Search Results (6,815)

The present work reports the bioguided isolation of constituents from the ethanol extract of Holarrhena floribunda stem bark, their identification by UHPLC-ESI-QTOF-MS/MS identification, and the in silico prediction of the pharmacokinetic and toxicity parameters. The crude extract, along with its n-hexane and alkaloid-rich fractions, displayed moderate to good antiplasmodial activity in vitro against chloroquine-sensitive (3D7) and multidrug-resistant (Dd2) strains of Plasmodium falciparum, with IC₅₀ values ranging from 6.54 to 43.54 µg/mL. Seventeen steroidal alkaloids (1–17) were identified in the most active fraction using UHPLC-ESI-QTOF-MS/MS, based on their fragmentation patterns and analysis with the Structural Similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS). Furthermore, bioguided isolation of the ethanol extract yielded twenty-one compounds (3, 5, 10, 14–16, 18–31), whose structures were elucidated by spectroscopic methods. Among them, compounds 5, 14, and 27 showed the highest potency against the two strains of P. falciparum, with IC₅₀ values between 25.97 and 55.78 µM. In addition, the in silico prediction of pharmacokinetic parameters and drug-likeness using the SwissADME web tool indicated that most of the evaluated compounds (1, 3–5, and 14–16) complied with Lipinski’s rule of five. Full article

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article

Introduction: Dry Eye Syndrome (DES) is a multifactorial disorder of the ocular surface, characterized by complex interactions between environmental factors, immune dysregulation, and potential genetic predispositions. Vitamin D deficiency, known for its immunomodulatory properties, has increasingly been implicated in the pathogenesis of DES; however, the underlying mechanisms remain insufficiently elucidated. Of particular interest is the vitamin D receptor (VDR) gene, whose polymorphisms may influence the bioavailability and biological activity of vitamin D. Objective: The aim of this study was to investigate the association between serum 25-hydroxyvitamin D [25(OH)D₃] levels and selected polymorphisms in the VDR gene (Taq, Fok, Apa, and Bsm) in patients with DES and to analyze their potential clinical and genetic interactions. Methods: This prospective observational study included 60 patients with a confirmed diagnosis of DES. Serum 25(OH)D₃ levels were measured, and genotyping of four VDR single-nucleotide polymorphisms (SNPs) was performed using PCR followed by restriction fragment length polymorphism analysis. Genotype distributions were assessed in relation to vitamin D status using appropriate statistical tests and Hardy–Weinberg equilibrium analysis. Results: Over 85% of patients exhibited insufficient or deficient vitamin D levels. Among the analyzed SNPs, only the ApaI polymorphism (rs7975232) showed a statistically significant association with vitamin D status (p = 0.0384), with the homozygous AA genotype being more prevalent among patients with hypovitaminosis. The remaining polymorphisms (TaqI, FokI, BsmI) did not reach statistical significance; however, potential trends were observed that may warrant further investigation in larger cohorts. Conclusion: The findings suggest a potential role for VDR gene variability in the regulation of systemic vitamin D levels in patients with DES. Identification of specific genotypes may contribute to the development of personalized diagnostic and therapeutic strategies, particularly for patients with treatment-resistant forms of the disease. These results support the integration of genetic biomarkers and nutritional parameters into modern ophthalmologic practice. Full article

Background: Bioelectrical impedance analysis (BIA) is a widely used field technique for assessing body composition in football. However, its reliance on population-specific regression equations limits its accuracy. Objective: This scoping review aimed to map the scientific literature on BIA applications in professional and semi-professional football, highlighting uses, limitations, and research opportunities. Methods: A comprehensive search was conducted in the scientific databases PubMed, EMBASE, Web of Science, and SPORTDiscus. Identified studies involved the use of BIA in professional and semi-professional football players (≥16 years) in the context of routine training and competition. Results: From 14,624 records, 39 studies met the inclusion criteria and were included. Three main applications were identified: (1) quantitative body composition assessment, (2) qualitative/semi-quantitative analysis (e.g., bioelectrical impedance vector analysis (BIVA)), and (3) muscle health and injury monitoring. Seven specific research areas emerged, including hydration monitoring, cross-method validation of body composition analyses, development of predictive models, sport phenotype identification, tracking training adaptations, performance/load assessment via phase angle, and localized BIA for injury diagnosis and recovery. Conclusions: While quantitative BIA estimates may lack individual-level precision, raw parameter analyses may offer valuable insights into hydration, cellular integrity, and muscle injury status, yet further research is needed to fully realize these applications. Full article

