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17 pages, 2025 KiB  
Article
Optimization of Ferimzone and Tricyclazole Analysis in Rice Straw Using QuEChERS Method and Its Application in UAV-Sprayed Residue Study
by So-Hee Kim, Jae-Woon Baek, Hye-Ran Eun, Ye-Jin Lee, Su-Min Kim, Mun-Ju Jeong, Yoon-Hee Lee, Hyun Ho Noh and Yongho Shin
Foods 2024, 13(21), 3517; https://doi.org/10.3390/foods13213517 - 4 Nov 2024
Viewed by 817
Abstract
Rice straw is used as livestock feed and compost. Ferimzone and tricyclazole, common fungicides for rice blast control, can be found in high concentrations in rice straw after unmanned aerial vehicle (UAV) spraying, potentially affecting livestock and human health through pesticide residues. In [...] Read more.
Rice straw is used as livestock feed and compost. Ferimzone and tricyclazole, common fungicides for rice blast control, can be found in high concentrations in rice straw after unmanned aerial vehicle (UAV) spraying, potentially affecting livestock and human health through pesticide residues. In this study, an optimized method for the analysis of the two fungicides in rice straw was developed using the improved QuEChERS method. After the optimization of water and solvent volume, extraction conditions including ethyl acetate (EtOAc), acetonitrile (MeCN), a mixed solvent, and MeCN containing 1% acetic acid were compared. Different salts, including unbuffered sodium chloride, citrate, and acetate buffer salts, were compared for partitioning. Among the preparation methods, the MeCN/EtOAc mixture with unbuffered salts showed the highest recovery rates (88.1–97.9%, RSD ≤ 5.1%). To address the severe matrix effect (%ME) of rice straw, which is characterized by low moisture content and cellulose-based complex matrices, samples were purified using 25 mg each of primary–secondary amine (PSA) and octadecylsilane (C18), without pesticide loss. The developed method was validated with a limit of quantification (LOQ) of 0.005 mg/kg for target pesticides, and recovery rates at levels of 0.01, 0.1, and 2 mg/kg met the permissible range (82.3–98.9%, RSD ≤ 8.3%). The %ME ranged from −17.6% to −0.3%, indicating a negligible effect. This optimized method was subsequently applied to residue studies following multi-rotor spraying. Fungicides from all fields and treatment groups during harvest season did not exceed the maximum residue limits (MRLs) for livestock feed. This confirms that UAV spraying can be safely managed without causing excessive residues. Full article
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Graphical abstract
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<p>Diagram of the rice straw sample preparation process to analyze target pesticides.</p>
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<p>Chromatograms for ferimzone and tricyclazole in rice straw. Panels (<b>a</b>–<b>d</b>) display chromatograms for ferimzone, where the peak that eluted earlier corresponds to the <span class="html-italic">E</span> form and the later peak to the <span class="html-italic">Z</span> form. Panels (<b>e</b>–<b>h</b>) exhibit chromatograms for tricyclazole. Specifically, (<b>a</b>,<b>e</b>) depict blank samples without pesticide, (<b>b</b>,<b>f</b>) show matrix-matched standard (MMSTD) at the limit of quantification (LOQ, 0.005 mg/kg), (<b>c</b>,<b>g</b>) illustrate recovery samples at 0.01 mg/kg, and (<b>d</b>,<b>h</b>) represent residue samples from Group A in Field 1.</p>
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<p>QuEChERS partitioning results based on the volume of water ((<b>a</b>–<b>c</b>) 6 mL, (<b>d</b>–<b>f</b>) 9 mL, (<b>g</b>–<b>i</b>) 12 mL) and organic solvent ((<b>a</b>,<b>d</b>,<b>g</b>) 6 mL; (<b>b</b>,<b>e</b>,<b>h</b>) 9 mL; (<b>c</b>,<b>f</b>,<b>i</b>) 12 mL) treated to the sample.</p>
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<p>Comparison of recovery rates and matrix effect (%ME) for ferimzone isomers and tricyclazole using extraction solvents with and without 0.1% formic acid (FA). (<b>a</b>) Recovery rate. (<b>b</b>) Matrix effect. M3 refers the method shown in <a href="#foods-13-03517-t002" class="html-table">Table 2</a>, while M3-FA denotes M3 with 0.1% FA in MeCN.</p>
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18 pages, 1183 KiB  
Article
Enhancing Transpiration Estimates: A Novel Approach Using SIF Partitioning and the TL-LUE Model
by Tewekel Melese Gemechu, Baozhang Chen, Huifang Zhang, Junjun Fang and Adil Dilawar
Remote Sens. 2024, 16(21), 3924; https://doi.org/10.3390/rs16213924 - 22 Oct 2024
Viewed by 1211
Abstract
Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. [...] Read more.
Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. However, these methods often face significant challenges, such as reliance on empirical coefficients, inadequate representation of canopy dynamics, and limitations due to cloud cover and sensor constraints. These issues can lead to inaccuracies in capturing ET’s spatial and temporal variability, highlighting the need for improved estimation techniques. This study introduces a novel approach to enhance ET estimation by integrating SIF partitioning with Photosynthetically Active Radiation (PAR) and leaf area index (LAI) data, utilizing the TL-LUE model (Two-Leaf Light Use Efficiency). Partitioning SIF data into sunlit and shaded components allows for a more detailed representation of the canopy’s functional dynamics, significantly improving ET modelling. Our analysis reveals significant advancements in ET modelling through SIF partitioning. At Xiaotangshan Station, the correlation between modelled ET and SIFsu is 0.71, while the correlation between modelled ET and SIFsh is 0.65. The overall correlation (R2) between the modelled ET and the combined SIF partitioning (SIF(P)) is 0.69, indicating a strong positive relationship at Xiaotangshan Station. The correlations between SIFsh and SIFsu with modelled ET show notable patterns, with R2 values of 0.89 and 0.88 at Heihe Daman, respectively. These findings highlight the effectiveness of SIF partitioning in capturing canopy dynamics and its impact on ET estimation. Comparing modelled ET with observed ET and the Penman–Monteith model (PM model) demonstrates substantial improvements. R2 values for modelled ET against observed ET were 0.68, 0.76, and 0.88 across HuaiLai, Shangqiu, and Yunxiao Stations. Modelled ET correlations to the PM model were 0.75, 0.73, and 0.90, respectively, at three stations. These results underscore the model’s capability to enhance ET estimations by integrating physiological and remote sensing data. This innovative SIF-partitioning approach offers a more nuanced perspective on canopy photosynthesis, providing a more accurate and comprehensive method for understanding and managing ecosystem water dynamics across diverse environments. Full article
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<p>XiaoTangshan Station Correlation Matrix Heatmap.</p>
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<p>Heihe Daman Station Correlation Matrix Heatmap.</p>
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<p>The Linear Regression between Model ET and Observed ET.</p>
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<p>Correlation Analysis between Model ET and the Penman–Monteith (PM) model across Different Stations.</p>
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<p>Correlation Analysis at Different Stations (ET vs. PM model).</p>
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20 pages, 3233 KiB  
Article
Climate-Informed Management of Irrigated Cotton in Western Kansas to Reduce Groundwater Withdrawals
by R. L. Baumhardt, L. A. Haag, R. C. Schwartz and G. W. Marek
Agronomy 2024, 14(6), 1303; https://doi.org/10.3390/agronomy14061303 - 16 Jun 2024
Viewed by 1108
Abstract
The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant [...] Read more.
