Association between the triglyceride to high-density lipoprotein cholesterol ratio and the risk of type 2 diabetes mellitus among Chinese elderly: The Beijing Longitudinal Study of Aging

Objective: Time-dependent covariates are generally available as longitudinal data were collected periodically in the cohort study. To examine whether time-dependent triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio could predict the future risk of type 2 diabetes mellitus (T2DM) and assess its potential impact on the risk of T2DM incidence. Research design and methods: This study enrolled 1460 participants without T2DM aged 55 or above in 1992 in the Beijing Longitudinal Study of Aging during 25 years. The questionnaire data were collected in nine surveys from 1992 to 2017. Physical examination and blood laboratory tests including TG and HDL-C concentrations were measured in five surveys. Incident T2DM cases were confirmed via a self-reported history of T2DM or the fasting plasma glucose level. Results: 119 new cases of T2DM were identified. In the Cox regression analysis with time-dependent TG/HDL-C ratios and covariates, the adjusted hazard ratios (95% confidence interval) of T2DM incidence were 1.90 (1.12 to 3.23), 2.75 (1.58 to 4.80) and 2.84 (1.69 to 4.77), respectively, for those with TG/HDL-C ratios (both TG and HDL-C were expressed in millimole per liter) in the ranges of 0.87-1.30, 1.31-1.74 and ≥ 1.75, compared with individuals with TG/HDL-C ratios \u3c 0.87. The similar results of subdistribution hazard ratios were obtained by performing the Fine-Gray model with time-dependent TG/HDL-C ratios. This positive association and the statistically significant trend with increased risk of T2DM incidence in the three categories of elevated TG/HDL-C ratio was confirmed by multiple sensitivity analyses. Furthermore, the T2DM discriminatory power of TG/HDL-C ratio combining with other risk factors was moderately high. Conclusions: We found that time-dependent TG/HDL-C ratios were positively associated with the risk of T2DM risk. The elevated TG/HDL-C ratios increased the future risk of T2DM incidence. Lowering the TG/HDL-C ratio could assist in the prevention of diabetes for older adults. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ

COX-2 suppresses tissue factor expression via endocannabinoid-directed PPARδ activation

Although cyclooxygenase (COX)-2 inhibitors (coxibs) are effective in controlling inflammation, pain, and tumorigenesis, their use is limited by the recent revelation of increased adverse cardiovascular events. The mechanistic basis of this side effect is not well understood. We show that the metabolism of endocannabinoids by the endothelial cell COX-2 coupled to the prostacyclin (PGI2) synthase (PGIS) activates the nuclear receptor peroxisomal proliferator–activated receptor (PPAR) δ, which negatively regulates the expression of tissue factor (TF), the primary initiator of blood coagulation. Coxibs suppress PPARδ activity and induce TF expression in vascular endothelium and elevate circulating TF activity in vivo. Importantly, PPARδ agonists suppress coxib-induced TF expression and decrease circulating TF activity. We provide evidence that COX-2–dependent attenuation of TF expression is abrogated by coxibs, which may explain the prothrombotic side-effects for this class of drugs. Furthermore, PPARδ agonists may be used therapeutically to suppress coxib-induced cardiovascular side effects

Multi-temporal remote sensing imagery semantic segmentation color consistency adversarial network

Using deep convolutional neural network (CNN) to intelligently extract buildings from remote sensing images is of great significance for digital city construction, disaster detection and land management. The color difference between multi-temporal remote sensing images will lead to the decrease of generalization ability of building semantic segmentation model. In view of this, this paper proposes the attention-guided color consistency adversarial network (ACGAN). The algorithm takes the reference color style images and the images to be corrected in the same area and different phases as the training set and adopts the consistency adversarial network with the U-shaped attention mechanism to train the color consistency model. In the prediction stage, this model converts the hue of the images to that of the reference color style image, which is based on the reasoning ability of the deep learning model, instead of the corresponding reference color style image. This model transforms the hue of the images to be corrected into that of the reference color style images. This stage is based on the reasoning ability of the deep learning model, and the corresponding reference color style image is no longer needed. In order to verify the effectiveness of the algorithm, firstly, we compare the algorithm of this paper with the traditional image processing algorithm and other consistency adversarial network. The results show that the images after ACGAN color consistency processing are more similar to that of the reference color style images. Secondly, we carried out the building semantic segmentation experiment on the images processed by the above different color consistency algorithms, which proved that the method in this paper is more conducive to the impro-vement of the generalization ability of multi-temporal remote sensing image semantic segmentation model

