Search Results (5,750)

Phyllosphere microorganisms interact with host plants to regulate growth, promote nutrient uptake and enhance stress tolerance with host specificity, while arbuscular mycorrhizal fungi facilitate plant nutrient absorption and stress adaptation. Current poplar microbial studies mostly focus on hermaphroditic species, with limited research on dioecious poplars. This study selected four poplar species commonly hybridized with Populuscathayana and Populus deltoides in the Xinmin area of Liaoning Province as research subjects: two female plants, DM-9-18 and DX-08-01, and two male plants, 2111 and Qingshan poplar. We performed MiSeq high-throughput sequencing targeting bacterial 16S rRNA, fungal ITS, and arbuscular mycorrhizal fungal (AMF) marker genes from poplar phyllosphere, coupled with chemical quantification of leaf, root and rhizosphere soil, to disentangle clone- and sex-associated divergence in microbial assemblages and their core environmental drivers. No significant gender differences were observed in leaf and rhizosphere nutrient levels and microbial α diversity, whereas male poplars had higher rhizosphere soil nutrients. Male and female poplars genotypes harbored distinct microbial ASVs. The dominant phyllosphere microbes and arbuscular mycorrhizal fungi exhibited gender-specific abundance variations, and nutrient content was the key factor shaping microbial communities. This study clarifies microbial community differences among the four selected hybrid poplar clones. While the experimental design confounds sex with host genotype, the observed patterns provide insights into potential sex-related variations. Our results advance the mechanistic understanding of how dioecious poplar genotype and sexual phenotype jointly filter leaf and root-associated microbial symbionts, with applied implications for hybrid poplar breeding and shelterbelt microbial regulation. Full article

Lonicerae japonicae flos (LJ), derived from Lonicera japonica Thunb., is a classic medicinal and edible plant traditionally used to clear heat, detoxify, and disperse wind-heat. It is widely consumed as a health tea or food additive and played a significant role during the COVID-19 pandemic. Its closely related species, Lonicerae flos (LFS), derived from L. macranthoides, L. hypoglauca, L. confusa, and L. fulvotomentosa, share similar functions. Distinguishing between these two is crucial to minimizing medication-related risks; however, studies on their differences in inorganic elements are currently insufficient. This study investigated the content differences in ten inorganic elements in 97 samples using inductively coupled plasma mass spectrometry and inductively coupled plasma atomic emission spectrometry. LJ exhibited significantly higher iron (Fe) content and considerably lower manganese (Mn) content than LFS. Fe, Mn and Na were identified as key discriminatory elements through orthogonal partial least squares-discriminant analysis, and cluster analysis based on the Fe/Mn ratio also achieved clear separation between the two groups. Network pharmacology and pathway enrichment analyses were subsequently conducted to explore the potential biological relevance of Fe- and Mn-associated differences. Additionally, molecular docking provided exploratory computational assessments of theoretical interactions between Fe/Mn and selected target proteins under simplified conditions. It is important to note that these computational results should not be interpreted as direct evidence of biological activity, since no in vitro or in vivo validation was performed. Overall, this study provides elemental profiling data to differentiate between LJ and LFS and highlights the potential value of the Fe/Mn ratio as a complementary chemical indicator for the authentication and quality evaluation of Lonicera medicinal materials. Full article

