The inhibitory effect of Aconiti Sinomontani Radix extracts on the proliferation and migration of human synovial fibroblast cell line SW982.

ETHNOPHARMACOLOGICAL RELEVANCE: Aconiti Sinomontani Radix is frequently used in the treatment of Bi syndrome in traditional Chinese medicine. Several reports indicate that Aconiti Sinomontani Radix has therapeutic effects for rheumatoid arthritis (RA). However, the cellular mode of action is still unclear. To investigate the effect of alkaloid extracts of Aconiti Sinomontani Radix on proliferation and migration of human synovial sarcoma SW982 cells as well as the molecular mechanism underlying. MATERIALS AND METHODS: SW982 cells were examined for proliferation by a 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) method. Wound scratch assays were performed to assess the migrated rate of SW982 cells. Quantitative real-time PCR was used to measure the mRNA expression levels of Wnt5a, Runx2, MMP3, and Bmp2. Western blotting was used to measure the phosphorylated levels of JNK and NF-κB as well as the expression of MMP3. RESULTS: The alkaloid extract from Aconiti Sinomontani Radix (MQA) and MQB, which removed lappaconitine from MQA significantly inhibited the proliferation of SW982 in a dose-dependent manner. The proliferation inhibitory effect of MQB was more potent. Incubation with 10µg/ml MQB for 12, 24, and 36h inhibited the migration of SW982 cells by 83%, 58%, and 42%, respectively. Treatment with different concentrations of MQB for 24h inhibited mRNA expression of Wnt5a, Runx2, and MMP3, but Bmp2 mRNA expression was elevated by MQB. Further, MQB inhibited phosphorylation of JNK and NF-κB p65 as well as MMP3 expression by Western blotting analysis. CONCLUSION: The results showed that MQB inhibited proliferation and migration of SW982 cells possibly through suppressing Wnt5a-mediated JNK and NF-κB pathways. These results indicated that MQB might be an active extract of Aconiti Sinomontani Radix for targeting fibroblast-like synoviocytes (FLS) and be potential for RA therapy.

[Spectra Classification Based on Local Mean-Based K-Nearest Centroid Neighbor Method].

In the present paper, a local mean-based K-nearest centroid neighbor (LMKNCN) technique is used for the classification of stars, galaxies and quasars (QSOS). The main idea of LMKNCN is that it depends on the principle of the nearest centroid neighborhood(NCN), and selects K centroid neighbors of each class as training samples and then classifies a query pattern into the class with the distance of the local centroid mean vector to the samples . In this paper, KNN, KNCN and LMKNCN were experimentally compared with these three different kinds of spectra data which are from the United States SDSS-DR8. Among these three methods, the rate of correct classification of the LMKNCN algorithm is higher than the other two algorithms or comparable and the average rate of correct classification is higher than the other two algorithms, especially for the identification of quasars. Experiment shows that the results in this work have important significance for studying galaxies, stars and quasars spectra classification.

[SKLOF: a new algorithm to reduce the range of supernova candidates].

Supernova (SN) is called the "standard candles" in the cosmology, the probability of outbreak in the galaxy is very low and is a kind of special, rare astronomical objects. Only in a large number of galaxies, we have a chance to find the supernova. The supernova which is in the midst of explosion will illuminate the entire galaxy, so the spectra of galaxies we obtained have obvious features of supernova. But the number of supernova have been found is very small relative to the large number of astronomical objects. The time computation that search the supernova be the key to weather the follow-up observations, therefore it needs to look for an efficient method. The time complexity of the density-based outlier detecting algorithm (LOF) is not ideal, which effects its application in large datasets. Through the improvement of LOF algorithm, a new algorithm that reduces the searching range of supernova candidates in a flood of spectra of galaxies is introduced and named SKLOF. Firstly, the spectra datasets are pruned and we can get rid of most objects are impossible to be the outliers. Secondly, we use the improved LOF algorithm to calculate the local outlier factors (LOF) of the spectra datasets remained and all LOFs are arranged in descending order. Finally, we can get the smaller searching range of the supernova candidates for the subsequent identification. The experimental results show that the algorithm is very effective, not only improved in accuracy, but also reduce the operation time compared with LOF algorithm with the guarantee of the accuracy of detection.

[Automatic Measurement of the Stellar Atmospheric Parameters Based Mass Estimation].

