Search Results (9,969)

Low-temperature stress adversely impairs rice germination and seedling establishment. This study assessed a nano-bio-priming strategy using lecithin (L) and silicon dioxide nanoparticles (SiO₂ NPs) to enhance chilling tolerance. Two fragrant rice cultivars (Xiangyaxiangzhan and Meixiangzhan 2) were primed with six combinations of lecithin (0, 50, and 100 μmol·L⁻¹, denoted as L0, L1, and L2) and SiO₂ NPs (0 and 100 mg·L⁻¹, denoted as S0 and S1) and exposed to optimal temperature (25 °C) or low-temperature stress (15 °C). Low-temperature stress delayed germination onset by two days. Combined priming treatments L1S1 and L2S1 significantly alleviated this inhibitory effect. Crucially, cultivar-specific responses were evident in Meixiangzhan 2, where L1S1 increased the germination vigor index by 50.97%. Meanwhile, the effect was less pronounced or inhibitory at normal temperature in Xiangyaxiangzhan. Priming substantially enhanced seedling growth, and L2S1 maximally increased root and shoot length in Meixiangzhan 2 by 55.30% and 15.82%, respectively. Furthermore, biomass accumulation was strongly promoted. L1S1 increased total dry weight and total fresh weight in Meixiangzhan 2 by 19.64% and 23.48%, respectively. Physiologically, priming elevated chlorophyll and carotenoid contents upregulated the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and increased levels of soluble protein and ascorbate (AsA), while maintaining nitrate reductase (NR) activity and hydrogen peroxide (H₂O₂) homeostasis. These physiological improvements were positively correlated with enhanced growth. Our findings demonstrate that co-priming with lecithin and SiO₂ NPs is a potent strategy for enhancing low-temperature tolerance, with efficacy depending on both the treatment combination and rice genotype. Full article

To address over-extrusion and forming defects at path corners caused by path overlap in additive manufacturing, this paper proposes an angle-adaptive look-ahead compensation algorithm based on a Sech attenuation curve. This method establishes a mapping model between the path angle and the adaptive look-ahead distance of the overlapping area, aiming to eliminate the material accumulation at the corner by precisely identifying the overlapping area and modulating the flow rate. By building a Beckhoff five-axis 3D-printing device and relying on the TwinCAT control platform, the compensation triggering logic based on PLC real-time Euclidean distance calculation was realized, and a slicing software with dynamic bias compensation was also developed. Experiments were conducted on triangular samples with extreme acute angles of 5°, universal acute angles of 85°, and 90° straight angles for printing verification. The results show that this algorithm can effectively suppress the material over-extrusion and accumulation at the path overlap in multiple angles, achieving a smooth transition of the sharp corners in the printed contour. The research confirms that the algorithm proposed in this study, together with the integrated software and hardware system, can ensure the forming accuracy of extreme and conventional geometric features while also guaranteeing the printing efficiency, providing an important reference for ensuring the quality coordination control of the formation process of extreme geometric features in additive manufacturing. Full article

Background: Acute Lung Injury/Acute Respiratory Distress Syndrome (ALI/ARDS) is a fatal inflammatory disorder driven by M1 macrophages and the associated inflammatory cascade. Targeted drug delivery to these cells is a promising therapeutic strategy. Methods: L-arginine was conjugated to chitosan of different molecular weights. The resulting curcumin nanocrystals (Arg-CS-Cur) were characterized for conjugation efficiency, zeta potential, stability, and drug release profile. Cellular uptake mechanisms and mitochondrial targeting were investigated in lipopolysaccharide (LPS)-induced M1 macrophages using specific endocytic inhibitors and confocal microscopy. Results: Low-molecular-weight chitosan (MW 50 kDa) showed the highest L-Arg conjugation efficiency (22.31%). The optimized Arg-CS-Cur nanocrystals exhibited high zeta potential (± 47.5 mV), excellent stability, and a superior drug release. They were internalized by M1 macrophages more efficiently than unmodified CS-Cur or free curcumin (p < 0.05). Uptake occurred via clathrin-mediated endocytosis (p < 0.001) and was mediated by CAT-2, which was highly expressed in M1 macrophages (p < 0.001). Arg-CS-Cur specifically targeted the mitochondria, reducing ROS and NLRP3 expression, thus inhibiting the NLRP3 inflammasome pathway (p < 0.001). Conclusions: This L-arginine-modified chitosan-based nanodelivery system synergistically exploits CAT-2 and clathrin pathways to deliver curcumin to M1 macrophage mitochondria, inhibiting the NLRP3 inflammasome. This dual-targeted strategy offers a promising approach for treating ALI/ARDS. Full article

Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and accurate prediction of mortality is essential for guiding clinical decision-making and optimizing ICU resource allocation. In this study, we utilized the MIMIC-III database and identified a final analytic cohort of 667 geriatric TBI patients, on which we developed a machine learning framework for 30-day mortality prediction. A rigorous preprocessing pipeline—including Random Forest-based imputation, feature engineering, and hybrid selection—was implemented to refine predictors from 69 to 9 clinically meaningful variables. CatBoost emerged as the top-performing model, achieving an AUROC of 0.867 (95% CI: 0.809–0.922), with a sensitivity of 0.752 and a specificity of 0.888 on the independent test set. SHAP analysis confirmed the importance of the GCS score, oxygen saturation, and prothrombin time as dominant predictors. These findings highlight the potential value of interpretable machine learning tools for early mortality risk stratification in elderly TBI patients and support further validation for future clinical use. Full article

Dermatophytosis is a superficial fungal skin disease of cats and dogs. The most common pathogens of small animals belong to the genera Microsporum and Trichophyton. It is an important skin disease because it is contagious and can be transmitted to people. Refractory dermatophytosis has become a disease of increasing concern in dermatological practice due to poor responses to standard antifungal therapy. The condition is characterised by chronicity, recurrence, or persistence despite adequate treatment. This report describes the clinical presentation and therapeutic management of refractory dermatophytosis in an 8-year-old intact male Spitz dog weighing 10 kg presenting with persistent alopecia, scaling, erythema, and pruritus despite multiple courses of systemic antifungal agents (itraconazole) and topical antifungal agents (2% miconazole shampoos and terbinafine-containing dusting powder). Diagnosis was confirmed by microscopic examination, culture, and punch biopsy. Due to the lack of response to standard therapy, posaconazole was initiated based on antifungal susceptibility testing (AFST). Marked clinical improvement was observed without adverse effects. This report documents a case of refractory dermatophytosis in which AFST informed the selection of posaconazole therapy. It highlights the diagnostic challenges of recurrent dermatophytosis, suggests that AFST-guided treatment strategies may help manage infections unresponsive to standard antifungal therapy, and demonstrates that posaconazole is a promising alternative antifungal agent for refractory dermatophytosis in dogs. Full article

Storage temperature (ST) and grain moisture content (GMC) critically influence cereal quality during storage. However, their interactive effects, the associations among oxidative indicators, quality components and major volatile organic compounds (VOCs) variations in millet during storage are not fully understood. In this study, foxtail millet was stored for 360 days at three STs (−18 °C, 4 °C and 25 °C) and three GMC levels (11.50%, 12.80% and 14.30%). Changes in oxidative indicators (malondialdehyde [MDA], electrical conductivity [EC] and catalase activity [CAT]) and quality components (crude protein [CP], yellow pigment [YP] and soluble sugar [SS]) were monitored. Viscosity characteristics and VOCs were analysed after storage. Under this study, ST was the primary factor driving the changes in oxidative indicators and quality components during the storage stage. The viscosity characteristics of stored millet are primarily influenced by ST, while the changes in major VOCs are mainly affected by ST, GMC, and their interaction effects. Significant negative correlations were observed between EC or MDA and dodecanenitrile and (E/Z)-4-heptenal, whereas the YP, CP, and SS were significantly positively correlated with both compounds. After day 360, the samples stored at −18 °C with 11.5% GMC exhibited 34.05% lower MDA content and 29.55% lower EC than those stored at 25 °C with 14.3% GMC. The treatment better preserved CAT, SS, YP, viscosity characteristics and major VOCs, including (E/Z)-4-heptenal. These findings provide a scientific basis for optimising storage conditions to maintain the nutritional and sensory quality of foxtail millet. Full article

