Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve). Additionally, the initial parameters of CNN are tuned using multiobjective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Kinesin-1 carries cargos including proteins, RNAs, vesicles, and pathogens over long distances within cells. The mechanochemical cycle of kinesins is well described, but how they establish cargo specificity is not fully understood. Transport of oskar mRNA to the posterior pole of the Drosophila oocyte is mediated by Drosophila kinesin-1, also called kinesin heavy chain (Khc), and a putative cargo adaptor, the atypical tropomyosin, aTm1. How the proteins cooperate in mRNA transport is unknown. Here, we present the high-resolution crystal structure of a Khc-aTm1 complex. The proteins form a tripartite coiled coil comprising two in-register Khc chains and one aTm1 chain, in antiparallel orientation. We show that aTm1 binds to an evolutionarily conserved cargo binding site on Khc, and mutational analysis confirms the importance of this interaction for mRNA transport in vivo. Furthermore, we demonstrate that Khc binds RNA directly and that it does so via its alternative cargo binding domain, which forms a positively charged joint surface with aTm1, as well as through its adjacent auxiliary microtubule binding domain. Finally, we show that aTm1 plays a stabilizing role in the interaction of Khc with RNA, which distinguishes aTm1 from classical motor adaptors.

The oxidation of ethylenediamine by diperiodatoargentate (III) ion has been studied by stopped-flow spectrophotometry. Kinetics of this reaction involves two steps. The first step is the complexation of silver (III) with the substrate and is over in about 10 ms. This is followed by a redox reaction in the second step that occurs intramolecularly from the substrate to the silver (III) center. The rate of reduction of silver (III) species by ethylenediamine, ethanolamine, and 1,2-ethanediol were observed to be 1.2 ϫ 10 4 , 1.1 ϫ 10 2 , and 0.14 dm 3 mol Ϫ1 s Ϫ1 , respectively, at 20ЊC.

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