“highly significant”

The read accuracy improvement of using ORP over single reads, revealed as a gap between their respective error profiles, was most prominent at Q10 and diminished with higher Q-score filtering; the advantage at Q30 was small yet still highly significant (two-sample Kolmogorov-Smirnov test p = 5×10 -36 and 2×10 -11 for rabies and BCV control, respectively), as any reduction in error rate can be used to detect viral variants with greater sensitivity.

CHUP1 Candidate Gene for bgm-1 The GWAS analysis of CIAT’s 412 breeding lines detected a highly significant interval on Pv03: 2.43–2.54 Mbp with a peak SNP S03_2524224 [−log10( P -value) = 35.23] ( Table 3 and Supplementary Table 3A ).

The correlation of IVW analysis is highly significant (β = −0.16, P = 5.91 × 10 −36 ).

These contrasting strategies are highly significant ( P < <0.001, Welch’s t- test, t = 54.6, P = 6 × 10 −36 ; n = 18 for D-lactate, n = 20 for L-lactate) and likely alter competitive dynamics in lactate-limited systems.

Our analysis revealed a highly significant correlation between the 12-protein panel and the classical AD biomarkers (297_CSF: positive correlation: Aβ 42 , R = 0.31, P = 7.5e-0.8; Aβ 42/tau , R = 0.64, P = 6.6e-36; negative correlation: p-tau, R =-0.62, P = 7.5e-33; t-tau, R =-0.76, P = 2.4e-57; Fig. 3 A).

We observed a marked elevation of EP300 protein abundance in HCC tissues compared to adjacent normal liver tissues, with a highly significant difference ( p ≈ 7.60 × 10^−36, Fig. 1 D).

This enrichment is highly significant with a P < 7.98E-36 using the hypergeometric test.

The t-test shows that the difference between DRNF and HHpred is statistically highly significant (P-value = 8.2E-36), so is the difference between DRNF and CNFpred (P-value = 8.5E-15).

IPA analysis also identifies, in both brain areas, several highly significant upstream regulators; that is, the transcription factors, Irf3 (F/M: p < 8.5E-36, z = 5.2; Cb: p < 6.6E-17, z = 4.7); Irf7 (F/M: p < 1.1E-28, z = 4.6; Cb: p < 5.7E-15, z = 4.1); the interleukin Ifng (interferon gamma; F/M: p < 9.9E-33, z = 6.3; Cb: p < 1.5E-26, z = 7.1); and interferon alpha/beta receptors ( Ifnar1 and Ifnar2 ; F/M: p < 1.4E-44, z = 6.2; Cb: p < 1.5E-25, z = 6.2).

rs13038305 in relation to plasma cystatin C and creatinine based measures of GFR In the MDC-CC, each copy of the major allele of rs13038305 was associated with a highly significant (P<1 x 10 −35 ) approximately one third of a SD increase of plasma concentration of cystatin C both in model 1 and model 2.

Of the top 100 proteins highest in the s11 subtype, 58 were among the top 10% brain tissue-specific genes, a highly significant overlap ( p < 1E−35, one-sided Fisher’s exact test).

The overlap of the downregulated genes in strains overexpressing the D303A or D346A variants compared to those in the strain overexpressing wild-type RNase E was statistically highly significant ( P < 10 –35 and < 10 –46 , respectively, hypergeometric test).

The results showed pathways involved in repairing damaged nucleotides/DNA as highly significant in determining alkylating agent sensitivity: the Fanconi anemia pathway, ( p = 1.04 × 10 −35 ), homologous recombination (2.00 × 10 −24 ), nucleotide excision repair (2.77 × 10 −9 ), and non-homologous end-joining (1.04 × 10 −7 ) ( Figure 6 ). 3.

Of note, in agreement with the known relationship between ΔAUC CP /ΔAUC GLU and GLUSENS in the general population, in our population of renal transplant patients, we also observed a quite high correlation between the two, as mirrored by the highly significant p -value ( p = 2.22 × 10 −35 ).

The average methylation level of the 4 hyper-methylated genes showed highly significant difference between breast tumor and matched normal tissues ( P = 3.54E-35) (Figure 1 ).

The different response to hc‐IEDs for sample entropy and delta power was highly significant ( p = 6.3 × 10 −35 , Wilcoxon rank‐sum test).

This may be the source of the highly significant association signal we observed in our dataset for all three genetic models (DOM: P = 9.33×10 −35 ; ADD: P = 7.48×10 −30 ; REC: P = 5.27×10 −6 ).

A transcriptional network was subsequently identified (Cell Morphology, Cellular Assembly and Organization, Nervous System Development and Function) which comprised 35 eV-enriched mRNA transcripts and was highly significant (p = 10 –34 ) (Fig. 5 and Table 4 ).

However, a single SNP on chromosome 9 (at position 20,550,439) was noticeable for its strong and highly significant effect (P = 10 −34 ).

Although the performance gain was quite small when DNA shape was already included in the model, it was highly significant ( P < 1.549 × 10 −34 for sequence+MGW+EP versus sequence+MGW models; P < 4.569 × 10 −41 for sequence+shape+EP versus sequence+shape models; P < 6.748 × 10 −4 for sequence+3shapes+EP versus sequence+shape).

Our analysis revealed a highly significant ( P = 1.67e–34) overlap between AS events regulated in si Ewsr1 -N2A cells and those differentially spliced upon neuron differentiation in vivo , with 49 events in common between the two datasets (Figure 6B ).

There was highly significant non-independence ( P-value = 1.8 × 10 −34 ) between the two predictions, driven primarily by the large proportion (66%) of SNPs predicted to be benign by PolyPhen and tolerant by SIFT.

In the liver, six highly significant modules were identified ( Figure 4 A–D and Figure S6 ): MEbrown (cor = 0.85, p = 2.3 × 10 −34 ; transcripts no. = 119), MEblack (cor = 0.83, p = 1 × 10 −200 ; transcripts no. = 925), MEcyan (cor = 0.54, p = 0.0012; transcripts no. = 33), MEsalmon (cor = 0.63, p = 8.5 × 10 −05 ; transcripts no. = 33), MEgreen (cor = 0.68, p = 3.1 × 10 −22 ; transcripts no. = 154), and MEgreenyellow (cor = 0.34, p = 0.049; transcripts no. = 34) ( Figure S7A–F and Figure 4 E–K).

The enrichment of upregulated SFs in the upregulated genes is statistically highly significant ( P < 3.517 × 10 −34 , hypergeometric test).

The final age prediction model, using simultaneously the 5 CpG sites, showed a high correlation coefficient (R = 0.982), highly significant (p = 3.63e-34), explaining 96.4% of age variation.