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Gibberellins (GAs) promote secondary cell wall (SCW) development in plants, but the underlying molecular mechanism is still to be elucidated. Here, we employed a new system, the first internode of cotton, and the virus-induced gene silencing method to address this problem. We found that knocking down major DELLA genes via VIGS phenocopied GA treatment and significantly enhanced SCW formation in the xylem and phloem of cotton stems. Cotton DELLA proteins were found to interact with a wide range of SCW-related NAC proteins, and virus-induced gene silencing of these NAC genes inhibited SCW development with downregulated biosynthesis and deposition of lignin. The findings indicated a framework for the GA regulation of SCW formation; that is, the interactions between DELLA and NAC proteins mediated GA signaling to regulate SCW formation in cotton stems.

Cotton stem is used to produce the high-efficient adsorbent (CSA) for Rhodamine B (RB) dye removal from wastewater, and hydrogen rich fuel gas via on-pot method using microwave heating. The adsorption data indicate that RB adsorption behavior follows the Langmuir model with the maximum adsorption capacity of 265.96 mg/g, whereas the adsorption kinetics follows the pseudo-second-order model. Thermodynamic calculations indicate that RB adsorption on CSA is spontaneous and endothermic process. The adsorption data are fitted to the Thomas and Yoon-Nelson model to predict the breakthrough curve in the column experiment. The RB removal could still be maintained at 71.22% of the original value after five cycles, demonstrating the reusability of CSA. The chemical functional groups, electrostatic interaction, and pore filling of CSA are found to be responsible for high RB adsorption capability. CSA exhibits excellent RB removal efficiency in treating actual water. The major components of byproduct gases collected from activation process are H 2 and CO.

The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating disease. Traditional diagnostic methods, which often rely on manual expertise or destructive sampling, are limited by low efficiency and high subjectivity. In recent years, Raman spectroscopy has emerged as a promising solution due to its rapid, non-destructive, and highly sensitive characteristics for plant disease detection. In this study, we analyzed cotton stems using Raman spectroscopy, applying Savitzky-Golay (SG) smoothing combined with multiple preprocessing methods including Scaling and Shifting (SS), Standard Normal Variate (SNV), inverse first-order differential (1/SG)′, and multiplicative scatter correction (MSC). For baseline correction, we employed polynomial fitting (PolyFit) and adaptive iterative weighted penalized least squares (airPLS). Feature selection was performed using principal component analysis (PCA), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS).Three optimized models were developed: support vector machine (SVM) with weighted mean of vectors (INFO) algorithm, random forest (RF) enhanced by particle swarm optimization (PSO), and long short-term memory (LSTM) network optimized via chameleon swarm algorithm (CSA).The results show that the INFO-SVM model with SG-airPLS-(1/SG)′ -CARS preprocessing demonstrated superior performance, achieving 97.5% accuracy (0.974 F1-score) on training data and 90.0% accuracy (0.867 F1-score) on validation data, outperforming both PSO-RF and CSA-LSTM models. These results confirm that Raman spectroscopy integrated with optimized machine learning enables accurate VW classification in cotton stems. This method enables early disease detection during infection, facilitating timely fungicide application and reducing yield losses.

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.

This article presents the results of research in the development of an effective technology for obtaining composite wood-plastic board materials for construction and furniture purposes based on fillers from cotton stalks, which are agricultural waste, and polymer binders based on modified urea-formaldehyde and gossypol resins, epichlorohydrin, benzyl chloride, and polyvinyl chloride. The study revealed a correlation between the ultimate bending strength, tensile strength and water absorption with the parameters of pressing polymer-fillers of the mass. At the same time, to obtain composite wood-plastic board materials, it is recommended to use a urea-formaldehyde resin modified with reactive structuring additives as a binder. It has been established that the optimal technological mode for pressing the mass of polymer fillers and cotton stalks is: specific pressure 35 kg/m², pressing temperature 168-170°C, heating duration 7-10 minutes. Key words: technology, composition, modified urea-formaldehyde resin, cotton stem filler, wood-plastic board, specific pressure, pressing temperature, heating duration, water absorption, tensile strength.

[...]it was found that under the influence of the external environment there are corresponding changes in the system of interactions between the quantitative characteristics of plants [1,2,3]. [...]it was found that the level of correlation between the quantitative characteristics of cotton increased as a result of mulching and minimal processing compared to the control option. [...]of mulching and minimal tillage, a change in the level of correlation bonds between quantitative characteristics of winter wheat was observed. [...]of mulching and minimal tillage, the degree of correlation between most quantitative characters increased.

