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20 pages, 1442 KiB  
Article
The Synergistic Effect of Limestone Powder and Rice Husk Ash on the Mechanical Properties of Cement-Based Materials
by Jialei Wang, Feifei Jiang, Juan Zhou and Zhongyang Mao
Materials 2024, 17(20), 5058; https://doi.org/10.3390/ma17205058 (registering DOI) - 16 Oct 2024
Abstract
Fully utilizing solid waste as supplementary cementitious materials (SCMs) while ensuring the mechanical properties of cement-based materials is one of the pathways for carbon reduction in the cement industry. Understanding the effects of the two solid wastes-limestone powder (LP) and rice husk ash [...] Read more.
Fully utilizing solid waste as supplementary cementitious materials (SCMs) while ensuring the mechanical properties of cement-based materials is one of the pathways for carbon reduction in the cement industry. Understanding the effects of the two solid wastes-limestone powder (LP) and rice husk ash (RHA) on the mechanical properties of cement-based materials is of great significance for their application in concrete. This study investigates the impact of LP and RHA on the strength of cement mortar at various ages and the microhardness of hardened cement paste. The results suggest that two materials have a certain synergistic effect on the mechanical properties of the cementitious materials. The addition of RHA effectively addresses the issues of slow strength development, insufficient late-stage strength of the cementitious material, and the low strength blended with a large amount of LP, while a suitable amount of LP can promote the strength increase in the cement-RHA system. Based on the comprehensive analysis of compressive strength and microhardness, the optimal solution for achieving high mechanical properties in composite cementitious materials is to use 10% each of LP and RHA, resulting in a 9.5% increase in 28 d strength compared to a pure cement system. The higher the content of LP, the greater the increase caused by 10% RHA in compressive strength of the composite system, which makes the strength growth rate of cementitious material mixed with 10% LP at 3–56 d 62.1%. When the LP content is 20% and 30%, the addition of 10% RHA increases the 28 d strength by 44.8% and 38.8%, respectively, with strength growth rates reaching 109.8% and 151.1% at 3–56 d. Full article
22 pages, 3894 KiB  
Article
Comparative Analysis of Domestic Production and Import of Hard Coal in Poland: Conclusions for Energy Policy and Competitiveness
by Izabela Jonek-Kowalska and Wieslaw Grebski
Energies 2024, 17(20), 5157; https://doi.org/10.3390/en17205157 - 16 Oct 2024
Abstract
In many energy policies, including Poland’s, environmental priorities clash with the issue of energy security. With these contradictions in mind, the main objective of the article is a comparative analysis of domestic production and imports of hard coal in Poland and the formulation [...] Read more.
In many energy policies, including Poland’s, environmental priorities clash with the issue of energy security. With these contradictions in mind, the main objective of the article is a comparative analysis of domestic production and imports of hard coal in Poland and the formulation of conclusions for energy policy and competitiveness. The analysis covers the years 2018–2023 and concerns three issues: the volume and directions of coal imports to Poland, the qualitative and price competitiveness of coal, and the possibility of substituting imported coal with domestic coal. The research used statistical analysis. Indicators of structure and dynamics as well as comparative analysis were also used. The analysis shows that the structure of coal importers to Poland is quite diverse and includes many geographic directions. However, until 2021, it was dominated by Russia, followed by Colombia, indicating a fairly homogeneous supply market and a continuing tendency to depend on a single importer. Analysis of qualitative competitiveness confirms the existence of balance and industrial resources whose quality parameters (sulfur content, ash content, and calorific value) are comparable to and better than those of imported coal. Polish hard coal can also compete with imported coal in terms of price. From 2021 to 2023, it was clearly cheaper than foreign coal. In the above circumstances, it is quite difficult to unequivocally assess the reasons for importing coal to Poland and to justify dependence on external suppliers. This is especially relevant since domestic mining in 2020–2023 remains stable (periodically even increasing), which does not indicate a decisive shift away from coal as an energy resource. Full article
(This article belongs to the Special Issue Circular Economy, Environmental and Energy Management)
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Figure 1
<p>Hard coal consumption in Poland in 2018–2023 [in million tonnes].</p>
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<p>Employment in Polish hard coal mining in 2008–2024 [in thousands]. Source: [<a href="https://polskirynekwegla.pl/raport-dynamiczny/stan-zatrudnienia" target="_blank">https://polskirynekwegla.pl/raport-dynamiczny/stan-zatrudnienia</a>]. [accessed on 1 July 2024].</p>
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<p>Determinants of energy resource imports: summary of the review.</p>
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<p>The size of hard coal import to Poland in 2018–2023 [in million tons]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2018 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2019 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2020 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2021 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2022 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Geographic structure of hard coal imports in Poland in 2023 [in %]. Source: own work based on [<a href="#B83-energies-17-05157" class="html-bibr">83</a>,<a href="#B84-energies-17-05157" class="html-bibr">84</a>,<a href="#B85-energies-17-05157" class="html-bibr">85</a>,<a href="#B86-energies-17-05157" class="html-bibr">86</a>,<a href="#B87-energies-17-05157" class="html-bibr">87</a>,<a href="#B88-energies-17-05157" class="html-bibr">88</a>].</p>
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<p>Ash content of imported coal and the size of the balance coal reserves with ash content below 10% in Poland in 2020–2023 Source: own work based on data [<a href="#B82-energies-17-05157" class="html-bibr">82</a>,<a href="#B89-energies-17-05157" class="html-bibr">89</a>,<a href="#B90-energies-17-05157" class="html-bibr">90</a>,<a href="#B91-energies-17-05157" class="html-bibr">91</a>,<a href="#B92-energies-17-05157" class="html-bibr">92</a>].</p>
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<p>Sulfur content of imported coal and the size of the balance coal reserves with sulfur content below 0.6% in Poland in 2020–2023 Source: own work based on data [<a href="#B82-energies-17-05157" class="html-bibr">82</a>,<a href="#B89-energies-17-05157" class="html-bibr">89</a>,<a href="#B90-energies-17-05157" class="html-bibr">90</a>,<a href="#B91-energies-17-05157" class="html-bibr">91</a>,<a href="#B92-energies-17-05157" class="html-bibr">92</a>].</p>
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<p>Calorific value of imported coal and size of balance coal reserves with calorific value above 25,000 kJ/kg in Poland in 2020–2023. Source: own work based on data [<a href="#B82-energies-17-05157" class="html-bibr">82</a>,<a href="#B89-energies-17-05157" class="html-bibr">89</a>,<a href="#B90-energies-17-05157" class="html-bibr">90</a>,<a href="#B91-energies-17-05157" class="html-bibr">91</a>,<a href="#B92-energies-17-05157" class="html-bibr">92</a>].</p>
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<p>Price of imported (free-at-frontier) and domestically produced (ex-mine) hard coal in 2020–2023 [PLN/ton] Source: own work based on data [<a href="#B82-energies-17-05157" class="html-bibr">82</a>,<a href="#B89-energies-17-05157" class="html-bibr">89</a>,<a href="#B90-energies-17-05157" class="html-bibr">90</a>,<a href="#B91-energies-17-05157" class="html-bibr">91</a>,<a href="#B92-energies-17-05157" class="html-bibr">92</a>].</p>
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22 pages, 3701 KiB  
Article
Physiological and Transcriptomic Analyses Reveal the Role of the Antioxidant System and Jasmonic Acid (JA) Signal Transduction in Mulberry (Morus alba L.) Response to Flooding Stress
by Xuejiao Bai, He Huang, Dan Li, Fei Yang, Xinyao Cong, Siqi Wu, Wenxu Zhu, Shengjin Qin and Yibo Wen
Horticulturae 2024, 10(10), 1100; https://doi.org/10.3390/horticulturae10101100 - 16 Oct 2024
Abstract
In recent decades, the frequency of flooding has increased as a result of global climate change. Flooding has become one of the major abiotic stresses that seriously affect the growth and development of plants. Mulberry (Morus alba L.) is an important economic [...] Read more.
