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Search Results (1,036)

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21 pages, 2551 KiB  
Article
The Diversity of Geochemical and Ecotoxicological Indices of Alluvial Deposits Reflects the Pattern of Landforms: The Case of the Vistula River Valley in the Małopolski Gorge (Poland)
by Agnieszka Kałmykow-Piwińska and Ewa Falkowska
Water 2025, 17(1), 64; https://doi.org/10.3390/w17010064 - 30 Dec 2024
Viewed by 473
Abstract
This study aimed to (1) determine the environmental risk resulting from the contamination of river valley sediments with trace elements of anthropogenic origin, (2) assess the relationship between this environmental risk and the geomorphology of the valley, and (3) identify areas that may [...] Read more.
This study aimed to (1) determine the environmental risk resulting from the contamination of river valley sediments with trace elements of anthropogenic origin, (2) assess the relationship between this environmental risk and the geomorphology of the valley, and (3) identify areas that may become a source of contamination. This research was conducted in the Vistula River Valley between Sulejów and Kazimierz Dolny (Poland). Geochemical and ecotoxicological indices (for fraction < 1 mm) were analyzed (EF, Igeo, PI, CF, Cd, PISum, PIAvg, PINemerow, PLI, ER, RI). Geomorphological mapping, supported by DEM and remote sensing analysis, was performed. High concentrations of trace elements in sediments, as determined by the ICP-OES and ICP-MS methods throughout the study area, indicate generally high environmental degradation and a moderate-to-considerable ecological risk. Contamination differs in the sediments of individual landforms: the highest levels are found in the sediments of the contemporary floodplain and oxbow lakes, while the lowest are observed in the Pleistocene terrace sediments. Only high concentrations of As, Pb, Zn, and Cd are of anthropogenic origin. Their source is probably the mining area of Upper Silesia (As, Pb, Zn) and agricultural activity (Cd). The differences in the values of geochemical indices in individual landforms confirm the influence of fluvial processes on the distribution of trace elements. Full article
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<p>Location of the study area. Study area against the background of aerial photos from 2003.</p>
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<p>Landforms of the analyzed section of the Vistula Valley. The contemporary floodplain takes the form of wide belts adjacent to the riverbed. It is composed of highly diverse deposits: sands, loams, and occasionally clays. (The Vistula River is marked in white).</p>
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<p>The degree of contamination (C<sub>d</sub>). LDC—low degree of contamination, MDC—moderate degree of contamination, CDC—considerable degree of contamination, VHDC—very high degree of contamination, pc—contemporary floodplain, mpr—meander plain reworked by flows of the contemporary braided river, ol—oxbow lakes, pt—Pleistocene terrace.</p>
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<p>Pollution load index (PLI). pc—contemporary floodplain, mpr—meander plain reworked by flows of the contemporary braided river, ol—oxbow lakes, pt—Pleistocene terrace.</p>
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<p>The potential ecological risk index (RI). LER—low ecological risk, MER—moderate ecological risk, CER—considerable ecological risk, pc—contemporary floodplain, mpr—meander plain reworked by flows of the contemporary braided river, ol—oxbow lakes, pt—Pleistocene terrace.</p>
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18 pages, 3071 KiB  
Article
Integrative Transcriptomic and Small RNA Analysis Uncovers Key Genes for Cold Resistance in Rice
by Fan Luo, Mengmeng Yin, Jianping Zhou, Xiaoli Zhou, Chunli Wang, Wenfeng Zhang, Lijuan Chen and Dongsun Lee
Genes 2025, 16(1), 38; https://doi.org/10.3390/genes16010038 - 29 Dec 2024
Viewed by 357
Abstract
Background/Objectives: Cold stress is the main environmental factor that affects the growth and development of rice, leading to a decrease in its yield and quality. However, the molecular mechanism of rice’s low-temperature resistance remains incompletely understood. Methods: In this study, we conducted a [...] Read more.
Background/Objectives: Cold stress is the main environmental factor that affects the growth and development of rice, leading to a decrease in its yield and quality. However, the molecular mechanism of rice’s low-temperature resistance remains incompletely understood. Methods: In this study, we conducted a joint analysis of miRNA and mRNA expression profiles in the cold-resistant material Yongning red rice and the cold-sensitive material B3 using high-throughput sequencing. Results: 194 differentially expressed miRNAs (DEMIs) and 14,671 differentially expressed mRNAs (DEMs) were identified. Among them, 19 DEMIs, including miR1437, miR1156, miR166, miR1861, and miR396_2 family members, showed opposite expression during the early or late stages of low-temperature treatment in two varieties, while 13 DEMIs were specifically expressed in Yongning red rice, indicating that these miRNAs are involved in rice’s resistance to low temperature. In the transcriptome analysis, 218 DEMs exhibited opposite expressions during the early or late stages of low-temperature treatment in two varieties. GO enrichment analysis indicated that these DEMs were enriched in biological processes such as a defense response to fungi, a defense response to bacteria, a plant-type cell wall modification, single-organism cellular processes, a response to chitin, and the regulation of a plant-type hypersensitive response, as well as in cellular components such as the apoplast, nucleus, vacuole, plasma membrane, and plasmodesma. Twenty-one genes were further selected as potential candidates for low-temperature resistance. The joint analysis of miRNA and mRNA expression profiles showed that 38 miRNAs corresponding to 39 target genes were candidate miRNA–mRNA pairs for low-temperature resistance. Conclusions: This study provides valuable resources for determining the changes in miRNA and mRNA expression profiles induced by low temperatures and enables the provision of valuable information for further investigating the molecular mechanisms of plant resistance to low temperatures and for the genetic improvement of cold-resistant varieties. Full article
(This article belongs to the Section Plant Genetics and Genomics)
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<p>Evaluation of cold tolerance in the rice seedling stage of the Yongning red rice and B3. (<b>A</b>) Image of cold tolerance identification of Yongning red rice and B3 seedlings. (<b>B</b>) Survival rates of Yongning red rice and B3 plants after 2 days of cold treatment at 5 °C and a 5-day recovery period. R stands for Yongning red rice; S stands for B3; DAL, days after low-temperature treatment; ** for <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>DEMIs in the comparisons. (<b>A</b>) Number of up- and downregulated miRNAs and target genes in comparing different low-temperature treatment periods (fold change &gt; 1.5, <span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) Venn diagrams of the unique and common DEMIs.</p>
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<p>Heatmap of DEMIs were opposite expressed in two varieties after low-temperature treatment. The heatmap is constructed based on log2fold change values.</p>
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<p>FKPM values of <span class="html-italic">GAPDH</span>, <span class="html-italic">SDHA</span>, <span class="html-italic">TBP</span>, <span class="html-italic">eEF1α</span>, <span class="html-italic">Ubiquitin</span>, <span class="html-italic">LSD1</span>, <span class="html-italic">β-tubulin</span>, and <span class="html-italic">HSP</span> from mRNA-sequencing data.</p>
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<p>Venn diagram of the unique and shared DEMs. (<b>A</b>) Venn diagram of downregulated genes in Se/S0, Sl/S0, Re/R0, and Rl/R0. (<b>B</b>) Venn diagram of upregulated genes in Se/S0, Sl/S0, Re/R0, and Rl/R0. (<b>C</b>) Venn diagram of upregulated genes in Se/S0, Sl/S0, and downregulated genes in Re/R0 and Rl/R0. (<b>D</b>) Venn diagram of downregulated genes in Se/S0, Sl/S0, and upregulated genes in Re/R0 and Rl/R0. DEMs were screened using a threshold of fold change ≥ 2, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>GO (Gene Ontology) analysis of DEMs with opposite expression in two materials during early or late stages of low-temperature treatment.</p>
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<p>Comparison of the expression patterns of miRNAs and their target genes. Data are means ± SD of three independent biological experiments.</p>
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16 pages, 3827 KiB  
Article
Effects of Steaming on Fresh Edible Kernels of Waxy and Normal Maize Determined by Metabolomic Analysis
by Yonghui He, Yingjie Zhu, Guangxuan Jiang, Mingyue Xu, Huanhuan Liu, Xuecai Zhang and Zhitong Yin
Foods 2024, 13(24), 4157; https://doi.org/10.3390/foods13244157 - 22 Dec 2024
Viewed by 400
Abstract
The understanding of the characteristics and metabolite changes in waxy and normal maize kernels after cooking is rather limited. This study was designed to meticulously analyze the differences in characteristics and metabolites of these kernels before and after steaming. To cut environmental impacts, [...] Read more.
