RESUMO
Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky-Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS-SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R 2 = 0.741-0.852), while the SVR and PLSR models performed similarly (R 2 = 0.733-0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R 2 was 0.849-0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R 2 increased by 14.39%, and the R 2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.
RESUMO
OBJECTIVE: To evaluate the effect of JAK/STAT signaling pathway activation on the transdifferentiation and secretion of transforming growth factor-beta1 (TGF-beta1) induced by high glucose in renal proximal tubular epithelial cells. METHODS: Human kidney cells (HKC) were cultured and then divided into four groups: low glucose (LG) group, high glucose (HG) group, high mannitol (LG + M) group, and HG + AG490 group. Immunoprecipitation and Western blot analysis were used to determine the expression of tryosine phosphorylated Janus kinase 2 ( p-JAK2). The protein expressions of STAT1, STAT3, p-STAT1, and p-STAT3 and the expressions of alpha-SMA and E-Cadherin were observed by Western blot. The contents of TGF-B1, fibronectin and type I collagen in the supernatants of the cultured HKC were detected by enzyme-linked immunosorbent assay (ELISA). The expression of TGF-beta1 mRNA was measured by reverse transcription and polymerase chain reaction (RT-PCR). RESULTS: Compared with LG group, the expressions of JAK2, p-STAT1, p-STAT3, and TGF-beta1, mRNA were significantly increased in HG group from 6 to 72 hours. Meanwhile, the contents of TGF-beta1 and collagen I in the supernatants and the expression of alpha-SMA increased and the expression of E-Cadherin decreased. The expressions of JAK2, p-STAT1, p-STAT3, and TGF-beta mRNA as well as the levels of TGF-beta1 and collagen I in the supernatant s in HG + AG490 group were significantly lower than in the HG group. The expressions of alpha-SMA and E-Cadherin were also decreased in HG + AG490 group. CONCLUSION: Activation of JAK/STAT signaling pathway may be involved in the high glucose-induced transdifferentiation and overproduction of TGF-beta1, and ECM proteins in HKCs.