In recent decades, the frequency of flooding has increased as a result of global climate change. Flooding has become one of the major abiotic stresses that seriously affect the growth and development of plants. Mulberry (Morus alba L.) is an important economic tree in China. Flooding stress is among the most severe abiotic stresses that affect the production of mulberry. However, the physiological and molecular biological mechanisms of mulberry responses to flooding stress are still unclear. In the present study, reactive oxygen species (ROS) metabolism, antioxidant mechanism, and plant hormones in mulberry associated with the response to flooding stress were investigated using physiological and transcriptomic analysis methods. The results showed significant increases in the production rate of superoxide anion (O2•−) and the content of hydrogen peroxide (H2O2) in leaves on the 5th day of flooding stress. This led to membrane lipid peroxidation and elevated malondialdehyde (MDA) levels. Antioxidant enzymes such as catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) exhibited enhanced activities initially, followed by fluctuations. The ascorbic acid–glutathione (AsA-GSH) cycle played a crucial role in scavenging ROS, promoting the reduction of oxidized glutathione (GSSG) to reduced glutathione (GSH). Transcriptomic analysis revealed the up-regulation of the gene-encoding antioxidant enzymes (APX, MDHAR, GPX, GR, GST) involved in ROS scavenging and stress tolerance mechanisms. Jasmonic acid (JA) levels and the expression of JA synthesis-related genes increased significantly in mulberry leaves under flooding stress. This activation of the JA signaling pathway contributed to the plant’s adaptability to flooding conditions. Proline (Pro) and soluble sugar (SS) contents increased notably in response to flooding stress. Proline helped maintain cell turgor and protected enzymes and membranes from damage, while soluble sugars supported anaerobic respiration and energy supply. However, soluble protein (SP) content decreased, suggesting inhibition of protein synthesis. The study provides insights into mulberry’s flooding tolerance mechanisms, guiding future molecular breeding efforts. This summary captures the key findings and implications of the study on mulberry’s response to flooding stress, focusing on physiological and molecular mechanisms identified in the research. Full article
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Figure 1
<p>Production rate of O<sub>2</sub><sup>•−</sup> (<b>A</b>), H<sub>2</sub>O<sub>2</sub> (<b>B</b>) and MDA content (<b>C</b>), SOD activity (<b>D</b>), POD activity (<b>E</b>), CAT activity (<b>F</b>), and heatmaps of genes expression of ROS scavenging enzymes (<b>G</b>) in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. Note: Heat map data are derived from the gene expression data of transcriptome analysis results, which are drawn according to the normalized gene expression amount under Ctl conditions. Under different waterlogging conditions, the gene expression amount higher than the average value under Ctl conditions is marked with red, and vice versa, and the gene expression amount lower than the average value is marked with blue. The color shading indicates the degree of difference between gene expression and Ctl. The darker the color, the more significant the difference. Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Activity key enzymes, content of metabolic substance, and heatmaps of gene expression in ASA-GSH cycle in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. The content of ascorbic acid (<b>A</b>), the content of dehydroascorbate (<b>B</b>), the ratio of the content of ascorbate to dehydroascorbate (<b>C</b>), the content of glutathione (<b>D</b>), the content of glutathiol (<b>E</b>), the ratio of the content of glutathione to glutathiol (<b>F</b>), the activity of ascorbate peroxidase (<b>G</b>), the activity of monodehydroascorbate reductase (<b>H</b>), the activity of dehydroascorbate reductase (<b>I</b>), the activity of glutathione peroxidase (<b>J</b>), the activity of glutathione reductase (<b>K</b>), the activity of glutathione S-transferase (<b>L</b>), heatmaps of the expression levels of relevant genes in the AsA-GSH cycle (<b>M</b>). APX: ascorbate peroxidase; MDHAR: monodehydroascorbate reductase; GPX: glutathione peroxidase; GR: gluathione reductase; DHAR: dehydroascorbate reductase; AsA: ascorbate; DHA: dehydroascorbate; GSH: glutathione; MDHA: monodehydroascorbate; GSSG: glutathiol. Note: Heat map data processing method is the same as above. Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Activity key enzymes and heatmaps of genes expression in Trx-Prx pathway in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. The activity of thioredoxin reductase (<b>A</b>), the activity of peroxiredoxin (<b>B</b>), heatmaps of the expression levels of related genes in the Trx-Prx pathway (<b>C</b>). Note: Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>JA content and heatmaps of gene expression in JA synthesis and JA signal in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. The content of JA (<b>A</b>), heatmaps of the expression levels of related genes in JA synthesis and JA signal (<b>B</b>). Note: Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Proline content (<b>A</b>), soluble sugar content (<b>B</b>), and soluble protein content (<b>C</b>) in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. Note: Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>RT-qPCR verified transcript expression levels of DEGs in mulberry (<span class="html-italic">Morus alba</span> L.) leaves under flooding stress. Data are represented as means ± SD of five replicate samples. Different lowercase letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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19 pages, 1475 KiB  
Article
Congruence and Discrepancy: Matching Effect of Searching and Integration on Green Product Development Performance
by Jianhua Yin and Ping Jiang
Sustainability 2024, 16(20), 8961; https://doi.org/10.3390/su16208961 - 16 Oct 2024
Abstract
The development of green products in manufacturing enterprises is challenged by limited independent innovation capabilities and insufficient green technology. Based on knowledge-based theory, this study investigates the influence of boundary-spanning green technology search (BGTS) and technology integration capability (TIC) on green product development [...] Read more.
