Sas 9.0 statistical software

Experiments 1 and 2 were analyzed using analysis of variance with a split-plot fitted into a completely randomized design with 8 replications. The factors (treatment, days of storage, and interaction between treatment and days of storage) were compared for differences using Duncan’s new multiple-range test. The overall differences between factor means were considered significant when p<0.05. Experiment 3 was analyzed using a Group t-test to determine the significant differences between the two experimental groups.
Data were analyzed using SAS 9.0 statistical software (SAS Institute, Inc., Cary, NC, USA). All data were first tested for normality and homogeneity of variance and then analyzed by the Proc general linear model procedure for Experiments 1 and 2 and by the Proc t-test procedure for Experiment 3.

All the data are expressed as means ± SD, except that the maximum binding capacity (B_max) and dissociation constant (Kd) are expressed as means ± SE. Differences between groups were determined by the one‐way anova followed by the Fisher's least significant difference test using SAS 9.0 statistical software (SAS Institute Inc., Cary, NC, USA). Ligand‐binding data were analysed by non‐linear regression with the one site‐specific binding option using Prism 5 software (GraphPad Software, Inc., San Diego, CA, USA). A P < 0.05 was considered statistically significant.

The minimum sample size of three samples per group (n = 3 in the study) was calculated using RNA-seq in the liver of adult pigs (Shen et al., 2018 (link)). Litter variation (P > 0.05) was tested using linear regression model (mixed effects). A logarithmic transformation was applied to the fold change of gene abundance in order to generate Figure 1B. Gene expression abundance of NBW sample at birth was used as the denominator, by which the gene expression abundance of all samples was compared. To compare the differences among the groups, one-way analysis of variance and Duncan post-hoc test for multiple comparisons were used for normally distributed data, whereas the Kruskal–Wallis test was used for non–normally distributed data. All analyses were performed using SAS 9.0 statistical software (SAS, Cary, NC, United States). Data are expressed as means with their SEM, and values of P < 0.05 were considered significant.

A linear regression mixed model, composed of random and fixed effects, was used to analyze data from hematological reconstitution and plasma marker levels. For variable frequency, the data underwent logarithmic transformation. This model allows multiple longitudinal observations per individual across a baseline period and subsequent time points after transplantation. Besides, this model applies to the analysis of data in which responses are grouped (more than one measure to the same individual), when the assumption of independence among observations in the same group is not adequate. The fixed effects were groups and periods. The random effects were associated with patients since it was necessary to control correlations among repeated measures. For variable frequency, we used a logarithmic transformation to fit the data to the proposed model. The analyses of each variable were controlled by patient’s age. Data analysis was performed using SAS^®9.0 statistical software (SAS Institute Inc., Cary, NC, USA). Receiver-operating characteristic (ROC) curves were calculated to identify potential markers of graft failure and area under the curves (AUC) ≥ 0.7 were considered. Significance was set at P<.05 and n≥3. Statistical significance was set at p < 0.05.