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14 pages, 3436 KiB  
Article
Chlorophyll and Carotenoid Metabolism Varies with Growth Temperatures among Tea Genotypes with Different Leaf Colors in Camellia sinensis
by Pengfei Xu, Jingbo Yu, Ruihong Ma, Yanyan Ji, Qiang Hu, Yihu Mao, Changqing Ding, Zhengzhen Li, Shibei Ge, Wei-Wei Deng and Xin Li
Int. J. Mol. Sci. 2024, 25(19), 10772; https://doi.org/10.3390/ijms251910772 (registering DOI) - 7 Oct 2024
Abstract
The phenotype of albino tea plants (ATPs) is significantly influenced by temperature regimes and light conditions, which alter certain components of the tea leaves leading to corresponding phenotypic changes. However, the regulatory mechanism of temperature-dependent changes in photosynthetic pigment contents and the resultant [...] Read more.
The phenotype of albino tea plants (ATPs) is significantly influenced by temperature regimes and light conditions, which alter certain components of the tea leaves leading to corresponding phenotypic changes. However, the regulatory mechanism of temperature-dependent changes in photosynthetic pigment contents and the resultant leaf colors remain unclear. Here, we examined the chloroplast microstructure, shoot phenotype, photosynthetic pigment content, and the expression of pigment synthesis-related genes in three tea genotypes with different leaf colors under different temperature conditions. The electron microscopy results revealed that all varieties experienced the most severe chloroplast damage at 15 °C, particularly in albino cultivar Baiye 1 (BY), where chloroplast basal lamellae were loosely arranged, and some chloroplasts were even empty. In contrast, the chloroplast basal lamellae at 35 °C and 25 °C were neatly arranged and well-developed, outperforming those observed at 20 °C and 15 °C. Chlorophyll and carotenoid measurements revealed a significant reduction in chlorophyll content under low temperature treatment, peaking at ambient temperature followed by high temperatures. Interestingly, BY showed remarkable tolerance to high temperatures, maintaining relatively high chlorophyll content, indicating its sensitivity primarily to low temperatures. Furthermore, the trends in gene expression related to chlorophyll and carotenoid metabolism were largely consistent with the pigment content. Correlation analysis identified key genes responsible for temperature-induced changes in these pigments, suggesting that changes in their expression likely contribute to temperature-dependent leaf color variations. Full article
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Figure 1
<p>The shoot phenotype (one bud and two leaves) of ‘Longjing 43’ (LJ), ‘Baiye 1’ (BY), and ‘Zhonghuang 1’ (ZH) tea cultivars under 15 °C, 20 °C, 25 °C, and 35 °C temperature conditions.</p>
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<p>The changes in chloroplast ultrastructure of ‘Longjing 43’ (LJ), ‘Baiye 1’ (BY), and ‘Zhonghuang 1’ (ZH) tea cultivars under 15 °C, 20 °C, 25 °C, and 35 °C temperature conditions. The black ellipse is the chloroplast; the white area within the ellipse is the gap caused by poorly aligned chloroplast basal granules; and the gray stripe is the cell wall. GT, grana thylakoid; PG, plastoglobule; V, void. All photographs are scaled to 1 μm.</p>
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<p>Effects of different temperature conditions on photosynthetic pigment content. (<b>A</b>) Chlorophyll a content. (<b>B</b>) Chlorophyll b content. (<b>C</b>) Carotenoid content. (<b>D</b>) Chlorophyll a Chlorophyll b ratio. FW, fresh weight (each bar shows the mean, n = 3). Lower case letters represent the level of significance (<span class="html-italic">p</span> &lt; 0.05) between treatment groups. Groups with non-repeated letters are statistically significant, while groups with the same letter are not significantly different from each other.</p>
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<p>Chlorophyll metabolic pathways and expression levels of chlorophyll metabolism-related genes in LJ, BY, and ZH under 15 °C, 20 °C, 25 °C, and 35 °C temperature conditions.</p>
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<p>Carotenoid metabolic pathways and expression levels of carotenoid metabolism-related genes in LJ, BY, and ZH under 15 °C, 20 °C, 25 °C and 35 °C temperature conditions.</p>
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<p>Pearson correlation analysis. (<b>A</b>) Chlorophyll content and expression of genes related to chlorophyll metabolism. (<b>B</b>) Pearson correlation analysis of carotenoid content and expression of genes related to carotenoid metabolism. Correlation expressed as R-value, with |R| ≥ 0.6 being moderately correlated and |R| ≥ 0.8 being highly correlated (the size of the circle shows the absolute value of R, red represents positive correlation, blue represents negative correlation, and * shows the level of significance: *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01).</p>
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23 pages, 2582 KiB  
Article
Effects of Salicylic Acid on Physiological Responses of Pepper Plants Pre-Subjected to Drought under Rehydration Conditions
by Fabrício Custódio de Moura Gonçalves, Luís Paulo Benetti Mantoan, Carla Verônica Corrêa, Nathália de Souza Parreiras, Luiz Fernando Rolim de Almeida, Elizabeth Orika Ono, João Domingos Rodrigues, Renato de Mello Prado and Carmen Sílvia Fernandes Boaro
Plants 2024, 13(19), 2805; https://doi.org/10.3390/plants13192805 - 7 Oct 2024
Abstract
Capsicum annuum L. has worldwide distribution, but drought has limited its production. There is a lack of research to better understand how this species copes with drought stress, whether it is reversible, and the effects of mitigating agents such as salicylic acid (SA). [...] Read more.
Capsicum annuum L. has worldwide distribution, but drought has limited its production. There is a lack of research to better understand how this species copes with drought stress, whether it is reversible, and the effects of mitigating agents such as salicylic acid (SA). Therefore, this study aimed to understand the mechanisms of action of SA and rehydration on the physiology of pepper plants grown under drought conditions. The factorial scheme adopted was 3 × 4, with three water regimes (irrigation, drought, and rehydration) and four SA concentrations, namely: 0 (control), 0.5, 1, and 1.5 mM. This study evaluated leaf water percentage, water potential of shoots, chlorophylls (a and b), carotenoids, stomatal conductance, chlorophyll a fluorescence, and hydrogen peroxide (H2O2) concentration at different times of day, water conditions (irrigation, drought, and rehydration), and SA applications (without the addition of a regulator (0) and with the addition of SA at concentrations equal to 0.5, 1, and 1.5 mM). In general, exogenous SA application increased stomatal conductance (gs) responses and modified the fluorescence parameters (ΦPSII, qP, ETR, NPQ, D, and E) of sweet pepper plants subjected to drought followed by rehydration. It was found that the use of SA, especially at concentrations of 1 mM in combination with rehydration, modulates gs, which is reflected in a higher electron transport rate. This, along with the production of photosynthetic pigments, suggests that H2O2 did not cause membrane damage, thereby mitigating the water deficit in pepper plants. Plants under drought conditions and rehydration with foliar SA application at concentrations of 1 mM demonstrated protection against damage resulting from water stress. Focusing on sustainable productivity, foliar SA application of 1 mM could be recommended as a technique to overcome the adverse effects of water stress on pepper plants cultivated in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Drought Responses and Adaptation Mechanisms in Plants)
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Figure 1
<p>Stomatal conductance (<span class="html-italic">gs</span>) in mmol m<sup>−2</sup>s<sup>−1</sup> throughout the day, 8:00 h (<b>A</b>), 10:00 h (<b>B</b>), 12:00 (<b>C</b>) h, 14:00 h (<b>D</b>), and 16:00 h (<b>E</b>) at seven days after treatments (7 DAT). Black bars represent irrigation treatment; white bars represent irrigation without treatment. Letters show difference between treatments by the 5% Tukey’s test. Values represent means ± standard deviation of four replicates. Capital letters compare means of water conditions, and lowercase letters compare means of salicylic acid concentrations.</p>
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<p>Stomatal conductance (<span class="html-italic">gs</span>) in mmol m<sup>−2</sup>s<sup>−1</sup> throughout the day, 8:00 h (<b>A</b>), 10:00 h (<b>B</b>), 12:00 h (<b>C</b>), 14:00 h (<b>D</b>), and 16:00 h (<b>E</b>) at twelve days after treatments (12 DAT). Black bars represent irrigation treatment; white bars represent rehydration treatment. Letters show the difference between treatments by the 5% Tukey’s test. Values represent means ± standard deviation of four replicates. Capital letters compare means of water conditions, and lowercase letters compare means of salicylic acid concentrations.</p>
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<p>Effective quantum yield—ΦPSII (<b>A</b>), electron transport efficiency—ETR (<b>B</b>), photochemical quenching—qP (<b>C</b>), non-photochemical quenching—NPQ (<b>D</b>), maximum quantum yield—Fv′/Fm′ (<b>E</b>), heat dissipation—D (<b>F</b>), and dissipation of unused energy—E (<b>G</b>) seven days after treatments (7 DAT), determined at 12:00 h. Black bars represent irrigation treatment; white bars represent drought treatment. Letters show the difference between treatments by the 5% Tukey’s test. Values represent means ± standard deviation of four replicates. Capital letters compare means of water conditions, and lowercase letters compare means of salicylic acid concentrations.</p>
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<p>Effective quantum yield—ΦPSII (<b>A</b>), electron transport efficiency—ETR (<b>B</b>), photochemical quenching—qP (<b>C</b>), non-photochemical quenching—NPQ (<b>D</b>), maximum quantum yield—Fv′/Fm′ (<b>E</b>), heat dissipation—D (<b>F</b>), and dissipation of unused energy—E (<b>G</b>) twelve days after treatments (12 DAT), determined at 12:00 h. Black bars represent irrigation treatment; white bars represent rehydration treatment. Letters show the difference between treatments by the 5% Tukey’s test. Values represent means ± standard deviation of four replicates. Capital letters compare means of water conditions, and lowercase letters compare means of salicylic acid concentrations.</p>
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<p>Hydrogen peroxide concentrations seven (<b>A</b>) and twelve (<b>B</b>) days after treatments. Black bars represent irrigation treatment; white bars represent rehydration treatment. Letters show the difference between treatments by the 5% Tukey’s test. Values represent means ± standard deviation of four replicates. Capital letters compare means of water conditions, and lowercase letters compare means of salicylic acid concentrations.</p>
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<p>Pepper plants (<span class="html-italic">Capsicum annuum</span> L.) subjected to irrigation. Plants from the drought at 7 days after treatments (DAT) were subjected to rehydration. At times 1 and 7, SA was applied at concentrations of 0.5, 1, and 1.5 mM. Control treatment without the addition of SA (0).</p>
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24 pages, 2371 KiB  
Review
Microalgal Phenolics: Systematic Review with a Focus on Methodological Assessment and Meta-Analysis
by Vasilis Andriopoulos and Michael Kornaros
Mar. Drugs 2024, 22(10), 460; https://doi.org/10.3390/md22100460 - 7 Oct 2024
Abstract
A critical review and analysis of the literature relevant to the phenolic content of eucaryotic microalgae was performed. Several issues were identified and discussed. In summary, the main problems with the reporting on the phenolic content of microalgae are the following: (1) despite [...] Read more.
