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Search Results (242)

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Keywords = FLUS model

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19 pages, 8020 KiB  
Article
Multi–Scenario Prediction of Land Cover Changes and Habitat Quality Based on the FLUS–InVEST Model in Beijing
by Xiaoyu Zhu, Zhongjun Wang, Tianci Gu and Yujun Zhang
Land 2024, 13(8), 1163; https://doi.org/10.3390/land13081163 - 29 Jul 2024
Viewed by 331
Abstract
As urbanization accelerates worldwide, understanding the impact of urban expansion on habitat quality has become increasingly critical in environmental science research. This study examines the impact of urban expansion on habitat quality in Beijing, forecasting land cover changes and ecological effects by 2030. [...] Read more.
As urbanization accelerates worldwide, understanding the impact of urban expansion on habitat quality has become increasingly critical in environmental science research. This study examines the impact of urban expansion on habitat quality in Beijing, forecasting land cover changes and ecological effects by 2030. Using CA–Markov and FLUS models, the research analyzes habitat quality from 2000 to 2030 through the InVEST model, revealing a significant urban land increase of 1316.47 km2 and a consequent habitat quality decline. Predictions for 2030 indicate varying habitat quality outcomes across three scenarios: ecological priority (0.375), natural growth (0.373), and urban development (0.359). We observed that the natural growth scenario forecasts a further decline in habitat quality, primarily due to increased low–value habitat regions. Conversely, the ecological priority scenario projects a notable improvement in habitat quality. To mitigate habitat degradation in Beijing and enhance regional habitat quality and ecological conditions, it is recommended to control urban land cover expansion, adopt effective ecological conservation policies, and systematically carry out national spatial restructuring and ecological restoration. This research provides vital decision–making support for urban planning and ecological conservation, emphasizing the need for comprehensive land cover and ecological strategies in urban development. Additionally, our findings and methodologies are applicable to other rapidly urbanizing cities worldwide. This demonstrates the broader applicability and relevance of our research, providing a framework for sustainable urban planning in diverse global contexts. Full article
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<p>Research framework.</p>
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<p>Location of study area.</p>
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<p>Land cover status and changes in Beijing City from 2000 to 2020.</p>
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<p>Land cover transfer of Beijing City from 2000 to 2020. Note: CL: Cropland; FL: Forest; GL: Grassland; WA: Water Area; CO: Construction Land; UL: Unused Land.</p>
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<p>Simulated predictions of land cover in Beijing by 2030 under various scenarios.</p>
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<p>The spatial distribution and changes in Beijing’s habitat quality from 2000 to 2020.</p>
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<p>Land cover simulation prediction map of Beijing City in 2030 under different scenarios.</p>
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21 pages, 9483 KiB  
Article
Exploring New Avenues in Sustainable Urban Development: Ecological Carbon Dynamics of Park City in Chengdu
by Lin Tang, Jing Wang, Luo Xu and Heng Lu
Sustainability 2024, 16(15), 6471; https://doi.org/10.3390/su16156471 - 29 Jul 2024
Viewed by 387
Abstract
The close relationship between land use and carbon stock is crucial for regional carbon balance, territorial and spatial planning, and the sustainable development of ecosystems. As a pioneer of Park Cities, Chengdu plays a vital role in Chinese cities. To investigate the impact [...] Read more.
The close relationship between land use and carbon stock is crucial for regional carbon balance, territorial and spatial planning, and the sustainable development of ecosystems. As a pioneer of Park Cities, Chengdu plays a vital role in Chinese cities. To investigate the impact of Park City construction on carbon stock, this study adopted a new perspective, the Park City perspective, using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to analyze the spatial and temporal differences in carbon stock. Additionally, we used Geographic Detector to analyze the driving factors of carbon stock in Chengdu. Based on the carbon peaking and carbon neutrality goals (peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060), we simulated the carbon stock in Chengdu for the years 2030 and 2060. Simultaneously, combining the Future Land Use Simulation (FLUS) model, we simulated the changing trends of carbon stock in Chengdu under three scenarios: the natural development scenario (NDS), cultivated land protection scenario (CLDS), and Park City scenario (PCS). The results show the following: (1) After the construction of the Park City, the quality of forest land improved, resulting in an increase in forest carbon stock by 1.19 × 106 tons. (2) Compared to the scenario without Park City construction, the implementation of the Park City led to a total carbon stock increase of 3.75 × 105 tons, with forest carbon stock increasing by 7.48 × 105 tons. (3) The PCS is the most conducive to achieving the carbon peaking and carbon neutrality goals, with the highest carbon stock. (4) Carbon stock is mainly driven by socio-economic factors. Land use/land cover (LULC) has the greatest explanatory power, with a q value of 0.9. The Park City is of great significance for an increase in carbon stock in Chengdu. Full article
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Figure 1
<p>Study area. (<b>a</b>) Chengdu City’s geographic location; (<b>b</b>) Chengdu City’s elevation; (<b>c</b>) Chengdu City’s administrative divisions; (<b>d</b>) Chengdu City’s land use distribution.</p>
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<p>Chengdu 2012–2022 land use.</p>
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<p>Land use transfer: (<b>a</b>) 2012–2017, (<b>b</b>) 2017–2022, (<b>c</b>) 2012–2022, (<b>d</b>) land use type transition changes from 2012 to 2022.</p>
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<p>Land use area with multiple scenic spots in Chengdu from 2030 to 2060.</p>
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<p>Land use in 2030 and 2060.</p>
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<p>Carbon stock changes in Chengdu from 2012 to 2022.</p>
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<p>Spatial distribution of carbon stock in Chengdu from 2012 to 2022.</p>
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<p>(<b>a</b>) Simulated carbon stock in 2022, (<b>b</b>) actual carbon stock in 2022.</p>
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<p>Future carbon stock in Chengdu under different scenarios.</p>
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<p>Driving factors of carbon stock. DEM, Digital Elevation Model; SOIL, soil type; SLOPE, slope; LULC, land use/land cover; POP, population; GDP, Gross Domestic Product; RW, distance to railway; HW, distance to highway.</p>
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<p>(<b>a</b>) Single-factor detection, (<b>b</b>) dual-factor detection.</p>
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16 pages, 2249 KiB  
Article
Integrating Genomic, Climatic, and Immunological Factors to Analyze Seasonal Patterns of Influenza Variants
by Anass Bouchnita and Behzad Djafari-Rouhani
Symmetry 2024, 16(8), 943; https://doi.org/10.3390/sym16080943 - 23 Jul 2024
Viewed by 569
Abstract
Influenza, often referred to as the flu, is an extremely contagious respiratory illness caused by influenza viruses, impacting populations globally with significant health consequences annually. A hallmark of influenza is its seasonal patterns, influenced by a mix of geographic, evolutionary, immunological, and environmental [...] Read more.
