Jmp 8

Statistical analyses were conducted using JMP 8.0, SAS, Inc. (Cary, NC, USA). The effect of experimental diets on shrimp growth rate and feed conversion ratio, and the effect of experimental diets on animal growth rate, was assessed using a one-factor analytical design. Normal data (indicated by a Jarque-Barre (JB) test for normality; Zaiontz, 2015 ) were tested with a one-way analysis of variance (ANOVA). If the JB test indicated non-normal data, the analyses were conducted nonparametrically using a Kruskal-Wallis test. Post-hoc comparisons of normal data were made and post hoc comparisons of orthogonal contrasts from ANOVA tests were examined using the Real Statistics Resource Pack software (Release 4.9, Zaiontz, 2015 ). The effect of experimental diet on shrimp taste was determined using a two-factor (treatment and order of preference) chi-square goodness-of-fit test. The effect of experimental diets on grunt microbiome was determined using adonis, a permutational multivariate analysis of variance, (PERMANOVA) implemented in R, and the similarities among treatments were calculated using the Bray-Curtis similarity metric and were visualized using a principal coordinates analysis in QIIME (Caporaso et al., 2012 (link)). The significance level for all analyses was set at P ≤ 0.05. All values are presented as mean ± standard error.

Data matrices were subjected to multivariate analyses using the SIMCA-P+ v.12.0 software (Umetrics, MKS Instruments Inc., Andover, MA, USA) for the detection of trends and corresponding metabolite-biomarkers as previously described [52 (link),53 (link)]. Initially, principal component analysis (PCA) was performed for the evaluation of data and detection of outliers.
The discovery of biomarkers was based on scaled and centered partial-least square-discriminant analysis (PLS-DA) regression coefficients (p < 0.05), since by applying PCA, it is not certain that the computed principal components (PCs) represent the largest sources of variation [54 ]. Standard errors were calculated using Jack-knifing (95% confidence interval). The performance of the obtained models was assessed by the cumulative fraction of the total variation of the X’s that could be predicted by the extracted components [Q2 (cum)] and the fraction of the sum of squares of all X’s (R2 X) and Y’s (R2 Y) explained by the current component. Additionally, data were subjected to One-Way ANOVA performing the Student’s t-test (p < 0.05) using the software JMP8.0 (SAS Institute Inc., Kari, NC, USA).

Statistical analyses between KK-LC-1 expression and each factor were performed using Fisher’s exact test. P-values of less than 0.01 were considered as significant. JMP8.0 (SAS institute Japan, Minato-ku, Japan) was used for the analysis.

First, the mean values for R₁, R₂, PD and rCBV for each individual ROI were calculated. These values were used to obtain the group mean and standard deviation of each ROI type (edema-ROI, NAWM-near-Tumor-ROI, and NAWM-contra-ROI).
In the edema-ROI, the relationship between the quantitative values and the distance of each individual voxel to the contrast-enhancing part of the tumor was investigated using mixed linear models. The voxel values of the R₁, R₂, PD, and rCBV values were used as dependent variables, the distance to the tumor-ROI was treated as fixed effect, and subject was treated as a random effect.
The difference in slope for R₁ post-Gd compared to the slope in R₁ pre-Gd was analyzed using Student’s t- test.
All statistical calculations were performed in JMP 8.0 (SAS Inc, Cary, North Carolina).