We reported our comparative observations on oligotrophs vs. copiotrophs from a hyper-alkaline and hypersaline habitat, Lonar Lake, situated in the Buldhana district of Maharashtra, India. Cell numbers of oligotrophic and copiotrophic microbes from the sediment were enumerated by the three-tube most probable number (MPN) method using an array of nutrient-rich and oligotrophic (≈10–20 mg carbon L−1) media offering simulated natural conditions of pH and salinity. A total of 50 strains from 15 different genera and 30 different species were isolated from the highest positive dilutions of MPN to identify the taxa of oligotrophs and copiotrophic microorganisms dominating in Lonar Lake. We did not get any true oligotrophs due to their adaptation to higher carbon levels during the isolation procedure. On the contrary, several true copiotrophs, which could not adapt and survive on a low-carbon medium, were isolated. It is also observed that changes in medium composition and nutrient level altered the selection of organisms from the same sample. Our data indicate that copiotrophic microorganisms dominate the eutrophic Lonar Lake, which is also supported by the past metagenomics studies from the same site. We also reported that quick depletion of carbon from oligotrophic medium worked as a limiting factor, inducing cell death after 2–3 generations and preventing the development of visible colonies on plates and sufficient optical density in liquid medium. Therefore, a long-term supply of low levels of carbon, followed by isolation on enriched media, can serve as a good strategy in isolation of novel taxa of microorganism, with industrial or environmental importance.
Electrocardiogram (ECG) Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. ECG is a non-linear, nonstationary signal. It is used for the primary diagnosis of heart abnormalities. The ECG signals were taken from MIT-BIH arrhythmias database for analysis. Noises in the ECG signal such as powerline interference, baseline wandering and muscle noises were removed using bandpass filter. Different statistical and morphological features were extracted for both normal as well as abnormal cases. These features include R-R interval, heart rate arithmetic mean, median, variance, skewness, kurtosis etc. The values of the feature vector reveal the information regarding status of cardiac health. Platform used for implementation is MATLAB. This paper includes comparative analysis of various transform-based techniques. For differentiating normal and abnormal signals, we have used KNN classifier. We have achieved an accuracy of 86.95%, sensitivity of 87.09% and specificity of 86.66% for 60% training dataset using this classifier.
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