The study examined the effects of different environmental stress on developmental competence and the relative abundance (RA) of various gene transcripts in oocytes and embryos of buffalo. Oocytes collected during cold period (CP) and hot period (HP) were matured, fertilized and cultured in vitro to blastocyst hatching stage. The mRNA expression patterns of genes implicated in developmental competence (OCT-4, IGF-2R and GDF-9), heat shock (HSP-70.1), oxidative stress (MnSOD), metabolism (GLUT-1), pro-apoptosis (BAX) and anti-apoptosis (BCL-2) were evaluated in immature and matured oocytes as well as in pre-implantation stage embryos. Oocytes reaching MII stage, cleavage rates, blastocyst yield and hatching rates increased (P \ 0.05) during the CP. In MII oocytes and 2-cell embryos, the RA of OCT-4, IGF-2R, GDF-9, MnSOD and GLUT-1 decreased (P \ 0.05) during the HP. In 4-cell embryos, the RA of OCT-4, IGF-2R and BCL-2 decreased (P \ 0.05) in the HP, whereas GDF-9 increased (P \ 0.05). In 8-to 16-cell embryos, the RA of OCT-4 and BCL-2 decreased (P \ 0. 05) in the HP, whereas HSP-70.1 and BAX expression increased (P \ 0.05). In morula and blastocyst, the RA of OCT-4, IGF-2R and MnSOD decreased (P \ 0.05) during the HP, whereas HSP-70.1 was increased (P \ 0.05). In conclusion, deleterious seasonal effects induced at the GVstage carry-over to subsequent embryonic developmental stages and compromise oocyte developmental competence and quality of developed blastocysts.

Interferon-tau (IFN-τ)-induced molecular markers such as ubiquitin-like modifier (ISG15), 2',5'-oligoadenylate synthetase 1 (OAS1) and myxovirus resistance genes (MX1 and MX2) have generated immense attention towards developing diagnostic tools for early diagnosis of pregnancy in bovine. These molecules are expressed at transcriptional level in peripheral nucleated cells. However, their presence in the serum is still a question mark. This study reports sequential changes in expression of MX2 transcript in whole blood and serum MX2 protein level on days 0, 7, 14, 21, 28 and 35 in pregnant (n = 9) buffalo heifers, and on days 0, 7 and 14 in non-inseminated (n = 8) and inseminated non-pregnant (n = 10) control animals. In non-inseminated and inseminated non-pregnant heifers, the differential expression of MX2 transcript and MX2 protein level remained similar between day 7 and 14 post-oestrus. However, in pregnant heifers, on 14th and 28th day post-insemination MX2 transcript was 16.38 ± 1.57 and 28.16 ± 1.91 times upregulated as compared to day 0. Similarly, serum MX2 protein concentration followed analogous trend as MX2 transcript and increased gradually with the progression of pregnancy. Correlation analysis between expression of MX2 transcript and its serum protein level showed a significant positive correlation in pregnant animals, while it was random in other two groups. Therefore, MX2 surge at transcriptional and serum protein level after day 14-28 of pregnancy in buffalo holds potential for its use in early pregnancy detection.

Pre-treatment of donor cells with oocyte extracts and selection of developmentally competent oocytes through BCB staining for recipient cytoplast preparations may enhance expression of developmentally important genes GLUT1, OCT4, DNMT1, BAX, and BCL2 in hand-made cloned embryos at levels similar to IVF counterparts. These results also support the notion that if developmental differences observed in HMC and in vitro fertilization produced foetuses and neonates are the results of aberrant gene expression during the pre-implantation stage, those differences in expression are subtle or appear after the maternal to zygotic transition stage of development.

Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.

Selection of high producing dairy animals is important for dairy profitability and future breeding stock. Thefarmers have relied on physical characters for identification of milk producing ability in animals. In the presentstudy feature selection algorithm were implemented to identify most relevant traits for prediction of peak milk yieldin buffaloes. Based on data recorded from 259 lactating Murrah buffaloes, 14 different body and udder conformation traits, viz. Body Length (BL), Height at Wither (HW), Heart Girth (HG), Body Depth (BD), Paunch Girth (PG), Naval-Udder Distance (NUD), Udder Depth (UD), Rear Udder Height (RUH), Fore Teat Distance (FTD), RearTeat Distance (RTD), Fore Rear Teat Distance (FRTD), Teat Length (TL), Rump Width (RW) and Rear UdderWidth (RUW) were selected. Descriptive statistical analysis revealed that the correlation with peak yield is highestfor RUH, followed RUW, lactation number (LN), NUD, FRTD, HG, RW, RTD, UD, TL, PG, BL, BD, HW andFTD. Correlation-based feature selection in ‘WEKA’ software platform suggested that nine parameters have highcorrelation with peak yield – UD, NUD, RTD, FRTD, TL, RW, RUW, RUH and TL. The Multiple linear regression(MLR) was implemented using the linear regression function available under function classifier in WEKA. TwoRegression models (Model 1 and Model 2) were developed using all fifteen input parameters and with subset of 9input parameters suggested in ‘feature selection’. All models were trained and validated with 10-fold cross validation method. The performance of models developed for prediction peak milk yield was evaluated using the metrics correlation coefficient and root mean squared error (RMSE). Comparison of the performance evaluation matrices revealed that the Model 2 requiring lesser number of inputs is good enough in predicting peak yield with 0.8429 correlation coefficient and 2.16 root mean squared error.