Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical metho...Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.展开更多
在传统无人侦察机模拟训练方法中,视景仿真环境逼真度不足,三维地形容易忽视动态目标。为此,提出一种基于谷歌地球(GE)和SketchUp(SU)模型的动态侦察环境模拟方法。通过GE场景建立三维仿真环境,利用SU制作三维模型,采用三次B样条插值方...在传统无人侦察机模拟训练方法中,视景仿真环境逼真度不足,三维地形容易忽视动态目标。为此,提出一种基于谷歌地球(GE)和SketchUp(SU)模型的动态侦察环境模拟方法。通过GE场景建立三维仿真环境,利用SU制作三维模型,采用三次B样条插值方法对路径进行平滑处理,计算模型的姿态角,使用Keyhole标记语言和GE COM API,完成飞行视景仿真。实验结果表明,该方法能生成逼真的动态三维场景,减少建模工作和开发周期。展开更多
目的探讨多模型迭代重建技术对早期肺癌低剂量GE revolution CT成像质量的影响。方法将2018年1月~2019年6月于北京世纪坛医院就诊的240例早期肺癌患者作为观察对象,所有患者均行低剂量GE revolution CT成像检查,并采用多模型迭代重建技...目的探讨多模型迭代重建技术对早期肺癌低剂量GE revolution CT成像质量的影响。方法将2018年1月~2019年6月于北京世纪坛医院就诊的240例早期肺癌患者作为观察对象,所有患者均行低剂量GE revolution CT成像检查,并采用多模型迭代重建技术对图像进行重建。比较图像重建前后肺窗图像质量、纵膈窗图像质量及图像质量参数。结果与低剂量GE revolution CT图像相比,应用多模型迭代重建技术重建后的肺窗和纵膈窗图像质量提高,图像噪声降低(8.83±1.95 Hu vs 9.21±2.17 Hu),信噪比升高(7.21±1.30 vs 6.89±1.22),差异均有统计学意义(P<0.05),而CT值比较差异无统计学意义(65.01±7.94 Hu vs 65.38±8.26 Hu,P>0.05)。结论多模型迭代重建技术能够提高早期肺癌低剂量GE revolution CT成像质量,对早期肺癌的筛查具有重要临床应用价值。展开更多
基金the bread wheat project of the Dryland Agricultural Research Institute (DARI)supported by the Agricultural Research and Education Organization (AREO) of Iran
文摘Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.
文摘在传统无人侦察机模拟训练方法中,视景仿真环境逼真度不足,三维地形容易忽视动态目标。为此,提出一种基于谷歌地球(GE)和SketchUp(SU)模型的动态侦察环境模拟方法。通过GE场景建立三维仿真环境,利用SU制作三维模型,采用三次B样条插值方法对路径进行平滑处理,计算模型的姿态角,使用Keyhole标记语言和GE COM API,完成飞行视景仿真。实验结果表明,该方法能生成逼真的动态三维场景,减少建模工作和开发周期。
文摘目的探讨多模型迭代重建技术对早期肺癌低剂量GE revolution CT成像质量的影响。方法将2018年1月~2019年6月于北京世纪坛医院就诊的240例早期肺癌患者作为观察对象,所有患者均行低剂量GE revolution CT成像检查,并采用多模型迭代重建技术对图像进行重建。比较图像重建前后肺窗图像质量、纵膈窗图像质量及图像质量参数。结果与低剂量GE revolution CT图像相比,应用多模型迭代重建技术重建后的肺窗和纵膈窗图像质量提高,图像噪声降低(8.83±1.95 Hu vs 9.21±2.17 Hu),信噪比升高(7.21±1.30 vs 6.89±1.22),差异均有统计学意义(P<0.05),而CT值比较差异无统计学意义(65.01±7.94 Hu vs 65.38±8.26 Hu,P>0.05)。结论多模型迭代重建技术能够提高早期肺癌低剂量GE revolution CT成像质量,对早期肺癌的筛查具有重要临床应用价值。