With the development of various emerging structures, concrete-filled steel tubular (CFST) columns have become critical load-bearing components in key infrastructures such as subways and underground utility tunnels. Accurately predicting their ultimate bearing capacity (N_u) is essential for guaranteeing structural safety. To address the limitations of traditional empirical formulas and code-based calculation approaches, this paper proposes a prediction model for ultimate bearing capacity based on the CatBoost algorithm optimized by Random Search. Furthermore, the marginal contribution of each key feature to the prediction results is measured through interpretability analysis. First, a database containing 438 CFST column ultimate bearing capacity test cases was established, with key parameters such as geometric dimensions and material properties as input variables. Second, the predictive performance of six machine learning algorithms—CatBoost, LightGBM, Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and XGBoost—was compared. A five-fold cross-validation integrated with a Random Search strategy was employed for joint hyperparameter optimization. The results show that the optimized CatBoost model significantly outperforms other algorithms and conventional design codes, achieving a coefficient of determination (R²) as high as 0.99 and a root mean square error (RMSE) of 174.29 kN. Furthermore, the SHAP (Shapley Additive exPlanations) method was used to perform global and local interpretability analyses of the prediction model. This not only quantified the individual contribution and interaction effects of each feature parameter on the bearing capacity but also revealed that geometric parameters are the primary influencing factor. This finding confirms a high degree of consistency between the prediction mechanism of the data-driven model and classical mechanical theories, effectively validating the model’s reliability. This study provides an efficient and reliable tool for the optimal design and rapid evaluation of CFST columns and establishes a new data-driven paradigm for the design and reinforcement of key components in underground structures. Full article

Industrial equipment data often exhibit high dimensionality and class imbalance, which make it difficult to achieve both accurate failure detection and identification of the factors contributing to failures. To address this issue, this study proposes an explainable failure classification framework, Feature-Wise Attention Graph Convolutional Network (FWA-GCN), which combines Feature-Wise Attention (FWA) with a Graph Convolutional Network (GCN) to provide both high classification performance and variable-level interpretability. In the proposed model, tabular sensor records are treated as nodes, and a similarity-based graph is constructed to capture relationships among samples. Feature-Wise Attention learns the importance of each feature and reweights node features accordingly, and the reweighted features are then used as input to the GCN to classify failure occurrences. To alleviate the class imbalance problem, a weighted loss function is applied during training by assigning a higher weight to the failure class. Experiments conducted on the Air Pressure System (APS) dataset demonstrate that the proposed FWA-GCN achieves Precision of 79.95%, Recall of 85.07%, and F1-score of 82.43%, outperforming conventional machine learning models including Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron, as well as a standard GCN model. Furthermore, an ablation study was conducted by removing the top features selected by the attention mechanism. The results show a significant decrease in recall, confirming the effectiveness of the attention-based feature importance and supporting the interpretability of the proposed framework. Full article

