Search Results (191)

Fibrillins (FBNs) are indispensable for plant growth and development, orchestrating multiple physiological processes. However, the precise functional role of FBNs in cotton fiber development remains uncharacterized. This study reports a genome-wide characterization of the FBN gene family in cotton. A total of 28 GhFBN genes were identified in upland cotton, with systematic analyses of their phylogenetic relationships, protein motifs, gene structures, and hormone-responsive cis-regulatory elements. Expression profiling of GhFBN1A during fiber development revealed stage-specific activity across the developmental continuum. Transcriptomic analyses following hormone treatments demonstrated upregulation of GhFBN family members, implicating their involvement in hormone-mediated regulatory networks governing fiber cell development. Collectively, this work presents a detailed molecular characterization of cotton GhFBNs and establishes a theoretical foundation for exploring their potential applications in cotton breeding programs aimed at improving fiber quality. Full article

Background: The B3-domain transcription factor ABI3 (ABSCISIC ACID INSENSITIVE 3) is a critical regulator of seed maturation, stress adaptation, and hormonal signaling in plants. However, its evolutionary dynamics and functional roles in cotton (Gossypium spp.) remain poorly characterized. Methods: We conducted a comprehensive genome-wide investigation of the ABI3 gene family across 26 plant species, with a focus on 8 Gossypium species. Analyses included phylogenetics, chromosomal localization, synteny assessment, gene duplication patterns, protein domain characterization, promoter cis-regulatory element identification, and tissue-specific/spatiotemporal expression profiling under different organizations of Gossypium hirsutum. Results: Phylogenetic and chromosomal analyses revealed conserved ABI3 evolutionary patterns between monocots and dicots, alongside lineage-specific expansion events within Gossypium spp. Syntenic relationships and duplication analysis in G. hirsutum (upland cotton) indicated retention of ancestral synteny blocks and functional diversification driven predominantly by segmental duplication. Structural characterization confirmed the presence of conserved B3 domains in all G. hirsutum ABI3 homologs. Promoter analysis identified key stress-responsive cis-elements, including ABA-responsive (ABRE), drought-responsive (MYB), and low-temperature-responsive (LTRE) motifs, suggesting a role in abiotic stress regulation. Expression profiling demonstrated significant tissue-specific transcriptional activity across roots, stems, leaves, and fiber developmental stages. Conclusions: This study addresses a significant knowledge gap by elucidating the evolution, structure, and stress-responsive expression profiles of the ABI3 gene family in cotton. It establishes a foundational framework for future functional validation and targeted genetic engineering strategies aimed at developing stress-resilient cotton cultivars with enhanced fiber quality. Full article

Background: Verticillium wilt (VW), caused by the fungal pathogen Verticillium dahliae, is a destructive disease that severely compromises cotton yield and fiber quality. Pyrimidine nucleotides, as essential metabolites and nucleic acid components, play critical roles in plant development and stress responses. However, genes involved in pyrimidine metabolism, especially their roles in disease resistance, remain largely uncharacterized in plants. Methods: Ghir_D05G039120, a gene encoding uridine kinase, shown to be associated with VW resistance in our previous study, was cloned and named as GhUKL4. The differential expression of GhUKL4 between the resistant and susceptible cultivars at multiple time points post-inoculation with V. dahliae was analyzed by quantitative real-time PCR (qRT-PCR), and the uracil phosphoribosyl transferase (UPRT) and uridine 5′-monophosphate kinase (UMPK) domains were verified by analyzing the amino acid sequences of GhUKL4. The role of GhUKL4 in the defense against VW infection was estimated by silencing GhUKL4 in the resistant and susceptible cultivars using virus-induced gene silencing (VIGS) analysis. Results: There were significant differences in the expression level of Ghir_D05G039120/ GhUKL4 among resistant and susceptible cotton lines. GhUKL4 contains UPRTase and UMPK domains, and there was one SNP between the resistant and susceptible cultivars in its 3′-UTR region. The silencing of GhUKL4 reduced cotton’s resistance to VW through mediating hormone signaling (JA) and oxidative stress (ROS) pathways. Conclusions: GhUKL4, encoding UMPK and UPRTase domain proteins, is a new regulatory factor associated with VW resistance in Gossypium hirsutum through fine-tuning JA-signalling and ROS bursting. Full article

Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests—thus supporting the development of effective control strategies and facilitating genetic breeding research—we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

Advances in spinning technology have increased the demand for upland cotton (Gossypium hirsutum L.) cultivars with superior fiber quality. However, progress in breeding for traits such as fiber length is constrained by limited phenotypic and genetic diversity within upland cotton. Introgression from Gossypium barbadense, a closely related species known for its superior fiber traits, offers a promising strategy. Sealand 883 is an obsolete upland germplasm developed through G. barbadense introgression and is known for its long and fine fibers. Previous studies have identified a fiber length quantitative trait locus (QTL) on Chromosome 25, designated qFL-Chr.25, in Sealand 883, conferred by an allele introgressed from G. barbadense. This study evaluated the effect of qFL-Chr.25 in near-isogenic introgression lines (NIILs) using Advanced Fiber Information System (AFIS) measurements. Across four genetic backgrounds, NIILs carrying qFL-Chr.25 consistently exhibited longer fibers, as reflected in multiple length parameters, including UHML, L(n), L(w), UQL(w), and L5%. Newly developed TaqMan SNP diagnostic markers flanking the QTL enable automated, reproducible, and scalable screening of large populations typical in commercial breeding programs. These markers will facilitate the incorporation of qFL-Chr.25 into commercial breeding pipelines, accelerating fiber quality improvement and enhancing the competitiveness of cotton against synthetic fibers. Full article

To identify optimal strategies for high-yield and high-efficiency cultivation under a “short-season direct-seeded cotton with once-only irrigation” regime, we conducted two-year field experiments (2022 and 2023) using a split-plot factorial design with three planting densities (30,000 (D1), 45,000 (D2), and 60,000 (D3) plants·ha⁻¹) and three nitrogen application rates (150 (N1), 180 (N2), and 210 (N3) kg·ha⁻¹). Our study systematically examined how these treatment combinations influenced canopy architecture, physiological traits, yield components, and fiber quality. The results showed that increased planting density significantly enhanced plant height, the leaf area index (LAI), and the number of fruiting branches, with the highest density (D3) contributing to a more compact and efficient canopy. Moderate nitrogen input (N2) significantly increased peroxidase (POD) activity, reduced malondialdehyde (MDA) accumulation, delayed functional leaf senescence, and prolonged the canopy’s photosynthetic performance. A significant interaction between planting density and nitrogen application was observed. The D3N2 treatment (high density with moderate nitrogen) consistently achieved the highest fruiting branch count, boll number per plant, and yields of both seed cotton and lint in both years, while maintaining stable fiber quality. This indicates its strong capacity to balance high yield with quality and maintain physiological resilience. By contrast, the D1N1 treatment (low density and low nitrogen) exhibited a loose canopy, premature photosynthetic decline, and the lowest yield. The D3N3 treatment (high density and high nitrogen) promoted vigorous early growth but reduced stress tolerance during later growth stages, leading to yield instability. These findings demonstrate that moderately increasing planting density while maintaining appropriate nitrogen levels can effectively optimize canopy structure, improve stress resilience, and enhance yield under short-season direct-seeded cotton systems with once-only irrigation. This provides both theoretical underpinning and practical guidance for achieving stable and efficient cotton production under such systems. Full article

