Data analysis was conducted using MS Excel 2021 (Microsoft Inc., Redmond, USA) and SPSS (v26.0) for Windows (SPSS Inc., Chicago, IL, USA). The concentration of potentially toxic elements in rice grains were presented as mean and standard deviations (SD). Differences among rice cultivars were assessed using a One-way ANOVA analysis. Association among metals in rice cultivars were determined by a Pearson correlation. A Principal component analysis (PCA) was applied to identify the potential sources of heavy metals in the rice cultivars. In addition, the Ward-algorithmic method was used for cluster analysis (CA) with a dendrogram to assess a detailed insight into the distribution of the potentially toxic elements in the rice grains. Statistical significance was set at p < 0.05 for all tests.
Spss v26.0 for windows
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19 protocols using spss v26.0 for windows
1
Toxic Elements in Rice Grains
2
Fatty Acid Composition Analysis
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All the measurements were performed in triplicates and the results were presented as mean ± standard deviation (SD). Differences among the samples were statistically analyzed using one-way ANOVA followed by Tukey’s test at level of p < 0.05. Regression models were used to analyze the straight-line relationships between SFC and saturated–unsaturated fatty composition of the oil blends. Statistical analyses were conducted using SPSS (v26.0) for Windows (SPSS Inc., Chicago, IL, USA).
3
Investigating Fungal and Bacterial Dynamics
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Data were analyzed using the statistical software SPSS V26.0 for Windows. Results were considered significant at the 5% significance level. For the characterization of the sample, frequency analysis (n, %) was used for qualitative data and mean and standard deviation for quantitative data. To test the normality of the data, the Shapiro–Wilk test was used. In order to study the relationship between bacterial and fungal counts, azole resistance, dust load, and Cq, the Spearman’s correlation coefficient was used, since the assumption of normality was not verified. The Kruskal–Wallis test was used to compare house divisions, since the assumption of normality was not confirmed. To compare the bacterial and fungal counts, azole resistance, and dust load between summer and winter, the Wilcoxon test was used, as the assumption of normality was also not observed.
4
Masculinity, Alexithymia, and Psychological Flexibility in Depression and Somatization
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Pearson intercorrelations between the study variables and demographics were calculated, followed by two four-step hierarchical regression analyses, one for depression and one for somatization. All independent variables were centered before entering the regression models. To statistically control age, education, family status, and religion, these were inserted into the equation in Step 1. Masculinity was entered in Step 2. After controlling for all other variables, alexithymia and psychological flexibility levels were entered in Step 3 to examine their contributions. To examine the combined contribution of masculinity–psychological flexibility and masculinity–alexithymia, these hypothesized two two-way interactions were entered in the final step. The same procedure was then carried out with somatization as a dependent variable. Following the regression, simple slopes post hoc analysis was performed using the PROCESS macro for SPSS (Model 1; A. F. Hayes, 2013 ) to examine the nature of the interactions within a regression framework. The statistical significance of the coefficients was confirmed by constructing 95% confidence intervals using a bootstrap procedure (see Efron & Tibshirani, 1986 (link)) with 1,000 resamples. SPSS (v26.0 for Windows) was used for all analyses. The level of statistical significance was set at p = .05.
5
Comparative Analysis of Immune Subclasses
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Continuous variables conforming to normal distribution were compared with Student t test, otherwise the Wilcoxon rank sum test was used. One-way ANOVA models and Kruskal-Wallis tests were used for multigroup comparison. The association between immune subclasses and the clinical parameters were evaluated by chi-squared test or Fisher-exact test. Overall survival (OS) curves were calculated according to the Kaplan-Meier method (R package survival) and differences between curves were assessed using the log-rank test. Statistical analyses were performed on R 3.6.2 software and SPSS V26.0 for Windows.
6
Metabolite Profiling of Rapeseed Lines
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The data for all metabolites were repeatedly collected 3 times, and all the data results were expressed as mean ± standard deviation (SD). Statistically significant differences among the nine rapeseed lines with different colors were assessed with analysis of variance (ANOVA) in SPSS v26.0 for Windows (SPSS Inc., Chicago, IL, USA) and Duncan’s test, and the p-values < 0.5 represented that the difference among groups was significant. Heatmap analysis, scatter matrix, violin plot, and histogram were drawn by using the Origin Pro 2023b for statistical computing (OriginLab, Northampton, MA, USA).
7
Comparative Analysis of MRN and EMG
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To study the difference in NA detection rate between MRN and EMG, mean values and standard deviation were calculated for patient age and time from symptom onset to imaging. Frequency data are presented using contingency tables, and pairwise associations between variables were computed using chi-square or Fisher exact tests. P < .05 was considered statistically significant. The consistency between clinical diagnosis, MRN and EMG were analyzed using the Cohen statistics (kappa concordance index). The κ values were interpreted according to the guidelines of Landis and Koch: mild (0–0.2), acceptable (0.21–0.4), moderate (0.41–0.6), substantial (0.61–0.8) and almost perfect (0.81–1). Data were analyzed using SPSS v. 26.0 for Windows (IBM, Portsmouth, UK).
8
Validation of Forgotten Joint Score-12 for Knee Disorders
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A power analysis was performed. Approximately, 80 patients were thought to have had surgical repair of the quadriceps tendon in a span of 5 years (from January 2015 to January 2020) in the respective hospital. The response rate was assumed between 50 and 80%, therefore 50–80 patients were eligible. With 5% alpha error and 80% power, the effect size correlation was r = 0.38 and r = 0.31 for 50 and 80 patients respectively. All data was collected using RedCap, which is a secure electronic data capture (EDC) software (that allows patients to complete questionnaires online through an individual QR code), or in paper form, which was subsequently added to the electronic database by the study team. The statistical analysis was performed on IBM SPSS v 26.0 for Windows. Descriptive statistics were used to define all quantitative variables. The criterion and construct validity were assessed by Pearson correlation, which analysed FJS-12 and the other used PROMs (WOMAC, Lysholm and TAS). The principal component factor analysis was performed to investigate the internal consistency of FJS-12 along with Cronbach’s alpha value.
9
Age Estimation from Craniofacial Measurements
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Statistical analysis was performed using SPSS®v. 26.0 for Windows (IBM Corporation, Armond, NY, USA). Intraobserver and interobserver variability were assessed using intraclass correlation coefficient. Descriptive statistics were performed. The Kolmogorov–Smirnov test and the Levene test were applied to check for normality and the homogeneity distribution of the sample. One-way analysis of variance (ANOVA) with post hoc Bonferroni multiple comparison tests were used to compare linear measurements and ratios between age groups. Multivariate linear regression analyses were performed for age estimation based on sagittal, coronal and axial linear measurements and ratios. Statistical significance was set at p ≤ 0.05.
10
Clinicopathological Associations of Coexisting Mutations
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Descriptive statistics are presented as median (range) for non-normally distributed variables and frequency (incidence) for categorical variables. The χ2 test and Mann-Whitney U test were used to calculate the significance of associations between coexisting mutations and clinicopathologic features. To extract independent factors, those with a P-value < 0.15 were included as covariates in the multivariate logistic model using the forward stepwise selection procedure. The results are expressed as odds ratios (ORs) together with 95% confidence intervals (CIs). All calculations were performed applying IBM SPSS v26.0 for Windows. In all analyses, P-values < 0.05 were considered significant. GraphPad Prism 8.4.2, Circos-0.69-9 and R version 4.0.4 were also used for figure plotting.
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