The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering s...The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.展开更多
Efficient and accurate acquisition of the rice grain protein content(GPC)is important for selecting high-quality rice varieties,and remote sensing technology is an attractive potential method for this task.However,the...Efficient and accurate acquisition of the rice grain protein content(GPC)is important for selecting high-quality rice varieties,and remote sensing technology is an attractive potential method for this task.However,the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands.Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics.However,the small size of typical datasets is a constraint for model construction for estimating GPC,limiting their accuracy and reducing their ability to generalize to a wide range of varieties.In this study,we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks(DCGANs)to generate simulated data to improve the model accuracy.Features sensitive to GPC were extracted after applying a continuous wavelet transform(CWT),and the estimated GPC model was constructed by partial least squares regression(PLSR).Finally,a genome-wide association study(GWAS)was applied to the measured and generated datasets to detect GPC loci.The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values.The wavelet feature(WF_(1743,2)),obtained from the data with the addition of 200 simulated samples,exhibited the highest GPC estimation accuracy(R^(2)=0.58 and RRMSE=6.70%).The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values,including the OsmtSSB1L gene related to grain storage protein.This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.展开更多
The distribution characteristics of the impact craters can provide a large amount of information on impact history and the lunar evolution process. In this research, based on the digital elevation model （DEM） data o...The distribution characteristics of the impact craters can provide a large amount of information on impact history and the lunar evolution process. In this research, based on the digital elevation model （DEM） data originating from Change＇E-1 CCD stereo camera, three automatic extraction methods for the impact craters are implemented in two research areas： direct extraction from flooded DEM data （the Flooded method）, object-oriented extraction from DEM data by using ENVI ZOOM function （the Object-Oriented method） and novel object-oriented extraction from flooded DEM data （the Flooded Object-Oriented method）. Accuracy assessment, extracted degree computation, cumulative frequency analysis, shape and age analysis of the extracted craters combined display the following results. （1） The Flooded Object-Oriented method yields better accuracy than the other two methods in the two research areas; the extraction result of the Flooded method offers the similar accuracy to that of the Object-Oriented method. （2） The cumulative frequency curves for the extracted craters and the confirmed craters share a simi- lar change trajectory. （3） The number of the impact craters extracted by the three methods in the Imbrian period is the largest and is of various types; as to their age earlier than lmbrain, it is difficult to extract because they could have been destroyed.展开更多
Background In March 2013,human cases of infection with a novel A （H7N9） influenza virus emerged in China.The epidemic spread quickly and as of 6 May 2013,there were 129 confirmed cases.The purpose of this study was ...Background In March 2013,human cases of infection with a novel A （H7N9） influenza virus emerged in China.The epidemic spread quickly and as of 6 May 2013,there were 129 confirmed cases.The purpose of this study was to analyze the epidemiology of the confirmed cases,determine the impacts of bird migration and temperature changes on the H7N9 epidemic,predict the future trends of the epidemic,explore the response patterns of the government and propose preventive suggestions.Methods The geographic,temporal and population distribution of all cases reported up to 6 May 2013 were described from available records.Risk assessment standard was established by analysing the temperature and relative humidity records during the period of extensive outbreak in three epidemic regions in eastern China,including Shanghai,Zhejiang and Jiangsu provinces.Risk assessment maps were created by combining the bird migration routes in eastern China with the monthly average temperatures from May 1993 to December 2012 nationwide.Results Among the confirmed cases,there were more men than women,and 50.4％ were elderly adults （age ＞61 years）.The major demographic groups were retirees and farmers.The temperature on the days of disease onset was concentrated in the range of 9℃-19℃; we defined 9℃-19℃ as the high-risk temperature range,0℃-9℃ or 19℃-25℃ as medium risk and ＜0℃ or ＞25℃ as low risk.The relative humidity on the days of disease onset ranged widely from 25％ to 99％,but did not correlate with the incidence of infection.Based on the temperature analysis and the eastern bird migration routes,we predicted that after May,the high-risk region would move to the northeast and inland,while after September,it would move back to north China.Conclusions Temperature and bird migration strongly influence the spread of the H7N9 virus.In order to control the H7N9 epidemic effectively,Chinese authorities should strengthen the surveillance of migrating birds,increase poultry and environmental sampling,improve live poultry selling and husbandry patterns and move from a ＂passive response pattern＂to an ＂active response pattern＂ in focused preventive measures.展开更多
基金supported by the National Natural Science Foundation of China(32371990,31971784)the Earmarked Fund for Jiangsu Agricultural Industry Technology System(JATS(2022)168,JATS(2022)468)+1 种基金the Jiangsu Provincial Cooperative Promotion Plan of Major Agricultural Technologies(2021-ZYXT-01-1)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0783)。
文摘The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.
