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polycystic ovary syndrome is the most common endocrine, reproductive disorder of women in their reproductive years. It is commonly considered as an androgenic disorder. Apart from high circulating testosterone levels, hyperandrogenism can also be attributed to the hyperactive androgen receptor (AR). Androgen receptor mediated signalling involves the association of multiple coactivators and corepressors and any aberrant activity of these factors may disrupt the normal transcriptional activity of AR and hence may act as contributing factors in the pathophysiology of various diseases. LncRNAs prostate cancer associated non-coding RNA 1 (PRNCR1) and prostate cancer gene expression marker 1 (PCGEM1) act as androgen receptor coactivator lncRNAs. The study was aimed at determining the association of lncRNAs PRNCR1 and PCGEM1 with PCOS. In this case-control study, a total of 178 participants were recruited these included 105 PCOS cases and 73 age-matched healthy controls. The Anthropometric, Metabolic and hormonal characteristics of the participants were determined. Total RNA was isolated from peripheral mononuclear blood cells and reverse transcribed into cDNA. Quantitative real-time PCR (qPCR) was done to study the expression pattern on lncRNAs. The association between different hormonal and metabolic parameters with the expression of PRNCR1 and PCGEM1 was determined by correlation analysis. Our study for the first time demonstrated that lncRNA PRNCR1 and PCGEM1 are overexpressed in Kashmiri women with PCOS. The expression levels of PRNCR1 (PRNCR1, 5.26 ± 1.77 versus 1.22 ± 0.73; P < 0.001) and PCGEM1 (PCGEM1, 3.39 ± 1.88 versus 1.23 ± 0.74; P < 0.001) were significantly higher in PCOS women compared to controls. Furthermore, lncRNA PRNCR1 showed a positive correlation with testosterone (P < 0.001).

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 13 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through PCA was 1. The correlation coefficients between true and predicted values for Artificial neural networks, Bayesian ridge regression, Classification and regression trees, Genetic Algorithms, Gradient boosting algorithm, K nearest neighbours, MARS algorithm, Polynomial regression, Principal component regression, Random forests, Support Vector Machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.762, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. A total of 13 machine learning models were developed from the prediction of breeding values in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies for breeding value prediction.

Background Long noncoding RNAs (lncRNAs) are now proven as essential regulatory elements, playing diverse role in many biological processes including mammary gland development. However, little is known about their roles in bovine lactation process. There are very few reports available to date on the role of lncRNAs in lactation physiology and mammary glands development in cattle. Results To identify and characterize the roles of lncRNAs in bovine lactation, milk derived mammary epithelial cells (MEC) from Jersey (high milk producer) and Kashmiri cattle (low milk producer) at early, mid and late lactation stages were used. The lncRNA transcriptome of the samples (n=18) was studied using next generation RNA sequencing technology. 633 putative lncRNAs were identified, 76 of which were differentially expressed (DE) between comparison between the three stages of lactation. Additionally, 56 DE lncRNAs were identified from 9 Jersey and 9 Kashmir samples. Correlation of DE lncRNAs with protein-coding genes resulted in a comprehensive list of lncRNA-mRNA co-expressed pairs. Most of the DE lncRNAs showed positive correlations with protein coding genes in Jersey compared to Kashmiri cattle where they were mainly negatively correlated, which could be one of the underlying mechanisms responsible for the differential milking performance between the two breeds. In addition, a number of the DE lncRNAs were paired with the most DE milk quality genes like GPAM, LPL, ABCG2, etc. indicative of their potential regulatory effects on milk quality genes. KEGG pathways analysis of potential cis and trans target genes of DE lncRNAs indicated that 27 and 48 pathways were significantly enriched in Kashmiri and Jersey respectively, including mTOR signaling, PI3K-Akt signaling and RAP1 signaling pathways. These pathways have been proven to play key roles in lactation biology and mammary gland development. Conclusions Our study mapped the expression profiles of lncRNAs across lactation stages and their relationships with candidate genes related to milk quality and yield traits in Jersey and Kashmiri cattle. These findings provide a valuable resource for the study of the regulatory mechanisms involved in the lactation process as well as facilitate understanding of the role of lncRNAs in bovine lactation biology.

Background: Keratin-associated protein’s (KRTAPs) are the major constituent proteins of cashmere fibre and have been implicated to have an important effect on the quality traits of this commercially valuable fibre. The objective of the present study was to determine the molecular characteristics of KRTAP9.2 gene in Pashmina (Cashmere) goat, known as Changthangi breed in J and K, India and well known for its finest fiber quality. Methods: We have studied the KRTAP9.2 gene expression status in hair follicles at different fiber development stages viz Anagen (growth) and Telogen (rest) in Changthangi goats of Kashmir and Ladakh region by performing quantitative Real Time PCR from fresh skin biopsies using gene specific primers. Furthermore, KRTAP9.2 structural models were generated and validated using various Bioinformatic tools. Result: Phylogenetic studies reveal close relation of KRTAP9.2 gene with Pantholops hodgsonii KRTAP9.2 (XM-005964828.1) and Ovis aries musimon KRTAP9.9 (XM-012152535.1). In our study we predicted a total of 20 KRTAP9.2 protein structures by QUARK and LOMET server and 5 structures by RaptorX, moreover, the validity of predicted structures was checked by using different parameters like RMSD value, C-Score, TM-value and ramachandran values, all parameters suggested structure predicted by RaptorX as better in our case. Real time PCR results showed expression of KRTAP9.2 in Pashmina hair follicles with a higher expression at the Telogen stage as compared to the Anagen stage. Owing to the structural importance of KRTAPs, such studies are indispensable for deciphering the molecular mechanism of pashmina growth which in turn will improve production, quality and diversity of pashmina fiber.

Background: Sheep farm data is often biased by extreme values which are generally introduced due to errors in manual measurement. These values interfere with the accuracy of estimations especially in state-of-the-art techniques like Machine Learning. Methods: Therefore, winsorization technique was attempted for the removal of outliers from sheep farm data data for 11 years (2011-2021) for body weights at different ages. Some outliers were deliberately introduced into the data to check the efficiency of the technique. This study was conducted during the year 2021. Result: Our results indicate that outlier values of 15.3, 42, 44, 60, 90 for birth weight, weaning weight, 6-month, 9 month and 12-month body weight which were far from the normal range were removed using this technique. The mean and standard deviation values were altered after winsorization. Winsorization technique works well for sheep farm data to remove the bias introduced by outliers and also removes, to a large extent, the need for manual outlier removal in data.

Objective: Polycystic ovary syndrome (PCOS) is one of the most common reproductive, endocrine and metabolic disorders in premenopausal women. Even though the pathophysiology of PCOS is complex and obscure, the disorder is prominently considered as the syndrome of hyperandrogenism. C-Terminal binding protein 1 antisense (CTBP1-AS) acts as a novel Androgen Receptor regulating long noncoding RNA (lncRNA). Therefore, the present study was aimed to establish the possible association of androgen receptor regulating long noncoding RNA CTBP1-AS with PCOS.Methods: A total of 178 subjects including 105 PCOS cases and 73 age-matched healthy controls were recruited for the study. The anthropometric, hormonal and biochemical parameters of all subjects were analysed. Total RNA was isolated from peripheral venous blood and expression analysis was done by quantitative real time PCR (qRT-PCR). The correlation analysis was performed to evaluate the association between and various clinical parameters and lncRNA CTBP1-AS expression.Results and conclusion: the mean expression level of CTBP1-AS was found to be significantly higher in the PCOS women than in the healthy controls (-lnCTBP1-AS, 4.23 ± 1.68 versus 1.24 ± 0.29 p