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17 pages, 2166 KiB  
Article
Immunogenic Cell Death Traits Emitted from Chronic Lymphocytic Leukemia Cells Following Treatment with a Novel Anti-Cancer Agent, SpiD3
by Elizabeth Schmitz, Abigail Ridout, Audrey L. Smith, Alexandria P. Eiken, Sydney A. Skupa, Erin M. Drengler, Sarbjit Singh, Sandeep Rana, Amarnath Natarajan and Dalia El-Gamal
Biomedicines 2024, 12(12), 2857; https://doi.org/10.3390/biomedicines12122857 (registering DOI) - 16 Dec 2024
Abstract
Background: Targeted therapies (e.g., ibrutinib) have markedly improved chronic lymphocytic leukemia (CLL) management; however, ~20% of patients experience disease relapse, suggesting the inadequate depth and durability of these front-line strategies. Moreover, immunotherapeutic success in CLL has been stifled by its pro-tumor microenvironment milieu [...] Read more.
Background: Targeted therapies (e.g., ibrutinib) have markedly improved chronic lymphocytic leukemia (CLL) management; however, ~20% of patients experience disease relapse, suggesting the inadequate depth and durability of these front-line strategies. Moreover, immunotherapeutic success in CLL has been stifled by its pro-tumor microenvironment milieu and low mutational burden, cultivating poor antigenicity and limited ability to generate anti-tumor immunity through adaptive immune cell engagement. Previously, we have demonstrated how a three-carbon-linker spirocyclic dimer (SpiD3) promotes futile activation of the unfolded protein response (UPR) in CLL cells through immense misfolded-protein mimicry, culminating in insurmountable ER stress and programmed CLL cell death. Method: Herein, we used flow cytometry and cell-based assays to capture the kinetics and magnitude of SpiD3-induced damage-associated molecular patterns (DAMPs) in CLL cell lines and primary samples. Result: SpiD3 treatment, in vitro and in vivo, demonstrated the capacity to propagate immunogenic cell death through emissions of classically immunogenic DAMPs (CALR, ATP, HMGB1) and establish a chemotactic gradient for bone marrow-derived dendritic cells. Conclusions: Thus, this study supports future investigation into the relationship between novel therapeutics, manners of cancer cell death, and their contributions to adaptive immune cell engagement as a means for improving anti-cancer therapy in CLL. Full article
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Figure 1
<p>CLL cells display ecto-CALR following SpiD3 treatment. HG-3 ((<b>A</b>,<b>B</b>); n = 3); OSU-CLL ((<b>C</b>,<b>D</b>); n = 3); or patient-derived CLL ((<b>E</b>,<b>F</b>); n = 5) cells were treated with vehicle (Veh), SpiD3 (0.25–2 µM), FeCl<sub>2</sub> (160 μM), or the positive control, etoposide (Etop; 20 µM) for the indicated durations. Viable cells were analyzed by flow cytometry for changes in surface CALR expression (ecto-CALR). Primary patient-derived CLL cells were additionally designated as CD19+/CD5+ by flow cytometry. Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>SpiD3 treatment evokes extracellular ATP release. HG-3 (<b>A</b>); and OSU-CLL (<b>B</b>) cells were treated over 24 h (n = 3) with vehicle (Veh), SpiD3 (0.5–2 µM), or the positive control, etoposide (Etop; 20 µM). Extracellular ATP measurements at 8, 16, and 24 h were parsed out to evaluate the average extracellular ATP measured at these timepoints in comparison to the matched timepoint vehicle. Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the matched timepoint average vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>SpiD3-treated cells release extracellular HMGB1. Supernatant from HG-3 ((<b>A</b>,<b>B</b>); n = 3); OSU-CLL ((<b>C</b>,<b>D</b>); n = 3); and primary CLL ((<b>E</b>); n = 10) cells were evaluated for extracellular HMGB1 after 24 h or 48 h of treatment with the vehicle (Veh), SpiD3 (0.5–2 µM), ibrutinib (1 µM), or positive control, etoposide (Etop; 20 µM). Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Chemotactic potential of SpiD3-treated cell supernatants. Bone marrow dendritic cells (BMDCs) were allowed to migrate for 6 h toward supernatant collected from HG-3 (<b>A</b>); and OSU-CLL (<b>B</b>) cells after 24 h treatment with the vehicle (Veh), SpiD3 (0.5–2 µM), or the positive control, etoposide (Etop; 20 µM). GM-CSF (20 ng/mL) stimulated media, and supernatant derived from heat-shocked CLL cells (HS) served as positive chemotactic controls. The number of migrated BMDCs were counted via flow cytometry analysis (n = 3). The chemotactic index is a comparison of the migrated events observed from treatment conditions to that of the vehicle condition. Data are represented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">In vivo</span> SpiD3 treatment yields an immunostimulatory response. (<b>A</b>) Schematic of experiment design: Eµ-TCL1 mice with comparable leukemia burden were treated intravenously with SpiD3 prodrug (SpiD3_AP, 10 mg/kg; n = 6) or equivalent vehicle (Veh; 50% PEG400, 10% DMSO, 40% water; n = 5) once daily for 3 days, as previously reported [<a href="#B20-biomedicines-12-02857" class="html-bibr">20</a>]. Following treatment, spleen cells were collected for flow cytometry analysis and plasma was isolated from murine blood; (<b>B</b>) leukemic (CD19+/CD5+) cells from murine spleens were analyzed by flow cytometry for changes in surface CALR expression (ecto-CALR) and compared to the percentage of leukemic cells detected in spleens of the same mice (as reported in Eiken, et al. [<a href="#B20-biomedicines-12-02857" class="html-bibr">20</a>]). The concentrations of plasma inflammatory cytokines and chemokines were assessed using Mouse Anti-Virus Response (<b>C</b>,<b>E</b>); and Mouse Pro-Inflammatory Chemokine (<b>D</b>,<b>F</b>) LEGENDplex™ flow cytometry-based multiplex immunoassays. (<b>C</b>,<b>D</b>) Heatmaps display fold change in the plasma analyte concentration compared to the average of vehicle-treated mice. Columns represent individual mice per treatment group. (<b>E</b>,<b>F</b>) Raw plasma analyte concentration and correlation with the percentage of CD19+/CD5+ spleen-derived cells are shown for select analytes. Individual data points (Veh = black circles; SpiD3_AP = blue triangles) in addition to summary statistics (mean ± SEM) are shown. Comparisons between treatment groups were analyzed by unpaired <span class="html-italic">t</span>-test. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Illustrative summary of SpiD3 anti-leukemic activity. CLL cell cytotoxicity via SpiD3 is demonstrated by: (i) inhibition of NF-κB signaling; and (ii) accumulation of unfolded proteins, promoting ER stress, activating a futile UPR and, subsequently, the associated programmed cell death pathways. ER stress is a proposed prerequisite for immunogenic DAMP emissions; we hypothesize it is this facet of SpiD3-associated effects that result in detectable hallmarks of immunogenic cell death from CLL cells. This diagram is adapted from Eiken, et al. CLL, chronic lymphocytic leukemia; DC, dendritic cell; iDAMP, immunogenic damage-associated molecular pattern; ER, endoplasmic reticulum; UPR, unfolded protein response.</p>
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10 pages, 229 KiB  
Article
Objective Voice Analysis in Partial Deafness: Comparison of Multi-Dimensional Voice Program (MDVP) and VOXplot Results
by Karol Myszel
J. Clin. Med. 2024, 13(24), 7631; https://doi.org/10.3390/jcm13247631 (registering DOI) - 14 Dec 2024
Viewed by 333
Abstract
Acoustic analysis of voice enables objective assessment of voice to diagnose changes in voice characteristics, and track the progress of therapy. In contrast to subjective assessment, objective measurements provide mathematical results referring to specific parameters and can be analyzed statistically. Changes in the [...] Read more.
