Search Results (150)

Pigskin, a major byproduct of pork processing, has high protein content and low fat, endowing it with considerable market value for food applications. In this study, bioaugmented fermentation with Latilactobacillus sakei YBZY-W5, a strain previously isolated from traditional fermented pigskin, was applied to pigskin to systematically evaluate its effects on physicochemical parameters, microbial community succession, and volatile flavor compound (VFC) profiles over 20 days. The results showed that moisture and pH significantly decreased, while total volatile basic nitrogen (TVB-N) and thiobarbituric acid reactive substances (TBARSs) increased with fermentation time. High-throughput sequencing revealed that Lactobacilli, Fusarium and Aspergillus dominated early fermentation and were gradually replaced by Bacillus, Hanseniaspora and Debaryomyces. A total of 493 VFCs were identified, among which terpenoids, heterocyclic compounds, and alcohols were the most abundant classes. Orthogonal partial least squares discriminant analysis (OPLS-DA) identified numerous differentially changed VFCs (DCVFCs) during fermentation. Odor activity value (OAV) analysis indicated that green, meaty, and woody notes dominated initially, while sour, floral, sweet, and fruity characteristics became increasingly prominent after fermentation. Pearson correlation analysis demonstrated significant associations between key microorganisms (Lactobacilli, Bacillus, Hanseniaspora, Debaryomyces) and DCVFCs (e.g., β-myrcene, ethyl hexanoate, hexanoic acid, ethyl ester, pyrazines). Collectively, bioaugmented fermentation with Ltb. sakei YBZY-W5 effectively modulated the physicochemical and microbial profiles of pigskin, enriched desirable flavor compounds, and reduced unpleasant odor, confirming its feasibility for producing high-quality fermented pigskin products. This study provides an experimental basis for the value-added utilization of pigskin and promotes sustainable development of the pork industry. Full article

Onion (Allium cepa) belongs to the genus Allium in the family Liliaceae and is widely cultivated worldwide for its nutritional and medicinal value. However, in Hohhot, Inner Mongolia, China, many onion plants exhibited severe virus-like symptoms, including yellow stripes and leaf distortion. Symptomatic plants were collected, and virus identification was conducted through mechanical inoculation of Nicotiana benthamiana and transmission electron microscopy. Two types of virus particles, rod-shaped and spherical, were observed. Mixed infection of Cucumber mosaic virus (CMV) and Cucumber green mottle mosaic virus (CGMMV) was confirmed by high-throughput sequencing and RT-PCR. The detection rates of CMV and CGMMV in the samples were 8/161 and 1/161, respectively. Recombination analysis indicated that no recombination events were detected in the CGMMV, whereas one recombination event was identified in CMV, occurring on RNA1 from nt 59 to 171. The major parent was CMV DSMZ PV-1255 (ON013910) in Greece, and the minor parent was CMV Am (JX993909) in China. This study reports, for the first time, the complete genome sequences of CMV infecting onions in China and CGMMV infecting onions worldwide. Full article

Aromatic amines (AAs) are potent carcinogens found in tattoo inks that pose significant health risks. Precise quantitative analysis is essential for safety. In this study, we developed and validated a robust method for the simultaneous quantification of 21 regulated AAs using both gas chromatography–electron ionization (GC-EI)–mass spectrometry (MS) and tandem MS (MS/MS). After optimizing separation conditions, MS-based selected ion monitoring (SIM) and MS/MS-based multiple reaction monitoring (MRM) were evaluated. While both methods were largely sufficient to ensure compliance with international safety thresholds, the MRM-based approach exhibited superior detection capability with comparable analytical accuracy and precision, providing a more effective tool for trace-level hazardous compound analysis. The developed MRM method was applied to eight commercial tattoo inks, identifying five AAs in five products. Notably, o-toluidine, o-anisidine, and 3,3′-dichlorobenzidine significantly exceeded the regulatory limit (5.0 mg/kg), particularly in green and yellow inks. The dual-capability GC-MS platform—combining high-performance MRM quantification with robust spectral confirmation—ensures the high throughput and analytical confidence necessary for regulatory compliance and public health protection. Full article