The development of green products in manufacturing enterprises is challenged by limited independent innovation capabilities and insufficient green technology. Based on knowledge-based theory, this study investigates the influence of boundary-spanning green technology search (BGTS) and technology integration capability (TIC) on green product development performance (GPDP). Using polynomial regression analysis with response surface methodology (PRA with RSM) on data from 341 companies, the following findings were obtained: (1) Higher levels of BGTS and TIC congruence are associated with higher GPDP compared to lower levels of congruence. (2) The degree of discrepancy between the BGTS and TIC has an inverted U-shaped relationship with GPDP. (3) GPDP is higher in the “low BGTS–high TIC” combination than in the “high BGTS–low TIC” combination. (4) Green technology innovation capability mediates the relationship between BGTS-TIC congruence and GPDP. (5) Strategic foresight positively moderates the relationship between the BGTS-TIC congruence and green technology innovation capability. These findings enrich the research content of organizational search theory, deepen the research of green technology innovation, expand research on the development of green products, and provide a decision-making reference for manufacturing enterprises’ green transition practices. Full article
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Figure 1
<p>Matching effect of BGTS and TIC.</p>
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<p>Theoretical model.</p>
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<p>Response surface for BGTS-TIC and GPDP.</p>
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<p>Response surface plot of the strategic foresight’s moderating effect.</p>
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14 pages, 420 KiB  
Review
Technological Interventions to Implement Prevention and Health Promotion in Cardiovascular Patients
by Ayisha Z. Bashir, Anji Yetman and Melissa Wehrmann
Healthcare 2024, 12(20), 2055; https://doi.org/10.3390/healthcare12202055 - 16 Oct 2024
Abstract
Background/Objectives: The aim of the narrative review is to identify information on the impact of technological interventions (such as telehealth and mobile health) on the health promotion of cardiac patients from diverse populations. Methods: The online databases of PubMed and the [...] Read more.
Background/Objectives: The aim of the narrative review is to identify information on the impact of technological interventions (such as telehealth and mobile health) on the health promotion of cardiac patients from diverse populations. Methods: The online databases of PubMed and the Cochrane Library were searched for articles in the English language regarding technological interventions for health promotion in cardiac patients. In addition, a methodological quality control process was conducted. Exclusion was based on first reading the abstract, and then the full manuscript was scanned to confirm that the content was not related to cardiac patients and technological interventions. Results: In all, 11 studies were included in this review after quality control analysis. The sample size reported in these studies ranged from 12 to 1424 subjects. In eight studies mobile phones, smartphones, and apps were used as mHealth interventions with tracking and texting components; two studies used videoconferencing as a digital intervention program, while three studies focused on using physical activity trackers. Conclusions: This review highlights the positive aspects of patient satisfaction with the technological interventions including, but not limited to, accessibility to health care providers, sense of security, and well-being. The digital divide becomes apparent in the articles reviewed, as individuals with limited eHealth literacy and lack of technological knowledge are not motivated or able participate in these interventions. Finding methods to overcome these barriers is important and can be solved to some extent by providing the technology and technical support. Full article
(This article belongs to the Special Issue Policy Interventions to Promote Health and Prevent Disease)
18 pages, 14457 KiB  
Article
Variations of Planetary Wave Activity in the Lower Stratosphere in February as a Predictor of Ozone Depletion in the Arctic in March
by Pavel Vargin, Andrey Koval, Vladimir Guryanov, Eugene Volodin and Eugene Rozanov
Atmosphere 2024, 15(10), 1237; https://doi.org/10.3390/atmos15101237 - 16 Oct 2024
Abstract
This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere [...] Read more.
This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere in March (Tmin) and the amplitude of the planetary wave with zonal number 1 (PW1) in February were calculated. Tmin determines the conditions for the formation of polar stratospheric clouds (PSCs) following the chemical destruction of the ozone layer. The NCEP and ERA5 reanalysis data and the modern and future climate simulations of the Earth system models INM CM5 and SOCOLv4 were employed. It is shown that the maximum significant CC value between Tmin at 70 hPa in the polar region in March and the amplitude of the PW1 in February in the reanalysis data in the lower stratosphere is 0.67 at the pressure level of 200 hPa. The CCs calculated using the model data are characterized by maximum values of ~0.5, also near the same pressure level. Thus, it is demonstrated that the change in the planetary wave activity in the lower extratropical stratosphere in February can be one of the predictors of the Tmin. For further analysis of the dynamic structure in the lower stratosphere, composites of 10 seasons with the lowest and highest Tmin of the Arctic lower stratosphere in March were assembled. For these composites, differences in the vertical distribution and total ozone content, surface temperature, and residual meridional circulation (RMC) were considered, and features of the spatial distribution of wave activity fluxes were investigated. The obtained results may be useful for the development of forecasting of the Arctic winter stratosphere circulation, especially for the late winter season, when substantial ozone depletion occurs in some years. Full article
(This article belongs to the Special Issue Measurement and Variability of Atmospheric Ozone)
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Figure 1
<p>The zonal mean temperature averaged over 70–90° N and <span class="html-italic">Tmin</span> in March over 1948–2024 (NCEP-R) (black and green lines 1–2, respectively) (<b>a</b>); vertical profile of the correlation coefficient between the amplitude of PW1 averaged over 45–75° N in the range of pressure levels from 700 hPa to 10 hPa in February and <span class="html-italic">Tmin</span> at 70 hPa in the polar cap 70–90° N in March in the following data sets: NCEP reanalysis data over the periods of 1948–2024, 1948–1978, 1979–2024, and ERA5 reanalysis (1979–2023) (<b>b</b>); INMCM5 historical simulations (1965–2014, mean over experiments HIST1–HIST5) under the SSP2-4.5 and SSP5-8.5 scenarios (2015–2100), ERA5 reanalysis (1979–2023), NCEP (1979–2024) reanalysis data, and SOCOLv4 simulations under SSP2-4.5 and SSP5-8.5 scenarios (2015–2099, 3 ensembles mean) (<b>c</b>); INM CM5 historical experiments HIST1–HIST5 and the mean for the period of 1965–2014 (<b>d</b>).</p>
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<p>Scatter diagram of PW1 amplitude in geopotential meters (gpm) at 200 hPa averaged over 45–75° N in February and <span class="html-italic">Tmin</span> in the polar cap 70–90° N at 70 hPa in March from 1979 to 2024 (years are marked by black squares). Selected in the next section for cold and warm composites 10 years with the lowest and highest <span class="html-italic">Tmin</span> in March are marked by blue and red colors, respectively.</p>
