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17 pages, 8620 KiB  
Article
Physiological Phenotyping and Biochemical Characterization of Mung Bean (Vigna radiata L.) Genotypes for Salt and Drought Stress
by Mayur Patel, Divya Gupta, Amita Saini, Asha Kumari, Rishi Priya and Sanjib Kumar Panda
Agriculture 2024, 14(8), 1337; https://doi.org/10.3390/agriculture14081337 (registering DOI) - 10 Aug 2024
Viewed by 285
Abstract
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence [...] Read more.
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence of salt and drought stress on mung bean genotypes by evaluating its morpho-physiological traits and biochemical characteristics. This phenotypic analysis revealed that both salt and drought stress adversely affected mung bean, which led to reduced plant height, leaf senescence, loss of plant biomass, and premature plant death. Reactive oxygen species (ROS) production increased under these abiotic stresses. In response, to prevent damage by ROS, the plant activates defense mechanisms to scavenge ROS by producing antioxidants. This response was validated through morpho-physiological, histological, and biochemical assays that characterized KVK Puri-3 and KVK Jharsuguda-1 as salt and drought sensitive genotypes, respectively, and Pusa ratna was identified as a drought and salt tolerant genotype. Full article
(This article belongs to the Section Crop Production)
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Figure 1

Figure 1
<p>This figure shows the physical changes that occur in response to salt and drought stress. (<b>A</b>) Salt tolerant germplasm: Pusa Ratna; (<b>B</b>) salt sensitive germplasm: KVK Puri-3; (<b>C</b>) drought tolerant germplasm: Pusa Ratna; (<b>D</b>) drought sensitive germplasm: KVK Jharsuguda.</p>
Full article ">Figure 2
<p>Comparing sensitive and tolerant plant genotypes under salt and drought stress reveals distinct differences in their RWC, PHTI, and Fv/Fm values. (<b>A</b>) Graphs between the salt sensitive vs. salt tolerant; (<b>B</b>) graphs between drought sensitive and drought tolerant genotypes. Here, ***—<span class="html-italic">p</span>-value-0.001, ****—<span class="html-italic">p</span>-value-0.0002.</p>
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<p>Heat map data analysis of 61 mung bean genotypes for sensitive and tolerant genotypes. This heat map was made by measuring RWC, PHTI, and Fv/Fm parameters for (<b>A</b>) salt and (<b>B</b>) drought stress to identify the most sensitive and most tolerant genotype among all 61 genotypes.</p>
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<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under salt stress. Here red dot corresponds to a named genotype.</p>
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<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under drought stress. Here, red dot corresponds to a named genotype.</p>
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<p>Biochemical and antioxidative analysis for salt stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, MDHAR, and DHAR, respectively. Here, *—<span class="html-italic">p</span>-value-0.01, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value &lt; 0.00001.</p>
Full article ">Figure 7
<p>Biochemical and antioxidative analysis for drought stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, and MDHAR, respectively. Here, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value &lt; 0.00001.</p>
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<p>The loss of plasma membrane during stress was depicted by Evan’s blue staining.</p>
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<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">Figure 9 Cont.
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">Figure 10
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">Figure 10 Cont.
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p>
Full article ">
20 pages, 14849 KiB  
Article
Comparison of the Impacts of Sea Surface Temperature in the Western Pacific and Indian Ocean on the Asian Summer Monsoon Anticyclone and Water Vapor in the Upper Troposphere
by Luyao Chao, Hongying Tian, Xiaoxu Tu, Jiaying Jiang and Kailong Shen
Remote Sens. 2024, 16(16), 2922; https://doi.org/10.3390/rs16162922 - 9 Aug 2024
Viewed by 237
Abstract
The variation in the Asian summer monsoon anticyclone (ASMA) has long been of interest due to its effects on the weather and climate, as well as the vertical transport of pollutants in South Asia and East Asia. This study employs composite analysis to [...] Read more.
The variation in the Asian summer monsoon anticyclone (ASMA) has long been of interest due to its effects on the weather and climate, as well as the vertical transport of pollutants in South Asia and East Asia. This study employs composite analysis to investigate the differences in the influences of sea surface temperature (SST) anomalies in the Western Pacific (WP) and the Indian Ocean (IO) on the ASMA and water vapor in the upper troposphere during summer. The underlying physical mechanisms were further explored. The results indicate that the warm SSTs in the WP have a greater impact on the intensity of the ASMA than those in the IO in summer. On the contrary, the cold SSTs in the IO have a greater impact on intensity of the ASMA than those in the WP in summer. The difference in the impact of SSTs in the WP and IO on the boundaries of the ASMA is relatively small. During positive SST anomalies in the WP, the increase in tropospheric temperature in South Asia and the strengthening of Walker circulation in the WP both contribute to the enhancement of the ASMA. The variations in tropospheric temperature and Walker circulation caused by positive SST anomalies in the IO are similar to those in the WP, except that the rising branch of the Walker circulation is located in the central and western IO. The decrease in SST in the WP region causes insignificant changes in the ASMA. During the cold SST period in the IO, the significant decrease in tropospheric temperature and the weakening of the Walker circulation in the IO region lead to a significant decrease in the intensity of the ASMA at the southern ASMA. When the SST in the WP and IO regions is warmer, the high value centers of water vapor in the troposphere generally coincide with the high value centers of temperature, accompanied by enhanced convection, significantly increasing the water vapor south of the ASMA. The anomalous sinking movement in the Western Pacific leads to relatively small changes in water vapor from the near-surface to 150 hPa over the southeast of the ASMA. Full article
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Figure 1
<p>Horizontal distributions of correlation coefficients (color shaded) of the (<b>a</b>) ASMA intensity–area index, (<b>b</b>) intensity index, and (<b>c</b>) area index with SST in the summer (June–August) of 1979–2021. The red contours show the JJA climatological geopotential height enclosed by the 16,750 gpm isoline at 100 hPa. The left black box indicates the selected study region over the Indian Ocean (IO: 40°–100°E, 25°S–25°N), and the right black box indicates the selected study region over the Western Pacific (WP: 110°–150°E, 0°–35°N). The thin black line indicates the Tibetan Plateau boundary (the same hereafter), and the black dots represent the corresponding values are significant at the 95% confidence level.</p>
