This epidemiological survey was a retrospective study on three nodes during the past three decades on fungal infections representing the China, including Taiwan. Owing to rare publications reporting on dynamic epidemiological trends in the pathogen epidemiology in China, we surveyed the isolation rates and pathogenic fungi from 8 representative districts in China using uniform identification with uniform methodology. The pathogenic fungi isolation rates and species obtained from 1986 (n=9,096), 1996 (n=19,009), and 2006 (n=33,022) suggested that Trichophyton rubrum was the commonest organism cultured in 1980s (45.4%) and 1990s (34.5%), but Candida albicans increased significantly and reaching to its peak (26.9%) in 2006s' survey, and has become the most common isolate of fungal infections in China currently. In addition, Candida glabrata became the most common non-albicans species of Candida in 2006s' survey. At the same time, the incidence of molds also gradually increased. According to comparative analysis of the results of these three surveys, we found apparent differences in the isolation rates of different pathogenic fungi and the forefront 10 species in China varied significantly, and the dermatophytes decreased markedly, while yeasts, especially the Candida species and the molds, increased gradually during the past three decades. Less dermatophytic infections may suggest better access to healthcare or increase in Candida species indicated higher incidence of hospital acquired infections.

Temperature is one of the most important parameters in climate modeling, as it has significant impacts on various geophysical processes such as evaporation and precipitation. Applying multiple climate models for prediction generally outperforms the use of individual climate models, and neural networks perform well at capturing nonlinear relationships, which can provide more reliable temperature projections. In this study, three neural network algorithms, including Multi-layer Perceptron (MLP), Time-lagged Feed-forward Neural Networks (TLFN) and Nonlinear Auto-Regressive Networks with exogenous inputs (NARX), were used to develop data-driven models for predicting daily mean near-surface temperature based on North American Coordinated Regional Downscaling Experiment (NA-CORDEX) output. A case study of Big Trout Lake in Ontario, Canada was carried out to demonstrate the applications and to evaluate the performance of the proposed neural network based models. The results showed that MLP, TLFN, and NARX performed well in generating accurate daily near-surface temperature predictions with the coefficient of determination (R 2) values above 0.84. The three neural network based models had similar performance with no significant difference in terms of root mean square error and R 2. Neural network based climate prediction models outperformed each of the individual regional climate models and generated smoother predicttions with less fluctuation. This study provides a technical basis for generating reliable predictions of daily temperature using neural networks based model.