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<p>Altitude–longitude cross-section of geopotential height difference (gpm) in the latitudinal belt 45–75° N (<b>а</b>); polar projections at the pressure levels 200 hPa (<b>b</b>) and 500 hPa (<b>c</b>) in February between “warm” and “cold” composites. The regions with significance at the 95% level for positive or negative changes are marked by gray dots.</p>
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<p>Altitude–latitudinal cross-section of PW1 amplitude (gpm) in February for “warm” and “cold” composites and the difference between them ((<b>a</b>–<b>c</b>) respectively). The same but for PW2 (<b>d</b>–<b>f</b>). The regions with significance at the 95% level for positive or negative changes are marked by gray dots.</p>
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<p>Altitude–latitudinal cross-sections of zonal mean heat flux (K m/s) in February for “warm” and “cold” composites (<b>a</b>,<b>b</b>) and the difference between them (<b>c</b>). The regions with significance at the 95% level for positive or negative changes are marked by gray dots.</p>
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<p>Altitude–latitudinal cross-sections of Plumb fluxes (<span class="html-italic">Fy</span>, <span class="html-italic">Fz</span> components, vectors) (m<sup>2</sup>/s<sup>2</sup>) and zonal mean wind (m/s, contours) in February of “warm” and “cold” composites and the difference between them (<b>a</b>–<b>c</b>). Altitude–longitudinal cross-sections of Plumb fluxes (<span class="html-italic">Fx</span>, <span class="html-italic">Fz</span> components, vectors) and geopotential height (contours) for “warm” and “cold” composites averaged over 45–75° N (<b>d</b>,<b>e</b>) and the difference between them for geopotential height and <span class="html-italic">Fz</span> (<b>f</b>). <span class="html-italic">Fz</span> is multiplied by 100. The area with the strongest upward propagation of wave activity fluxes from the troposphere to the stratosphere is highlighted by a purple oval (<b>f</b>).</p>
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<p>Vertical Plumb fluxes <span class="html-italic">Fz</span> component of “warm” and “cold” composites in February at the pressure level of 100 hPa (<b>a</b>,<b>b</b>). Difference of <span class="html-italic">Fz</span> (m<sup>2</sup>/s<sup>2</sup>) in February between “warm” and “cold” composites at 100 hPa (<b>c</b>) and 30 hPa (<b>d</b>).</p>
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<p>Temperature difference (K) (<b>a</b>) and ozone concentration at 70 hPa (%) (<b>a</b>,<b>b</b>) and total ozone content (%) in March between “warm” and “cold” composites (<b>b</b>,<b>c</b>).</p>
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<p>Temperature difference between ”warm” and “cold” composites at 1000 hPa in February (<b>a</b>), March (<b>b</b>), April (<b>c</b>), and May (<b>d</b>). The regions with significance at the 95% level for positive or negative changes are marked by gray dots.</p>
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<p>Temperature difference between ”warm” and “cold” composites at 1000 hPa in February (<b>a</b>), March (<b>b</b>), April (<b>c</b>), and May (<b>d</b>). The regions with significance at the 95% level for positive or negative changes are marked by gray dots.</p>
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<p>Altitude–latitude distribution of temperature (K, contours) and the RMC components (m/s, arrows) for “warm” and “cold” composites ((<b>a</b>,<b>b</b>), respectively); the eddy term of the RMC (m/s, arrows) and its vertical component (contours) for the “warm” and “cold” composites ((<b>d</b>,<b>e</b>), respectively); the differences in the corresponding values are shown in panels (<b>c</b>,<b>f</b>). The vertical components are multiplied by 200.</p>
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25 pages, 4786 KiB  
Systematic Review
Systematic Review of Instruments to Assess Computational Thinking in Early Years of Schooling
by Lina Marcela Ocampo, Milena Corrales-Álvarez, Sergio Augusto Cardona-Torres and María Zapata-Cáceres
Educ. Sci. 2024, 14(10), 1124; https://doi.org/10.3390/educsci14101124 - 16 Oct 2024
Abstract
Computational thinking (CT) is considered a key competence in today’s digital era. It is an emerging construct that relates to critical thinking and creativity. Research on its assessment is in the process of consolidation. This systematic review aims to analyze studies that have [...] Read more.
Computational thinking (CT) is considered a key competence in today’s digital era. It is an emerging construct that relates to critical thinking and creativity. Research on its assessment is in the process of consolidation. This systematic review aims to analyze studies that have used CT assessment instruments for children and adolescents aged 4 to 16 years in order to identify which variables, they assess and their psychometric properties. The search and analysis were carried out following the PRISMA statement protocol, analyzing 50 articles published between 2006 and March 2023. An increase in the publication of CT measurement instruments is observed, with 54% of them supported by evidence of validity and 88% by reliability, highlighting construct validity, followed by content and criteria validity. China leads in the number of publications, while Asia and Europe concentrate most of the research. There is a noticeable contribution from South America, evidencing the lack of participation from Central and South American countries in this field of study. Full article
(This article belongs to the Special Issue Measuring Children’s Computational Thinking Skills)
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<p>Literature Selection Flow Diagram. Source: Own elaboration following the PRISMA Methodology [<a href="#B26-education-14-01124" class="html-bibr">26</a>].</p>
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<p>Distribution over time of the number of publications.</p>
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<p>Word cloud of titles.</p>
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<p>Map of title and keywords.</p>
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<p>Co-citation Network.</p>
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<p>Country where the study was conducted.</p>
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<p>Age distribution targeted by the instruments.</p>
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<p>Distribution of authors and/or associations that have contributed to the construction of the instruments.</p>
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<p>Skills, concepts, and attitudes evaluated.</p>
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<p>Distribution of assessed skills, concepts, perspectives, and attitudes.</p>
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10 pages, 1847 KiB  
Communication
The Effect of Temperature and Humidity on Yellow Tea Volatile Compounds during Yellowing Process
by Weiwei Wang, Zhihui Feng, Rui Min, Junfeng Yin and Heyuan Jiang
Foods 2024, 13(20), 3283; https://doi.org/10.3390/foods13203283 - 16 Oct 2024
Abstract
Yellowing is the key processing technology of yellow tea, and environmental conditions have a significant impact on the yellowing process. In this study, volatile compounds of the yellowing process under different environmental conditions were analyzed by GC–MS. Results showed that a total of [...] Read more.
