Discernment of grape rootstocks base on their response to salt stress using selected characteristics in combination with chemometric tools

Rational design of ROS scavenging and fluorescent gold nanoparticles to deliver siRNA to improve plant resistance to Pseudomonas syringae

Bacterial diseases are one of the most common issues that result in crop loss worldwide, and the increasing usage of chemical pesticides has caused the occurrence of resistance in pathogenic bacteria and environmental pollution problems. Nanomaterial mediated gene silencing is starting to display powerful efficiency and environmental friendliness for improving plant disease resistance. However, the internalization of nanomaterials and the physiological mechanisms behind nano-improved plant disease resistance are still rarely understood. We engineered the polyethyleneimine (PEI) functionalized gold nanoparticles (PEI-AuNPs) with fluorescent properties and ROS scavenging activity to act as siRNA delivery platforms. Besides the loading, protection, and delivery of nucleic acid molecules in plant mature leaf cells by PEI-AuNPs, its fluorescent property further enables the traceability of the distribution of the loaded nucleic acid molecules in cells. Additionally, the PEI-AuNPs-based RNAi delivery system successfully mediated the silencing of defense-regulated gene AtWRKY1. Compared to control plants, the silenced plants performed better resistance to Pseudomonas syringae, showing a reduced bacterial number, decreased ROS content, increased antioxidant enzyme activities, and improved chlorophyll fluorescence performance. Our results showed the advantages of AuNP-based RNAi technology in improving plant disease resistance, as well as the potential of plant nanobiotechnology to protect agricultural production.

Similar and divergent responses to salinity stress of jamun (Syzygium cumini L. Skeels) genotypes

Background

Genetic variation for salt tolerance remains elusive in jamun (Syzygium cumini).

Methods

Effects of gradually increased salinity (2.0-12.0 dS/m) were examined in 20 monoembryonic and 28 polyembryonic genotypes of jamun. Six genotypes were additionally assessed for understanding salt-induced changes in gas exchange attributes and antioxidant enzymes.

Results

Salt-induced reductions in leaf, stem, root and plant dry mass (PDM) were relatively greater in mono- than in poly-embryonic types. Reductions in PDM relative to control implied more adverse impacts of salinity on genotypes CSJ-28, CSJ-31, CSJ-43 and CSJ-47 (mono) and CSJ-1, CSJ-24, CSJ-26 and CSJ-27 (poly). Comparably, some mono- (CSJ-5, CSJ-18) and poly-embryonic (CSJ-7, CSJ-8, CSJ-14, CSJ-19) genotypes exhibited least reductions in PDM following salt treatment. Most polyembryonic genotypes showed lower reductions in root than in shoot mass, indicating that they may be more adept at absorbing water and nutrients when exposed to salt. The majority of genotypes did not exhibit leaf tip burn and marginal scorch despite significant increases in Na+ and Cl-, suggesting that tissue tolerance existed for storing excess Na+ and Cl- in vacuoles. Jamun genotypes were likely more efficient in Cl- exclusion because leaf, stem and root Cl- levels were consistently lower than those of Na+ under salt treatment. Leaf K+ was particularly little affected in genotypes with high leaf Na+. Lack of discernible differences in leaf, stem and root Ca2+ and Mg2+ contents between control and salt treatments was likely due to their preferential uptake. Correlation analysis suggested that Na+ probably had a greater inhibitory effect on biomass in both mono- and poly-embryonic types. Discriminant analysis revealed that while stem and root Cl- probably accounted for shared responses, root Na+, leaf K+ and leaf Cl- explained divergent responses to salt stress of mono- and poly-embryonic types. Genotypes CSJ-18 and CSJ-19 seemed efficient in fending off oxidative damage caused by salt because of their stronger antioxidant defences.

Conclusions

Polyembryonic genotypes CSJ-7, CSJ-8, CSJ-14 and CSJ-19, which showed least reductions in biomass even after prolonged exposure to salinity stress, may be used as salt-tolerant rootstocks. The biochemical and molecular underpinnings of tissue tolerance to excess Na+ and Cl- as well as preferential uptake of K+, Ca2+, and Mg2+ need to be elucidated.

gc-ims-tools - A new Python package for chemometric analysis of GC-IMS data

Due to its high sensitivity and resolving power, gas chromatography ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for non-target screening of complex sample materials. Given the wide range of applications, such as food authenticity, custom data analysis workflows are needed. As a common basis, they necessarily share many functionalities such as file input/output, preprocessing methods, exploratory or supervised analysis and visualizations. This study introduces a new open source, fully customizable Python package for handling and analysis of GC-IMS data. A workflow to classify olive oils by geographical origin exemplarily demonstrates functionality and ease of use. Key preprocessing steps, exploratory - and supervised data analysis and feature selections are visualized. All code and detailed documentation are freely available as open source under the BSD 3-clause license at https://github.com/Charisma-Mannheim/gc-ims-tools.

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.