The understanding of the characteristics and metabolite changes in waxy and normal maize kernels after cooking is rather limited. This study was designed to meticulously analyze the differences in characteristics and metabolites of these kernels before and after steaming. To cut environmental impacts, samples were obtained by pollinating one ear with mixed pollen. Non-targeted metabolomics was used to analyze metabolites comprehensively. The results demonstrated that a total of 4043 annotated metabolites were identified. Principal component analysis (PCA) indicated distinct variances between kernels before and after steaming and between the two maize types. Steaming led to an increase in differential metabolites (DEMs) for both maize varieties, noticeably in waxy maize. In waxy maize, the down-regulated DEMs were associated with lipid metabolism, while the up-regulated ones were related to amino acid, phenylpropanoid, and flavone metabolism. Compared to steamed normal maize kernels, waxy maize had more DEMs in purine and steroid pathways, fewer in fatty acid, α-linolenic acid, and phenylpropanoid ones, with marked differences in secondary metabolites like those in amino acid metabolism. This study offers a vital foundation and direction for future research on metabolic pathways regarding maize quality improvement and flavor regulation. Full article
(This article belongs to the Section Foodomics)
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<p>Comparison of characteristics of waxy and normal maize kernels before and after steaming. (<b>A</b>) Raw/steamed normal (yellow) and waxy (white) maize kernels from ear 25 days after pollination. Scale bars: 1 cm. Helps observe initial color differences before/after steaming. Blue arrow: waxy corn kernels. Red arrow: normal corn kernels. (<b>B</b>) Individual fresh/steamed normal and waxy maize kernels. Scale bars: 1 cm. Picture clearly shows visual transformation from raw to steamed state. (<b>C</b>) Weight change in individual waxy and normal maize kernels before and after steaming. “a” indicates no significant differences (&gt;0.05) by Duncan’s test. <span class="html-italic">n</span> = 20. CO, fresh normal maize kernels (labeled as control, CO); Wa, fresh waxy maize kernels; Qco, steamed normal maize kernels with respect to kernel quality; Qwa, steamed waxy maize kernels.</p>
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<p>Global analysis of detected metabolites in waxy and normal maize kernels. (<b>A</b>) Principal component analysis (PCA) of metabolites in raw normal maize kernels (CO), raw waxy maize kernels (Wa), steamed normal maize kernels (Qco), and steamed waxy maize kernels (QWa). Axes show components and contribution, with points for samples (same group in same color). The dash line indicates the position of zero. (<b>B</b>) Correlation plot of metabolites in CO, Wa, Qco, and QWa. Vertical axis for sample names, color for <span class="html-italic">r</span> magnitude, replicates with strong positive correlation for data reproducibility. (<b>C</b>) Use top 20 KEGG KO pathway level 3 entries to display 4043 metabolites’ classification. Box items denote KEGG pathway annotations, column length shows metabolite numbers in pathways, parentheses figures show proportion. (<b>D</b>) Clustering heatmap of 1246 common single differential metabolites (filtered by fold change ≥ 1, VIP ≥ 1, <span class="html-italic">p</span>-value ≤ 0.05) in CO, Wa, Qco, and QWa.</p>
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<p>Identification and comparative analysis of differential metabolites in waxy and normal maize kernels pre-/post-steaming. (<b>A</b>) Screened all metabolites pairwise by fold change ≥ 1, VIP ≥ 1, <span class="html-italic">p</span>-value ≤ 0.05. (<b>B</b>) Venn diagram shows common single metabolites in CO_vs_Wa, CO_vs_Qco, Wa_vs_QWa, Qco_vs_QWa groups; overlapping part and numbers mark common metabolites and their quantity. CO, fresh normal maize kernels; Wa, fresh waxy maize kernels; Qco, steamed normal maize kernels; Qwa, steamed waxy maize kernels; DEM, differential metabolites; vs, versus; DEMs_all, all the DEMs in an analytical group; DEMs_up, up-regulated DEMs; DEMs_down, down-regulated DEMs.</p>
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<p>Comprehensive analysis of steaming’s impact on waxy and normal maize kernel metabolites. (<b>A</b>) Venn diagram of differential metabolites in CO_vs_Qco and Wa_vs_Qwa. (<b>B</b>) Top 20 KO pathway level 3 annotations of 1076 identified differential metabolites using KEGG. (<b>C</b>) KEGG—enriched metabolic pathways of metabolites. Analyzing 20 most significantly enriched pathways in DEG metabolites. Each dot represents a KEGG pathway. <span class="html-italic">X</span>-axis: enrichment factor (Rich_factor). <span class="html-italic">Y</span>-axis: pathway name. (<b>D</b>) Heatmap of DEMs in significant metabolic KO pathway in (<b>C</b>). Black boxes indicate significant differences compared to the corresponding raw kernels. CO, fresh normal maize kernels; Wa, fresh waxy maize kernels; Qco, steamed normal maize kernels; Qwa, steamed waxy maize kernels.</p>
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<p>Differential metabolite profiles in steamed waxy and normal maize kernels. (<b>A</b>) The comparison of the number of differential metabolites (DEMs) in steamed waxy and normal maize kernels. (<b>B</b>) KEGG enrichment pathways of the metabolites between the Qco_vs_QWa. (<b>C</b>) Depicts the differences in metabolite abundances in specific metabolic pathways between the waxy and normal kernels after steaming. CO, fresh normal maize kernels; Wa, fresh waxy maize kernels; Qco, steamed normal maize kernels; Qwa, steamed waxy maize kernels; vs, versus.</p>
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23 pages, 3484 KiB  
Article
Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models
by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li and Yucheng Zhang
Remote Sens. 2024, 16(24), 4742; https://doi.org/10.3390/rs16244742 - 19 Dec 2024
Viewed by 414
Abstract
Gully erosion is one of the significant environmental issues facing the black soil regions in Northeast China, and its formation is closely related to various environmental factors. This study employs multiple machine learning models to assess gully erosion susceptibility in this region. The [...] Read more.
Gully erosion is one of the significant environmental issues facing the black soil regions in Northeast China, and its formation is closely related to various environmental factors. This study employs multiple machine learning models to assess gully erosion susceptibility in this region. The primary objective is to evaluate and optimize the top-performing model under high-resolution UAV data conditions, utilize the optimized best model to identify key factors influencing the occurrence of gully erosion from 11 variables, and generate a local gully erosion susceptibility map. Using 0.2 m resolution DEM and DOM data obtained from high-resolution UAVs, 2,554,138 pixels from 64 gully and 64 non-gully plots were analyzed and compiled into the research dataset. Twelve models, including Logistic Regression, K-Nearest Neighbors, Classification and Regression Trees, Random Forest, Boosted Regression Trees, Adaptive Boosting, Extreme Gradient Boosting, an Artificial Neural Network, a Convolutional Neural Network, as well as optimized XGBOOST, a CNN with a Multi-Head Attention mechanism, and an ANN with a Multi-Head Attention Mechanism, were utilized to evaluate gully erosion susceptibility in the Dahewan area. The performance of each model was evaluated using ROC curves, and the model fitting performance and robustness were validated through Accuracy and Cohen’s Kappa statistics, as well as RMSE and MAE indicators. The optimized XGBOOST model achieved the highest performance with an AUC-ROC of 0.9909, and through SHAP analysis, we identified roughness as the most significant factor affecting local gully erosion, with a relative importance of 0.277195. Additionally, the Gully Erosion Susceptibility Map generated by the optimized XGBOOST model illustrated the distribution of local gully erosion risks. Full article
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<p>(<b>a</b>) The schematic location of the study area; (<b>b</b>) a display of the study area; (<b>c</b>) a field photograph of the gully; and (<b>d</b>) a UAV-captured gully image with the highlighted gully area.</p>
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<p>Flowchart of the methodology used in this study.</p>
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<p>Maps of geo-environmental factors (GEFs): (<b>a</b>) Altitude, (<b>b</b>) slope, (<b>c</b>) Aspect, (<b>d</b>) Profile curvature, (<b>e</b>) Plan curvature, (<b>f</b>) Topographic Ruggedness Index, (<b>g</b>) Topographic Position Index, (<b>h</b>) roughness, (<b>i</b>) LS Factor, (<b>j</b>) Topographic Wetness Index, and (<b>k</b>) Stream Power Index.</p>
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<p>Multicollinearity analysis of the geo-environmental factors.</p>
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<p>Relative importance of different geo-environmental factors.</p>
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<p>Gully erosion susceptibility mapping using the optimized XGBOOST mode.</p>
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24 pages, 3111 KiB  
Article
Effect of Seminal Plasma on the Freezability of Boar Sperm
by Kuanfeng Zhu, Yukun Song, Zhi He, Peng Wang, Xuguang Wang and Guoshi Liu
Animals 2024, 14(24), 3656; https://doi.org/10.3390/ani14243656 - 18 Dec 2024
Viewed by 373
Abstract
Background: Seminal plasma is an important component of semen and has a significant effect on sperm function. However, the relationship between seminal plasma and sperm freezing capacity has not been fully studied. Purpose: Exploring metabolites and proteins related to the boar sperm freezing [...] Read more.