Acoustic analysis of voice enables objective assessment of voice to diagnose changes in voice characteristics, and track the progress of therapy. In contrast to subjective assessment, objective measurements provide mathematical results referring to specific parameters and can be analyzed statistically. Changes in the voice of patients with partial deafness (PD) were not widely described in the literature, and recent studies referred to the voice parameters measured in this group of patients only using the multi-dimensional voice program (MDVP) by Kay Pentax. This paper describes the results of acoustic analysis of voice in patients with PD using VOXplot, and compares the results with those achieved with MDVP. Background/Objectives: The purpose of this study was a VOXplot objective analysis of voice in individuals with PD and to assess consistency with results obtained using MDVP and with perceptual assessment. Methods: Voice samples from 22 post-lingual PD individuals were recorded. They included continuous speech (cs) and sustained vowels (sv). The control group consisted of 22 healthy individuals with no history of voice or hearing dysfunction. The samples were analyzed with MDVP followed by VOXplot version 2.0.0 Beta. Statistical analysis was performed using a t-test paired with two samples for means. All individuals were also subjected to a perceptual voice assessment using the GRBAS by Hirano. Results: Differences were observed in 13 VOXplot parameters measured in voice samples of adults with PD compared with those in the control group. Both multiparametric indices, AVQI and ABI, showed a statistical increase. When it comes to MDVP parameters correlating with breathiness, all of them (shim dB, APQ, NHR, SPI, and NSH) increased in patients with partial deafness, reflecting a breathy voice. Only one increase in the SPI was not statistically significant. Seven MDVP parameters correlating with hoarseness were elevated, and five (Jitt%, vF0, Shim dB, APQ, and NHR) showed a statistically significant increase. Correlations were found of VOXplot and MDVP parameters with perceptual voice assessment. Conclusions: Both programs for objective assessment showed voice abnormalities in patients with PD compared with the control groups. There was a poor to moderate level of consistency in the results achieved using both systems. Correlations were also found with GRBAS assessment results. Full article
(This article belongs to the Section Otolaryngology)
28 pages, 10117 KiB  
Article
The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors
by Fernando Maliti Chivangulula, Malik Amraoui and Mário Gonzalez Pereira
Climate 2024, 12(12), 221; https://doi.org/10.3390/cli12120221 - 13 Dec 2024
Viewed by 431
Abstract
Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based [...] Read more.
Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based on vegetation and meteorological indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971–2020 period. The results revealed the following: (i) droughts of various classes at different timescales occurred throughout the study period and region; (ii) a greater Sum of Drought Intensity and Number, in all classes, but lower duration and severity of droughts with the SPI than with the SPEI; (iii) drought frequency varies from 1.3 droughts/decade to 4.5 droughts/decade, for the SPI at 12- to 3-month timescales; (iv) the number, duration, severity and intensity of drought present high spatial variability, which tends to decrease with the increasing timescale; (v) the area affected by drought increased, on average, 6.6%/decade with the SPI and 9.1%/decade with the SPEI; and (vi) a high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers and managers in defining legislation and strategies to manage drought and water resources. Full article
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<p>Political map, ecoregions (<b>a</b>), and Köppen–Geiger climate type (<b>b</b>) in Southern Africa. Adapted from Olson et al. [<a href="#B28-climate-12-00221" class="html-bibr">28</a>] and Kottek et al. [<a href="#B24-climate-12-00221" class="html-bibr">24</a>], with the following: equatorial monsoon (Am); equatorial savannah with dry summer (As); equatorial savannah with dry winter (Aw), arid, steppe with hot arid (BSh); arid, steppe with cold arid (BSk); arid, desert with hot arid (BWh); arid, desert with cold arid (BWk); warm temperate, fully humid with hot summer (Cfa); warm temperate, fully humid with warm summer (Cfb); warm temperate, dry summer with hot summer (Csa); warm temperate, dry summer with warm summer (Csb); warm temperate, dry winter with hot summer (Cwa); and warm temperate, dry winter with warm summer (Cwb).</p>
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<p>Sum of the Drought Number (Sum DN, from (<b>a</b>–<b>d</b>)), Sum of the Drought Duration (Sum DD, panels (<b>e</b>–<b>h</b>)), Drought Severity (Sum DS, panels (<b>i</b>–<b>l</b>)) and Drought Intensity (Sum DI, panels (<b>m</b>–<b>p</b>)), and assessed based on the SPI for the 3-, 6-, 9- and 12-month timescales (from left to right), during the 1971–2020 period.</p>
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<p>Sum of the Drought Number (Sum DN) assessed with the SPI, at the 3-, 6-, 9- and 12-month timescales (panels left to right), during the 1971–2020 period for each Drought Class (DC), namely, abnormally dry (DC 1, panels (<b>a</b>–<b>d</b>)), mild drought (DC 2, panels (<b>e</b>–<b>h</b>)), moderate drought (DC 3, panels (<b>i</b>–<b>l</b>)), severe drought (DC 4, panels (<b>m</b>–<b>p</b>)) and extreme drought (DC 5, panels (<b>q</b>–<b>t</b>)).</p>
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<p>Interannual distribution of the Sum of Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with the SPI at timescales of 3, 6, 9 and 12 months (panels (<b>a</b>–<b>d</b>)), for the 1971–2020 period.</p>
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<p>Spatial distribution of the annual Sum of Drought Months (SDM) for 2018 (panels (<b>a</b>–<b>d</b>) and 2019 (panels (<b>e</b>–<b>h</b>)) and the Mean Drought Severity (MDS) also for 2018 (panels (<b>i</b>–<b>l</b>)) and 2019 (panels <b>m</b>–<b>p</b>)), computed with the SPI at timescales of 3, 6, 9 and 12 months (from left to right).</p>
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<p>Anomalies of the NDVI (panels (<b>a</b>–<b>d</b>)), the EVI (panels (<b>e</b>–<b>h</b>)) and the VCI (panels (<b>i</b>–<b>l</b>)) in Southern Africa during the rainy season, from the December 2018 to February 2019 period.</p>
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<p>Drought severity (DS) for November (panels (<b>a</b>–<b>d</b>)) and December (panels (<b>e</b>–<b>h</b>)) 2018, January (panels (<b>i</b>–<b>l</b>)) and February (panels (<b>m</b>–<b>p</b>)) 2019, computed with the SPI, for the timescales of 3, 6, 9 and 12 months (from left to right).</p>
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<p>The difference between the annual MDE evaluated with the SPEI (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>D</mi> <mi>E</mi> </mrow> <mrow> <mi>S</mi> <mi>P</mi> <mi>E</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>) and the SPI (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>D</mi> <mi>E</mi> </mrow> <mrow> <mi>S</mi> <mi>P</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>) at timescales of 3, 6, 9 and 12 months, in SA for the 1971–2020 period.</p>
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<p>Work and results flow diagram of this study.</p>
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19 pages, 4317 KiB  
Article
Comparison of Interactions Between Soy Protein Isolate and Three Folate Molecules: Effect on the Stabilization, Degradation, and Oxidization of Folates and Protein
by Linlin He, Yuqian Yan, Dandan Song, Shuangfeng Li, Yanna Zhao, Zhuang Ding and Zhengping Wang
Foods 2024, 13(24), 4033; https://doi.org/10.3390/foods13244033 - 13 Dec 2024
Viewed by 438
Abstract
This study selected three approved folate sources—folic acid (FA), L-5-methyltetrahydrofolate (MTFA), and calcium 5-methyltetrahydrofolate (CMTFA)—to explore their interaction mechanisms with soy protein isolate (SPI) through spectrofluorometric analysis and molecular docking simulations. We investigated how these interactions influence the structural and physicochemical stability of [...] Read more.
