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31 pages, 3494 KiB  
Article
Age-Dependent Pleomorphism in Mycobacterium monacense Cultures
by Malavika Ramesh, Phani Rama Krishna Behra, B. M. Fredrik Pettersson, Santanu Dasgupta and Leif A. Kirsebom
Microorganisms 2025, 13(3), 475; https://doi.org/10.3390/microorganisms13030475 - 20 Feb 2025
Viewed by 114
Abstract
Changes in cell shape have been shown to be an integral part of the mycobacterial life cycle; however, systematic investigations into its patterns of pleomorphic behaviour in connection with stages or conditions of growth are scarce. We have studied the complete growth cycle [...] Read more.
Changes in cell shape have been shown to be an integral part of the mycobacterial life cycle; however, systematic investigations into its patterns of pleomorphic behaviour in connection with stages or conditions of growth are scarce. We have studied the complete growth cycle of Mycobacterium monacense cultures, a Non-Tuberculous Mycobacterium (NTM), in solid as well as in liquid media. We provide data showing changes in cell shape from rod to coccoid and occurrence of refractive cells ranging from Phase Grey to phase Bright (PGB) in appearance upon ageing. Changes in cell shape could be correlated to the bi-phasic nature of the growth curves for M. monacense (and the NTM Mycobacterium boenickei) as measured by the absorbance of liquid cultures while growth measured by colony-forming units (CFU) on solid media showed a uniform exponential growth. Based on the complete M. monacense genome we identified genes involved in cell morphology, and analyses of their mRNA levels revealed changes at different stages of growth. One gene, dnaK_3 (encoding a chaperone), showed significantly increased transcript levels in stationary phase cells relative to exponentially growing cells. Based on protein domain architecture, we identified that the DnaK_3 N-terminus domain is an MreB-like homolog. Endogenous overexpression of M. monacense dnaK_3 in M. monacense was unsuccessful (appears to be lethal) while exogenous overexpression in Mycobacterium marinum resulted in morphological changes with an impact on the frequency of appearance of PGB cells. However, the introduction of an anti-sense “gene” targeting the M. marinum dnaK_3 did not show significant effects. Using dnaK_3-lacZ reporter constructs we also provide data suggesting that the morphological differences could be due to differences in the regulation of dnaK_3 in the two species. Together these data suggest that, although its regulation may vary between mycobacterial species, the dnaK_3 might have a direct or indirect role in the processes influencing mycobacterial cell shape. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Bacterial Infection)
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Figure 1
<p>The representative growth curves for <span class="html-italic">Mmon</span><sup>T</sup> and <span class="html-italic">Mmon</span><sup>RFPHyg</sup> cultivated under various conditions. (<b>A</b>) The growth curve for <span class="html-italic">Mmon</span><sup>T</sup> (marked with squares) and <span class="html-italic">Mmon</span><sup>RFPHyg</sup> (marked with circles) cultivated on 7H10 (supplemented with hygromycin in the case of <span class="html-italic">Mmon</span><sup>RFPHyg</sup>) plates with average generation times ± deviations in hours (h). WT_Expo and RFP_Expo represent exponential growth phases and the trend lines representing the slope of the growth curves are marked in red. The growth was generated by plotting the average values for each time point. The average CFU/mL was calculated from two biological replicates for each strain (see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a>) and the generation times are given in hours ± error range. (<b>B</b>) The growth curve for <span class="html-italic">Mmon</span><sup>RFPHyg</sup> in liquid 7H9 media with two exponential growth phases, Expo I and Expo II (highlighted dark) and trend lines representing the slope of the curve in red used to calculate the generation times (see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a>) ± error range as indicated. (<b>C</b>) The “normal” growth curve for <span class="html-italic">Mmon</span><sup>RFPHyg</sup> (in grey) and the growth curves after subjecting Expo II cells to various conditions, “re-suspended” in orange, addition of “Tween” in green, and addition of “glycerol” in red (for details see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a>). The red arrow marks the time when cells were harvested and subjected to various conditions as outlined in see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a>. (<b>C</b>,<b>D</b>) show representative best fit curves (with R<sup>2</sup>-values ≈ 1) to the observed bi-phasic growth pattern (see also <a href="#app1-microorganisms-13-00475" class="html-app">Table S3</a>). (<b>D</b>) The growth curves for Expo I cells (<span class="html-italic">Mmon</span><sup>RFPHyg</sup>) inoculated into “fresh media” (blue triangles) and “spent media” (blue circles) and for Expo II cells inoculated into “fresh media” (red triangles) and “spent media” (red circles).</p>
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<p>Microscopy of <span class="html-italic">Mmon</span><sup>T</sup> and <span class="html-italic">Mmon</span><sup>RFPHyg</sup> grown on 7H10 media and 37 °C. (<b>A</b>) Cells stained with FM4-64 (red) and DAPI (blue). Yellow arrows represent the PGB morphologies observed at different time points. Scale bar = 1 µm. (<b>B</b>) <span class="html-italic">Mmon</span><sup>RFPHyg</sup> cells expressing RFP (red) stained with MTG (green) and DAPI (blue). Yellow arrows indicate the PGB cell morphology. Scale bar = 1 µm. (<b>C</b>,<b>D</b>) Statistical representation of average percentage of occurrence of the various cell morphology types, <span class="html-italic">Mmon</span><sup>T</sup> (<b>C</b>) and <span class="html-italic">Mmon</span><sup>RFPHyg</sup> (<b>D</b>). A minimum of 350 cells were analysed for each time point. “d” = number of days. (<b>E</b>) One day old <span class="html-italic">Mmon</span><sup>T</sup> cells (rods and coccoids, yellow arrows mark the division site) stained with FM4-64 and DAPI depicting asymmetric septum formation (see <b>top</b> panel). Scale bar = 1 mm. (<b>F</b>) TEM image of a two days old culture showing dividing cells in the top panel. The bottom panel shows a TEM image of 2 and 17 days old coccoid <span class="html-italic">Mmon</span><sup>T</sup> cells showing asymmetric division sites. For each time point and condition, a minimum of 350 cells were counted. Error bars represent standard deviation. Plots represent the final averages of percentage of occurrence (see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a> for details).</p>
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<p>Visualisation of old <span class="html-italic">Mmon</span><sup>T</sup> and <span class="html-italic">Mmon</span><sup>RFPHyg</sup> cells grown on 7H10 media at 37 °C by phase contrast and transmission electron microscopy as indicated. (<b>A</b>) Phase contrast microscopy of 28 days old <span class="html-italic">Mmon</span><sup>T</sup> cells after enrichment for spores (see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a>). Yellow arrows mark refractive PGB cells. Scale bar = 1 mm. (<b>B</b>) TEM images of 28 days old <span class="html-italic">Mmon</span><sup>T</sup> cells after spore enrichment. Red arrows mark internal membrane structures. (<b>C</b>) Internal structures (yellow arrows) observed in PGB cells detected in old <span class="html-italic">Mmon</span><sup>RFPHyg</sup> cultures (48 days old, <b>top</b> row; 14 days old, <b>middle</b> and <b>bottom</b> rows). Cells were stained with MTG (green) while red is the result of the presence of <span class="html-italic">rfp</span>. Scale bar = 1 mm. (<b>D</b>) <span class="html-italic">Mmon</span><sup>T</sup> cells one week after growth of enriched PGB cells on fresh 7H10 media. Phase contrast microscopy (<b>top</b> panels; Scale bar = 1 mm) and TEM (<b>bottom</b> panel; Scale bar = 2 mm). Pink arrows mark appearance of rod-shaped cells, see (<b>A</b>) for comparison.</p>
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<p><span class="html-italic">Mmon</span><sup>T</sup> genome and functional classification of genes. (<b>A</b>) Overview of the <span class="html-italic">Mmon</span><sup>T</sup> complete genome. From the outer to inner circle: Green track illustrates the genome overlapping with scale along the genome length and position of genes (red and blue arrow heads mark the direction of transcription) listed in <a href="#app1-microorganisms-13-00475" class="html-app">Table S4</a>. The next two circles represent genes in forward (brown) and reversed (purple) strands. The next circle shows the GC-content distribution calculated with a sliding window of 1000 bp, blue (higher than mean value) and grey (lower than mean value) “spikes” correspond to variations of the mean GC-content 68.4% in ±10 and ±20 units, i.e., outer grey circle = 88.4% and inner grey circle = 48.4%. The inner circle, red (positive) and green (negative) correspond to the GC-skew obtained using a sliding window of 1000 bp. Generation of circus plot, see <a href="http://circos.ca" target="_blank">http://circos.ca</a> (last accessed on 9 July 2019). (<b>B</b>) Subsystem classification of 3557 <span class="html-italic">Mmon</span><sup>T</sup> genes as indicated.</p>
