[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.

[Correction of Fiber Throughput Variation Due to the Focal Ratio Degradation].

The focal ratio degradation (FRD) of optical fiber is one of major sources causing light loss in multi-fiber astronomical instruments. Meanwhile, the sky subtraction is crucial to multi-fiber spectra reduction, especially for the objects which are as faint as the sky background, not to mention for those even fainter ones. To improve the accuracy of sky subtraction, it is necessary to normalize the throughput among object fibers and sky sampling fibers. The rotation and twist during mounting and rotating could change the FRD of individual fibers, which means the variation of the transmission throughput among fibers. We investigate such throughput variation among LAMOST fibers and its correlation with the intensity of sky emission lines on all wavelength coverage in this paper. On the basis of this work, we present an approach to correcting the varied fiber throughput by measuring the intensity of the sky emission lines as the secondary throughput correction. This approach has been applied to LAMOST 2D Pipeline.

[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.

[The radial velocity measurement accuracy of different spectral type low resolution stellar spectra at different signal-to-noise ratio].

The radial velocity of the star is very important for the study of the dynamics structure and chemistry evolution of the Milky Way, is also an useful tool for looking for variable or special objects. In the present work, we focus on calculating the radial velocity of different spectral types of low-resolution stellar spectra by adopting a template matching method, so as to provide effective and reliable reference to the different aspects of scientific research We choose high signal-to-noise ratio (SNR) spectra of different spectral type stellar from the Sloan Digital Sky Survey (SDSS), and add different noise to simulate the stellar spectra with different SNR. Then we obtain theradial velocity measurement accuracy of different spectral type stellar spectra at different SNR by employing a template matching method. Meanwhile, the radial velocity measurement accuracy of white dwarf stars is analyzed as well. We concluded that the accuracy of radial velocity measurements of early-type stars is much higher than late-type ones. For example, the 1-sigma standard error of radial velocity measurements of A-type stars is 5-8 times as large as K-type and M-type stars. We discuss the reason and suggest that the very narrow lines of late-type stars ensure the accuracy of measurement of radial velocities, while the early-type stars with very wide Balmer lines, such as A-type stars, become sensitive to noise and obtain low accuracy of radial velocities. For the spectra of white dwarfs stars, the standard error of radial velocity measurement could be over 50 km x s(-1) because of their extremely wide Balmer lines. The above conclusion will provide a good reference for stellar scientific study.

[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.

[Preparation technology and dissolution research of Blumea volatile oil suppository].

OBJECTIVE: To research the preparation technology and dissolution of Blumea volatile oil suppository. METHOD: In order to establish the content determination and methodology inspection method of Blumea volatile oil plug, the extraction process of Blumea volatile oil was optimized by using orthogonal test. Optimization on the investigation to the suppository matrix by melting time, appearance and dissolution was carried on. The best prescription craft was determined by determining the best molding temperature, dosage of the matrix and complementary makings. The determination method of dissolution was established by investigating different dissolution method and its impact on the preparation of dissolution. RESULT: The best conditions of steam distillation extracted Blumea volatile oil was as followed, the ratio of gardenia to liquor 1:6, 2.5% drug amount of sodium, 8 hours of extracting time. The optimum temperature for mold was 60-65 degrees C. Preparation technique of Blumea volatile oil suppository was stable, which after 45 minutes and 3 h in pH 4.5 PBS released at least 70% and 90%. CONCLUSION: Blumea volatile oil suppository with rational prescription, simple preparation and good stability.

Correlation of serum and local CXCL13 levels with disease severity in patients with non-traumatic osteonecrosis of femoral head.

