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24 pages, 4996 KiB  
Article
Research and Performance Evaluation of Environmentally Friendly Shale Inhibitor TIL-NH2 for Shale Gas Horizontal Wells
by Yuexin Tian, Xiangjun Liu, Yintao Liu, Haifeng Dong, Guodong Zhang, Biao Su, Xiaofeng Liu, Yifan Hu, Jinjun Huang and Zeze Lu
Molecules 2024, 29(24), 5950; https://doi.org/10.3390/molecules29245950 - 17 Dec 2024
Viewed by 230
Abstract
Wellbore instability caused by hydration during the development of shale gas reservoirs poses significant challenges to drilling engineering. In this study, a novel and environmentally friendly shale inhibitor, TIL-NH2, was synthesized via free radical polymerization using 1-vinylimidazole and N-(2-bromoethyl)-1,3-propanediamine dihydrobromide as [...] Read more.
Wellbore instability caused by hydration during the development of shale gas reservoirs poses significant challenges to drilling engineering. In this study, a novel and environmentally friendly shale inhibitor, TIL-NH2, was synthesized via free radical polymerization using 1-vinylimidazole and N-(2-bromoethyl)-1,3-propanediamine dihydrobromide as the main raw materials. The molecular structure of TIL-NH2 was characterized by infrared spectroscopy and nuclear magnetic resonance. Incorporating imidazole cations and amino bifunctional groups, TIL-NH2 exhibits excellent inhibitory performance and environmental friendliness. Its performance was systematically evaluated through linear swelling tests, shale cuttings rolling recovery tests, permeability recovery experiments, and dynamic adsorption analyses. The results indicate the following: (1) At a concentration of 1.2 wt%, TIL-NH2 reduced the linear swelling height of shale by 65.69%, significantly outperforming traditional inhibitors like KCl and NW-1. (2) Under conditions of 140 °C, the cuttings rolling recovery rate of TIL-NH2 reached 88.12%, demonstrating excellent high-temperature resistance. (3) Permeability recovery experiments showed that at a concentration of 2.0 wt%, TIL-NH2 achieved a permeability recovery rate of 90.58%, effectively mitigating formation damage. (4) Dynamic adsorption experiments indicated that at a concentration of 2.5 wt%, the adsorption capacity tended toward saturation, reaching 26.00 mg/g, demonstrating stable adsorption capability. Additionally, environmental friendliness evaluations revealed that TIL-NH2 has a degradation rate exceeding 90% within 28 days, and its acute toxicity is significantly lower than that of traditional inhibitors like KCl (the LC50 of TIL-NH2 is 1080.3 mg/L, whereas KCl is only 385.4 mg/L). This research provides a high-efficiency and environmentally friendly new inhibitor for green drilling fluid systems in horizontal shale gas wells, offering important references for technological advancements in unconventional energy development. Full article
(This article belongs to the Topic Petroleum and Gas Engineering, 2nd edition)
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<p>Reaction mechanism equation.</p>
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<p>TIL-NH<sub>2</sub> inhibitor infrared spectra [<a href="#B39-molecules-29-05950" class="html-bibr">39</a>].</p>
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<p><sup>1</sup>H-NMR spectrum of TIL-NH<sub>2</sub> [<a href="#B39-molecules-29-05950" class="html-bibr">39</a>].</p>
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<p>Relationship between shale swelling height and immersion time in TIL-NH<sub>2</sub> solutions with different concentrations.</p>
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<p>Relationship between shale swelling height and immersion time under different concentrations of inhibitor solution.</p>
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<p>Variation in heat rolling recovery with TIL-NH<sub>2</sub> addition at different temperatures.</p>
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<p>Heat roll recovery for each inhibitor at 140 °C.</p>
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<p>Expansion stress of illite in response to different solution treatments.</p>
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<p>Influence of TIL-NH<sub>2</sub> concentration on the swelling stress of illite.</p>
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<p>Effect of combined KCl/TIL-NH<sub>2</sub> solutions on illite swelling stress.</p>
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<p>Triaxial stress diagrams of downhole shale of the Longmaxi Formation soaked by different treatments ((<b>a</b>) water, (<b>b</b>) diesel fuel, (<b>c</b>) white oil, (<b>d</b>) 2% DEM, (<b>e</b>) 2% polyetheramine, (<b>f</b>) TIL-NH<sub>2</sub> solution).</p>
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<p>Variation in shale permeability recovery rates at different TIL-NH<sub>2</sub> concentrations.</p>
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<p>Dynamic adsorption as a function of TIL-NH<sub>2</sub> concentration.</p>
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<p>Influence of KCl concentration on the anti-swelling effectiveness of TIL-NH<sub>2</sub>.</p>
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<p>Biodegradation rates of different concentrations of TIL-NH<sub>2</sub> as a function of time.</p>
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<p>Flowchart summarizing the experimental design and workflow of this study.</p>
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9 pages, 3640 KiB  
Proceeding Paper
Theoretical Study of Intermolecular Interactions in Benzopyrans Substituted with Polyhaloalkyl Groups
by Lissette A. Haro-Saltos, Pablo M. Bonilla-Valladares and Christian D. Alcívar-León
Chem. Proc. 2024, 16(1), 32; https://doi.org/10.3390/ecsoc-28-20209 - 13 Dec 2024
Viewed by 158
Abstract
A study of the solid-state intermolecular interactions of twenty-nine benzopyrans substituted with polyhaloalkyl groups was carried out by quantum chemical calculations using the Mercury and WinGX computer programs. Molecular structures were obtained from crystallographic information files (CIF) of the CCDC database. C-H—O, C-H—X, [...] Read more.
A study of the solid-state intermolecular interactions of twenty-nine benzopyrans substituted with polyhaloalkyl groups was carried out by quantum chemical calculations using the Mercury and WinGX computer programs. Molecular structures were obtained from crystallographic information files (CIF) of the CCDC database. C-H—O, C-H—X, C-X—O and C-X—X type contacts, characterized as unconventional hydrogen bonds, were identified and calculated. The criteria used for distances and angles were d(D—A) < R(D) + R(A) + 0.50 and d(H—A) < R(H) + R(A)—0.12°, where D-H—A > 100.0°. D is the donor atom, A is the acceptor atom, R is the Van der Waals radius and d is the interatomic distance. In addition, Etter’s notation was used to describe sets of hydrogen bonds in organic crystals, detailing the intermolecular contacts and periodic arrangements of the crystal packing. It was corroborated that certain positions of halogen atoms and their interactions play an important role in stabilizing the crystal lattice. Full article
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<p>3D Scatter Plot: R vs ETOTAL with color coding for <b>1</b>–<b>29</b> compounds.</p>
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<p>Geometric parameters that involving π-π interactions (A°, °) between heterocycles of chromone ring of <b>1</b>, <b>3</b>–<b>16</b>, <b>20–23</b>, <b>25</b> and <b>26</b> compounds.</p>
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<p>Hydrogen bonding interaction C—H ··· O and C—H ··· F of the <b>8</b> compound.</p>
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<p>π···π stacking and C—O ··· π interactions showing intercentroid interaction and O ··· Cg1 distance of the <b>8</b> compound.</p>
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<p>View of Hirshfeld surfaces in two orientations for compound <b>8</b>. (1) C—H ··· O; (2) C—H ···F of the <b>8</b> compound.</p>
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<p>Hirshfeld surfaces evaluated with the shape index and curvature (curvedness) of the <b>8</b> compound.</p>
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<p>2D fingerprint plot of the close contacts with the greatest contribution as (1) O ··· H, (2) F ··· H (3) H ···H and (4) C ··· C of the <b>8</b> compound.</p>
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<p>Relative contributions of the main intermolecular contacts on the Hirshfeld surface for the set of compounds.</p>
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15 pages, 1689 KiB  
Article
Identifying Significant SNPs of the Total Number of Piglets Born and Their Relationship with Leg Bumps in Pigs
by Siroj Bakoev, Lyubov Getmantseva, Maria Kolosova, Faridun Bakoev, Anatoly Kolosov, Elena Romanets, Varvara Shevtsova, Timofey Romanets, Yury Kolosov and Alexander Usatov
Biology 2024, 13(12), 1034; https://doi.org/10.3390/biology13121034 - 11 Dec 2024
Viewed by 372
Abstract
The aim of this study was to identify genetic variants and pathways associated with the total number of piglets born and to investigate the potential negative consequences of the intensive selection for reproductive traits, particularly the formation of bumps on the legs of [...] Read more.