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<p>Analysis of mRNA levels of selected genes and <span class="html-italic">dnaK</span> paralogs in <span class="html-italic">Mmon</span><sup>T</sup>. (<b>A</b>–<b>D</b>) Change in mRNA levels as indicated comparing <span class="html-italic">Mmon</span><sup>RFPHyg</sup> cells of different ages (6, 14 and 48 days) relative to 3 days old cells. The changes are expressed as log<sub>2</sub>-fold change and the selected genes were grouped as indicated. (<b>A</b>) Peptidoglycan-associated genes, (<b>B</b>) divisome- and elongation-associated genes, (<b>C</b>) arabinogalactan-associated genes and (<b>D</b>) <span class="html-italic">dnaK</span> homologs, the highlighted bars correspond to the change observed for <span class="html-italic">dnaK</span>_3. (<b>E</b>) Illustration of the domain architecture of the four DnaK proteins in <span class="html-italic">Mmon</span><sup>T</sup>. Along with the expected Hsp70 domain, DnaK_3 (highlighted) is suggested to carry a MreB-Mbl domain while the other three lack this element. Notably, according to the Pfam database the average length of MreB-Mbl proteins encompass 313 amino acids. (<b>F</b>) Gene synteny for <span class="html-italic">dnaK</span>_3, <span class="html-italic">grpE</span>, <span class="html-italic">dnaJ</span> and <span class="html-italic">hspR</span> in <span class="html-italic">Mmon</span><sup>T</sup>, <span class="html-italic">Mmar</span><sup>T</sup> and <span class="html-italic">Mtb</span><sup>H37Rv</sup>. The arrows represent the genes as indicated. (<b>G</b>) Change in DnaK_3, GrpE, DnaJ and HspR mRNA levels expressed as log2-fold change as indicated. For <span class="html-italic">Mmon</span><sup>RFPHyg</sup>, mRNA levels at different time points (6, 14 and 48 days of growth) relative to levels in exponentially growing cells (3 days of growth). In the case of <span class="html-italic">Mmar</span><sup>RFPHyg</sup> mRNA levels in exponentially growing cells (OD<sub>600</sub> = 0.5) compared to levels in stationary cells (OD<sub>600</sub> ≈ 3). Statistical significance, ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Analysis of the over-expression of <span class="html-italic">dnaK_3</span><sup>Mmon</sup> and <span class="html-italic">dnaK</span>_3<sup>Mmar</sup> in <span class="html-italic">Mmar</span><sup>T</sup>. (<b>A</b>) Represents time course microscopy of <span class="html-italic">Mmar</span><sup>RFPHyg</sup> (<b>top</b> panel) with the corresponding average frequencies of occurrence of the different cell morphotypes (<b>bottom</b> panel). Scale bar = 1 mm. (<b>B</b>) Statistical distribution of the different cell morphologies in <span class="html-italic">Mmar</span><sup>pBS401-dnaK3Mmon</sup> (un-induced and induced conditions) observed in the cell cultures. (<b>C</b>) Staining and microscopy of samples corresponding to (<b>B</b>). <b>Left</b> panel: microscopy of <span class="html-italic">Mmar</span><sup>pBS401-dnaK3Mmon</sup> at two time points (1 day and 3 days) exhibiting occurrence of PGB cells (yellow arrows) and coccoids (black arrows) under un-induced condition. Scale bar = 1 mm. <b>Middle</b> panel: MTG and FM4-64 staining of <span class="html-italic">Mmar</span><sup>pBS401-dnaK3Mmon</sup> cells showing internal membrane formation and compartmentalisation (red arrows). Scale bar = 1 mm. <b>Right</b> panel: TEM images of 4 days old <span class="html-italic">Mmar</span><sup>pBS401-dnaK3Mmon</sup> cells (un-induced and after 24 h induction). Red arrows mark the internal membrane formation and presence of internal structures. All cultures were grown on 7H10 media supplemented with hygromycin (100 μg mL<sup>−1</sup>) at 30 °C. (<b>D</b>) Statistical distribution of the different cell morphologies in <span class="html-italic">Mmar</span><sup>pBS401-dnaK3Mmar</sup> (un-induced and ‘Tet’ induced conditions) observed in the cell cultures. (<b>E</b>) Expression of <span class="html-italic">dnaK</span>_3<sup>Mmon</sup> carried on pBS401 in <span class="html-italic">Mmar</span><sup>T</sup> as determined by qPCR. Cell extracts from three different time points (1 day, 3 days and 5 days) with and without tetracycline induction were considered, “d” = days. The log<sub>2</sub>-fold change was normalised to the “1 day-induction”. (<b>F</b>) Analysis of the expression of DnaK_3<sup>Mmar</sup> and DnaK_3<sup>Mmon</sup> in <span class="html-italic">Mmar</span><sup>T</sup> by β-galactosidase assay. β-galactosidase activity (DnaK_3-LacZ fused protein) upon addition of the CPRG substrate with (substrate) was measured using a spectrophotometer at 595 nm. The measured absorbance normalised with the total protein content for each sample as shown in the plots. The samples were protein extracts from <span class="html-italic">Mmar</span> cells carrying pIGN vector with <span class="html-italic">dnaK3</span><sup>Mmar</sup>-<span class="html-italic">lacZ</span> or <span class="html-italic">dnaK3</span><sup>Mmon</sup>-<span class="html-italic">lacZ</span>. The empty vector was used as control (see <a href="#sec2-microorganisms-13-00475" class="html-sec">Section 2</a> for details). The numbers represent an average based on two independent experiments (round 1 and round 2) with two biological replicates.</p>
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18 pages, 33036 KiB  
Article
Three-Dimensional Magnetotelluric Forward Modeling Using Multi-Task Deep Learning with Branch Point Selection
by Fei Deng, Hongyu Shi, Peifan Jiang and Xuben Wang
Remote Sens. 2025, 17(4), 713; https://doi.org/10.3390/rs17040713 - 19 Feb 2025
Viewed by 150
Abstract
Magnetotelluric (MT) forward modeling is a key technique in magnetotelluric sounding, and deep learning has been widely applied to MT forward modeling. In three-dimensional (3-D) problems, although existing methods can predict forward modeling results with high accuracy, they often use multiple networks to [...] Read more.
Magnetotelluric (MT) forward modeling is a key technique in magnetotelluric sounding, and deep learning has been widely applied to MT forward modeling. In three-dimensional (3-D) problems, although existing methods can predict forward modeling results with high accuracy, they often use multiple networks to simulate multiple forward modeling parameters, resulting in low efficiency. We apply multi-task learning (MTL) to 3-D MT forward modeling to achieve simultaneous inference of apparent resistivity and impedance phase, effectively improving overall efficiency. Furthermore, through comparative analysis of feature map differences in various decoder layers of the network, we identify the optimal branching point for multi-task learning decoders. This enhances the feature extraction capabilities of the network and improves the prediction accuracy of forward modeling parameters. Additionally, we introduce an uncertainty-based loss function to dynamically balance the learning weights between tasks, addressing the shortcomings of traditional loss functions. Experiments demonstrate that compared with single-task networks and existing multi-task networks, the proposed network (MT-FeatureNet) achieves the best results in terms of Structural Similarity Index Measure (SSIM), Mean Relative Error (MRE), and Mean Absolute Error (MAE). The proposed multi-task learning model not only improves the efficiency and accuracy of 3-D MT forward modeling but also provides a novel approach to the design of multi-task learning network structures. Full article
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<p>Comparison between (<b>a</b>) single-task networks and (<b>b</b>) multi-task networks.</p>
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<p>U-Net single-task network model.</p>
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<p>U-Net single-task network model.</p>
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<p>Feature map of layer (<b>a</b>) A and (<b>b</b>) B and (<b>c</b>) C.</p>
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<p>Loss per Epoch Comparison.</p>
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<p>Comparison of single anomaly results.</p>
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<p>Comparison of the results of the two anomalies.</p>
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<p>Comparison of the results of the three anomalies.</p>
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<p>Loss per Epoch Comparison.</p>
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12 pages, 3542 KiB  
Article
Study on the Magnetic Contact Mechanical Properties of Polyurethane-Based Magnetorheological Elastomer Sealing Materials
by Xiuxu Zhao, Emmanuel Appiah and Haile Tang
Lubricants 2025, 13(2), 88; https://doi.org/10.3390/lubricants13020088 - 16 Feb 2025
Viewed by 324
Abstract
In order to meet the dual requirements of hydraulic dynamic sealing to ensure a reduction in friction, this study prepared polyurethane-based magnetorheological elastomers (MREs). The compression performance of isotropic and anisotropic samples under a magnetic field was tested in samples containing carbonyl iron [...] Read more.
