We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes ...We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m-2·a-1), 34.33% (79 g C·m-2·a-1) and 5.4% (92 g C·m-2·a-1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m-2·a-1), 2.1% (5.7 g C·m-2·a-1), and 0.26% (4.3 g C·m-2·a-1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%-8%, 7%-22% and 2%-4% respectively at annual timescale.展开更多
目的采用半定量分析方法对制首乌与9个补虚药配伍后成分含量及DPPH自由基清除能力变化进行研究。方法以UPLC-DAD建立制首乌多成分半定量分析方法,对制首乌与9味常用补虚药(当归、熟地黄、白芍、党参、黄芪、甘草、麦冬、枸杞子、墨旱莲...目的采用半定量分析方法对制首乌与9个补虚药配伍后成分含量及DPPH自由基清除能力变化进行研究。方法以UPLC-DAD建立制首乌多成分半定量分析方法,对制首乌与9味常用补虚药(当归、熟地黄、白芍、党参、黄芪、甘草、麦冬、枸杞子、墨旱莲)配伍后成分含量变化进行分析。采用DPPH法分别测定单味药以及配伍药对的自由基清除能力,绘制量-效曲线并计算半数清除浓度(EC50)。采用多元统计分析方法建立制首乌中多成分含量与DPPH自由基清除能力间的量-效回归模型,筛选量-效关系中影响显著的化学标志物,并通过质谱进行定性分析。结果线性范围、准确度、精密度、重复性及稳定性5项方法学验证结果表明,半定量分析方法可用于制首乌中12个成分在配伍前后的含量对比分析。含量对比分析结果表明,制首乌与不同药物配伍后,12个成分的含量均发生了不同程度的变化,且与墨旱莲配伍后制首乌中有33%的成分含量显著降低(P<0.05)、42%的成分含量显著升高外(P<0.05),与另8味药配伍后制首乌中至少50%的成分含量显著降低(P<0.05)。DPPH自由基清除能力实验结果显示,制首乌DPPH自由基清除能力高于其他9味中药,配伍后9个制首乌药对的DPPH自由基清除能力低于制首乌,但高于相应的配伍药物。量-效回归正交偏最小二乘法(orthogonal projections to latentstructures,OPLS)模型中R2X、R2Y及Q2值分别为0.841、0.981及0.962,筛选出4个量-效关系化学标志物,分别为反式-2,3,5,4′-四羟基二苯乙烯-2-O-β-D-吡喃葡萄糖苷(trans-THSG)、大黄素甲醚、顺式-2,3,5,4′-四羟基二苯乙烯-2-O-β-D-吡喃葡萄糖苷(cis-THSG)、大黄素-8-O-β-D-吡喃葡萄糖苷(EG)。结论建立的多成分半定量分析方法可用于何首乌在药对配伍过程中多成分含量变化的对比分析,trans-THSG、大黄素甲醚、cis-THSG、EG是影响制首乌在上述药对中发挥DPPH自由基清除作用的化学标志物,可为制首乌药对配伍机制的深入研究提供参考。展开更多
The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-t...The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term.A modified Incidence Decay and Exponential Adjustment(m-IDEA)model was developed to predict the progression of infectious disease outbreaks.The modification allows for the production of precise daily estimates,which are critical during a pandemic of this scale for planning purposes.The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19.Both deterministic and stochastic approaches were applied.Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts,and performance was retrospectively assessed.The performance of the m-IDEA for projection cases in the nearterm was improved using shorter serial intervals(1e4 days)at early stages of the pandemic,and longer serial intervals at mid-to late-stages(5e9 days)thus far.This,coupled with epidemiological reports,suggests that the serial interval of COVID-19 might increase as the pandemic progresses,which is rather intuitive:Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures,public caution and social distancing,thus increasing the time between transmission events.In most cases,the stochastic approach captured the majority of future reported incidence data,because it accounts for the uncertainty around the serial interval of COVID-19.As such,it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.展开更多
基金Supported by National Natural Science Foundation of China (Grant No. 30570347)Innovative Research International Partnership Project of the Chinese Academy of Sciences (Grant No. CXTD-Z2005-1)National Basic Research Program of China (Grant No. 2002CB412502)
文摘We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m-2·a-1), 34.33% (79 g C·m-2·a-1) and 5.4% (92 g C·m-2·a-1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m-2·a-1), 2.1% (5.7 g C·m-2·a-1), and 0.26% (4.3 g C·m-2·a-1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%-8%, 7%-22% and 2%-4% respectively at annual timescale.
