Background Atherosclerosis (AS) is a common frequently-occurring disease in the clinic and a serious threat to human health. This research aimed to explore the value between GASL1 and AS. Methods The expression and values of GASL1 in AS patients were revealed by qRT-PCR and ROC curve. The HUVEC cells were induced by ox-LDL to construct in-vitro models. Cell viability was detected by MTT assay, and apoptosis was detected by flow cytometry. The inflammatory situation was reflected by the ELISA assay. Double luciferase reporter gene assay verified the regulatory relationship between GASL1 and miR-106a, miR-106a and LKB1. Results The levels of GASL1 was lower in AS group than those in control group. The value of GASL1 in predicting AS patients was also tested by the ROC curve. After HUVEC cells were induced by ox-LDL, the levels of GASL1 and LKB1 decreased significantly, while the level of miR-106a increased significantly. Upregulation of LKB1 reversed the effect of upregulation of GASL1 on viability, apoptosis, and inflammation of HUVEC cells induced by ox-LDL. Conclusion Overexpression of GASL1 might suppress ox-LDL-induced HUVEC cell viability, apoptosis, and inflammation by regulating miR-106a/LKB1 axis.
Relation extraction aims at discovering relations between entities from free text, and it is a crucial part of information extraction. Recently, kernel methods have seen successfully applied in relation extraction.The paper proposes two novel composite kernels for relation extraction, namely linear and polynomial kernels, based on three individual kernels: an entity kernel that allows for structured features, a string kernel for parse tree, and Zelenko's parse tree kernel.In experiments, the kernels mentioned above are used in conjunction with Support Vector Machines for extracting person-affiliation relations from 500 sentences. In order to improve the training speed, trees parsed from Stanford Parser are pruned before using. Finally, the outcome shows that though linear composite kernel's precision (77.0%) and recall (82.2%) are not the highest, its F-measure with 79.4% significantly outperforms the best record, which is 72.6%, of three previous kernels. This result indicates that the linear composite kernel performs better than the three individual kernels.
Frequent itemset mining is a well studied and important problem in the datamining community. An abundance of different mining algorithms exists, all with different flavor and characteristics, but almost all suffer from two major shortcomings. First, in general frequent itemset mining algorithms perform exhaustive search over a huge pattern space. Second, most algorithms assume that the input data fits into main memory. The first problem was recently tackled in the work of [2], by direct sampling the required number of patterns over the pattern space. This paper extends the direct sampling approach by casting the algorithm into the MapReduce framework, effectively ceasing the memory requirements that the data should fit into main memory. The results show that the algorithm scales well for large data sets, while the memory requirements are solely dependent on the required number of patterns in the output.
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