Spss v25

The success of the newly developed LAMP assay in detecting G. truncatula DNA in extracted eDNA samples compared to PCR was analysed statistically using a generalized logistic regression mixed model in SPSS (v.25). The subject variable was each eDNA sample, with DNA amplification method (LAMP or PCR) the within subject variable. The dependent variable was the outcome of each DNA amplification test (positive or negative). The DNA amplification method (LAMP or PCR) was inserted as a fixed factor in the model to identify if any differences in DNA amplification from each sample was seen between both methods. The habitat and sampling time-point were inserted into the model as random factors to identify if any differences in test performance was influenced by sampling location and period. Outcomes were deemed significant if P < 0.05. Kappa coefficient analysis with 95% confidence level was undertaken to assess the agreement between LAMP and PCR assays in amplifying eDNA using SPSS (v.25).

To develop a second-order model of attitudes towards adopting EBI to use as outcome variables for predictive analysis, a CFA was conducted in Mplus v8. The model specification was based on the 12 subscales of the recently developed EBPAS-36. The parameters were estimated with the full maximum likelihood estimation procedure (FIML). Robust standard errors (MLR) were obtained to accommodate non-normal item distributions. The following model fit indices were used: χ², root mean square error of approximation (RMSEA), standardized root mean error (SRMR) and comparative fit indices (CFI). In line with Hu and Bentlerʼs cutoff recommendations [42 ], RMSEA values < .06, SRMR < .08 and CFI > 0.95 indicate an acceptable model fit. The primary factor scores were saved in Mplus and subjected to an exploratory second-order principal component analysis (PCA), using SPSS v25. Correlation analysis, t-tests and hierarchical regression analysis were conducted in SPSS v25. Missing EBPAS-36 and QPSnordic item scores were imputed using the Expectation Maximization (EM) method. Values were imputed separately for each subscale’s set of items. Bivariate associations were calculated as Pearson correlation coefficients.

All data were presented as mean ± standard error of three replicates. For only two treatments, the difference was checked by using independent-sample t-test. Differences among three or more treatments were examined by one-way analysis of variance (ANOVA), and means were compared using Duncan’s multiple-range test. All analyses were carried out through the IBM SPSS v.25 statistical software (SPSS Inc., Chicago, IL, USA).
To find key genes in AM fungal regulation on alleviating the effect of pathogen infection, the correlations between DEGs and lesion indexes, together with antioxidant indexes, were discovered by performing redundancy analysis (RDA) and visualized in CANOCO 5 software (Ter Braak and Šmilauer, 2002 ). Pearson correlation coefficients were also calculated using SPSS v.25. Additionally, we proposed a hypothetical model by the results of previous studies, which was specified and analyzed with partial least squares structural equation modeling (PLS-SEM) with the support of WarpPLS (version 6.0) software (Kock, 2017 ), to explore the relationships between AMF inoculation, pathogen infection, biomass, and metabolisms of auxin, ethylene, pathogenesis-related (PR) protein and the antioxidant system.

A The percentage of polymorphic loci (%P) generated by MSTD for donor plants and regenerants and the marker system informativeness evaluated by Shannon’s information index (I) was assessed used GenAlEx6.501 (Excel add-in software) [130 (link)].
A one-way analysis of variance (ANOVA) was applied to the RP-HPLC results. Two-way ANOVA was conducted for the molecular characteristics for MSTD. The presence of outliers was evaluated via visual inspection of box-plots, Cook’s distances, and Leverage coefficients. The Shapiro–Wilk tests were performed to test for the normality. Homogeneity assumption of ANOVA was verified using the Levene’s test of equality of error variance. Interaction and simple main effects were tested. The SPSS v 25 software [131 (link)] was used for ANOVA.
Regression analysis for three models A (CHH_SV: CHH_DNM, CHH_DNMV, CHH_DMV * CHH_DNMV), B (CHG_SV: CHG_DNM, CHG_DNMV, CHG_DMV * CHG_DNMV), and C (CG_SV: CG_DNM, CG_DNMV, CG_DMV*CG_DNMV), including regression assumption testing, as well as linear regression analysis (SV: Global DNA methylation based on RP-HPLC data), was conducted in the SPSS v 25 software.
Automated linear regression analysis combining models A, B, C, and D were conducted in the SPSS software using default settings.

Statistical analyses were performed using the SPSS v.25 software (accessed on 19 November 2021), and graphs were generated using GraphPad Prism v.8.0.2. (accessed on 19 November 2021) Student’s t-test was used when comparing two groups, and more than two groups were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc analysis. SPSS v.25 (accessed on 19 November 2021) was used for hierarchical clustering with the between-group linkage method and Pearson correlation analyses.