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<p>Standardized time series of SST (unit: °C) averaged over the (<b>a</b>) WP region and the (<b>b</b>) IO region in the summers of 1979–2021. The red dots represent years with warm SST, the blue dots represent years with cold SST, and the black dots represent years with normal SST.</p>
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<p>Horizontal distribution of anomalies in geopotential height (unit: gpm) between warm SST and climate state at (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) 200 hPa and (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>) 100 hPa in the (<b>a</b>,<b>c</b>) WP region and (<b>e</b>,<b>g</b>) IO region during summer, as well as differences in geopotential height between cold SST and climate state in the (<b>b</b>,<b>d</b>) WP region and (<b>f</b>,<b>h</b>) IO region. Green, purple, and black contours in (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) indicate the 12,520 gpm geopotential height contours at 200 hPa for warm SST years, cold SST years, and the climatological state, respectively. Green, purple, and black contours in (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>) indicate the 16,750 gpm geopotential height contours at 100 hPa for warm SST years, cold SST years, and the climatological state, respectively. Dotted regions represent that the anomalies are statistically significant at the 90% confidence level (the same hereafter).</p>
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<p>Horizontal distribution of anomalies in tropospheric temperature (defined as the mean air temperature from 850 to 100 hPa, color shaded, unit: K) between warm SST and climate state in the (<b>a</b>) WP region and (<b>c</b>) IO region during summer, as well as differences between cold SST and climate state in the (<b>b</b>) WP region and (<b>d</b>) IO region. Green, purple, and black contours indicate the 12,520 gpm geopotential height contour at 200 hPa for years of warm, cold, and climate mean SSTs, respectively.</p>
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<p>Vertical profiles of regional averaged temperature anomalies between warm SST and climate state in the (<b>a</b>,<b>b</b>) WP region (average along the selected WP region) and (<b>e</b>,<b>f</b>) IO region (average along the selected IO region), as well as differences between cold SST and climate state in the (<b>c</b>,<b>d</b>) WP region and (<b>g</b>,<b>h</b>) IO region. In panels (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>), purple arrows represent meridional wind and vertical velocity passing the significance test, while green contours represent zonal wind passing the significance test. In panels (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>), purple arrows represent zonal wind and vertical velocity passing significance test, while green contours represent meridional wind passing significance test. Positive values are displayed as solid green contours, and negative values as dashed green lines. Magenta contour lines indicate the ASMA, and black dashed lines denote the selected WP region and IO region.</p>
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<p>Meridional-vertical profiles of Walker circulation (averaged between 5°S and 5°N) anomalies (color shaded) and winds field anomalies (purple arrows indicate latitudinal wind and vertical velocity) between warm SST and climate state in the (<b>a</b>) WP region and (<b>c</b>) IO region during summer, as well as differences between cold SST and climate state in the (<b>b</b>) WP region and (<b>d</b>) IO region. Black dashed lines delineate the selected WP region in panel (<b>a</b>,<b>b</b>), and the selected IO region in panel (<b>c</b>,<b>d</b>).</p>
Full article ">Figure 7
<p>Horizontal distributions of outward longwave radiation anomalies (color shaded, unit: W/m<sup>2</sup>) between warm SST and climate state in the (<b>a</b>) WP region and (<b>c</b>) IO region during summer, as well as differences between cold SST and climate state in the (<b>b</b>) WP region and (<b>d</b>) IO region.</p>
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<p>Horizontal distribution of anomalies in geopotential height (color shaded, unit: gpm) between warm SST and climate state in the (<b>a</b>) WP region and (<b>c</b>) IO region during summer, as well as differences between cold SST and climate state in the (<b>b</b>) WP region and (<b>d</b>) IO region. The (<b>e</b>–<b>h</b>) are similar to (<b>a</b>–<b>d</b>), but the repetitive years were removed from the warm and cold years of SST in the WP region and IO region. The green arrows indicate abnormal horizontal winds (u and v). The area shaded in black represents the Tibetan Plateau.</p>
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<p>Horizontal distribution of anomalies in MLS water vapor (unit: ppmv) at 216 hPa between warm SST and climate state in (<b>a</b>) WP region and (<b>b</b>) IO region during summer, as well as differences in ERA5 water vapor at 200 hPa in (<b>c</b>) WP region and (<b>d</b>) IO region. Green, blue, and black contours indicate the 12,520 gpm geopotential height contours at 200 hPa for warm SST years, cold SST years, and the climatological state, respectively. Dotted regions represent that the anomalies are statistically significant at the 90% confidence level.</p>
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<p>Vertical profiles of water vapor anomalies (color shaded, unit: 10<sup>−8</sup>%) along 80°E, 30°N, and 15°N between warm SST and climate state in (<b>a</b>–<b>c</b>) WP region and (<b>d</b>–<b>f</b>) IO region during summer. The green line represents the height of the tropopause (2.5 PVU contour line), the dotted area indicates passing the 90% significance test, and the black contour line represents the range of the ASMA. Black shadow represents topography.</p>
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<p>Vertical profiles of temperature anomalies (color shaded, unit: K) along 80°E, 30°N, and 15°N between warm SST and climate state in the (<b>a</b>–<b>c</b>) WP region and (<b>d</b>–<b>f</b>) IO region during summer. Purple arrows represent the vertical velocity differences. The green line represents the height of the tropopause (2.5 PVU contour line), the dotted area indicates passing the 90% significance test, and the black contour line represents the range of the ASMA. Black shadow represents topography.</p>
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<p>Horizontal distribution of anomalies in divergence (unit: 10<sup>−6</sup> s<sup>−1</sup>) at 500 and 200 hPa between warm SST and climate state in (<b>a</b>,<b>b</b>) WP region and (<b>c</b>,<b>d</b>) IO region during summer. Purple arrows represent the horizontal winds (u and v, units: m/s). Green, blue, and black contours indicate the 12,520 gpm geopotential height contours at 200 hPa for warm SST years, cold SST years, and the climatological state, respectively.</p>
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<p>Summary schematic.</p>
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11 pages, 2752 KiB  
Article
Detection of Dengue Virus 1 and Mammalian Orthoreovirus 3, with Novel Reassortments, in a South African Family Returning from Thailand, 2017
by Petrus Jansen van Vuren, Rhys H. Parry and Janusz T. Pawęska
Viruses 2024, 16(8), 1274; https://doi.org/10.3390/v16081274 - 9 Aug 2024
Viewed by 276
Abstract
In July 2017, a family of three members, a 46-year-old male, a 45-year-old female and their 8-year-old daughter, returned to South Africa from Thailand. They presented symptoms consistent with mosquito-borne diseases, including fever, headache, severe body aches and nausea. Mosquito bites in all [...] Read more.
