Stata statistical software release 10

Statistical analyses were performed using Stata 10.1 (StataCorp. 2007; Stata Statistical Software: Release 10; StataCorp LP, College Station, TX, USA). Gene expression in G₀-arrested cells was set as baseline and the expression in 21% O₂ was compared to 1% O₂ for day 1, 2 and 3 using a linear mixed-effects model with subjects as random effects and with time (day 1, 2 and 3) and group (1% O₂ versus 21% O₂) as fixed effects. Model assumptions about normal distribution of residuals and homogeneity of variance were satisfied by log-transformation of data. Significance level was set at a = 0.05.

Categorical data were presented as absolute frequencies and percent values. Quantitative measurements were expressed as mean ± SD and were checked for testing the normality of distribution (Shapiro–Wilk test) either in the overall patients as well as in the two groups of patients separately considered. In addition, Levene's test was used for testing the homogeneity of variance between groups. Due to non-normality of data and/or dishomogeneity of variance for some variables, the two groups of patients (Group LT: arrest circulation temperature < 24°C; Group HT: higher arrest temperature ≥24) were compared by Mann-Whitney test for the quantitative variables. The categorical variables were compared by Fisher's exact probability test (in case of two by-two contingency tables) or chi-square test. Bonferroni's correction was applied in case of multiple comparisons to control the experiment wise Type I error probability. H₁ was postulated bidirectional for all the analyses. A p value of <0.05 was considered statistically significant. Statistical analysis was performed using the STATA Statistical Software: Release 10, College Station, TX: StataCorp LP.

Descriptive statistics (proportions, averages, and standard deviations) were presented for spawning rates (number of fragments spawned and time to spawn) with associated 95% confidence intervals (denoted as 95% CI). 95% CIs were calculated using summary statistics for the population (N) and number of successes (n) using the Clopper–Pearson formula, producing interval, mean and standard error for each observation. Comparisons between treatments (trials, genotype and mortality rates) were performed using case controlled odd’s ratios (OR). Significance was determined using chi-square probability calculations which accounted for the degrees of freedom and variance. Analyses were selected to best express the result as a meaningful representation of the tested objectives. All analyses were performed using Stata Statistical Software: Release 10 (StataCorp, 2007 ).

All statistical analyses were conducted using STATA software version 10 (StataCorp 2009; Stata Statistical Software: Release 10. College Station, TX: StataCorp LP). Standard descriptive analysis was done using Pearson's Chi-square test. We first examined sociodemographic differentials and premigratory and migration related experiences in the prevalence of psychological distress among the migrants just after migration and after settlement. Associations between psychological distress and various covariates were analyzed using multivariate logistic regression models. The analysis is based on 2112 rural to urban migrants aged ≥18 years which has been extracted from the total IMS sample of 7067 who reported their reasons for migration. The analysis was done separately for men and women as it was found that there is a strong evidence of gender differential in mental distress between men and women in our study both after migration and after settlement [Table 2].