The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data...The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass.展开更多
目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究...目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究组),研究组依据认知功能障碍严重程度分为轻度组45例、中度组66例和重度组39例。另选取同期武汉市第一医院就诊的急性脑梗死未合并认知功能障碍患者125例(对照组)。采用Spearman相关性分析血清SIRT2水平与认知功能障碍及病情程度的相关性,ROC曲线分析血清SIRT2水平对认知功能障碍及病情的评估价值,并计算曲线下面积(area under curve,AUC)。结果研究组血清SIRT2水平明显高于对照组,差异有统计学意义[(20.38±5.19)mg/L vs(14.66±4.49)mg/L,P<0.05]。重度组血清SIRT2水平明显高于中度组和轻度组,中度组血清SIRT2水平明显高于轻度组,差异有统计学意义(P<0.05)。Spearman相关性分析显示,血清SIRT2水平与认知功能障碍发生及严重程度呈正相关(r=0.510,r=0.527,P<0.01)。ROC曲线分析显示,血清SIRT2水平评估急性脑梗死患者认知功能障碍发生的AUC为0.796(95%CI:0.743~0.842),临界值为19.0 mg/L,敏感性为63.33%,特异性为84.00%;血清SIRT2水平评估急性脑梗死患者认知功能障碍严重程度的AUC为0.747(95%CI:0.655~0.824),临界值为17.2 mg/L,敏感性为75.76%,特异性为71.11%(P<0.05)。结论急性脑梗死患者血清SIRT2水平与认知功能障碍发生及病情密切相关。展开更多
Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with P...Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.展开更多
Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(...Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.展开更多
In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based ...In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.展开更多
This paper reviews the CO2emissions data for China provided by various international organizations and databases(namely IEA,BP,EDGAR/PBL/JRC,CDIAC,EIA and CAIT)and compares them with China’s official data and estimat...This paper reviews the CO2emissions data for China provided by various international organizations and databases(namely IEA,BP,EDGAR/PBL/JRC,CDIAC,EIA and CAIT)and compares them with China’s official data and estimation.The difference among these data is due to different scopes,methods and underlying data,and particularly the difference in fossil fuel consumption.Compared with data from other databases,IEA and CAIT data have the best comparability with China’s official data.The paper recommends that China enhance its coal statistics,raise the frequency of official data publication and improve the inventory completeness.展开更多
Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to bette...Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard.In this work,we develop a Data Assimilation(DA)method integrating Volunteered Geographic Information(VGI)and a 2D hydraulic model and we test its performances.The proposed framework seeks to extend the capabilities and performances of standard DA works,based on the use of traditional in situ sensors,by assimilating VGI while managing and taking into account the uncertainties related to the quality,and the location and timing of the entire set of observational data.The November 2012 flood in the Italian Tiber River basin was selected as the case study.Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI,even in the case of use of few-selected observations gathered from social media.This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide.展开更多
Chinese FengYun-2C(FY-2C) satellite data were combined into the Local Analysis and Prediction System(LAPS) model to obtain three-dimensional cloud parameters and rain content. These parameters analyzed by LAPS were us...Chinese FengYun-2C(FY-2C) satellite data were combined into the Local Analysis and Prediction System(LAPS) model to obtain three-dimensional cloud parameters and rain content. These parameters analyzed by LAPS were used to initialize the Global/Regional Assimilation and Prediction System model(GRAPES) in China to predict precipitation in a rainstorm case in the country. Three prediction experiments were conducted and were used to investigate the impacts of FY-2C satellite data on cloud analysis of LAPS and on short range precipitation forecasts. In the first experiment, the initial cloud fields was zero value. In the second, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS without combining the satellite data. In the third experiment, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS including satellite data. The results indicated that the FY-2C satellite data combination in LAPS can show more realistic cloud distributions, and the model simulation for precipitation in 1–6 h had certain improvements over that when satellite data and complex cloud analysis were not applied.展开更多
In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RM...In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.展开更多
文摘The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass.
