The fusion glycoprotein (F) and hemagglutinin-neuraminidase (HN) genes of human parainfluenza virus type 2 (P12) were molecularly cloned and expressed in HeLa-T4 cells by using the vaccinia virus-T7 transient expression system. Expression of the F and HN proteins was detected by using immunoprecipitation and surface inmunofluorescence staining. Although the F protein was found to be cleaved into F1 and F2 and expressed on cell surfaces, no cell fusion was observed. However, cotransfection of the F-protein gene together with the P12 HN gene resulted in significant levels of cell fusion. Cell fusion was also observed when separate cell cultures were transfected with the HN and F genes and the F-expressing ceLls were mixed with the HN-expressing cells. Surprisingly, when the PI2 F protein was expressed together with the parainfluenza virus type 3 (P13) HN protein, no fusion was detectable in the transfected cells. Similarly, no fusion was found upon coexpression of the P12 HN and P13 F proteins. However, coexpression of the PI3 F and HN proteins resulted in extensive cell fusion, which resembled the P12 coexpression result. These results indicate that under the conditions used, the F protein is unable to cause fusion by itself and the HN protein provides a specific function in cell fusion which cannot be provided by another paramyxovirus attachment protein. Further, the results suggest that a type-specific functional interaction between the F and HN proteins is involved in mediating cell fusion.

Abstract:The vibration signals of rolling bearings are often nonlinear and non-stationary. Multiscale entropy (MSE) has been widely applied to measure the complexity of nonlinear mechanical vibration signals, however, at present only the single channel vibration signals are used for fault diagnosis by many scholars. In this paper multiscale entropy in multivariate framework, i.e., multivariate multiscale entropy (MMSE) is introduced to machinery fault diagnosis to improve the efficiency of fault identification as much as possible through using multi-channel vibration information. MMSE evaluates the multivariate complexity of synchronous multi-channel data and is an effective method for measuring complexity and mutual nonlinear dynamic relationship, but its statistical stability is poor. Refined composite multivariate multiscale fuzzy entropy (RCMMFE) was developed to overcome the problems existing in MMSE and was compared with MSE, multiscale fuzzy entropy, MMSE and multivariate multiscale fuzzy entropy by analyzing simulation data. Finally, a new fault diagnosis method for rolling bearing was proposed based on RCMMFE for fault feature extraction, Laplacian score and particle swarm optimization support vector machine (PSO-SVM) for automatic fault mode identification. The proposed method was compared with the existing methods by analyzing experimental data analysis and the results indicate its effectiveness and superiority.

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In recent years, a steady increase has been detected in the incidence of acute cerebral infarction (ACI). ACI is caused by blood flow disruption, leading to high disability and mortality rates. Understanding the underlying molecular mechanisms is critical toward developing effective therapeutic approaches. Circular RNAs (circRNAs) are an important class of non-coding RNAs, which have been implicated in several molecular pathways, and their dysregulation has been described in several disease conditions. Here, we set out to explore the possible regulatory role of circRNAs in ischemic stroke and study their molecular function in disease. First, we applied high-throughput sequencing techniques to identify the differential changes of plasma circRNAs expression in patients with acute cerebral infarction. Next, we used GO and KEGG pathway analysis to predict the function of differentially expressed circRNAs. Moreover, we have assessed the possible interaction between the identified differentially expressed circRNAs and miRNAs. Finally, we have selected and validated five downregulated circRNAs by RT-qPCR. Together, the results of this study provide evidence that circRNAs are potential biomarkers for early diagnosis of cerebral infarction and have to be considered as targets for drug treatment.

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