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Elevated remnant cholesterol (RC) is considered a risk factor for atherosclerotic cardiovascular disease, but the evidence on this association applies to the Chinese population with hypertension is limited. We aimed to explore the association between RC levels and carotid plaque in old adults with hypertension. 8523 hypertensive patients aged ≥ 60 years with serum lipids and carotid ultrasonography data were included in this community-based screening. Fasting RC was calculated as total cholesterol minus high-density lipoprotein cholesterol minus low-density lipoprotein cholesterol (LDLC). The associations of RC levels with carotid plaque risk were evaluated using Logistic regression and restricted cubic spline models. Carotid plaque was screened in 4821 (56.56%) subjects. After multivariable-adjusted, RC was significantly related to carotid plaque [Odd ratio (OR)] = 1.043 per 0.1 mmol/L increase, 95% confidence interval (CI): 1.030–1.056). The highest versus the lowest quartile of RC was 1.928 (1.673–2.223) for carotid plaque. A nonlinear association was found between serum RC levels and the risk of carotid plaque (P for nonlinearity < 0.001). Moreover, an RC > 0.78 mmol/L differentiated patients at a higher risk of carotid plaque compared to those at lower concentrations, regardless of whether LDLC was on target at 2.59 mmol/L. In old adults with hypertension, elevated RC was positively associated with carotid plaque, independent of LDLC and other conventional risk factors. [ABSTRACT FROM AUTHOR]

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi'an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods. [ABSTRACT FROM AUTHOR]