基金国家自然科学基金项目“青藏高原露天煤矿排土场地形-土壤-植被响应机理及地貌重塑研究”(编号:41977415)中国地质调查局项目“全球冰川及荒漠化遥感地质调查”(编号:DD20190515)+1 种基金中欧科技合作“龙计划”五期项目“Integration of multi-source Remote Sensing Data to detect and monitoring large and rapid landslides and use of Artificial Intelligence for Cultural Heritage preservation”(编号:56796)JAXA EO-RA2项目“Application of Radar Remote Sensing Technology in Resource Environment Monitoring”(编号:P3073002)共同资助。
文摘目的分析急性脑梗死患者沉默信息调节因子2(silent information regulator 2,SIRT2)与认知功能障碍发生及病情程度的关系。方法回顾性选择2022年5月至2024年5月武汉市第一医院神经内科收治的急性脑梗死合并认知功能障碍患者150例(研究组),研究组依据认知功能障碍严重程度分为轻度组45例、中度组66例和重度组39例。另选取同期武汉市第一医院就诊的急性脑梗死未合并认知功能障碍患者125例(对照组)。采用Spearman相关性分析血清SIRT2水平与认知功能障碍及病情程度的相关性,ROC曲线分析血清SIRT2水平对认知功能障碍及病情的评估价值,并计算曲线下面积(area under curve,AUC)。结果研究组血清SIRT2水平明显高于对照组,差异有统计学意义[(20.38±5.19)mg/L vs(14.66±4.49)mg/L,P<0.05]。重度组血清SIRT2水平明显高于中度组和轻度组,中度组血清SIRT2水平明显高于轻度组,差异有统计学意义(P<0.05)。Spearman相关性分析显示,血清SIRT2水平与认知功能障碍发生及严重程度呈正相关(r=0.510,r=0.527,P<0.01)。ROC曲线分析显示,血清SIRT2水平评估急性脑梗死患者认知功能障碍发生的AUC为0.796(95%CI:0.743~0.842),临界值为19.0 mg/L,敏感性为63.33%,特异性为84.00%;血清SIRT2水平评估急性脑梗死患者认知功能障碍严重程度的AUC为0.747(95%CI:0.655~0.824),临界值为17.2 mg/L,敏感性为75.76%,特异性为71.11%(P<0.05)。结论急性脑梗死患者血清SIRT2水平与认知功能障碍发生及病情密切相关。
文摘Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.
基金The National Key Research and Development Programme of China under contract No.2017YFA0603004the Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(Zhanjiang Bay Laboratory)under contract No.ZJW-2019-08+1 种基金the National Natural Science Foundation of China under contract Nos 41825014,41676172 and 41676170the Global Change and Air-Sea Interaction Project of China under contract Nos GASI-02-SCS-YGST2-01,GASI-02-PACYGST2-01 and GASI-02-IND-YGST2-01。
文摘Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.
文摘In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.
基金supported by the project of research on key technologies for synthesis problems during climate change negotiations under 12FYP organized by Ministry of Science and Technology(No.2012BAC20B02)
文摘This paper reviews the CO2emissions data for China provided by various international organizations and databases(namely IEA,BP,EDGAR/PBL/JRC,CDIAC,EIA and CAIT)and compares them with China’s official data and estimation.The difference among these data is due to different scopes,methods and underlying data,and particularly the difference in fossil fuel consumption.Compared with data from other databases,IEA and CAIT data have the best comparability with China’s official data.The paper recommends that China enhance its coal statistics,raise the frequency of official data publication and improve the inventory completeness.
文摘Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard.In this work,we develop a Data Assimilation(DA)method integrating Volunteered Geographic Information(VGI)and a 2D hydraulic model and we test its performances.The proposed framework seeks to extend the capabilities and performances of standard DA works,based on the use of traditional in situ sensors,by assimilating VGI while managing and taking into account the uncertainties related to the quality,and the location and timing of the entire set of observational data.The November 2012 flood in the Italian Tiber River basin was selected as the case study.Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI,even in the case of use of few-selected observations gathered from social media.This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide.
基金supported by the National Natural Science Foundation of China (41375025, 41275114, and 41275039)the National High Technology Research and Development Program of China (863 Program, 2012AA120903)+1 种基金the Public Benefit Research Foundation of the China Meteorological Administration (GYHY201106044 and GYHY201406001)the China Meteorological Administration Torrential Flood Project
文摘Chinese FengYun-2C(FY-2C) satellite data were combined into the Local Analysis and Prediction System(LAPS) model to obtain three-dimensional cloud parameters and rain content. These parameters analyzed by LAPS were used to initialize the Global/Regional Assimilation and Prediction System model(GRAPES) in China to predict precipitation in a rainstorm case in the country. Three prediction experiments were conducted and were used to investigate the impacts of FY-2C satellite data on cloud analysis of LAPS and on short range precipitation forecasts. In the first experiment, the initial cloud fields was zero value. In the second, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS without combining the satellite data. In the third experiment, the initial cloud fields were cloud liquid water, cloud ice, and rain content derived from LAPS including satellite data. The results indicated that the FY-2C satellite data combination in LAPS can show more realistic cloud distributions, and the model simulation for precipitation in 1–6 h had certain improvements over that when satellite data and complex cloud analysis were not applied.
基金Supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(grant no.2019QZKK0105)the National Key Research and Development Program of China(2018YFC1506603).
文摘In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.