The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd−1 and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields. Full article
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<p>Southwest, west-central, and northwest Kansas locations at Garden City, Tribune, and Colby (respectively) where cotton responses to irrigation period and capacity scenarios were modeled for El Niño, Neutral, and La Niña phases of the El Niño southern oscillation (ENSO).</p>
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<p>Southwestern Kansas, Division 7, mean annual air temperatures, Ta, for 1961–2020 shows a pronounced +0.16 °C decadal trend (dashed line) with an overall 12.8 °C mean (solid line). The generally non-trending, &lt;0.04 °C per decade, or stationary Ta series averaging 12.6 °C for the period 1961–2000 (red) compares with a static 13.3 °C for the 2000–2020 period (blue) following a temperature step increase around 2000 [<a href="#B34-agronomy-14-01303" class="html-bibr">34</a>].</p>
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<p>Incrementally larger total application depths are shown for irrigation capacities of 2.5, 3.75, and 5.0 mm d<sup>−1</sup> and increasing period duration from 4 to 8 weeks that provide common depths for comparing capacity by period length effects.</p>
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<p>Location-specific cumulative thermal energy, CGDD °C, (<b>A</b>–<b>C</b>) and precipitation, mm, (<b>D</b>–<b>F</b>) plotted as a function of exceedance probability for the 1961–2000 cotton growing seasons of variable length separated into El Niño, Neutral, and La Niña ENSO phases.</p>
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<p>Mean simulated dryland cotton lint yield for the 1961–2000 El Niño, Neutral, and La Niña ENSO phases plotted as a function of exceedance probability for Colby, Tribune, and Garden City in northwestern (<b>A</b>), west-central (<b>B</b>), and southwestern (<b>C</b>) Kansas (respectively).</p>
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<p>The 1961–2000 location-specific mean simulated cotton leaf area index, LAI, at first open boll plotted as a function of scenario irrigation periods and capacities for the El Niño, Neutral, and La Niña ENSO phases. Bar patterns for dryland or 0 weeks, and irrigation capacities of 2.5, 3.75, and 5.0 mm d<sup>−1</sup> are solid black, hashed, solid white, and solid gray, respectively. Error bars are the LSD, <span class="html-italic">p</span> = 0.05, from the model-based standard error.</p>
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<p>The 1961–2000 location-specific mean simulated cotton lint yield and crop water use or evapotranspiration, ET, plotted as a function of scenario irrigation periods and capacities for the El Niño, Neutral, and La Niña ENSO phases. Bar patterns for dryland or 0 weeks, and irrigation capacities of 2.5, 3.75, and 5.0 mm d<sup>−1</sup> are solid black, hashed, solid white, and solid gray, respectively. The error bar represents a common LSD, <span class="html-italic">p</span> = 0.05, from the model-based standard error.</p>
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<p>The 1961–2000 location-specific mean simulated cotton crop water productivity, CWP, plotted as a function of scenario irrigation periods and capacities for El Niño, Neutral, and La Niña ENSO phases. Bar patterns for dryland or 0 weeks, and irrigation capacities of 2.5, 3.75, and 5.0 mm d<sup>−1</sup> are solid black, hashed, solid white, and solid gray, respectively. Error bars are the LSD, <span class="html-italic">p</span> = 0.05, from the model-based standard error.</p>
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14 pages, 3712 KiB  
Article
Theoretical Evaluation of Fluorinated Resazurin Derivatives for In Vivo Applications
by Amílcar Duque-Prata, Carlos Serpa and Pedro J. S. B. Caridade
Molecules 2024, 29(7), 1507; https://doi.org/10.3390/molecules29071507 - 28 Mar 2024
Viewed by 1116
Abstract
Primarily owing to the pronounced fluorescence exhibited by its reduced form, resazurin (also known as alamarBlue®) is widely employed as a redox sensor to assess cell viability in in vitrostudies. In an effort to broaden its applicability for in vivo studies, [...] Read more.
Primarily owing to the pronounced fluorescence exhibited by its reduced form, resazurin (also known as alamarBlue®) is widely employed as a redox sensor to assess cell viability in in vitrostudies. In an effort to broaden its applicability for in vivo studies, molecular adjustments are necessary to align optical properties with the near-infrared imaging window while preserving redox properties. This study delves into the theoretical characterisation of a set of fluorinated resazurin derivatives proposed by Kachur et al., 2015 examining the influence of fluorination on structural and electrochemical properties. Assuming that the conductor-like polarisable continuum model mimics the solvent effect, the density functional level of theory combining M06-2X/6-311G* was used to calculate the redox potentials. Furthermore, (TD-)DFT calculations were performed with PBE0/def2-TZVP to evaluate nucleophilic characteristics, transition states for fluorination, relative energies, and fluorescence spectra. With the aim of exploring the potential of resazurin fluorinated derivatives as redox sensors tailored for in vivo applications, acid–base properties and partition coefficients were calculated. The theoretical characterisation has demonstrated its potential for designing novel molecules based on fundamental principles. Full article
(This article belongs to the Section Computational and Theoretical Chemistry)
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<p>Resazurin and resorufin chemical structures labelling the carbon atoms.</p>
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<p>Different forms of resazurin: (<b>a</b>) protonated; (<b>b</b>,<b>c</b>) zwitterionic; and (<b>d</b>) deprotonated.</p>
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<p>Transition states for the different fluorination reactions: (<b>a</b>) monoflurination <span class="html-italic">x</span>-MFRA<sup>+</sup>, (<b>b</b>) diflourination 2,<span class="html-italic">x</span>-DFRA<sup>+</sup>, (<b>c</b>) diflourination <span class="html-italic">x</span>,4-DFRA<sup>+</sup>, (<b>d</b>) triflourination 2,5,<span class="html-italic">x</span>-TFRA<sup>+</sup>, (<b>e</b>) triflourination 2,5,<span class="html-italic">x</span>-TFRA<sup>+</sup>, (<b>f</b>) triflourination 4,5,<span class="html-italic">x</span>-TFRA<sup>+</sup>. Also reported are the different imaginary frequency for each transition (‡) state.</p>
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<p>Minimum energy path for the 1 (in red) and 5 (in black) fluorination reactions.</p>
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<p>Relative concentration of different resazurin forms as a function of the pH.</p>
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<p>Calculated molecular orbitals of RA and 2,4,5-TFRA from HOMO−1 to LUMO+1.</p>
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<p>Calculated fluorescence spectra of resorufin and its derivatives. The experimental spectrum of resorufin is displayed in the background, normalised to the maximum intensity calculated.</p>
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20 pages, 6705 KiB  
Article
Environmental Controls on Evapotranspiration and Its Components in a Qinghai Spruce Forest in the Qilian Mountains
by Guanlong Gao, Xiaoyun Guo, Qi Feng, Erwen Xu, Yulian Hao, Rongxin Wang, Wenmao Jing, Xiaofeng Ren, Simin Liu, Junxi Shi, Bo Wu, Yin Wang and Yujing Wen
Plants 2024, 13(6), 801; https://doi.org/10.3390/plants13060801 - 12 Mar 2024
Cited by 1 | Viewed by 1023
Abstract
Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration ( [...] Read more.
Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration (T), evaporation (Es) and canopy interceptions (Ei), of the Qinghai spruce, which may provide rich information for improving water resource management. In this study, we partitioned ET based on the assumption that total ET equals the sum of T, Es and Ei, and then we analyzed the environmental controls on ET, T and Es. The results show that, during the main growing seasons of the Qinghai spruce (from May to September) in the Qilian mountains, the total ET values were 353.7 and 325.1 mm in 2019 and 2020, respectively. The monthly dynamics in the daily variations in T/ET and Es/ET showed that T/ET increased until July and gradually decreased afterwards, while Es/ET showed opposite trends and was mainly controlled by the amount of precipitation. Among all the ET components, T always occupied the largest part, while the contribution of Es to ET was minimal. Meanwhile, Ei must be considered when partitioning ET, as it accounts for a certain percentage (greater than one-third) of the total ET values. Combining Pearson’s correlation analysis and the boosted regression trees method, we concluded that net radiation (Rn), soil temperature (Ts) and soil water content (SWC) were the main controlling factors for ET. T was mainly determined by the radiation and soil hydrothermic factors (Rn, photosynthetic active radiation (PAR) and TS30), while Es was mostly controlled by the vapor pressure deficit (VPD), atmospheric precipitation (Pa), throughfall (Pt) and air temperature (Ta). Our study may provide further theoretical support to improve our understanding of the responses of ET and its components to surrounding environments. Full article
(This article belongs to the Special Issue Responses of Vegetation to Global Climate Change)
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<p>Location of the study area.</p>
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<p>Diurnal variations in environmental factors, including (<b>a</b>) net radiation (<span class="html-italic">R<sub>n</sub></span>) and photosynthetic active radiation (PAR), (<b>b</b>) air temperature (<span class="html-italic">T<sub>a</sub></span>) and vapor pressure deficit (VPD), (<b>c</b>) soil temperature (<span class="html-italic">T<sub>s</sub></span>) at different depths, (<b>d</b>) precipitation (<span class="html-italic">P<sub>a</sub></span>) and throughfall (<span class="html-italic">P<sub>t</sub></span>), (<b>e</b>) soil water content (SWC) at different depths, and (<b>f</b>) relative humidity (RH) and wind speed (<span class="html-italic">u</span>), in 2019 and 2020 at the study site.</p>
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<p>The dynamics of monthly mean diurnal variations in (<b>a</b>) evapotranspiration (ET) and (<b>b</b>) transpiration (<span class="html-italic">T</span>) of the Qinghai spruce in the Qilian mountains in 2019 and 2020.</p>
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<p>The monthly dynamics of daily variations in (<b>a</b>) evapotranspiration (ET), (<b>b</b>) transpiration (<span class="html-italic">T</span>) and (<b>c</b>) evaporation (<span class="html-italic">E<sub>s</sub></span>) during the main growing season of the Qinghai spruce in the Qilian mountains in 2019 and 2020.</p>
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<p>Monthly cumulative values of (<b>a</b>) evapotranspiration (ET), (<b>b</b>) transpiration (<span class="html-italic">T</span>) and (<b>c</b>) evaporation (<span class="html-italic">E<sub>s</sub></span>) for the Qinghai spruce in the Qilian mountains during the study periods in 2019 and 2020.</p>
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<p>Monthly dynamics of daily variations in the proportions of (<b>a</b>) transpiration to evapotranspiration (<span class="html-italic">T</span>/ET) and (<b>b</b>) evaporation to evapotranspiration (<span class="html-italic">E<sub>s</sub></span>/ET) of the Qinghai spruce in the Qilian mountains in 2019 and 2020.</p>
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<p>Proportions of transpiration to evapotranspiration (<span class="html-italic">T</span>/ET), evaporation to evapotranspiration (<span class="html-italic">E<sub>s</sub></span>/ET) and canopy interception to evapotranspiration (<span class="html-italic">E<sub>i</sub></span>/ET) of the Qinghai spruce in each month in 2019 and 2020.</p>
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<p>Proportions of transpiration to evapotranspiration (<span class="html-italic">T</span>/ET), evaporation to evapotranspiration (<span class="html-italic">E<sub>s</sub></span>/ET) and canopy interception to evapotranspiration (<span class="html-italic">E<sub>i</sub></span>/ET) of the Qinghai spruce during the study periods in 2019 and 2020.</p>
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<p>Pearson’s correlation analysis between (<b>a</b>) evapotranspiration (ET), (<b>b</b>) transpiration (<span class="html-italic">T</span>), (<b>c</b>) evaporation (<span class="html-italic">E<sub>s</sub></span>) and the environmental factors, including air temperature (<span class="html-italic">T<sub>a</sub></span>), relative humidity (RH), vapor pressure deficit (VPD), net radiation (<span class="html-italic">R<sub>n</sub></span>), photosynthetic active radiation (PAR), wind speed (<span class="html-italic">u</span>), precipitation (<span class="html-italic">P<sub>a</sub></span>) and throughfall (<span class="html-italic">P<sub>t</sub></span>). ** means significant correlation.</p>
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<p>Contributions of environmental factors to (<b>a</b>) evapotranspiration (ET), (<b>b</b>) transpiration (<span class="html-italic">T</span>) and (<b>c</b>) evaporation (<span class="html-italic">E<sub>s</sub></span>) based on the boosted regression trees method. The environmental factors included net radiation (<span class="html-italic">R<sub>n</sub></span>); soil temperature at depths of 30 cm (<span class="html-italic">T<sub>s</sub></span><sub>30</sub>) and 80 cm (<span class="html-italic">T<sub>s</sub></span><sub>80</sub>); soil water content at depths of 10 cm (SWC<sub>10</sub>), 40 cm (SWC<sub>40</sub>), 60 cm (SWC<sub>60</sub>) and 80 cm (SWC<sub>80</sub>); vapor pressure deficit (VPD); relative humidity (RH); wind speed (<span class="html-italic">u</span>); precipitation (<span class="html-italic">P<sub>a</sub></span>); and throughfall (<span class="html-italic">P<sub>t</sub></span>).</p>
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1 pages, 504 KiB  
Correction
Correction: Volkova et al. Cyclodextrin’s Effect on Permeability and Partition of Nortriptyline Hydrochloride. Pharmaceuticals 2023, 16, 1022
by Tatyana Volkova, Olga Simonova and German Perlovich
Pharmaceuticals 2024, 17(1), 57; https://doi.org/10.3390/ph17010057 - 29 Dec 2023
Viewed by 911
Abstract
In the original publication [...] Full article
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Figure 2

Figure 2
<p>Distribution coefficients log<math display="inline"><semantics> <mrow> <msubsup> <mi>D</mi> <mrow> <mi>a</mi> <mi>p</mi> <mi>p</mi> </mrow> <mrow> <mi>o</mi> <mi>c</mi> <mi>t</mi> <mo>/</mo> <mi>b</mi> <mi>u</mi> <mi>f</mi> </mrow> </msubsup> </mrow> </semantics></math>, log<math display="inline"><semantics> <mrow> <msubsup> <mi>D</mi> <mrow> <mi>a</mi> <mi>p</mi> <mi>p</mi> </mrow> <mrow> <mi>h</mi> <mi>e</mi> <mi>x</mi> <mo>/</mo> <mi>b</mi> <mi>u</mi> <mi>f</mi> </mrow> </msubsup> </mrow> </semantics></math>, and ΔlogD parameter without cyclodextrins (1), with 0.0115 M of HP-β-CD (2), and with 0.0115 M of SBE-β-CD (3) in the aqueous phase for NTT•HCl at 37 °C: (<b>a</b>) pH 6.8, (<b>b</b>) pH 4.0 of the buffer phase.</p>
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11 pages, 3610 KiB  
Article
Phenylalkyl Glycosides from the Flowers of Brugmansia arborea L. and Their Radical Scavenging Effect and Protective Effect on Pancreatic Islets Damaged by Alloxan in Zebrafish (Danio rerio) Larvae
by Hyoung-Geun Kim, Youn Hee Nam, Tong Ho Kang, Nam-In Baek, Min-Ho Lee and Dae Young Lee
Plants 2023, 12(24), 4075; https://doi.org/10.3390/plants12244075 - 5 Dec 2023
Viewed by 1120
Abstract
The study aimed to investigate the antioxidant and antidiabetic activity of Brugmansia arborea L. flower extracts, solvent fractions, and isolated compounds. B. arborea L flowers were extracted with aqueous methanol, and concentrated extract was successively partitioned into EtOAc, n-BuOH, and H2 [...] Read more.
