Short communication: Multivariate outlier detection for routine Nordic dairy cattle genetic evaluation in the Nordic Holstein and Red population

Survey data assessing the junior high school students' learning attitudes toward home-based education amidst the Covid-19 pandemic

This dataset was obtained to determine the learning attitudes of junior high school (JHS) students toward Home-Based Education (HBE). A descriptive - survey approach was used employing a proportional stratified random sampling to determine the samples n = 398 drawn from a total population of N = 75,542 junior high school students enrolled in 42 public secondary schools in Zamboanga City Division for SY 2020-2021. The data collection was conducted from August 2021 to September 2021 amidst lockdown thus, a combined data collection - Online & Offline was conducted using an adopted validated instrument. Out of 398 samples considered, only 383 eligible consenting JHS students completed the survey with a response rate of 96.23% to include 274 (71.54%) Online & 109 (28.46%) Offline. There were two problems investigated to include determining the learning attitudes of JHS students measured in terms of Nature, Anxiety, Expectations, & Openness to Learning, and to determine whether there exists a significant difference between the learning attitudes of junior high school students across four Independent Variables (IVs): gender, grade level, age and socioeconomic status (SES). Mean, Standard Deviation, and MANOVA were used to analyze the data gathered. Data assumptions were employed before employing MANOVA, and based on the results obtained from the data analysis, the overall learning attitudes of junior high school students toward HBE is High; and learning attitude varies significantly across grade level and age in terms of Nature and Anxiety of Learning, and in the SES in terms of Expectations of Learning. .

An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between 'physiologically normal', 'physiologically extreme' and 'implausible' observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.

A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits

A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the [Formula: see text] vectors of regression coefficients (T: number of traits) is assigned the same T-variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO when [Formula: see text] The covariance matrix of the residual distribution is assigned a multivariate Jeffreys prior, and Σ is given an inverse-Wishart prior. The unknown quantities in the model are learned using a Markov chain Monte Carlo sampling scheme constructed using a scale-mixture of normal distributions representation. MBL is demonstrated in a bivariate context employing two publicly available data sets using a bivariate genomic best linear unbiased prediction model (GBLUP) for benchmarking results. The first data set is one where wheat grain yields in two different environments are treated as distinct traits. The second data set comes from genotyped Pinus trees, with each individual measured for two traits: rust bin and gall volume. In MBL, the bivariate marker effects are shrunk differentially, i.e., "short" vectors are more strongly shrunk toward the origin than in GBLUP; conversely, "long" vectors are shrunk less. A predictive comparison was carried out as well in wheat, where the comparators of MBL were bivariate GBLUP and bivariate Bayes Cπ-a variable selection procedure. A training-testing layout was used, with 100 random reconstructions of training and testing sets. For the wheat data, all methods produced similar predictions. In Pinus, MBL gave better predictions that either a Bayesian bivariate GBLUP or the single trait Bayesian LASSO. MBL has been implemented in the Julia language package JWAS, and is now available for the scientific community to explore with different traits, species, and environments. It is well known that there is no universally best prediction machine, and MBL represents a new resource in the armamentarium for genome-enabled analysis and prediction of complex traits.