A critical review and analysis of the literature relevant to the phenolic content of eucaryotic microalgae was performed. Several issues were identified and discussed. In summary, the main problems with the reporting on the phenolic content of microalgae are the following: (1) despite its usefulness in the determination of phenolic content in plant samples, the Folin–Ciocalteu assay is non-suitable for microalgal research due to the high presence of interfering compounds in microalgal extracts such as chlorophyll and its derivatives in organic extracts and free aromatic amino acids or nucleotides in aqueous extracts; (2) while there is chromatographic evidence for the presence of simple phenolic acids in most microalgal clades, the lack of critical enzymes of phenolic biosynthesis in most microalgae, as well as the high variability of phenolic profiles even in the same genus, require more extensive research before conclusions are drawn; (3) the accumulation and metabolism of external phenolics by microalgae has been almost universally neglected in studies focusing on the phenolic content of microalgae, even when natural seawater or complex organic media are used in the cultivation process. Despite these issues, the literature focusing on the bioremediation of waste streams rich in phenolics through microalgae demonstrates the ability of those organisms to adsorb, internalize, and in many cases oxidize or transform a wide range of phenolic compounds, even at very high concentrations. Simple phenolics found in waste streams, such as olive mill waste, have been shown to enhance the antioxidant activity and various bioactivities of microalgal extracts, while complex biotransformation products of phenolics have also been characterized. In conclusion, the de novo biosynthesis of phenolic compounds via eucaryotic microalgae requires further investigation with better designed experiments and suitable analytical methods, while the response of microalgae to phenolic compounds in their growth medium is of great practical interest, both in terms of waste treatment and for the production of functional foods, cosmetics, and pharmaceuticals. Full article
(This article belongs to the Special Issue High-Value Algae Products)
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Figure 1
<p>Gallic acid equivalents (GAEs) to chlorophyll ratio for various solvents and growth medium compositions (defined or not). Data were collected and processed according to the <a href="#sec5-marinedrugs-22-00460" class="html-sec">Section 5</a>, subsections “Data Collection” and “Estimation of chlorophyll interference”. All data are available in <a href="#app1-marinedrugs-22-00460" class="html-app">Supplementary Data—GAE-Chl_ratio</a>. The dotted vertical line indicates the interference observed by Ben Hamouda et al., ~0.88 GAE:Chl w:w (detailed explanation provided in the <a href="#sec5-marinedrugs-22-00460" class="html-sec">Section 5</a>, subsection “Estimation of chlorophyll interference”). Significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated by a compact letter display. The TPC of the data points on the left of the interference line can be attributed solely to the chlorophyll content. Boxes contain values between the first and third quartiles, while the minimum and maximum values are indicated with vertical bars at the end of lines that extend from the boxes. Values more than 1.5 times greater or lower than the interquartile range (the difference between the first and third quartiles) shown for the boxes are considered outliers.</p>
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<p>Reported TPC in terms of the dry weight of the biomass for the different references used in this study. The dotted vertical lines indicate the minimum and maximum chlorophyll interference expected assuming a chlorophyll content ~35 mg Chl/g DW (detailed explanation provided in the <a href="#sec5-marinedrugs-22-00460" class="html-sec">Section 5</a>, subsection “Estimation of chlorophyll interference”). The total phenolic content below the upper limit of the interference (31 mg GAE/g DW) could be attributed to the presence of chlorophyll rather than the presence of phenolic compounds. Significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated by a compact letter display. Boxes contain values between the first and third quartiles, while the minimum and maximum values are indicated with vertical bars at the end of the lines that extend from the boxes. Values that are more or less than 1.5 times the interquartile range (the difference between the first and third quartiles) are considered outliers.</p>
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<p>Chord diagram of major types of phenolic compounds (and some non-phenolic compounds like polyketides and isoprenoids) identified chromatographically in different microalgal Phyla. Microalgal phyla are distinguished from identified compounds with an asterisk at the end (*). The arc length of every item on the diagram is proportional to the number of times the item is present in the data (all data are provided in the <a href="#app1-marinedrugs-22-00460" class="html-app">Supplementary Data, Sheet “Figures S3 and S6_Data”</a>).</p>
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<p>Total identified phenolics in different references used in this article. Significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated by compact letter displays. Boxes contain values between the first and third quartiles, while the minimum and maximum values are indicated by the vertical bars at the end of lines that extend from the boxes. Outliers are considered values more than 1.5 times greater or smaller than the interquartile range (the difference between the first and third quartiles).</p>
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<p>Some key aspects of the biosynthesis of phenolics in plants. Phenylalanine ammonia lyase (PAL) and tyrosine ammonia lyase (TAL) are shown. Multiple reaction steps are indicated with dotted lines. Compounds were retrieved from KEGG (<a href="https://www.genome.jp/kegg/" target="_blank">https://www.genome.jp/kegg/</a>, accessed on 27 September 2024).</p>
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<p>Sankey plot of the distribution of identified compounds in different metabolic pathways and microalgal Phyla. The height of bars on the left is proportional to the number of times a compound was identified in a specific Phylum. The height of bars on the right is proportional to the number of times a compound originating from a given metabolic pathway was identified.</p>
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<p>Bubble chart of Kruskal–Wallis test results for the effect of various parameters on the biomass concentration in individual phenolics. Bubble diameter is proportional to the effect size, Epsilon squared (a detailed explanation is provided in the <a href="#sec5-marinedrugs-22-00460" class="html-sec">Section 5</a>, subsection “Statistical analysis”). Only results deemed significant using the Benjamani–Hochberg procedure (detailed explanation provided in the <a href="#sec5-marinedrugs-22-00460" class="html-sec">Section 5</a>, subsection “Statistical analysis”) are presented. Molecules are sorted by their biosynthesis pathway (from left to right; Shikimate pathway, starting with gallic acid; Phenylpropanoid pathway, staring with caffeic acid; Flavonoid pathway; starting with catechin; and Stilbenoid pathway, represented by resveratrol).</p>
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<p>Concentration of total identified phenolics in microalgae cultivated under different light intensities in a defined or undefined medium. Data points corresponding to conditions with copper stress have been excluded to rule out interference. Significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated through compact letter displays. Boxes contain values between the first and third quartiles, while the minimum and maximum values are indicated with horizontal bars at the end of the lines that extend from the boxes. Outliers (marked with rhombi) are considered to be values that are 1.5 times outside the interquartile range (the difference between the first and third quartiles).</p>
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<p>Concentration of total identified phenolics in microalgae cultivated in defined or undefined media based on DI water, natural seawater, or saline water of unknown origin. A further distinction is made between cases where cells were known to be washed or not washed, and cases where this information is unknown. Data points corresponding to conditions with copper stress have been excluded to rule out interference. Significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated with compact letter display. Boxes contain values between the first and third quartiles, while the minimum and maximum values are indicated with horizontal bars at the end of the lines that extend from the boxes. Outliers (marked with rhombi) are considered when values are more than 1.5 times outside the interquartile range (the difference between the first and third quartiles).</p>
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19 pages, 5873 KiB  
Article
An Excessive K/Na Ratio in Soil Solutions Impairs the Seedling Establishment of Sunflower (Helianthus annuus L.) through Reducing the Leaf Mg Concentration and Photosynthesis
by Yu Cheng, Tibin Zhang, Weiqiang Gao, Yuxin Kuang, Qing Liang, Hao Feng and Saparov Galymzhan
Agronomy 2024, 14(10), 2301; https://doi.org/10.3390/agronomy14102301 - 6 Oct 2024
Viewed by 408
Abstract
In saline conditions, establishing healthy seedlings is crucial for the productivity of sunflowers (Helianthus annuus L.). Excessive potassium (K+) from irrigation water or overfertilization, similar to sodium (Na+), could adversely affect sunflower growth. However, the effects of salt [...] Read more.