The aim of this study was to identify genetic variants and pathways associated with the total number of piglets born and to investigate the potential negative consequences of the intensive selection for reproductive traits, particularly the formation of bumps on the legs of pigs. We used genome-wide association analysis and methods for identifying selection signatures. As a result, 47 SNPs were identified, localized in genes that play a significant role during sow pregnancy. These genes are involved in follicle growth and development (SGC), early embryonic development (CCDC3, LRRC8C, LRFN3, TNFRSF19), endometrial receptivity and implantation (NEBL), placentation, and embryonic development (ESRRG, GHRHR, TUSC3, NBAS). Several genes are associated with disorders of the nervous system and brain development (BCL11B, CDNF, ULK4, CC2D2A, KCNK2). Additionally, six SNPs are associated with the formation of bumps on the legs of pigs. These variants include intronic variants in the CCDC3, ULK4, and MINDY4 genes, as well as intergenic variants, regulatory region variants, and variants in the exons of non-coding transcripts. The results suggest important biological pathways and genetic variants associated with sow fertility and highlight the potential negative impacts on the health and physical condition of pigs. Full article
(This article belongs to the Special Issue Reproductive Physiology and Pathology in Livestock)
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<p>The results of enrichment analysis of the genes on the basis of the DL algorithm. Legend: (<b>A</b>) path tree; (<b>B</b>) distribution of paths by degree of enrichment.</p>
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<p>The results of enrichment analysis of the genes on the basis of the RR algorithm. Legend: (<b>A</b>) path tree; (<b>B</b>) distribution of paths by degree of enrichment.</p>
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<p>Distribution of selection signals. Legend: (<b>A</b>) distribution of iHS (integrated haplotype score) signals; (<b>B</b>) distribution of iHH12 (integrated haplotype homozygosity pooled) signals; (<b>C</b>) distribution of nSl (number of segregating sites by length) signals.</p>
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<p>Enrichment of selection signals with quantitative trait loci. Legend: <span class="html-italic">(</span><b>A</b>) enrichment at the QTLs type; <span class="html-italic">(</span><b>B</b>) enrichment at the feature level.</p>
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22 pages, 27970 KiB  
Article
Monthly Prediction of Pine Stress Probability Caused by Pine Shoot Beetle Infestation Using Sentinel-2 Satellite Data
by Wen Jia, Shili Meng, Xianlin Qin, Yong Pang, Honggan Wu, Jia Jin and Yunteng Zhang
Remote Sens. 2024, 16(23), 4590; https://doi.org/10.3390/rs16234590 - 6 Dec 2024
Viewed by 414
Abstract
Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to [...] Read more.
Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures. Full article
(This article belongs to the Section Forest Remote Sensing)
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<p>Location of the study area in Ning’er County, Puer City, Yunnan Province, China, overlaid on a false-color Sentinel-2 image (R, G, B = SWIR1, NIR, Red bands). The yellow dashed line delineates the study area’s boundaries.</p>
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<p>Field survey of pine stress.</p>
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<p>Overall technical workflow for predicting monthly pine stress probability.</p>
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<p>Reference data based on stress disturbance results. (<b>a</b>) The monthly stress disturbance results from 2019 to 2023; (<b>b</b>) An example of reference sample points displayed on the GF-1, GF-2, Sentinel-2, and Landsat-8 imagery; (<b>c</b>) The spatial distribution of non-stress sample points selected through visual interpretation; (<b>d</b>) The spatial distribution of pine stress sample points selected through visual interpretation.</p>
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<p>Comparison of monthly cloud-free Sentinel-2 composite images and vegetation indices from January to March 2023. The images are displayed as false-color composites (RGB = SWIR1, NIR, Red). A specific site was selected for detailed close-up analysis, showing the imagery, NDVI, and NDWI of the pine stress area affected by beetle infestation.</p>
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<p>Feature importance ranking for pine stress prediction model.</p>
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<p>Predicted pine stress probability for January, February, and March 2023 (<b>left</b>) and spatial distribution of areas with probability greater than 80% (<b>right</b>).</p>
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<p>Site 1: Monthly increase in pine stress level and area from January to March 2023, with Sentinel-2 imagery and stress probability distribution. The dash circles are key focus areas of forest stress.</p>
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<p>Site 2: Monthly decrease in pine stress level and area from January to March 2023, with Sentinel-2 imagery and stress probability distribution. The dash circles are key focus areas of forest stress.</p>
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<p>Site 3: Monthly changes (increase and decrease) in pine stress levels and areas from January to March 2023, with Sentinel-2 imagery and stress probability distribution. The dash circles are key focus areas of forest stress.</p>
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6 pages, 849 KiB  
Case Report
Identification and Characterization of a Novel CCDC6::CASP7 Gene Rearrangement in an Advanced Colorectal Cancer Patient: A Case Report
by Juan Carlos Montero, Raquel Tur, Andrea Jiménez-Perez, Elena Filipovich, Susana Alcaraz, Marta Rodríguez, Mar Abad and José María Sayagués
Int. J. Mol. Sci. 2024, 25(23), 12665; https://doi.org/10.3390/ijms252312665 - 26 Nov 2024
Viewed by 461
Abstract
Despite the existence of effective therapy options for patients with localized colorectal cancer, advanced-stage patients have limited therapies. Genomic profiling is a promising tool for guiding treatment selection as well as patient monitoring. Here, we describe a novel gene rearrangement (CCDC6::CASP7) [...] Read more.
Despite the existence of effective therapy options for patients with localized colorectal cancer, advanced-stage patients have limited therapies. Genomic profiling is a promising tool for guiding treatment selection as well as patient monitoring. Here, we describe a novel gene rearrangement (CCDC6::CASP7) detected in a patient with advanced colorectal cancer that could be a therapeutic target. The patient underwent surgical resection but died after the operation from fecal peritonitis. To our knowledge, this is the first report in which the CCDC6::CASP7 gene rearrangement has been described in an advanced colorectal adenocarcinoma patient. Full article
(This article belongs to the Section Molecular Oncology)
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<p>Molecular characterization of the <span class="html-italic">CCDC6::CASP7</span> rearrangement detected in a patient with aCRC by NGS. Panel (<b>A</b>): In the aCRC specimen, a chromosomal inversion in chromosome 10 juxtaposes the 5′ end of exon 1 of the <span class="html-italic">CCDC6</span> gene with the 3′ end of exon 2 of the <span class="html-italic">CASP7</span> gene, resulting in the fusion oncogene <span class="html-italic">CCDC6::CASP7</span>. The NGS results show that the rearrangement produces two different transcripts: (i) the first 95 amino acids of exon 1 of <span class="html-italic">CCDC6</span> are fused with the first two amino acids of exon 2 of <span class="html-italic">CASP7</span> (70% of reads), and (ii) the second transcript preserves the first 82 amino acids of exon 1 of CCDC6, and due to a deletion of a base, a frameshift occurs that allows exon 2 of <span class="html-italic">CASP7</span> to be read correctly (30% of reads). Panel (<b>B</b>): Representative pictures of nuclei counterstained with DAPI (blue) and hybridized with a probe for the centromere of chromosome 10 (green spots) and a probe for the <span class="html-italic">PTEN</span> gene (red spots). All the nuclei analyzed showed a normal number of hybridization signals: two for the centromere of chromosome 10 and two for the <span class="html-italic">PTEN</span> gene.</p>
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13 pages, 3557 KiB  
Article
Preparation and Performance Evaluation of Small-Molecule Ammonium as a Shale Hydration Inhibitor
by Quande Wang, Huifeng He, Yarui Zhao, Jing Rui, Ruichen Jiang, Michal Slaný, Gang Chen and Xuefan Gu
Minerals 2024, 14(11), 1117; https://doi.org/10.3390/min14111117 - 3 Nov 2024
Viewed by 798
Abstract
In this paper, small-molecule quaternary ammonium salts were synthesized by N-alkylation to inhibit hydration swelling and hydration dispersion. The prepared small-molecule quaternary ammonium salt was characterized by Fourier transform infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), particle size analysis and Scanning electron microscopy (SEM), [...] Read more.
