Search Results (139,800)

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

In this work, a lightweight framework enabled by the modified YOLOv11s-seg model for tea shoot detection and plucking point localization is proposed. Detecting tea shoots and localizing plucking points with higher accuracy generally require larger model size and more model parameters, making it difficult to balance accuracy and lightweighting. To overcome this limitation, a modified lightweight YOLOv11s-seg model is developed. First, the multi-scale edge information enhancement is introduced into the conventional YOLOv11s-seg to extract edge feature better and improve the detection accuracy of tea shoots. Meanwhile, context anchor attention is utilized to modify the cross stage partial spatial attention module in a backbone network to improve the detection capability for small objects. Moreover, the detail calibration reconstruction feature pyramid network is proposed. It utilizes spatial and contextual semantic information to reconstruct and calibrate features in key regions, enhancing the capability for object fusion and recognition at various scales. Furthermore, with the modified model performing instance segmentation to acquire the contour of each tea shoot, the coordinates of the three lowest pixel points in the contour are captured to localize the plucking point based on the average coordinates. In addition, the layer-adaptive magnitude-based pruning (LAMP) method is used to lighten the model. The experimental results show that the LAMP-pruned modified YOLOv11s-seg model with a speedup ratio of 1.5 achieves a mAP@0.5 of 86.5% for tea shoot detection, exhibiting a 4.7 percentage point improvement over the conventional YOLOv11s-seg model. Moreover, it exhibits an accuracy of 81.9% for plucking point localization on the validation and test subsets with 232 images in total, and its number of parameters, model size and floating point operations (FLOPs) separately achieve reductions of 67.3%, 66.2%, and 24.9% over the conventional model as well. Therefore, the proposed LAMP-pruned modified model shows good balance between lightweighting and detection accuracy. Finally, the modified LAMP-pruned YOLOv11s-seg model is deployed on a Jetson Orin NX edge module and measured in a tea plantation, with the measured results exhibiting a detection speed of 34.1 FPS and verifying its availability in practical applications. Full article

Simultaneous multi-ion detection is important for interpreting leaching, corrosion, hydration, and solidification processes in cement-based materials, because these processes are controlled by coupled ion migration, binding, and precipitation–dissolution reactions. Conventional methods such as pore-solution extraction, ion chromatography, inductively coupled plasma optical emission spectroscopy, and single-ion potentiometric measurements provide useful chemical information, but they generally rely on discrete sampling or isolated ion channels and therefore have limited ability to capture time-aligned multi-ion evolution. In this study, an IoT-based in situ multi-ion detection system was developed by integrating ion-selective electrodes for Cl⁻, Ca²⁺, F⁻, and H⁺ with an ADS1115 analog-to-digital converter, an ESP32 microcontroller, and a voltage amplification module. The system achieved minimum resolvable concentrations of 10⁻⁵ M for Cl⁻ and F⁻ and 10⁻⁴ M for Ca²⁺, while maintaining pH measurement over the range of 2–12. Ten consecutive measurements at 0.01 M showed relative standard deviations below 0.12%, indicating good short-term repeatability under laboratory calibration conditions. Interference and temperature tests showed that Br⁻ and NO₃⁻ affected the chloride channel at high concentrations, Ca²⁺ reduced free F⁻ activity through Ca–F precipitation equilibrium, and the temperature drift of Cl⁻ and F⁻ electrodes changed direction with concentration, whereas the Ca²⁺ response decreased monotonically with increasing temperature. When applied to phosphogypsum–cement hardened pastes, the system captured rapid Ca²⁺ release, low-level F⁻ fluctuation controlled by Ca–F interaction, non-monotonic Cl⁻ release, and alkaline pH evolution on the same time axis. Compared with existing single-ion or offline methods, the proposed system provides synchronized in situ evidence for interpreting coupled ion leaching in cement-based solid-waste systems.: Full article

