Distribution of alpha‐neoendorphin,ACTH (18–39) and beta‐endorphin (1–27) in the alpaca brainstem

Using an immunocytochemical technique, we have studied in the alpaca brainstem the distribution of immunoreactive structures containing prodynorphin (alpha‐neoendorphin)‐ and pro‐opiomelanocortin (adrenocorticotrophin hormone (18–39) (ACTH), beta‐endorphin (1–27))‐derived peptides. No peptidergic‐immunoreactive cell body was observed. Immunoreactive fibres were widely distributed, although in most of the brainstem nuclei the density of the peptidergic fibres was low or very low. In general, the distribution of the immunoreactive fibres containing the peptides studied was very similar. A close anatomical relationship occurred among the fibres containing alpha‐neoendorphin, ACTH or beta‐endorphin (1–27), suggesting a functional interaction among the three peptides in many of the brainstem nuclei. The number of fibres belonging to the prodynorphin system was higher than that of the pro‐opiomelanocortin system. A moderate/low density of immunoreactive fibres was observed in 65.11% (for alpha‐neoendorphin (1–27)), 18.18% (for ACTH) and 13.95% (for beta‐endorphin) of the brainstem nuclei/tracts. In the alpaca brainstem, a high density of immunoreactive fibres was not observed. The neuroanatomical distribution of the immunoreactive fibres suggests that the peptides studied are involved in auditory, motor, gastric, feeding, vigilance, stress, respiratory and cardiovascular mechanisms, taste response, sleep‐waking cycle and the control of pain transmission.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

A Parametric Transfer Function for Grain-Flow Within a Conventional Combine Harvester

Grain yield monitoring is an integral tool in the Precision Agriculture management system. When used in conjunction with a satellite-based navigation system, it provides spatial information on output variability, output response to managed inputs and is used to identify limiting resources in the crop production process. Accurately matching measured yield quantities with spatial units within a field is therefore important. At present, a simple linear time shift is employed by all commercial monitoring systems to account for the delay between GPS recorded positions and subsequent yield measurements. This study examines the internal process of grain transport to the sensor by monitoring the flow of strategically coloured grain. The flow is shown to be significantly influenced by mixing induced by threshing and auger transport processes. In contrast to the common assumption that grain moves as a spatially related cohort through to the sensor, the results suggest that a diffusion process is more realistic. A parametric model for the diffusion process is provided which suggests that from each individual yield measurement a maximum 20% of the mass could be assigned to a single spatial unit of the size that is typically allocated. The results imply that for further analyses, the inconclusive spatial origin and artificially smoothed quantities of instantaneous yield measurements should be considered.