Parallel Processing Transport Model MT3DMS by Using OpenMP.

Solute transport modeling resolves advection, dispersion, and chemical reactions in groundwater systems with its accuracy depending on the resolution of domain at all scales, thus the computational efficiency of a simulator becomes a bottleneck for the wide application of numerical simulations. However, the traditional serial numerical simulators have reached their limits for the prohibitive computational time and memory requirement in solving large-scale problems. These limitations have greatly hindered the wide application of groundwater solute transport modeling. Thus, the development of an efficient method for handling large-scale groundwater solute transport simulation is urgently required. In this study, we developed and assessed a parallelized MT3DMS (Modular Three-Dimensional Multispecies Transport Model) by using OpenMP (Open specifications for Multi-Processing) to accelerate the solute transport simulation process. The parallelization was achieved by adding OpenMP compile directives (i.e., defining various types of parallel regions) into the most time-consuming packages, including the Advection package (ADV), Dispersion package (DSP), and Generalized Conjugate Gradient Solver package (GCG). This allows parallel processing on shared-memory multiprocessors, i.e., both the memory requirement and computing efforts are automatically distributed among all processors. Moreover, we discussed two different parallelization strategies for handling numerical models with either many layers or few layers. The performance of parallelized MT3DMS was assessed by two benchmark numerical models with different model domain sizes via a workstation with two quad-core processors. Results showed that the running time of parallelized MT3DMS can be 4.15 times faster than that using sequential MT3DMS. The effects of using different preconditioners (procedures that transform a given problem into a form that is more suitable for numerical solving methods) in the GCG package were additionally evaluated. The modified strategy for handling numerical models with few layers also achieved satisfactory results with running time two times faster than that via sequential simulation. Thus, the proposed parallelization allows high-resolution groundwater transport simulation with higher efficiency for large-scale or multimillion-cell simulation problems.

LncRNATCF7 promotes the growth and self-renewal of glioma cells via suppressing the miR-200c-EpCAM axis.

Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are involved in tumor initiation and development. Recent studies illustrated that lncRNATCF7 was highly expressed in lung cancer and liver cancer, however, the expression pattern and function of lncRNATCF7 in glioma remains to be elucidated. Here, we found that lncTCF7 was highly expressed in glioma tissues and cell lines. Overexpression of lncTCF7 promoted the proliferation and migration of glioma cells. Down-regulation of lncTCF7 significantly suppressed the tumorigenesis of glioma. Mechanistically, lncTCF7 enhanced the self-renewal of glioma cells via up-regulating the expression of epithelial cell adhesion molecule (EpCAM). The detailed molecular mechanism uncovered that lncTCF7 bound to miR-200c and decreased the abundance of miR-200c, which consequently attenuated the negative regulation of miR-200c on EpCAM. Collectively, these data provide evidence to demonstrate the critical role of lncTCF7 in the tumorigeneis of glioma, which suggested that lncTCF7 might be a promising target in the treatment of glioma.