Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods

Hanwoo was originally raised for draft purposes, but the increase in local demand for red meat turned that purpose into full-scale meat-type cattle rearing; it is now considered one of the most economically important species and a vital food source for Koreans. The application of genomic selection in Hanwoo breeding programs in recent years was expected to lead to higher genetic progress. However, better statistical methods that can improve the genomic prediction accuracy are required. Hence, this study aimed to compare the predictive performance of three machine learning methods, namely, random forest (RF), extreme gradient boosting method (XGB), and support vector machine (SVM), when predicting the carcass weight (CWT), marbling score (MS), backfat thickness (BFT) and eye muscle area (EMA). Phenotypic and genotypic data (53,866 SNPs) from 7324 commercial Hanwoo cattle that were slaughtered at the age of around 30 months were used. The results showed that the boosting method XGB showed the highest predictive correlation for CWT and MS, followed by GBLUP, SVM, and RF. Meanwhile, the best predictive correlation for BFT and EMA was delivered by GBLUP, followed by SVM, RF, and XGB. Although XGB presented the highest predictive correlations for some traits, we did not find an advantage of XGB or any machine learning methods over GBLUP according to the mean squared error of prediction. Thus, we still recommend the use of GBLUP in the prediction of genomic breeding values for carcass traits in Hanwoo cattle.

The Effects of Hot Blocks Geometry and Particle Migration on Heat Transfer and Entropy Generation of a Novel I-Shaped Porous Enclosure

This paper studied the cooling performance of a hot electronic chip using nanofluids (NF) mixed convection, implementing Buongiorno’s model of the NF simulation. The NF were assumed water-Al2O3 nanoparticles (NP) in the range of 0 to 4% of volume concentration. Six different problems of the combinations of three internal hot blocks, including triangular, square, and circular geometries, and two porous media, including sand and compact metallic powder, were numerically solved. To discretize the governing equations, a finite control volume method was applied. As most of the proposed correlations for the thermophysical properties of the NF were inaccurate, especially for thermal conductivity, a new predictive correlation was proposed using the multi-variable regression method with acceptable accuracy. It was found that the cooling performance improved with any increase in the NP loading. A higher nanoparticle concentration yielded better cooling characteristics, which was 11.93% for 4% volume. The sand porous medium also yielded a much higher value of the normalized Nusselt number (Nu) compared to the other medium. The entropy generation (EG) enhancement was maximum for the triangular hot block in a sand porous cavity.

The dominant frequency analysis of interfacial wave in wet-gas pipeline transportation based on NIR absorption

During the long-distance transportation of wet-gas, the dominant frequency is of great significance for the study of pipeline fatigue and damage, and the safety production. Therefore, the theoretical and experimental researches for dominant frequency are carried out increasingly. However, most of the current prediction correlation of dominant frequency are mainly applicable to atmospheric pressure conditions (0.1 MPa), and the prediction accuracy is not accurate enough. The paper obtains the time series signal of liquid film thickness by near-infrared (NIR) sensor, and then calculates the wave frequency by the power spectrum density (PSD). The performance of typical predictive correlation is evaluated and analyzed by utilizing the experimental data at different flow and pressure conditions (0.1–0.8) MPa. The structure of Strouhal number and Lockhart-Martinelli (L-M) parameter are optimized reasonably, the mean velocity of the liquid film surface, the density increment of gas core, the gas core mass flow and average liquid film velocity are considered in the L-M parameter, a modified interfacial wave frequency correlation is proposed. The results indicate that the mean absolute error of the predictive correlation is 9.06% (current data) and 25.64% (literature data). The new correlation has a better predictive accuracy.

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

Predictive Correlation between Apparent Sensory Properties and the Formation of Heterocyclic Amines in Chicken Breast as a Function of Grilling Temperature and Time

In the present set of experiments, we studied the correlation between the heterocyclic amine (HCA) concentration and the color changes of the chicken breast with or without skin during grilling under open or closed conditions as a function of the applied temperature and time. The concentration of the HCAs formed during grilling was measured by a validated LC–MS/MS method, whereas the color changes were determined either instrumentally or by visual observation. In general, higher temperatures and longer heat treatment times resulted in a more substantial HCA formation, especially on the surface of the samples and in the skin, where the total levels reached 746 ng/g. Results of regression analysis demonstrate a strong correlation (r > 0.7) between the HCA content of the grilled chicken breast and the L* and a* values indicating the significance of brightness and the red parameter of the color scale, respectively. In the case of open grilling, the skinless breast samples showed correlation (r > 0.7) between the HCA content and the color analysis results in both the full sample and the crust, respectively. Breast samples with skin exhibited the same level of correlation when they were grilled closed. In the case of open grilling the breast with skin, and closed-grilling the skinless breast, the linear regression analysis yielded a weaker correlation (0.7 > r > 0.4 or less) between the HCA concentrations and the color. Our results demonstrate that there is a predictive correlation between the color changes perceptible for the consumers and the HCA formation during grilling of chicken breast as a function of time and temperature depending on the type of grilling and the presence of skin.

Identifying Significant Personal and Program Factors that Predict Online EdD Students’ Program Integration

Based on a synthesis of persistence theory and the empirical literature, an online doctoral program integration model was developed using data from 232 online EdD students. A predictive, correlation design and regression analysis were used to examine if personal factors (sex, race, age, marital status, and presence of children in the home) and program factors (stage in doctoral journey, synchronous interactions, cohorts, and orientations) could predict program integration. The entire model was significant. The variables of sex, race, participation in a cohort, and engagement in synchronous communication individually contributed to the variance in program integration.

Combined vEEG and Cerebral Oximetry Results to Determine the Severity of Hypoxic–Ischemic Encephalopathy

The objectives of the study were to evaluate the prognostic utility of bedside monitoring tools for hypoxic–ischemic encephalopathy (HIE) outcome and develop a prognostic predictive model. This retrospective study reviewed neonatal HIE treated with hypothermia between 2013 and 2016. Continuous video electroencephalography (vEEG) recordings scored for background electrocerebral activity, seizure, and sleep–wake cycles, and rSO2 data were stratified by magnetic resonance imaging (MRI) severity. The vEEG and rSO2 were combined in a predictive model. The analysis included 38 patients. The rSO2 was significantly higher in the severe group. vEEG showed early and persistent maximal suppression in the severe group. The predictive correlation of the rSO2 improves when combined with the vEEG.