Search

The Leaf Area Index (LAI) is a key parameter in crop yield estimation and growth monitoring. LAI is also an important canopy structural characteristic of crops that controls their biophysical processes and respiration. Researchers have mostly used remote sensing images and LiDAR to estimate the LAI; however, these two estimation methods do not consider estimation accuracy or efficiency. This study seeks to remedy the shortcomings of these methods using UAV photogrammetry with tobacco in the mature stage as an example. UAV photogrammetry is convenient and efficient in generating images that provide a good spatial description. In this study the collected UAV images were matched with feature points to generate a dense point cloud, which was then used to construct three-dimensional tobacco points. The cloud phenotype model uses the Lambert spherical coordinate system to convert the three-dimensional coordinates into spherical coordinates, and extracts the target plants to calculate the porosity, effective leaf area index, and clustering index to obtain the real leaf area index. The results calculated by the hemispheric photography method were used as reference values to examine the accuracy of the leaf area index calculation at different spatial resolutions at the individual plant and plot scales. The results show that: (1) The LAI estimated from the three-dimensional point cloud data was generally higher than the calculation results of the hemispheric image, but the overall calculation accuracy was higher. The calculation results of the four spatial resolution models were compared with those of the hemispheric images, yielding coefficients of determination R² of 0.959, 0.931, 0.967, and 0.985; the relative errors RE were 11.87%, 19.74%, 14.96%, and 11.79%; the root mean square errors (RMSE) were 0.150, 0.195, 0.136, and 0.094; and the rRMSE values were 20.81%, 26.97%, 18.87%, and 13.10%. (2) Of the four spatial resolutions, the three-dimensional point cloud model with the highest calculation accuracy (87.29%) had 2.15 cm spatial resolution. (3) The results of the leaf area calculation at the plot scale showed that the calculation results of the four three-dimensional point cloud models were all within 0.05 of the hemispheric image calculation results, and the 2.15 cm spatial resolution model showed the best calculation accuracy, 94.24%. (4) Therefore, it is feasible, accurate, and efficient to obtain orthophotos through UAV photogrammetry, match feature points, and build a three-dimensional point cloud phenotype model to calculate the leaf area index of field plants. This method can provide important technical support and methodological references for the management and accurate yield estimation of other field crops and can also provide a technical means and scientific basis for precision agricultural planting and high-quality agricultural development.

Objective To investigate the mechanism and the effect of miR-524-5p regulating HEG1 expression on the proliferation and epithelial-mesenchymal transition of esophageal cancer cells. Methods The expression levels of miR-524-5p and HEG1 mRNA in esophageal cancer cells and normal esophageal epithelial cells were detected by qRT-PCR. KYSE30 cells were divided into miR-524-5p mimic group, miR-524-5p NC group, miR-524-5p mimic+pcDNA3.1 group, and miR-524-5p mimic+pcDNA3.1-HEG1 group. Non-transfected cells were set as the normal control group (group Control). CCK-8 method was applied to detect the proliferation ability of KYSE30 cells. Western blot analysis was conducted to detect the expression of proteins related to EMT, invasion, and migration and the HEG1 protein. Scratch and Transwell assays were applied to detect the migration and invasion abilities of KYSE30 cells. A dual-luciferase reporter gene was used to examine the targeting relationship between miR-524-5p and HEG1. Results miR-524-5p was lowly expressed in four esophageal cancer cell lines, namely, TE-1, KYSE30, KYSE150, and NEC (P < 0.05). KYSE30 cells with the lowest expression level were selected for subsequent experiments. HEG1 mRNA was highly expressed in four esophageal cancer cell lines (P < 0.05). The GEPIA database showed that HEG1 was highly expressed in esophageal cancer tumor tissues (P < 0.05). KYSE30 cells in the miR-524-5p mimic group had lower proliferation ability, colony formation number, mesenchymal marker protein expression, and migration and invasion abilities and upregulated epithelial marker protein E-cadherin level than cells in the miR-524-5p NC group (P < 0.05). The miR-524-5p mimic+pcDNA3.1-HEG1 group significantly reversed the inhibitory effect of overexpression of miR-524-5p on the proliferation, epithelial–mesenchymal transformation, invasion, and metastasis of KYSE30 cells (P < 0.05). The luciferase activity of cells in the miR-524-5p mimic and WT-HEG1 co-transfection groups was lower than that in the miR-524-5p NC and WT-HEG1 co-transfection groups (P < 0.05). Conclusion miR-524-5p is lowly expressed in EC cells and tissues. The overexpression of miR-524-5p can negatively regulate the expression of HEG1 in esophageal cancer cell line (KYSE30 cells) and reduce the proliferation, EMT process, and invasion and migration abilities of KYSE30 cells.

Non-thermal processing technology has been widely used in starch modification because of its advantages such as environmental friendliness, safety and high efficiency. After non-thermal modification, changes in the granular morphology, crystal shape, crystallinity and helical structure of starch will affect its gelatinization and retrogradation characteristics, solubility and digestibility. Changes in functional characteristics can further improve the practical application of starch. In this paper, the principles of six non-thermal processing technologies for starch modification, namely, ball milling, pulse electric field, high pressure, ultrasound, radiation and plasma are reviewed, the importance of non-thermal technology for starch granule structure as well as the effect of starch structural changes at different levels on its functional properties are discussed, and future prospects for the application of non-thermal technology in starch deep processing are proposed.