Local Gromov-Witten Invariants are Log Invariants

We prove a simple equivalence between the virtual count of rational curves in the total space of an anti-nef line bundle and the virtual count of rational curves maximally tangent to a smooth section of the dual line bundle. We conjecture a generalization to direct sums of line bundles.Comment: 15 pages, version accepted for publication in Advances in Mathematic

An Encapsulation of Gene Signatures for Hepatocellular Carcinoma, MicroRNA-132 Predicted Target Genes and the Corresponding Overlaps

<div><p>Objectives</p><p>Previous studies have demonstrated that microRNA-132 plays a vital part in and is actively associated with several cancers, with its tumor-suppressive role in hepatocellular carcinoma confirmed. The current study employed multiple bioinformatics techniques to establish gene signatures for hepatocellular carcinoma, microRNA-132 predicted target genes and the corresponding overlaps.</p><p>Methods</p><p>Various assays were performed to explore the role and cellular functions of miR-132 in HCC and a successive panel of tasks was completed, including NLP analysis, miR-132 target genes prediction, comprehensive analyses (gene ontology analysis, pathway analysis, network analysis and connectivity analysis), and analytical integration. Later, HCC-related and miR-132-related potential targets, pathways, networks and highlighted hub genes were revealed as well as those of the overlapped section.</p><p>Results</p><p>MiR-132 was effective in both impeding cell growth and boosting apoptosis in HCC cell lines. A total of fifty-nine genes were obtained from the analytical integration, which were considered to be both HCC- and miR-132-related. Moreover, four specific pathways were unveiled in the network analysis of the overlaps, i.e. adherens junction, VEGF signaling pathway, neurotrophin signaling pathway, and MAPK signaling pathway.</p><p>Conclusions</p><p>The tumor-suppressive role of miR-132 in HCC has been further confirmed by <i>in vitro</i> experiments. Gene signatures in the study identified the potential molecular mechanisms of HCC, miR-132 and their established associations, which might be effective for diagnosis, individualized treatments and prognosis of HCC patients. However, combined detections of miR-132 with other bio-indicators in clinical practice and further <i>in vitro</i> experiments are needed.</p></div

Network analysis for HCC.

<p>In NLP analysis, a network of multiple genes was established for HCC.</p

Proliferation test.

<p>Time-dependent effects of miR-132 were assessed on proliferation in various HCC cell lines, i.e. HepG2 (A), SMMC-7221 (B), HepB3 (C) and SNU449 (D). Points represent the averages of sets of three single, independent experiments while bars stand for the standard deviations. *P < 0.05, ** P < 0.01 and ***P < 0.001, compared to blank and negative controls at the same time point.</p

Connectivity analysis for HCC.

<p>Connectivity analysis demonstrated that the top connectivities of PIK3CA and PIK3R2.</p

The integration systematically analyzed the overlaps and featured 59 genes that were both potentially HCC-related and probably regulated by miR-132.

<p>The integration systematically analyzed the overlaps and featured 59 genes that were both potentially HCC-related and probably regulated by miR-132.</p

Flow chart of NLP analysis for HCC.

<p>The NLP analysis procedure of HCC includes document mining, data processing and statistical analysis.</p

Network analysis for miR-132 predicted target genes.

<p>Network analysis provided insights into the potential interacting and regulatory networks of miR-132.</p

Flow chart of bioinformatic processes.

<p>A series of tasks, i.e. natural language processing (NLP) analysis of HCC, prediction of miRNA-132 target genes, comprehensive gene analyses and analytical integration was conducted successively.</p

All the miR-132 predicted target genes were sorted out according to molecular function, cellular component and biological process by GO analysis.

<p>All the miR-132 predicted target genes were sorted out according to molecular function, cellular component and biological process by GO analysis.</p