This study selected three approved folate sources—folic acid (FA), L-5-methyltetrahydrofolate (MTFA), and calcium 5-methyltetrahydrofolate (CMTFA)—to explore their interaction mechanisms with soy protein isolate (SPI) through spectrofluorometric analysis and molecular docking simulations. We investigated how these interactions influence the structural and physicochemical stability of folates and SPI. Three folates spontaneously bound to SPI, forming complexes, resulting in a decrease of approximately 30 kJ·mol−1 in Gibbs free energy and an association constant (Ka) of 105 L·mol−1. The thermodynamic parameters and molecular docking study revealed the unique binding mechanisms of FA and MTFA with SPI. FA’s planar pteridine ring and conjugated double bonds facilitate hydrophobic interactions, whereas MTFA’s reduced ring structure and additional polar groups strengthen hydrogen bonding. Although the formation of SPI–folate complexes did not result in substantial alterations to the SPI structure, their binding has the potential to enhance both the physical and thermal stability of the protein by stabilizing its conformation. Notably, compared with free FA, the FA-SPI complexes significantly enhanced FA’s stability, exhibiting 71.1 ± 1.2% stability under light conditions after 9 days and 63.2 ± 2.6% stability in the dark after 60 days. In contrast, no similar effect was observed for MTFA. This discrepancy can be ascribed to the distinct degradation pathways of the Fa and MTFA molecules. This study offers both theoretical and experimental insights into the development of folate-loaded delivery systems utilizing SPI as a matrix. Full article
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Figure 1
<p>Fluorescence emission spectra of 5 g/L soy protein isolation (SPI) in the presence of folic acid (FA), (<b>A</b>) L-5-methyltetrahydrofolate (MTFA), (<b>B</b>) and MTFA with 25 μM calcium ion (Ca<sup>2+</sup>) (<b>C</b>) at different concentrations ranging from 0 to 25 μM (T = 298.2 K, pH = 7.4, λex = 280 nm). (<b>D</b>–<b>F</b>) Stern–Volmer plots of the interactions between SPI and three folate molecules at 298.2–308.2 K.</p>
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<p>(<b>A</b>) Surface hydrophobicity (H<sub>0</sub>) of soy protein isolation (SPI) and SPI–folate complexes. (<b>B</b>) Linearity curve plotting the relative fluorescence intensity of the ANS probe with concentrations from 0 to 1.5 g/L of the SPI and SPI–folate complexes. FA, MTFA, and CMTFA denote folic acid, L-5-methyltetrahydrofolate, and calcium L-5-methyltetrahydrofolate, respectively.</p>
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<p>(<b>A</b>) Fourier transform infrared (FTIR) spectra and (<b>B</b>) curve-fitted analysis of the amide I region (1600–1700 cm<sup>−1</sup>) for soy protein isolate (SPI) and SPI–folate complexes. The table in (<b>B</b>) summarizes protein secondary structure contents derived from integrating the area under each band in the curve-fitting analysis.</p>
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<p>Binding model diagram of folic acid (FA) and L-5-methyltetrahydrofolate (MTFA) to soy β-conglycinin (7S globulins) (<b>A</b>,<b>C</b>) and glycinin (11S globulins) (<b>B</b>,<b>D</b>). The left and right images show the binding site and the interactions between folates and surrounding amino acid residues, respectively.</p>
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<p>Differential scanning calorimetry (DSC) thermograms of soy protein isolation (SPI) (<b>A</b>) and three SPI–folate complexes (<b>B</b>–<b>D</b>).</p>
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<p>Stability of folic acid (FA) (<b>A</b>,<b>D</b>), L-5-methyltetrahydrofolate (MTFA) (<b>B</b>,<b>E</b>), and calcium L-5-methyltetrahydrofolate (CMTFA) (<b>C</b>,<b>F</b>) in the absence and presence of soy protein isolation (SPI) under light and dark conditions at 25 °C.</p>
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<p>Liquid chromatography chromatograms of the soy protein isolation (SPI)-L-5-methyltetrahydrofolate (MTFA) complexes after 3 days of light (<b>A</b>) and 7 days of dark storage (<b>B</b>) at 25 °C. (<b>C</b>) Degradation products of MTFA during storage and their corresponding <span class="html-italic">m/z</span> values determined via mass spectrometry.</p>
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<p>(<b>A</b>) Variation in sulfhydryl content of SPI and SPI-FA complexes over 9 days under light exposure and 60 days under dark storage conditions. (<b>B</b>) Variation in sulfhydryl content of SPI and SPI-MTFA/CMTFA complexes over 3 days under light exposure and 7 days under dark storage conditions.</p>
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19 pages, 3527 KiB  
Article
Establishment of Sample-to-Answer Loop-Mediated Isothermal Amplification-Based Nucleic Acid Testing Using the Sampling, Processing, Incubation, Detection and Lateral Flow Immunoassay Platforms
by Lilas Pommiès, Hervé Boutal, David Fras and Hervé Volland
Biosensors 2024, 14(12), 609; https://doi.org/10.3390/bios14120609 - 13 Dec 2024
Viewed by 439
Abstract
Diagnostics often require specialized equipment and trained personnel in laboratory settings, creating a growing need for point-of-care tests (POCTs). Among the genetic testing methods available, Loop-mediated Isothermal Amplification (LAMP) offers a viable solution for developing genetic POCT due to its compatibility with simplified [...] Read more.
Diagnostics often require specialized equipment and trained personnel in laboratory settings, creating a growing need for point-of-care tests (POCTs). Among the genetic testing methods available, Loop-mediated Isothermal Amplification (LAMP) offers a viable solution for developing genetic POCT due to its compatibility with simplified devices. This study aimed to create a genetic test that integrates all steps from sample processing to analyzing results while minimizing the complexity, handling, equipment, and time required. Several challenges were addressed to achieve this goal: (1) the development of a buffer for bacterial DNA extraction that is compatible with both LAMP and immunochromatographic tests; (2) the adaption of the LAMP protocol for use with the SPID device; and (3) the optimization of the detection protocol for specific test conditions, with a lateral flow immunoassay format selected for its POCT compatibility. Following these developments, the test was validated using Escherichia coli (E. coli) and non-E. coli strains. A portable heating station was also developed to enable amplification without costly equipment. The resulting genetic POCT achieved 100% sensitivity and 85% specificity, with results available in 60 to 75 min. This study demonstrated that our POCT efficiently performs DNA extraction, amplification, and detection for bacterial identification. The test’s simplicity and cost-effectiveness will support its implementation in various settings. Full article
(This article belongs to the Special Issue Biosensing for Point-of-Care Diagnostics)
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Figure 1
<p>Schematic representation of the test strips. The test strip comprises a sample pad, a nitrocellulose membrane, and an absorption pad. The detection zone uses immobilized anti-digoxigenin antibodies as a test line and anti-mouse antibodies or biotinylated BSA as a control line.</p>
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<p>The SPID (Sampling, Processing, Incubation, Detection) platform elements. The SPID platform is composed of two parts: (i) the sample processing part, which includes a filtration/concentration unit consisting of a syringe adaptor, a cup, and a lower part and an extraction unit, consisting of a cap and a tank (SPID Device); and (ii) the detection part, which consists of a SPID adaptor to connect the cassette to the tank, and a plastic cassette integrating a lateral flow immunochromatographic strip.</p>
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<p>Heating station. The heating station consists of a metal part heated by resistors and adapted to the shape of the tank. The operator has access to the on/off button and a display showing the set temperature, the real-time temperature and the station’s IP address. The IP address can be used to connect to an application to set the desired temperature. All the components are assembled inside a plastic housing.</p>
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<p>Test workflow. A 1 mL bacterial suspension at 10<sup>8</sup> CFU/mL is drawn into a syringe and attached to a filtration device. The sample is then pushed through the filter (<b>a</b>). The filter cup is subsequently placed in a tank (<b>b</b>), and 180 μL of LAMP reaction solution is added (<b>c</b>). After sealing the tank (<b>d</b>), the system is heated to 63 °C for 30 min (<b>e</b>). Once heated, the unit is placed onto a SPID adaptor (<b>f</b>,<b>g</b>), which punctures the operculum, allowing the liquid to flow onto a strip for migration (<b>g</b>). After 5 min, the adapter and reservoir are removed (<b>h</b>), and 100 μL of diluted conjugate is applied to the strip (<b>i</b>). Results are visually interpreted after 15 and 30 min (<b>j</b>).</p>
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<p>Amplification curves of the <span class="html-italic">malB</span> gene using two primer sets. Black curves: amplification using the primer set developed by Hill et al. [<a href="#B41-biosensors-14-00609" class="html-bibr">41</a>]; red curves: amplification using the set primer design of the current study. The solid lines correspond to the amplification using <span class="html-italic">E. coli</span> solution at 10<sup>8</sup> cfu/mL and the dotted lines correspond to the amplification in LB broth. No amplification was observed for the LB broth. For <span class="html-italic">E. coli</span>, amplification began after 10 min of incubation for both primer sets. The amplification curves reached a peak at 20 min and then began to decrease. We observed that the decrease was faster for the primer set used by Hill et al. [<a href="#B41-biosensors-14-00609" class="html-bibr">41</a>].</p>
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<p>Amplification curves for the <span class="html-italic">malB</span> gene. Red curve: amplification of <span class="html-italic">E. coli</span>; blue curve: amplification of <span class="html-italic">C. freundii</span>. No amplification was observed for <span class="html-italic">C. freundii</span>. For <span class="html-italic">E. coli</span>, amplification began after 10 min of incubation. The amplification curve reached a peak at 15 min and then began to decrease.</p>
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<p>Comparison of streptavidine and mAb anti-biotin as a conjugate. After the extraction/filtration and amplification steps, 10 µL of conjugate was added to the LAMP solution in the tank. The tank was reclosed and pressed onto the SPID adaptor positioned on the cassette. After 30 min the results were read: (<b>a</b>) results using streptavidin–colloïdal gold; (<b>b</b>) results using mAb anti-biotin–colloïdal gold.</p>
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<p>Comparison of different methods for the conjugate deposition. (<b>a</b>) One-stage deposition. The tank was opened and 10 µL of either streptavidin or mAb anti-biotin conjugates were added. The tank was reclosed and pressed onto the SPID adaptor positioned on the cassette. (<b>b</b>) Dried conjugate deposition. The conjugate was dried on a Standard 14 membrane which was inserted between the sample pad and the nitrocellulose membrane. The tank was pressed onto the SPID adaptor positioned on the cassette. (<b>c</b>) Two-stage deposition. The tank was pressed onto the SPID adaptor and after 5 min of migration the tank and the SPID adaptor were removed. A volume of 100 µL of diluted conjugate (prepared by mixing 10 µL of conjugate with 90 µL of conjugate buffer) was then applied to the strip. For all these conditions, the results were read after 30 min.</p>
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<p>Comparison of different concentrations of FIP–digoxigenin. In this experiment all the primer mixes contained 1.6 µM of BIP–biotin, 0.2 µM of B3 and F3, and 0.4 µM of LB and LF. Mix 1 contained 1.6 µM of FIP–digoxingenin; Mix 2 contained 0.8 µM of FIP–digoxingenin and 0.8 µM of FIP; Mix 3 contained 0.4 µM of FIP–digoxingenin and 1.2 µM of FIP; and mix 4 contained 0.2 µM of FIP–digoxingenin and 1.4 µM of FIP.</p>
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<p>Evaluation of the limit of detection. Different concentrations of <span class="html-italic">E. coli</span> were tested for 30 min of amplification at 63 °C. A test line was visible for the 10<sup>8</sup> and 10<sup>7</sup> cfu/mL concentrations.</p>
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15 pages, 2588 KiB  
Communication
Quantification of Staphylococcal Enterotoxin A Variants at Low Level in Dairy Products by High-Resolution Top-Down Mass Spectrometry
by Nina Aveilla, Cécile Feraudet-Tarisse, Dominique Marcé, Abdelhak Fatihi, François Fenaille, Jacques-Antoine Hennekinne, Stéphanie Simon, Yacine Nia and François Becher
Toxins 2024, 16(12), 535; https://doi.org/10.3390/toxins16120535 - 11 Dec 2024
Viewed by 361
Abstract
Food poisoning outbreaks frequently involve staphylococcal enterotoxins (SEs). SEs include 33 distinct types and multiple sequence variants per SE type. Various mass spectrometry methods have been reported for the detection of SEs using a conventional bottom-up approach. However, the bottom-up approach cannot differentiate [...] Read more.