This study provides a practical and replicable improvement model for productivity and inspection reliability improvement in resource-constrained food logistics environments. This study presents an engineering-based optimization of productivity and process reliability in an agro-industrial post-harvest processing system for canned young green jackfruit using an integrated Define–Measure–Analyze–Improve–Control (DMAIC) and Failure Mode and Effects Analysis (FMEA) framework. The case-study production system experienced high raw-material loss, prolonged blanching cycles, and low inter-operator inspection agreement, which reduced process yield and logistics throughput. Root causes were identified through process mapping and fishbone analysis and prioritized using FMEA Risk Priority Number (RPN) scoring. Key improvement actions included optimizing blanching time, standardizing supplier grading to reduce material variability, and strengthening inspection decisions through Attribute Gage Repeatability and Reproducibility (Gage R&R)-based training and criteria alignment. After implementation, productivity increased by 2.31%, raw-material loss decreased by 1.90%, and inter-operator inspection agreement improved by 16%, exceeding the benchmark. Blanching time was reduced from 3 to 1 min at ≥90 °C, shortening cycle time by 67% and generating an estimated annual cost saving of USD 7200 without major capital investment. The results demonstrate that structured, risk-based improvement combined with validated measurement systems can enhance workforce consistency, process stability, and logistics flow efficiency in agro-industrial food processing environments, providing a replicable improvement model for agro-industrial processing small and medium-sized enterprises (SMEs). Full article

The transition from petrochemical plastics to sustainable biopolymers has created a critical demand for functional materials that do not compromise on performance. Starch and zein, due to their abundance and complementary nature, represent not just a chemical pair, but a techno-economic symbiosis: zein provides the hydrophobic shield, while starch offers the cost-effective structural volume. This review adopts a “Puzzle Theory” framework to synthesize over 80 peer-reviewed studies published between 2014 and 2025, categorizing the literature into established structural knowledge and unresolved functional limitations. Our analysis reveals that while fabrication protocols and molecular synergy are well-defined in approximately 65% of the surveyed literature, critical functional data remain largely absent. Specifically, fewer than 15% of studies investigate hydro-stability in high-humidity environments or bio-interface behavior, creating a disconnect between laboratory success and industrial application. We identify that current research disproportionately prioritizes dry-state morphology over wet-state mechanical integrity. To bridge the gap between academic prototypes and industrial reality, this article moves beyond general recommendations to propose concrete experimental benchmarks, including specific targets for wet mechanical integrity (>1 MPa), regulatory solvent compliance (<50 ppm), and scalable throughput. This article concludes by providing a strategic roadmap to bridge these gaps, arguing that future research must pivot from simple morphological characterization to developing “smart response” mechanisms and “green manufacturing” protocols to ensure commercial viability. Full article

Soil fungi play a pivotal role in maintaining ecosystem functions and regulating plant health. Although plant root traits can significantly impact the abundance and diversity of different fungal groups, the mechanism by which plant root strategies drive the assembly of soil fungal guilds remains limited. Utilizing Root Economics Space theory, this study investigates how four green manures (hairy vetch, rye, radish, and rapeseed) with contrasting root functional strategies (along the ‘fast–slow’ and ‘outsourcing–DIY’ axes) regulate the composition and functional structure of soil fungal communities. Community characteristics of three functional guilds (plant pathogens, saprophytes, and arbuscular mycorrhizal fungi), as well as relationships between these communities and plant root traits, were evaluated using a combination of Illumina high-throughput sequencing, functional annotation, and multivariate statistical analysis. Overall, different root strategies were associated with distinct fungal community patterns, potentially related to differences in root-derived resource inputs and soil properties. The ‘slow’ and ‘DIY’ strategies were associated with lower relative abundance of plant pathogenic fungi and higher relative abundance of saprotrophic fungi, whereas the ‘fast’ and ‘outsourcing’ strategies were associated with higher relative abundance of plant pathogens and AMF. These findings suggest that root functional strategies may help explain variation in fungal guild composition under different green manure species. From a practical perspective, the results provide a basis for selecting green manure species to help manage soil-borne disease risk, regulate beneficial soil microbial communities, and support more sustainable soil management in agricultural production. Full article

Lead-free double perovskites possess the capabilities of wide bandgap control, excellent photoelectric performance, and environmental friendliness. They are an ideal alternative system for addressing the heavy metal toxicity of lead-based perovskites and promoting their large-scale application. Precise control of their bandgap is key to the green transformation of optoelectronic devices. Bandgap, as a key parameter determining the photoelectric properties of materials, has limitations in traditional experimental determination and DFT calculation methods, such as being time consuming, labour intensive, costly, and difficult to achieve high-throughput screening. Deep learning provides an efficient solution to this problem, but current research has issues such as a single-model architecture and poor interpretability, which cannot effectively support bandgap regulation. This study utilised 2367 valid datasets of lead-free double perovskites sourced from the Materials Project database and relevant literature. Following preprocessing steps, including MinMaxScaler normalisation and Pearson correlation coefficient screening, the dataset was divided into a ratio of 7:1:2. The bandgap prediction capabilities of four models—MLP, deep ensemble learning, PINN, and Transformer—were systematically compared, with feature importance analysed using the SHAP method. The results show that the MLP model performs the best in medium-scale, structured feature prediction. The R² value of the test set is 0.9311, while the MAE, MSE, and RMSE are 0.1915 eV, 0.0975 eV², and 0.3122 eV, respectively. A total of 98% of the test samples have a prediction error of ≤0.4 eV, highlighting the stability of low bandgap systems. The Transformer is more suitable for large-scale, sequential feature prediction, while the MLP has limited generalisation ability for medium-to-high bandgap systems containing elements such as Si and Mg. The SHAP analysis revealed that the five electronic structure descriptors, such as B_HOMO+ and A_LUMO+, are the key influencing factors of the bandgap. The research results are helpful for the high-precision prediction and mechanism explanation of the bandgap of lead-free double perovskites, providing theoretical support for rational material design, performance optimisation, and bandgap-oriented regulation. They also point out the direction for subsequent model improvement. Full article

Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties. Full article

Drought stress severely constrains cotton yield and fiber quality, but conventional evaluation methods are inefficient and time-consuming. To address this, we developed a high-throughput, non-destructive phenotyping framework by integrating UAV-based multispectral remote sensing with machine learning, using 225 upland cotton (Gossypium hirsutum L.) accessions. The accessions were subjected to well-watered (CK) and drought stress (DS) treatments at the flowering and boll-setting stage. Canopy multispectral imagery (Green/Red/Red_edge/Near-infrared bands) was acquired via DJI Mavic 3 Multispectral UAV, and 16 vegetation indices (VIs) were derived. Concurrently, 15 agronomic and fiber quality traits were measured to calculate drought resistance coefficients (DRCs), which were used for principal component analysis (PCA) and comprehensive drought tolerance index (D) construction. Hierarchical clustering categorized the accessions into 6 drought tolerance grades (Groups I–VI). Variable importance analysis identified GNDVI, NGRVI, and NDRE as the most drought-sensitive VIs (% IncMSE > 11). Among four regression models (LR, KNN, LGBM, XGBoost), XGBoost achieved the best performance for D prediction (test set: R² = 0.785, RMSE = 0.032, MAE = 0.024). This study demonstrates that UAV multispectral data coupled with XGBoost enables accurate, efficient drought tolerance assessment, providing a robust tool for high-throughput germplasm screening and smart agricultural management. Full article

The design and optimization of microalgae processes are usually focused on maximizing biomass productivity, neglecting the impact of cell-to-cell heterogeneity. Flow cytometry (FCM) represents a powerful and high-throughput tool for analyzing and examining microalgae intrinsic characteristics, such as their physiology, metabolism and response at the single-cell level. The aim of this work is to develop a novel FCM sensor-based single-cell analysis method to monitor and study the effect of several process conditions, mainly variations of light spectral composition (blue, red and green), nitrogen depletion and moderate osmotic stress conditions (0.2 M NaCl), on the subpopulation structure and dynamics of the green microalgae Chromochloris zofingiensis, a natural source for lipids, proteins and carotenoids. The FCM procedures developed in this study proved to be effective for monitoring the population dynamics of microalgae, demonstrating how the process conditions have a direct and significant impact on population heterogeneity of C. zofingiensis on a single-cell level. Cell division was found to be adversely affected by the moderate osmotic stress (N⁺S⁺), nitrogen depletion (N⁻), and their combined occurrence (N⁻S⁺), independent of the light spectral composition used for culture illumination. In terms of cell-to-cell heterogeneity, a higher proportion of large cells (~20 µm) was observed under green light across all conditions with 21%, 29%, 35% and 52% under N⁻, N⁻S⁺, N⁺S⁺ and N⁺ conditions, respectively, followed by red light combined with osmotic stress (46%), whereas blue light consistently led to a predominance of smaller cells (≤4 µm) with 30%, 47%, 50% and 55% under N⁺S⁺, N⁺, N⁻S⁺ and N⁻ conditions, respectively. Full article

Methane (CH₄) emissions present a significant challenge to both environmental sustainability and energy efficiency in ruminants, including beef cattle that are born in dairy herds. Although numerous approaches, including alterations in feed and the use of additives, are under investigation to mitigate these emissions, the genetic selection of animals that produce lower levels of methane offers the potential for enduring and cumulative advantages. Transcriptome analysis represents a crucial advancement in elucidating the networks and mechanisms through which the ruminant genome influences methane emissions. In the present study, methane emissions were measured using a GreenFeed system in beef-on-dairy cattle (n = 11). High-throughput RNA sequencing was conducted on animal blood samples, followed by differential gene expression analysis using methane production (g/d) as a continuous trait. The analysis identified eleven differentially expressed genes (DEGs), including six downregulated (KIAA1211L, LOC107131224, OSCP1, IL12B, LOC618859, FREM1) and five upregulated (DSCAML1, OSBP2, ACAN, PRSS16, CD1B) genes (Padj < 0.05) with one gene exhibiting potential biomarker characteristics. Gene and cell enrichment, as well as pathway analysis, suggested that nervous, immune, and endocrine systems may be involved in ruminal methane production by beef-on-dairy cattle. These findings highlight the potential of transcriptomic biomarkers to guide genetic selection strategies, offering a sustainable pathway to reduce methane emissions and enhance both environmental and agricultural efficiency. Full article

