Notes

Genomic skimming and also nanopore sequencing learn cryptic hybridization in a single associated with world's nearly all threatened primates.
f the influence of different microorganisms on the excellent traits formation of " sweating" Magnolia Officinalis Cortex.The study aimed to investigate the effect of processing on lectin protein in four toxic Chinese medicines tubers of Pinellia ternata,P. pedatisecta,Arisema heterophyllum and Typhonium giganteum. Western blot was used to semi-quantitatively analyze the content of lectin in the four kinds of toxic Chinese medicines and their different processed products. Raw products and lectin were treated by heating or soaking in ginger juice or alum solution. The effects of different excipients and the heating methods on lectin proteins were investigated. The results showed that the content of lectin in raw products of P. pedatisecta,P. ternata,A. heterophyllum,and T. giganteum were 7. 3%,4. 9%,2. 7%,2. 3%,respectively. And the content of lectin in Pinelliae Rhizoma praeparatum cum alumine was 0. 027%. Lectin was not detected in the Pinelliae Rhizoma Praeparatum cum Zingibere et Alumine,Arisaematis Rhizma Praeparatum and Typhonii Rhizoma Praeparatum,which indicated that processing could significantly reduce the content of active lectin in raw products. The results also showed that with the prolongation of soaking and heating time,the content of lectin in raw products decreased gradually,while the content was almost unchanged when soaked in ginger juice alone. The effects of different excipients and heating on lectin were the same as those on raw products. Therefore,the method with alum soaking and heating can reduce the content of active lectin,which is the key to reduce the toxicity of toxic Chinese medicines. In this paper,Western blot was used to study the content of toxic protein in Araceae toxic Chinese medicines as an evaluation method of the processing degree.This paper constructs a prediction model of material attribute-tensile strength based on principal component analysis-radial basis neural network( PCA-RBF),in order to predict the formability of traditional Chinese medicine tablets. Firstly,design Expert8. 0 software was used to design the dosage of different types of extracts,the mixture of traditional Chinese medicine with different physical properties was obtained,the powder properties of each extract and the tensile strength of tablets were determined,the correlation of the original input layer data was eliminated by PCA,the new variables unrelated to each other were trained as the input data of RBF neural network,and the tensile strength of the tablets was predicted. The experimental results showed that the PCA-RBF model had a good predictive effect on the tensile strength of the tablet,the minimum relative error was 0. 25%,the maximum relative error was2. 21%,and the average error was 1. 35%,which had a high fitting degree and better network prediction accuracy. This study initially constructed a prediction model of material properties-tensile strength of Chinese herbal tablets based on PCA-RBF,which provided a reference for the establishment of effective quality control methods for traditional Chinese medicine preparations.A minimal data set( MDS) for soil fertility evaluation of Chrysanthemum plantation areas of Macheng city was established by principal component analysis( PCA) combined with Norm values of soil fertility indices and correlation coefficients among indices. A radar map was used to visually reflect the fertility level of individual indicators. Then,the comprehensive index model was used to calculate the soil fertility quality index( SFQI),and the values of SFQI was used to cluster,and the results showed that MDS was composed of five indicators organic matter( OM),total phosphate( TP),available phosphorus( Av P),available magnesium( Av Mg) and available ferrum( Av Fe). Radar maps showed that the fertility of available phosphorus( Av P) and available copper( Av Cu) was mostly different in the two town,and the fertility of available ferrum( Av Fe) is smallest different. Except for the effective manganese( Av Mn) fertility level of Huangtugang town was higher than that of Futianhe town,the rest were lower than that otilizer and nitrogen fertilizer should be increased appropriately. At the same time,the amount of organic fertilizer should be increased to enhance soil fertility and improve soil physical and chemical properties.This article aims to identify four commonly applied herbs from Curcuma genus of Zingiberaceae family,namely Curcumae Radix( Yujin),Curcumae Rhizoma( Ezhu),Curcumae Longae Rhizoma( Jianghuang) and Wenyujin Rhizoma Concisum( Pianjianghuang). The odor fingerprints of those four herbal medicines were collected by electronic nose,respectively. Meanwhile,XGBoost algorithm was introduced to data analysis and discriminant model establishment,with four indexes for performance evaluation,including accuracy,precision,recall,and F-measure. The discriminant model was established by XGBoost with positive rate of returning to 166 samples in the training set and 69 samples in the test set were 99. 39% and 95. 65%,respectively. The top four of the contribution to the discriminant model were LY2/g CT,P40/1,LY2/Gh and LY2/LG,the least contributing sensor was T70/2. Compared with support vector machine,random forest and artificial neural network,XGBoost algorithms shows better identification capacity with higher recognition efficiency. The accuracy,precision,recall and F-measure of the XGBoost discriminant model forecast set were 95. 65%,95. find more 25%,93. 07%,93. 75%,respectively. The superiority of XGBoost in the identification of Curcuma herbs was verified. Obviously,this new method could not only be suitable for digitization and objectification of traditional Chinese medicine( TCM) odor indicators,but also achieve the identification of different TCM based on their odor fingerprint in electronic nose system. The introduction of XGBoost algorithm and more excellent algorithms provide more ideas for the application of electronic nose in data mining for TCM studies.The study is aimed to clarify the spatial distribution of Epimedium koreanum( Ek) high-quality production areas. Through visiting and field investigation,collecting the distribution information of Ek samples,and based on the four kinds of flavonoids in Ek,the high-quality production areas and distribution of Ek distribution of the main environmental factors were drawn using GIS technology,the maximum entropy model( MaxEnt),geographical detector statistical analysis method,and the statistical significance of regression equation were obtained. Considering the content of 4 main flavonoids in Ek,the results of this study showed that the main environmental factors,such as precipitation,annual precipitation variation coefficient,annual average temperature and clay content exhibited the greatest influence on the growth suitability of Ek. Ek materials quality concentrated distribution in southeastern Jilin province Changbai mountain hinterland and northeastern Liaoning province. Ek with high content of epimedine A and epimedine C are mainly distributed in the southeastern Jilin province and northeastern Liaoning province,Ek with high epimedine B is distributed in eastern Liaoning province; high icariin Ek was found in most area of northeastern Liaoning province,a small amount distributed in the southeast of Jilin province.
Homepage: https://www.selleckchem.com/products/Dihydroartemisinin(DHA).html