Search Results (6,229)

Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with multiple clinical and inflammatory phenotypes. The coexistence of bronchiectasis, known as bronchiectasis–COPD overlap (BCO), identifies a subgroup with increased morbidity and mortality. Non-invasive breath analysis using electronic noses (e-noses) has shown promise in identifying disease-specific volatile organic compound (VOC) patterns (“breathprints”). Our aim was to evaluate the ability of an e-nose to differentiate between COPD and BCO patients, and to assess its utility in detecting inflammatory endotypes (neutrophilic vs. eosinophilic). In a monocentric, prospective, real-life study, 98 patients were enrolled over nine months. Forty-two patients had radiologically confirmed BCO, while fifty-six had COPD without bronchiectasis. Exhaled breath samples were analyzed using the Cyranose 320 e-nose. Principal component analysis (PCA) and discriminant analysis were used to identify group-specific breathprints and inflammatory profiles. PCA revealed significant breathprint differences between BCO and COPD (p = 0.021). Discriminant analysis yielded an overall accuracy of 69.6% (AUC 0.768, p = 0.037). The highest classification performance (76.8%) was achieved when distinguishing eosinophilic COPD from neutrophilic BCO. These findings suggest distinct inflammatory profiles that may be captured non-invasively. E-nose technology holds potential for the non-invasive endotyping of COPD, especially in identifying neutrophilic BCO as a unique inflammatory entity. Breathomics may support early, personalized treatment strategies. Full article

Temperate forest ecosystems in Korea are currently undergoing a successional transition from Pinus densiflora Siebold & Zucc. (evergreen conifer) communities to Quercus mongolica Fisch. ex Ledeb. (deciduous broadleaf) communities. This study aimed to assess interspecific differences in ecological responses to climate change [Representative Concentration Pathway (RCP) 4.5] by evaluating changes in ecological niche characteristics and species distribution. Controlled-environment experiments, principal component analysis (PCA), and MaxEnt species distribution modeling were employed to quantify and predict ecological shifts in the two dominant species under climate change scenarios. Both species exhibited increases in niche breadth and interspecific overlap under climate change conditions. However, Q. mongolica showed a more pronounced increase in niche breadth compared to P. densiflora, indicating greater ecological flexibility and adaptive potential to warming conditions. According to the MaxEnt model projections, climate change is expected to result in an approximate 30% reduction in suitable habitat for P. densiflora in lowland areas. In contrast, Q. mongolica is projected to expand its suitable habitat by over 80%, notably in both low-elevation (below 800 m) and high-elevation (above 1400 m) zones, without being restricted to any specific altitudinal range. Our findings suggest that climate change may increase ecological similarity between P. densiflora and Q. mongolica, thereby raising the potential for interspecific competition. This convergence in niche traits could contribute to an accelerated successional transition, although actual competitive interactions in natural ecosystems require further empirical validation. Consequently, Korean forests are likely to transform into predominantly deciduous forest ecosystems under future climate conditions. Full article

Sea cucumbers, important members of the Echinoderm phylum, play a crucial role in sediment mixing and nutrient cycling on the seafloor. They also hold significant economic value, particularly in Asian food and pharmaceutical markets. In the Mediterranean Sea, the harvesting of sea cucumbers has recently intensified, often without regulation, threatening both species populations and benthic ecosystem health. This study investigated the potential of using fatty acid (FA) profiles as ecological biomarkers to trace the different origin and feeding ecology of two sea cucumber species, Holothuria polii and H. tubulosa, collected from ten coastal sites in Italy. A total of 285 individuals were analyzed by extracting and characterizing lipids from their body walls using gas chromatography (GC-FID and GC-MS). Key fatty acids identified included arachidonic acid, eicosapentaenoic acid, eicosenoic acid, palmitic acid, palmitoleic acid, stearic acid, and nervonic acid. Principal Component Analysis (PCA) revealed patterns consistent with geographic origin, suggesting that FA profiles can reflect site-specific trophic conditions. The analysis also indicated that sea cucumbers primarily feed on diatoms, bacteria, and blue-green algae, with notable regional variation. This study is the first to successfully apply FA-based trophic markers to differentiate Italian populations of these species, providing insights for ecological monitoring and fishery management. Full article

