Colorectal cancer is the 4th common cancer in China. Most colorectal cancers are due to modifiable lifestyle factors, but few studies have provided a systematic evidence-based assessment of the ...burden of colorectal cancer incidence and mortality attributable to the known risk factors in China.
We estimated the population attributable faction (PAF) for each selected risk factor in China, based on the prevalence of exposure around 2000 and relative risks from cohort studies and meta-analyses.
Among 245,000 new cases and 139,000 deaths of colorectal cancer in China in 2012, we found that 115,578 incident cases and 63,102 deaths of colorectal cancer were attributable to smoking, alcohol drinking, overweight and obesity, physical inactivity and dietary factors. Low vegetable intake was the main risk factor for colorectal cancer with a PAF of 17.9%. Physical inactivity was responsible for 8.9% of colorectal cancer incidence and mortality. The remaining factors, including high red and processed meat intake, low fruit intake, alcohol drinking, overweight/obesity and smoking, accounted for 8.6%, 6.4%, 5.4%, 5.3% and 4.9% of colorectal cancer, respectively. Overall, 45.5% of colorectal cancer incidence and mortality were attributable to the joint effects of these seven risk factors.
Tobacco smoking, alcohol drinking, overweight or obesity, physical inactivity, low vegetable intake, low fruit intake, and high red and processed meat intake were responsible for nearly 46% of colorectal cancer incidence and mortality in China in 2012. Our findings could provide a basis for developing guidelines of colorectal cancer prevention and control in China.
Image dehazing is a crucial image processing step for outdoor vision systems. However, images recovered through conventional image dehazing methods that use either haze-relevant priors or heuristic ...cues to estimate transmission maps may not lead to sufficiently accurate haze removal from single images. The most commonly observed effects are darkened and brightened artifacts on some areas of the recovered images, which cause considerable loss of fidelity, brightness, and sharpness. This paper develops a variational image dehazing method on the basis of a color-transfer image dehazing model that is superior to conventional image dehazing methods. By creating a color-transfer image dehazing model to remove haze obscuration and acquire information regarding the coefficients of the model by using the devised convolutional neural network-based deep framework as a supervised learning strategy, an image fidelity, brightness, and sharpness can be effectively restored. The experimental results verify through quantitative and qualitative evaluations of either synthesized or real haze images, and the proposed method outperforms existing single image dehazing methods.
Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of ...existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.
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•Coix seed polysaccharides were evaluated for their hypoglycemic effects.•These polysaccharides can modulate the gut microbial composition in T2DM mice.•The polysaccharides activated ...IGF1/PI3K/AKT signaling by increasing SCFAs.
Type 2 diabetes mellitus (T2DM) has become a worldwide concern in recent years. Coix seed (CS) as a homologous substance of traditional Chinese medicine and food, its polysaccharides can improve the symptoms of patients with metabolic disorders. Since most plant polysaccharides are difficult to digest and absorb, we hypothesized that Coix seed polysaccharides (CSP) exert hypoglycemic effects through the gut. In this study, the underlying mechanisms regulating hypoglycemic effects of CSP on a T2DM mouse model were investigated. After treatment with CSP, serum insulin and high-density lipoprotein cholesterol levels were increased, while total cholesterol, triglycerides and low-density lipoprotein cholesterol levels were decreased in T2DM mice. In addition, CSP treatment helped repair the intestinal barrier and modulated the gut microbial composition in T2DM mice, mainly facilitating the growth of short-chain fatty acid (SCFA)-producing bacteria, Spearman’s analysis revealed these bacteria were positively related with the hypoglycemic efficacy of CSP. Colonic transcriptome analysis indicated the hypoglycemic effect of CSP was associated with the activation of the IGF1/PI3K/AKT signaling pathway. Correlative analysis revealed that this activation may result from the increase of SCFAs-producing bacteria by CSP. GC–MS detection verified that CSP treatment increased fecal SCFAs levels. Molecular docking revealed that SCFAs could bind with IGF1, PI3K, and AKT. Our findings demonstrated that CSP treatment modulates gut microbial composition, especially of the SCFAs-producing bacteria, activates the IGF1/PI3K/AKT signaling pathways, and exhibits hypoglycemic efficacy.
We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma ...were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. "Estimate" was used for immune infiltrating estimation and "xCell" was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.
Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic ...disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.
In order to improve the welding quality of robotic gas metal arc welding (GMAW), a welding system for seam tracking is developed based on a purpose-built vision sensor. By analyzing the features of ...robotic GMAW, a novel software program is developed for welding seam tracking. The program includes specific modules, such as the welding power control, the intelligent parameter setting, the image capturing and processing based on improved algorithms, the welding expert database, robot communication and path planning modules. To evaluate the feasibility of the developed seam tracking method, the accuracy and the real-time nature are verified using experiments on different types of typical weldments. The results prove that the proposed welding seam tracking method is able to achieve a good tracking accuracy for most welding applications.
Objective
This study aims to develop an artificial intelligence‐based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data ...alone.
Methods
Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12‐lead ECG and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG‐TTE pairs from a single individual, only the earliest data pair was included. All the ECG‐TTE pairs were randomly divided into the training, validation, or testing data set in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
Results
We retrospectively enrolled a total of 26 786 ECG‐TTE pairs and randomly divided them into training (n = 21 732), validation (n = 2 530), and testing data set (n = 2 530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%.
Conclusion
Our results demonstrate that a well‐trained CNN algorithm may be used as a low‐cost and noninvasive method to identify patients with left ventricular dysfunction.
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