The study aimed to investigate the antioxidant and antidiabetic activity of Brugmansia arborea L. flower extracts, solvent fractions, and isolated compounds. B. arborea L flowers were extracted with aqueous methanol, and concentrated extract was successively partitioned into EtOAc, n-BuOH, and H2O fractions. Repeated silica gel and octadecyl silica gel column chromatographies for EtOAc and n-BuOH fractions led to the isolation of a new phenylalkyl glycoside (6), along with five known ones. Several spectroscopic data led to the structure determination of one new phenylalky glycoside as brugmansioside C (named) (6) and five known ones as benzyl-O-β-D-glucopyranoside (1), benzyl-O-β-D-glucosyl-(1→6)-β-D-glucopyranoside (2), 2-phenylethyl-O-β-D-glucopyranoside (3), 2-phenylethyl-O-β-D-glucosyl-(1→6)-β-D-glucopyranoside (4), and 3-phenylpropyl-O-β-D-glucopyranoside (5). The five known ones (15) were isolated from B. arborea flowers for the first time in this study. The extract, solvent fractions, and all isolated compounds showed radical scavenging activities using ABTS radical, and EtOAc fraction showed the highest scavenging capacity, whereas compounds 2, 4, and 6 did not display the capacity to use the DPPH radical. The extract, solvent fractions, and all isolated compounds showed a protective effect on pancreatic islets damaged by alloxan treatment in zebrafish larvae. The pancreatic islet size treated with EtOAc, n-BuOH fractions, and all compounds significantly increased by 64.0%, 69.4%, 82.0%, 89.8%, 80.0%, 97.8%, 103.1%, and 99.6%, respectively, compared to the alloxan-induced group. These results indicate that B. arborea flowers and their isolated compounds are useful as potential antioxidant and antidiabetic agents. Full article
(This article belongs to the Special Issue Antioxidant Activity of Medicinal and Aromatic Plants 2023)
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<p>Chemical structures of phenylalkyl glucosides <b>1</b>–<b>6</b> isolated from the flowers of <span class="html-italic">Brugmansia arborea</span>. Glc: <span class="html-italic">β</span>-D-glucopyranosyl; the gHMBC key correlations are represented by single-headed arrows from H to C.</p>
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<p>Protective effect of EtOAc and <span class="html-italic">n</span>-BuOH fractions and compounds <b>1</b>–<b>6</b> from <span class="html-italic">Brugmansia arborea</span> flowers on alloxan-induced pancreatic islets in zebrafish. (<b>a</b>) Size of the pancreatic islets. (<b>b</b>) Pancreatic islet image: NOR: normal group, AX: alloxan group, GLM: glimepiride+AX group, BAF: Extract+AX, BAFE: EtOAc fraction+AX, BAFB: <span class="html-italic">n</span>-BuOH fraction+AX, BAP1-6: compound <b>1</b>–<b>6</b>+AX. (<sup>###</sup> <span class="html-italic">p</span> &lt; 0.001; compared to the normal group), (*** <span class="html-italic">p</span> &lt; 0.001 compared to the alloxan-treated group). Scale bar = 100 μm.</p>
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<p>Action of diazoxide (DZ) for pancreatic islets damage by alloxan (AX) in zebrafish fractions and compounds. (<b>a</b>) Size of the pancreatic islets. (<b>b</b>) Pancreatic islet image. Normal (NOR), NOR+alloxan-treatment (AX), AX+glimepiride, a positive control (GLM), AX+Extract, EtOAc, and <span class="html-italic">n</span>-BuOH fractions of <span class="html-italic">B. arborea</span> flowers (BAF, BAFE, BAFB), AX + phenylalkyl glucosides <b>1</b>–<b>6</b> (BAP1~BAP6), treated with and without diazoxide (DZ). (<sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001; compared to NOR), (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001; compared to AX), (+ <span class="html-italic">p</span> &lt; 0.05, ++ <span class="html-italic">p</span> &lt; 0.01). Scale bar = 100 μm.</p>
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23 pages, 5692 KiB  
Article
Evapotranspiration Partitioning and Estimation Based on Crop Coefficients of Winter Wheat Cropland in the Guanzhong Plain, China
by Xiongbiao Peng, Xuanang Liu, Yunfei Wang and Huanjie Cai
Agronomy 2023, 13(12), 2982; https://doi.org/10.3390/agronomy13122982 - 2 Dec 2023
Cited by 2 | Viewed by 1472
Abstract
Accurate estimation and effective portioning of actual evapotranspiration (ETa) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on [...] Read more.
Accurate estimation and effective portioning of actual evapotranspiration (ETa) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on ETa rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (Kc, α, and KHc) were quantified by combining the ETa and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (Kc, α, and KHc) was proposed to estimate the ETa rates. According to the obtained results, the average cumulative ETa, T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/ETa ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the ETa rates at a threshold of 1.27 KPa. The average Kc, α, and KHc values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured ETa rates and ETa rates estimated using the adjusted Kc, α, and KHc showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R2) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use. Full article
(This article belongs to the Section Water Use and Irrigation)
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Figure 1

Figure 1
<p>Geographical location of the experiment site and an image of the installed EC experimental device. The blue line is the Yellow River, and the red line is the contour line of the Guanzhong area.</p>
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<p>Seasonal variation in the monitored environmental variables over the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons at the experimental site; air temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>air</mi> </mrow> </msub> </mrow> </semantics></math>), maximum temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math>), and minimum temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math>) (<b>a</b>); solar radiation (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> </mrow> </msub> </mrow> </semantics></math>), net radiation (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>), and vapor pressure deficit (VPD) (<b>b</b>); wind speed (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">U</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>) at 2 m (<b>c</b>); precipitation amounts, irrigations amounts, and volumetric soil water contents at the 10 cm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>10</mn> </mrow> </msub> </mrow> </semantics></math>), 20 cm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msub> </mrow> </semantics></math>), 40 cm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>40</mn> </mrow> </msub> </mrow> </semantics></math>), and 60 cm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>60</mn> </mrow> </msub> </mrow> </semantics></math>) soil depths (<b>d</b>); leaf area index (LAI) (<b>e</b>). Ini, Ove, Dev, Mid, and Lat denote the initial, overwintering, development, middle, and late winter wheat growth stages, respectively.</p>
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<p>Daily energy balance closure in the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons, represented by H + LE versus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math> − G.</p>
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<p>Diurnal variation in the net radiation (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>), latent heat flux (LE), sensible heat flux (H), and soil heat flux (G) in the 2020–2021 (<b>a</b>), 2021–2022 (<b>b</b>), and 2022–2023 (<b>c</b>) winter wheat growing seasons.</p>
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<p>Ninety-fifth quantile regression between the seasonal half-hourly GPP∙<math display="inline"><semantics> <mrow> <msup> <mrow> <mi>VPD</mi> </mrow> <mrow> <mn>0.5</mn> </mrow> </msup> </mrow> </semantics></math> and ET data observed at the study site during the 2020–2021 (<b>a</b>), 2021–2022 (<b>b</b>), and 2022–2023 (<b>c</b>) winter wheat growing seasons, as well as those derived from the three-year average of the winter wheat growing seasons (<b>d</b>). The intercept values of the quantile regressions were set to zero.</p>
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<p>Seasonal variation in the actual evapotranspiration (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>), transpiration (T), and evaporation (E) over the 2020–2021 (<b>a</b>), 2021–2022 (<b>b</b>), and 2022–2023 (<b>c</b>) winter wheat growing seasons. Ini, Ove, Dev, Mid, and Lat denote the initial, overwintering, development, middle, and late winter wheat growth stages, respectively.</p>
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<p>Pearson correlation coefficients between the daily actual evapotranspiration (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>) and measured environmental factors at different stages of the three winter wheat growing seasons. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>air</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi mathvariant="normal">n</mi> </mrow> </msub> </mrow> </semantics></math>, VPD, U<sub>2</sub>, P, G, VPD, and LAI denote air temperature, maximum air temperature, minimum air temperature, net radiation, vapor pressure deficit, wind speed at 2 m, precipitation, soil heat fluxes, and leaf area index, respectively; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>10</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>40</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>SWC</mi> </mrow> <mrow> <mn>60</mn> </mrow> </msub> </mrow> </semantics></math> denote volumetric soil water contents at 10, 20, 40, and 60 cm. Ini, Ove, Dev, Mid, and Lat denote the initial, overwintering, development, middle, and late winter wheat growth stages.</p>
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<p>Seasonal variation in the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>), α (<b>b</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mrow> <mi>Hc</mi> </mrow> </msub> </mrow> </semantics></math> (<b>c</b>) coefficients obtained using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations, respectively, in the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons. Ini, Ove, Dev, Mid, and Lat denote the initial, overwintering, development, middle, and late winter wheat growth stages, respectively. The black solid, grey solid, and black dashed lines represent the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> </mrow> </msub> </mrow> </semantics></math>, α, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mrow> <mi>Hc</mi> </mrow> </msub> </mrow> </semantics></math> curves, respectively.</p>
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<p>Seasonal variation in the actual (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>) and estimated ET rates in the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eq</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePT</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePM</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">H</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eH</mi> </mrow> </msub> </mrow> </semantics></math> denote reference ET determined using the PT equation, PT coefficients, PM equation, PM coefficients, HS equation, and HS coefficients. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eq</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePT</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePM</mi> </mrow> </msub> </mrow> </semantics></math> (<b>b</b>); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">H</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eH</mi> </mrow> </msub> </mrow> </semantics></math> (<b>c</b>).</p>
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<p>Performances of the PM equation, PM coefficients, PM equation, PM coefficients, HS equation, and HS coefficients in estimating the daily <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eq</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePT</mi> </mrow> </msub> </mrow> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> (<b>c</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>ePM</mi> </mrow> </msub> </mrow> </semantics></math> (<b>d</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi mathvariant="normal">H</mi> </mrow> </msub> </mrow> </semantics></math> (<b>e</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ET</mi> </mrow> <mrow> <mi>eH</mi> </mrow> </msub> </mrow> </semantics></math> (<b>f</b>) rates, respectively, in the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons.</p>
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<p>Relationships between the daily evapotranspiration (ET) and vapor pressure deficit (VPD) values of the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons.</p>
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<p>Relationships of the daily T/ET rates with the vapor pressure deficit (VPD) (<b>a</b>) and leaf area index (LAI) (<b>b</b>) values from the development growth stage to the end of the middle growth stage in the 2020–2021, 2021–2022, and 2022–2023 winter wheat growing seasons.</p>
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14 pages, 1471 KiB  
Article
The Decreased Availability of Soil Moisture and Canopy Conductance Dominate Evapotranspiration in a Rain-Fed Maize Ecosystem in Northeastern China
by Hui Zhang, Tianhong Zhao, Ruipeng Ji, Shuting Chang, Quan Gao and Ge Zhang
Agronomy 2023, 13(12), 2941; https://doi.org/10.3390/agronomy13122941 - 29 Nov 2023
Cited by 2 | Viewed by 1460
Abstract
Evapotranspiration (ET) determines the crop productivity in rain-fed agriculture. Global climate change alters the trade-off between soil water supply and atmospheric demand, energy partitioning, and community biophysical and structural properties; however, the interactive effects of these biotic and abiotic factors on ET and [...] Read more.