OBJECTIVE: The primary aim of the present study was to explore the potential correlation of serum / local CXCL13 expressions and disease severity in non-traumatic osteonecrosis of the femoral head (NT-ONFH). METHODS: In total, NT-ONFH patients (n = 130) together with healthy controls (HCs, n = 130) were included in this investigation. Radiographic progression was evaluated based on the imaging criteria outlined in the ARCO classification system. To assess the diagnostic value of serum CXCL13 in relation to radiographic progression, Receiver operating characteristic (ROC) curve analysis was conducted. Serum CXCL13 levels were quantified utilizing ELISA in all participants. Furthermore, local protein/mRNA expressions of CXCL13 were examined employing immunohistochemistry, western blot, as well as RT-PCR techniques. Clinical severity was appraised using the visual analogue scale (VAS), Harris Hip Score (HHS), and Western Ontario as well as McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: The findings revealed a significant reduction in serum CXCL13 levels among NT-ONFH patients in contrast with HCs. Moreover, both mRNA and protein expressions of CXCL13 were markedly decreased in the necrotic area (NA) than the non-necrotic area (NNA) as well as the healthy femoral head tissues. Additionally, serum CXCL13 levels were substantially lower among patients classified as ARCO stage 4 than those at ARCO stage 3. The concentrations of CXCL13 in stage 3 patients were notably diminished relative to those at ARCO stage 2. Notably, serum CXCL13 levels demonstrated a negative association with ARCO grade. Furthermore, these levels were also inversely linked to VAS scores as well as WOMAC scores while displaying a positive association with HHS scores. The findings of ROC curve suggested that reduced serum CXCL13 levels could be an underlying indicator for ARCO stage. CONCLUSIONS: The reduced levels of either serum CXCL13 or local CXCL13 were intricately linked to disease severity for patients with NT-ONFH.

Sedum aizoon L.: a review of its history, traditional uses, nutritional value, botany, phytochemistry, pharmacology, toxicology, and quality control.

In China, Russia, Mongolia, Japan, North Korea, and Mexico, Sedum aizoon L. (S. aizoon) is used as an edible plant. Up to now, over 234 metabolites, including phenolic acids, flavonoids, triterpenes, phytosterols, and alkaloids, among others, have been identified. In addition to its antioxidant, anti-inflammatory, anti-fatigue, antimicrobial, anti-cancer, and hemostatic activities, S. aizoon is used for the treatment of cardiovascular disease. This paper provides an overview of the history, botany, nutritional value, traditional use, phytochemistry, pharmacology, toxicology, and quality control of S. aizoon.

[Data mining approach to cataclysmic variables candidates based on random forest algorithm].

An automatic and efficient method for cataclysmic variables candidates is presented in the present paper. The identified CVs were selected as templates. A model was constructed by random forest algorithm with templates and random selected spectra. Wavelength ranking was described by the model and the classifier was constructed afterwards. Most of the non-candidates were excluded by the method. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. 16 new CVs candidates were found in the experiment that shows that our approach to finding special celestial bodies can be feasible in LAMOST.

[Effects of spectral resolution and noise on the measurement of indices].

In the present paper, we analysed the effects of spectral resolutions and signal-to-noise ratios (SNRs) on 19 atomic absorption line indices of Lick index system. First of all, adopting method of convolving a spectrum with a Gaussian profile, we transformed spectra into those under different resolutions and then measured the line indices on them. Comparisons of the indices under various resolutions allow to investigate the impact of spectral resolution change on the accuracy of measurements of indices. Secondly, by adding random noises with different Gaussian distribution to a spectrum, the authors transformed theoretical spectra with no noises into those under diverse SNRs and then measured line indices on them. Comparisons of the indices under different SNRs greatly helped analyse the influence of SNR on the precision of the measurements of line indices. It comes from comparisons and analysis that the spectral resolution change can cause an index measurement change depending on the extent of the change of spectral resolution. Such a kind of change relationship varies with the indices. The lower the SNR, the less precise the measurements of indices. The effect of SNR on the measurements of indices can be ignored if SNR is larger than 25.

[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.

[A new automated method to identify emission line star from massive spectra].

Stellar spectra are characterized by obvious absorption lines or absorption bands, while those with emission lines are usually special stars such as cataclysmic variable stars (CVs), HerbigAe/Be etc. The further study of this kind of spectra is meaningful. The present paper proposed a new method to identify emission line stars (ELS) spectra automatically. After the continuum normalization is done for the original spectral flux, line detection is made by comparing the normalized flux with the mean and standard deviation of the flux in its neighbor region The results of the experiment on massive spectra from SDSS DR8 indicate that the method can identify ELS spectra completely and accurately. Since no complex transformation and computation are involved in this method, the identifying process is fast and it is ideal for the ELS detection in large sky survey projects like LAMOST and SDSS.

