Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae.

Ageratina adenophora, Eupatorium odoratum, and Mikania micrantha are three highly destructive invasive plants of Compositae in China. Through the screening of SDMs, random forest (RF), gradient boosting model (GBM), artificial neural network (ANN), and flexible discriminant analysis (FDA) with TSS greater than 0.8 are selected to construct a high-precision ensemble model (EM) as the prediction model. We use specimen sites and environmental variables containing climate, soil, terrain, and human activities to simulate and predict the invasion trend of three invasive weeds in China in current, the 2050s, and the 2070s. Results indicate that the highly invasive risk area of three exotic plants is mostly distributed along the river in the provinces south of 30° N. In the future scenario, the three exotic plants obviously invade northwards Yunnan, Sichuan, Guizhou, Jiangxi and Fujian. Climate is the most important variable that affects the spread of three kinds of alien plant invasions. Temperature and precipitation variables have a similar effect on A. adenophora and E. odoratum, while M. micrantha is more sensitive to temperature. It has been reported that Ipomoea batatas and Vitex negundo can prevent the invasion of three invasive plants. Hence, we also simulate the suitable planting areas for I. batatas and V. negundo. The results show that I. batatas and V. negundo are suitable to be planted in the areas where the three weeds show invasion tendency. In the paper, predicting invasion trends of exotic plants and simulating the planting suitability of crops that can block invasion, to provide a practical significance reference and suggestion for the management, prevention, and control of the invasion of exotic plants in China.

Effect of Tripterygium Wilfordii Polyglycoside on Experimental Prostatitis Caused by Ureaplasma Urealyticum in Rats.

BACKGROUND Prostatitis is a common and refractory urological disease with complicated etiology. Ureaplasma urealyticum (UU) has a close relationship with human urinary tract infection that can induce nonbacterial prostatitis. Tripterygium wilfordii polyglycoside (TWP) is a non-steroidal immune inhibitor that causes significant immune suppression and anti-inflammatory effects. Its role in prostatitis caused by UU has not yet been established. The aim of this study was to investigate the effect of TWP on UU-infected prostatitis in a rat model. MATERIAL AND METHODS UU-infected prostatitis SD model rats were randomly divided into 2 groups: the prostatitis group (model group) and the TWP treatment group (treatment group). At 7 days after treatment, prostate weight, leucocyte count, lecithin corpuscles, UU infection rate, and UU microbe count were compared between the 2 groups. Serum inflammatory cytokines TNF-α was determined by ELISA, and ICAM-1 and NF-κB expression were detected. RESULTS UU infection rate was 80% after modeling. The rat prostate weight and leucocyte count in the model group increased significantly, while lecithin corpuscles decreased. Compared with controls, inflammatory factor TNF-α, ICAM-1, and NF-κB expression were obviously higher (P<0.05). TWP markedly reduced prostate weight and leucocyte count, increased lecithin corpuscles, and decreased UU microbe count and TNF-α, ICAM-1, and NF-κB expression (P<0.05). CONCLUSIONS TWP can inhibit expression of inflammatory factors and may be useful in treating UU-infected prostatitis through reducing UU infection rate.