In order to meet the dual requirements of hydraulic dynamic sealing to ensure a reduction in friction, this study prepared polyurethane-based magnetorheological elastomers (MREs). The compression performance of isotropic and anisotropic samples under a magnetic field was tested in samples containing carbonyl iron powder (CIP) particles with different volume contents and particle sizes. The compression performance of isotropic and anisotropic samples under the magnetic field was tested under static loading, and the friction coefficient changes in isotropic and anisotropic samples under a magnetic field were analyzed by a friction testing machine. The test results show that under static compression load, the contact stress of isotropic and anisotropic specimens increases with the increase in magnetic field strength, and the magnitude of the contact stress changes when the increase in magnetic field strength is proportional to the CIP content and CIP particle size of the specimen. The friction test results of the samples showed that an increase in magnetic field strength, CIP particle diameter, and CIP content reduces the friction coefficient of the CIP particle polyurethane-based magnetorheological elastomer samples, and the variation in the magnetic friction coefficient of anisotropic samples is greater than that of isotropic samples. This research result indicates that utilizing the magneto-mechanical properties of polyurethane-based magnetorheological elastomers can provide an innovative solution to the inherent contradiction between increasing contact stress and avoiding wear in the dynamic sealing of hydraulic systems, which can provide controllable sealing performance for hydraulic dynamic sealing components in specific application scenarios, enabling them to have a better sealing ability while reducing the friction coefficient of the sealing pair. Full article
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<p>Sample preparation process.</p>
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<p>Static magnetic compression performance testing. (<b>a</b>) Testing machine; (<b>b</b>) magnetization device.</p>
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<p>(<b>a</b>) Friction experiments. (<b>b</b>) COF plot for PU, isotropic and anisotropic MRE samples.</p>
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<p>Static compression test results of isotropic specimens (<b>a</b>) 5 µm with varying CIP concentration and its corresponding contact stress response (<b>b</b>). (<b>c</b>) 30% CIP concentration with varying CIP sizes and its corresponding contact stress response (<b>d</b>).</p>
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<p>Static compression test results of isotropic specimens (<b>a</b>) 5 µm with varying CIP concentration and its corresponding contact stress response (<b>b</b>). (<b>c</b>) 30% CIP concentration with varying CIP sizes and its corresponding contact stress response (<b>d</b>).</p>
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<p>Static compression test results of anisotropic specimens (<b>a</b>) 5 µm with varying CIP concentration and its corresponding contact stress response (<b>b</b>). (<b>c</b>) 30% CIP concentration with varying CIP sizes and its corresponding contact stress response (<b>d</b>).</p>
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<p>Friction coefficient test results of the samples (<b>a</b>) Isotropic 5 µm with varying CIP concentration. (<b>b</b>) Anisotropic 5 µm with varying CIP concentration.</p>
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15 pages, 1470 KiB  
Article
Magnetic Resonance Elastography of Invasive Breast Cancer: Evaluating Prognostic Factors and Treatment Response
by Jin Joo Kim, Jin You Kim, Yeon Joo Jeong, Suk Kim, In Sook Lee, Nam Kyung Lee, Taewoo Kang, Heeseung Park and Seokwon Lee
Tomography 2025, 11(2), 18; https://doi.org/10.3390/tomography11020018 - 14 Feb 2025
Viewed by 422
Abstract
Objectives: To assess the elasticity values in breast tissues using magnetic resonance elastography (MRE) and examine the association between elasticity values of invasive breast cancer with prognostic factors and the pathologic response to neoadjuvant systemic therapy (NST). Methods: A total of 57 patients [...] Read more.
Objectives: To assess the elasticity values in breast tissues using magnetic resonance elastography (MRE) and examine the association between elasticity values of invasive breast cancer with prognostic factors and the pathologic response to neoadjuvant systemic therapy (NST). Methods: A total of 57 patients (mean age, 54.1 years) with invasive breast cancers larger than 2 cm in diameter on ultrasound were prospectively enrolled. The elasticity values (mean, minimum, and maximum) of invasive breast cancers, normal fibroglandular tissues, and normal fat tissues were measured via MRE using a commercially available acoustic driver and compared. Elasticity values of breast cancers were compared according to prognostic factors and pathologic responses in patients who received NST before surgery. Receiver operating curve analysis was performed to evaluate the predictive efficacy of elasticity values in terms of pathological response. Results: Among the 57 patients, the mean elasticity value of invasive breast cancers was significantly higher than that of normal fibroglandular tissue and normal fat tissue (7.90 ± 5.80 kPa vs. 2.54 ± 0.80 kPa vs. 1.32 ± 0.33 kPa, all ps < 0.001). Invasive breast cancers with a large diameter (>4 cm) exhibited significantly higher mean elasticity values relative to tumors with a small diameter (≤4 cm) (11.65 ± 7.22 kPa vs. 5.87 ± 3.58 kPa, p = 0.002). Among 24 patients who received NST, mean, minimum, and maximum elasticity values significantly differed between the pathologic complete response (pCR) and non-pCR groups (all ps < 0.05). For the mean elasticity value, the area under the curve value for distinguishing pCR and non-pCR groups was 0.880 (95% confidence interval, 0.682, 0.976; p < 0.001). Conclusions: The elasticity values of invasive breast cancers measured via breast MRE showed a positive correlation with tumor size and showed potential in predicting the therapeutic response in patients receiving NST. Full article
(This article belongs to the Section Cancer Imaging)
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<p>Pretreatment MRI of the breasts in 45 years-old-woman with invasive breast cancer. (<b>A</b>) Early phase of dynamic contrast-enhanced T1-weighted image shows an irregular heterogeneous enhancing mass in the left breast, (<b>B</b>) T2-weighted image shows irregular hyperintense mass, (<b>C</b>) the processed color map of elastography with a range of 0–8 kPa shows a mass with red color, and (<b>D</b>) the process color map of elastography with a range of 0–20 kPa shows a mass with green color. The elasticity values were measured via (<b>E</b>) elastography by drawing region of interest. The mean elasticity value of the breast cancer was 8.49 kPa. After completion of neoadjuvant chemotherapy, no residual cancer was found on surgical histopathology (ypT0N0).</p>
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<p>Pretreatment MRI of the breasts in 45 years-old-woman with invasive breast cancer. (<b>A</b>) Early phase of the dynamic contrast-enhanced T1-weighted image shows an irregular heterogeneous enhancing mass in the right breast, (<b>B</b>) T2-weighted image shows an irregular hyperintense mass in the right breast, the processed color maps of elastography with a range of (<b>C</b>) 0–8 kPa and (<b>D</b>) 0–20 kPa show a stiff mass with red color. The elasticity values were measured via (<b>E</b>) elastography by drawing region of interest. The mean elasticity value of the breast cancer was 17.18 kPa. After completion of neoadjuvant chemotherapy, a 5.2 cm-sized residual mixed mucinous and invasive ductal carcinoma was found on surgical histopathology (ypT3N1).</p>
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<p>Box-and-whisker plots of the pretreatment (<b>A</b>) mean, (<b>B</b>) maximum, and (<b>C</b>) minimum elasticity values according to the response to neoadjuvant systemic therapy. The horizontal line within each box is the median; the top and bottom edges of each box are the 25% and 75% percentiles of elasticity values, respectively.</p>
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<p>Receiver operating characteristic curves for the mean, maximum, and minimum elasticity values used as predictors of pathological complete response in 24 patients who received neoadjuvant systemic therapy. The areas under the curves for pathological complete response prediction were 0.880 (95% confidence interval [CI] 0.682, 0.976) for mean elasticity value, 0.907 (95% CI 0.718, 0.987) for maximum elasticity value, and 0.843 (95% CI 0.637, 0.958) for minimum elasticity value.</p>
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11 pages, 546 KiB  
Technical Note
The Density Profile of a Neutron Star
by Allan D. Woodbury
Entropy 2025, 27(2), 194; https://doi.org/10.3390/e27020194 - 13 Feb 2025
Viewed by 284
Abstract
The problem posed in this study is to determine the density distribution within an ideal spherically symmetric neutron star based on only two constraints: the volumetrically averaged density and a moment of inertia factor, f. In order to deal with the above, [...] Read more.
The problem posed in this study is to determine the density distribution within an ideal spherically symmetric neutron star based on only two constraints: the volumetrically averaged density and a moment of inertia factor, f. In order to deal with the above, it is recognized that space within these objects is heavily curved, and thus lengths, densities, and the moment of inertia have to be adjusted for relativistic effects. For the first time, the minimum relative entropy methodology (MRE) is used to find the expected value of a series of effective densities within a neutron star. In numerical experiments, we use the data from the star PSR J0737-3039A, which has a mass of 2.6×1030 kg and a radius of 13.75 km. Here, the factor f is based on a range of values of moments of inertia (MOI): 1.30–1.63 ×1045 g cm2. For f=0.324, at no time do densities cross over 1×1015 gm/cc. For the most part, densities > 6×1014 gm/cc are shown at radial dimensions of less than about 4 km. When f=0.258, densities closer to the core are pushed higher, as one might expect, and peak at slightly over 4×1015 gm/cc. If recent values of MOI are more appropriate at 1.15×1045 g cm2, this then suggests core densities greater than 4×1015 gm/cc. These various density models lead to quantitative statements about qualitative interpretations, and as time goes on, any internal density models should satisfy the two constraints posed. Also, since the model presented here is probabilistic, it can be established that density at a certain depth is constrained within a certain confidence limit. The expected values of densities for PSR J0737-3039A are in reasonable agreement with current conceptual neutron star models but are highly sensitive to assumed MOI values. It is emphasized that the probabilities and the mean values of density obtained are conditional on the imposed moments, namely, M and f, and also the radius R. Full article
(This article belongs to the Section Multidisciplinary Applications)
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<p>Conceptual model of a neutron star and its interior. Credit NASA/B. Link, <a href="https://heasarc.gsfc.nasa.gov" target="_blank">https://heasarc.gsfc.nasa.gov</a>.</p>
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<p>Star J0737-3039A. Expected values of density for different values of moment of inertia (MOI) factors, <span class="html-italic">f</span>. The dashed line is the expected value of prior probability, which is a uniform prior <math display="inline"><semantics> <mrow> <mn>2.4</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>14</mn> </msup> </mrow> </semantics></math> gm/cc in the radial direction.</p>
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<p>SR J0737-3039A. Expected values of density for two different values of moment of inertia factor <span class="html-italic">f</span>. The dashed line is the expected value of prior probability, which, in this case, is a zoned conceptual model loosely based on the conceptual model of <a href="#entropy-27-00194-f001" class="html-fig">Figure 1</a>.</p>
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<p>Assumed conceptual model of a neutron star studied by [<a href="#B20-entropy-27-00194" class="html-bibr">20</a>]. The expected value of density for assumed values of moment of inertia factor <span class="html-italic">f</span>. The dashed line is the expected value of prior probability from nuclear model V18. The dashed line is a uniform prior in the radial direction.</p>
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30 pages, 6091 KiB  
Article
Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis
by Ganglong Duan, Yutong Du, Yanying Shang, Hongquan Xue and Ruochen Zhang
Appl. Sci. 2025, 15(4), 1779; https://doi.org/10.3390/app15041779 - 10 Feb 2025
Viewed by 401
Abstract
Short-time traffic flow prediction is essential for intelligent traffic management. By accurately predicting traffic conditions in the near future, it helps to alleviate congestion, improve road efficiency, reduce accidents, and support timely traffic control. Short-time traffic flow exhibits uncertainty and randomness, and this [...] Read more.
