The soil-water characteristic curve (SWCC) is the basis for obtaining the hydraulic conductivity parameters of a soil as well as for using soil water and heat transport models. At present, the curve can be obtained by two methods: by direct measurement and by empirical formula. Direct measurement is both difficult and time-consuming. By contrast, fitting the SWCC with a suitable empirical formula is stable and convenient. The van Genuchten (VG) model has the advantage of universal applicability due to its use of a statistical aperture distribution model for estimating hydraulic conductivity. This study selected the Mu Us Bottomland as a study area. Data on the water content and water potential of undisturbed soil from this site were obtained with a Ku-pF instrument and a self-designed soil column experiment with temperature settings of 13 °C, 18 °C, 23 °C, 27 °C, and 30 °C. The variation of four main parameters in the VG model with temperature was analyzed based on thermodynamic theory and considering the effect of temperature on soil capillary pressure via its effects on surface tension and contact angle. A prediction model for the soil-water characteristic curve of the Mu Us Bottomland was then constructed, and its applicability was further analyzed. The temperature dependence of the SWCC demonstrated here provides an important scientific basis for agricultural production, farmland water conservancy, and the design of soil and water conservation engineering projects.

Background Gastric cancer (GC) is a primary reason for cancer death in the world. At present, GC has become a public health issue urgently to be solved to. Prediction of prognosis is critical to the development of clinical treatment regimens. This work aimed to construct the stable gene set for guiding GC diagnosis and treatment in clinic. Methods A public microarray dataset of TCGA providing clinical information was obtained. Dimensionality reduction was carried out by selection operator regression on the stable prognostic genes discovered through the bootstrap approach as well as survival analysis. Findings A total of 2 prognostic models were built, respectively designated as stable gene risk scores of OS (SGRS-OS) and stable gene risk scores of PFI (SGRS-PFI) consisting of 18 and 21 genes. The SGRS set potently predicted the overall survival (OS) along with progression-free interval (PFI) by means of univariate as well as multivariate analysis, using the specific risk scores formula. Relative to the TNM classification system, the SGRS set exhibited apparently higher predicting ability. Moreover, it was suggested that, patients who had increased SGRS were associated with poor chemotherapeutic outcomes. Interpretation The SGRS set constructed in this study potentially serves as the efficient approach for predicting GC patient survival and guiding their treatment.

Background Gastric cancer is one of the leading causes of cancer-related death worldwide. How to eliminate gastric cancer is an urgent public health problem. Prediction of prognosis is critical to the development of clinical treatment regimens. The aim of this study was to establish a stable prognostic gene set to guide the clinical diagnosis and treatment of gastric cancer. Methods A public microarray dataset of TCGA providing clinical information was obtained. The selection operator regression method was used to reduce the dimensionality of stable prognostic genes identified via the bootstrap method and survival analysis. Conclusion We established two prognostic models, respectively designated as stable gene risk score of OS(SGRS-OS) and stable gene risk score of PFI(SGRS-PFI) consisting of 18 and 21 genes. With specific risk score formulae, the SGRS set possesses a strong ability to predict overall survival and progression-free interval through both univariate and multivariate analyses. Compared with the TNM stage, the SGRS set showed much higher predictive accuracy. Further analysis revealed that patients with higher SGRS exhibited worse chemotherapy outcomes. Our SGRS set may be an effective tool to predict survival and guide treatment in patients with gastric cancer.