Originpro 2016g

The statistical program SPSS^® (IBM SPSS Statistics, Armonk, NY, USA) was used for statistical analysis. The Mann–Whitney U-Test was performed to compare the optical densities of the ipsilateral and contralateral hemispheres. Furthermore, the 12-month group was compared with the non-BoNT-A-treated sham group, and the groups (of different survival times) were compared with each other. The test for normal distribution was carried out with the Shapiro–Wilk test. Depending on whether a normal distribution was present, the correlation coefficient was calculated according to Pearson, otherwise the rank correlation coefficient was determined according to Spearman.
The minima, maxima, and mean values of the optical densities of each investigated structure at each time point were plotted as curves in graphs using Origin^® (OriginPro 2016G, OriginLab, Northampton, MA, USA) and its cubic-spline interpolation function. Correlation analyses were also performed using Origin^®.

OriginPro 2016G (OriginLab Corp., Northampton, MA, USA) was used for the analysis of the absorption data. The effect of treatments was examined in a one-way analysis of variance (ANOVA); the null hypothesis was rejected with a significance level of 0.05.
The presented results denote the arithmetic means of at least three replicate samples; error bars represent the standard deviation.

For parametric data, either a two-tailed unpaired Student’s t-test (two groups) or a one- or two-way ANOVA followed by Tukey’s post-test (more than two groups) was performed (OriginPro 2016 G, OriginLab and Prism 7, GraphPad Software). Differences were considered to be significant when P < 0.05. All summary data are displayed as means and SD unless indicated otherwise.
General linear models, with and without repeated measures, were applied to analyse associations of CD4⁺ T cell frequencies, the type of antidepressants (strong versus weak/no ASM inhibition) and response after 4 weeks of treatment with the antidepressants. Clinical and sociodemographic variables were compared between patients with strong versus weak/no ASM activity-inhibiting antidepressants with t-tests for independent samples in case of quantitative data and with Fisher exact or Pearson’s chi-square tests in case of qualitative data. The analyses were performed using SPSS for Windows (Releases 25, SPSS Inc.). Only complete data sets were included in the analysis.

For the cytotoxicity assay, 20 mM stock solutions and 200-fold concentrations of the used concentration range (0.1–100 μM) of 1–3 were prepared in DMSO. These stock solutions were diluted to the respective concentrations with serum containing culture medium. Human hepatic cancer cells (HepG2, ACC 180, DSMZ, Braunschweig, Germany) were cultivated in DMEM using standardized culture conditions (37 °C, 5% CO₂, saturated humidified atmosphere). For evaluation of cytotoxic effects, the resazurin reduction assay (Alamar Blue assay) based on O’Brien was performed in 96-well plates (O’Brien et al. 2000 (link)). Cells were treated with 1–3 (0.1–100 μM) and incubated for 24 h, whereas 0.01% (w/v) saponin from Quillaja bark (Sigma-Aldrich, Steinheim, Germany) served as positive control. Viability was calculated after blank subtraction as test over control (T/C). Six replicates of all experiments were performed for each of three independent passages (n = 3). The data are presented as the mean ± standard deviation (SD) in Fig. S22 (Supplementary Material). The IC₅₀ and significance values were calculated with OriginPro 2016G (OriginLab Corporation, Northampton, USA): significant at *p ≤ 0.05 and highly significant at **p ≤ 0.01 and ***p ≤ 0.001.

The ROIs were analysed and sorted using three steps, that is the i) determination of ROI origins, ii) determination of master-ROIs, and iii) assigning IDs to ROIs. For the determination of ROI origins, a late reference frame was defined in which all non-germinated spores are visible. From these ROIs coordinates are extracted that serve as origins. Then master-ROIs were determined, i.e. ROIs in the last frame. These ROIs were verified by checking that they only have a single spore origin. Finally, all ROIs were assigned to a master-ROI, resulting in different integer for each master-ROI (ID). Eventually occurring holes within the germling shape are recovered during the analysis by the ImageJ binary filter “fill holes”.
Finally, a text-file was outputted stating area, temporal information (number of frame), and ID along with additional geometrical information of all ROIs. These data were then further analysed using OriginPro2016G (OriginLab Corporation, Northampton, USA) or Mathematica (Wolfram Inc., Version 11.1). Furthermore, a filtered TIF-file was outputted as binary time series that displayed the growth of each germling.

Statistical analyses were performed using GraphPad Prism 9.0 (GraphPad Software, Inc.), and OriginPro 2016G (OriginLab Corporation). Assumptions of normality and homogeneity of variance were tested before conducting the following statistical approaches. Student’s t-test was used to measure the significance of the differences between two distributions. In case the results failed the test of normality, Mann-Whitney or Kolmogorov-Smirnov test was performed. Multiple groups were compared using a two- or three-way repeated-measures analysis of variance (ANOVA) with Bonferonni’s tests as post hoc comparison. Kruskal-Wallis test with Dunn’s multiple comparisons, or Friedman multiple comparisons in case of paired values, were used for non-normal distributions. Categorical data were analyzed with Fisher’s exact test, and correlations were assessed by the Pearson coefficient. The probability of error level (alpha) was chosen to be 0.05. Unless otherwise stated, results are presented as means ± SEM and documentation of individual data points.

The kinetic parameters of the enzymes were determined by recording enzyme activity with respect to the substrate concentration, followed by nonlinear regression analysis. The regression analysis was performed in OriginPro 2016G by fitting the experimental data to the original Michaelis–Menten equation using the Levenberg–Marquardt algorithm. The error values in the kinetic data represent the standard error from the nonlinear regression analysis.