The European legislation sets the maximum residue levels for glyphosate in sesame seeds at 0.1 mg/kg (EU Regulation n. 293/2013) and for glufosinate and N-Acetyl-glufosinate expressed as glufosinate at 0.03 mg/kg (EU Regulation n. 2016/1002). The present work describes a rapid methodology to determine glyphosate, glufosinate and its metabolite and N-Acetyl-glufosinate in sesame seeds by LC/MS/MS. The method was studied in the framework of EU proficiency tests on sesame seeds. The analytical method was developed using methanol acidified with formic acid (1%, v/v) extraction with an isotope internal standard, followed by LC/MS/MS detection. The recoveries were performed in the range of 0.05–0.5 mg/kg for glyphosate and 0.02–0.2 mg/kg for glufosinate and N-Acetyl-glufosinate. All the recovery values were between 70 and 114%, which is the acceptable interval according to SANTE/11312/2021; the relative standard deviation (%RSD) values met the requirement of <20%. Linearity for each substance in solvent and matrix was studied, and the response was linear with R2 > 0.999. We considered precision, matrix effect, LOD and LOQ in the validation. All the parameters were in agreement with the acceptability criteria of the document SANTE/11312/2021. The method was considered suitable for the determination of the studied substances on sesame seeds. Full article

Mycotoxins are fungal secondary metabolites with dual significance: they threaten health via food contamination yet hold potential as biopesticides. Their isolation from complex matrices remains a critical challenge. This review analyzes classical methods (liquid–liquid extraction, SPE including QuEChERS, chromatography). Traditional techniques suffer from poor selectivity, multi-step processing, large toxic solvent volumes, and matrix effects. As alternatives, emerging strategies based on rational design are considered: directed cocrystallization, supercritical fluid extraction, smart MOF/COF membranes, and AI integrated with physicochemical modeling. The concept of “precision” extraction enabling prediction of target isolation at the molecular level is developed. Recommendations for standardizing experimental reporting to create machine-readable datasets for neural networks are provided. The review concludes that while most still require experimental validation for mycotoxins, these approaches point toward selective, sustainable mycotoxin isolation technologies for analytical control and pure standard production. Full article

Microplastics (MPs) are increasingly detected in dairy products, raising food-safety concerns. Their behavior in complex food matrices and interactions with probiotic microorganisms remain poorly understood. This exploratory study evaluated storage-dependent changes in operationally defined, digestion-resistant recoverable residues in yogurt containing Bifidobacterium longum subsp. infantis (ATCC 15697). Yogurt samples were prepared with polypropylene (PP), polyethylene (PE), and polystyrene (PS), individually and in combination, and analyzed over 21 days of refrigerated storage. Gravimetric values served as relative, operational indicators of recoverable residues—not validated absolute polymer masses—while polymer identity was qualitatively confirmed by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS). B. longum subsp. infantis remained viable throughout storage (6.3–8.2 log₁₀ CFU/g). All MP-containing groups showed consistent storage-associated decreases in recoverable residue fractions, greatest in PP, followed by PE and PS; probiotic-free controls remained stable. Polymer-specific Py-GC/MS signals were detectable at all time points. Because polymer identity was retained and the workflow was not validated for absolute recovery, findings are interpreted as storage-associated changes in extractability, filterability, and/or residue recovery—not as polymer degradation, mineralization, or biological removal. These in vitro observations are limited to the yogurt matrix and do not support extrapolation to livestock exposure, human dietary risk, or farm-to-fork transfer. Within these limits, the findings provide a preliminary, hypothesis-generating perspective on probiotic–microplastic interactions in fermented dairy products. Full article

Due to their diverse phytochemical composition, medicinal plants belonging to the families Amaryllidaceae, Lamiaceae, and Myrtaceae possess antimicrobial and antioxidant properties. In this study, six ethanolic extracts of Allium ursinum, Allium sativum, Allium cepa, Salvia rosmarinus, Ocimum basilicum, and Syzygium aromaticum were analyzed by HS-SPME GC-MS and HPLC. Their chemical composition was evaluated and compared by chemometrics and their biological activity determined by an antimicrobial assay. A total of 72 compounds was detected (terpenoids, phenolic derivatives, fatty acids, and phytosterols). In Allium species, phytosterols were mainly abundant, whereas O. basilicum extracts were characterized by high contents of linalool and S. rosmarinus by 2-hydroxychalcone and 4-hydroxybutanoic acid lactone. Principal component analysis distinguished chemically species-specific chemical profiles, whilst the HPLC evaluation resulted in the highest quercetin content in S. rosmarinus extracts, which also displayed the best antibacterial effect against Staphylococcus aureus. Despite the observed correlation between the quercetin content and antibacterial activity, no definitive relation could be established without biological replicates, MIC evaluation, and tests with isolated compounds. Full article

