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11 pages, 723 KiB  
Article
Hot-Pressing Process of Flat-Pressed Wood–Polymer Composites: Theory and Experiment
by Pavlo Lyutyy, Pavlo Bekhta, Yurii Protsyk and Vladimír Gryc
Polymers 2024, 16(20), 2931; https://doi.org/10.3390/polym16202931 (registering DOI) - 18 Oct 2024
Abstract
The objective of this research was to develop a mathematical model of the hot-pressing process for making flat-pressed wood–polymer composites (FPWPCs). This model was used to calculate and predict the temperature and time required for FPWPC pressing. The model’s performance was analysed using [...] Read more.
The objective of this research was to develop a mathematical model of the hot-pressing process for making flat-pressed wood–polymer composites (FPWPCs). This model was used to calculate and predict the temperature and time required for FPWPC pressing. The model’s performance was analysed using the experimental results of hot pressing FPWPCs. It was found that an increase in the content of wood particles led to a rapid increase in the pressing time. The model and experiment showed that the core temperature of the wood–polymer mat remained nearly constant for the first 20–30 s of the hot-pressing process. After this period, this temperature increased rapidly until it reached 100 °C, after which the rate of increase began to decelerate sharply. This transition was more distinct in FPWPCs with a high wood-particle content, while in those with a high thermoplastic-polymer content, it was smoother. Increasing the pressing temperature contributed to a reduction in the time required to heat the FPWPC, as confirmed by both experimental data and the modelling of the hot-pressing process. A decrease in the predicted density of the FPWPC resulted in a directly proportional increase in the time required to heat the mat. Validation of the mathematical model revealed a mean absolute percentage error (MAPE) of only 2.5%, confirming its high precision and reliability. The developed mathematical model exhibited a high degree of accuracy and can be used for further calculations of the time required for FPWPC pressing, considering variable parameters such as pressing temperature, wood–polymer ratio, mat thickness, and density. Full article
(This article belongs to the Special Issue New Challenges in Wood and Wood-Based Materials III)
12 pages, 3047 KiB  
Article
Green-Dyeing Processes of Plant and Animal Fibers Using Folium, an Ancient Natural Dye
by Andrea Marangon, Francesca Robotti, Elisa Calà, Alessandro Croce, Maurizio Aceto, Domenico D’Angelo and Giorgio Gatti
Appl. Sci. 2024, 14(20), 9518; https://doi.org/10.3390/app14209518 - 18 Oct 2024
Abstract
In recent decades, fabric-dyeing processes involved greener processes because, since ancient times, dyers used mordants based on metals to make the color better adhere to the textile fibers, but this is the reason for their increased pollution. To develop new strategies, attention was [...] Read more.
In recent decades, fabric-dyeing processes involved greener processes because, since ancient times, dyers used mordants based on metals to make the color better adhere to the textile fibers, but this is the reason for their increased pollution. To develop new strategies, attention was focused on finding the best condition for a dyeing method for natural fibers of vegetable and animal origin (cotton and wool) using an ancient natural dye known as folium. Folium was used mostly in miniature painting in an attempt to avoid the use of classical mordants and solvents. To this purpose, plasma treatment and chitosan coating were employed. Firstly, the textile fibers were analyzed through infrared spectroscopies to verify surface modifications; subsequently, the post-treatment morphological variations were observed via scanning electron microscopy. Both techniques highlighted a significant variation of the surface functional groups due to plasma treatments with He-O2 mixtures, which allowed a greater adhesion of chitosan on the fiber’s surface. Finally, the color strength of samples dyed with folium was tested through fiber optic reflectance spectroscopy, and the folium absorbance peaks were still detected after fabric washing. It is thus shown how an ancient, traditional raw matter has become relevant for developing new modern technologies. Full article
(This article belongs to the Section Green Sustainable Science and Technology)
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<p>SEM images of (<b>A</b>) raw cotton, (<b>B</b>) raw wool, (<b>C</b>) cotton treated with He plasma and chitosan 10 g/L, (<b>D</b>) wool treated with He plasma and chitosan 10 g/L, (<b>E</b>) cotton treated with He-O<sub>2</sub> plasma and chitosan 10 g/L, (<b>F</b>) wool treated with He-O<sub>2</sub> plasma and chitosan 10 g/L.</p>
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<p>SEM images of (<b>A</b>) raw cotton, (<b>B</b>) raw wool, (<b>C</b>) cotton treated with He plasma and chitosan 10 g/L, (<b>D</b>) wool treated with He plasma and chitosan 10 g/L, (<b>E</b>) cotton treated with He-O<sub>2</sub> plasma and chitosan 10 g/L, (<b>F</b>) wool treated with He-O<sub>2</sub> plasma and chitosan 10 g/L.</p>
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<p>(<b>A</b>) FT-IR spectra of raw cotton treated with He plasma at different times: (a) untreated, (b) treated for 1 min, (c) 2 min, and (d) 5 min; (<b>B</b>) FT-IR spectra of raw cotton treated with a mixture of He-O<sub>2</sub> plasma at different times: (a′) untreated, (b′) treated for 1 min, (c′) 2 min, and (d′) 5 min. Asterisks mark the bands of cotton that are subjected to changes after plasma treatment.</p>
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<p>(<b>A</b>) FT-IR spectra of raw wool treated with He plasma at different times: (a) untreated, (b) treated for 1 min, (c) 2 min, and (d) 5 min; (<b>B</b>) FT-IR spectra of raw wool treated with a mixture of He-O<sub>2</sub> plasma at different times: (a′) untreated, (b′) treated for 1 min, (c′) 2 min, and (d′) 5 min. Asterisks mark the bands of wool that are subjected to changes after plasma treatment.</p>
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<p>FORS spectrum of folium in Kubelka-Munk coordinates.</p>
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<p>(<b>A</b>) FORS spectra of raw cotton (a) after plasma treatment with He, (b) dyed with folium, (c) washed; (<b>B</b>) FORS spectra of raw cotton (a′) after plasma treatment with He-O<sub>2</sub>, (b′) dyed with folium, (c′) washed; (<b>C</b>) FORS spectra of raw wool (a*) after plasma treatment with He, (b*) dyed with folium, (c*) washed; (<b>D</b>) FORS spectra of raw wool (a**) after plasma treatment with He-O<sub>2</sub>, (b**) dyed with folium, (c**) washed.</p>
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<p>(<b>A</b>) left pane, plasma- (left) and chitosan-treated (right) cotton; right pane, cotton dyed with folium (left) and subsequently washed (right), samples CHe1.5-10 and CHeO1.5-10. (<b>B</b>) let pane, plasma- (left) and chitosan-treated (right) wool; right pane, wool dyed with folium (left) and subsequently washed (right), samples WHe1.5-10 and WHeO1.5-10.</p>
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18 pages, 6828 KiB  
Article
Characterization of Atlantic Forest Tucum (Bactris setosa Mart.) Leaf Fibers: Aspects of Innovation, Waste Valorization and Sustainability
by Taynara Thaís Flohr, Eduardo Guilherme Cividini Neiva, Marina Proença Dantas, Rúbia Carvalho Gomes Corrêa, Natália Ueda Yamaguchi, Rosane Marina Peralta, Afonso Henrique da Silva Júnior, Joziel Aparecido da Cruz, Catia Rosana Lange de Aguiar and Carlos Rafael Silva de Oliveira
Plants 2024, 13(20), 2916; https://doi.org/10.3390/plants13202916 - 18 Oct 2024
Viewed by 115
Abstract
This study investigates the fibers of tucum (Bactris setosa Mart.), a palm species native to the Atlantic Forest. The fibers manually extracted from tucum leaves were characterized to determine important properties that help with the recognition of the material. The fibers were [...] Read more.
