Based on Glycine gracilis growth and ion homeostasis testing, neutral salt (NS)and alkaline salt (AS) stress were characterized and the responses of G. gracilis were investigated.The injurious effects of AS on G. gracilis were obviously stronger than those of NS.The effects of both stresses on the Na+ content and Na+/K+ ratio were similar at low concentrations, but as the stress increased, the effects of a greater Na+ content and Na+/K+ratio increased slowly under NS conditions, but sharply under AS. The roots of G. gracilis accumulated NO3−and H2PO4−, while the stems and leaves accumulated C2O4(2−) and H2PO4−to maintain thein tracellular ion balance. The dominant intracellular anions in the stipes were NO3- and C2O4(2-) under control conditions, and NO3- and H2PO4- under salt stress. With the increasing AS, the Cl−, NO3− and H2PO4− concentrations decreased, and G.gracilis might have increased SO4(2−) and C2O4(2−)levels to compensate for the shortage of inorganic anions. Under NS, the NO3− and C2O4(2−) concentrations decreased, and G. gracilis might have increased Cl−, H2PO4− and SO4(2−)levels to compensate for the shortage of inorganic anions. G. gracilis seedling showed a special nutritional metabolism and some growth adaptability under salt stress.

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.