This study aimed to identify areas at risk of deforestation in the Cerrado biome of the Brazilian Midwest (states of Mato Grosso, Mato Grosso do Sul, and Goiás) and to estimate potential soil losses due to water erosion under land-use change scenarios. The methodology integrated the Universal Soil Loss Equation (USLE), spatializing rainfall erosivity (R), soil erodibility (K), topographic factor (LS), and cover-management factor (CP), with the ACEU (Accessibility, Cultivability, Extractability and Unprotected/protection status) model to assess deforestation risk based on accessibility, agricultural suitability, extractive activities, and legal protection status. Results indicated an average soil loss of 0.11 t ha⁻¹ year⁻¹ under natural vegetation cover, with 90% of the area presenting losses below 0.25 t ha⁻¹ year⁻¹. However, 27.5% of the remaining natural cover is located in areas classified as high or very high deforestation risk, indicating significant environmental vulnerability. Simulated scenarios of land-use conversion to pasture and annual crops revealed substantial increases in soil loss, particularly under annual cropping systems, potentially exceeding soil loss tolerance thresholds across millions of hectares. The findings demonstrate that integrating deforestation risk assessment with erosion modeling is a strategic tool for environmental planning, reinforcing the importance of preserving native vegetation to maintain ecosystem services and ensure long-term environmental sustainability. Full article

Exploitation of deep (>4000 m) tight geothermal reservoirs is constrained by low native permeability and premature thermal breakthrough, limiting sustainable heat recovery. Here, we investigate THM (thermo–hydro–mechanical) controls on fluid flow and heat transport during cold-water reinjection in deep tight sandstone reservoirs through an integrated framework linking two-dimensional mechanistic analysis with three-dimensional field-scale modeling. A two-dimensional thermo-poroelastic model reveals that strong thermal contrasts induced by cold-fluid injection cause contraction of the rock framework and transient pore-space dilation under confinement, producing short-term permeability enhancement. This process alters local flow capacity and redirects early cold-front migration, with persistent impacts on subsequent heat transport. Field-scale simulations further quantify the coupled effects of well spacing and reinjection temperature on thermal breakthrough, defined as a 1 °C decline in production-well temperature. Increased well spacing delays cold-front arrival and significantly retards breakthrough, whereas lower reinjection temperature enhances early heat extraction but accelerates convective transport, leading to earlier breakthrough. The combination of thermally enhanced permeability and intensified convection promotes preferential flow channels, increasing breakthrough risk. Balancing thermal-breakthrough delay against the heat-extraction driving force, the simulations delineate a favorable engineering window for the investigated conditions and clarify how cooling-sensitive permeability evolution affects preferential flow and reservoir-scale thermal response. Full article

The exponential increase in photovoltaic panel (PV) waste highlights the urgent need to develop efficient and sustainable recycling processes. It is estimated that by 2030, 8 million tons of PV modules will reach their end-of-life stage, posing a significant environmental challenge and requiring the development of green technologies for resource recovery. This study assessed the performance of imidazolium-based ionic liquids (ILs) as “designer solvents” for the selective leaching of copper and silver from disused PV panels. Specifically, four quaternary imidazolium salts were evaluated: [Bmim]HSO₄, [Emim]HSO₄, [Bmim]Cl, and [Emim]Cl. Leaching tests were conducted on silicon wafers containing 0.28% Ag and 0.19% Cu under varying temperatures (25, 50, and 80 °C), IL concentrations (20% and 60% v/v), and hydrogen peroxide (H₂O₂) dosages (0% and 3% v/v) as an oxidizing agent. The results identified [Bmim]HSO₄ as the most effective leaching agent. The system achieved a maximum copper extraction of 96.70% at 60% v/v concentration and 80 °C. For silver, the highest extraction of 45.13% was obtained using [Bmim]HSO₄ at 20% v/v and 80 °C. The addition of H₂O₂ was crucial, demonstrating a clear synergistic effect with the imidazolium-based ILs by promoting oxidative dissolution. These findings confirm that imidazolium-based ionic liquids represent a promising and environmentally friendly alternative for the recovery of high-value metals in the circular economy of photovoltaic recycling. Full article

