Spss 21

The difference of top 30 genera between two groups was assessed using the STAMP software v2.1.3. The student t test was conducted to compare the growth properties, physicochemical properties, and alpha diversity indices between two groups using the software SPSS 21.0 (IBM Co., Armonk, NY, USA). Correlation analysis between physicochemical properties and bacterial populations were also analyzed with the Spearman method using the software SPSS 21.0 (IBM Co., Armonk, NY, USA). The principal coordinate analysis (pCoA) and the dissimilarity tests (MRPP, Adonis, and ANOSIM) were used to demonstrate the bacterial community differences among the five groups (Anderson 2010 (link); Caporaso et al. 2010 (link)). Canonical correspondence analysis (CCA) was used to measure the correlation between the bacterial community and different environmental factors. FAPROTAX was used to establish Functional Annotation of Prokaryotic Taxa (Louca et al. 2016 (link)).

The sample size in this study was calculated based on the sample size determination theory of statistics. Sample size was calculated using an effect size of 0.75, beta value of 0.2, and alpha value of 0.05. Demographic data were analyzed using SPSS 21.0 (IBM Corporation; Armonk, NY, USA). All data are presented as mean ± SD. The significance level threshold was set to P < 0.05. The data were analyzed using the non-parametric Student’s t-test and the Spearman rank correlation coefficient to account for non-normal distributions of some variables. An overall generalized estimating equation (GEE) analysis [29 (link)] (SPSS 21.0, IBM Corp.) was performed during the random distention. The fixed main effects included those of group (IBS or HC), drug (CRH or saline), condition (no distention, 20 mmHg distention, or 40 mmHg distention), and sex (female or male), as well as their potential interactions with the dependent variables of interest. Network analyses within the neuroendocrine system were conducted using structural equation modeling in Amos 22.0 (IBM Corp.). The strengths of relationships between two factors are indicated as standardized beta weights using an arrow path. A satisfactory model usually has a comparative fit index ≥ 0.95 and a root mean square error of approximation < 0.05. The significance level threshold was set to P < .0125.

The raw data for copulation duration, number of eggs, and longevity of the F₀, F_1, and F₂ generation individuals were analyzed using SPSS 21 (SPSS 21.0, IBM). All data were tested for normality using the Kolmogorov–Smirnov test and met the assumption of ANOVA. The number of eggs’ data was transformed into a logarithmic transformation to approximate a normal distribution. The number of eggs and longevity were subsequently analyzed by one‐way analysis of variance (ANOVA) followed by Tukey's HSD test (p < .05). The distribution of the copulation duration data was skewed and was analyzed by nonparametric Kruskal–Wallis analysis of variance. All data are presented as the mean ± standard error (SE). Survival curve analysis was evaluated by log‐rank test for the different mating treatments. The parameters of intrinsic rates of increase, finite rate of increase, net reproductive rates, and mean generation time were calculated using the bootstrap method included in the computer program TIMING–MSChart (Chi, 2020 ). Because bootstrap analysis uses random resampling, a small number of replications will generate variable means and standard errors. To generate fewer variable results, 100,000 replications were used in this study.

Statistical analysis was performed with SPSS 21.0 (IBM, Inc., USA). First, adjustment to normal distribution was tested for all continuous variables as per one-sample Kolgomorov-Smirnov test and the normal quantil-quantil (QQ) plots. Data represents the mean ± standard deviation (SD) or the median (interquartile range). In the discovery cohort, univariate analysis of demographic and clinical characteristics was performed by ANOVA or Kruskall-Wallis test along with Chi square test. In the validation cohort, univariate analysis of demographic and clinical characteristics was performed by Student t-test or U Mann–Whitney test along with Chi square test. circRNA expression differences between two groups was estimated by Mann–Whitney U test. For all the comparisons, significance level was set at p-value < 0.05. SPSS 21.0 (IBM, Inc., USA) was used to draw graphs.

The imported cases were simulated as transmission sources and the secondary cases were employed for the curve fitting. Berkeley Madonna 8.3.18 (developed by Robert Macey and George Oster of the University of California at Berkeley. Copyright ©1993–2001 Robert I. Macey & George F. Oster, CA, USA) was employed to perform the procedures of curve fitting and the simulation. The simulation methods (Runge–Kutta method of order four with tolerance set at 0.001) were the same as the previously published researches^{11 (link),12 (link),14 (link)–16 (link),18 (link)}. The goodness of fit was judged by Chi-square (χ²) value calculated by SPSS 21.0 (IBM Corp, Armonk, NY, USA). Epidemiological characteristics analysis was also performed by SPSS 21.0 (IBM Corp, Armonk, NY, USA). Differences in epidemiological characteristics of COVID-19 were analyzed by two side way tests.