Spss v19

Mosquito survival was analysed using Kaplan-Meier survival analysis in SPSS (v.19) with significant differences between different isolates estimated using a Log Rank Test. Differences between the control and the infected mosquitoes were examined using a Cox Regression analysis in SPSS. Hazard ratios (HR; the daily chance of death) in comparison with the isolate IMI391510 were calculated. One-way ANOVA was conducted in R (2.12.2) to detect differences in conidia size, sporulation and linear growth rate, with the sporulation data being Log transformed before analysis. A General Lineal Model was used to analyse the correlation between phenotypic characteristics of isolates and their virulence on mosquitoes using R (2.12.2). To further check for any correlation among the phenotypic characteristics themselves, a Pearson’s Correlation analysis was done. Mosquito survival exposed to the same fungal isolates at different time points were analysed using Kaplan-Meier survival analysis in SPSS (v.19) and significant differences were estimated using a Log Rank Test.

Sample size calculations were based on showing differences of four main parameters (OSS, NIKBUT, lid margin, and meibum quality) through multiple comparisons of two treatments vs a control-simulation. A priori power analysis showed that the maximum sample size required for 80% power of detecting those differences as significant at the two-side 5% level was 21 subjects using the PASS 2008 calculation software. We enrolled 27 eyes and found the power of our study was 90.3%. All statistical analyses were performed using SPSS V.19.0 software. A linear mixed model with Bonferroni post hoc analysis was used to evaluate repeated measurements of continuous variables, including OSS, NIKBUT, and Schirmer I test score. Generalized linear mixed model analysis with Bonferroni post hoc analysis was used for repeated measurements of discrete variables, including the FL score, lid margin findings, and MGs findings. The Student t-test and Mann–Whitney U test were used to compare differences in outcomes between every two cohorts. Chi-square test was used to analyze the enumeration data. Spearman's correlation analysis was used to estimate the correlations between various factors. All statistical analyses were performed using SPSS V.19.0 software. P values less than 0.05 were considered statistically significant.

Spectrophotometric data for comparing sterilization methods were first analyzed with the Shapiro-Wilk and Levene tests (IBM SPSS v19; SPSS Inc., IBM Company, Armonk, NY) (α=0.05). Then, spectrophotometric measurements (mAbs) were tested with one-way ANOVA followed by Tukey’s HSD post-hoc test (α=0.05) (IBM SPSS v19; SPSS Inc.). Sample power analysis was performed using GPower 3.1 (G*Power; Universität Düsseldorf, Düsseldorf, North Rhine-Westphalia, Germany).

All experiments were performed in triplicate. Data were statistically analyzed using Graphpad Prism5 and SPSS v. 19.0 and are presented as the means ± standard deviations (SDs). Independent-samples t tests, assuming equal variances, and one-way analysis of variance (ANOVA) were used for microarray and ELISA data comparisons between the SA and HC groups. Post hoc tests of one-way ANOVA were performed with the least-significant difference (LSD) and Student-Newman-Keuls (S-N-K) tests for data with equal variances or the Tamhane test for data with unequal variances. Correlation analysis and regression analysis were used to analyze plasma concentrations in association with BMI and leptin levels, and analysis of covariance was performed to eliminate the impact of BMI on leptin concentrations in serum samples. Receiver operating characteristic (ROC) curves of ELISA data were plotted using SPSS v. 19.0 software, and quantification was based on the optimal cutoff value with the maximum Youden index (Youden index = sensitivity + specificity—1). Combination diagnosis was evaluated with a prediction equation constructed by logistic regression, and serial and parallel tests were performed to verify the accuracy of diagnosis. Differences or associations with P values of less than 0.05 were considered statistically significant.

The differences of data related to growth performance, serum parameters, rumen morphology, mRNA expressions, rumen fermentations and bacterial abundances between groups were analyzed using One-way ANOVA followed Duncan’s post hoc testing in SPSS v.19.0, results were presented as means ± SEM. P-values less than 0.05 were regarded as statistical significance. The correlation between rumen VFA concentrations or bacteria abundances and epithelium gene expression was analyzed by using Spearman’s correlation analysis in SPSS v.19.0, P-values less than 0.05 and the absolute value of correlation coefficient more than 0.8 were regarded as significant correlation.