RESUMO
Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China's seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R2: 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.
RESUMO
Fatty liver disease, a type of metabolic disorder, frequently occurs in dairy cows during the parturition period, causing a high culling rate and, therefore, considerable economic losses in the dairy industry owing to the lack of effective diagnostic methods. Here, metabolite biomarkers were identified and validated for the diagnosis of metabolic disorders. A total of 58 participant cows, including severe fatty liver disease and normal control groups, in the discovery set (liver biopsy tested, n = 18), test set (suspected, n = 20) and verification set (liver biopsy tested, n = 20), were strictly recruited and a sample collected for their feces, urine, and serum. Non-targeted GC-MS-based metabolomics methods were used to characterize the metabolite profiles and to screen in the discovery set. Eventually, ten novel biomarkers involved in bile acid, amino acid, and fatty acid were identified and validated in the test set. Each of them had a higher diagnostic ability than the traditional serum biochemical indicators, with an average area under the receiver operating characteristic curve of 0.830 ± 0.0439 (n = 10) versus 0.377 ± 0.182 (n = 9). Especially, combined biomarker panels via different metabolic pipelines had much better diagnostic sensitivity and specificity than every single biomarker, suggesting their powerful utilization potentiality for the early detection of fatty liver disease. Intriguingly, the serum biomarkers were confirmed perfectly in the verification set. Moreover, common biological pathways were found to be underlying the pathogenesis of fatty liver syndrome in cattle via different metabolic pipelines. These newly-discovered and non-invasive metabolic biomarkers are meaningful in reducing the high culling rate of cows and, therefore, benefit the sustainable development of the dairy industry.
