Estimation of biomass in wheat using random forest regression algorithm and remote sensing data

Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China

Effects of Soil Water Deficit on Insecticidal Protein Expression in Boll Shells of Transgenic Bt Cotton and the Mechanism

This study was conducted to investigate the effects of soil water deficit on insecticidal protein expression in boll shells of cotton transgenic for a Bt gene. In 2014, Bt cotton cultivars Sikang 1 (a conventional cultivar) and Sikang 3 (a hybrid cultivar) were planted in pots and five soil water content treatments were imposed at peak boll stage: 15% (G1), 35% (G2), 40% (G3), 60% (G4), and 75% field capacity (CK), respectively. Four treatments (G2, G3, G4, and CK) were repeated in 2015 in the field. Results showed that the insecticidal protein content of boll shells decreased with increasing water deficit. Compared with CK, boll shell insecticidal protein content decreased significantly when soil water content was below 60% of maximum water holding capacity for Sikang 1 and Sikang 3. However, increased Bt gene expression was observed when boll shell insecticidal protein content was significantly reduced. Activity assays of key enzymes in nitrogen metabolism showed that boll shell protease and peptidase increased but nitrogen reductase and glutamic-pyruvic transaminase (GPT) decreased. Insecticidal protein content exhibited significant positive correlation with nitrogen reductase and GPT activities; and significant negative correlation with protease and peptidase activities. These findings suggest that the decrease of insecticidal protein content associated with increasing water deficit was a net result of decreased synthesis and increased decomposition

An enzyme-based system for extraction of small extracellular vesicles from plants

Abstract Plant-derived nanovesicles (NVs) and extracellular vesicles (EVs) are the next generation of nanocarrier platforms for biotherapeutics and drug delivery. EVs exist not only in the extracellular space, but also within the cell wall. Due to the limitations of existing isolation methods, the EVs extraction efficiency is low, and a large amount of plant material is wasted, which is of concern for rare and expensive medicinal plants. We proposed and validated a novel method for isolation of plant EVs by enzyme degradation of the plant cell wall to release the EVs. The released EVs can easily be collected. The new method was used for extraction of EVs from the roots of Morinda officinalis (MOEVs). For comparison, nanoparticles from the roots (MONVs) were extracted using the grinding method. The new method yielded a greater amount of MOEVs, and the vesicles had a smaller diameter compared to MONVs. Both MOEVs and MONVs were readily absorbed by endothelial cells without cytotoxic effect and promoted the expression of miR-155. The promotion of miR-155 by MOEVs was dose-dependent. More importantly, we found that MOEVs and MONVs were enriched toward bone tissue. These results support our hypothesis that EVs in plants could be efficiently extracted by enzymatic cell wall digestion and confirm the potential of MOEVs as therapeutic agents and drug carriers

Kinetics and mechanism of jack bean urease inhibition by Hg²⁺

<p>Abstract</p> <p>Background</p> <p>Jack bean urease (EC 3.5.1.5) is a metalloenzyme, which catalyzes the hydrolysis of urea to produce ammonia and carbon dioxide. The heavy metal ions are common inhibitors to control the rate of the enzymatic urea hydrolysis, which take the Hg²⁺ as the representative. Hg²⁺ affects the enzyme activity causing loss of the biological function of the enzyme, which threatens the survival of many microorganism and plants. However, inhibitory kinetics of urease by the low concentration Hg²⁺ has not been explored fully. In this study, the inhibitory effect of the low concentration Hg²⁺ on jack bean urease was investigated in order to elucidate the mechanism of Hg²⁺ inhibition.</p> <p>Results</p> <p>According to the kinetic parameters for the enzyme obtained from Lineweaver–Burk plot, it is shown that the <it>K</it>_m is equal to 4.6±0.3 mM and <it>V</it>_m is equal to 29.8±1.7 μmol NH₃/min mg. The results show that the inhibition of jack bean urease by Hg²⁺ at low concentration is a reversible reaction. Equilibrium constants have been determined for Hg²⁺ binding with the enzyme or the enzyme-substrate complexes (<it>K</it>_i =0.012 μM). The results show that the Hg²⁺ is a noncompetitive inhibitor. In addition, the kinetics of enzyme inhibition by the low concentration Hg²⁺ has been studied using the kinetic method of the substrate reaction. The results suggest that the enzyme first reversibly and quickly binds Hg²⁺ and then undergoes a slow reversible course to inactivation. Furthermore, the rate constant of the forward reactions (<it>k</it>₊₀) is much larger than the rate constant of the reverse reactions (<it>k</it>_-0). By combining with the fact that the enzyme activity is almost completely lost at high concentration, the enzyme is completely inactivated when the Hg²⁺ concentration is high enough.</p> <p>Conclusions</p> <p>These results suggest that Hg²⁺ has great impacts on the urease activity and the established inhibition kinetics model is suitable.</p