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Hot springs are considered to be a unique environment with extremophiles, that are sources of industrially important enzymes, and other biotechnological products. The objective of this study was to undertake, analyze, and characterize the microbiome of two major hot springs located in the state of Madhya Pradesh explicitly, Chhoti Anhoni (Hotspring 1), and Badi Anhoni (Hotspring 2) to find out the inhabitant microbial population, and their functional characteristics. The taxonomic analysis of the microbiome of the hot springs revealed the phylum Proteobacteria was the most abundant taxa in both the hot-springs, however, its abundance in hot-spring 1 (~88%) was more than the hot-spring 2 (~52%). The phylum Bacteroides (~10–22%) was found to be the second most abundant group in the hot-springs followed by Spirocheates (~2–11%), Firmicutes (~6–8%), Chloroflexi (1–5%), etc. The functional analysis of the microbiome revealed different features related to several functions including metabolism of organics and degradation of xenobiotic compounds. The functional analysis showed that most of the attributes of the microbiome was related to metabolism, followed by cellular processes and environmental information processing functions. The functional annotation of the microbiomes at KEGG level 3 annotated the sequences into 279 active features that showed variation in abundance between the hot spring samples, where hot-spring 1 was functionally more diverse. Interestingly, the abundance of functional genes from methanogenic bacteria, was higher in the hot-spring 2, which may be related to the relatively higher pH and temperature than Hotspring 1. The study showed the presence of different unassigned bacterial taxa with high abundance which indicates the potential of novel genera or phylotypes. Culturable isolates (28) were bio-prospected for industrially important enzymes including amylase, protease, lipase, gelatinase, pectinase, cellulase, lecithinase, and xylanase. Seven isolates (25%) had shown positive results for all the enzyme activities whereas 23 isolates (82%) produced Protease, 27 isolates (96%) produced lipase, 27 isolates produced amylase, 26 isolates (92%) produced cellulase, 19 isolates (67%) produced pectinase, 19 isolates (67%) could produce lecithinase, and 13 isolates (46%) produced gelatinase. The seven isolates, positive for all the enzymes were analyzed further for quantitative analysis and identified through molecular characterization.

The concept of dual frame-based estimators has been already developed in sample surveys. These dual frame estimators are theoretically optimal in some cases but difficult to apply in practice, while the others are generally applicable but may have larger variances. In this chapter, we propose Joint Calibration Estimator (JCE) under minimum entropy distance function for the dual frame surveys. The proposed estimator has smaller bias and considerable decrement in variance under Lahiri–Midzuno design as compared to Simple Random Sampling Without Replacement when sample size increases. In addition, we obtain the optimal weights along with its sensitive weighing interval for the combined JCE under non-overlapping frames for which it is more efficient than individual frame-based estimator.