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The bilobal protein kinase-like fold in pseudokinases lack one or more catalytic residues, conserved in canonical protein kinases, and are considered enzymatically deficient. Tertiary structures of pseudokinases reveal that their loops topologically equivalent to activation segments of kinases adopt contracted configurations, which is typically extended in active conformation of kinases. Herein, anisotropic network model based normal mode analysis (NMA) was conducted on 51 active conformation structures of protein kinases and 26 crystal structures of pseudokinases. Our observations indicate that although backbone fluctuation profiles are similar for individual kinase-pseudokinase families, low intensity mean square fluctuations in pseudo-activation segment and other sub-structures impart rigidity to pseudokinases. Analyses of collective motions from functional modes reveal that pseudokinases, compared to active kinases, undergo distinct conformational transitions using the same structural fold. All-atom NMA of protein kinase-pseudokinase pairs from each family, sharing high amino acid sequence identities, yielded distinct community clusters, partitioned by residues exhibiting highly correlated fluctuations. It appears that atomic fluctuations from equivalent activation segments guide community membership and network topologies for respective kinase and pseudokinase. Our findings indicate that such adaptations in backbone and side-chain fluctuations render pseudokinases competent for catalysis-independent roles.

Smoking has severe negative health impact and leads to complications like chronic obstructive pulmonary diseases (COPD), anxiety, mood stimulations, lung cancer, bronchitis, and infertility. The effects are believed to be more profound in case of females. The current study investigates the effects of smoking on cardiac activity in female smokers during the 3 distinct menstrual cycle phases, i.e. Follicular Phase (FP), Ovulatory Phase (OP) and Luteal Phase (LP). The Electrocardiogram (ECG) signal was acquired before and after smoking from the female volunteers who were habitual smokers. The acquired signal was analyzed using Empirical Mode Decomposition (EMD) technique to understand the alterations taking place in the cardiac activity due to smoking. EMD is a recently proposed technique and is quite efficient for the analysis of non-stationary and nonlinear signals like ECG. Statistical analysis was performed using both linear (t-test) and non-linear decision tree methods, namely, Classification And Regression Tree (CART), Boosted Tree (BT) and Random Forest (RF). Further, Artificial Neural Network (ANN)-based classification of the signals obtained from different states were achieved using predominant features that were obtained by the above statistical analysis methods. For all the 3 stages of menstrual cycle, a classification efficiency of more than 90% was obtained evincing a change in the cardiac activity before and after smoking.

In this paper, Recurrence Quantification Analysis (RQA) method has been implemented on RR interval signals to study the changes in the cardiac autonomic regulation among female smokers and non-smokers across different menstrual phases. The 6 min ECG signals of 24 different female volunteers, among which 12 are smokers and 12 are non-smokers, were recorded during different stages of menstruation. The RR interval signals were extracted from these ECG Signals and the analysis of these RR intervals were performed using recurrence plot and RQA. The statistically significant RQA features extracted during each menstrual cycle were identified using a series of statistical tests (t-test, Classification And Regression Tree (CART), Boosted Tree (BT) and Random Forest (RF)). The statistically significant features were applied as input to Multilayer Perceptron (MLP)-based artificial neural networks (ANNs) to classify smokers and nonsmokers. The classifiers could provide the classification accuracies of ≥ 80% during all the different stages of menstruation, supporting the variation in the cardiac autonomic regulation among female smokers and non-smokers.

This chapter is an attempt to understand the effect of audio-visual stimulus with a humorous content on the cardiac electrophysiology. Electrocardiogram (ECG) signals were acquired from 11 female volunteers under the pre- and the post-stimulus conditions. Artificial neural network (ANN)-based classification of the ARMA model coefficients computed from the RR interval signals suggested significant variation in the autonomic nervous system activity. Analysis of the Gabor denoised ECG signals indicated a change in the electrical activity of the heart in the post-stimulus condition, which was confirmed by the ANN-based classification. Recurrence analysis of the RR interval suggested plausible differences of the cardiac activity amongst both the conditions. The audio-visual stimulus has resulted in significant alterations in the ANS and the cardiac physiology.

Detection of analytes (biological markers) is an important process for the confirmation of some disease and/or overdose of drugs. Without the detection of the analytes, the treatment of the patients cannot be initiated immediately. In this regard, various detection methodologies have been proposed. One such method is the colorimetric estimation of the analytes. This chapter discusses the advancements made in the field of devising of instruments for colorimetric estimation. Keeping a view of above, a case study to device a low-cost colorimetric instrument, capable of measuring the optical density (OD) of a solution, has also been reported. The suitability of the developed device for estimating rhodamin-B dye and potassium indigo sulphate and salicylic acid was ascertained by making a standard curve plot. The results suggested that the developed device can be used with great efficiency in estimating analytes by colorimetric assays.

This study portrays the effect of the regional state anthem of Odisha, used as an audio stimulus, on the autonomic nervous system (ANS) and cardiac conduction pathway in Odia females during the first stage of their menstrual cycle. Electrocardiogram (ECG) signal was acquired from eighteen female volunteers, prior to and after exposure to the state anthem (stimulus). In order to study the consequence of the stimulus on the ANS, heart rate variability (HRV) analysis was performed. The results suggested an increase in the parasympathetic activity and a corresponding reduction in the sympathetic activity. ECG signal processing was performed using well founded coherent methods like time-domain, Gabor denoised time-domain and joint time-frequency domain analysis to understand the effect of the stimulus on the cardiac electrophysiology. An alteration in the cardiac electrophysiology in the post-stimulus condition was observed. Artificial neural network (ANN) provided a classification efficiency of ≥ 90% in all the analyses.