Nortodzhi Abdikholikovich Khujamshukurov

The degradation of Calcisols in the arid regions of Central Asia constrains sustainable agricultural production because of low organic matter content, poor aggregate stability, nutrient limitations, and increasing climatic stress. This study evaluated a rhizosphere engineering approach based on the native plant growth-promoting bacterium Pseudomonas veronii Ps-S/Sh-1503/2022 for the rehabilitation of degraded Calcisols. Four-year field experiments (2022–2025) using Sorghum bicolor assessed plant growth, rhizosphere microbial indicators, physiological responses, pathogen suppression, crop productivity, and implementation feasibility through economic and environmental assessments. Inoculation with P. veronii increased root depth by 45%, improved aboveground biomass, increased the ratio of culturable bacteria to Fusarium spp. from 6.1 to 10.3, and reduced Fusarium abundance by 29.4%. Structural equation modeling suggested that trophic support (42.1%), aggregate stabilization (27.4%), biocontrol (23.3%), and defense-related responses (7.2%) were the principal pathways associated with soil rehabilitation. Economic assessment indicated that the combined inoculation and mineral fertilization treatment provided the highest profitability, while environmental assessment estimated potential reductions in mineral fertilizer use and greenhouse gas emissions. These findings suggest that rhizosphere engineering using a native P. veronii strain represents a promising, economically viable, and climate-smart approach for improving the biological functioning of degraded Calcisols and supporting sustainable agricultural production.

Highlights• Water temperature rise (21.3-27.7°C) strongly increased Daphnia abundance (r = 0.91).• Dissolved oxygen correlated negatively with Daphnia density (r = -0.82).• Daphnia abundance peaked in June, reaching up to 168 ind./L in Group 4 lakes.• High mineralization (11.25 mS/cm) was linked to lower Daphnia abundance.• Calcium and nitrate were identified as the main drivers of Daphnia development.

Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring.

Lycium barbarum L. is a widely used medicinal and edible Chinese medicinal material. However, with consumers’ heightened concern for health and food safety, pesticide residues have become one of the major challenges affecting its quality and safety. Cyhalothrin is a pyrethroid insecticide and a typical type of pesticide with excessive pesticide residues in Lycium barbarum L. Rapid detection of pesticide residues is an effective way to ensure the quality and safety of traditional Chinese medicinal materials. In this work, a molecularly imprinted polymer electrochemiluminescence (ECL) sensor based on gold nanoparticles (AuNPs)@Ru-ZIF-8 was constructed for rapid detection of cyhalothrin residues. The prepared cyhalothrin molecularly imprinted polymers (MIPs) were used as a recognition element and modified on the surface of a glassy carbon electrode (GCE) by an electrochemical polymerization method. AuNPs were utilized to promote the excitation of Ru(bpy)₃²⁺ and TPrA in the ECL system, which improved the observability of the light signal. The GCE modified with the metal–organic frameworks (MOFs) ZIF-8 was employed to increase the specific surface area and enhance the electron transfer capacity on the electrode, thereby improving the sensing sensitivity of the sensor. In addition, the luminescent reagent of Ru(bpy)₃²⁺ was introduced into the synthesis process of ZIF-8, which caused Ru(bpy)₃²⁺ to be tightly bound around it and enhanced the stability of the sensor. Under optimal conditions, the linear detection range of the sensor is 1 × 10⁻¹~1 × 10⁴ nM, with a limit of detection (LOD) of 10 pM. The accuracy of the ECL-MIP sensor has been verified through spiked recovery experiments and actual sample detection. This study has opened up a new approach to rapid detection of pesticide residues in traditional Chinese medicinal materials used for both food and medicine.