[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. In view of this, Fuzzy Large Margin and Minimum Ball Classification Model (FLM-MBC) is proposed in this paper. In FLM-MBC, part of general data and outlier data are trained to construct the minimum ball model and the fuzzy technique is introduced to reduce the noise influence to classification. Comparative experiments with C-SVM, KNN, and SVDD on the SDSS spectral datasets verify the effectiveness of the proposed FLM-MBC.

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. In the experiment, we investigate the performance of the proposed method on the K-type, F-type, G-type, M-type datasets from Sloan Digital Sky Survey (SDSS), Data Release 8. It can be concluded that the proposed method can complete the rare spectra distinguishing task compared with several traditional classifiers.

[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. The advantage of TSVM is that the time cost is reduced to 1/4 of that of SVM. While all the methods mentioned above only focus on the global characteristics and neglect the local characteristics. In view of this, an automatic classification method of star spectra data based on manifold fuzzy twin support vector machine (MF-TSVM) is proposed in this paper. In MF-TSVM, manifold-based discriminant analysis (MDA) is used to obtain the global and local characteristics of the input data and the fuzzy membership is introduced to reduce the influences of noise and singular data on the classification results. Comparative experiments with current classification methods, such as C-SVM and KNN, on the SDSS star spectra datasets verify the effectiveness of the proposed method.

[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. Based on the above analysis, the corresponding optimal problem can be established, and then MDA transforms the original optimization problem to the QP dual form and we can obtain the support vectors and decision function. The classes of test samples are decided by the decision function. The advantage of the proposed method is that it not only focuses on the information between classes and distribution characteristics, but also preserves the manifold structure of each class. Experiments on SDSS star spectra datasets verify the effectiveness of the proposed method.

[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. The effects of NPY on L-type Ca2+ channel currents (I(Ca-L)) were evaluated in some studies and different results were presented, which might be attributed to the different species of animal tested and different methods used. It is necessary, therefore, to study the effects of NPY on ion channels in cardiac myocytes systematically and further to discuss the biological significance of their coexistence with NE in sympathetic terminals. The single ventricular myocytes from adult rat or guinea pig (only for measuring I(K)) were prepared using enzymatic dispersion. I(Ca-L), I(to), I(Na/Ca), I(Na) and I(K) in the cellular membrane were observed using whole cell voltage-clamp recording. In the present study, NPY from 1.0 to 100 nmol/L dose-dependently inhibited I(Ca-L) (P<0.01, n=5). The maximal rate of inhibition in this study reached 39% and IC(50) was 1.86 nmol/L. NPY had no effect on the voltage-dependence of calcium current amplitude and on the voltage-dependence of the steady-state gating variables. I(Ca-L) was activated at -30 mV, reaching the maximum at 0 mV. When both NE and NPY were applied with a concentration ratio of 500:1, 10 nmol/L NPY inhibited I(Ca-L) that had been increased by 5 mumol/L NE, which was consistent with the effect of NPY only on I(Ca-L). NPY also inhibited I(Na/Ca). At a concentration of 10 nmol/L, NPY inhibited inward and outward I(Na/Ca) from (0.27+/-0.11) pA/pF and (0.45+/-0.12) pA/pF to (0.06+/-0.01) pA/pF and (0.27+/-0.09) pA/pF, respectively (P<0.05, n=4). NPY at 10 nmol/L increased I(to) from (12.5+/-0.70) pA/pF to (14.7+/-0.59) pA/pF(P<0.05, n=4). NPY at 10 nmol/L did not affect I(Na) in rat myocytes and I(K) in guinea pig myocytes. NPY increased the speed of action potential depolarization and reduced action potential duration of I(Ca-L), I(Na/Ca) and I(to), which contributed to the reduction of contraction. These results indicate that the effects of NPY are opposite to the effects of NE on ion channels of cardiac myocytes.

[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 < 0.05). The treatment with losartan not only inhibited structural and functional changes, but also the frequencies and titres of AT1R-AA was significantly lower (P < 0.05) than RVH group. CONCLUSION: It is suggested that the losartan significantly inhibits generation of the AT1R-AA.