Supervised learning techniques for dairy cattle body weight prediction from 3D digital images.

Introduction: The use of automation and sensor-based systems in livestock production allows monitoring of individual cows in real-time and provides the possibility of early warning systems to take necessary management actions against possible anomalies. Among the different RT monitoring parameters, body weight (BW) plays an important role in tracking the productivity and health status. Methods: In this study, various supervised learning techniques representing different families of methods in the machine learning space were implemented and compared for performance in the prediction of body weight from 3D image data in dairy cows. A total of 83,011 records of contour data from 3D images and body weight measurements taken from a total of 914 Danish Holstein and Jersey cows from 3 different herds were used for the predictions. Various metrics including Pearson's correlation coefficient (r), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE) were used for robust evaluation of the various supervised techniques and to facilitate comparison with other studies. Prediction was undertaken separately within each breed and subsequently in a combined multi-breed dataset. Results and discussion: Despite differences in predictive performance across the different supervised learning techniques and datasets (breeds), our results indicate reasonable prediction accuracies with mean correlation coefficient (r) as high as 0.94 and MAPE and RMSE as low as 4.0 % and 33.0 (kg), respectively. In comparison to the within-breed analyses (Jersey, Holstein), prediction using the combined multi-breed data set resulted in higher predictive performance in terms of high correlation coefficient and low MAPE. Additional tests showed that the improvement in predictive performance is mainly due to increase in data size from combining data rather than the multi-breed nature of the combined data. Of the different supervised learning techniques implemented, the tree-based group of supervised learning techniques (Catboost, AdaBoost, random forest) resulted in the highest prediction performance in all the metrics used to evaluate technique performance. Reported prediction errors in our study (RMSE and MAPE) are one of the lowest in the literature for prediction of BW using image data in dairy cattle, highlighting the promising predictive value of contour data from 3D images for BW in dairy cows under commercial farm conditions.

MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes' regulatory interactions.

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes' regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.

The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance.

This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

Controlling mechanisms in directional growth of aggregated archaeal cells.

Members of the family Methanosarcinaceae are important archaeal representatives due to their broad functionality, ubiquitous presence, and functionality in harsh environments. A key characteristic is their multicellular (packet) morphology represented by aggregates of spatially confined cells. This morphology is driven by directed growth of cells in confinement with sequential variation in growth direction. To further understand why spatially confined Methanosarcina cells (and in general, confined prokaryotes) change their direction of growth during consecutive growth-division stages, and how a particular cell senses its wall topology and responds to changes on it a theoretical model for stress dependent growth of aggregated archaeal cells was developed. The model utilizes a confined elastic shell representation of aggregated archaeal cell and is derived based on a work-energy principle. The growth law takes into account the fine structure of archaeal cell wall, polymeric nature of methanochondroitin layer, molecular-biochemical processes and is based on thermodynamic laws. The developed model has been applied to three typical configurations of aggregated cell in 3D. The developed model predicted a geometry response with delayed growth of aggregated archaeal cells explained from mechanistic principles, as well as continuous changes in direction of growth during the consecutive growth-division stages. This means that cell wall topology sensing and growth anisotropy can be predicted using simple cellular mechanisms without the need for dedicated cellular machinery.