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The empirical expressions for predicting the small-strain shear modulus (G0) of granular soils in current engineering practice are established mainly on experimental data under isotropic stress conditions. In most geotechnical applications, however, soils are subjected to anisotropic stress conditions. The impact of stress anisotropy on G0 is a critical concern but is not yet fully understood. In this paper, we present a specifically designed experimental study to address the question. Various principal stress ratios were applied to isotropically consolidated sand specimens in a triaxial apparatus, and the elastic shear waves were generated by the bender elements installed in the apparatus such that the variations of G0 from isotropic stress states to anisotropic stress states were determined. Three quartz sands with different particle shapes were tested under a range of states in terms of void ratio, axial stress, and radial stress. The study shows that the impact of stress anisotropy is much more complicated than commonly thought. It depends on the magnitude of the stress ratio and the loading mode. A simple model that accounts for two primary mechanisms associated with the impact of stress anisotropy is proposed, and its performance is evaluated using various sources of data in the literature. [ABSTRACT FROM AUTHOR]

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC. [ABSTRACT FROM AUTHOR]