Background: Seminal plasma is an important component of semen and has a significant effect on sperm function. However, the relationship between seminal plasma and sperm freezing capacity has not been fully studied. Purpose: Exploring metabolites and proteins related to the boar sperm freezing capacity in seminal plasma, by metabolomic and proteomic approaches, and directly verifying the protective effect of seminal plasma on the cryopreservation of boar sperm using high and low freezability seminal plasma as base freezing extender. Methods: Semen samples were collected from 30 different boars, 11 high and 11 low freezing-resistant boars were selected after freezing 2~4 times, and seminal plasma was selected at the same time. Sperm motility and movement parameters were analyzed using a CASA system. Reproductive hormones (Testosterone, progesterone, estradiol, prolactin, prostaglandin F2α, luteinoid hormone) in seminal plasma were detected by ELISA. Analysis of proteins and metabolites in high and low freezing-resistant seminal plasma by proteomics and metabolomics techniques. Results: The six reproductive hormones tested were not significantly associated with sperm freezing resistance. A total of 13 differentially expressed metabolites (DEMs) and 38 differentially expressed proteins (DEPs) were identified, while a total of 348 metabolites and 1000 proteins were identified. These DEMs were related to energy metabolism, drugs, or environmental pollutants, while the DEPs were mainly involved in the cytoskeletal dynamics and cell adhesion processes. There were 33 metabolites and 70 proteins significantly associated with mean progress motility (PM) at 10 min and 2 h after thawing. The 70 related proteins were associated with cell division and cycle regulation in gene ontology (GO) terms, as well as KEGG pathways, thermogeneration, and pyruvate metabolism. Using highly freezable boar SP as a base freezing extender made no difference from using lowly freezable boar SP, and both were not as good as the commercial control. Conclusion: There were significant differences in seminal plasma with different freezability, but the similarity was much greater than the difference. The protection effect of seminal plasma is not remarkable, and it does not exhibit superior cryoprotective properties compared to commercial semen cryoelongators. Significance: This study provides a deeper understanding of how seminal plasma composition affects sperm freezabilty. It provides potential biomarkers and targets for improving sperm cryopreservation techniques. Full article
(This article belongs to the Special Issue Advances in Animal Fertility Preservation—Second Edition)
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<p>Comparison of the motility of highly and lowly freezable groups before and after freezing. (<b>A</b>,<b>B</b>) PM and TM of groups H and L after pre-diluted before freezing. (<b>C</b>,<b>D</b>) PM and TM of groups H and L after thawing. H, high freezability. L, low freezability. PM, progress motility. TM, total motility. “ns”, not significant. “**” “***” means significant difference and “<span class="html-italic">p</span> &lt; 0.01” “<span class="html-italic">p</span> &lt; 0.001”, respectively. Sample size <span class="html-italic">n</span> = 11.</p>
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<p>Quality control and repeatability analysis of metabolome of highly and lowly freezable seminal plasma. (<b>A</b>) Principal component analysis (PCA) including samples and quality control group. (<b>B</b>) The relative standard deviation of samples. (<b>C</b>) PCA of group H and L. (<b>D</b>) Heatmap of relativity between samples. H, high freezability; L, low freezability. H1,H2,H3, samples of group H;L1, L2, L3, samples of group L. QC, quality control. Each sample was mixed with 3 boar seminal plasma samples with equal volume.</p>
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<p>Quality control and reproducibility analysis of the proteome of highly and lowly freezable seminal plasma. (<b>A</b>) Signal intensity distribution of samples. (<b>B</b>) Distribution of peptide lengths. (<b>C</b>) RSD of group H and L. (<b>D</b>) PCA of group H and L. (<b>E</b>) Heatmap of relativity between samples. (<b>F</b>) Number of identified peptides and proteins. H, high freezability group; L, low freezability group. H1, H2, H3, samples of group H; L1, L2, L3, samples of group L. Each sample was mixed with 3 boar seminal plasma samples with equal volume.</p>
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<p>Differential expressed proteins (DEPs) analysis and GO enrichment. (<b>A</b>) Volcano plot for DEPs screening. The top 5 upregulated proteins (URPs) (red dots) and downregulated proteins (DRPs) (blue dots) are labeled with Uniprot IDs. The thresholds of fold_change were set as 1.5 and 1/1.5. <span class="html-italic">p</span>-value &lt; 0.05 was defined as statistically significant. (<b>B</b>) Heatmap of DEP expression levels of each sample. (<b>C</b>) Number of regulated proteins. (<b>D</b>) GO_BP enrichment of DEPs. (<b>E</b>) GO_CC enrichment of DEPs. (<b>F</b>) GO_MF enrichment of DEPs. (<b>G</b>) Protein domain enrichment of DEPs. H, high freezability group; L, low freezability group. H1, H2, H3, samples of group H; L1, L2, L3, samples of group L. Each sample was mixed with 3 boar seminal plasma with equal volume.</p>
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<p>GO and KEGG pathway enrichment of proteins associated with mean PM (PAPMs) of 10 min and 2 h after thawing. (<b>A</b>) GO_BP enrichment of PAPMs. (<b>B</b>) GO_CC enrichment of PAPMs. (<b>C</b>) GO_MF enrichment of PAPMs. (<b>D</b>) Protein domain enrichment of PAPMs. (<b>E</b>) KEGG pathway enrichment of PAPMs.</p>
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<p>KEGG pathway enrichment in a combination of proteins and metabolites associated with mean PM of 10 min and 2 h after thawing.</p>
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<p>Effect of seminal plasma as base freezing extender on thawing motility and movement parameters of frozen semen. (<b>A</b>–<b>I</b>): TM, PM, ALH, VCL, VAP, VSL, STR, WOB and LIN at 10 min and 2 h after thawing of groups with different base freezing extender. H, high freezability seminal plasma (SP) group. L, low freezability SP group. PM, progress motility. TM, total motility. VCL, velocity of curve line. VSL velocity of straight line. VAP, velocity of path rate. ALH. Amplitude of lateral head. STR, straightness. WOB, wobble. LIN, linear. Individual boar differences were removed for all data. <span class="html-italic">n</span> = 20. “ns”, not significant. “*” “**” “***” “****” means significant difference with control and “<span class="html-italic">p</span> &lt; 0.05” “<span class="html-italic">p</span> &lt; 0.01” “<span class="html-italic">p</span> &lt; 0.001” “<span class="html-italic">p</span> &lt; 0.0001”, respectively.</p>
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13 pages, 4249 KiB  
Article
Spatial (Mis)Matches Between Biodiversity and Habitat Quality Under Multi-Scenarios: A Case Study in Shandong Province, Eastern China
by Xiaoyin Sun, Ruifeng Shan, Qingxin Luan, Yuee Zhang and Zhicong Chen
Land 2024, 13(12), 2215; https://doi.org/10.3390/land13122215 - 18 Dec 2024
Viewed by 363
Abstract
Despite declines in biodiversity and habitat quality (HQ) at a global scale, our understanding of the HQ and matches between HQ and biodiversity under management scenarios is incomplete. To address this deficiency, the study examined trends in HQ and (mis)matches between biodiversity and [...] Read more.