In July 2017, a family of three members, a 46-year-old male, a 45-year-old female and their 8-year-old daughter, returned to South Africa from Thailand. They presented symptoms consistent with mosquito-borne diseases, including fever, headache, severe body aches and nausea. Mosquito bites in all family members suggested recent exposure to arthropod-borne viruses. Dengue virus 1 (Genus Orthoflavivirus) was isolated (isolate no. SA397) from the serum of the 45-year-old female via intracerebral injection in neonatal mice and subsequent passage in VeroE6 cells. Phylogenetic analysis of this strain indicated close genetic identity with cosmopolitan genotype 1 DENV1 strains from Southeast Asia, assigned to major lineage K, minor lineage 1 (DENV1I_K.1), such as GZ8H (99.92%) collected in November 2018 from China, and DV1I-TM19-74 isolate (99.72%) identified in Bangkok, Thailand, in 2019. Serum samples from the 46-year-old male yielded a virus isolate that could not be confirmed as DENV1, prompting unbiased metagenomic sequencing for virus identification and characterization. Illumina sequencing identified multiple segments of a mammalian orthoreovirus (MRV), designated as Human/SA395/SA/2017. Genomic and phylogenetic analyses classified Human/SA395/SA/2017 as MRV-3 and assigned a tentative genotype, MRV-3d, based on the S1 segment. Genomic analyses suggested that Human/SA395/SA/2017 may have originated from reassortments of segments among swine, bat, and human MRVs. The closest identity of the viral attachment protein σ1 (S1) was related to a human isolate identified from Tahiti, French Polynesia, in 1960. This indicates ongoing circulation and co-circulation of Southeast Asian and Polynesian strains, but detailed knowledge is hampered by the limited availability of genomic surveillance. This case represents the rare concurrent detection of two distinct viruses with different transmission routes in the same family with similar clinical presentations. It highlights the complexity of diagnosing diseases with similar sequelae in travelers returning from tropical areas. Full article
(This article belongs to the Section Human Virology and Viral Diseases)
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Figure 1
<p>Phylogenetic analysis of imported DENV1 SA strain in 2017. The maximum likelihood (ML) tree was constructed based on aligned DENV1 strains with a nucleotide substitution model: TIM2+F+I+G4 with 1000 ultrafast bootstrap replicates. DENV1 strains are indicated as the 6 major genotypes (I–VI), highlighted in different colors with major and minor lineages if assigned on the label. Sequence accession number, country, and reported year are indicated, and the imported South African case is shown with arrowhead. Bootstrap support values are shown on nodes exceeding 75. The tree is rooted at the midpoint. Branch length corresponds to nucleotide substitutions per site.</p>
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<p>Genome organization and coverage, and phylogenetic inference of the S1 segment of the South African Human/SA395/SA/2017 MRV-3 strain. (<b>A</b>) Coverage and genome organization of the 10 MRV3 segments. (<b>B</b>) Consensus maximum likelihood phylogeny GTR+F+G4 model with a bootstrap of 1000 replicates. Accession number, host species, country and year are indicated for each strain. MRV-3 lineages are indicated in lowercase, a, b and c, and MRV-2 and MRV-1 serotypes are indicated. The South African Human/SA395/SA/2017 MRV-3 strain is indicated with an arrowhead. Bootstrap support values are shown on nodes exceeding 75. The tree is rooted at the midpoint. Branch length corresponds to nucleotide substitutions per site.</p>
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20 pages, 15047 KiB  
Article
Gottschelia (Gottscheliaceae, Marchantiophyta) in Indochina
by Vadim A. Bakalin, Anna A. Vilnet, Ksenia G. Klimova, Van Sinh Nguyen and Seung Se Choi
Plants 2024, 13(16), 2198; https://doi.org/10.3390/plants13162198 - 8 Aug 2024
Viewed by 259
Abstract
Gottschelia, collected for the first time in Indochina, inspired an attempt to review the genus phylogeny to identify a more precise position of Indochinese plants. The genetic distance between African and Asian populations of G. schizopleura sensu lato was confirmed. The two [...] Read more.
Gottschelia, collected for the first time in Indochina, inspired an attempt to review the genus phylogeny to identify a more precise position of Indochinese plants. The genetic distance between African and Asian populations of G. schizopleura sensu lato was confirmed. The two groups should be treated as different species. A new combination, G. microphylla comb. nov., has been proposed for Asian plants. Aside from molecular genetics, distinguishing this species from the presumable strictly African G. schizopleura is also possible by morphological characteristics, as well as by its distribution. At the same time, at least three groups are distinguished among Asian haplotypes of G. microphylla, each of which can be interpreted as a species or, at least, subspecies. A morphological description, intravital photographs of the general habitat, and details of the morphological structures are provided. The position of Gottschelia in the phylogenetic schema of Jungermanniales does not allow us to attribute it to any of the known families and forces us to describe a new family, Gottscheliaceae, which is phylogenetically somewhat related to the Chaetophyllopsidaceae re-evaluated here and very different from Gottscheliaceae morphologically. Full article
(This article belongs to the Section Plant Systematics, Taxonomy, Nomenclature and Classification)
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Figure 1
<p>The phylogram for the Jungermanniidae obtained from the maximum likelihood approach based on <span class="html-italic">rbc</span>L + <span class="html-italic">trn</span>L-F cpDNA. Bootstrap support values and posterior probabilities more than 50% (0.50) are indicated. The branches stretched from nodes with 100% bootstrap support values and 1.00 posterior probabilities are in bold. A—order Jungermanniales, B—order Porellales, and C—order Ptilidiales.</p>
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<p>Statistical parsimony network for the <span class="html-italic">rbc</span>L cpDNA (451 base pairs) for the genus <span class="html-italic">Gottschelia</span>. Solid points indicate missing haplotypes. The specimen vouchers, according to <a href="#plants-13-02198-t002" class="html-table">Table 2</a>, are marked.</p>
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<p>The distribution of pairwise <span class="html-italic">p</span>-distances between <span class="html-italic">rbc</span>L sequences (451 b.p.) of the genus <span class="html-italic">Gottschelia</span> specimens. Dist. Dt—distance value, and Nbr—number of runs.</p>
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<p><span class="html-italic">Gottschelia microphylla</span> (Nees) Bakalin, Vilnet, Klimova et S.S. Choi: (<b>A</b>) Perianthous shoot, dorsal view. (<b>B</b>) Part of shoot, dorsal view. (<b>C</b>) Part of shoot, ventral view. (<b>D</b>–<b>I</b>) Leaves, (<b>J</b>) perianth cross-section in its middle part, and (<b>K</b>,<b>L</b>) female bracts. Scales: a—2 mm for (<b>A</b>), b—2 mm for (<b>B</b>,<b>C</b>), c—1 mm for (<b>D</b>–<b>I</b>,<b>K</b>,<b>L</b>), and d—100 µm for (<b>J</b>). All from V-88-15-23 (VBGI, HN).</p>
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<p><span class="html-italic">Gottschelia microphylla</span> (Nees) Bakalin, Vilnet, Klimova et S.S. Choi: (<b>A</b>) Mat of <span class="html-italic">Gottschelia microphylla</span> covering rocks in natural conditions. (<b>B</b>) Mat with perianthous shoots in dry condition. (<b>C</b>) Shoots, dorsal view. (<b>D</b>) Fragment of perianthous shoot, dorsal view. (<b>E</b>) Perianthous shoots in a mat, dorsal view. Scales: 5 mm for (<b>A</b>,<b>B</b>) and 2 mm for (<b>C</b>–<b>E</b>). (<b>B</b>,<b>D</b>) From V-88-15-23, and (<b>C</b>,<b>E</b>) from V-88-7-23 (VBGI, NH).</p>
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<p><span class="html-italic">Gottschelia microphylla</span> (Nees) Bakalin, Vilnet, Klimova et S.S. Choi: (<b>A</b>–<b>C</b>) Leaves, curled up and torn under the slide. (<b>D</b>) Perianth mouth armature. (<b>E</b>) Perianth cells in lower part. (<b>F</b>) Stem cross-section, fragment. (<b>G</b>) Leaf cells along leaf margin. (<b>H</b>,<b>I</b>,<b>L</b>) Mid-leaf cells with oil bodies. (<b>J</b>) Papillose cuticle in lower part of the leaf. (<b>K</b>) Papillose cuticle in the mid-leaf. Scales: 1 mm for (<b>A</b>–<b>C</b>), 3 mm for (<b>F</b>), 100 µm for (<b>D</b>–<b>K</b>), and 50 µm for (<b>L</b>). (<b>A</b>–<b>F</b>,<b>I</b>–<b>K</b>) From V-88-15-23, and (<b>G</b>,<b>H</b>) from V-88-6-23 (VBGI, NH).</p>
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4 pages, 171 KiB  
Editorial
Zebrafish Models in Toxicology and Disease Studies
by Ida Ferrandino
Int. J. Mol. Sci. 2024, 25(16), 8608; https://doi.org/10.3390/ijms25168608 - 7 Aug 2024
Viewed by 236
Abstract
Danio rerio is a small tropical freshwater fish, also known as Brachydanio rerio and commonly referred to as zebrafish, described for the first time in 1822 by Francis Hamilton in the Ganges River but widespread throughout the entire Great Himalayan region of Southeast [...] Read more.
