Image Comparison Based On Local Pixel Clustering

Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. This problem, however, is complicated because (i) the observed images often contain noise, (ii) the image intensity functions are discontinuous and have spatial structures. In the literature, a vast majority of the methods are intensity-based. However, such an approach is often questionable in real life situations where small changes in the background may not indicate an actual meaningful change in the images as long as the boundaries of the image objects remain the same. In this article, we propose a flexible and effective image comparison method based on local pixel clustering and construct a test statistic based on the Variation of Information metric. This is a feature based image comparison technique where edges or the jump points are considered as the primary features. Numerical examples and statistical properties show that the proposed image comparison method performs well in various real life scenarios.</p

Identification and structural studies of natural inhibitors against SARS-CoV-2 viral RNA methyltransferase (NSP16)

Pathogenic RNA viruses are emerging as one of the major threats and posing challenges to human community. RNA viruses have an exceptionally shorter generation time and easy to adapt in host cells. The recent emergence of SARS-CoV-2, a long RNA virus, has shown us how difficult it is to overcome this kind of pandemic without understanding the viral infection and replication mechanisms. It is essential to comprehend replications of the viral genome, including RNA polymerization and the final capping process. The mRNAs of SARS-CoV-2 coronaviruses are protected at their 5′-ends by cap structure. The cap-like system plays a significant role in viral translational process, viral RNA stability, and scatting in detecting innate immune recognition in host cells. Two coronavirus enzymes, Nsp14 and Nsp16, critically help in the formation of capping and are considered as potential drug targets for antiviral therapy. Natural and herbal medicines have a past record of treating various acute respiratory diseases. In this work, we have exploited 56000 natural compounds to screen potential inhibitors against NSP16. In silico virtual screening, docking and Molecular Dynamics (MD) simulation studies were performed to understand how these potential inhibitors are bound to NSP16. We observed that the most highly screened compound binds to protein molecules with a high dock score, primarily through hydrophobic interactions and hydrogen bonding, as previously reported for NSP16. Compound-13 (2-hydroxy-N-({1-[2-hydroxy-1-(hydroxymethyl)ethyl]piperidin-3-yl}methyl)-5-methylbenzamide) and compound-51 (N-(2-isobutoxybenzyl)-N,2-dimethyl-2,8-diazaspiro[4.5]decane-3-carboxamide) occupied in active site along with good pharmokinetices properties. In conclusion, the selected compounds could be used as a novel therapeutic against SARS-CoV-2. Communicated by Ramaswamy H. Sarma</p