Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explici...Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.展开更多
Wheat grains contain various bioactive substances,of which,condensed tannins(CT)are polymeric flavan-3-ols that accumulate in wheat seed coat influencing the end-use quality and nutritional value.However,the genetic a...Wheat grains contain various bioactive substances,of which,condensed tannins(CT)are polymeric flavan-3-ols that accumulate in wheat seed coat influencing the end-use quality and nutritional value.However,the genetic architecture underlying CT biosynthesis in wheat grain remains unclear.Here,we studied the deposition and genetic regulation of CT in wheat grains,and found that CT deposited specifically in the testa layer of red-grained wheat as catechin-and epicatechin-formed polymers.Genome-wide association study identified 22 genetic loci affecting CT content,one of which,TaTAN,a single dominant gene controlling CT presence,was mapped to chromosome 3A in a segregation population.Further pan-genome analysis,transcriptome profiling and ethyl methanesulfonate induced mutants sequencing revealed a R2R3-MYB transcription factor,TaMYB10-3A,as the causal gene.Three loss-of-function alleles in TaMYB10-3A caused by large fragment inversion-deletion and insertion were identified which abolish both CT deposition and red pigmentation,demonstrating the pleiotropic effect of TaMYB10-3A on CT presence and grain color.TaMYB10-3A directly trans-activates core flavonoid genes such as chalcone synthase and dihydroflavonol 4-reductase to initiate CT biosynthesis.Our investigation provides a comprehensive understanding of CT presence in wheat grains and lays a solid foundation for manipulating CT metabolites to improve wheat grain end-use quality and nutrition values in wheat.展开更多
对基于空间可分辨光谱的番茄成熟度分类判别方法进行了试验研究。首先根据番茄的内部颜色,将600个番茄分为6个不同成熟度(green,breaker,turning,pink,light red and red),然后用自行开发的多通道高光谱成像探头采集番茄的空间可分辨(SR...对基于空间可分辨光谱的番茄成熟度分类判别方法进行了试验研究。首先根据番茄的内部颜色,将600个番茄分为6个不同成熟度(green,breaker,turning,pink,light red and red),然后用自行开发的多通道高光谱成像探头采集番茄的空间可分辨(SR)光谱,建立基于空间可分辨光谱的番茄成熟度偏最小二乘判别(PLSDA)模型和支持向量机判别(SVMDA)模型。结果显示,对于PLSDA模型,SR光谱15为最佳分类光谱,分类正确率达到81.3%;对于SVMDA模型,SR光谱10为最佳预测分类光谱,分类正确率为86.3%。对六个成熟度等级番茄的判别分类,SVMDA模型要明显优于PLSDA模型。此外,相对于较小的光源-检测器距离SR光谱,较大的光源-检测器距离SR光谱可以获得更好的判别效果,显示出空间可分辨光谱在果蔬品质检测方面的应用潜力。展开更多
基金supported by United States Department of Agriculture,Agricultural Research Service(No.58-8042-9-072)United States Department of Agriculture-National Institute of Food and Agriculture(No.2019-34263-30552)+1 种基金Management Information System(No.043050)United States Department of Agriculture-Agricultural Research Service-Non-Assistance Cooperative Agreement(No.58-6066-2-030).
文摘Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.
基金supported by the Key R&D Program of Shandong Province (2024LZGC007, 2024CXPT072, 2022LZGC001)Shandong Provincial Natural Science Foundation (ZR2024YQ069)+1 种基金the National Natural Science Foundation of China (32201863,32272181)the Taishan Scholars Program
文摘Wheat grains contain various bioactive substances,of which,condensed tannins(CT)are polymeric flavan-3-ols that accumulate in wheat seed coat influencing the end-use quality and nutritional value.However,the genetic architecture underlying CT biosynthesis in wheat grain remains unclear.Here,we studied the deposition and genetic regulation of CT in wheat grains,and found that CT deposited specifically in the testa layer of red-grained wheat as catechin-and epicatechin-formed polymers.Genome-wide association study identified 22 genetic loci affecting CT content,one of which,TaTAN,a single dominant gene controlling CT presence,was mapped to chromosome 3A in a segregation population.Further pan-genome analysis,transcriptome profiling and ethyl methanesulfonate induced mutants sequencing revealed a R2R3-MYB transcription factor,TaMYB10-3A,as the causal gene.Three loss-of-function alleles in TaMYB10-3A caused by large fragment inversion-deletion and insertion were identified which abolish both CT deposition and red pigmentation,demonstrating the pleiotropic effect of TaMYB10-3A on CT presence and grain color.TaMYB10-3A directly trans-activates core flavonoid genes such as chalcone synthase and dihydroflavonol 4-reductase to initiate CT biosynthesis.Our investigation provides a comprehensive understanding of CT presence in wheat grains and lays a solid foundation for manipulating CT metabolites to improve wheat grain end-use quality and nutrition values in wheat.
文摘对基于空间可分辨光谱的番茄成熟度分类判别方法进行了试验研究。首先根据番茄的内部颜色,将600个番茄分为6个不同成熟度(green,breaker,turning,pink,light red and red),然后用自行开发的多通道高光谱成像探头采集番茄的空间可分辨(SR)光谱,建立基于空间可分辨光谱的番茄成熟度偏最小二乘判别(PLSDA)模型和支持向量机判别(SVMDA)模型。结果显示,对于PLSDA模型,SR光谱15为最佳分类光谱,分类正确率达到81.3%;对于SVMDA模型,SR光谱10为最佳预测分类光谱,分类正确率为86.3%。对六个成熟度等级番茄的判别分类,SVMDA模型要明显优于PLSDA模型。此外,相对于较小的光源-检测器距离SR光谱,较大的光源-检测器距离SR光谱可以获得更好的判别效果,显示出空间可分辨光谱在果蔬品质检测方面的应用潜力。