This study investigates the fibers of tucum (Bactris setosa Mart.), a palm species native to the Atlantic Forest. The fibers manually extracted from tucum leaves were characterized to determine important properties that help with the recognition of the material. The fibers were also subjected to pre-bleaching to evaluate their dyeing potential. The extraction and characterization of these fibers revealed excellent properties, making this material suitable not only for manufacturing high-quality textile products but also for various technical and engineering applications. The characterization techniques included SEM (Scanning Electron Microscopy), FTIR (Fourier Transform Infrared Spectroscopy), TGA (Thermogravimetric Analysis), and tensile strength tests. These analyses showed that tucum fibers possess desirable properties, such as high tensile strength, with values comparable to linen but with a much finer diameter. The fibers also demonstrated good affinity for dyes, comparable to cotton fibers. An SEM analysis revealed a rough surface, with superficial phytoliths contributing to their excellent mechanical strength. FTIR presented a spectrum compatible with cellulose, confirming its main composition and highly hydrophilic nature. The dyeing tests indicated that tucum fibers can be successfully dyed with industrial direct dyes, showing good color yield and uniformity. This study highlights the potential of tucum fibers as a renewable, biodegradable, and sustainable alternative for the transformation industry, promoting waste valorization. Full article
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<p>Tucum palm (<span class="html-italic">Bactris setosa</span> Mart.): (<b>a</b>) records of occurrences of the species <span class="html-italic">Bactris setosa</span> Mart. in Brazil (reproduced with permission from [<a href="#B25-plants-13-02916" class="html-bibr">25</a>]); (<b>b</b>) map of the geographic distribution of the species <span class="html-italic">Bactris setosa</span> Mart. in the Brazilian state of Santa Catarina (Reproduced with permission from [<a href="#B26-plants-13-02916" class="html-bibr">26</a>]); (<b>c</b>) clump from which samples were taken; (<b>d</b>) green fruit of the tucum palm; (<b>e</b>) detail of the thorns on the rachis of the tucum leaf; (<b>f</b>) ripe fruit of the tucum palm, from left to right: first, the fruit with its outer skin; next, the same fruit without the skin, exposing the pulp; and finally, the whole and broken almond, from which tucum oil is extracted.</p>
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<p>SEM analysis of raw tucum fibers: (<b>a</b>) panoramic view of the cross-section of a tucum fiber from its end, magnification of 3600×; (<b>b</b>) longitudinal view of tucum fibers, magnification of 7000×; (<b>c</b>) EDS analysis of tucum fiber, the black curve represents the EDS spectrum of the surface in a region without phytoliths, while the red curve represents the spectrum from the region containing the phytolith.</p>
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<p>SEM analysis of manually extracted raw tucum fibers and pre-bleached fiber: (<b>a</b>) photographic image of a bundle of raw fibers (before pre-bleaching); (<b>b</b>,<b>c</b>) longitudinal view of the manually extracted raw fiber, with magnifications of 300× and 5800×, respectively. (<b>d</b>) photographic image of a bundle of pre-bleached fibers; (<b>e</b>,<b>f</b>) longitudinal view of the pre-bleached fiber with H<sub>2</sub>O<sub>2</sub>, with magnifications of 295× and 5300×, respectively.</p>
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<p>Determination of the average fiber length: (<b>a</b>) shape of the tucum palm leaf × length of the leaflets; (<b>b</b>) variation in leaflet sizes depending on the region of the leaf; (<b>c</b>) histogram of fiber length distribution of the analyzed sample.</p>
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<p>Characterization analysis of manually-extracted tucum fibers: (<b>a</b>) FTIR analysis of the raw tucum fiber; (<b>b</b>) TGA-DTG analysis of the raw tucum fiber; (<b>c</b>) Zeta Potential analysis of the raw tucum fiber; (<b>d</b>) stress–strain analysis of the raw tucum fiber.</p>
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<p>Comparison of dyed samples and their respective residual dye baths: (<b>a</b>) cotton fabric; (<b>b</b>) viscose fabric; (<b>c</b>) tucum fiber.</p>
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<p>Steps of the manual process of fiber extraction from the leaves of the genus <span class="html-italic">Bactris setosa</span> Mart.: (<b>a</b>) tucum leaf; (<b>b</b>) manual process of tucum fiber extraction; (<b>c</b>) the leaflets were removed from the rachis, then a fold was made near the tip of the leaflet, creating a crease; (<b>d</b>,<b>e</b>) after folding the crease, the epidermis of the leaflet was pulled, promoting the release of the fibers along the entire leaflet, and this process could be repeated up to five times for complete extraction.</p>
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<p>Treatment parameters of the raw fibers: (<b>a</b>) placement of the fibers in a small tulle fabric pouch; (<b>b</b>) temperature curve of the pre-bleaching process; (<b>c</b>) molecule of the dye used in the dyeing of fiber and fabric samples; (<b>d</b>) temperature curve of the dyeing process.</p>
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21 pages, 8413 KiB  
Article
Design and Testing of a Crawler Chassis for Brush-Roller Cotton Harvesters
by Zhenlong Wang, Fanting Kong, Qing Xie, Yuanyuan Zhang, Yongfei Sun, Teng Wu and Changlin Chen
Agriculture 2024, 14(10), 1832; https://doi.org/10.3390/agriculture14101832 - 17 Oct 2024
Viewed by 194
Abstract
In China’s Yangtze River and Yellow River basin cotton-growing regions, the complex terrain, scattered planting areas, and poor adaptability of the existing machinery have led to a mechanized cotton harvesting rate of less than 10%. To address this issue, we designed a crawler [...] Read more.
In China’s Yangtze River and Yellow River basin cotton-growing regions, the complex terrain, scattered planting areas, and poor adaptability of the existing machinery have led to a mechanized cotton harvesting rate of less than 10%. To address this issue, we designed a crawler chassis for a brush-roller cotton harvester. It is specifically tailored to meet the 76 cm row spacing agronomic requirement. We also conducted a theoretical analysis of the power transmission system for the crawler chassis. Initially, we considered the terrain characteristics of China’s inland cotton-growing regions and the current cotton agronomy practices. Based on these, we selected and designed the power system and chassis; then, a finite element static analysis was carried out on the chassis frame to ensure safety during operation; finally, field tests on the harvester’s operability, stability, and speed were carried out. The results show that the inverted trapezoidal crawler walking device, combined with a hydraulic continuously variable transmission and rear-drive design, enhances the crawler’s passability. The crawler parameters included a ground contact length of 1650 mm, a maximum ground clearance of 270 mm, a maximum operating speed of 6.1 km/h, and an actual turning radius of 2300 mm. The maximum deformation of the frame was 2.198 mm, the deformation of the walking chassis was 1.0716 mm, the maximum equivalent stress was 216.96 MPa, and the average equivalent stress of the entire frame was 5.6356 MPa, which complies with the physical properties of the selected material, Q235. The designed cotton harvester crawler chassis features stable straight-line and steering performance. The vehicle’s speed can be adjusted based on the complexity of the terrain, with timely steering responses, minimal compaction on cotton, and reduced soil damage, meeting the requirements for mechanized harvesting in China’s inland small plots. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Schematic diagram of the operating mode of the brush-roller-type crawler cotton harvester: (1) crawler.</p>
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<p>Brush-roller-crawler cotton harvester: (1) cutterhead assembly; (2) cotton boll collecting device; (3) blower; (4) propulsion chassis; (5) cotton bin base; (6) cotton bin; (7) air conveyance channel; (8) operator’s platform.</p>
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<p>Hydraulic drive system of the crawler cotton harvester chassis: (1) oil filter; (2) HST (hydrostatic transmission); (3) steering cylinder; (4) oil tank; (5) fuel tank radiator; (6) three-position four-way valve; (7) engine.</p>
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<p>Schematic diagram of the walking drive system: (1) engine; (2) pulley; (3) HST (hydrostatic transmission); (4) mechanical gearbox; (5) drive axle; (6) steering cylinder; (7) Drive wheel. Annotation: In the figure, “M” means engine.</p>
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<p>Steering cylinder.</p>
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<p>Schematic diagram of the turning process.</p>
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<p>Turning in place.</p>
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<p>Overall diagram of the walking chassis: (<b>a</b>) chassis frame; (<b>b</b>) crawler-type walking chassis.</p>
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<p>Overall assembly of the chassis transmission.</p>
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<p>Force diagram of the chassis frame.</p>
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<p>Equivalent stress deformation contour map.</p>
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<p>Total deformation contour map of the frame.</p>
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<p>Safety factor contour map.</p>
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<p>Field site of the experiment.</p>
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<p>Field operation effect test: (<b>a</b>) field harvesting operation test; (<b>b</b>) effect of field harvesting test.</p>
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<p>Maneuverability test: (<b>a</b>) field turnaround test; (<b>b</b>) transition operation.</p>
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<p>Field operation effect test: (<b>a</b>) passability test; (<b>b</b>) crawler operation travel effect.</p>
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27 pages, 17001 KiB  
Article
Experimental Study on the Application of “Dry Sowing and Wet Emergence” Drip Irrigation Technology with One Film, Three Tubes, and Three Rows
by Hongxin Wang and Chunxia Wang
Agronomy 2024, 14(10), 2406; https://doi.org/10.3390/agronomy14102406 - 17 Oct 2024
Viewed by 183
Abstract
In order to alleviate the shortage of water in Xinjiang cotton fields, to ensure the sustainable development of the cotton industry in southern Xinjiang, it is necessary to determine a suitable “dry sowing and wet emergence” water quantity plan for cotton fields in [...] Read more.