We have collected massive stellar spectral data in recent years, which leads to the research on the automatic measurement of stellar atmospheric physical parameters (effective temperature Teff, surface gravity log g and metallic abundance [Fe/ H]) become an important issue. To study the automatic measurement of these three parameters has important significance for some scientific problems, such as the evolution of the universe and so on. But the research of this problem is not very widely, some of the current methods are not able to estimate the values of the stellar atmospheric physical parameters completely and accurately. So in this paper, an automatic method to predict stellar atmospheric parameters based on mass estimation was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]. This method has small amount of computation and fast training speed. The main idea of this method is that firstly it need us to build some mass distributions, secondly the original spectral data was mapped into the mass space and then to predict the stellar parameter with the support vector regression (SVR) in the mass space. we choose the stellar spectral data from the United States SDSS-DR8 for the training and testing. We also compared the predicted results of this method with the SSPP and achieve higher accuracy. The predicted results are more stable and the experimental results show that the method is feasible and can predict the stellar atmospheric physical parameters effectively.

[The backgroud sky subtraction around [OIII] line in LAMOST QSO spectra].

At present, most sky-subtraction methods focus on the full spectrum, not the particular location, especially for the backgroud sky around [OIII] line which is very important to low redshift quasars. A new method to precisely subtract sky lines in local region is proposed in the present paper, which sloves the problem that the width of Hß-[OIII] line is effected by the backgroud sky subtraction. The exprimental results show that, for different redshift quasars, the spectral quality has been significantly improved using our method relative to the original batch program by LAMOST. It provides a complementary solution for the small part of LAMOST spectra which are not well handled by LAMOST 2D pipeline. Meanwhile, This method has been used in searching for candidates of double-peaked Active Galactic Nuclei.

[Stellar spectrum parameter measurement based on line index by linear regression].

Using the Lick line index, according to the magnanimity characteristics of the spectrum an efficient algorithm of the atmospheric physical parameters measurement by the linear regression method from the point of view of statistical regression was designed. The linear regression was used to achieve the best regression effect by selecting the type of regression and the composition of line index. The formula obtained from the regression model makes the computation speed fast when applied to new data, and the clarity and ease of analysis processing can not be reached by other methods. The experimental results show that through the line index regression method to get the atmospheric physical parameters is feasible.

[An automated method to fit stellar continuum based on statistic windows].

A novel statistic window based method to fit stellar continuum is proposed. First a stellar spectrum is divided into a series of statistic windows in which a certain percent of flux points is selected according to S/N ratio; then low order polynomial iteration fitting is carried out based on the selected flux points to obtain the stellar continuum. Experimental results show that the continuum obtained by the proposed method is more close to the real continuum, compared to other existed methods. This method has a better practical applicability and robustness to all kinds of spectra (except M-type spectrum) in SDSS. It also works well for Guoshoujing Telescope (LAMOST) pilot survey spectra.

[A novel method to determine the redshifts of active galaxies based on wavelet transform].

Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.

[Galaxy spectrum subtraction of a mixed spectrum based on two class PCA eigen-spectra].

The authors present a new method called two class PCA for decomposing the mixed spectra, namely, for subtracting the host galaxy contamination from each SN spectrum. The authors improved the quality of reconstructed galaxy spectrum and computational efficiency, and these improvements were realized because we used both the PCA eigen spectra of galaxy templates library and SN templates library to model the mixed spectrum. The method includes mainly three steps described as follows. The first step is calculating two class PCA eigen spectra of galaxy templates and SN templates respectively. The second step is determining all reconstructed coefficients by the SVD matrix decomposition or orthogonal transformation. And the third step is computing a reconstructed galaxy spectrum and subtracting it from each mixed spectrum. Experiments show that this method can obtain an accurate decomposition of a mixed synthetic spectrum, and is a method with low time-consumption to get the reliable SN spectrum without galaxy contamination and can be used for spectral analysis of large amount of spectra. The time consumption using our method is much lower than that using chi2-template fitting for a spectrum.

[Reducing the searching range of supernova candidates automatically in a flood of spectra of galaxies].

Supernova (SN) is one of the most intense astronomical phenomena among the known stellar activities, but compared with several billion astronomical objects which people have probed, the number of supernova the authors have observed is very small. Therefore, the authors need to find faster and higher-efficiency approaches to searching supernova. In the present paper, we present a novel automated method, which can be successfully used to reduce the range of searching for 1a supernova candidates in a huge number of galaxy spectra. The theoretical basis of the method is clustering and outlier picking, by introducing and measuring local outlier factors of data samples, description of statistic characters of SN emerges in low dimension space. Firstly, eigenvectors of Peter's 1a supernova templates are acquired through PCA projection, and the description of la supernova's statistic characters is calculated. Secondly, in all data set, the local outlier factor (LOF) of each galaxy is calculated including those SN and their host galaxy spectra, and all LOFs are arranged in descending order. Finally, spectra with the largest first one percent of all LOFs should be the reduced 1a SN candidates. Experiments show that this method is a robust and correct range reducing method, which can get rid of the galaxy spectra without supernova component automatically in a flood of galaxy spectra. It is a highly efficient approach to getting the reliable candidates in a spectroscopy survey for follow-up photometric observation.