Interactions of pseudorabies virus and swine influenza virus with porcine respiratory mucus

Pseudorabies virus (PrV) and swine influenza virus (SIV) both initiate their infection in the respiratory mucosa. Crossing the mucus layer which covers the respiratory mucosa is a crucial step for the mucosal invasion of PrV and SIV. In this thesis, interactions of PrV and SIV with porcine respiratory mucus were investigated, and the association between the viral behavior in mucus and the viral pathogenesis was discussed. In the first part, it is demonstrated whether and how PrV invades the host via mucus barrier. We first set up a virus tracking model using single particle tracking. The diffusion of PrV in mucus was determined and compared with that of negatively, positively and neutrally charged nanoparticles. It was shown that PrV was almost completely obstructed, with 96% of viral particles being immobilized in the porcine respiratory mucus. The negatively and positively charged particles were similarly trapped, in contrast to the neutral particles that moved freely. This suggests that immobilization of PrV was at least partly due to charge-charge interactions between its surface proteins and mucus. Since PrV had difficulties in crossing the mucus barrier, we further identified which component mediates the interactions, by using an explant model. We found that MUC5AC was a dominant mucin expressed in the apical epithelium. The content of MUC5AC on the apical epithelium was inversely related to the attachment and infection of PrV to/in porcine trachea explants, suggesting an important role of MUC5AC in blocking PrV to reach the epithelium. The MUC5AC present above the epithelium was able to block more than 50% of virus infection, which further confirmed a strong inhibition of respiratory mucus against PrV infection. However, the mucus barrier could be overcome by PrV at low temperature (4 oC), determined by a virus-mucus binding system, a virus in-capsule-mucus penetration system and the explant model. We found that, compared to 37 oC, less viral particles were bound to the respiratory mucus at 4 oC, which resulted in deeper viral penetration in mucus, leading to a higher percentage of PrV that overcame the mucus of the explants and caused infection eventually. These results may explain winter seasonality of PrV infection. In the second part, single particle tracking and the virus in-capsule-mucus penetration system were applied to track SIV H1N1 in porcine respiratory mucus. Results showed that 70% of SIV particles were entrapped, while the rest diffused freely in mucus. In addition, SIV was partially able to penetrate through the respiratory mucus over time. Moreover, both the microscopic diffusion and macroscopic penetration were enhanced by the addition of exogenous neuraminidase, while they were in contrast diminished by the use of a neuraminidase inhibitor, indicating that neuraminidase helps SIV to move through the porcine respiratory mucus. In summary, PrV was highly hindered in porcine respiratory mucus. Thus, it may depend on mucus defects or physiological changes which for example may be caused by low temperature to invade the mucus barrier. On the other hand, SIV has evolved to produce neuraminidase which is able to release SIV particles which may be bound to mucins, thereby enabling the virus to move through the respiratory mucus. The in-depth investigation of virus-mucus interaction may provide novel insights into the study of prophylactic treatment for swine influenza and Aujeszky’s diseas

Computational Identification of Novel MicroRNAs and Their Targets in Vigna unguiculata

MicroRNAs (miRNAs) are a class of endogenous, noncoding, short RNAs directly involved in regulating gene expression at the posttranscriptional level. High conservation of miRNAs in plant provides the foundation for identification of new miRNAs in other plant species through homology alignment. Here, previous known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) and Genomic Survey Sequence (GSS) databases of Vigna unguiculata, and according to a series of filtering criteria, a total of 47 miRNAs belonging to 13 miRNA families were identified, and 30 potential target genes of them were subsequently predicted, most of which seemed to encode transcription factors or enzymes participating in regulation of development, growth, metabolism, and other physiological processes. Overall, our findings lay the foundation for further researches of miRNAs function in Vigna unguiculata

Failure Mode and Effect Analysis of Power Transformer Based on Cloud Model of Weight

As the key component of a power system, the power transformer directly impacts the reliability and safety of the system. Failure mode and effects analysis (FMEA) is a methodology used to analyze potential failure modes within a system and has been used extensively to examine the power transformer’s performance in various potential failure scenarios. However, the FMEA method has several flaws; for example, the non-differential analysis of evaluation index and the impossibility of evaluating the actual risk among risk priority number (RPN) values that on the surface are equal. The cloud model of weight incorporates the relative importance of index. This paper proposes applying FMEA based on the cloud model of weight to assess a power transformer for risk, and shows that this method can effectively overcome the defects of traditional FMEA assessment methods

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods

Numerical simulation of secondary breakup of shear-thinning droplets

The breakup of non-Newtonian droplets is ubiquitous in numerous applications. Although the non-Newtonian property can significantly change the droplet breakup process, most previous studies consider Newtonian droplets, and the effects of the non-Newtonian properties on the breakup process are still unclear. This study focuses on the secondary breakup of shear-thinning droplets by numerical simulation. The volume of fluid method is used to capture interface dynamics on adaptive grids. To compare shear-thinning droplets and Newtonian droplets, a new definition of the Ohnesorge number is proposed by considering the characteristic shear rate in the droplet induced by the airflow. The results show that compared with the Newtonian fluid, the shear-thinning properties can change the effective viscosity distribution inside the droplet, alter the local deformation, change the droplet morphology, and affect the transition in the droplet breakup regime.Comment: 14 pages, 15 figure