Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP)

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR

Rationalisation of the Differences between APOBEC3G Structures from Crystallography and NMR Studies by Molecular Dynamics Simulations

The human APOBEC3G (A3G) protein is a cellular polynucleotide cytidine deaminase that acts as a host restriction factor of retroviruses, including HIV-1 and various transposable elements. Recently, three NMR and two crystal structures of the catalytic deaminase domain of A3G have been reported, but these are in disagreement over the conformation of a terminal β-strand, β2, as well as the identification of a putative DNA binding site. We here report molecular dynamics simulations with all of the solved A3G catalytic domain structures, taking into account solubility enhancing mutations that were introduced during derivation of three out of the five structures. In the course of these simulations, we observed a general trend towards increased definition of the β2 strand for those structures that have a distorted starting conformation of β2. Solvent density maps around the protein as calculated from MD simulations indicated that this distortion is dependent on preferential hydration of residues within the β2 strand. We also demonstrate that the identification of a pre-defined DNA binding site is prevented by the inherent flexibility of loops that determine access to the deaminase catalytic core. We discuss the implications of our analyses for the as yet unresolved structure of the full-length A3G protein and its biological functions with regard to hypermutation of DNA