Despite declines in biodiversity and habitat quality (HQ) at a global scale, our understanding of the HQ and matches between HQ and biodiversity under management scenarios is incomplete. To address this deficiency, the study examined trends in HQ and (mis)matches between biodiversity and HQ over four decades in Shandong province, China, identified the key drivers, and assessed the effectiveness of ecological policies, including Ecological Redlines (ERLs) and the Grain for Green (GG) program. During the 40-year period, HQ and matching degrees (indicated by related coefficients) between biodiversity and HQ decreased obviously. Correlation analysis showed that related coefficients between HQ and four biodiversity indices (vertebrate, vascular plant, and vegetation formation type richness, and comprehensive biodiversity index) were all significant (p < 0.01), and coefficients were highest for the biodiversity composite index. An analysis of relative importance by the random forest algorithm indicated significant variation in driving factors for spatial distribution of HQ, biodiversity, and matches between them. The key determinants of biodiversity distribution were biophysical factors, such as NDVI (normalized difference vegetation index), DEM (digital elevation model), and temperature. However, the main drivers of HQ distribution were social factors, such as the accessibility of anthropogenic activities, urbanization, and population density. Ecological policy scenarios, ERLs and GG, are clearly effective and could improve HQ and the matching degree between HQ and biodiversity significantly. Furthermore, the improvement in HQ under ERLs was less than that under GG, while the increase in the matching degree was opposite. The results of this study can be integrated by ecological managers and planners for biodiversity conservation. Full article
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<p>Location and topographic subregions of Shandong province in China.</p>
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<p>Habitat quality in 1980–2020 in Shandong Province ((<b>a</b>) 1980; (<b>b</b>) 1990; (<b>c</b>) 2000; (<b>d</b>) 2010; (<b>e</b>) 2020; (<b>f</b>) 1980–2020.).</p>
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<p>Distribution of biodiversity indices in Shandong province ((<b>a</b>) Vertebrates; (<b>b</b>) Vascular plants; (<b>c</b>) Vegetation formations; (<b>d</b>) Biodiversity index.).</p>
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<p>Spatial matches between HQ and biodiversity in 2020 ((<b>a</b>) Vertebrates; (<b>b</b>) Vascular plants; (<b>c</b>) Vegetation formations; (<b>d</b>) Biodiversity index.).</p>
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<p>Relative importance of various factors for biodiversity, habitat quality, and matches between them ((<b>a</b>) Biodiversity; (<b>b</b>) Habitat quality; (<b>c</b>) Matches between biodiversity and habitat quality.).</p>
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<p>Variations in habitat quality under ecological policy scenarios ((<b>a</b>) Ecological redlines; (<b>b</b>) Variations from 2020; (<b>c</b>) Grain for Green; (<b>d</b>) variation from 2020.).</p>
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26 pages, 46995 KiB  
Article
New Evidence of Holocene Faulting Activity and Strike-Slip Rate of the Eastern Segment of the Sunan–Qilian Fault from UAV-Based Photogrammetry and Radiocarbon Dating, NE Tibetan Plateau
by Pengfei Niu, Zhujun Han, Peng Guo, Siyuan Ma and Haowen Ma
Remote Sens. 2024, 16(24), 4704; https://doi.org/10.3390/rs16244704 - 17 Dec 2024
Viewed by 436
Abstract
The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with [...] Read more.
The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics. Full article
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<p>The distribution of the major active faults and earthquake epicenters (M ≥ 6.0) along the northeastern margin of the Tibetan Plateau. (<b>a</b>) The red box indicates the area shown in panel (<b>b</b>), while the black arrows indicate the direction of block movement. Abbreviations: ATF, Altyn Tagh fault; KF, Kunlun fault; QHF, Qilian-Haiyuan fault; XF, Xianshuihe fault. (<b>b</b>) The locations and characteristics of the faults are based on [<a href="#B9-remotesensing-16-04704" class="html-bibr">9</a>]. The seismic data are sourced from the China Earthquake Information Network (<a href="https://news.ceic.ac.cn/index.html?time=1698442872" target="_blank">https://news.ceic.ac.cn/index.html?time=1698442872</a>, accessed on 3 October 2024), while the GPS velocity field relative to the stable Eurasian continent is derived from [<a href="#B21-remotesensing-16-04704" class="html-bibr">21</a>]. Abbreviations: ATF, Altyn Tagh fault; CMF, Changma fault; TLSF, Tuolaishan fault; HLHF, Halahu fault; SN-QLF, Sunan-Qilian fault; ES-SQF, Eastern segment of the Sunan–Qilian Fault; LLLF, Lenglongling fault; JQHF, Jinqianghe fault; MMSF, Maomaoshan fault; LHSF, Laohushan fault; HYF, Haiyuan fault; GLF, Gulang fault; XS-TJSF, Xiangshan-Tianjingshan fault.</p>
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<p>The distribution map of the eastern segment of the Sunan–Qilian Fault. (<b>a</b>) A fault distribution map, with the fault trace based on [<a href="#B9-remotesensing-16-04704" class="html-bibr">9</a>], primarily interpreted using high-resolution remote sensing images (Google Earth, 0.4 m resolution). (<b>b</b>) Geomorphic features along and on both sides of the fault.</p>
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<p>The faulted geomorphic features north of Ebao town (base map: Google Earth 2024 image). (<b>a</b>) Google Earth imagery; (<b>b</b>) fault trace with Google Earth imagery as the base map.</p>
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<p>A shaded relief map of the mountainous area north of Ebao town, captured using UAVs.</p>
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<p>Fault and displaced geomorphic features in the Qingsha River section. (<b>a</b>) Shaded relief map generated from the Unmanned Aerial Vehicle (UAV)-derived digital elevation model (DEM), with a resolution of 0.24 m. The contour interval is 2 m. (<b>b</b>) Interpreted map of displaced geomorphic features; (<b>c</b>–<b>f</b>) are close-up views.</p>
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<p>The measurement and restoration of T2/T1 riser displacement in the Qingsha River section using LaDiCaozsoftware (V2.1). (<b>a</b>) Shaded relief map of the T2/T1 riser on the left bank of the Qingsha River; the cyan line indicates the fault location, the light yellow lines show the trend of the risers on both sides of the fault, and the red and blue lines mark the locations of topographic profiles of the risers; (<b>b</b>) the optimal displacement restoration map of the T2/T1 riser; (<b>c</b>) the original riser and gully topographic profile (top left), the restored riser and gully topographic profile (bottom left), and the misfit distribution map for displacement measurements (right).</p>
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<p>Trench profile mosaic (<b>a</b>) and interpretation map (<b>b</b>) at the bend of the Qingsha River section.</p>
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<p>Close-up photo and interpretation map of the Qingsha River trench profile. (<b>a</b>) Close-up photo. (<b>b</b>) Fault interpretation map.</p>
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<p>The stratigraphic profile of the top of the T2 terrace in the Qingsha River section and sampling locations.</p>
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<p>Fault and displacement geomorphology features in the Dangzhong River section: (<b>a</b>) Shaded relief map generated from the Unmanned Aerial Vehicle (UAV)-derived digital elevation model (DEM), with a resolution of 0.24 m. The contour interval is 2 m. (<b>b</b>) Interpreted map of displaced geomorphic features.</p>
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<p>The displacement measurement and restoration of the T2/T1 riser in the Dangzhong River section based on LaDiCaoz software (V2.1). (<b>a</b>) Shaded relief map of the T2/T1 riser on the left bank of the Dangzhong River; the cyan line indicates the fault location, the light yellow lines show the trend of the risers on both sides of the fault, and the red and blue lines mark the locations of topographic profiles of the risers; (<b>b</b>) the optimal displacement restoration map of the T2/T1 riser; (<b>c</b>) the original riser and gully topographic profile (top left), the restored riser and gully topographic profile (bottom left), and the misfit distribution map for displacement measurements (right).</p>
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<p>Trench profile mosaic (<b>a</b>) and interpretation map (<b>b</b>) at the bend of the Dangzhong River.</p>
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<p>The stratigraphic profile of the top of the T2 terrace in the Dangzhong River section and sampling locations.</p>
