Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds.However,the levels of metabolites in different lettuce cultivars are poorly characterized.In...Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds.However,the levels of metabolites in different lettuce cultivars are poorly characterized.In this study,we used combined GC×GC-TOF/MS and UPLC-IMS-QTOF/MS to detect and relatively quantify metabolites in 30 lettuce cultivars representing large genetic diversity.Comparison with online databases,the published literature,standards as well using collision cross-section values enabled putative identification of 171 metabolites.Sixteen of these 171 metabolites(including phenolic acid derivatives,glycosylated flavonoids,and one iridoid)were present at significantly different levels in leaf and head type lettuces,which suggested the significant metabolomic variations between the leaf and head types of lettuce are related to secondary metabolism.A combination of the results and metabolic network analysis techniques suggested that leaf and head type lettuces contain not only different levels of metabolites but also have significant variations in the corresponding associated metabolic networks.The novel lettuce metabolite library and novel non-targeted metabolomics strategy devised in this study could be used to further characterize metabolic variations between lettuce cultivars or other plants.Moreover,the findings of this study provide important insight into metabolic adaptations due to natural and human selection,which could stimulate further research to potentially improve lettuce quality,yield,and nutritional value.展开更多
Free air CO2 enrichment(FACE) and nitrogen(N) have marked effects on rice root growth,and numerical simulation can explain these effects. To further define the effects of FACE on root growth of rice, an experiment was...Free air CO2 enrichment(FACE) and nitrogen(N) have marked effects on rice root growth,and numerical simulation can explain these effects. To further define the effects of FACE on root growth of rice, an experiment was performed, using the hybrid indica cultivar Xianyou63. The effects of increasing atmospheric CO2 concentration [CO2], 200 μmol mol-1higher than ambient, on the growth of rice adventitious roots were evaluated, with two levels of N: low(LN, 125 kg ha-1) and normal(NN, 250 kg ha-1). The results showed a significant increase in both adventitious root number(ARN) and adventitious root length(ARL) under FACE treatment. The application of nitrogen also increased ARN and ARL, but these increases were smaller than that under FACE treatment. On the basis of the FACE experiment, numerical models for rice adventitious root number and length were constructed with time as the driving factor. The models illustrated the dynamic development of rice adventitious root number and length after transplanting, regulated either by atmospheric [CO2] or by N application.The simulation result was supported by statistical tests comparing experimental data from different years, and the model yields realistic predictions of root growth. These results suggest that the models have strong predictive potential under conditions of atmospheric [CO2] rises in the future.展开更多
To estimate the leaf area index(LAI)in large areas,this paper analyzes the relationships between normalized difference vegetation index(NDVI)and the grassland LAI based on MODIS data in the southern grassy mountains a...To estimate the leaf area index(LAI)in large areas,this paper analyzes the relationships between normalized difference vegetation index(NDVI)and the grassland LAI based on MODIS data in the southern grassy mountains and slopes of China.By using nonlinear fitting equation we constructed the basic estimation model of grassland LAI with NDVI as the independent variable and introduced precipitation and temperature as regulatory factors.The model was validated with observed data in different years and the results showed that there was a good correlation between the simulated and observed LAI value with a statistically significant level of R2.RMSE was 0.302 and RRMSE was 0.154.It was also found that the spatial distribution of grassland LAI in south China showed a remarkable zonal characterization,and temporal distribution showed a single peak curve.These results provided a theoretical basis for the effective management of southern grassland resources and the carbon sink estimation of the nationwide grasslands.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.61233006)the Seed Industry Development Project of Shanghai,China(Grant No.2016,1-8)+1 种基金Shanghai Agriculture Applied Technology Development Program,China(Grant No.20170304)X.Y.was supported by the State Scholarship Fund of China Scholarship Council(No.201706230173).
文摘Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds.However,the levels of metabolites in different lettuce cultivars are poorly characterized.In this study,we used combined GC×GC-TOF/MS and UPLC-IMS-QTOF/MS to detect and relatively quantify metabolites in 30 lettuce cultivars representing large genetic diversity.Comparison with online databases,the published literature,standards as well using collision cross-section values enabled putative identification of 171 metabolites.Sixteen of these 171 metabolites(including phenolic acid derivatives,glycosylated flavonoids,and one iridoid)were present at significantly different levels in leaf and head type lettuces,which suggested the significant metabolomic variations between the leaf and head types of lettuce are related to secondary metabolism.A combination of the results and metabolic network analysis techniques suggested that leaf and head type lettuces contain not only different levels of metabolites but also have significant variations in the corresponding associated metabolic networks.The novel lettuce metabolite library and novel non-targeted metabolomics strategy devised in this study could be used to further characterize metabolic variations between lettuce cultivars or other plants.Moreover,the findings of this study provide important insight into metabolic adaptations due to natural and human selection,which could stimulate further research to potentially improve lettuce quality,yield,and nutritional value.
基金funded by the National Natural Science Foundation of China(No.30270777)the Key Direction Research of Knowledge Innovation in Chinese Academy of Science(No.KZCX3-SW-440)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Free air CO2 enrichment(FACE) and nitrogen(N) have marked effects on rice root growth,and numerical simulation can explain these effects. To further define the effects of FACE on root growth of rice, an experiment was performed, using the hybrid indica cultivar Xianyou63. The effects of increasing atmospheric CO2 concentration [CO2], 200 μmol mol-1higher than ambient, on the growth of rice adventitious roots were evaluated, with two levels of N: low(LN, 125 kg ha-1) and normal(NN, 250 kg ha-1). The results showed a significant increase in both adventitious root number(ARN) and adventitious root length(ARL) under FACE treatment. The application of nitrogen also increased ARN and ARL, but these increases were smaller than that under FACE treatment. On the basis of the FACE experiment, numerical models for rice adventitious root number and length were constructed with time as the driving factor. The models illustrated the dynamic development of rice adventitious root number and length after transplanting, regulated either by atmospheric [CO2] or by N application.The simulation result was supported by statistical tests comparing experimental data from different years, and the model yields realistic predictions of root growth. These results suggest that the models have strong predictive potential under conditions of atmospheric [CO2] rises in the future.
基金Supported by the Key Project of National Programs for Fundamental Research and Development(973 Program)of China(2010CB950702)the Asia-Pacific Network for Global Change Research Project(ARCP2011-06CMY-LI)
文摘To estimate the leaf area index(LAI)in large areas,this paper analyzes the relationships between normalized difference vegetation index(NDVI)and the grassland LAI based on MODIS data in the southern grassy mountains and slopes of China.By using nonlinear fitting equation we constructed the basic estimation model of grassland LAI with NDVI as the independent variable and introduced precipitation and temperature as regulatory factors.The model was validated with observed data in different years and the results showed that there was a good correlation between the simulated and observed LAI value with a statistically significant level of R2.RMSE was 0.302 and RRMSE was 0.154.It was also found that the spatial distribution of grassland LAI in south China showed a remarkable zonal characterization,and temporal distribution showed a single peak curve.These results provided a theoretical basis for the effective management of southern grassland resources and the carbon sink estimation of the nationwide grasslands.