LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.Comment: WWW 201

Suppression of long non-coding RNA H19 inhibits proliferation, cell migration and invasion in human cervical cancer cells

Purpose: To determine the expression profile of lncRNA H19 in different cervical cancers, and to decipher its function in the growth and metastasis of cervical cancer.Methods: The analysis LncRNA H19 expression was performed using quantitative real timepolymerase chain reaction (qRT-PCR). Cell counting kit 8 (CCK8) assay was used to assess the viability of the cells. The cells were transfected with Si-H19 using Lipofectamine 2000 and the metastasis of cells was determined by cell migration and invasion assay. Immunoblotting was used to evaluate the protein expression.Results: The lncRNA H19 expression was considerably enhanced in cervical cancer cells, and was about 2.6 to 5.3 times more in cervical cancer cells relative to non-cancer cells. Inhibition of lncRNA caused significant reduction in cervical cancer cell growth in a time-dependent manner. In addition while silencing of lncRNA inhibited the metastasis of HeLa cells. Cell migration and invasion was about 26 % in Si-H19 transfected cervical cancer cells, relative to 65 % in Si-NC cervical HeLa cells. Similarly, cell invasion was 45 % in Si-H19 cervical HeLa cells relative to the negative control (Si-NC). Inhibition of HeLa cell metastasis was also concomitant with decline of metalloproteinases (MMP)-2 and 9expression.Conclusion: lncRNA regulates the growth and metastasis of cervical cancer cells. Thus, IncRNA may be an important therapeutic agent for cervical cancer.Keywords: Cervical cancer, lncRNA, Proliferation, Invasio

Evidence for quasi-one-dimensional charge density wave in CuTe by angle-resolved photoemission spectroscopy

We report the electronic structure of CuTe with a high charge density wave (CDW) transition temperature Tc = 335 K by angle-resolved photoemission spectroscopy (ARPES). An anisotropic charge density wave gap with a maximum value of 190 meV is observed in the quasi-one-dimensional band formed by Te px orbitals. The CDW gap can be filled by increasing temperature or electron doping through in situ potassium deposition. Combining the experimental results with calculated electron scattering susceptibility and phonon dispersion, we suggest that both Fermi surface nesting and electron-phonon coupling play important roles in the emergence of the CDW