In this paper, small-molecule quaternary ammonium salts were synthesized by N-alkylation to inhibit hydration swelling and hydration dispersion. The prepared small-molecule quaternary ammonium salt was characterized by Fourier transform infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), particle size analysis and Scanning electron microscopy (SEM), and its performance as an inhibitor in clay was evaluated by an anti-swelling test and a linear swelling test. The results show that small-molecule quaternary ammonium salt (TEE-2) synthesized by triethanolamine and epichlorohydrin in ethanol with a molar ratio of 1:1.5 can successfully inhibit the hydration swelling and dispersion of clay. The anti-swelling rate of TEE-2 was 84.94%, the linear swelling rate was 36.42%, and the linear swelling rate of 0.5% TEE-2 was only 29.34%. The hydration swelling of clay in 0.5% TEE-2 solution was significantly inhibited. The hydration inhibition mechanism of the small-molecule quaternary ammonium salt inhibitor 0.5% TEE-2 was analyzed by FTIR, SEM and TGA. It was considered that 0.5% TEE-2 has strong hydration inhibition, which was realized by infiltration and adsorption on the clay surface. Small-molecule quaternary ammonium salts were beneficial for maintaining wellbore stability and reducing the risk of wellbore instability. Full article
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<p>Synthesis mechanism of inhibitors.</p>
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<p>The effect of inhibitor concentration on anti-swelling rate.</p>
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<p>The effect of different solvents on linear swelling of bentonite.</p>
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<p>The effect of TEE with different molar ratios on linear swelling of bentonite.</p>
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<p>The effects of different concentrations of TEE-2 on linear swelling of bentonite.</p>
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<p>FTIR spectra of the bentonite treated with water and TEE-2.</p>
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<p>Effect of 0.5% TEE-2 on particle size distribution of sodium bentonite BH and AH.</p>
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<p>Effect of 0.5% TEE-2 concentration on zeta potential of electric double layer adsorbed on bentonite surface.</p>
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<p>SEM image of bentonite under different conditions.</p>
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<p>TGA curve of inhibitor TEE-2.</p>
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<p>Wellbore instability mechanism 1.</p>
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<p>Wellbore instability mechanism 2.</p>
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12 pages, 2594 KiB  
Article
Study on the Effectiveness of Okra as an Environmentally Friendly and Economical Lubricant for Drilling Fluid
by Huifeng He, Xiaofeng Chang, Yan Sun, Le Xue, Bingbing Bai and Gang Chen
Processes 2024, 12(11), 2417; https://doi.org/10.3390/pr12112417 - 1 Nov 2024
Viewed by 889
Abstract
With the gradual improvement and implementation of unconventional wells drilling and environmental regulations, there is an urgent need for high-performance and more environmentally friendly lubricants for water-based drilling fluids (WD). Developing green oilfield chemicals from natural products is a shortcut. In this work, [...] Read more.
With the gradual improvement and implementation of unconventional wells drilling and environmental regulations, there is an urgent need for high-performance and more environmentally friendly lubricants for water-based drilling fluids (WD). Developing green oilfield chemicals from natural products is a shortcut. In this work, Abelmoschus esculentus (L.) Moench/okra has been studied as the lubricant in WD. The green drilling fluid lubricant developed demonstrates excellent lubrication performance, as well as good filtration loss reduction and inhibition of bentonite hydration expansion. The results show that with the addition of 2.5% okra slurry to water-based drilling fluid, the coefficient of friction decreased by 51.68%, the apparent viscosity (AV) increased by 51.32%, the plastic viscosity (PV) increased by 42.99%, and the fluid loss decreased by 39.88%. Moreover, through TGA, SEM, FT-IR, particle distribution tests, and contact angle tests, the lubrication mechanism of okra slurry was discussed. Finally, the economic feasibility of using okra as an environmentally friendly lubricant for drilling fluids was analyzed. This work combines agricultural products with industrial production, which not only solves industrial problems but also enhances the added value of agricultural products, providing a reference for the coordinated development of industry and agriculture. Full article
(This article belongs to the Special Issue Oil and Gas Drilling Rock Mechanics and Engineering)
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<p>Schematic of the application of okra in improving the lubricity of WD.</p>
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<p>Structure of polyrhamnogalacturonic acid I (RG-I).</p>
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<p>Structure of polygalacturonic acid I (HG).</p>
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<p>Effect of okra slurry dosage on COF of water-based drilling fluid.</p>
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<p>Distribution of bentonite particle size under various conditions.</p>
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<p>SEM images of bentonite treated with different methods: (<b>a</b>) bentonite treated with 2.5% okra slurry and (<b>b</b>) bentonite treated with tap water.</p>
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<p>FT-IR spectra of bentonite treated with water and 2.5% okra slurry.</p>
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<p>TGA of bentonite treated with tap water and okra slurry.</p>
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<p>Contact angle of tap water on steel sheet: (<b>a</b>) water-based drilling fluid and (<b>b</b>) drilling fluid treated by okra slurry.</p>
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17 pages, 8188 KiB  
Article
Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest
by Miaohang Zhou, Xujun Han, Jinghan Wang, Xiangyu Ji, Yuefei Zhou and Meng Liu
Forests 2024, 15(11), 1866; https://doi.org/10.3390/f15111866 - 24 Oct 2024
Viewed by 655
Abstract
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the [...] Read more.
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies. Full article
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<p>Elevation and geographic location of Qinzhou City.</p>
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<p>Workflow for mapping eucalyptus plantations.</p>
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<p>Reflectance (colored) and mean reflectance (black) of candidate endmembers collected for NDFI calculation.</p>
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<p>Visualization results of the NBR fitting for seven typical land cover types in 2020.</p>
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<p>Schematic representation of band correlations with selected normalized intercepts and slopes shown.</p>
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<p>The top 20 parameters ranked by importance and their contribution ratios. The green color represents the top six important parameters that contributed to over 70% of the importance, while the yellow color represents the remaining parameters.</p>
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<p>Differential representation of eucalyptus, other vegetation, and non-vegetation categories under the top 6 ranked features of importance.</p>
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<p>Spatial distribution of eucalyptus plantations in Qinzhou City in 2020.</p>
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<p>Distribution of elevation and slope of eucalyptus plantations in Qinzhou City in 2020.</p>
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<p>Comparative analysis between high-resolution imagery and classification results. (<b>a</b>) Validation Area 1; (<b>b</b>) Validation Area 2.</p>
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16 pages, 1698 KiB  
Article
Functional Targets for Epstein-Barr Virus BART MicroRNAs in B Cell Lymphomas
by Devin N. Fachko, Bonnie Goff, Yan Chen and Rebecca L. Skalsky
Cancers 2024, 16(20), 3537; https://doi.org/10.3390/cancers16203537 - 19 Oct 2024
Viewed by 1148
Abstract
MicroRNAs are key post-transcriptional regulators of gene expression and their dysregulation is often linked to cancer. Epstein-Barr virus encodes 22 BamHI A Rightward Transcript (BART) miRNAs, which are expressed in nearly all EBV-associated cancers and implicated in viral pathogenesis. To investigate biological targets [...] Read more.
