Genotyping of Toxoplasma gondii in domestic animals from Campeche, México, reveals virulent genotypes and a recombinant ROP5 allele.

Toxoplasma gondii has at least 318 genotypes distributed worldwide, and tropical regions usually have greater genetic diversity. Campeche is a state located in the southeastern region of México and has favourable climate conditions for the replication and dissemination of this protozoan, similar to those in South American countries where broad genetic diversity has been described. Thus, in this study, 4 T. gondii isolates were obtained from tissues of stray dogs and free-range chickens in Campeche, México, and were genotyped by Mn-PCR-RFLP with 10 typing markers (SAG1, altSAG2, SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1 and Apico) and 5 virulence markers (CS3, ROP16, ROP17, ROP18 and ROP5) to provide new information about the distribution and virulence prediction of T. gondii genotypes. Two isolates of T. gondii genotype #116 and 2 of genotype #38 were obtained from stray dogs and chickens, respectively. The parasite load found in these species was between <50 and more than 35 000 tachyzoites per mg of tissue. Virulence marker genotyping revealed a recombinant 1&3 ROP5 RFLP pattern in 2 ToxoDB #116 isolates with no prediction of virulence in a murine model, while in the 2 ToxoDB #38 isolates, the ROP18/ROP5 combination predicted high virulence. Considering all the typed markers, there is a predominance of type I and III alleles, as constantly reported for the isolates characterized in various regions of México. It is crucial to determine their phenotype to corroborate the genetic virulence profile of the T. gondii isolates obtained in this study.

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico.

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.