In saline conditions, establishing healthy seedlings is crucial for the productivity of sunflowers (Helianthus annuus L.). Excessive potassium (K+) from irrigation water or overfertilization, similar to sodium (Na+), could adversely affect sunflower growth. However, the effects of salt stress caused by varying K/Na ratios on the establishment of sunflower seedlings have not been widely studied. We conducted a pot experiment in a greenhouse, altering the K/Na ratio of a soil solution to grow sunflower seedlings. We tested three saline solutions with K/Na ratios of 0:1 (P0S1), 1:1 (P1S1), and 1:0 (P1S0) at a constant concentration of 4 dS m−1, along with a control (CK, no salt added), with five replicates. The solutions were applied to the pots via capillary rise through small holes at the bottom. The results indicate that different K/Na ratios significantly influenced ion-selective uptake and transport in crop organs. With an increasing K/Na ratio, the K+ concentration in the roots, stems, and leaves increased, while the Na+ concentration decreased in the roots and stems, with no significant differences in the leaves. Furthermore, an excessive K/Na ratio (P1S0) suppressed the absorption and transportation of Mg2+, significantly reducing the Mg2+ concentration in the stems and leaves. A lower leaf Mg2+ concentration reduced chlorophyll concentration, impairing photosynthetic performance. The lowest plant height, leaf area, dry matter, and shoot/root ratio were observed in P1S0, with reductions of 27%, 48%, 48%, and 13% compared to CK, respectively. Compared with CK, light use efficiency and CO2 use efficiency in P1S0 were significantly reduced by 13% and 10%, respectively, while water use efficiency was significantly increased by 9%. Additionally, improved crop morphological and photosynthetic performance was observed in P1S1 and P0S1 compared with P1S0. These findings underscore the critical role of optimizing ion composition in soil solutions, especially during the sensitive seedling stage, to enhance photosynthesis and ultimately to improve the plant’s establishment. We recommend that agricultural practices in saline regions incorporate tailored irrigation and fertilization strategies that prioritize optimal K/Na ratios to maximize crop performance and sustainability. Full article
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Figure 1
<p>Experimental device in this study. P1S0, P1S1, and P0S1 indicate treatments with K/Na ratios of 1:0, 1:1, and 0:1 at the same external concentration (4 dS m<sup>−1</sup>), respectively. CK indicates no added salt in the tap water.</p>
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<p>Daily air temperature and relative humidity during the seedling stage of sunflower. (<b>A</b>) Daily air temperature and relative humidity of the first batch of experiments from 13 March (planting date) to 27 April (harvest date). Sunflower was harvested at the end of the seedling stage. (<b>B</b>) Daily air temperature and relative humidity of the second batch of experiments from 10 May to 24 June. (<b>C</b>) Data dispersion of air temperature and relative humidity in two batches of experiments by box plot.</p>
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<p>Plant height and leaf area of sunflower during the experiments, dry matter, and site photos at the end of the seeding stage under different K/Na ratios in the first batch experiment (<b>A</b>–<b>D</b>) and second batch experiment (<b>E</b>–<b>H</b>). P0S1, P1S1, and P1S0 indicate treatments with K/Na ratios of 0:1, 1:1, and 1:0 at the same external concentration (4 dS m<sup>−1</sup>), respectively. CK indicates no added salt in the tap water. Different small letters on the same color bar or point in (<b>C</b>,<b>G</b>) indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Na, K, Ca, Mg, and Cl concentration of each organ of sunflower at the end of the seeding stage under different K/Na ratios in the first (<b>A</b>–<b>E</b>) and second (<b>F</b>–<b>J</b>) batch experiments. P0S1, P1S1, and P1S0 indicate treatments with K/Na ratios of 0:1, 1:1, and 1:0 at the same external concentration (4 dS m<sup>−1</sup>), respectively. CK indicates no added salt in the tap water. Different small letters on the same organ in the subfigure indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Na, K, Ca, Mg, and Cl accumulation of each organ of sunflower at the end of the seeding stage under different K/Na ratios in the first (<b>A</b>) and second (<b>B</b>) batch experiments. P0S1, P1S1, and P1S0 indicate treatments with K/Na ratios of 0:1, 1:1, and 1:0 at the same external concentration (4 dS m<sup>−1</sup>), respectively. CK indicates no added salt in the tap water.</p>
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<p>The photosynthetic characteristics (<span class="html-italic">Evap</span>, <span class="html-italic">P<sub>n</sub></span>, <span class="html-italic">g<sub>s</sub></span>, and <span class="html-italic">C<sub>i</sub></span>), <span class="html-italic">Ls</span>, and resource use efficiency (<span class="html-italic">WUE</span>, <span class="html-italic">LUE</span>, and <span class="html-italic">CUE</span>) under different K/Na ratios in the first batch experiment (<b>A</b>–<b>H</b>) and second batch experiment (<b>I</b>–<b>P</b>). <span class="html-italic">Evap</span>, transpiration rate; <span class="html-italic">P<sub>n</sub></span>, net photosynthetic rate; <span class="html-italic">g<sub>s</sub></span>, stomatal conductance; <span class="html-italic">C<sub>i</sub></span>, intercellular CO<sub>2</sub> concentration; <span class="html-italic">Ls</span>, stomatal limitation; <span class="html-italic">WUE</span>, water use efficiency; <span class="html-italic">LUE</span>, light use efficiency; <span class="html-italic">CUE</span>, CO<sub>2</sub> use efficiency. P0S1, P1S1, and P1S0 indicate treatments with K/Na ratios of 0:1, 1:1, and 1:0 at the same external concentration (4 dS m<sup>−1</sup>), respectively. CK indicates no added salt in the tap water. Different small letters in the subfigure indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation matrix between the K/Na ratio, ion concentration of plant organs, gas exchange parameters (<span class="html-italic">Evap</span>, <span class="html-italic">P<sub>n</sub></span>, <span class="html-italic">g<sub>s</sub></span>, and <span class="html-italic">C<sub>i</sub></span>), PH, LA, and DM in the first batch experiment (<b>A</b>) and second batch experiment (<b>B</b>). Root-Na, Root-K, Root-Ca, and Root-Mg indicate the Na, K, Ca, and Mg concentration in the roots, respectively. Stem-Na, Stem-K, Stem-Ca, and Stem-Mg indicate the Na, K, Ca, and Mg concentration in the stems, respectively. Leaf-Na, Leaf-K, Leaf-Ca, and Leaf-Mg indicate the Na, K, Ca, and Mg concentration in the leaves, respectively. <span class="html-italic">Evap</span>, transpiration rate; <span class="html-italic">P<sub>n</sub></span>, net photosynthetic rate; <span class="html-italic">g<sub>s</sub></span>, stomatal conductance; <span class="html-italic">C<sub>i</sub></span>, intercellular carbon dioxide concentration; PH, plant height; LA, leaf area; DM, dry matter weight. The gradient of the legend is a function of the strength of the correlation (darker colors indicate stronger correlations); ellipse slopes indicate a negative or positive correlation (i.e., increasing towards the right indicates a positive correlation, and decreasing towards the right indicates a negative correlation). The shape of the ellipse also indicates the strength of the correlation; a wide shape indicates a weak correlation and a narrow shape indicates a strong correlation. * indicates <span class="html-italic">p</span> &lt; 0.05. For example, the DM and <span class="html-italic">P<sub>n</sub></span> were significantly and strongly positively correlated.</p>
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<p>Response of crop organ ion concentration and leaf photosynthesis to an excessive K/Na ratio.</p>
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19 pages, 5703 KiB  
Article
Physiological Parameters and Transcriptomic Levels Reveal the Response Mechanism of Maize to Deep Sowing and the Mechanism of Exogenous MeJA to Alleviate Deep Sowing Stress
by Fang Wang, Zhijin Feng, Xinyi Yang, Guangkuo Zhou and Yunling Peng
Int. J. Mol. Sci. 2024, 25(19), 10718; https://doi.org/10.3390/ijms251910718 - 5 Oct 2024
Viewed by 218
Abstract
Deep sowing, as a method to mitigate drought and preserve soil moisture and seedlings, can effectively mitigate the adverse effects of drought stress on seedling growth. The elongation of the hypocotyl plays an important role in the emergence of maize seeds from deep-sowing [...] Read more.
Deep sowing, as a method to mitigate drought and preserve soil moisture and seedlings, can effectively mitigate the adverse effects of drought stress on seedling growth. The elongation of the hypocotyl plays an important role in the emergence of maize seeds from deep-sowing stress. This study was designed to explore the function of exogenous methyl jasmonate (MeJA) in the growth of the maize mesocotyl and to examine its regulatory network. The results showed that the addition of a 1.5 μ mol L−1 MeJA treatment significantly increased the mesocotyl length (MES), mesocotyl and coleoptile length (MESCOL), and seedling length (SDL) of maize seedlings. Transcriptome analysis showed that exogenous MeJA can alleviate maize deep-sowing stress, and the differentially expressed genes (DEGs) mainly include ornithine decarboxylase, terpene synthase 7, ethylene responsive transcription factor 11, and so on. In addition, candidate genes that may regulate the length of maize hypocotyls were screened by Weighted Gene Co-expression Network Analysis (WGCNA). These genes may be involved in the growth of maize hypocotyls through transcriptional regulation, histones, ubiquitin protease, protein binding, and chlorophyll biosynthesis and play an important role in maize deep-sowing tolerance. Our research findings may provide a theoretical basis for determining the tolerance of maize to deep-sowing stress and the mechanism of exogenous hormone regulation of deep-sowing stress. Full article
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<p>Effects of exogenous MeJA on endogenous hormones of maize inbred line seedlings under deep-sowing stress. CK: distilled water treatment at 3 cm sowing depth; CM: 3 cm sowing depth with 1.5 μmol·L<sup>−1</sup> exogenous MeJA treatment; DS: distilled water treatment at 15 cm sowing depth; DM: 15 cm sowing depth with 1.5 μmol·L<sup>−1</sup> exogenous MeJA treatment. Different lowercase letters represent the same inbred line with significant differences under different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Longitudinal structure and cell length of mesocotyl cells of maize inbred lines treated with exogenous MeJA under deep-sowing stress. <b>A</b>: 3 cm sowing depth + distilled water treatment (Qi319); <b>B</b>: 3 cm sowing depth + MeJA treatment (Qi319); <b>C</b>: 15 cm sowing depth + distilled water treatment (Qi319); <b>D</b>: 15 cm sowing depth + MeJA treatment (Qi319); <b>E</b>: 3 cm sowing depth + distilled water treatment (Zi330); <b>F</b>: 3 cm seeding depth + MeJA treatment (Zi330); <b>G</b>: 15 cm sowing depth + distilled water treatment (Zi330); <b>H</b>: 15 cm seeding depth + MeJA treatment (Zi330); <b>I</b>: cell lengths of mesocotyls. CK: distilled water treatment at 3 cm sowing depth; CM: 3 cm sowing depth with 1.5 μmol·L<sup>−1</sup> exogenous MeJA treatment; DS: distilled water treatment at 15 cm sowing depth; DM: 15 cm sowing depth with 1.5 μmol·L<sup>−1</sup> exogenous MeJA treatment. Different lowercase letters represent the same inbred line with significant differences under different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Differential gene expression analysis. (<b>a</b>) Number distribution of down-regulated DEGs in different comparison groups. (<b>b</b>,<b>c</b>) Venn diagram analysis of the normal sowing depth and deep-sowing stress and the normal sowing depth and deep-sowing stress with exogenous MeJA applied. Qi_CK3: Qi319 under 3 cm sowing depth and distilled water treatment; Qi_CK15: Qi319 distilled water treatment at 15 cm sowing depth; Qi_T3: Qi319 under 3 cm sowing depth and MeJA treatment; Qi_T15: Qi319 under 15 cm sowing depth and MeJA treatment; Zi_CK3: distilled water treatment of Zi330 at 3 cm sowing depth; Zi_CK15: distilled water treatment of Zi330 at 15 cm sowing depth; Zi_T3: MeJA treatment of Zi330 at 3 cm sowing depth; Zi_T15: MeJA treatment of Zi330 at 15 cm sowing depth.</p>
