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Abstract Purpose Previous studies suggest an association between chronic kidney disease (CKD) and cognitive impairment. The purpose of this study was to explore the association between the diverse stages of CKD and the cognitive performance of elderly American adults. Methods Data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 were used. Multivariate adjusted logistic regression, subgroup analysis, and the restricted cubic spline model were used to assess the associations of CKD stage and estimated glomerular filtration rate (eGFR) with cognitive performance. The measures used to evaluate cognitive function included the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test, the Animal Fluency test, and the Digit Symbol Substitution test (DSST). Results This study included 2234 participants aged ≥ 60 years. According to the fully adjusted model, stages 3–5 CKD were significantly associated with the CERAD test score (OR = 0.70, 95% CI [0.51, 0.97], p = 0.033), the Animal Fluency test score (OR = 0.64, 95% CI [0.48, 0.85], p = 0.005), and the DSST score (OR = 0.60, 95% CI [0.41, 0.88], p = 0.013). In addition, the incidence of poor cognitive function increased with decreasing eGFR, especially for individuals with low and moderate eGFRs. Both the DSST score (p nonlinearity

Effective extraction of building edge information based on high-resolution remote sensing images is the basis for efficient urban 3D modeling. Existing building extraction methods still have some problems, such as an uncertain segmentation scale, effective feature selection, and sample selection. In this paper, we propose a practical building extraction method based on convolutional network edge-enhanced attention U2-Net (EA U2-Net) to accurately achieve multi-scale extraction of buildings from remote sensing imagery. First, the U2-Net is used as the backbone network for building extraction because each stage of the network is filled by residual U-block (RSU), and the network can better aggregate multi-scale features. Second, the building edge feature map is introduced into the generation network to compensate for the problems of insufficient extracted building edge features and loss of detail. Finally, the convolutional block attention module is used to achieve effective feature extraction of buildings. We performed the experiment on the WHU building dataset, and the experimental results showed that the EA U2-Net model has significantly improved the ability to extract buildings, with an accuracy of 96.30%, a recall rate of 94.91%, f1 of 95.26%, and iou of 91.57%. This proves that EA U 2 - Net can achieve better remote sensing image-building segmentation results. Finally, in view of the problem that the deep learning network relies on training samples, this study examined the influence of the number of building samples, sample purity, and sample resolution on the effect of building extraction. The results confirmed that reasonable sample parameter settings can improve the target extraction accuracy and the optimal sample parameter combination was verified in this experiment.