This study evaluated the effect of mixed meal replacement of soybean meal on growth conditions, carcass traits, and meat quality of finishing pigs by partially and entirely replacing soybean meal with equal proportions of rapeseed, cotton, and sunflower meal. A total of fifty-four pigs with an average initial weight of 97.60 ± 0.30 kg were selected and randomly divided into three groups according to their initial weight, with six pens in each group and three pigs in each pen. The experimental groups were as follows: control group (CON), fed corn–soybean meal type basal diet; corn–soybean mixed meal group (CSM), using equal proportions of rapeseed meal, cotton meal, and sunflower meal (3.52% each) to replace 9.06% of soybean meal in the basal diet; and corn mixed meal group (CMM), using equal proportions of rapeseed meal, cotton meal, and sunflower meal (6.46% each) to replace soybean meal in the basal diet completely. According to the results, the use of mixed meal as a replacement for soybean meal did not have a significant impact (p > 0.05) on the average daily weight gain, average daily feed intake, feed-to-weight ratio, body size, carcass traits, and meat quality of finishing pigs. The entire replacement of soybean meal with a mixed meal resulted in a significant increase (p < 0.05) in leaf fat weight. The use of mixed meal as a substitute for soybean meal had no significant effect (p > 0.05) on the antioxidant capacity and fatty acid composition of the longissimus thoracis in finishing pigs. However, longissimus thoracis muscle fiber diameter was reduced in the mixed meal partially replaced soybean meal group compared to the mixed meal completely replaced soybean meal group (p < 0.05). In addition, mixed meal replacing soybean meal did not significantly affect (p > 0.05) the expression of the longissimus thoracis muscle fiber type genes MYHC1 and MYHC2. Mixed meal replacement of soybean meal did not significantly affect (p > 0.05) the expression of ACACA, FASN, and PPARG genes in the longissimus thoracis. This study showed that mixed meal as an alternative to soybean meal in diets did not have significant negative effects on the growth performance and meat quality of finishing pigs. These results can help develop further mixed meals as a functional alternative feed ingredient for soybean meals in pig diets. Full article

Expanding genetic variability of cultivated cotton (Gossypium hirsutum) is essential for improving fiber quality and pest resistance. This study synthesized allotetraploids through interspecific hybridization between G. herbaceum (A₁) and G. nelsonii (G₃). Upon chromosome doubling using 0.2% colchicine, fertile F₁C allotetraploids (A₁A₁G₃G₃) were developed. Cytogenetic analysis confirmed chromosome stability of synthetic allotetraploids, and 74 polymorphic SSR markers verified hybridity and parental contributions. The F₁C hybrids exhibited enhanced resistance to cotton aphids (Aphis gossypii) and whiteflies (Aleyrodidae), with respective infestation rates of 5.2–5.6% and 5.4–5.8%, lower than those of G. hirsutum cv. Ravnak-1 (22.1% and 23.9%). Superior fiber length (25.0–26.0 mm) was observed in complex hybrids and backcross progeny, confirming the potential for trait introgression into elite cultivars. Phylogenetic analysis based on SSR data clearly differentiated G. herbaceum from Australian wild species, demonstrating successful bridging of divergent genomes. The F₁C hybrids consistently expressed dominant G. nelsonii-derived traits regardless of the hybridization direction and clustered phylogenetically closer to the wild parent. These synthetic allotetraploids could broaden the genetic base of G. hirsutum, addressing cultivation constraints through improved biotic stress resilience and fiber quality traits. The study establishes a robust framework for utilizing wild Gossypium species to overcome genetic bottlenecks in conventional cotton breeding programs. Full article

Cotton exhibits indeterminate growth potential at its apical meristem. In field cultivation, it is often necessary to restrict plant height by the foliar application of plant growth regulators or artificial topping. The genetic engineering of cotton architecture offers an efficient, environmentally friendly, and low-cost alternative to current field management. Our study aimed to improve the plant architecture of transgenic cotton by the suppression of GhSP, a key flowering repressor, via the RNA interference method. Sixteen independent transgenic lines were generated and classified as mildly, moderately, and severely suppressed, according to GhSP expression levels. Field evaluation revealed the dose-dependent effects of GhSP silencing on plant height. The mildly suppressed line GhSPi-#5 exhibited a semi-dwarf phenotype of approximately 70~100 cm in height. Negative phenotypes, including excessive dwarf plant architecture and inferior fiber quality and yield traits, were observed in severely GhSP-suppressed transgenic lines. Notably, the mild silencing of GhSP in GhSPi-#5 did not negatively affect leaf and flower organ growth, pollen fertility, major agronomic traits, or fiber quality compared with the wild type. These observations demonstrate the feasibility of manipulating the architecture of transgenic cotton via GhSP silencing. Full article

