Search Results (3,056)

Alheira is a traditionally smoked, fermented, non-ready-to-eat meat sausage produced in the Trás-os-Montes region of Portugal, where lactic acid bacteria (LAB) are major determinants of product safety and quality. This study used whole-genome sequencing to characterize 59 LAB isolates collected from artisanal alheira produced in six municipalities. Eight species belonging to six genera were identified, and average nucleotide identity analysis resolved 24 non-redundant strain groups. Functional annotation revealed broad repertoires of carbohydrate-active enzymes, proteases, lipases, transport systems, and genes associated with tolerance to acid, oxidative, bile, heat, and salt stress. Genotype–phenotype inference was performed with a subset of 22 strain groups represented by unflagged genome assemblies. After false-discovery-rate correction, carbon-metabolism, transport, energy-metabolism, total functional-gene, acid-stress, bile-stress, and total-stress counts were positively associated with total pH decline. A mixed-effects model showed a positive association between standardized total functional-gene count and acidification ($b=0.137\pm 0.059$, $p=0.049$; marginal $R^2=0.266$, conditional $R^2=0.582$), but within–between decomposition indicated that the association was detectable between species rather than within species. Regularized regression, Random Forest, AICc model comparison, and partial least-squares regression converged on carbon metabolism, transport, energy metabolism, and acid- or bile-stress functions as the principal exploratory signals; however, sparse LASSO regression for variable selection was unstable, and prediction of unobserved species was limited. None of the five targeted pathways: acetaldehyde, citrate, diacetyl, exopolysaccharide, and lactose metabolism, remained significant after multiple-testing correction. These findings provide a genomic and phenotypic basis for selecting native starter-culture candidates while emphasizing the need for independent functional and safety validation. Full article

Historical pax demand estimation is important for understanding long-term demand dynamics and supporting airline planning, strategic decision-making, and transportation policy analysis. However, current decomposition approaches either estimate trends without explicitly modeling seasonality or jointly estimate trend and seasonal components without providing user control over trend smoothness. This paper proposes a Guerrero–Holt–Winters (GHW) hybrid smoothing model to estimate and reconstruct historical route-level airline pax demand. The GHW model combines Guerrero’s user-controlled trend estimation with the multiplicative seasonal structure of the Holt–Winters (HW) model to produce an interpretable decomposition of historical demand into trend, seasonal, and irregular components. The model is applied to quarterly pax demand data for ten representative U.S. domestic air routes covering the period 2015–2023. The results show that the proposed GHW model achieves lower in-sample reconstruction errors than the multiplicative HW model at lower degrees of trend smoothness within the evaluated range. Among the evaluated specifications, λ = 1, corresponding to a Guerrero smoothness index of S(1; 36) = 58.82%, produces the lowest in-sample reconstruction error. The historical reconstruction error, measured by the root mean squared error (RMSE), is reduced by approximately 54.4% relative to the multiplicative HW model. Similar improvements are observed under MAE, MAPE, and sMAPE, indicating that the reduction in reconstruction error at this smoothness level is consistent across the four error measures. An expanded benchmark comparison with HW, ETS state-space exponential smoothing, STL decomposition, and HP1600 further shows that the GHW model produces the lowest in-sample reconstruction errors across all four evaluation error metrics and all ten routes analyzed. A sensitivity analysis further demonstrates that reconstruction accuracy declines as the degree of trend smoothness increases from S(λ; 36) = 58.82% to 88.63%, whereas the estimated seasonal factors remain remarkably stable across the evaluated smoothness range. A COVID-19 robustness analysis further shows that reconstruction errors are substantially lower in the Pre-COVID period than in the Post-COVID period across all ten routes, indicating that reconstruction accuracy is sensitive to the period analyzed. Full article

Exposure to atmospheric particulate matter (PM) is a major public-health concern, in part because PM can perturb cellular redox homeostasis. This study evaluates an in vitro A549/DCFH-DA approach using repeated fluorescence readouts to assess PM2.5-induced oxidative activity. Untreated and assay-specific controls were combined with zymosan and NIST Standard Reference Material^® 2584 suspended in PBS, and fluorescence was monitored at multiple readout times over a 15 min–6 h window. Method performance was characterized using the coefficient of variation (CV) and signal-to-noise ratio (SNR). A dedicated three-concentration SRM 2584 series (0.02, 0.05 and 0.10 mg mL⁻¹) further showed readout-dependent concentration behaviour: at 60 min the untreated-control-corrected mean response increased across the tested concentrations and followed an approximate descriptive linear trend (R² = 0.90), whereas earlier readouts were non-monotonic. Substrate-related effects were examined using paired PTFE and quartz filters. Among the eight matched PTFE–quartz pairs included in the regression analysis, zero-intercept fits showed slopes close to unity for both mass- and air-volume-normalized responses (0.90 and 0.99, respectively; R² ≈ 0.99), demonstrating strong proportional agreement within this comparison set; the limited number of pairs does not support universal substrate interchangeability. Application to chemically characterized field PM2.5 samples from an urban-background site and a high-altitude site showed that DCFH-DA fluorescence did not track PM mass alone and is interpreted in terms of exploratory associations with particle composition, rather than causal effects of individual constituents. Taken together, these findings support the use of the method-performance-characterized workflow for assessing oxidative responses to field-collected PM2.5 across multiple readout times and for investigating their associations with particle chemical characteristics. Full article

