Spss 19.0 for window

SPSS 19.0 for Windows was used to analyze the data. Data were generally reported as mean ± SD. A Chi-square test and the independent t-test were performed to compare demographic data and the serum S100B/cytokines levels between GAD patients and HC. The distributions of all variables were checked by the Kolmogorov–Smirnov test, and all showed equal or nearly equal distribution. A receiver operating characteristic (ROC) curve was applied to compare the predicting value of baseline S100B and cytokines levels in GAD, Relationships between S100B/cytokines and clinical variables (age, HAMA scores, and illness duration) were evaluated using Pearson correlations.

The data were presented as the means ± SD. Differences among samples were defined using one-way ANOVA followed by a Tukey’s multiple comparison post hoc test using SPSS 19.0 for Windows^® (SPSS Inc., IL, USA). The %apoptotic cells in each treatment groups are expressed as the mean ± SD of three experiments and were analyzed by Kruskal Wallis nonparametric statistics. Any differences with a p value of <0.05 were considered statistically significant.

Data analysis was made for the overall study population by region and ethnicity. To enable comparisons between subgroups, results were adjusted by age and sex. Continuous variables were presented as mean (SD) deviations and were analyzed using ANOVA. Categorical variables were expressed as frequency (n) and proportion (%) and were analyzed using the Chi-square test. As for hypertension, multivariable logistic analysis was used to analyze the associated factors for prevalence, awareness, treatment, and control of hypertension; adjusted odds ratio (OR) with associated 95% confidence interval (95% CI) were calculated. For each dependent variable, the independent variables included in the multivariable logistic model were mainly the variables with statistical significance in the univariable analysis, which is set as p values ≤0.10. All statistical tests were two-tailed, and differences were considered statistically significant when the p value was <0.05. All statistical analyses were performed using SPSS 19.0 for Windows.

When performing the statistical analysis, 41 samples were divided into 6 groups, which were branch river control (n = 3), upstream control (n = 6), sewage outfall (n = 6), dosing sites (n = 7), midstream (n = 16), and farmland (n = 2) (Table S1). The physicochemical indices were statistically analysed with separate one-way analyses of variance (ANOVA). The correlations (correlation between Cd content of sludge and Cd concentration of river water; the correlation between Cd content of five different phases in sludge and the total Cd content in sludge; the correlation between Cd content in sludge and microbial richness (Chao1 index); the correlation between Cd content in sludge and the alpha diversity (Shannon index) of the communities in the sludge samples; the correlation between Cd content in sludge and microbial structure) were analysed with bivariate correlation, and these statistical analysis were performed using SPSS 19.0 for Windows^{81 (link)}. To evaluate the effects of environmental factors on overall functional community structures, CCA was implemented with the CANOCO 4.5 software package^{82 (link)}.

SPSS 19.0 for Windows (SPSS Inc, Chicago, IL, USA) and MedCalc 9.5.0.0 for Windows (MedCalc Software, Mariakerke, Belgium) were used to calculate Cronbach α, dimension coefficients, and other scale quality indicators used in this study. The cloud computation was programmed using the active server pages on the website (see Multimedia Appendix 3). MS Excel Visual Basic for Application (Microsoft Corporation, Redmond, WA, USA) was used to organize the study data.

All experiments were conducted in triplicate. Values shown are mean ± standard deviation (SD). Statistical analyses (one-way analysis of variance, ANOVA, and Dunnett’s test) were conducted using SPSS 19.0 for windows (SPSS, Chicago, IL, USA). In all cases, a value of p<0.05 was considered to be statistically significant.

Statistical analysis was performed by using SPSS 19.0 for Windows (version 19.0; SPSS Inc., Chicago, IL, USA). The quantitative data were expressed as means ± standard deviations (SD). Categorical variables, expressed as numbers and percentages, were compared by the Chi-square or Fisher’s exact test. A value of P ≤ 0.05 was considered statistically significant. Antimicrobial resistance data was analyzed by using WHONET 5.6.