Distinguishing the Rare Spectra with the Unbalanced Classification Method Based on Mutual Information.

Distinguishing the rare spectra from the majority of stellar spectra is one of quite important issues in astronomy. As the size of the rare spectra is much smaller than the majority of the spectra, many traditional classifiers can’t work effectively because they only focus on the classification accuracy and have not paid enough attentions on the rare spectra. In view of this, the relationship between the decision tree and mutual information is discussed on the basis of summarizing the traditional classifiers, and the cost-free decision tree based on mutual information is proposed in this paper to improve the performance of distinguishing the rare spectra.

[Study on Stellar Spectral Outliers Mining Based on Fuzzy Large Margin and Minimum Ball Classification Model].

It’s one of the main goals in universe exploration to find unknown and special celestial bodies. The spectra outlier data is analyzed based on the traditional classification approaches, which is a general method of special celestial body exploration. But it’s depressed that many traditional classification approaches are insensitive to the outlier data, which even influence the classification efficiencies, therefore, these methods can’t accomplish the task of special celestial body exploration.

[Automatic classification method of star spectra data based on manifold fuzzy twin support vector machine].

Support vector machine (SVM) with good leaning ability and generalization is widely used in the star spectra data classification. But when the scale of data becomes larger, the shortages of SVM appear: the calculation amount is quite large and the classification speed is too slow. In order to solve the above problems, twin support vector machine (TWSVM) was proposed by Jayadeva.

[Automatic classification method of star spectra data based on manifold-based discriminant anaysis and Support Vector Machine].

Although Support Vector Machine (SVM) is widely used in astronomy, it only takes the margin between classes into consideration while neglects the data distribution in each class, which seriously limits the classification efficiency. In view of this, a novel automatic classification method of star spectra data based on manifold-based discriminant analysis (MDA) and SVM is proposed in this paper. Two important concepts in MDA, manifold-based within-class scatter (MWCS) and manifold-based between-class scatter (MBCS), are introduced in the proposed method, the separating hyperplane found by which ensures MWCS is minimized and MBCS is maximized.

[Effects of neuropeptide Y on ion channels in ventricular myocytes].

Neuropeptide Y (NPY) co-exists with norepinephrine (NE) in sympathetic terminals, and is the most abundant neuropeptide in myocardium. Many studies have focused on the effects of NE on ion channels in cardiac myocytes and its physiological significance has been elucidated relatively profoundly. There have been few investigations, however, on the physiological significance of NPY in myocardium.

[Effect of losartan on produce of sera autoantibodies to angiotensin II-1 receptor in renovascular hypertension rats].

Aim And Methods: The effects of losartan (after operation 2 week to 10 week, 5 mg/kg d ig) on generation of AT1R-AA in sera were observed during development of hypertension in rats. The renovascular hypertension (RVH) model was established by two-kidney one-clip method, a synthetic peptide corresponding to amino acid sequence 165-191 of the second extracellular loop of the angiotensin II-1 receptor (AT1R) was used as antigen, SA-ELISA were used to examine sera AT1R autoantibody (AT1R-AA).

Results: The frequencies and titres of AT1R-AA after operation one week rats were significantly increased (P < 0.