Semantics-and-Primitives-Guided Indoor 3D Reconstruction from Point Clouds

The automatic 3D reconstruction of indoor scenes is of great significance in the application of 3D-scene understanding. The existing methods have poor resilience to the incomplete and noisy point cloud, which leads to low-quality results and tedious post-processing. Therefore, the objective of this work is to automatically reconstruct indoor scenes from an incomplete and noisy point-cloud base on semantics and primitives. In this paper, we propose a semantics-and-primitives-guided indoor 3D reconstruction method. Firstly, a local, fully connected graph neural network is designed for semantic segmentation. Secondly, based on the enumerable features of indoor scenes, a primitive-based reconstruction method is proposed, which retrieves the most similar model in a 3D-ESF indoor model library by using ESF descriptors and semantic labels. Finally, a coarse-to-fine registration method is proposed to register the model into the scene. The results indicate that our method can achieve high-quality results while remaining better resilience to the incompleteness and noise of point cloud. It is concluded that the proposed method is practical and is able to automatically reconstruct the indoor scene from the point cloud with incompleteness and noise

An OSM Data-Driven Method for Road-Positive Sample Creation

Determining samples is considered to be a precondition in deep network training and learning, but at present, samples are usually created manually, which limits the application of deep networks. Therefore, this article proposes an OpenStreetMap (OSM) data-driven method for creating road-positive samples. First, based on the OSM data, a line segment orientation histogram (LSOH) model is constructed to determine the local road direction. Secondly, a road homogeneity constraint rule and road texture feature statistical model are constructed to extract the local road line, and on the basis of the local road lines with the same direction, a polar constraint rule is proposed to determine the local road line set. Then, an iterative interpolation algorithm is used to connect the local road lines on both sides of the gaps between the road lines. Finally, a local texture self-similarity (LTSS) model is implemented to determine the road width, and the centerpoint autocorrection model and random sample consensus (RANSAC) algorithm are used to extract the road centerline; the road width and road centerline are used to complete the creation of the road-positive samples. Experiments are conducted on different scenes and different types of images to demonstrate the proposed method and compare it with other approaches. The results demonstrate that the proposed method for creating road-positive samples has great advantages in terms of accuracy and integrity

An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance

Effects of dietary xylan on growth performance, digestive enzyme activity and intestinal morphology of juvenile turbot (Scophthalmus maximus L.)

A 12-week feeding trial was conducted to evaluate the effects of dietary xylan on growth performance, digestive enzyme activity, and intestinal morphology of juvenile turbot (Scophthalmus maximus L.) with a mean initial body weight of 4.63 ± 0.01 g. Five isonitrogenous and isolipidic diets were formulated to contain 0%, 0.625%, 1.25%, 2.5% and 5% xylan, respectively. The dietary supplementation of 5% xylan significantly decreased (P<0.05) fish feed intake, growth performance and feed utilization, but these parameters were significantly improved (P<0.05) by 1.25% dietary xylan supplement. Similar trends were observed in whole-body protein and lipid contents of experimental fish. The activity of intestinal caseinolytic, trypsin, and intestinal amylase were inversely related to the supplemented dietary xylan (P<0.05). The integrity of the distal intestine was impaired and the length of intestinal epithelium (lIE) significantly declined (P<0.05) when 5% xylan was added to the diet. Results of the present study suggest that dietary xylan affected the growth performance and feed utilization of juvenile turbot, with beneficial effects at an intermediate supplemental level of 1.25% but with adverse effects at higher supplemental levels (5%)

Fast and Intelligent Seamline Detection for Orthoimage Mosaicking Based on Minimum Spanning Tree

A method of fast and intelligent seamline detection is presented that based on minimum spanning tree for high resolution orthoimage mosaicking. The image gradient and difference of homonymy pixels in the overlap area are calculated to build the differential image, which is deemed as a weighted undirected graph. According to the Bottleneck model, the optimal seamline is detected on the differential image by finding the minimum spanning tree of the weighted undirected graph. This method discards the conventional iterative process, thus achieves high speed. Experiment results illustrate the value of the proposed method which achieves great efficiency and guarantees the quality of the seamlines at the same time

Extraction of Buildings from Multiple-View Aerial Images Using a Feature-Level-Fusion Strategy

Aerial images are widely used for building detection. However, the performance of building detection methods based on aerial images alone is typically poorer than that of building detection methods using both LiDAR and image data. To overcome these limitations, we present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching (DIM) point clouds, orthophoto and original aerial images. The proposed framework is divided into three stages. In the first stage, the features from the original aerial image and DIM points are fused to detect buildings and obtain the so-called blob of an individual building. Then, a feature-level fusion strategy is applied to match the straight-line segments from original aerial images so that the matched straight-line segment can be used in the later stage. Finally, a new footprint generation algorithm is proposed to generate the building footprint by combining the matched straight-line segments and the boundary of the blob of the individual building. The performance of our framework is evaluated on a vertical aerial image dataset (Vaihingen) and two oblique aerial image datasets (Potsdam and Lunen). The experimental results reveal 89% to 96% per-area completeness with accuracy above almost 93%. Relative to six existing methods, our proposed method not only is more robust but also can obtain a similar performance to the methods based on LiDAR and images