Evapotranspiration (ET) determines the crop productivity in rain-fed agriculture. Global climate change alters the trade-off between soil water supply and atmospheric demand, energy partitioning, and community biophysical and structural properties; however, the interactive effects of these biotic and abiotic factors on ET and its components remain unclear. ET was measured in 2005–2020 in a rain-fed maize ecosystem in northeastern China using the eddy covariance method. By decomposing ET into transpiration (T) and evaporation (E) with the Shuttleworth–Wallace model, we investigated the abiotic and biotic interactive effects on ET and its components at annual levels. Results showed that available energy and albedo exhibited no significant time-series trends, but the Bowen ratio exhibited an increasing trend. Precipitation exhibited no significant trends; however, soil water content (SWC) decreased with time, accompanied by significantly increased air temperature (Ta) and a vapor pressure deficit (VPD). The ET decline was controlled by T, rather than E. The T decline was mainly controlled by canopy conductance and SWC. CO2 concentrations and the VPD exhibited indirect effects on T by reducing canopy conductance, while Ta and precipitation had indirect effects on T by reducing SWC. Our results indicated that decreasing ET may be more severe with crop physiological adaptability for a decreased SWC. Aiming to enhance water resource efficiency, the practice of returning crop residues to the field to reduce soil evaporation, coupled with adjusting the sowing time to mitigate the risk of seasonal droughts during critical growth stages, represents an effective strategy in agricultural water resource management. Full article
(This article belongs to the Section Water Use and Irrigation)
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Figure 1

Figure 1
<p>Inter-annual variation in annual net radiation (Rn) above the canopy (<b>a</b>); soil heat flux (<span class="html-italic">G</span>), and net radiation (Rns) above the soil surface (<b>b</b>); and sensible heat flux (<span class="html-italic">H</span>) (<b>c</b>), latent heat flux (LE) and Bowen ratio (<span class="html-italic">β</span>) (<b>d</b>) during 2005–2020 in the study area and at a nearby national weather station.</p>
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<p>Inter-annual variation in annual downward shortwave (DR) and photosynthetically active radiation (PAR) (<b>a</b>); upward shortwave radiation (UR) and albedo (Albedo) (<b>b</b>); and downward (DLR) (<b>c</b>) and upward longwave radiation (ULR) and air temperature (Ta) above the canopy (<b>d</b>) during 2005–2020 at the study site and at a nearby national weather station.</p>
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<p>Inter-annual variation in annual precipitation (PPT) (<b>a</b>); soil water content (SWC) obtained from a datalogger (<b>b</b>) and with sampling, vapor pressure deficit (VPD) (<b>c</b>); and evapotranspiration (ET), transpiration (<span class="html-italic">T</span>), and evaporation (<span class="html-italic">E</span>) (<b>d</b>) during 2005–2020.</p>
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<p>Structural equation modeling results of the relationships among environmental factors with annual evapotranspiration (ET) and its components for years 2005–2020 (<span class="html-italic">χ</span><sup>2</sup> = 18.002, <span class="html-italic">p</span> = 0.389, df = 16). <span class="html-italic">E</span>, evaporation; <span class="html-italic">T</span>, transpiration; Rns, net soil radiation; SWC, soil water content; VPD, vapor pressure deficit; Ta, air temperature; PPT, annual precipitation. Black arrows indicate significant positive relationships, while gray arrows indicate significant negative relationships. ***, <span class="html-italic">p</span> &lt; 0.001; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Annual carbon dioxide (CO<sub>2</sub>) (<b>a</b>), leaf area index (LAI) (<b>b</b>), canopy stomatal conductance (g<sub>sc</sub>) (<b>c</b>), and soil conductance (g<sub>ss</sub>) (<b>d</b>) for years 2005–2020.</p>
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<p>Structural equation modeling results of the relationships among environmental and biotic factors with annual evapotranspiration (ET) and its components for years 2005–2020 (<span class="html-italic">χ</span><sup>2</sup> = 33.529, <span class="html-italic">p</span> = 0.393 df = 16). <span class="html-italic">E</span>, evaporation; <span class="html-italic">T</span>, transpiration; g<sub>sc</sub>, canopy stomatal conductance; Rns, net soil radiation; SWC, soil water content; VPD, vapor pressure deficit; Ta, air temperature; PPT, annual precipitation; CO<sub>2</sub>, CO<sub>2</sub> concentration; LAI, leaf area index; g<sub>sc</sub>, canopy stomatal conductance; g<sub>ss</sub>, soil surface conductance. Black arrows indicate significant positive relationships, while gray arrows indicate significant negative relationships. ***, <span class="html-italic">p</span> &lt; 0.001; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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38 pages, 26977 KiB  
Article
Ecosystem Evapotranspiration Partitioning and Its Spatial–Temporal Variation Based on Eddy Covariance Observation and Machine Learning Method
by Linjun Lu, Danwen Zhang, Jie Zhang, Jiahua Zhang, Sha Zhang, Yun Bai and Shanshan Yang
Remote Sens. 2023, 15(19), 4831; https://doi.org/10.3390/rs15194831 - 5 Oct 2023
Cited by 2 | Viewed by 1817
Abstract
Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be [...] Read more.
Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation. Full article
(This article belongs to the Section Remote Sensing for Geospatial Science)
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Figure 1

Figure 1
<p>Spatial distributions of the 55 flux sites used in this study. (<b>a</b>,<b>b</b>) are detailed explanations of black box (<b>a</b>) and black box (<b>b</b>) in the figure above.</p>
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<p>The workflow of this study. <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>n</mi> <mi>i</mi> <mi>g</mi> <mi>h</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> represents night-time vegetation transpiration; <math display="inline"><semantics> <mrow> <mi>E</mi> <msub> <mi>T</mi> <mrow> <mi>n</mi> <mi>i</mi> <mi>g</mi> <mi>h</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> represents night-time ecosystem transpiration; E represents soil evaporation; <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> represents daytime vegetation transpiration; <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>d</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> represents daytime soil evaporation; NSE, R, and RMSE W/m<sup>2</sup> are the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and root mean square error, respectively. They are used to evaluate the model accuracy.</p>
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<p>Importance of model features for the ten ecosystems.</p>
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<p>Performance of the XGBoost model when estimating the values of E for all ecosystems using the growing season dataset. RMSE unit: W/m<sup>2</sup>; ENF: evergreen needleleaf forests; EBF: evergreen broadleaf forests; DBF: deciduous broadleaf forests; MF: mixed forests; CSH: closed shrublands; OSH: open shrublands; WSA: woody savannas; GRA: grasslands; CRO: croplands; WET: wetlands.</p>
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<p>Performance of the XGBoost model when estimating the values of E for the nine ecosystems using the non-growing season dataset. RMSE unit: W/m<sup>2</sup>.</p>
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<p>Performance of the XGBoost model when estimating the values of E for CRO ecosystem using the crops fallow period dataset. RMSE unit: W/m<sup>2</sup>. CRO: croplands.</p>
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<p>T/ET values for different ecosystems. The diamonds and solid lines in the boxes indicate the mean and median values, respectively. The solid black diamonds are outliers.</p>
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<p>The seasonal variations in T/ET grouped by ecosystems. The line is the mean values across sites and shading area is the 95% confidence intervals.</p>
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<p>The impacts of mean annual LAI (<b>a</b>) and VPD (<b>b</b>) on the interannual variations in T/ET. Solid line is the regression line. R<sup>2</sup> is the correlation coefficient, and *** is the significant level at 0.001.</p>
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<p>Validation results of the XGBoost model in growing season and non-growing season for nine ecosystems.</p>
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<p>Performance of RF, LightGBM, ANN, and XGBoost models for estimating E values in the cropland sites with the subtropical humid climate (CRO + Cf): (1) using the test dataset; (2) using the crop fallow period dataset. RMSE unit: W/m<sup>2</sup>.</p>
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<p>Performance of RF, LightGBM, ANN, and XGBoost models for estimating E values in the cropland sites in the Mediterranean climate (CRO + Cs): (1) using the test dataset; (2) using the crop fallow period dataset. RMSE unit: W/m<sup>2</sup>.</p>
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<p>The range of T/ET obtained in our study and the values published in the previous literature. Bold solid line inside the bar represents the mean T/ET for each study, while the extension of the box represents the plus or minus standard deviation, or indicates ranges reported in the published literature.(Wang et al. [<a href="#B52-remotesensing-15-04831" class="html-bibr">52</a>], Schlesinger and Jasechko [<a href="#B51-remotesensing-15-04831" class="html-bibr">51</a>], Li et al. [<a href="#B15-remotesensing-15-04831" class="html-bibr">15</a>], Good et al. [<a href="#B53-remotesensing-15-04831" class="html-bibr">53</a>], Maxwell and Condon [<a href="#B54-remotesensing-15-04831" class="html-bibr">54</a>], Fatichi and Pappas [<a href="#B55-remotesensing-15-04831" class="html-bibr">55</a>], Zhou et al. [<a href="#B10-remotesensing-15-04831" class="html-bibr">10</a>] and Gu et al. [<a href="#B50-remotesensing-15-04831" class="html-bibr">50</a>]).</p>
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<p>Relationship between average daily T/ET and average LAI at 14 sites in ENF ecosystem. Solid line is regression for the individual data points.</p>
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<p>Relationship between average daily T/ET and average LAI at 2 sites in EBF ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 7 sites in DBF ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 1 site in MF ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 11 sites in CRO ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 10 sites in GRA ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 4 sites in WET ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 1 site in WSA ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 1 site in CSH ecosystem.</p>
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<p>Relationship between average daily T/ET and average LAI at 2 sites in OSH ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 14 sites in ENF ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 2 sites in EBF ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 7 sites in DBF ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 1 site in MF ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 11 sites in CRO ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 10 sites in GRA ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 4 sites in WET ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 1 site in WSA ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 1 site in CSH ecosystem.</p>
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<p>Relationship between average daily T/ET and average VPD at 2 sites in OSH ecosystem.</p>
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17 pages, 2989 KiB  
Article
Evaluation of Lipid Extraction Protocols for Untargeted Analysis of Mouse Tissue Lipidome
by Ashraf M. Omar and Qibin Zhang
Metabolites 2023, 13(9), 1002; https://doi.org/10.3390/metabo13091002 - 9 Sep 2023
Cited by 2 | Viewed by 2400
Abstract
Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery [...] Read more.
Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid–liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl3 (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine. Full article
(This article belongs to the Section Metabolomic Profiling Technology)
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<p>Extraction recoveries of ISTD in mouse tissues by the six tested extraction methods. Displayed are mean and SD (<span class="html-italic">n</span> = 3).</p>
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<p>Mean extraction recoveries of ISTD per lipid class. Displayed are mean and SD (<span class="html-italic">n</span> = 3).</p>
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<p>The sum concentrations of identified lipids in each mouse tissue using the six extraction methods. Displayed are mean and SD (<span class="html-italic">n</span> = 3).</p>
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<p>The sum of concentrations of identified lipids in mouse pancreas by the six extraction methods in relation to the total no. of carbons in the fatty acyl side chain(s). Since SM species were identified as the sum composition without providing information on the composition of the sphingoid base, the sum concentrations were plotted against the total no. of carbons rather than the no. of carbons in the fatty acyl chain. Displayed are mean and SD (<span class="html-italic">n</span> = 3).</p>
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<p>PCA analysis of absolute concentrations of endogenous mouse lipids extracted by different methods.</p>
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<p>Percentage of identified lipids with absolute concentration CV ≥ 25%.</p>
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13 pages, 5830 KiB  
Article
Neuroprotective Effects of Sparassis crispa Ethanol Extract through the AKT/NRF2 and ERK/CREB Pathway in Mouse Hippocampal Cells
by Malk Eun Pak and Wei Li
J. Fungi 2023, 9(9), 910; https://doi.org/10.3390/jof9090910 - 7 Sep 2023
Cited by 1 | Viewed by 1446
Abstract
Sparassis crispa, known as the “Cauliflower mushroom”, is an edible medicinal fungus found in Asia, Europe, and North America. Its fruiting bodies contain active biological and pharmacological ingredients with antitumor and anti-inflammatory properties. In this study, we investigated the neuroprotective effect of [...] Read more.
Sparassis crispa, known as the “Cauliflower mushroom”, is an edible medicinal fungus found in Asia, Europe, and North America. Its fruiting bodies contain active biological and pharmacological ingredients with antitumor and anti-inflammatory properties. In this study, we investigated the neuroprotective effect of various Sparassis crispa extract against glutamate-induced toxicity and oxidative stress in hippocampal HT22 cells. Cell viability and reactive oxygen species (ROS) analyses served to evaluate the neuroprotective effects of Sparassis crispa ethanol extract (SCE) and their fractions partitioned with ethyl acetate (EtOAc; SCE-E) and water (SCE-W) in HT22 cells. SCE and SCE-E treatment reduced glutamate-induced cell death and ROS generation. SCE-E reduced apoptosis and ROS levels by regulating anti-apoptotic proteins. Under glutamate treatment, SCE-E activated nuclear factor erythroid-derived 2-related factor 2 (Nrf2) and regulated extracellular signal-regulated kinase (ERK) and AKT signals at late stages. SCE-E increased the protein expression of cAMP response element binding (CREB), brain-derived neurotrophic factor (BDNF), and Kelch-like ECH-associated protein 1 (Keap1), and decreased the Nrf2 protein expression. Moreover, co-treatment of SCE-E and wortmannin did not activate Nrf2 expression. Thus, the neuroprotective effect of SCE-E is likely due to Nrf2 and CREB activation through AKT and ERK phosphorylation, which effectively suppress glutamate-induced oxidative stress in HT22 cells. Accordingly, a daily supplement of SCE-E could become a potential treatment for oxidative-stress-related neurological diseases. Full article
(This article belongs to the Special Issue Edible Mushroom 3.0)
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<p><span class="html-italic">Sparassis crispa</span> material and sample preparation of <span class="html-italic">Sparassis crispa</span>. (<b>A</b>) Dried fruiting body and (<b>B</b>) extraction and separation scheme of <span class="html-italic">Sparassis crispa</span>. SCE: <span class="html-italic">Sparassis crispa</span> ethanol extract; SCE-E: SCE EtOAc fraction; SCE-W: SCE water fraction; EtOH: ethanol; EtOAc: ethyl acetate.</p>
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<p>Effects of <span class="html-italic">Sparassis crispa</span> ethanol extract and fractions on glutamate-induced cell death and ROS production. (<b>A</b>) Cell viability assay and morphological changes after SCE with glutamate treatment. (<b>B</b>) Cell viability assay after SCE-E and SCE-W with glutamate treatment. (<b>C</b>) Intracellular ROS levels were detected and (<b>D</b>) photomicrographs using carboxy-H<sub>2</sub>DCFDA after SCE, SCE-E, and SCE-W with glutamate treatment. SCE significantly enhanced cell survival dose dependently, and specially SCE-E reduced cell death and ROS production. All data are expressed as the mean ± SEM. <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. Con; ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. GLU. N = 6. Scale bar = 100 μm. Con: Control; GLU: glutamate; SCE: <span class="html-italic">Sparassis crispa</span> ethanol extract; SCE-E: SCE EtOAc fraction; SCE-W: SCE water fraction; ROS: intracellular reactive oxygen species; -: glutamate treatment cells</p>