This study presents a comparative evaluation of traditional machine-learning (ML) and deep-learning (DL) approaches for detecting and counting Varroa destructor mites in hyperspectral images. As Varroa infestations pose a serious threat to honeybee health, accurate and efficient detection methods are essential. The ML pipeline—based on Principal Component Analysis (PCA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM)—was previously published and achieved high performance (precision = 0.9983, recall = 0.9947), with training and inference completed in seconds on standard CPU hardware. In contrast, the DL approach, employing Faster R-CNN with ResNet-50 and ResNet-101 backbones, was fine-tuned on the same manually annotated images. Despite requiring GPU acceleration, longer training times, and presenting a reproducibility challenges, the deep-learning models achieved precision of 0.966 and 0.971, recall of 0.757 and 0.829, and F1-Score of 0.848 and 0.894 for ResNet-50 and ResNet-101, respectively. Qualitative results further demonstrate the robustness of the ML method under limited-data conditions. These findings highlight the differences between ML and DL approaches in resource-constrained scenarios and offer practical guidance for selecting suitable detection strategies. Full article

Background/Objectives: The progression of antibiotic resistance is increasingly recognized as a dynamic and time-dependent phenomenon, challenging conventional diagnostics that define resistance as a binary trait. Methods: Biomolecules have fingerprints in Fourier-transform infrared spectroscopy (FTIR). The targeting of specific molecular groups, combined with principal component analysis (PCA) and machine learning algorithms (ML), enables the identification of bacteria resistant to antibiotics. Results: In this work, we investigate how effective classification depends on the use of different numbers of principal components, spectral regions, and defined resistance thresholds. Additionally, we explore how the time-dependent behavior of certain spectral regions (different biomolecules) may demonstrate behaviors that, independently, do not capture a complete picture of resistance development. FTIR spectra were obtained from Staphylococcus aureus exposed to azithromycin, trimethoprim/sulfamethoxazole, and oxacillin at sequential time points during resistance induction. Combining spectral windows substantially improved model performance, with accuracy reaching up to 96%, depending on the antibiotic and number of components. Early resistance patterns were detected as soon as 24 h post-exposure, and the inclusion of all three biochemical windows outperformed single-window models. Each spectral region contributed distinctively, reflecting biochemical remodeling associated with specific resistance mechanisms. Conclusions: These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers. Full article

This study aimed to develop a novel cream formulation incorporating pectin-based microcapsules loaded with lemon essential oil (LEO), with the goal of enhancing both sensory attributes and microbiological quality. The capsules were added at increasing concentrations (0%, 0.25%, 0.5%, 0.75%, and 1%) to assess their impact. Physicochemical analysis revealed that higher capsule content significantly improved consistency and viscosity. Microbiological evaluations confirmed the absence of key foodborne pathogens, including Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, and Enterobacteriaceae, in all formulations. Additionally, the antibacterial efficacy of the encapsulated LEO was validated against Escherichia coli and Staphylococcus aureus strains. Sensory analysis using paired comparison, ranking, and hedonic tests demonstrated a clear preference for samples enriched with the 0.5% and 0.75% capsules, noted for their enhanced creaminess, pleasant lemon aroma, and well-balanced flavour. Statistical analysis (ANOVA and principal component analysis, PCA) confirmed significant differences among samples, particularly in texture and aroma attributes. These findings highlight the potential of LEO-loaded pectin capsules as a clean-label strategy to improve both the sensory appeal and microbial safety of cream formulations. Full article

Men have limited options for contraception, despite the widely accepted public health benefits of it, placing the contraceptive burden solely on women. The current study focuses on inhibiting the PP1γ2 enzyme, which plays a role in sperm maturation and motility. The study considered three top compounds based on the findings of molecular docking. The three compounds exhibited a good interaction profile with a binding affinity score of D751-0223 (−8.7 kcal/mol), D751-014 (−8.1 kcal/mol), and N117-0087 (−8 kcal/mol) measured in kcal/mol. Molecular dynamics simulation (MDS) were performed on the PP1γ2–ligand complexes along with the Apo form. The results suggested that all the complexes were stable with no major deviations observed compared to Apo. The average RMSDs for PP1γ2-D751-0223, D751-014, and Apo were 1.27 Å, 1.73 Å, 1.39 Å, and 1.69 Å, respectively. The PP1γ2–ligand complexes were observed with unique salt bridge interactions such as Glu133-Arg137, Asp4-Lys107, Asp188-Arg116, and Glu120-Arg90. The principal component analysis (PCA) findings indicated that every complex had a distinctive motion state. Furthermore, the net MM/PBSA scores for D751-0223, D751-0143, and N117-0087 were −80.01 kcal/mol, −72.18 kcal/mol, and −64.26 kcal/mol, respectively, while the MM/GBSA and MM/PBSA values were −82, −73.07,−67.26 and −80.01, −72.18, −64.26, measured in kcal/mol, respectively. The WaterSwap energy estimation was performed to validate the former technique, and the findings demonstrated that PP1γ2-D751-0223 is a stable complex, with a value of −51.05 kcal/mol. This work provides a baseline to researchers for the identification of novel therapeutic approaches for non-hormonal male contraceptives. Full article