In order to alleviate the shortage of water in Xinjiang cotton fields, to ensure the sustainable development of the cotton industry in southern Xinjiang, it is necessary to determine a suitable “dry sowing and wet emergence” water quantity plan for cotton fields in southern Xinjiang to change the current situation. In this study, to explore the irrigation regime of “dry sowing and wet emergence” for cotton in Korla, Xinjiang, by combining field experiments and modeling simulations, the effects of different irrigation amounts on the water–heat–salt and seedling emergence characteristics of “dry sowing and wet emergence” cotton fields were investigated; the soil, water, and salt transport under different irrigation regimes was simulated by using HYDRUS-2D, and the seedling emergence rate of the cotton under different irrigation regimes was obtained through the establishment of a regression model. The results indicated that, in the field experiment, the soil water content of the 0−40 cm soil layer showed an overall trend of first increasing and then decreasing with time, while the soil salt content showed an overall trend of first decreasing and then increasing over time. The soil water content at the drip heads and cotton rows position, as well as on the 15th day, increased by an average of 5.58 cm3·cm−3 compared to before irrigation, and the soil salt content decreased by an average of 2.74 g/kg compared to before irrigation. In the irrigation water range of 675−825 m3/hm2, reducing the irrigation water amount increased the cotton emergence rate by 3.86% and the cotton vigor index by 70.53%. After the model simulation, it is recommended to choose the cotton “dry sowing and wet emergence” irrigation regime with a low to medium water amount (300−450 m3/hm2) at 14-day intervals or a low to medium water amount (300−375 m3/hm2) at 7-day intervals, which can obtain a higher seedling emergence rate. Full article
(This article belongs to the Section Water Use and Irrigation)
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<p>Meteorological data chart.</p>
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<p>Cotton planting pattern (unit: cm).</p>
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<p>Layout plan.</p>
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<p>Schematic diagram of the simulation area.</p>
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<p>Changes in the soil water content under the different water gradients. Note: the horizontal coordinate date refers to the date after the start of irrigation; T1, T2, and T3 represent the three treatments with the irrigation amounts of 525 m<sup>3</sup>/hm<sup>2</sup>, 675 m<sup>3</sup>/hm<sup>2</sup>, and 825 m<sup>3</sup>/hm<sup>2</sup>, respectively; each processed subgraph represents, from left to right, the changes in the soil water content between two drip bands, between drip irrigation belts and cotton rows, and between bare land positions in the film.</p>
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<p>Changes in the soil salt content under the different water gradients. Note: the horizontal coordinate date refers to the date after the start of irrigation; T1, T2, and T3 represent the three treatments with irrigation amounts of 525 m<sup>3</sup>/hm<sup>2</sup>, 675 m<sup>3</sup>/hm<sup>2</sup>, and 825 m<sup>3</sup>/hm<sup>2</sup>, respectively; each processed subgraph represents, from left to right, the changes in the soil salt content between two drip bands, between drip irrigation belts and cotton rows, and between bare land positions in the film.</p>
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<p>Changes in the soil salt content under the different water gradients. Note: the horizontal coordinate date refers to the date after the start of irrigation; T1, T2, and T3 represent the three treatments with irrigation amounts of 525 m<sup>3</sup>/hm<sup>2</sup>, 675 m<sup>3</sup>/hm<sup>2</sup>, and 825 m<sup>3</sup>/hm<sup>2</sup>, respectively; each processed subgraph represents, from left to right, the changes in the soil salt content between two drip bands, between drip irrigation belts and cotton rows, and between bare land positions in the film.</p>
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<p>Temperature changes under different water amounts. Note: The label (<b>a</b>) in the image represents the temperature change of the T1 treatment (irrigation amounts of 525 m<sup>3</sup>/hm<sup>2</sup>), The label (<b>b</b>) in the image represents the temperature change of the T2 treatment (irrigation amounts of 675 m<sup>3</sup>/hm<sup>2</sup>), The label (<b>c</b>) in the image represents the temperature change of the T3 treatment (irrigation amounts of 825 m<sup>3</sup>/hm<sup>2</sup>).</p>
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<p>Changes in effective accumulated temperature and average temperature under different water amounts. Note: The bar chart represents the effective accumulated temperature changes at different soil depths under different treatments, while the line chart represents the average ground temperature changes at different soil depths under different treatments.</p>
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<p>Changes in seedling emergence rate under different water amounts.</p>
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<p>Seedling emergence index under different water amounts. Note: The error bar represents the standard error. The differences between different treatments were determined through Duncan’s test of variance. The different letters above the bar chart indicate significant differences between treatments when <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Changes in seedling height under different water amounts.</p>
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<p>Correlation analysis between seedling emergence rate, soil water, heat, and salt, and plant height. Note: “*” indicates <span class="html-italic">p</span> &lt; 0.05, significant correlation, “**” indicates <span class="html-italic">p</span> &lt; 0.01, significant correlation. ER (cotton seedling emergence rate), SWC<sub>10</sub> (average water content of 10 cm soil layer), SWC<sub>20</sub> (average water content of 20 cm soil layer), SSC<sub>10</sub> (average salt of 10 cm soil layer), SSC<sub>20</sub> (average salt of 20 cm soil layer), SGT<sub>5–15</sub> (average soil temperature within 5–15 cm), SGT<sub>15–25</sub> (average soil temperature within 15–25 cm), CH (plant height of cotton seedlings).</p>
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<p>Soil water changes under different scenarios. Note: the simulated soil water content in this figure is the soil water content of the 10–40 cm soil layer between the drip head and the cotton row.</p>
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<p>Soil salt changes under different scenarios. Note: the simulated soil salt content in this figure is the soil salt content of the 10–40 cm soil layer between the drip head and the cotton row.</p>
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<p>Water and salt changes under different planting patterns in S2 scenario. Note: The numbers on the left side of the image represent the cotton planting mode. For example, 1-3-3 represents one film, three tubes, and three rows. The number at the top of the image represents the number of days after stopping watering. For example, 1 represents the first day after stopping watering. The first to third lines of the image show changes in soil water content, while the fourth to sixth lines show changes in soil salt content.</p>
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<p>Water and salt changes under different planting patterns in S13 scenario. Note: The numbers on the left side of the image represent the cotton planting mode. For example, 1-3-3 represents one film, three tubes, and three rows. The number at the top of the image represents the number of days after stopping watering. For example, 1 represents the first day after stopping watering. The first to third lines of the image show changes in soil water content, while the fourth to sixth lines show changes in soil salt content.</p>
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13 pages, 9828 KiB  
Article
Examining Carotenoid Metabolism Regulation and Its Role in Flower Color Variation in Brassica rapa L.
by Guomei Liu, Liuyan Luo, Lin Yao, Chen Wang, Xuan Sun and Chunfang Du
Int. J. Mol. Sci. 2024, 25(20), 11164; https://doi.org/10.3390/ijms252011164 - 17 Oct 2024
Viewed by 247
Abstract
Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and [...] Read more.
Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and root colors. Integrating physiological and biochemical assessments, transcriptome profiling, and quantitative metabolomics, we examined carotenoid accumulation in the flowers, roots, stems, and seeds of YB1 throughout its growth and development. The results indicated that carotenoids continued to accumulate in the roots and stems of YBI, especially in its cortex, throughout plant growth and development; however, the carotenoid levels in the petals decreased with progression of the flowering stage. In total, 54 carotenoid compounds were identified across tissues, with 30 being unique metabolites. Their levels correlated with the expression pattern of 22 differentially expressed genes related to carotenoid biosynthesis and degradation. Tissue-specific genes, including CCD8 and NCED in flowers and ZEP in the roots and stems, were identified as key regulators of color variations in different plant parts. Additionally, we identified genes in the seeds that regulated the conversion of carotenoids to abscisic acid. In conclusion, this study offers valuable insights into the regulation of carotenoid metabolism in B. rapa, which can guide the selection and breeding of carotenoid-rich varieties. Full article
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<p>Dynamics of the phenotype and total carotenoid content in the mutants. (<b>A</b>): YB1 flowers; (<b>B</b>): TY7 flowers; (<b>C</b>): comparison of total carotenoid content at different flowering stages; CB, CO and CA represent the bud stage, semi-open stage, and full bloom stage of TY7 petals, respectively; YB, YO, and YA represent the bud stage, semi-open stage, and full bloom stage of YB1 petals, respectively; (<b>D</b>): YB1 rhizomes; (<b>E</b>): TY7 rhizomes; (<b>F</b>): comparison of total carotenoids at different stages of rhizome fertility; CP and YP denote the TY7 and YB1 cortices, respectively, and CW and YW denote the TY7 and YB1 vascular bundles, respectively; November 2022 is referred to as the 11th, December 2022 as the 12th, January 2023 as the 1st, February 2023 as the 2nd, March 2023 as the 3rd, April 2023 as the 4th, and May 2023 as the 5th. Data are expressed as the mean of three biological replicates. Differences between the two varieties were considered statistically significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Identification and clustering analysis of carotenoid differential metabolites. (<b>A</b>): OPLS-DA supervised analysis; CK1, CK2, and CK3 denote the petal, rhizome, and seed samples of the TY7 variety, respectively; YB1, YB2, and YB3 denote the petal, rhizome, and seed samples of the YB1 variety, respectively; (<b>B</b>): metabolite Wayne plots; comparisons between CSM (TY7 seed) and YSM (YB1 seed); CRM (TY7 root) and YRM (YB1 root); and CFM (TY7 petal) and YFM (YB1 petal); (<b>C</b>): heatmap of carotenoid metabolite clustering in different tissues; CF1−1, CF1−2, and CF1−3 represent the three biological replicates of TY7 petal samples; CR2−1, CR2−2, and CR2−3 represent the three biological replicates of TY7 root samples; CS3−1, CS3−2, and CS3−3 represent the three biological replicates of TY7 seed samples; YF1−1, YF1−2, YF1−3 represent the three biological replicates of YB1 petal samples; YR2−1, YR2−2, and YR2−3 represent the three biological replicates of YB1 root samples; and YS3−1, YS3−2, and YS3−3 represent the three biological replicates of YB1 seed samples; (<b>D</b>): KEGG analysis of differential metabolites. Note: CF stands for TY7 flower, YF stands for YB1 flower, CR stands for TY7 rhizome, YR stands for YB1 rhizome, CS stands for TY7 seed, and YS denotes YB1 seed.</p>
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<p>Transcriptome analysis of different samples. (<b>A</b>): Wayne plots of differentially expressed genes (DEGs) in different tissues of the control and mutant plants; (<b>B</b>): transcriptome DEGs; CF_vs._YF denotes the comparison between petals of TY7 and YB1; CR_vs._YR denotes the comparison between the roots of TY7 and YB1; and CS_vs._YS denotes the comparison between the seeds of TY7 and YB1. (<b>C</b>): GO classification of DEGs. (<b>D</b>): KEGG pathway enrichment of the DEGs.</p>
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<p>Weighted gene co-expression network analysis of the genes associated with carotenoid metabolism. (<b>A</b>): Hierarchical clustering tree diagram of co-expressed genes in WGCNA, with each leaf corresponding to one gene, and the main branches from seven modules labeled in different colors; (<b>B</b>): relationship between modules and carotenoid metabolism-related DEGs, with each row representing one module. Each column represents the carotenoid biosynthesis-related DEGs; the value of each cell at the intersection of rows and columns represents the coefficient of correlation between the modules and carotenoid metabolism DEGs (shown on the right side of the color scale), whereas the value in parentheses in each cell represents the <span class="html-italic">p</span> value; (<b>C</b>): KEGG enrichment analysis of turquoise module DEGs; (<b>D</b>): KEGG enrichment analysis of green module DEGs; (<b>E</b>): KEGG enrichment analysis of yellow module DEGs.</p>
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<p>Pearson correlation analysis of DEGs with carotenoid differential metabolites (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Carotenoid regulatory networks in different tissues. Note: CF: TY7 petals; YF: YB1 petals; CR: TY7 rhizomes; YR: YB1 rhizomes; CS: TY7 seeds; YS: YB1 seeds; PDS: 15-cis-octahydroxylycopene desaturase; crtL2: lycopene e-cyclase; CYP97A3: β-cyclohydroxylase; crtZ: β-carotenoids 3-lightening enzyme; CCD8: carotenoid cleavage dioxygenase; NCED: 9-cis-epoxycarotenoid dioxygenase; ABA2: xanthoxin dehydrogenase; CYP707A: (+)−abscisic acid 8′-hydroxylase; ZEP, ABA1: zeaxanthin epoxidase. Orange color indicates upregulation and light blue color indicates downregulation.</p>
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<p>qRT-PCR assay for the differential expression profiles of genes in the seeds, petals, and roots of the control and mutant plants and transcriptome heat map. *** Significantly Note: CF: TY7 petals; YF: YB1 petals; CR: TY7 rhizomes; YR: YB1 rhizomes; CS: TY7 seeds; YS: YB1 seeds.</p>
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16 pages, 1905 KiB  
Article
Are Microfibers a Threat to Marine Invertebrates? A Sea Urchin Toxicity Assessment
by Jennifer Barbosa dos Santos, Rodrigo Brasil Choueri, Francisco Eduardo Melo dos Santos, Laís Adrielle de Oliveira Santos, Letícia Fernanda da Silva, Caio Rodrigues Nobre, Milton Alexandre Cardoso, Renata de Britto Mari, Fábio Ruiz Simões, Tomas Angel Delvalls and Paloma Kachel Gusso-Choueri
Toxics 2024, 12(10), 753; https://doi.org/10.3390/toxics12100753 - 17 Oct 2024
Viewed by 284
Abstract
The rise of “fast fashion” has driven up the production of low-cost, short-lived clothing, significantly increasing global textile fiber production and, consequently, exacerbating environmental pollution. This study investigated the ecotoxicological effects of different types of anthropogenic microfibers—cotton, polyester, and mixed fibers (50% cotton: [...] Read more.
