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Zhongjing Wang,1,* Shu Fang,1,2,* Sheng Ding,1 Qin Tan,1 Xuyan Zhang1 1Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China; 2School of Medicine, Jianghan University, Wuhan, 430056, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuyan Zhang, Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No. 26 of Shengli Street, Jiang’an District, Wuhan, 430014, People’s Republic of China, Tel +86 027 6569 6337, Fax +86 027 8276 1417, Email wish66zh@126.comAbstract: Iron is one of the most important trace elements in life activities. It participates in a variety of important physiological processes in the body through oxidation-reduction reaction. A large number of studies show that iron overload (IO) is closely related to the progression of diabetes and its various chronic complications. However, the mechanism of iron overload in the pathogenesis of diabetes and the mechanism of iron overload in atherosclerosis (AS) are still controversial, and the relationship between iron overload and diabetic lower extremity arterial disease (LEAD) remains still unclear. Some recent reviews and original research articles suggest further studies to explain the complex relationship between iron metabolism and atherosclerosis. This article reviews the relationship between iron overload and diabetes and its relationship with LEAD, and discusses its mechanisms from various aspects, such as lipid peroxidation induced by iron overload, so as to provide clinical diagnosis and treatment ideas for diabetic lower extremity arterial disease. It is hoped that early evaluation, diagnosis and treatment of LEAD will be inspired.Keywords: iron overload, lower extremity arterial disease, atherosclerosis, serum ferritin, type 2 diabetes mellitus

Runoff forecast with high precision is important for the efficient utilization of water resources and regional sustainable development, especially in the arid area. The monthly runoff of Changmabao (CMB) station has an upwards trend and an abrupt point in 1998. The impact factor analysis shows that it is highly correlated with the current precipitation and temperature in the wet season while the previous runoff and previous global land temperature in the dry season. Three models including the time-series decomposition model, the model based on teleconnection coupled with the support vector machine, and the model based on teleconnection coupled with the artificial neural network are used to predict the runoff of CMB station. An indicator β is constructed with the correlation coefficient (R) and mean relative deviation (rBias) to evaluate the model performance more conveniently and intuitively. The results suggest that the model based on teleconnection coupled with the support vector machine preforms best. This forecasting method could be applied to the management and dispatch of water resources in arid areas.

Estimating reference evapotranspiration (ET0) is a fundamental requirement of agricultural water management. The FAO Penman–Monteith (FAO-PM) equation has been used as the standard for ET0 estimation. However, the lack of necessary meteorological data makes it difficult to estimate spatially distributed ET0 using the FAO-PM method in the wider ungauged areas. In this study, the aim is to explore the methodology for estimating reference evapotranspiration based on remote sensing data. In this method, remote sensing data are combined with machine learning algorithms to establish a model for spatially distributed ET0 estimation. Three machine learning algorithms were tested, including support vector machine (SVM), back-propagation neural network (BP), and adaptive neuro fuzzy inference system (ANFIS). Results showed this method had good ability in estimating ET0. Application of the method in Northwest China indicated that the land surface temperature (LST) can be used to accurately estimate ET0 with high correlation coefficients (r2 of 0.897–0.915). The surface reflectance has potential for estimating ET0 with LST and can slightly improve model accuracy based on LST. Evaluation showed LST was more essential than surface reflectance and the model only based on LST had satisfactory performance. This method could be applicability in worldwide with available remote sensing and meteorological data due to the relationship between LST and ET0.