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<p>Effects of SCE-E on glutamate-induced apoptotic cell death. (<b>A</b>) Representative flow cytometry analysis scatter-gram and (<b>B</b>) histogram for Annexin Ⅴ/PI staining. (<b>C</b>) Western blot and (<b>D</b>) its histogram for Bcl-2 and Bax. All data are expressed as the mean ± SEM. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. Con; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. GLU. N = 6 or 3. Con: Control; GLU: glutamate; SCE-E: SCE EtOAc fraction.</p>
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<p>Effects of SCE-E on glutamate-induced ROS production. Representative flow cytometry analysis for (<b>A</b>) carboxy-H<sub>2</sub>DCFDA and (<b>B</b>) it’s histograms. (<b>C</b>) Western blot and (<b>D</b>) its histogram for HO-1 and NQO-1. All data are expressed as the mean ± SEM. <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. Con; * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 vs. GLU. N = 3. Con: Control; GLU: glutamate; SCE-E: SCE EtOAc fraction; ROS: intracellular reactive oxygen species; HO-1: heme oxygenase-1; NQO1: NAD[P]H: quinone oxidoreductase 1.</p>
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<p>Effects of SCE-E on expression of antioxidant-related gene and phosphorylation of ERK/AKT signaling in glutamate-induced oxidative stress. (<b>A</b>) Gene expression of Creb, Bdnf, Nrf2, catalase, Sod1, and Sod2. (<b>B</b>) Western blot for pERK, ERK, pAKT, AKT, and Nrf2 in time-course in glutamate and SCE-E-treated cells. Data for gene expression are expressed as the mean ± SEM. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. Con; * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 vs. Glutamate. N = 3. Con: Control; GLU: glutamate; SCE-E: SCE EtOAc fraction; ERK: extracellular signal-regulated kinase; pERK: phosphorylated ERK; pAKT: phosphorylated AKT; Nrf2: nuclear factor erythroid-2-related factor 2.</p>
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<p>SCE-E regulates ERK/CREB and AKT/NRF2 signaling in glutamate-induced oxidative stress. (<b>A</b>) Western blot and its histogram for (<b>B</b>) BDNF, pCREB, NRF2, and Keap1. (<b>C</b>) Protein expression and (<b>D</b>) its densitometric histogram for pERK/ERK and pAKT/AKT using Western blot. (<b>E</b>) Expression of NRF2 protein by SCE-E in HT22 cells treated with glutamate with/without wortmannin or PD98059 and (<b>F</b>) its representative photographs. All data are expressed as the mean ± SEM. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 vs. Con; * <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 vs. GLU. N = 3. Con: Control; GLU: glutamate; SCE-E: SCE EtOAc fraction. BDNF: brain-derived neurotrophic factor; pCREB: phosphorylated cAMP response element binding protein; Nrf2: nuclear factor erythroid-2-related factor 2; Keap1: Kelch-like ECH-associated protein 1, ERK: extracellular signal-regulated kinase; pERK: phosphorylated ERK; pAKT: phosphorylated AKT.</p>
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<p>HPLC chromatogram of SCE-E at UV wavelengths of 254 nm.</p>
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<p>Schema of a hypothesized mechanism by neuroprotective effect of SCE-E in glutamate-induced hippocampal cells. SCE-E: SCE EtOAc fraction; ERK: extracellular signal-regulated kinase; CREB: ERK: extracellular signal-regulated kinase; NRF2: Nuclear factor erythroid-2-related factor 2; Keap1: Kelch-like ECH-associated protein 1; NQO1: NAD[P]H: quinone oxidoreductase 1; SOD: Superoxide Dismutase; ROS: intracellular reactive oxygen species.</p>
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27 pages, 4106 KiB  
Article
Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment
by Alexandre Maniçoba da Rosa Ferraz Jardim, José Edson Florentino de Morais, Luciana Sandra Bastos de Souza, Carlos André Alves de Souza, George do Nascimento Araújo Júnior, Cléber Pereira Alves, Gabriel Ítalo Novaes da Silva, Renan Matheus Cordeiro Leite, Magna Soelma Beserra de Moura, João L. M. P. de Lima and Thieres George Freire da Silva
Plants 2023, 12(13), 2562; https://doi.org/10.3390/plants12132562 - 6 Jul 2023
Cited by 9 | Viewed by 1719
Abstract
The in-situ quantification of turbulent flux and evapotranspiration (ET) is necessary to monitor crop performance in stressful environments. Although cacti can withstand stressful conditions, plant responses and plant–environment interactions remain unclear. Hence, the objective of our study was to investigate the interannual and [...] Read more.
The in-situ quantification of turbulent flux and evapotranspiration (ET) is necessary to monitor crop performance in stressful environments. Although cacti can withstand stressful conditions, plant responses and plant–environment interactions remain unclear. Hence, the objective of our study was to investigate the interannual and seasonal behaviour of components of the surface energy balance, environmental conditions, morphophysiological parameters, biomass yield and water relations in a crop of Nopalea cochenillifera in the semi-arid region of Brazil. The data were collected from a micrometeorological tower between 2015 and 2017. The results demonstrate that net radiation was significantly higher during the wet season. Latent heat flux was not significant between the wet season and dry season. During the dry-wet transition season in particular, sensible heat flux was higher than during the other seasons. We observed a large decline in soil heat flux during the wet season. There was no difference in ET during the wet or dry seasons; however, there was a 40% reduction during the dry-wet transition. The wet seasons and wet-dry transition showed the lowest Evaporative Stress Index. The plants showed high cladode water content and biomass during the evaluation period. In conclusion, these findings indicate high rates of growth, high biomass and a high cladode water content and explain the response of the cactus regarding energy partitioning and ET. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Hourly daytime mean energy flux over the area of cactus for different periods: wet season (<b>a</b>,<b>e</b>,<b>i</b>), wet-dry transition (<b>b</b>,<b>f</b>), dry season (<b>c</b>,<b>g</b>) and dry-wet transition (<b>d</b>,<b>h</b>). Here, <span class="html-italic">R<sub>n</sub></span> is the net radiation, <span class="html-italic">LE</span> is the latent heat flux, <span class="html-italic">H</span> is the sensible heat flux and <span class="html-italic">G</span> is the soil heat flux, all calculated in W m<sup>−2</sup>. Note: panels (<b>a</b>–<b>c</b>) show the seasons for 2015; (<b>d</b>–<b>g</b>) for 2016; and (<b>h</b>,<b>i</b>) for 2017.</p>
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<p>Time series for the energy budget in an area of cactus from 2015 to 2017. <span class="html-italic">R<sub>g</sub></span> is the global solar radiation (<b>a</b>), <span class="html-italic">R<sub>n</sub></span> is the net radiation (<b>b</b>), <span class="html-italic">LE</span> is the latent heat flux (<b>c</b>), <span class="html-italic">H</span> is the sensible heat flux (<b>d</b>), <span class="html-italic">G</span> is the soil heat flux (<b>e</b>) and ET is the evapotranspiration (<b>f</b>). The boxplots show the median; horizontal bars represent the 25th, 50th and 75th percentiles; whiskers (lower and upper) represent the 1.5× interquartile ranges. Corresponding data are represented by circles.</p>
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<p>Boxplot of seasonal variations in energy and water exchange in an area of cactus. <span class="html-italic">R<sub>n</sub></span> is the net radiation (<b>a</b>), <span class="html-italic">LE</span> is the latent heat flux (<b>b</b>), <span class="html-italic">H</span> is the sensible heat flux (<b>c</b>), <span class="html-italic">G</span> is the soil heat flux (<b>d</b>), ET is the evapotranspiration (<b>e</b>) and VPD is the vapour pressure deficit (<b>f</b>). The boxplots show the median; horizontal bars represent the 25th, 50th and 75th percentiles; whiskers (lower and upper) represent the 1.5× interquartile ranges; dots represent outliers. Significance was calculated using one-way analysis of variance (ANOVA) with Tukey’s honestly significant difference (HSD) post hoc test. Different letters above each box indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Temporal evolution of the Normalised Difference Vegetation Index [NDVI] (<b>a</b>), reference evapotranspiration [ET<sub>0</sub>] (<b>c</b>), Evaporative Stress Index [ESI] (<b>e</b>), available water fraction [AWF] (<b>g</b>) and rainfall (<b>i</b>) for an area cultivated with cactus. The five panels (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) show monthly results over each year for NDVI, ET<sub>0</sub>, ESI, AWF and rainfall during the experimental period, respectively. Data with error bars represent the mean ± SD (standard deviation).</p>
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<p>Absolute growth rate—AGR and relative growth rate—RGR (<b>a</b>), net assimilation rate—NAR (<b>b</b>), specific cladode area—SCA (<b>c</b>) and cladode emission rate (<b>d</b>) in <span class="html-italic">N. cochenillifera</span> (L.) Salm-Dyck under a semi-arid environment.</p>
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<p>Cladode water content (<b>a</b>) and cladode succulence (<b>b</b>) in <span class="html-italic">N. cochenillifera</span> (L.) Salm-Dyck over time (2015 to 2017) under a semi-arid environment. The boxplots show the median; horizontal bars represent the 25th, 50th and 75th percentiles; whiskers (lower and upper) represent the 1.5× interquartile ranges. The colouring of each boxplot indicates the sampling period.</p>
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<p>Principal component analysis (PCA) ordered biplot of environmental and plant factors. Score plot (<b>a</b>) and loading plot (<b>b</b>) of the first two principal components (PC) during the wet and dry seasons and transition periods. P (phosphorus), Mg<sup>2+</sup> (magnesium), Ca<sup>2+</sup> (calcium), K<sup>+</sup> (potassium) and Na<sup>+</sup> (sodium) refer to the efficiency of use of each nutrient. The following abbreviations are used: net radiation (<span class="html-italic">R<sub>n</sub></span>), latent heat flux (<span class="html-italic">LE</span>), sensible heat flux (<span class="html-italic">H</span>), soil heat flux (<span class="html-italic">G</span>), evapotranspiration (ET), vapour pressure deficit (VPD), Evaporative Stress Index (ESI), Normalised Difference Vegetation Index (NDVI), water use efficiency (WUE), reference evapotranspiration (ET<sub>0</sub>), absolute growth rate (AGR), relative growth rate (RGR), net assimilation rate (NAR), specific cladode area (SCA), cladode water content (CWC), cladode succulence (CS), radiation use efficiency (RUE) and biomass yield (Yield).</p>
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<p>Geographic location of the study area in the district of Mirandiba, Pernambuco, Brazil. UTM projection (zone 24S), with the SIRGAS2000 Datum.</p>
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16 pages, 8003 KiB  
Article
Partitioning Evapotranspiration in a Cotton Field under Mulched Drip Irrigation Based on the Water-Carbon Fluxes Coupling in an Arid Region in Northwestern China
by Yanxue Liu and Changlu Qiao
Agriculture 2023, 13(6), 1219; https://doi.org/10.3390/agriculture13061219 - 9 Jun 2023
Cited by 1 | Viewed by 1400
Abstract
Measuring evapotranspiration (ET) components in cotton fields under mulched drip irrigation is needed to improve water use efficiency and promote the development of water-saving agriculture. In this study, an Eddy Covariance (EC) system was used to observe the water-carbon [...] Read more.