Short-time traffic flow prediction is essential for intelligent traffic management. By accurately predicting traffic conditions in the near future, it helps to alleviate congestion, improve road efficiency, reduce accidents, and support timely traffic control. Short-time traffic flow exhibits uncertainty and randomness, and this paper proposes an SVR model for short-time traffic flow prediction on non-main and branch roads, using correlations between associated roads to improve accuracy. Association Rule Analysis: First, we use Pearson correlation to identify strongly correlated roads. This step helps in understanding the relationships between different roads and their traffic patterns. SVR Model Construction: Second, based on the identified correlations, we construct an SVR model using traffic data from the target road and its associated roads. The model parameters are optimized using grid search and cross-validation to ensure the best performance. Simulation and Evaluation: Third, we conduct simulation experiments using real traffic data from Xi’an city. The performance of our model is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE). Simulation experiments show that our model outperforms existing methods. Specifically, our model achieved an RMSE of 11.422, an MAE of 7.017, and an MRE of 0.139. In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. For instance, the LSTM model had an RMSE of 14.5, an MAE of 8.2, and an MRE of 0.165; the Random Forest model had an RMSE of 13.8, an MAE of 7.8, and an MRE of 0.152; and the GBDT model had an RMSE of 13.2, an MAE of 7.5, and an MRE of 0.148. These results demonstrate that our proposed SVR model, combined with association rules, is highly effective in predicting short-time traffic flow on non-main and branch roads, which are often overlooked in existing research. Full article
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<p>The schematic diagram of the optimal hyperplane.</p>
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<p><math display="inline"><semantics> <mi>ε</mi> </semantics></math>-insensitive loss function.</p>
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<p>The schematic of SVR with the addition of relaxation variables.</p>
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<p>SVR short-term traffic flow prediction model based on road correlation.</p>
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<p>SVR model prediction flowchart.</p>
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<p>Variation curve of prediction error (<math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>).</p>
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<p>Variation curve of training error (<math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>).</p>
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<p>Variation curve of prediction error (C = 0~200, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>).</p>
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<p>Variation curve of training error (C = 0~200, <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>).</p>
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<p>Error variation curves with 95% confidence intervals.</p>
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<p>The structure of the association rule–SVR model.</p>
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<p>Prediction results of selected trunk roads based on SVR mode. Note: * are SVR predictions for associated roads.</p>
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<p>SVR model-based road prediction results for selected feeder roads. Note: * are SVR predictions for associated roads.</p>
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<p>Comparison of model results.</p>
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<p>Comparison of different models at 95% confidence intervals.</p>
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<p>Comparison of SVR modeled feeder road tests.</p>
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13 pages, 750 KiB  
Article
Diagnostic Performance of Serum Mac-2-Binding Protein Glycosylation Isomer as a Fibrosis Biomarker in Non-Obese and Obese Patients with MASLD
by Prooksa Ananchuensook, Kamonchanok Moonlisarn, Bootsakorn Boonkaew, Chalermarat Bunchorntavakul and Pisit Tangkijvanich
Biomedicines 2025, 13(2), 415; https://doi.org/10.3390/biomedicines13020415 - 9 Feb 2025
Viewed by 451
Abstract
Background: Serum mac-2-binding protein glycosylation isomer (M2BPGi) is a new biomarker for liver fibrosis. However, its performance in metabolic dysfunction-associated steatotic liver disease (MASLD), particularly in obese patients, remains to be explored. Methods: This study evaluated the role of M2BPGi in predicting liver [...] Read more.
Background: Serum mac-2-binding protein glycosylation isomer (M2BPGi) is a new biomarker for liver fibrosis. However, its performance in metabolic dysfunction-associated steatotic liver disease (MASLD), particularly in obese patients, remains to be explored. Methods: This study evaluated the role of M2BPGi in predicting liver fibrosis in 205 patients with MASLD using magnetic resonance elastography (MRE) as a reference. The performance of M2BPGi was compared to vibration-controlled transient elastography (VCTE), FIB-4, APRI, and NFS. The PNPLA3, TM6SF2, and HSD17B13 polymorphisms were assessed by allelic discrimination assays. Results: The area under the ROC curves for VCTE, M2BPGi FIB-4, APRI, and NFS in differentiating significant fibrosis were 0.95 (95% CI; 0.91–0.98), 0.85 (0.79–0.92), 0.81 (0.74–0.89), 0.79 (0.71–0.87), and 0.80 (0.72–0.87) (all p < 0.001), respectively. The optimal cut-off values of M2BPGi in predicting significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.95, and 1.23 cut-off index (COI); yielding satisfactory sensitivity, specificity, and diagnostic accuracy. The performance of M2BPGi was consistent among subgroups according to BMI, while the AUROCs of FIB-4, APRI, and NFS were remarkably decreased in patients with BMI ≥ 30 kg/m2. Patients with the PNPLA3 GG genotype had significantly higher M2BPGi than those with the CC/CG genotypes. In multivariate analysis, the independent factors associated with significant liver fibrosis were VCTE, M2BPGi, and PNPLA3 rs738409. Conclusions: Our data demonstrated that serum M2BPGi accurately assessed liver fibrosis across different BMI, indicating that this biomarker could apply to non-obese and obese patients with MASLD in clinical settings. Full article
(This article belongs to the Section Endocrinology and Metabolism Research)
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<p>Comparison of AUROCs between VCTE and serum biomarkers (<b>a</b>) F0–F1 vs. F2–F4; (<b>b</b>) F0–F2 vs. F3–F4; (<b>c</b>) F0–F3 vs. F4. AUROCs, area under the receiver operator curves; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase/platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score; COI, cut-off index.</p>
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<p>Serum M2BPGi values for each fibrosis stage. Data are presented as mean ± S.E.M. *** <span class="html-italic">p</span>-value &lt; 0.001.</p>
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25 pages, 10868 KiB  
Article
Study on the Automatic Selection of Sensitive Hyperspectral Bands for Rice Nitrogen Retrieval Based on a Maximum Inscribed Rectangle
by Yaobing Fan, Youxing Chen, Shangrong Wu, Wei Kuang, Jieyang Tan, Yan Zha, Baohua Fang and Peng Yang
Agronomy 2025, 15(2), 406; https://doi.org/10.3390/agronomy15020406 - 5 Feb 2025
Viewed by 419
Abstract
Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is [...] Read more.
Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R2 between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R2) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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<p>Distribution of test plots and nitrogen application levels.</p>
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<p>The specific technical flowchart.</p>
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<p>Schematic diagram of inscribed rectangular band expansion.</p>
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<p>Distribution of rice N-VIs in a two-dimensional plot. (<b>a</b>) Jointing stage N–NDVI. (<b>b</b>) Jointing stage N-DVI. (<b>c</b>) Jointing stage N-RVI. (<b>d</b>) Flowering stage N-NDVI. (<b>e</b>) Flowering stage N-DVI. (<b>f</b>) Flowering stage N-RVI.</p>
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<p>Two-dimensional plot of fitted <span class="html-italic">R</span><sup>2</sup> between N-VIs and rice plant nitrogen at the jointing stage. (<b>a</b>) N-NDVI. (<b>b</b>) N-DVI. (<b>c</b>) N-RVI.</p>
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<p>Two-dimensional plot of fitted <span class="html-italic">R</span><sup>2</sup> between N-VIs and rice plant nitrogen at the flowering stage. (<b>a</b>) N-NDVI. (<b>b</b>) N-DVI. (<b>c</b>) N-RVI.</p>
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<p>Two-dimensional isopotential plot of fitted <span class="html-italic">R</span><sup>2</sup> between N-VIs and rice plant nitrogen at the jointing stage. (<b>a</b>) N-NDVI. (<b>b</b>) N-DVI. (<b>c</b>) N-RVI.</p>
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<p>Two-dimensional isopotential plot of fitted <span class="html-italic">R</span><sup>2</sup> between N-VIs and rice plant nitrogen at the flowering stage. (<b>a</b>) N-NDVI. (<b>b</b>) N-DVI. (<b>c</b>) N-RVI.</p>
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<p>Results of plant nitrogen inversion based on N-VIs (jointing stage): (<b>a</b>) Plant nitrogen inversion results based on the N-NDVI. (<b>b</b>) Plant nitrogen inversion results based on the N-DVI. (<b>c</b>) Plant nitrogen inversion results based on the N-RVI.</p>
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<p>Results of plant nitrogen inversion based on N-VIs (flowering stage): (<b>a</b>) Plant nitrogen inversion results based on the N-NDVI. (<b>b</b>) Plant nitrogen inversion results based on the N-DVI. (<b>c</b>) Plant nitrogen inversion results based on the N-RVI.</p>
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<p>Verification results of rice N-VI estimation accuracy. (<b>a</b>) Accuracy validation of plant nitrogen based on the N-NDVI. (<b>b</b>) Accuracy validation of plant nitrogen based on the N-DVI. (<b>c</b>) Accuracy validation of plant nitrogen based on the N-RVI.</p>
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21 pages, 2523 KiB  
Article
Macroporous Resin Recovery of Antioxidant Polyphenol Compounds from Red Onion (Allium cepa L.) Peel
by Khanafina Aliya, Ha-Seong Cho, Ibukunoluwa Fola Olawuyi, Ju-Hwi Park, Ju-Ock Nam and Won-Young Lee
Antioxidants 2025, 14(2), 145; https://doi.org/10.3390/antiox14020145 - 26 Jan 2025
Viewed by 605
Abstract
In this study, polyphenols in the crude extract (CE) from red onion peel were recovered by macroporous resin, and their antioxidant and anti-inflammatory activities were evaluated. Among the four resins screened (SP850, XAD2, XAD7HP, and XAD16N), XAD7HP showed the highest desorption and recovery [...] Read more.
In this study, polyphenols in the crude extract (CE) from red onion peel were recovered by macroporous resin, and their antioxidant and anti-inflammatory activities were evaluated. Among the four resins screened (SP850, XAD2, XAD7HP, and XAD16N), XAD7HP showed the highest desorption and recovery ratios, and it was used to optimize polyphenol recovery through single-factor experiments. The optimal conditions were established as 1 g resin, pH 4, 25 °C, 7 h for adsorption, followed by desorption with 70% ethanol for 1 h at 25 °C. These conditions achieved 85.00% adsorption ratio, 87.10% desorption ratio, and 20.9% yield of the macroporous resin-recovered extract (MRE) from the CE. HPLC analysis revealed that rosmarinic acid, quercetin, and myricetin were major compounds in the MRE, with the content of these compounds higher (about 7-fold) compared to the CE, confirming enhanced recovery of polyphenols by macroporous resin. Moreover, FT-IR and ¹H-NMR analysis confirmed the successful recovery of these polyphenol compounds in the MRE. Furthermore, the MRE displayed significantly improved antioxidant activities (DPPH, ABTS, and FRAP) and anti-inflammatory activities (inhibition of nitric oxide synthesis and reactive oxygen species production) compared to the CE. In summary, our findings suggest that macroporous resin can effectively recover polyphenol compounds from red onion peel extract and enhance their biological activities. Full article
(This article belongs to the Section Natural and Synthetic Antioxidants)
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<p>Single-factor experiments on the adsorption and desorption. (<b>A</b>) Effect of resin amounts; (<b>B</b>) Effect of pH; (<b>C</b>) Effect of the adsorption temperature; (<b>D</b>) Effect of adsorption time; (<b>E</b>) Effect of the ethanol concentration; (<b>F</b>) Effect of the desorption time; and (<b>G</b>) Effect of the adsorption temperature. * indicates plateau phase where equilibrium adsorption or desorption had been achieved. Error bars with different letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) by Duncan’s multiple range test.</p>
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<p>Chromatogram of standard (<b>top</b>), crude extract (<b>middle</b>), and macroporous resin-recovered extract (<b>bottom</b>) (condition: column C18 (250 × 4.6 mm, 5 μm), mobile phase: 1% aqueous acetic acid solution and acetonitrile, flow rate: 0.7 mL/min, UV detection at 254 nm).</p>
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<p>FT-IR spectra of the crude extract (CE) and macroporous resin-recovered extract (MRE) from red onion peel.</p>
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<p><sup>1</sup>H-NMR spectra of macroporous resin-recovered extract (MRE) from red onion peel.</p>
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<p>Antioxidant activity of the crude extract (CE) and macroporous resin-recovered extract (MRE) from red onion peel (<b>A</b>) ABTS assay; (<b>B</b>) DPPH assay; (<b>C</b>) FRAP assay. Different letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) by Duncan’s multiple range test.</p>
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<p>Anti-inflammatory activities of CE and MRE in LPS-induced activated RAW264.7 cells. (<b>A</b>) RAW264.7 cells were treated with various concentrations of CE or MRE for 24 h, and the cell viability was analyzed by CCK-8 assay. (<b>B</b>–<b>D</b>). Cells were pretreated with various concentrations of CE or MRE for 1 h, then cotreated with LPS for 24 h. The nitric oxide synthesis was analyzed by Griess reagent, and the intracellular ROS production was measured by DCF-DA assay using flow cytometry. The data are presented as mean ± SD. Different letters indicate statistically significant different. If the measurement value was too low and there was no result, it was marked as ‘N.D (Not detected)’.</p>
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32 pages, 20828 KiB  
Article
Time-Variation Damping Dynamic Modeling and Updating for Cantilever Beams with Double Clearance Based on Experimental Identification
by Yunhe Zhang, Fanjun Meng, Xueguang Li, Wei Song, Dashun Zhang and Faping Zhang
Actuators 2025, 14(2), 58; https://doi.org/10.3390/act14020058 - 26 Jan 2025
Viewed by 398
Abstract
The accuracy of a space manipulator’s end trajectory and stability is significantly affected by joint clearance. Aiming to improve the prediction accuracy of vibration caused by clearance, a dynamic clearance modeling method is developed based on parameter identification in this study. First, a [...] Read more.
The accuracy of a space manipulator’s end trajectory and stability is significantly affected by joint clearance. Aiming to improve the prediction accuracy of vibration caused by clearance, a dynamic clearance modeling method is developed based on parameter identification in this study. First, a dynamic model framework for manipulator arms is established based on the Hamilton principle and hypothetical mode method with time-variation damping. Then, a multi-resolution identification is performed for identifying the instantaneous frequency and damping ratio to estimate stiffness and damping by the sensors. The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. Finally, a case study is conducted to verify the presented model. In comparison with the initial dynamic model based on constant damping, the modal assurance criterion (MAC) of the proposed improved model based on time-variation damping is improved by 43.97%, the mean relative error (MRE) of the frequency response function (FRF) is reduced by 32.6%, and the root mean square error (RMSE) is reduced by 18.19%. The comparison results indicate the advantages of the proposed model. This modeling method could be used for vibration prediction in control systems for space manipulators to improve control accuracy. Full article
(This article belongs to the Special Issue Power Electronics and Actuators—Second Edition)
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<p>Simplified model.</p>
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<p>Parameter identification.</p>
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<p>Iterative process.</p>
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<p>Identification results and residual curves of the continuous− and step−change damping processes. (<b>a</b>) Identification results of the damping model. (<b>b</b>) Residual curve of the damping model.</p>
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<p>Comparison of the identification accuracy and efficiency. (<b>a</b>) Stacked histogram of multiscale identification. (<b>b</b>) Objective function curve of the QGA.</p>
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<p>Dimensions of contact rings.</p>
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<p>Test model.</p>
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<p>FRF result comparison of the simulation and test models. (<b>a</b>) Clearance position 1. (<b>b</b>) Clearance position 2. (<b>c</b>) Front position. (<b>d</b>) End position.</p>
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<p>Comparison of the FRF correlation coefficients of the two models. In (<b>A</b>) The CSF result comparison. (<b>a</b>) Clearance position 1. (<b>b</b>) Clearance position 2. (<b>c</b>) Front position. (<b>d</b>) End position. In (<b>B</b>) The CSAC result comparison. (<b>a</b>) Clearance position 1. (<b>b</b>) Clearance position 2. (<b>c</b>) Front position. (<b>d</b>) End position.</p>
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<p>Comparison of the FRF correlation coefficients of the two models. In (<b>A</b>) The CSF result comparison. (<b>a</b>) Clearance position 1. (<b>b</b>) Clearance position 2. (<b>c</b>) Front position. (<b>d</b>) End position. In (<b>B</b>) The CSAC result comparison. (<b>a</b>) Clearance position 1. (<b>b</b>) Clearance position 2. (<b>c</b>) Front position. (<b>d</b>) End position.</p>
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<p>FRF results for different clearance values. (<b>a</b>) Clearance 1. (<b>b</b>) Clearance 2. (<b>c</b>) Front position. (<b>d</b>) End position.</p>
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<p>Coincidence degree between the measured and calculated frequencies. (<b>a</b>) First order. (<b>b</b>) Second order. (<b>c</b>) Third order. (<b>d</b>) Fourth order.</p>
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<p>FRF comparison of the simulation and test models (clearance = 0.05 mm).</p>
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<p>FRF comparison of the simulation and test models (clearance = 0.1 mm).</p>
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<p>FRF comparison of the simulation and test models (clearance = 0.5 mm).</p>
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<p>FRF comparison of the simulation and test models (clearance = 1 mm).</p>
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<p>FRF comparison of the simulation and test models (clearance = 1.5 mm).</p>
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<p>FRF comparison of the simulation and test models (clearance = 2 mm).</p>
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<p>CSF result comparison (clearance = 0.05 mm).</p>
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<p>CSAC result comparison (clearance = 0.05 mm).</p>
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<p>SF result comparison (clearance = 0.1 mm).</p>
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<p>CSAC result comparison (clearance = 0.1 mm).</p>
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<p>CSF result comparison (clearance = 0.5 mm).</p>
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<p>CSAC result comparison (clearance = 0.5 mm).</p>
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<p>CSF result comparison (clearance = 1 mm).</p>
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<p>CSAC result comparison (clearance = 1 mm).</p>
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<p>CSF result comparison (clearance = 1.5 mm).</p>
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<p>CSAC result comparison (clearance = 1.5 mm).</p>
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<p>CSF result comparison (clearance = 2 mm).</p>
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<p>CSAC result comparison (clearance = 2 mm).</p>
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<p>Test FRF comparison results at a different clearance.</p>
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21 pages, 4555 KiB  
Article
Adaptive Simplified Calculation of Algal Bloom Risk Index for Reservoir-Type Drinking Water Sources Based on Improved TOPSIS and Identification of Risk Areas
by Shuyi Ji, Jihong Xia, Yue Wang, Jiayi Zu, Kejun Xu, Zewen Liu, Qihua Wang and Guofu Lin
Water 2025, 17(2), 267; https://doi.org/10.3390/w17020267 - 18 Jan 2025
Viewed by 356
Abstract
As a result of global climate change and human production activities, algal blooms are occurring in aquatic environments. The problem of eutrophication in water bodies is becoming increasingly severe, affecting the safety of drinking water sources. In this study, an algal bloom risk [...] Read more.