The synergistic effects of glucose (Glu) concentration and clay mineral type (kaolinite [Kao], montmorillonite [Mon]) on abiotic humification via the polyphenol-Maillard reaction remain poorly understood. To address these scientific challenges, a series of controlled, sterile batch experiments was conducted. Specifically, a glucose concentration gradient (0, 0.03, 0.06, 0.12, and 0.24 mol/L) was established; Kao and Mon were separately introduced as mineral catalysts; and the Maillard reaction was facilitated in the presence of catechol and glycine under strictly abiotic conditions to preclude any potential biological interference. Comprehensive analyses were performed on the reaction products—namely, the supernatant and the dark-brown residue generated during the reaction process. These analyses included: the E₄/E₆ ratio and total organic carbon (TOC) content of the supernatant; the carbon-based ratio of humic-like acid to fulvic-like acid (C_HLA/C_FLA); and the structural characteristics of humic-like acid (HLA) isolated from the dark-brown residue. Results showed dynamic E₄/E₆ ratio and TOC changes in the supernatant were accurately described by the Logistic function. Kao favored soluble organic C accumulation and enhanced retention of early-stage, low-molecular-weight intermediates in the dark-brown residue, while Mon promoted humic-like substances (HLS) polymerization and aromatic condensation. FTIR spectroscopy analysis identified optimal Glu thresholds for maximal HLS formation—0.03 mol/L for Kao and 0.06 mol/L for Mon—indicating non-linear, rather than monotonic, dependence on Glu dosage. Comparative pre- and post-reaction Fourier-transform infrared (FTIR) spectroscopy further demonstrated that Mon, owing to Mg–OH octahedral sites arising from isomorphic substitution, formed more stable Cat chelates than Kao. These chelates effectively stabilized surface-bound hydroxyl-associated water molecules and modulated the electron cloud distribution around Si–O bonds. Collectively, this study clarified the dual regulatory role of Glu concentration and clay mineral identity in abiotic humification pathways, advanced mechanistic understanding of clay mineral-mediated polyphenol-Maillard reactions, and established a scientific foundation for optimizing humification efficiency in both engineered and natural systems. Full article

Co-pyrolysis of sludge and plastics has gradually emerged as a crucial technical approach for waste reduction and resource recovery. This study develops high-precision, interpretable prediction models and quantifies the contributions of core risk factors to environmental risks. Based on the experimental datasets from 2015 to 2025, which include operational parameters and eight potential toxic elements (PTEs) with four chemical speciation fractions: acid-soluble/exchangeable (F1), reducible (F2), oxidizable (F3), and residual (F4), we constructed six machine learning models. Based on the experimental datasets from 2015 to 2025, which include operational parameters and eight potential toxic elements (PTEs) chemical speciation (F1–F4), we constructed six machine learning models. Feature importance analysis and Shapley Additive Explanation (SHAP) analysis were employed to identify core risk factors and interpret the model’s decision logic. Results indicate that XGBoost, Random Forest and CatBoost outperform other models, achieving test accuracies of 0.94, 0.92, and 0.90, with weighted F1-Scores of 0.94, 0.92, and 0.90, respectively. Feature importance highlights the most important features for the six different models, with Cd-F4, As-F1, and Cu-F4 contributing most significantly to the model predictions. SHAP analysis quantified the contributions of each feature to the model predictions, verified Cd-F4 as the primary risk discriminant, and further revealed that F1 and F4 of PTEs are key factors in distinguishing risk levels. This study proposes an interpretable machine learning framework, providing a theoretical basis for the optimization of the sludge and plastic co-pyrolysis process and the assessment of potential risks. Full article