MicroRNAs are key post-transcriptional regulators of gene expression and their dysregulation is often linked to cancer. Epstein-Barr virus encodes 22 BamHI A Rightward Transcript (BART) miRNAs, which are expressed in nearly all EBV-associated cancers and implicated in viral pathogenesis. To investigate biological targets for BART miRNAs in B cell lymphomas, we performed a meta-analysis of publicly available Ago-CLIP datasets from EBV-positive Burkitt lymphomas (BLs), primary effusion lymphomas (PELs), AIDS-associated diffuse large B cell lymphomas (DLBCLs), and lymphoblastoid cell lines (LCLs). Our analysis focused on comparing targets of EBV BART miRNAs across the different types of transformed B cells. Using reporter assays, we then experimentally validated over 50 functional interactions between BART miRNAs and cellular protein-coding transcripts involved in activities such as B cell differentiation (PRDM1, IRF4, and MYC), cell cycle regulation (UHMK1, CDKN1A, MDM2, and NPAT), apoptosis (MCL1), signaling and intracellular trafficking (GAB1, SOS1, MAPK1, RAB11A, CAV1, and RANBP9), and tumor suppression (CCDC6). Moreover, ectopic BART miRNA expression in several EBV-negative BL cells induced transcriptional changes that may influence molecular signatures of EBV-associated BLs. Collectively, our findings reveal novel, functional interactions for BART miRNAs in lymphomas and provide insights into their roles in these B cell cancers. Full article
(This article belongs to the Special Issue Epstein–Barr Virus (EBV) Associated Cancers)
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<p><b>Meta-analysis of Ago-CLIP interactions in EBV-transformed B cells.</b> (<b>A</b>) Distribution of Ago interaction sites across various transformed B cell lines. Ago interaction sites were identified in 41 Ago-CLIP libraries using PIPE-CLIP. A total of 94,828 unique sites were found, of which 52,043 were found in at least two cell types, and 4988 were found in all cell types. (<b>B</b>) Distribution of Ago interaction sites by viral infection status. A total of 26% of sites were specific to B cells infected with EBV only, and 13% were specific to B cells infected with KSHV. (<b>C</b>) Distribution of unique Ago interaction sites mapping to human genes. Annotation of Ago-CLIP interaction sites determined that 21% mapped to the 3′UTRs of protein-coding genes, 11% mapped to the CDS, (coding sequence) and 51% mapped to non-protein coding regions of the human genome. (<b>D</b>) The tSNE method was applied to the 16,020 genes harboring Ago interaction sites to visualize the similarities and differences of the Ago interactions across the various B cell types.</p>
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<p><b>EBV miRNA targets in transformed B cells.</b> (<b>A</b>) Distribution of EBV miRNA targets in the EBV-positive Ago-CLIP datasets. 3′UTRs were scanned for canonical seed matches to the BART and BHRF1 miRNAs. A total of 1869 targets were found in two or more cell types, and 148 targets were found in all cell types. (<b>B</b>) The number of 3′UTR targets assigned to each EBV miRNA using both PARalyzer and PIPECLIP. An average of 355 cellular genes were detected for each pre-miRNA.</p>
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<p><b>Validation of BART miRNA targets by luciferase reporter assays.</b> HEK293T cells were co-transfected with a 3′UTR luciferase reporter and either a BART miRNA expression vector or an empty control vector. After 48–72 h post-transfection, cells were lysed and assayed for dual luciferase activity. For each 3′UTR reporter, luciferase values in the presence of a given miRNA are shown relative to the empty control vector (pLCE). The reported average is at least three independent experiments. RLU = relative light units. By Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05. (<b>A</b>) 75 miRNA:3UTR pairs for which there is biochemical evidence of an interaction by Ago-CLIP. (<b>B</b>) 28 miRNA:3UTR pairs for which there is biochemical evidence of interaction by Ago-CLIP; however, the site was also detected in miRNA-negative cells. (<b>C</b>) 34 miRNA:3UTR pairs that were tested as controls. (<b>D</b>) EBV miRNA targets were predicted by either DIANA microT or TargetScan. Shown is the overlap of predicted targets with the number of miRNA:3UTR pairs exhibiting luciferase inhibition or showing no response.</p>
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<p><b>Individual EBV BART miRNA target interactions.</b> 3′UTR interactions are shown with individual EBV BART miRNAs. Luciferase assays are from <a href="#cancers-16-03537-f003" class="html-fig">Figure 3</a>, with targets arranged according to biological function. Values are reported relative to the pLCE empty control vector. RLU = relative light units. By Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p><b>Reduced gene expression levels in the presence of EBV BART miRNAs.</b> RNA was harvested from BL cell lines stably expressing individual EBV BART miRNAs or empty vector (EV). Transcript levels of indicated EBV miRNA targets were assessed by qRT-PCR. Values are normalized to GAPDH and reported relative to control cells (EV) for each BL cell line. <span class="html-italic">n</span>.d. = not determined. By Student’s <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.1, * <span class="html-italic">p</span> &lt; 0.05.</p>
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19 pages, 2515 KiB  
Article
Descriptive Analysis of Common Fusion Mutations in Papillary Thyroid Carcinoma in Hungary
by Richard Armos, Bence Bojtor, Janos Podani, Ildiko Illyes, Bernadett Balla, Zsuzsanna Putz, Andras Kiss, Andrea Kohanka, Erika Toth, Istvan Takacs, Janos P. Kosa and Peter Lakatos
Int. J. Mol. Sci. 2024, 25(19), 10787; https://doi.org/10.3390/ijms251910787 - 8 Oct 2024
Cited by 1 | Viewed by 1042
Abstract
Thyroid cancer is the most common type of endocrine malignancy. Papillary thyroid carcinoma (PTC) is its predominant subtype, which is responsible for the vast majority of cases. It is true that PTC is a malignant tumor with a very good prognosis due to [...] Read more.
Thyroid cancer is the most common type of endocrine malignancy. Papillary thyroid carcinoma (PTC) is its predominant subtype, which is responsible for the vast majority of cases. It is true that PTC is a malignant tumor with a very good prognosis due to effective primary therapeutic approaches such as thyroidectomy and radioiodine (RAI) therapy. However, we are often required to indicate second-line treatments to eradicate the tumor properly. In these scenarios, molecular therapies are promising alternatives, especially if specifically targetable mutations are present. Many of these targetable gene alterations originate from gene fusions, which can be found using molecular diagnostics like next-generation sequencing (NGS). Nonetheless, molecular profiling is far from being a routine procedure in the initial phase of PTC diagnostics. As a result, the mutation status, except for BRAF V600E mutation, is not included in risk classification algorithms either. This study aims to provide a comprehensive analysis of fusion mutations in PTC and their associations with clinicopathological variables in order to underscore certain clinical settings when molecular diagnostics should be considered earlier, and to demonstrate yet unknown molecular–clinicopathological connections. We conducted a retrospective fusion mutation screening in formalin-fixed paraffin-embedded (FFPE) PTC tissue samples of 100 patients. After quality evaluation by an expert pathologist, RNA isolation was performed, and then NGS was applied to detect 23 relevant gene fusions in the tumor samples. Clinicopathological data were collected from medical and histological records. To obtain the most associations from the multivariate dataset, we used the d-correlation method for our principal component analysis (PCA). Further statistical analyses, including Chi-square tests and logistic regressions, were performed to identify additional significant correlations within certain subsets of the data. Fusion mutations were identified in 27% of the PTC samples, involving nine distinct genes: RET, NTRK3, CCDC6, ETV6, MET, ALK, NCOA4, EML4, and SQSTM1. RET and CCDC6 fusions were associated with type of thyroidectomy, RAI therapy, smaller tumor size, and history of Hashimoto’s disease. NCOA4 fusion correlated with sex, multifocality, microcarcinoma character, history of goiter, and obstructive pulmonary disease. EML4 fusion was also linked with surgical procedure type and smaller tumor size, as well as the history of hypothyroidism. SQSTM1 fusion was associated with multifocality and a medical history of thyroid/parathyroid adenoma. NTRK3 and ETV6 fusions showed significant associations with Hashimoto’s disease, and ETV6, also with endometriosis. Moreover, fusion mutations were linked to younger age at the time of diagnosis, particularly the fusion of ETV6. The frequent occurrence of fusion mutations and their associations with certain clinicopathological metrics highlight the importance of integrating molecular profiling into routine PTC management. Early detection of fusion mutations can inform surgical decisions and therapeutic strategies, potentially improving clinical outcomes. Full article
(This article belongs to the Special Issue Current Research on Cancer Biology and Therapeutics: 2nd Edition)
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<p>Calculated relative distribution of partner genes associated with detected driver fusion mutations (<span class="html-italic">n</span> = 27) in the PTC cohort without representing fusion non-carrier cases (<span class="html-italic">n</span> = 73). The relative frequency of occurrence was significantly different (Chi-square test) between <span class="html-italic">RET</span> and <span class="html-italic">SQSTM1</span> fusions (<span class="html-italic">p</span> = 0.026) as marked (*) on the plot. The most frequently identified fusion genes were <span class="html-italic">RET</span> (28.57%) and <span class="html-italic">NTRK3</span> (16.33%) and their common gene partners <span class="html-italic">CCDC6</span> and <span class="html-italic">ETV6</span>, respectively.</p>