Yellowing is the key processing technology of yellow tea, and environmental conditions have a significant impact on the yellowing process. In this study, volatile compounds of the yellowing process under different environmental conditions were analyzed by GC–MS. Results showed that a total of 75 volatile compounds were identified. A partial least squares discriminant analysis (PLS-DA) determined that 42 of them were differential compounds, including 12 hydrocarbons, 8 ketones, 8 aldehydes, 6 alcohols, and 8 other compounds, and compared the contents of differential compounds under the conditions of 40 °C with 90% humidity, 50 °C with 50% humidity, and 30 °C with 70% humidity, then analyzed the variation patterns of hydrocarbons under different yellowing environmental conditions. A 40 °C with 90% humidity treatment reduced the content of more hydrocarbons and increased the aldehydes. The content of 3-hexen-1-ol was higher when treated at 50 °C with 50% humidity and was consistent with the results of sensory evaluation. This study could provide a theoretical basis for future research on the aroma of yellow tea. Full article
(This article belongs to the Section Food Engineering and Technology)
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<p>Flowchart of yellow tea process.</p>
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<p>(<b>a</b>) Tea and tea infusion. (<b>b</b>) Radar map of the sensory of yellow tea.</p>
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<p>Metabolomics analysis of volatile compounds in different yellow teas: PLS-DA score plot (<b>a</b>), cross-validation plot (<b>b</b>), VIP plot (<b>c</b>).</p>
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<p>Heat map of differential volatile compounds in yellow teas.</p>
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<p>Trend chart of changes in alkanes during the yellowing process.</p>
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15 pages, 2513 KiB  
Article
The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory
by Ya-Sin Yang, Hsin-Fu Yeh, Chien-Chung Ke, Lun-Wei Wei and Nai-Chin Chen
Geosciences 2024, 14(10), 274; https://doi.org/10.3390/geosciences14100274 - 16 Oct 2024
Abstract
Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall [...] Read more.
Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall on slope stability, focusing on the dynamic hydraulic behavior of unsaturated soils. By simulating the soil water content and slope stability under four different rainfall scenarios based on observational data and historical thresholds, this study reveals that higher rainfall intensity significantly increases the soil water content, leading to reduced slope stability. The results show a strong correlation between the soil water content and slope stability, with a 20 mm/h rainfall intensity threshold emerging as a reliable predictor of potential slope instability. This study contributes to a deeper understanding of slope stability dynamics and emphasizes the importance of proactive risk management in response to changing rainfall patterns while also validating current management practices and providing essential insight for improving early warning systems to effectively mitigate landslide risk. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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<p>The location of the stations in Babaoliao area.</p>
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<p>Mohr circle-based conceptual illustration of Local Factor of Safety [<a href="#B66-geosciences-14-00274" class="html-bibr">66</a>].</p>
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<p>Flowchart of the modeling analysis process.</p>
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<p>Conceptual model, boundary condition, and mesh configuration: (<b>a</b>) zone A, (<b>b</b>) zone D.</p>
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<p>Comparison of SWCC obtained from pressure plate tests and SWCC used in the model.</p>
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<p>Results of simulated and observed values for (<b>a</b>) groundwater level in zone A, (<b>b</b>) soil water content in zone A, and (<b>c</b>) soil water content in zone D.</p>
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<p>Four hypothetical rainfall scenarios: (<b>a</b>) extreme intensity, (<b>b</b>) high intensity, (<b>c</b>) moderate intensity, and (<b>d</b>) low intensity.</p>
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<p>Simulation results for (<b>a</b>) soil water content in zone A, (<b>b</b>) LFS in zone A, (<b>c</b>) soil water content in zone D, and (<b>d</b>) LFS in zone D.</p>
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14 pages, 5509 KiB  
Article
Ti-Ta-Cu Biocompatible Alloy System Development via Selective Laser Melting for Prosthetic Applications
by Igor Polozov, Victoria Sokolova, Anna Gracheva, Anton Zolotarev, Victoria Nefyodova and Anatoly Popovich
Metals 2024, 14(10), 1177; https://doi.org/10.3390/met14101177 - 16 Oct 2024
Abstract
This study investigated the development of Ti-Ta-Cu alloys via selective laser melting (SLM) for potential prosthetic applications. Ti-Ta-Cu alloys with 10, 15, and 20 wt.% Ta were fabricated using in situ alloying of elemental powders. We examined the effects of Ta content and [...] Read more.
This study investigated the development of Ti-Ta-Cu alloys via selective laser melting (SLM) for potential prosthetic applications. Ti-Ta-Cu alloys with 10, 15, and 20 wt.% Ta were fabricated using in situ alloying of elemental powders. We examined the effects of Ta content and SLM processing parameters on microstructure, phase composition, mechanical properties, and corrosion resistance. X-ray diffraction analysis revealed an increase in β-phase content with increasing Ta concentration. Microstructural analysis showed a dendritic structure in Ta-rich areas, with remelting strategies improving chemical homogeneity and Ta dissolution. The Ti-20Ta-5Cu alloy exhibited the best balance of strength and ductility, with an ultimate tensile strength of 1011 MPa and elongation of 5.7%. All compositions demonstrated lower elastic moduli (103–109 GPa) compared to traditional titanium alloys. Microhardness values were highest for Ti-15Ta-5Cu, ranging from 359 to 410 HV0.5 depending on SLM parameters. Corrosion testing in Hank’s solution showed improved pitting resistance for Ti-15Ta-5Cu and Ti-20Ta-5Cu compared to Ti-10Ta-5Cu. The study demonstrates the feasibility of producing Ti-Ta-Cu alloys with tailored properties via SLM, offering potential for customized prosthetic applications with improved biomechanical compatibility and functionality. Full article
(This article belongs to the Section Additive Manufacturing)
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<p>SEM images of initial titanium (<b>a</b>) tantalum (<b>b</b>) and copper (<b>c</b>) powder.</p>
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<p>Density dependence of samples on the SLM mode for three alloy compositions.</p>
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<p>Microstructure of as-SLMed Ti15Ta5Cu alloy at different magnifications: (<b>a</b>) general view of the microstructure, (<b>b</b>) microstructure at a higher magnification.</p>
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<p>Microstructure of as-SLMed Ti-Ta-Cu alloys: (<b>a</b>) ME-S mode for Ti10Ta5Cu, (<b>b</b>) ME-R2 mode for Ti15Ta5Cu, (<b>c</b>) ME-S mode for Ti10Ta5Cu, (<b>d</b>) ME-R2 mode for Ti15Ta5Cu.</p>
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<p>XRD diffractograms of Ti-Ta-Cu samples: (<b>a</b>) with various Ta content (wt.%), (<b>b</b>) Ti-15Ta-5Cu at different SLM modes.</p>
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<p>Electrochemical characterization of Ti-Ta-Cu alloys in Hank’s solution at 36.5 ± 0.5 °C: (<b>a</b>) open circuit potential (OCP) curves and (<b>b</b>) potentiodynamic polarization curves.</p>
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<p>Microhardnesses of Ti-xTa-5Cu samples with various Ta (wt.%) content at different SLM scanning modes.</p>
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<p>Tensile diagrams for Ti-10Ta-5Cu, Ti-15Ta-5Cu, and Ti-20Ta-5Cu samples after SLM processing with ME-S scanning mode.</p>
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11 pages, 438 KiB  
Article
Rapid Classification of Milk Using a Cost-Effective near Infrared Spectroscopy Device and Variable Cluster–Support Vector Machine (VC-SVM) Hybrid Models
by Eleonora Buoio, Valentina Colombo, Elena Ighina and Francesco Tangorra
Foods 2024, 13(20), 3279; https://doi.org/10.3390/foods13203279 - 16 Oct 2024
Abstract
Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly [...] Read more.
Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud. Full article
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<p>Mean absorbance spectra (solid line) and variation between the mean minus one standard deviation and mean plus one standard deviation of all spectra (shaded areas).</p>
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<p>Mean absorbance spectrum (solid line) after being processed with SNV and variation between mean minus one standard deviation and mean plus one standard deviation of all spectra (shaded areas).</p>
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15 pages, 4033 KiB  
Article
NaCl Stress Stimulates Phenolics Biosynthesis and Antioxidant System Enhancement of Quinoa Germinated after Magnetic Field Pretreatment
by Shufang Wang, Xuejiao Zhang, Yiting Wang, Jirong Wu, Yin-Won Lee, Jianhong Xu and Runqiang Yang
Foods 2024, 13(20), 3278; https://doi.org/10.3390/foods13203278 - 16 Oct 2024
Abstract
Our previous study showed that magnetic field pretreatment promoted germination and phenolic enrichment in quinoa. In this study, we further investigated the effects of NaCl stress on the growth and phenolic synthesis of germinated quinoa after magnetic field pretreatment (MGQ). The results showed [...] Read more.
Our previous study showed that magnetic field pretreatment promoted germination and phenolic enrichment in quinoa. In this study, we further investigated the effects of NaCl stress on the growth and phenolic synthesis of germinated quinoa after magnetic field pretreatment (MGQ). The results showed that NaCl stress inhibited the growth of MGQ, reduced the moisture content and weight of a single plant, but increased the fresh/dry weight. The higher the NaCl concentration, the more obvious the inhibition effect. In addition, NaCl stress inhibited the hydrolysis of MGQ starch, protein, and fat but increased the ash content. Moreover, lower concentrations (50 and 100 mM) of NaCl stress increased the content of MGQ flavonoids and other phenolic compounds. This was due to the fact that NaCl stress further increased the enzyme activities of PAL, C4H, 4CL, CHS, CHI, and CHR and up-regulated the gene expression of the above enzymes. NaCl stress at 50 and 100 mM increased the DPPH and ABTS scavenging capacity of MGQ and increased the activities of antioxidant enzymes, including SOD, POD, CAT, APX, and GSH-Px, further enhancing the antioxidant system. Furthermore, principal component analysis showed that NaCl stress at 100 mM had the greatest combined effect on MGQ. Taken together, NaCl stress inhibited the growth of MGQ, but appropriate concentrations of NaCl stress, especially 100 mM, helped to further increase the phenolic content of MGQ and enhance its antioxidant system. Full article
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<p>Effects of NaCl stress on the sprout length (<b>A</b>), germination percentage (<b>B</b>), single plant weight (<b>C</b>), moisture content (<b>D</b>), and fresh weight/dry weight (<b>E</b>) of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the starch (<b>A</b>), reducing sugar (<b>B</b>), soluble protein (<b>C</b>), free amino acid (<b>D</b>), crude fat (<b>E</b>), and ash (<b>F</b>) content of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the phenolics (<b>A</b>) and flavonoids (<b>B</b>) content of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the activities of PAL (<b>A</b>), C4H (<b>B</b>), 4CL (<b>C</b>), CHS (<b>D</b>), CHR (<b>E</b>), and CHI (<b>F</b>) of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the gene expression of <span class="html-italic">PAL</span> (<b>A</b>), <span class="html-italic">C4H</span> (<b>B</b>), <span class="html-italic">4CLs</span> (<b>C</b>), <span class="html-italic">CHS</span> (<b>D</b>), <span class="html-italic">CHR</span> (<b>E</b>), and <span class="html-italic">CHIs</span> (<b>F</b>) of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the scavenging capacity of DPPH (<b>A</b>) and ABTS (<b>B</b>) of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of NaCl stress on the antioxidative enzyme activity of SOD (<b>A</b>), POD (<b>B</b>), CAT (<b>C</b>), APX (<b>D</b>), and GSH-Px (<b>E</b>) of germinated quinoa after magnetic field pretreatment. Values are expressed as mean ± SD. Lowercase letters represent significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>A comprehensive evaluation of the effects of various NaCl concentrations on germinated quinoa following pretreatment with a magnetic field (<b>A</b>). Rotated component matrix of principal component analysis (<b>B</b>). Indicators of significant differences under NaCl stress (<b>C</b>). Correlation analysis of indices of germinated quinoa after magnetic field pretreatment under different NaCl concentrations (<b>D</b>).</p>
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22 pages, 23991 KiB  
Article
Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns
by Fernando A. M. Marinho, Yuri Corrêa, Rosiane Soares, Inácio Diniz Carvalho and João Paulo de S. Silva
Geosciences 2024, 14(10), 273; https://doi.org/10.3390/geosciences14100273 - 16 Oct 2024
Abstract
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability [...] Read more.
