The postembryonic developmental program of the plant root system is plastic and allows changes in root architecture to adapt to environmental conditions such as water and nutrient availability. Among essential nutrients, phosphorus (P) often limits plant productivity because of its low mobility in soil. Therefore, the architecture of the root system may determine the capacity of the plant to acquire this nutrient. We studied the effect of P availability on the development of the root system in Arabidopsis. We found that at P-limiting conditions (<50 microM), the Arabidopsis root system undergoes major architectural changes in terms of lateral root number, lateral root density, and primary root length. Treatment with auxins and auxin antagonists indicate that these changes are related to an increase in auxin sensitivity in the roots of P-deprived Arabidopsis seedlings. It was also found that the axr1-3, axr2-1, and axr4-1 Arabidopsis mutants have normal responses to low P availability conditions, whereas the iaa28-1 mutant shows resistance to the stimulatory effects of low P on root hair and lateral root formation. Analysis of ethylene signaling mutants and treatments with 1-aminocyclopropane-1-carboxylic acid showed that ethylene does not promote lateral root formation under P deprivation. These results suggest that in Arabidopsis, auxin sensitivity may play a fundamental role in the modifications of root architecture by P availability.
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http://dx.doi.org/10.1104/pp.010934 | DOI Listing |
Biodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
December 2024
Department of Agronomy, Faculty of Agricultural Sciences, SGT University, Gurugram, India.
Chromium (Cr) is an ever-present abiotic stress that negatively affects crop cultivation and production worldwide. High rhizospheric Cr concentrations inhibit nutrients uptake and their translocation to aboveground parts, thus can affect the growth and development of crop plants. This experiment was designed to evaluate the effects of sole and combined zinc-lysine and iron-lysine applications on photosynthetic efficacy, antioxidative defense, oxidative stress, and nutrient uptake and translocation under Cr stress.
View Article and Find Full Text PDFJ Agric Food Chem
December 2024
College of Horticulture, Henan Agricultural University, Zhengzhou 450046, PR China.
Drought limits crop growth and yield. Inoculation with plant growth-promoting rhizobacteria (PGPR) emerges as a promising strategy to protect crops against drought. However, the number of drought-tolerant PGPR is limited, and the regulation mechanisms remain elusive.
View Article and Find Full Text PDFTree Physiol
December 2024
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, SE-901 83 Umeå.
Isotopic pulse-labelling of photosynthate allows tracing of carbon (C) from tree canopies to belowground biota and calculations of its turnover in roots and recipient soil microorganisms. A high concentration of label is desirable, but is difficult to achieve in field studies of intact ecosystem patches with trees. Moreover, root systems of trees overlap considerably in most forests, which requires a large labelled area to minimize the impact of C allocated belowground by un-labelled trees.
View Article and Find Full Text PDFJ Med Eng Technol
December 2024
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.
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