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Although pyroptosis is critical for macrophages against pathogen infection, its role and mechanism in cancer cells remains unclear. PD-L1 has been detected in the nucleus, with unknown function. Here we show that PD-L1 switches TNFα-induced apoptosis to pyroptosis in cancer cells, resulting in tumour necrosis. Under hypoxia, p-Stat3 physically interacts with PD-L1 and facilitates its nuclear translocation, enhancing the transcription of the gasdermin C (GSDMC) gene. GSDMC is specifically cleaved by caspase-8 with TNFα treatment, generating a GSDMC N-terminal domain that forms pores on the cell membrane and induces pyroptosis. Nuclear PD-L1, caspase-8 and GSDMC are required for macrophage-derived TNFα-induced tumour necrosis in vivo. Moreover, high expression of GSDMC correlates with poor survival. Antibiotic chemotherapy drugs induce pyroptosis in breast cancer. These findings identify a non-immune checkpoint function of PD-L1 and provide an unexpected concept that GSDMC/caspase-8 mediates a non-canonical pyroptosis pathway in cancer cells, causing tumour necrosis.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults and is a leading cause of worldwide cancer mortality. Intrahepatic dissemination and extrahepatic metastasis are key factors in malignant growth of HCC. Reducing HCC-associated metastasis is critically dependent on uncovering molecular signaling pathways that promote HCC metastasis. In this study, we explored the effect of TGF-β1 and RELN on cell migration, and the relationship between TGF-β1 and RELN in HCC cells. The data presented that TGF-β1 and RELN showed an opposite expression pattern, and either increased expression of TGF-β1 or decreased expression of RELN increased HCC cell migration ability. We also found TGF-β1 enhanced cell migration ability was through repressing RELN expression, as overexpression of RELN impaired TGF-β1 enhanced cell migration. Our work revealed the relationship between TGF-β1 and RELN and uncovered the important role of RELN in suppressing cell migration in HCC cells.

Doxorubicin (DOX) is a member of the anthracycline class of chemotherapeutic agents that are used for the treatment of many common human cancers. A self-assembled functionalized metal-organic nanotubes, SWMONTs could be loaded with the anticancer drug DOX. Via the modification of SWMONTs, DOX/SWMONTs-SiO 2 , DOX/SWMONTs-SiO 2 -NH 2 , DOX/SWMONTs-SiO 2 -NH 2 -FA samples could be obtained. The SEM characterization of the samples indicated that the particle size of DOX/SWMONTs-SiO 2 NH 2 samples were smaller than 200 nm. Drug-release experiments implied that DOX from the DOX/SWMONTs-SiO 2 -NH 2 -FA samples could be released faster at acidic tumor tissue than at normal body fluid (pH7.4). DOX has strong cytotoxicity, and at 20 μg/mL dosage of DOX large amount of apoptotic cells could be seen. Cellular uptaking experiments were used to study the apoptotic mechanism, while for DOX/SWMONTs-SiO 2 -NH 2 -FA samples, the strong drug fluorescence was found in the cytoplasm rather than in the nucleus.

Chronic copper exposure impaired spermatogenesis in adult male mice. The aim of this study was to determine whether chronic copper exposure can induce apoptosis of testicular cell and hypospermatogenesis via disturbing testosterone synthesis in adult male mice. In the present study, sixty CD-1 male mice were randomly divided into four groups, and were continuously administered for 8 weeks by oral gavage with copper sulfate at a dose of 0, 25, 100, and 150 mg/kg/day, respectively. We determined the content of serum and testicular copper, testicular coefficient, testicular histopathology, sperm count and motility, the mRNA and protein levels of Caspase-3, Bax, and Bcl-2, Leydig cell count, testosterone content, testosterone synthetase, and testosterone synthesis–related genes. The results showed that the copper levels in serum increased in a dose-dependent manner, and the copper levels in testes were significantly related to serum copper levels. Male mice given copper sulfate 100 and 150 dosage groups showed significant decreased in sperm motility and sperm number as well as increased in testes damage, and there was no significant change in testicular coefficient in the four groups. The mRNA levels of Bcl-2 decreased and Caspase-3 increased in 150 dosage group, and Bax increased in two higher dosage groups. Meanwhile, Caspase-3 and Bax proteins increased in 150 dosage group, and Bcl-2 protein decreased in three copper treatment groups. Nevertheless, there were no differences on the levels of testosterone content and testosterone synthetase of 3β-HSD, 17β-HSD, 17α-Hyd, and 20α-Hyd, mRNA levels of Cyp11a1, Cyp17a1, and Star, and quantity of Leydig cells in four groups. Overall, these data showed that chronic copper exposure led to copper residues in the testes, and the doses of 100 and 150 mg/kg/day copper sulfate may induce hypospermatogenesis by increasing apoptosis without affecting testosterone secretion.