Influenza, often referred to as the flu, is an extremely contagious respiratory illness caused by influenza viruses, impacting populations globally with significant health consequences annually. A hallmark of influenza is its seasonal patterns, influenced by a mix of geographic, evolutionary, immunological, and environmental factors. Understanding these seasonal trends is crucial for informing public health decisions, including the planning of vaccination campaigns and their formulation. In our study, we introduce a genotype-structured infectious disease model for influenza transmission, immunity, and evolution. In this model, the population of infected individuals is structured according to the virus they harbor. It considers a symmetrical fitness landscape where the influenza A and B variants are considered. The model incorporates the effects of population immunity, climate, and epidemic heterogeneity, which makes it suitable for investigating influenza seasonal dynamics. We parameterize the model to the genomic surveillance data of flu in the US and use numerical simulations to elucidate the scenarios that result in the alternating or consecutive prevalence of flu variants. We show that the speed of virus evolution determines the alternation and co-circulation patterns of seasonal influenza. Our simulations indicate that slow immune waning reduces how often variants change, while cross-immunity regulates the co-circulation of variants. The framework can be used to predict the composition of future influenza outbreaks and guide the development of cocktail vaccines and antivirals that mitigate influenza in both the short and long term. Full article
(This article belongs to the Special Issue Mathematical Modeling in Biology and Life Sciences)
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<p>(<b>A</b>) The mode dynamics are schematically represented. They illustrate the progression from the susceptible compartment (<span class="html-italic">S</span>) to the infected (<span class="html-italic">I</span>) and recovered (<span class="html-italic">R</span>). Susceptible individuals transition into the infected compartment upon contracting the virus. During infection, individuals in the infected state experience genotypic alterations resulting from viral mutations. These infected individuals recover at a constant rate. Similarly to previous works [<a href="#B17-symmetry-16-00943" class="html-bibr">17</a>], individuals in the recovered state rapidly lose immunity, but they contribute to the overall population immunity level, denoted by <span class="html-italic">M</span>. This population immunity level modulates the average susceptibility and disease severity, which varies based on the phenotype of circulating strains and the extent of cross-immunity. (<b>B</b>) The fitness landscape for the basic reproduction number considered in the simulation to study variant emergence and competition. It shows two symmetrical viable spaces corresponding to influenza A and B, separated by a genotypic distance <math display="inline"><semantics> <mrow> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mo>|</mo> </mrow> <msub> <mi>x</mi> <mi>A</mi> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi>B</mi> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> taken equal to 4 G.U. The two variants are considered to have the same transmissibility level.</p>
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<p>(<b>A</b>) A comparison between the simulation result for an influenza A-dominated season with the genomic surveillance data for the weekly confirmed cases in the US [<a href="#B3-symmetry-16-00943" class="html-bibr">3</a>]. (<b>B</b>) The simulated number of weekly confirmed cases during four consecutive seasons showing an alternation between seasons dominated by influenza A (in blue) and B (in orange). (<b>C</b>) The underlying immunological dynamics for both influenza A (in blue) and B (in orange) during four seasons of a simulation.</p>
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<p>The number of weekly infected cases caused by variants A (in blue) and B (in orange) for four values of the mutation rate: 25 (<b>A</b>), 50 (<b>B</b>), 75 (<b>C</b>), and 100 (<b>D</b>) <math display="inline"><semantics> <mrow> <mo>×</mo> <mspace width="3.33333pt"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>9</mn> </mrow> </msup> </mrow> </semantics></math> G.U.<sup>2</sup>. The change in the mutation rate influences the timing of variant B’s emergence. This, in turn, determines the co-circulation or alternation patterns of variants A and B.</p>
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<p>(<b>A</b>) The number of weekly confirmed cases for three different values of the immune waning half-life time: 4 months (top), 8 months (middle), and 12 months (bottom). (<b>B</b>) The mean number of confirmed cases in each season for the three considered immune waning speeds. (<b>C</b>) The number of seasons in which influenza B was more dominant during ten consecutive seasons.</p>
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<p>Results of numerical simulations for the number of weekly confirmed cases generated by influenza A (in blue) and B (in orange) during twelve seasons for four values of the cross-immunity broadness (<math display="inline"><semantics> <msub> <mi>d</mi> <mn>0</mn> </msub> </semantics></math>): 1 G.U. (<b>A</b>), 2 G.U. (<b>B</b>), 3 G.U. (<b>C</b>), and 4 G.U. (<b>D</b>).</p>
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<p>Cross-section of the number of infected individuals during peaks of two consecutive influenza seasons in the cases where there is alternation in the predominance of variants (<math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> G.U. and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>/</mo> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>) (<b>A</b>) and when there is co-circulation of influenza A and B (<math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> G.U. and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>/</mo> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>) (<b>B</b>).</p>
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<p>A consistency analysis for the considered numerical schemes obtained by repeating the same simulation but for different values of the discretization step for the genotype space (<math display="inline"><semantics> <mrow> <mi>d</mi> <mi>x</mi> </mrow> </semantics></math>).</p>
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23 pages, 2523 KiB  
Article
Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China
by Maomao Zhang, Enqing Chen, Cheng Zhang, Chen Liu and Jianxing Li
Sustainability 2024, 16(14), 6237; https://doi.org/10.3390/su16146237 - 22 Jul 2024
Viewed by 477
Abstract
Changes in land use patterns, types, and intensities significantly impact ecosystem services. This study follows the time series logic from history to the expected future to investigate the spatial and temporal characteristics of land use changes in Ezhou and their potential impacts on [...] Read more.
Changes in land use patterns, types, and intensities significantly impact ecosystem services. This study follows the time series logic from history to the expected future to investigate the spatial and temporal characteristics of land use changes in Ezhou and their potential impacts on the ecosystem services value (ESV). The results show that the Markov–FLUS model has strong applicability in predicting the spatial pattern of land use, with a Kappa coefficient of 0.9433 and a FoM value of 0.1080. Between 2000 and 2020, construction land expanded continuously, while water area remained relatively stable, and other land types experienced varying degrees of contraction. Notably, the area of construction land expanded significantly compared to 2000, and it expanded by 70.99% in 2020. Moreover, the watershed area expanded by 9.30% from 2000 to 2010, but there was very little change in the following 10 years. Under the three scenarios, significant differences in land use changes were observed in Ezhou City, driven by human activities, particularly the strong expansion of construction land. In the inertial development scenario, construction land expanded to 313.39 km2 by 2030, representing a 38.30% increase from 2020. Conversely, under the farmland protection scenario, construction land increased to 237.66 km2, a 4.89% rise from 2020. However, in the ecological priority development scenario, the construction land area expanded to 253.59 km2, a 10.13% increase from 2020. Compared to 2020, the ESV losses in the inertia development and farmland protection scenarios were USD 4497.71 and USD 1072.23, respectively, by 2030. Conversely, the ESV under the ecological protection scenario increased by USD 2749.09, emphasizing the importance of prioritizing ecological protection in Ezhou City’s development. This study may provide new clues for the formulation of regional strategies for sustainable land use and ecosystem restoration. Full article
(This article belongs to the Special Issue Sustainable Land Use and Management, 2nd Edition)
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<p>Geographical Area Map (<b>a</b>) and Elevation Map of Ezhou (<b>b</b>).</p>
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<p>Statistical map of the area of land use types.</p>
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<p>Land use transfer chord map of Ezhou.</p>
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<p>Map of the transfer of LULC types. Notes: Codes 1–6 indicate cropland, woodland, grassland, waters, construction land, and unused land. The mapping code is a combination of two converted secondary space codes. For example, “cropland → woodland” (code 12).</p>
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<p>Land use change in 2030 under three scenarios; (<b>a</b>) for the inertial development scenario, (<b>b</b>) for the cultivated land protection scenario, (<b>c</b>) for the ecological priority scenario.</p>
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<p>The ESV under different scenarios; (<b>a</b>) for the inertial development scenario, (<b>b</b>) for the cultivated land protection scenario, (<b>c</b>) for the ecological priority scenario.</p>
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<p>The ESV change rate under different scenarios in 2030.</p>
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<p>Changes in individual ESV in 2030 (compared to 2020).</p>
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25 pages, 3975 KiB  
Article
Exploring a New Generation of Pyrimidine and Pyridine Derivatives as Anti-Influenza Agents Targeting the Polymerase PA–PB1 Subunits Interaction
by Ilaria Giacchello, Annarita Cianciusi, Chiara Bertagnin, Anna Bonomini, Valeria Francesconi, Mattia Mori, Anna Carbone, Francesca Musumeci, Arianna Loregian and Silvia Schenone
Pharmaceutics 2024, 16(7), 954; https://doi.org/10.3390/pharmaceutics16070954 - 18 Jul 2024
Viewed by 550
Abstract
The limited range of available flu treatments due to virus mutations and drug resistance have prompted the search for new therapies. RNA-dependent RNA polymerase (RdRp) is a heterotrimeric complex of three subunits, i.e., polymerase acidic protein (PA) and polymerase basic proteins 1 and [...] Read more.