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

This study investigates the transverse loads acting on high-speed train bogie frames under actual service conditions. To enable direct identification, the locating arms were instrumented as bending sensors and calibrated under realistic lateral-stop constraints, ensuring robustness of the measurement channels. Field tests were conducted on a CR400BF high-speed EMU over a 226 km route at six speed levels (260–390 km/h), with gyroscope and GPS signals employed to recognize typical operating conditions, including straights, curves, and switches (straight movement and diverging movements). The results show that the proposed recognition method achieves high accuracy, enabling rapid and effective identification and localization of typical operating conditions. Under switch conditions, the bogie frame transverse loads are characterized by low-frequency, large-amplitude fluctuations, with overall RMS levels being higher in diverging switches and straight-through depot switches. Curve parameters and speed levels exert significant influence on the amplitude of the transverse-load trend component. On curves with identical parameters, the trend-component amplitude exhibits a quadratic nonlinear relationship with train speed, decreasing first and then increasing in the opposite direction as speed rises. In mainline curves and straight sections, the RMS values of transverse loads on Axles 1 and 2 scale proportionally with speed level, with the leading axle in the direction of travel consistently producing higher transverse loads than the trailing axle. When load samples are balanced across both running directions, the transverse load spectra of Axles 1 and 2 at the same speed level show negligible differences, while the spectrum shape index increases proportionally with speed level. Full article

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment. Full article

Highly Hazardous Pesticides (HHPs) pose severe risks to human health and the environment, making it essential to understand their molecular stability and degradation pathways. In this study, the Quantum Theory of Atoms in Molecules (QTAIM) was applied to four representative organophosphate pesticides, allowing the identification of electronically weak bonds as intrinsic sites of lability. These findings are consistent with reported hydrolytic, oxidative, enzymatic, and microbial degradation routes. Importantly, QTAIM descriptors proved largely insensitive to solvation, confirming their intrinsic character within the molecular electronic structure. To complement QTAIM, conceptual DFT (Density Functional Theory) reactivity indices were analyzed, revealing that solvent effects induce more noticeable variations in global and local descriptors than in topological parameters. In addition, a Topological Analysis of the Fukui Function (TAFF) was performed, which mapped nucleophilic, electrophilic, and radical susceptibilities directly onto QTAIM basins. The TAFF analysis confirmed that bonds identified as weak by QTAIM (notably P–O, P–S, and P–N linkages) also coincide with the most reactive sites, thereby reinforcing their mechanistic role in degradation pathways. This integrated framework highlights the robustness of QTAIM, the sensitivity of global and local reactivity descriptors to solvation revealed by conceptual DFT, and the complementary insights provided by TAFF, contributing to risk assessment, remediation strategies, and the rational design of safer pesticides. Full article

Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on its genetic variability and agronomic potential. This study aimed to estimate genetic parameters, predict genotypic values, and identify superior ecotypes with desirable forage traits, integrating stability and adaptability analyses. Methods: A total of 84 ecotypes were evaluated over three consecutive years for twelve morphological and forage-related traits. Genetic parameters, genotypic values, and selection gains were estimated using mixed models (REML/BLUP). Stability was assessed through harmonic means of genotypic performance, and the multi-trait genotype–ideotype distance index (MGIDI) was applied to identify ecotypes with balanced performance across traits. Results: Substantial genetic variability was detected for most traits, particularly those related to biomass accumulation, such as total dry matter, the number of tillers, fresh matter, and leaf dry matter. These traits exhibited medium to high heritability and strong potential for selection. Ecotype N3.10 consistently showed superior performance across productivity traits while other ecotypes, such as N4.14 and N1.09, stood out for quality-related attributes and cold tolerance, respectively. The application of the MGIDI index enabled the identification of 17 ecotypes with balanced multi-trait performance, supporting the simultaneous selection for productivity, quality, and adaptability. Comparisons with P. notatum suggest that P. nicorae harbors competitive genetic potential, despite its lower level of domestication. Conclusions: The integration of REML/BLUP analyses, stability parameters, and ideotype-based multi-trait selection provided a robust framework for identifying elite P. nicorae ecotypes. These findings reinforce the strategic importance of this species as a valuable genetic resource for the development of adapted and productive forage cultivars in subtropical environments. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article

