Spss statistics software version

All data were presented as medians with range (minimum and maximum), as appropriate. The change of discrepancy in size of hyperfluorescent areas was estimated using Wilcoxon rank sum test, and the correlations were evaluated using Spearman correlation coefficients (ρ). Multivariate analyses were performed using a forward stepwise linear regression, where the decrease in discrepancy between the imaging modalities was used as dependent variable and all associated parameters as explanatory variables. The best linear model for grading of discrepancy decrease was calculated. The diagnostic value of each test was assessed by the area under the receiver operating characteristic curve (AUROC). Optimal cut-off values for the two imaging modalities were set at the maximum of total both sensitivity and specificity. Likelihood ratios for the appropriate cut-offs were calculated. A p value of < 0.05 was considered to be statistically significant. Data were analysed using SPSS statistics software version 20.0 (SPSS Inc., Chicago, IL, USA) and MedCalc statistical package version 16.2.0 (MedCalc, MariaKerke, Belgium).

All statistical analyses were performed using SPSS statistics software (version 23.0). Descriptive analysis was reported as mean with standard deviation (SD), median with interquartile range (IQR) for quantitative variables, and frequency with percent for qualitative variables. The normal distribution of ISRs was checked using the Shapiro–Wilk test. The Friedman test was used to test antibody titer differences during sampling and between groups. Antibody titer was compared between patients and controls for the three-time point sampling using the Mann–Whitney U test.
We used a logistic regression approach to investigate the predictive impact of selected baseline clinical parameters and laboratory indicators for strength of serologic response following the second vaccine dose based on the median level of ISR rising (Subtracting the baseline value from the post-second dosage yielded the ISR rising). In univariate analysis, predictors associated with strong immune response (p ≤ 0.20) are then incorporated into a multivariable logistic regression model using stepwise forward selection. All the tests were considered two-way, and a p-value < 0.05 was reported as statistically significant. The graphs were plotted by GraphPad Prism software (Version 8).

SPSS Statistics software (version 20.0, USA) was used to perform statistical analyses. Data were presented as the means ± standard deviation (SD). Student t-tests were used to assess the differences between two groups, whereas one-way ANOVA followed by Tukey's multiple comparisons test was used to evaluate the differences among multiple groups. Pearson’s correlation analysis was used to determine the correlations among LINC00460, miR-503-5p and ANLN in PC tissues. Survival curves were calculated by the Kaplan-Meier method and compared using the log-rank test. P-value less than 0.05 indicated a statistically significant difference. All experiments were conducted in triplicate in at least three independent trials.

The data are expressed as mean (standard deviation) for normally distributed continuous variables and as median (range) for non-normally distributed continuous variables. Categorical data are expressed as number (percent). The normal distribution of continuous variables was assessed with the Kolmogorov-Smirnov method. The unpaired t test and the Mann–Whitney U test were used to compare means and medians, respectively. For polychotomous outcome parameters, we used one-way analysis of variance (ANOVA) with the Bonferroni correction or Kruskall-Wallis ANOVA with a Tamhane T2 test. A contingency table was generated to assess potential significant differences between the groups in categorical variables, and Fisher’s exact test was applied. Based on clinical judgement and after univariate analysis, we entered all parameters that could likely be associated with disturbed glucose metabolism in a logistic regression model, and assessed the corrected effect of those elements using bootstrapping. Odds ratios were converted to risk ratios. A two-tailed P value <0.05 was considered significant. Statistical analyses were performed with SPSS Statistics software, version 20.0 for Mac (Armonk, NY, USA).

All statistical analyses were performed in SPSS statistics software (version 20.0). The Shapiro-Wilk probability test was used to assess the normality of the distributions. We used the Wilcoxon non-parametric test to compare the values of MA_{1_24h} and MA_{2_24h} indices between the paretic and unaffected arms and to compare NIHSS_T0 and NIHSS_T1 scores. We used the Mann-Whitney U Test to compare the asymmetry indices between controls and patients.
In order to evaluate the agreement between the deficit laterality measured by AR_{1_24h} and AR_{2_24h} and the clinically defined laterality, we used the Phi Coefficient. The Pearson’s test was used to correlate the degree of asymmetry between arms as measured by the absolute value of AR_{1_24h} and AR_{2_24h} with NIHSS scores (either before or after having removed the epochs with passive movements). A p < 0.05 was set as the level of significance.

Statistical analysis was performed using the SPSS Statistics software (Version 25). Quantitative variables were expressed as means (± SD) and qualitative variables were presented as percentages. The mean value in the two groups (Duette and Captivator) was compared using Student’s t test. Fisher’s exact test used to compare R0 and R1 between the two groups.

qRT-PCR data was analyzed in accordance with the procedure of Zheng & Hu (2016) (link). Error analysis was conducted with SPSS Statistics Software version 18.0 (SPSS18.0, IBM, USA) based on the biological replicates of three individual plants. The related indicators of agronomic traits were also statistically analyzed using SPSS18.0 software. The data of all graphs was represented as the mean ± standard error. The graphics were analyzed and produced with OriginPro 2018C SR1 and Excel 2010 software.