The limited range of available flu treatments due to virus mutations and drug resistance have prompted the search for new therapies. RNA-dependent RNA polymerase (RdRp) is a heterotrimeric complex of three subunits, i.e., polymerase acidic protein (PA) and polymerase basic proteins 1 and 2 (PB1 and PB2). It is widely recognized as one of the most promising anti-flu targets because of its critical role in influenza infection and high amino acid conservation. In particular, the disruption of RdRp complex assembly through protein–protein interaction (PPI) inhibition has emerged as a valuable strategy for discovering a new therapy. Our group previously identified the 3-cyano-4,6-diphenyl-pyridine core as a privileged scaffold for developing PA–PB1 PPI inhibitors. Encouraged by these findings, we synthesized a small library of pyridine and pyrimidine derivatives decorated with a thio-N-(m-tolyl)acetamide side chain (compounds 2an) or several amino acid groups (compounds 3an) at the C2 position. Interestingly, derivative 2d, characterized by a pyrimidine core and a phenyl and 4-chloro phenyl ring at the C4 and C6 positions, respectively, showed an IC50 value of 90.1 μM in PA–PB1 ELISA, an EC50 value of 2.8 μM in PRA, and a favorable cytotoxic profile, emerging as a significant breakthrough in the pursuit of new PPI inhibitors. A molecular modeling study was also completed as part of this project, allowing us to clarify the biological profile of these compounds. Full article
(This article belongs to the Section Drug Targeting and Design)
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<p>Structures of pyridine and pyrimidine compounds <b>1a</b>–<b>e</b> [<a href="#B23-pharmaceutics-16-00954" class="html-bibr">23</a>,<a href="#B24-pharmaceutics-16-00954" class="html-bibr">24</a>,<a href="#B25-pharmaceutics-16-00954" class="html-bibr">25</a>].</p>
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<p>Predicted binding mode of the reference <b>1a</b> on PA at the PA–PB1 interface. (<b>A</b>) Docking pose of <b>1a</b> on PA, which is represented as a grey surface. Shape complementarity between <b>1a</b> and PA is satisfactory; hydrophobic regions, as described in [<a href="#B24-pharmaceutics-16-00954" class="html-bibr">24</a>], are highlighted in red. (<b>B</b>) Structural superimposition of the docking pose of <b>1a</b> on the crystallographic pose of PB1<sub>N</sub>, which is shown as magenta cartoon and lines.</p>
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<p>Docking-based binding mode of bioactive inhibitors <b>2d</b> (<b>A</b>), <b>2i</b> (<b>B</b>), <b>2n</b> (<b>C</b>), <b>3d</b> (<b>D</b>), and inactive molecules <b>3a</b> (<b>E</b>) and <b>3n</b> (<b>F</b>), towards the crystallographic structure of PA–PB1 PPI complex coded by PDB-ID: 3CM8. PB1 coordinates were removed in docking simulations. PA is shown as green cartoon coils and lines. Residues within 5 Å from the ligands are shown as lines. Two key residues, Lys-643 and Trp-706, that are discussed in the text are labeled. Residues numbering is taken from the crystallographic structure 3CM8. Small molecules are shown as cyan sticks, non-polar H atoms are omitted.</p>
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<p>Synthesis of pyrimidine intermediates <b>5a</b>–<b>l</b>. <b>Reagents and conditions:</b> (i) opportune phenylboronic acid (2 eq), 1 M Na<sub>2</sub>CO<sub>3</sub>, PPh<sub>3</sub>, Pd(OAc)<sub>2</sub>, THF, N<sub>2</sub> atm, reflux, 3 h (to obtain <b>5a</b>,<b>b</b>), 47–61%; (ii) opportune phenylboronic acid (1 eq), 1 M Na<sub>2</sub>CO<sub>3</sub>, PPh<sub>3</sub>, Pd(OAc)<sub>2</sub>, THF, N<sub>2</sub> atm, reflux, 3–6 h (to obtain <b>6a</b>–<b>d</b>, 35–90% and <b>5c</b>–<b>e</b>, 24–63%); (iii) 1-naphtalenboronic acid, aq. sol Na<sub>2</sub>CO<sub>3</sub>, PPh<sub>3</sub>, Pd(OAc)<sub>2</sub>, DME, N<sub>2</sub> atm, reflux, 3 h, 36–73%; (iv) Method A: thiophenol, H<sub>2</sub>O/Acetone, NaOH, rt, 3 h (to obtain <b>5i</b>, 71%); Method B: thiophenol, dry DMF, NaH, N<sub>2</sub> atm, 0 °C to rt, 3 h (to obtain <b>5j</b>,<b>k</b>, 47–71%); (v) aniline, EDC, HOBt, DMF, N<sub>2</sub> atm, rt, 3 h, 20%.</p>
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<p>Synthesis of pyridine intermediates <b>8a</b>–<b>d</b>. <b>Reagents and conditions:</b> (<b>Route A</b>) (i) phenylboronic acid (2 eq), 1 M K<sub>2</sub>CO<sub>3</sub>, Pd(PPh<sub>3</sub>)<sub>4</sub>, DME, N<sub>2</sub> atm, reflux, 16 h, 60%; (ii) opportune phenylboronic acid (1.1 eq), 1 M Na<sub>2</sub>CO<sub>3</sub>, PPh<sub>3</sub>, Pd(OAc)<sub>2</sub>, THF, N<sub>2</sub> atm, reflux, 3 h, 73–74%; (iii) thiophenol, dry DMF, NaH, N<sub>2</sub> atm, 0 °C 1 h, then rt, 3 h, 82–95%; (<b>Route B</b>) (i) 4 M NaOH, EtOH, rt, 3 h, 80%; (ii) ethyl 2-nitroacetate, NH<sub>4</sub>OAc, EtOH, N<sub>2</sub> atm, reflux, 6 h, 25%; (iii) POCl<sub>3</sub>/DMF (1:1), CHCl<sub>3</sub>, reflux, 12 h, 60%.</p>
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<p>The final step for the synthesis of the pyrimidine and pyridine derivatives <b>2a</b>–<b>n</b>. <b>Reagents and conditions:</b> (i) dry K<sub>2</sub>CO<sub>3</sub>, dry ACN, N<sub>2</sub> atm, rt, 5 h, 20–62%; (ii) dry K<sub>2</sub>CO<sub>3</sub>, CuI, L-proline, dry DME, N<sub>2</sub> atm, reflux, 24 h, 20%.</p>
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<p>Final steps for the synthesis of the pyrimidine and pyridine derivatives <b>3a</b>–<b>n</b>. <b>Reagents and conditions:</b> (i) Method A: ethyl-thioglycolate, Cs<sub>2</sub>CO<sub>3</sub>, dry ACN, 30–40 °C, 1 h (to obtain <b>15a</b>,<b>b</b>, 68–76%); Method B: ethyl-thioglycolate, Cs<sub>2</sub>CO<sub>3</sub>, dry DMF, 100 °C, 6 h (to obtain <b>15c</b>,<b>d</b>, 25–40%); (ii) aq. sol. NaOH 5% <span class="html-italic">w</span>/<span class="html-italic">v</span>, THF, rt, 16 h; (iii) opportune L-aa-methyl ester, EDC, HOBt, dry DIPEA, dry DMF, N<sub>2</sub> atm, rt, 4–6 h, 20–64%.</p>
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26 pages, 4741 KiB  
Article
Spatiotemporal Dynamics and Scenario Simulation of Regional Green Spaces in a Rapidly Urbanizing Type I Large City: A Case Study of Changzhou, China
by Chenjia Xu, Yao Xiong, Ziwen Liu and Yajuan Chen
Sustainability 2024, 16(14), 6125; https://doi.org/10.3390/su16146125 - 17 Jul 2024
Viewed by 537
Abstract
The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources [...] Read more.
The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources across urban and rural areas. This study focuses on conducting a comprehensive analysis of the evolution, driving factors, and future scenarios of regional green spaces in Changzhou, which serves as a representative Type I large city in China. To accomplish this analysis, Landsat satellite images from 1992, 2002, 2012, and 2022 were utilized. Various methodologies, including landscape pattern indices for quantitative evaluation, the CLUE-S model, logistic regression for qualitative evaluation, and the Markov–FLUS model, were employed. The findings indicate a continuous decline in the area of regional green spaces in Changzhou, decreasing from 248.23 km2 in 1992 to 204.46 km2 in 2022. Landscape pattern analysis reveals an increase in fragmentation, complexity, irregularity, and human interference within these green spaces. Logistic regression analysis identifies key driving factors influencing regional green spaces, including elevation, urban population, and proximity to water bodies and transportation. The scenario simulations provide valuable insights into potential future trends of regional green spaces. According to the economic priority scenario, a modest increase in regional green spaces is anticipated, while the ecological priority scenario indicates substantial growth. Conversely, the inertial development scenario predicts a continued decline in regional green spaces. This research emphasizes the significance of achieving a harmonious coexistence between economic progress and environmental preservation. It emphasizes the necessity of optimizing the arrangement of green areas within a region while fostering public engagement in the conservation of these spaces. The findings contribute to the protection and sustainable development of the urban environment in the Yangtze River Delta region. Full article
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<p>Geographical location of Changzhou in China.</p>
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<p>Contour maps of the 11 driving factors including (<b>a</b>) aspect, (<b>b</b>) DEM, (<b>c</b>) slope, (<b>d</b>) temperature, (<b>e</b>) precipitation, (<b>f</b>) urban population, (<b>g</b>) GDP, (<b>h</b>) distance to residential area, (<b>i</b>) distance to railway, (<b>j</b>) distance to roads, and (<b>k</b>) distance to waterways considered in the qualitative analysis of regional green spaces in Changzhou, China, showing low and high value ranges for each factor.</p>
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<p>Sankey diagram of land-use type transformation from 1992 to 2022.</p>
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<p>Changzhou regional green space in (<b>a</b>) 1992, (<b>b</b>) 2002, (<b>c</b>) 2012, and (<b>d</b>) 2022.</p>
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<p>Distribution of green space reduction rate in Changzhou from (<b>a</b>) 1992 to 2002, (<b>b</b>) 2002 to 2012, and (<b>c</b>) 2012 to 2022.</p>
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<p>Distribution of green space increase rate in Changzhou from (<b>a</b>) 1992 to 2002, (<b>b</b>) 2002 to 2012, and (<b>c</b>) 2012 to 2022.</p>
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<p>Land-use map of Changzhou in 2032 under three scenarios. (<b>a</b>) Economic priority scenario simulation, (<b>b</b>) ecological priority scenario simulation, and (<b>c</b>) inertial development scenario simulation.</p>
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<p>Three simulated scenarios of the area of land-use change in Changzhou from 2022 to 2032.</p>
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20 pages, 9435 KiB  
Article
Spatial and Temporal Dynamics and Multi-Scenario Forecasting of Habitat Quality in Gansu–Qinghai Contiguous Region of the Upper Yellow River
by Xuan Zhang, Huali Tong, Ling Zhao, Enwei Huang and Guofeng Zhu
Land 2024, 13(7), 1060; https://doi.org/10.3390/land13071060 - 15 Jul 2024
Viewed by 460
Abstract
Human activities exert a profound influence on land use and land cover, and these changes directly influence habitat quality and ecosystem functioning. In the Gansu–Qinghai contiguous region of the upper Yellow River, habitat quality has undergone substantial transformations in recent years due to [...] Read more.
