The accurate estimation of reference evapotranspiration (ET0) is crucial for water resource management and crop water requirements. This study aims to develop an efficient and accurate model to estimate the monthly ET0 in the Jialing River Basin, China. For this purpose, a relevance vector machine, complex extreme learning machine (C-ELM), extremely randomized trees, and four empirical equations were developed. Monthly climatic data including mean air temperature, solar radiation, relative humidity, and wind speed from 1964 to 2014 were used as inputs for modeling. A total comparison was made between all constructed models using four statistical indicators, i.e., the coefficient of determination (R2), Nash efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The outcome of this study revealed that the Hargreaves equation (R2 = 0.982, NSE = 0.957, RMSE = 7.047 mm month−1, MAE = 5.946 mm month−1) had better performance than the other empirical equations. All machine learning models generally outperformed the studied empirical equations. The C-ELM model (R2 = 0.995, NSE = 0.995, RMSE = 2.517 mm month−1, MAE = 1.966 mm month−1) had the most accurate estimates among all generated models and can be recommended for monthly ET0 estimation in the Jialing River Basin, China.
Circulating exosomal miRNAs released into all body fluids have incredible functionality and stability. Their expression is associated with multiple pathological conditions, hence can be used as informative biomarkers when assessing and monitoring the body’s physiopathological status. However, there is no consensus on reference miRNAs for circulating exosomal reference and abundance normalization. The present study aimed to quantify 16 potential reference miRNAs in ten porcine body fluids using qRT-PCR. Further, their stability was quantified by combining multiple gold-standard statistical tools, including BestKeeper, GeNorm, and NormFinder. The identified miRNAs were comprehensively ranked. The top-ranked miRNA was recommended as the optimal reference miRNAs for data normalization. To identify more stable genes, the body fluids were assigned into three groups based on the collection point, they are in vivo (bile, bladder fluid, and gastric juice), in vitro (colostrum, ordinary milk, semen, and urine) and in the blood (UVBP, UABP and PBS). The most stable optimal circulating exosomal reference miRNAs in the body fluids were let-7b-5p (miR-93) in bile, miR-92a in bladder fluid, miR-93 in gastric juice, let-7b-5p in colostrum, miR-92a in ordinary milk and urine, miR-25 in semen, let-7b-5p in UVBP, miR-25 in UABP and U6 in PBS. Overall, miR-93, miR-451 (miR-92a), and miR-25 are the bona fide reference miRNA for qRT-PCR data normalization of body fluids in vivo, in vitro, and the blood, respectively. Across all body fluids, miR-451 was the most stable when determining the miRNA abundance in the circulating exosomes.
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