Danio rerio is a small tropical freshwater fish, also known as Brachydanio rerio and commonly referred to as zebrafish, described for the first time in 1822 by Francis Hamilton in the Ganges River but widespread throughout the entire Great Himalayan region of Southeast Asia [...] Full article
(This article belongs to the Special Issue Zebrafish Models in Toxicology and Disease Studies)
16 pages, 7922 KiB  
Article
Ecosystem Resilience Trends and Its Influencing Factors in China’s Three-River Headwater Region: A Comprehensive Analysis Using CSD Indicators (1982–2023)
by Zishan Wang, Wenli Huang and Xiaobin Guan
Land 2024, 13(8), 1224; https://doi.org/10.3390/land13081224 - 7 Aug 2024
Viewed by 363
Abstract
Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area [...] Read more.
Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the “Water Tower of China”. We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world. Full article
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Figure 1
<p>Location, terrain, and vegetation types of the study area.</p>
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<p>AR(1) trends across the subregions of the TRHR.</p>
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<p>Average AR(1) distribution maps and Kendall τ distribution maps of different periods.</p>
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<p>Histograms of AR(1) Kendall τ distribution in subregions from 1996 to 2011 and 2012 to 2021 (The color mapping is consistent with <a href="#land-13-01224-f003" class="html-fig">Figure 3</a>c,d.).</p>
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<p>Mean AR(1) trends of each major vegetation type.</p>
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<p>Mean AR(1) trend under different levels of grazing intensity, temperature, precipitation, and vegetation growth condition (NDVI).</p>
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<p>Spatial distribution of temporal correlation between AR(1) and temperature, precipitation, NDVI, and grazing intensity (1984–2015).</p>
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<p>Possible tipping points showed by annual mean NDVI trend of the TRHR.</p>
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21 pages, 686 KiB  
Article
Understanding the Economic Drivers of Climate Change in Southeast Asia: An Econometric Analysis
by Agung Suwandaru, Widhiyo Sudiyono, Ahmed Shawdari and Yuntawati Fristin
Economies 2024, 12(8), 200; https://doi.org/10.3390/economies12080200 - 5 Aug 2024
Viewed by 470
Abstract
This study analyses macroeconomic trends in Southeast Asian countries and their implications for climate change, focusing on urbanisation, GDP per capita, energy intensity, FDI, inflation, and trade. Using panel data from 1970 to 2020, we investigate climate change drivers across Indonesia, Malaysia, the [...] Read more.
This study analyses macroeconomic trends in Southeast Asian countries and their implications for climate change, focusing on urbanisation, GDP per capita, energy intensity, FDI, inflation, and trade. Using panel data from 1970 to 2020, we investigate climate change drivers across Indonesia, Malaysia, the Philippines, Singapore, and Thailand through panel ARDL with PMG and MG analyses, along with Hausman tests. Our results highlight the need for tailored urbanisation policies for sustainability, as the consistent positive correlation between GDPs per capita and emissions, underscores the challenge of decoupling economic growth from emissions. Urbanisation’s varying impact calls for proactive planning, and mixed FDI results suggest nuanced investment approaches aligned with sustainability. Inflation’s negative impact hints at environmental benefits during price increases, necessitating integrated economic and climate policies. The positive relationship between trade openness and emissions emphasises the need for eco-conscious trade agreements to mitigate emissions from industrial activity. Our study stresses the importance of considering macroeconomic heterogeneity in crafting climate policies. Policymakers must adopt multifaceted approaches that prioritise sustainability across economic growth, energy efficiency, technology adoption, and trade to balance development with environmental preservation. This approach enables Southeast Asian countries to contribute effectively to global climate change mitigation. Full article
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<p>Total carbon emissions from selected countries in ASEAN in 1970–2020.</p>
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<p>Graphical representation of macroeconomic variables impacting climate change.</p>
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20 pages, 6228 KiB  
Article
Evaluation of Future Changes in Climate Extremes over Southeast Asia Using Downscaled CMIP6 GCM Projections
by Sophal Try and Xiaosheng Qin
Water 2024, 16(15), 2207; https://doi.org/10.3390/w16152207 - 4 Aug 2024
Viewed by 510
Abstract
This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme [...] Read more.
This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study’s findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region. Full article
(This article belongs to the Special Issue Analysis of Extreme Precipitation under Climate Change)
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<p>Study area of Southeast Asia: (<b>a</b>) five geographical masks for regional analysis including Mainland Southeast Asia (MSEA), Sumatra and Malay Peninsula (SRMP), Java Island (JAVA), Borneo and Sulawesi Islands (BORS), and the Philippines (PHLP); and (<b>b</b>) digital elevation map for the study region. The country administrative boundaries were accessed from open data provided by the World Food Programme (a UN agency) at <a href="https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries" target="_blank">https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries</a> (accessed on 30 May 2023). The digital elevation data was freely available from HydroSHEDS at <a href="https://www.hydrosheds.org/hydrosheds-core-downloads" target="_blank">https://www.hydrosheds.org/hydrosheds-core-downloads</a> (accessed on 30 May 2023).</p>
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<p>Comparison of precipitation indices from APHRODITE with historical NEX-GDDP-CMIP6 dataset. The bias was calculated by using the NEX-GDDP-CMIP6 dataset subtracted from the APHRODITE dataset.</p>
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<p>Comparison of temperature indices from CPC with historical NEX-GDDP-CMIP6 dataset. The bias was calculated by using the NEX-GDDP-CMIP6 dataset subtracted from the CPC dataset.</p>
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<p>Comparison of precipitation (<b>a</b>,<b>b</b>) and temperature changes (<b>c</b>,<b>d</b>) from the future projection period (2021–2085) compared to the baseline period (1950–2014). The hatch fills represent the significance of K-S test at a significant level of 1%.</p>
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<p>Monthly changes in precipitation (<b>left column</b>) and temperature (<b>right column</b>) over the five main regions in SEA in the far future (2061–2100) compared to the baseline period (1950–2014).</p>
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<p>Changes in extreme and frequency of precipitation in SEA cities. The dots and lines represent the empirical and GEV fitting for the future projections under the SSP245 (blue) and SSP585 (red) scenarios compared to the historical period (black).</p>
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<p>Changes in extreme and frequency of temperature in SEA cities. The dots and lines represent the empirical and GEV fitting for the future projections under the SSP245 (blue) and SSP585 (red) scenarios compared to the historical period (black).</p>
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14 pages, 2460 KiB  
Article
Genomic Selection for Growth and Wood Traits in Castanopsis hystrix
by Weihua Zhang, Ruiyan Wei and Yuanzhen Lin
Forests 2024, 15(8), 1342; https://doi.org/10.3390/f15081342 - 2 Aug 2024
Viewed by 252
Abstract
Castanopsis hystrix, a precious tree species in Southeast Asia, has the advantages of rapid growth and high-quality wood materials. However, there are problems such as its long breeding cycle and low efficiency, and being time-consuming, which greatly restricts the industrial development of [...] Read more.
