Sas statistical package 9

Data from the experiments were analyzed, based on a completely randomized factorial design, using the SAS Statistical Package 9.3 (SAS Institute Inc., Cary, NC, USA). For germination data, observed values were analysed similarly to other variables and a non-linear regression model was fitted to predict the probability of germination, in Figure 3 (Piegorsch and Bailer, 2005 ; Schwinghamer et al., 2015 (link)). Duncan’s multiple comparison test was used when there was a significant difference at the 95% confidence level.

Analyses were performed using SAS statistical package 9.2 (SAS Institute Inc., 2007). After calculating descriptive statistics, bivariate analysis was used to examine the association of each socio-demographic factors and chronic diseases status with ADL/IADL disability in unadjusted models. Then, we used multiple logistic regression models to examine the association between multimorbidity and ADL/IADL disability after adjusting age, gender, income, and living arrangement.

The VFA and the CNCPS carbohydrate fractions of the second dataset were used for validating the MLR models and the BP3 models. The validation was carried out in three ways: Comparing the observed and the predicted VFA values using the t-test; analyzing the linear regression relationship between the observed and the predicted VFA values using the model: where x refers to the observed acetate, propionate, butyrate, or total VFA production, mmol/g DM of feed mixtures; y refers to the predicted acetate, propionate, butyrate, or total VFA production, mmol/g DM of feed mixtures; calculating the root mean square prediction error (RMSPE) between the observed and the predicted VFA production:
where i = 1, 2, …, n; O_i refers to the observed value; P_i, the predicted value; n, the number of determinations. RMSPE, the ratio of the observed mean used to indicate the whole prediction error, %.
The SAS Statistical Package 9.2 (SAS Institute Inc., Carey, NC, USA) was used for statistical analysis. The MLR relationships between the VFA production and the CNCPS carbohydrate fractions were analyzed using the PROC GLM Procedure. The comparisons between the observed and the predicted values were performed using a t-test. The linear relationships between the observed and the predicted values were analyzed using the PROC REG Procedure.

Experiments were conducted following a completely randomized design with sufficient experimental replicates. The SAS statistical package 9.4 (SAS Institute Inc., Cary, NC, USA.) was used for one-way ANOVA, and Tukey's multiple means comparison was used when there was significance at the 95% confidence level, for hormone, plate screening, proline estimation, and elemental analysis data. Data transformation was applied when necessary to meet the criteria for analysis of variance. Data obtained for untargeted proteomics were analyzed using embedded statistical programs in Scaffold 4.4.8. After normalization of the quantitative spectra, the data were subjected to false discovery rate (FDR), fold change between samples, and Fisher's exact test performed with the Benjamini-Hochberg procedure. The FASTA file generated was analyzed using OmicsBox, for the functional annotation and analysis of the protein sequences (Götz et al., 2008 (link), 2011 (link)). Apart from these, Enzyme code (EC), KEGG maps, and InterPro motifs were queried directly using the InterProScan web service. The mass spectrometry proteomics data have been deposited to Mass Spectrometry Interactive Virtual Environment (MassIVE), with the dataset identifier PXD040670 and doi: 10.25345/C5H98ZP6R

Each piglet was considered an experimental unit for BW evaluation. After transformation, placental and milk transfer values, as well as piglet BW were analyzed using the TTEST procedure of the SAS statistical package 9.4 (SAS Inst. Inc., Cary, NC, US). All data were analyzed considering the treatment as the main effect, and the results are presented as least square (LS) means with their corresponding SEM. Finally, mean significant differences were declared at p < 0.05, while 0.05 ≤ p < 0.10 were considered significant trends.

Experiments were structured following a completely randomized design. The SAS statistical package 9.4 (SAS Institute Inc., Cary, NC, USA.) was utilized. The Proc Mixed procedure and Tukey’s multiple means comparison were used to determine differences among means at the 95% confidence level for the germination and elemental analysis data.
Scaffold 4 was used to analyze the proteomics data for fold change and Fisher’s exact test of the identified proteins, after subjecting the quantitative value of the spectra to the embedded normalization. The FASTA file generated was analyzed using Blast2GO-Pro 3.1.3 [54 (link)–57 (link)], for the functional annotation and analysis of the protein sequences. Apart from these, Enzyme code (EC), KEGG maps and InterPro motifs were queried directly using the InterProScan web service. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [58 (link)] with the dataset identifier PXD004106.

All data were subjected to statistical analysis using two-way ANOVA (SAS Statistical package 9.4). The main effects of gut barrier dysfunction and probiotic, as well as their interaction, were assessed. When significant difference was detected, means were separated and compared using Least Square Differences test. Data were checked for normal distribution. For ELISA assays, occasional outliers were removed from the data if there were ± 3× standard deviations from the mean. Each individually housed bird and its respective sample was considered an experimental unit. The level of significance was considered P < 0.05 and tendency was considered for 0.05 ≤ P ≤ 0.10.