A novel position-patch face super-resolution method for face super-resolution is developed.We developed a novel patch representation method based on Tikhonov regularized neighbor representation.The proposed patch representation model achieves sparsity and locality simultaneously.The effectiveness of our algorithms was shown on public face databases and some real-world images. In human-machine interaction, human face is one of the core factors. However, due to the limitations of imaging conditions and low-cost imaging sensors, the captured faces are often low-resolution (LR). This will seriously degrade the performance of face detection, expression analysis, and face recognition, which are the basic problems in human-machine interaction applications. Face super-resolution (SR) is the technology of inducing a high-resolution (HR) face from the observed LR one. It has been a hot topic of wide concern recently. In this paper, we present a novel face SR method based on Tikhonov regularized neighbor representation (TRNR). It can overcome the technological bottlenecks (e.g., instable solutionand noise sensitive) of the patch representationscheme in traditional neighbor embedding based image SR methods. Specifically, we introduce the Tikhonov regularization term to regularize the representation of the observation LR patches, leading to a unique and stable solution for the least squares problem. Furthermore, we show a connection of the proposed model to the neighbor embedding model, least squares representation,sparse representation, and locality-constrained representation. Extensive experiments on face SR are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results on the public FEI face database and surveillance images show that the proposed method achieves better performance in terms of reconstruction error and visual quality than existing state-of-the-art methods.

Sparse coding based face image Super-Resolution (SR) approaches have received increasing amount of interest recently. However, most of the existing sparse coding based approaches fail to consider the geometrical structure of the face space, as a result, artificial effects on reconstructed High Resolution (HR) face images come into being. In this paper, a novel sparse coding based face image SR method is proposed to reconstruct a HR face image from a Low Resolution (LR) observation. In training stage, it aims to get a more expressive HR-LR dictionary pair for certain input LR patch. The intrinsic geometric structure of training samples is incorporated into the sparse coding procedure for dictionary learning. Unlike the existing SR methods which use the graph constructed in LR Manifold (LRM) as regularization term, the proposed method uses graph constructed in HR Manifold (HRM) as regularization term. In reconstruction stage, K selection mean constrains is used in l1 convex optimization, aiming at finding an optimal weight for HR face image patch reconstruction. Experimental results on both simulation and real world images suggest that our proposed one achieves better quality when compared with other state-of-the-art methods.

Previous studies have demonstrated that microRNAs (miRs) are involved in cell apoptosis. However, the role of miR-519 in acute myeloid leukemia (AML) has yet to be elucidated. The present study identified the effects of miR-519 on HL60 human acute myeloid leukemia cell growth and apoptosis. The expression levels of miR-519 were examined in AML cells, as well as AML tissue samples. Furthermore, cell viability and apoptosis were examined in HL60 cells transfected with miR-519 mimics, miR-519 inhibitors or a negative control. In addition, the effects of human antigen R (HuR) on cell apoptosis were investigated using specific small interfering RNA targeting HuR. The results demonstrated that the expression levels of miR-519 were significantly increased in the AML cells and the tissue samples, suggesting that miR-519 may contribute to abnormal HL60 cell proliferation. Upregulation of miR-519 expression decreased HL60 cell viability and induced cell apoptosis. Furthermore, knockdown of HuR reduced cell migration and enhanced cell apoptosis. The results of the present study indicate that miR-519 may contribute to HL60 cell apoptosis by regulating the expression of HuR.

It is important to enhance the resolution of face images from video surveillance for recognization and other post processing. In this paper, a novel sparse representation based face image superresolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a LR observation. First, it gets a HR-LR dictionary pair for certain input LR patch via position patch clustering and locality preserving projection (LPP). LPP is used to further refine the training samples by taking into account the geometry structure between training patches. It can make the dictionary more expressive. Second, it sparse codes the input patch over LR dictionary by solving L1-regularized least squares optimization. F eature-sign algorithm is used to get the stable solution. Third, it maps the sparse coding coefficitons obtain from the LR dictionary to the HR dictionary, and reconstruct the HR face image patch with the coefficitions and HR dictionary. By integrating the hallucinated HR patches together with overlapped in adjacent, we can obtain the final HR face image. Experiments conducted on CAS-PEAL-R1 database validate the proposed method both in subjective and objective quality.

The article introduces the Web excavation technology that uses a Web log framework and database data mining, and it analyzes the methods of the Waikato environment for knowledge analysis (WEKA) mining tool to a simulation e-commerce sites in the process of data mining and its key techniques, discusses the Web data mining for e-commerce sites, the importance finally points out the development trend of Web mining and the existing defects of mining tool, the paper proposes developing intelligent, and popularization of the reality of mining tool and necessity [1, 2].