Human activities exert a profound influence on land use and land cover, and these changes directly influence habitat quality and ecosystem functioning. In the Gansu–Qinghai contiguous region of the upper Yellow River, habitat quality has undergone substantial transformations in recent years due to the synergistic impacts of natural processes and human intervention. Therefore, evaluating the effects of land use changes on habitat quality is crucial for advancing regional sustainable development and improving the worth of ecosystem services. In response to these challenges, we devised a two-pronged approach: a land use simulation (FLUS) model and an integrated valuation of ecosystem services and trade-offs (InVEST) model, leveraging remote sensing data. This integrated methodology establishes a research framework for the evaluation and simulation of spatial and temporal variations in habitat quality. The results of the study show that, firstly, from 1980 to 2020, the habitat quality index in the Gansu–Qinghai contiguous region of the upper Yellow River decreased from 0.8528 to 0.8434. Secondly, our predictions anticipate a decrease in habitat quality, although the decline is not pronounced across all scenarios. The highest habitat quality values were projected under the EP (Ecology Priority) scenario, followed by the CLP (Cultivated Land Priority) scenario, while the BAU (Business as Usual) scenario consistently yielded the lowest values in all three scenarios. Finally, the ecological land, including forest land and grassland, consistently occupied areas characterized by high habitat quality. In contrast, Construction land consistently appeared in regions associated with low habitat quality. The implementation of conservation measures emerges as a crucial strategy, effectively limiting the expansion of construction land and promoting the augmentation of forest land and grassland cover. This approach serves to enhance overall habitat quality. These outcomes furnish a scientific foundation for the judicious formulation of future land-use policies and ecological protection measures. Full article
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<p>Diagram of the study area (Projected Coordinate System: Krasovosky_1940_Albers).</p>
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<p>The research framework of FLUS- InVEST models.</p>
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<p>Actual land use and simulated land use in 2020.</p>
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<p>Land use change during 1980–2020 and land use simulation during 2030–3040 under three scenarios.</p>
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<p>The string diagram of the land use transfer during 1980–2040.</p>
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<p>Habitat quality during 1980–2020.</p>
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<p>Habitat quality simulation during 2030–2040 under three scenarios.</p>
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<p>The diagram of habitat quality grade proportion from 1980 to 2040.</p>
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<p>The diagram of habitat quality changes from 1980 to 2040.</p>
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19 pages, 1470 KiB  
Article
Quantitative Risk Assessment of Wind-Supported Transmission of Highly Pathogenic Avian Influenza Virus to Dutch Poultry Farms via Fecal Particles from Infected Wild Birds in the Environment
by Clazien J. de Vos and Armin R. W. Elbers
Pathogens 2024, 13(7), 571; https://doi.org/10.3390/pathogens13070571 - 8 Jul 2024
Viewed by 1047
Abstract
A quantitative microbial risk assessment model was developed to estimate the probability that the aerosolization of fecal droppings from wild birds in the vicinity of poultry farms would result in the infection of indoor-housed poultry with highly pathogenic avian influenza virus (HPAIv) in [...] Read more.
A quantitative microbial risk assessment model was developed to estimate the probability that the aerosolization of fecal droppings from wild birds in the vicinity of poultry farms would result in the infection of indoor-housed poultry with highly pathogenic avian influenza virus (HPAIv) in the Netherlands. Model input parameters were sourced from the scientific literature and experimental data. The availability of data was diverse across input parameters, and especially parameters on the aerosolization of fecal droppings, survival of HPAIv and dispersal of aerosols were uncertain. Model results indicated that the daily probability of infection of a single poultry farm is very low, with a median value of 7.5 × 10−9. Accounting for the total number of poultry farms and the length of the bird-flu season, the median overall probability of at least one HPAIv-infected poultry farm during the bird-flu season is 2.2 × 10−3 (approximately once every 455 years). This is an overall estimate, averaged over different farm types, virus strains and wild bird species, and results indicate that uncertainty is relatively high. Based on these model results, we conclude that it is unlikely that this introduction route plays an important role in the occurrence of HPAIv outbreaks in indoor-housed poultry. Full article
(This article belongs to the Special Issue Pathogenesis, Epidemiology, and Control of Animal Influenza Viruses)
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<p>Outline of the quantitative microbial risk assessment model to estimate the HPAI transmission risk from wild birds to domestic poultry via aerosolized fecal droppings.</p>
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<p>Box-and-whisker plot of model results for the daily probability of infection of a single poultry farm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>f</mi> <mo>,</mo> <mi>p</mi> <mi>f</mi> <mo>,</mo> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>) and the overall probability of at least one infected poultry farm during the bird-flu season (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>Spider plot showing the relation between the median overall probability of at least one infected poultry farm during the bird-flu season (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>) and the percentile values of input parameters that had a correlation coefficient &gt; |0.1| with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>. These input parameters were: fraction of virus retained after the dispersion of aerosols over a short distance (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>); concentration of HPAIv in wild bird feces (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>A</mi> <mi>I</mi> </mrow> <mrow> <mi>f</mi> <mi>e</mi> <mi>c</mi> <mo>_</mo> <mi>c</mi> <mi>o</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>); fraction of the day that wild birds spent at the farm yard (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>p</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>); number of wild birds at the farm yard on a day that birds are present (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>w</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>); bird infectious dose (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>B</mi> <mi>I</mi> <mi>D</mi> </mrow> <mrow> <mn>50</mn> </mrow> </msub> </mrow> </semantics></math>); ventilation rate of poultry house (<math display="inline"><semantics> <mrow> <mi>V</mi> <mi>R</mi> </mrow> </semantics></math>); and daily probability that wild birds are present at the farm (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>w</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>Tornado chart showing the relative increase or decrease (expressed as log<sub>10</sub> difference) in the overall probability of at least one infected poultry farm during the bird-flu season (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>) compared to the baseline scenario for 10 what-if scenarios. Parameters considered in the what-if scenarios were: apparent HPAI prevalence in wild birds (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> <mi>r</mi> <mi>e</mi> <mi>v</mi> </mrow> <mrow> <mi>w</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>); daily amount of feces excreted by wild birds (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> <mi>e</mi> <mi>c</mi> </mrow> <mrow> <mi>w</mi> <mi>b</mi> <mo>_</mo> <mi>d</mi> <mi>r</mi> <mi>y</mi> <mo>,</mo> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>); concentration of HPAIv in wild bird feces (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>A</mi> <mi>I</mi> </mrow> <mrow> <mi>f</mi> <mi>e</mi> <mi>c</mi> <mo>_</mo> <mi>c</mi> <mi>o</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>); survival of HPAIv during the drying of feces (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>v</mi> <mo>_</mo> <mi>d</mi> <mi>r</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math>); survival of HPAIv during air transport (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>v</mi> <mo>_</mo> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>); fraction of virus retained after the dispersion of aerosols over a short distance (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>); ventilation rate of poultry house (<math display="inline"><semantics> <mrow> <mi>V</mi> <mi>R</mi> </mrow> </semantics></math>); and bird infectious dose (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>B</mi> <mi>I</mi> <mi>D</mi> </mrow> <mrow> <mn>50</mn> </mrow> </msub> </mrow> </semantics></math>). The arrows indicate an increase (↑) or a decrease (↓) of the input parameter’s value. A more detailed description of each scenario is given in <a href="#pathogens-13-00571-t002" class="html-table">Table 2</a>.</p>
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21 pages, 19047 KiB  
Article
Evolution and Driving Forces of Ecological Service Value in Response to Land Use Change in Tarim Basin, Northwest China
by Aynur Mamat, Muhetaer Aimaiti, Muattar Saydi and Jianping Wang
Remote Sens. 2024, 16(13), 2311; https://doi.org/10.3390/rs16132311 - 25 Jun 2024
Viewed by 473
Abstract
The main objective of protecting ecosystems and enhancing the supply of ecosystem services (ESs) is to quantify the value of ecological services. This article calculates the ecological service value (ESV) of the Tarim Basin over the past 40 years using the improved benefits [...] Read more.
