The expression of BECLIN1 is significantly reduced in non‑small cell lung cancer (NSCLC) compared with non‑cancerous tissue. However, the role of BECLIN1 in lung cancer is unclear. Using the RNA interference (RNAi) technique the present study investigated the effect of the knockdown of BECLIN1 on the cell growth and proliferation of the A549 human lung cancer cell line. The target site for the RNAi technique was designed and the lentivirus vector for the small interfering (si)RNA expression was constructed according to the encoding sequence of the mRNA for BECLIN1. The A549 cells were transfected with the siRNA virus against BECLIN1. BECLIN1 expression was detected by reverse transcription (RT)‑PCR and western blot analysis. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method was applied to detect cell growth. Flow cytometry was used to determine cell apoptosis. The activity of caspase‑3 and caspase‑9 was also detected in the A549 cells with BECLIN1 knockdown. The results showed that siRNA virus transfection significantly decreased the mRNA and protein expression of BECLIN1 in the transfected A549 cells. The knockdown of BECLIN1 promoted cell growth and decreased apoptosis. Caspase‑3 and ‑9 activity in the A549 cells with BECLIN1 knockdown was significantly reduced. In conclusion, the siRNA expression vector effectively inhibited the expression of BECLIN1 in the A549 human lung cancer cell line and promote the growth and proliferation of the A549 cells.

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy .

No abstract

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

Tongue diagnosis is an important way of monitoring human health status in traditional Chinese medicine. As a key step of achieving automatic tongue diagnosis, the major challenges for robust and accurate segmentation and identification of tongue body in tongue images lay in the large variations of tongue appearance, e.g., tongue texture and tongue coating, caused by different diseases for different patients. To cope with these challenges, we propose a novel end-to-end model for multi-task learning of tongue localization and segmentation, named TongueNet, in which pixel-level prior information is utilized for supervised training of deep convolutional neural network. Firstly, we introduce a feature pyramid network based on the designed context-aware residual blocks for the extraction of multi-scale tongue features. Then, the region of interests (ROIs) of tongue candidates are located in advance from the extracted feature maps. Finally, finer localization and segmentation of tongue body are conducted based on the feature maps of ROIs. Quantitative and qualitative comparisons on real-world datasets show that the proposed TongueNet achieves state-of-the-art performance for the segmentation of tongue body in terms of both robustness and accuracy.

Abstract.Although Beclin 1 has been demonstrated to exert an important role in cell autophagy during carcinogenesis, its biological function in lung cancer has yet to be fully elucidated. A previous study by our laboratory identified that knockdown of Beclin 1 promoted cell growth and inhibited apoptosis in the A549 lung cancer cell line. In the present study, a Beclin 1 lentiviral expression vector was constructed, and an A549 cell line was established with a steady expression of Beclin 1. Furthermore, the effect of Beclin 1 overexpression on cell invasion and apoptosis, changes in the activities of the apoptosis-associated caspases-3 and -9, and the overexpression of esophageal cancer-related gene 4 (ECRG4) were examined. The results demonstrated that the overexpression of Beclin 1 in A549 cells reduced cell invasion by Matrigel invasion assay and promoted apoptosis by flow cytometric analysis (P<0.01) compared with Lenex-packaged lentiviral particles and non-transfected control groups. Furthermore, the overexpression of Beclin 1 in A549 cells increased the activities of caspases-3 and -9 and the expression of ECRG4 (P<0.01) compared with Lenex-packaged lentiviral particles and non-transfected control groups. In conclusion, the overexpression of Beclin 1 promoted apoptosis and decreased invasion by upregulating the expression of ECRG4 in A549 lung adenocarcinoma cells. Therefore, the selection of Beclin l as a target for gene therapy represents a more effective method for the treatment of lung cancer. IntroductionBeclin l (the mammalian counterpart of the yeast Atg6 gene) is an essential player in autophagy. Allelic loss or deficiency of the Beclin 1 gene has been demonstrated in human breast cancer, ovarian cancer and prostate cancer; in lung cancer, hepatocellular carcinoma, cervical cancer and lymphoma, the expression of Beclin 1 is very low/almost undetectable (1-4). A previous study identified that Beclin 1 -/-mice died early in embryonic development (5). Although Beclin 1 +/-mice were able to survive, the incidence of cancer was much higher in these animals. In addition, the measured in vivo cell autophagy activity was markedly decreased, and cells reproduced faster in Beclin 1-deficient animals. These findings clearly suggested that there is a close correlation between the inhibition of autophagy activity and the occurrence of cancer.Although Beclin 1 has been demonstrated to exert an important role in cell autophagy during carcinogenesis, its biological function in lung cancer has yet to be fully elucidated. A previous study by our laboratory identified that knockdown of Beclin 1 promoted cell growth and inhibited apoptosis in the A549 lung cancer cell line (6). In the present study, a Beclin 1 lentiviral expression vector was constructed, and an A549 cell line was established with a steady expression of Beclin 1. The effects of Beclin 1 overexpression on cell invasion and apoptosis, changes in the activities of the apoptosis-associated caspases-3 and -9, and the expression of esophageal cancer-related...

Image dehazing on a hazy image aims to remove the haze and make the image scene clear, which attracts more and more research interests in recent years. Most existing image dehazing methods use a classic atmospheric scattering model and natural image priors to remove the image haze. In this paper, we propose an end-to-end image dehazing model termed as DRHNet (Deep Residual Haze Network), which restores the haze-free image by subtracting the learned negative residual map from the hazy image. Specifically, DRHNet proposes a context-aware feature extraction module to aggregate the contextual information effectively. Furthermore, it proposes a novel nonlinear activation function termed as RPReLU (Reverse Parametric Rectified Linear Unit) to improve its representation ability and to accelerate its convergence. Extensive experiments demonstrate that DRHNet outperforms state-of-the-art methods both quantitatively and qualitatively. In addition, experiments on image deraining task show that DRHNet can also serve for image deraining. INDEX TERMS Image dehazing, image deraining, negative residual map, context-aware feature extraction, reverse parametric rectified linear unit (RPReLU).

Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively.Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be