In this paper, we propose a manifold learning based algorithm using 'Nearest Feature Line - NFL' to hallucinate high-resolution face image. According to the fact that existing NFL can effectively characterize the geometrical proportions to the face samples, we propose using NFL metric to define the neighborhood relations between face samples. Our algorithm can solve the problem that traditional method cannot effectively reveal the similar local geometry between high-resolution and low-resolution face manifolds under the condition that the training sample size is small. Moreover, in order to enhance the representation capacity of available face samples and reduce the computational complexity, we select neighborhood samples for each input LR image. Experimental results demonstrate that our algorithm can generates clearer local feature details, and the PSNR is 1.4 dB higher than that of the best manifold learning based method reported so far.

Instead of using probabilistic graph based or manifold learning based models, some approaches based on position-patch have been proposed for face hallucination recently. In order to obtain the optimal weights for face hallucination, they represent image patches through those patches at the same position of training face images by employing least square estimation or convex optimization. However, they can hope neither to provide unbiased solutions nor to satisfy locality conditions, thus the obtained patch representation is not the best. In this paper, a simpler but more effective representation scheme- Locality-constrained Representation (LcR) has been developed, compared with the Least Square Representation (LSR) and Sparse Representation (SR). It imposes a locality constraint onto the least square inversion problem to reach sparsity and locality simultaneously. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art face hallucination approaches.

We explore in this paper efficient algorithmic solutions to single image super-resolution (SR). We propose the GESR, namely Graph Embedding Super-Resolution, to super-resolve a high-resolution (HR) image from a single low-resolution (LR) observation. The basic idea of GESR is to learn a projection matrix mapping the LR image patch to the HR image patch space while preserving the intrinsic geometrical structure of original HR image patch manifold. While GESR resembles other manifold learning-based SR methods in persevering the local geometric structure of HR and LR image patch manifold, the innovation of GESR lies in that it preserves the intrinsic geometrical structure of original HR image patch manifold rather than LR image patch manifold, which may be contaminated because of image degeneration (e.g., blurring, down-sampling and noise). Experiments on benchmark test images show that GESR can achieve very competitive performance as Neighbor Embedding based SR (NESR) and Sparse representation based SR (SSR). Beyond subjective and objective evaluation, all experiments show that GESR is much faster than both NESR and SSR.

Super-resolution methods based on sparse easily lead to over-smoothing at the edges of reconstructed image. A novel super-resolution method based on local sparse and global gradient is proposed to solve the problem. First, it represents the input low-resolution (LR) image patches with sparse coefficients and LR over-complete dictionary. Then it maps the coefficients to high resolution (HR) over-complete dictionary and reconstructs the HR texture. At last, it enhances the main edge using global natural image statistics' prior information and merges it together with the texture. By using the local sparse representation and global gradient transformation, it can obtain the result image with clean texture and clear edge. Experimental results validate the proposed method, both in subjective and objective quality.

Human faces in surveillance video images usually have low resolution and poor quality. They need to be reconstructed in super-resolution for identification. The traditional subspace-based face super-resolution algorithms are sensitive to light. For solving the problem, this paper proposes a face super-resolution method based on illumination invariant feature. The method firstly extracts the illumination invariant features of an input low resolution image by using adaptive L1–L2 total variation model and self-quotient image in logarithmic domain. Then it projects the feature onto non-negative basis obtained by Nonnegative Matrix Factorization(NMF) in face image database. Finally it reconstructs the high resolution face images under the framework of Maximum A Posteriori (MAP) probability. Experimental results demonstrate that the proposed method outperforms the compared methods both in subjective and objective quality under poor light conditions.

Based on teaching practice of Educational Techonology the paper presents some suggestions and measures of improving the course teaching.

E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the E-learning behavior.It classified E-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing E-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction.

Resources construction is the key element of modern distance education project in rural K-12. But how to design and develop the high-quality and easy-to-use resources is still an important problem. In order to make the resources more applicable and useable, Resources construction should pay attention to resources’ actual using situation from the users’ real need. This essay presented a new model: situation analysis, function extracting, and system framework for design and developing modern distance resource, and give some suggestion from this perspective.

SOA is a software architecture. It is a real solution to integrate education resources and enhance resource utilizes. This essay introduced SOA's principle, structure and reference model of application. Based on this, it designed a SOA-based education resource service system. It used Web Service, WSDL, UDDI, SOAP and CELTS to implement the system. Through the system, we can use and develop the education resource much more easier and quicker.

E-learning is a new learning model. Understand the learner's behavior is the foundation of e-learning. This essay present a multi-dimensional and multi-hierarchy model of e-learning behavior, based on this, it designed an E-learning behavior mine system. It used Web Services, data mine and CELTS to implement the system. Through the system, we can master the learner's characteristics and push them with personalized learning materials.

In the collaborative learning environment, role behavior brings on occurrence, development, and disappearance of collaborative learning. In this paper, we first introduce WF-net to describe role behavior in collaborative learning, and then indicate relations among some notions in collaborative learning. We focus on dynamic organization of learning pattern by awareness of role. We represent a mechanism of generating tasks oriented role to ensure persistence of collaborative learning.