Excerpts

Highly significant differences in the levels of composite biomarkers were found between AD groups and normal controls ( p < 0.000001, one-way ANOVA), whereas differences in the levels of a single biomarker were only significant at the level of 0.001 (Fig. 4a–f and Supplementary Fig. 28 ).

The Friedman test yields highly significant P -values (F-measure: P = 1.009 × 1 0 − 6 , Accuracy: P = 6.852 × 1 0 − 8 , and F1 + ACC: P = 1.713 × 1 0 − 7 ), confirming statistically significant differences among the algorithms.

In all these analyses, we observed highly significant networks (p < 1.01E-06, p < 3.6E-14, p < 0.000975, respectively).

VitD-dependent DE genes in Teff cells from B6 females exhibited highly significant enrichment ( p = 1.02e−6) within genes downregulated in MS-CIS CD4 cells, while marginal or no enrichment was observed for genes that were upregulated in MS-CIS (Figure 3 E).

3 , the effects of this region were also very close to significance for the 0/1/2 phenotype in the same breed (−log 10 ( P ) = 5.99).

These correlations were strong and highly significant (r s >0.5, p-value <1.05E-06).

The difference in occurrence of the 295C allele in probands with triphalangeal thumb (3/5), compared with controls (0/381), was highly significant ( P = 1.05 × 10 −6 , Fisher's exact test).

Image-based HSPC fractions showed highly significant concordance with the clinically determined peripheral blood (PB) blast percentages ( p < 1.06 −6 ; Supplementary Fig. 2d, e ).

Similarly, 25% MCL showed a highly significant increase compared to 10% MCL (mean difference = 13.579, p = 1.07 × 10−6).

The test showed a highly significant difference in median slopes ( H = 27.50, P = 1.07 × 10 −6 ; Extended Data Fig. 1 ), indicating that the slope populations of the three elevation zones are statistically distinct.

RESULTS AND CONCLUSION This preliminary study showed a highly significant association of DRB1*08:02 with BI-Pro ( P = 1.09 × 10 −6 , corrected P [ Pc ] = 1.96 × 10 −5 , odds ratio [OR] 25.17, 95% confidence interval [CI] 7.98–79.38).

Linear mixed modelling revealed a highly significant effect of PTT on the DPOAE level (r = −0.82, p = 1∙10 −6 ), but no effect of frequency (r = −0.15, p = 0.90).

50%; one-sample t-test for Self retrieval: p = 10 −6 ; one-sample t-test for Semantic retrieval: p = 3.10 −5 ), and the paired t-test between the two conditions revealed a highly significant difference (self reference > semantic; p = 1.10 −6 ).

The quadratic model was a highly significant fit (P = 1.1×10 −6 ).

Conditional analyses on rs17366743 and rs6773957 yielded highly significant results for rs17366653 ( P < 1.1E−06 in GEMS and P < 2.0E−09 in CoLaus), indicating its effect is independent of the other two significant associations in the gene.

This ratio is highly significant ( P =1.1×10 −6 ) ( Figure 2 ).

Given that the statistical power in the top1 datasets was relatively low due to the small sample size (4017 individuals), we investigated the p-values of the two eGFRcrecys-specific signals, and found that one (rs9532933 on chromosome 13) was borderline significant for the other two traits ( P = 1.1 × 10 −6 for creatinine and 3.0 × 10 −7 for cystatin C, Table S7 ), while the other (rs116542696 on chromosome 20) was borderline significant only for cystatin C ( P = 0.20 for creatinine and 1.3 × 10 −7 for cystatin C, Fig. 5d–f , Table S7 ), suggesting that these signals are likely not eGFRcrecys-specific, but shared with at least one for the two biomarkers.

Criteria such as high levels of missing data (SNP call rate < 98%), highly significant departures from Hardy-Weinberg equilibrium ( P < 1.10–6), or low Minor Allele Frequencies (MAF < 1%) were used to determine which SNPs to include in the imputation step.

As we anticipated, Geminin staining was strongest in the region closest to the surface, and we found a strong and highly significant correlation between these results and those obtained from SPoRTS analysis of rFUCCI-expressing spheroids (Pearson’s r 2 adj = 0.9979, p = 1.1e−6; Figure 5 D).

However, the interaction between the tile dandelion density and the average dandelion density was highly significant ( p = 1.12e‐06), indicating that the effect of the local dandelion density strongly depends on the average dandelion density at the quadrat level (Figure 4b ).

The top association signal for rs12671707 remained nominally significant in the 90 non-European panelists (effect size ± standard error = 28.48 ± 5.38; p-value = 1.14 × 10 − 6 ).

This displays an increasing trend of trisomy with increasing maternal age, with Cochran-Armitage trend test results of z = 4.8607 and p -value = 1.17e-06.

As shown in Figure 5c , Kaplan–Meier curves showed a highly significant difference in relapse-free survival between the two groups (P value = 1.17e − 06).

For older human subjects, the average glycogen content was 3.5-fold higher and was highly significant (one-sided t-test, p = 1.2 × 10 −6 ) ( Figure 3 a).

For older human subjects, the ratio between the signals from PAS stained regions and cell areas showed that the average glycogen content was 3.5 times higher and this difference was statistically highly significant (one-sided t -test, p = 1.2 × 10 –6 ).