Prognostic role of GPER/Ezrin in triple-negative breast cancer is associated with menopausal status

The role of G protein-coupled estrogen receptor 1 (GPER) signaling, including promotion of Ezrin phosphorylation (which could be activated by estrogen), has not yet been clearly identified in triple-negative breast cancer (TNBC). This study aimed to evaluate the prognostic value of GPER and Ezrin in TNBC patients. Clinicopathologic features including age, menopausal status, tumor size, nuclear grade, lymph node metastasis, AJCC TNM stage, and ER, PR and HER-2 expression were evaluated from 249 TNBC cases. Immunohistochemical staining of GPER and Ezrin was performed on TNBC pathological sections. Kaplan–Meier analyses, as well as logistic regressive and Cox regression model tests were applied to evaluate the prognostic significance between different subgroups. Compared to the GPER-low group, the GPER-high group exhibited higher TNM staging (P = 0.021), more death (P < 0.001), relapse (P < 0.001) and distant events (P < 0.001). Kaplan–Meier analysis showed that GPER-high patients had a decreased OS (P < 0.001), PFS (P < 0.001), LRFS (P < 0.001) and DDFS (P < 0.001) than GPER-low patients. However, these differences in prognosis were not statistically significant in post-menopausal patients (OS, P = 0.8617; PFS, P = 0.1905; LRFS, P = 0.4378; DDFS, P = 0.2538). There was a significant positive correlation between GPER and Ezrin expression level (R = 0.508, P < 0.001) and the effect of Ezrin on survival prognosis corresponded with GPER. Moreover, a multivariable analysis confirmed that GPER and Ezrin level were both significantly associated with poor DDFS (HR: 0.346, 95% CI 0.182–0.658, P = 0.001; HR: 0.320, 95% CI 0.162–0.631, P = 0.001). Thus, overexpression of GPER and Ezrin may contribute to aggressive behavior and indicate unfavorable prognosis in TNBC; this may correspond to an individual’s estrogen levels

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Benchmarking non-photorealistic rendering of portraits

We present a set of images for helping NPR practitioners evaluate their image-based portrait stylisation algorithms. Using a standard set both facilitates comparisons with other methods and helps ensure that presented results are representative. We give two levels of difficulty, each consisting of 20 images selected systematically so as to provide good coverage of several possible portrait characteristics. We applied three existing portrait-specific stylisation algorithms, two general-purpose stylisation algorithms, and one general learning based stylisation algorithm to the first level of the benchmark, corresponding to the type of constrained images that have often been used in portrait-specific work. We found that the existing methods are generally effective on this new image set, demonstrating that level one of the benchmark is tractable; challenges remain at level two. Results revealed several advantages conferred by portrait-specific algorithms over general-purpose algorithms: portrait-specific algorithms can use domain-specific information to preserve key details such as eyes and to eliminate extraneous details, and they have more scope for semantically meaningful abstraction due to the underlying face model. Finally, we provide some thoughts on systematically extending the benchmark to higher levels of difficulty

17β-Estradiol Enhances Breast Cancer Cell Motility and Invasion via Extra-Nuclear Activation of Actin-Binding Protein Ezrin

Estrogen promotes breast cancer metastasis. However, the detailed mechanism remains largely unknown. The actin binding protein ezrin is a key component in tumor metastasis and its over-expression is positively correlated to the poor outcome of breast cancer. In this study, we investigate the effects of 17β-estradiol (E2) on the activation of ezrin and its role in estrogen-dependent breast cancer cell movement. In T47-D breast cancer cells, E2 rapidly enhances ezrin phosphorylation at Thr567 in a time- and concentration-dependent manner. The signalling cascade implicated in this action involves estrogen receptor (ER) interaction with the non-receptor tyrosine kinase c-Src, which activates the phosphatidylinositol-3 kinase/Akt pathway and the small GTPase RhoA/Rho-associated kinase (ROCK-2) complex. E2 enhances the horizontal cell migration and invasion of T47-D breast cancer cells in three-dimensional matrices, which is reversed by transfection of cells with specific ezrin siRNAs. In conclusion, E2 promotes breast cancer cell movement and invasion by the activation of ezrin. These results provide novel insights into the effects of estrogen on breast cancer progression and highlight potential targets to treat endocrine-sensitive breast cancers