Background: Colorectal cancer is the third most diagnosed cancer and a leading cause of cancer-related deaths worldwide. Early detection significantly improves patient outcomes, yet many cases are identified only at late stages. The high molecular and genetic heterogeneity of colorectal cancer presents major challenges in accurate diagnosis, prognosis, and therapeutic stratification. Recent advances in gene expression profiling offer new opportunities to discover genes that play a role in colorectal cancer carcinogenesis and may contribute to early diagnosis, prognosis prediction, and the identification of novel therapeutic targets. Methods: This study involved 142 samples: 84 primary tumor samples, 27 liver metastases, and 31 adjacent non-tumor tissues serving as controls. RNA sequencing was performed on a subset of tissues (12 liver metastases and 3 adjacent non-tumor tissues) using a targeted RNA panel covering 395 cancer-related genes. Data processing and differential gene expression analysis were carried out using the DRAGEN RNA and DRAGEN Differential Expression tools. The expression of six genes involved in hypoxia and epithelial-to-mesenchymal transition (EMT) pathways (SLC16A3, ANXA2, P4HA1, SPP1, KRT19, and LGALS3) identified as significantly differentially expressed was validated across the whole cohort via quantitative real-time PCR. The relative expression levels were determined using the ΔΔct method and log2FC, and compared between different groups based on the sample type; clinical parameters; and mutational status of the genes KRAS, PIK3CA, APC, SMAD4, and TP53. Results: Our results suggest that the expression of all the validated genes is significantly altered in metastases compared to non-tumor control samples (p < 0.05). The most pronounced change occurred for the genes P4HA1 and SPP1, whose expression was significantly increased in metastases compared to non-tumor and primary tumor samples, as well as between clinical stages of CRC (p < 0.001). Furthermore, all genes, except for LGALS3, exhibited significantly altered expression between non-tumor samples and samples in stage I of the disease, suggesting that they play a role in the early stages of carcinogenesis (p < 0.05). Additionally, the results suggest the mutational status of the KRAS gene did not significantly affect the expression of any of the validated genes, indicating that these genes are not involved in the carcinogenesis of KRAS-mutated CRC. Conclusions: Based on our results, the genes P4HA1 and SPP1 appear to play a role in the progression and metastasis of colorectal cancer and are candidate genes for further investigation as potential biomarkers in CRC. Full article

Background: Osteoarthritis (OA) is a major global health issue, increasing with aging and obesity. Current therapies mainly address symptoms without modifying disease progression. Platelet-rich growth factor (PRGF) therapy has potential regenerative effects through high cytokines and growth factors, but the outcomes of these therapies remain heterogeneous. This study explores the relationship between patient nutritional status, PRGF characteristics, and clinical outcomes in knee OA treatment. Methods: Baseline anthropometric, metabolic, and nutritional assessments of 41 patients with knee OA who underwent PRGF treatment were conducted. Blood samples were analyzed for metabolic and inflammatory markers. PRGF composition was assessed by protein content and extracellular vesicle (EV) markers. KOOS and VAS pain scores were collected at 2, 6, and 12 months. Responders improved KOOS by ≥10 points. An elastic-net regularized logistic model allowed the identification of the predictors of treatment response. Results: KOOS and VAS scores improved significantly at all follow-ups. At 2 months, the PRGF of responder patients showed higher PRGF G-CSF levels; at 12 months, increased CD49e and HLA-ABC expression. Higher BMI correlated with increased IL-6, IL-1ra, and resistin in PRGF samples. Hypercholesterolemic patients displayed altered EV profiles, with elevated levels of CD8 but reduced CD49e, HLA-ABC, CD42a, and CD31. Multivariate analysis identified BMI, biceps fold, fat percentage, red blood cell, platelet, and neutrophil counts as predictors of early response. Conclusions: Metabolic and immunological factors influence PRGF composition and clinical efficacy in knee OA. Baseline body composition and hematological parameters as key predictors of response, highlighting the potential of personalized PRGF therapy. Full article

The subject of this paper is the computational homogenisation and identification of heterogeneous materials in the form of auxetic structures made of materials with nonlinear characteristics. It is assumed that some of the material and topological parameters of the auxetic structures are uncertain and are modelled as interval numbers. Directed interval arithmetic is used to minimise the width of the resulting intervals. The finite element method is employed to solve the boundary value problem, and artificial neural network response surfaces are utilised to reduce the computational effort. In order to solve the identification task, the Pareto approach is adopted, and a multi-objective evolutionary algorithm is used as the global optimisation method. The results obtained from computational homogenisation under uncertainty demonstrate the efficacy of the proposed methodology in capturing material behaviour, thereby underscoring the significance of incorporating uncertainty into material properties. The identification results demonstrate the successful identification of material parameters at the microscopic scale from macroscopic data involving the interval description of the process of deformation of auxetic structures in a nonlinear regime. Full article