Food poisoning outbreaks frequently involve staphylococcal enterotoxins (SEs). SEs include 33 distinct types and multiple sequence variants per SE type. Various mass spectrometry methods have been reported for the detection of SEs using a conventional bottom-up approach. However, the bottom-up approach cannot differentiate between all sequence variants due to partial sequence coverage, and it requires a long trypsin digestion time. While the alternative top-down approach can theoretically identify any sequence modifications, it generally provides lower sensitivity. In this study, we optimized top-down mass spectrometry conditions and incorporated a fully 15N-labeled SEA spiked early in the protocol to achieve sensitivity and repeatability comparable to bottom-up approaches. After robust immunoaffinity purification of the SEA, mass spectrometry signals were acquired on a Q-Orbitrap instrument operated in full-scan mode and targeted acquisition by parallel reaction monitoring (PRM), enabling the identification of sequence variants and precise quantification of SEA. The protocol was evaluated in liquid and solid dairy products and demonstrated detection limits of 0.5 ng/mL or ng/g in PRM and 1 ng/mL or ng/g in full-scan mode for milk and Roquefort cheese. The top-down method was successfully applied to various dairy products, allowing discrimination of contaminated versus non-contaminated food, quantification of SEA level and identification of the variant involved. Full article
(This article belongs to the Section Bacterial Toxins)
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<p>Intact purified <sup>15</sup>N SEA<sub>3</sub> (<b>A</b>) and SEA<sub>3</sub> (<b>B</b>) at 10 µg/mL analyzed by HRMS/MS. Intact <sup>15</sup>N SEA<sub>3</sub> (<b>A1</b>) and SEA<sub>3</sub> (<b>B1</b>) analyzed by full-scan HRMS and corresponding deconvoluted spectra (<b>A2</b>,<b>B2</b>). Spectra were deconvoluted with the software «Protein Deconvolution» version 4.0 from Thermo Scientific. The observed mass matched with a theoretical mass of 28,494.70 Da corresponding to 341 labeled nitrogens, a loss of one histidine in the His-Tag, and the expected disulfide bridge between the two cysteines. Blue star: additional peaks attributed to a dimeric form. HRMS/MS spectra of intact <sup>15</sup>N SEA<sub>3</sub> (<b>C2</b>) and SEA<sub>3</sub> (<b>D2</b>) using HCD and observed retention times (<b>C1</b>,<b>D1</b>).</p>
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<p>Measurement of the SEA<sub>3</sub> to <sup>15</sup>N SEA<sub>3</sub> ratio using different filtration conditions, with and without the 10 kDa ultrafiltration device (UD10kDa). SEA<sub>3</sub> was spiked at 10 ng/mL in milk, n = 3.</p>
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<p>Sample preparation protocol for SEA followed by top-down analysis. Created in BioRender. <a href="https://BioRender.com/q37i192" target="_blank">https://BioRender.com/q37i192</a> accessed on 29 October 2024.</p>
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<p>Isolation of multiple charge states for PRM acquisition. (<b>A</b>) MS spectra of native SEA<sub>3</sub>. (<b>B</b>) Peak area of SEA<sub>3</sub> with 3 to 9 selected charge states using MSX. MSX was limited to 10 charge states with the Orbitrap instrument. (<b>C</b>) Peak area of SEA<sub>3</sub> with 3 to 17 selected charge states using WIW isolation. In <a href="#app1-toxins-16-00535" class="html-app">Supplementary Data Table S3</a>, the different charge states isolated in MSX and WIW are reported. ** MSX3 isolation was previously used [<a href="#B17-toxins-16-00535" class="html-bibr">17</a>] and corresponded to the initial conditions in this work.</p>
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<p>Linearity of the top-down assay for SEA<sub>3</sub> spiked in milk and analyzed in PRM mode using the WIW9 isolation (<b>A</b>) or full-scan mode (<b>B</b>) (n = 4).</p>
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<p>Linearity of SEA<sub>3</sub> in Roquefort analyzed in PRM mode using the WIW9 isolation (<b>A</b>) or full-scan mode (<b>B</b>) (n = 2).</p>
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21 pages, 2159 KiB  
Article
Multi-Secular Trend of Drought Indices in Padua, Italy
by Francesca Becherini, Claudio Stefanini, Antonio della Valle, Francesco Rech, Fabio Zecchini and Dario Camuffo
Climate 2024, 12(12), 218; https://doi.org/10.3390/cli12120218 - 10 Dec 2024
Viewed by 375
Abstract
The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). [...] Read more.
The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified. Full article
(This article belongs to the Special Issue Climate Variability in the Mediterranean Region)
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<p>Map of Italy indicating the location of Padua inside the Veneto Region.</p>
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<p>Flowchart of the methodology used to calculate the drought indices.</p>
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<p>Building procedure for the final precipitation series from 1951 to 2023 [<a href="#B26-climate-12-00218" class="html-bibr">26</a>].</p>
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<p>(<b>a</b>) Mean bias error (MBE) between the SPEI values estimated with standard (SA), “general”(BFAg) and “monthly” (BFAm) best-fit approaches for each accumulation period. For SPEI12, BFAg = BFAm. (<b>b</b>) Relative error (RE) of the number of classes detected by SPI and SPEI estimated with SA and the average between BFAg and BFAm. SPI6 is not reported, as for all approaches, gamma was the best-fitting function.</p>
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<p>Difference between SPI and SPEI time series calculated with the best-fit approach applied month by month: (<b>a</b>) December values of SPI12-SPEI12 for yearly analysis; (<b>b</b>) February values of SPI3-SPEI3 for winter (DJF); (<b>c</b>) May values of SPI3-SPEI3 for spring (MAM); (<b>d</b>) August values of SPI3-SPEI3 for summer (JJA); (<b>e</b>) November values of SPI3-SPEI3 for fall (SON). Red and blue lines indicate positive and negative differences, respectively.</p>
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<p>Innovative trend analysis performed for SPI12 (<b>a</b>) and SPEI12 (<b>b</b>) time series calculated with the best-fit approach applied month by month. The colored rectangles indicate the area where the SPI and SPEI belong to the severely wet (blue) and severe drought (red) classes.</p>
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<p>Wavelet transform of the SPEI12 time series calculated with the “monthly” standard approach (SA) and “general”(BFAg) and “monthly” (BFAm) best-fit approaches: (<b>a</b>) continuous wavelet scalogram with the area outside of the cone of influence masked; (<b>b</b>) red-noise spectrum of the time series at the 90<sup>th</sup>, 95<sup>th</sup> and 99<sup>th</sup> significance levels.</p>
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<p>Critical drought intensity–duration–frequency curves for SPI3 (<b>a</b>), SPI12 (<b>b</b>), SPEI3 (<b>c</b>) and SPEI12 (<b>d</b>) for RP = 5, 10, 25, 50, 100, 300 years.</p>
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<p>Critical drought intensity–duration–frequency curves for SPI3 (<b>a</b>), SPI12 (<b>b</b>), SPEI3 (<b>c</b>) and SPEI12 (<b>d</b>) for RP = 5, 10, 25, 50, 100, 300 years.</p>
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15 pages, 1246 KiB  
Article
Effect of Spirulina Nigrita® on Exercise-Induced Oxidative Stress in Humans: A Breath Analysis Study
by Anastasios Krokidas, Katerina Mikedi, Athanasios G. Gakis, Spyridon Methenitis, Tzortzis Nomikos and Magdalini Krokida
Appl. Sci. 2024, 14(24), 11501; https://doi.org/10.3390/app142411501 - 10 Dec 2024
Viewed by 364
Abstract
In the current work, the non-invasive approach of breath analysis is implemented for the first time in an eccentric exercise protocol that investigated the effect of spirulina supplementation on exercise-induced oxidative stress. We assessed whether volatile alkanes in exhaled breath can serve as [...] Read more.
