This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R², RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R² value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

The food industry is always looking for ways to innovate in elaborating and packaging food products to ensure the consumer receives the highest quality. The new proposals include the use of polycaprolactone (PCL), a commonly used biopolymer th...

Reliable and cost-effective live weight determination is critical for improving both management and selection efficiency in small-scale sheep farming. In r

Dragon fruit peel is a by-product rich in bioactive compounds, such as polyphenols and betalains. In this study, ultrasound-assisted enzyme extraction (UAEE) was proposed to exploit this, combining the advantages of the enzymatic hydrolysis and ultrasound extraction. The effect of extraction time, temperature, and enzyme quantity were evaluated using a Box–Behnken design. Total betalains and polyphenol contents were determined spectrophotometrically. The results show that the extraction of total polyphenols was significantly affected (p ≤ 0.05) by the enzyme quantity, while temperature had a significant effect (p ≤ 0.05) on the extracted betalains. The optimal conditions for the extraction of total betalains and polyphenols were a temperature of 20 °C, an extraction time of 20 min, and an enzyme/substrate ratio of 400 mg/g. Under optimized conditions, the extraction efficiency reached 565.6 ± 12.9 µg/g for total betalains and 14.9 ± 2.4 mg/g for total polyphenols. In addition, UAEE showed the best extraction yields compared to other methodologies, such as microwave, ultrasound, and enzymatic hydrolysis extraction (p ≤ 0.05). This study helps us to understand how the temperature, time, and amount of enzymes affect the extraction of total polyphenols and betalains present in the peel of the dragon fruit using the UAEE technique.

Understanding the functional mechanisms of genes influencing economically important traits in the domestic pig is essential for optimizing marker-assisted selection (MAS). This study aimed to characterize the biological functions, molecular mechanisms, and metabolic pathways of genes associated with morphological, productive, reproductive, and carcass quality traits through a functional bioinformatics approach. Genes were compiled from 116 peer-reviewed studies published between 2000 and 2024, and subsequently grouped according to trait. A de novo functional bioinformatics analysis was performed on this dataset. Functional enrichment analysis was conducted using DAVID and the clusterProfiler package in R, applying FDR correction (≤0.05). Protein-protein interaction (PPI) networks were explored using STRING. No individual gene was consistently reported with high frequency. Among the most frequently reported genes were VRTN (17 studies) for teat number, HOMER1 (3 studies) for leg strength, and BMPR1B (3 studies) for litter size. Enriched GO terms included processes such as positive regulation of transcription (GO:0045944), chondrocyte differentiation (GO:0032331), and SMAD signaling (GO:0060391; an FDR = 7.34 × 10⁻⁷). The PPI networks revealed key genes involved in signaling and immune regulation. In conclusion, this bioinformatics analysis provides an integrated functional overview of the genes underlying key economic traits in pigs, identifying pleiotropic pathways such as SMAD/TGF-β signaling, which supports the development of more effective MAS strategies in pig breeding programs.

Male turkeys are raised mainly for meat production due to their high carcass yields and good capacity to convert food into meat. However, their carcass characteristics remain poorly understood. The objective of the study was to describe the carcass traits of 45 male native Mexican turkeys raised in the municipality of Champoton, Mexico, using principal component analysis (PCA). Fourteen carcass traits, namely, slaughter weight (SW), hot carcass weight (HCW), cold carcass weight (CCW), dressing percentage (DP), neck weight (NEW), foot weight (FEW), breast weight (BRW), thigh weight (THW), drumstick weight (DRW), wing weight (WIW), back weight (BAW), gizzard weight (GIW), heart weight (HEW), and liver weight (LIW), were collected. Pearson’s correlation analysis revealed strong positive relationships among carcass variables, with the highest correlations observed between CCW and HCW (r = 0.99; p < 0.001), SW and HCW (r = 0.98; p < 0.001), and SW and CCW (r = 0.98; p < 0.001). Hierarchical clustering identified four main groups of variables with similar correlation patterns. Three principal components (PCs) with eigenvalues greater than 1.0 were extracted, explaining 85.48% of the total variance in carcass traits. The first principal component (PC1) contributed 72.81% of the total variation (eigenvalue = 10.19), with high loadings (>0.70) for CCW (0.98), HCW (0.98), SW (0.98), DRW (0.95), BRW (0.91), WIW (0.90), THW (0.89), HEW (0.87), BAW (0.81), and FEW (0.82), representing a general size factor. PC2 explained 6.86% of the variance (eigenvalue = 0.96), characterized by a negative loading for DP (−0.64) and positive loadings for GIW (0.35) and LIW (0.34). PC3 accounted for 5.81% of the variance (eigenvalue = 0.81), with a negative loading for LIW (−0.63) and positive loadings for NEW (0.51) and FEW (0.46). Communality values exceeded 0.85 for all variables, indicating adequate representation in the reduced dimensional space. It was concluded that PCA effectively reduced dimensionality while retaining 85.48% of original information and can be used for the improvement of the carcass traits of male native Mexican turkey breeding programs.