COVID-19 and the Middle East respiratory syndrome-related coronavirus (MERS) viruses are from coronaviridae family; the former became a pandemic while the latter confined to a limited region. Their pathogenicity and infection rates are also different; the high mortality rate for MERS with low spreading capability. To investigate the possible structural changes at RNA sequences of both virus, 1621 and 125 sequences of COVID-19 and MERS downloaded and converted to polynomial datasets and seven attribute weighting (feature selection) approaches have been used for the analysis of genomic sequences of COVID-19 and MERS viruses. The end nucleotide sequences (from 29288 to the end genome positions) selected by the most attribute weighting models to be significantly different between two virus classes followed by smaller piece at 5700 and 1750 and 7600 nucleotide positions. These parts encode Nucleocapsid (N), Papin-like protease (NSP3) and NSP4 proteins of COVID-19. The finding for the first time reports the structural differences between two important viruses at the sequential level and paves the road to decipher new emerging COVID-19 virus high pathogenicity.
Local feature matching has become a commonly used method to compare images. For tracking and human detection, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. two different types of image feature algorithms, Scale-Invariant Feature Transform (SIFT) and the more recent Speeded Up Robust Features (SURF), have been used to compare the images. In this paper, we propose the use of a rich set of feature descriptors based on SIFT and SURF in the different color spaces.
Fog computing (FC) is a promising paradigm to use as an efficientarchitecture for the Internet of Things applications. Proximity, low latency,flexible resource power, and distributed structure of this architecture are somebenefits of it. A huge number of generated data and their requisites to real-timeprocess causes fog nodes offload number of tasks to the others that make trustissues. Here, each clients prefers to offload task to a trusted server, also eachserver tends to service the trusted clients. This may takes a long especiallywhen we want to consume less energy. In order to encounter this problem,in this paper, we propose a two-way trust management strategy based onBayesian learning automata. The proposed approach outperforms the otherstate-of-the-art approaches in terms of the energy consumption, network usage,latency, response time, and trust value.
Increase in influenza A virus host range throughout its evolution has given rise to major concerns worldwide. Although the increasing host range mechanism of the virus is largely unknown; persistent genetic mutations have been blamed as a key factor in the re-organization of the host response and the host range. To uncover the underlying core bases of the two important antigenic proteins of influenza virus (HA and NA), functional data mining and image processing analysis of over 8000 protein sequences of different HA and NA subtypes were performed. Each amino acid sequence in HA or NA proteins sat as a feature or variable and two polynomial datasets were created and subjected into conventional prediction models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.