[Data mining of cataclysmic variables candidates in massive spectra].

An automatic and efficient method for LAMOST's massive spectral data reduction is presented in this paper. The identified cataclysmic variables were selected as templates to construct the feature space by PCA (the principal component analysis), and most of the non-candidates were excluded by the method using support vector machine. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. Fifty eight new CVs candidates were found in the experiment, showing that our approach to finding special celestial bodies can be practical in LAM-OST.

[Searching QSO candidates and calculating their redshfit from a flood of spectra].

In the present paper the author offers a method to search the QSO candidates and calculate their redshfit using their broad emission lines which are the most important character of quasars. It is hard to identify the lines in the quasar's spectra due to their redshifts distributing on a broad range. Spectra contain two components. One is continuum and the other is lines. The author uses a method of LFPS (low frequency points set) to build the continuum and detect the obvious emission lines, a method that can avoid the broad emission lines as a part of the continuum. The redshift can be calculated by comparing the extracted lines with the line table. The classification can be done with both emission lines and the redshift. For a better accurate rate to recognize the lines, this paper provides a method to estimate the level of the local noise. The method this paper used is independent of the flux calibration of the spectra. It can work for the spectra of the present LAMOST.

[A fractal denoising method for astronomical spectral signal].

To restore the continuum and the spectral lines from a noisy astronomical spectrum, then to measure the equivalent widths of the spectral lines, the fractal denoising method was firstly used in astronomical spectra in the present paper. The method is based on the distinguishing features, that is the local self-similarities exist in an astronomical spectrum, while not in a random white noise signal. The experimental results show that the fractal denoising method is efficient in parameter measurements, such as equivalent widths for spectral lines, redshift of galaxies, and so on. In addition, the method can achieve data compression. The fractal method can be used in the mass spectra of LAMOST.

Identification of Biomarkers for Predicting Allograft Rejection following Kidney Transplantation Based on the Weighted Gene Coexpression Network Analysis.

Kidney transplantation is the promising treatment of choice for chronic kidney disease and end-stage kidney disease and can effectively improve the quality of life and survival rates of patients. However, the allograft rejection following kidney transplantation has a negative impact on transplant success. Therefore, the present study is aimed at screening novel biomarkers for the diagnosis and treatment of allograft rejection following kidney transplantation for improving long-term transplant outcome. In the study, a total of 8 modules and 3065 genes were identified by WGCNA based on the GSE46474 and GSE15296 dataset from the Gene Expression Omnibus (GEO) database. Moreover, the results of Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these genes were mainly involved in the immune-related biological processes and pathways. Thus, 317 immune-related genes were selected for further analysis. Finally, 5 genes (including CD200R1, VAV2, FASLG, SH2D1B, and RAP2B) were identified as the candidate biomarkers based on the ROC and difference analysis. Furthermore, we also found that in the 5 biomarkers an interaction might exist among each other in the protein and transcription level. Taken together, our study identified CD200R1, VAV2, FASLG, SH2D1B, and RAP2B as the candidate diagnostic biomarkers, which might contribute to the prevention and treatment of allograft rejection following kidney transplantation.

[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.

[An automated stellar spectra classification system based on non-parameter regression and nearest neighbor method].

The automated classification and recognition of stellar spectra is an important research for the spectra processing system of modem telescope survey project. For the spectra without flux calibration, the authors present an automated stellar spectra classification system to achieve two goals: one is the spectral class and spectral subclass classification, and the other is luminosity type recognition. The system is composed of three units: (1) continuum normalization method based on wavelet technique; (2) non-parameter regression method for spectral class and spectral subclass classification; (3) chi2 method based on nearest neighbor for luminosity type determination. The experiments on low-resolution spectra show that the system achieves 3.2 spectral subclass precision for spectral and spectral subclass classification, 60% correct rate for luminosity recognition, and 78% rate for the luminosity recognition with error less than or equal to 1. The system is easy, rapid in training, and feasible for the automated spectra classification.

[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.