Background/Objectives: Niaprazine is widely used for the management of sleep disorders in pediatric and geriatric patients; however, no commercially available oral liquid formulation is currently available in Italy, making extemporaneous compounding necessary. In routine practice, the beyond-use date (BUD) is often limited to 30 days, potentially affecting therapeutic continuity. This study aimed to evaluate the chemical, physical, and microbiological stability of a compounded niaprazine syrup to support evidence-based BUD and to assess the suitability of selected ready-to-use compounding vehicles for preparing alternative niaprazine oral liquid formulations. Methods: A niaprazine syrup (3 mg·mL⁻¹) was prepared in a sucrose-based vehicle acidified with tartaric acid and preserved with potassium sorbate. Chemical stability of niaprazine was evaluated by high-performance liquid chromatography coupled with diode array detector (HPLC-DAD), whose specificity was confirmed by forced degradation studies. Stability was monitored for up to 9 months at 4–8, 25, and 40 °C and confirmed after 12 months by HPLC-DAD and mass spectrometry (MS). Physical stability of the formulation was monitored by pH and visual inspection up to 12 months. Microbiological quality was assessed for 2 months at 4–8 and 25 °C according to the European Pharmacopoeia. In parallel, four selected ready-to-use compounding vehicles were evaluated for their suitability to prepare stable niaprazine oral liquid formulations. Results: Niaprazine concentrations in the syrup remained within pharmacopeial acceptance limits (±10%) at all temperatures, although a decrease was observed under accelerated conditions (40 °C). pH remained stable (≤0.5-unit variation) and the formulation stayed clear and homogeneous throughout the study, with only minor visual changes after prolonged storage at 40 °C. Statistically significant differences (p < 0.05) were observed in both the HPLC-DAD and HPLC-MS datasets. Microbiological testing confirmed compliance up to 2 months at 4–8 and 25 °C (TAMC ≤ 10³ CFU/mL; TYMC ≤ 10² CFU/mL; Escherichia coli absent). Comparable chemical and physical stability was observed for three of the four formulations prepared with the ready-to-use vehicles over at least two months. Conclusions: The compounded niaprazine syrup demonstrated chemical, physical, and microbiological stability under refrigerated and room-temperature storage, supporting evidence-based beyond-use dating of up to two months under the tested conditions. Ready-to-use vehicles may represent a practical complementary approach, offering standardized alternatives for the preparation of niaprazine oral liquid formulations. Full article

Excessive nitrate discharge causes water eutrophication and public health risks, which has become a core challenge in global water environment governance. Conventional nitrogen removal technologies suffer from limitations such as carbon source dependence and secondary pollution, and can hardly meet the requirements of low-carbon water treatment. Driven by electric energy and free of additional chemical reagents, electrocatalytic nitrate reduction enables flexible regulation of product selectivity. Among all possible reaction pathways, selective N₂ production is the nitrogen removal route with the highest environmental benefits. However, constrained by the high energy barrier of N–N coupling and intense competition from side reactions, achieving highly selective N₂ production remains a major technical difficulty, and most existing reviews in this field focus on ammonia synthesis. This paper systematically reviews the research progress in this field, elucidates the reaction network and nitrogen production mechanism, compares the advantages and disadvantages of three types of selectivity evaluation methods, summarizes the design strategies of multi-scale electrocatalysts, and analyzes how operational parameters (including applied potential, electrolyte composition, pH, etc.) and reactor configuration regulate the reaction selectivity. Finally, the existing challenges are concluded and future development directions are prospected, so as to provide a reference for the research, development and engineering application of electrocatalytic nitrogen removal technology. Full article

Membrane separation has become a key technology for advanced water purification and control of emerging contaminants because of its high separation efficiency, low chemical demand, and ease of integration. However, organic membrane fouling induced by the coupling of dissolved organic matter and coexisting metal ions remains a major obstacle to stable and efficient membrane operation. This review focuses on metal ion-mediated formation of organic aggregates in the bulk solution and their governing role in membrane fouling behavior. The review summarizes how metal ions regulate organic aggregate formation through distinct dominant mechanisms. Na⁺ mainly screens electrostatic repulsion, Ca²⁺ promotes ion bridging and cross-linking, and Mg²⁺ often induces weaker bridging or hydration-mediated effects due to its stable hydration shell. The review further discusses the dual effects of mixed foulants and dynamic fouling layers on the rejection of emerging contaminants. In addition, current control strategies, including pre-coagulation, pre-oxidation, and catalytic functional membranes, are evaluated from the perspective of regulating aggregate structures and interrupting interfacial deposition. Finally, future research should shift from membrane-interface-centered analysis to bulk-phase aggregation, establish quantitative structure–effect relationships between aggregate properties and fouling behavior, and promote fouling-control strategies from mechanistic effectiveness toward engineering practicality. Full article

