| 1 | The RppC-AvrRppC NLR-effector interaction mediates the resistance to southern corn rust in maize显示文摘Southern corn rust(SCR),caused by the fungal pathogen Puccinia polysora,is a major threat to maize pro-duction worldwide.Efficient breeding and deployment of resistant hybrids are key to achieving durable control of SCR.Here,we report the molecular cloning and characterization of RppC,which encodes an NLR-type immune receptor and is responsible for a major SCR resistance quantitative trait locus.Further-more,we identified the corresponding avirulence effector,AvrRppC,which is secreted by P.polysora and triggers RppC-mediated resistance.Allelic variation of AvrRppC directly determines the effectiveness of RppC-mediated resistance,indicating that monitoring of AvrRppC variants in the field can guide the rational deployment of RppC-containing hybrids in maize production.Currently,RppC is the most frequently deployed SCR resistance gene in China,and a better understanding of its mode of action is crit-ical for extending its durability. | Ce Deng April Leonard James Cahill Meng Lv Yurong Li Shawn Thatcher Xueying Li Xiaodi Zhao Wenjie Du Zheng Li Huimin Li Victor Llaca Kevin Fengler Lisa Marshall Charlotte Harris Girma Tabor Zhimin Li Zhiqiang Tian Qinghua Yang Yanhui Chen Jihua Tang Xintao Wang Junjie Hao Jianbing Yan Zhibing Lai Xiaohong Fei Weibin Song Jinsheng Lai Xuecai Zhang Guoping Shu Yibo Wang Yuxiao Chang Weiling Zhu Wei Xiong Juan Sun Bailin Li Junqiang Ding | 2022 | Molecular Plant2022，15，5： | 6 |
| 3 | Grain Yield Predict Based on GRA-AdaBoost-SVR Model显示文摘Grain yield security is a basic national policy of China,and changes in grain yield are influenced by a variety of factors,which often have a complex,non-linear relationship with each other.Therefore,this paper proposes a Grey Relational Analysis-Adaptive Boosting-Support Vector Regression(GRA-AdaBoost-SVR)model,which can ensure the prediction accuracy of the model under small sample,improve the generalization ability,and enhance the prediction accuracy.SVR allows mapping to high-dimensional spaces using kernel functions,good for solving nonlinear problems.Grain yield datasets generally have small sample sizes and many features,making SVR a promising application for grain yield datasets.However,the SVR algorithm’s own problems with the selection of parameters and kernel functions make the model less generalizable.Therefore,the Adaptive Boosting(AdaBoost)algorithm can be used.Using the SVR algorithm as a training method for base learners in the AdaBoost algorithm.Effectively address the generalization capability problem in SVR algorithms.In addition,to address the problem of sensitivity to anomalous samples in the AdaBoost algorithm,the GRA method is used to extract influence factors with higher correlation and reduce the number of anomalous samples.Finally,applying the GRA-AdaBoost-SVR model to grain yield forecasting in China.Experiments were conducted to verify the correctness of the model and to compare the effectiveness of several traditional models applied to the grain yield data.The results show that the GRA-AdaBoost-SVR algorithm improves the prediction accuracy,the model is smoother,and confirms that the model possesses better prediction performance and better generalization ability. | Diantao Hu Cong Zhang Wenqi Cao Xintao Lv Songwu Xie | 2021 | Journal on Big Data2021，3，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员