Attachment styles, rooted in Bowlby’s Attachment Theory, significantly influence our romantic relationships, workplace behavior, and decision-making processes. Traditional methods like self-report questionnaires often have biases, so we aimed to develop a predictive model using objective physiological data. In our study, participants engaged in the Secretary problem, a sequential decision-making task, while their brain activity was recorded with a 16-electrode EEG device. We transformed this data into coherence graphs and used Node2Vec and PCA to convert these graphs into feature vectors. These vectors were then used to train a machine learning model, XGBoost, to predict attachment styles. Using participant-level nested 5-fold cross-validation, our first model achieved 80% accuracy for Secure and 88% for Fearful-avoidant styles but had difficulty distinguishing between Avoidant and Anxious styles. Analysis of the first three principal components showed these two groups overlapped in coherence space, explaining the confusion. To address this, we created a second model that categorized participants as Secure, Insecure, or Extremely Insecure, improving the overall accuracy to about 92%. Together, the results highlight (i) large-scale EEG connectivity as a viable biomarker of attachment, and (ii) the empirical similarity between Anxious and Avoidant profiles when measured electrophysiologically. This method shows promise in using EEG data and machine learning to understand attachment styles. Our findings suggest that future research should include larger and more diverse samples to refine these models. If validated in multi-site cohorts, such graph-based EEG markers could guide personalised interventions by objectively assessing attachment-related vulnerabilities. This study demonstrates the potential for using EEG data to classify attachment styles, which could have important implications for both research and therapeutic practices. Full article

Kiwifruit brown spot, caused by the fungus Corynespora cassiicola, has recently emerged as a problematic foliar disease of kiwifruit, causing premature defoliation. The objective of this study was to determine the effects of kiwifruit brown spot on the yield and quality of kiwifruit. Principal component analysis (PCA) was used to conduct a comprehensive evaluation of the fruit quality of ‘Hongyang’ kiwifruit in the main producing regions. The first principal component for PCA included the weight of individual fruit, soluble solids content, and dry matter content, which were negative significantly correlated with disease index. The significant differences among different disease levels indicated that the impact of the disease on fruit quality was largely determined by these three intrinsic flavor indices. Due to kiwifruit brown spot, the average yield loss was 22.652%, which leads to kiwifruit quality being downgraded by one grade, resulting in an economic loss of 73,591 yuan/ha. The Pearson correlation coefficient between disease index and comprehensive score of fruit quality was −0.762 (p < 0.01), indicating a significant relationship. Accordingly, the disease loss model was constructed, and the damage threshold based on disease index for kiwifruit brown spot was calculated to be 36.14. In conclusion, this study found that kiwifruit brown spot could have a significant impact on yield and fruit quality. Full article

Evaluating the response of maize crops to different nitrogen fertilization rates is essential to ensure their agronomic, environmental, and economic efficiency. In this study, the spectral information of maize plants subjected to five distinct nitrogen fertilization strategies was analyzed. The fertilization strategies were based on the practices commonly used in maize fields in the study area, with the aim of ensuring the research findings’ applicability. The spectral reflectance was measured using a spectroradiometer covering the 350–2500 nm range, and the results enabled the identification of optimal spectral regions for monitoring plants’ nitrogen status, particularly in the visible and infrared ranges. A Principal Component Analysis (PCA) of the reflectance data revealed the key wavelengths most sensitive to the nitrogen availability: 555 nm and 720 nm during the vegetative stage and 680 nm during the reproductive stage. This information will support the development of drone-mounted multispectral sensor systems for large-scale monitoring, as well as the design of low-cost sensors for early nitrogen deficiency detection. Furthermore, the study demonstrated the feasibility of estimating the cornstalk nitrate content based on direct reflectance measurements of maize stems. The prediction model showed satisfactory performance, with a coefficient of determination (R²) of 0.845 and a root mean square error of prediction (RMSECV) of 2035.3 ppm, indicating its strong potential for predicting the NO₃-N concentrations in maize stems. Full article

