Spss software package 16

Variations in the physicochemical parameters and APases activities of water samples among different experimental groups were compared using repeated measures ANOVA test with the Statistical Program for Social Sciences (SPSS) software package 16.0 (IBM, USA), and the plots were drawn using Sigmaplot v12.5 software (Systat Software Inc, USA). Canonical correspondence analysis (CCA) in the software CANOCO 4.53 for Windows [40 ] was used to investigate the correlation of physicochemical parameters with variations of genotype constitution. Before the analysis, the matrix of genotypes and physicochemical parameters were log₁₀ (x+1) transformed to ensure they met the requirements for subsequent statistical analysis [41 (link)]. The forward selection method in the CANOCO software was used to screen the physicochemical parameters significantly correlated with the genotype constitution. The significance (P<0.05) of the calculated results was verified with an unrestricted Monte Carlo permutation [40 ]. The Pearson correlation and linear regression between the log₁₀-transformed bacterial phoX gene abundances and water physicochemical parameters were also analysed using the SPSS v16.0 and Sigmaplot v12.5, respectively. Only the significant correlations were plotted with the Sigmaplot v12.5 software.

Wilcoxon signed-rank test was used to analyze mRNA levels of CRBP-1 in HCC tissue samples and normal liver tissue samples. Single variable analysis was performed using a two-sided Fisher’s exact test. Multivariate analysis of overall survival odds ratio (OR) and 95% confidence intervals (CI) were performed with Cox proportional-hazards regression. Overall survival analysis for the patients was performed using the Kaplan-Meier method with a log-rank test. The unpaired student’s t-test was used to determine the statistical significance of differences between two groups. Data were presented as mean ± standard deviation (SD), p < 0.05 was considered statistically significant, data analysis was performed with SPSS software package 16.0 (IBM SPSS Software, Inc., IL, USA) or GraphPad Prism 8.0 (GraphPad Software, Inc., CA, USA). Gene Set Enrichment Analysis (GSEA) was performed using the Bioconductor package clusterProfiler based on KEGG pathways (minimal set size = 15, maximal set size = 500).

The observed data were normally distributed and presented as means ± standard deviation (SD). The differences between groups were tested using the unpaired Student's t-test or one-way analysis of variance (ANOVA) analysis, followed by Tukey's post hoc test for multiple comparisons, as indicated. A linear regression analysis was performed to calculate telomere shortening rates. All P values presented are 2-tailed, and a P < 0.05 was chosen for levels of significance. Statistical analyses were performed using spss 16 software package (SPSS, Inc., Chicago, IL, USA) or GraphPad Prism software version 5.0 (San Diego, CA, USA).

The S_DM and SNC data for each sampling date, year and variety was separated and subjected to analysis of variance (ANOVA) using GLM procedures in SPSS-16 software package (SPSS Inc., Chicago. IL, USA). The differences among treatment means were measured by using the least significant difference (LSD) test at 90% level of significance, instead of classically used 95% in order to reduce the occurrence of Type II errors that could be high in such field experiments. For each measurement date, year and variety, the variation in the SNC versus S_DM across the different N levels was combined into a bilinear relation composed of a linear regression representing the joint increase in SNC and S_DM and a vertical line corresponding to an increase in SNC without significant variation in S_DM. The theoretical N_c points corresponds to the ordinate of the breakout of the bilinear regression. Regression analysis was performed using Microsoft Excel (Microsoft Cooperation, Redmond, WA, USA).