Nickel oxide nanoparticles induce cell wall modifications, root anatomical changes, and nitrosative signaling in ecotypes of Ni hyperaccumulator Odontarrhena lesbiaca.

The industrial application and environmental release of nickel oxide NPs (NiO NPs) is increasing, but the details of their relationship with plants are largely unknown. In this work, the cellular, tissue, organ, and molecular level responses of three ecotypes of Ni hyperaccumulator Odontarrhena lesbiaca grown in the presence of high doses of NiO NP (250 mg/L and 500 mg/L) were studied. All three ecotypes showed a similar accumulation of Ni in the presence of nano Ni, and in the case of NiO NPs, the root-to-shoot Ni translocation was slighter compared to the bulk Ni. In all three ecotypes, the walls of the root cells effectively prevented internalization of NiO NPs, providing cellular defense against Ni overload. Exposure to NiO NP led to an increase in cortex thickness and the deposition of lignin-suberin and pectin in roots, serving as a tissue-level defense mechanism against excessive Ni. Exposure to NiO NP did not modify or cause a reduction in some biomass parameters of the Ampeliko and Loutra ecotypes, while it increased all parameters in Olympos. The free salt form of Ni exerted more negative effects on biomass production than the nanoform, and the observed effects of NiO NPs can be attributed to the release of Ni ions. Nitric oxide and peroxynitrite levels were modified by NiO NPs in an ecotype-dependent manner. The changes in the abundance and activity of S-nitrosoglutathione reductase protein triggered by NiO NPs suggest that the enzyme is regulated by NiO NPs at the post-translational level. The NiO NPs slightly intensified protein tyrosine nitration, and the slight differences between the ecotypes were correlated with their biomass production in the presence of NiO NPs. Overall, the Odontarrhena lesbiaca ecotypes exhibited tolerance to NiO NPs at the cellular, tissue, organ/organism and molecular levels, demonstrating various defense mechanisms and changes in the metabolism of reactive nitrogen species metabolism and nitrosative protein modification.

Selenate triggers diverse oxidative responses in Astragalus species with diverse selenium tolerance and hyperaccumulation capacity.

Selenium (Se) hyperaccumulators are capable of uptake and tolerate high Se dosages. Excess Se-induced oxidative responses were compared in Astragalus bisulcatus and Astragalus cicer. Plants were grown on media supplemented with 0, 25 or 75 µM selenate for 14 days. Both A. bisulcatus and A. cicer accumulated >2000 µg/g dry weight Se to the shoot but the translocation factors of A. cicer were below 1 suggesting its non hyperaccumulator nature. A. cicer showed Se sensitivity indicated by reduced seedling fresh weight, root growth and root apical meristem viability, altered element homeostasis in the presence of Se. In Se-exposed A. bisulcatus, less toxic organic Se forms (mainly MetSeCys, γ-Glu-MetSeCys, and a selenosugar) dominated, while these were absent from A. cicer suggesting that the majority of the accumulated Se may be present as inorganic forms. The glutathione-dependent processes were more affected, while ascorbate levels were not notably influenced by Se in either species. Exogenous Se triggered more intense accumulation of malondialdehyde in the sensitive A. cicer compared with the tolerant A. bisulcatus. The extent of protein carbonylation in the roots of the 75 µM Se-exposed A. cicer exceeded that of A. bisulcatus indicating a correlation between selenate sensitivity and the degree of protein carbonylation. Overall, our results reveal connection between oxidative processes and Se sensitivity/tolerance/hyperaccumulation and contribute to the understanding of the molecular responses to excess Se.

Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition.

The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs matrix-matched external calibration supported by a pixel-by-pixel automatic matrix (tissue type) recognition performed by linear discriminant analysis of the spatially resolved LIBS hyperspectral data set. On a swine (porcine) brain sample, we successfully performed this matrix recognition with an accuracy of 98% for the grey and white matter and we converted a LIBS intensity map of a tissue sample to a correct concentration map for the elements Na, K and Mg. Found concentrations in the grey and white matter agreed the element concentrations published in the literature and our reference measurements. Our results revealed that the actual concentration distribution in tissues can be quite different from what is suggested by the LIBS signal intensity map, therefore this conversion is always suggested to be performed if an accurate concentration distribution is to be assessed.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.