Spatial-Temporal Variation Characteristics and Influencing Factors of Vegetation in the Yellow River Basin from 2000 to 2019

Vegetation is a crucial and intuitive index that can be used to evaluate the ecological status. Since the 20th century, land use has changed significantly in Yellow River Basin (YRB), along with great changes of vegetation, serious soil erosion, and gradual ecological deterioration. To improve the ecological environment in the YRB, China has carried out a series of ecological protection projects since the 1970s. Therefore, long-term sequence monitoring of vegetation in YRB is necessary to show the conservation effect and better support the further protection and restoration. This study analysed vegetation changes from 2000 to 2019 based on an annual mean fractional vegetation cover (FVC) dataset. The Theil–Sen median trend analysis method was used to analyse trends in FVC. The results showed that the vegetation in the YRB has improved significantly, with an average annual growth rate of 0.65%, and the ‘green line’ of vegetation has moved approximately 300 km westward. The influence of climate on vegetation is essential; therefore, this study also analysed the influence of temperature and precipitation on vegetation over time and space. Ecological control and afforestation are important anthropogenic factors that affect vegetation. The growth trend (0.6%/a) in key ecological function regions (KEFRs) was the fastest, and even though the protection measures are not strict, they provide space for afforestation. The China Ecological Conservation Red Line (CECRL) and the national nature reserves (NNRs) showed relatively flat trends. Ecological afforestation projects were closely correlated with the growth trend of the FVC. The correlation between FVC and the intensity of ecological engineering was significant in typical areas

Spatial-Temporal Variation Characteristics and Influencing Factors of Vegetation in the Yellow River Basin from 2000 to 2019

Vegetation is a crucial and intuitive index that can be used to evaluate the ecological status. Since the 20th century, land use has changed significantly in Yellow River Basin (YRB), along with great changes of vegetation, serious soil erosion, and gradual ecological deterioration. To improve the ecological environment in the YRB, China has carried out a series of ecological protection projects since the 1970s. Therefore, long-term sequence monitoring of vegetation in YRB is necessary to show the conservation effect and better support the further protection and restoration. This study analysed vegetation changes from 2000 to 2019 based on an annual mean fractional vegetation cover (FVC) dataset. The Theil–Sen median trend analysis method was used to analyse trends in FVC. The results showed that the vegetation in the YRB has improved significantly, with an average annual growth rate of 0.65%, and the ‘green line’ of vegetation has moved approximately 300 km westward. The influence of climate on vegetation is essential; therefore, this study also analysed the influence of temperature and precipitation on vegetation over time and space. Ecological control and afforestation are important anthropogenic factors that affect vegetation. The growth trend (0.6%/a) in key ecological function regions (KEFRs) was the fastest, and even though the protection measures are not strict, they provide space for afforestation. The China Ecological Conservation Red Line (CECRL) and the national nature reserves (NNRs) showed relatively flat trends. Ecological afforestation projects were closely correlated with the growth trend of the FVC. The correlation between FVC and the intensity of ecological engineering was significant in typical areas

Prediction of Myocardial Infarction From Patient Features With Machine Learning

International audienceThis study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI

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