In the current work, the non-invasive approach of breath analysis is implemented for the first time in an eccentric exercise protocol that investigated the effect of spirulina supplementation on exercise-induced oxidative stress. We assessed whether volatile alkanes in exhaled breath can serve as alternative biomarkers of oxidative stress. A randomized, double-blinded, placebo-controlled, crossover supplementation study was carried out enrolling 14 participants. The volunteers consumed 42 mg·kg−1 body weight of either Spirulina Nigrita® or maltodextrin, as a placebo, daily for 15 days. Afterward, they followed a damaging eccentric exercise protocol of the upper limbs. Expired breath samples were collected from them just before supplementation (baseline measurement), prior to exercise, and 1 h, 24 h, 48 h, and 72 h after exercise. The samples were analyzed by Gas Chromatography/Mass Spectrometry (GC-MS) coupled with a thermal desorption unit (TDU) to determine the alveolar gradient (AG) of several alkanes, C5–C14, that are known to be related to oxidative stress. Apart from breath analysis, TBARSs were also determined as a crude marker of lipid peroxidation. Two-way repeated measures ANOVA tests were applied to the alkanes’ AGs between the spirulina (SPI) and placebo (PL) groups across time. In the PL group, a trend of increasing almost all alkanes immediately after exercise, with a gradual return to pre-exercise levels up to 72 h later was revealed. A statistically significant time effect was observed for 2-methylhexane, 3-methylhexane, heptane, octane, and undecane. The administration of spirulina appeared to reduce the increases in alkanes after exercise, and a statistically significant attenuation was observed for 2-methylpentane and 2-methylhexane. An examination of TBARSs confirmed that the reduced increases observed in the SPI group were due to changes in lipid peroxidation, while a positive correlation between the iAUC of TBARSs and that of 2-methylhexane and 3-methylhexane was revealed. In conclusion, the analysis of volatile alkanes in exhaled breath may serve as an attractive alternative for assessing redox changes after eccentric exercise compared to traditional blood biomarkers. Full article
(This article belongs to the Special Issue Chemical and Physical Properties in Food Processing)
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<p>Timeline of this intervention study; sampling points PreSup, PreEx, PostEx, 24 h, 48 h, and 72 h.</p>
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<p>Spirulina supplementation effect on the kinetics of volatile alkanes’ AGs after an eccentric exercise protocol: (<b>a</b>) 2-methylpentane and (<b>b</b>) 2-methylhexane.</p>
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<p>Effect of spirulina supplementation on TBARSs: (<b>a</b>) absolute values, (<b>b</b>) % change, and (<b>c</b>) net area under the curve.</p>
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23 pages, 11639 KiB  
Article
Projected Drought Prevalence in Malawi’s Lufilya Catchment: A Study Using Regional Climate Models and the SPI Method
by Lenard Kumwenda, Patsani Gregory Kumambala, Lameck Fiwa, Grivin Chipula, Stanley Phiri, Righteous Kachali and Sangwani Mathews Mfune
Water 2024, 16(24), 3548; https://doi.org/10.3390/w16243548 - 10 Dec 2024
Viewed by 790
Abstract
Droughts are caused either by a deficiency in precipitation compared to normal levels or by excessive evapotranspiration exceeding long-term averages. Therefore, assessing future drought prevalence based on projected climatic variables is essential for effective drought preparedness. In this study, an ensemble of three [...] Read more.
Droughts are caused either by a deficiency in precipitation compared to normal levels or by excessive evapotranspiration exceeding long-term averages. Therefore, assessing future drought prevalence based on projected climatic variables is essential for effective drought preparedness. In this study, an ensemble of three Regional Climate Models (REMO2009, RCA4, and CCLM4-8-17) was used for Representative Concentration Pathways (RCP 4.5 and RCP 8.5), covering two future time periods (2025–2069 and 2070–2100). The quantile distribution mapping technique was employed to bias-correct the RCMs. The ensemble of RCMs projected an increase in rainfall, ranging from 40% to 85% under both RCP 8.5 and RCP 4.5. Both RCPs indicated an increase in daily average temperatures. RCP 4.5 projects an increase in average daily temperature by 1% between 2025 and 2069 and 6.5% between 2070 and 2100, while under RCP 8.5, temperatures are expected to rise by 3.7% between 2025 and 2069 and 12.7% between 2070 and 2100. The Standard Precipitation Index (SPI) was used to translate these projected climatic anomalies into future drought prevalence. The results suggest that RCP 4.5 forecasts an 8% increase in drought prevalence, while RCP 8.5 projects an 11% increase in drought frequency, with a greater rise in moderate and severe droughts and a decrease in extreme drought occurrences. Full article
(This article belongs to the Section Water and Climate Change)
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<p>Lufilya River catchment.</p>
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<p>Q-Q plot of observed rainfall data for Lufilya catchment.</p>
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<p>Q-Q plot of simulated rainfall data of CCLM4-8-17 model for Lufilya catchment.</p>
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<p>Q-Q plot of simulated rainfall data of RCA4 model for Lufilya catchment.</p>
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<p>Q-Q plot of simulated rainfall data of REMO2009 model for Lufilya catchment.</p>
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<p>Monthly average change in precipitation for CCLM4-8-17 model under (RCP4.5).</p>
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<p>Monthly average change in precipitation for CCLM4-8-17 model under (RCP8.5).</p>
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<p>Monthly average change in precipitation for REMO2009 model under (RCP4.5).</p>
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<p>Monthly average change in precipitation for REMO2009 model under (RCP8.5).</p>
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<p>Monthly average change in precipitation for RECA model under (RCP4.5).</p>
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<p>Monthly average change in precipitation for RECA model under (RCP8.5).</p>
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<p>Variations in average temperature for CCLM4-8-17 model under (RCP4.5).</p>
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<p>Variations in average temperature for CCLM4-8-17 model under (RCP8.5).</p>
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<p>Variations in average temperature for REMO2009 model under (RCP4.5).</p>
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<p>Variations in average temperature for REMO2009 model under (RCP8.5).</p>
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<p>Variations in average temperature for RCA4 model under (RCP8.5).</p>
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<p>Variations in average temperature for RCA4 model under (RCP8.5).</p>
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<p>Temporal variations in SPIs, Historical (1976–1999).</p>
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<p>Temporal variations in SPIs, Historical (2000–2022): Where the red spikes means drought occurrence while the blue ones means no drought occurrence.</p>
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<p>Temporal variations in SPIs, RCP 4.5 (2025–2069): Where the red spikes means drought occurrence while the blue ones means no drought occurrence.</p>
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<p>Temporal variations in SPIs, RCP 4.5 (2070–2100): Where the red spikes means drought occurrence while the blue ones means no drought occurrence.</p>
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<p>Temporal variations in SPIs, RCP 8.5 (2025–2069): Where the red spikes means drought occurrence while the blue ones means no drought occurrence.</p>
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18 pages, 16516 KiB  
Article
RRM2 Regulates Hepatocellular Carcinoma Progression Through Activation of TGF-β/Smad Signaling and Hepatitis B Virus Transcription
by Dandan Wu, Xinning Sun, Xin Li, Zongchao Zuo, Dong Yan and Wu Yin
Genes 2024, 15(12), 1575; https://doi.org/10.3390/genes15121575 - 6 Dec 2024
Viewed by 622
Abstract
Background: Hepatocellular carcinoma (HCC) is a type of malignant tumor with high morbidity and mortality. Untimely treatment and high recurrence are currently the major challenges for HCC. The identification of potential targets of HCC progression is crucial for the development of new therapeutic [...] Read more.
