Genstat

Data are represented as mean ± SEM and were analyzed using GenStat (GenStat, 10th Edition, VSN International Ltd, Oxford). In the case of experiments testing the influence of a single factor a one-way ANOVA was performed. Where two factors were involved data were analysed by two-way ANOVA. P<0.05 was considered statistically significant.

Data are presented as mean ± SEM and were analysed using GenStat (GenStat, Eighth Edition (2005), VSN International Ltd., Oxford). In the case of experiments testing the influence of a single factor, a one-way ANOVA was performed. Where two or three factors were compared in a single experiment, a two- or three-way ANOVA followed by post hoc Student’s t tests based on the LSD were performed. In this case, the ANOVA results are expressed in the figure legend and the results of the Student t test are represented on the graph. P < 0.05 was considered statistically significant.

Initial ejaculate characteristics, motility (Exp 1), viability, acrosome integrity and percent normal morphology (Exp 2 only) assessed prior to freezing, were analysed using an ANOVA and means were compared using a two-sample t-test comparison of means assuming unequal variances. Post-thaw motility kinematics (Exp 1) were assessed using an ANOVA and means were compared using a paired two-sample t-test. t-tests were conducted in Genstat (Version 18, VSN International, Hemel Hempstead, UK).
In Experiment 2, sperm motility, viability, acrosome integrity, lipid membrane disorder, intracellular ROS production and DNA integrity were analysed using a restricted maximum likelihood model (REML) in Genstat. Treatment and timepoint (0, 1.5 or 3) were specified as fixed effects, while individual bull was specified as a random effect. REMLS were used in the current study to investigate whether there was any interaction between the influence of treatments on changes in sperm parameters over time. These interactions were dropped from the model if not significant (p > 0.05).
Normal distribution of data and homogeneity of variances were assessed within Genstat. For both experiments and variables, means were reported with ± S.E.M and p < 0.05 was considered statistically significant.

Unbalanced analysis of variance (ANOVA) was conducted on cooking loss, texture, collagen characteristics, and IMF content with purchase day as a blocking factor using GenStat (16th Edition, VSN International). Correlation matrices were obtained using RStudio (RStudio, PBC) between cooking loss, texture parameters, IMF, and collagen characteristics of both and individual muscles. Principal component analysis (PCA) was performed on both muscles in RStudio (RStudio, PBC) and the PCA loading plot was plotted.

The statistical comparisons between the aCO₂- and eCO₂-derived residues were performed for C and N concentrations, C:N ratio and ¹³C abundances using Paired Student’s t-test at the 0.05 significance level (SPSS 20.0 for Windows).
The significance in α diversity of prokaryotic community and relative abundance at the genus level over time between the residue treatments was tested using the two-way ANOVA (GenStat, version 13.0, VSN International, Hemel Hemspstead, United Kingdom) and Duncan’s multiple range test at a 0.05 significance level (SPSS 20.0 for Windows). The data of the relative abundances of genera in ¹³C-DNA fractions were log-transformed to follow a normal distribution before variation analysis. Changes over time in prokaryotic community structure in response to residue amendments were analyzed using the principal co-ordinates analysis (PCoA) based on Bray-Curtis distance. The effect of residue amendment on prokaryotic community structure in ¹³C-DNA fractions over time were analyzed using PCoA based on Unweighted-Unifrac distance. The permutational multivariate ANOVA (PERMANOVA) was performed to assess the significance of difference in the microbial community structure between treatments. It was achieved by running the adonis function with the “vegan” package in the R version 3.3.1 for Windows (R Development Core Team, 2010 ; ADONIS, Oksanen et al., 2014 ).

Data were analyzed via a combination of descriptive techniques (means, frequencies, percentages) using Microsoft Excel, including segmentation of consumers on the basis of factors such as sex, age, ethnicity, prior consumption of insects, and food neophobia, as well as analysis of variance (ANOVA) using GenStat (15th Edition, VSN International Limited, Herts, UK).