When growing in search for light, plants can experience continuous or occasional shading by other plants. Plant proximity causes a decrease in the ratio of R to far-red light (low R:FR) due to the preferential absorbance of R light and reflection of FR light by photosynthetic tissues of neighboring plants. This signal is often perceived before actual shading causes a reduction in photosynthetically active radiation (low PAR). Here, we investigated how several Brassicaceae species from different habitats respond to low R:FR and low PAR in terms of elongation, photosynthesis, and photoacclimation. Shade-tolerant plants such as hairy bittercress (Cardamine hirsuta) displayed a good adaptation to low PAR but a poor or null response to low R:FR exposure. In contrast, shade-avoider species, such as Arabidopsis (Arabidopsis thaliana), showed a weak photosynthetic performance under low PAR but they strongly elongated when exposed to low R:FR. These responses could be genetically uncoupled. Most interestingly, exposure to low R:FR of shade-avoider (but not shade-tolerant) plants improved their photoacclimation to low PAR by triggering changes in photosynthesis-related gene expression, pigment accumulation, and chloroplast ultrastructure. These results indicate that low R:FR signaling unleashes molecular, metabolic, and developmental responses that allow shade-avoider plants (including most crops) to adjust their photosynthetic capacity in anticipation of eventual shading by nearby plants.
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http://dx.doi.org/10.1093/plphys/kiab206 | DOI Listing |
Environ Geochem Health
December 2024
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China.
Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau.
View Article and Find Full Text PDFFront Plant Sci
November 2024
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is a crucial parameter for evaluating crop growth and informing fertilization management in agricultural fields. Compared to traditional methods, UAV-based hyperspectral imaging technology offers significant advantages for non-destructive, rapid monitoring of crop LAI by simultaneously capturing both spectral information and two-dimensional images of the crop canopy, which reflect changes in its structure. While numerous studies have demonstrated that various texture features, such as the Gray-Level Co-occurrence Matrix (GLCM), can be used independently or in combination with crop canopy spectral data for LAI estimation, limited research exists on the application of Haralick textures for evaluating crop LAI across multiple growth stages.
View Article and Find Full Text PDFPlants (Basel)
November 2024
Institute for Plant Molecular and Cell Biology (IBMCP), CSIC-Universitat Politècnica de València, 46022 Valencia, Spain.
Plants of several species, including crops, change their volatilome when exposed to a low ratio of red to far-red light (low R/FR) that informs about the presence of nearby plants (i.e., proximity shade).
View Article and Find Full Text PDFBull Math Biol
November 2024
Theoretical Bioinformatics, ICube, C.N.R.S., University of Strasbourg, 300 Boulevard Sébastien Brant, 67400, Illkirch, France.
Sci Rep
November 2024
Department of Computer Sciences, Faculty of Mathematics, Statistics and Computer Science, Semnan University, P.O. Box: 35195-363, Semnan, Iran.
Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps.
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