Background: Hepatocellular carcinoma (HCC) is a type of malignant tumor with high morbidity and mortality. Untimely treatment and high recurrence are currently the major challenges for HCC. The identification of potential targets of HCC progression is crucial for the development of new therapeutic strategies. Methods: Bioinformatics analyses have been employed to discover genes that are differentially expressed in clinical cases of HCC. A variety of pharmacological methods, such as MTT, colony formation, EdU, Western blotting, Q-PCR, wound healing, Transwell, cytoskeleton F-actin filaments, immunohistochemistry (IHC), hematoxylin–eosin (HE) staining, and dual-luciferase reporter assay analyses, were utilized to study the pharmacological effects and potential mechanisms of ribonucleotide reductase regulatory subunit M2 (RRM2) in HCC. Results: RRM2 expression is significantly elevated in HCC, which is well correlated with poor clinical outcomes. Both in vitro and in vivo experiments demonstrated that RRM2 promoted HCC cell growth and metastasis. Mechanistically, RRM2 modulates the EMT phenotype of HCC, and further studies have shown that RRM2 facilitates the activation of the TGF-β/Smad signaling pathway. SB431542, an inhibitor of TGF-β signaling, significantly inhibited RRM2-induced cell migration. Furthermore, RRM2 expression was correlated with diminished survival in HBV-associated HCC patients. RRM2 knockdown decreased the levels of HBV RNA, pgRNA, cccDNA, and HBV DNA in HepG2.2.15 cells exhibiting sustained HBV infection, while RRM2 knockdown inhibited the activity of the HBV Cp, Xp, and SpI promoters. Conclusion: RRM2 is involved in the progression of HCC by activating the TGF-β/Smad signaling pathway. RRM2 increases HBV transcription in HBV-expressing HCC cells. Targeting RRM2 may be of potential value in the treatment of HCC. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>RRM2 upregulation in HCC is associated with poor prognosis. (<b>A</b>) RRM2 expression in different types of cancer was analyzed using the TIMER database (“<a href="https://cistrome.shinyapps.io/timer/" target="_blank">https://cistrome.shinyapps.io/timer/</a> (accessed on 5 May 2023)”). The black box shows HCC-related data. (<b>B</b>) The GEPIA2 database (“<a href="http://gepia2.cancer-pku.cn/#index" target="_blank">http://gepia2.cancer-pku.cn/#index</a> (access date: 5 May 2023)”) was used to analyze the expression levels of RRM2 in HCC tissue (<span class="html-italic">n</span> = 369) and precancerous tissue (<span class="html-italic">n</span> = 160). (<b>C</b>) Representative RRM2 IHC staining images from a clinical TMA of HCC (<span class="html-italic">n</span> = 15). Magnification: 20× and 200×. (<b>D</b>) Kaplan–Meier analysis was used to examine the relationship between RRM2 expression levels and OS, RFS, and PFS in HCC patients (“<a href="http://kmplot.com/analysis/" target="_blank">http://kmplot.com/analysis/</a> (accessed on 5 May 2023)”), with a low RRM2 expression group (<span class="html-italic">n</span> = 29/129/56) and a high RRM2 expression group (<span class="html-italic">n</span> = 26/45/62). (<b>E</b>) Kaplan–Meier analysis was utilized to analyze RRM2 expression in HCC patients with variable degrees of differentiation (“<a href="http://kmplot.com/analysis/" target="_blank">http://kmplot.com/analysis/</a> (accessed on 5 May 2023)”), with a low RRM2 expression group (<span class="html-italic">n</span> = 237/193/135) and a high RRM2 expression group (<span class="html-italic">n</span> = 127/123/235). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>RRM2 expression promotes the proliferation of HCC cells. (<b>A</b>,<b>B</b>) Western blotting and Q-PCR tests were used to assess the expression of RRM2 in HCC cell lines, such as HepG2.2.15, HepG2, SMMC7721, Huh7, and the immortalized hepatocyte cell line LO2. (<b>C</b>) Western blotting and Q-PCR tests were utilized to evaluate the expression of RRM2 in SMMC7721 and Huh7 cells transfected with RRM2 siRNA (siRRM2#1 and siRRM2#2). (<b>D</b>) MTT test was applied to evaluate RRM2 siRNA’s impact on SMMC7721 and Huh7 cell viability. (<b>E</b>) Colony-forming test was utilized to evaluate RRM2 siRNA’s impact on SMMC7721 and Huh7 cell colony formation. (<b>F</b>) Representative EdU staining (red) images of SMMC7721 and Huh7 cells expressing low RRM2 levels. Nuclei are counterstained with DAPI (blue). Scale bar: 200 µm. (<b>G</b>) Western blotting and Q-PCR tests were utilized to evaluate the expression of RRM2 in SMMC7721 and Huh7 cells transfected with Flag-RRM2. (<b>H</b>) MTT test was utilized to evaluate SMMC7721 and Huh7 cell viability after RRM2 overexpression. (<b>I</b>) A colony-forming test was utilized to evaluate SMMC7721 and Huh7 cell colony formation after RRM2 overexpression. (<b>J</b>) Representative EdU staining (red) images of SMMC7721 and Huh7 cells expressing high RRM2 levels. Nuclei are counterstained with DAPI (blue). Scale bar: 200 µm. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>RRM2 promotes HCC cell migration. (<b>A</b>–<b>D</b>) Wound healing and Transwell assays were performed to assess the migration of SMMC7721 and Huh7 cells after RRM2 knockdown or overexpression. (<b>E</b>,<b>F</b>) Representative F-actin (green) images of SMMC7721 and Huh7 cells after RRM2 knockdown or overexpression. Nuclei are counterstained with DAPI (blue). Scale bar: 200 µm. (<b>G</b>–<b>J</b>) Western blot and Q-PCR tests revealed the impact of RRM2 knockdown or overexpression on EMT markers in SMMC7721 and Huh7 cells. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>RRM2 promotes HCC migration by activating TGF-β/Smad signaling. (<b>A</b>) The GEPIA2 database was analyzed for correlations between RRM2 and TGFB1, Smad2, and Smad3 gene expression (“<a href="http://gepia2.cancer-pku.cn/#index" target="_blank">http://gepia2.cancer-pku.cn/#index</a> (accessed on 19 July 2023)”). (<b>B</b>) Q-PCR assay to detect TGF-β1 mRNA levels in SMMC7721 and Huh7 cells after RRM2 knockdown. (<b>C</b>) Western blotting assay was utilized to evaluate the protein expression of TGF-β1, p-Smad2, and p-Smad3 in SMMC7721 and Huh7 cells transfected with RRM2 siRNA. (<b>D</b>) Q-PCR assay to detect TGF-β1 mRNA levels in SMMC7721 and Huh7 cells after RRM2 overexpression. (<b>E</b>) Western blotting assay was utilized to evaluate RRM2 protein expression in SMMC7721 and Huh7 cells transfected with Flag-RRM2. (<b>F</b>,<b>G</b>) SMMC7721 and Huh7 cells underwent a 24 h preliminary treatment with siRRM2#1, followed by a 12 h treatment with TGF-β1 (5 ng/mL). Transwell and wound healing tests assessed cell migration. (<b>H</b>,<b>I</b>) SMMC7721 and Huh7 cells underwent a 12 h preliminary treatment with Flag-RRM2, followed by a 24 h treatment with SB431542 (5 μM). SMMC7721 and Huh7 cells were transfected with Flag-RRM2 for 12 h and subsequently treated with SB431542 (5 μM) for 24 h. Transwell and wound healing tests assessed cell migration. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>RRM2 knockdown inhibits tumorigenesis in a mouse model of HCC. (<b>A</b>) Q-PCR test was utilized to evaluate the RRM2 mRNA levels in mouse liver tissue in the control, AAV-shNC, and AAV-shRRM2 groups (<span class="html-italic">n</span> = 6). (<b>B</b>) Western blotting analysis was used to evaluate RRM2 and PCNA protein levels in the liver tissue of the control, AAV-shNC, and AAV-shRRM2 groups (<span class="html-italic">n</span> = 3). (<b>C</b>) Images of the morphology of the liver tissue from the orthotopic transplantation mouse model of HCC are shown (<span class="html-italic">n</span> = 6). (<b>D</b>) Mouse liver weight in the control, AAV-shNC, and AAV-shRRM2 groups. (<b>E</b>) The AST and ALT levels in the serum of the control, AAV-shNC, and AAV-shRRM2 groups (<span class="html-italic">n</span> = 6). (<b>F</b>) Representative HE images of liver tissue from control, AAV-shNC, and AAV-shRRM2 mice. (<b>G</b>) Representative HE images of lung histomorphology and number of lung metastases in orthotopic transplantation tumor mouse models (<span class="html-italic">n</span> = 6). (<b>H</b>) Representative HE images of lung tissue from control, AAV-shNC, and AAV-shRRM2 mice. (<b>I</b>) Representative IHC images of RRM2, p-Smad2, p-Smad3, and PCNA expression in liver tissue of control, AAV-shNC, and AAV-shRRM2 mice. Scale bars: 100 and 35 μm. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>RRM2 promotes HBV replication. (<b>A</b>) Kaplan–Meier survival plots of HBV-associated HCC patients with different RRM2 mRNA levels in the low RRM2-expressing group (<span class="html-italic">n</span> = 97) and the high RRM2-expressing group (<span class="html-italic">n</span> = 70). (<b>B</b>) The mRNA levels of pgRNA, HBV DNA, HBV RNA, and cccDNA were detected by Q-PCR in HepG2.2.15 cells transfected with RRM2 siRNA (siRRM2#1). (<b>C</b>,<b>D</b>) The mRNA levels of RRM2, HBV RNA, pgRNA, HBV DNA, and cccDNA were measured by Q-PCR in SMMC7721 and HepG2 cells transfected with the HBV plasmid. (<b>E</b>) Schematic representation of luciferase reporter plasmid containing HBV promoter. (<b>F</b>) The HBV plasmid was transfected into SMMC7721 and HepG2 cells and continued to culture for 24 h, and then pGL3 basic, Cp, Xp, spI, and spII were co-transfected with siNC or siRRM2#1 and continued to culture for 36 h. The promoter activity of Cp, Xp, spI, and spII was measured using a dual-luciferase reporter assay. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns represents nonsignificant effects.</p>
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12 pages, 4880 KiB  
Article
Climate Fluctuations and Growing Sensitivity of Grape Production in Abruzzo (Central Italy) over the Past Sixty Years
by Vincenzo Guerriero, Anna Rita Scorzini, Bruno Di Lena, Mario Di Bacco and Marco Tallini
Geographies 2024, 4(4), 769-780; https://doi.org/10.3390/geographies4040042 - 4 Dec 2024
Viewed by 469
Abstract
The sensitivity of the agricultural production system to short- and long-term climate variations significantly affects the availability and prices of food resources, raising relevant issues of sustainability and food security. Globally, productive systems have adapted to climate change, leading to increased yields over [...] Read more.