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<p>Principal component analysis (PCA) of fusion genes and clinicopathological variables using <span class="html-italic">d</span>-correlation for mixed scale types. Black points (variable positions) are labeled and color-coded (bottom left corner) to reflect the grouping of different individual variables into larger categories. Variables related to gene fusion status are indicated with red rectangles. It is well demonstrated that most fusion mutation-related variables tended to cluster with specific clinicopathological variables (middle right side). Therapy-related and prognostics-related variables (middle left side), however, correlated negatively with gene fusions.</p>
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<p>This horizontal bar chart displays the impact of carrying <span class="html-italic">ETV6</span> and/or <span class="html-italic">NTRK3</span> gene fusions on having certain comorbidities. The values are derived from a logistic regression analysis of the <span class="html-italic">ETV6</span> and <span class="html-italic">NTRK3</span> gene fusion partners (independent variables) and those binary/nominal-type clinicopathological features (dependent variables) that were associated with these fusions in a significant manner (<span class="html-italic">p</span> &lt; 0.05). The length of the bars depends on the strength of the associations relative to other variable constellations in the cohort. All links represented are above the 0 value threshold on the x-axis, indicating that the directions of all the correlations are positive.</p>
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<p>Using Chi-square test (<span class="html-italic">p</span> &lt; 0.05), the type of surgical procedure performed was found to be significantly different when <span class="html-italic">NTRK3</span> and/or <span class="html-italic">ETV6</span> fusions occurred compared to those cases without these fusions. This vertical bar chart of these two significant fusions, generated by applying multinomial logistic regression, illustrates the potential impact of the <span class="html-italic">NTRK3</span> (blue column) and <span class="html-italic">ETV6</span> (orange column) fusion genes on surgical decision-making across three different categories: primary total thyroidectomy, not-total thyroidectomy (usually lobectomy), and secondary total thyroidectomy (completion of a not-total thyroidectomy). The likelihoods of the indications for total thyroidectomies (primary or secondary) are represented relative to not-total thyroidectomies (with a baseline value of 0). PTC patients with both <span class="html-italic">NTRK3</span> and/or <span class="html-italic">ETV6</span> fusion mutations underwent total thyroidectomies more frequently than not-total thyroidectomies. The number of <span class="html-italic">NTRK3</span> and/or <span class="html-italic">ETV6</span> fusion-positive patients who needed a secondary completion surgery was greater than the number of those with primary total thyroidectomy increasing the risks related to the repeated procedures.</p>
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<p>This bar chart illustrates a comparative analysis of the mean age at the diagnosis of PTC between patients carrying those gene fusions occurring at least 6 times in the cohort compared to the mean age of those patients not carrying any gene fusions. The height of the bars along the y-axis represents the mean age of the patients carrying gene fusions. The specific genes are indicated under the corresponding columns with the last column representing an overall positive status for any studied gene fusions (including those mutations with minimal occurrence as well). The red dashed line marks the mean age of the fusion-negative patients. All evaluated fusion mutations were associated with a younger age at the time of diagnosis than the age of patients without any gene fusions, with <span class="html-italic">NTRK3</span>, <span class="html-italic">ETV6</span>, and general fusion-positive status being significant as marked (*) on the plot. Data are presented as mean ± standard deviation (SD).</p>
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<p>Heatmap listing significant associations between binary-type clinicopathological variables (x-axis and y-axis) of the PTC study cohort. The color scale illustrates the direction of the correlations ranging from strongly positive correlations (red) to strongly negative correlations (blue). Empty (white) cells mark no significant associations. Significant associations mostly tend to occur as clinically expected (e.g., strong positive correlation between lymphovascular invasion and lymph node dissection surgery). Medical indication of molecular therapies explicitly correlated with variables, such as relapse, thyroid capsule invasion, extrathyroidal extension, lymphovascular extension, or need for EBRT, usually related to a more advanced state of illness.</p>
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14 pages, 2555 KiB  
Article
Associations between Disc Hemorrhage and Primary Open-Angle Glaucoma Based on Genome-Wide Association and Mendelian Randomization Analyses
by Je Hyun Seo, Young Lee and Hyuk Jin Choi
Biomedicines 2024, 12(10), 2253; https://doi.org/10.3390/biomedicines12102253 - 3 Oct 2024
Viewed by 879
Abstract
Background/Objectives: We aimed to investigate the genetic loci related to disc hemorrhage (DH) and the relationship of causation between DH and primary open-angle glaucoma (POAG) using a genome-wide association study (GWAS) in East Asian individuals. Methods: The GWAS included 8488 Koreans who underwent [...] Read more.
Background/Objectives: We aimed to investigate the genetic loci related to disc hemorrhage (DH) and the relationship of causation between DH and primary open-angle glaucoma (POAG) using a genome-wide association study (GWAS) in East Asian individuals. Methods: The GWAS included 8488 Koreans who underwent ocular examination including fundus photography to determine the presence of DH and POAG. We performed a GWAS to identify significant single-nucleotide polymorphisms (SNPs) associated with DH and analyzed the heritability of DH and genetic correlation between DH and POAG. The identified SNPs were utilized as instrumental variables (IVs) for two-sample Mendelian randomization (MR) analysis. The POAG outcome dataset was adopted from Biobank Japan data (n = 179,351). Results: We found that the rs62463744 (TMEM270;ELN), rs11658281 (CCDC42), and rs77127203 (PDE10A;LINC00473) SNPs were associated with DH. The SNP heritability of DH was estimated to be 6.7%, with an absence of a genetic correlation with POAG. MR analysis did not reveal a causal association between DH and POAG for East Asian individuals. Conclusions: The novel loci underlying DH in the Korean cohort revealed SNPs in the ELN, CCDC41, and LINC00473 genes. The absence of a causal association between DH and POAG implies that DH is a shared risk factor, rather than an independent culprit factor, and warrants further investigation. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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<p>Diagram presentation of the study design. POAG, primary open-angle glaucoma; DH, disc hemorrhage; GENIE cohort, Gene-Environmental Interaction and Phenotype; SNP, single-nucleotide polymorphism.</p>
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<p>Quantile-quantile and Manhattan plots for disc hemorrhage in the genome-wide association study. (<b>A</b>). Quantile-quantile (Q-Q) plot. The expected line is shown in red and confidence bands are shown in gray. (<b>B</b>). Manhattan plot. The red line indicates the preset threshold of <span class="html-italic">p</span> = 1.0 × 10<sup>−6</sup>.</p>
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<p>Regional association plots for top 4 SNPs. SNP, single-nucleotide polymorphism. (<b>A</b>): rs62463744, (<b>B</b>): rs11658281, (<b>C</b>): rs77127203, (<b>D</b>): rs7589033.</p>
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<p>Schematic design of Mendelian randomization analysis. SNP, single-nucleotide polymorphism. Solid lines indicate the presence of an association, dashed lines indicate the absence of an association.</p>
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<p>MR visualizations of the effect of DH on POAG. CI, confidence interval; DH, disc hemorrhage; POAG, primary open-angle glaucoma; OR, odds ratio; SIMEX, simulation extrapolation; SNP, single-nucleotide polymorphism.</p>
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<p>Scatter plot of MR analyses using different MR methods evaluating the impact of the existence of DH on POAG. Light blue, dark blue, light green, and dark green regression lines represent IVW, MR-Egger, MR-Egger (SIMEX), and weighted median estimate, respectively. SNP, single-nucleotide polymorphism; DH, disc hemorrhage; POAG, primary open-angle glaucoma; MR, Mendelian randomization; SIMEX, simulation extrapolation.</p>
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24 pages, 8603 KiB  
Article
CCDC78: Unveiling the Function of a Novel Gene Associated with Hereditary Myopathy
by Diego Lopergolo, Gian Nicola Gallus, Giuseppe Pieraccini, Francesca Boscaro, Gianna Berti, Giovanni Serni, Nila Volpi, Patrizia Formichi, Silvia Bianchi, Denise Cassandrini, Vincenzo Sorrentino, Daniela Rossi, Filippo Maria Santorelli, Nicola De Stefano and Alessandro Malandrini
Cells 2024, 13(17), 1504; https://doi.org/10.3390/cells13171504 - 8 Sep 2024
Viewed by 1463
Abstract
CCDC78 was identified as a novel candidate gene for autosomal dominant centronuclear myopathy-4 (CNM4) approximately ten years ago. However, to date, only one family has been described, and the function of CCDC78 remains unclear. Here, we analyze for the first time a family [...] Read more.
