVA testing main effects of TBI status (RMBI, TBI, Control), memory status (Impaired, Normal) and the interaction of TBI and memory yielded a highly significant overall effect [ F (5,180) = 10.70, p < 0.001], the main effect of memory status [ F (1,180) = 24.11, p < 0.001] and the interaction of TBI*memory [ F (2,180) = 13.57, p < 0.001], but non-significant main effects of TBI [ F (2,180) = 1.12, p > 0.3].

The variable ‘Do you have a physical health condition or disability that limits you, and that has lasted for 6 months or more?’ showed a highly significant relationship with PCS ( p = 0.001).

When the data were disaggregated by the window, there was a moderately significant correlation between windows 1 and 2 ( r = 0.383, p < 0.001).

Furthermore, highly significant changes were observed in the scores of images compared to those of videos and text messages ( P < 0.001) at T2.