The rise of “fast fashion” has driven up the production of low-cost, short-lived clothing, significantly increasing global textile fiber production and, consequently, exacerbating environmental pollution. This study investigated the ecotoxicological effects of different types of anthropogenic microfibers—cotton, polyester, and mixed fibers (50% cotton: 50% polyester)—on marine organisms, specifically sea urchin embryos. All tested fibers exhibited toxicity, with cotton fibers causing notable effects on embryonic development even at environmentally relevant concentrations. The research also simulated a scenario where microfibers were immersed in seawater for 30 days to assess changes in toxicity over time. The results showed that the toxicity of microfibers increased with both concentration and exposure duration, with polyester being the most toxic among the fibers tested. Although synthetic fibers have been the primary focus of previous research, this study highlights that natural fibers like cotton, which are often overlooked, can also be toxic due to the presence of harmful additives. These natural fibers, despite decomposing faster than synthetic ones, can persist in aquatic environments for extended periods. The findings underline the critical need for further research on both natural and synthetic microfibers to understand their environmental impact and potential threats to marine ecosystems and sea urchin populations. Full article
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<p>Photomicrographs of cotton microfibers (<b>i</b>) and polyester microfibers (<b>ii</b>).</p>
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<p>FT-IR (UATR) spectra of polyester and cotton samples covering the spectral range from 550 to 4000 cm<sup>−1</sup>. (<b>i</b>) Chemical structure of polyester. (<b>ii</b>) Chemical structure of cotton cellulose.</p>
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<p>Mean ± standard deviation (SD) of normal sea urchin embryo–larval development exposed to different concentrations (g) of microfibers of (<b>i</b>) freshly added cotton and (<b>ii</b>) aged cotton. The asterisks indicate significant differences between new and aged microfibers. Different letters represent significant differences between concentrations.</p>
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<p>Mean ± standard deviation (SD) of normal sea urchin embryo–larval development exposed to different concentrations (g) of microfibers of (<b>i</b>) freshly added polyester and (<b>ii</b>) aged polyester. The asterisks indicate significant differences between new and aged microfibers. Different letters represent significant differences between concentrations.</p>
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<p>Mean ± standard deviation (SD) of normal sea urchin embryo–larval development exposed to different concentrations (g) of microfibers of (<b>i</b>) freshly added mixed microfibers and (<b>ii</b>) aged mixed microfibers. The asterisks indicate significant differences between new and aged microfibers. Different letters represent significant differences between concentrations.</p>
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17 pages, 6604 KiB  
Article
Preparation of Lyocell Fibers from Solutions of Miscanthus Cellulose
by Igor S. Makarov, Vera V. Budaeva, Yulia A. Gismatulina, Ekaterina I. Kashcheyeva, Vladimir N. Zolotukhin, Polina A. Gorbatova, Gennady V. Sakovich, Markel I. Vinogradov, Ekaterina E. Palchikova, Ivan S. Levin and Mikhail V. Azanov
Polymers 2024, 16(20), 2915; https://doi.org/10.3390/polym16202915 - 16 Oct 2024
Viewed by 329
Abstract
Both annual (cotton, flax, hemp, etc.) and perennial (trees and grasses) plants can serve as a source of cellulose for fiber production. In recent years, the perennial herbaceous plant miscanthus has attracted particular interest as a popular industrial plant with enormous potential. This [...] Read more.
Both annual (cotton, flax, hemp, etc.) and perennial (trees and grasses) plants can serve as a source of cellulose for fiber production. In recent years, the perennial herbaceous plant miscanthus has attracted particular interest as a popular industrial plant with enormous potential. This industrial crop, which contains up to 57% cellulose, serves as a raw material in the chemical and biotechnology sectors. This study proposes for the first time the utilization of miscanthus, namely Miscanthus Giganteus “KAMIS”, to generate spinning solutions in N-methylmorpholine-N-oxide. Miscanthus cellulose’s properties were identified using standard methods for determining the constituent composition, including also IR and atomic emission spectroscopy. The dry-jet wet method was used to make fibers from cellulose solutions with an appropriate viscosity/elasticity ratio. The structural characteristics of the fibers were studied using IR and scanning electron microscopy, as well as via X-ray structural analysis. The mechanical and thermal properties of the novel type of hydrated cellulose fibers demonstrated the possibility of producing high-quality fibers from miscanthus. Full article
(This article belongs to the Special Issue Advances in Cellulose-Based Polymers and Composites, 2nd Edition)
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<p>IR spectra of miscanthus cellulose no. I and II.</p>
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<p>Diffraction patterns of cellulose samples from miscanthus no. I and no. II (reflection mode scanning).</p>
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<p>Micrographs of 18% spinning solution of miscanthus cellulose no. I (<b>a</b>) and no. II (<b>b</b>).</p>
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<p>Dependence of viscosity on time for (a) 5, (b) 8, and (c) 16% solutions of cellulose no. I and (d) 18% solution of cellulose no. II in NMMO; T = 120 °C.</p>
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<p>Flow curves of solutions of cellulose samples no. I and II.</p>
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<p>Concentration dependence of viscosity for cellulose solutions no. I and no. II at T = 120 °C (<math display="inline"><semantics> <mover> <mi>γ</mi> <mo>·</mo> </mover> </semantics></math> = 0.01 s<sup>−1</sup>).</p>
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<p>Temperature dependences of the viscosity of solutions of cellulose samples no. I and no. II in the Arrhenius coordinates.</p>
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<p>Dependence of the activation energy of miscanthus cellulose solutions no. I and no. II on concentration.</p>
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<p>Frequency dependences of the elastic modulus G′ (solid line) and the loss modulus G″ (dashed line) for the studied solutions of miscanthus cellulose no. I and no. II.</p>
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<p>Fibers spun from solutions of miscanthus cellulose no. I (<b>left</b> filament bundle) and no. II (<b>right</b> filament bundle).</p>
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<p>IR spectra of fibers spun from cellulose no. I and no. II.</p>
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<p>Equatorial diffraction patterns of fibers spun from 18% solutions of miscanthus cellulose no. I and no. II in NMMO.</p>
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<p>SEM images of the surface and cleavages of fibers (<b>a</b>,<b>b</b>) no. I and (<b>c</b>,<b>d</b>) no. II at different zoom.</p>
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12 pages, 2556 KiB  
Article
Impact of Zinnia elegans Cultivation on the Control Efficacy and Distribution of Aphidius colemani Viereck (Hymenoptera: Braconidae) against Aphis gossypii Glover (Hemiptera: Aphididae) in Cucumber Greenhouses
by Eun-Jung Han, Sung-Hoon Baek and Jong-Ho Park
Insects 2024, 15(10), 807; https://doi.org/10.3390/insects15100807 - 15 Oct 2024
Viewed by 322
Abstract
This study aimed to evaluate the enhancement of A. gossypii control by A. colemani when Z. elegans was planted as a companion crop in cucumber greenhouses. The density and spatial distribution of A. gossypii and parasitized mummies were investigated across three treatment plots: [...] Read more.