Castanopsis hystrix, a precious tree species in Southeast Asia, has the advantages of rapid growth and high-quality wood materials. However, there are problems such as its long breeding cycle and low efficiency, and being time-consuming, which greatly restricts the industrial development of C. hystrix. Performing genome selection (GS) for growth and wood traits for the early selection of superior progeny has great significance for the rapid breeding of new superior varieties of C. hystrix. We used 226 clones in the main distribution and 479 progenies within 23 half-sib families as experimental materials in this study. Genotyping datasets were obtained by high-throughput re-sequencing technology, and GS studies were conducted on the growth (tree height (H), diameter at breast height (DBH)) and wood (wood density (WD), fiber length (FL), and fiber length–width ratio (LWR)) traits. The coefficient of variation (CV) of five phenotypic traits ranged from 10.1% to 22.73%, the average CV of growth traits was 19.93%, and the average CV of wood traits was 9.72%. The Pearson correlation coefficients between the five traits were almost all significantly positive. Based on the Genomic Best Linear Unbiased Prediction (GBLUP) model, the broad-sense heritabilities of growth traits were higher than those of wood quality traits, and the different number of SNPs had little effect on the heritability estimation. GS prediction accuracy first increased and then reached a plateau at around 3K SNPs for all five traits. The broad-sense heritability of these five traits was significantly positively correlated with their GS predictive ability (r = 0.564, p < 0.001). Bayes models had better GS prediction accuracy than the GBLUP model. The 15 excellent progeny individuals were selected, and their genetic gain ranged from 0.319% to 2.671%. These 15 superior offspring individuals were 4388, 4438, 4407, 4468, 4044, 4335, 4410, 4160, 4212, 4461, 4052, 4014, 4332, 4389, and 4007, mainly from three families F5, F6, and F11. Our research lays out the technical and material foundation for the rapid breeding of new superior varieties of C. hystrix in southern China. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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<p>Descriptive statistics of growth and wood traits of <span class="html-italic">C</span>. <span class="html-italic">hystrix.</span> The diagonal plots are histograms of height (H), diameter at breast (DBH), wood density (WD), fiber length (FL), and fiber length–width ratio (LWR); CV is coefficient of variance. Pearson’s correlation coefficients between the five traits are depicted in the top-right corner; ** and *** indicates significant correlation at 0.01 and 0.001 levels, respectively. The bottom-left corner plots are the scatter plots between the five traits, respectively.</p>
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<p>The distribution of SNPs after quality control in <span class="html-italic">C</span>. <span class="html-italic">hystrix</span> genome. The ordinate is 12 chromosomes, and the abscissa is the length of the chromosome. The legend presents the marker density.</p>
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<p>Effect of different SNP numbers on trait heritability (<span class="html-italic">H</span><sup>2</sup>) in <span class="html-italic">C</span>. <span class="html-italic">hystrix.</span> H<sup>2</sup> is heritability, H is height, DBH is diameter at breast, WD is wood density, FL is fiber length, and LWR is fiber length–width ratio, respectively. K is for 1000.</p>
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<p>Comparison of GS prediction accuracy under different SNP numbers in <span class="html-italic">C</span>. <span class="html-italic">hystrix.</span> H is height, DBH is diameter at breast, WD is wood density, FL is fiber length, and LWR is fiber length–width ratio, respectively. K is for 1000.</p>
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<p>Effect of trait heritability on GS prediction ability in <span class="html-italic">C</span>. <span class="html-italic">hystrix.</span> H is height, DBH is diameter at breast, WD is wood density, FL is fiber length, and LWR is fiber length–width ratio, respectively. K is for 1000.</p>
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<p>Comparison of GS prediction accuracy under different models in <span class="html-italic">C</span>. <span class="html-italic">hystrix.</span> H is height, DBH is diameter at breast, WD is wood density, FL is fiber length, and LWR is fiber length–width ratio, respectively. BRR is Bayes ridge regression. The error bar is standard error.</p>
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18 pages, 12871 KiB  
Article
A Survey of Changes in Grasslands within the Tonle Sap Lake Landscape from 2004 to 2023
by Monysocheata Chea, Benjamin T. Fraser, Sonsak Nay, Lyan Sok, Hillary Strasser and Rob Tizard
Diversity 2024, 16(8), 448; https://doi.org/10.3390/d16080448 - 29 Jul 2024
Viewed by 1059
Abstract
The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened [...] Read more.
The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite decades of recognition by several government and international agencies and the fact that nine protected areas have been established within this region, natural land cover such as grasslands have experienced considerable decline since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery using Google Earth Engine. The time series of thematic maps were then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10 km buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5–87.9%), with grasslands averaging 76.1% user’s accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (>59.5% of the 2004 area), with approx. 89.3% of this loss being attributed to cropland expansion. The results of this project will inform conservation efforts focused on local-scale planning and the management of commercial agriculture. Full article
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<p>Tonle Sap Lake (TSL) landscape and protected areas. Protected areas include the following: A—Ang Trapeng Thmor; B—Bakan; C—Prek Toal; D—Kampong Thom; E—Phnom Neang Kong Rey-Phnom Touk Meas; F—Phnom Krang Dey Meas; G—Stung Sen core area (Ramsar Site); H—Angkor; I—Boeng Chhmar core area (Ramsar Site). All protected areas are regulated by the Royal Cambodian Government (RCG) Ministry of Environment (MoE). Those given in green are supported by the Wildlife Conservation Society (WCS).</p>
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<p>European Union (EU) Joint Research Centre (JRC) surface water occurrence ≥ 20% layer was used to define corresponding grassland areas as wet grasslands. Grassland areas located outside of this polygon were defined as dry grasslands.</p>
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<p>Flow chart of the methods used to complete the Tonle Sap Lake landscape level classification, change detection, and accuracy assessment. * The classification parameters were iteratively tested and tuned for each land cover map using the results of the error matrix and feature importance test.</p>
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<p>Classified map of the Tonle Sap Lake landscape based on 2004 satellite imagery.</p>
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<p>Classified map of the Tonle Sap Lake landscape based on 2023 satellite imagery.</p>
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<p>Grassland loss (dry only) between 2004 and 2023. Grassland loss is symbolized based on four distinct time periods: yellow (grassland lost between 2004 and 2008), orange (2009–2014 losses), dark orange (2015–2018), and red (2019–present).</p>
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18 pages, 3483 KiB  
Article
Epiphytic Patterns Impacting Metabolite Diversity of Drynaria roosii Rhizomes Based on Widely Targeted Metabolomics
by Nana Chang, Xianping Yang, Xiaoqing Wang, Chao Chen, Chu Wang, Yang Xu, Hengyu Huang and Ye Wang
Metabolites 2024, 14(8), 409; https://doi.org/10.3390/metabo14080409 - 26 Jul 2024
Viewed by 289
Abstract
Drynaria roosii Nakaike, a fern widely distributed in China and some countries in Southeast Asia, is a commonly used herbal medicine in tonic diets and Chinese patented medicine. The metabolites of its dried rhizomes are easily affected by the epiphytic pattern, whether on [...] Read more.
