ABSTRACT
GRAS transcription factors play important roles in the regulation of plant root growth and GA signaling. In this study,SmGRAS3 gene was cloned,which open reading frame was 2 247 bp,and encoding 748 amino acids. The physicochemical properties and structure of SmGRAS3 and its encoded protein were analyzed by bioinformatics software. This gene belongs to the SCL9 subfamily of the GRAS family,and its promoter sequence mainly contains the light response,stress response,and hormone response elements. It may interact with the GA signal pathway and anti-stress related proteins. The subcellular localization showed that SmGRAS3 protein was mainly located in the nucleus. The expression pattern analysis showed that the expression of Sm GRAS3 was the highest in the root and the lowest in the stem,and both light and low temperature could induce the high expression level of SmGRAS3. This study provides a foundation for further study on the roles of SmGRAS3 gene in the root growth and stress tolerance of Salvia miltiorrhiza.
Subject(s)
Salvia miltiorrhiza/genetics , Cloning, Molecular , Gene Expression Regulation, Plant , Phylogeny , Plant Proteins , Transcription FactorsABSTRACT
A new method was put forward to diagnose chronic enteritis of alpine musk deer (Moschus chrysogaster) by visible-near infrared reflectance spectra of feces. A total of 125 feces samples, including 70 samples from healthy individuals (healthy samples) and 55 samples from chronic enteritis sufferers (diseased samples), were collected in Xinglongshan musk deer farm, Gansu province. The spectral scan was carried out in the darkroom (temperature 18 degrees C-22 degrees C, humidity 22%-25% and halogen lamp as a sole light source) with an ASD FieldSpec 3 spectrometer. All the samples were divided randomly into two groups, one with 95 samples as the calibration set, and another with 30 samples as the validation set. The samples data were pretreated by the methods of S. Golay smoothing and first derivative. The pretreated spectra were analyzed by principal component analysis (PCA), and the top 6 principal components, which were computed by PCA and accounted for 95.16% variation of the original spectral information, were used for modeling as the new variables. The data of the calibration set were used to build models for diagnosing the chronic enteritis of alpine musk deer by means of back-propagation artificial neural network (ANN-BP), fuzzy pattern recognition, Fisher linear discriminant and Bayes stepwise discriminant, respectively. The predicted outcomes of the 30 unknown samples in validation set showed that the accuracy was 86.7% by themethod of Fisher linear discriminant, 90% by fuzzy pattern recognition and ANN-BP model, and 93.3% by stepwise discrimination. Further analysis found that all misdiagnosed samples were derived from the healthy samples, which were treated as disease samples, and the detection rates of diseased samples were 100% by the four different methods. The results indicated that it was feasible to diagnose the chronic enteritis of alpine musk deer by visible-near infrared reflectance spectra of feces as a rapid and non-contact way, and the PCA combined with Bayes stepwise discriminant was a preferred method.