Spectroelectrochemistry (SEC) provides unique information-rich datasets by coupling molecular spectroscopic fingerprints with electrochemical activity. Despite its richness, direct machine-learning (ML) analysis of SEC data under realistic experimental constraints and scarcity remains unexplored. This work examines how much spectroscopically encoded electrochemical information can be learned from minimal SEC training data in a chemically reversible two-electron redox system, and how model choice interacts with limited experimental diversity across scan rate. Using purely experimental SEC datasets collected at four scan rates (2, 3, 5, and 7 mV/s), partial least squares (PLS) and convolutional neural networks (CNNs) regressors are evaluated under several SEC-level training, validation, and test configurations. Model performance is assessed across three targets of increasing physical complexity, including species concentrations, derivative cyclic voltabsorptometry (DCVA) current, and experimental cyclic voltammetry (CV) current. Under single-SEC training, both models achieve the expected near-quantitative concentration prediction (R² ~0.99), while performance decreases for DCVA (R² ~0.93) and most substantially for CV current (R² ~0.78), reflecting the progressively weaker and more indirect encoding of these targets within the absorbance data. Introducing minimal experimental diversity with only two distinct training SEC datasets enables both PLS and CNN models to generalize strongly to an unseen third SEC dataset, achieving maximum CV R² values approaching ~0.98 in the most favorable configurations. CNN models extend the apparent linear performance ceiling observed for PLS by capturing localized, scan-rate-conditioned nonlinear correlations between spectral evolution and the experimentally measured CV response, yielding improved waveform reconstruction and greater robustness to training SEC dataset selection. These results demonstrate that, within the present chemically reversible and spectroscopically well-resolved SEC system, high-fidelity prediction of electrochemical targets can be achieved without large datasets when limited but strategically selected electrochemical diversity is introduced. SEC dataset linearity is further shown to be target-dependent and becomes operationally meaningful only when scan-rate space is sufficiently sampled. More broadly, this work establishes a controlled framework for investigating ML-enabled SEC dataset analysis under experimentally scarce conditions and provides guidance for experimental design and calibration in low-data spectroelectrochemical settings. Full article

Fresh pork remains susceptible to psychrotrophic spoilage during superchilled storage. Although oregano essential oil (OEO), nisin, and active absorbent pads have each been studied, their effects on spoilage-community proliferation and concurrent quality loss remain poorly resolved. Here, we coupled longitudinal 16S rRNA gene profiling with conventional microbiological and quality measurements to evaluate a poly(vinyl alcohol)/soy protein isolate pad containing OEO-loaded soluble soybean polysaccharide-nisin nanoparticles (PS-NPs). Pork was stored at −1 °C for 24 days without a pad (CK), with a nanoparticle-free pad (PS), or with PS-NPs. Bacterial communities in CK and PS-NPs were profiled alongside total viable count, TVB-N, TBARS, protein carbonyls, color, water-holding capacity, and sensory quality. Compared with CK, PS-NPs slowed the increase in viable counts and delayed physicochemical and sensory deterioration. On day 24, Pseudomonas relative abundance was 39.48% with PS-NPs and 51.02% in CK. TBARS and protein carbonyl contents were 36.96% and 19.44% lower than CK, respectively, while cooking loss was 28.49% versus 32.18%. Among the evaluated taxon-quality pairs, Pseudomonas psychrophila showed the strongest positive association with protein carbonyl content. Unlike earlier performance-focused studies, this work links packaging-associated community shifts with concurrent chemical, physical, and sensory changes under superchilling. The findings support PS-NPs as a preservation strategy. Full article