The rapid increase in companion animal ownership has intensified the demand for innovative tools that support animal health and overall welfare. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach in veterinary medicine. However, its application beyond clinical diagnostics, especially in behaviour and personality assessment, remains fragmented and insufficiently integrated into routine practice. This systematic review aims to synthesise current knowledge on AI-based applications in companion animal care, with a focus on behavioural monitoring, personality prediction, and welfare-related challenges. Following PRISMA guidelines, a structured literature search was conducted in the Scopus and PubMed databases from 2020 to 2025. In addition, grey literature sources were searched to capture relevant non-peer-reviewed data. A total of 115 studies met the inclusion criteria and were included in the analysis. Eligibility criteria included studies applying AI methods (machine learning or deep learning) to companion animals (dogs, cats, and exotic pets), while studies on humans, farm animals, or without AI methods were excluded. Due to the heterogeneity of included studies, no formal risk of bias assessment was performed, and results were synthesised narratively. The findings indicate that AI applications are most advanced in diagnostic imaging and clinical decision support, where data availability and methodological maturity are highest. In contrast, AI-based approaches for behaviour and personality prediction remain limited, particularly in cats and exotic companion animals, largely due to small, heterogeneous datasets, potential bias, and a lack of external validation. Emerging technologies such as wearable sensors, computer vision, and multimodal data integration demonstrate substantial potential for continuous behavioural monitoring and early detection of welfare-related issues in real household environments. Nevertheless, significant challenges persist, including data heterogeneity, limited model explainability, ethical considerations, and the absence of regulatory frameworks specifically addressing AI-based veterinary applications. Overall, this review highlights a substantial gap between the technical potential of AI and its current readiness for widespread application in companion animal behaviour and welfare assessment. Future research should prioritise large-scale and standardised data collection, cross-species validation, and interdisciplinary collaboration to ensure that AI-driven tools effectively support veterinary decision-making, animal welfare, and the well-being of owners. Full article

Objective: The aim of this systematic review was to evaluate the skeletal stability, predictability, and safety of using autogenous iliac crest bone grafts (ICBG) during large maxillary advancement performed with Le Fort I osteotomy. Methods: A systematic literature search was performed in November 2025 using PubMed, Scopus, Embase, Web of Science, and WorldCat databases. Clinical studies reporting large maxillary advancement performed with Le Fort I osteotomy and incorporating ICBG were included. Study selection followed PRISMA guidelines. Data extraction focused on the magnitude of maxillary advancement, surgical protocols, stabilization methods, skeletal stability, relapse patterns, graft integration, implant-related outcomes, and complications. Methodological quality was assessed using the Mixed-Methods Appraisal Tool (MMAT). Results: The review included clinical studies predominantly consisting of case reports, case series, and retrospective cohort studies. ICBG were consistently used in complex clinical scenarios, such as severe maxillary atrophy, hypoplasia, and congenital craniofacial deformities. Large maxillary advancements were generally associated with favorable postoperative skeletal stability, with most relapse occurring during the early healing phase and minimal changes observed during long-term follow-up when rigid fixation and adequate graft integration were achieved. Interpositional grafting facilitated predictable advancement by bridging extensive osteotomy gaps. Donor-site morbidity related to iliac crest harvesting was typically mild and transient. Implant-related outcomes, reported as secondary findings, were generally favorable when implants were placed after an adequate healing period. Conclusions: Despite predominantly observational evidence, ICBG during large maxillary advancement with Le Fort I osteotomy appears to offer predictable advancement, acceptable skeletal stability, and a favorable safety profile, warranting further prospective investigation. Full article

