Introduction: Obesity is a chronic disease characterized by complex alterations in body composition and metabolic health. Traditional reliance on body weight may overlook important physiological adaptations during lifestyle intervention. Methodology: A cohort of 99 participants at programme entry underwent body composition assessment using bioelectrical impedance analysis. Parameters included percent body fat (PBF), visceral fat area (VFA), skeletal muscle index (SMI), and phase angle (PhA). Correlation analyses were performed to explore relationships between adiposity, muscle mass, and cellular health. Results: Participants demonstrated elevated adiposity (PBF 38.9 ± 8.9%; VFA 142.9 ± 56.1 cm²). PhA was inversely associated with VFA (r ≈ −0.23, p <0.05) and positively associated with SMI (r ≈ 0.49, p <0.001). Notably, a subset of overweight individuals exhibited relatively preserved PhA, indicating heterogeneity in metabolic health despite similar adiposity levels. Conclusion Body composition and PhA provide complementary insights into metabolic health beyond body weight. These findings support the role of bioimpedance-derived parameters in identifying metabolic heterogeneity and guiding personalized obesity management.
Body Composition ; Body Weight ; Workplace ; Obesity

The escalating pressure from global population growth, climate change, and resource consumption is intensifying the burden on traditional agricultural production. Against this backdrop, soil degradation and pollution present increasingly severe challenges, creating a vicious cycle with rising food demands. Maintaining soil health and its ecosystem services has thus become a critical prerequisite for achieving sustainable agriculture in the future. This review explores the impacts of soil carbon (C) and nitrogen (N) dynamics on soil microbial communities and their interactions. Soil C and N are key determinants of microbial diversity and community structure, intrinsically linked to soil C/N cycling, crop productivity, and ecological balance. Environmental factors such as nitrogen fertilizer application, organic matter amendment application, litter decomposition, elevated CO₂ concentrations, and nitrogen deposition significantly influence soil C and N dynamics. Changes in soil C and N content regulate microbial community dynamics and the synergistic, competitive, and antagonistic interactions among microorganisms. Meanwhile, microbial communities actively respond to alterations in soil C and N availability. The resulting shifts in microbial communities and their interactions subsequently regulate soil C/N cycling and ecosystem stability, ultimately influencing ecosystem functions. By elucidating the mechanisms underlying soil carbon-nitrogen-microbial interactions, this review significantly advances our understanding of soil ecosystem responses and feedback mechanisms in the context of global change, while also providing crucial practical guidance for enhancing soil fertility and promoting sustainable agricultural development through microbial regulation.
Soil Microbiology ; Nitrogen/metabolism* ; Carbon/metabolism* ; Soil/chemistry* ; Bacteria/growth & development* ; Fungi/metabolism* ; Ecosystem ; Fertilizers ; Agriculture

In recent years, potato scab caused by Streptomyces scabies is aggravating year by year, becoming an industrial problem urgently to be resolved. Screening antagonistic bacteria with good inhibitory effect and wide adaptability is the main measure to realize effective prevention and control of the disease. This study screened three strains of antagonistic bacteria DXT2-4, T2-1 and 21-14 with good inhibitory effect on S. scabies by using plate standoff test, and identified them as Bacillus altitudinis, Bacillus safensis and Bacillus pumilus, respectively, based on morphological characteristics, physiological and biochemical properties, and 16S rRNA gene sequences. DXT2-4, T2-1 and 21-14 showed the pot control efficacy of 68.83%, 48.57%, and 57.14%, respectively. The field control efficacy of the three strains was 59.48%, 34.58% and 51.75% in Hulun Buir, Inner Mongolia Autonomous Region and 55.14%, 36.05%, and 49.05% in Huizhou, Guangdong. The three strains could grow normally in the media with pH 1.0-13.0 and with 1%-11% NaCl, and they had inhibitory effects on Rhizoctonia solani, Verticillium dahliae, Alternaria solani, and Fusarium oxysporum. The indole-3-acetic acid yields of DXT2-4, T2-1, and 21-14 were 2.23, 1.11, and 1.67 mg/L, respectively. DXT2-4 and 21-14 demonstrated strong abilities to solubilize phosphorus. The optimal carbon source, nitrogen source, and inorganic salt for fermentation of strain DXT2-4 were 2% molasses+2% corn starch, 2% soybean meal, and 0.3% MgSO₄·7H₂O, respectively. These findings suggest the three strains of bacteria can efficiently inhibit the growth of S. scabies and have strong environmental adaptability. Particularly, DXT2-4 has the best effects of inhibiting the disease and promoting plant growth, showing a high development value and broad application prospects, this is of great significance for promoting sustainable potato production and ensuring the environmentally sound utilization of resources.
Streptomyces/metabolism* ; Fermentation ; Plant Diseases/prevention & control* ; Solanum tuberosum/growth & development* ; Bacillus/growth & development* ; Antibiosis

Fusarium head blight (FHB), caused by Fusarium graminearum, not only leads to severe yield losses but also poses a threat to food safety due to the mycotoxins produced by the pathogen. Since this disease is preventable but not curable, the current control mainly relies on chemical fungicides, the long-term use of which may lead to pathogen resistance and environmental pollution. To develop green control methods, we screened 13 biocontrol strains from the rhizosphere soil of wheat, among which strain No. 12 (identified as Pythium aphanidermatum) showed significant antifungal effects. In the plate confrontation test, this strain reduced the colony diameter of the pathogen by 69.2% (1.47 mm vs. 4.78 mm in the control group), with an inhibition rate of 77% (P < 0.01). Microscopic observation revealed obvious deformations in the pathogen hyphae, suggesting a lysing effect. The coleoptile experiment further confirmed that the pre-treatment with this strain reduced the incidence rate to 0. These findings provide new candidate strains for the biocontrol of FHB and offer a scientific basis for reducing the use of chemical fungicides and promoting sustainable agricultural development.
Triticum/growth & development* ; Fusarium/growth & development* ; Plant Diseases/prevention & control* ; Soil Microbiology ; Pest Control, Biological/methods* ; Pythium/physiology* ; Biological Control Agents ; Rhizosphere ; Fungicides, Industrial