This study presents targeted contributions to the development of macromolecular silane coupling agents (MSCAs) and high-performance fiber-reinforced polyimide (PI) composites. Three novel MSCAs were synthesized via chemical imidation and transamidation reactions, using hexafluoroisopropylidene diphthalic anhydride and 2,3,3′,4′-diphenyl ether tetracarboxylic acid as dianhydride monomers, 4,4′-diaminodiphenyl ether and 1,3-bis(4′-aminophenoxy)benzene as diamine monomers, and aminopropyltriethoxysilane (KH550) as the capping agent. Structural characterization by FTIR, ¹H NMR, and XPS confirmed the successful synthesis of the target products, with silicon contents of 3.29%, 3.37%, and 3.66%, respectively. The MSCAs exhibited excellent thermal stability, with 10% weight loss temperatures ranging from 462 °C to 543.3 °C, and good solubility in most polar organic solvents, addressing the poor processability of conventional macromolecular coupling agents. Compared with small-molecule KH550, the MSCAs significantly enhanced interfacial properties: the average tensile and flexural strengths of the composites increased by 11.0% and 9.8%, respectively, compared to 3.9% and 4.0% for KH550. SEM analysis demonstrated that MSCAs improved resin adhesion to fibers and fiber–resin compatibility. Additionally, the T5, T10, and glass transition temperatures of the composites were further optimized due to polymer chain diffusion and entanglement. This work provides a feasible strategy for interfacial design in high-performance polyimide composites. Full article

Starch-modifying glycoside hydrolases (GHs) typically operate via a retaining double-displacement mechanism, involving formation of a covalent glycosyl–enzyme intermediate. This intermediate can be resolved either by water, resulting in hydrolysis, or by a glucan acceptor, leading to transglucosylation. Many GHs exhibit both catalytic activities, although they are classified according to their predominant reaction. For example, branching enzymes (BEs) catalyse α-(1→4) bond cleavage and α-(1→6) branch formation via transglucosylation, while also exhibiting minor hydrolytic and disproportionation activities that broaden their catalytic repertoire. More recent research indicates that the different catalytic activities of BEs can be interconnected, thereby collectively determining the final α-glucan architecture. This challenges the classical view that the predominant branching activity of BEs is catalysed independently. Moreover, the balance between these coupled activities influences substrate specificity and can broaden the substrate scope to include chemically modified starches. Furthermore, the co-application of BEs with other GHs reveals synergistic interactions between catalytic activities, enabling the generation of α-glucan structures that cannot be produced by any of the enzymes individually. In this perspective paper, and based on recent developments, we argue that the catalytic framework of BEs provides multiple strategies for tailoring diverse α-glucan architectures. This enables modulation of structural features across hierarchical levels, from supramolecular to macromolecular organisation. As a result, BEs represent versatile tools for engineering starch functionality beyond digestibility, extending their potential toward pharmaceutical and non-food applications. Looking ahead, we discuss how enzyme-designed and chemically functionalised α-glucan polymers may emerge as a new class of sustainable materials. These materials could provide biodegradable, water-soluble, and renewable alternatives to petrochemical-derived polymers used in personal and home care products. Full article

Lethal foodborne illness outbreaks represent a critical intersection of public health surveillance, environmental health, and forensic toxicology. When acute gastrointestinal syndromes lead to sudden death, traditional postmortem investigation techniques are often hindered by tissue autolysis and the overgrowth of putrefactive microflora, which complicate conventional microbiological assays. This review establishes a comprehensive framework for chemical forensics by evaluating the utility of stable isotope analysis (SIA) as a supportive, probabilistic chemical proxy to complement traditional epidemiological investigations of postmortem food contamination sources. Following JBI scoping review guidelines and the PRISMA-ScR reporting framework, data from 42 peer-reviewed articles (2000–2026) were charted and synthesized to map natural isotopic variations (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H and δ³⁴S) across both forensic decedents and environmental reservoirs. The findings outline a structured, multi-tissue diagnostic cascade governed by biological metabolic turnover rates: unabsorbed gastric chyme provides a direct chemical match to contaminated source food items within a hyper-acute 0–6 h window; high-turnover visceral matrices (liver, blood plasma) shift to reflect acute exposure profiles within 1–7 days; and continuously fixed keratinized matrices (hair, nails) archive multi-month dietary and transcontinental transit histories. Furthermore, compound-specific isotope analysis (CSIA) of individual amino acids offers unprecedented structural resolution, utilizing the carbon discrimination metric (Δ¹³C_glu-phe) to differentiate pristine agricultural signatures from endogenous metabolic distortions while biochemically verifying pre-mortem physiological stress and hyper-catabolic muscle wasting. Taphonomic thresholds were explicitly defined, establishing that bulk visceral soft tissues remain isotopically stable (±0.3‰) for up to 48 h at room temperature (~21 °C) before microbially induced nitrogen enrichment (δ¹⁵N > +2.8‰) alters native profiles, whereas hair and nail keratin maintain absolute isotopic stability for over 180 days postmortem. When pristine multi-isotope signatures are coupled with mandatory chloroform–methanol lipid extraction and processed through spatial Bayesian assignment models, geographic provenance tracking via environmental isoscapes achieves a predictive accuracy of 97%. This review introduces a standardized environmental health protocol designed to harmonize field environmental sampling with medical autopsies. This protocol provides a legally robust strategy for investigating unresolved lethal foodborne illness case-outbreaks, particularly those involving pediatric mortalities linked to the consumption of counterfeit or fraudulent food products in low- and middle-income countries. Furthermore, it aims to strengthen national and municipal legal frameworks and international biosecurity enforcement. Full article

