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Azakheli is relatively smaller riverine breed with a very peculiar characteristics kept under unique traditional husbandry practices in comparison with rest of the Pakistani buffalo breeds; however, milk production is comparable. The present study was conducted to evaluate the genetic diversity of Azakheli breed. A total of sixty-six blood samples were collected for the amplification of mtDNA D-loop region and MC1R gene sequencing analysis. Median-joining network analysis of 191 mtDNA D-loop sequences of Azakheli and eight Indian riverine buffalo breeds clustered into three clades. Ancient Azakheli Region 1 clade was the oldest with the highest mutation steps and was present close to the root of UPGMA phylogenetic tree. There was 5 mutated lines distance between Pakistan buffalo and Indian riverine buffaloes. The populations of neighboring countries did not share any haplotypes with Azakheli buffalo of Pakistan. Possibly, residing for so long in the cold atmosphere and high elevation regions caused the mutation in mtDNA D-loop, though these conditions did not affect the overall performance of Azakheli as milch buffalo breed of Pakistan. MC1R analyses showed high mutations in Azakheli of Albino phenotype and all the black phenotype individuals of Azakheli buffalo share haplotypes with dominant Chinese and Indian black phenotypes buffaloes in MC1R median-joining network, indicating the reason of black coat color is due to MC1R gene. The haplotype diversity and nucleotide diversity was (H. 0.923, Pi: 0.00895) in Azakheli. Current results illustrated Asian ancestry for Azakheli buffalo, and mtDNA and MC1R analyses provided further evidence. Additional genetic analyses and archeological studies may provide further insight into the domestication period and history of Azakheli buffalo breed. The further studies are required on different coat colors with different genes on Azakheli buffalo to understand the phenotype variation. [ABSTRACT FROM AUTHOR]

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants. [ABSTRACT FROM AUTHOR]