Drynaria roosii Nakaike, a fern widely distributed in China and some countries in Southeast Asia, is a commonly used herbal medicine in tonic diets and Chinese patented medicine. The metabolites of its dried rhizomes are easily affected by the epiphytic pattern, whether on rock tunnels (RTs) or tree trunks (TTs). The current research focused on rhizomes from these two patterns, RTs and TTs (further divided into subclasses TA, TB, TC, and TD, based on trunk differences) and conducted a widely targeted metabolomics analysis. A total of 1435 components were identified across 13 categories, with flavonoids, amino acids, and their derivative, lipids, identified as the main components. They accounted for 19.96%, 12.07%, and 12.14% of all metabolites, respectively. The top five flavonoids in TB were eriodicty-ol-7-O-(6″-acetyl)glucoside, quercetin-3-O-sophoroside (baimaside), dihydrochar-cone-4′-O-glucoside, morin, and hesperetin-7-O-glucoside, with relative contents 76.10, 24.20, 17.02, 15.84, and 14.64 times higher than in RTs. Principal component analysis revealed that samples with different epiphytic patterns clustered into five groups. The RT patterns revealed unique metabolites that were not detected in the other four epiphytic species (TA, TB, TC, and TD), including 16 authenticated metabolites: 1 alkaloid, 1 amino acid derivative, 7 flavonoids, 2 lignans, 1 lipid, 1 alcohol, 1 aldehyde, and 2 phenolic acids. These differences in epiphytic patterns considerably affected the accumulation of both primary and secondary metabolites. The comparison of diversity between RTs and TTs can guide the selection of a cultivation substance and the grading of collective rhizomes in the wild. This comprehensive analysis of D. roosii rhizome metabolites also offers fundamental insights for identifying active components and understanding the mechanisms underlying their potential pharmacological activities. Full article
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<p><span class="html-italic">Drynaria roosii</span> under two epiphytic pattern and metabolite comparison profiles. (<b>A1</b>) <span class="html-italic">D. roosii</span> grown on rock tunnels (RT group), where adhesion relies on moss and bits of soil in the rock tunnels. (<b>A2</b>) <span class="html-italic">D. roosii</span> grown on a tree trunk (four different tree species are labeled as TA, TB, TC, and TD), where rough bark provides enough adhesion surface. (<b>A3</b>) Fresh rhizome covering golden tomentum after removing fronds, and old parts are the main experimental materials. (<b>B</b>) Biochemical categories and the proportion of authenticated metabolites in these rhizomes. (<b>C</b>) Score plot of principal components analysis using all metabolite profiles based on five groups. (<b>D</b>) Heatmap of biochemical categories among various epiphytic patterns.</p>
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<p>Differential metabolite analysis of <span class="html-italic">Drynaria roosii</span> under two epiphytic patterns. (<b>A</b>) Venn plot based on all metabolites detected in five kinds of rhizomes. (<b>B</b>) Heatmap of different biochemical categories between RT and TB rhizomes. (<b>C</b>) Score plot of orthogonal partial least square discriminate analysis using all metabolites between RT and TB. (<b>D</b>) Permutation test of partial least square discriminate analysis between RT and TB based on 500 permutation times. R2 means explanation percentages and Q2 means prediction ability.</p>
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<p>Differential metabolite analysis and enrichment results of DEMs of <span class="html-italic">Drynaria roosii</span> under two epiphytic patterns (RT and TB). (<b>A</b>) Volcano plot displaying the amounts of differential metabolites between RT and TB. (<b>B</b>) Enrichment pathway of differential metabolites between RT and TB. (<b>C</b>) Differential abundance score plot between RT and TB. RT is the control group; the negative differential abundance score indicated that the counts of upregulated metabolites were lower than those of downregulated metabolites between RT and TB.</p>
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16 pages, 3732 KiB  
Technical Note
Study of the Long-Lasting Daytime Field-Aligned Irregularities in the Low-Latitude F-Region on 13 June 2022
by Pengfei Hu, Gang Chen, Chunxiao Yan, Shaodong Zhang, Guotao Yang, Qiang Zhang, Wanlin Gong and Zhiqiu He
Remote Sens. 2024, 16(15), 2738; https://doi.org/10.3390/rs16152738 - 26 Jul 2024
Viewed by 278
Abstract
The unusual daytime F-region Field-Aligned Irregularities (FAIs) were observed by the HCOPAR and the satellites at low latitudes on 13 June 2022. These irregularities survived from night-time to the following afternoon at 15:00 LT. During daytime, they appeared as fossil structures with low [...] Read more.
The unusual daytime F-region Field-Aligned Irregularities (FAIs) were observed by the HCOPAR and the satellites at low latitudes on 13 June 2022. These irregularities survived from night-time to the following afternoon at 15:00 LT. During daytime, they appeared as fossil structures with low Doppler velocities and narrow spectral widths. These characteristics indicated that they drifted along the magnetic field lines without apparent zonal velocity to low latitudes. Combining the observations of the ICON satellite and the Hainan Digisonde, we derived the movement trails of these daytime irregularities. We attributed their generation to the rapid ascent of the F-layer due to the fluctuation of IMF Bz during the quiet geomagnetic conditions. Subsequently, the influence of the substorm on the low-latitude ionosphere was investigated and simulated. The substorm caused the intense Joule heating that enhanced the southward neutral winds, carrying the neutral compositional disturbances to low latitudes and resulting in a negative storm effect in Southeast Asia. The negative storm formed a low-density circumstance and slowed the dissipation of the daytime FAIs. These results may provide new insights into the generation of post-midnight irregularities and their relationship with daytime fossil structures. Full article
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<p>Projections of all the beams of the HCOPAR on the geographic map. The black solid and dashed lines denote the radar beams and observation altitudes of the beams of the HCOPAR, respectively.</p>
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<p>Time variations in (<b>a</b>) Z component of Interplanetary Magnetic Field, IMF Bz (nT); (<b>b</b>) geomagnetic storm index, SYM-H (nT) and Kp index; (<b>c</b>) SuperMAG Auroral Electrojet (SME) index (nT); (<b>d</b>) Polar Cap index (mV/m); (<b>e</b>) equatorial electric field (EEF, mV/m) at 110°E for quiet time (red dashed curve) and penetration (black solid curve) (mV/m); (<b>f</b>) vertical velocity of F-layer measured by the Hainan Digisonde during 12–13 June, LT = UT + 7 h. Yellow/gray block marks period of turning of IMF Bz/daytime FAIs.</p>
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<p>Observations of daytime F-region irregularities observed by the HCOPAR in seven beams. (<b>a</b>–<b>g</b>) The Range-Time-Intensity (Signal-to-Noise Ratio, SNR) and (<b>h</b>–<b>n</b>) Doppler velocity of the daytime irregularities observed during 11:00–15:00 LT (LT = UT + 7 h) on 13 June 2022.</p>
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<p>Typical Doppler spectra distribution of these daytime irregularities at (<b>a</b>) 05:05 UT and (<b>b</b>) 07:35 UT on 13 June 2022. LT = UT + 7 h. The vertical and horizontal axes represent the altitude and Doppler velocity of the irregularity, respectively.</p>
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<p>Simultaneous observations of the daytime irregularities by the ICON satellite on 13 June 2022. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>): Orbits of the ICON satellite. Red lines and blocks indicate the region around the HCOPAR (100–120°E) and the depleted region of ion density, respectively. The altitudes of the ICON satellite are indicated by the pink dashed lines. The dashed lines denote the magnetic dip equator. (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>): Ion density. Black and pink lines indicate the logarithm of ion density and perturbation index S, respectively. D1 and D2 denote the depleted regions. The horizontal dotted line is used as the threshold to recognize the irregularities at night-time (0.02) and daytime (0.002). (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>): The velocities of ion flows. Black and red lines denote the vertical velocities (upward is positive) and horizontal velocities (eastward is positive). The longitudes indicated by red curves are zoomed out by the shaded regions in the middle and right columns.</p>
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<p>Temporal variations in (<b>a</b>) changes in the ion densities (*10<sup>5</sup> cm<sup>−3</sup>, the “*” means multiplication) observed by the ICON compared to the quiet day on 12 June 2022. The Universal Time of the ICON passing through this area is labeled. (<b>b</b>,<b>c</b>) Thermospheric ∑O/N<sub>2</sub> measured by the GUVI on 12 June and 13 June 2022. The Universal Time of the satellite’s equatorial crossings is shown at the bottom.</p>
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<p>The ICON MIGHTI observation tracks and corresponding Universal Time, zonal wind (positive eastward), and meridional wind (positive northward) profiles for three consecutive orbits on 12 June and 13 June. The geomagnetic dip equators are shown by black dashed lines.</p>
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<p>The maps of gridded Total Electron Content (TEC) with a 20 min interval during 02:20–06:00 UT on 13 June 2022. The red dotted lines indicate the dividing line standardized at about 30 TECu between the high and low values of TEC.</p>
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21 pages, 21462 KiB  
Article
Mapping Urban Landscapes Prone to Hosting Breeding Containers for Dengue-Vector Mosquitoes: A Case Study in Bangkok
by Eric Daudé, Alexandre Cebeillac, Kanchana Nakhapakorn and Rick Paul
Urban Sci. 2024, 8(3), 98; https://doi.org/10.3390/urbansci8030098 - 25 Jul 2024
Viewed by 576
Abstract
Dengue fever is an urban, tropical, and semi-tropical disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes. One significant challenge lies in identifying reliable intra-urban indicators of their densities. Following standardized sampling protocols that adequately take into account the spatial heterogeneity of the [...] Read more.
Dengue fever is an urban, tropical, and semi-tropical disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes. One significant challenge lies in identifying reliable intra-urban indicators of their densities. Following standardized sampling protocols that adequately take into account the spatial heterogeneity of the geographical contexts which may influence mosquito habitats is therefore fundamental to compare studies and follow such relevant indicators. We develop a method for subdividing urban territory based on environmental factors which are susceptible to influence the density of potential mosquito-breeding containers. Indeed, the presence of these containers, most of which are produced by humans, is essential for the renewal of mosquito populations. Land-uses variables and their local variations are determinant in this analysis. Starting from each building and its immediate neighborhood described in terms of vegetation and open area, we computed the local landscape metrics of a million buildings in Bangkok. We then used segmentation and clustering techniques to generate homogeneous zones based on these components and physiognomy. Subsequently, a classification process was conducted to characterize these zones according to land-use and composition indicators. We applied this automatic clustering method within Bangkok’s urban area. This classification built from hypotheses on the existence of links between the types of urban landscape and the presence of outdoor containers must be evaluated and will serve as a foundation for the spatial sampling of field studies for vector surveillance in Bangkok. The choice of sampling zones, even if it must be based on an administrative division due to the decentralization of health agencies in Bangkok, can then be enriched by this new, more functional division. This method, due to the genericity of the factors used, could be tested in other cities prone to dengue vectors. Full article
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<p>Location of Bangkok (red outline) in the Gulf of Thailand (top map) and spatial extent of the Bangkok Metropolitan Administration (bottom map, outline in white dots) with its built-up (shaded gray) and vegetation density (shaded green)—obtained, respectively, by summing the area of buildings provided by Bing Map and the vegetation from Pléiades (CNES) images (NDVI &gt; 0.22) in a 125 m grid. The study area (black outline) is centered on the highly urbanized area.</p>
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<p>Example of polygons representing buildings (orange color) provided by Bing Map, with Pléiades satellite image as base map.</p>
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<p>Starting from building polygons in red provided by Bing Map (<b>a</b>) to compute distance between buildings (from blue to red) associated with roads (<b>b</b>) in order to select roads provided by Open Street Map with a width superior to 20 m (in white, (<b>c</b>)).</p>
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<p>Construction of urban blocks. The red contours represent the main roads and river that are considered as major barriers to mosquito movement.</p>
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<p>Vegetation extraction <b>on the right</b> (NDVI) from Pléiades images <b>on the left</b>. Large areas of vegetation can be distinguished, such as small urban gardens (<b>bottom images</b>), and distinct plant clusters, groves or isolated trees (<b>top images</b>).</p>
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<p>Estimation of building heights (<b>right</b>, in meters) from Pléiades tri-stereoscopic images (<b>left</b>, one of the three images).</p>
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<p>Methodology developed to describe the city on the basis of buildings, elementary entities of the urban territory.</p>
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<p>Example of the constructed area of influence (black contour, Voronoï polygon) of each building (red) with their height (red gradient), vegetated areas (green) and free spaces between and around buildings (white).</p>
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<p>Example of buildings grouping into two zones by clustering. From built polygons from Bing Map (<b>A</b>), Voronoï polygons of each building (<b>B</b>), result of the minimum spanning tree based on the completed graph where each link is weighted (<b>C</b>) and clustering by proximity and similarity (<b>D</b>).</p>
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<p>Examples of segmentation of different blocks into homogeneous zones in terms of composition and landscape configuration.</p>
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<p>Importance of vegetation (<b>left</b>) and building densities (<b>right</b>) in Bangkok’s zones.</p>
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<p>Inertia of the 3 factorial axes (respectively, 47.9%, 24.8% and 19.4%) and variable contributions to each axis.</p>
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<p>Examples of zones associated with Axis 1 and Axis 3 of the PCA.</p>
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<p>Contributions of the 4 descriptive variables (Elevation as a supplementary variable) to inter-zone landscape differentiation (6 classes) for all 1472 zones within our study area.</p>
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<p>A typology for the center of Bangkok that takes into account the density and height of the buildings, the extent of vegetation cover and the configuration of open spaces between and around the buildings.</p>
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<p>Subdistricts (Kwaengs) of Bangkok with varying degrees of internal spatial heterogeneity. Histogram of the number of subdistricts according to the number of classes (<b>left</b>) and spatial organization of these subdistricts (<b>right</b>).</p>
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11 pages, 468 KiB  
Article
Cutaneous Adverse Reactions and Survival Outcomes of Advanced Melanoma Treated with Immune Checkpoint Inhibitors in an Academic Medical Centre in Singapore
by Agnes Yeok-Loo Lim, Jason Yongsheng Chan and Choon Chiat Oh
Diagnostics 2024, 14(15), 1601; https://doi.org/10.3390/diagnostics14151601 - 25 Jul 2024
Viewed by 315
Abstract
Programmed cell death-1 (PD1) inhibitors, a form of immune checkpoint inhibitor, are efficacious for metastatic melanoma but are associated with cutaneous adverse reactions (CARs). Studies in Europe and North America showed that CARs are associated with an increased overall survival. However, studies from [...] Read more.
