Originpro 9

Data were analyzed using SPSS software version 21.0 (IBM Inc. NC, USA). The differences in selected soil physicochemical properties and D values among the MPPs were compared using multiple comparison and one-way analysis of variance. A least-significant difference test (at p < 0.05) was used to compare the means of soil variables. Pearson’s correlation coefficient and a two-tailed test were used to distinguish correlation (significantly correlated at p < 0.05 (0.05 level) and p < 0.01 (0.01 level)) and significant differences (at the 0.05 level and 0.01 level). Simple linear regression and correlation analysis were performed using OriginLab OriginPro 9.0 software (OriginLab Inc., Northampton, MA, USA) to identify the relationships between D and the selected soil properties and stand density (at the 0.05 level and 0.01 level). Data processing and plotting were also completed using OriginLab OriginPro 9.0 software.

The results were summarized as the mean values ± standard deviation (SD). The data were processed using Microsoft Excel 2016 and OriginPro 9.5 software (OriginLab Co., Northampton, MA, USA). The phytochemical data of the JB1 and JB2 extracts were compared pairwise using Student’s t-test (normal distribution, Kolmogorov–Smirnov and Shapiro–Wilk criteria, p > 0.05). Repeated measures using analysis of covariance (ANOVA) and Post-hoc Tukey test were used to examine the extraction yield and the main phytochemicals (total sugars, total phenolic compounds, total flavonoids, total terpenoids and ascorbic acid content) between berries of the 1st and 2nd year of maturity. Data analysis was performed using IBM SPSS 23.0 statistical software (Chicago, IL, USA). The level of p < 0.05 was considered statistically significant. Pearson correlations (p < 0.05) were used to identify correlations between phytochemicals and antioxidant activity of acetone, methanol and 70% ethanol extracts of JB1 and JB2.
The statistical processing of nematocidal activity results was performed using Fisher’s angular transformation. The regression analysis of calibration characteristics and the mathematical prediction of the peak area response for chromatographic analysis were performed using OriginPro 9.5 software (Origin Lab Corp, Northampton, MA, USA) and processed according to standards [94 ].

The results were summarized as the mean values ± standard deviation (SD). The data were processed using Microsoft Excel 2016 and OriginPro 9.5 software (OriginLab Co., Northampton, MA, USA). The phytochemical data of the JB1 and JB2 extracts were compared pairwise using Student’s t-test (normal distribution, Kolmogorov–Smirnov and Shapiro–Wilk criteria, p > 0.05). Repeated measures using analysis of covariance (ANOVA) and Post-hoc Tukey test were used to examine the extraction yield and the main phytochemicals (total sugars, total phenolic compounds, total flavonoids, total terpenoids and ascorbic acid content) between berries of the 1st and 2nd year of maturity. Data analysis was performed using IBM SPSS 23.0 statistical software (Chicago, IL, USA). The level of p < 0.05 was considered statistically significant. Pearson correlations (p < 0.05) were used to identify correlations between phytochemicals and antioxidant activity of acetone, methanol and 70% ethanol extracts of JB1 and JB2.
The statistical processing of nematocidal activity results was performed using Fisher’s angular transformation. The regression analysis of calibration characteristics and the mathematical prediction of the peak area response for chromatographic analysis were performed using OriginPro 9.5 software (Origin Lab Corp, Northampton, MA, USA) and processed according to standards [94 ].

Samples sizes were calculated using the formula described in [32 (link)] assuming α and β values of 0.05 and 0.2, respectively. We used standard deviation obtained in our preliminary experiments and estimated 150% difference in means.
To evaluate the statistical significance of differences between groups we performed the nonparametric Mann-Whitney U test using the OriginPro 9.1 software (OriginLab, USA) or the Chi-square test (χ²) using Microsoft Excel 2007 in the case of categorical variables.
Differences were considered statistically significant if p < 0.05. To evaluate the discriminative power of the parameters studied for kidney cancer diagnostics we built binary logistic regression models for the selected predicting variables and all their possible combinations using SPSS version 22 (IBM, USA). From these models, the probabilities of positive outcome (i.e., cancer occurrence) were calculated. These probabilities were used for Receiver-operating characteristics (ROC) analysis. Building of ROC and evaluation of AUC (Area Under Curve) was performed using the GraphPad Prism 6.07 (GraphPad Software, La Jolla, CA, USA) or the OriginPro 9.1 software (OriginLab, USA).

Analysis of the AFM images and FCs was done in the JPK Data Processing software (Version spm-5.1.4), which enabled automated detection and processing of jumps and plateaus. Following this, only those isolated, individual jumps and plateaus not convoluted by other interactions were considered for analysis of the forces and rupture lengths. Histograms of the force distributions were plotted and primarily fitted to either Lorenzian or Lognormal functions using OriginPro 9.1 to extract the peak distribution values. Histograms were prepared using the same bin size and peak distribution values obtained from the fitting. ANOVA and post-hoc Tukey were performed using statistical packages of OriginPro 9.1 and Igor Pro (Wavemetrics). To quantify the cell modulus, we fitted the contact region of the approaching curves to the Hertz model using the JPK Data Processing software.