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<p>GO analysis of two inbred lines in different comparison groups. (<b>a</b>) GO analysis of Qi319 maize inbred line at normal sowing depth under deep-sowing stress. (<b>b</b>) GO analysis of Zi330 maize inbred line at normal sowing depth under deep-sowing stress. (<b>c</b>) GO analysis of Qi319 maize inbred line after adding exogenous MeJA under deep-sowing stress. (<b>d</b>) GO analysis of Zi330 maize inbred line after adding exogenous MeJA under deep-sowing stress. The treatments and abbreviations are the same as those given in <a href="#ijms-25-10718-f001" class="html-fig">Figure 1</a>.</p>
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<p>GO analysis of two inbred lines in different comparison groups. (<b>a</b>) GO analysis of Qi319 maize inbred line at normal sowing depth under deep-sowing stress. (<b>b</b>) GO analysis of Zi330 maize inbred line at normal sowing depth under deep-sowing stress. (<b>c</b>) GO analysis of Qi319 maize inbred line after adding exogenous MeJA under deep-sowing stress. (<b>d</b>) GO analysis of Zi330 maize inbred line after adding exogenous MeJA under deep-sowing stress. The treatments and abbreviations are the same as those given in <a href="#ijms-25-10718-f001" class="html-fig">Figure 1</a>.</p>
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<p>Pathway enrichment analysis of the two inbred lines in different comparison groups. (<b>a</b>) Pathway enrichment analysis of maize inbred line Qi319 at a normal sowing depth under deep-sowing stress. (<b>b</b>) Pathway enrichment analysis of maize inbred line Zi330 at a normal sowing depth under deep-sowing stress. (<b>c</b>) Pathway enrichment analysis of maize inbred line Qi319 after adding exogenous MeJA under deep-sowing stress. (<b>d</b>) Pathway enrichment analysis of maize inbred line Zi330 adding exogenous MeJA under deep-sowing stress. The treatments and abbreviations are the same as those given in <a href="#ijms-25-10718-f001" class="html-fig">Figure 1</a>.</p>
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<p>GO and KEGG analysis of expressed genes in different treatment groups. (<b>a</b>) GO analysis of two varieties under normal sowing and deep-sowing stress. (<b>b</b>) Pathway enrichment analysis of two cultivars under normal sowing and deep-sowing stress. (<b>c</b>) GO analysis of two varieties under deep-sowing stress after adding exogenous MeJA. (<b>d</b>) Pathway enrichment analysis of two cultivars under deep-sowing stress after adding exogenous MeJA.</p>
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<p>Real-time quantitative PCR validation of significantly up-regulated differentially expressed genes between two varieties under deep-sowing stress treatment. (<b>a</b>) The expression changes in response to the QCK, QDS, ZCK, and ZDS treatments for each candidate gene as measured by qRT-PCR. (<b>b</b>) Scatter plot showing the changes in the expression (log fold changes) of selected genes based on RNA-seq via qRT-PCR. The red line in the figure represents RNA seq, and the blue dots represent qRT-PCR. QCK: distilled water treatment of Qi319 at 3 cm sowing depth; QDS: distilled water treatment of Qi319 at 15 cm sowing depth; ZCK: distilled water treatment of Zi330 at 3 cm sowing depth; ZDS: distilled water treatment of Zi330 at 15 cm sowing depth.</p>
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<p>Gene cluster analysis and correlation analysis of phenotypes and modules. (<b>a</b>) Hierarchical clustering analysis of co-expression genes. Different colors represent all modules, with gray indicating genes that cannot be classified into any module by default. (<b>b</b>) Correlated heat maps between modules. A color block in the picture represents a numerical value. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (<b>c</b>) Correlations between gene modules and phenotypes. Each tree diagram in the figure represents a module, each branch represents a gene, and the darker the color of each point (white → yellow → red), the stronger the connectivity between the two genes corresponding to the row and column. (<b>d</b>) Heat map of correlations between gene modules and phenotypes. The leftmost color block represents the module, and the rightmost color bar represents the correlation range. In the heatmap of the middle part, the darker the color, the higher the correlation, with red indicating positive correlation and blue indicating negative correlation. The numbers in each cell represent correlation and significance. MES: mesocotyl length; COL: coleoptile length; MESCOL: mesocotyl length and coleoptile length; MEWCOW: mesocotyl weight and coleoptile weight; RL: root length; RW: root fresh weight; SDL: seedling length; SDW: seedling fresh weight.</p>
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<p>Gene cluster analysis and correlation analysis of phenotypes and modules. (<b>a</b>) Hierarchical clustering analysis of co-expression genes. Different colors represent all modules, with gray indicating genes that cannot be classified into any module by default. (<b>b</b>) Correlated heat maps between modules. A color block in the picture represents a numerical value. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. (<b>c</b>) Correlations between gene modules and phenotypes. Each tree diagram in the figure represents a module, each branch represents a gene, and the darker the color of each point (white → yellow → red), the stronger the connectivity between the two genes corresponding to the row and column. (<b>d</b>) Heat map of correlations between gene modules and phenotypes. The leftmost color block represents the module, and the rightmost color bar represents the correlation range. In the heatmap of the middle part, the darker the color, the higher the correlation, with red indicating positive correlation and blue indicating negative correlation. The numbers in each cell represent correlation and significance. MES: mesocotyl length; COL: coleoptile length; MESCOL: mesocotyl length and coleoptile length; MEWCOW: mesocotyl weight and coleoptile weight; RL: root length; RW: root fresh weight; SDL: seedling length; SDW: seedling fresh weight.</p>
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<p>Analysis of hub gene network interaction in phenotypic significant enrichment modules. (<b>a</b>) Network interaction analysis of hub genes in royalblue module. (<b>b</b>) Network interaction analysis of hub genes in bisque4 module. The color gradients of the dots represent high or low soft thresholds of connectivity, with a redder dot color representing a higher soft threshold of connectivity. The color gradients of the dots represent high or low soft thresholds of connectivity, with a redder dot color representing a higher soft threshold of connectivity.</p>
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<p>Model of the molecular mechanisms underlying deep-sowing tolerance and MeJA mitigation of deep-sowing-stress-induced damage in maize.</p>
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15 pages, 987 KiB  
Article
Origanum majorana L. as Flavoring Agent: Impact on Quality Indices, Stability, and Volatile and Phenolic Profiles of Extra Virgin Olive Oil (EVOO)
by Panagiota Kyriaki Revelou, Spyridon J. Konteles, Anthimia Batrinou, Marinos Xagoraris, Petros A. Tarantilis and Irini F. Strati
Foods 2024, 13(19), 3164; https://doi.org/10.3390/foods13193164 - 4 Oct 2024
Viewed by 694
Abstract
The flavoring of olive oils with aromatic plants is commonly used to enrich the oils with aromatic and antioxidant compounds. Origanum majorana L. was applied as a flavoring agent for extra virgin olive oil (EVOO), at concentrations of 20 g L−1 and [...] Read more.
The flavoring of olive oils with aromatic plants is commonly used to enrich the oils with aromatic and antioxidant compounds. Origanum majorana L. was applied as a flavoring agent for extra virgin olive oil (EVOO), at concentrations of 20 g L−1 and 40 g L−1, via ultrasound-assisted maceration. The aim of this study was to evaluate the impact of flavoring on the EVOOs’ quality indices, oxidative stability, and antioxidant, antiradical and antifungal activities, as well as on the oils’ volatile and phenolic profile. The flavored EVOO maintained the quality indices (free fatty acids, peroxide value, extinction coefficients) below the maximum permitted levels, whereas the addition of marjoram enhanced the oxidative stability, the levels of chlorophyll and b-carotene and the total phenolic content. The incorporation of marjoram into the EVOO did not have a significant impact on the antioxidant and antiradical activities. Concerning the antifungal activity, no Zygosaccharomyces bailli cell growth was observed for two weeks in a mayonnaise prepared with the flavored EVOO at a 40 g L−1 concentration. SPME-GC-MS analysis revealed the presence of 11 terpene compounds (hydrocarbon and oxygenated monoterpenes) that had migrated from marjoram in the flavored EVOO. Twenty-one phenolic compounds were tentatively characterized by LC-QToF-MS in the EVOO samples; however, hesperetin and p-coumaric acid, originating from marjoram, were only detected in the flavored EVOO. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Yeast counts of <span class="html-italic">Zygosaccharomyces bailli</span> in samples of two batches of mayonnaise over an incubation period of 20 days at 25 °C. Yeast colonies were measured on YPD agar plates incubated at 25 °C for 48 h. MY B_0: mayonnaise samples prepared with EVOO flavored with 40 g L<sup>−1</sup> dry marjoram; MY C: mayonnaise samples prepared with the unflavored EVOO.</p>
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<p>Chromatograms of (<b>A</b>) EVOO and (<b>Β</b>) EVOO flavored with 40 g L<sup>−1</sup> dry marjoram. Peak numbers correspond to compounds in <a href="#foods-13-03164-t002" class="html-table">Table 2</a> (I.S.: internal standard).</p>
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23 pages, 6299 KiB  
Article
Impact of Pulse Disturbances on Phytoplankton: How Four Storms of Varying Magnitude, Duration, and Timing Altered Community Responses
by Noah Claflin, Jamie L. Steichen, Darren Henrichs and Antonietta Quigg
Environments 2024, 11(10), 218; https://doi.org/10.3390/environments11100218 - 4 Oct 2024
Viewed by 391
Abstract
Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) [...] Read more.
Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) shifts in phytoplankton communities associated with four pulse disturbances (Tax Day Flood in 2016, Hurricane Harvey in 2017, Tropical Storm Imelda in 2019, and Winter Storm Uri in 2021) that occurred in Galveston Bay (TX, USA). Water samples collected daily were processed using an Imaging FlowCytobot (IFCB), along with concurrent measurements of temperature, salinity, and chlorophyll-a. Stronger storm events with localized heavy precipitation and flooding had greater impacts on community composition, increasing diversity (Shannon–Weiner and Simpson Indices) while a cold wave event lowered it. Diatoms and dinoflagellates accounted for the largest fraction of the community, cyanobacteria and chlorophytes varied mostly with salinity, while euglenoids, cryptophytes, and raphidophytes, albeit at lower densities, fluctuated greatly. The unconstrained variance of the redundancy analysis models pointed to additional environmental processes than those measured being responsible for the changes observed. These findings provide insights into the impact of pulse disturbances of different magnitudes, durations, and timings on phytoplankton communities. Full article
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Graphical abstract

Graphical abstract
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<p>Galveston Bay, Texas located in the northwestern Gulf of Mexico. The IFCB samples and water quality data were collected from the marina located on the Texas A&amp;M University at Galveston Campus.</p>
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<p>Relative abundances of the major taxonomic groups pre- and post-major storm events (denoted with a vertical dashed line). Phytoplankton were categorized as diatoms (orange), chlorophytes (dark green), cyanobacteria (blue), dinoflagellates (red), cryptophytes (pink), euglenoids (light green), and raphidophytes (light red) are shown. (<b>a</b>) Tax Day Flood in 2016, (<b>b</b>) Hurricane Harvey in 2017, (<b>c</b>) Tropical Storm Imelda in 2019, and (<b>d</b>) Winter Storm Uri in 2021.</p>
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<p>Redundancy analysis of the major taxonomic groups and significant water quality parameters pre- (green) and post- (blue) storm. Phytoplankton category codes: DIA—diatoms, CHL—chlorophytes, CYA—cyanobacteria, DIN—dinoflagellates, CRY—cryptophytes, EUG—euglenoids, and RAP—raphidophytes. Vector codes: Temp—temperature (°C), Sal—salinity, and Chl—chlorophyll-a (μg/L). (<b>a</b>) Tax Day Flood in 2016, (<b>b</b>) Hurricane Harvey in 2017, (<b>c</b>) Tropical Storm Imelda in 2019, and (<b>d</b>) Winter Storm Uri in 2021. These ellipsoids represent normalized 95% confidence intervals of community composition by multivariate t-distribution (<span class="html-italic">p</span>-value = 0.05).</p>
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<p>Diversity indices calculated for the phytoplankton community pre- (left) and post- (right) major storm events: Tax Day Flood in 2016 (green), Hurricane Harvey in 2017 (blue), Tropical Storm Imelda in 2019 (red), and Winter Storm Uri in 2021 (orange). (<b>a</b>) Species richness, (<b>b</b>) Shannon–Weiner index, (<b>c</b>) Simpson index, and (<b>d</b>) Pielou index.</p>
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<p>Bubble plots were used to examine changes in the abundance of the major phytoplankton categories classified with the IFCB pre- and post-major storm events (denoted with a vertical dashed line). (<b>a</b>) Tax Day Flood in 2016, (<b>b</b>) Hurricane Harvey in 2017, (<b>c</b>) Tropical Storm Imelda in 2019, and (<b>d</b>) Winter Storm Uri in 2021. Bubble size and the continuous color scale represent the abundance (cells mL<sup>−1</sup>) (data no transformed). No bubble was displayed when the abundance was equal to zero. Data is shown as day to day (MM/DD) in the <span class="html-italic">x</span>-axis. A side color bar refers to each major phytoplankton category: diatoms (orange), chlorophytes (dark green), cyanobacteria (blue), dinoflagellates (red), cryptophytes (pink), euglenoids (light green), and raphidophytes (light red).</p>
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<p>Bubble plots were used to examine changes in the abundance of the major phytoplankton categories classified with the IFCB pre- and post-major storm events (denoted with a vertical dashed line). (<b>a</b>) Tax Day Flood in 2016, (<b>b</b>) Hurricane Harvey in 2017, (<b>c</b>) Tropical Storm Imelda in 2019, and (<b>d</b>) Winter Storm Uri in 2021. Bubble size and the continuous color scale represent the abundance (cells mL<sup>−1</sup>) (data no transformed). No bubble was displayed when the abundance was equal to zero. Data is shown as day to day (MM/DD) in the <span class="html-italic">x</span>-axis. A side color bar refers to each major phytoplankton category: diatoms (orange), chlorophytes (dark green), cyanobacteria (blue), dinoflagellates (red), cryptophytes (pink), euglenoids (light green), and raphidophytes (light red).</p>
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<p>Bubble plots were used to examine changes in the abundance of the major phytoplankton categories classified with the IFCB pre- and post-major storm events (denoted with a vertical dashed line). (<b>a</b>) Tax Day Flood in 2016, (<b>b</b>) Hurricane Harvey in 2017, (<b>c</b>) Tropical Storm Imelda in 2019, and (<b>d</b>) Winter Storm Uri in 2021. Bubble size and the continuous color scale represent the abundance (cells mL<sup>−1</sup>) (data no transformed). No bubble was displayed when the abundance was equal to zero. Data is shown as day to day (MM/DD) in the <span class="html-italic">x</span>-axis. A side color bar refers to each major phytoplankton category: diatoms (orange), chlorophytes (dark green), cyanobacteria (blue), dinoflagellates (red), cryptophytes (pink), euglenoids (light green), and raphidophytes (light red).</p>
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23 pages, 18099 KiB  
Article
Alleviative Effect of Exogenous Application of Fulvic Acid on Nitrate Stress in Spinach (Spinacia oleracea L.)
by Kangning Han, Jing Zhang, Cheng Wang, Youlin Chang, Zeyu Zhang and Jianming Xie
Agronomy 2024, 14(10), 2280; https://doi.org/10.3390/agronomy14102280 (registering DOI) - 3 Oct 2024
Viewed by 206
Abstract
Salt stress could be a significant factor limiting the growth and development of vegetables. In this study, Fulvic Acid (FA) (0.05%, 0.1%, 0.15%, 0.2%, and 0.25%) was applied under nitrate stress (150 mM), with normal Hoagland nutrient solution as a control to investigate [...] Read more.
Salt stress could be a significant factor limiting the growth and development of vegetables. In this study, Fulvic Acid (FA) (0.05%, 0.1%, 0.15%, 0.2%, and 0.25%) was applied under nitrate stress (150 mM), with normal Hoagland nutrient solution as a control to investigate the influence of foliar spray FA on spinach growth, photosynthesis, and oxidative stress under nitrate stress. The results showed that nitrate stress significantly inhibited spinach growth, while ROS (reactive oxygen species) accumulation caused photosystem damage, which reduced photosynthetic capacity. Different concentrations of FA alleviated the damage caused by nitrate stress in spinach to varying degrees in a concentration-dependent manner. The F3 treatment (0.15% FA + 150 mM NO3) exhibited the most significant mitigating effect. FA application promoted the accumulation of biomass in spinach under nitrate stress and increased chlorophyll content, the net photosynthetic rate, the maximum photochemical quantum yield of PSII (Photosystem II) (Fv/Fm), the quantum efficiency of PSII photochemistry [Y(II)], the electron transport rate, and the overall functional activity index of the electron transport chain between the PSII and PSI systems (PItotal); moreover, FA decreased PSII excitation pressure (1 − qP), quantum yields of regulated energy dissipation of PSII [Y(NPQ)], and the relative variable initial slope of fluorescence. FA application increased superoxide dismutase, peroxidase, and catalase activities and decreased malondialdehyde, H2O2, and O2 levels in spinach under nitrate stress. FA can enhance plant resistance to nitrate by accelerating the utilization of light energy in spinach to mitigate excess light energy and ROS-induced photosystem damage and increase photosynthetic efficiency. Full article
(This article belongs to the Special Issue Crop and Vegetable Physiology under Environmental Stresses)
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Figure 1
<p>The effect of FA on the growth of spinach under nitrate stress. Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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<p>The effect of FA on chlorophyll contents of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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<p>The effect of FA on gas exchange parameters of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). (<b>A</b>) Net photosynthetic rate, Pn. (<b>B</b>) Stomatal conductance, Gs. (<b>C</b>) Intercellular CO<sub>2</sub> concentration, Ci. (<b>D</b>) Transpiration rate, Tr. Gray, CK; Blue, N treatment; Green, F1 treatment; Purple, F2 treatment; Red, F3 treatment; Pink, F4 treatment; Yellow, F5 treatment.</p>
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<p>The effect of FA on chlorophyll fluorescence parameters of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). (<b>A</b>) The maximum photochemical quantum yield of PSII, Fv/Fm. (<b>B</b>) The efficiency of excitation energy capture by open PSII reaction centers, Fv′/Fm′. (<b>C</b>) Non-photochemical quenching, NPQ. (<b>D</b>) Photochemical quenching coefficient, qP. (<b>E</b>) PSII excitation pressure, 1 − qP. (<b>F</b>) Excess excitation energy, (1 − qP)/NPQ. Gray, CK; Blue, N treatment; Green, F1 treatment; Purple, F2 treatment; Red, F3 treatment; Pink, F4 treatment; Yellow, F5 treatment.</p>
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<p>The effects of FA on PSII energy distribution and the electron transport rate of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). (<b>A</b>) The effective quantum yield of PSII, Y(II); the quantum yield of regulatory energy dissipation, Y(NPQ); the quantum yield of non-regulated energy dissipation, Y(NO). (<b>B</b>) The electron transfer rate, ETR, Gray, CK; Blue, N treatment; Green, F1 treatment; Purple, F2 treatment; Red, F3 treatment; Pink, F4 treatment; Yellow, F5 treatment.</p>
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<p>The effects of FA on rETR-PAR response curves and fitting parameters of spinach under nitrate stress. Different lower case letters in the same column indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). rETR<sub>max</sub>: maximum relative electron transport rate; α: the initial slope of the light curve; and Ik = rETR<sub>max</sub>/α: half-saturation light intensity. Curves were fitted with Origin software (version 2022); the fitted parameters are shown in the embedded table.</p>
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<p>The effect of FA on the performance of rapid induction kinetics of spinach under nitrate stress. Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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<p>The effects of FA on Vt curves and ΔVt curves of spinach under nitrate stress. Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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<p>The effects of FA on V<sub>J</sub> and V<sub>I</sub> of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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<p>The effects of FA on JIP test parameters, energy distribution per unit cross-sectional area, and energy distribution per RC of PSII in spinach leaves under nitrate stress. Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). (<b>A</b>) JIP test parameters. (<b>B</b>) Energy distribution per unit cross-sectional area, and energy distribution per RC of PSII.</p>
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<p>The effects of FA on the activity of SOD, POD, and CAT of spinach under nitrate stress. Mean values of different letters indicate significant differences through Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5). (<b>A</b>) superoxide dismutase, SOD; (<b>B</b>) peroxidase, POD; and (<b>C</b>) catalase, CAT. Gray, CK; Blue, N treatment; Green, F1 treatment; Purple, F2 treatment; Red, F3 treatment; Pink, F4 treatment; Yellow, F5 treatment.</p>
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<p>Correlation analysis of spinach in response to nitrate stress. * Significant at the 5% level.</p>
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<p>Principal component analysis of spinach in response to nitrate stress. Control (CK), 150 mM NO<sub>3</sub><sup>−</sup> (N), 0.05% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F1), 0.1% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F2), 0.15% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F3), 0.2% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F4), and 0.25% FA + 150 mM NO<sub>3</sub><sup>−</sup> (F5).</p>
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19 pages, 834 KiB  
Review
Chlorophyll Fluorescence in Wheat Breeding for Heat and Drought Tolerance
by Firuz Abdullaev, Polina Pirogova, Vladimir Vodeneev and Oksana Sherstneva
Plants 2024, 13(19), 2778; https://doi.org/10.3390/plants13192778 - 3 Oct 2024
Viewed by 338
Abstract
The constantly growing need to increase the production of agricultural products in changing climatic conditions makes it necessary to accelerate the development of new cultivars that meet the modern demands of agronomists. Currently, the breeding process includes the stages of genotyping and phenotyping [...] Read more.
