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| 1 | Integration of Metabolomics and Subcellular Organelle Expression Microarray to Increase Understanding the Organic Acid Changes in Post-harvest Citrus Fruit显示文摘Citric acid plays an important role in fresh fruit favor and its adaptability to post-harvest storage conditions.In order to explore organic acid regulatory mechanisms in post-harvest citrus fruit,systematic biological analyses were conducted on stored Hirado Buntan Pummelo(HBP;Citrus grandis)fruits.High-performance capillary electrophoresis,subcellular organelle expression microarray,real-time quantitative reverse transcription polymerase chain reaction,gas chromatography mass spectrometry(GC-MS),and conventional physiological and biochemical analyses were undertaken.The results showed that the concentration of organic acids in HBP underwent a regular fuctuation.GC-MS-based metabolic profling indicated that succinic acid,g-aminobutyric acid(GABA),and glutamine contents increased,but 2-oxoglutaric acid content declined,which further confrmed that the GABA shunt may have some regulatory roles in organic acid catabolism processes.In addition,the concentration of organic acids was signifcantly correlated with senescence-related physiological processes,such as hydrogen peroxide content as well as superoxide dismutase and peroxidase activities,which showed that organic acids could be regarded as important parameters for measuring citrus fruit post-harvest senescence processes. | Xiaohua Sun Andan Zhu Shuzhen Liu Ling Sheng Qiaoli Ma Li Zhang Elsayed Mohamed Elsayed Nishawy Yunliu Zeng Juan Xu Zhaocheng Ma Yunjiang Cheng Xiuxin Deng | 2013 | Journal of Integrative Plant Biology2013，55，11： | 14 |
| 2 | Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO_2 in China显示文摘Observations of atmospheric carbon dioxide (CO2 ) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO2 can be analyzed and modeled by geostatistical methods, and CO2 values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO2 , and does not consider temporal variation in the satellite-observed CO2 data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO2 in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO2 data sets in China. Prediction of monthly CO2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient (r2 ), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO2 than those from the spatial-only approach. | ZENG ZhaoCheng LEI LiPing GUO LiJie ZHANG Li ZHANG Bing | 2013 | Chinese Science Bulletin2013，58，16： | 12 |
| 3 | A comparison of atmospheric CO_2 concentration GOSAT-based observations and model simulations显示文摘Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model. | LEI LiPing GUAN XianHua ZENG ZhaoCheng ZHANG Bing RU Fei BU Ran | 2014 | Science China Earth Sciences2014，57，6： | 6 |
| 4 | Specific patterns of XC02 observed by GOSAT during 2009-2016and assessed with model simulations over China显示文摘Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset(2009-2016)derived from the Greenhouse gases Observing SATellite(GOSAT).XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model.The following results are found:Firstly,the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn.Secondly,on average,XCO2 increases by 2.08 ppm every year from2010 to 2015,with a sharp increase of 2.6 ppm in 2013.Lastly,in the analysis of three typical regions,the GOSAT XCO2 time series is inbetter agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region(the least difference with bias 0.65±0.78 ppm),compared with the northern urban agglomerationregion(-1.3±1.2 ppm)and the northeastern forest region(-1.4±1.4 ppm).The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data. | Nian BIE Liping LEI Zhonghua HE Zhaocheng ZENG Liangyun LIU Bing ZHANG Bofeng CAI | 2020 | Science China Earth Sciences2020，63，3： | 2 |
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