Auxin is one type of phytohormones that plays important roles in nearly all aspects of plant growth and developmental processes. The glycosylation of auxins is considered to be an essential mechanism to control the level of active auxins. Thus, the identification of auxin glycosyltransferases is of great significance for further understanding the auxin regulation. In this study, we biochemically screened the group L of Arabidopsis thaliana glycosyltransferase superfamily for enzymatic activity toward auxins. UGT74D1 was identified to be a novel auxin glycosyltransferase. Through HPLC and LC-MS analysis of reaction products in vitro by testing eight substrates including auxins and other compounds, we found that UGT74D1 had a strong glucosylating activity toward indole-3-butyric acid [IBA], indole-3-propionic acid [IPA], indole-3-acetic acid [IAA] and naphthaleneacetic acid [NAA], catalyzing them to form corresponding glucose esters. Biochemical characterization showed that this enzyme had a maximum activity in HEPES buffer at pH 6.0 and 37°C. In addition, the enzymatic activity analysis of crude protein and the IBA metabolite analysis from transgenic Arabidopsis plants overexpressing UGT74D1 gene were also carried out. Experimental results indicated that over-production of the UGT74D1 in plants indeed led to increased level of the glucose conjugate of IBA. Moreover, UGT74D1 overexpression lines displayed curling leaf phenotype, suggesting a physiological role of UGT74D1 in affecting the activity of auxins. Our current data provide a new target gene for further genetic studies to understand the auxin regulation by glycosylation in plants.

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Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for

The existing evaluation methods and indexes of noise of electric motors can not comprehensively reflect all the physical characteristics of noise of hub permanent magnet synchronous motors (HPMSM) and human subjective sensations. In this paper, a method of the sound quality (SQ) evaluation of HPMSM for electric vehicles is proposed. The method is divided into three steps. In the first step, a noise objective evaluation of HPMSM with seven acoustical objective indexes, including loudness, roughness, A-weighted sound pressure level (A-W SPL), tonality, sharpness, articulation index (AI) and fluctuation strength, is made. In the second step, a noise subjective evaluation of HPMSM by using the grade evaluation method is made. The subjective annoyances (SA) of noise samples of HPMSM are obtained. In the last step, the SQ evaluation model of HPMSM by using the BP neural network theory is established. The average error rate of the proposed model is only 3.97%. By means of weight analysis, a comparison between the relative importance of acoustical objective parameters is found out. The main original contribution of this paper is that the proposed method can comprehensively reflect all the physical sound characteristics of HPMSM as well as their influence on human subjective sensations.