Background/Objectives: Loneliness is a clinically relevant psychosocial factor in later life and has been associated with adverse mental, cognitive, and physical health outcomes. Views on ageing may shape social engagement, coping, and perceived belonging, but evidence from geriatric patient populations remains limited. This study examined whether views on ageing are associated with loneliness in geriatric patients. Secondary analyses explored subjective age, including felt age and the perceived onset of old age. Methods: This cross-sectional analysis used baseline data from two prospective observational studies in geriatric patients (N = 459). Loneliness was assessed with the three-item UCLA Loneliness Scale. Views on ageing were measured using five items from the German Ageing Survey. Associations were examined using Spearman correlations, non-parametric group comparisons, and multiple linear regression. Results: Participants had a mean age of 83.1 years and 65.6% were female. The mean UCLA loneliness score was 1.54 ± 2.07, and the mean views on ageing sum score was 13.21 ± 2.72. More negative views on ageing were associated with higher loneliness (ρ = 0.122, p = 0.009). In the fully adjusted regression model, views on ageing remained independently associated with loneliness after adjustment for chronological age, sex, and depressive symptoms (β = 0.117, p = 0.014; adjusted R² = 0.051). Depressive symptoms were also independently associated with loneliness (β = 0.182, p < 0.001), whereas chronological age and sex were not. At the item level, associations with loneliness were mainly driven by reduced positive ageing-related beliefs. Higher felt age was associated with higher loneliness (ρ = 0.151, p = 0.003), while a later perceived onset of old age was associated with lower loneliness (ρ = −0.136, p = 0.017). Conclusions: In geriatric patients, more negative views on ageing were weakly associated with higher loneliness independent of depressive symptoms. The small effect size suggests that views on ageing should be interpreted as one modest component within a broader biopsychosocial understanding of loneliness in geriatric care. Full article

Time series anomaly detection is one of the core tasks in time series data analysis, aiming to identify abnormal events or behaviors from normal temporal data, and plays a critically important role across numerous domains. In seismic data analysis, the identification of anomaly intervals preceding earthquake occurrences holds significant research value for subsequent earthquake prediction. This study evaluates the effectiveness of a large time series model named Timer in detecting anomalies within seismic time series data collected by tiltmeters. As a multi-task time series model, Timer has achieved state-of-the-art performance on forecasting, imputation, and anomaly detection tasks across multiple benchmark datasets, and is therefore selected as the core model in this study. This study employs the Timer model combined with the Constrained Gaussian-Noise Optimization and Smoothing (COGNOS) method for anomaly detection in seismic time series data, and compares the results with those of the original Timer model. Additionally, several mainstream time series anomaly detection models are selected as baselines for comparison. To comprehensively validate the model’s performance, comparative experiments were conducted on both the publicly available SIRGAS GNSS dataset and a real-world seismic dataset collected by tiltmeters. The experimental results demonstrate that the Timer model exhibits outstanding performance in anomaly detection on seismic time series data, and achieves further improvement when integrated with the COGNOS method. Full article

Single-cell RNA sequencing (scRNA-seq) studies are frequently organized around comparisons—disease versus control, treatment response, or genetic perturbation—yet biological interpretation still depends on integrating multiple independent downstream analyses for differential expression, functional enrichment, regulatory network inference, and cell–cell communication analysis. Applying these tools consistently across comparisons typically requires substantial custom scripting, and their heterogeneous outputs must be manually harmonized before the results can be compared or reported together. We present scFlowReport, a lightweight, configuration-driven workflow that propagates a single user-defined comparison across cell-level and sample-aware pseudobulk differential expression, over-representation and ranked functional enrichment, transcription-factor regulon export for SCENIC, and group-resolved LIANA cell–cell communication analysis, starting from an already annotated Seurat object. The workflow automatically compiles complementary downstream results into standardized figures, summary tables, and a self-contained static HTML report that can be readily inspected and shared without requiring a persistent server. Application of scFlowReport to a publicly available Atopic Dermatitis scRNA-seq dataset demonstrated its utility by enabling researchers to obtain complementary biological evidence from multiple established downstream analyses. By coordinating complementary downstream analyses under a shared comparison framework, scFlowReport provides a practical and reproducible workflow for systematic interpretation of comparative single-cell transcriptomic data. Full article