The effect of pH, dose of adsorbent, concentration of Cu(II), and contact time has been studied well on adsorption rate of Cu(II) from aqueous solution using commercially available activated carbon (CAC), prepared activated carbon from cotton stem (PACC) and prepared activated carbon from bagasse (PACB). Increased adsorption of Cu(II) by CAC, PACC and PACB was found at pH 6. The adsorption of Cu(II) was observed to be highest at 4g, 5g and 6g dose of CAC, PACC and PACB respectively. Adsorption was observed maximum at PACC (90.5%)>PACB (87.5 %)>CAC (85.5%) in a solution containing 25 mg/L concentration of Cu(II) for all PACs. Comparable adsorption was observed at contact time for CAC (30 min), PACC (60 min) and PACB (90 min). The results revealed that all the adsorbents have ability to adsorb Cu(II) significantly at particular pH, dose, concentration and time.

In search of an efficient and no-cost adsorbent for removal of methylene blue (MB) from water, cotton stem ash (CSA) was evaluated. The effect of process variables, namely, contact time, solution pH and adsorbent dose on adsorption of MB onto CSA were investigated. The adsorption kinetics and mechanism were tested with pseudo-first order and pseudo-second order model, and with intraparticle diffusion model, respectively. Based on error analysis (chi-square and percent sum of error) the suitability order of adsorption isotherms was Langmuir>Temkin>Freundlich>Jovanoic. The maximum adsorption capacity of CSA was 20.42 mg g−1. The feasibility parameter and free energy change were also calculated. The present study demonstrated that the CSA could be used as a potential, efficient, low-cost, and easily available adsorbent for the treatment of MB containing industrial wastewater. Highlights • For the first time, this study reported CSA as a potential, efficient and no-cost adsorbent for removal of methylene blue (MB) from water. • Effects of different process variables were investigated and optimized. • The maximum MB adsorption capacity of CSA was 20.42 mg g−1.

Background Mesenchymal stem cells (MSCs) can improve cutaneous wound healing via the secretion of growth factors. However, the therapeutic efficacy of MSCs varies depending upon their source. Induced pluripotent stem cells are emerging as a promising source of MSCs with the potential to overcome several limitations of adult MSCs. This study compared the effectiveness of conditioned medium of MSCs derived from induced pluripotent stem cells (iMSC-CdM) with that derived from umbilical cord MSCs (uMSC-CdM) in a mouse cutaneous wound healing model. We also investigated the mechanisms of protection. Methods The iMSC-CdM or uMSC-CdM were topically applied to mice cutaneous wound model. The recovery rate, scar formation, inflammation and angiogenesis were measured. We compared angiogenesis cytokine expression between iMSC-CdM and uMSC-CdM and their protective effects on human umbilical vein endothelial cells (HUVECs) under H 2 O 2 -induced injury. The effects of iMSC-CdM on energy metabolism, mitochondria fragmentation and apoptosis were measured. Results Topical application of iMSC-CdM was superior to the uMSC-CdM in accelerating wound closure and enhancing angiogenesis. Expression levels of angiogenetic cytokines were higher in iMSC-CdM than they were in uMSC-CdM. The iMSC-CdM protected HUVECs from H 2 O 2 induced injury more effectively than uMSC-CdM did. Administration of iMSC-CdM stimulated HUVEC proliferation, tube formation and energy metabolism via the ERK pathway. Mechanistically, iMSC-CdM inhibited H 2 O 2 -induced mitochondrial fragmentation and apoptosis of HUVECs. Conclusion Collectively, these findings indicate that iMSC-CdM is more effective than uMSC-CdM in treating cutaneous wounds, and in this way, iMSC-CdM may serve as a more constant and sustainable source for cell-free therapeutic approach. Graphical abstract

Although cultivated for over 7000 years, mainly for production of cotton fibre, the cotton plant has not been fully explored for potential uses of its other parts. Despite cotton containing many important chemical compounds, limited understanding of its phytochemical composition still exists. In order to add value to waste products of the cotton industry, such as cotton gin trash, this review focuses on phytochemicals associated with different parts of cotton plants and their biological activities. Three major classes of compounds and some primary metabolites have been previously identified in the plant. Among these compounds, most terpenoids and their derivatives (51), fatty acids (four), and phenolics (six), were found in the leaves, bolls, stalks, and stems. Biological activities, such as anti-microbial and anti-inflammatory activities, are associated with some of these phytochemicals. For example, β-bisabolol, a sesquiterpenoid enriched in the flowers of cotton plants, may have anti-inflammatory product application. Considering the abundance of biologically active compounds in the cotton plant, there is scope to develop a novel process within the current cotton fibre production system to separate these valuable phytochemicals, developing them into potentially high-value products. This scenario may present the cotton processing industry with an innovative pathway towards a waste-to-profit solution.