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities. Full article
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<p>(<b>a</b>) Ore-pile draining and (<b>b</b>) water content variation along the pile.</p>
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<p>Relationship between the water content and the amount of fines.</p>
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<p>(<b>a</b>) Physical model of a soil column with a water table (<b>b</b>) Relationships between free energy and water content in a soil column with a fixed water table (<b>c</b>) Variation of water content with the height of the column (modified from Edlefesen and Anderson [<a href="#B7-geosciences-14-00273" class="html-bibr">7</a>]).</p>
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<p>Suction (<b>a</b>) and water content (<b>b</b>) profile in the field.</p>
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<p>(<b>a</b>) PVC column; (<b>b</b>) schematic drawing of the column; (<b>c</b>) suction equilibrium profile, and (<b>d</b>) water content profiles for three hypothetical materials [<a href="#B15-geosciences-14-00273" class="html-bibr">15</a>].</p>
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<p>Soil water retention curve of the material (data from Jesus et al. [<a href="#B22-geosciences-14-00273" class="html-bibr">22</a>]).</p>
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<p>Segments for the column assembly.</p>
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<p>Drainage segment. Placement of (<b>a</b>) gravel and (<b>b</b>) medium sand.</p>
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<p>Column compaction process: (<b>a</b>) Details of the compaction; (<b>b</b>) column at its 6th segment.</p>
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<p>First completed column: (<b>a</b>) Image of the completed column; (<b>b</b>) sensor positions.</p>
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<p>Time lag graphical analysis between sensors WC6 and TE6 during (<b>a</b>) saturation, (<b>b</b>) drainage, (<b>c</b>) infiltration, and (<b>d</b>) evaporation.</p>
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<p>Stages imposed in the columns.</p>
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<p>Profiles at the end of construction and before saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during drainage: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the first infiltration and evaporation.</p>
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<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the second infiltration and evaporation.</p>
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<p>Profiles during the first infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the first evaporation: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the second infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p>
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<p>Profiles during the second evaporation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p>
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<p>Measured water flux at the base of the column.</p>
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<p>A closer look at the sensor readings plotted with the retention curve.</p>
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<p>Water retention curve with the sensor readings.</p>
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<p>SWRC versus infiltration and evaporation data.</p>
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17 pages, 5606 KiB  
Article
Combining Physiology and Transcriptome Data Screening for Key Genes in Actinidia arguta Response to Waterlogging Stress
by Jiaqi Geng, Guangli Shi, Xiang Li, Yumeng Liu, Wenqi An, Dan Sun, Zhenxing Wang and Jun Ai
Agronomy 2024, 14(10), 2391; https://doi.org/10.3390/agronomy14102391 - 16 Oct 2024
Abstract
Actinidia arguta is a cold-resistant fruit tree but intolerant to waterlogging. Waterlogging stress is the major abiotic stress in A. arguta growth, and several pathways are involved in the response mechanisms. Fifteen physiological indices and transcriptome data of two A. arguta cultivars, which [...] Read more.
Actinidia arguta is a cold-resistant fruit tree but intolerant to waterlogging. Waterlogging stress is the major abiotic stress in A. arguta growth, and several pathways are involved in the response mechanisms. Fifteen physiological indices and transcriptome data of two A. arguta cultivars, which showed two forms under waterlogging, were used to identify the major factor following the leaf senescence in waterlogging. Through principal component analysis (PCA) of 15 physiological indices in ‘Kuilv’ and ‘Lvwang’, the hormone contents were selected as the most important principal component (PCA 2) out of four components in response to waterlogging stress. According to the analysis of transcriptome data, 21,750 differentially expressed genes were identified and 10 genes through WGCNA, including hormone metabolism and sucrose metabolism, were screened out on the 6th day of waterlogging. In particular, the ABA signal transduction pathway was found to be closely related to the response to waterlogging based on the correlation analysis between gene expression level and plant hormone content, which may have regulated physiological indicators and morphological changes together with other hormones. Overall, the phenomenon of leaves falling induced by ABA might be a protective mechanism. The results provided more insights into the response mechanism of coping with waterlogging stress in A. arguta. Full article
(This article belongs to the Section Water Use and Irrigation)
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<p>Morphological changes in ‘Kuilv’ and ‘Lvwang’ under different numbers of waterlogged days. (<b>a</b>) Photos of ‘Kuilv’ waterlogging for 0, 3, 6, 9, and 12 days. (<b>b</b>) Photos of ‘Lvwang’ waterlogging for 0, 3, 6, 9, and 12 days.</p>
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<p>PCA cluster analysis of PCA 1 and PCA 2 to show the changes in main indicators.</p>
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<p>Changes in hormones content of different varieties under waterlogging. (<b>a</b>) Content of GA<sub>3.</sub> (<b>b</b>) Content of IAA. (<b>c</b>) Content of ABA. (<b>d</b>) GA<sub>3</sub>+IAA/ABA ratio of content. K and L represent the treatment of ‘Kuilv’ and ‘Lvwang’, respectively. Mean values followed by the same letter within a colum do not deffer significantly according to analysis of variance at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Comparison of NR database.</p>
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<p>DEG number statistics and Venn diagram between different waterlogging days. (<b>a</b>) DEG number. (<b>b</b>) Venn diagram.</p>
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<p>GO enrichment barplot (<b>a</b>) and KEGG enrichment scatterplot. (<b>b</b>) of the waterlogging-responsive DEGs in ‘Lvwang’ and ‘Kuilv’.</p>
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<p>Co-expression network visualization results. (<b>a</b>) The visualization of gene modules, the same color means that these genes correspond to the same module; (<b>b</b>) module and associated biological characteristics. (<b>c</b>) Co-expression network of genes in the MEblue module.The color of yellow represent the interaction frequency.</p>
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<p>Gene-expression-related plant hormone signal pathway pattern under waterlogging.</p>
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<p>Gene-expression-related starch and sucrose metabolism pathway pattern under waterlogging.</p>
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<p>Log 2 fold changes of ten genes in quantitative real-time PCR (qRT-PCR) and RNA-seq.</p>
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<p>A schematic model of transcriptional regulation in <span class="html-italic">A. arguta</span> in response to waterlogging. Red and blue text indicate up- and down-regulated genes.</p>
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22 pages, 20819 KiB  
Article
Single-Base Methylome Analysis of Sweet Cherry (Prunus avium L.) on Dwarfing Rootstocks Reveals Epigenomic Differences Associated with Scion Dwarfing Conferred by Grafting
by Yi Hong, Zhuang Wen, Guang Qiao, Tian Tian and Xiaopeng Wen
Int. J. Mol. Sci. 2024, 25(20), 11100; https://doi.org/10.3390/ijms252011100 - 16 Oct 2024
Abstract
Plant grafting using dwarfing rootstocks is one of the important cultivation measures in the sweet cherry (Prunus avium) industry. In this work, we aimed to explore the effects of the dwarfing rootstock “Pd1” (Prunus tomentosa) on sweet cherry ‘Shuguang2’ [...] Read more.
