Faculty Publications
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Item Expression of Bacillus licheniformis chitin deacetylase in E. coli pLysS: Sustainable production, purification and characterisation(Elsevier B.V., 2019) Bhat, P.; Pawaskar, G.-M.; Raval, R.; Cord-Landwehr, S.; Moerschbacher, B.; Raval, K.Chitosan obtained by enzymatic deacetylation of chitin using chitin deacetylase (CDA) holds promise primarily due to the possibility to yield chitosan with non-random patterns of acetylation and more environmentally friendly process compared to chemical deacetylation. In the present study, a sustainable bioprocess is reported for over-expression of a bacterial CDA in E. coli pLysS cells. A Bacillus licheniformis CDA gene is identified in the genome of the bacterium, cloned, and expressed, yielding enzymatically active recombinant protein. For enzyme production, a growth medium is formulated using carbon and nitrogen sources, which do not compete with the human food chain. The maximum enzyme activity of 320 ± 20 U/mL is achieved under optimized conditions. The CDA productivity is improved by about 23 times in shake flask culture by optimizing operating conditions and medium components. The CDA is purified and the enzyme kinetic values i.e. Km, Vmax and Kcat are reported. Also the effect of cofactors, temperature, and pH on the enzyme activity is reported. Further, economic yield is proposed for production of CDA through this bioprocess. © 2019 Elsevier B.V.Item Detection of heart arrhythmia with electrocardiography(Springer, 2024) Jat, T.; Patil, N.; Bhat, P.Early detection of cardiac arrhythmia, a prevalent form of cardiovascular disease (CVD) impacting millions globally, is heavily reliant on the accurate analysis of heartbeats. Physicians often recommend that patients wear Holter monitors for 24 h or longer to observe concerning cardiac issues, resulting in the collection of substantial amounts of electrocardiogram (ECG) data. Consequently, there is a need to automate the process of interpreting ECGs to detect cardiac abnormalities efficiently. Current state-of-the-art studies rely on handcrafted feature extraction, which may not effectively capture the intricate temporal relationships inherent in ECG signal data. To address this limitation and facilitate the diagnosis of cardiac diseases, this study proposes a technique that converts electrocardiogram signals into images, subsequently training a deep learning model on the generated images. Image encoding techniques such as Gramian Angular Difference Field (GADF), Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF) are employed to translate the ECG signals into images. The highest accuracy, 96.71%, was achieved by training the Convolutional Neural Network (CNN) model using the concatenation of these three image encoding techniques. The proposed approach is assessed using ECG recordings from the MIT-BIH Arrhythmia Database to detect heart arrhythmia, demonstrating the efficacy of the approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.