No abstract

No abstract

No abstract

Objectives Our study purpose was to detect the distribution of anti-nuclear antibody (ANA) IgG subclasses in patients with systemic lupus erythematosus (SLE) and to evaluate their influence on the inflammatory process in SLE. Methods We determined the serum levels of ANA IgG subclasses from 70 SLE patients, 25 patients with other autoimmune diseases (OAD), and 25 healthy controls using ELISA. The serum level of total ANA IgG and the avidity of ANA IgG, dsDNA IgG, and dsDNA IgG subclasses were analysed by ELISA. Results The results indicated that levels of four ANA IgG subclasses (IgG1, IgG2, IgG3 and IgG4) and total IgG were significantly higher in SLE patients than in OAD patients and healthy controls ( p < 0.001). Moreover, the level of each ANA IgG subclass and the prevalence of high-avidity IgG ANAs (HA IgG ANAs) were significantly higher in the active cases than in the inactive cases of SLE and LN. Furthermore, level of ANA IgG subclasses decreased as level of dsDNA IgG subclasses decreased in 30 patients with SLE. In comparison, ANA IgG3 was significantly effective in high-dose prednisone combined with hydroxychloroquine ( p = 0.025). Additionally, it revealed that level of dsDNA IgG had a significant influence on four ANA IgG subclasses, especially on ANA IgG3 (β coefficient = 0.649, p < 0.001). Level of ANA IgG3 was also positively related to the serum level of dsDNA IgG (r = 0.729, p < 0.001) and RAI of HA IgG ANAs (r = 0.504, p < 0.001). However, the level of ANA IgG4 was positively related to the serum level of albumin (r = 0.572, p < 0.001) and RAI of HA IgG ANAs (r = 0.549, p < 0.001). Moreover, the results revealed that cutaneous and renal involvement were mainly associated with the ANA IgG1 and IgG4 subclasses. Although, arthritic involvement was mainly associated with ANA IgG3. Conclusions First, we demonstrated that the ANA IgG subclasses were diagnostic tools in SLE patients. Furthermore, HA IgG ANAs might affect the distribution of ANA IgG3 and IgG4. Moreover, ANA IgG3 might play a particular role in the activity of SLE disease and therapy. Therefore, an altered ANA IgG subclass distribution might be a risk factor influencing the inflammatory process in SLE.

Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

In order to improve exact recognition ratios for aerial targets, this paper presents a novel algorithm for target recognition based on interval-valued intuitionistic fuzzy sets with grey correlation. Drawbacks of some previously proposed methods are analyzed, and then a novel algorithm is presented. Recognition matrix of an aerial target is established first. Every entry associated with the matrix is an interval-valued intuitionistic fuzzy number, which is composed of interval-valued membership and nonmembership, representing the relation of the target to one category in terms of one characteristic parameter. Then grey correlation theory is used to analyze the recognition matrix to obtain the grey correlation degree of this unknown target to every category. 200 sets of target recognition data are used to compare the proposed algorithm with traditional methods. Experimental results verify that the correct recognition ratio can be up to 99.5% that satisfies the expectations, which shows the proposed algorithm can solve the target recognition problems better. The proposed algorithm can be used to solve the uncertain inference problems, such as target recognition, threat assessment, and decision making.

The spatial information in a remotely sensed image is often characterized by the texture features, which have been regarded as an important visual primitive to search through large collections of natural visually similar patterns in the image. This paper presents an automated process of extract and classify the texture patterns observed in the remote sensing images such as Landsat TM multispectral images. After the principal component analysis, the first component image, which preserves the largest percentage of the variance, is divided into sub regions. The feature vectors representing the textures of each region are computed by Gabor wavelets and classified using a Bayes Point Machine classifier. The preliminary results show the effectiveness of the process and its potentials in practical remote sensing applications.

No abstract