The textile industry is under increasing pressure to adopt sustainable practices due to the significant environmental impacts associated with fiber production, including high energy consumption, water usage, and substantial greenhouse gas emissions. The recycling of textile waste, particularly cotton, is a promising solution that has the potential to reduce landfill waste and decrease the demand for virgin fibers. However, mechanically recycled cotton fibers frequently demonstrate diminished mechanical properties compared to virgin fibers, which limits their potential for high-quality textile applications. This study explores the use of cross-linking agents (citric acid (CA) and sodium hypophosphite (SHP)), polymers (polyethylene glycol (PEG), chitosan (CH), carboxymethyl cellulose (CMC) and starch (ST)), and silicas (anionic (SA) and cationic (SC)) to enhance the mechanical properties of recycled cotton fibers. The treatments were then subjected to a hierarchical ranking, with the effectiveness of each treatment determined by its impact on enhancing fiber tenacity. The findings of this research indicate that the most effective treatment was starck (ST_50), which resulted in an enhancement of tenacity from 14.63 cN/tex to 15.34 cN/tex (+4.9%), closely followed by CA-SHP_110/110, which also reached 15.34 cN/tex (+4.6%). Other notable improvements were observed with CMC_50 (15.23 cN/tex), PEG_50 (14.91 cN/tex), and CA_50 (14.89 cN/tex), all in comparison to the control. In terms of yarn quality, the CA-SHP_110/110 treatment yielded the most substantial reductions in yarn irregularities, including thin places, thick places, and neps with decreases of 36%, 10%, and 7%, respectively. Furthermore, CA_50 exhibited moderate enhancements in yarn regularity, thin places (−12%), thick places (−6.1%), and neps (−8.9%). The results of this study demonstrate that combining CA with SHP, particularly when preceded by the heating of the solution before the addition of the fibers, results in a substantial enhancement of the structural integrity, strength, and overall quality of recycled cotton fibers. This approach offers a viable pathway for the improvement of the performance of recycled cotton, thereby facilitating its wider utilization in high-quality textile products. Full article

Contamination with foreign fibers—such as mulch films and polypropylene strands—during cotton harvesting and processing severely compromises fiber quality. The traditional detection methods often fail to identify fine impurities under visible light, while full-spectrum hyperspectral imaging (HSI) techniques—despite their effectiveness—tend to be prohibitively expensive and computationally intensive. Specifically, the vast amount of redundant spectral information in full-spectrum HSI escalates both the system’s costs and processing challenges. To address these challenges, this study presents an intelligent detection framework that integrates optimized spectral band selection with a lightweight neural network. A novel hybrid Harris Hawks–Whale Optimization Operator (HWOO) is employed to isolate 12 discriminative bands from the original 288 channels, effectively eliminating redundant spectral data. Additionally, a lightweight attention mechanism, combined with a depthwise convolution module, enables real-time inference for online production. The proposed attention-enhanced CNN architecture achieves a 99.75% classification accuracy with real-time processing at 12.201 μs per pixel, surpassing the full-spectrum models by 11.57% in its accuracy while drastically reducing the processing time from 370.1 μs per pixel. This approach not only enables the high-speed removal of impurities in harvested seed cotton production lines but also offers a cost-effective pathway to practical multispectral solutions. Moreover, this methodology demonstrates broad applicability for quality control in agricultural product processing. Full article

