Abundance considerations for modeling yield of rapeseed at the flowering stage.

Introduction: To stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation. Methods: A field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield. Results: For the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales. Discussion: The results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage.

Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data (R2 = 0.03, RMSE = 603.33 g/m2) and canopy height (R2 = 0.79, RMSE = 283.33 g/m2), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice (R2 = 0.95, RMSE = 136.81 g/m2). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R2 above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.

Sivelestat sodium attenuates acute lung injury by inhibiting JNK/NF-κB and activating Nrf2/HO-1 signaling pathways.

Sivelestat sodium (SIV), a neutrophil elastase inhibitor, is mainly used for the clinical treatment of acute respiratory distress syndrome (ARDS) or acute lung injury (ALI). However, studies investigating the effects of SIV treatment of ALI are limited. Therefore, this study investigated the potential molecular mechanism of the protective effects of SIV against ALI. Human pulmonary microvascular endothelial cells (HPMECs) were stimulated with tumor necrosis factor α (TNF-α), and male Sprague-Dawley rats were intratracheally injected with Klebsiella pneumoniae (KP) and treated with SIV, ML385, and anisomycin (ANI) to mimic the pathogenetic process of ALI in vitro and in vivo, respectively. The levels of inflammatory cytokines and indicators of oxidative stress were assessed in vitro and in vivo. The wet/dry (W/D) ratio of lung tissues, histopathological changes, inflammatory cells levels in bronchoalveolar lavage fluid (BALF), and survival rates of rats were analyzed. The JNK/NF-κB (p65) and Nrf2/HO-1 levels in the HPMECs and lung tissues were analyzed by western blot and immunofluorescence analyses. Administration of SIV reduced the inflammatory factors levels, intracellular reactive oxygen species (ROS) production, and malondialdehyde (MDA) levels and increased the levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in lung tissues. Meanwhile, SIV alleviated pathological injuries, decreased the W/D ratio, and inflammatory cell infiltration in lung tissue. In addition, SIV also inhibited the activation of JNK/NF-κB signaling pathway, promoted nuclear translocation of Nrf2, and upregulated the expression of heme oxygenase 1 (HO-1). However, ANI or ML385 significantly reversed these changes. SIV effectively attenuated the inflammatory response and oxidative stress. Its potential molecular mechanism was related to the JNK/NF-κB activation and Nrf2/HO-1 signaling pathway inhibition. This further deepened the understanding of the protective effects of SIV against ALI.

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery.

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes.

As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.

Transcriptomics combined with metabolomics analysis of the mechanism of agmatine in the treatment of septic liver injury.

Background: Acute liver injury can occur at any stage of sepsis and is an important sign of multiple organ dysfunction syndrome (MODS). Studies have shown that agmatine (AGM) can effectively improve liver injury caused by sepsis. However, due to the numerous metabolites and metabolic pathways of AGM in the human body, its mechanism in treating septic liver injury is unclear. Methods: In this study, a liver injury model of septic Sprague-Dawley rats was established by cecal ligation and perforation (CLP). After AGM treatment, transcriptomics combined with metabolomics was employed to analyze the gene expression levels and metabolite changes. Results: The results showed that AGM decreased the expression levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), procalcitonin (PCT), and inflammatory factors [interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß)] in the serum of septic rats. It also reduced liver inflammatory cell infiltration and abnormal lipid metabolism, and promoted the survival rate of septic rats. In addition, 17 differentially-expressed genes were identified by transcriptomics, mainly in arginine and proline metabolism, the arachidonic acid metabolism pathway, as well as the nuclear factor kappa B (NF-κB) and AMP-activated protein kinase (AMPK)-peroxisome proliferator-activated receptor α (PPARα) signal transduction pathways. Metabolomics analysis was carried out to study the potential liver metabolism spectrum changes induced by AGM treatment. The results showed significant changes in 26 metabolites in the rat liver samples, mainly involved in arginine and proline metabolism, arachidonic acid metabolism, linoleic acid metabolism, and fatty acid metabolism. Conclusions: The integrated transcriptomics and metabolomics analysis demonstrated that AGM improved septic liver injury by regulating lipid metabolism, and reduced the inflammatory reaction by affecting fatty acid metabolism, amino acid metabolism, and the arachidonic acid metabolism pathway. The integration of transcriptomics and metabolomics provides an effective means to elucidate AGM's therapeutic pathways and biomarkers.

The synergistic Reduning and cefmetazole sodium treatment of severe pneumonia is mediated by the AhR-Src-STAT3 pathway.

Background: Reduning (RDN) is a common Chinese medicine preparation with antibacterial, anti-inflammatory, antiviral and immunomodulatory effects in respiratory infectious diseases. Clinically, it is used in combination with antibiotics, but its synergistic effect and mechanism in treating severe pneumonia remain unclear. Methods: A rat model of severe pneumonia and an in vitro coculture model consisting of A549 and THP-1 cells were used to observe the synergistic effect of RDN on severe pneumonia. The inflammatory cytokines were tested by enzyme-linked immunosorbent assay (ELISA). The localization of Aryl hydrocarbon receptor (AhR) in A549 cells was observed by immunofluorescence, and the interaction of AhR and signal transducer and activator of transcription 3 (STAT3) proteins was observed by co-immunoprecipitation. AhR-Src tyrosine kinase (Src)-STAT3 pathway in rats and A549 cells were examined by Western Blot. Histopathological changes were observed by Hematoxylin-eosin (HE) staining, X-ray and survival rates were used to evaluate the effects of paclitaxel on severe pneumonia rats. Results: RDN regulation of Src-STAT3-interleukin 10 (IL-10) signaling pathway activation and macrophage polarization were mediated through the nuclear receptor AhR. The expression of AhR was significantly increased after RDN treatment, and this effect was accompanied by STAT3 expression increasing. Coimmunoprecipitation confirmed an interaction between AhR and STAT3 and upregulated IL-10 expression. Silencing AhR decreased Src, STAT3, and IL-10 expression. RDN activated AhR and increased Src, STAT3, and IL-10 expression. In addition, RDN regulated the polarization of macrophages RDN combined with cefmetazole sodium significantly reduced the pulmonary bacterial load, alleviated lung injury, and reduced o inflammatory factors expression, improving their survival. Conclusions: RDN can synergistically enhance the effect of cefmetazole sodium treatment in severe pneumonia, and the mechanism may involve increasing the expression level of IL-10 mediated through the AhR-Src-STAT3 pathway, driving the polarization of macrophages, and attenuating the cytokine storm to control inflammation in severe pneumonia.

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network.

BACKGROUND: The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. METHODS: In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. RESULTS: The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. CONCLUSION: The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage.

BACKGROUND: The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. METHODS: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. RESULTS: The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. CONCLUSION: This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.