Significant associations in GWAS. by Dai Nogimura (21445649)

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eQTL analysis of significant SNPs. by Dai Nogimura (21445649)

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Significant associations in the candidate gene approach. by Dai Nogimura (21445649)

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Suggestive significant associations in the candidate gene approach. by Dai Nogimura (21445649)

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The significance of the specific Nusselt number on a variety of factors. by Siva Sankari (2827295)

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The significance of the specific Nusselt number on a variety of factors. by Siva Sankari (2827295)

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The significance of the specific Nusselt number on a variety of factors. by Siva Sankari (2827295)

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The significance of the specific Nusselt number on a variety of factor. by Siva Sankari (2827295)

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Comparison of questionnaire scores among genotypes for the significant associations. by Dai Nogimura (21445649)

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County-level geographical septile distribution for the first 12 statistically significant factors with at least one statistically significant coefficient ranked by decreasing ΔIC. by Yanan Long (11784717)

Significant disease phenotypes with LADA loci. by Margaret Sunitha Selvaraj (13994016)

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Application of Machine Learning-Based Models to Understand and Predict Critical Flux of Oil-in-Water Emulsion in Crossflow Microfiltration by Henry J. Tanudjaja (12106715)

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The significance of variation in cortical bone elastic modulus within a region. by Linje Wang (18064951)

Raw data used to calculate the statistical significances stated in the result section. by Tom Adriaenssens (397863)

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Bexarotene pretreatment significantly suppressed HBV and HDV infections in primary human hepatocytes (PHH). by Sameh A. Gad (12269979)

Table_1_COVID-19 vaccine booster significantly decreases the risk of intensive care unit hospitalization in heart failure patients during the Omicron variant wave: A population-based study.docx by Jiri Parenica (293645)

Genome-wide significant SNPs for meat L*15 min and a*15 min. by Jiahong Sun (6497603)

Demographic of the study participants, urinary arsenic species and Th17 cells. by Faruque Parvez (13136)

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Cat Stress Score (CSS) was significantly predicted by intake type and days in the study. by Allison Andrukonis (11846404)

“…The dots represent outliers. B) More days in the study predicted lower CSS (<i>p</i> <  0.001). The black line represents the line of best fit based on the reduced model with marginal means. …”

Demographic of the study participants, urinary arsenic species and IL-17A cytokine. by Faruque Parvez (13136)

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