Acquired resistance inevitably limits the curative effects of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), which represent the classical paradigm of molecular-targeted therapies in non-small-cell lung cancer (NSCLC). How to break such a bottleneck becomes a pressing problem in cancer treatment. The epithelial-mesenchymal transition (EMT) is a dynamic process that governs biological changes in various aspects of malignancies, notably drug resistance. Progress in delineating the nature of this process offers an opportunity to develop clinical therapeutics to tackle resistance toward anticancer agents. Herein, we seek to provide a framework for the mechanistic underpinnings on the EMT-mediated acquisition of EGFR-TKI resistance, with a focus on NSCLC, and raise the question of what therapeutic strategies along this line should be pursued to optimize the efficacy in clinical practice.

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Abstract. Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia.

The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.

Despite impressive progress in highresource settings, Neural Machine Translation (NMT) still struggles in lowresource and out-of-domain scenarios, often failing to match the quality of phrasebased translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard backtranslation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.

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The peritoneum, especially the omentum, is a common site for gastric cancer (GC) metastasis. Our aim was to expound the role and mechanisms of Piezo1 on GC omentum metastasis. A series of functional assays were performed to examine cell proliferation, clone formation, apoptosis, Ca2+ influx, mitochondrial membrane potential (MMP) and migration after overexpression or knockdown of Piezo1. A GC peritoneal implantation and metastasis model was conducted. After infection by si‐Piezo1, the number and growth of tumours were observed in abdominal cavity. Fibre and angiogenesis were tested in metastatic tumour tissues. Piezo1 had higher expression in GC tissues with omentum metastasis and metastatic lymph node tissues than in GC tissues among 110 patients. High Piezo1 expression was associated with lymph metastasis, TNM and distant metastasis. Overexpressed Piezo1 facilitated cell proliferation and suppressed cell apoptosis in GC cells. Moreover, Ca2+ influx was elevated after up‐regulation of Piezo1. Piezo1 promoted cell migration and Calpain1/2 expression via up‐regulation of HIF‐1α in GC cells. In vivo, Piezo1 knockdown significantly inhibited peritoneal metastasis of GC cells and blocked EMT process and angiogenesis. Our findings suggested that Piezo1 is a key component during GC omentum metastasis, which could be related to up‐regulation of HIF‐1α.