For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective.Although screening methods exist to detect some types of cancer at an early stage,there are...For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective.Although screening methods exist to detect some types of cancer at an early stage,there are currently no effective methods to screen for most types of cancer.Biomarkers have the potential to improve detection of early-stage cancers,risk stratification,and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive.Although thousands of research articles on biomarkers for early detection are published every year,few of these biomarkers have been validated and shown to be clinically useful.This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic.To overcome this limitation the US National Cancer Institute created the Early Detection Research Network.It is a highly collaborative program that brings together biomarker discoverers,assay developers,and clinicians.It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.展开更多
Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large ...Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km<sup>2</sup> catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.展开更多
文摘For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective.Although screening methods exist to detect some types of cancer at an early stage,there are currently no effective methods to screen for most types of cancer.Biomarkers have the potential to improve detection of early-stage cancers,risk stratification,and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive.Although thousands of research articles on biomarkers for early detection are published every year,few of these biomarkers have been validated and shown to be clinically useful.This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic.To overcome this limitation the US National Cancer Institute created the Early Detection Research Network.It is a highly collaborative program that brings together biomarker discoverers,assay developers,and clinicians.It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.
基金the financial support from the China Scholarship Council(CSC)through a scholarship for the first author
文摘Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km<sup>2</sup> catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.