The main objective of protecting ecosystems and enhancing the supply of ecosystem services (ESs) is to quantify the value of ecological services. This article calculates the ecological service value (ESV) of the Tarim Basin over the past 40 years using the improved benefits transfer method of satellite remote sensing data, such as Landsat, analyzes the spatiotemporal evolution characteristics of ESV, and studies the driving mechanism of ESV changes using GeoDetector. Finally, the FLUS model was selected to predict the ecosystem service value until 2030, setting up three scenarios: the Baseline Scenario (BLS), the Cultivated Land Protection Scenario (CPS), and the Ecological Protection Scenario (EPS). The results indicate that (1) the ESV in the Tarim Basin decreased by USD 1248.21 million (−2.29%) from 1980 to 2020. The top three contributors are water bodies, wetlands, and grassland. (2) Waste treatment and water supply functions had the highest service value, accounting for 44.53% of the total contribution. The rank order of ecosystem functions in terms of their contribution to the total value of ESV was as follows, refining from high to low importance: water supply, waste treatment, biodiversity protection, climate regulation, soil formation, recreation and culture, gas regulation, food production, raw material. (3) The spatial differentiation driving factors of ESV were detected, with the following Q-values in descending order: net primary productivity (NPP) > normalized difference vegetation index (NDVI) > precipitation > aspect > temperature > slope > soil erosion > GDP > land use intensity > per capita GDP > population > human activity index. (4) The ESVs simulated under the three scenarios (BLS, CPS, and EPS) for 2030 were USD 51,133.9 million, USD 53,624.99 million, and USD 54,561.26 million, respectively. Compared with 2020, the ESVs of the three scenarios decreased as follows: BLS (USD 4209.33 million), CPS (USD 1718.24 million), and EPS USD (−781.97 million). These findings are significant for maintaining the integrity and sustainability of the large-scale ecosystem, where socioeconomic development and the fragile features of the natural ecosystem interact. Additionally, the study results provide a crucial foundation for governmental decision-makers, local residents, and environmental researchers in northwest China to promote sustainable development. Full article
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<p>The location of the Tarim Basin.</p>
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<p>Land use maps of Tarim Basin in 1980, 1990, 2000, 2010, and 2020.</p>
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<p>Land use maps of Tarim Basin in 1980, 1990, 2000, 2010, and 2020.</p>
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<p>Land use change patterns of Tarim Basin in1980, 1990, 2000, 2010, and 2020.</p>
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<p>Transition matrix figure of LUCC from 1980 to 2020 in the Tarim Basin (10<sup>6</sup> ha).</p>
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<p>The results of single-element detection and two elements’ interaction detection.</p>
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<p>Land use and cover changes under the in BLS, CPS, and EPS scenarios.</p>
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<p>Land use transfer map of Tarim Basin in 1980–2020. Note: In this map, 1, 2, 3, 4, 5, 6, 7, and 8 denote cultivated land, woodland, grassland, water body, construction land, wetland, and unused land, respectively. The “1–2” denotes the land use change from cultivated land to woodland, and the same for other expressions.</p>
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<p>Contribution of ESV drivers in the Tarim Basin.</p>
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<p>Spatial distribution of predicted ESV in the Tarim Basin region in 2030 under <span class="html-italic">BLS</span>, <span class="html-italic">CPS,</span> and <span class="html-italic">EPS</span>.</p>
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<p>Spatial distribution of predicted ESV in the Tarim Basin region in 2030 under <span class="html-italic">BLS</span>, <span class="html-italic">CPS,</span> and <span class="html-italic">EPS</span>.</p>
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26 pages, 1536 KiB  
Article
A Broad Influenza Vaccine Based on a Heat-Activated, Tissue-Restricted Replication-Competent Herpesvirus
by Nuria Vilaboa, David C. Bloom, William Canty and Richard Voellmy
Vaccines 2024, 12(7), 703; https://doi.org/10.3390/vaccines12070703 - 23 Jun 2024
Viewed by 646
Abstract
Vaccination with transiently activated replication-competent controlled herpesviruses (RCCVs) expressing influenza A virus hemagglutinins broadly protects mice against lethal influenza virus challenges. The non-replicating RCCVs can be activated to transiently replicate with high efficiency. Activation involves a brief heat treatment to the epidermal administration [...] Read more.
Vaccination with transiently activated replication-competent controlled herpesviruses (RCCVs) expressing influenza A virus hemagglutinins broadly protects mice against lethal influenza virus challenges. The non-replicating RCCVs can be activated to transiently replicate with high efficiency. Activation involves a brief heat treatment to the epidermal administration site in the presence of a drug. The drug co-control is intended as a block to inadvertent reactivation in the nervous system and, secondarily, viremia under adverse conditions. While the broad protective effects observed raise an expectation that RCCVs may be developed as universal flu vaccines, the need for administering a co-activating drug may dampen enthusiasm for such a development. To replace the drug co-control, we isolated keratin gene promoters that were active in skin cells but inactive in nerve cells and other cells in vitro. In a mouse model of lethal central nervous system (CNS) infection, the administration of a recombinant that had the promoter of the infected cell protein 8 (ICP8) gene of a wild-type herpes simplex virus 1 (HSV-1) strain replaced by a keratin promoter did not result in any clinical signs, even at doses of 500 times wild-type virus LD50. Replication of the recombinant was undetectable in brain homogenates. Second-generation RCCVs expressing a subtype H1 hemagglutinin (HA) were generated in which the infected cell protein 4 (ICP4) genes were controlled by a heat switch and the ICP8 gene by the keratin promoter. In mice, these RCCVs replicated efficiently and in a heat-controlled fashion in the epidermal administration site. Immunization with the activated RCCVs induced robust neutralizing antibody responses against influenza viruses and protected against heterologous and cross-group influenza virus challenges. Full article
(This article belongs to the Special Issue The Recent Development of Influenza Vaccine)
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<p>RCCV structure, dually-controlled gene switch, and characterization of KRT1 constructs. (<b>a</b>) Diagrammatic representation of the genome structure of HSV-1-derived RCCV HSV-GS19. The RCCV contains a CMV immediate early-promoter (CMV IE)-driven full-length HA gene of influenza virus strain A/California/07/2009 (CA09 HA) inserted into the intergenic region between UL37 and UL38. A gene for transactivator GLP65 functionally linked to a promoter assembly comprising a human HSP70B promoter (HSP70B) and a GAL-responsive promoter (GAL4) is inserted into the intergenic region between UL43 and UL44. The promoters of the replication-essential HSV-1 genes ICP4 and ICP8 are replaced with GAL4 promoters. TR<sub>L</sub>, TR<sub>S</sub>: long and short terminal repeats; U<sub>L</sub>, U<sub>S</sub>: long and short unique regions; IR<sub>L</sub>, IR<sub>S</sub>: long and short internal repeats. (<b>b</b>) Dually-responsive gene switch in HSV-GS19: a promoter assembly comprising an HSP70B promoter (HSP70B) and a GAL4-responsive promoter (GAL4) controls a gene for AP-activated transactivator GLP65. The replication-essential ICP4 and ICP8 genes are controlled by GAL4 promoters. Heat treatment of a cell infected with HSV-GS19 transiently activates the cellular heat shock factor (HSF1) that then transactivates the GLP65 gene. Newly synthesized, inactive GLP65 molecules are activated when bound by an AP. Activated GLP65 transactivates the GAL4 promoter-controlled ICP4 and ICP8 genes as well as its own gene. (<b>c</b>,<b>d</b>) Characterization of KRT1 promoters: Triplicate, subconfluent cultures (25,000 cells per well; 96-well plates) of HEK293T, HaCaT, and Neuro-2a cells (<b>c</b>) or HEK293T, SH-SY5Y, and Neuro-2a cells (<b>d</b>) were co-transfected with 159 ng of plasmid pmKRT1 1.2, pmKRT1 2.3, phKRT1 S3, phKRT1 L1, or pGL4.16 and 1 ng of plasmid pDRIVE-mROSA using a standard lipofectamine transfection protocol. Relative activities of the KRT1 promoters expressed as ratios of luciferase activities (after subtraction of pGL4.16 background activity) to β-galactosidase activities were determined one day after transfection. Relative activities (actual and mean values) and standard deviations from representative experiments are shown. <span class="html-italic">p</span> ≤ 0.05, comparing to HEK293T or HaCaT cells transfected with plasmid pmKRT1 1.2 (*), pmKRT1 2.3 (#), phKRT1 S3 (&amp;), or phKRT1 L1 (<span>$</span>).</p>
