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maius and M. variabilis. Fungal inoculation was especially effective in reducing NaCl effects on Pn in lingonberry. Oidiodendron maius and M. variabilis were also equally effective in reversing NaCl-induced declines of E in velvetleaf blueberry and lingonberry. However, in Labrador tea, O. maius reversed the decline of E in NaCl-treated plants less compared with M. variabilis resulting in high photosynthetic water use efficiency values. The results support the hypothesis that, similarly to arbuscular mycorrhizal and ectomycorrhizal associations, ERM association increases salt tolerance of plants.INTRODUCTION Gastric cancer (GC) is the fifth most common cancer worldwide, and every year approximately 950,000 individuals are diagnosed worldwide. Our study aimed to establish an effective nomogram to predict the prognosis of GC based on inflammation biomarkers. METHODS We retrospectively analysed GC patients from the Sun Yat-sen University Cancer Center. Bezafibrate mouse The nomogram was developed with a primary cohort (n = 1067), and 537 patients were included in the validation cohort. The univariate survival analyses included 19 biomarkers. RESULTS The multivariate analysis showed that tumour stage, metastasis stage and C-reactive protein (CRP), albumin (ALB), carcinoembryonic antigen (CEA) and carbohydrate antigen-199 (CA199) levels as well as the lymphocyte (LYM) count were independent risk factors for the prognosis of GC patients. The nomogram was based on the above factors. In the primary cohort, the nomogram had a concordance index (C-index) of 0.825 (95% CI 0.796-0.854), which was higher than the C-index of the AJCC TNM stage and that of the other biomarkers (CEA and CA199). The calibration plot suggested good agreement between the actual and nomogram-predicted overall survival (OS) probabilities, and the decision curve analyses showed that the nomogram model had a higher overall net benefit in predicting OS than the AJCC TNM stage. Moreover, we divided the patients into the following three distinct risk groups for OS based on the nomogram points low, middle and high risk. The differences in OS rates were significant among the subgroups (P  less then  0.001). CONCLUSION A novel nomogram integrated with inflammatory prognostic factors was proposed, which is highly predictive of OS in GC patients.The mechanism of the non-agricultural transfer of rural labor to agricultural production efficiency and their interrelationships is a problem worthy of further discussion at this stage in China. The mediating effect model is constructed, the least square method is used for regression, and the instrumental variable method is used to solve the possible endogeneity problem. Through the investigation of farmers in Loess Plateau region, this paper analyzes the effects of factor substitution and planting structure adjustment after agricultural labors work outside and its impact on agricultural land output from the theoretical and empirical aspects, as well as the impact of off-farm employment on agricultural land output under different constraints. Results showed that the negative influence of labor non-agricultural transfer on farmland land output rate is - 7.264, and farmers' participation in returning farmland to forests can alleviate the negative impact of non-agricultural transfer on the agricultural land output. Labor substitution factor investment plays a part in the mediating effect, and part of the mediating effect is - 0.879. The adjustment of agricultural planting structure plays the whole mediating effect, that is to say, the larger the scale of labor non-agricultural transfer in Loess Plateau area, the more unfavorable for farmers to invest in labor substitution agricultural factors of production, and the more they are inclined to grow food crops. When the constraint conditions of factor substitution difficulty and planting structure adjustment space are considered, the higher the factor substitution difficulty is, the smaller the planting structure adjustment space; the negative effect of the non-agricultural transfer of labor on the agricultural land output rate is more obvious. It provides effective reference value to judge the development stage and trend of regional agriculture and puts forward relevant policy suggestions to guarantee the development of regional agriculture and farmers' life.Removal of arsenic from water is of utmost priorities on a global scenario due to its ill effects. Therefore, in the present study, aluminium oxide nano-particles (nano-alumina) were synthesised via solution combustion method, which is self-propagating and eco-friendly in nature. Synthesised nano-alumina was further employed for arsenate removal from water. Usually, pre-oxidation of arsenite is performed for better removal of arsenic in its pentavalent form. Thus, arsenate removal as a function of influencing parameters such as initial concentration, dose, pH, temperature, and competing anions was the prime objective of the present study. The speciation analysis showed that H2AsO4- and HAsO42- were co-existing anions between pH 6 and 8, as a result of which higher removal was observed. Freundlich isotherm model was well suited for data on adsorption. At optimal temperature of 298 K, maximum monolayer adsorption capacity was found as 1401.90 μg/g. The kinetic data showed film diffusion step was the controlling mechanism. In addition, competing anions like nitrate, bicarbonate, and chloride had no major effect on arsenate removal efficiency, while phosphate and sulphate significantly reduced the removal efficiency. The negative values of thermodynamic parameters ΔH° (- 23.15 kJ/mol) established the exothermic nature of adsorption, whereas the negative values of ΔG° (- 7.05, - 6.51, - 5.97, and - 5.43 kJ/mol at 298, 308, 318, and 328 K respectively) indicated the spontaneous nature of the process. The best-fitted isotherm was used to design a batch adsorber to estimate the required amount of aluminium oxide nano-particles for achieving the desired equilibrium arsenate concentration. Nano-alumina was also applied to treat the collected arsenic-contaminated groundwater from actual field. Experimental data were used to develop a neural network-based model for the effective prediction of removal efficiency without carrying out any extra experimentation.