Genome-wide characterization of the xyloglucan endotransglucosylase/hydrolase family genes and their response to plant hormone in sugar beet.

Xyloglucan endotransglucosylase/hydrolases (XTHs) play a crucial role in plant growth and development. However, their functional response to phytohormone in sugar beet still remains obscure. In this study, we identified 30 putative BvXTH genes in the sugar beet genome. Phylogenetic and evolutionary relationship analysis revealed that they were clustered into three groups and have gone through eight tandem duplication events under purifying selection. Gene structure and motif composition analysis demonstrated that they were highly conserved and all contained one conserved glycoside hydrolase family 16 domain (Glyco_hydro_16) and one xyloglucan endotransglycosylase C-terminus (XET_C) domain. Transcriptional expression analysis exhibited that all BvXTHs were ubiquitously expressed in leaves, root hairs and tuberous roots, and most of them were up-regulated by brassinolide (BR), jasmonic acid (JA), abscisic acid (ABA) and gibberellic acid (GA3). Further mutant complementary experiment demonstrated that expression of BvXTH17 rescued the retarded growth phenotype of xth22, an Arabidopsis knock out mutant of AtXTH22. The findings in our work provide fundamental information on the structure and evolutionary relationship of the XTH family genes in sugar beet, and reveal the potential function of BvXTH17 in plant growth and hormone response.

Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries.

Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the accuracy on the test set were 0.8355, 0.7611, and 0.7940, respectively. This method could be applied to remote sensing classification of fruit trees in a large area, and could provide effective technical means for monitoring fruit tree planting areas with medium and high resolution remote sensing imageries.

Analysis and Research on Sustainable Development Factors of the Sports Industry Based on Chaos Theory.

The sports industry is an emerging industry with broad development prospects, and it is also full of competition. The sports industry has the characteristics of fluctuation, intermittence, and randomness, which are suitable for the analysis of chaos theory in order to find out the internal development law of the sports industry. In order to solve the above problems, an improved chaos theory method is proposed in this paper and the K-cluster analysis method is integrated into chaos calculation, in order to reduce the occurrence rate of the "local extreme value" and improve the accuracy of calculation results. The model uses nonlinear and irregular chaos theory to analyze the aggregation degree of sports industry, industrial spatial distribution, and the spatial governance effect and find out the best optimization decision. When selecting the optimization indicators, not only the European distance of each indicator cluster but also the spatial correlation of the indicators are considered to realize the comprehensive analysis of the sports industry and improve the accuracy of optimization. In the simulation analysis of optimization decision-making, the decision-making model based on chaos theory is compared with the previous first-order decision-making model. The results show that the improved chaos theory can control the data aggregation range of sports industry between (0∼3), the data fusion degree of industrial space between 95 and 99%, and the variation range between 0 and 0.2%, which is significantly better than (0∼9), 90∼95%, and 0∼0.4% of the genetic algorithm. Therefore, the aggregation degree, spatial governance, and decision optimization of the optimization decision-making model proposed in this paper are better than those of the previous genetic algorithm.