The constantly growing need to increase the production of agricultural products in changing climatic conditions makes it necessary to accelerate the development of new cultivars that meet the modern demands of agronomists. Currently, the breeding process includes the stages of genotyping and phenotyping to optimize the selection of promising genotypes. One of the most popular phenotypic methods is the pulse-amplitude modulated (PAM) fluorometry, due to its non-invasiveness and high information content. In this review, we focused on the opportunities of using chlorophyll fluorescence (ChlF) parameters recorded using PAM fluorometry to assess the state of plants in drought and heat stress conditions and predict the economically significant traits of wheat, as one of the most important agricultural crops, and also analyzed the relationship between the ChlF parameters and genetic markers. Full article
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<p>The distribution of the genes characterized as involved in photosynthesis along the chromosomes of <span class="html-italic">T. aestivum</span> based on the NCBI Genes database (<a href="https://www.ncbi.nlm.nih.gov/gene" target="_blank">https://www.ncbi.nlm.nih.gov/gene</a> (accessed on 5 June 2024)). * indicates chromosomes on which loci associated with ChlF parameters and chlorophyll content have been identified; the letters A, B and D indicate genomes; 1–7 indicate chromosomes; the number of genes described is indicated by the numbers above the corresponding columns.</p>
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<p>Relationship between ChlF parameters and significant breeding traits through some candidate genes under control and stress conditions for <span class="html-italic">T. aestivum</span>. The scheme is based on data from [<a href="#B127-plants-13-02778" class="html-bibr">127</a>,<a href="#B129-plants-13-02778" class="html-bibr">129</a>,<a href="#B132-plants-13-02778" class="html-bibr">132</a>,<a href="#B133-plants-13-02778" class="html-bibr">133</a>,<a href="#B135-plants-13-02778" class="html-bibr">135</a>], a detailed description of the measured ChlF parameters and associated genes is described in <a href="#app1-plants-13-02778" class="html-app">Table S1</a>.</p>
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18 pages, 2610 KiB  
Article
Overexpression of GmXTH1 Enhances Salt Stress Tolerance in Soybean
by Yang Song, Kun Wang, Dan Yao, Qi Zhang, Boran Yuan and Piwu Wang
Agronomy 2024, 14(10), 2276; https://doi.org/10.3390/agronomy14102276 - 3 Oct 2024
Viewed by 226
Abstract
Soybean is an important grain, oil and feed crop, which plays an important role in ensuring national food security. However, soil salinization hinders and destroys the normal physiological metabolism of soybean, resulting in the abnormal growth or death of soybean. The XTH gene [...] Read more.
Soybean is an important grain, oil and feed crop, which plays an important role in ensuring national food security. However, soil salinization hinders and destroys the normal physiological metabolism of soybean, resulting in the abnormal growth or death of soybean. The XTH gene can modify the plant cell wall and participate in the response and adaptation of plants to negative stress. To elucidate the role of the overexpressed GmXTH1 gene under NaCl-induced stress in soybean, we determined the germination rate, the germination potential, the germination index, seedling SOD activity, POD activity, the MDA content and the MDA content during the germination stage of the overexpressed lines of the GmXTH1 gene, the OEAs (OEA1, OEA2 and OEA3), the interference expression line IEA2, the control mutant M18, the CAT content and the chlorophyll content. The relative expression of the GmXTH1 gene in the material OEA1 and the contents of Na+ and K+ in the roots after stress were also determined. The results showed that OEAs exhibited enhanced germination indices, including the germination rate and germination potential, and were less sensitive to stress compared with the mutant M18. In contrast, the inhibitory effect of NaCl was more pronounced in the line with a disturbed expression of GmXTH1 (IEA2). The OEAs exhibited more enzyme activities and a lower MDA content, indicating reduced oxidative stress, and maintained higher chlorophyll levels, suggesting improved photosynthetic capacity. Relative expression analysis showed that the GmXTH1 gene was rapidly up-regulated in response to NaCl, peaking at 4 h after treatment, and subsequently declining. This temporal expression pattern correlated with the enhanced salt tolerance observed in OEA1. Notably, OEA1 accumulated more Na+ and maintained higher K+ levels, indicating effective ionic homeostasis under stress. Collectively, these results suggest that the overexpression of the GmXTH1 gene may positively regulate plant responses to salt stress by modulating the antioxidant defense and ion transport mechanisms. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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<p>Germination phenotype of each material under different treatment conditions.</p>
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<p>Seedling phenotypes of each material under different treatment conditions.</p>
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<p>The physiological and biochemical indexes of the seedling leaves of each strain. (<b>A</b>) shows the SOD activity of each strain under the different treatment conditions. (<b>B</b>) shows the POD activity of each strain under the different treatment conditions. (<b>C</b>) shows the CAT activity of each strain under the different treatment conditions. (<b>D</b>) shows the MDA content of each strain under the different treatment conditions.</p>
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<p>The chlorophyll content of each strain under the different treatment conditions. (<b>A</b>) shows the chlorophyll a content of each strain in the different treatments. (<b>B</b>) shows the chlorophyll b content of each strain in the different treatments. (<b>C</b>) shows the total chlorophyll content of each strain in the different treatments.</p>
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<p>Root growth of each strain at V2 stage.</p>
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<p>The measurement of the root indexes of each strain at the V2 stage. (<b>A</b>) shows the total root length of each strain for the different treatment conditions. (<b>B</b>) shows the root volume of each strain in the different treatment conditions. (<b>C</b>) shows the root surface area of each strain in the different treatment conditions. (<b>D</b>) shows the mean root diameter of each strain in the different treatment conditions.</p>
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<p>Relative expression of <span class="html-italic">GmXTH1</span> gene in roots and leaves of OEA1 over time.</p>
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<p>(<b>A</b>) shows the Na<sup>+</sup> and K<sup>+</sup> contents under the different treatment conditions. (<b>B</b>) shows the ratio of K<sup>+</sup>/Na<sup>+</sup>.</p>
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11 pages, 813 KiB  
Article
Extraction and Concentration of Spirulina Water-Soluble Metabolites by Ultrafiltration
by Claudia Salazar-González, Carolina Mendoza Ramos, Hugo A. Martínez-Correa and Hugo Fabián Lobatón García
Plants 2024, 13(19), 2770; https://doi.org/10.3390/plants13192770 - 3 Oct 2024
Viewed by 388
Abstract
Spirulina (Arthospira platensis) is known for its rich content of natural compounds like phycocyanin, chlorophylls, carotenoids, and high protein levels, making it a nutrient-dense food. Over the past decade, research has aimed to optimize the extraction, separation, and purification of these [...] Read more.
Spirulina (Arthospira platensis) is known for its rich content of natural compounds like phycocyanin, chlorophylls, carotenoids, and high protein levels, making it a nutrient-dense food. Over the past decade, research has aimed to optimize the extraction, separation, and purification of these valuable metabolites, focusing on technologies such as high-pressure processing, ultrasound-assisted extraction, and microwave-assisted extraction as well as enzymatic treatments, chromatographic precipitation, and membrane separation. In this study, various extraction methods (conventional vs. ultrasound-assisted), solvents (water vs. phosphate buffer), solvent-to-biomass ratios (1:5 vs. 1:10), and ultrafiltration (PES membrane of MWCO 3 kDa, 2 bar) were evaluated. The quantities of total protein, phycocyanin (PC), chlorophyll a (Cla), and total carotenoids (TCC) were measured. The results showed that ultrasound-assisted extraction (UAE) with phosphate buffer at a 1:10 ratio yielded a metabolite-rich retentate (MRR) with 37.0 ± 1.9 mg/g of PC, 617 ± 15 mg/g of protein, 0.4 ± 0.2 mg/g of Cla, and 0.15 ± 0.14 mg/g of TCC. Water extraction in the concentration process achieved the highest concentrations in MRR, with approximately 76% PC, 92% total protein, 62% Cla, and 41% TCC. These findings highlight the effective extraction and concentration processes to obtain a metabolite-rich retentate from Spirulina biomass, reducing the volume tenfold and showing potential as a functional ingredient for the food, cosmetic, and pharmaceutical industries. Full article
(This article belongs to the Special Issue Microalgae Photobiology, Biotechnology, and Bioproduction)
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<p>Permeate flux during the ultrafiltration of crude extract.</p>
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<p>Lyophilized retentates (<b>a</b>) from buffer extraction and (<b>b</b>) from water extraction.</p>
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20 pages, 12438 KiB  
Article
Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning
by Zhixin Wang, Zhenqi Zhang, Hailong Li, Hong Jiang, Lifei Zhuo, Huiwen Cai, Chao Chen and Sheng Zhao
J. Mar. Sci. Eng. 2024, 12(10), 1742; https://doi.org/10.3390/jmse12101742 - 3 Oct 2024
Viewed by 356
Abstract
Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need to study marine water quality. The use of remote sensing for water quality inversion offers a precise, timely, and comprehensive way to evaluate the [...] Read more.
Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need to study marine water quality. The use of remote sensing for water quality inversion offers a precise, timely, and comprehensive way to evaluate the present state and future trajectories of water quality. In this paper, a remote sensing inversion model utilizing machine learning was developed to evaluate water quality variations in the Ma’an Archipelago Marine Special Protected Area (MMSPA) over a long-time series of Landsat images. The concentrations of chlorophyll-a (Chl-a), phosphate, and dissolved inorganic nitrogen (DIN) in the sea area from 2002 to 2022 were inverted and analyzed. The spatial and temporal characteristics of these variations were investigated. The results indicated that the random forest model could reliably predict Chl-a, phosphate, and DIN concentrations in the MMSPA. Specifically, the inversion results for Chl-a showed the coefficient of determination (R2) of 0.741, the root mean square error (RMSE) of 3.376 μg/L, and the mean absolute percentage error (MAPE) of 16.219%. Regarding spatial distribution, the concentrations of these parameters were notably elevated in the nearshore zones, especially in the northwest, contrasted with lower concentrations in the offshore and southeast areas. Predominantly, the nearshore regions with higher concentrations were in proximity to the aquaculture zones. Additionally, nutrients originating from land sources, transported via rivers such as the Yangtze River, as well as influenced by human activities, have shaped this nutrient distribution. Over the long term, the water quality in the MMSPA has shown considerable interannual fluctuations during the past two decades. As a sanctuary, preserving superior water quality and a healthy ecosystem is very important. Efforts in protection, restoration, and management will demand considerable labor. Remote sensing has demonstrated its worth as a proficient technology for real-time monitoring, capable of supporting the sustainable exploitation of marine resources and the safeguarding of the marine ecological environment. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Sampling sites in the study area.</p>
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<p>Technical workflow depicting the study methods.</p>
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<p>Accuracy evaluation of the Chl-a concentration retrieval by the RF model.</p>
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<p>Accuracy evaluation of the phosphate concentration retrieval by the RF model.</p>
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<p>Accuracy evaluation of the DIN concentration retrieval by the RF model.</p>
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<p>Distribution of Chl-a concentration in Ma’an Archipelago Marine Special Protected Area from 2002 to 2022. (<b>a</b>) 2002; (<b>b</b>) 2007; (<b>c</b>) 2013; (<b>d</b>) 2018; (<b>e</b>) 2022.</p>
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<p>Distribution of reactive phosphate concentrations in the Ma’an Archipelago Marine Special Protected Area from 2002 to 2022 (<b>a</b>) 2002; (<b>b</b>) 2007; (<b>c</b>) 2013; (<b>d</b>) 2018; (<b>e</b>) 2022.</p>
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<p>Distribution of DIN concentrations in Ma’an Archipelago Marine Special Protected Area from 2002 to 2022. (<b>a</b>) 2002; (<b>b</b>) 2007; (<b>c</b>) 2013; (<b>d</b>) 2018; (<b>e</b>) 2022.</p>
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12 pages, 4316 KiB  
Article
Iodine-Enriched Urea Reduces Volatilization and Improves Nitrogen Uptake in Maize Plants
by João Victor da Costa Cezar, Everton Geraldo de Morais, Jucelino de Sousa Lima, Pedro Antônio Namorato Benevenute and Luiz Roberto Guimarães Guilherme
Nitrogen 2024, 5(4), 891-902; https://doi.org/10.3390/nitrogen5040057 - 2 Oct 2024
Viewed by 304
Abstract
Urea is the primary source of nitrogen (N) used in agriculture. However, it has a high N loss potential through volatilization. Various mechanisms can be employed to reduce N volatilization losses by inhibiting urease. When added to urea, iodine (I) has high potential [...] Read more.
Urea is the primary source of nitrogen (N) used in agriculture. However, it has a high N loss potential through volatilization. Various mechanisms can be employed to reduce N volatilization losses by inhibiting urease. When added to urea, iodine (I) has high potential for this purpose. Thus, this study aimed to determine whether adding I to urea reduces volatilization losses and increases N uptake in maize plants. Maize plants were cultivated in greenhouse conditions for 36 days. Urea treatments were applied at 15 days of testing, including iodine-enriched urea, conventional urea, and no urea application. Additionally, a study concerning N volatilization from urea was conducted using the same treatments under the same environmental conditions. Iodine was incorporated and adhered to urea, at an I concentration of 0.2%, using potassium iodate (KIO3). Under controlled conditions and over a short period of time, it was observed that the application of iodine-enriched urea increased the chlorophyll b content, root N accumulation, and total N accumulation in maize plants compared with conventional urea. Moreover, iodine-enriched urea reduced N losses from volatilization by 11% compared with conventional urea. The reduction in N volatilization correlated positively with the increased chlorophyll b, total chlorophyll, root N accumulation, and total N accumulation favored by the iodine-enriched urea treatment. Our findings demonstrated that adding I to urea is an efficient and promising strategy to reduce N losses and increase N uptake in plants. Full article
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<p>Conventional urea and iodine-enriched urea photos.</p>
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<p>A scheme for the nitrogen volatilization study that was carried out.</p>
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<p>Chlorophyll content, dry matter production, and nitrogen accumulation in maize according to the urea fertilization. Full bar: total chlorophyll (<b>a</b>) or total biomass production (<b>b</b>) or total nitrogen accumulation (<b>c</b>). <b><span class="html-italic">NS</span></b>, NS, and ns: without a statistical difference in total, shoot, and root dry matter production (<span class="html-italic">p</span> &gt; 0.05). The means in <a href="#nitrogen-05-00057-f003" class="html-fig">Figure 3</a>a follow the same scheme with bold–italic capitals, capital letters, and minuscule letters, not differentiating the treatments for total chlorophyll, chlorophyll <span class="html-italic">a</span> (Chl <span class="html-italic">a</span>), and chlorophyll <span class="html-italic">b</span> (Chl <span class="html-italic">b</span>) contents, respectively (<span class="html-italic">p</span> &gt; 0.05). The means in <a href="#nitrogen-05-00057-f003" class="html-fig">Figure 3</a>c follow the same scheme with bold–italic capitals, capital letters, and minuscule letters, not differentiating the treatments for total, shoot, and root nitrogen accumulations, respectively (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Cumulative nitrogen (N) volatilization according to the urea fertilization treatments. Treatments whose bootstrap-generated confidence intervals do not overlap in the figure were statistically different (<span class="html-italic">p</span> &lt; 0.05). The amount of N added was 150 mg per mini-lysimeter.</p>
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<p>Principal component analysis (<b>a</b>) and Pearson correlation (<b>b</b>) for variables analyzed in the maize cultivation and N volatilization studies. Significant correlation coefficients (<span class="html-italic">p</span> &lt; 0.05) are indicated by bold numbers, with positive and negative correlations distinguished by red and blue, respectively. White boxes indicate non-significance without numbers.</p>
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18 pages, 3892 KiB  
Article
Differential Drought Responses of Soybean Genotypes in Relation to Photosynthesis and Growth-Yield Attributes
by Md. Saddam Hossain, Md. Arifur Rahman Khan, Apple Mahmud, Uttam Kumar Ghosh, Touhidur Rahman Anik, Daniel Mayer, Ashim Kumar Das and Mohammad Golam Mostofa
Plants 2024, 13(19), 2765; https://doi.org/10.3390/plants13192765 - 2 Oct 2024
Viewed by 302
Abstract
Water scarcity leads to significant ecological challenges for global farming production. Sustainable agriculture depends on developing strategies to overcome the impacts of drought on important crops, including soybean. In this present study, seven promising soybean genotypes were evaluated for their drought tolerance potential [...] Read more.
Water scarcity leads to significant ecological challenges for global farming production. Sustainable agriculture depends on developing strategies to overcome the impacts of drought on important crops, including soybean. In this present study, seven promising soybean genotypes were evaluated for their drought tolerance potential by exposing them to water deficit conditions. The control group was maintained at 100% field capacity (FC), while the drought-treated group was maintained at 50% FC on a volume/weight basis. This treatment was applied at the second trifoliate leaf stage and continued until maturity. Our results demonstrated that water shortage exerted negative impacts on soybean phenotypic traits, physiological and biochemical mechanisms, and yield output in comparison with normal conditions. Our results showed that genotype G00001 exhibited the highest leaf area plant−1 (483.70 cm2), photosynthetic attributes like stomatal conductance (gs) (0.15 mol H2O m−2 s−1) and photosynthetic rate (Pn) (13.73 μmol CO2 m−2 s−1), and xylem exudation rate (0.25 g h−1) under drought conditions. The G00001 genotype showed greater leaf greenness by preserving photosynthetic pigments (total chlorophylls (Chls) and carotenoids; 4.23 and 7.34 mg g−1 FW, respectively) in response to drought conditions. Soybean plants accumulated high levels of stress indicators like proline and malondialdehyde when subjected to drought stress. However, genotype G00001 displayed lower levels of proline (4.49 μg g−1 FW) and malondialdehyde (3.70 μmol g−1 FW), indicating that this genotype suffered from less oxidative stress induced by drought stress compared to the other investigated soybean genotypes. Eventually, the G00001 genotype had a greater yield in terms of seeds pod−1 (SP) (1.90) and 100-seed weight (HSW) (14.60 g) under drought conditions. On the other hand, BD2333 exhibited the largest decrease in plant height (37.10%), pod number plant−1 (85.90%), SP (56.20%), HSW (54.20%), gs (90.50%), Pn (71.00%), transpiration rate (59.40%), relative water content (34.40%), Chl a (79.50%), total Chls (72.70%), and carotenoids (56.70%), along with the maximum increase in water saturation deficit (290.40%) and malondialdehyde content (280.30%) under drought compared to control conditions, indicating its higher sensitivity to drought stress. Our findings suggest that G00001 is a promising candidate to consider for field trials and further evaluation of its molecular signature may help breeding other elite cultivars to develop drought-tolerant, high-yielding soybean varieties. Full article
(This article belongs to the Special Issue Drought Responses and Adaptation Mechanisms in Plants)
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<p>Effects of different water regimes on the morphological and yield traits of soybean genotypes. Plants were retained at 100% field capacity (FC) (control) (<b>A</b>) and at 50% FC (drought) (<b>B</b>) after 25 days of exposure to water-shortage treatment. Effect on plant height (<b>C</b>), pod number plant<sup>−1</sup> (<b>D</b>), and seed yield plant<sup>−1</sup> (<b>E</b>) of investigated soybean genotypes under different water regimes. (<b>F</b>) Heatmap illustrates the percent reductions in plant height (PH), leaf area plant<sup>−1</sup> (LA), leaf number plant<sup>−1</sup> (LP), branch number plant<sup>−1</sup> (BP), pod number plant<sup>−1</sup> (PP), seeds pod<sup>−1</sup> (SP), 100-seed weight (HSW), and seed yield plant<sup>−1</sup> (SY) in investigated soybean genotypes under drought compared to control conditions. Statistical analyses were conducted separately for control and drought treatments, with values derived from five biological replicates (<span class="html-italic">n</span> = 5; 10 plants per replicate). Bars symbolize means with standard errors for both control and drought-stressed soybean genotypes. Different letters are displayed on the bars to specify significant differences (<span class="html-italic">p</span> &lt; 0.05) among the investigated soybean genotypes.</p>