Background/Objectives: T-cell lymphomas are relatively common in veterinary species, yet current diagnostic tools such as PCR-based clonality assays often lack sensitivity and specificity. In humans, we recently developed two related tissue-based diagnostic approaches based on the differential detection of the mutually exclusively expressed TCRbeta1 and 2 (TCRβ1 and 2) constant region proteins, or the corresponding TRBC1 and TRBC2 transcripts. Analogous to the detection of kappa/lambda light chains for the diagnosis of B-cell/plasma cell neoplasms in human clinical practice, our TCRβ1/2 diagnostic assay has the potential to transform veterinary diagnostic workflows. Methods: We identified and confirmed the sequences of the relevant TRBC1 and TRBC2 sequences in both cats and dogs, focusing on the 3′ untranslated region (UTR), where there is the least sequence homology between TRBC1 and TRBC2. To allow us to design appropriate probe sequences, we confirmed a lack of 3′UTR in either species, and we observed limited 3′ untranslated region UTR sequence polymorphism in the cat but not in the dog 3′UTR. We designed BaseScope™ RNA in situ hybridization probes targeting the 3′ UTR to distinguish between TRBC1 and TRBC2 transcripts in formalin-fixed paraffin-embedded tissues. Results: In normal tissues, we found the TRBC2:TRBC1 expression ratio to be similar to the 1.2:1 ratio in humans, between 1:1 and 3:1, skewing towards TRBC2, in both dogs and cats. These findings were corroborated using quantitative reverse transcription PCR. Applying our in situ hybridization probes to cases of T-cell lymphoma in dogs and cats, we demonstrated that an assay for differential expression of TRBC1 and TRBC2 in T-cell populations could identify clonal T-cell populations, as in human diagnostics. If further studies corroborate this proof-of-concept study, TRBC1/2 detection could obviate the need for slow, complex and expensive multiplexed PCR-based (PCR for antigen receptor rearrangements (PARR)) clonality assays. Conclusions: This study provides proof-of-concept data for a novel diagnostic approach that could simplify and substantially improve the accuracy of lymphoma diagnostics in veterinary medicine, by detecting TRBC1/2 transcripts. Full article

Background/Objectives: Edible bird’s nest (EBN) benefits skin, but its transgenerational effects are unknown. This study investigated whether maternal EBN or its key component, sialic acid (SA), could program offspring skin pigmentation and antioxidant capacity. Methods: Pregnant Sprague-Dawley rats were supplemented with EBN or equi-sialic acid SA. Offspring skin brightness (L*, ITA°), melanin content, and key molecular targets (e.g., MITF, TYR, TRP1/2, PMEL, RAB27A, p-CREB, p-ERK, CAT, GCS, MDA) were assessed at postnatal days 0–21. Results: Maternal EBN induced a dose-dependent skin-brightening effect in offspring. High-dose EBN increased skin L* by 10.46% and ITA° by 14.28%, while reducing total melanin by 26.77%. This was mediated by downregulation of the MITF-TYR/TRP axis and its upstream CREB/ERK signaling, suppression of melanosome transport proteins (PMEL, RAB27A), and enhancement of antioxidant defenses (increased CAT/GCS, decreased MDA). SA alone showed similar but weaker effects. Conclusions: This study demonstrates that maternal EBN intake programs offspring skin towards a lighter phenotype and enhanced antioxidant status through multi-faceted regulation of melanogenesis. The superior efficacy of whole EBN over pure SA highlights the value of the intact food matrix, suggesting EBN as a promising functional food for maternal nutrition. Full article

This review classifies plant-derived functional ingredients in pet food according to phytochemical groups and application forms, including direct oral supplementation and incorporation into complete diets. Polyphenols and plant extracts exert prominent antioxidant (singular), anti-inflammatory, immunomodulatory, and microbiome-regulating effects. Microalgae and omega-3 sources support lipid metabolism, cardiovascular function, and skin integrity. Cannabinoids demonstrate dose-dependent responses in dogs, while cats generally tolerate long-term administration and exhibit notable benefits in chronic pain management. Combinations of botanical extracts with complementary bioactives and fermented botanical preparations exhibit multi-target functionality, with dogs showing pronounced biochemical and microbiome modulation, whereas cats display more behavioral and functional improvements. Phytochemicals operate through integrated multi-level regulation, including activation of antioxidant enzymes, modulation of inflammatory cytokines and T-lymphocyte ratios, microbial metabolic shifts toward short-chain fatty acid production, and regulation of lipid metabolism. Dogs demonstrate marked effects on hepatic function, reproductive resilience, microbiome diversity, CD4+/CD8+ balance, and cholesterol control. In contrast, cats show greater benefits in inflammation reduction, pain relief, intestinal integrity, and long-term safety. These species-specific responses underscore the importance of precision formulation and highlight the emergence of plant-based “pharma-pet nutrition” integrating nutritional and biochemical strategies for targeted health promotion. Full article