Due to the increasing demand of capacity in wireless cellular networks, the small cells such as pico and femto cells are becoming more popular to enjoy a spatial reuse gain, and thus cells with different sizes are expected to coexist in a complex manner. In such a heterogeneous environment, the role of interference management (IM) becomes of more importance, but technical challenges also increase, since the number of cell-edge users, suffering from severe interference from the neighboring cells, will naturally grow. In order to overcome low performance and/or high complexity of existing static and other dynamic IM algorithms, we propose a novel low-complex and fully distributed IM scheme, called REFIM (REFerence based Interference Management), in the downlink of heterogeneous multi-cell networks. We first formulate a general optimization problem that turns out to require intractable computation complexity for global optimality. To have a practical solution with low computational and signaling overhead, which is crucial for low-cost small-cell solutions, e.g., femto cells, in REFIM, we decompose it into per-BS (base station) problems based on the notion of reference user and reduce feedback overhead over backhauls both temporally and spatially. We evaluate REFIM through extensive simulations under various configurations, including the scenarios from a real deployment of BSs. We show that, compared to the schemes without IM, REFIM can yield more than 40% throughput improvement of cell-edge users while increasing the overall performance by 10∼107%. This is equal to about 95% performance of the existing centralized IM algorithm (MC-IIWF) that is known to be near-optimal but hard to implement in practice due to prohibitive complexity. We also present that as long as interference is managed well, the spectrum sharing policy can outperform the best spectrum splitting policy where the number of subchannels is optimally divided between macro and femto cells.
Background Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.
N-myc downstream-regulated gene 2 (NDRG2), which is known to have tumor suppressor functions, is frequently down-regulated in breast cancers and potentially involved in preventing the migration and invasion of malignant tumor cells. In the present study, we examined the inhibitory effects of NDRG2 overexpression, specifically focusing on the role of cyclooxygenase-2 (COX-2) in the migration of breast cancer cells. NDRG2 overexpression in MDA-MB-231 cells inhibited the expression of the COX-2 mRNA and protein, the transcriptional activity of COX-2, and prostaglandin E2 (PGE2) production, which were induced by a treatment with phorbol-12-myristate-13-acetate (PMA). Nuclear transcription factor-κB (NF-κB) signaling attenuated by NDRG2 expression resulted in a decrease in PMA-induced COX-2 expression. Interestingly, the inhibition of COX-2 strongly suppressed PMA-stimulated migration and invasion in MDA-MB-231-NDRG2 cells. Moreover, siRNA-mediated knockdown of NDRG2 in MCF7 cells increased the COX-2 mRNA and protein expression levels and the PMA-induced COX-2 expression levels. Consistent with these results, the migration and invasion of MCF7 cells treated with NDRG2 siRNA were significantly enhanced following treatment with PMA. Taken together, our data show that the inhibition of NF-κB signaling by NDRG2 expression is able to suppress cell migration and invasion through the down-regulation of COX-2 expression.
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