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Named entity recognition (NER) is a wellstudied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundaryadjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

BackgroundETS variant 1 (ETV1) and E3 ubiquitin ligase constitutive photomorphogenetic 1 (COP1) have been proposed to be a pair of oncogene and tumor suppressor. However, the co-existing status of ETV1 and COP1 in triple-negative breast cancer (TNBC) and their predictive role in determining the patient’s outcome are uncertain.MethodsWe examined the abundance of COP1 and ETV1 proteins and their clinicopathologic significance in archival TNBC tissues from 105 patients by tissue microarray. The potential function link between COP1 and ETV1 was observed in MDA-MB-231 cells by cell proliferation, invasion and migration assays.ResultsETV1 expression was higher in TNBC tissues compared to normal tissues, while COP1 was lower. ETV1 expression was negatively associated with COP1 abundance in TNBCs. Overexpression of COP1 led to significant reduction of ETV1 in MDA-MB-231 cells, and suppressed the cells migration and invasion. Rescue of ETV1 expression in the presence of COP1 notably regained the cells behaviors. ETV1-positive group was associated with a markedly poor overall survival. Meanwhile, we had observed favourable prognosis in COP1-positive cases for the first time. Multivariate analysis showed that COP1 together with ETV1 were independent risk factors in the prognosis of TNBC patients.ConclusionsCOP1 might be a tumor suppressor by negative regulating ETV1 in patients with TNBCs. COP1 and ETV1 are a pair of independent predictors of prognosis for TNBC cases. Thus, targeting them might be a potential strategy for personalized TNBC treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1151-y) contains supplementary material, which is available to authorized users.

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Named entity recognition (NER) is a wellstudied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundaryadjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

Evidence is presented to show that the thiosuiphate-oxidising multi-enzyme system from Thiobacillus versutus has a periplasmic location, and that the oxygen-binding site of the cytochrome oxidase (aa3) is on the inner surface of the membrane. A scheme for the mechanism of generation of a proton motive force during electron flow from thiosulphate to oxygen via cytochrome c and aa 3 is proposed.

In this paper, we presented a service infrastructure based on distributed file system for massive storage in digital library. In addition, we addressed the small-file problem by merging small files into big ones, and proposed a novel dynamic replica number adjustment scheme to ensure the maximal availability and reliability in a limited storage space.

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