This study aimed to evaluate the enhancement of A. gossypii control by A. colemani when Z. elegans was planted as a companion crop in cucumber greenhouses. The density and spatial distribution of A. gossypii and parasitized mummies were investigated across three treatment plots: (1) the simultaneous application of A. colemani and cultivation of Z. elegans (parasitoid-zinnia plot); (2) the application of A. colemani alone (parasitoid plot); and (3) a control plot (no application of both). A. gossypii maintained low densities in the parasitoid–zinnia plots, while its densities in the parasitoid plots initially decreased but rapidly increased thereafter. The spatial distribution patterns of A. gossypii and parasitized mummies showed similar trends across treatments. However, the parasitism rate of A. gossypii exhibited random distribution in parasitoid and control plots, while showing uniform distribution in the parasitoid–zinnia treatment. These results supported the idea that cultivating Z. elegans alongside cucumber could enhance the effectiveness of A. colemani as a biocontrol agent against A. gossypii, highlighting the importance of such companion planting in pest management strategies. Full article
(This article belongs to the Section Insect Pest and Vector Management)
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<p>The densities (mean ± SE) of <span class="html-italic">A. gossypii</span> in cucumber greenhouses during the spring seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) according to different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>Th densities (mean ± SE) of parasitized mummies per leaf in cucumber greenhouses during the spring seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) according to different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>A comparison of <span class="html-italic">A. gossypii</span> parasitism rates (mean ± SE) on cucumber in a greenhouse during the spring seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) under three different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (Parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>The densities (mean ± SE) of <span class="html-italic">A. gossypii</span> in cucumber greenhouses during the fall seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) according to different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>The densities (mean ± SE) of parasitized mummies per leaf in cucumber greenhouses during the fall seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) according to different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>A comparison of <span class="html-italic">A. gossypii</span> parasitism rates (mean ± SE) on cucumber in a greenhouse during the fall seasons of 2016 (<b>a</b>) and 2017 (<b>b</b>) under three different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (Parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)). The values on the same date followed by different letters are significantly different at the 95% confidential level. “NS” means that values are not significantly different at the 95% confidential level.</p>
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<p>The distribution maps of <span class="html-italic">A. gossypii</span> densities (<b>a</b>), its parasitized mummy densities (<b>b</b>), and parasitism rates (<b>c</b>) under different treatments (i.e., the simultaneous application of <span class="html-italic">A. colemani</span> and cultivation of <span class="html-italic">Z. elegans</span> (parasitoid–zinnia), the application of <span class="html-italic">A. colemani</span> alone (parasitoid), and the control (no <span class="html-italic">A. colemani</span> and <span class="html-italic">Z. elegans</span>)) in the spring of 2016. DACT indicates days after cucumber transplantation.</p>
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21 pages, 15596 KiB  
Article
Assessing the Pathogenicity of Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum on Cotton (Gossypium hirsutum) Using a Rapid and Robust Seedling Screening Method
by Andrew Chen, Duy P. Le, Linda J. Smith, Dinesh Kafle, Elizabeth A. B. Aitken and Donald M. Gardiner
J. Fungi 2024, 10(10), 715; https://doi.org/10.3390/jof10100715 - 15 Oct 2024
Viewed by 366
Abstract
Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by [...] Read more.
Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by soil-borne pathogens has always been a challenge. To increase the uniformity of infection, we adapted a seedling screening method that directly uses liquid cultures to inoculate the plant roots and the soil. Four isolates, each of B. rouxiae and Fov, were collected from cotton fields in Australia and were characterised for virulence on cotton under controlled plant growth conditions. While the identities of all four B. rouxiae isolates were confirmed by multilocus sequencing, only two of them were found to be pathogenic on cotton, suggesting variability in the ability of isolates of this species to cause disease. The four Fov isolates were phylogenetically clustered together with the other Australian Fov isolates and displayed both external and internal symptoms characteristic of Fusarium wilt on cotton plants. Furthermore, the isolates appeared to induce varied levels of plant disease severity indicating differences in their virulence on cotton. To contrast the virulence of the Fov isolates, four putatively non-pathogenic Fusarium oxysporum (Fo) isolates collected from cotton seedlings exhibiting atypical wilt symptoms were assessed for their ability to colonise cotton host. Despite the absence of Secreted in Xylem genes (SIX6, SIX11, SIX13 and SIX14) characteristic of Fov, all four Fo isolates retained the ability to colonise cotton and induce wilt symptoms. This suggests that slightly virulent strains of Fo may contribute to the overall occurrence of Fusarium wilt in cotton fields. Findings from this study will allow better distinction to be made between plant pathogens and endophytes and allow fungal effectors underpinning pathogenicity to be explored. Full article
(This article belongs to the Special Issue Current Research in Soil Borne Plant Pathogens)
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<p>Colony morphology of <span class="html-italic">Berkeleyomyces rouxiae</span> and <span class="html-italic">Fusarium oxysporum</span> isolates used in this study. (<b>A</b>) <span class="html-italic">B. rouxiae</span> isolates RVB4.1, BRR4, 22BRR77, and StrB22 grown for 2 weeks on 10% carrot agar. (<b>B</b>) <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">vasinfectum</span> isolates <span class="html-italic">Fov</span> SG1, <span class="html-italic">Fov</span> SG26, <span class="html-italic">Fov</span> SG55, and <span class="html-italic">Fov</span> TH1 grown for 2 weeks on half-strength potato dextrose agar (PDA). (<b>C</b>) <span class="html-italic">Fusarium oxsporum</span> isolates <span class="html-italic">Fo</span> BRF1, <span class="html-italic">Fo</span> BRF2, <span class="html-italic">Fo</span> WRF2, and <span class="html-italic">Fo</span> SHF6 grown for 2 weeks on half-strength PDA.</p>
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<p>Compound microscopic images of <span class="html-italic">Berkeleyomyces rouxiae</span> and <span class="html-italic">Fusarium oxysporum</span> isolates used in this study. (<b>A</b>,<b>B</b>) <span class="html-italic">B. rouxiae</span> isolate RVB4.1. (<b>C</b>,<b>D</b>) <span class="html-italic">B. rouxiae</span> isolate 22BRR77. (<b>E</b>,<b>F</b>) <span class="html-italic">B. rouxiae</span> isolate BRR4. (<b>G</b>,<b>H</b>) <span class="html-italic">B. rouxiae</span> isolate StrB22. (<b>I</b>) <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">Vasinfectum</span> isolate (<span class="html-italic">Fov</span>) SG1. (<b>J</b>) <span class="html-italic">Fov</span> isolate SG26. (<b>K</b>) <span class="html-italic">Fov</span> isolate SG55. (<b>L</b>) <span class="html-italic">Fov</span> isolate TH1. (<b>M</b>) <span class="html-italic">Fusarium oxsporum</span> (<span class="html-italic">Fo</span>) isolate BRF1. Inset: a cluster of conidia. (<b>N</b>) <span class="html-italic">Fo</span> isolate BRF2. Inset: microconidia. (<b>O</b>) <span class="html-italic">Fo</span> isolate SHF6. Inset: a cluster of macroconidia. (<b>P</b>) <span class="html-italic">Fo</span> isolate WRF2. Inset: microconidia. e = endoconidia; ec = endoconidia chains; a = aleuriospores; c = microconidia; ma = macroconidia. Bars indicate a scale of 15 µm.</p>
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<p>Phylogenetic positions of <span class="html-italic">Berkeleyomyces rouxiae</span> isolates within the genus <span class="html-italic">Berkeleyomyces</span> determined using Bayesian inference. Phylogenetic relationships were reconstructed using concatenated sequences of <span class="html-italic">MCM7</span> and <span class="html-italic">RPB2</span> genes. Isolates examined in this study are highlighted in red. Orange circles indicate accessions that were used to define the genus <span class="html-italic">Berkeleyomyces</span> [<a href="#B4-jof-10-00715" class="html-bibr">4</a>]. The bar indicates a scale range of 0.1. Node values show the posterior probability. Genus is abbreviated as Ch. for <span class="html-italic">Chalaropsis</span>, C. for <span class="html-italic">Ceratocystis</span>, B. for <span class="html-italic">Berkeleyomyces</span>, and L. for <span class="html-italic">Lignincola</span>.</p>
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<p>Bayesian phylogeny of the <span class="html-italic">Fusarium oxysporum</span> isolates inferred from combined analysis of translation elongation factor, mitochondrial small subunit rDNA, nitrate reductase, and phosphate permease gene sequences. <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">vasinfectum</span> isolates are classified into lineages I–V [<a href="#B7-jof-10-00715" class="html-bibr">7</a>,<a href="#B9-jof-10-00715" class="html-bibr">9</a>]. Isolates examined in this study are highlighted in red. The endophytic and slightly pathogenic <span class="html-italic">F. oxysporum</span> isolates from cotton were classified as a distinct lineage [<a href="#B18-jof-10-00715" class="html-bibr">18</a>], and these isolates were characterised to be either non-pathogenic (green circle) or slightly pathogenic (orange circles) on cotton plants. VCG01111 and VCG01112 are abbreviated as VCG11 and VCG12, respectively. The bar indicates a scale range of 0.001. Node values show the posterior probability.</p>