Background/Objectives: Cerebral small vessel disease (SVD) is a heterogeneous condition in which similar conventional MRI findings may be associated with different clinical manifestations and pathogenetic mechanisms. Previously, hierarchical clustering of structural MRI features in patients with severe white matter hyperintensities (Fazekas 3) identified two MRI phenotypes, designated MRI Type 1 and MRI Type 2. Diffusion MRI (dMRI) may provide additional information about the microstructural differences between these phenotypes. To compare white matter microstructure between MRI Type 1 and MRI Type 2 of sporadic age-related SVD using signal-based and biophysical dMRI models. Methods: This cross-sectional study included 75 patients with SVD and 36 age- and sex-matched healthy controls. Among the patients with SVD, 43 had MRI Type 1 and 32 had MRI Type 2. All participants underwent structural and multi-shell dMRI on a 3 Tesla MRI scanner. Diffusion metrics were derived using multiple models: Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), White Matter Tract Integrity (WMTI), and the Multi-compartment Spherical Mean Technique (MC-SMT). Tract-profile analysis was performed in three corpus callosum segments: the forceps major, forceps minor, and body. Group differences were assessed using age- and sex-adjusted general linear models with correction for multiple comparisons. The combined discriminative value of dMRI metrics was evaluated using regularized Elastic Net logistic regression with repeated nested five-fold cross-validation. Results: After adjustment for age and sex, the overall group effect remained significant for 45 of 48 global dMRI measures following Benjamini–Hochberg correction. Compared with MRI Type 2, MRI Type 1 showed lower fractional anisotropy (FA), neurite density index (NDI), intra-axonal volume fraction (INTRA), axonal water fraction (AWF), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK), and higher mean diffusivity (MD), radial diffusivity (RD), extra-axonal mean diffusivity (EXTRA_MD), extra-axonal transverse diffusivity (EXTRA_TRANS), and extra-axonal radial diffusivity (radEAD). These differences were generally most pronounced in the body of the corpus callosum. In the segmental analysis, 131 of 144 values showed a significant overall group effect after correction, and 108 demonstrated significant differences between MRI Type 1 and MRI Type 2. The largest effects were observed in the 60–80% interval of the corpus callosum body, particularly for AWF, MK, INTRA, EXTRA_TRANS, RK, FA, RD, radEAD, and MD. An Elastic Net model combining age, sex, and 48 global dMRI measures discriminated MRI Type 1 from MRI Type 2 with an internally validated area under the curve of 0.866 (95% CI, 0.762–0.953), accuracy of 86.7%, sensitivity of 75.0%, and specificity of 95.3%. Ten dMRI features showed a selection frequency of at least 70% across repeated model construction. Conclusions: MRI Type 1 is characterized by more severe and spatially extensive corpus callosum microstructural abnormalities than MRI Type 2, despite broadly similar vascular risk-factor profiles. The findings support the heterogeneity of sporadic age-related SVD and indicate that combined signal-based and biophysical dMRI metrics may improve MRI phenotyping. The observed associations should be interpreted as indirect markers of tissue microstructure and require confirmation in larger, independent, and longitudinal cohorts. Full article

Stakeholder workshops often produce diverse qualitative and ordinal data that are difficult to process consistently, transparently, and reproducibly, particularly in multilingual settings. To address these challenges, we developed an end-to-end workflow for systematic processing of multilingual participatory workshop data. The workflow integrates preprocessing, structured data management, computational analysis, automated reporting, and interactive dissemination. It incorporates a range of data analysis methods, including large language models (LLMs), and supports both qualitative exploration and quantitative comparison of stakeholder perspectives. We also propose an LLM-based approach for topic extraction and intensity scoring, which transforms qualitative workshop inputs into quantitative representations. The workflow is demonstrated in the EU BENCHMARKS project, which involves multiple workshops, stakeholder groups, land-use contexts, and languages. The main contribution of this work is a transparent and adaptable workflow for systematic processing of multilingual participatory workshop data, supporting reproducible analysis, scalable dissemination, and cross-workshop comparison. Full article

