Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical engineering applications, but are facing ever-growing demands for more accurate turbulence models. Recently, emerging machine learning techniques have had a promising impact on turbulence modeling, but are still in their infancy regarding widespread industrial adoption. Toward their extensive uptake, this paper presents a universally interpretable machine learning (UIML) framework for turbulence modeling, which consists of two parallel machine learning-based modules to directly infer the structural and parametric representations of turbulence physics, respectively. At each phase of model development, data reflecting the evolution dynamics of turbulence and domain knowledge representing prior physical considerations are converted into modeling knowledge. The data- and knowledge-driven UIML is investigated with a deep residual network. The following three aspects are demonstrated in detail: (i) a compact input feature parameterizing a new turbulent timescale is introduced to prevent nonunique mappings between conventional input arguments and output Reynolds stress; (ii) a realizability limiter is developed to overcome the under-constrained state of modeled stress; and (iii) fairness and noise-insensitivity constraints are included in the training procedure. Consequently, an invariant, realizable, unbiased, and robust data-driven turbulence model is achieved. The influences of the training dataset size, activation function, and network hyperparameter on the performance are also investigated. The resulting model exhibits good generalization across two- and three-dimensional flows, and captures the effects of the Reynolds number and aspect ratio. Finally, the underlying rationale behind prediction is explored.
Abstract. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. The results of the present study demonstrate that high expression of microRNA (miR)-137 and low expression of steroid receptor coactivator-3 (SRC3) had a significant negative correlation in 40 NSCLC tissue samples. In addition, cell colony formation and proliferation was significantly reduced in miR-137-transfected A549 and NCI-H838 cells compared with scramble-transfected NSCLC cell lines. miR-137 was identified to induce G 1 /S cell cycle arrest and dysregulate the mRNA expression of cell cycle-associated proteins (proliferating cell nuclear antigen, cyclin E, cyclin A1, cyclin A2 and p21) in NSCLC cells. Notably, miR-137 could significantly suppress SRC3 3' untranslated region (UTR) luciferase-reporter activity, an effect that was not detectable when the putative 3'-UTR target-site was mutated, further clarifying the molecular mechanisms underlying the role of miR-137 in NSCLC. In conclusion, the results of the present study suggest that miR-137 suppresses NSCLC cell proliferation by partially targeting SRC3.
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