文摘目的采用半定量分析方法对制首乌与9个补虚药配伍后成分含量及DPPH自由基清除能力变化进行研究。方法以UPLC-DAD建立制首乌多成分半定量分析方法,对制首乌与9味常用补虚药(当归、熟地黄、白芍、党参、黄芪、甘草、麦冬、枸杞子、墨旱莲)配伍后成分含量变化进行分析。采用DPPH法分别测定单味药以及配伍药对的自由基清除能力,绘制量-效曲线并计算半数清除浓度(EC50)。采用多元统计分析方法建立制首乌中多成分含量与DPPH自由基清除能力间的量-效回归模型,筛选量-效关系中影响显著的化学标志物,并通过质谱进行定性分析。结果线性范围、准确度、精密度、重复性及稳定性5项方法学验证结果表明,半定量分析方法可用于制首乌中12个成分在配伍前后的含量对比分析。含量对比分析结果表明,制首乌与不同药物配伍后,12个成分的含量均发生了不同程度的变化,且与墨旱莲配伍后制首乌中有33%的成分含量显著降低(P<0.05)、42%的成分含量显著升高外(P<0.05),与另8味药配伍后制首乌中至少50%的成分含量显著降低(P<0.05)。DPPH自由基清除能力实验结果显示,制首乌DPPH自由基清除能力高于其他9味中药,配伍后9个制首乌药对的DPPH自由基清除能力低于制首乌,但高于相应的配伍药物。量-效回归正交偏最小二乘法(orthogonal projections to latentstructures,OPLS)模型中R2X、R2Y及Q2值分别为0.841、0.981及0.962,筛选出4个量-效关系化学标志物,分别为反式-2,3,5,4′-四羟基二苯乙烯-2-O-β-D-吡喃葡萄糖苷(trans-THSG)、大黄素甲醚、顺式-2,3,5,4′-四羟基二苯乙烯-2-O-β-D-吡喃葡萄糖苷(cis-THSG)、大黄素-8-O-β-D-吡喃葡萄糖苷(EG)。结论建立的多成分半定量分析方法可用于何首乌在药对配伍过程中多成分含量变化的对比分析,trans-THSG、大黄素甲醚、cis-THSG、EG是影响制首乌在上述药对中发挥DPPH自由基清除作用的化学标志物,可为制首乌药对配伍机制的深入研究提供参考。
基金I would like to thank the Knowledge Synthesis team members within the Public Health Risk Sciences Division of Public Health Agency of Canada.Their daily literature scans and summarization of Sars-CoV-2 publications contributed to the quick preparation of the work presented here.Thanks to Charly Phillips(Public Health Risk Sciences Division of Public Health Agency of Canada)for her assistance summarizing serial interval values from the literature.
文摘The SARS-CoV-2 virus causes the disease COVID-19,and has caused high morbidity and mortality worldwide.Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term.A modified Incidence Decay and Exponential Adjustment(m-IDEA)model was developed to predict the progression of infectious disease outbreaks.The modification allows for the production of precise daily estimates,which are critical during a pandemic of this scale for planning purposes.The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19.Both deterministic and stochastic approaches were applied.Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts,and performance was retrospectively assessed.The performance of the m-IDEA for projection cases in the nearterm was improved using shorter serial intervals(1e4 days)at early stages of the pandemic,and longer serial intervals at mid-to late-stages(5e9 days)thus far.This,coupled with epidemiological reports,suggests that the serial interval of COVID-19 might increase as the pandemic progresses,which is rather intuitive:Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures,public caution and social distancing,thus increasing the time between transmission events.In most cases,the stochastic approach captured the majority of future reported incidence data,because it accounts for the uncertainty around the serial interval of COVID-19.As such,it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.