L. pratensis is a significant pest of cotton. Clarifying the intestinal bacterial structure of L. pratensis can provide a theoretical basis for the development of new pest biological control strategies. In this study, high-throughput sequencing was employed to characterize the intestinal bacterial communities across five L. pratensis populations, and the functions of their core metabolic pathways were predicted. The results showed that the intestinal bacterial communities of the five L. pratensis populations comprised 16 phyla, 25 classes, 54 orders, 85 families, 133 genera, and 187 species. Diversity analysis revealed that the diversity of the intestinal bacterial community exhibited a dynamic trend of first increasing and then decreasing during the pest’s growth and development. Specifically, the Shannon and Simpson diversity indices of the nymphal stage were significantly higher than those of the egg and adult stages (p < 0.05). The dominant phylum, class, order, family, genus and species shared by the five groups were Proteobacteria (93.17%), Gammaproteobacteria (48.71%), Rickettsiales (43.83%), Anaplasmataceae (49.39%), Wolbachia (43.83%) and Wolbachia (43.82%). Among them, Acinetobacter was mainly found in the first instar nymph stage, and Serratia was mainly distributed in the fifth instar nymph and female and male adults. Functional prediction results showed that the intestinal bacterial community was mainly enriched in core pathways, including metabolism, genetic information processing, and environmental information processing. This study provides a new target for green prevention and control of L. pratensis and also provides a theoretical basis for further elucidating the succession law and functional mechanism of its gut microbiota. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

To elucidate the potential of integrated multi-omics approaches for studying systemic mechanisms of mycorrhizal fungi in mediating plant-microbe interactions, this study employed the Tuber-inoculated Corylus system as a model to demonstrate how high-throughput profiling can investigate how fungal inoculation reshapes the rhizosphere microbial community and correlates with host metabolism. A pot experiment was conducted comparing inoculated (CTG) and non-inoculated (CK) plants, followed by integrated multi-omics analysis involving high-throughput sequencing (16S/ITS), functional prediction (PICRUSt2/FUNGuild), and metabolomics (UPLC-MS/MS). The results demonstrated that inoculation significantly restructured the fungal community, establishing Tuber as a dominant symbiotic guild and effectively suppressing pathogenic fungi. Although bacterial alpha diversity remained stable, the functional profile shifted markedly toward symbiotic support, including antibiotic biosynthesis and environmental adaptation. Concurrently, root metabolic reprogramming occurred, characterized by upregulation of strigolactones and downregulation of gibberellin A5, suggesting a potential “symbiosis-priority” strategy wherein carbon allocation shifted from structural growth to energy storage, and plant defense transitioned from broad-spectrum resistance to targeted regulation. Multi-omics correlation analysis further revealed notable associations between microbial communities and root metabolites, proposing a model in which Tuber acts as a core regulator that collaborates with the host to assemble a complementary micro-ecosystem. In summary, the integrated approach successfully captured multi-level changes, suggesting that Tuber-Corylus symbiosis constitutes a fungus-driven process that transforms the rhizosphere from a competitive state into a mutualistic state, thereby illustrating the role of mycorrhizal fungi as “ecosystem engineers” and providing a methodological framework for green agriculture research. Full article

The expansion of green hydrogen requires technologies that are both manufacturable at a GW-to-TW power scale and adaptable for decentralized, renewable-driven energy systems. Recent advances in proton exchange membrane, alkaline, and solid oxide electrolysis reveal persistent bottlenecks in catalysts, membranes, porous transport layers, bipolar plates, sealing, and high-temperature ceramics. Emerging fabrication strategies, including roll-to-roll coating, spatial atomic layer deposition, digital-twin-based quality assurance, automated stack assembly, and circular material recovery, enable high-yield, low-variance production compatible with multi-GW power plants. At the same time, these developments support decentralized hydrogen systems that demand compact, dynamically operated, and material-efficient electrolyzers integrated with local renewable generation. The analysis underscores the need to jointly optimize material durability, manufacturing precision, and system-level controllability to ensure reliable and cost-effective hydrogen supply. This paper outlines a convergent approach that connects critical-material reduction, high-throughput manufacturing, a digitalized balance of plant, and circularity with distributed energy architectures and large-scale industrial deployment. Full article