CCDC78 was identified as a novel candidate gene for autosomal dominant centronuclear myopathy-4 (CNM4) approximately ten years ago. However, to date, only one family has been described, and the function of CCDC78 remains unclear. Here, we analyze for the first time a family harboring a CCDC78 nonsense mutation to better understand the role of CCDC78 in muscle. Methods: We conducted a comprehensive histopathological analysis on muscle biopsies, including immunofluorescent assays to detect multiple sarcoplasmic proteins. We examined CCDC78 transcripts and protein using WB in CCDC78-mutated muscle tissue; these analyses were also performed on muscle, lymphocytes, and fibroblasts from healthy subjects. Subsequently, we conducted RT-qPCR and transcriptome profiling through RNA-seq to evaluate changes in gene expression associated with CCDC78 dysfunction in muscle. Lastly, coimmunoprecipitation (Co-Ip) assays and mass spectrometry (LC-MS/MS) studies were carried out on extracted muscle proteins from both healthy and mutated subjects. Results: The histopathological features in muscle showed novel histological hallmarks, which included areas of dilated and swollen sarcoplasmic reticulum (SR). We provided evidence of nonsense-mediated mRNA decay (NMD), identified the presence of novel CCDC78 transcripts in muscle and lymphocytes, and identified 1035 muscular differentially expressed genes, including several involved in the SR. Through the Co-Ip assays and LC-MS/MS studies, we demonstrated that CCDC78 interacts with two key SR proteins: SERCA1 and CASQ1. We also observed interactions with MYH1, ACTN2, and ACTA1. Conclusions: Our findings provide insight, for the first time, into the interactors and possible role of CCDC78 in skeletal muscle, locating the protein in the SR. Furthermore, our data expand on the phenotype previously associated with CCDC78 mutations, indicating potential histopathological hallmarks of the disease in human muscle. Based on our data, we can consider CCDC78 as the causative gene for CNM4. Full article
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<p>Pedigree of the <span class="html-italic">CCDC78</span>-mutated patients (<b>a</b>), photographs of the proband showing slight hypertrophy of the calves bilaterally (<b>b</b>–<b>d</b>), muscle biopsy with E-H staining (20X) indicating nuclear centralizations (<b>e</b>,<b>f</b>), and TEM (scale bar = 200 nm) revealing peculiar dilated terminal SR (red arrows), whirls of redundant membranes (green arrow), and areas of dilated and swollen SR with numerous abnormal accumulations of membranous material (blue arrows) (<b>g–j</b>). Immunofluorescent analysis on muscle tissue (20X): by comparing control (<b>k</b>–<b>m</b>) and <span class="html-italic">CCDC78</span>-mutated muscles (<b>n</b>–<b>p</b>) after CCDC78 (<b>k</b>,<b>n</b>) and RyR1 staining (<b>l</b>,<b>o</b>), we showed CCDC78 aggregates, overlapping with RyR1.</p>
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<p>PCR amplification of <span class="html-italic">CCDC78</span> exons 10–14 showed the presence of four transcripts in healthy controls (<b>a</b>). Agarose gel electrophoresis (2% agarose) of PCR-amplified products using specific PCR primer sets. Lanes 1–3 display the sequenced PCR products in control muscle; lanes 4, 5, and 6 show the transcript pattern of exon regions 10–14 in PBLs, muscle, and fibroblasts from controls, respectively. Tissues transcripts in muscle, fibroblast, and blood from controls are shown (v = transcript sequenced, X = transcript not sequenced (<b>b</b>). RT-qPCR in muscle showed a significant increase in our patient (<b>c</b>) calculated by setting the ratio of <span class="html-italic">CCDC78</span>/reference genes expression in the control group to 1. Relative expression levels were calculated relative to <span class="html-italic">HPRT1</span>, <span class="html-italic">SERPINC1</span>, and <span class="html-italic">ZNF80</span> mRNA levels. The bars show the mean ± SD (n = 2, ** <span class="html-italic">p</span> &lt; 0.01). RT-qPCR showed the relative expression levels of <span class="html-italic">CCDC78</span> in lymphocytes, fibroblasts, and muscle of control samples (<b>d</b>). <span class="html-italic">CCDC78</span> expression in muscle tissue was set to 1 (SD ± 0.09), and the relative expressions in fibroblasts and lymphocytes were, respectively, 2.3 (SD ± 1.30045) and 25.62 (SD ± 9.39). Relative expression levels were calculated in relation to <span class="html-italic">HPRT1</span> and <span class="html-italic">ZNF80</span> mRNA levels (n = 2). To assess NMD due to the p.W402* mutation, as suggested by an apparently lower level of transcript NM_001031737.3 from muscle sample (<b>e</b>), we analyzed the <span class="html-italic">CCDC78</span> expression in lymphocytes of the <span class="html-italic">CCDC78</span>-mutated patient and control subjects in basal conditions and after cycloheximide treatment. The patient showed a significant increase in transcript values compared to the control (<b>f</b>). Relative expression levels were calculated relative to <span class="html-italic">HPRT1</span>, <span class="html-italic">SERPINC1</span>, and <span class="html-italic">ZNF80</span> mRNA levels and set to 1. (n = 3, <span class="html-italic">p</span> &lt; 0.01). Western blot (WB) analysis showing the expression levels of CCDC78 isoforms in muscle tissue of patient and three controls (<b>g</b>). The bar graph (<b>h</b>) shows the isoform expression fold change in CCDC78, calculated by setting the ratio of CCDC78 protein/GAPDH protein band intensities in the control group to 100. The bars show the mean ± SD. (n ≥ 5, * <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Curves obtained through pairwise comparison of the proteins in the test set in an “each against the rest” fashion (<b>a</b>,<b>b</b>): there are two cutoffs indicated on the plots: a “strict” one at 2.2 DAS score, and a “loose” one at 1.7. The hit at 2.2 is informative in terms of the number of matching segments, while the hit at 1.7 gives the actual location of the transmembrane segment (TS). The segments reported in the feature table (FT) records of the SwissProt database are marked at 1.0 DAS score (“FT lines”). In (<b>a</b>), we report a staggered superposition of the potential TS predictions for both the WT 48 kDa protein (NM_001031737.3) (black lines) and the relative mutated protein (p.Trp402* (NM_001031737.3)) (orange lines): WT TS starts at 209 and stops at 216 (length ~8, cutoff ~1.7); in the mutated protein, TS starts at 210 and stops at 216 (length ~7, cutoff ~1.7). The plots are very similar; however, the TS is shorter in the mutated protein. In (<b>b</b>), we report a staggered superposition of the potential TS predictions for both the WT 52kDa protein (NM_001378030.1) (black lines) and the relative mutated protein (p.Glu404Lys (NM_001378030.1)) (blue lines): both WT and mutated TS start at 209 and stops at 216 (length ~8, cutoff ~1.7). The plots are identical and perfectly overlapping. Three-dimensional modelling: both the WT isoforms and the relative mutated protein are shown (A2IDD5_CCDC78_HUMAN, 438aa, WT (<b>c</b>), A2IDD5_CCDC78_HUMAN, 401aa, p.Trp402* (<b>d</b>), H3BLT8_CCDC78_HUMAN, 470aa, WT. (<b>e</b>), H3BLT8_CCDC78_HUMAN, 470aa, p.Glu404Lys (<b>f</b>)). For both the variants, we observed a different spatial orientation of the first (Glu56-Asp105) and the fourth (Asp381-Ser401) alpha-helices with respect to the second one (Asn156-Asp259).</p>
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<p>Volcano plots showing gene expression differences between the <span class="html-italic">CCDC78</span> mutated patient and controls (<b>a</b>). The plot shows the global transcriptional change across the groups compared. All the genes are plotted, and each data point represents a gene. The log2 fold change in each gene is represented on the x-axis, and the log10 of its adjusted <span class="html-italic">p</span>-value is on the y-axis. Genes with an adjusted <span class="html-italic">p</span>-value less than 0.05 and a log2 fold change greater than 1 are indicated by red dots and represent upregulated genes. Genes with an adjusted <span class="html-italic">p</span>-value less than 0.05 and a log2 fold change less than −1 are indicated by blue dots and represent downregulated genes. Hierarchical clustering analysis of the <span class="html-italic">CCDC78</span> mutated patients and controls with heatmap density color representation of differentially expressed genes (DEGs) (<b>b</b>). This analysis was performed to visualize the expression profile of the top 30 genes sorted by their adjusted <span class="html-italic">p</span>-values. This analysis is useful to identify co-regulated genes across the conditions. In (<b>c</b>,<b>d</b>), a series of downregulated and upregulated genes associated with muscular function and SR are reported. Normalized counts for each gene in the controls and the <span class="html-italic">CCDC78</span> mutated patient are reported. Gene ontology (GO) analysis (<b>e</b>): top GO terms of genes associated with differentially expressed transcripts identified in muscles from the <span class="html-italic">CCDC78</span>-mutated patient as compared to controls. <span class="html-italic">p</span>-values were adjusted by false discovery rate (FDR) multiple testing correction.</p>