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<p>Photosynthetic responses of investigated soybean genotypes under different water regimes. Effect on stomatal conductance (<b>A</b>), photosynthetic rate (<b>B</b>), transpiration rate (<b>C</b>), and leaf temperature (<b>D</b>) of investigated soybean genotypes under different water regimes. (<b>E</b>) Heatmap illustrates the percent reductions in stomatal conductance (<span class="html-italic">gs</span>), photosynthetic rate (<span class="html-italic">Pn</span>), intercellular CO<sub>2</sub> concentration (<span class="html-italic">Ci</span>), transpiration rate (<span class="html-italic">E</span>), leaf temperature (LT), and instantaneous water use efficiency (WUEins) in investigated soybean genotypes under drought compared to control conditions. Statistical analyses were conducted separately for control and drought treatments, with values derived from five biological replicates (<span class="html-italic">n</span> = 5; 10 plants per replicate). Bars symbolize means with standard errors for both control and drought-stressed soybean genotypes. Different letters are displayed on the bars to specify significant differences (<span class="html-italic">p</span> &lt; 0.05) among the investigated soybean genotypes.</p>
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<p>Effect on relative water content (<b>A</b>), water saturation deficit (<b>B</b>), and xylem exudation rate (<b>C</b>) of investigated soybean genotypes under different water regimes. (<b>D</b>) Heatmap illustrates the percent reductions in relative water content (RWC), water saturation deficit (WSD), water retention capacity (WRC), water uptake capacity (WUC), and xylem exudation rate (XER) of investigated soybean genotypes under drought compared to control conditions. Statistical analyses were conducted separately for control and drought treatments, with values derived from five biological replicates (<span class="html-italic">n</span> = 5; 10 plants per replicate). Bars symbolize means with standard errors for both control and drought-stressed soybean genotypes. Different letters are displayed on bars to specify significant differences (<span class="html-italic">p</span> &lt; 0.05) among investigated soybean genotypes.</p>
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<p>Changes in levels of photosynthetic pigments, proline, and malondialdehyde in leaves of investigated soybean genotypes under different water regimes. Effect on chlorophyll <span class="html-italic">a</span> (<b>A</b>), chlorophyll <span class="html-italic">b</span> (<b>B</b>), total chlorophylls (<b>C</b>), carotenoids (<b>D</b>), proline (<b>E</b>), and malondialdehyde (<b>F</b>) of investigated soybean genotypes under different water regimes. (<b>G</b>) Heatmap illustrates the percent reductions in chlorophyll <span class="html-italic">a</span> (Chl <span class="html-italic">a</span>), chlorophyll <span class="html-italic">b</span> (Chl <span class="html-italic">b</span>), total chlorophylls (Total Chls), carotenoids, proline, and malondialdehyde (MDA) of soybean genotypes under drought compared to control conditions. Statistical analyses were conducted separately for control and drought treatments, with values derived from five biological replicates (<span class="html-italic">n</span> = 5; 10 plants per replicate). Bars symbolize means with standard errors for both control and drought-stressed soybean genotypes. Different letters are displayed on the bars to specify significant differences (<span class="html-italic">p</span> &lt; 0.05) among investigated soybean genotypes.</p>
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<p>Pearson’s correlation coefficient among studied parameters under control (<b>A</b>) and drought stress (<b>B</b>) conditions. The correlations (positive to negative) among the traits were visualized by the color range green to gray in both control and drought stress conditions. Strong positive (+1) and negative (−1) associations between the two variables were presented in grey and green colored circles, respectively. Larger circles indicate a highly strong correlation, and smaller circles indicate a weaker relationship between the two traits. BP, branch number plant<sup>−1</sup>; Chl <span class="html-italic">a</span>, chlorophyll a; Chl <span class="html-italic">b</span>, chlorophyll b; <span class="html-italic">Ci</span>, intercellular CO<sub>2</sub> concentration; <span class="html-italic">E</span>, transpiration rate; <span class="html-italic">gs</span>, stomatal conductance; HSW, 100-seed weight; LA, leaf area plant<sup>−1</sup>; LP, leaf number plant<sup>−1</sup>; LT, leaf temperature; MDA, malondialdehyde; PH, plant height; PP, pod number plant<sup>−1</sup>; <span class="html-italic">Pn</span>, photosynthetic rate; RWC, relative water content; SP, seeds pod<sup>−1</sup>; SY, seed yield plant<sup>−1</sup>; Total Chls, total chlorophylls; WRC, water retention capacity; WSD, water saturation deficit; WUC, water uptake capacity; WUEint, intrinsic water use efficiency; WUEins, instantaneous water use efficiency; XER, xylem exudation rate.</p>
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15 pages, 2936 KiB  
Article
Effects of Planting Density and Nitrogen Fertilization on Growth Traits and Leaf and Wood Characteristics of Three Poplar Clones
by Hongxing Wang, Luping Jiang, Feifan Zhang and Xiyang Zhao
Sustainability 2024, 16(19), 8561; https://doi.org/10.3390/su16198561 - 2 Oct 2024
Viewed by 378
Abstract
A comprehension of the effects planting density and nitrogen (N) fertilization have on the physiological and morphological characteristics of trees is critical for optimizing the require size and characteristics of wood products. We evaluated the growth traits and the leaf and wood characteristics [...] Read more.
A comprehension of the effects planting density and nitrogen (N) fertilization have on the physiological and morphological characteristics of trees is critical for optimizing the require size and characteristics of wood products. We evaluated the growth traits and the leaf and wood characteristics of three clone poplars including Populus simonii × P. nigra ‘Xiaohei’, ‘Xiaohei-14’ and ‘Bailin-3’ under five planting densities (1666, 1111, 833, 666, and 555 tree ha−1) and four N fertilization rates (0, 100, 160, and 220 g tree−1 year−1). The results show that the clone type significantly affected all observed indicators, while planting density and N fertilization treatments had a significant effect on growth traits and leaf characteristics, but not on wood characteristics. Specifically, the clone ‘Bailin-3’ exhibited the largest annual increments in tree height and diameter at breast height (DBH), leaf width, N content, and soluble protein content. A decrease in initial planting density (from 1666 to 555 tree ha−1) led to an increased annual incremental tree height and DBH, regardless of clone type and N fertilization treatment. N fertilization treatment significantly impacted the annual increment in DBH, but not that of tree height. Further, the annual increments in tree height and DBH were positively correlated with leaf width, N content, chlorophyll content, and soluble protein content, and negatively correlated with hemicellulose content. In addition, the chlorophyll and soluble protein contents were identified as the most reliable predictors of the annual increments in tree height and DBH. Our results demonstrate the clone ‘Bailin-3’ with 555 tree ha−1 under 160 g N tree−1 yr−1 showed superior growth traits and leaf characteristics. Thus, it is recommended for future poplar silviculture of larger diameter timber production at similar sites. The results contribute to understanding of the effects of planting density and fertilization on the growth traits and the leaf and wood characteristics of three poplar clones, offering valuable guidance for the sustainable development and long-term productivity of poplar plantations. Full article
(This article belongs to the Section Sustainable Forestry)
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Figure 1

Figure 1
<p>Regression relationships between planting density and height (<b>a</b>) and DBH (<b>b</b>) under different N fertilization levels in three poplar clone plantations. D2: 3 m × 2 m (1666 tree ha<sup>−1</sup>); D3: 3 m × 3 m (1111 tree ha<sup>−1</sup>); D4: 3 m × 4 m (833 tree ha<sup>−1</sup>); D5: 3 m × 5 m (666 tree ha<sup>−1</sup>); and D6: 3 m × 6 m (555 tree ha<sup>−1</sup>, D6), and four N fertilization levels included 0 (N0), 100 (N1), 160 (N2), and 220 g N tree<sup>−1</sup> yr<sup>−1</sup> (N3). R<sup>2</sup> and <span class="html-italic">p</span> values are shown.</p>
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<p>The wood characteristics under different density and N fertilization levels in three poplar clone plantations. Data are means ± SE (<span class="html-italic">n</span> = 3). D2: 3 m × 2 m (1666 tree ha<sup>−1</sup>); D3: 3 m × 3 m (1111 tree ha<sup>−1</sup>); D4: 3 m × 4 m (833 tree ha<sup>−1</sup>); D5: 3 m × 5 m (666 tree ha<sup>−1</sup>); and D6: 3 m × 6 m (555 tree ha<sup>−1</sup>, D6), and four N fertilization levels included 0 (N0), 100 (N1), 160 (N2), and 220 g N tree<sup>−1</sup> yr<sup>−1</sup> (N3).</p>
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<p>The leaf characteristics (leaf mass per area, N content, chlorophyll, soluble sugar, and soluble protein) under different planting density and N fertilization levels in three poplar clone plantations. Different letters present significant differences for the same parameter among different N fertilization levels within the same clone type and planting density based on Tukey’s HSD test at <span class="html-italic">p</span> &lt; 0.05 level. D2: 3 m × 2 m (1666 tree ha<sup>−1</sup>); D3: 3 m × 3 m (1111 tree ha<sup>−1</sup>); D4: 3 m × 4 m (833 tree ha<sup>−1</sup>); D5: 3 m × 5 m (666 tree ha<sup>−1</sup>); and D6: 3 m × 6 m (555 tree ha<sup>−1</sup>, D6), and four N fertilization levels included 0 (N0), 100 (N1), 160 (N2), and 220 g N tree<sup>−1</sup> yr<sup>−1</sup> (N3).</p>
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<p>The leaf characteristics (leaf length, leaf width, and chlorophyll a, chlorophyll b, and carotenoids content) under different planting density levels in three poplar clone plantations. Different letters present significant differences for the same parameter among different planting densities within the same clone type based on Tukey’s HSD test at <span class="html-italic">p</span> &lt; 0.05 level. Data for different nitrogen fertilization levels are merged because neither ANOVA nor Tukey’s HSD test at <span class="html-italic">p</span> &lt; 0.05 level showed any significant differences. D2: 3 m × 2 m (1666 tree ha<sup>−1</sup>); D3: 3 m × 3 m (1111 tree ha<sup>−1</sup>); D4: 3 m × 4 m (833 tree ha<sup>−1</sup>); D5: 3 m × 5 m (666 tree ha<sup>−1</sup>); and D6: 3 m × 6 m (555 tree ha<sup>−1</sup>, D6), and four N fertilization levels included 0 (N0), 100 (N1), 160 (N2), and 220 g N tree<sup>−1</sup> yr<sup>−1</sup> (N3).</p>
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<p>Correlations between growth traits and leaf and wood characteristics. Asterisks indicate the statistical significance (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Random forest analysis showing the main growth traits, wood properties, and leaf physiological characteristics drivers of tree height (<b>a</b>) and diameter at breast height (DBH) (<b>b</b>). Significance levels of each predictor are as follows: * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. MSE: mean square error.</p>
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