As a result of global climate change and human production activities, algal blooms are occurring in aquatic environments. The problem of eutrophication in water bodies is becoming increasingly severe, affecting the safety of drinking water sources. In this study, an algal bloom risk index model combining the Improved Fuzzy Analytic Hierarchy Process (IFAHP), Entropy Weight Method (EWM), and Game Theory (GT) was proposed for the Shanxi Reservoir based on the TOPSIS method. After the seasonal and spatial variability in algal bloom risk from 2022 to 2023 was analyzed, an adaptive simplification of the algal bloom risk index calculation was proposed to optimize the model. To enhance its practical applicability, this study proposed an adaptive simplification of the algal bloom risk index calculation based on an improved TOPSIS approach. The error indexes R2 for the four seasons and the annual analysis were 0.9884, 0.9968, 0.9906, 0.9946, and 0.9972, respectively. Additionally, the RMSE, MAE, and MRE values were all below 0.035, indicating the method’s high accuracy. Using the adaptively simplified risk index, a risk grading and a spatial delineation of risk areas in Shanxi Reservoir were conducted. A comparison with traditional risk classification methods showed that the error in the risk levels did not exceed one grade, demonstrating the effectiveness of the proposed calculation model and risk grading approach. This study provides valuable guidance for the prevention and control of algal blooms in reservoir-type drinking water sources, contributing to the protection of drinking water sources and public health. Full article
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<p>Layout of sampling positions in the Shanxi Reservoir.</p>
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<p>Construction of the model used to calculate the algal bloom risk index.</p>
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<p>Seasonal variations in <span class="html-italic">RI-S</span> in the Shanxi Reservoir.</p>
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<p>Spatial variations in <span class="html-italic">RI-S</span> in the Shanxi Reservoir.</p>
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<p>Comparison of the initial algal bloom risk index (<span class="html-italic">RI-I</span>) and the adaptive simplified algal bloom risk index (<span class="html-italic">RI-S</span>). (<b>a</b>) Spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter.</p>
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<p>Error index of the adaptive simplified method.</p>
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<p>Distribution of algal bloom risk areas in the Shanxi Reservoir by risk level.</p>
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25 pages, 1811 KiB  
Article
Symmetric and Asymmetric Expansion of the Weibull Distribution: Features and Applications to Complete, Upper Record, and Type-II Right-Censored Data
by Mahmoud El-Morshedy, M. El-Dawoody and Adel A. El-Faheem
Symmetry 2025, 17(1), 131; https://doi.org/10.3390/sym17010131 - 17 Jan 2025
Viewed by 457
Abstract
This paper introduces a new continuous lifetime model called the Odd Flexible Weibull-Weibull (OFW-W) distribution, which features three parameters. The new model is capable of modeling both symmetric and asymmetric datasets, regardless of whether they are positively or negatively skewed. Its hazard rate [...] Read more.
This paper introduces a new continuous lifetime model called the Odd Flexible Weibull-Weibull (OFW-W) distribution, which features three parameters. The new model is capable of modeling both symmetric and asymmetric datasets, regardless of whether they are positively or negatively skewed. Its hazard rate functions can exhibit various behaviors, including increasing, decreasing, unimodal, or bathtub-shaped. The key characteristics of the OFW-W model are discussed, including the quantile function, median, reliability and hazard rate functions, kurtosis and skewness, mean waiting (residual) lifetimes, moments, and entropies. The unknown parameters of the model are estimated using eight different techniques. A comprehensive simulation study evaluates the performance of these estimators based on bias, mean squared error (MSE), and mean relative error (MRE). The practical usefulness of the OFW-W distribution is demonstrated through four real datasets from the fields of engineering and medicine, including complete data, upper record data, and type-II right-censored data. Comparisons with five other lifetime distributions reveal that the OFW-W model exhibits superior flexibility and capability in fitting various data types, highlighting its advantages and improvements. In conclusion, we anticipate that the OFW-W model will prove valuable in various applications, including human health, environmental studies, reliability theory, actuarial science, and medical sciences, among others. Full article
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<p>The graphs of the HRF and PDF for the OFW-W model.</p>
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<p>Graphs of the <math display="inline"><semantics> <msub> <mi>S</mi> <mi>k</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mi>u</mi> </msub> </semantics></math> for the OFW-W distribution.</p>
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<p>Graphs of the <math display="inline"><semantics> <msub> <mi>S</mi> <mi>k</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mi>u</mi> </msub> </semantics></math> for the OFW-W distribution.</p>
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<p>The mean and standard deviation for the ranking of the various estimation methods.</p>
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<p>The QQ, box, TTT, KD, histogram, and the Violin plots of data I.</p>
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<p>The appreciated PDF, PP, CDF, and SF plots of data I.</p>
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<p>The contour and profile of log-likelihood functions plots for <math display="inline"><semantics> <mi>α</mi> </semantics></math>, <math display="inline"><semantics> <mi>β</mi> </semantics></math>, and <math display="inline"><semantics> <mi>γ</mi> </semantics></math> of dataset I.</p>
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<p>The appreciated PDFs, CDFs, and SFs of data I.</p>
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<p>The QQ, box, TTT, KD, histogram, and the Violin plots of data II.</p>
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<p>The appreciated PDF, PP, CDF, and SF plots of data II.</p>
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<p>The contour and profile of log-likelihood functions plots for <math display="inline"><semantics> <mi>α</mi> </semantics></math>, <math display="inline"><semantics> <mi>β</mi> </semantics></math>, and <math display="inline"><semantics> <mi>γ</mi> </semantics></math> of dataset II.</p>
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<p>The appreciated CDFs, PDFs, and SFs for data II.</p>
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23 pages, 20381 KiB  
Article
In and out of Replication Stress: PCNA/RPA1-Based Dynamics of Fork Stalling and Restart in the Same Cell
by Teodora Dyankova-Danovska, Sonya Uzunova, Georgi Danovski, Rumen Stamatov, Petar-Bogomil Kanev, Aleksandar Atemin, Aneliya Ivanova, Radoslav Aleksandrov and Stoyno Stoynov
Int. J. Mol. Sci. 2025, 26(2), 667; https://doi.org/10.3390/ijms26020667 - 14 Jan 2025
Viewed by 530
Abstract
Replication forks encounter various impediments, which induce fork stalling and threaten genome stability, yet the precise dynamics of fork stalling and restart at the single-cell level remain elusive. Herein, we devise a live-cell microscopy-based approach to follow hydroxyurea-induced fork stalling and subsequent restart [...] Read more.
