Both cell polarity and c-Jun N-terminal kinase (JNK) activity are essential to the maintenance of tissue homeostasis, and disruption of either is commonly seen in cancer progression. Despite the established connection between loss-of-cell polarity and JNK activation, much less is known about the molecular mechanism by which aberrant cell polarity induces JNK-mediated cell migration and tumor invasion. Here we show results from a genetic screen using an in vivo invasion model via knocking down cell polarity gene in Drosophila wing discs, and identify Rho1-Wnd signaling as an important molecular link that mediates loss-of-cell polarity-triggered JNK activation and cell invasion. We show that Wallenda (Wnd), a protein kinase of the mitogen-activated protein kinase kinase kinase family, by forming a complex with the GTPase Rho1, is both necessary and sufficient for Rho1-induced JNK-dependent cell invasion, MMP1 activation and epithelial-mesenchymal transition. Furthermore, Wnd promotes cell proliferation and tissue growth through wingless production when apoptosis is inhibited by p35. Finally, Wnd shows oncogenic cooperation with Ras(V12) to trigger tumor growth in eye discs and causes invasion into the ventral nerve cord. Together, our data not only provides a novel mechanistic insight on how cell polarity loss contributes to cell invasion, but also highlights the value of the Drosophila model system to explore human cancer biology.

Loss of the cell polarity gene could cooperate with oncogenic Ras to drive tumor growth and invasion, which critically depends on the c-Jun N-terminal Kinase (JNK) signaling pathway in Drosophila. By performing a genetic screen, we have identified Src42A, the ortholog of mammalian Src, as a key modulator of both RasV12/lgl−/− triggered tumor invasion and loss of cell polarity gene-induced cell migration. Our genetic study further demonstrated that the Bendless (Ben)/dUev1a ubiquitin E2 complex is an essential regulator of Src42A-induced, JNK-mediated cell migration. Furthermore, we showed that ectopic Ben/dUev1a expression induced invasive cell migration along with increased MMP1 production in wing disc epithelia. Moreover, Ben/dUev1a could cooperate with RasV12 to promote tumor overgrowth and invasion. In addition, we found that the Ben/dUev1a complex is required for ectopic Src42A-triggered cell death and endogenous Src42A-dependent thorax closure. Our data not only provide a mechanistic insight into the role of Src in development and disease but also propose a potential oncogenic function for Ubc13 and Uev1a, the mammalian homologs of Ben and dUev1a.

In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy. up objects of different sizes, then extract them by using the unique spectral information, shape and texture features of the buildings [3]. Due to different shooting angles, light and other factors, remote sensing images in different periods have considerable internal variability, and buildings have a variety of structure, texture and spectral information, therefore, the methods above cannot perform well in the extraction of complex buildings.In recent years, deep learning technology has ushered in a new wave of revival. At present, deep learning technology represented by deep convolutional neural networks has achieved excellent results in the field of computer vision [4][5][6]. Compared with the traditional method of feature extraction in artificial design, deep convolutional neural networks can obtain the structure, texture, semantics and other information of the object through multiple convolutional layers, and their performance is closer to visual interpretation in object recognition. The experiments in [7,8] had the advantages of deep convolutional neural networks in the field of object detection, but also revealed the problems of local absence and edge blur in image segmentation. This phenomenon is especially serious when the hardware equipment is insufficient or the dataset is small. Figure 1 shows this phenomenon using a mask image [9]. The main reasons for poor...

Many intellectual disability disorders are due to copy number variations, and, to date, there have been no treatment options tested for this class of diseases. MECP2 duplication syndrome (MDS) is one of the most common genomic rearrangements in males and results from duplications spanning the methyl-CpG binding protein 2 (MECP2) gene locus. We previously showed that antisense oligonucleotide (ASO) therapy can reduce MeCP2 protein amount in an MDS mouse model and reverse its disease features. This MDS mouse model, however, carried one transgenic human allele and one mouse allele, with the latter being protected from human-specific MECP2-ASO targeting. Because MeCP2 is a dosage-sensitive protein, the ASO must be titrated such that the amount of MeCP2 is not reduced too far, which would cause Rett syndrome. Therefore, we generated an “MECP2 humanized” MDS model that carries two human MECP2 alleles and no mouse endogenous allele. Intracerebroventricular injection of the MECP2-ASO efficiently down-regulated MeCP2 expression throughout the brain in these mice. Moreover, MECP2-ASO mitigated several behavioral deficits and restored expression of selected MeCP2-regulated genes in a dose-dependent manner without any toxicity. Central nervous system administration of MECP2-ASO is therefore well tolerated and beneficial in this mouse model and provides a translatable approach that could be feasible for treating MDS.