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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2020

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Quorum quenching (QQ) blocks bacterial cell-to-cell communication (i.e., quorum sensing), and is a promising antipathogenic strategy to control bacterial infection via inhibition of virulence factor expression and biofilm formation. QQ enzyme AiiO-AIO6 from Ochrobactrum sp. M231 has several excellent properties and shows biotherapeutic potential against important bacterial pathogens of aquatic species. AiiO-AIO6 can be secretory expressed in Bacillus subtilis via a non-classical secretion pathway. To improve AiiO-AIO6 production, four intracellular protease-deletion mutants of B. subtilis 1A751 were constructed by individually knocking out the intracellular protease-encoding genes (tepA, ymfH, yrrN and ywpE). The AiiO-AIO6 expression plasmid pWB-AIO6BS was transformed into the B. subtilis 1A751 and its four intracellular protease-deletion derivatives. Results showed that all recombinant intracellular protease-deletion derivatives (BSΔtepA, BSΔymfH, BSΔyrrN and BSΔywpE) had a positive impact on AiiO-AIO6 production. The highest amount of AiiO-AIO6 extracellular production of BSΔywpE in shake flask reached 1416.47 U/mL/OD600, which was about 121% higher than that of the wild-type strain. Furthermore, LC–MS/MS analysis of the degrading products of 3-oxo-C8-HSL by purification of AiiO-AIO6 indicated that AiiO-AIO6 was an AHL-lactonase which hydrolyzes the lactone ring of AHLs. Phylogenetic analysis showed that AiiO-AIO6 was classified as a member of the α/β hydrolase family with a conserved “nucleophile-acid-histidine” catalytic triad. In summary, this study showed that intracellular proteases were responsible for the reduced yields of heterologous proteins and provided an efficient strategy to enhance the extracellular production of AHL lactonase AiiO-AIO6.

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Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

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The current study provides an in vivo analysis of the production of reactive oxygen species (ROS) and oxidative stress in the nematode Caenorhabditis elegans following exposure to EU reference silver nanoparticles NM300K and AgNO3. Induction of antioxidant defenses was measured through the application of a SOD-1 reporter, and the HyPer and GRX biosensor strains to monitor changes in the cellular redox state. Both forms of Ag resulted in an increase in sod-1 expression, elevated H2O2 levels and an imbalance in the cellular GSSG/GSH redox status. Microscopy analysis of the strains revealed that AgNO3 induced ROS-related effects in multiple tissues, including the pharynx, intestinal cells and muscle tissues. In contrast, NM300K resulted in localized ROS production and oxidative stress, specifically in tissues surrounding the intestinal lumen. This indicates that Ag from AgNO3 exposure was readily transported across the whole body, while Ag or ROS from NM300K exposure was predominantly confined within the luminal tissues. Concentrations resulting in an increase in ROS production and changes in GSSG/GSH ratio were in line with the levels associated with observed physiological toxic effects. However, sod-1 was not induced at the lowest Ag concentrations, although reprotoxicity was seen at these levels. While both forms of Ag caused oxidative stress, impaired development, and reprotoxicity, the results suggest different involvement of ROS production to the toxic effects of AgNO3 versus NM300K.