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<p>Absence of spread and replication of a KRT1 promoter-restricted HSV-1 recombinant in the CNS in vivo and vaccine efficacy of a KRT1 promoter-restricted RCCV. (<b>a</b>) Survival of mice inoculated with recombinant 17syn+/mKRT1. Groups (n = 10) of adult BALB/c mice were inoculated on the rear footpads with 5 × 10<sup>3</sup> PFU/animal of HSV-1 wild-type strain 17syn+ or 5 × 10<sup>3</sup>, 1 × 10<sup>4</sup>, or 2.5 × 10<sup>5</sup> PFU/animal of recombinant 17syn+/mKRT1, or vehicle (mock). The data are presented as percent survival for each treatment group. (<b>b</b>) Replication of virus in the feet and brains of BALB/c mice infected with 17syn+ or 17syn+/mKRT1 assessed by qPCR. DNA was isolated from mouse feet (2 days after inoculation) and brains (6 days after inoculation), and TaqMan PCR was performed using primers/probe specific for the HSV-1 DNA polymerase gene. Results are relative to quantities of HSV-1 DNA in brains infected with 17syn+ and are presented as mean values of relative genomes/mg tissue with standard deviations. A relative amount of 100 corresponded to 6.2 × 10<sup>3</sup> genomes/mg tissue. ND: not detected. Sensitivity: 0.15 genomes/mg tissue. * <span class="html-italic">p</span> ≤ 0.05, comparing to mouse feet infected with 17syn+. (<b>c</b>) Vaccine efficacy of mKRT1 promoter-restricted RCCV HSV-GS19. The diagrammatic structures of HSV-GS19 and HSV-GS19/mKRT1 are shown on top. See <a href="#vaccines-12-00703-f001" class="html-fig">Figure 1</a> for additional explanations. Groups (n = 10) of adult BALB/c mice were inoculated on their rear footpads with 2.5 × 10<sup>5</sup> PFU/animal of RCCV HSV-GS19 or HSV-GS19/mKRT1, or vehicle (mock), and the RCCV-inoculated groups were subjected, 3 h later, to a 10 min heat treatment of their hindlimbs at 44.5 °C. Ulipristal (50 µg/kg body weight) was administered IP at the time of inoculation as well as on the following day. Three weeks after inoculation, the mice were immunized again, and, after three further weeks, all mice were challenged by intranasal administration of a lethal dose of heterologous influenza virus strain FM47(H1N1). Animals were observed daily, and weights were recorded. Left graph: survival (≤20% weight loss) after challenge; center graph: averaged relative weights of surviving animals after challenge. Weights are relative to weights on the day of the challenge. Relative values (down to the nadir in the case of the mock group comprising a survivor) and standard deviations are shown. * <span class="html-italic">p</span> ≤ 0.05, comparing to the HSV-GS19 or the HSV-GS19/mKRT1 group; right graph: relative weights after the challenge of all animals in the RCCV-immunized groups. Weights are relative to weights on the day of the challenge.</p>
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<p>Modified promoter–transactivator cassettes and heat regulation conferred by them. (<b>a</b>) Diagrams of the structures of recombination plasmids (transactivator gene plasmids) are shown containing the original HSP70B/GAL4-GLP65 cassette present in HSV-GS19 (pINTA), a cassette lacking the GAL4 promoter and the sequences encoding the AP-binding domain of GLP65 (pINTAΔPRL-BDΔGAL4), and cassettes additionally containing different hairpin sequences inserted in the 5′ UTR of the modified GLP65 gene (pINTAΔPRL-BDΔGAL4 Hairpin 1-4). (<b>b</b>) Activities of the different promoter–transactivator cassettes at mass ratios of transactivator gene plasmid to reporter gene plasmid pGAL4-fLuc of 1:5 or 1:20 (amounts of reporter gene plasmid and total DNA kept constant). Two sets of triplicate, subconfluent cultures of Vero cells (25,000 cells per well; 96-well plates) were co-transfected with a transactivator gene plasmid, reporter gene plasmid pGAL4-fLuc, plasmid pRL-CMV (containing a CMV IE promoter-driven Renilla luciferase gene) for normalization, and plasmid pBluescript II using a standard lipofectamine transfection protocol. On the following day, one of the two sets of parallel cultures was subjected to a 30 min heat treatment at 43.5 °C. Six hours later, luciferase activities were determined. Data from a representative experiment are presented as normalized relative firefly luciferase activities (actual values and mean values with standard deviations). Shown below the graph are ratios of heat-induced and not-induced firefly luciferase activities. (<b>c</b>) Comparison of the activities of the transactivator cassettes of recombination plasmids pINTAΔPRL-BDΔGAL4 and pINTAΔPRL-BDΔGAL4 Hairpin 4 co-transfected with pGAL4-fLuc at a 1:1 molar ratio. Transactivator gene and reporter gene plasmids were transfected in decreasing amounts, as indicated in the graph. Cells transfected with plasmid pHsp70-fLuc were included for comparison. Total amounts of transfected DNA were kept constant by the addition of plasmid pBluescript II DNA. Results from a representative experiment performed as described under (<b>b</b>) are presented. Shown below the graph are ratios of heat-induced and not-induced firefly luciferase activities.</p>
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<p>Heat-regulated replication of RCCVs HSV-GS61, HSV-GS62, and HSV-GS63, neutralizing antibody and protective responses induced by vaccination with the latter RCCVs, and lung viral titers after challenge. (<b>a</b>) Diagrammatic structures of RCCVs HSV-GS19, HSV-GS61, HSV-GS62, and HSV-GS63. See <a href="#vaccines-12-00703-f001" class="html-fig">Figure 1</a> for additional explanations. (<b>b</b>) Single-step growth experiments with RCCVs HSV-GS61 (<b>left</b>), HSV-GS62 (<b>center</b>), and HSV-GS63 (<b>right</b>). For each RCCV, two sets of parallel confluent cultures of HEK293T cells were infected with the RCCV at an MOI of 3. One set was heat-treated by partially immersing the sealed dishes in a 43.5 °C water bath for 30 min (heat). All cultures were then incubated further at 37 °C. At 0, 4, 12, and 24 h post-treatment, three dishes from each set were removed, and virus was harvested from cells and medium. Infectious virus from each culture was titered on E5 cells transfected 24 h prior with ICP8 expression plasmid pICP8. Data are presented as total PFU. * <span class="html-italic">p</span> ≤ 0.05, comparing to the heat-activated group at 12 or 24 h. (<b>c</b>) Neutralizing antibodies. Groups (n = 10) of adult BALB/c mice were pre-bled and bled 3 wk after the first immunization with the indicated RCCV (each administered at 2.5 × 10<sup>5</sup> PFU/animal) or vehicle (mock) and, again, 3 wk after the second immunization (immediately prior to challenge), and sera were prepared. Serial 2-fold dilutions of 1:16-diluted sera were analyzed for neutralizing antibodies against the indicated influenza virus strains by the microneutralization assay described in ref. [<a href="#B12-vaccines-12-00703" class="html-bibr">12</a>]. Data are presented as neutralization ID<sub>50</sub> titers (reciprocal dilutions where infection was reduced by 50% relative to normal serum expressed as geometric mean ID<sub>50</sub>). ND: not detected (below the limit of detection). <span class="html-italic">p</span> ≤ 0.05, comparing to the mock group (*) or the not-activated RCCV group (#). (<b>d</b>,<b>e</b>) Challenge experiments. Groups (n = 10) of adult BALB/c mice were inoculated on their rear footpads with 2.5 × 10<sup>5</sup> PFU/animal of RCCVs HSV-GS19, HSV-GS61, HSV-GS62, or HSV-GS63, or vehicle (mock). As part of the activation treatment, HSV-GS19-inoculated animals received ulipristal (50 µg/kg) IP at the time of inoculation and, again, one day later. Three hours after inoculation, all animals of the indicated groups (i.e., the groups labeled as immunized with an activated RCCV) were subjected to a ten-minute heat treatment of their hindlimbs at 44.5 ˚C. This immunization and activation procedure was repeated 3 wk later. Three weeks after the second immunization, all animals were challenged intranasally with a lethal dose of either influenza virus strain FM47(H1N1) (<b>d</b>) or mouse-adapted strain HK14(H3N2) (<b>e</b>). Animals were observed daily, and weights were recorded. Left graphs: survival (≤20% weight loss) after challenge; center graphs: averaged relative weights of surviving animals after challenge. Weights are relative to weights on the day of the challenge. Relative values and standard deviations are shown; <span class="html-italic">p</span> ≤ 0.05, comparing activated and not-activated HSV-GS19 (†), HSV-GS61 (Φ), HSV-GS62 (Ω), or HSV-GS63 (&amp;) groups or comparing the mock-treated group and either of the activated HSV-GS19, HSV-GS61, HSV-GS62, and HSV-GS63 groups (*); right graphs: relative weights after the challenge of all animals of the groups immunized with activated RCCVs. Weights are relative to weights on the day of the challenge. (<b>f</b>) Further challenge experiment performed as described in (d-e), except that the dose of RCCV HSV-GS61 administered was 1 × 10<sup>6</sup> PFU/animal. Twice-HSV-GS61-immunized or mock-immunized animals were challenged with a lethal dose of influenza virus HK14(H3N2). * <span class="html-italic">p</span> ≤ 0.05, comparing to the HSV-GS61-immunized group. (<b>g</b>) Lung titers of challenge virus HK14. The results are presented as TCID<sub>50</sub> values per g of tissue. Limit of detection: 210 TCID<sub>50</sub>/g tissue. ND: not detected. * <span class="html-italic">p</span> ≤ 0.05, comparing to groups immunized with RCCV.</p>