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<p>Bayesian inference phylogenetic reconstruction of <span class="html-italic">Secreted in Xylem 6</span> gene (<span class="html-italic">SIX6</span>) in <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">vasinfectum</span> isolates and isolates from other <span class="html-italic">formae speciales</span> of <span class="html-italic">Fusarium oxysporum</span>. Isolates examined in this study are highlighted in red. <span class="html-italic">Focuc</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">cucumerinum</span>; <span class="html-italic">Fol</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">lycopersici</span>; <span class="html-italic">Fopas</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">passiflora</span>; <span class="html-italic">Fon</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">niveum</span>; <span class="html-italic">Fop</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">pisi</span>; <span class="html-italic">Foradc</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">radicis-cucumerinum</span>; <span class="html-italic">Foses</span> = <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">sesami</span>. Bars indicate a scale range of 0.05. Node values show the posterior probability.</p>
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<p>Virulence testing of <span class="html-italic">Berkeleyomyces rouxiae</span> isolates on cotton cv. Sicot746 B3F. (<b>A</b>) Plants at harvest (15–20 days post inoculation). (<b>B</b>) Total (above and below ground) plant weight. (<b>C</b>) Plant height. (<b>D</b>) Percentage of roots discoloured. (<b>E</b>) Total stem discoloured. (<b>F</b>) Percentage of leaves wilted or dropped. (<b>G</b>) Reisolations of <span class="html-italic">B. rouxiae</span>-like colonies on half-strength potato dextrose agar from the roots, stems, and petioles of uninoculated plants and plants inoculated with the <span class="html-italic">B. rouxiae</span> isolates. Numbers indicate the number of samples assayed per tissue type. Letters indicate a significant separation of means by Tukey’s range test at <span class="html-italic">p</span> = 0.05. Error bars indicate a 95% confidence interval.</p>
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<p>Symptomatology of <span class="html-italic">Berkeleyomyces rouxiae</span> and <span class="html-italic">Fusarium oxysporum</span> isolates on cotton cv. Sicot746 B3F at harvest. (<b>A</b>) Stem and root symptomatology of Sicot746 B3F plants inoculated with <span class="html-italic">B. rouxiae</span> isolates StrB22, BRR4, RVB4.1 and 22BRR77. (<b>B</b>) Stem and root symptomatology of Sicot746 B3F plants inoculated with <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">vasinfectum</span> (<span class="html-italic">Fov</span>) isolates <span class="html-italic">Fov</span> SG26, <span class="html-italic">Fov</span> TH1, <span class="html-italic">Fov</span> SG55 and <span class="html-italic">Fov</span> SG1. (<b>C</b>) Stem and root symptomatology of Sicot746 B3F plants inoculated with <span class="html-italic">F. oxysporum</span> (<span class="html-italic">Fo</span>) isolates <span class="html-italic">Fo</span> SHF6, <span class="html-italic">Fo</span> BRF2, <span class="html-italic">Fo</span> WRF2, <span class="html-italic">Fo</span> BRF1 and <span class="html-italic">Fov</span> SG1 (positive control). Plants inoculated with water served as a negative control. Red vertical bars indicate a scale of 10 cm.</p>
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<p>Virulence testing of <span class="html-italic">Fusarium oxysporum</span> f. sp. <span class="html-italic">vasinfectum</span> (<span class="html-italic">Fov</span>) isolates on cotton cv. Sicot746 B3F. (<b>A</b>) Plants at harvest (27–34 days post inoculation). (<b>B</b>) Total (above and below ground) plant weight. (<b>C</b>) Plant height. (<b>D</b>) Percentage of roots discoloured. (<b>E</b>) Total stem discoloured. (<b>F</b>) Percentage of leaves wilted or dropped. (<b>G</b>) Reisolations of <span class="html-italic">F. oxysporum</span>-like colonies on half-strength potato dextrose agar from the roots, stems, and petioles of uninoculated plants and plants inoculated with the <span class="html-italic">Fov</span> isolates. Numbers indicate the number of samples assayed per tissue type. Letters indicate a significant separation of means by Tukey’s range test at <span class="html-italic">p</span> = 0.05. Error bars indicate a 95% confidence interval.</p>
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<p>Virulence testing of <span class="html-italic">Fusarium oxysporum</span> (<span class="html-italic">Fo</span>) isolates on cotton cv. Sicot746 B3F. (<b>A</b>) Plants at harvest (17–22 days post inoculation). (<b>B</b>) Total (above and below ground) plant weight. (<b>C</b>) Plant height. (<b>D</b>) Percentage of roots discoloured. (<b>E</b>) Total stem discoloured. (<b>F</b>) Percentage of leaves wilted or dropped. (<b>G</b>) Reisolations of <span class="html-italic">F. oxysporum</span>-like colonies on half-strength potato dextrose agar from the roots, stems, and petioles of uninoculated plants and plants inoculated with the <span class="html-italic">Fov</span> isolates. Numbers indicate the number of samples assayed per tissue type. Letters indicate a significant separation of means by Tukey’s range test at <span class="html-italic">p</span> = 0.05. Error bars indicate a 95% confidence interval.</p>
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20 pages, 7061 KiB  
Article
Evaluating the Effect of Pulse Width Modulation-Controlled Spray Duty Cycles on Cotton Fiber Quality Using Principal Component Analysis
by Joe Mari Maja, Jyoti Neupane, Van Patiluna, Gilbert Miller, Aashish Karki, Michael W. Marshall, Matthew Cutulle, Jun Luo and Edward Barnes
AgriEngineering 2024, 6(4), 3719-3738; https://doi.org/10.3390/agriengineering6040212 - 14 Oct 2024
Viewed by 309
Abstract
The optimization of cotton defoliant application is critical for enhancing fiber quality and yield. This study aims to assess the impact of different defoliant duty cycles on cotton fiber quality by applying Principal Component Analysis (PCA) to High-Volume Instrument (HVI) data from two [...] Read more.
The optimization of cotton defoliant application is critical for enhancing fiber quality and yield. This study aims to assess the impact of different defoliant duty cycles on cotton fiber quality by applying Principal Component Analysis (PCA) to High-Volume Instrument (HVI) data from two fields. Three duty cycles—20%, 40%, and 60%—along with a control treatment were evaluated. PCA was used to identify the key factors influencing cotton quality, with a focus on parameters such as fiber length, strength, and uniformity. The results revealed that the 40% duty cycle consistently produced the most stable and uniform cotton fiber quality across both fields, minimizing variability in critical parameters. In contrast, the 20% and 60% duty cycles, as well as the control, introduced greater variability, with the control treatment showing the most significant outliers. These findings suggest that a 40% duty cycle is optimal for balancing effective defoliation with high-quality cotton production. Future research should explore the robustness of the 40% duty cycle across different environmental conditions and investigate the integration of advanced technologies to further optimize defoliant applications. This study provides valuable insights for improving cotton production practices and ensuring consistent fiber quality. Full article
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Figure 1
<p>Map showing the locations of Field 1 and Field 2 at the Edisto Research and Education Center, Blackville, SC, USA. Map data: Google, 2024.</p>
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<p>Detailed layout of Field 1, illustrating the division of 6 planted rows, marked in red, into two main groups (G1 and G2), with each group further subdivided into three smaller rows, marked in orange, each measuring 9.8 m in length.</p>
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<p>Field 2 layout showing the selected six rows of cotton (marked in red), with lengths ranging from 21 to 27 m.</p>
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<p>(<b>a</b>) Husky A200 mobile platform pulling a retrofitted sprayer unit featuring the (<b>b</b>) pump and a close-up of the (<b>c</b>) lower two nozzles on the left side.</p>
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<p>The (<b>a</b>) main controller is connected to the (<b>b</b>) sprayer board, highlighting the integration and function of both components within the sprayer controller system.</p>
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<p>Block diagram of the sprayer system, illustrating the power distribution and connections between the main controller, sprayer board, and key components.</p>
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<p>Correlation loading plot of Field 1 illustrating the relationships among key cotton fiber quality parameters, with PC-1 and PC-2 explaining the majority of the variance.</p>
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<p>Score plot of Field 1 displaying the distribution of cotton quality parameters across different duty cycle treatments and control groups.</p>
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<p>Influence plot of Field 1 highlighting the impact of different treatments on the PCA model, with a notable outlier from the control group.</p>
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<p>Explained variance plot of Field 1 showing the proportion of variance captured by each principal component, with PC-1 and PC-2 accounting for the majority.</p>
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<p>The correlation loading plot of Field 2 illustrates the relationships among key cotton fiber quality parameters, with PC-1 and PC-2 explaining the majority of the variance.</p>
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<p>Score plot of Field 2 displaying the distribution of cotton quality parameters across different duty cycle treatments and control groups.</p>
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<p>Influence plot of Field 2 highlighting the impact of different treatments on the PCA model, with a notable outlier from the control group.</p>
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<p>Explained variance plot of Field 2 showing the proportion of variance captured by each principal component, with PC-1 and PC-2 accounting for the majority.</p>
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21 pages, 7847 KiB  
Article
Unusual Metal–organic Multicomponent Ni(II) and Mononuclear Zn(II) Compounds Involving Pyridine dicarboxylates: Supramolecular Assemblies and Theoretical Studies
by Kamal K. Dutta, Pranay Sharma, Subham Banik, Rosa M. Gomila, Antonio Frontera, Miquel Barcelo-Oliver and Manjit K. Bhattacharyya
Inorganics 2024, 12(10), 267; https://doi.org/10.3390/inorganics12100267 - 14 Oct 2024
Viewed by 447
Abstract
In the present work, we reported the synthesis and characterization [single crystal X-ray diffraction technique, spectroscopic, etc.] of two new Ni(II) and Zn(II) coordination compounds, viz. [Ni(2,6-PDC)2]2[Ni(en)2(H2O)2]2[Ni(en)(H2O)4 [...] Read more.
In the present work, we reported the synthesis and characterization [single crystal X-ray diffraction technique, spectroscopic, etc.] of two new Ni(II) and Zn(II) coordination compounds, viz. [Ni(2,6-PDC)2]2[Ni(en)2(H2O)2]2[Ni(en)(H2O)4]·4H2O (1) and [Zn(2,6-PDC)(Hdmpz)2] (2) (where 2,6-PDC = 2,6-pyridinedicarboxylate, en = ethylene-1,2-diamine, and Hdmpz = 3,5-dimethyl pyrazole). Compound 1 is found to crystallize as a multicomponent Ni(II) compound with five discrete complex moieties, whereas compound 2 is isolated as a mononuclear Zn(II) compound. A deep analysis of the crystal structure of 1 unfolds unusual dual enclathration of guest complex cationic moieties within the supramolecular host cavity stabilized by anion–π, π-stacking, N–H⋯O, C–H⋯O, and O–H⋯O hydrogen bonding interactions. Again, the crystal structure of compound 2 is stabilized by the presence of unconventional C–H⋯π(chelate ring) interactions along with C–H⋯O, C–H⋯N hydrogen bonding, π-stacking, and C–H⋯π(pyridyl) interactions. These non-covalent interactions were further studied theoretically using density functional theory (DFT) calculations, molecular electrostatic potential (MEP) surfaces, non-covalent interaction (NCI) plot index, and quantum theory of atoms in molecules (QTAIM) computational tools. The computational study displays that π-stacking or H bonds greatly tune the directionality of compound 1, although non-directional electrostatic forces dominate energetically. For compound 2, a combined QTAIM/NCI plot analysis confirms the presence of unconventional C–H⋯π(chelate ring) interactions along with other weak interactions obtained from the crystal structure analysis. Further, the individual energy contributions of these weak yet significant non-covalent interactions have also been determined computationally. Full article
(This article belongs to the Special Issue Metal Complexes with N-donor Ligands, 2nd Edition)
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Graphical abstract

Graphical abstract
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<p>Molecular structure of [Ni(2,6-PDC)<sub>2</sub>]<sub>2</sub>[Ni(en)<sub>2</sub>(H<sub>2</sub>O)<sub>2</sub>]<sub>2</sub>[Ni(en)(H<sub>2</sub>O)<sub>4</sub>]·4H<sub>2</sub>O (<b>1</b>).</p>
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<p>Formation of supramolecular 1D chain of compound <b>1</b> with the help of non-covalent anion–π, π-stacking, and C–H⋯O hydrogen bonding contacts along the crystallographic c axis.</p>
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<p>Unusual dual enclathration of guest complex cationic moieties within the supramolecular host cavity of compound <b>1</b> stabilized by anion–π, π-stacking, N–H⋯O, C–H⋯O, and O–H⋯O hydrogen bonding interactions.</p>
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<p>Layered architecture of compound <b>1</b> guided by dual enclathration of complex cationic moieties inside the self-assembled host cavity of <b>1</b> along the crystallographic ac plane.</p>
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<p>Enclathration of cationic (Ni5) complex moiety within the non-covalent host cavity of compound <b>1</b> strengthened by N–H⋯O, O–H⋯O, and C–H⋯O hydrogen bonding interactions.</p>
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<p>Layered assembly of compound <b>1</b> assisted by enclathration of cationic moieties inside self-assembled host cavities along the crystallographic bc plane.</p>
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<p>Molecular structure of [Zn(2,6-PDC)(Hdmpz)<sub>2</sub>] (<b>2</b>).</p>
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<p>1D chain of compound <b>2</b> involving intermolecular C–H⋯O, C–H⋯N H bonding interactions and π-stacking, CH<sub>3</sub>···π(CR) and CH<sub>3</sub>···π(pyridyl) interactions along the crystallographic c axis.</p>
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<p>Layered structure of compound <b>2</b> involving C–H⋯O and N–H⋯O hydrogen bonding interactions along the crystallographic bc plane.</p>
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<p>Layered assembly of <b>2</b> involving non-covalent C–H⋯π and C–H⋯C interactions along the crystallographic ac plane.</p>
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<p>QTAIM (bond CPs in red and bond paths as orange lines) and NCI plot analyses (RDG = 0.6, ρ<sub>cut-off</sub> = 0.04, color range −0.04 a.u. ≤ (signλ<sub>2</sub>)ρ ≤ 0.04 a.u.) for the π-stacking and H-bonded assemblies of compound <b>1</b>. The H bonding energies evaluated using the Vr energy predictor are indicated in parenthesis.</p>
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<p>MEP surface of compound <b>2</b>. The values at selected points of the surface are given in kcal/mol. Isovalue of 0.001 a.u.</p>
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<p>QTAIM (bond CPs in red and bond paths as orange lines) and NCI plot analyses (RDG = 0.6, ρ<sub>cut-off</sub> = 0.04, color range –0.04 a.u. ≤ (signλ<sub>2</sub>)ρ ≤ 0.04 a.u.) for the π-stacking (<b>a</b>) and CH<sub>3</sub>···π (<b>b</b>) assemblies of compound <b>2</b>. The H bonding energies evaluated using the Vr energy predictor are indicated in parenthesis.</p>
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<p>Preparation of compounds <b>1</b> and <b>2</b>.</p>
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18 pages, 6737 KiB  
Article
Genome-Wide Identification and Functional Validation of Actin Depolymerizing Factor (ADF) Gene Family in Gossypium hirsutum L.
by Jingxuan Guo, Qingtao Zeng, Ying Liu, Zhaoyuan Ba and Xiongfeng Ma
Agronomy 2024, 14(10), 2349; https://doi.org/10.3390/agronomy14102349 - 11 Oct 2024
Viewed by 408
Abstract
The Actin Depolymerizing Factor (ADF) protein, highly conserved among eukaryotes, is essential for plant growth, development, and stress responses. Cotton, a vital economic crop with applications spanning oilseed, textiles, and military sectors, has seen a limited exploration of its ADF gene family. This [...] Read more.
The Actin Depolymerizing Factor (ADF) protein, highly conserved among eukaryotes, is essential for plant growth, development, and stress responses. Cotton, a vital economic crop with applications spanning oilseed, textiles, and military sectors, has seen a limited exploration of its ADF gene family. This research has identified 118 unique ADF sequences across four principal cotton species: Gossypium hirsutum L., Gossypium barbadense Linn, Gossypium raimondii, and Asiatic cotton. The study found that the structural domains and physicochemical properties of these proteins are largely uniform across species. The ADF genes were classified into four subfamilies with a notable expansion in groups III and IV due to tandem and chromosomal duplication events. A thorough analysis revealed a high degree of conservation in gene structure, including exon counts and the lengths of introns and exons, with the majority of genes containing three exons, aligning with the characteristics of the ADF family. RNA-seq analysis uncovered a spectrum of responses by GhADFs to various abiotic stresses with GhADF19 showing the most significant reaction. Virus-induced gene silencing (VIGS) experiments were conducted to assess the role of GhADF19 in plant growth under abiotic stress. The results demonstrated that plants with silenced GhADF19 exhibited significantly slower growth rates and lower dry weights when subjected to cold, salt, and drought stress compared to the control group. This marked reduction in growth and dry weight under stress conditions highlights the potential importance of GhADF19 in stress tolerance mechanisms. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Figure 1
<p>Phylogenetic tree of ADF proteins from four cotton species and other species. Using MEGA 7.0 software, the phylogenetic tree was constructed with 1000 bootstrap replicates using the neighbor-joining method, where only bootstrap values &gt; 50% are shown. Different colored lines and regions indicate ADF protein scores in different subgroups. Red stars represent ADF proteins of <span class="html-italic">Gossypium hirsutum</span> L. All ADFs were classified into four groups (I, II, III, IV).</p>
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<p>Distribution of ADF gene on cotton chromosome. The vertical bar on the far left indicates chromosome size in megabases (Mbs) with chromosome numbering to the left of each chromosome.</p>
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<p>Collinearity analysis. (<b>a</b>) Intraspecies collinearity analysis, where red lines indicate segmentally duplicated pairs of ADF genes. (<b>b</b>) Interspecies collinearity analysis.</p>
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<p>The exon–intron structures and conserved motifs of ADF. Structural triplets of cotton ADF proteins. Protein and DNA sequence lengths are estimated using the scale at the bottom with black lines indicating non-conserved amino acids or introns. The (<b>left</b>) panel shows the phylogenetic relationship of cotton ADF proteins; the (<b>right</b>) panel shows the conserved motifs and gene structure of the ADF gene family.</p>
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<p>Cis-acting element analysis of the ADF gene. The promoter region (2000 bp upstream of ATG) of each cotton ADF family member was analyzed by PlantCARE.</p>
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<p>Expression profiles of ADF genes under different stresses. Cold treatment (<b>a</b>), heat treatment (<b>b</b>), salt treatment (<b>c</b>), drought treatment (<b>d</b>). The red color represents a high expression and the green color represents a low expression. (The detailed FPKM values are present in <a href="#app1-agronomy-14-02349" class="html-app">Supplementary Additional File S1</a>).</p>
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<p>The expression of GhADFs under different stress treatments. (<b>a</b>) NaCl represents salt stress; (<b>b</b>) COLD represents low-temperature stress; (<b>c</b>) PEG represents drought stress. (The error line in the graph represents the standard deviation (SD) with a sample size of <span class="html-italic">n</span> = 3. The data presented here represent the mean of three biological experiments with the standard error of the mean indicated. The root relative expression values were standardized to a value of 1. A one-way ANOVA test was employed to perform the significance analyses, with a significance level of <span class="html-italic">p</span> &lt; 0.05). In the context of reference controls, the selected control was actin.</p>
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<p>Abiotic stress phenotypic characteristics, silencing efficiency and expression of <span class="html-italic">GhADF19</span> after gene silencing. (<b>a</b>) Plant growth after four genes were silenced under three abiotic stresses. (<b>b</b>) Dry weight of single plant after four genes were silenced under three abiotic stresses. (The error line in the graph represents the standard deviation (SD) with a sample size of <span class="html-italic">n</span> = 3. The data presented here represent the mean of three biological experiments with the standard error of the mean indicated. The root relative expression values were standardized to a value of 1. A one-way ANOVA test was employed to perform the significance analyses with a significance level of <span class="html-italic">p</span> &lt; 0.05. As indicated by Duncan’s multiple range test, the presence of different lowercase letters signifies a statistically significant distinction between groups at the <span class="html-italic">p</span> &lt; 0.05 level of significance.) (<b>c</b>) Silence efficiency of <span class="html-italic">GhADF19</span>.</p>
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<p>Abiotic stress phenotypic characteristics, silencing efficiency and expression of <span class="html-italic">GhADF19</span> after gene silencing. (<b>a</b>) Plant growth after four genes were silenced under three abiotic stresses. (<b>b</b>) Dry weight of single plant after four genes were silenced under three abiotic stresses. (The error line in the graph represents the standard deviation (SD) with a sample size of <span class="html-italic">n</span> = 3. The data presented here represent the mean of three biological experiments with the standard error of the mean indicated. The root relative expression values were standardized to a value of 1. A one-way ANOVA test was employed to perform the significance analyses with a significance level of <span class="html-italic">p</span> &lt; 0.05. As indicated by Duncan’s multiple range test, the presence of different lowercase letters signifies a statistically significant distinction between groups at the <span class="html-italic">p</span> &lt; 0.05 level of significance.) (<b>c</b>) Silence efficiency of <span class="html-italic">GhADF19</span>.</p>
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16 pages, 2485 KiB  
Article
The Distribution of Reniform Nematode (Rotylenchulus reniformis) in Cotton Fields in Central Queensland and Population Dynamics in Response to Cropping Regime
by Linda J. Smith, Linda Scheikowski and Dinesh Kafle
Pathogens 2024, 13(10), 888; https://doi.org/10.3390/pathogens13100888 - 11 Oct 2024
Viewed by 368
Abstract
Reniform nematode (Rotylenchulus reniformis) causes significant yield loss in cotton worldwide. In 2012, its detection in the Dawson-Callide region of Central Queensland prompted extensive surveys of cotton fields. The nematode was confirmed in 68% of sampled fields, with populations ranging from [...] Read more.