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<p>The geological slip rate distribution map of the QHF [<a href="#B5-remotesensing-16-04704" class="html-bibr">5</a>,<a href="#B7-remotesensing-16-04704" class="html-bibr">7</a>,<a href="#B8-remotesensing-16-04704" class="html-bibr">8</a>,<a href="#B24-remotesensing-16-04704" class="html-bibr">24</a>,<a href="#B27-remotesensing-16-04704" class="html-bibr">27</a>,<a href="#B28-remotesensing-16-04704" class="html-bibr">28</a>,<a href="#B30-remotesensing-16-04704" class="html-bibr">30</a>,<a href="#B31-remotesensing-16-04704" class="html-bibr">31</a>,<a href="#B32-remotesensing-16-04704" class="html-bibr">32</a>,<a href="#B33-remotesensing-16-04704" class="html-bibr">33</a>,<a href="#B34-remotesensing-16-04704" class="html-bibr">34</a>,<a href="#B35-remotesensing-16-04704" class="html-bibr">35</a>,<a href="#B36-remotesensing-16-04704" class="html-bibr">36</a>,<a href="#B37-remotesensing-16-04704" class="html-bibr">37</a>,<a href="#B38-remotesensing-16-04704" class="html-bibr">38</a>,<a href="#B62-remotesensing-16-04704" class="html-bibr">62</a>,<a href="#B63-remotesensing-16-04704" class="html-bibr">63</a>,<a href="#B64-remotesensing-16-04704" class="html-bibr">64</a>,<a href="#B65-remotesensing-16-04704" class="html-bibr">65</a>,<a href="#B66-remotesensing-16-04704" class="html-bibr">66</a>,<a href="#B67-remotesensing-16-04704" class="html-bibr">67</a>]. Abbreviations: CMF, Changma fault; TLSF, Tuolaishan fault; HLHF, Halahu fault; SN-QLF, Sunan-Qilian fault; ES-SQF, Eastern segment of the Sunan–Qilian Fault; LLLF, Lenglongling fault; JQHF, Jinqianghe fault; MMSF, Maomaoshan fault; LHSF, Laohushan fault; HYF, Haiyuan fault; GLF, Gulang fault; XS-TJSF, Xiangshan-Tianjingshan fault.</p>
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<p>The influence of the 2022 Menyuan earthquake on the Coulomb stress of ES-SQF. Abbreviations: ES-SQF, Eastern segment of the Sunan–Qilian Fault. (<b>a</b>) A depth of 5 km; (<b>b</b>) A depth of 10 km.</p>
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14 pages, 17262 KiB  
Article
Analyzing the Accuracy of Satellite-Derived DEMs Using High-Resolution Terrestrial LiDAR
by Aya Hamed Mohamed, Mohamed Islam Keskes and Mihai Daniel Nita
Land 2024, 13(12), 2171; https://doi.org/10.3390/land13122171 - 13 Dec 2024
Viewed by 407
Abstract
The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological [...] Read more.
The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological modeling. A comparative analysis with Terrestrial Laser Scanning (TLS) measurements assessed the agreement between these datasets. Multiple linear regression models were utilized to evaluate the relationships between the datasets and provide detailed insights into their accuracy and biases. The results indicate significant correlations between satellite DEMs and TLS measurements, with adjusted R-square values of 0.8478 for ALOS and 0.955 for the SRTM. To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. Additionally, slope and aspect analyses were performed to highlight terrain characteristics across the DEMs. Slope analysis showed a statistically significant negative correlation between SRTM and TLS slopes (R2 = 0.16, p < 4.47 × 10−10 indicating a weak relationship, while no significant correlation was observed between ALOS and TLS slopes. Aspect analysis showed significant positive correlations for both ALOS and the SRTM with TLS aspect, capturing 30.21% of the variance. These findings demonstrate the accuracy of satellite-derived elevation models in representing terrain features relative to high-resolution terrestrial data. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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<p>Geographical location of study area.</p>
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<p>Summary of data processing and analysis workflow.</p>
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<p>Analyzing the datasets using a grid-cell-based approach.</p>
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<p>Slope analysis of satellite-derived products (ALOS and SRTM) using grid cell analysis.</p>
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<p>Analyzing the TLS slope using a grid-cell-based approach.</p>
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<p>Aspect analysis of satellite-derived products (ALOS and SRTM).</p>
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<p>Analyzing the TLS aspect using a grid-cell-based approach.</p>
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<p>Comparison of satellites (SRTM and ALOS) with TLS measurements. (<b>a</b>) Elevation values, (<b>b</b>) slope values, and (<b>c</b>) aspect values for comparison of each model.</p>
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<p>Comparison of satellites (SRTM and ALOS) with TLS measurements. (<b>a</b>) Elevation values, (<b>b</b>) slope values, and (<b>c</b>) aspect values for comparison of each model.</p>
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22 pages, 13321 KiB  
Article
Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points
by Barbora Černilová, Jiří Kuře, Rostislav Chotěborský and Miloslav Linda
Technologies 2024, 12(12), 257; https://doi.org/10.3390/technologies12120257 - 12 Dec 2024
Viewed by 782
Abstract
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is [...] Read more.
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R2 values of 0.9988, 0.9972, and 0.9982 for the X–, Y–, and Z–axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach. Full article
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<p>The ChArUco chessboard pattern was used to obtain the intrinsic camera parameters. The image was captured from the side camera in the Caliscope software. The reference points were highlighted in red within the software.</p>
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<p>Location of GoPro cameras within the sand box. Cam 1 refers to the side camera and cam 2 refers to the front camera.</p>
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<p>The ChArUco chessboard pattern was used to obtain extrinsic camera parameters in the sand box. (<b>a</b>) The side camera view on the ChArUco chessboard pattern; (<b>b</b>) the front camera view on the ChArUco chessboard pattern.</p>
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<p>The arrangement of particles on the surface of the sand in the sand box. The particles were arranged with a distance of 50 mm between each other. The particles were labeled with numbers 1 to 8 for better identification in the future.</p>
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<p>To obtain trajectories from the video recordings, the Tracker tool was utilized. (<b>a</b>) Selected cluster of pixels for tracing Particle 1. (<b>b</b>) Particle paths from the side camera view after Tracker tool processing.</p>
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<p>The world co-ordinate system and camera co-ordinate system. View from side camera and front camera.</p>
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<p>The rotation around the Z–axis. The original co-ordinate system is presented by x<sub>2</sub>, y<sub>2</sub> (solid lines) and the new co-ordinate system is presented by x, y (dotted lines).</p>
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<p>Three-dimensional model of the sand box walls and tillage tool for import into Ansys Rocky.</p>
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<p>The particles were of two sizes: (<b>a</b>) the upper layer of particles contained 2-mm particles (shown by the blue inlet box); (<b>b</b>) the lower particle layer contained 10-mm particles (shown by the blue inlet box).</p>
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<p>3D chart illustrating the trajectories of the particles with number: 1, 2, 3, 5, 6, 7, and 8.</p>
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<p>Comparison of particle trajectories 5, 6, 7, and 8.</p>
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<p>Comparison between the experimental sand pile and the results from the Ansys Rocky script of the model number 40.</p>
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<p>The comparison of the verified particle system during tillaging (<b>a</b>) from the experiment (with contrast points in the sand) and (<b>b</b>) the result of its digital twin (colored based on particle height in the Y axis for better comprehensibility).</p>
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<p>Comparison of the trajectory of particle 7 in the Y– and Z–axes with the particles from Ansys Rocky.</p>
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<p>Comparison of the trajectory of particle 7 in the X– and Z–axes with the particles from Ansys Rocky.</p>
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<p>Comparison of the trajectory of particle 8 in the Y– and Z–axes with the particles from Ansys Rocky.</p>
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<p>Comparison of the trajectory of particle 8 in the X– and Z–axes with the particles from Ansys Rocky.</p>
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27 pages, 3310 KiB  
Article
Evaluation of Correction Algorithms for Sentinel-2 Images Implemented in Google Earth Engine for Use in Land Cover Classification in Northern Spain
by Iyán Teijido-Murias, Marcos Barrio-Anta and Carlos A. López-Sánchez
Forests 2024, 15(12), 2192; https://doi.org/10.3390/f15122192 - 12 Dec 2024
Viewed by 739
Abstract
This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern [...] Read more.