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<p>Challenge experiments to compare the vaccine efficacies of RCCV HSV-GS19 and recombinants HSV-GC2, HSV-GC3, and HSV-GC9. (<b>a</b>) Diagrammatic representation of the structures of HSV-GS19 and recombinants HSV-GC2, 3, and 9. Deletion of the gH gene is indicated (ΔgH). See <a href="#vaccines-12-00703-f001" class="html-fig">Figure 1</a> for additional explanations. (<b>b</b>,<b>c</b>) Challenge experiment. Groups (n = 10) of adult BALB/c mice were immunized twice with 2.5 × 10<sup>5</sup> PFU/animal of RCCV HSV-GS19 (heat- and AP-co-activated), recombinants HSV-GC2 or HSV-GC3, or vehicle (mock), as described in <a href="#vaccines-12-00703-f004" class="html-fig">Figure 4</a>d,e. Animals were challenged intranasally with influenza virus strain FM47(H1N1) in (<b>b</b>) or (HK14)(H3N2) in (<b>c</b>). Left graphs: survival (≤ 20% weight loss) after challenge; center graphs: averaged relative weights of surviving animals after challenge. Weights are relative to weights on the day of the challenge. Relative values and standard deviations are shown. <span class="html-italic">p</span> ≤ 0.05, comparing the activated HSV-GS19 group and the mock group (*) or the activated HSV-GS19 group and the HSV-GC2 (&amp;) or the HSV-GC3 (Φ) group; right graphs: relative weights after the challenge of all animals of the RCCV- and HSV-GC-immunized groups. Weights are relative to weights on the day of the challenge. (<b>d</b>) Further challenge experiment. Animals twice-immunized with HSV-GC9 or activated HSV-GS19 or vehicle were challenged with influenza virus strain FM47(H1N1). See (<b>b</b>,<b>c</b>) for details. Note that in the center graph, relative weights for the mock group are only shown down to the nadir. <span class="html-italic">p</span> ≤ 0.05, comparing the activated HSV-GS19 group and the mock group (*) or the HSV-GC9 group (#).</p>
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21 pages, 8399 KiB  
Article
Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China
by Jinlin Lai, Jiashun Li and Li Liu
Forests 2024, 15(6), 1039; https://doi.org/10.3390/f15061039 - 16 Jun 2024
Cited by 1 | Viewed by 598
Abstract
Revealing the relationship between land use changes and soil erosion provides a reference for formulating future land use strategies. This study simulated historical and future soil erosion changes based on the RULSE and GeoSOS-FLUS models and used a random forest model to explain [...] Read more.
Revealing the relationship between land use changes and soil erosion provides a reference for formulating future land use strategies. This study simulated historical and future soil erosion changes based on the RULSE and GeoSOS-FLUS models and used a random forest model to explain the relative importance of natural and anthropogenic factors on soil erosion. The main conclusions are as follows: (1) From 1990 to 2020, significant changes in land use occurred in Kunming, with a continuous reduction in woodland, grassland, and cropland, being converted into construction land, which grew by 195.18% compared with 1990. (2) During this period, the soil erosion modulus decreased from 133.85 t/(km²·a) in 1990 to 130.32 t/(km²·a) in 2020, with a reduction in soil loss by 74,485.46 t/a, mainly due to the conversion of cropland to construction and ecological lands (woodland, grassland). (3) The expansion of construction land will continue, and it is expected that by 2050, the soil erosion modulus will decrease by 3.77 t/(km²·a), 4.27 t/(km²·a), and 3.27 t/(km²·a) under natural development, rapid development, and ecological protection scenarios, respectively. However, under the cropland protection scenario, the soil erosion modulus increased by 0.26 t/(km²·a) compared with 2020. (4) The spatial pattern of soil erosion is influenced by both natural and anthropogenic factors, and as human activities intensify in the future, the influence of anthropogenic factors will further increase. Traditionally, the expansion of construction land is thought to increase soil loss. Our study may offer a new perspective and provide a reference for future land use planning and soil loss management in Kunming. Full article
(This article belongs to the Section Forest Soil)
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<p>Geographic location map: (<b>a</b>) China’s boundary; (<b>b</b>) Yunnan province’s boundary; (<b>c</b>) Kunming city’s boundary.</p>
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<p>Spatial distribution of land use types in (<b>a</b>) 1990, (<b>b</b>) 1995, (<b>c</b>) 2000, (<b>d</b>) 2005, (<b>e</b>) 2010, (<b>f</b>) 2015, (<b>g</b>) 2020; (<b>h</b>) area proportion.</p>
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<p>Land use change trajectory: (<b>a</b>) represents land transferred out, and (<b>b</b>) represents land transferred in.</p>
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<p>Spatial distribution of SEM in (<b>a</b>) 1990, (<b>b</b>) 1995, (<b>c</b>) 2000, (<b>d</b>) 2005, (<b>e</b>) 2010, (<b>f</b>) 2015, (<b>g</b>) 2020; (<b>h</b>) SEM change from 1990 to 2020.</p>
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<p>Soil loss due to land use changes from 1990 to 2020.</p>
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<p>Spatial distribution of land use under different scenarios in (<b>a</b>) 2030, (<b>b</b>) 2040, (<b>c</b>) 2050. Note: natural development scenarios (NDS), rapid development scenarios (RDS), cropland protection scenarios (CPS), and ecological protection scenarios (EPS).</p>
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<p>Land use change trajectories under different scenarios from 2020 to 2050; (<b>a</b>) represents land transferred out and (<b>b</b>) represents land transferred in. Note: natural development scenarios (NDS), rapid development scenarios (RDS), cropland protection scenarios (CPS), and ecological protection scenarios (EPS).</p>
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<p>Spatial distribution of SEM under different scenarios in (<b>a</b>) 2030, (<b>b</b>) 2040, (<b>c</b>) 2050; and (<b>d</b>) SEM change from 2020 to 2050. Note: natural development scenarios (NDS), rapid development scenarios (RDS), cropland protection scenarios (CPS), and ecological protection scenarios (EPS).</p>
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<p>Soil loss due to land use change under different scenarios from 2020 to 2050. Note: natural development scenarios (NDS), rapid development scenarios (RDS), cropland protection scenarios (CPS), and ecological protection scenarios (EPS).</p>
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<p>Importance of factors influencing soil erosion. Notes: natural development scenarios (NDS), rapid development scenarios (RDS), cropland protection scenarios (CPS), ecological protection scenarios (EPS), land use type (Lut), slope (Slop), elevation (Ele), human footprint (Hf), rainfall (Rain), soil erodibility factor (Se), temperature (Tem), population density (Pop), and normalized difference vegetation index (NDVI).</p>
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20 pages, 16712 KiB  
Article
Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030
by Jiejun Zhang, Jie Yang, Pengfei Liu, Yi Liu, Yiwen Zheng, Xiaoyu Shen, Bingchen Li, Hongquan Song and Zongzheng Liang
Remote Sens. 2024, 16(10), 1810; https://doi.org/10.3390/rs16101810 - 20 May 2024
Viewed by 736
Abstract
Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the [...] Read more.
Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB. Full article
(This article belongs to the Special Issue Assessment of Ecosystem Services Based on Satellite Data)
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<p>Overview of the Yellow River Basin in China.</p>
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<p>Spatial distribution of land use in the Yellow River Basin, 2000–2020.</p>
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<p>LUCC in the Yellow River Basin from 2000 to 2020. (<b>a</b>) Area of land use type, (<b>b</b>) percentage of area in land use type.</p>
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<p>Changes in carbon storage in the Yellow River Basin of various land use types and carbon pools from 2000 to 2020. (<b>a</b>,<b>b</b>) Carbon storage of land use type, (<b>c</b>,<b>d</b>) changes in carbon storage in land use type.</p>
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<p>Changes in carbon storage of LUCC and carbon pools in the sub-basins from 2000 to 2020.</p>
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<p>Spatial distribution of carbon storage in the Yellow River Basin from 2000 to 2020.</p>
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<p>Spatial distribution of future LUCC in the Yellow River Basin under different scenarios.</p>
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<p>Future LUCC in the Yellow River Basin under different scenarios. (<b>a</b>) Area of land use type, (<b>b</b>) percentage of area in land use type.</p>
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<p>Carbon storage and changes in land use and carbon pools in the Yellow River Basin under different future scenarios. (<b>a</b>,<b>b</b>) Carbon storage of land use type, (<b>c</b>,<b>d</b>) changes of carbon storage in land use type.</p>
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<p>Spatial distribution of carbon storage in the Yellow River Basin under different future scenarios.</p>
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20 pages, 99360 KiB  
Article
Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin
by Shuangying Li, Yanyan Zhou, Dongxia Yue and Yan Zhao
Sustainability 2024, 16(10), 3901; https://doi.org/10.3390/su16103901 - 7 May 2024
Viewed by 628
Abstract
We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative [...] Read more.