Replication forks encounter various impediments, which induce fork stalling and threaten genome stability, yet the precise dynamics of fork stalling and restart at the single-cell level remain elusive. Herein, we devise a live-cell microscopy-based approach to follow hydroxyurea-induced fork stalling and subsequent restart at 30 s resolution. We measure two distinct processes during fork stalling. One is rapid PCNA removal, which reflects the drop in DNA synthesis. The other is gradual RPA1 accumulation up to 2400 nt of ssDNA per fork despite an active intra-S checkpoint. Restoring the nucleotide pool enables a prompt restart without post-replicative ssDNA and a smooth cell cycle progression. ATR, but not ATM inhibition, accelerates hydroxyurea-induced RPA1 accumulation nine-fold, leading to RPA1 exhaustion within 20 min. Fork restart under ATR inhibition led to the persistence of ~600 nt ssDNA per fork after S-phase, which reached 2500 nt under ATR/ATM co-inhibition, with both scenarios leading to mitotic catastrophe. MRE11 inhibition had no effect on PCNA/RPA1 dynamics regardless of ATR activity. E3 ligase RAD18 was recruited at stalled replication forks in parallel to PCNA removal. Our results shed light on fork dynamics during nucleotide depletion and provide a valuable tool for interrogating the effects of replication stress-inducing anti-cancer agents. Full article
(This article belongs to the Special Issue DNA Damage and DNA Repair Pathways in Cancer Development)
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<p>Nuclear distribution of PCNA and RPA1 during hydroxyurea-induced replication stress. (<b>A</b>) Comparison of the expression level of BAC-tagged PCNA-mCherry and RPA1-EGFP versus their endogenous counterparts via Western blotting. (<b>B</b>) Representative timelapse airyscan images of RPA1-EGFP and PCNA-mCherry before and during HU-induced replication stress. Scale bar = 5 µm. (<b>C</b>) Same as (<b>B</b>), but in the presence of ATR inhibitor AZD6738 before and during HU treatment. Scale bar = 5 µm. Abbreviations: HU: hydroxyurea; AZD: AZD6738.</p>
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<p>Dynamics of RPA1 and PCNA during HU-induced replication fork stalling and restart. (<b>A</b>) Representative time-lapse images of RPA1 and PCNA before, during, and after HU treatment. Arrows indicate timepoints of HU addition and washout. Scale bar = 5 µm. (<b>B</b>) Same as (<b>A</b>), but with inhibition of ATR (3 µM AZD6738) throughout the experimental period. (<b>C</b>) Normalized intensity of PCNA and RPA1 at replication foci during HU-induced replication fork stalling and restart, with or without ATR inhibition. The maximum intensity of PCNA/RPA at replication foci is normalized to 1. (<b>D</b>) Fraction of PCNA and RPA1 bound at replication foci during HU-induced replication fork stalling and restart, with or without ATR inhibition, relative to the total nuclear intensity of PCNA/RPA1, which is normalized to 1. (<b>E</b>) Estimated number of PCNA homotrimer and RPA heterotrimer complexes engaged at replication foci with or without ATR inhibition. (<b>F</b>) Estimated number of nucleotides covered by RPA heterotrimers with or without ATR inhibition. For HU only: n = 17 cells; for HU + AZD: n = 10 cells. Abbreviations: HU: hydroxyurea; AZD: AZD6738.</p>
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<p>Detailed overview for the measurement and quantification of PCNA and RPA1 engaged at replication foci during fork stalling and restart. (<b>A</b>) Regions of interest applied for analysis. ‘A’ represents the cell nucleus (white lining), ‘B’ represents a region within the nucleus without replication foci (blue ellipse), and ‘C’ represents noise outside of cells (orange square). (<b>B</b>) Constants and formulas for the calculation of variables used for quantifying RPA1 and PCNA kinetics. (<b>C</b>) Mean intensity of diffusing RPA1-EGFP/mPCNA-mCherry within region ‘B’, calculated as shown in the formula for D<sub>t</sub>. (<b>D</b>) Mean intensity of RPA1-EGFP/mPCNA-mCherry bound at replication foci, calculated as per the formula for E<sub>t</sub> (2). (<b>E</b>) Same as (<b>D</b>), but with the maximum intensity normalized to 1, calculated as per the formula for nE<sub>t</sub> (3). (<b>F</b>) Fraction of the RPA1-EGFP/mPCNA-mCherry bound at replication foci when the total cellular pool of RPA1/PCNA is normalized to 1, as per formula (4). (<b>G</b>) Estimated number of RPA and PCNA complexes engaged at a single replication fork, calculated as per the equation for G<sub>t</sub> (5). (<b>H</b>) Average length of ssDNA (nt) covered by RPA at a replication fork, calculated as per the formula for H<sub>t</sub> (6). (<b>I</b>) Representative timelapse images of single RPA1/PCNA foci tracking. After a focus is tracked, a square region (orange square, K) surrounding is cropped, and a kymogram is created (below). The background noise is measured in a region indicated by the blue circle, L. (<b>J</b>) Formula (7) for calculating the mean intensity of the RPA1-EGFP/mPCNA-mCherry signal in a single focus (Q<sub>t</sub>) normalized to 1. (<b>K</b>) Normalized intensity of the single tracked focus (nM<sub>t</sub>) from (<b>I</b>).</p>
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<p>Exchange of PCNA and RPA1 at replication foci during unperturbed and stalled replication. (<b>A</b>) Representative timelapse images depicting simultaneous FRAP of RPA1 and residual PCNA co-localized at stalled replication forks under nucleotide depletion. (<b>B</b>) Same as (<b>A</b>), but under conditions of ATR inhibition. (<b>C</b>) Replication timelapse images of PCNA FRAP at replication foci during unperturbed replication (upper panel) or in the presence of ATR inhibitor AZD6738. (<b>D</b>) FRAP curves of PCNA under the following conditions: untreated, HU alone, AZD alone, HU + AZD. The mean intensity is normalized as described in <a href="#app1-ijms-26-00667" class="html-app">Figure S5</a>. (<b>E</b>) Contribution of distinct PCNA fractions (freely diffusing [F1] and replisome-bound [F2]) to the FRAP curve under nucleotide depletion, as determined via fitting of two single exponential curves. (<b>F</b>) Same as (<b>E</b>), but in the presence of both AZD6738 and HU. (<b>G</b>) Recovery of the replisome-bound fraction of PCNA under HU treatment, as derived based on (<b>E</b>). (<b>H</b>) Recovery of the replisome-bound fraction of PCNA under HU treatment, as derived based on (<b>F</b>). (<b>I</b>) Comparison of PCNA recovery under the following conditions: untreated, AZD alone, HU alone, HU + AZD. The contribution of freely diffusing PCNA has been subtracted from the HU and HU + AZD curves, as per (<b>G</b>,<b>H</b>). (<b>J</b>) Number of PCNA complexes at a single replication fork, recovered after photobleaching. (<b>K</b>) Enlarged view of HU and HU + AZD curves from (<b>J</b>). (<b>L</b>) FRAP curves of RPA1 at replication foci under HU alone and HU + AZD. (<b>M</b>) Number of RPA complexes at a single replication fork recovered after photobleaching under HU alone and HU + AZD. For HU: n = 11 cells; for HU + AZD: n = 16 cells; for AZD (PCNA only): n = 13 cells; untreated (PCNA only): n = 15 cells. Abbreviations: HU: hydroxyurea; AZD: AZD6738.</p>
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<p>Influence of ATM activity on RPA1 and PCNA dynamics during HU-induced replication fork stalling and restart under conditions of ATR inhibition. (<b>A</b>) Representative time-lapse images of RPA1 and PCNA before, during, and after 10 mM HU treatment under combined ATM (10 µM Ku55933) and ATR (3 µM AZD6738) inhibition. Arrows indicate timepoints of HU addition and washout. Scale bar = 5 µm. (<b>B</b>) Normalized intensity of PCNA and RPA1 at replication foci during HU-induced replication fork stalling and restart under ATM inhibition with or without ATR co-inhibition. The maximum intensity of PCNA/RPA1 engaged at replication foci is normalized to 1. (<b>C</b>) Fraction of PCNA and RPA1 bound at replication foci during HU-induced replication fork stalling and restart under ATM inhibition with or without ATR inhibition, relative to the total nuclear intensity of PCNA/RPA1, which is normalized to 1. (<b>D</b>) Normalized intensity of PCNA and RPA1 at replication foci during HU-induced replication fork stalling and restart under ATR inhibition with or without ATM co-inhibition. The maximum intensity of PCNA/RPA1 engaged at replication foci is normalized to 1. (<b>E</b>) Fraction of PCNA and RPA1 bound at replication factories during HU-induced replication fork stalling and restart under ATR inhibition with or without ATM inhibition, relative to the total nuclear intensity of PCNA/RPA1, which is normalized to 1. (<b>F</b>) Same as (<b>B</b>,<b>D</b>), but with or without combined ATR + ATM inhibition. (<b>G</b>) Same as (<b>C</b>,<b>E</b>), but with or without combined ATR + ATM inhibition. (<b>H</b>) Estimated number of PCNA homotrimer and RPA heterotrimer complexes engaged at replication foci during HU-induced fork stalling and subsequent restart. (<b>I</b>) Estimated number of nucleotides covered by RPA heterotrimers under combined ATM and ATR inhibition. Dashed orange lines indicate the timepoints of HU addition and wash-out. For HU + AZD + KU, n = 10 cells; for HU + KU: n = 19 cells. Abbreviations: HU: hydroxyurea; AZD: AZD6738; KU: Ku55933.</p>
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<p>Dynamics of PCNA and RPA1 during hydroxyurea-induced replication stress in HeLa, DU145, and PC3 cell lines. (<b>A</b>) Normalized intensity of PCNA and RPA1 during, before, and after HU treatment. (<b>B</b>) Same as (<b>A</b>), but under conditions of ATM inhibition (10 µM Ku55933). (<b>C</b>) Same as (<b>A</b>), but under conditions of ATR inhibition (3 µM AZD6738). (<b>D</b>) Same as (<b>A</b>), but under combined ATR and ATM inhibition. Dashed green lines indicate the timepoints of HU addition and washout. Data are presented as the mean ± SD. For HeLa: n = 11 cells (HU), n = 16 cells (HU + AZD), n = 19 cells (HU + KU), n = 10 cells (HU + AZD + KU); for DU145: n = 11 cells (HU), n = 10 cells (HU + AZD), n = 10 cells (HU + KU), n = 18 cells (HU + AZD + KU); for PC3: n = 18 cells (HU), n = 10 cells (HU + AZD), n = 13 cells (HU + KU), n = 15 cells (HU + AZD + KU). Abbreviations: HU: hydroxyurea; AZD: AZD6738; KU: Ku55933.</p>
