To date, many strategies involving graph theory have been proposed to solve the targeted immunization problem. Among them, the well-known relationship-related (RR) method makes use of the sum rule and the product rule from the perspective of the network explosive percolation. However, the RR method needs to carefully consider all nodes within a network, leading to high computational time. To close this gap, we propose the fringe node set: it is applied to an immunization strategy such as RR to remove noncritical nodes before optimizing the node sequence. Besides adapting this algorithm for strategies, such as RR and degree centrality strategy, we further propose a novel reconstruction method (RM) under the percolation perspective, which ranks critical nodes by measuring their contribution to the giant component in the network reconstruction or node reoccupying process. Experimental results based on our proposed identification method have demonstrated the feasibility of using the fringe node set. The competitive advantage of our proposed RM is also demonstrated in comparison with other existing methods.
Objective:To observe the dynamic expression of mRNA of TLR4 and TLR9 in Lewis rats with experimental autoimmune neuritis(EAN) and the effect of TWP on the disease.Methods:Male Lewis rats were immunized with P0 180-199(100 microgram),TWP was profused into post-immunization rats’ stomach daily.The clinical signs of rats and pathological changes in the sciatic nerves were observed.TLR4 and TLR9 were detected by RT-PCR dynamically which spleens,sciatic nerves and peripheral blood lymphonodes as sample.Results:EAN group got the peak of clinical score at the 17 d.p.i,and ameliorated obviously at 33 d.p.i,and the mRNA expression of TLR4 got the peak at the 16 d.p.i,then reduced gradually(P0.05).The mRNA expression of TLR9 was up-regulated during the whole process of experiment,and was higher compared with that of CFA group,P0.05.Clinical manifestation of EAN+TWP group was ameliorated,expression of TLR4 and TLR9 was lower than in EAN+NS group (P0.05).CFA group and NS group didn’t show any clinical manifestations,and when compared with NS group,the mRNA expression of TLR4 and TLR9 of CFA group was higher in any tissue,which got the peak at the 7 d,and then reduced gradually.Conclusion:TLR4 and TLR9 may play a role in the pathogenesis of EAN by taking part in the inducing stage and effecting stage.TWP may ameliorate EAN through inhibiting TLR4 and TLR9 activation.
Abstract Blast caused by fungal Magnaporthe oryzae is a devastating disease of rice (Oryza sativa ) worldwide, and this fungus also infects barley (Hordeum vulgare). At least 11 rice WRKY transcription factors have been reported to regulate rice response to M. oryzae either positively or negatively. However, the relationships of these WRKYs in the rice defense signaling pathway against M. oryzae are unknown. Previous studies have revealed that rice WRKY13 (as a transcriptional repressor) and WRKY45-2 enhance resistance to M. oryzae. Here, we show that rice WRKY42, functioning as a transcriptional repressor, suppresses resistance to M. oryzae. WRKY42-RNA interference (RNAi) and WRKY42-overexpressing (oe) plants showed increased resistance and susceptibility to M. oryzae, accompanied by increased or reduced jasmonic acid (JA) content, respectively, compared with wild-type plants. JA pretreatment enhanced the resistance of WRKY42-oe plants to M. oryzae. WRKY13 directly suppressed WRKY42. WRKY45-2, functioning as a transcriptional activator, directly activated WRKY13. In addition, WRKY13 directly suppressed WRKY45-2 by feedback regulation. The WRKY13-RNAi WRKY45-2-oe and WRKY13-oe WRKY42-oe double transgenic lines showed increased susceptibility to M. oryzae compared with WRKY45-2-oe and WRKY13-oe plants, respectively. These results suggest that the three WRKYs form a sequential transcriptional regulatory cascade. WRKY42 may negatively regulate rice response to M. oryzae by suppressing JA signaling-related genes, and WRKY45-2 transcriptionally activates WRKY13, whose encoding protein in turn transcriptionally suppresses WRKY42 to regulate rice resistance to M. oryzae.
A mathematics model of constant probability event in product space conditional event algebra was proposed. It is showed that the numerical based fusion and the algebraic based fusion has the consistent result by modeling the weighs of relevant contributing events given by expert as constant probability event in data fusion system. Based on the model, a novel Boolean similarity measure was presented to determine the similarity between experts opinion. A numeric example was illustrated to show the validity of the similarity measure.
Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system which leads to loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model Differential Fuzzy Convolutional Neural Network (DFCNN) utilizes a convolution neural network to re-implement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of non-stationary time series due to the trend of non-stationary time series. The trend of non-stationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the Difference algorithm to weaken the non-stationary of time series so that DFCNN can forecast the non-stationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.
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