Peripheral Blood and Milk Leukocytes Subsets of Lactating Sarda Ewes

Leukocytes subpopulations in blood and milk of lactating Sarda ewes were investigated. Animals characterized by a SSC level <500×103cells/mL and a negative bacteriological examination were sampled in early, mid and late lactation. Milk differential cell count evidenced that macrophage represented the main population (42.8%±3.5) followed by lymphocytes (40.2%±3.4) and neutrophils (8,6%±2.1). Flow cytometry analysis showed that lymphocytes subsets in milk were quite different from blood. High CD8+ and low CD4+ lymphocytes percentages determined a CD4/CD8 ratio inversion in milk compared to blood (0.3%±0.03 vs 1.8%±0.08). CD8+ decreased while, conversely, CD4+ increased in late lactation. γδ T cells were more represented in milk (12.6%±1.3) than in blood (6.8%±0.3) and their proportions appeared similar throughout lactation in both compartments. IL-2 receptor was mainly expressed in milk on T cytotoxic lymphocytes. Data obtained in uninfected mammary glands could allow an early discrimination between physiological and pathological changes occurring in ewe milk. Further phenotypical and functional studies on milk leukocytes subsets might help to understand defense mechanisms of the ovine mammary gland against IMI

Kinetics of fat and protein secretion in dairy cattle, sheep, goats and buffaloes

The negative correlations of fat and protein concentrations and milk yield, existing in all ruminants dairy species (Oftedal, 1984; Mepham, 1987), reflect a deep mechanism regulating the respective kinetics of secretion of carrier (mainly lactose which is the major responsible for the water drawn to the milk) and of fat and protein. Whereas the correlation coefficients are low (from –0.2 to – 0.4), fat and protein daily yield and milk production are positively and strongly linked (r = 0.8÷0.9). It means that more productive animals have higher fat and protein yield, but their milk has lower concentration of these components. The aim of this work is to investigate the relationships between milk, fat and protein yield in all main ruminant dairy species by using a simple mathematical model

Comparison of parametric, orthogonal, and spline functions to model individual lactation curves for milk yield in Canadian Holsteins

Test day records for milk yield of 57,390 first lactation Canadian Holsteins were analyzed with a linear model that included the fixed effects of herd-test date and days in milk (DIM) interval nested within age and calving season. Residuals from this model were analyzed as a new variable and fitted with a five parameter model, fourth-order Legendre polynomials, with linear, quadratic and cubic spline models with three knots. The fit of the models was rather poor, with about 30%-40% of the curves showing an adjusted R-square lower than 0.20 across all models. Results underline a great difficulty in modelling individual deviations around the mean curve for milk yield. However, the Ali and Schaeffer (5 parameter) model and the fourth-order Legendre polynomials were able to detect two basic shapes of individual deviations among the mean curve. Quadratic and, especially, cubic spline functions had better fitting performances but a poor predictive ability due to their great flexibility that results in an abrupt change of the estimated curve when data are missing. Parametric and orthogonal polynomials seem to be robust and affordable under this standpoint

A Multivariate measure of lactation persistency for dairy sheep

The persistency of lactation, i.e. the ability of animals to maintain a rconstant level of production after the lactation peak, represents an interesting trait for animal breeding strategies, allowing for the increase of profitability of animal husbandry via the reduction of production costs. Dairy cattle with flatter curves show a higher reproductive efficiency, a better metabolic status and have their nutritional requirements more constantly spread throughout lactation, allowing for the use of cheaper feeds (Dekkers et al., 1998; Solkner and Fucks, 1987). Also in dairy sheep the persistency could represent an interesting trait for breeding purposes. A main problem for the introduction of this trait in an aggregate genotype is represented by the difficulty in finding an objective measure: several measurements of lactation persistency have been proposed but none of them is widely accepted (Gengler, 1996). Recently a new index of the persistency based on multivariate Factor analysis, has been proposed for dairy cattle (Macciotta et al., 2002). Aim of the present work is to check the suitability of this index to discriminate lactation curves with different persistency and to analyse the effect of some environmental factor on this trait

Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs

Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs) were compared using simulated data provided by the XII QTL-MAS workshop: i) Bonferroni correction of the significance threshold and ii) Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P&lt;0.01 and P&lt;0.001 significance thresholds. From the set of markers significant at P&lt;0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%). The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively). Interestingly, halving or dividing by four the number of SNPs significant at P&lt;0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method

Use of a partial least squares regression model to predict Test Day of milk, fat and protein yields in dairy goats

A model able to predict missing test day data for milk, fat and protein yields on the basis of few recorded tests was proposed, based on the partial least squares (PLS) regression technique, a multivariate method that is able to solve problems related to high collinearity among predictors. A data set of 1731 lactations of Sarda breed dairy Goats was split into two data sets, one for model estimation and the other for the evaluation of PLS prediction capability. Eight scenarios of simplified recording schemes for fat and protein yields were simulated. Correlations among predicted and observed test day yields were quite high (from 050 to 088 and from 053 to 096 for fat and protein yields, respectively, in the different scenarios). Results highlight great flexibility and accuracy of this multivariate technique

The Mathematical description of lactation curves in dairy cattle

This review gives an overview of the mathematical modelling of lactation curves in dairy cattle. Over the last ninety years, the development of this field of study has followed the main requirements of the dairy cattle industry. Non-linear parametric functions have represented the preferred tools for modelling average curves of homogeneous groups of animals, with the main aim of predicting yields for management purposes. The increased availability of records per individual lactations and the genetic evaluation based on test day records has shifted the interest of modellers towards more flexible and general linear functions, as polynomials or splines. Thus the main interest of modelling is no longer the reconstruction of the general pattern of the phenomenon but the fitting of individual deviations from an average curve. Other specific approaches based on the modelling of the correlation structure of test day records within lactation, such as mixed linear models or principal component analysis, have been used to test the statistical significance of fixed effects in dairy experiments or to create new variables expressing main lactation curve traits. The adequacy of a model is not an absolute requisite, because it has to be assessed according to the specific purpose it is used for. Occurrence of extended lactations and of new productive and functional traits to be described and the increase of records coming from automatic milking systems likely will represent some of the future challenges for the mathematical modelling of the lactation curve in dairy cattle

A Six-year investigation on reproductive performance of hybrid rabbits: 1.: pregnancy rate and numerical productivity at weaning as affected by season

With the aim to clarify the effect of seasonal variation on reproductive performance of hybrid rabbits, a six-years investigation was carried out. Traits analysed were pregnancy rate of does and numerical productivity at weaning. The data set included: 33588 matings and subsequent pregnancy diagnosis; 245743 young rabbits at weaning. From the statistical analysis, pregnancy rate and numerical productivity at weaning appeared to be significantly (P&lt;0.001) affected by seasonal variation. Furthermore a statistically significant (P&lt;0.001) month influence was also found. Nevertheless a correlation between the two parameters needs to be performed to supplement our analysis

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed