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15 pages, 5903 KiB  
Article
Insights into the Stearoyl-Acyl Carrier Protein Desaturase (SAD) Family in Tigernut (Cyperus esculentus L.), an Oil-Bearing Tuber Plant
by Zhi Zou, Xiaowen Fu, Chunqiang Li, Xiaoping Yi, Jiaquan Huang and Yongguo Zhao
Plants 2025, 14(4), 584; https://doi.org/10.3390/plants14040584 - 14 Feb 2025
Abstract
Plant oils rich in oleic acid (OA) are attracting considerable attention for their high nutritional value and significant industrial potential. Stearoyl-acyl carrier protein desaturases (SADs) are a class of soluble desaturases that play a key role in OA accumulation in plants. In this [...] Read more.
Plant oils rich in oleic acid (OA) are attracting considerable attention for their high nutritional value and significant industrial potential. Stearoyl-acyl carrier protein desaturases (SADs) are a class of soluble desaturases that play a key role in OA accumulation in plants. In this study, the first genome-wide characterization of the SAD gene family was conducted in tigernut (Cyperus esculentus L. var. sativus Baeck., Cyperaceae), an oil-rich tuber plant typical for its high OA content. Six SAD genes identified from the tigernut genome are comparative to seven reported in two model plants Arabidopsis thaliana and Oryza sativa, but relatively more than four were found in most Cyperaceae species examined in this study. A comparison of 161 SAD genes from 29 representative plant species reveals the monogenic origin and lineage-specific family evolution in Poales. C. esculentus SAD genes (CeSADs) were shown to constitute two evolutionary groups (i.e., FAB2 and AAD) and four out of 12 orthogroups identified in this study, i.e., FAB2a, FAB2b, FAB2c, and AAD1. Whereas FAB2a and AAD1 are widely distributed, FAB2b and FAB2c are specific to Cyperaceae, which may arise from FAB2a via tandem and dispersed duplications, respectively. Though FAB2d and AAD2 are also broadly present in monocots, they are more likely to be lost in the Cyperaceae ancestor sometime after the split with its close family, Juncaceae. In tigernut, FAB2a appears to have undergone species-specific expansion via tandem duplication. Frequent structural variation and apparent expression divergence were also observed. Though FAB2a and AAD1 usually feature two and one intron, respectively, gain of certain introns was observed in CeSAD genes, all of which have three introns. Despite recent expansion of the FAB2 group, CeFAB2-1 has evolved into the dominant member that was highly and constitutively expressed in all tested organs. Moreover, CeFAB2-1, CeAAD1, as well as CeFAB2-5 have evolved to be predominantly expressed in tubers and thus contribute to high OA accumulation. These findings highlight lineage-specific evolution of the SAD family and putative roles of CeSAD genes in tuber oil accumulation, which facilitate further functional analysis and genetic improvement in tigernut and other species. Full article
(This article belongs to the Special Issue Advances in Oil Regulation in Seeds and Vegetative Tissues)
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<p>Multiple sequence alignment of CeSAD proteins with structure-resolved RcSAD1. Sequence alignment was conducted using MUSCLE, where the mature peptide of RcSAD1 is under the NCBI accession number of 1OQ4_A. Identical and similar amino acids are highlighted in black or dark grey, respectively. The positions of the phase 2 intron in <span class="html-italic">CeFAB2-1–5</span> are boxed in black, whereas the phase 0 introns are indicated using solid (<span class="html-italic">CeFAB2-1–5</span>) and hollow (<span class="html-italic">CeAAD1</span>) arrows (see more in <a href="#app1-plants-14-00584" class="html-app">Figure S1</a>). The conserved FA_desaturase_2 domain is underlined, and several typical structural features are boxed in different colors, i.e., chloroplast transit peptide (gold), two histidine boxes (i.e., EENRHG and DEKRHE) (purple), and key residues determining substrate specificity and double-bond position (red) (AAD: acyl-ACP desaturase; Ce: <span class="html-italic">C. esculentus</span>; FA: fatty acid; FAB2: fatty acid biosynthesis 2; Rc: <span class="html-italic">R. communis</span>; SAD: stearoyl-ACP desaturase).</p>
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<p>Chromosomal localization, duplication events, and structural and phylogenetic analyses of <span class="html-italic">CeSAD</span> genes. (<b>A</b>) Shown is an unrooted phylogenetic tree resulting from full-length Ce/AtSAD proteins with MEGA6 (maximum likelihood method and bootstrap of 1000 replicates), where the distance scale denotes the number of amino-acid substitutions per site. The name of each clade is indicated next to the corresponding group. (<b>B</b>) Shown are the exon-intron structures, where the numbers indicate intron phases. Whereas 0 indicates the intron that is located between codons, 2 indicates the intron that is located between the second and third bases of a codon. (<b>C</b>) Shown is the distribution of conserved motifs among Ce/AtSAD proteins, where different motifs are represented by different color blocks, as indicated, and the same color block in different proteins indicates a certain motif. (<b>D</b>) Shown are chromosomal localization and duplication events of <span class="html-italic">CeSAD</span> genes, where dispersed and tandem repeats are connected using gold and blue lines, respectively (AAD: acyl-ACP desaturase; At: <span class="html-italic">A. thaliana</span>; Ce: <span class="html-italic">C. esculentus</span>; FA: fatty acid; FAB2: fatty acid biosynthesis 2; Mb: Megabase; SAD: stearoyl-ACP desaturase; Scf: scaffold).</p>
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<p>Species-specific distribution of 12 orthogroups in 29 representative plant species. The species tree is referred to NCBI Taxonomy (<a href="https://www.ncbi.nlm.nih.gov/taxonomy" target="_blank">https://www.ncbi.nlm.nih.gov/taxonomy</a>, accessed on 20 August 2024) and recent WGDs or triplications resulting in polyploidy (CoGepedia; <a href="https://genomevolution.org/coge/" target="_blank">https://genomevolution.org/coge/</a>, accessed on 20 August 2024) are marked. Names of tested plant families are indicated next to the corresponding branches (AAD: acyl-ACP desaturase; FAB2: fatty acid biosynthesis 2; WGD: whole-genome duplication).</p>
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<p>Synteny analysis within and between <span class="html-italic">C. esculentus</span> and representative plant species. (<b>A</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">C. littledalei</span>, <span class="html-italic">C. scoparia</span>, <span class="html-italic">R. breviuscula</span>, and <span class="html-italic">J. inflexus</span>. (<b>B</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">J. ascendens</span>, <span class="html-italic">S. stoloniferum</span>, and <span class="html-italic">A. comosus</span>. (<b>C</b>) Synteny analysis within and between <span class="html-italic">J. inflexus</span>, <span class="html-italic">P. latifolius</span>, <span class="html-italic">E. guineensis</span>, and <span class="html-italic">D. alata</span>. (<b>D</b>) Synteny analysis within and between <span class="html-italic">J. ascendens</span>, <span class="html-italic">P. latifolius</span>, <span class="html-italic">O. sativa</span>, and <span class="html-italic">S. bicolor</span>. Shown are <span class="html-italic">SAD</span> gene-encoding chromosomes/scaffolds and only syntenic blocks containing <span class="html-italic">SAD</span> genes are marked, where red and purple lines indicate intra- and inter-species, respectively. The scale is in Mb (AAD: acyl-ACP desaturase; Ac: <span class="html-italic">A. comosus</span>; Ce: <span class="html-italic">C. esculentus</span>; Cl: <span class="html-italic">C. littledalei</span>; Cs: <span class="html-italic">C. scoparia</span>; Da: <span class="html-italic">D. alata</span>; Eg: <span class="html-italic">E. guineensis</span>; FAB2: fatty acid biosynthesis 2; Ja: <span class="html-italic">J. ascendens</span>; Ji: <span class="html-italic">J. inflexus</span>; Mb: megabase; Os: <span class="html-italic">O. sativa</span>; Pl: <span class="html-italic">P. latifolius</span>; Rb: <span class="html-italic">R. breviuscula</span>; Sb: <span class="html-italic">S. bicolor</span>; Ss: <span class="html-italic">S. stoloniferum</span>).</p>
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<p>Expression profiles of <span class="html-italic">CeSAD</span> genes. (<b>A</b>) Organ-specific expression profiles of six <span class="html-italic">CeSAD</span> genes. The heatmap was generated using the R package implemented with a row-based standardization. Color scale represents FPKM normalized log<sub>2</sub> transformed counts, where blue indicates low expression and red indicates high expression. (<b>B</b>) Expression profiles of <span class="html-italic">CeFAB2-1</span>, <span class="html-italic">CeFAB2-5</span>, and <span class="html-italic">CeAAD1</span> at different stages of tuber development. All values for three tested genes at DAI1 were normalized to 1, whereas bars indicate SD (N = 3) and uppercase letters indicate difference significance tested following Duncan’s one-way multiple-range post hoc ANOVA (<span class="html-italic">p</span> &lt; 0.01) (AAD: acyl-ACP desaturase; Ce: <span class="html-italic">C. esculentus</span>; DAI: days after tuber initiation; FAB2: fatty acid biosynthesis 2; FPKM: Fragments per kilobase of exon per million fragments mapped).</p>
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27 pages, 1800 KiB  
Review
Current State of Greenhouse Waste Biomass Disposal Methods, with a Focus on Essex County Ontario
by Robyn Jadischke and William David Lubitz
Sustainability 2025, 17(4), 1476; https://doi.org/10.3390/su17041476 - 11 Feb 2025
Abstract
Managing organic waste produced from agricultural greenhouse production is becoming an increasing concern for growers and communities that contain significant greenhouse production. Currently, in North America, the waste vines, leaves and stems, and fruit grade-outs that are produced during in-season greenhouse production and [...] Read more.
Managing organic waste produced from agricultural greenhouse production is becoming an increasing concern for growers and communities that contain significant greenhouse production. Currently, in North America, the waste vines, leaves and stems, and fruit grade-outs that are produced during in-season greenhouse production and post-harvest processes are most commonly sent to local landfills. With landfills rapidly filling and increasing pressures to improve the sustainability and circularity of greenhouse production, alternative waste management solutions are needed. This review examines greenhouse organic waste characteristics and composition, focusing on Essex County, Ontario, Canada, which has the highest density of greenhouse production in North America. Current worldwide research on greenhouse waste disposal methods is reviewed, including landfilling, land application, incineration and waste-to-energy, anaerobic digestion, char production, organic fertilizer production and composting, and insect digestion. Seasonal timing, waste composition, cost, space, and the state of research influence the feasibility of implementing these solutions on an industrial scale. This review also contains a case study of greenhouse organic waste characteristics and quantity, and the most suitable management strategies for Essex County (containing the Leamington and Kingsville areas) in southern Ontario, Canada, where this issue is becoming an increasing concern to the local community. Gaps in policy and data are highlighted, including barriers that may limit the adoption of the innovative solutions proposed. Full article
(This article belongs to the Special Issue Emerging Trends in Waste Management and Sustainable Practices)
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<p>(<b>a</b>) Young conventional hydroponic pepper plants in rock wool growing media; (<b>b</b>) young organic tomato plants in grow bags.</p>
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<p>Typical polypropylene plastic string and clip system found in greenhouses.</p>
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19 pages, 2183 KiB  
Article
In-Season Price Forecasting in Cotton Futures Markets Using ARIMA, Neural Network, and LSTM Machine Learning Models
by Jeffrey Vitale and John Robinson
J. Risk Financial Manag. 2025, 18(2), 93; https://doi.org/10.3390/jrfm18020093 - 10 Feb 2025
Abstract
This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers [...] Read more.
This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers in hedging and marketing decisions, particularly in the Texas Gulf region. The models evaluated included ARIMA, basic feedforward neural networks, basic LSTM, bidirectional LSTM, stacked LSTM, CNN LSTM, and 2D convolutional LSTM models. The forecasts were generated for five-, ten-, and fifteen-day periods using historical data spanning 2009 to 2023. The results demonstrated that advanced LSTM architectures outperformed other models across all forecast horizons, particularly during periods of significant price volatility, due to their enhanced ability to capture complex temporal and spatial dependencies. The findings suggest that incorporating advanced LSTM architectures can significantly improve forecasting accuracy, providing a robust tool for producers and market analysts to better navigate price risks. Future research could explore integrating additional contextual variables to enhance model performance further. Full article
(This article belongs to the Special Issue Financial Innovations and Derivatives)
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<p>Seasonal index of ICE seasonal December prices, averaging daily settlements (1987–2023).</p>
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<p>ICE December futures prices: 2009–2023.</p>
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<p>LSTM architecture illustrating the flow of information across the forget input, candidate, and output gates.</p>
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<p>K-fold (k = 5) model hyperparameter random search results for 2D convolution LSTM model (5-day forecast) and Pareto frontier.</p>
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<p>Five-day forecast cumulative RMSE performance results.</p>
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<p>Ten-day forecast cumulative RMSE performance results.</p>
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<p>Fifteen-day forecast cumulative RMSE performance results.</p>
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23 pages, 7522 KiB  
Article
Scalable Prediction of Northern Corn Leaf Blight and Gray Leaf Spot Diseases to Predict Fungicide Spray Timing in Corn
by Layton Peddicord, Alencar Xavier, Steven Cryer, Jeremiah Barr and Gerie van der Heijden
Agronomy 2025, 15(2), 328; https://doi.org/10.3390/agronomy15020328 - 27 Jan 2025
Abstract
Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and [...] Read more.
Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and predicted leaf wetness duration (LWD) intervals based on meteorological conditions, can help growers to anticipate and manage crop diseases effectively. Effective crop disease management programs integrate crop rotation, tillage practices, hybrid selection, and fungicides. However, growers often struggle with correctly timing fungicide applications, achieving only a 30–55% positive return on investment (ROI). This paper describes the development of a disease-warning and fungicide timing system, equally effective at predicting NLB and GLS with ~70% accuracy, that utilizes historical and forecast hourly weather data. This scalable recommendation system represents a valuable tool for proactive, practicable crop disease management, leveraging in-season weather data and advanced modeling techniques to guide fungicide applications, thereby improving profitability and reducing environmental impact. Extensive on-farm trials (>150) conducted between 2020 and 2023 have shown that the predicted fungicide timing out-yielded conventional grower timing by 5 bushels per acre (336 kg/ha) and the untreated check by 9 bushels per acre (605 kg/ha), providing a significantly improved ROI. Full article
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<p>Schematic decision tree diagram for logic used to determine hourly leaf wetness.</p>
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<p>Disease units (DUs) measured from consecutive hours (LWD) of ideal infection conditions under the threshold model and the extended model.</p>
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<p>Trial locations from which 5 observational disease ratings were taken in 2019.</p>
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<p>Geographical distribution of data collected in 2019 (blue) and 2020 (orange).</p>
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<p>Framework for spray timing in which an economic threshold line is shown with respect to early or late disease infection possibilities. Illustration of disease severity (<span class="html-italic">y</span>-axis) by days after planting (<span class="html-italic">x</span>-axis) where this can extend to upwards of 90–120 days after planting (×9–12).</p>
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<p>Performance fungicide ROI trials across the US testing the Corteva model’s timing recommendations versus the farmer’s conventional timing.</p>
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<p>Locations across 2019 and 2020 from which GLS and NLB disease observations were taken; these Corteva on-farm agronomy trials show the average rating on the 1–9 scoring scale, where 9 indicates no symptoms and 1 indicates very severe symptoms.</p>
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<p>Locations across 2019 and 2020 from which GLS and NLB disease observations were taken; these Corteva on-farm agronomy trials show the average rating on the 1–9 scoring scale, where 9 indicates no symptoms and 1 indicates very severe symptoms.</p>
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<p>Performance ROI trials across 2020–2023, which are colored by year, are shown in the piano-style descending bar graphs. The first figure represents the conventional GT compared to the UTC in bu/acre advantage; the second figure shows the CT versus the UTC, and the third figure demonstrates the CT vs. GT advantage.</p>
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<p>Dennis (1987) model based on temperature and hours of leaf wetness [<a href="#B25-agronomy-15-00328" class="html-bibr">25</a>].</p>
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<p>NLB development from Bowen and Pederson (1989) model Corn-IL1 [<a href="#B35-agronomy-15-00328" class="html-bibr">35</a>].</p>
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<p>Correlation among different sources of weather data.</p>
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<p>Illustration of NLB and GLS scores on a 1–9 scale. Lower values represent higher disease severity. Image source example: <a href="https://www.pioneer.com/us/agronomy/Managing-Northern-Corn-Leaf-Blight.html" target="_blank">https://www.pioneer.com/us/agronomy/Managing-Northern-Corn-Leaf-Blight.html</a> (accessed on 22 January 2025).</p>
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<p>Disease infection levels (medium to high) observed in 2019 from southern Illinois (GLS) and eastern Iowa (NLB). These leaf samples were submitted to the Corteva Agriscience pathology and diagnostics lab located in Johnston, Iowa.</p>
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<p>Spray timing governance decision tree representative of non-irrigated acres.</p>
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<p>Probability of NLB infection based on disease units (2019 and 2020 data).</p>
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17 pages, 3684 KiB  
Article
Identification of Mango Cultivars’ Resistance Against Red Spider Mite: Impact of Climate Elements on Resistance Performance
by Xiao Liang, Xuelian Xu, Ying Liu, Chunling Wu, Mufeng Wu and Qing Chen
Agronomy 2025, 15(2), 324; https://doi.org/10.3390/agronomy15020324 - 27 Jan 2025
Abstract
The use of resistant plants is recognized as an environmentally friendly measure for mite control. Oligonychus mangiferus, known as the mango red spider mite (MRSM), is a dangerous pest for mango production. To date, the resistance levels of the mango germplasms against [...] Read more.
The use of resistant plants is recognized as an environmentally friendly measure for mite control. Oligonychus mangiferus, known as the mango red spider mite (MRSM), is a dangerous pest for mango production. To date, the resistance levels of the mango germplasms against the MRSM remain largely unknown. Furthermore, the environmental factors potentially influencing resistance performance have been seldom discussed. To fill those knowledge gaps, this study aimed to identify the resistance level of twelve mango cultivars against the MRSM. Based on three rounds of greenhouse and five seasons of field tests, cultivars with distinct resistant levels were identified. When exploring the climate impact, we found that for the susceptible cultivars, precipitation is the primary external environment factor altering the resistance performance, while temperature presents a secondary effect, and air humidity did not show a significant impact on MRSM resistance. By contrast, MRSM-resistant cultivars were not prone to be affected by changing climate conditions. Furthermore, yield tests indicated that the resistant cultivars can better reduce the yield losses compared with the susceptible ones. This study illustrated the climate element-driven effect on mango tree resistance performance against the MRSM, which can provide insight into insect pest management under changing climate conditions. Full article
(This article belongs to the Special Issue Green Control of Pests and Pathogens in Tropical Plants)
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<p>The method of evaluation of resistant mango cultivars to the mango red spider mite (MRSM) in the greenhouse. (<b>A</b>) Typical damage of the MRSM on a mango leaf. An adult MRSM is shown at the bottom left corner. (<b>B</b>) The leaf damage rate of mango plants was analyzed using the Leaf Image Analyzer (YMJ-E) (<a href="https://www.hzdj17.com/timemodel/product/2021-05-29/2064055189.html" target="_blank">https://www.hzdj17.com/timemodel/product/2021-05-29/2064055189.html</a> accessed on 19 January 2025). (<b>C</b>) The classification of mango leaf damage caused by MRSM, in which the upper panel indicates the whole original leaves, while the lower panel represents the MRSM damage area, which was visualized by a specialized leaf area analysis software.</p>
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<p>The effect of different mango cultivars on the reproduction and development of the mango red spider mite (MRSM). (<b>A</b>) Mortalities; (<b>B</b>) fecundity, (<b>C</b>) hatching rate; (<b>D</b>) developmental duration. Homogeneity tests were first conducted for all data, and those that did not meet the assumptions of normality and homoscedasticity were subjected to log- or square root-transformation. Different letters indicate significant differences according to one-way analysis of variance with Tukey’s Honestly Significant Difference (HSD) tests (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Field identification results of MRSM resistance of 12 mango cultivars. Different resistance levels were presented with different color zones. The effect of cultivars and time on the MDI was evaluated by the GLMM. EMC and EY stand for the experimental mango cultivar and experimental year, respectively.</p>
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<p>Mann–Kendall trend test on (<b>A</b>) sum of precipitation per month (P<span class="html-italic"><sub>SUM</sub></span>), (<b>C</b>) sum of temperature per day (T<span class="html-italic"><sub>SUM</sub></span>), and (<b>E</b>) average relative air humidity per month (RAH). Values for MK Tau, Sen’s slope, and trend lines are shown in each panel. Mann–Kendall mutation test on (<b>B</b>) statistic of P<span class="html-italic"><sub>SUM</sub></span>, (<b>D</b>) statistic of T<span class="html-italic"><sub>SUM</sub></span>, and (<b>F</b>) statistic of RAH. UF and UB are two important dimensionless parameters in Mann–Kendall mutation test according to Equations (E1)–(E6) (see <a href="#app1-agronomy-15-00324" class="html-app">Supplementary Materials</a>); intersections with a 99% confidence level (α = 0.01) from time series represent mutation points (marked with dashed circles), indicating specific month after which the time series shows abrupt changes.</p>
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<p>Annual population dynamics of mango red spider mites on the highly susceptible cultivar Dashehari from December 2018 to April 2023. Months with high mite density were marked with red font and shadowed.</p>
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<p>Adjusted R<sup>2</sup> (<b>A</b>) and respective <span class="html-italic">p</span> values (<b>B</b>) of cultivar-wise multiple linear regression models from December (previous year) to April (current year) in the planting seasons from 2019 to 2023. The multiple linear least-squares regression equation was used to determine the effects of weather conditions on the MDI of mango tree leaves. A single asterisk and a double asterisk indicates a significant (<span class="html-italic">p</span> &lt; 0.05) and extremely significant level (<span class="html-italic">p</span> &lt; 0.01) in fitting the regression equation, respectively.</p>
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<p>The capacity of 12 mango cultivars in reducing the yield losses during the field tests. (<b>A</b>) Dashehari. (<b>B</b>) Keitt. (<b>C</b>) Yuexi. (<b>D</b>) Zihua. (<b>E</b>) India 901. (<b>F</b>) Red ivory. (<b>G</b>) Hongguang. (<b>H</b>) Kent. (<b>I</b>) Golden Phoenix. (<b>J</b>) Sanya. (<b>K</b>) Hongmamg. (<b>L</b>) Tainong No. 1. “+” indicates acaricide application, while “−” indicates without acaricide application. A generalized linear mixed model was used to analyze the effects of acaricide application and years of experiments on the mango yield. The F- and <span class="html-italic">p</span>-values are indicated within panels (<b>A</b>–<b>L</b>), significance level = 0.05. AA and EY stand for acaricide application and experimental year.</p>
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11 pages, 636 KiB  
Article
Seasonal Variations in the Physical Fitness of South African Premier Soccer League Players over an Annual Training Macrocycle (Nine Months)
by Mduduzi Rhini, Robert Charles Hickner, Rowena Naidoo and Takshita Sookan-Kassie
J. Funct. Morphol. Kinesiol. 2025, 10(1), 38; https://doi.org/10.3390/jfmk10010038 - 21 Jan 2025
Viewed by 304
Abstract
Background: Anecdotal data indicate that the physical fitness of soccer players fluctuates across the season. This is often a concern for coaches, since players are expected to be at optimal fitness during matches on weekly basis across the season. Objectives: To [...] Read more.
Background: Anecdotal data indicate that the physical fitness of soccer players fluctuates across the season. This is often a concern for coaches, since players are expected to be at optimal fitness during matches on weekly basis across the season. Objectives: To analyze the physical fitness variation in South African Premier Soccer League players over an annual training macrocycle. Methods: Twenty-four Premier Soccer League players belonging to the same team participated in the study. Players went through fitness assessments at three stages of the season: at the beginning of pre-season (T1); mid-first round in-season (T2); and mid-second round in-season (T3). The assessments included body fat percentage; sit and reach; vertical jump; 10 and 30 m sprints; and YoYo Intermittent Recovery Level 2 (YoYo IR2). Results: There was a significant increase in body fat percentage from T1 to T2 (p < 0.001), and a slight decline was evident at T3 (p = 0.04). Flexibility was significantly greater at T2 (p < 0.001) compared to T1 and T3. Vertical jump significantly improved at T3 (p = 0.004) compared to T1 and T2. A similar trend was evident in the YoYo IR2, where players reached the highest levels at T3 (p < 0.001). However, there were no significant changes in the 10 and 30 m sprints across the season. Conclusions: These results indicate that, indeed, some parameters, such as body fat percentage and flexibility, are likely to fluctuate as the season progresses. However, it is also evident that a gradual improvement can be achieved, as seen in vertical jump and YoYo IR2. Full article
(This article belongs to the Special Issue Optimizing Post-activation Performance Enhancement)
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<p>Physical characteristics of players per playing position across the three stages of the season (pre-season; 1st round in-season and 2nd round in-season). Fitness tests: (<b>a</b>) Body fat percentage (%); (<b>b</b>) Sit and reach (cm); (<b>c</b>) Vertical jump (cm); (<b>d</b>) YoYo Intermittent Recovery Test Level 2 (m). Playing positions: ACM—attacking central midfielders; CB—center backs; CF—center forwards; CM—central midfielders; FB—full backs; GK—goalkeepers.</p>
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<p>Physical characteristics of players per playing position across the three stages of the season (pre-season; 1st round in-season and 2nd round in-season). Fitness tests: (<b>a</b>) Body fat percentage (%); (<b>b</b>) Sit and reach (cm); (<b>c</b>) Vertical jump (cm); (<b>d</b>) YoYo Intermittent Recovery Test Level 2 (m). Playing positions: ACM—attacking central midfielders; CB—center backs; CF—center forwards; CM—central midfielders; FB—full backs; GK—goalkeepers.</p>
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18 pages, 3454 KiB  
Article
Estimating Switchgrass Biomass Yield and Lignocellulose Composition from UAV-Based Indices
by Daniel Wasonga, Chunhwa Jang, Jung Woo Lee, Kayla Vittore, Muhammad Umer Arshad, Nictor Namoi, Colleen Zumpf and DoKyoung Lee
Crops 2025, 5(1), 3; https://doi.org/10.3390/crops5010003 - 16 Jan 2025
Viewed by 574
Abstract
Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced [...] Read more.
Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha−1). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R2 = 0.88). Cellulose was best predicted using the NDRE (R2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration. Full article
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<p>Seasonal trajectories of the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), green ratio vegetation index (GRVI), and simple ratio (SR) index as influenced by cultivar and N treatments. The indices were calculated from multispectral images taken over large-scale switchgrass plots at the Urbana Energy Farm, IL, during the 2021, 2022, and 2023 growing seasons. Error bars represent the mean standard errors of three replicates.</p>
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<p>Pearson correlation coefficients between vegetation indices measured at different times (June, July, August, and September) and biomass yield and lignocellulose components. NDVI, normalized difference vegetation index; GNDVI, green normalized difference vegetation index; NDRE, normalized difference red-edge index; GRVI, green ratio vegetation index; SR, simple ratio index. Significance levels *, **, *** indicate <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Scatter plots of predicted and actual switchgrass biomass yields based on linear, exponential, partial least square, and random forest regression models for the training and validation sets using the univariate GNDVI from mid-August imagery. The shaded areas indicate the 95% confidence intervals associated with model fit. The dots (blue points) represent the actual data points from the dataset, while the red lines indicate how well the model fits or predicts the data.</p>
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<p>Scatter plots of predicted and actual switchgrass biomass yields based on linear, exponential, partial least square, and random forest regression models for the training and validation sets using a multivariate combination of the GNDVI and NDRE from mid-August imagery. The shaded areas indicate the 95% confidence intervals associated with model fit. The dots (blue points) represent the actual data points from the dataset, while the red lines indicate how well the model fits or predicts the data.</p>
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<p>Scatter plots of predicted and actual cellulose concentration based on linear, exponential, partial least square (PLSR), and random forest (RF) regression models for the training and validation sets using the normalized difference red-edge index (NDRE) from mid-August imagery. The shaded areas indicate the 95% confidence intervals associated with model fit. The dots (blue points) represent the actual data points from the dataset, while the red lines indicate how well the model fits or predicts the data.</p>
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<p>Scatter plots of predicted and actual hemicellulose concentration based on linear, exponential, partial least square (PLSR), and random forest (RF) regression models for the training and validation sets using a multivariate combination of the GNDVI, NDRE, NDVI, and GRVI from mid-July imagery. The shaded areas indicate the 95% confidence intervals associated with model fit. The dots (blue points) represent the actual data points from the dataset, while the red lines indicate how well the model fits or predicts the data.</p>
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<p>Scatter plots of predicted and actual acid detergent lignin (ADL) based on linear, exponential, partial least square (PLSR), and random forest (RF) regression models for the training and validation sets using a combination of the GNDVI, NDVI, and GRVI from mid-July imagery. The shaded areas indicate the 95% confidence intervals associated with model fit. The dots (blue points) represent the actual data points from the dataset, while the red lines indicate how well the model fits or predicts the data.</p>
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17 pages, 1690 KiB  
Review
Contractile and Mechanical Properties of Quadriceps Muscles Measured by the Method of Tensiomyography (TMG) in Professional Soccer Players: A Systematic Review, Meta-Analysis, and Meta-Regression
by Daniel Fernández-Baeza, Germán Díaz-Ureña and Cristina González-Millán
Bioengineering 2024, 11(12), 1295; https://doi.org/10.3390/bioengineering11121295 - 20 Dec 2024
Viewed by 1179
Abstract
Tensiomyography (TMG) is a non-invasive tool used to assess contractile properties. This systematic review aimed to accomplish the following: (1) Analyze quadriceps TMG parameters in professional football players during the season and compare them with reference values. (2) Assess the differences in TMG [...] Read more.
Tensiomyography (TMG) is a non-invasive tool used to assess contractile properties. This systematic review aimed to accomplish the following: (1) Analyze quadriceps TMG parameters in professional football players during the season and compare them with reference values. (2) Assess the differences in TMG parameters between quadriceps muscles. A PRISMA-guided search in PubMed, Web of Science, and Sport Discus (up to March 2024) identified 139 studies. Twelve in-season professional soccer players (20–29 years old) and quadriceps tensiomyography parameters were included (muscle displacement, delay time, and contraction time). All the studies were assessed using the Newcastle–Ottawa scale, scoring 7/9 to 8/9, indicating good quality. The findings of this study were that of the nine parameters analyzed, three variables were found to differ significantly. The weighted mean values were as follows: rectus femoris (contraction time 30.11 ms, muscle displacement 8.88 mL, delay time, 24.68 ms), vastus medialis (contraction time 25.29 ms, muscle displacement 7.45 mL, delay time, 21.27 ms), and vastus lateralis (contraction time 23.21 ms, muscle displacement 5.31 mL, delay time, 21.89 Â ms). Furthermore, significant differences were observed in muscle displacement between the rectus femoris and vastus medialis, and between the rectus femoris and vastus lateralis. The TMG can serve as a valuable device for assessing neuromuscular function in soccer players. Full article
(This article belongs to the Special Issue Biomechanics and Motion Analysis)
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<p>PRISMA flow diagram of the included studies.</p>
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<p>Results of the meta-regression analysis investigating the moderating effect of age on the time of contraction of the vastus lateralis (<b>A</b>), rectus femoris (<b>B</b>), and vastus medialis (<b>C</b>) muscles in soccer players. Blue dots represent primary studies, solid lines denote meta-regression prediction lines, and the gray area indicates the 95% confidence intervals around the mean.</p>
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<p><b>VM forest plot.</b> Note: (<b>A</b>) Dm (muscle displacement), (<b>B</b>) Tc (contraction time), (<b>C</b>) Td (delay time) [<a href="#B26-bioengineering-11-01295" class="html-bibr">26</a>,<a href="#B32-bioengineering-11-01295" class="html-bibr">32</a>,<a href="#B33-bioengineering-11-01295" class="html-bibr">33</a>,<a href="#B34-bioengineering-11-01295" class="html-bibr">34</a>,<a href="#B35-bioengineering-11-01295" class="html-bibr">35</a>].</p>
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<p><b>RF forest plot.</b> Note: (<b>A</b>) Dm (muscle displacement), (<b>B</b>) Tc (contraction time), (<b>C</b>) Td (delay time) [<a href="#B19-bioengineering-11-01295" class="html-bibr">19</a>,<a href="#B20-bioengineering-11-01295" class="html-bibr">20</a>,<a href="#B22-bioengineering-11-01295" class="html-bibr">22</a>,<a href="#B23-bioengineering-11-01295" class="html-bibr">23</a>,<a href="#B24-bioengineering-11-01295" class="html-bibr">24</a>,<a href="#B26-bioengineering-11-01295" class="html-bibr">26</a>,<a href="#B33-bioengineering-11-01295" class="html-bibr">33</a>,<a href="#B34-bioengineering-11-01295" class="html-bibr">34</a>,<a href="#B35-bioengineering-11-01295" class="html-bibr">35</a>].</p>
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<p><b>VL forest plot.</b> Note: (<b>A</b>) Dm (muscle displacement), (<b>B</b>) Tc (contraction time), (<b>C</b>) Td (delay time) [<a href="#B26-bioengineering-11-01295" class="html-bibr">26</a>,<a href="#B32-bioengineering-11-01295" class="html-bibr">32</a>,<a href="#B33-bioengineering-11-01295" class="html-bibr">33</a>,<a href="#B34-bioengineering-11-01295" class="html-bibr">34</a>,<a href="#B35-bioengineering-11-01295" class="html-bibr">35</a>].</p>
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13 pages, 559 KiB  
Article
Risk Factors for Low Back Pain in Youth Inline Hockey Players During the Season—A Prospective Cohort Research
by Antonio Cejudo, Víctor Jesús Moreno-Alcaraz and Pilar Sainz de Baranda
Children 2024, 11(12), 1517; https://doi.org/10.3390/children11121517 - 14 Dec 2024
Viewed by 706
Abstract
Background: Low back pain is one of the most common musculoskeletal complaints in team sports. A screening test can help understand why injuries occur and predict who is at risk for non-contact low back pain. The objectives of the research were (1) to [...] Read more.
Background: Low back pain is one of the most common musculoskeletal complaints in team sports. A screening test can help understand why injuries occur and predict who is at risk for non-contact low back pain. The objectives of the research were (1) to create models using logistic regression analysis of limited lower-extremity ranges of motion to prospectively identify potential factors for in-season non-contact non-contact low back pain and (2) to determine a training threshold (cut-off) for the identified factors in inline hockey players. Methods: A prospective cohort research was performed with 49 male inline hockey players aged 8 to 15 years. Data were collected regarding age, body composition, sports antecedents, competition level, and lower-limb ranges of motion (ROM-SPORT battery, n = 11 tests). A prospective measurement of non-contact low back pain was performed after 1 year (outcome) by asking the players supervised by the medical staff team (questionnaire). Results: Sixteen players (32.7%) experienced non-contact low back pain during the 1-year surveillance period. The model showed a significant relationship (χ2(39) = 43.939; p < 0.001) between the low back pain and the predictor variable hip flexion with the knee extended range of motion (OR = 3.850 [large]; 95% CI = 1.293 to 11.463; p = 0.015). The Bayesian Information Criteria and the Akaike Information Criteria for model fit were 56.885 and 37.967, respectively. The training threshold for hip flexion with the knee extended of ≤67° was set, which has an acceptable (area under the curve ≥ 94.1%) discriminatory ability for the development of non-contact low back pain for the screening test. Conclusions: Hamstring extensibility at 67° or less, as determined by hip flexion with knee extension, is a predictor of non-contact low back pain in youth inline hockey players. Full article
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<p>Flow chart of the follow-up cohort research.</p>
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11 pages, 1156 KiB  
Article
Relationship of Individual Athlete External Load, Session Rating of Perceived Exertion, and Athlete Playing Status Across a Collegiate Women’s Basketball Season
by Faith S. A. Brown, Jennifer B. Fields, Andrew R. Jagim, Erica L. King, Robert E. Baker, Angela Miller and Margaret T. Jones
Sports 2024, 12(12), 340; https://doi.org/10.3390/sports12120340 - 6 Dec 2024
Viewed by 681
Abstract
External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate [...] Read more.
External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate the effect of athlete playing status and individual in-season practices on EL and IL across a collegiate women’s basketball season. Female basketball athletes were classified as high-minute (HMA; ≥15 min/game) or low-minute (LMA; <15 min/game) and wore microsensors during 53 practices for a total of 583 data points. EL was obtained via an inertial measurement unit (IMU) device that contained a triaxial accelerometer to obtain three-dimensional positioning data. IL and strength training (ST) load were determined via session rating of perceived exertion (sRPE) to create a daily summated value. Descriptive statistics indicate that athletes experienced individual differences in EL, ST, and IL throughout the season. A growth model showed that HMAs experienced higher EL than LMAs at the start of the season for practices (90.21 AU). Across all athletes, IL increased across the season (40.11 AU) and for each 1 unit of change in EL, IL increased by 1.04 AU. Repeated measures correlations identified a large relationship between IL and EL (r = 0.51, p < 0.001). A location-scale model indicated that the within-person variability of IL across all athletes was 3.29 AU but was not due to athlete playing status. It is recommended to base in-season training on individual loads and game demands to promote athlete readiness and improved sport performance. Full article
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<p>Practice sRPE (P-sRPE) and practice Playerload™ (P-PL) across all practice sessions and all participants.</p>
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<p>Athlete practice sRPE (P-sRPE) variability across all practice sessions.</p>
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13 pages, 5830 KiB  
Article
Insights into Genes Encoding LEA_1 Domain-Containing Proteins in Cyperus esculentus, a Desiccation-Tolerant Tuber Plant
by Yongguo Zhao, Xiaowen Fu and Zhi Zou
Plants 2024, 13(20), 2933; https://doi.org/10.3390/plants13202933 - 19 Oct 2024
Cited by 2 | Viewed by 1033
Abstract
LEA_1 domain-containing proteins constitute a class of late-embryogenesis-abundant proteins that are highly hydrophilic and predominantly accumulate in mature seeds. Though LEA_1 proteins have been proven to be essential for seed desiccation tolerance and longevity, little information is available on their roles in non-seed [...] Read more.
LEA_1 domain-containing proteins constitute a class of late-embryogenesis-abundant proteins that are highly hydrophilic and predominantly accumulate in mature seeds. Though LEA_1 proteins have been proven to be essential for seed desiccation tolerance and longevity, little information is available on their roles in non-seed storage organs. In this study, a first genome-wide characterization of the LEA_1 gene family was conducted in tigernut (Cyperus esculentus L., Cyperaceae), whose underground tubers are desiccation tolerant with a moisture content of less than 6%. Five family members identified in tigernut are comparative to four to six found in seven other Cyperaceae plants, but relatively more than three reported in Arabidopsis. Further comparison of 125 members from 29 plant species supports early divergence of the LEA_1 family into two phylogenetic groups before angiosperm radiation, and gene expansion in tigernut was contributed by whole-genome duplications occurring after the split with the eudicot clade. These two phylogenetic groups could be further divided into six orthogroups in the momocot clade, five of which are present in tigernut and the remaining one is Poaceae specific. Frequent structural variation and expression divergence of paralogs were also observed. Significantly, in contrast to seed-preferential expression of LEA_1 genes in Arabidopsis, rice, and maize, transcriptional profiling and qRT-PCR analysis revealed that CeLEA1 genes have evolved to predominantly express in tubers, exhibiting a seed desiccation-like accumulation during tuber development. Moreover, CeLEA1 transcripts in tubers were shown to be considerably more than that of their orthologs in purple nutsedge, another Cyperaceae plant producing desiccation-sensitive tubers. These results imply species-specific activation and key roles of CeLEA1 genes in the acquisition of desiccation tolerance of tigernut tubers as observed in orthodox seeds. Our findings not only improve the understanding of lineage-specific evolution of the LEA_1 family, but also provide valuable information for further functional analysis and genetic improvement in tigernut. Full article
(This article belongs to the Section Plant Molecular Biology)
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<p>Structural and phylogenetic analyses of <span class="html-italic">LEA_1</span> family genes in <span class="html-italic">C. esculentus</span>. (<b>A</b>) Amino acid composition of CeLEA1 proteins. (<b>B</b>) The unrooted phylogenetic tree resulting from full-length Ce/AtLEA1 proteins with MEGA6 (maximum likelihood method and bootstrap of 1000 replicates), where the distance scale denotes the number of amino acid substitutions per site. The name of each clade is indicated next to the corresponding group. (<b>C</b>) Exon–intron structures, where 0 and 1 indicate intron phases. (<b>D</b>) The distribution of conserved motifs among Ce/AtLEA1 proteins, where different motifs are represented by different color blocks as indicated and the same color block in different proteins indicates a certain motif. (At: <span class="html-italic">A. thaliana</span>; CDS: coding sequence; Ce: <span class="html-italic">C. esculentus</span>; LEA: late embryogenesis abundant).</p>
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<p>Species-specific distribution of six orthogroups in 29 representative plant species. The species tree is referred to NCBI Taxonomy (<a href="https://www.ncbi.nlm.nih.gov/taxonomy" target="_blank">https://www.ncbi.nlm.nih.gov/taxonomy</a> (accessed on 20 August 2023)) and recent whole-genome duplications or triplications resulting in polyploidy (CoGepedia; <a href="https://genomevolution.org/wiki/index.php/Plant_paleopolyploidy" target="_blank">https://genomevolution.org/wiki/index.php/Plant_paleopolyploidy</a> (accessed on 20 August 2023)) are marked. “?” indicates unknown. (LEA: late embryogenesis abundant.)</p>
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<p>Synteny analysis within and between <span class="html-italic">C. esculentus</span> and representative plant species. (<b>A</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">A. gramineus</span>, <span class="html-italic">A. thaliana</span>, and <span class="html-italic">A. trichopoda</span>. (<b>B</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">C. littledalei</span>, <span class="html-italic">C. scoparia</span>, and <span class="html-italic">R. breviuscula</span>. (<b>C</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">J. effusus</span>, <span class="html-italic">J. ascendens</span>, <span class="html-italic">S. stoloniferum</span>, and <span class="html-italic">A. comosus</span>. (<b>D</b>) Synteny analysis within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">B. distachyon</span>, <span class="html-italic">O. sativa</span>, and <span class="html-italic">S. italica</span>. <span class="html-italic">LEA_1</span> gene-encoding chromosomes/scaffolds and only syntenic blocks containing <span class="html-italic">LEA_1</span> genes are marked, where red and purple lines for intra- and inter-species, respectively. The scale is in Mb. (Ac: <span class="html-italic">A. comosus</span>; Ag: <span class="html-italic">A. gramineus</span>; At: <span class="html-italic">A. thaliana</span>; Atr: <span class="html-italic">A. trichopoda</span>; Bd: <span class="html-italic">B. distachyon</span>; Ce: <span class="html-italic">C. esculentus</span>; Cl: <span class="html-italic">C. littledalei</span>; Cs: <span class="html-italic">C. scoparia</span>; Ja: <span class="html-italic">J. ascendens</span>; Je: <span class="html-italic">J. effuses</span>; Mb: megabase; Os: <span class="html-italic">O. sativa</span>; Rb: <span class="html-italic">R. breviuscula</span>; Si: <span class="html-italic">S. italic</span>; Ss: <span class="html-italic">S. stoloniferum</span>).</p>
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<p>Expression profiles of <span class="html-italic">CeLEA1 and CrLEA1</span> genes. (<b>A</b>) Tissue-specific expression profiles of five <span class="html-italic">CeLEA1</span> genes. (<b>B</b>) Expression profiles of <span class="html-italic">CeLEA1-2</span>, <span class="html-italic">-3</span>, and <span class="html-italic">-4</span> at different stages of tuber development. (<b>C</b>) Expression profiles of <span class="html-italic">CeLEA1 and CrLEA1</span> genes at three representative stages of tuber development. The heatmap was generated using the R package implemented with a row-based standardization. Color scale represents FPKM normalized log<sub>2</sub> transformed counts, where blue indicates low expression and red indicates high expression. Bars indicate SD (N = 3) and uppercase letters indicate a difference significance following Duncan’s one-way multiple-range post hoc ANOVA (<span class="html-italic">p</span> &lt; 0.01). (Ce: <span class="html-italic">C. esculentus</span>; Cr: <span class="html-italic">C. rotundus</span>; DAI: days after tuber initiation; DAS: days after sowing; FPKM: Fragments per kilobase of exon per million fragments mapped.)</p>
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26 pages, 1687 KiB  
Review
Research Progress on Viruses of Passiflora edulis
by Wenhua Wu, Funing Ma, Xiaoyan Zhang, Yuxin Tan, Te Han, Jing Ding, Juyou Wu, Wenting Xing, Bin Wu, Dongmei Huang, Shaoling Zhang, Yi Xu and Shun Song
Biology 2024, 13(10), 839; https://doi.org/10.3390/biology13100839 - 19 Oct 2024
Viewed by 1036
Abstract
Passiflora edulis, also known as passion fruit, is celebrated for its rich nutritional content, distinctive flavour, and significant medicinal benefits. At present, viral diseases pose a major challenge to the passion fruit industry, affecting both the production and quality of the fruit. [...] Read more.
Passiflora edulis, also known as passion fruit, is celebrated for its rich nutritional content, distinctive flavour, and significant medicinal benefits. At present, viral diseases pose a major challenge to the passion fruit industry, affecting both the production and quality of the fruit. These diseases impede the sustainable and healthy growth of the passion fruit sector. In recent years, with the expansion of P. edulis cultivation areas, virus mutations, and advances in virus detection technology, an increasing number of virus species infecting P. edulis have been discovered. To date, more than 40 different virus species have been identified; however, there are different strains within the same virus. This poses a challenge for the control and prevention of P. edulis virus disease. Therefore, this review discusses the different types of viruses and their characteristics, modes of transmission, and effects on the growth of the passion fruit plant, as well as the mechanisms of virus generation and preventive measures, with the hope that these discussions will provide a comprehensive understanding of and countermeasures for viruses in passion fruit. Full article
(This article belongs to the Special Issue Advances in Research on Diseases of Plants)
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<p>Global distribution map of passion fruit-growing areas. Maps based on longitude (automatically generated) and latitude (automatically generated).</p>
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<p>The genome structure of TeMV [<a href="#B52-biology-13-00839" class="html-bibr">52</a>].</p>
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<p>Phylogenetic tree of viruses infecting Passiflora based on nucleotide sequences of the coat protein (CP) gene.</p>
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<p>Symptoms of TeMV virus infection in passion fruit. (<b>A</b>) Healthy passion fruit leaves. (<b>B</b>–<b>E</b>) Leaf symptoms after infection with TeMV. (<b>F</b>) Fruit after infection with TeMV.</p>
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12 pages, 561 KiB  
Review
‘Supporting the Support Staff’: A Narrative Review of Nutritional Opportunities to Enhance Recovery and Wellbeing in Multi-Disciplinary Soccer Performance Staff
by Christopher Curtis, Christopher Carling, Edward Tooley and Mark Russell
Nutrients 2024, 16(20), 3474; https://doi.org/10.3390/nu16203474 - 14 Oct 2024
Viewed by 1854
Abstract
Background: With ever-increasing training, match-play and travel demands in professional soccer, recovery is vital for athletic performance, a statement amplified in tournament and in-season scenarios. However, alongside supporting the tasks associated with these increased demands, the recovery and wellbeing strategies recommended for playing [...] Read more.
Background: With ever-increasing training, match-play and travel demands in professional soccer, recovery is vital for athletic performance, a statement amplified in tournament and in-season scenarios. However, alongside supporting the tasks associated with these increased demands, the recovery and wellbeing strategies recommended for playing staff are often unavailable to their support staff counterparts, who routinely experience extended working hours over and above scheduled player attendance. Methods: Focusing on the contributions of nutrition to this undoubtedly multifactorial issue, this narrative review aimed to (1) identify potential strategies to enhance recovery and wellbeing in multi-disciplinary soccer support staff and (2) highlight future research opportunities exploring the benefits of nutrition for those staff in soccer performance-related support roles. Results: The potential health and wellbeing consequences of chronic sub-optimal practices suggest that chrononutrition strategies may be an area of future interest. Notably, nutritional strategies that enhance sleep hygiene and immune function warrant consideration. Individualizing such strategies to maximize recovery and wellbeing in multi-disciplinary soccer support staff should offer an adjunct and complementary strategy to the holistic performance-focused support provided to professional soccer players. Conclusions: Policymakers responsible for organizational and club structures aligned with soccer performance could consider ‘Supporting the Support Staff’ when seeking to improve overall performance. Full article
(This article belongs to the Section Nutrition and Public Health)
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<p>A theoretical framework of nutritional opportunities to enhance recovery and wellbeing in multi-disciplinary soccer performance staff. The framework focuses on key considerations (white circle) and proposed key considerations for practitioners (grey boxes). Directional arrows represent the interlinking between the framework categories.</p>
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9 pages, 741 KiB  
Article
Pruning and In-Season Canopy Manipulation Affects MidSouth Juice and Wine Phenolic Content
by Haley Williams, Eric Stafne, Yan Zhang and Sam Chang
Beverages 2024, 10(4), 98; https://doi.org/10.3390/beverages10040098 - 10 Oct 2024
Viewed by 942
Abstract
Low total soluble solids and high titratable acidity limit MidSouth use as a varietal red wine grape. While canopy management practices were reported not to have enough of an effect on these primary metabolites, they could potentially improve MidSouth secondary metabolites, broadening its [...] Read more.
Low total soluble solids and high titratable acidity limit MidSouth use as a varietal red wine grape. While canopy management practices were reported not to have enough of an effect on these primary metabolites, they could potentially improve MidSouth secondary metabolites, broadening its potential as a wine grape. Two studies assessed the effects of different canopy management treatments on monomeric anthocyanin pigments and total phenolic content in MidSouth juice and wine. The first study compared early pruning, early pruning with leaf removal, normal pruning with leaf removal, and normal pruning. Early pruning with leaf removal showed higher total phenolics in juice and wine in 2021 but lower levels in 2020. The second study evaluated leaf removal, shoot thinning, or neither leaf removal nor shoot thinning. Leaf removal resulted in higher anthocyanins and total phenolics in 2021 juice, while shoot thinning increased total phenolics in 2021 juice and both anthocyanins and phenolics in 2021 wine. Shoot thinning demonstrated the most consistent improvement in phenolic content. MidSouth grapes can produce a range of wine phenolic content, depending on canopy management and postharvest treatment. Further investigation is needed to understand yearly variations and optimize MidSouth for regional red wine production. Full article
(This article belongs to the Section Wine, Spirits and Oenological Products)
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<p>Flow diagram of the treatment of <span class="html-italic">MidSouth</span> grapes after harvesting in 2020 and 2021.</p>
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12 pages, 253 KiB  
Article
Effects of In-Season Velocity-Based vs. Traditional Resistance Training in Elite Youth Male Soccer Players
by Veselin Sekulović, Tatjana Jezdimirović-Stojanović, Nikola Andrić, Andoni Elizondo-Donado, Diego Martin, Mladen Mikić and Marko D. M. Stojanović
Appl. Sci. 2024, 14(20), 9192; https://doi.org/10.3390/app14209192 - 10 Oct 2024
Viewed by 1575
Abstract
The objectives of this study were to compare the effects of two in-season velocity loss training methods (VBT) on performance outcomes and to evaluate the effects of velocity-based training compared to traditional resistance training (TRT) on performance outcomes in young elite soccer players. [...] Read more.
The objectives of this study were to compare the effects of two in-season velocity loss training methods (VBT) on performance outcomes and to evaluate the effects of velocity-based training compared to traditional resistance training (TRT) on performance outcomes in young elite soccer players. VBT utilized the same relative load but varied in the extent of velocity loss during the set: 15% (VL15%) and 30% (VL30%). Thirty-four players were recruited and randomly distributed into three groups: the VL15% group (n = 12; age = 18.50 ± 0.67 years; stature = 183.41 ± 4.25 cm; body mass = 75.08 ± 5.57 kg), the VL30% group (n = 11; age = 17.91 ± 0.60 years; stature = 181.21 ± 6.56 cm, body mass = 73.58 ± 6.22 kg), and the traditional strength training group TRT (n = 11; age = 18.14 ± 0.74 years; stature = 182.17 ± 5.52 cm; body mass = 74.86 ± 6.68 kg). Alongside regular soccer sessions and matches, the groups underwent a four-week (2 sesions per week) resistance training intervention with back squats involved. Changes in leg strength (SQ1RM), 20 m sprint time (SPR 20 m), countermovement jump height (CMJ), reactive strength index (RSI), and change of direction (COD) from before and after were evaluated using a 3 × 2 ANOVA. While no significant interaction was found for SQ1RM and SPR20, all of the groups showed significant pre to post improvements. Significant interactions were observed for CMJ (F = 38.24, p = 0.000), RSI (F = 8.33; p = 0.001), and change of direction agility test (COD) (F = 3.64; p = 0.038), with a post hoc analysis showing differences between the VL15 (6.0%) and TRT (1.7%) groups (p = 0.034); VL15 (12.2%) and VL30 (3.2%) groups (p = 0.004); VL15 and TRT (0.4%) (p = 0.018); VL15 (2.4%) and VL30 (1.5%) (p = 0.049); and between the VL15 and TRT (0.4%) groups (p = 0.015). Four weeks of VL15% training during the season induced similar strength increases to VL30% and TRT, superior improvements in RSI and COD compared to VL30%, and superior improvements in CMJ, RSI, and COD tests compared to TRT. Thus, incorporating the VL15% training method may be recommended to improve power-related performance metrics in elite young soccer players. Full article
(This article belongs to the Special Issue Innovative Approaches in Sports Science and Sports Training)
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