Development and application of a novel suspension concentrate for seed coating of rice for controlling bakanae disease and seedling rot disease.

The main rice planting areas in the middle and lower reaches of the Yangtze River are primarily affected by two types of rice seedling diseases: bakanae disease and seedling rot disease. These diseases lead to considerable losses. Seed coating technology effectively protects rice from these diseases and mitigates environmental pollution. We determined the antifungal activity of six fungicides, including phenamacril, azoxystrobin, fludioxonil, metconazole, thifluzamide and prothioconazole against Fusarium moniliforme Sheldon and Curvularia lunata in this study. In addition, the impact of fungicides and surfactants on rice seed germination were determined. Furthermore, phenamacril and fludioxonil were selected as the active components of suspension concentrate for seed coating. The antifungal activity of phenamacril against F. moniliforme Sheldon was 0.139 mg/L and fludioxonil against C. lunata was 0.110 mg/L. PEG-2000 was selected as the surfactant due to its promoting effect on rice seedling. Based on the above findings, 6% phenamacril fludioxonil FS was developed via the wet sand grinding method. The toxicity of 6% phenamacril fludioxonil FS to zebrafish was verified, and field experiments were conducted in five different regions of the Yangtze River Basin. The results indicated minimal toxicity of 6% phenamacril fludioxonil FS to zebrafish. Relative to the control agent consisting of 6.25% phenamacril metalaxyl-M FS, 6% phenamacril fludioxonil FS showed better control effect and exhibited superior efficacy in promoting growth and increasing yield in all five regions. Specifically, the control effect of 6% phenamacril fludioxonil FS on bakanae exceeded 84.83% with the highest yield increasing value recorded at 30.48%. Currently, the market offers a limited selection of suspension concentrate for seed coating of rice. The findings of this study may offer a viable alternative formulation and directions for further research concerning the application of suspension concentrate for seed coating of rice.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.