目的:分析甲型H1N1流感(简称"甲流")患儿血常规、血清淀粉样蛋白A(serum amyloid A,SA A)及C反应蛋白(Creactiveprotein,CRP)水平,为初步诊断儿童甲流提供帮助。方法:选择138例甲流患儿(A组)、126例普通流感患儿(B组)和120名...目的:分析甲型H1N1流感(简称"甲流")患儿血常规、血清淀粉样蛋白A(serum amyloid A,SA A)及C反应蛋白(Creactiveprotein,CRP)水平,为初步诊断儿童甲流提供帮助。方法:选择138例甲流患儿(A组)、126例普通流感患儿(B组)和120名健康对照儿童(C组),统计分析白细胞(white blood cells,WBC)计数、血小板(platelet,PLT)计数、平均血小板体积(mean plateletv o l u m e, M P V)、中性粒细胞百分比、淋巴细胞百分比、单核细胞百分比以及全血S A A和C R P水平。三组间计量资料比较采用F检验,两两组间计量资料比较采用L SD检验。结果:3组W B C计数差异无统计学意义(F=2.698, P>0.05); A组PLT计数较B组和C组显著降低(F=6.598,P <0. 0 5); A组M P V水平与B, C两组相比,差异有统计学意义(F=5. 9 8 9, P <0. 0 5)。采用L S D检验进行两两组间比较:A组PLT计数显著低于B组(q=6.26,P<0.05),但与C组相比差异无统计学意义(q=3.02,P>0.05);A组MPV水平显著低于C组(q=6.23,P<0.05),B组MPV水平也显著低于C组(q=6.84,P<0.05),但A组与B组MPV水平无显著差异(q=1.06,P>0.05)。三组中性粒细胞含量、单核细胞含量、淋巴细胞含量差异均有统计学意义(F值分别为5.364,5.261,6.169,均P <0.05)。A组淋巴细胞和中性粒细胞含量与C组比较差异无统计学意义(q值分别为2.36、 1.94,均P>0.05),但A组单核细胞百分比明显高于B组和C组,差异有统计学意义(q值分别为6.29,6. 1 0,均P <0. 0 5)。A组S A A水平与B组和C组比较,差异有统计学意义(F=8. 1 9 8, P <0. 0 5)。A组与B组SAA水平差异有统计学意义(q=6.97,P<0.05);A,B两组SAA水平均显著高于C组,差异有统计学意义(q值分别为6.99、6.07,均P<0.05)。A组CRP水平与B组和C组比较,差异有统计学意义(F=7.654,P<0.05)。A组与B组间CRP水平差异无统计学意义(q=1.94,P>0.05);但A,B两组C R P水平均显著高于C组,差异有统计学意义(q值分别为6. 4 8, 6. 6 1,均P <0. 0 5)。通过RO C分析得知,SA A诊断甲流的RO C曲线下面积(AUC)为0.823(95%CI0.701~0.944),最佳诊断临界值为121.34mg/L;CRP诊断甲流的AUC为0.904(95%CI0.814~0.994),最佳诊断临界值为11.06mg/L。结论:甲流患儿血常规中单核细胞比例、SA A和CRP水平增高,而PLT总数低于普通流感患儿,这可为初步鉴别普通流感和甲流提供参考。展开更多
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 geostati...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.展开更多
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 atmo...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.展开更多
文摘目的:分析甲型H1N1流感(简称"甲流")患儿血常规、血清淀粉样蛋白A(serum amyloid A,SA A)及C反应蛋白(Creactiveprotein,CRP)水平,为初步诊断儿童甲流提供帮助。方法:选择138例甲流患儿(A组)、126例普通流感患儿(B组)和120名健康对照儿童(C组),统计分析白细胞(white blood cells,WBC)计数、血小板(platelet,PLT)计数、平均血小板体积(mean plateletv o l u m e, M P V)、中性粒细胞百分比、淋巴细胞百分比、单核细胞百分比以及全血S A A和C R P水平。三组间计量资料比较采用F检验,两两组间计量资料比较采用L SD检验。结果:3组W B C计数差异无统计学意义(F=2.698, P>0.05); A组PLT计数较B组和C组显著降低(F=6.598,P <0. 0 5); A组M P V水平与B, C两组相比,差异有统计学意义(F=5. 9 8 9, P <0. 0 5)。采用L S D检验进行两两组间比较:A组PLT计数显著低于B组(q=6.26,P<0.05),但与C组相比差异无统计学意义(q=3.02,P>0.05);A组MPV水平显著低于C组(q=6.23,P<0.05),B组MPV水平也显著低于C组(q=6.84,P<0.05),但A组与B组MPV水平无显著差异(q=1.06,P>0.05)。三组中性粒细胞含量、单核细胞含量、淋巴细胞含量差异均有统计学意义(F值分别为5.364,5.261,6.169,均P <0.05)。A组淋巴细胞和中性粒细胞含量与C组比较差异无统计学意义(q值分别为2.36、 1.94,均P>0.05),但A组单核细胞百分比明显高于B组和C组,差异有统计学意义(q值分别为6.29,6. 1 0,均P <0. 0 5)。A组S A A水平与B组和C组比较,差异有统计学意义(F=8. 1 9 8, P <0. 0 5)。A组与B组SAA水平差异有统计学意义(q=6.97,P<0.05);A,B两组SAA水平均显著高于C组,差异有统计学意义(q值分别为6.99、6.07,均P<0.05)。A组CRP水平与B组和C组比较,差异有统计学意义(F=7.654,P<0.05)。A组与B组间CRP水平差异无统计学意义(q=1.94,P>0.05);但A,B两组C R P水平均显著高于C组,差异有统计学意义(q值分别为6. 4 8, 6. 6 1,均P <0. 0 5)。通过RO C分析得知,SA A诊断甲流的RO C曲线下面积(AUC)为0.823(95%CI0.701~0.944),最佳诊断临界值为121.34mg/L;CRP诊断甲流的AUC为0.904(95%CI0.814~0.994),最佳诊断临界值为11.06mg/L。结论:甲流患儿血常规中单核细胞比例、SA A和CRP水平增高,而PLT总数低于普通流感患儿,这可为初步鉴别普通流感和甲流提供参考。
基金supported by the National Natural Science Foundation of China (41071234)the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (XDA05040401)
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.
基金supported by the National Natural Science Foundation of China(Grant No.41071234)"Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues"of the Chinese Academy of Sciences(Grant No.XDA05040401)the National High Techondogy Research and Development Program of China(Grant No.2012AA12A301)
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.