This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain. Full article
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<p>Workflow adopted in this study to analyze different combinations of Sentinel-2 imagery corrections. In Algorithm_AT00B, Algorithm_ is the name or abbreviation of the algorithm used, A denotes “atmospheric correction”, T “topographic correction”, the number 00 refers to the spatial resolution of the digital elevation model (DEM) (90, 30, and 10 m per pixel, respectively) and B refers to “application of BRDF correction”. The datasets are shown in three different colours: datasets available in the GEE repository, in blue, the dataset developed in Sentinel Application Platform—SNAP 11.0.0 and uploaded in GEE assets, in purple; and the Level 1 C datasets derived from the GEE platform, in orange. In all cases, the Random Forest algorithm was used for fitting each processing dataset.</p>
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<p>Overview of (<b>a</b>) the location of the study area overlapping the Spanish National Forest Inventory plots used in this study, (<b>b</b>) Sentinel-2 granules for the study area, and (<b>c</b>) location of the region of interest in northern Spain. WGS 84/UTM zone 29N (EPSG: 32629).</p>
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<p>Visual comparison into the 4 datasets.</p>
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<p>Box plots of the overall accuracy (Accuracy) of the whole land cover classification corresponding to different levels of S2 image processing: absence of atmospheric, topographic, or BRDF correction (1C), atmospheric correction with the Sen2Cor algorithm and topographic correction with the Sen2Cor algorithm with DEM of 90 m per pixel (S2C_AT90) and atmospheric correction with the Py6S algorithm, topographic correction with the SCS + C algorithm with DEM of 10 m per pixel and the BRDF correction (Py6S_AT10B). The letters at the top of the box indicate the results of Tukey’s HSD multiple comparison test (different letters indicate significant differences between the difference levels of database processing and/or correction algorithms used).</p>
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14 pages, 3544 KiB  
Article
Enhanced Synthesis of Volatile Compounds by UV-B Irradiation in Artemisia argyi Leaves
by Haike Gu, Zhuangju Peng, Xiuwen Kuang, Li Hou, Xinyuan Peng, Meifang Song and Junfeng Liu
Metabolites 2024, 14(12), 700; https://doi.org/10.3390/metabo14120700 - 11 Dec 2024
Viewed by 597
Abstract
Background: Volatile compounds have a deep influence on the quality and application of the medicinal herb Artemisia argyi; however, little is known about the effect of UV-B radiation on volatile metabolites. Methods: We herein investigated the effects of UV-B exposure on the [...] Read more.
Background: Volatile compounds have a deep influence on the quality and application of the medicinal herb Artemisia argyi; however, little is known about the effect of UV-B radiation on volatile metabolites. Methods: We herein investigated the effects of UV-B exposure on the volatile compounds and transcriptome of A. argyi to assess the potential for improving its quality and medicinal characteristics. Results: Out of 733 volatiles obtained, a total of 133 differentially expressed metabolites (DEMs) were identified by metabolome analysis. These were classified into 16 categories, primarily consisting of terpenoids, esters, heterocyclic compounds, alcohols, and ketones. Sensory odor analysis indicated that green was the odor with the highest number of annotations. Among the 544 differentially expressed genes (DEGs) identified by transcriptome analysis, most DEGs were linked to “metabolic pathways” and “biosynthesis of secondary metabolites”. Integrated analysis revealed that volatiles were mainly synthesized through the shikimate pathway and the MEP pathway. RNA-seq and qPCR results indicated that transcription factors HY5, bHLH25, bHLH18, bHLH148, MYB114, MYB12, and MYB111 were upregulated significantly after UV-B radiation, and were therefore considered key regulatory factors for volatiles synthesis under UV-B radiation. Conclusions: These findings not only provide new insights into UV-induced changes in volatile compounds, but also provide an exciting opportunity to enhance medicinal herbs’ value, facilitating the development of products with higher levels of essential oils, flavor, and bioactivity. Full article
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<p>Overview of volatile metabolite changes in <span class="html-italic">A. argyi</span> leaves in response to UV-B radiation. (<b>A</b>) PCA of metabolites. (<b>B</b>) Cluster heatmap of all metabolite contents. The horizontal axis represents the sample name, and the vertical axis represents the metabolite information. Different colors are filled with different values obtained after standardizing the relative content (red represents high content, green represents low content). (<b>C</b>) Volcano plot of DEMs. (<b>D</b>) KEGG enrichment analysis of DEMs. (<b>E</b>) Top 20 upregulated DEMs.</p>
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<p>Odorous compounds analysis. (<b>A</b>) Category and number of DEMs with sensory flavor. (<b>B</b>) Radar chart of sensory flavor characteristics of differential volatile compounds. (<b>C</b>) Correlation network diagram between sensory flavor characteristics and DEMs.</p>
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<p>Overview of transcriptome analysis of <span class="html-italic">A. argyi</span> responsive to UV-B irradiation. (<b>A</b>) PCA analysis of samples taken at 0 h, 4 h, 8 h, and 6 days. (<b>B</b>) Changes in the total number of genes and DEGs. (<b>C</b>) Volcano map of DEGs from the pairwise comparison of UV0 vs. UV6d. (<b>D</b>) Venn graph for up- and downregulated DEGs from the pairwise comparisons of UV0 vs. UV4h, UV0 vs. UV8h, and UV0 vs. UV6d.</p>
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<p>Enrichment analysis of metabolic pathways in comparison of UV0 vs. UV6d. (<b>A</b>) Top 20 enriched GO pathways of DEGs. (<b>B</b>) Top 20 enriched KEGG pathways of DEGs. The color and size of the solid circles represent the significant value of the enrichment factor and the number of transcripts involved in the specific pathway, respectively. (<b>C</b>) Classification of enriched metabolic pathways. The numbers in the figure represent the number of DEGs annotated to this pathway, and the parentheses indicate the ratio of DEGs annotated to this pathway to the number of background genes annotated to this pathway.</p>
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<p>Metabolic analysis of volatile compounds in <span class="html-italic">A. argyi</span> leaves. HK, hexokinase; PFK, 6-phosphofructokinase; PK, pyruvate kinase; MEP, 2-C-methyl-D-erythrin-4-phosphate; IPP, isopentenyl pyrophosphate; DXS, 1-Deoxy-D-xylulose-5-phosphate synthase; DXR, 1-deoxy-d-xylulose-5-phosphate reductoisomerase; CMS, 2-C-methyl-D-erythritol 4-phosphate cytidine synthase; CMK, 2-C-Methyl-D-erythritol 4-phosphate cytidine kinase; MCS, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, Hydroxymethylbutene-4-phosphate synthase; HDR, 1-Hydroxy-2-methyl-2 (E)-butenyl-4-diphosphate reductase; GGPPS, geranylgeranyl diphosphate synthases; FPPS, Farnesyl pyrophosphate synthase; Cit2, citrate synthase; Icl, isocitrate lyase; Idh, isocitrate dehydrogenase; Kgd, alpha-ketoglutarate dehydrogenase; Sdh, succinate dehydrogenase; Fum, fumarase; Mdh, malate dehydrogenase; DS, DAHP synthase; DAS, 3-dehydroquinic acid synthase; DAD, 3-dehydroquinic acid dehydratase; SDH, shikimate dehydrogenase; SK, shikimate kinase; ES, EPSP synthase; BAS, branched acid synthase; ICS, isochorismate synthase; PBS3, avrPphB susceptible 3; PAL, phenylalanine ammonia-lyase; C4H, cinnamate4-hydroxylase; 4CL, 4-coumarate-CoA ligase. The results were expressed as mean ± SD of triplicate measurements.</p>
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<p>Transcriptional regulation of volatile compounds induced by UV-B. Gene expression of transcription factors analyzed by RNA-seq (<b>A</b>) and qPCR (<b>B</b>). Red characters indicate the upregulated metabolites. (<b>C</b>) A regulation model of volatile compounds-related genes. Red characters indicate the upregulated genes. Black characters indicate expression with insignificant differences. UVR8, UV Resistance Locus 8; COP1, Constitutively Photomorphogenetic 1; HY5, Elongated Hypocotyl 5.</p>
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35 pages, 19129 KiB  
Article
Mapping Lithology with Hybrid Attention Mechanism–Long Short-Term Memory: A Hybrid Neural Network Approach Using Remote Sensing and Geophysical Data
by Michael Appiah-Twum, Wenbo Xu and Emmanuel Daanoba Sunkari
Remote Sens. 2024, 16(23), 4613; https://doi.org/10.3390/rs16234613 - 9 Dec 2024
Viewed by 740
Abstract
Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature [...] Read more.
Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature diagnosis, interpretation, and identification across varied remote sensing datasets. To address these limitations, a hybrid-attention-integrated long short-term memory (LSTM) network is employed to diagnose, interpret, and identify lithological feature representations in a remote sensing-based geological analysis using multisource data fusion. The experimental design integrates varied datasets including Sentinel-2A, Landsat-9, ASTER, ALOS PALSAR DEM, and Bouguer anomaly gravity data. The proposed model incorporates a hybrid attention mechanism (HAM) comprising channel and spatial attention submodules. HAM utilizes an adaptive technique that merges global-average-pooled features with max-pooled features, enhancing the model’s accuracy in identifying lithological units. Additionally, a channel separation operation is employed to allot refined channel features into clusters based on channel attention maps along the channel dimension. The comprehensive analysis of results from comparative extensive experiments demonstrates HAM-LSTM’s state-of-the-art performance, outperforming existing attention modules and attention-based models (ViT, SE-LSTM, and CBAM-LSTM). Comparing HAM-LSTM to baseline LSTM, the HAM module’s integrated configurations equip the proposed model to better diagnose and identify lithological units, thereby increasing the accuracy by 3.69%. Full article
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<p>An overview of this study’s workflow: The multisource data fusion technique is employed to fuse the gravity anomaly data and remote sensing data. Channel and spatial attention mechanisms are modeled to learn the spatial and spectral information of pixels in the fused data and the resultant attention features, fed into the LSTM network for sequential iterative processing to map lithology.</p>
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<p>Location of study area and regional geological setting. (<b>a</b>) Administrative map of Burkina Faso; (<b>b</b>) administrative map of Bougouriba and Ioba Provinces within which the study area is located; (<b>c</b>) geological overview of Burkina Faso (modified from [<a href="#B44-remotesensing-16-04613" class="html-bibr">44</a>]) indicating the study area; (<b>d</b>) color composite image of Landsat-9 covering the study area.</p>
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<p>False color composite imagery of remote sensing data used: (<b>a</b>) Sentinel-2A (bands 4-3-2); (<b>b</b>) Landsat-9 (bands 4-3-2); (<b>c</b>) ASTER (bands 3-2-1); and (<b>d</b>) 12.5 m spatial resolution high-precision ALOS PALSAR DEM.</p>
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<p>Vegetation masking workflow.</p>
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<p>The HAM structure. It comprises three sequential components: channel attention submodule, feature separation chamber, and spatial attention submodule. One-dimensional and two-dimensional feature maps are produced by the channel and spatial attention submodules, respectively.</p>
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<p>Framework of HAM’s channel attention submodule. Dimensional feature information is generated by both max-pooling and average-pooling operations. The resultant features are then fed through a one-dimensional convolution with a sigmoid activation to deduce the definitive channel feature.</p>
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<p>Framework of HAM’s spatial attention. Two feature clusters of partitioned refined channel features from the separation chamber are fed into the submodule. Average-pooling and max-pooling functions subsequently synthesize two pairs of 2D maps into a shared convolution layer to synthesize spatial attention maps.</p>
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<p>The structural framework of the proposed HAM-LSTM model.</p>
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<p>Gravity anomaly maps of the terrane used: (<b>a</b>) complete Bouguer anomaly; (<b>b</b>) residual gravity.</p>
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<p>Band imagery: (<b>a</b>) Landsat-9 band 5; (<b>b</b>) Sentinel-2A band 5; (<b>c</b>) ASTER band 5; (<b>d</b>) fused image; (<b>e</b>) partial magnification of (<b>a</b>) (<math display="inline"><semantics> <mrow> <mn>279</mn> <mo>×</mo> <mn>235</mn> </mrow> </semantics></math> pixels); (<b>f</b>) partial magnification of (<b>b</b>) (<math display="inline"><semantics> <mrow> <mn>279</mn> <mo>×</mo> <mn>235</mn> </mrow> </semantics></math> pixels); (<b>g</b>) partial magnification of (<b>c</b>) (<math display="inline"><semantics> <mrow> <mn>279</mn> <mo>×</mo> <mn>235</mn> </mrow> </semantics></math> pixels); and (<b>h</b>) partial magnification of (<b>d</b>) (<math display="inline"><semantics> <mrow> <mn>279</mn> <mo>×</mo> <mn>235</mn> </mrow> </semantics></math> pixels).</p>
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<p>Resultant multisource fusion imagery.</p>
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<p>Annotation map of the study area.</p>
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<p>An illustration of the sliding window method implementation.</p>
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<p>Graphs of training performance of the varied model implementations in this study: (<b>a</b>) accuracy and (<b>b</b>) loss.</p>
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<p>Classification maps derived from implementing (<b>a</b>) HAM-LSTM, (<b>b</b>) CBAM-LSTM, (<b>c</b>) SE-LSTM, (<b>d</b>) ViT, and (<b>e</b>) LSTM on the multisource fusion dataset.</p>
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<p>Confusion matrices of (<b>a</b>) HAM-LSTM, (<b>b</b>) CBAM-LSTM, (<b>c</b>) SE-LSTM, (<b>d</b>) LSTM, and (<b>e</b>) ViT implementation.</p>
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16 pages, 8780 KiB  
Article
Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy
by Leonardo Pace, Simone Priori, Monica Zanini and Valerio Cristofori
Soil Syst. 2024, 8(4), 128; https://doi.org/10.3390/soilsystems8040128 - 7 Dec 2024
Viewed by 619
Abstract
Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost [...] Read more.
Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. Full article
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<p>Framework for the study area.</p>
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<p>Pretreatments applied to the spectral library with local samples (n = 377): on the left the Savitzky–Golay filter; on the right, the application of the standard normal variate.</p>
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<p>Maps of electrical conductivity (ECa) measured at different depths by EMI sensor. The black dots show the soil profiles (P24 and P25), whereas the polygons show the two STUs delineated by k-means clustering.</p>
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<p>Digital elevation model (DEM) with selected profiles.</p>
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<p>Profile P24.</p>
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<p>Profile P25.</p>
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<p>Total random augerings. The blue dots are the points selected for the local calibration set.</p>
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<p>Maps of clay (g·100 g<sup>−1</sup>), sand (g·100 g<sup>−1</sup>) and SOC (g·100 g<sup>−1</sup>), interpolated by KED, using the values of the sampling datapoints predicted by NIR spectroscopy and the most related covariates according to Pearson’s correlation index.</p>
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<p>Maps of TN (g·kg<sup>−1</sup>), CEC (meq·100 g<sup>−1</sup>) and CaCO<sub>3</sub> (g·100 g<sup>−1</sup>), interpolated by KED, using the values of the sampling datapoints predicted by NIR spectroscopy and the most related covariates according to Pearson’s correlation index.</p>
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<p>Error map of clay (g·100 g<sup>−1</sup>), sand (g·100 g<sup>−1</sup>), SOC (g·100 g<sup>−1</sup>)<sub>,</sub> TN (g·kg<sup>−1</sup>), CEC (meq·100 g<sup>−1</sup>), and CaCO<sub>3</sub> (g·100 g<sup>−1</sup>), interpolated by KED, using the values of the sampling datapoints predicted by NIR spectroscopy and the most related covariates according to Pearson’s index.</p>
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<p>Total random augerings. Blue dots are the points selected for the local calibration set; red dots are the points collected for the local validation set.</p>
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15 pages, 3309 KiB  
Article
Multi-Omics Analysis Provides Insights into Green Soybean in Response to Cold Stress
by Yanhui Lin, Guangping Cao, Jing Xu, Honglin Zhu and Liqiong Tang
Metabolites 2024, 14(12), 687; https://doi.org/10.3390/metabo14120687 - 7 Dec 2024
Viewed by 572
Abstract
Green soybean (Glycine max (L.) Merrill) is a highly nutritious food that is a good source of protein and fiber. However, it is sensitive to low temperatures during the growing season, and enhancing cold tolerance has become a research hotspot for breeding [...] Read more.