The sensitivity of the agricultural production system to short- and long-term climate variations significantly affects the availability and prices of food resources, raising relevant issues of sustainability and food security. Globally, productive systems have adapted to climate change, leading to increased yields over the past century. However, the extent to which these adaptations mitigate the impacts of short-term climate fluctuations, both extreme and ordinary, remains poorly studied. To evaluate the vulnerability of crop yield to short-term climate fluctuations and to determine whether it changes over time, we conducted a statistical analysis focusing on one of the main crops in the Abruzzo region (central Italy) as a case study: grape. The study involves correlation analysis between opportune climatic indices (SPI and SPEI) and grape yield data over the sixty-year period from 1952 to 2014, aimed at evaluating the impact of short-term climatic fluctuations—both extreme and ordinary—on crop yield. Our findings reveal an increasing correlation, mainly in the summer–autumn season, which suggests a rising sensitivity of the productive system over time. The observed increase is indicative of the Abruzzo grape production system’s adaptation to climate change, resulting in higher overall yields but not enhancing the response to short-term climatic fluctuations. Full article
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<p>The agricultural productive system viewed as a simple dynamic system, constituted by three main subsystems.</p>
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<p>Location and land use of the study area. Modified from [<a href="#B23-geographies-04-00042" class="html-bibr">23</a>].</p>
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<p>(<b>a</b>) Flowchart summarizing the analysis process. (<b>b</b>) Example of correlation analysis between SYRS and quarterly SPEI (SPEI3) of September, for grape in the Chieti province, over the time range 1960–1990.</p>
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<p>(<b>a</b>) Time series of total grape production (tons) in the four Abruzzo provinces. Red dashed line denotes the only wine grape produced in Chieti province. (<b>b</b>) Crop yield (t/h) of the Chieti province grape. (<b>c</b>) Time series of standardized yield residuals for grape yield.</p>
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<p>Time series of maximum and minimum daily temperatures and Péguy climate classification in the two considered time ranges, 1952–1982 and 1983–2014, for L’Aquila and Chieti provinces. Dotted lines denote trendlines over about thirty-year time ranges (from [<a href="#B23-geographies-04-00042" class="html-bibr">23</a>], modified).</p>
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<p>(<b>a</b>) Identified correlation coefficients between detrended crop yield (SYRS) and relevant SPI and SPEI indices for grape in the province of Chieti and for five sequential time windows. The correlation threshold (absolute) values of 0.3 and 0.46 are associated with a statistical significance of 10% and 1%, respectively. An increase in the number of colored cells, and mainly of dark red or blue ones, denotes a growing correlation between crop and climatic oscillations. (<b>b</b>) Correlation values between SYRS and SPEI3 of September, calculated in a time window of thirty years, sliding over the whole studied time range (1952–2014), with the trendline.</p>
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10 pages, 3406 KiB  
Article
Development and Characterization of an Environmentally Friendly Soy Protein-Modified Phenol–Formaldehyde Resin for Plywood Manufacturing
by Taotao Li, Zhanjun Liu, Shiquan Liu and Cheng Li
Forests 2024, 15(12), 2130; https://doi.org/10.3390/f15122130 - 1 Dec 2024
Viewed by 495
Abstract
Most wood-based panels were currently prepared using aldehyde-based adhesives, making the development of natural, renewable, and eco-friendly biomass-based adhesives a prominent area of research. Herein, the phenolic resin was modified using a soybean protein isolate (SPI) treated with a NaOH/urea solution through a [...] Read more.
Most wood-based panels were currently prepared using aldehyde-based adhesives, making the development of natural, renewable, and eco-friendly biomass-based adhesives a prominent area of research. Herein, the phenolic resin was modified using a soybean protein isolate (SPI) treated with a NaOH/urea solution through a copolymerization method. The physicochemical properties, chemical structure, bonding properties, and thermal properties of the soybean protein-modified phenolic resin (SPF-U) were analyzed using Fourier transform infrared spectroscopy, thermogravimetric analysis, and formaldehyde emission tests. The results indicated that the molecular structure of the soy protein isolate degraded after NaOH/urea solution treatment, while the gel time was gradually shortened with increasing NaOH/urea solution-treated soy protein isolate (SPI-U) dosages. Although the thermal stability of the soy protein isolate was lower than that of the phenolic resin, the 20% SPF-U resin demonstrated better thermal stability than other modified resins. The PF modified with 30% SPI-U (SPF-U-3) exhibited the lowest curing peak temperature of 139.69 °C than that of the control PF resin. In addition, all modified PF resins exhibited formaldehyde emissions ranging from 0.18 to 0.38 mg/L when the SPI-U dosage varied between 20% and 50%, thereby meeting the E0 plywood grade standard (≤0.5 mg/L). Full article
(This article belongs to the Section Wood Science and Forest Products)
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<p>FTIR spectra of (<b>a</b>) SPI and SPI-U and (<b>b</b>) different SPF-U resins.</p>
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<p>TG and DTG curves of PF and different SPF-U resins.</p>
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<p>DSC curves of SPF-U-2 (<b>a</b>), SPF-U-3(<b>b</b>), SPF-U-4 (<b>c</b>),and SPF-U-5 (<b>d</b>) resins.</p>
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<p>(<b>a</b>) Bonding strength and (<b>b</b>) formaldehyde emission of PF and different SPF-U resins.</p>
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26 pages, 10256 KiB  
Article
Propagation Characteristics and Influencing Factors of Meteorological Drought to Soil Drought in the Upper Reaches of the Shiyang River Based on the Copula Function
by Junju Zhou, Anning Gou, Shizhen Xu, Yuze Wu, Xuemei Yang, Wei Wei, Guofeng Zhu, Dongxia Zhang and Peiji Shi
Land 2024, 13(12), 2050; https://doi.org/10.3390/land13122050 - 29 Nov 2024
Viewed by 378
Abstract
Drought propagation is a complex process, and understanding the propagation mechanisms of meteorological drought to soil drought is crucial for early warning, disaster prevention, and mitigation. This study focuses on eight tributaries in the upper reaches of the Shiyang River. Based on the [...] Read more.