Programmed cell death-1 (PD1) inhibitors, a form of immune checkpoint inhibitor, are efficacious for metastatic melanoma but are associated with cutaneous adverse reactions (CARs). Studies in Europe and North America showed that CARs are associated with an increased overall survival. However, studies from Asia showed mixed results. There is a paucity of data regarding the efficacy of PD1 inhibitors and the effect of CARs on overall survival from Southeast Asia. A retrospective study of patients in the National Cancer Centre Singapore who were diagnosed with melanoma between 2015 and 2020 was conducted. Patients were included in the study if they had stage IV melanoma (advanced melanoma). Sixty-two patients were included in the study. The median age was 62.5 years and acral melanoma was the commonest subtype. Forty-three patients received PD1 inhibitors. Comparing patients who did not receive PD1 inhibitors to patients who received PD1 inhibitors, the former had a median overall survival of 6 months (95% CI: 5.07, 6.93), whereas the latter had a median overall survival of 21 months (95% CI: 13.33, 28.67; p < 0.001) (Hazard ratio 0.32; 95% CI: 0.16, 0.63; p = 0.001). Amongst patients who received PD1 inhibitors, patients who developed CARs had a greater median overall survival of 33 months (95% CI: 17.27, 48.73) compared to 15 months (95% CI: 9.20, 20.80; p = 0.013) for patients who did not (HR 0.29; 95% CI: 0.098, 0.834; p = 0.022). This study provides insight into the outcomes of metastatic melanoma in Singapore, and adds to the body of evidence supporting the use of PD1 inhibitors in Asians. Full article
(This article belongs to the Special Issue Latest Advances in Diagnosis and Management of Skin Cancer)
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<p>Kaplan–Meier curves for overall survival. (<b>a</b>) Patients who received PD1 inhibitors (<span class="html-italic">n</span> = 43) had increased overall survival compared to patients who did not (<span class="html-italic">n</span> = 19). (<b>b</b>) For patients who received PD1 inhibitors, the development of cutaneous adverse reactions (<span class="html-italic">n</span> = 11) was associated with increased overall survival compared to no adverse reactions (<span class="html-italic">n</span> = 32). <span class="html-italic">p</span> values were calculated using the log-rank test.</p>
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Article
A Mobile Application for Enhancing Caregiver Support and Resource Management for Long-Term Dependent Individuals in Rural Areas
by Niruwan Turnbull, Chanaphol Sriruecha, Ruchakron Kongmant, Le Ke Nghiep and Kukiat Tudpor
Healthcare 2024, 12(15), 1473; https://doi.org/10.3390/healthcare12151473 - 24 Jul 2024
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Abstract
The “SmartCaregivers” 1.0 mobile application is a beacon of hope for caregivers (CG) in rural areas, often facing limited access to facilities and support. This study, conducted from February to August 2021, aimed to comprehensively analyze the need for developing a [...] Read more.
The “SmartCaregivers” 1.0 mobile application is a beacon of hope for caregivers (CG) in rural areas, often facing limited access to facilities and support. This study, conducted from February to August 2021, aimed to comprehensively analyze the need for developing a database system and a mobile application tailored to enhance caregiver support and resource management for long-term dependent individuals in the rural areas of Maha Sarakham province, Thailand. The research followed a rigorous research and development (R & D) approach, specifically the ADDIE model (analysis, design, development, implementation, and evaluation). Data were collected from 402 caregivers and 10 key informants through surveys and interviews, as well as from 402 caregivers during the implementation and evaluation phases. The application’s impact was assessed using a quasi-experimental design with a one-group pre–post-test, and its acceptance was evaluated through the technology acceptance model (TAM). The application significantly improved caregivers’ knowledge scores, with a mean increase from 10.49 ± 2.53 to 12.18 ± 2.76 post-intervention. High scores for perceived usefulness (4.36 ± 0.62) and ease of use (4.31 ± 0.59) reassure the audience about the application’s effectiveness in providing rapid access to health information, aiding decision-making, and improving care coordination. The system quality was also highly rated, with users appreciating the variety of functions and structural design. This potential for transformation and improvement instills hope and optimism for the future of caregiving in rural areas. Full article
(This article belongs to the Special Issue Mobile Technology-Based Interventions in Healthcare)
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<p>ADDIE model, participants, and timeframe.</p>
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<p>The research areas of the participants.</p>
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<p>Patient responsibility chart by caregivers.</p>
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<p>Flowchart of the data retrieval in the system. ADL, activities of daily living, refers to daily activities that people do without assistance. These activities include eating, bathing, dressing, toileting, transferring (walking), and continence. In the context of our study, ADL scores help assess the functional status and independence level of the dependent individuals. TAI, typology of the aged with illustration, is a tool for measuring the ability to do activities (function) of older adults by measuring four functions: (1) movement (motility); (2) mental and intellectual aspects (mental); (3) eating aspect; and (4) toilet.</p>
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<p>Homepage and login page. The screen shows LTC (long-term care), and Thai texts read “Application for senior care”.</p>
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<p>Illustration of health assessment menus in the “<span class="html-italic">SmartCaregivers</span>” mobile application. On the left panel, orange and green texts read “Healthy city design” and “Older society support”. The red oval and brown rectangle texts show the “Press” and Care plan assessment forms. The four tabs below show the health assessment form (green), health information (orange), home visit (blue), and screening tools (red). These menus are designed to facilitate comprehensive health assessments by caregivers and include the following components: (1) Health Status Overview: Provides a summary of the overall health status of the dependent individual, including vital signs and general health indicators; (2) ADL (Activities of Daily Living) Assessment: Evaluates the individual’s ability to perform daily activities independently, such as eating, bathing, dressing, toileting, transferring, and continence; (3) TAI (Thai ADL Index) Assessment: This assessment uses a standardized tool tailored to the Thai population to measure the individual’s functional status and independence level in daily activities; (4) Cognitive Function Assessment: Includes tests and evaluations to assess the dependent individual’s cognitive abilities and mental health status; (5) Nutritional Assessment: Monitors dietary intake and nutritional status, ensuring that the individual receives adequate nutrition; (6) Medication Management: Tracks medication schedules, dosages, and adherence to prescribed treatments; and (7) Health Records: Maintains a comprehensive record of all health assessments, medical history, and treatment plans for easy access and reference by caregivers and healthcare professionals. These health assessment menus are integrated into the application to provide caregivers with a robust tool for managing and monitoring the health of dependent individuals effectively. The middle panel, inside the health assessment form (green) menu, below the profile picture, will show the name of the older adult under the supervision of the CG. The status of the assessment is shown in red. The green button can be clicked to start the assessment. On the right panel, the top of the screen indicates that the assessment is in progress. The green bar shows the client’s health status (healthy, etc.). Next to the profile picture, name, last name, age, and number are displayed. Below are the health statuses (healthy, waiting for assessment, home visits, screening, and care planning). Next are 11 screening tools of choice (1, diabetic screening tool; 2, hypertension screening tool; and 3, fall risk screening tool).</p>
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