Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen'an County, Shan'xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert's knowledge and the opinions of decision-makers.

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets 1 .

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Fatty acid-binding protein 5 (FABP5) was found in our previous study to be a potential biomarker for lymph node metastasis of cervical cancer. However, the roles of FABP5 in cervical cancer remain unclear. In the present study, FABP5 expression was found to be significantly upregulated in cervical cancer tissues, and high FABP5 expression was significantly correlated with lymph node metastasis, lymphovascular space invasion, the International Federation of Gynecology and Obstetrics (FIGO) stage, and tumor size. Moreover, FABP5 was an independent factor for poor prognosis in cervical cancer patients. Silencing of FABP5 inhibited cell proliferation, colony formation, cell migration, and invasion in vitro. Furthermore, FABP5 silencing significantly reduced tumor growth and lung metastases in a murine allograft model in vivo. In addition, FABP5 silencing decreased the expression of matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9) in vitro and in vivo. Collectively, these findings indicated that FABP5 plays an important role in the carcinogenesis and metastasis of cervical cancer, and FABP5 may be a novel predictor for prognostic assessment of cervical cancer patients.

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