Drought propagation is a complex process, and understanding the propagation mechanisms of meteorological drought to soil drought is crucial for early warning, disaster prevention, and mitigation. This study focuses on eight tributaries in the upper reaches of the Shiyang River. Based on the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSMI), the Drought Propagation Intensity Index (DIP) and Copula function were applied to quantify the intensity and time of drought propagation from meteorological to soil drought and explored the drought propagation patterns at different temporal and spatial scales in these tributaries. Results showed that, in the 0–10 cm soil layer, the propagation intensity of meteorological drought to soil drought was peer-to-peer, with a propagation time of one month. In the middle (10–40 cm) and deep (40–100 cm) soil layers, propagation characteristics differed between the eastern and western tributaries. The western tributaries experienced stronger drought propagation intensity and shorter propagation times (2–4 months), while the eastern tributaries exhibited peer-to-peer propagation intensity with longer times (4–10 months). The large areas of forests and grasslands in the upper reaches of the Shiyang River contributed to strong land–atmosphere interactions, leading to peer-to-peer drought propagation intensity in the 0–10 cm soil layer. The eastern tributaries had extensive cultivated land, where irrigation during meteorological drought enhanced soil moisture, resulting in peer-to-peer propagation intensity in the middle (10–40 cm) and deep (40–100 cm) soil layers. In contrast, the western tributaries, with larger forest areas and widespread permafrost, experienced high water consumption and limited recharge in the 10–40 cm and 40–100 cm soil layers, leading to strong drought propagation. Full article
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<p>Overview of the study area.</p>
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<p>Marginal fitting of SPI and in eight tributaries of 10−40 cm soil layer.</p>
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<p>Marginal fitting of SSMI in eight tributaries of 10−40 cm soil layer.</p>
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<p>Annual−scale change characteristics of dry–wet meteorological and dry–wet soil in the eight tributaries((<b>a</b>–<b>d</b>) represent dry–wet meteorological, dry–wet soil in 0–10 cm layer, dry–wet soil in 10–40 cm layer, and dry–wet soil in 40–100 cm layer, respectively).</p>
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<p>The annual-scale change tendency rate of dry–wet meteorological and dry–wet soil in the eight tributaries.</p>
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<p>Spatial variation of meteorological drought (<b>a</b>) and soil drought frequency (<b>b</b>): The (<b>A</b>–<b>D</b>) of the left Figure corresponds to the frequency of MD (meteorological drought), frequency of SD1 (soil drought of 0–10 cm layer), frequency of SD2 (soil drought of 10–40 cm layer), frequency of SD3 (soil drought of 40–100 cm layer).</p>
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<p>The drought frequency change characteristics (<b>a</b>) and change tendency rate (<b>b</b>) from 1982 to 2021 (MD, SD1, SD2 and SD3 represent meteorological drought frequency, soil drought frequency of 0–10 cm layer, soil drought frequency of 10–40 cm layer, and soil drought frequency of 40–100 cm layer, respectively).</p>
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<p>The Copula joint probability of eight tributaries (* is the best joint probability for eight tributaries. According to the right color axis, red indicates a higher correlation, and blue indicates a lower correlation).</p>
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<p>Spatial characteristics of DPT.</p>
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<p>Spatial characteristics of DIP.</p>
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<p>Driving factors of drought propagation characteristic.</p>
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<p>Changes in climate factors in the eight tributaries (P, AE, P-AE represent precipitation, actual evapotranspiration, and the difference value between precipitation and actual evapotranspiration, respectively).</p>
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<p>The proportion of land use (<b>a</b>) and soil types (<b>b</b>) in the eight tributaries (CL, F, GL, UL and BL in (<b>a</b>) represents the Cultivated Land, Forest, Grass Land, Unused Land and Building Land, respectively; SL in (<b>b</b>) represents the soil layer).</p>
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<p>Relationship between DIP and land use ((<b>a</b>–<b>e</b>) represent for grassland, forest, cultivated land, unused land and building land respectively).</p>
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<p>Relationship between land–atmosphere interaction and main drought propagation partition ((<b>a</b>) represents strong land–atmosphere interaction, (<b>b</b>) represents weak land–atmosphere interaction. AET represents the actual evapotranspiration, PRE represents the precipitation).</p>
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28 pages, 11196 KiB  
Article
Surface Charging Analysis of Ariel Spacecraft in L2-Relevant Space Plasma Environment and GEO Early Transfer Orbit
by Marianna Michelagnoli, Mauro Focardi, Maxsim Pudney, Ian Renouf, Pierpaolo Merola, Vladimiro Noce, Marina Vela Nunez, Giacomo Dinuzzi and Simone Chiarucci
Aerospace 2024, 11(12), 988; https://doi.org/10.3390/aerospace11120988 - 29 Nov 2024
Viewed by 328
Abstract
Ariel (Atmospheric Remote-sensing Infrared Exoplanet Large-survey) is the ESA Cosmic Vision M4 mission, selected in March 2018 and officially adopted in November 2020, whose launch is scheduled by 2029. It aims at characterizing the atmospheres of hundreds of exoplanets orbiting nearby stars by [...] Read more.
Ariel (Atmospheric Remote-sensing Infrared Exoplanet Large-survey) is the ESA Cosmic Vision M4 mission, selected in March 2018 and officially adopted in November 2020, whose launch is scheduled by 2029. It aims at characterizing the atmospheres of hundreds of exoplanets orbiting nearby stars by low-resolution primary and secondary transit spectroscopy. The Ariel spacecraft’s operational orbit is baselined as a large-amplitude, eclipse-free halo orbit around the second Lagrangian (L2) point, a virtual point located at about 1.5 million km from the Earth in the anti-Sun direction, as it offers the possibility of long uninterrupted observations in a fairly stable radiative and thermo-mechanical environment. A direct escape injection with a single passage through the Van Allen radiation belts is foreseen. During both the injection trajectory and the final orbit around L2, Ariel will be immersed in and interact with Sun radiation and the plasma environment. These interactions usually result in the accumulation of net electrostatic charge on the external surfaces of the spacecraft, leading to a potentially hazardous configuration for the nominal operation and survivability of the Ariel platform and its payload, as it may induce harmful electrostatic discharges (ESDs). This work presents the latest results collected from surface charging analyses conducted using the SPIS tool of the European SPINE community along the GEO insertion orbit segment and operational orbit. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>Overview of Ariel spacecraft. (<b>a</b>) Main structural components; (<b>b</b>) details of PLM and SVM elements.</p>
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<p>Ariel spacecraft in a halo orbit around L2, ensuring no eclipses occur from Earth or the Moon throughout the mission duration.</p>
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<p>Ariel’s injection trajectory concept: perigee located in Low Earth Orbit (LEO) shown by the green line, apogee reaching geostationary orbit (GEO) altitudes represented by the red line, with the intermediate transfer phase marked by the yellow line.</p>
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<p>Spacecraft external surfaces’ charging mechanisms [<a href="#B14-aerospace-11-00988" class="html-bibr">14</a>].</p>
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<p>Near-Earth regions of concern for surface charging hazards for a shadowed spacecraft. Data derived from the Defense Meteorological Satellite Program (DMSP—800 km altitude) and Freja satellite observations [<a href="#B10-aerospace-11-00988" class="html-bibr">10</a>].</p>
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<p>(<b>a</b>) Schematic simplified structure of Earth’s magnetosphere; (<b>b</b>) Earth–Moon system, magnetotail, magnetopause, bow shock, and halo orbit around L2 [<a href="#B15-aerospace-11-00988" class="html-bibr">15</a>].</p>
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<p>(<b>a</b>) Schematic simplified structure of Earth’s magnetosphere; (<b>b</b>) Earth–Moon system, magnetotail, magnetopause, bow shock, and halo orbit around L2 [<a href="#B15-aerospace-11-00988" class="html-bibr">15</a>].</p>
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<p>Ariel geometrical model used for SPIS simulations: (<b>a</b>) front view; (<b>b</b>) bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution in GEO orbit.</p>
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<p>Three-dimensional map of plasma potential in GEO orbit: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—solar wind (1 A.U.)—average conditions.</p>
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<p>Three-dimensional map of plasma potential—solar wind (1 A.U.)—average conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—solar wind (1 A.U.)—5% conditions.</p>
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<p>Three-dimensional map of plasma potential—solar wind (1 A.U.)—5% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—solar wind (1 A.U.)—95% conditions.</p>
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<p>Three-dimensional map of plasma potential—solar wind (1 A.U.)—95% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetosheath (1 A.U.)—average conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetosheath (1 A.U.)—average conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetosheath (1 A.U.)—5% conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetosheath (1 A.U.)—5% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetosheath (1 A.U.)—90% conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetosheath (1 A.U.)—90% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetotail (1 A.U.)—average conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetotail (1 A.U.)—average conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetotail (1 A.U.)—5% conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetotail (1 A.U.)—5% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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<p>Surface potential evolution—magnetotail (1 A.U.)—90% conditions.</p>
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<p>Three-dimensional map of plasma potential—magnetotail (1 A.U.)—90% conditions: (<b>a</b>) Ariel front view; (<b>b</b>) Ariel bottom view; (<b>c</b>) close-up of the FPE-IC connector and FPE strut.</p>
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19 pages, 8662 KiB  
Article
Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress
by Hui Yin, Fuqing Bai, Huiming Wu, Meng Yan and Shuai Zhou
Sustainability 2024, 16(23), 10489; https://doi.org/10.3390/su162310489 - 29 Nov 2024
Viewed by 477
Abstract
With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article [...] Read more.
With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change. Full article
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<p>Geographical location, meteorological stations, and spatial distribution of water systems in the Hai River Basin.</p>
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<p>Research flow chart of the study.</p>
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<p>The trend test results for annual precipitation and annual average temperature in the watershed.</p>
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<p>The results of the variability test for the annual precipitation and annual average temperature in the watershed.</p>
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<p>Evolutionary patterns of the multi-scale (1–12 months) meteorological drought time history based on the SPI (blue represents a humid climate, while red indicates a dry climate. The same color appearing in consecutive periods signifies the duration of either drought or humidity).</p>
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<p>Results of the correlation analysis between the SPI at multiple scales (1–12 months) and STI at the 1-month scale.</p>
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<p>Identification results for the optimal marginal distribution functions for the 3-month SPI and 1-month STI.</p>
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<p>The optimal Copula functions for each station in the Haihe River Basin.</p>
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<p>Joint cumulative probability distributions of the 3-month-scale SPI and 1-month-scale STI.</p>
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<p>Joint return period of the 3-month-scale SPI and 1-month-scale STI.</p>
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<p>Distribution of correlation coefficients between the SPEI, STI, and sNDVI on a 3-month time scale from June to August in the Haihe River Basin (CC stands for correlation coefficient).</p>
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<p>Probability distribution of vegetation loss in response to drought, high temperatures, and compound dry and hot climates in the Haihe River Basin from June to August (PVL stands for the probability of vegetation loss).</p>
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