Background/Objectives: Heracleum millefolium Diels (HMD) is a traditional Tibetan medicinal herb that has been reported to possess analgesic, anti-edematous, and anti-inflammatory activities. However, its chemical composition and anti-inflammatory mechanisms remain unclear. Methods: In this study, ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS), combined with database searching and “seed”-based molecular networking, was used to systematically characterize the chemical constituents of HMD. Furthermore, an integrated strategy combining network pharmacology, molecular docking, and cellular validation was applied to explore potential anti-inflammatory mechanisms associated with HMD. Results: A total of 472 compounds were identified or tentatively identified from HMD. Network pharmacology analysis predicted five key targets, namely TNF, IL-6, IL-1β, GAPDH, and AKT1, together with four potential bioactive constituents, including velutin, artemitin, kaempferol, and naringenin. Pathway enrichment analysis indicated that the potential anti-inflammatory effects of HMD were mainly associated with lipid and atherosclerosis and the AGE-RAGE signaling pathway. Molecular docking suggested potential favorable interactions between the selected constituents and key targets. Moreover, cellular experiments demonstrated that the four compounds significantly inhibited inflammatory responses in LPS-stimulated RAW264.7 macrophages. Conclusions: This study systematically characterized the chemical profile of HMD and preliminarily explored its potential anti-inflammatory mechanisms, providing a scientific basis for further investigation of its bioactive constituents and pharmacological properties. Full article

Small experimental datasets make synthesis-condition modelling particularly sensitive to overfitting and data leakage. Here, a graph convolutional neural network (GCNN) was coupled with approximate attribution enhancement (AAE) and evaluated on 54 gold-nanocluster synthesis records using a strict fold-local pipeline. All preprocessing, supervised representation learning, augmentation, model selection, and calibration were confined to each training fold, and metrics were calculated only on untouched original records. The strongest no-augmentation model achieved a Matthews correlation coefficient (MCC) of 0.729. With 20,000 fold-local AAE samples, Logistic Regression reached an accuracy of 0.864, an F1 score of 0.862, and an MCC of 0.758. The corresponding MCC values decreased to 0.579, 0.526, and 0.652 under leave-one-ligand-out, leave-one-publication-out, and leave-region-out evaluation, respectively, indicating that extrapolation beyond represented chemistry remained more difficult than random-fold interpolation. Feature, label-weight, augmentation-baseline, covariance, calibration, and stability analyses consistently identified ligand aromaticity, temperature, and HAuCl₄ concentration as the most influential factors. An interpretable analysis of a 34-record aqueous Au₂₅ subset further produced condition rules and a pH–temperature map, while nine literature formulations provided an external audit within the represented domain. The results position AAE as a local regularisation strategy for data-limited synthesis modelling rather than as a source of new chemical information. Full article