The widespread deployment of Internet of Things (IoT) devices has expanded the attack surface of modern networks, making IoT botnets a persistent cybersecurity threat. Although machine learning-based intrusion detection systems (IDSs) have demonstrated promising detection capability, their performance is often hindered by redundant high-dimensional features and limited model interpretability. This study presents an explainability-guided feature selection framework that employs SHAP values derived from an XGBoost model to rank features and systematically compares its effectiveness with Mutual Information (MI), Analysis of Variance (ANOVA), and Principal Component Analysis (PCA) across nine machine learning classifiers using the N-BaIoT dataset. Experimental results indicate that LightGBM with SHAP-selected features achieves an accuracy of 0.9994 using only the top 10 features. Among the evaluated feature selection methods, SHAP achieves the highest classification performance on seven of the nine classifiers, while Wilcoxon signed-rank tests indicate a statistically significant difference between SHAP and ANOVA (p = 3.03 × 10⁻⁵); the difference between SHAP and MI is significant at the conventional threshold (p = 2.86 × 10⁻²) but does not remain significant after Bonferroni correction for multiple comparisons. Furthermore, SHAP-based analysis reveals distinct traffic statistical signatures between the Mirai and Gafgyt botnet families, offering actionable insights for explainability-aware IoT intrusion detection. The proposed framework provides a practical solution for balancing detection performance and model interpretability in IoT botnet detection. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

Climate change perception among youth has gained increasing scholarly attention; however, immigrant youth remain underrepresented in empirical research despite navigating climate change impacts across multiple sociocultural and geopolitical contexts. This study investigates (1) immigrant youths’ beliefs about climate change, (2) how climate risks are perceived across space and time, and (3) how they construct their climate change beliefs and associated risks. Drawing on Social Constructivism, and Risk Perception Theory, the study employs a qualitative descriptive design using eight focus group discussions with 41 immigrant youth aged 14–18 in Calgary, Canada. Reflexive thematic analysis reveals that immigrant youth widely recognise climate change as real and intensifying, with beliefs shaped through lived and observed environmental experiences, social interaction, family narratives, and transnational comparisons rather than abstract scientific reasoning alone. Climate risk perception emerges as complex and relational. Participants associated climate change with impacts on health, wellbeing, food security, and everyday life; however, they often perceived its most severe consequences as temporally and spatially distant. This produces a notable “belief–risk gap,” where acceptance of climate change does not translate into a strong sense of personal vulnerability or urgency. Risk perceptions are mediated by everyday experiences, institutional trust in the host country, and comparisons with other global crises. The findings highlight the critical role of transnational experiences in shaping climate understanding and advance an integrated interpretation linking socially constructed climate knowledge with perceptions of risk and psychological distance, with implications for climate communication and public health communication strategies for immigrant youth. Full article

Accurate conditional volatility forecasting is essential for risk management, asset pricing, and portfolio optimization. Despite the widespread use of GARCH family models and the proliferation of deep learning architectures, a methodological gap persists: neural networks are rarely trained under criteria statistically coherent with the distributional properties of financial returns. This paper makes two contributions. First, we establish formal structural equivalences between classical heteroscedastic models and neural architectures, showing that ARCH(p) is equivalent to a single-layer linear MLP and GARCH(1,1) to a constrained LSTM, with an explicit parameter correspondence. Second, we propose LSTM-SSE-t-Student, a parsimonious LSTM trained with a hybrid loss that combines the sum of squared errors with the Student-t negative log-likelihood, penalizing errors in the tails of the return distribution. The model is evaluated on six daily series spanning three asset classes—Bitcoin, Ethereum, Gold, Oil, the DJIA, and the S&P 500—across diverse regimes, including the COVID-19 period, against a broad set of econometric and deep learning benchmarks. Relative to GARCH(1,1), it significantly improves point forecast accuracy and probabilistic calibration over naive, short-memory, and regime-switching specifications, while matching the strongest GARCH family and deep learning competitors; a sensitivity analysis shows that the likelihood term lowers the QLIKE loss for most series, with a market-dependent optimal weighting. Statistical significance is assessed via Diebold–Mariano tests with a correction for multiple comparisons, and Value-at-Risk and Expected Shortfall backtests confirm adequate tail calibration for the equity and cryptocurrency series. Interpretability is preserved through the GARCH-consistent structure, whose learned gate dynamics are stable across random seeds. Full article