Flavor esters are valuable compounds widely used in the food, beverage, and cosmetics industries for their aroma and flavor-enhancing properties. Traditional methods of obtaining these compounds, such as extraction from natural sources or chemical synthesis, present challenges related to cost and toxicity, respectively. Enzymatic synthesis, particularly using immobilized lipases, offers a sustainable and efficient alternative. This study investigates the application of CRL immobilized on Diaion HP-20 for geranyl butyrate synthesis via esterification of geraniol and butanoic acid using Candida rugosa lipase (CRL) immobilized on Diaion HP-20 (CRL-DHP-20). The immobilization process resulted in a protein loading of 29.6 ± 2.2 mg/g support from an initial 40 mg/g, and the immobilized biocatalyst exhibited a hydrolytic activity of 124.0 ± 2.5 U/g using olive oil emulsion. Reaction conditions were optimized through a central composite design, evaluating the influence of biocatalyst concentration, temperature, and agitation on ester conversion. The optimal conditions (13.4% CRL-DHP-20, 48.2 °C, and 220.1 rpm) led to 85.4% conversion in 360 min. Additionally, CRL-DHP-20 retained 84% of its initial activity after six reaction cycles, indicating good operational stability. These findings highlight the potential of CRL-DHP-20 as an effective and reusable biocatalyst for green synthesis of flavor esters. Full article

Methods of ridge preservation following tooth extraction, aiming to maintain alveolar bone volume and support tissue regeneration, have been extensively researched. Continuously, new approaches and materials are being explored in this context. To scientifically evaluate outcomes, the pre-implant situation is usually assessed radiologically, histologically, and/or clinically. However, the influence of ridge preservation on implant placement itself is rarely examined in depth, and if at all, the focus has been on implant stability or survival rates. Based on the assumption that preoperative radiological assessment, including cone beam computed tomography, provides only an indirect and inherently limited approximation of actual intraoperative bone condition, undetected factors such as insufficient bone density, mechanically unfavorable trabecular structure, or incompletely resorbed residual biomaterial may necessitate a shift of the implant from the preferred position originally occupied by the tooth root. We therefore established a method that evaluates and categorizes implant position in three dimensions based on radiological data post-implantation. Our data, derived from a multicenter randomized clinical trial (RCT), demonstrate that the greatest positional deviations are observed without preservation, whereas the combination of biomaterial and PRF most frequently allowed for central implant placement. The proposed method proves well suited for evaluating the outcome of ridge preservation procedures. The findings demonstrate that both the absence and presence, and further the type, of preservation have a measurable influence on the final implant positioning. Full article

Accurate and efficient detection of cyanobacteria in microscopic images is important for automated water-quality monitoring, but remains challenging because of complex aquatic backgrounds, large scale variation, and uneven sample quality. This study proposes an adaptive multi-scale fusion enhanced RT-DETR framework for cyanobacteria detection. The baseline RT-DETR-R18 is improved by incorporating the SeFaster module for efficient feature extraction, the high-level screening-feature fusion pyramid network for semantic-guided multi-scale fusion, and the Wise-IoU loss for more stable localization learning under mixed-quality samples. Experiments on the reorganized EMDS-7 dataset show that the proposed method achieved 79.05% mAP@0.5, 66.03% mAP@0.5:0.95, 16.31 M parameters, 54.6 G FLOPs, and 70.85 FPS. The proposed model also obtained the highest mAP@0.5 across the seven cyanobacteria categories. Moreover, cross-dataset evaluations further suggest the stability and transferability of the model. These results indicate that the proposed framework demonstrates potential for effective cyanobacteria detection in microscopic images with a good balance between detection accuracy and computational efficiency. Full article

