Fusarium crown rot (FCR), predominantly caused by the soil-borne pathogen Fusarium pseudograminearum, poses a severe and escalating threat to global wheat (Triticum aestivum L.) production and food security. This review consolidates recent advances in understanding the pathogen’s biology, infection strategies, host–pathogen interactions, and sustainable management practices. F. pseudograminearum deploys trichothecene mycotoxins, particularly deoxynivalenol (DON), to undermine host immunity, with its virulence governed by transcription factors and functional proteins involved in fungal growth, toxin biosynthesis, and stress adaptation. In response, wheat initiates a multilayered defense mechanism, primarily by upregulating genes related to toxin detoxification and transport, hormonal signaling, and structural reinforcement. Integrated disease management (IDM) practices aimed at mitigating disease severity include agronomic interventions, fungicides, biocontrol agents, and breeding efforts supported by quantitative trait locus (QTL) mapping, genome-wide association (GWAS) studies, and genome editing. To address the dynamic nature of F. pseudograminearum populations under changing environmental conditions, future directions should prioritize climate-resilient strategies, integrated multi-omics approaches, drone-based early detection technologies, and global pathogen surveillance systems. This review provides an updated framework to guide sustainable and effective FCR control.
Soil microbial carbon use efficiency (CUE) plays a critical role in carbon (C) cycling and ecosystem functioning, yet its response to nitrogen (N) deposition remains poorly understood, particularly in planted forests. This study investigates how N addition affects microbial CUE and its underlying mechanisms in Populus deltoides plantations in coastal eastern China. Using a long-term field experiment with five levels of N addition (0–30 g N·m−2·yr−1), we measured microbial CUE, soil chemical properties, enzyme activities, and microbial community composition from 2018 to 2020. We found that N addition significantly reduced microbial CUE, primarily through N-induced stoichiometric imbalances and soil acidification. Excess N increased available N and decreased the DOC:AN ratio, driving microbial carbon limitation and reducing metabolic efficiency. Furthermore, N addition suppressed bacterial diversity and shifted microbial communities toward taxa with lower CUE. Model selection identified soil pH, available N, and DOC:AN as key predictors of microbial CUE. These findings highlight the dominant role of soil environmental factors—particularly nutrient stoichiometry and pH—in regulating microbial CUE. Our results suggest that excessive N deposition may compromise soil C sequestration in poplar plantations by altering microbial resource allocation and reducing microbial metabolic efficiency. Managing nutrient balance and maintaining microbial diversity are thus critical for sustaining soil health and carbon storage in forest ecosystems under increasing N deposition.
Hydrogels, despite their potential in flexible electronics and wearable sensors, often suffer from inadequate mechanical robustness under sustained loading. This study aims to overcome this limitation by developing a novel nanocomposite hydrogel system through the integration of calcium-polyoxometalate sub-nanometer wires (Ca-POM SNWs) into a polyvinyl alcohol (PVA) matrix. Utilizing a H2O/ethylene glycol (EG) binary solvent, the hydrogel achieves uniform dispersion of Ca-POM SNWs, which enhances mechanical properties through dual reinforcement mechanisms: stress dissipation via polymer-mimetic flexibility and crystallinity improvement via hydrophobic ligand-induced chain alignment. The resulting PVA/Ca-POM hydrogel exhibits exceptional performance, including a 2.4-fold increase in fracture stress (0.85 MPa), 3.8-fold toughness enhancement (2.76 MJ m−3), and high ionic conductivity (3.6 S m−1). As a strain sensor, it achieves a gauge factor of 2.56 with rapid response, enabling precise detection of both large joint movements and subtle physiological vibrations. A prototype Morse code communication system further demonstrates its potential in assistive healthcare technologies, facilitating barrier-free, real-time communication between disabled patients and clinicians. This work highlights a breakthrough in inorganic-organic interface compatibility, offering a versatile platform for next-generation wearable technologies and extreme-environment applications. The innovative design principles and multifunctional performance underscore its significance in advancing soft material engineering.
TaDJA-6D boosts wheat regeneration and transformation efficiency across genotypes, offering a new molecular breeding resource. As a key member of the heat shock protein 40 family, the co-chaperone DnaJ proteins play a crucial role in plant growth, development, and stress responses. However, very few studies have focused on the function of DnaJ protein in wheat (Triticum aestivum L.). Here, we characterized a DnaJ family gene from wheat, TaDJA-6D, (TraesCS6D02G232600) and analyzed its spatiotemporal expression pattern. During wheat regeneration, the transcriptional expression level of the TaDJA-6D gene was continuously upregulated with the extension of tissue culture time; in situ hybridization revealed that the genes were highly expressed in the vigorously dividing regions of wheat calli. Functional analysis demonstrated that, compared with the control vector, overexpression of TaDJA-6D significantly improved the wheat regeneration and transformation efficiency across diverse wheat genotypes, including Fielder, Kenong 199, Yanda 1817 and Xiaoyan 6. Our findings suggest that TaDJA-6D plays an important role in the process of wheat regeneration and genetic transformation. This study deepens our understanding of the functions of wheat co-chaperone proteins and provides novel genetic resources for efficient wheat transformation.
Soil organic carbon (SOC) is a key indicator for assessing soil quality. Rapid and accurate acquisition of the spatial distribution of SOC content can guide production management in reclaimed farmland in mining areas. This study takes the Xinglongzhuang Coal Mine in Yanzhou District, Jining City, as the study area, with reclaimed farmland SOC as the research subject. Based on unmanned aerial vehicle (UAV) multispectral remote sensing images and ground sampling point data, three sets of feature variables were constructed: a full variable set, a variance inflation factor (VIF) screened variable set, and a genetic algorithm (GA) screened variable set. Four deep learning algorithms, namely convolutional neural network (CNN), graph neural network (GNN), recurrent neural network (RNN), and transformer, are employed to construct a total of 12 SOC content estimation models. Based on the optimal estimation model CNN-GA, the model was optimized and improved by generating pseudo-reflectance samples through Kernel-SMOTE algorithm oversampling. The results indicate that (1) following variable selection through the use of GA, the accuracy of the majority of estimation models saw a significant improvement. (2) Among the four deep learning algorithms, the model constructed using the CNN algorithm achieved the highest accuracy. (3) CNN-GA-SMOTE was identified as the optimal estimation model, achieving an R2 of 0.864 and RMSE of 0.827 g kg−1 for the modeling set, with R2 of 0.857 and RMSE of 0.796 g kg−1 for the validation set. The SOC content of reclaimed farmland was estimated using this model, with an overall range of 7.53 13.40 g kg−1. This study provides technical support for soil quality assessment in reclaimed mining areas.