Reniform nematode (Rotylenchulus reniformis) causes significant yield loss in cotton worldwide. In 2012, its detection in the Dawson-Callide region of Central Queensland prompted extensive surveys of cotton fields. The nematode was confirmed in 68% of sampled fields, with populations ranging from 2 to 3870 R. reniformis/200 mL of soil. Soil monitoring revealed increasing populations associated with consecutive cotton crops. However, when corn or sorghum replaced cotton, soil nematode populations significantly decreased. A two-year replicated field trial demonstrated that growing a non-host crop (such as biofumigant sorghum ‘Fumig8tor’, grain sorghum, or corn) significantly reduced nematode populations in the top 15 cm of soil compared to cotton. Unfortunately, when cotton was replanted the following season, nematode populations rebounded regardless of the previous crop. Only the ‘Fumig8tor’-cotton rotation resulted in significantly lower nematode populations than continuous cotton. Vertical soil sampling showed that rotating with a non-host crop significantly reduced nematode densities to a depth of 100 cm compared to cotton. However, when the field was replanted with cotton, nematode populations recovered, unaffected by cropping or soil depth. This study emphasises the importance of monitoring reniform nematodes in cotton soils for early detection and defining distribution patterns within a field, which may improve the effectiveness of management practices. These results suggest that one rotation out of cotton is not sufficient, as populations return to high levels when cotton is grown again. Therefore, two or more rotations out of cotton should be considered to manage this nematode. Full article
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<p>Proportion of soil samples collected post-harvest cotton in 2013 that represent a nil detection of <span class="html-italic">Rotylenchulus reniformis</span> (Rr), populations less than 500, between 500 and 1000 nematodes, or greater than 1000 nematodes per 200 mL of soil for each of the four sub-regions in the Dawson-Callide region.</p>
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<p>Mean reniform nematode (<span class="html-italic">Rotylenchulus reniformis</span>) field population/200 mL of soil in the top 15 cm post-harvest of crop from four fields on three commercial cotton farms in the Theodore region in Queensland. First crop in sequence was grown in the 2012/13 season and included cotton-cotton-corn-cotton, cotton-cotton-corn, cotton-corn-cotton, and continuous cotton.</p>
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<p>Mean reniform nematode (<span class="html-italic">Rotylenchulus reniformis</span>) field population/200 mL of soil in the top 15 cm post-harvest cotton grown back-to-back from five fields on four commercial cotton farms in the Theodore region in Queensland.</p>
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<p>The mean population of <span class="html-italic">Rotylenchulus reniformis</span> [log<sub>10</sub>(x + 1)] per 200 mL of soil in the top 15 cm post-harvest following cotton in 2013 and sorghum in 2014 in fields 7 and 8 on a commercial cotton farm in Emerald, Queensland. Error bars represent the standard error of the mean (n = 6 for field 7, n = 4 for field 8).</p>
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<p>The effect of cotton and non-host summer crops on density of <span class="html-italic">Rotylenchulus reniformis</span> [log<sub>10</sub>(x + 1)] per 200 mL of soil in the top 15 cm pre-plant and post-harvest of crop. Pre = Soil sampling pre-planting of crop; Post = Soil sampling post-harvest of crop. C = cotton, F = biofumigation sorghum ‘Fumig8tor’, S = sorghum (grain), Co = corn. Cropping cycle over two seasons: C-C = cotton-cotton, F-C = biofumigation sorghum ‘Fumig8tor’-cotton, S-C = sorghum (grain)-cotton, Co-C = corn-cotton. Error bars represent the standard deviation of the mean (n = 6), and treatments followed by a different letter are significantly different from one another (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Mean reniform nematode densities (<span class="html-italic">Rotylenchulus reniformis</span>/200 g of soil) at three sampling depths for four crops post-harvest in 2015. Error bars represent the standard deviation of the mean (n = 6). The figure represents raw data, but the statistical comparisons are based on the transformed data [log<sub>10</sub>(x + 1)]. Treatments (Crop × Depth) followed by different letters are statistically different from one another (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Mean reniform nematode densities (<span class="html-italic">Rotylenchulus reniformis</span>/200 g of soil) at three sampling depths for four cropping sequences post-harvest. Cropping cycle over two seasons: C-C = cotton-cotton, F-C = ‘Fumig8tor’-cotton, S-C = sorghum-cotton, Co-C = corn-cotton. Error bars represent the standard deviation of the mean (n = 6). The figure represents raw data, but the statistical comparisons are based on the transformed data [√X]. Treatments (Crop × Depth) were not statistically different from one another (<span class="html-italic">p</span> ≤ 0.05).</p>
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14 pages, 2348 KiB  
Article
Use of Beauveria bassiana and Bacillus amyloliquefaciens Strains as Gossypium hirsutum Seed Coatings: Evaluation of the Bioinsecticidal and Biostimulant Effects in Semi-Field Conditions
by Vasileios Papantzikos, Spiridon Mantzoukas, Alexandra Koutsompina, Evangelia M. Karali, Panagiotis A. Eliopoulos, Dimitrios Servis, Stergios Bitivanos and George Patakioutas
Agronomy 2024, 14(10), 2335; https://doi.org/10.3390/agronomy14102335 - 10 Oct 2024
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Abstract
There are many challenges in cotton cultivation, which are mainly linked to management practices and market demands. The textile commerce requirements are increasing but the effects of climate change on cotton cultivation are becoming an issue, as its commercial development depends significantly on [...] Read more.
There are many challenges in cotton cultivation, which are mainly linked to management practices and market demands. The textile commerce requirements are increasing but the effects of climate change on cotton cultivation are becoming an issue, as its commercial development depends significantly on the availability of favorable climatic parameters and the absence of insect pests. In this research, it was studied whether the use of two commercial strains as cotton seed coatings could effectively contribute to the previous obstacles. The experiment was carried out in semi-field conditions at the University of Ioannina. It used a completely randomized design and lasted for 150 days. The following treatments were tested: (a) coated seeds with a commercial strain of Beauveria bassiana (Velifer®); (b) coated seeds with a combination of Velifer® and a commercial strain of Beauveria bassiana (Selifer®); and (c) uncoated cotton seeds (control). The biostimulant effect of the two seed coatings was assessed against the growth characteristics of cotton, and the total chlorophyll and proline content. The bioinsecticidal effect was evaluated by measuring the population of Aphis gossypii on the cotton leaves. The proline effect increased by 15% in the treated plants, whereas the total chlorophyll was higher in the use of both Velifer® and Velifer®–Selifer® treatments by 32% and 19%, respectively. Aphid populations also decreased in the treated plants compared to the control plants (29.9% in Velifer® and 22.4% in Velifer®–Selifer®). Based on an assessment of the above parameters, it follows that the two seed coatings can significantly enhance the growth performance of cotton and reduce the abundance of A. gossypii. Full article
(This article belongs to the Special Issue Pests, Pesticides, Pollinators and Sustainable Farming)
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Figure 1

Figure 1
<p>The mean number of <span class="html-italic">A. gossipii</span> aphids on cotton plant leaves up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control.</p>
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<p>The mean length (cm) of cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean shoot number of cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean number of internodes on cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean number of cotton bolls up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean number of leaves on cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean stem diameter (mm) of cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer-Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean proline values (μmol g<sup>−1</sup>) for cotton plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The mean values of the leaves’ total chlorophyll content (μg cm<sup>−2</sup>) up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control.</p>
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<p>The mean fresh (<b>A</b>) and dry (<b>B</b>) weight (g) of roots, shoots, leaves, and seedcotton of <span class="html-italic">G. hirsutum</span> plants up to 150 days after treatment: <b>V</b>—Velifer; <b>VS</b>—Velifer–Serifel; and <b>C</b>—control. Different letters among treatments indicate statistically significant differences (Tukey test, <span class="html-italic">p</span> &lt; 0.05).</p>
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