Climate variability can affect forage production through changes in temperature, precipitation, and the frequency of stressful weather conditions. We examined climatic variability and alfalfa (Medicago sativa L.) productivity in three Romanian development regions—South-Muntenia, South-East, and North-East—over 2006–2024. The climatic variables included mean, maximum, and minimum air temperature, annual precipitation, and maximum 24 h precipitation. Regional relationships between climatic variables and alfalfa yield were examined using descriptive statistics, Kendall’s rank-based trend analysis with Sen’s slope, Pearson correlation, and multiple linear regression with diagnostic testing. We also developed an exploratory Alfalfa Climate Resilience Index (ACRI) to compare relative climate-resilience profiles of regional alfalfa production systems. The index combines two performance-related components, normalized mean yield and yield stability, with two climatic-context components, normalized precipitation and inverse normalized mean temperature. Its sensitivity to alternative component weights was also evaluated. Mean annual temperature increased significantly in all regions, with Sen’s slopes ranging from +0.071 to +0.100 °C year⁻¹, whereas annual precipitation showed no significant monotonic trend. Yield slopes were negative in all regions, but only North-East showed a statistically significant decline (τ = −0.794, p < 0.001). Mean annual temperature was negatively correlated with yield in all three regions (r = −0.617 to −0.749), whereas the precipitation indicators showed no significant bivariate relationships with yield. The regional multiple regression models accounted for 64.3–85.1% of the observed variation in annual yield (R² = 0.643–0.851), with the relative importance and statistical significance of climatic predictors differing among regions. ACRI ranked North-East first (0.760), South-Muntenia second (0.469), and South-East third (0.295), and this ordering remained unchanged across the alternative weighting scenarios examined. These scores represent relative differences among the three regional production systems within the study dataset and should not be interpreted as absolute measures of intrinsic alfalfa resilience. The findings show that climate–yield relationships vary among regions and that temperature was more consistently associated with yield than the precipitation indicators considered here. ACRI is therefore presented as an exploratory, dataset-dependent framework for within-dataset comparison of regional alfalfa production-system climate resilience and requires validation using broader reference datasets, common normalization criteria, and additional management and environmental variables before wider application. Full article

Playing position influences soccer match demands, but comparisons may be biased by unequal playing time and repeated observations from the same players. This retrospective observational study compared GPS-derived running and change-of-velocity demands across five positional groups while accounting for within-player clustering and match exposure. The primary analysis included 84 player-match observations from 21 male professional outfield players across six competitive matches. Linear mixed-effects models included playing position and match as fixed effects, log-transformed playing duration as a covariate, and player as a random intercept. After Holm correction, position was associated with total distance, high-intensity running distance at ≥19 km/h, distance at ≥25 km/h, sprint count, peak speed, accelerations at ≥3 m/s², and decelerations at ≤−3 m/s² (adjusted p ≤ 0.020). At equivalent exposure, central defenders recorded approximately 49% less high-intensity running than wingers and fewer sprints, accelerations, and decelerations than several other groups. Midfielders achieved a 3.06 km/h lower adjusted peak speed than side defenders/full-backs. Findings were robust to two sensitivity analyses. Position-specific monitoring should combine accumulated match dose with exposure-adjusted estimates and account for repeated observations when players contribute data from multiple matches. Full article

Background: This study aimed to compare serum levels of nitric oxide (NO) related metabolites, glial fibrillary acidic protein (GFAP), and ubiquitin (UBI) between children with controlled epilepsy (CE) and drug-resistant epilepsy (DRE) of unknown etiology, and to investigate the associations between these biomarkers and electroclinical features. Methods: Eighty-five children aged 2–18 years with epilepsy of unknown etiology who had been receiving antiseizure treatment for at least six months were enrolled; 58 were classified as CE and 27 as DRE. Serum GFAP and UBI concentrations were measured using enzyme-linked immunosorbent assay, and serum NO-related metabolites were assessed using a Griess-based colorimetric assay. Between-group comparisons were performed using the Mann–Whitney U test, and associations between biomarkers and clinical variables were evaluated using Spearman rank correlation analysis. Bonferroni correction was applied for multiple comparisons. Results: Historical seizure frequency, comorbidity rate, and seizure detection on initial video-EEG differed significantly between the CE and DRE groups. However, serum NO-related metabolite, GFAP, and UBI levels did not differ significantly between the CE and DRE groups. Correlation analyses revealed a significant positive correlation between serum NO-related metabolite levels and age exclusively in the DRE group (ρ = 0.594, p = 0.001), which remained significant after Bonferroni correction. No such association was observed in the CE group. Conclusion: No statistically significant differences in the measured peripheral serum biomarkers were detected between the CE and DRE groups in this sample. The age-related increase in NO-related metabolites observed in the DRE group should be interpreted cautiously and does not establish progressive NO upregulation or cumulative neuroinflammatory burden. Studies incorporating a healthy control group, standardized sampling intervals, and longitudinal designs are needed to clarify the clinical utility of these biomarkers in childhood epilepsy. Full article