Originpro

The outcomes of independent experiment were performed in triplicates and were expressed as mean ± SD. To analyse the statistical differences in EO yield (%) and quality across Curcuma species, a one-way ANOVA followed by a post-hoc test was carried out using the statistical software (Minitab 17). The data were also analysed using OriginPro (2023 version), GraphPad Prism 8.0.2 and Microsoft Office Excel (version 2304, 2019).

Data were analyzed in Matlab, GraphPad Prism and OriginPro software. Standard error of the mean (S.E.M.) and Student’s t-test (two-tailed with criteria of significance: *p < 0.05; **p < 0.01, and ***p < 0.001) were calculated when applicable, and n.s. denotes “not significant”.

Single-molecule fluorescence data were analysed using SPARTAN analysis software in MATLAB^{21 (link)}. FRET trajectories were calculated from the emitted donor and acceptor fluorescence intensities (I_D and I_A, respectively) as E_FRET = I_A/(I_A + I_D). FRET trajectories were selected for further analysis on the basis of the following criteria: single-step donor photobleaching; a signal-to-noise ratio >8; fewer than 4 donor-blinking events; and FRET efficiency above baseline for at least 50 frames. Further, single-molecule traces exhibiting FRET values above 0.8 were excluded from analysis. This subpopulation was insensitive to phosphorylation and nucleotide, and probably reflected denatured molecules. For kinetic analysis, traces were also manually curated to remove obvious photophysical artefacts. FRET trajectories were idealized using the segmental k-means algorithm^{52 (link)} with a model containing two non-zero-FRET states with FRET values of 0.25 ± 0.1 and 0.48 ± 0.1. Data were further analysed with GraphPad Prism and OriginPro.

All trajectory data was analyzed blindly. Each experiment contains data from at least n = 5 cells and were repeated at least three times. Statistical analysis, performed with OriginPro or GraphPad Prism 8, included all data points obtained for each cell, cells that failed to internalize fluorescent cargo were not included. No test for outliers was employed and the outliers are part of the plots. Data are presented as mean ± standard deviation, unless otherwise noted. Differences in population means was assessed using unpaired t-tests, probability (p) values < 0.01 were considered as significant (*<0.01, **<0.001, ***<0.0001, ****<0.00001).

Data are expressed as mean ± SEM. GraphPad Prism 8.2.1 software, Excel and OriginPro (v.9.8) were utilized in data presentation. Statistical analyses were conducted using SPSS 26 (IBM, Armonk, NY). Kolmogorov–Smirnov normality test was used to analyze the normal distribution of the data. The frequency distribution histogram of total distance, average velocity and central distance/total distance was fitted using simple Gaussian algorithm. Two-tailed paired t-tests were applied to compare the mean value between pre-training and post-training. Two-tailed unpaired t-test, Wilcoxon Matched-Pairs Signed-Ranks Test were applied to compare other mean values. Bivariate correlation analysis was performed to understand the relations between parameters from swimming exercise and the biological indices of the swimming training. The correlation coefficient was expressed as Pearson’s r. The number of independent experiments and the statistical tests employed are indicated in the respective figure legends. p < 0.05 was considered statistically significant.

Statistical analyses were performed via analysis of variance (ANOVA) and/or student’s t-test using OriginPro^® and GraphPad Prism. Statistically significant differences were considered for $p<0.05$ .