To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.
Objective. Coronary slow/no reflow is not rare after successfully undergoing primary percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI), and shock index (SI) is an important factor for adverse cardiovascular prognosis. In this study, we are to explore whether SI is associated with coronary slow/no reflow in patients with AMI following primary PCI. Methods. A total of 153 consecutive AMI patients undergoing primary PCI within 24 hours of symptom onset were included in this study. The participants were divided into normal flow group (n=124) and slow/no reflow group (n=29) according to cineangiograms recorded during the period of PCI. Cardiovascular risk factors, hematologic parameters, preoperative management of antithrombotic therapy, and baseline angiography were collected. Results. SI, plasma glucose, white blood cells (WBC) and neutrophil count, neutrophil to lymphocyte ratio (PLR), high sensitivity C-reactive protein (hs-CRP), probrain natriuretic peptide (pro-BNP), and Killip classification on admission and thrombus burden on initial angiography were significantly different between patients with and without slow/no reflow. Multivariate analysis revealed that SI≥0.66, thrombus burden, and plasma glucose on admission were independent predictors for coronary slow/no reflow. Preoperative management of tirofiban therapy improves initial thrombolysis in myocardial infarction (TIMI). However, it has no effect on prognosis of slow/no reflow. Conclusion. Our findings demonstrated that slow/no reflow in patients with AMI following primary PCI was more likely associated with SI≥0.66, thrombus burden, and plasma glucose on admission. SI as a predictor for coronary slow/no reflow should be further confirmed in the following more large-scale and prospective studies. The clinical registration number is ChiCTR1900024447.
The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.
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