目的采用Meta分析方法评价肾上腺髓质素前体中段肽对心力衰竭(心衰)患者全因死亡率的影响。方法检索PubMed、EMbase、Web of Science和The Cochrane Library、中国期刊全文数据库、维普数据库和万方数字化期刊全文数据库,搜集肾上腺髓...目的采用Meta分析方法评价肾上腺髓质素前体中段肽对心力衰竭(心衰)患者全因死亡率的影响。方法检索PubMed、EMbase、Web of Science和The Cochrane Library、中国期刊全文数据库、维普数据库和万方数字化期刊全文数据库,搜集肾上腺髓质素前体中段肽对心衰患者全因死亡率的前瞻性研究,检索时限均为建库至2020年5月23日。由两名研究者独立筛选文献后提取数据进行评价,并对研究人群、危险比因素、随访时间进行亚组分析,评估肾上腺髓质素前体中段肽对心衰患者预后预测价值。所有数据采用STATA 16.0软件进行Meta分析。结果共纳入12项前瞻性队列研究,包括5950例心衰患者。Meta分析结果显示:肾上腺髓质素前体中段肽水平升高与心衰患者死亡风险增加相关(HR=1.86,95%CI:1.68~2.06)。结论肾上腺髓质素前体中段肽水平的升高与心衰患者不良预后有关。肾上腺髓质素前体中段肽水平对于心衰患者的预后有着稳定、敏感、独立的预测价值,可作为心衰患者风险分层及预后的特异性指标。指南推荐强度可考虑适当上升。展开更多
Analysis of leaf hemispherical radiative properties for retrieval of its biochemical and mineral nutrients could lead to a powerful monitoring approach for precise farm management.This study explores the potential of ...Analysis of leaf hemispherical radiative properties for retrieval of its biochemical and mineral nutrients could lead to a powerful monitoring approach for precise farm management.This study explores the potential of leaf spectral modeling techniques for estimation of key biochemical and nutritional traits in grapevine leaves.Hyperspectral data spanning the 400-2500 nm range were collected from around 1000 leaf grapevine leaf samples across three growing seasons.Certain traits,including leaf structural parameter(Nstruct),anthocyanins,carotenoids,and chlorophyll,were imputed using the PROSPECT-PRO radiative transfer model in the inverse mode to enrich the dataset.An imputation model was developed to address missing labels for part of the dataset,employing a Convolutional Neural Network(CNN)with 23 principal components derived from the spectral data as inputs.This model enabled the completion of the dataset by predicting missing trait values,providing a comprehensive foundation for subsequent modeling efforts.For the primary trait prediction models,the spectral data were then reduced from 2101 bands to 204 bands through band merging based on pairwise correlations.Two predictive modeling approaches were evaluated:a single-trait model,where each trait is predicted inde-pendently,and a multi-trait model,where all traits are predicted simultaneously.Both models employed a hybrid of CNN and Long Short-Term Memory(LSTM)networks designed to capture spatial and sequential patterns in spectral data.The single-trait model utilized CNN-LSTM architecture with a single output node,requiring in-dependent training for each trait.In contrast,the multi-trait model employed the same architecture but featured 16 output nodes,enabling the simultaneous prediction of all traits.A weighting strategy was implemented to balance the influence of fully measured and imputed samples during training,ensuring reliable predictions.The multi-trait model demonstrated superior predictive performance across most traits,achieving a higher coefficient of determination(R^(2))and RPD(Residual Predictive Deviation),and lower normalized root mean squared error(NRMSE)values than the single-trait models.Some traits,such as nitrogen,phosphorus,Nstruct,and manganese benefited significantly in the multi-trait model with R^(2) values of 0.42,0.81,0.90,and 0.62,respectively,compared to 0.26,0.64,0.25,and 0.30 in single-trait models.The results highlight the advantages of multi-trait modeling in leveraging shared spectral information and inter-trait dependencies,offering an efficient and ac-curate approach to predicting grapevine traits.展开更多
基金supported by USDA-NIFA Specialty Crop Research Initiative Award No.2020-51181-32159the California Table Grape Commission.
文摘Analysis of leaf hemispherical radiative properties for retrieval of its biochemical and mineral nutrients could lead to a powerful monitoring approach for precise farm management.This study explores the potential of leaf spectral modeling techniques for estimation of key biochemical and nutritional traits in grapevine leaves.Hyperspectral data spanning the 400-2500 nm range were collected from around 1000 leaf grapevine leaf samples across three growing seasons.Certain traits,including leaf structural parameter(Nstruct),anthocyanins,carotenoids,and chlorophyll,were imputed using the PROSPECT-PRO radiative transfer model in the inverse mode to enrich the dataset.An imputation model was developed to address missing labels for part of the dataset,employing a Convolutional Neural Network(CNN)with 23 principal components derived from the spectral data as inputs.This model enabled the completion of the dataset by predicting missing trait values,providing a comprehensive foundation for subsequent modeling efforts.For the primary trait prediction models,the spectral data were then reduced from 2101 bands to 204 bands through band merging based on pairwise correlations.Two predictive modeling approaches were evaluated:a single-trait model,where each trait is predicted inde-pendently,and a multi-trait model,where all traits are predicted simultaneously.Both models employed a hybrid of CNN and Long Short-Term Memory(LSTM)networks designed to capture spatial and sequential patterns in spectral data.The single-trait model utilized CNN-LSTM architecture with a single output node,requiring in-dependent training for each trait.In contrast,the multi-trait model employed the same architecture but featured 16 output nodes,enabling the simultaneous prediction of all traits.A weighting strategy was implemented to balance the influence of fully measured and imputed samples during training,ensuring reliable predictions.The multi-trait model demonstrated superior predictive performance across most traits,achieving a higher coefficient of determination(R^(2))and RPD(Residual Predictive Deviation),and lower normalized root mean squared error(NRMSE)values than the single-trait models.Some traits,such as nitrogen,phosphorus,Nstruct,and manganese benefited significantly in the multi-trait model with R^(2) values of 0.42,0.81,0.90,and 0.62,respectively,compared to 0.26,0.64,0.25,and 0.30 in single-trait models.The results highlight the advantages of multi-trait modeling in leveraging shared spectral information and inter-trait dependencies,offering an efficient and ac-curate approach to predicting grapevine traits.