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<p>Dynamics of RAD18 during hydroxyurea-induced replication fork stalling and restart. (<b>A</b>) Representative time-lapse images of RAD18 and PCNA before, during, and after HU treatment. Arrows indicate timepoints of HU addition and washout. Scale bar = 5 µm. (<b>B</b>) Normalized intensity of RAD18 and PCNA during HU-induced replication fork stalling and restart, with or without ATR inhibition (3 µM AZD6738). The maximum intensity of PCNA/RAD18 foci is normalized to 1. For HU: n = 17 cells; for HU + AZD: n = 12 cells. (<b>C</b>) Normalized intensity of single RAD18 and PCNA foci before and after HU addition (max intensity = 1). Single foci were tracked using SPARTACUSS, and a representative kymogram is shown. This panel presents a case where RAD18 accumulates at a replication focus, while PCNA dissociates after HU treatment. (<b>D</b>) Same as (<b>C</b>), but under conditions of ATR inhibition. (<b>E</b>) Same as (<b>C</b>), but this panel presents a case where RAD18 is already present at the replication focus and dissociates from the replication focus in parallel to PCNA upon HU addition. (<b>F</b>) Same scenario as shown in (<b>E</b>), but under conditions of ATR inhibition. Dashed green lines indicate timepoints of HU addition and removal. Abbreviations: HU: hydroxyurea; AZD: AZD6738.</p>
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<p>Visual summary of PCNA and RPA dynamics during replication fork stalling and restart.</p>
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21 pages, 816 KiB  
Article
An Integrated Stacking Ensemble Model for Natural Gas Purchase Prediction Incorporating Multiple Features
by Junjie Wang, Lei Jiang, Le Zhang, Yaqi Liu, Qihong Yu and Yuheng Bu
Appl. Sci. 2025, 15(2), 778; https://doi.org/10.3390/app15020778 - 14 Jan 2025
Viewed by 448
Abstract
Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in [...] Read more.
Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models—Informer, multiple linear regression (MLR), and support vector regression (SVR)—for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including R2, MRE, and RMSE. Notably, the R2 values improved by 4–15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model’s predictive strength in the energy sector. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)
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<p>Data visualization.</p>
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<p>Correlation matrix of regional features, basic features, and gas purchase volume.</p>
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<p>Correlation matrix of usage types, basic features, and gas purchase volume.</p>
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<p>Stacking ensemble model architecture.</p>
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<p>Comparison of actual and predicted gas purchase volumes using the INF model with different feature combinations. (<b>a</b>) INF; (<b>b</b>) INF-RB; (<b>c</b>) INF-UB; (<b>d</b>) INF-RB-UB.</p>
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<p>Comparison of actual and predicted gas purchase volumes using the MLR model with different feature combinations. (<b>a</b>) MLR; (<b>b</b>) MLR-RB; (<b>c</b>) MLR-UB; (<b>d</b>) MLR-RB-UB.</p>
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<p>Comparison of actual and predicted gas purchase volumes using the SVR model with different feature combinations. (<b>a</b>) SVR; (<b>b</b>) SVR-RB; (<b>c</b>) SVR-UB; and (<b>d</b>) SVR-RB-UB.</p>
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<p>Comparison of actual and predicted gas purchase volumes using stacking.</p>
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27 pages, 13997 KiB  
Article
Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach
by Rui-Zi Dong, Yin Fan, Jiapeng Bian and Zhili Chen
Polymers 2025, 17(2), 180; https://doi.org/10.3390/polym17020180 - 13 Jan 2025
Viewed by 604
Abstract
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes [...] Read more.
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters. Original lightning strike images, preprocessed with the Sobel operator for edge enhancement, are fed into a UNet neural network using four channels to detect damaged areas. These identified areas, along with lightning parameters and layup details, are inputs for a neural network predicting the damage depth in CFRP laminates. Due to its close relation to residual strength, damage depth is then used to estimate the residual strength of lightning-damaged CFRP laminates. The effectiveness of the current method is confirmed, with the mean Intersection over Union (mIoU) achieving over 93% for damage identification, the Mean Absolute Error (MAE) reducing to 5.4% for damage depth prediction, and the Mean Relative Error (MRE) reducing to 7.6% for residual strength prediction, respectively. Full article
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<p>Flowchart of the overall methodology.</p>
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<p>Schematic diagram of a CFRP sample used in the lightning strike experiment.</p>
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<p>Comparative images of lightning damage samples: (<b>a</b>) original image, (<b>b</b>) cropping, (<b>c</b>) adding Noise, (<b>d</b>) mirroring, (<b>e</b>) translation, (<b>f</b>) rotation, (<b>g</b>) changing brightness, and (<b>h</b>) mosaic augmentation (the colored rectangular blocks are randomly generated mosaic occlusions designed to enhance the complexity of images).</p>
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<p>Sobel operator preprocessing of an image of a lightning strike-damaged CFRP laminate: (<b>a</b>) original image, (<b>b</b>) horizontal gradient image, (<b>c</b>) vertical gradient image, and (<b>d</b>) combined gradient image.</p>
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<p>Original, preprocessed, and damage-identified images. (<b>a</b>) original images of typical lightning strike damage, (<b>b</b>) images after Sobel operator preprocessing, and (<b>c</b>) images of damage areas identified by the neural network.</p>
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<p>Schematic diagram of C-scan test and mapping images of damages for various injected peak currents. (<b>a</b>) The specific principle of conducting a C-scan on CFRP laminates after lightning strike damage and (<b>b</b>) Partial results of C-scan images for lightning struck CFRP laminates under different I<sub>peak</sub> conditions, the transition from red to blue represents the percentage of ultrasound reflections that can be detected.</p>
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<p>The structure of the UNet neural network for identifying lightning strike damage areas on CFRP laminates.</p>
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<p>The structure of FCNN for predicting the damage depth in CFRP laminates.</p>
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<p>The structure of the FCNN for predicting the residual strength of lightning strike damage in CFRP laminates.</p>
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<p>The relationship between the damage area and peak current under the same conditions. (<b>a</b>) Thickness = 2.39 mm, (<b>b</b>) thickness = 4.64 mm, and (<b>c</b>) thickness = 6.13 mm.</p>
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<p>Relationship between the compressive residual strength of CFRP laminates and damage characterization parameters (damage depth and area) under waveform A: (<b>a</b>) linear relationships between compressive residual strength and damage area, (<b>b</b>) linear relationships between compressive residual strength and damage depth.</p>
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<p>CE loss curve of identifying lightning strike damage areas on CFRP laminates.</p>
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<p>Predicted values and true values for test sets using the model predicting the damage depth in CFRP laminates. (<b>a</b>) Bar chart of the true and predicted damage depth for 10 data groups, (<b>b</b>) Fitting line between true and predicted damage depth.</p>
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<p>Predicted values and true values for test sets using the model predicting the residual strength of lightning strike damage in CFRP laminates. (<b>a</b>) Bar chart of the true residual strength and predicted residual strength for 10 data groups. (<b>b</b>) Fitting line between true residual strength and predicted residual strength.</p>
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<p>Comparison of mIoU for identifying lightning strike damage areas on CFRP laminates before and after data augmentation.</p>
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<p>Comparison of mIoU for identifying lightning strike damage areas on CFRP laminates before and after Sobel pretreatment.</p>
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<p>Results of the predictive model for the residual strength after lightning strike damage in CFRP laminates, before and after data augmentation. (<b>a</b>) MSE curve results of the predictive model and (<b>b</b>) R<sup>2</sup> curve results of the predictive model.</p>
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<p>Comparison of the model results predicting the residual strength of CFRP materials after lightning strike damage, with and without considering damage depth as an input parameter. (<b>a</b>) MSE curve results of the predictive model and (<b>b</b>) R<sup>2</sup> curve results of the predictive model.</p>
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