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Synthetic biology, recognized as one of the most revolutionary interdisciplinary fields in the 21st century, has established innovative strategies for the genetic improvement of rice through the integration of multidisciplinary technologies including genome editing, genetic circuit design, metabolic engineering, and artificial intelligence. This review systematically summarizes recent research advancements and breakthrough achievements in the application of synthetic biology in the genetic improvement of rice, focusing on three critical domains: yield improvement, nutritional quality fortification, and reinforcement of disease resistance and abiotic stress tolerance. It elucidates that synthetic biology enables precise genomic and metabolic pathway engineering through modular, standard, and systematic approaches, effectively overcoming the limitations of conventional breeding methods characterized by prolonged cycles and restricted trait modification capabilities. The implementation of synthetic biology has facilitated synergistic improvement of multi-traits, thereby providing critical technical references for developing elite rice cultivars with superior productivity and nutritional value. These technological breakthroughs hold significant implications for ensuring global food security and promoting green and sustainable development of agriculture.
Oryza/growth & development* ; Synthetic Biology/methods* ; Metabolic Engineering ; Plant Breeding/methods* ; Gene Editing ; Genetic Engineering/methods* ; Plants, Genetically Modified/genetics* ; Disease Resistance/genetics*

Genetically modified (GM) crops, as a pivotal innovation in modern agriculture, exhibit significant advantages such as pest and disease resistance, herbicide tolerance, stress tolerance, and yield enhancement. However, their widespread adoption has been associated with potential ecological risks, including weediness of transgenic plants, gene flow, emergence of novel viral strains in virus-resistant crops, impacts on non-target organisms and soil ecosystems, and evolution of target pest resistance. This review focuses on the dual characteristics of GM crops, systematically examining their agronomic benefits and the underlying mechanisms of ecological risks. This review provides a theoretical foundation for optimizing the development of GM crops and ecological risk management, facilitating sustainable agricultural practices.
Plants, Genetically Modified/growth & development* ; Crops, Agricultural/growth & development* ; Ecosystem ; Ecology

Nitrogen use efficiency (NUE) is a pivotal indicator for achieving high wheat yields and sustainable resource utilization. This paper reviews recent research advances in the NUE of wheat, emphasizing genotypic variations, physiological mechanisms, molecular regulation, and agronomic management practices. Furthermore, this paper analyzes the critical regulatory nodes in nitrogen uptake, transport, assimilation, and redistribution, summarizes the current research bottlenecks, and makes an outlook on the future research directions. We then propose a strategy integrating emerging biotechnologies with precision agronomic management to enhance both wheat yields and NUE. This review aims to offer a theoretical framework for breeding nitrogen-efficient wheat cultivars and promoting the eco-friendly production of wheat.
Triticum/growth & development* ; Nitrogen/metabolism* ; Plant Breeding

Rice (Oryza sativa L.), as a thermophilic crop, is highly susceptible to cold stress during its growth process. Chilling injury at the plumule stage and seedling stage often affects the morphological development and leads to yield reduction of rice. The exploration and utilization of cold tolerance genes are among the most direct and effective approaches to address cold stress in rice. To identify quantitative trait loci (QTLs) associated with cold tolerance at plumule and seedling stages, in this study, we measured the seedling rates and survived seedling rates of the indica rice cultivar 'HZ', the japonica cultivar 'Nekken2', and their 120 recombinant inbred lines (RILs) under cold stress. A previously constructed high-density genetic linkage map was used for the mapping of the QTLs conferring cold tolerance at the plumule and seedling stages. A total of 4 QTLs for plumule-stage cold tolerance and 9 QTLs for seedling-stage cold tolerance were detected, with the maximum limit of detection reaching 5.20. Notably, a genetically overlapping QTL for both plumule and seedling stages was identified on chromosome 8, spanning a physical interval of 24 432 953-25 295 129 bp. Candidate genes within the detected QTL intervals were screened, and quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to analyze the gene expression during the plumule and seedling stages. The results revealed that LOC_Os03g06570, LOC_Os03g07100, LOC_Os06g08280, LOC_Os08g38440, LOC_Os08g39100, and LOC_Os08g39540 exhibited significantly differential expression between the parental lines. These genes were either significantly downregulated or upregulated under cold stress. Among them, the first three gene (LOC_Os03g06570, LOC_Os03g07100, and LOC_Os06g08280) were hypothesized to be key candidates regulating the cold tolerance of rice seedlings, while the latter three genes (LOC_Os08g38440, LOC_Os08g39100, and LOC_Os08g39540) were identified as comprehensive regulators of cold tolerance during both plumule and seedling stages. These findings lay a foundation for the fine mapping and cloning of cold tolerance genes at the plumule and seedling stages, providing valuable insights for breeding cold-tolerant rice varieties.
Quantitative Trait Loci/genetics* ; Oryza/growth & development* ; Seedlings/growth & development* ; Cold Temperature ; Chromosome Mapping ; Gene Expression Regulation, Plant