Cotton is a vital economic crop in global agriculture and the textile industry, contributing significantly to food security, industrial competitiveness, and sustainable development. Traditional technologies such as spectral imaging and machine learning improved cotton cultivation and processing, yet their performance often falls short in complex agricultural environments. Deep learning (DL), with its superior capabilities in data analysis, pattern recognition, and autonomous decision-making, offers transformative potential across the cotton value chain. This review highlights DL applications in seed quality assessment, pest and disease detection, intelligent irrigation, autonomous harvesting, and fiber classification et al. DL enhances accuracy, efficiency, and adaptability, promoting the modernization of cotton production and precision agriculture. However, challenges remain, including limited model generalization, high computational demands, environmental adaptability issues, and costly data annotation. Future research should prioritize lightweight, robust models, standardized multi-source datasets, and real-time performance optimization. Integrating multi-modal data—such as remote sensing, weather, and soil information—can further boost decision-making. Addressing these challenges will enable DL to play a central role in driving intelligent, automated, and sustainable transformation in the cotton industry. Full article

Efficient extraction of high-quality DNA from plants is a critical challenge in molecular research, especially in species such as Gossypium barbadense L., native to Peru, due to the presence of inhibitors such as polysaccharides and phenolic compounds. This study presents a modified CTAB-based protocol with silica columns that is designed to overcome these limitations without the need for liquid nitrogen or expensive reagents. Native cotton samples were collected in Lambayeque, Peru, and processed using a simplified procedure that optimizes the purity and concentration of the extracted DNA. Eight cultivars of G. barbadense L. with colored fibers (cream, fifo, light brown, dark brown, orange-brown, reddish, fine reddish, and white) were evaluated, yielding DNA with A260/A280 ratios between 2.14 and 2.19 and A260/A230 ratios between 1.8 and 3.14; these values are higher than those obtained with the classical CTAB method. DNA quality was validated by PCR amplification using ISSR and RAPD molecular markers, which yielded clear and well-defined banding patterns. Furthermore, the extracted DNA was suitable for advanced applications, such as Sanger sequencing, by which high-quality electropherograms were obtained. The results demonstrate that the proposed protocol is an efficient, economical, and adaptable alternative for laboratories with limited resources, allowing the extraction of high-quality DNA from Gossypium barbadense L. and other plant species. This simplified approach facilitates the development of genetic and biotechnological research, contributing to the knowledge and valorization of the genetic resources of Peruvian native cotton. Full article

The continuous provision of nitrogen (N) to the crop is critical for optimal cotton production; however, the constant and excessive application of synthetic fertilizers causes adverse impacts on soil, plants, animals, and human health. The current study focused on the short-term effects (one-year study) of adding different rates of clinoptilolite zeolite, as part of an integrated nutrient management plan, and different rates of inorganic N fertilizer to improve soil and crop performance of cotton in three locations (ATH, MES, and KAR) in Greece. Each experiment was set up according to a split-plot design with three replications, three main plots (zeolite application at rates of 0, 5, and 7.5 t ha⁻¹), and four sub-plots (N fertilization regimes at rates of 0, 100, 150, and 200 kg N ha⁻¹). The results of this study indicated that increasing rates of the examined factors increased cotton yields (seed cotton yield, lint yield, and lint percentage), with the greatest lint yield recorded under the highest rates of zeolite (7.5 t ha⁻¹: 1808, 1723, and 1847 kg ha⁻¹ in ATH, MES, and KAR, respectively) and N fertilization (200 kg N ha⁻¹: 1804, 1768, and 1911 kg ha⁻¹ in ATH, MES, and KAR, respectively). From the evaluated parameters, most soil parameters (soil organic matter, soil total nitrogen, and total porosity), root and shoot development (root length density, plant height, leaf area index, and dry weight), fiber maturity traits (micronaire, maturity, fiber strength, and elongation), fiber length traits (upper half mean length, uniformity index, and short fiber index), as well as color (reflectance and spinning consistency index) and trash traits (trash area and trash grade), were positively impacted by the increasing rates of the evaluated factors. In conclusion, the results of the present research suggest that increasing zeolite and N fertilization rates to 7.5 t ha⁻¹ and 200 kg N ha⁻¹, respectively, improved soil properties (except mean weight diameter), stimulated crop development, and enhanced cotton and lint yield, as well as improved the fiber maturity, length, and color parameters of cotton grown in clay-loam soils in the Mediterranean region. Full article