We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level. Full article
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<p>Location map of the BRB.</p>
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<p>Taylor plot between GCMs and observed precipitation.</p>
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<p>Taylor plot between maximum and minimum temperatures for GCMs and observational data.</p>
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<p>Spatial distribution of land use in the BRB under the SSP126-EP scenario during 2040–2100.</p>
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<p>Spatial distribution of land use in the BRB under the SSP245-ND scenario during 2040–2100.</p>
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<p>Spatial distribution of land use in the BRB under the SSP585-EG scenario during 2040–2100.</p>
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<p>Sangji map of land use area transfer under SSP1–2.6 scenarios from 2040 to 2100.</p>
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<p>Sangji map of land use area transfer under SSP2–4.5 scenarios from 2040 to 2100.</p>
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<p>Sangji map of land use area transfer under SSP5–8.5 scenarios from 2040 to 2100.</p>
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<p>Annual streamflow in the BRB in 2020.</p>
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<p>Annual streamflow changes under various scenarios (SSP1–2.6, SSP2–4.5, and SSP5–8.5) in the BRB from 2021 to 2100.</p>
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<p>Boxplot of the mean monthly runoff and its variation under different scenarios from 2021 to 2100. The error bars represent 75% (upper limit) and 25% (lower limit) of streamflow variance.</p>
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<p>Box plots of maximum monthly runoff and its variations under different scenarios from 2021 to 2100. The error bars represent 75% (upper limit) and 25% (lower limit) of streamflow variance.</p>
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<p>Spatial distribution of streamflow changes influenced by climate (<b>a</b>) and land use changes (<b>b</b>).</p>
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<p>Monthly measurement and verification results of SWAT models at Wudu and Bikou stations.</p>
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<p>Daily measurement and verification results of SWAT models at Wudu and Bikou stations.</p>
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<p>Interannual variation of streamflow in the BRB from 2008 to 2020.</p>
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<p>Climate data under different scenarios in the Bailong River Basin from 2020 to 2100.</p>
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25 pages, 10465 KiB  
Article
Multi-Scenario Prediction of Land-Use Changes and Ecosystem Service Values in the Lhasa River Basin Based on the FLUS-Markov Model
by Bing Qi, Miao Yu and Yunyuan Li
Land 2024, 13(5), 597; https://doi.org/10.3390/land13050597 - 29 Apr 2024
Cited by 1 | Viewed by 669
Abstract
The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused [...] Read more.
The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future. Full article
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<p>Location of the study area.</p>
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<p>Driving factors of land use.</p>
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<p>Actual and simulated land-use patterns in 2020. (<b>A</b>) The actual land-use pattern in 2020; (<b>B</b>) the simulated land-use pattern in 2020 of model 1; (<b>C</b>) the simulated land-use pattern in 2020 supplemented with the drivers of model 2.</p>
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<p>Land-use patterns in the Lhasa River Basin for the years 2010 to 2030: (<b>a</b>) an area located in Dangxiong county; (<b>b</b>) an area located in Dazi district.</p>
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<p>The (<b>A</b>) transitions between different land-use types, and (<b>B</b>) area change rates for different land-use types in the Lhasa River Basin for the years 2010 to 2020.</p>
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<p>The (<b>C</b>) transitions between different land-use types, and (<b>D</b>) area change rate for different land-use types in the natural development scenario for the years 2010 to 2030.</p>
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<p>The (<b>E</b>) transitions between different land-use types, and (<b>F</b>) area change rate for different land-use types in the ecological protection scenario for the years 2010 to 2030.</p>
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<p>The (<b>G</b>) transitions between different land-use types, and (<b>H</b>) area change rate for different land-use types in the agricultural development scenario for the years 2010 to 2030.</p>
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<p>Changes in the ecosystem service values (ESVs) for different land-use types in the Lhasa River Basin for the years 2010 to 2030 (10<sup>8</sup> CNY).</p>
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<p>Changes in the ecosystem service values (ESVs) of different ecosystem service types for the years 2010 to 2030.</p>
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<p>Spatial distribution of the ecosystem service values (ESVs) in the Lhasa River Basin for the years 2010 to 2030.</p>
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<p>Spatial distribution of the degree of change in the ecosystem service values (ESVs) in the Lhasa River Basin for the years 2010 to 2030.</p>
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<p>Changes in the ecosystem service values (ESVs) in different regions of the Lhasa River Basin for the years 2010 to 2030.</p>
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14 pages, 3814 KiB  
Article
Optimized Synthesis of Poly(Lactic Acid) Nanoparticles for the Encapsulation of Flutamide
by Duarte Almeida, Mariana Dias, Beatriz Teixeira, Carolina Frazão, Mónica Almeida, Gil Gonçalves, Miguel Oliveira and Ricardo J. B. Pinto
Gels 2024, 10(4), 274; https://doi.org/10.3390/gels10040274 - 18 Apr 2024
Viewed by 1255
Abstract
Biopolymeric nanoparticles (NPs) have gained significant attention in several areas as an alternative to synthetic polymeric NPs due to growing environmental and immunological concerns. Among the most promising biopolymers is poly(lactic acid) (PLA), with a reported high degree of biocompatibility and biodegradability. In [...] Read more.
Biopolymeric nanoparticles (NPs) have gained significant attention in several areas as an alternative to synthetic polymeric NPs due to growing environmental and immunological concerns. Among the most promising biopolymers is poly(lactic acid) (PLA), with a reported high degree of biocompatibility and biodegradability. In this work, PLA NPs were synthesized according to a controlled gelation process using a combination of single-emulsion and nanoprecipitation methods. This study evaluated the influence of several experimental parameters for accurate control of the PLA NPs’ size distribution and aggregation. Tip sonication (as the stirring method), a PLA concentration of 10 mg/mL, a PVA concentration of 2.5 mg/mL, and low-molecular-weight PLA (Mw = 5000) were established as the best experimental conditions to obtain monodisperse PLA NPs. After gelification process optimization, flutamide (FLU) was used as a model drug to evaluate the encapsulation capability of the PLA NPs. The results showed an encapsulation efficiency of 44% for this cytostatic compound. Furthermore, preliminary cell viability tests showed that the FLU@PLA NPs allowed cell viabilities above 90% up to a concentration of 20 mg/L. The comprehensive findings showcase that the PLA NPs fabricated using this straightforward gelification method hold promise for encapsulating cytostatic compounds, offering a novel avenue for precise drug delivery in cancer therapy. Full article
(This article belongs to the Special Issue Advanced Hydrogels for Tissue Engineering and Drug Delivery)
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<p>Schematic representation of the synthesis of PLA and FLU@PLA NPs.</p>
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<p>SEM images (×10.0 k) and histograms of the size distribution of the PLA particles obtained using PLA HMw (<b>A</b>,<b>B</b>) and PLA LMw (<b>C</b>,<b>D</b>).</p>
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<p>SEM images (×10.0 k) and histograms of the size distribution of PLA UT (<b>A</b>,<b>B</b>) and PLA TS (<b>C</b>,<b>D</b>) samples.</p>
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<p>SEM images (×20.0 k) and histograms of the size distribution of PLA<sub>1</sub> (<b>A</b>,<b>B</b>), PLA<sub>10</sub> (<b>C</b>,<b>D</b>), and PLA<sub>50</sub> (<b>E</b>,<b>F</b>).</p>
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<p>SEM images (×10.0 k) and histograms of the size distribution of PVA<sub>0.1</sub> (<b>A</b>,<b>B</b>), PVA<sub>2.5</sub> (<b>C</b>,<b>D</b>), and PVA<sub>10</sub> (<b>E</b>,<b>F</b>).</p>
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<p>SEM images (×25.0 k) and size histograms of pristine PLA NPs (<b>A</b>,<b>B</b>) and FLU@PLA NPs (<b>C</b>,<b>D</b>).</p>
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<p>Cell viability of HCT-116 cell line incubated with different concentrations of PLA NPs and FLU@PLA NPs for 24 h. Error bars correspond to the standard deviation. Dots on the graph represent the values obtained for each experiment, n = 3.</p>
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