In semi-humid loess regions, young apple–soybean alley-cropping is constrained by water competition and phenolic accumulation, but how emitter-line layout reorganizes soil biochemical environment remains unclear. This study evaluated whether locally used management systems differed in the spatial coupling of phenolic load, biological activity, and productivity. A two-year field experiment compared rainfed apple monoculture, rainfed soybean monoculture, rainfed apple–soybean alley-cropping, and drip-irrigated alley-cropping with one emitter line per soybean row (DL1), per two rows (DL2), or per three rows. Drip-irrigated monoculture was not included because monocultures are generally rainfed locally owing to lower water demand relative to regional precipitation. Soil phenolics, enzyme activities, microbial abundances, yield, and WUE were assessed using spatial profiling, PLS-SEM, SHAP-based model interpretation, and a network-informed soil quality index (SQI). Drip-irrigated alley-cropping, particularly DL1 and DL2, was associated with lower phenolic load and higher biological activity than rainfed systems. Under DL treatments, phenolics were concentrated mainly in the 40–60 cm layer, whereas enzymes and microorganisms remained enriched in the 0–20 cm layer and shifted horizontally away from the tree row, indicating reduced spatial overlap between chemical constraints and biological activity. Productivity was more closely associated with microbial abundance under rainfed conditions but with enzyme activity under drip irrigation. The network-informed SQI discriminated treatments more clearly than conventional indices. DL1 had the highest SQI, whereas DL2 achieved the highest productivity. Among locally relevant systems, DL2 provides a practicable emitter-line layout that balances soil biochemical improvement with crop productivity. Full article

Continuous medium-density fiberboard (MDF) production presents a persistent quality-assurance problem: destructive laboratory tests return results too late to prevent off-specification material from accumulating before a corrective response is possible. This study develops an inspection-oriented predictive quality framework using industrial Distributed Control System (DCS) data and automated machine learning, treating the production line as an integrated nine-stage system in which upstream process disturbances propagate through coupled thermomechanical and chemical operations before becoming visible in final panel properties. Two quality targets were modeled across Ultralight (UL) and Standard Thin (STD) panels using 3365 production batches and 327 DCS process variables. The pipeline combined Random Forest imputation, Pearson collinearity filtering ($\left|r\right|\ge 0.8$), target-specific feature selection, and stacked ensemble regression via H2O AutoML. The Vertical Density Profile Index (VSC), a plant-reported scalar derived from X-ray density profiling, was predicted accurately in both product families (test RMSE: 1.39 and 1.78, index units for UL and STD respectively), reflecting its close coupling to drying stability, resin dosing, and thermal conditions. Internal Bond strength (IB) was harder to predict, especially for thin STD panels (test RMSE: 78.93 kPa vs. 27.41 kPa for UL), as core-layer bonding mechanisms are only indirectly observable through standard DCS instrumentation. Model-agnostic feature importance rankings were physically coherent across both product families, with dominant predictors concentrated in drying, resin application, forming, and hot pressing, consistent with the coupled-subsystem nature of MDF quality formation. The historical dataset was dominated by acceptable and over-quality IB production, which precluded conformity classification and sampling-reduction analysis; a prospective dataset with near-threshold observations is required for those evaluations. Within that scope, the framework provides continuous quality estimates, identifies deviations from the desired operating range, and supports inspection planning as a complement to formal laboratory testing. Full article

This study describes the microstructural and chemical characteristics of luster glazes applied to white clay and chamotte clay bodies using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS) and X-ray diffraction (XRD). Surface and cross-sectional analyses were performed to evaluate glaze morphology, the glaze–body interface, and the formation of metallic phases responsible for the luster effect. SEM observations indicated that the glaze applied to the white clay body exhibited greater thickness uniformity and a well-defined transition zone. In contrast, the chamotte clay body, due to its higher porosity, showed a more heterogeneous glaze distribution and increased interfacial irregularities. EDS analyses revealed the localized distribution of metallic elements, particularly silver and copper, and demonstrated the influence of body composition on glaze–body interactions. XRD results confirmed the presence of silver- and copper-rich crystalline phases on the glaze surface. Overall, the results demonstrate that the optical and esthetic performance of luster glazes depends on both glaze formulation and the microstructural properties of the ceramic body, providing relevant insights for optimizing artistic and industrial applications. Full article