Genetic and non-genetic factors influencing KLH binding natural antibodies and specific antibody response to Newcastle disease in Kenyan chicken populations

This study aimed at investigating the influence of genetic and non-genetic factors on immune traits to inform on possibilities of genetic improvement of disease resistance traits in local chicken of Kenya. Immune traits such as natural and specific antibodies are considered suitable indicators of an individual's health status and consequently, used as indicator traits of disease resistance. In this study, natural antibodies binding to Keyhole Limpet Hemocyanin (KLH-NAbs) was used to measure general disease resistance. Specific antibodies binding to Newcastle disease virus (NDV-IgG) post vaccination was used to measure specific disease resistance. Titers of KLH-NAbs isotypes (KLH-IgM, KLH-IgG and KLH-IgA) and NDV-IgG were measured in 1,540 chickens of different ages ranging from 12 to 56 weeks. A general linear model was fitted to determine the effect of sex, generation, population type, phylogenetic cluster, line, genotype and age on the antibody traits. A multivariate animal mixed model was fitted to estimate heritability and genetic correlations among the antibody traits. The model constituted of non-genetic factors found to have a significant influence on the antibody traits as fixed effects, and animal and residual effects as random variables. Overall mean (±SE) concentration levels for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 10.33 ± 0.04, 9.08 ± 0.02, 6.00 ± 0.02 and 10.12 ± 0.03, respectively. Sex, generation and age (linear covariate) significantly (p < 0.05) influenced variation across all the antibody traits. Genotype effects (p < 0.05) were present in all antibody traits, apart from KLH-IgA. Interaction between generation and line was significant (p < 0.05) in KLH-IgM and NDV-IgG while nesting phylogenetic cluster within population significantly (p < 0.05) influenced all antibody traits, apart from KLH-IgA. Heritability estimates for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 0.28 ± 0.08, 0.14 ± 0.06, 0.07 ± 0.04 and 0.31 ± 0.06, respectively. There were positive genetic correlations (0.40–0.61) among the KLH-NAbs while negative genetic correlations (−0.26 to −0.98) were observed between the KLH-NAbs and NDV-IgG. Results from this study indicate that non-genetic effects due to biological and environmental factors influence natural and specific antibodies and should be accounted for to reduce bias and improve accuracy when evaluating the traits. Subsequently, the moderate heritability estimates in KLH-IgM and NDV-IgG suggest selection possibilities for genetic improvement of general and specific immunity, respectively, and consequently disease resistance. However, the negative correlations between KLH-NAbs and NDV-IgG indicate the need to consider a suitable approach that can optimally combine both traits in a multiple trait selection strategies.     

1-Octanol/Water Partition Coefficient (Kow) and pKa for Ionisable Pesticides Measured by apH-Metric Method

pK_a values for a wide range of commonly used ionisable pesticides, together with the log K_ow values of the most lipophilic form of each, have been measured using pH-metric techniques. Examples of acids, bases and multiprotic compounds from the major classes of herbicides, and a number of insecticides and fungicides that contain ionisable groups, are included. The pK_a and log K_ow values so obtained were generally in good agreement with values taken from the literature that were measured by other methods. The lower limit of log K_ow that could be measured by the pH-metric method lay below the -0·97 obtained for amitrole, but the method could not be applied to glyphosate for which shake-flask measurements indicated log K_ow below -3. The highest log K_ow obtained in this study was 5·12 for pentachlorophenol. The pH-metric technique offers a rapid and convenient method to determine pK_a and log K_ow for ionisable compounds, especially when utilising an automatic titration system linked to a dedicated computer.     

Using the RUSLE to identify factors controlling erosion rates of mine soils

Observable differences in particle size, smoothness and compaction between cap site (slope 2·8 per cent) and batter site (slope 20·7 per cent) surfaces on the waste rock dump at Ranger Uranium Mine were quantified in terms of revised universal soil loss equation (RUSLE) parameter values. Cap site surface material had a K_m (erodibility corrected for sediment density) of 0·030 and batter site surface material had a K_m of 0·0056. Using these K_m values (derived from particle size distributions), slope length and steepness (LS) factors of 0·36 for the cap site and 3·66 for the batter site, and a cover (C) factor of 0·45 for the cap site and 0·16 for the batter site, the RUSLE predicts an erosion rate from the cap site that is 1·9 times greater than erosion from the much steeper batter site. The RUSLE indicates that the finer particle size and blocky soil structure of the cap site (D₅₀ = 0·91 mm) compared with the looser granular structure of the batter site (D₅₀ = 1·74 mm) strongly influence erosion. The predictions are similar to observed soil losses from erosion plots on these sites under rainfall simulation events, for which the measured erosion rate from the cap site was approximately twice that from the batter site. For the RUSLE to predict the observed erosion rates, the support practice (P) factor for the cap site would have to be approximately 30 per cent greater than the P factor for the batter site. The higher cap site P factor probably results from smoothing and compaction caused by vehicle movement across the surface. Compaction is considered to have greatly reduced infiltration capacity, thus increasing the erodibility of the cap site. Vehicles probably also crushed the surface material at the cap site, creating the observed finer particle size distribution and further increasing the erodibility. Compaction, through its effects on erodibility (K_m) and surface roughness (P), is concluded to be the major cause of higher erosion from the cap site, even though the slope steepness is 10 times less. Parameterisation of the RUSLE quantifies the differences between sites and explains the unexpected erosion rates observed. The results highlight the need for careful management of rehabilitated sites to avoid increases in erosion which may arise from compaction by machinery.