The Use of Fullerenol as a Seed Treatment to Maintain the Intensity of Physiological and Biochemical Processes of Wheat in Control and Low-Temperature Conditions
It is of utmost importance to investigate the effect of carbon-based nanostructures on plants in order to fully unlock their potential for enhancing productivity and stress tolerance. In this study, a comprehensive assessment of the effect of pre-sowing treatment of wheat seeds with fullerenol (PHF [C60(OH)24], 0.1 mg/L) solution on a number of physiological and biochemical parameters in seedlings under optimal and low temperature (LT, 4 °C, 5 days) conditions was carried out. Seedlings primed with PHF differed from control variant in accelerated growth (by 27–34%), increased biomass (by 10%), larger area of chloroplasts and mesophyll cells (by 40%), more intensive photosynthesis (1.5-fold higher), increased content of chlorophyll a (by 11%), proteins (by 28%) and ascorbic acid, reduced level of MDA and H2O2, decreased SOD activity (by 45%) and a higher level of COR-genes (WCOR15, WCOR726) transcription (p < 0.05). Under LT conditions, PHF nanopriming enhanced photosynthesis intensity by increasing chloroplast area, content of chlorophyll b (by 22%) and proportion of chlorophyll in LHC (by 11%), as well as increasing the expression of RBCS (fourfold higher), content of proline, and COR-gene expression (p < 0.05). All these changes are adaptive and expand the adaptive potential of plants. It is concluded that nanopriming with PHF is an active metabolic modulator that reduces ROS generation and enhances cold acclimation of wheat.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.