Background: Abnormalities in energy and amino acid metabolism are potentially involved in hepatocellular carcinoma (HCC) development. This study aimed to identify serum metabolites predictive of HCC following sustained virological response (SVR) with hepatitis C virus (HCV) treatment. Methods: Comparative metabolomics was conducted using time-course serum samples from patients who failed interferon-based therapy but subsequently achieved SVR with direct-acting antivirals (DAAs), minimizing inter-individual variability. Predictive biomarkers for post-SVR HCC were extracted from the results and validated by comparing 29 patients who developed post-SVR HCC with 58 age-matched patients who remained HCC-free during follow-up. Results: Metabolite concentrations changed more markedly after treatment in SVR cases than in non-SVR cases. Significant changes in methionine (Met), methionine sulfoxide (MetO), and ornithine (Orn) levels before and after treatment (Pre- and Post-Tx) were found only in the non-HCC group. Regression and survival analyses identified high levels of Pre- and Post-Tx Orn, Pre-Tx Met, and Post-Tx MetO as predictors of post-SVR HCC and enabled risk stratification. The integration of these metabolites with the fibrosis-4 (FIB-4) index and alpha-fetoprotein (AFP) facilitated risk stratification and discriminated between high- and low-risk patients. The Pre-Tx FIB-4/Met model and the Post-Tx AFP/MetO/Orn model identified low- and high-risk groups with 3-year HCC incidence rates of 6.4% and 81.8%, respectively. Conclusions: Serum Met, MetO, and Orn were identified as candidate biomarkers associated with post-SVR HCC development, which remains a concern in the fight against hepatitis C. Combining these metabolites with established clinical markers may improve post-SVR HCC risk stratification. Full article

Background/Objectives: The emergence of drug-resistant Plasmodium falciparum highlights the need for new antiplasmodial compounds with distinct mechanisms of action. Microbial secondary metabolites, particularly from Streptomyces species, remain important sources of bioactive molecules. This study aimed to evaluate antiplasmodial metabolites associated with a Mongolian Streptomyces isolate. Methods: Streptomyces sp. strain D10 was isolated from Mongolian soil samples and extracted with ethyl acetate. Bioassay-guided fractionation was performed, followed by LC–HRMS analysis and GNPS-based spectral dereplication. Antiplasmodial activity was evaluated against P. falciparum 3D7, K1, and Dd2 strains using a SYBR Green I assay. Cytotoxicity was assessed in HSF cells. Stage-specific susceptibility assays were conducted using synchronized 3D7 parasites. Comparative docking analyses against β-hematin and the chloroquine resistance transporter (PfCRT), together with target prediction and molecular docking analyses, were performed to explore potential mechanisms. In vivo efficacy was evaluated using a Plasmodium yoelii 17XNL mouse model. Results: Fractionation yielded an active fraction (C2), and LC–HRMS and GNPS-based dereplication suggested a bile acid-like metabolite, with ursodeoxycholic acid (UDCA) returned as a putative spectral library candidate associated with fraction C2. Fraction C2 and UDCA showed comparable antiplasmodial activity against P. falciparum 3D7 (IC₅₀ = 6.55 ± 3.00 and 4.68 ± 0. 65 µg/mL, respectively) without detectable cytotoxicity up to 200 µg/mL. Activity was retained against multidrug-resistant K1 and Dd2 strains. Stage-specific assays demonstrated inhibitory activity across ring, trophozoite, and schizont stages without significant stage-dependent differences. Comparative docking analyses suggested interaction profiles distinct from chloroquine in β-hematin and PfCRT models. Additional docking analyses identified PfGluPho, PfMAPK, and PfPFT-β as potential targets. In vivo, UDCA reduced parasitemia in a dose-dependent manner without significant toxicity. Conclusions: UDCA exhibited moderate antiplasmodial activity across in vitro, in silico, and in vivo evaluations with a favorable selectivity profile, supporting further investigation of bile acid-like metabolites as potential antimalarial scaffolds. Full article