Matlab 2015

An intraclass correlation (ICC) was conducted to assess the reliability of the contouring and segmentation from three independent measurements. The operator is blinded to the age and utilized scanner. Paired t-tests were conducted to detect the differences between the data from the different scanners. Pearson’s correlations were used to determine the relationship and predictiveness between microCT-1µm and microCT-6 µm measures. One-way ANOVA with Tukey’s LSD post-hoc comparisons was used to determine the effects of aging on notochord percentage volume and S.a./V. Statistical analyses were conducted using, Prism 6.0 h (GraphPad Software, La Jolla CA), Excel 2011 (Microsoft, Redmond WA), and Matlab 2015 (Mathworks, Natick MA).

Statistical analysis was performed using Matlab 2015 (Version 8.1.0.604, the Mathworks, Nattick, MA, USA). Bland-Altman plots were generated to analyze perfusion parameters for every single patient for all possible pairs of two different models, respectively. Lilliefors test was conducted to test for normal distribution within the three groups. Since not all data was normally distributed, nonparametric paired Wilcoxon sign rank test was employed for further analysis of the quantitative perfusion values within different models. A significance level of P < 0.05 was set.

Statistical analysis was performed with SAS 9.0 (SAS Institute, Cary, NC, USA) and Matlab 2015 (Mathworks Inc., Natick, MA, USA). Differences in D_EE and D_I computed from the AIM, diary, and push button at time resolutions 0.1–30 s were analyzed with a linear mixed model with participant as a random factor. If the mixed model showed a significant difference among methods, Tukey–Kramer post hoc multiple comparisons analysis was performed to determine which methods differed from each other. Data for the N_B at different time resolutions were analyzed by one-way repeated ANOVA to determine whether different time resolution yielded differing results. Since the parametric method did not pass the residual diagnostic criteria, a non-parametric Friedman test method was adopted for repeated ANOVA. If the ANOVA results were significant, Tukey–Kramer post hoc multiple comparisons test was performed to determine which time resolutions differed from each other. To assess the relative bias (mean difference) and random error (1.96 SD of the difference) between methods, the Bland and Altman plots (39 (link)) were investigated. Statistical significance was assumed at p-value <0.05.

The calculation of RDF, also known as pair-correlation function, was performed based on an established method written in MATLAB 2015 (MathWorks) (Caetano et al., 2015 (link)). In brief, for each cell nucleus, we divided the entire segmented cell nucleus into a maximum possible number of non-overlapping sub-regions with a size of 500 nm × 500 nm. Within each sub-region, we calculated the RDF by adapting the function “spatialStats” in the published software package MIiSR (Caetano et al., 2015 (link)). The RDF quantifies the density of localized spots as a function of distance (r) to other localized spots (self-clustering) based on the original coordinates of localized spots (or single molecules) from a single color. It illustrates the presence of multiple cluster sizes and intercluster distance without any assumptions on the shape of the clusters, and the relative degree of clustering is indicated by the height of the peaks corresponding to the molecular clusters (Caetano et al., 2015 (link)).