基金supported by the National Key Research and Development Program of China(2021YFD2000100)the National Natural Science Foundation of China(32101617)+3 种基金the Fundamental Research Funds for the Central Universities(JSJL2023005)the Zhongshan Biological Breeding Laboratory(ZSBBL-KY2023-05)the Key Independent Research Project of Jjiangsu Key Laboratory of Information Agriculture(KLIAZZ2301)the Jiangsu Collaborative Innovation Center for Modern Crop Production(JCICMCP).
文摘Efficient and accurate acquisition of the rice grain protein content(GPC)is important for selecting high-quality rice varieties,and remote sensing technology is an attractive potential method for this task.However,the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands.Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics.However,the small size of typical datasets is a constraint for model construction for estimating GPC,limiting their accuracy and reducing their ability to generalize to a wide range of varieties.In this study,we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks(DCGANs)to generate simulated data to improve the model accuracy.Features sensitive to GPC were extracted after applying a continuous wavelet transform(CWT),and the estimated GPC model was constructed by partial least squares regression(PLSR).Finally,a genome-wide association study(GWAS)was applied to the measured and generated datasets to detect GPC loci.The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values.The wavelet feature(WF_(1743,2)),obtained from the data with the addition of 200 simulated samples,exhibited the highest GPC estimation accuracy(R^(2)=0.58 and RRMSE=6.70%).The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values,including the OsmtSSB1L gene related to grain storage protein.This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.
基金supported by the National Natural Science Foundation of China (Grant Nos. 40871177 and 41171332)the Knowledge Innovation Project of the Institute of Geographic and Natural Resources Research, the Chinese Academy of Sci-ences (Grant No. 201001005)
文摘The distribution characteristics of the impact craters can provide a large amount of information on impact history and the lunar evolution process. In this research, based on the digital elevation model （DEM） data originating from Change＇E-1 CCD stereo camera, three automatic extraction methods for the impact craters are implemented in two research areas： direct extraction from flooded DEM data （the Flooded method）, object-oriented extraction from DEM data by using ENVI ZOOM function （the Object-Oriented method） and novel object-oriented extraction from flooded DEM data （the Flooded Object-Oriented method）. Accuracy assessment, extracted degree computation, cumulative frequency analysis, shape and age analysis of the extracted craters combined display the following results. （1） The Flooded Object-Oriented method yields better accuracy than the other two methods in the two research areas; the extraction result of the Flooded method offers the similar accuracy to that of the Object-Oriented method. （2） The cumulative frequency curves for the extracted craters and the confirmed craters share a simi- lar change trajectory. （3） The number of the impact craters extracted by the three methods in the Imbrian period is the largest and is of various types; as to their age earlier than lmbrain, it is difficult to extract because they could have been destroyed.
基金This study was supported by a grant from the National Natural Science Foundation of China （No. 81172735）.
文摘Background In March 2013,human cases of infection with a novel A （H7N9） influenza virus emerged in China.The epidemic spread quickly and as of 6 May 2013,there were 129 confirmed cases.The purpose of this study was to analyze the epidemiology of the confirmed cases,determine the impacts of bird migration and temperature changes on the H7N9 epidemic,predict the future trends of the epidemic,explore the response patterns of the government and propose preventive suggestions.Methods The geographic,temporal and population distribution of all cases reported up to 6 May 2013 were described from available records.Risk assessment standard was established by analysing the temperature and relative humidity records during the period of extensive outbreak in three epidemic regions in eastern China,including Shanghai,Zhejiang and Jiangsu provinces.Risk assessment maps were created by combining the bird migration routes in eastern China with the monthly average temperatures from May 1993 to December 2012 nationwide.Results Among the confirmed cases,there were more men than women,and 50.4％ were elderly adults （age ＞61 years）.The major demographic groups were retirees and farmers.The temperature on the days of disease onset was concentrated in the range of 9℃-19℃; we defined 9℃-19℃ as the high-risk temperature range,0℃-9℃ or 19℃-25℃ as medium risk and ＜0℃ or ＞25℃ as low risk.The relative humidity on the days of disease onset ranged widely from 25％ to 99％,but did not correlate with the incidence of infection.Based on the temperature analysis and the eastern bird migration routes,we predicted that after May,the high-risk region would move to the northeast and inland,while after September,it would move back to north China.Conclusions Temperature and bird migration strongly influence the spread of the H7N9 virus.In order to control the H7N9 epidemic effectively,Chinese authorities should strengthen the surveillance of migrating birds,increase poultry and environmental sampling,improve live poultry selling and husbandry patterns and move from a ＂passive response pattern＂to an ＂active response pattern＂ in focused preventive measures.