Green soybean (Glycine max (L.) Merrill) is a highly nutritious food that is a good source of protein and fiber. However, it is sensitive to low temperatures during the growing season, and enhancing cold tolerance has become a research hotspot for breeding improvement. Background/Objectives: The underlying molecular mechanisms of cold tolerance in green soybean are not well understood. Methods: Here, a comprehensive analysis of transcriptome and metabolome was performed on a cold-tolerant cultivar treated at 10 °C for 24 h. Results: Compared to control groups, we identified 17,011 differentially expressed genes (DEGs) and 129 differentially expressed metabolites (DEMs). The DEGs and DEMs were further subjected to KEGG functional analysis. Finally, 11 metabolites (such as sucrose, lactose, melibiose, and dehydroascorbate) and 17 genes (such as GOLS, GLA, UGDH, and ALDH) were selected as candidates associated with cold tolerance. Notably, the identified metabolites and genes were enriched in two common pathways: ‘galactose metabolism’ and ‘ascorbate and aldarate metabolism’. Conclusions: The findings suggest that green soybean modulates the galactose metabolism and ascorbate and aldarate metabolism pathways to cope with cold stress. This study contributes to a deeper understanding of the complex molecular mechanisms enabling green soybeans to better avoid low-temperature damage. Full article
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<p>Transcriptome analysis of green soybeans (QXD15). (<b>A</b>) MA plot of the DEGs in response to cold stress. (<b>B</b>) Functional Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification of DEGs (top 20 listed).</p>
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<p>Metabolomics analysis of green soybeans (QXD15). (<b>A</b>) Classification of metabolites. (<b>B</b>) PCA of metabolites. (<b>C</b>) Volcano plot of the DEMs in response to cold stress. (<b>D</b>) KEGG pathway classification of DEMs (top 20 listed). (<b>E</b>) Heatmap of 31 DAMs under cold stress. Scaled values of the relative contents of metabolites were used for z-scale normalization.</p>
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<p>The genes and metabolites identified in the galactose metabolism pathway in response to cold stress. Blue represents the metabolites or genes that changed under cold stress. <span class="html-italic">GOLS</span>: inositol 3-alpha-galactosyltransferase [EC:2.4.1.123]; <span class="html-italic">RFS</span>: raffinose synthase [EC:2.4.1.82]; <span class="html-italic">GLA</span>: alpha-galactosidase [EC:3.2.1.22]; <span class="html-italic">INV</span>: beta-fructofuranosidase [EC:3.2.1.26]; <span class="html-italic">UGP2:</span> UTP-glucose-1-phosphate uridylyltransferase [EC:2.7.7.9].</p>
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<p>The genes and metabolites identified in the ascorbate and aldarate metabolism pathways in response to cold stress. Blue represents the metabolites or genes that changed under cold stress. <span class="html-italic">UGDH</span>: UDPglucose 6-dehydrogenase [EC:1.1.1.22]; <span class="html-italic">USP</span>: UDP-sugar pyrophosphorylase [EC:2.7.7.64]; <span class="html-italic">GLCAK</span>: glucuronokinase [EC:2.7.1.43]; <span class="html-italic">GULO</span>: L-gulonolactone oxidase [EC:1.1.3.8]; DHAR: glutathione dehydrogenase/transferase [EC:1.8.5.1 2.5.1.18]; ALDH: aldehyde dehydrogenase (NAD+) [EC:1.2.1.3]; <span class="html-italic">GME</span>: GDP-D-mannose 3′, 5′-epimerase [EC:5.1.3.18 5.1.3.-]; <span class="html-italic">VTC2_5</span>: GDP-L-galactose phosphorylase [EC:2.7.7.69]; <span class="html-italic">VTC4</span>: inositol-phosphate phosphatase/L-galactose 1-phosphate phosphatase [EC:3.1.3.25 3.1.3.93]; <span class="html-italic">GalDH</span>: L-galactose dehydrogenase [EC:1.1.1.316]; <span class="html-italic">APX</span>: L-ascorbate peroxidase [EC:1.11.1.11]; <span class="html-italic">AO</span>: L-ascorbate oxidase [EC:1.10.3.3].</p>
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20 pages, 14738 KiB  
Article
Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach
by Zeyuan Liao, Xiujun Dong and Qiulin He
Remote Sens. 2024, 16(23), 4563; https://doi.org/10.3390/rs16234563 - 5 Dec 2024
Viewed by 576
Abstract
Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method to quantify DEM quality by combining the [...] Read more.
Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method to quantify DEM quality by combining the RMSE of elevation and terrain complexity, analyzing the DEM quality error curves constructed with different point cloud densities by a discrete difference peak-seeking method, to determine the optimal ground point density, and then constructing an ICP-NN algorithm for predicting the collected point cloud density. After analysis of DEM quality at eight point cloud dilution levels, the optimal ground point cloud densities were determined to be 2.43 pts/m2 (0.2 m resolution), 2.08 pts/m2 (1 m and 0.5 m resolution), and 1.84 pts/m2 (2 m resolution). Using the obtained optimal ground point densities, survey area slopes, canopy density, and elevation differences as eigenvalues, the ICP-NN model can be used to directly predict the collected point cloud density intervals in other regions, and the model has interval lengths ranging from 36 to 70.33 pts/m2 at 5 CLs. This method solves the problem of determining point cloud density in landslide surveys using airborne LiDAR and provides direct guidance for practical applications. Full article
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<p>Overview of the study area. (<b>a</b>) The province and its boundaries where the survey area is located; (<b>b</b>) 3D terrain model of the survey area; (<b>c</b>) optical image of the Limushan landslide; (<b>d</b>) characteristic vegetation of the region (trees); (<b>e</b>) shrubs; (<b>f</b>) grassland.</p>
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<p>Work flow chart.</p>
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<p>Data acquisition and processing process (The numbers in the figure indicate the order of the processing steps).</p>
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<p>ICP-NN algorithm framework.</p>
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<p>Location map of micro-topographic features (Region a in the yellow box shows the crack at the back edge of the landslide; region b in the red box shows the right boundary of the landslide; and region c in the blue box shows the gully at the front edge of the landslide).</p>
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<p>Visually interpreted results–DEM comparison chart.</p>
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<p>Quantitative analysis of elevation RMSE.</p>
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<p>Quantitative analysis of elevation RMSE. ** indicates significant differences at <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Results of terrain complexity calculation. (<b>a</b>) Relatively flat area; (<b>b</b>) area of high terrain complexity.</p>
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<p>Complexity error fitting curve.</p>
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<p>The elevation RMSE curve discrete difference peak-seeking plot (a–d is an enlarged detail view of the last peak on the error curve).</p>
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<p>The TCI error curve discrete difference peak-seeking plot (a–d is an enlarged detail view of the last peak on the error curve).</p>
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<p>Overview of canopy density by 2D canopy height model. (<b>a</b>) Overall vegetation cover in the DOM; (<b>b</b>) canopy density inversion result.</p>
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<p>The loss curve of the trained model.</p>
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<p>Distribution of MPIW under different CLs.</p>
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