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      Influence of Silver Nanoparticles on Photosynthetic Pigment Content and Mineral Uptake in Pineapple Seedlings Grown In Vitro under Aluminum Stress

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      Agronomy
      MDPI AG

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          Abstract

          The presence of toxic metals such as aluminum is described as a factor that could lead to a significant decrease in crop productivity, particularly for the cultivation of acidophilic plants. In the present study, in vitro cultivation of pineapple was used as a model to evaluate the role of silver nanoparticles (0.005, 0.01, 0.025, 0.05, and 0.1 g L−1 Ag NPs) against the negative effects of aluminum (0, 100, 300, and 500 μM AlCl3). The results showed that the presence of 0.025 g L−1 Ag NPs stimulated a higher concentration of photosynthetic pigments “a”, “b”, “a + b” and carotenoids in treatments with high levels of AlCl3. The application of Ag NPs allowed better shoot formation, improved chlorophyll a/b, and total/carotenoid ratios, as well as better levels of proline biosynthesis in response to stress. The synergistic interaction of Ag NPs and AlCl3 increased the availability and assimilation of mineral elements (K, Ca, Mg, Fe, Cu, Mn, and Zn) while decreasing Al translocation. In conclusion, the implementation and validation of Ag NPs in agricultural fields would be revolutionary because they represent a novel alternative for overcoming the limitations imposed by the presence of Al.

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          Rapid determination of free proline for water-stress studies

          Plant and Soil, 39(1), 205-207
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            A Revised Medium for Rapid Growth and Bio Assays with Tobacco Tissue Cultures

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              Generalized linear mixed models: a practical guide for ecology and evolution.

              How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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                Author and article information

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                Journal
                ABSGGL
                Agronomy
                Agronomy
                MDPI AG
                2073-4395
                May 2023
                April 23 2023
                : 13
                : 5
                : 1186
                Article
                10.3390/agronomy13051186
                2911b7ee-5869-4708-bafa-698b47029ad9
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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