For the study of the ionomic parameters of needles, fourteen sites covering most of the territory of Lithuania and belonging to distinct habitats (coastal brown dunes covered with natural Scots pine forests (G), scrubs (F), transition mires and quaking bogs (D), subcontinental moss Scots pine forests (G), and xero-thermophile fringes) were selected. Concentrations of macro-, micro-, and non-essential elements were analyzed in current-year needles, sampled in September. According to the concentrations of elements in needles, the differences between the most contrasting populations were as follows: up to 2-fold for Mg, N, K, Ca, and Zn; 2- to 7-fold for P, Na, Fe, Cu, Al, Cr, Ni, and Pb; and 26- to 31-fold for Mn and Cd. The concentrations of Cd, Cr, and Ni in needles of did not reach levels harmful for conifers. When compared to all other habitats (B, F, G, and E), the populations from transition mires and quaking bogs (D) had significantly lower concentrations of main nutritional elements N (12176 µg/g d. m.), P (1054 µg/g d. m.), and K (2916 µg/g d. m.). In scrubs (F), a habitat protected by EUNIS, the concentration of K in the needles was highest, while Zn and Cu concentrations were the lowest. Principal component (PC) analyses using concentrations of 15 elements as variables for the discrimination of populations or habitats allowed authors to distinguish F and B habitats from the E habitat (PC1) and F and D habitats from the G habitat (PC2). Discriminating between populations, the most important variables were concentrations of P, N, Mg, Ca, Cu, and K. Discriminating between habitats, the important variables were concentrations of N and P.
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http://dx.doi.org/10.3390/plants12040961 | DOI Listing |
Biometals
December 2024
Institue of Molecular Biology and Biotechnology, The University of Lahore, Lahore, 54590, Pakistan.
Phytoextraction of lead (Pb) is a challenging task due to its extremely low mobility within soil and plant systems. In this study, we tested the influence of some novel chelating agents for Pb-phytoextraction using sunflower. The Pb was applied at control (0.
View Article and Find Full Text PDFToxics
July 2024
Center for Natural and Human Sciences (CCNH), Federal University of ABC (UFABC), Santo André 09210-580, SP, Brazil.
Nanotechnology has been increasingly used in plant sciences, with engineered nanoparticles showing promising results as fertilizers or pesticides. The present study compared the effects in the foliar application of Se nanoparticles (SeNPs) or sodium selenite-Se(IV) on rice seedlings. The degree of plant growth, photosynthetic pigment content, and concentrations of Se, Na, Mg, K, Ca, Mn, Co, Cu, Zn, As, Cd, and Pb were evaluated.
View Article and Find Full Text PDFBMC Plant Biol
July 2024
College of Agriculture & Life Science, School of Applied Biosciences, Kyungpook National University, 80 Daehak-ro, Buk-Gu, Daegu, 41566, Korea.
Recent studies have exhibited a very promising role of copper nanoparticles (CuNPs) in mitigation of abiotic stresses in plants. Arbuscular mycorrhizae fungi (AMF) assisted plants to trigger their defense mechanism against abiotic stresses. Arsenic (As) is a non-essential and injurious heavy-metal contaminant.
View Article and Find Full Text PDFPlants (Basel)
June 2024
Plant Omics Laboratory, Department of Biotechnology, Life Science Building, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa.
-Coumaric acid (-CA) is a phenolic compound that plays a crucial role in mediating multiple signaling pathways. It serves as a defense strategy against plant wounding and is also presumed to play a role in plant development and lignin biosynthesis. This study aimed to investigate the physiological and ionomic effect of -CA on chia seedlings under salt stress.
View Article and Find Full Text PDFSci Rep
June 2024
ESAT-STADIUS, KU Leuven, 3001, Leuven, Belgium.
Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs.
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