Additional file 2 of Whole genome sequencing of the pulmonary microbiome in interstitial lung disease subtypes by Kristel S. Knudsen (10535406)

Image 5_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…After data cleaning, 3933 samples were analyzed for age subgroup partitioning of trace elements from 1 month through 18 years. The difference between the age sub-groups was statistically significant according to the Harris-Boyd method and the Lahti method. …”

Image 3_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…After data cleaning, 3933 samples were analyzed for age subgroup partitioning of trace elements from 1 month through 18 years. The difference between the age sub-groups was statistically significant according to the Harris-Boyd method and the Lahti method. …”

Image 2_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…After data cleaning, 3933 samples were analyzed for age subgroup partitioning of trace elements from 1 month through 18 years. The difference between the age sub-groups was statistically significant according to the Harris-Boyd method and the Lahti method. …”

Image 4_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…After data cleaning, 3933 samples were analyzed for age subgroup partitioning of trace elements from 1 month through 18 years. The difference between the age sub-groups was statistically significant according to the Harris-Boyd method and the Lahti method. …”

Image 6_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…After data cleaning, 3933 samples were analyzed for age subgroup partitioning of trace elements from 1 month through 18 years. The difference between the age sub-groups was statistically significant according to the Harris-Boyd method and the Lahti method. …”

Differences between patients and controls. by Jolanda Malamud (19727472)

“…The bars indicate the mean and the dots individual subjects. Significance *≤0.5, **≤0.1, ***≤0.001, ****≤0.0001. Black frames survive correction for multiple comparisons.…”

Table 9_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 2_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 10_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 3_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 8_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 6_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 5_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 4_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Table 7_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…In horses, two multivariable models explained the maximum amount of data variability. Model 1 demonstrated increased odds of seropositivity in mares [odds ratio (OR) = 2.90 95% CI 1.70–4.95, p < 0.001] and decreased odds in horses <2.5 years (OR = 0.46 95% CI 0.22–0.95, p = 0.04; reference: ≥4.5 years). …”

Coded Data.xlsx by Mohana Vignesh P (20399198)

“…Significant differences in turnover intention by income categories were found, indicating that financial considerations are significant determinants of retention decisions. …”

DataSheet2_Mineral nutrition for Cannabis sativa in the vegetative stage using response surface analysis.docx by Patrick Yawo Kpai (20376234)

“…The optimum concentrations of total nitrogen, P, K, calcium, and sulfur in the cannabis leaves were 0.54, 0.073, 0.27, 0.56, and 0.38 mg g^-1, respectively. An increase in P and K concentrations decreased the magnesium concentration in the leaves, but it was unaffected by the increase in N concentration. …”

Relating the biophysical forces that govern bacterial adhesion to experimental data. by Mikkel Anbo (15287803)

“…This is the case for PA14 (left) and PAO1 (right) strains expressing different serotypes. …”

Non-random tree species loss shifts soil fungal communities by Sirong Zhang (20831093)