Measuring evapotranspiration (ET) components in cotton fields under mulched drip irrigation is needed to improve water use efficiency and promote the development of water-saving agriculture. In this study, an Eddy Covariance (EC) system was used to observe the water-carbon fluxes of cotton fields under mulched drip irrigation in an arid region during two years (2021–2022). The Underlying Water Use Efficiency (uWUE) method was used to partition the ET into transpiration (T) and evaporation (E) in order to reveal the changing characteristics of ET and its components in cotton fields under mulched drip irrigation and analyze the effects of environmental factors on each component. The results showed that the diurnal variation of ET was the same as gross primary productivity (GPP), and their course of change showed a bimodal curve at budding, blooming, and boll stages. The relationship of T at different growth stages was the same as ET, which is blooming and boll stage > budding stage > boll opening stage > seedling stage. ET and its components were mainly affected by temperature (Tair) and net radiation (Rn). This study can provide a theoretical and practical basis for the application of uWUE in cotton fields under mulched drip irrigation and a scientific basis for the rational allocation of water resources and the formulation of a scientific water-saving irrigation system for farmland in an arid region. Full article
(This article belongs to the Section Agricultural Water Management)
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<p>Cultivation pattern of cotton fields under mulched drip irrigation (unit: cm).</p>
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<p>The exponential fitting results of <span class="html-italic">T<sub>air</sub></span> and <span class="html-italic">NEE</span> in 2021 (<b>a</b>) and 2022 (<b>b</b>).</p>
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<p>Day-to-day variation of environmental factors in 2021 (<b>a</b>) and 2022 (<b>b</b>).</p>
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<p>Day-to-day variation of the <span class="html-italic">ET</span> and <span class="html-italic">GPP</span> in 2021 (<b>a</b>) and 2022 (<b>b</b>).</p>
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<p>Diurnal variation of the <span class="html-italic">ET</span> (<b>a</b>) and <span class="html-italic">GPP</span> (<b>b</b>).</p>
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<p>Results of the <span class="html-italic">uWUE<sub>p</sub></span> in 2021 (<b>a</b>) and 2022 (<b>b</b>).</p>
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<p><span class="html-italic">E</span> and <span class="html-italic">T</span> of cotton fields under mulched drip irrigation at different growth stages in 2021 (<b>a</b>) and 2022 (<b>b</b>).</p>
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<p>Hyperbola fitting of <span class="html-italic">ET</span> and different environmental factors.</p>
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<p>Hyperbola fitting of <span class="html-italic">GPP</span> and different environmental factors.</p>
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13 pages, 3757 KiB  
Article
The Influence of the Osmotic Potential on Evapotranspiration
by Adil K. Salman, Wolfgang Durner, Mahyar Naseri and Deep C. Joshi
Water 2023, 15(11), 2031; https://doi.org/10.3390/w15112031 - 26 May 2023
Cited by 1 | Viewed by 2465
Abstract
Climate change is expected to affect the quality of soil and water, resulting in a significant impact on soil water balance in various regions around the world. Soil water potential plays a significant role in influencing evapotranspiration (ET), which is a crucial component [...] Read more.
Climate change is expected to affect the quality of soil and water, resulting in a significant impact on soil water balance in various regions around the world. Soil water potential plays a significant role in influencing evapotranspiration (ET), which is a crucial component of the soil water balance. The matric potential and the osmotic potential are the main components of the soil water potential. The osmotic potential is particularly important in dry soils, salt-affected soils, coastal lands, or when low-quality water is utilized for irrigation. Despite its importance, the impact of osmotic potential on ET has not been well-studied compared to other factors. Therefore, we conducted a study to investigate the impact of osmotic potential on ET from small-scale lab lysimeters planted with grass and equipped with scales and data loggers connected to computers. To create different osmotic potential levels, we irrigated the lysimeters with two different water qualities: distilled water and 4.79 dS.m−1 NaCl solution. The lysimeters were kept in well-watered conditions, and daily ET was monitored. Our results indicate a strong correlation between osmotic potential and ET. After three months of applying the treatments, the lysimeters with lower osmotic potential had a 39% reduction in cumulative ET compared to those irrigated with distilled water. Moreover, the osmotic stress affected plant health, leading to a notable decrease in the leaf area index and exerting a significant influence on evapotranspiration partitioning components, including transpiration and evaporation. Full article
(This article belongs to the Special Issue Monitoring, Reclamation and Management of Salt-Affected Lands)
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Figure 1

Figure 1
<p>Experimental setup. (<b>Left</b>): Moist soil with the grass seeds. (<b>Right</b>): Four mini-lysimeters with DL6 data loggers mounted on scales after 18 days of initial growth.</p>
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<p>Retention curve and hydraulic conductivity curve of the material used in the small lysimeters. pF is defined by <math display="inline"><semantics> <mrow> <mi>pF</mi> <mo>=</mo> <mi>log</mi> <mn>10</mn> <mrow> <mo>(</mo> <mrow> <mo>−</mo> <mi>h</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, where <math display="inline"><semantics> <mi>h</mi> </semantics></math> is the matric potential expressed as pressure head in cm.</p>
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<p>Original and processed images used in GC estimation. The images are labeled as (<b>A</b>–<b>C</b>) for the distilled water treatment at days 1, 20, and 86 after treatment application, and (<b>D</b>–<b>F</b>) for the saline water treatment at days 1, 20, and 86 after treatment application.</p>
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<p>Evolution in Ψo values for the lysimeters during the experiment.</p>
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<p>Average daily evapotranspiration values for the treatments during the experiment.</p>
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<p>Reduced plant health of the saline irrigated variant, 20 days after applying the treatments.</p>
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<p>Cumulative evapotranspiration in response to the experimental treatments.</p>
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<p>Partitioning of ET.</p>
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<p>The influence of Ψo on the ETa/ETo ratio.</p>
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<p>Electrical conductivity at the soil surface (1), mid layer (2), and lower layer (3).</p>
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