Objective To screen the asymmetric dimethyl arginines (ADMA)-containing proteins which could combine with protein arginine methyltransferase 1 (PRMT1). Methods Western blot was adopted to identify the expression of PR...Objective To screen the asymmetric dimethyl arginines (ADMA)-containing proteins which could combine with protein arginine methyltransferase 1 (PRMT1). Methods Western blot was adopted to identify the expression of PRMT1 and the proteins with ADMA in glioma cell lines and normal brain tissues, and then to detect the changes of ADMA level after knock-down of PRMT1 with RNAi transfection in U87MG cells. Co-Immunoprecipitation (Co-IP), western blot, and sliver staining were employed to screen the candidate binding proteins of PRMT1. Then liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify the binding proteins of PRMT1. Results The expression of PRMT1 and some levels of ADMA were higher in glioma cell lines than in normal brain tissues. After knocking down PRMT1, some ADMA levels were found declined. After screening the binding proteins of PRMT1 with Co-IP and LC-MS/MS, 26 candidate binding proteins were identified. Among them, 6 candidate proteins had higher ions scores (>38) and bioinformation analysis predicted that SEC23-IP, ANKHD1-EIF4EBP3 protein, and 1-phosphatidylinositol-3-phosphate 5-kinase isoform 2 had possible methylated aginine sites. Conclusions The high expression of PRMT1 in glioma may induce the change of ADMA levels. Altogether 26 candidate proteins were identified, which contain ADMA and specifically bind with PRMT1.展开更多
There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the tru...There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.展开更多
Citrus sinensis commonly called sweet orange belongs to the family Rutaceae. Nutritionally, it is highly recommended due to its high content of micronutrients. However, the rejection of a large amount of epicarp in na...Citrus sinensis commonly called sweet orange belongs to the family Rutaceae. Nutritionally, it is highly recommended due to its high content of micronutrients. However, the rejection of a large amount of epicarp in nature contributes to the emission of greenhouse gas and the development of leachate which contaminate surface water and groundwater. The aim of this work was to identify the essential oil components from Citrus sinensis epicarp, and then look after the biological activity of these components in order to underline the worth to reuse the Citrus sinensis epicarp as a gainful mean. The essential oil of 4,000 g of Citrus sinensis epicarp was done through the water steam distillation and 0.0287 g of essential oil was obtained; so a yield of 0.0007%. The essential oil was then submitted to gas chromatography-flame ionization detector （GC-F1D）. The result revealed that the essential oil was teemed with 28 volatile compounds, including terpene compounds （50%）, aldehydes （32%） and alcohols （18%） whose anti-inflammatory, anti-diabetic, larvicidal and antioxidant activities were underlined.展开更多
基金Supported by National Natural Science Foundation of China(30825023)
文摘Objective To screen the asymmetric dimethyl arginines (ADMA)-containing proteins which could combine with protein arginine methyltransferase 1 (PRMT1). Methods Western blot was adopted to identify the expression of PRMT1 and the proteins with ADMA in glioma cell lines and normal brain tissues, and then to detect the changes of ADMA level after knock-down of PRMT1 with RNAi transfection in U87MG cells. Co-Immunoprecipitation (Co-IP), western blot, and sliver staining were employed to screen the candidate binding proteins of PRMT1. Then liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify the binding proteins of PRMT1. Results The expression of PRMT1 and some levels of ADMA were higher in glioma cell lines than in normal brain tissues. After knocking down PRMT1, some ADMA levels were found declined. After screening the binding proteins of PRMT1 with Co-IP and LC-MS/MS, 26 candidate binding proteins were identified. Among them, 6 candidate proteins had higher ions scores (>38) and bioinformation analysis predicted that SEC23-IP, ANKHD1-EIF4EBP3 protein, and 1-phosphatidylinositol-3-phosphate 5-kinase isoform 2 had possible methylated aginine sites. Conclusions The high expression of PRMT1 in glioma may induce the change of ADMA levels. Altogether 26 candidate proteins were identified, which contain ADMA and specifically bind with PRMT1.
文摘There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.
文摘Citrus sinensis commonly called sweet orange belongs to the family Rutaceae. Nutritionally, it is highly recommended due to its high content of micronutrients. However, the rejection of a large amount of epicarp in nature contributes to the emission of greenhouse gas and the development of leachate which contaminate surface water and groundwater. The aim of this work was to identify the essential oil components from Citrus sinensis epicarp, and then look after the biological activity of these components in order to underline the worth to reuse the Citrus sinensis epicarp as a gainful mean. The essential oil of 4,000 g of Citrus sinensis epicarp was done through the water steam distillation and 0.0287 g of essential oil was obtained; so a yield of 0.0007%. The essential oil was then submitted to gas chromatography-flame ionization detector （GC-F1D）. The result revealed that the essential oil was teemed with 28 volatile compounds, including terpene compounds （50%）, aldehydes （32%） and alcohols （18%） whose anti-inflammatory, anti-diabetic, larvicidal and antioxidant activities were underlined.
