To explore interleukin-17 (IL-17) and its epigenetic regulation during the progression of chronic hepatitis B virus (HBV) infection. A total of 162 patients with chronic HBV infection, including 75 with chronic hepatitis B (CHB), 54 with hepatitis B-associated liver cirrhosis and 33 with hepatitis B-associated hepatocellular carcinoma (HBV-HCC), were enrolled in this study. Thirty healthy adults of the same ethnicity were enrolled in the control group. Whole venous blood was obtained from the patients and normal controls (n = 30). Clinical and laboratory parameters were assessed, and we performed enzyme-linked immunosorbent assay and quantitative real-time PCR to measure the serum levels and relative mRNA expression of IL-17, respectively. IL-17 promoter methylation in peripheral blood mononuclear cells was assessed by methylation-specific PCR. We analyzed the serum and mRNA levels of IL-17 and IL-17 promoter methylation in the 4 groups as well as the effect of methylation on serum IL-17 levels. Correlations between the IL-17 promoter methylation status and clinical parameters were analyzed by Spearman correlation analysis. Compared to the normal control group, the patient groups exhibited significantly higher serum and relative mRNA levels of IL-17. The methylation distribution among the patients was significantly lower than that among the normal controls ( P < .05), with the HBV-HCC group showing the lowest IL-17 gene methylation frequency. The average IL-17 promoter CG methylation level was negatively correlated with IL-17 mRNA expression ( r = −0.39, P = .03), and negative correlations between IL-17 promoter methylation and prothrombin time activity ( r = −0.585, P = .035), alanine aminotransferase ( r = −0.522, P < .01), aspartate aminotransferase ( r = −0.315, P < .05), and the model for end-stage liver disease score ( r = −0.461, P < .05) were observed. IL-17 serum levels in the methylated-promoter groups were significantly lower than those in the unmethylated-promoter groups. IL-17 expression and promoter methylation were associated with chronic HBV infection progression, especially in the HBV-HCC group. The IL-17 promoter status may help clinicians initiate the correct treatment strategy at the CHB stage.

This paper proposes a novel smart parking scheme for the parking lot. Automatic car detection is the core technology of the proposed scheme. However, new challenges arise in car detection in aerial views, such as a large number of tiny objects and complex backgrounds. In order to solve these issues, this paper proposes a car detection method based on multi-task cost-sensitive-convolutional neural network (MTCS-CNN). In the proposed network framework, multi-task partition layer which is composed of some sub-task selection units is first developed. The sub-task selection unit is constructed by introducing local mask and non-zero pooling, which can divide the complex detection task into many simple sub-tasks. To tackle the obtained sub-tasks, cost-sensitive sub-network is proposed based on faster R-CNN framework with the introduction of cost-sensitive loss. In the proposed Multi-task partition layer, the sub-task selection unit is used to capture the local map of the original aerial view image. In each local map, the scale and the number of objects are enlarged and decreased, respectively. Therefore, multi-task partition layer can divide a complex tiny objects detection task into many simple enlarged objects detection sub-tasks, which is helpful for performance improvement. In addition, the proposed cost-sensitive loss can effectively discount the effect of easy examples and focus attention on the hard examples, which may improve the detection performance on hard examples. Therefore, the model with incorporation of proposed cost-sensitive loss is robust to the complex background, further improving the performance. On our dataset, the proposed method obtained an mAP of 85.3%, outperformed state-of-the-art method. INDEX TERMS Smart parking, car detection, multi-task cost-sensitive-convolutional neural network.

Breast ultrasound image segmentation is the foundation of the diagnosis and treatment of breast cancer. The level set method is widely used for medical image segmentation. However, it remained a challenge for traditional level set methods because they cannot fully understand the tumor regions with complex characteristics by only low-level features. Considering that contextual features can provide complementary discriminative information to low-level features, this paper proposed a contextual level set method for breast tumor segmentation. Firstly, an encoder-decoder architecture network such as UNet is developed to learn high-level contextual features with semantic information. After that, the contextual level set method has been proposed to incorporate the novel contextual energy term. The proposed term has the ability to embed the high-level contextual knowledge into the level set framework. The learned contextual features with semantic information can provide more discriminative information, which has been directly associated with category labels, instead of the original intensity. Therefore, it is robust to serious intensity inhomogeneity, which is helpful to improve segmentation performance. The experiments had taken place with the help of three databases, which indicates that the proposed method outperformed traditional methods. INDEX TERMS Breast ultrasound images, contextual feature, Level-set method, tumor segmentation.