Background/Objectives: Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly associated with gastrointestinal (GI) adverse events. This study aimed to assess the incidence and patterns of NSAID-induced GI disorders using the FDA Adverse Event Reporting System (FAERS) database and to compare the risks among different NSAIDs. Methods: NSAID-related reports were extracted from FAERS, focusing on 21 ulcer-related GI events with ≥1000 reports each, based on MedDRA v26.0. The number of reports, reporting odds ratios, and p-values were calculated and visualized using a volcano plot. Principal component analysis(PCA) was carried out to reduce the dimensionality of the dataset and revealed under-lying patterns in the data.PCA was performed to identify patterns related to risk, severity, and injury site, whereas hierarchical clustering was used to group NSAIDs based on these patterns. Hierarchical cluster analysis is a method of grouping similar data to generate a classification. Results: Statistically significant signals were identified for 19 of the 21 GI-related adverse events, including the serious condition of perforation. PCA revealed that the first component represented risk, the second severity, and the third the site of injury (upper vs. lower GI tract). Cyclooxygenase-2 (COX-2) selective NSAIDs (e.g., celecoxib, rofecoxib) were associated with a lower incidence but greater severity, primarily in the upper GI tract. Conversely, nonselective NSAIDs (e.g., acetylsalicylic acid, lornoxicam) showed higher incidence rates, though the events were generally milder. In our dataset, acetylsalicylic acid had the highest incidence, whereas meloxicam showed the highest severity. Clustering analysis revealed three distinct NSAID groups with differing patterns in risk, severity, and affected GI site. Mild adverse events may be underreported in FAERS. Dosage-related effects were not assessed in this study. Conclusions: NSAIDs differ significantly in their gastrointestinal adverse event profiles, attributable to COX selectivity. When selecting an NSAID, both the likelihood and the nature of potential GI harm should be considered. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) includes significant feeding difficulties, behavioral issues, and communication deficits that are linked to serious medical complications and developmental challenges. The Brief Autism Mealtime Behavior Inventory (BAMBI) is a commonly used tool to screen for mealtime behavior problems in children with ASD; however, it lacks validation for use within the Greek-Cypriot population. The current study sought to present the translation, cultural adaptation, and validation of the BAMBI for Greek-Cypriot parents of children with ASD. Methods: Three bilingual experts translated the inventory into Greek, following the translation guidelines by the World Health Organization. The inventory was then administered to 117 parents: 42 children with ASD and 75 typically developing children. Principal Component Analysis was used to obtain the tool’s statistical reliability and validity. Results: BAMBI-Gr demonstrated strong internal consistency, as indicated by a Cronbach’s alpha of 0.755, and showed excellent test–retest reliability, with an intraclass correlation coefficient of 0.999. PCA identified three key factors: General Refusals, Refusing Food, and Autism-Related Features. Significant differences in BAMBI-Gr scores of the comparative group of parents of children with ASD and parents of typically developing children highlighted the tool’s sensitivity in detecting mealtime behavior problems. Receiver Operating Characteristics analysis set the cut-off points for optimum distinguishing of feeding problems at 46.00 (sensitivity 0.738, 1-specificity 0.000). Conclusions: The Greek-translated version of the BAMBI demonstrates validity and effectiveness as a parent-reported assessment tool for identifying feeding and mealtime difficulties in children with ASD. Full article

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article

Lightning nowcasting is crucial for ensuring safety and operational continuity in weather-exposed industries such as mining. This study evaluates three machine learning (ML)-based approaches for predicting lightning using dual-polarimetric weather radar data collected in the eastern Amazon, Brazil. The strategies propose advances in literature in three ways by involving (i) grouping radar variables by temperature layers, (ii) statistical summaries at key altitudes, and (iii) analyzing all the 18 levels of reflectivity data combined with Principal Component Analysis (PCA) dimensionality reduction and ensemble models. For each approach, models such as Random Forest, Support Vector Machines, and XGBoost were trained and tested using data from 2021–2022 with class balancing and feature engineering techniques. Among the approaches, the PCA-based ensemble achieved the best generalization (recall = 0.89, F1 = 0.77), while the layer-based method had the highest recall (0.97), and the altitude-based strategy offered a computationally efficient alternative with competitive results. These findings confirm the predictive value of radar-derived features and emphasize the role of feature representation in model performance. Additionally, the best model was integrated into the operational LEWAIS alert system, and four integration strategies were tested. The strategy that combined alerts from both ML and LEWAIS systems reduced the failure-to-warn rate to 0.0531 and increased the lead time to 10.18 min, making it ideal for safety-critical applications. Overall, the results show that ML models based solely on radar inputs can achieve robust lightning nowcasting, supporting both scientific advancement and industrial risk mitigation. Full article