ROS generation due to the interaction between bacteria and azorubine could be responsible for the biofilm inhibitory action of the food colorant. Findings of the in vitro studies were well supported by molecular docking and simulation analysis of azorubine and QS virulence proteins. Azorubine showed strong binding to PqsA as compared to other virulent proteins (LasR, Vfr, and QscR). Thus, it is concluded that azorubine is a promising candidate to ensure food safety by curbing the menace of bacterial QS and biofilm-based spoilage of food and reduce economic losses. © 2020 The Authors.Investigating the application of CT images when diagnosing lung cancer based on finite mixture model is the objective. METHOD 120 clean healthy rats were taken as the research objects to establish lung cancer rat model and carry out lung CT image examination. After the successful CT image data preprocessing, the image is segmented by different methods, which include lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization - Gaussian mixture model (APSO-GMM), lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization - gamma mixture model (APSO-GaMM), lung nodule segmentation based on statistical information and self-selected mixed distribution model, and lung nodule segmentation based on neighborhood information and self-selected mixed distribution model. The segmentation effect is evaluated. RESULTS Compared with the results of lung nodule segmentation based on statistical information and self-selected mixed distribution model, the Dice coefficient of lung nodule segmentation based on neighborhood information and self-selected mixed distribution model is higher, the relative final measurement accuracy is smaller, the segmentation is more accurate, but the running time is longer. Compared with APSO-GMM and APSO-GaMM, the dice value of self-selected mixed distribution model segmentation method is larger, and the final measurement accuracy is smaller. CONCLUSION Among the five methods, the dice value of the self-selected mixed distribution model based on neighborhood information is the largest, and the relative accuracy of the final measurement is the smallest, indicating that the segmentation effect of the self-selected mixed distribution model based on neighborhood information is the best. © 2020 The Authors.Objective Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients. Method CT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared. Results 30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher. Conclusion The prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC. © 2020 The Authors.Despite the knowledge regarding allelopathy, known as a major ecological mechanism for biological weed control, had increased greatly, the role of soil microorganisms in that field remained controversial. The study sought to evaluate the interference potential of soil microorganisms, residues-derived allelochemicals and their interaction on seed germination and understand the variation of microbial community in allelopathic activities. Three different rice residues-derived fractions from variety PI312777 (extracts, straw fraction and fresh residue) were applied to sterile and live soils to disentangle the interference potential of soil microorganisms, residues-derived allelochemicals and their interaction concerned allelopathic activities. The results demonstrated that microbe-only and residues-only exerted onefold promotion and inhibition effects on lettuce (Lactuca sativa Linn.) seed germination, respectively, whereas, microbe-by-residues interaction showed an inhibition at the beginning, and a feeble promotion later. The 20 most dominant genera of microbes were classified into three clusters, with 13 genera in one cluster, only 1 in the second cluster and 6 in the third one. The genera in the first cluster commonly exerted negative effects on phenol content, while showed positive correlation with seed germination. Interestingly, Bacillus, clustered in the second cluster, had an opposite effect alone. The third cluster genera somehow had a weak correlation with both germination as well as the release of the allelochemicals. Overall, we incorporated molecular methodology for tracking bacterial impacts during incubation with allelochemicals, and demonstrated the mutable role of soil microbes in allelopathy. It may be potentially important for stimulating the beneficial roles of microbes for environmentally friendly weed management.  https://www.selleckchem.com/products/CP-690550.html  © 2020 The Author(s).In order to explore the predictive model for analyzing clinical pregnancy outcomes based on IVF-ET (in vitro fertilization and embryo transfer) and ICSI (Intracytoplasmic sperm injection) assisted reproductive technology (ART). METHODS this study selected the embryo transfer (fresh) patients who received IVF-ET or ICSI treatment in the First Affiliated Hospital of Guangxi Medical University as the subjects. Moreover, the controlled ovarian stimulation (COS) and follow-up were conducted to collect relevant data for analysis, and finally a prediction model was established. RESULTS The results showed that the patients were divided into different ovarian response groups at first. The age, bFSH and bFSH/bLH were the highest in the poor ovarian response group (POR), followed by the normal ovarian response group (NOR) and the lowest in the high ovarian response group (HOR). The area under the ROC curve was 0.669 according to the predictive model of pregnancy-related factors. The confidence interval of 94% was 0.629-0.