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<p>Co-immunoprecipitation (Co-Ip) of CCDC78-interacting proteins (<b>a</b>): muscle lysates were subjected to Co-Ip using anti-CCDC78 antibody and analyzed by SDS-PAGE followed by blue Coomassie staining. Two bands with molecular weights of around 50 and 110 kDa were detected (band 2, in red, and 4, in green). Lane 1, protein molecular weight ladder; lane 2, muscle lysate of healthy control. Identification of CCDC78-interacting proteins by nLC-nESI-HRMS/MS (<b>b</b>). Mass spectrometry (MS) analysis of band 2 (<b>c</b>): fragmentation spectra of 781.42053 m/z identifying AT2A1_HUMAN. The corresponding putative amino acid sequences were taken from MASCOT search. The VGEATETALTTLVEK producing an Ions Score of 52 (expect: 5.9 × 10<sup>−5</sup>). The m/z values of detected positive ion fragments are in red. The ‘b’ and ‘y’ ions are singly charged fragments (molecule + 1 H +) produced by fragmentation from the N- and C-terminus, respectively; ‘b ++’ and ‘y ++’ ions are the corresponding doubly charged fragments (molecule + 2 H +). The y* ions are y ions with loss of water. MS analysis of band 4 (<b>d</b>): fragmentation spectra of 789.38879 m/z identifying CASQ1_HUMAN and the corresponding putative amino acid sequences taken from MASCOT search. The ELQAFENIEDEIK producing an Ions Score of 47 (expect: 3.9 × 10<sup>−5</sup>). Western blot analysis (<b>e</b>). Proteins immunoprecipitated using CCDC78 antibody were immunoblotted on membranes using anti-SERCA1 and anti-CASQ1 antibodies. SERCA1 and CASQ1 were detected in wild-type pulldowns (WT1, WT2) but not in <span class="html-italic">CCDC78</span>-mutated patient pulldown (PT). ATP2A1 and CASQ1 were also detected in the total cell lysate (+Cnt) but not in the IgG control (−Cnt.). Membranes were immunoblotted with anti-GAPDH (negative Co-IP control) and anti-CCDC78 (positive Co-IP control). Colocalization between SERCA1 and CCDC78 in control muscle tissue (<b>f</b>–<b>h</b>). In the muscle seriated sections, we found a colocalization of CCDC78, SERCA1, and NADPH diaphorase (<b>i</b>–<b>k</b>).</p>
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<p>SERCA1 and CCDC78 aggregates (asterisks) in CCDC78-mutated muscle (<b>d</b>–<b>f</b>) compared to control (<b>a</b>–<b>c</b>). RYR1-mutated muscle (<b>g</b>–<b>i</b>): costaining of SERCA1 and CCDC78 with cores (arrow heads).</p>
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<p>Co-Ip of CCDC78-interacting proteins (<b>a</b>). Five bands were further detected: ~250 kDa (band 1, in red), ~100 kDa (band 3, in green), ~45 kDa (band 5, in blue), and two bands in the 25–37 kDa range (band 6, in orange, band 7, in purple). Lane 1, protein molecular weight ladder; lane 2, muscle lysate of healthy patient. Identification of CCDC78-interacting proteins by nLC-nESI-HRMS/MS (<b>b</b>). Mass spectrometry (MS) analysis of band 1 (<b>c</b>): fragmentation spectra of 859.91791 m/z identifying MYH1_HUMAN. The LQNEVEDLMIDVER sequence produced an Ions Score of 81 (expect: 6.7 × 10<sup>−8</sup>). Mass spectrometric analysis of band 3 (<b>d</b>): fragmentation spectra of 908.42621 m/z identifying ACTN2_HUMAN. The ISSSNPYSTVTMDELR sequence produced an Ions Score of 82 (expect: 2.1 × 10<sup>−8</sup>). Mass spectrometric analysis of band 5 (<b>e</b>): fragmentation spectra of 1276.58142 m/z identifying ACTS_HUMAN. The LCYVALDFENEMATAASSSSLEK sequence produced an Ions Score of 77 (expect: 5.6 × 10<sup>−8</sup>). Western blot analysis (<b>f</b>,<b>g</b>). Proteins immunoprecipitated using CCDC78 antibody were immunoblotted using anti-ACTN2, anti-MYH1, anti-ACTA1, anti-TPM1, and anti-TPM2 antibodies. ACTN2, MYH1, and ACTA1 were detected in wild-type pulldowns (WT1, WT2). ACTN2, MYH1, and ACTA1 were also detected in the total cell lysate (CTR+) but not in the IgG control (CTR−). Membranes immunoblotted with anti-TPM1 and anti-TPM2 detected protein only in the total cell lysate (CTR+) but not in the WT muscles and IgG control (CTR−). Membranes were immunoblotted with anti-GAPDH (negative Co-IP control) and anti-CCDC78 (positive Co-IP control). MYH1 staining in control (<b>h</b>–<b>j</b>) and <span class="html-italic">CCDC78</span>-mutated (<b>k</b>–<b>m</b>) muscles (20×): perinuclear small MYH1 aggregates were present in mutated muscle. HeLa cells showing a costaining of CCDC78 and gamma-tubulin (<b>n</b>).</p>
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<p>Schematic representation of cytosolic shell (CS, in green) and channel and activation core (CAC, in yellow) of RyR1 (<b>a</b>): black star signs represent the analyzed <span class="html-italic">RYR1</span> mutations. CCDC78 expression analysis by WB in RYR1-mutated patients (n = 4) compared to controls (n = 3) (<b>b</b>): significant reduction (**, <span class="html-italic">p</span> &lt; 0.01) in 37 kDa, 48 kDa, and 52 kDa isoforms compared to controls. The two <span class="html-italic">RYR1</span>-mutated patients harboring a mutation in the CS showed a significant reduction in 37, 48, and 52 kDa isoforms compared to controls (<span class="html-italic">p</span> &lt; 0.01); the patients carrying a <span class="html-italic">RYR1</span> mutation in CAC showed a significant reduction only for 37 and 52 kDa isoforms. Western blot analysis (<b>c</b>): Proteins immunoprecipitated using CCDC78 antibody were immunoblotted using anti-RyR1 and anti-CCDC78 antibodies. CCDC78 was detected in wild-type pulldowns (WT1, WT2), in the total cell lysate (positive control) but not in the IgG control (negative control). Membranes immunoblotted with anti-RyR1 detected the protein only in the total cell lysate. Morphometric analysis of muscle fiber cross-sectional area (<b>d</b>): in <span class="html-italic">CCDC78</span>-mutated muscle, we observed a significant increase in %positive RyR1 and Trisk95 nuclei compared to controls. RyR1, DAPI, and Trisk95 staining in the <span class="html-italic">CCDC78</span>-mutated muscle (<b>f</b>) and control (<b>e</b>): in mutated muscle, RyR1 aggregates co-stained with DAPI both in peripheral and central regions of the fibers; we also observed Trisk95 aggregates that colocalized with RyR1.</p>
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11 pages, 640 KiB  
Review
Contrasting Retromer with a Newly Described Retriever in Arabidopsis thaliana
by Connor D. Lewis and Mary L. Tierney
Plants 2024, 13(17), 2470; https://doi.org/10.3390/plants13172470 - 4 Sep 2024
Viewed by 724
Abstract
The tight regulation of protein composition within the plasma membranes of plant cells is crucial for the proper development of plants and for their ability to respond to a changing environment. Upon being endocytosed, integral membrane proteins can be secreted, sorted into multivesicular [...] Read more.
The tight regulation of protein composition within the plasma membranes of plant cells is crucial for the proper development of plants and for their ability to respond to a changing environment. Upon being endocytosed, integral membrane proteins can be secreted, sorted into multivesicular bodies/late endosomes, and degraded in the lytic vacuole, or recycled back to the plasma membrane to continue functioning. The evolutionarily conserved retromer complex has attracted the interest of plant cell biologists for over a decade as it has emerged as a key regulator of the trafficking of endocytosed integral plasma membrane proteins. Recently, a related recycling complex that shares a subunit with retromer was described in metazoan species. Named “retriever”, homologs to the proteins that comprise this new recycling complex and its accessory proteins are found within plant lineages. Initial experiments indicate that there is conservation of function between metazoan and plant retriever proteins, suggesting that it is prudent to re-evaluate the available plant retromer data with the added potential of a plant retriever complex. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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<p>An overview model of retriever and retromer structures, functions, and localizations in plants. (<b>A</b>) Retriever and (<b>B</b>) retromer are both described as trimers that oligomerize into higher-order structures. Two trimers come together to form an arch-like structure with the largest subunit, VPS35, being connected to VPS26 homologs at the base and VPS29 at the top. Retromer and retriever are differentiated by the VPS26 member present; VPS26C is part of retriever and VPS26A and VPS26B are part of retromer. Furthermore, in plants, a putative retriever complex has been described as containing VPS35A, while retromer complexes can comprise VPS35A, VPS35B, or VPS35C. A homolog to VPS35L of <span class="html-italic">Homo sapiens</span> has recently been identified but has not been studied in plants to date. We have placed a ? next to VPS35L in the model because there is no data showing an interaction between VPS35L and either VPS29 or VPS26C in plants to date. (<b>C</b>) When bound to membranes, retriever and retromer create a tubular–vesicular structure and provide a scaffold-like matrix for protein–protein interactions to take place. (<b>D</b>) Retromer is central to vesicular trafficking in plant cells, having been demonstrated to play a role the recycling of PIN proteins to the plasma membrane in collaboration with BLISTER (BLI), which localizes predominantly to the Golgi but also the TGN and MVB. Furthermore, retromer is essential for MVB/tonoplast fusion, a process in which ALIX and RABG3f also play major roles due to their abilities to regulate retromer’s localization to MVBs. Retromer localization represents a gradient of VPS29 and VPS26A colocalization with SYP32 (Golgi), VHA-a1 (TGN), and Rha1 (MVB) markers, as reported by Hu et al., 2022 [<a href="#B39-plants-13-02470" class="html-bibr">39</a>].</p>
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21 pages, 15871 KiB  
Article
Tracking Forest Disturbance in Northeast China’s Cold-Temperate Forests Using a Temporal Sequence of Landsat Data
by Yueting Wang, Xiang Jia, Xiaoli Zhang, Lingting Lei, Guoqi Chai, Zongqi Yao, Shike Qiu, Jun Du, Jingxu Wang, Zheng Wang and Ran Wang
Remote Sens. 2024, 16(17), 3238; https://doi.org/10.3390/rs16173238 - 1 Sep 2024
Viewed by 1161
Abstract
Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading [...] Read more.
Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. Full article
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<p>Overview of the study area.</p>
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<p>Forest disturbance analysis workflow.</p>
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<p>Mapping the forest. (<b>a</b>) OTSU value and forest area every year (Summer &amp; EVI); (<b>b</b>) forest cover synthesis map from 1990 to 2021 (Summer &amp; EVI).</p>
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<p>Schematic diagram of typical disturbance types and disturbance processes. (<b>a</b>) Logging in 1999; (<b>b</b>) anthropogenic fire in 2003; (<b>c</b>) wildfires in 2003 and 2010, respectively; (<b>d</b>) logging in 1990 and anthropogenic fire in 2003; (<b>e</b>) EVI curves for the sample points in disturbance areas from (<b>a</b>–<b>d</b>), with red boxes indicating disturbance events.</p>
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<p>Forest disturbance extraction. (<b>a</b>) Forest disturbance zone; (<b>b</b>) disturbance caused by logging after 1990; (<b>c</b>) disturbance caused by man-made fire in 2003; (<b>d</b>) other disturbances caused by multiple factors such as wildfire, etc.; <b>b1</b>–<b>d1</b> show the results of extracting forest disturbance information, <b>b2</b>–<b>d2</b> display the satellite images that correspond to these areas after the disturbance has occurred; (<b>e</b>) forest disturbance zone after fire-induced disturbances have been removed; (<b>f</b>) annual forest disturbance area caused by fires and other factors; the lines in the plot are the univariate linear trendlines.</p>
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<p>Distance of forest disturbance patches from roads and rivers. (<b>a</b>,<b>c</b>) represent the distance of disturbance patches from roads; (<b>b</b>,<b>d</b>) represent the distance of disturbance patches from rivers.</p>
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<p>Number of forest disturbance events. (<b>a</b>) Number of forest disturbance events in Genhe; (<b>b</b>) number of forest disturbance events in each administrative unit.</p>
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<p>The relationship between forest disturbance and its influencing factors. a<sub>i</sub>, b<sub>i</sub>, c<sub>i</sub>, d<sub>i</sub>, and e<sub>i</sub> are models of the area of disturbance and its influencing factors (annual precipitation, annual average temperature, annual snow cover days, the annual number of fires, and annual commercial logging output, respectively) for every year; the pink circles are for the anomalous years (2002 and 2003); the period of (<b>a<sub>1</sub></b>–<b>d<sub>1</sub></b>) is from 1991 to 2020; the period of (<b>a<sub>2</sub></b>–<b>d<sub>2</sub></b>) is from 2011 to 2020; the period of (<b>a<sub>3</sub></b>–<b>c<sub>3</sub></b>) is from 1991 to 2020, the disturbance area of (<b>a<sub>3</sub></b>–<b>c<sub>3</sub></b>) is the disturbance caused by factors other than fire; (<b>d<sub>3</sub></b>) is the model of the disturbance area and burned area for every year from 1991 to 2020; (<b>e<sub>1</sub></b>) annual commercial logging output; the period of (<b>e<sub>2</sub></b>,<b>e<sub>3</sub></b>) is from 1991 to 2015; the disturbance area of (<b>e<sub>3</sub></b>) is the disturbance caused by factors other than fire; the red line in the figure is the univariate linear trendline.</p>
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<p>Time and location of forest disturbance and fires. Note: the blue, pink, and purple areas are the areas where disturbances and fires occurred. It should be noted that the purple areas are areas where disturbances and fires occurred in the same year, and other areas with colors (except white and gray) are all where disturbances were detected but the region of fire did not exist in the auxiliary dataset.</p>
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23 pages, 8757 KiB  
Article
The Automated Inspection of Precast Utility Tunnel Segments for Geometric Quality Based on the BIM and LiDAR
by Zhigang Guo, Gang Wang, Zhengxiong Liu, Lingfeng Liu, Yakun Zou, Shengzhen Li, Ran Yang, Xin Hu, Shenghan Li and Daochu Wang
Buildings 2024, 14(9), 2717; https://doi.org/10.3390/buildings14092717 - 30 Aug 2024
Viewed by 761
Abstract
The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. [...] Read more.
The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. In order to achieve a high-precision and high-efficiency geometric quality inspection for multi-type precast utility tunnel segments, this paper proposes an automated inspection method based on the Building Information Model (BIM) and Light Detection and Ranging (LiDAR). Initially, the point cloud data (PCD) of the precast utility tunnel segment are acquired through LiDAR and preprocessed to obtain independent point clouds of the precast utility tunnel segment. Then, the shape of the precast utility tunnel segment is identified using the proposed Cross-Sectional Geometric Ratio Feature Identification (CSGRFI) algorithm. Subsequently, the geometric features of the components are extracted based on preset conditions, and the geometric dimensions are calculated. Finally, the quality inspection results are obtained by comparing with the design information provided by the BIM. The proposed method was validated in a real precast component factory. The results indicate that the method achieved a 100% success rate in identifying the cross-sectional shapes of the segments. Compared with the manual measurement method, the proposed method demonstrated a higher accuracy in the geometric quality assessment and an improved time efficiency by 44%. The proposed method enables the efficient geometric quality inspection of tunnel segments, effectively addressing the construction industry’s need for large-scale, high-quality tunnel projects. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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<p>Workflow of the proposed method.</p>
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<p>Inspection items for geometric quality.</p>
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<p>Position arrangement for PCD acquisition.</p>
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<p>Ground point removal.</p>
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<p>Coordinate alignment: (<b>a</b>) initial position; (<b>b</b>) calculate rotation angle; (<b>c</b>) perform rotation.</p>
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<p>Workflow of the CSGRFI algorithm.</p>
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<p>Cross-Sectional Geometric Ratio Feature: (<b>a</b>) the circular cross-section; (<b>b</b>) the rectangular cross-section.</p>
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<p>Process of feature extraction.</p>
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<p>Two types of edges.</p>
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<p>Edge detection: (<b>a</b>) upper edge detection; (<b>b</b>) lower edge detection.</p>
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<p>Type II edge extraction.</p>
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<p>Corner point extraction.</p>
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<p>The schematic diagram of the flatness detection results.</p>
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<p>Corner point numbering annotation.</p>
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<p>BIM-based quality inspection.</p>
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<p>The acquisition of the point cloud data: (<b>a</b>) the rectangular tunnel; (<b>b</b>) the circular tunnel.</p>
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<p>Precast utility tunnel point cloud data: (<b>a</b>) the original point cloud of the rectangular tunnel; (<b>b</b>) the original point cloud of the circular tunnel; (<b>c</b>) the point cloud of the rectangular tunnel after extracting the region of interest; and (<b>d</b>) the point cloud of the circular tunnel after extracting the region of interest.</p>
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<p>Preprocessing of point cloud: (<b>a</b>) ground removal from the rectangular tunnel point cloud; (<b>b</b>) ground removal from the circular tunnel point cloud; (<b>c</b>) Euclidean algorithm segmentation of the rectangular tunnel segments; and (<b>d</b>) Euclidean algorithm segmentation of the circular tunnel segments.</p>
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<p>Application of the proposed geometric information extraction method: (<b>a</b>) the plane extraction of the rectangular segment; (<b>b</b>) the edge extraction of the rectangular segment; (<b>c</b>) the corner point extraction of the rectangular segment; (<b>d</b>) the section fitting of the circular segment; and (<b>e</b>) the geometric information extraction of the circular segment.</p>
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<p>The manual measurement methods: (<b>a</b>) the rectangular tunnel; (<b>b</b>) the circular tunnel.</p>
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<p>Comparison of geometric quality assessment results between the proposed method and manual measurements: (<b>a</b>) rectangular segments; and (<b>b</b>) circular segments.</p>
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<p>The results of the flatness detection: (<b>a1</b>) detection results for one side of segment 1; (<b>b1</b>) detection results for the other side of segment 1; (<b>a2</b>) detection results for one side of segment 2; (<b>b2</b>) detection results for the other side of segment 2; (<b>a3</b>) detection results for one side of segment 3; (<b>b3</b>) detection results for the other side of segment 3; (<b>a4</b>) detection results for one side of segment 4; (<b>b4</b>) detection results for the other side of segment 4.</p>
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