Plant grafting using dwarfing rootstocks is one of the important cultivation measures in the sweet cherry (Prunus avium) industry. In this work, we aimed to explore the effects of the dwarfing rootstock “Pd1” (Prunus tomentosa) on sweet cherry ‘Shuguang2’ scions by performing morphological observations using the paraffin slice technique, detecting GA (gibberellin) and IAA (auxin) contents using UPLC-QTRAP-MS (ultra-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer), and implementing integration analyses of the epigenome and transcriptome using whole-genome bisulfite sequencing and transcriptome sequencing. Anatomical analysis indicated that the cell division ability of the SAM (shoot apical meristem) in dwarfing plants was reduced. Pd1 rootstock significantly decreased the levels of GAs and IAA in sweet cherry scions. Methylome analysis showed that the sweet cherry genome presented 15.2~18.6%, 59.88~61.55%, 28.09~33.78%, and 2.99~5.28% methylation at total C, CG, CHG, and CHH sites, respectively. Shoot tips from dwarfing plants exhibited a hypermethylated pattern mostly due to increased CHH methylation, while leaves exhibited a hypomethylated pattern. According to GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, DMGs (differentially methylated genes) and DEGs (differentially expressed genes) were enriched in hormone-related GO terms and KEGG pathways. Global correlation analysis between methylation and transcription revealed that mCpG in the gene body region enhanced gene expression and mCHH in the region near the TSS (transcription start site) was positively correlated with gene expression. Next, we found some hormone-related genes and TFs with significant changes in methylation and transcription, including SAURs, ARF, GA2ox, ABS1, bZIP, MYB, and NAC. This study presents a methylome map of the sweet cherry genome, revealed widespread DNA methylation alterations in scions caused by dwarfing rootstock, and obtained abundant genes with methylation and transcription alterations that are potentially involved in rootstock-induced growth changes in sweet cherry scions. Our findings can lay a good basis for further epigenetic studies on sweet cherry dwarfing and provide valuable new insight into understanding rootstock–scion interactions. Full article
(This article belongs to the Collection Advances in Molecular Plant Sciences)
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Figure 1

Figure 1
<p>Growth and anatomical observation of shoot tips. (<b>A</b>) Grafting plants at the growth stagnation stage (195 days after grafting). Red circles mark shoot tips. (<b>B</b>) Anatomical observation of shoot tips. SG-Pd1: “Shuguang2” sweet cherry grafted onto Pd1 rootstock (dwarf); SG-WT: “Shuguang2” sweet cherry grafted onto wild-type rootstock (vigorous).</p>
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<p>The effect of rootstock on scion growth. (<b>A</b>) The growth of “Shuguang2” grafted buds. (<b>B</b>) New branch growth of 4-year-old grafted plants. Significant differences from the fifteen biological replicates were calculated using <span class="html-italic">t</span>-test and indicated by * (<span class="html-italic">p</span> value &lt; 0.05). SG-Pd1: “Shuguang2” sweet cherry grafted onto Pd1 rootstock (dwarf); SG-WT: “Shuguang2” sweet cherry grafted onto wild-type rootstock (vigorous).</p>
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<p>The content of GAs and IAA in shoot tips. ND indicates “not detected”. Significant differences were evaluated using <span class="html-italic">t</span>-test and indicated by ** (<span class="html-italic">p</span> value &lt; 0.01), *** (<span class="html-italic">p</span> value &lt; 0.001), and **** (<span class="html-italic">p</span> value &lt; 0.0001). SG-Pd1: “Shuguang2” grafted onto Pd1 rootstock (dwarf); SG-WT: “Shuguang2” grafted onto wild-type rootstock (vigorous).</p>
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<p>Relative expression analysis of genes encoding DNA methylation-related enzymes using RT-qPCR. (<b>A</b>) Genes encoding de novo methylation-related enzymes. (<b>B</b>) Genes encoding demethylation-related enzymes. (<b>C</b>) Genes encoding methylation maintenance-related enzymes. Significant differences were assessed by <span class="html-italic">t</span>-test and are indicated by * (<span class="html-italic">p</span> value &lt; 0.05), ** (<span class="html-italic">p</span> value &lt; 0.01), and *** (<span class="html-italic">p</span> value &lt; 0.001), with ns indicating “not significant”.</p>
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<p>Features of the sweet cherry scion methylome. (<b>A</b>) The methylation level of sweet cherry scions. (<b>B</b>) Relative proportions of mCpG, mCHG, and mCHH in total mC. (<b>C</b>) DNA methylation of chromosomes in the sweet cherry genome. (<b>D</b>) The methylation level of each chromosome in sweet cherry. (<b>E</b>) Methylation density. (<b>F</b>) The DNA methylation features of genes and TEs. Mds_S and Mds_L indicated shoot tips and leaves from SG-Pd1, respectively; Mwt_S and Mwt_L indicated shoot tips and leaves from SG-WT, respectively.</p>
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<p>Dynamic changes in DNA methylation after grafting. (<b>A</b>) The DNA methylation level of each sample. (<b>B</b>) DNA methylation characteristics of genes and TEs. (<b>C</b>) Principal component analysis (PCA) of total mC, mCpG, mCHG, and mCHH. Mds_S and Mds_L indicate shoot tips and leaves from SG-Pd1, respectively; Mwt_S and Mwt_L indicate shoot tips and leaves from SG-WT, respectively.</p>
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<p>Identification of DMRs. (<b>A</b>) The number of DMRs identified in Mds_S vs. Mwt_S (shoot tips from SG-Pd1 vs. shoot tips from SG-WT) and Mds_L vs. Mwt_L (leaves from SG-Pd1 vs. leaves from SG-WT). (<b>B</b>) The proportion of CpG-DMRs, CHG-DMRs, and CHH-DMRs. (<b>C</b>) The proportion of hyper-DMRs and hypo-DMRs. (<b>D</b>) The number of DMRs on chromosomes (chr1–chr8). (<b>E</b>) Boxplot of DNA methylation changes in DMRs.</p>
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<p>Differentially methylated gene (DMG) analysis. (<b>A</b>) The number of DMGs identified in Mds_S vs. Mwt_S (shoot tips from SG-Pd1 vs. shoot tips from SG-WT) and Mds_L vs. Mwt_L (leaves from SG-Pd1 vs. leaves from SG-WT). (<b>B</b>) Venn diagram analysis of DMGs in different sequence contexts (mCpG-DMGs, mCHG-DMGs, and mCHH-DMGs) and regions (promoter, exon, and intronic and intergenic regions). (<b>C</b>) Venn diagram analysis of DMGs. (<b>D</b>) KEGG pathway enrichment analysis of DMGs.</p>
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<p>Differentially expressed gene (DEGs) analysis. (<b>A</b>) The number of DEGs. (<b>B</b>) Venn diagram of DEGs in shoot tips and leaves. (<b>C</b>) Volcano plot showing DEGs between shoot tips and leaves. (<b>D</b>) KEGG enrichment analysis was separately conducted on the DEGs in the shoot tips and leaves. WT_S and DS_S indicate shoot tips of SG-WT and SG-Pd1, respectively; WT_L and DS_L indicate leaves of SG-WT and SG-Pd1, respectively.</p>
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<p>Correlation analysis between DNA methylation and gene expression. (<b>A</b>) Venn diagram analysis of DEGs and DMGs in shoot tips and leaves. (<b>B</b>) Venn diagram analysis of overlapping genes in shoot tips and leaves. (<b>C</b>) Distributions of methylation levels within genes partitioned by different expression levels: rank1 is the lowest, and rank6 is the highest. (<b>D</b>) Differential expression levels of all genes, hypermethylated genes, and hypomethylated genes, displayed as boxplots. Wilcoxon <span class="html-italic">p</span> values indicated by ** (<span class="html-italic">p</span> value &lt; 0.005). In addition, ns indicates “not significant”.</p>
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