Search Results (10,903)

Chronic inflammation is a hallmark of the breast cancer tumor microenvironment and is also known to be associated with disease progression and therapeutic response. Interleukin-1 (IL-1) signaling has been widely studied in breast cancer biology; however, the long-term effect of sustained IL-1 exposure on hormone receptor-positive breast cancer cells remain poorly understood. In this study, we investigated how chronic IL-1 exposure influences inflammatory response, hormone dependency, and therapeutic sensitivity in ERα+/PR+ breast cancer models, MCF7 and T47D. Chronic IL-1 exposure attenuated response to subsequent acute IL-1 treatment, but the chronically exposed cells remained sensitive to serum deprivation, retained dependence on estrogen or progesterone receptor signaling, and responded robustly to endocrine and chemotherapeutic treatments. Extensive changes in basal gene expression and histone modification revealed that chronic IL-1 exposure alters transcriptional reprogramming and chromatin remodeling. Together, these findings demonstrate that chronic IL-1 signaling drives selective inflammatory response in hormone receptor-positive MCF7 and T47D breast cancer cells. This work underscores the continued therapeutic relevance of hormone receptor-targeted strategies in chronically inflamed tumors and provides insight into how sustained inflammatory stress shapes tumor behavior and gene regulation predicted to promote tumor progression. Full article

Traffic signal control increasingly faces disturbed operating conditions such as incidents, abrupt demand surges, sensing degradation, and abnormal driving patterns. Under these nonstationary regimes, classical fixed-time and actuated strategies may exhibit slow recovery, while purely data-driven controllers can be brittle when disturbance characteristics shift. This paper proposes an LLM-in-the-loop architecture for disturbed traffic signal control that integrates (i) deep learning for disturbance detection and short-horizon traffic forecasting, (ii) a disturbance-aware candidate generation and scoring layer (template/retrieval-based), and (iii) a constrained large language model (LLM) that selects or minimally repairs signal plans only within constraint-screened action templates. A deterministic validator enforces safety and operational constraints, including minimum/maximum greens, cycle feasibility, and clearance rules, by checking action feasibility before execution. The method is formulated as constrained decision making under uncertainty, where disturbance estimates and predictive confidence shape both retrieval/scoring and LLM supervision. The originally reported SUMO evaluation considered multiple disturbance categories, including capacity drops, demand shocks, and sensing dropouts as well as reported network delay, queue spillback, recovery time, and switching stability. Within the originally reported SUMO scenarios, descriptive results suggest that among the selected baselines, the proposed DL + LLM framework reported lower mean values of delay, spillback frequency, and recovery time than the fixed-time, actuated, and retrieval-only baselines. The reported validator-detected action-feasibility violations were zero; this result concerns timing-action feasibility rather than an absence of traffic-state risks such as spillback. Full article

Background: Chronic atrophic gastritis (CAG) is a prevalent precancerous gastric disorder characterized by persistent inflammation, glandular atrophy, and progressive mucosal damage, for which effective multi-target therapeutic strategies remain insufficient. The Lian-Huo-Hua-Zhuo formula (LHHZ), a traditional Chinese herbal prescription, has demonstrated potential anti-inflammatory and gastrointestinal protective effects in clinical practice; however, its active constituents and mechanisms of action against CAG remain undefined. This study aimed to clarify the absorbed bioactive components of LHHZ and explore its therapeutic mechanism for CAG. Methods: Ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry was employed to identify the absorbed components of LHHZ in the gastric and intestinal tissues of mice. The therapeutic effects of LHHZ on CAG were assessed through histopathological staining, ultrastructural observation, and evaluation of serum and gastric functional indicators. Network pharmacology, molecular docking, and molecular dynamics simulations were integrated to predict the core targets and key signaling pathways, while the regulatory effects on the interleukin-17 (IL-17) signaling pathway were further validated by immunofluorescence staining, real-time quantitative polymerase chain reaction, and Western blotting. Additionally, 16S ribosomal RNA gene sequencing and targeted metabolomics were applied to investigate the effects of LHHZ on gut microbiota composition and short-chain fatty acid (SCFA) metabolism. Results: The results revealed that 55 and 48 absorbed components were identified in the gastric and intestinal tissues, respectively, predominantly derived from Coptis chinensis Franch. and Pogostemon cablin (Blanco) Benth. LHHZ significantly alleviated gastric mucosal lesions, reduced intestinal metaplasia, restored the ultrastructure of gastric mucosal cells, improved gastric functional indicators including pepsinogen I (PG I), pepsinogen II (PG II), and gastrin-17 (GAS-17), and decreased the levels of pro-inflammatory cytokines. Network pharmacology combined with in vitro and in vivo experiments demonstrated that the core bioactive components of LHHZ can target and regulate interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), attenuate activation of the IL-17 signaling pathway, and suppress the secretion of downstream pro-inflammatory factors. Furthermore, LHHZ enhanced the alpha diversity of gut microbiota, reduced the Firmicutes to Bacteroidetes (F/B) ratio, restored the abundance of SCFA-producing bacteria such as Bacteroidales and Oscillospirales, and normalized the aberrant levels of eight SCFAs. Significant correlations were also observed between gut microbiota composition and SCFA metabolism. Conclusions: These findings suggest that LHHZ alleviates CAG by inhibiting inflammation via the IL-17 signaling pathway and by modulating the gut microbiota–SCFA axis, thereby providing preclinical evidence supporting its further investigation and development for multi-target therapeutic strategies against CAG. Full article

At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R² = 0.654 ± 0.123, temporal holdout R² = 0.710, and long-jump R² = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R² values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R² values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal. Full article

Accurate forecasting of high-dimensional meteorological fields remains challenging due to the complex spatio-temporal dynamics of atmospheric systems and the presence of heterogeneous training difficulty across space and lead time. Existing deep forecasting approaches usually optimize all prediction units uniformly, which may overemphasize low-benefit or weakly generalizable supervision signals. To address this issue, we propose Spatio-Temporal Selective Learning (ST-SL), an online training framework that estimates the learnability of each prediction unit by comparing the main model with a frozen reference model and computes the loss only over selected high-benefit spatio-temporal units. To provide an effective forecasting backbone, we further introduce VASTFormer, a variable-aware spatio-temporal Transformer that models cross-variable dependencies, incorporates physics-enhanced Solar Positional Encoding, and captures atmospheric trajectories with an efficient temporal translator. Experiments on the ERA5 reanalysis dataset show that VASTFormer outperforms representative spatio-temporal baselines, while ST-SL further improves accuracy without adding inference-time parameters or computational cost. Compared with the strongest baseline, VASTFormer+ST-SL reduces MSE, MAE, and RMSE by 8.84%, 6.70%, and 4.54%, respectively. Meteorological skill evaluation further shows an average ACC of 0.9801 and RMSESS of 0.8104, and percentile-based extreme-condition evaluations confirm consistent improvements across standard and high-impact forecasting scenarios. These results indicate that selective supervision can improve generalization in dense meteorological forecasting. Full article

Objectives: To assess the feasibility and reproducibility of predicting antimicrobial resistance (AMR) in Escherichia coli from MALDI-TOF mass spectrometry data using a standardized, open-source machine learning (ML) workflow, we systematically compared four ML algorithms, evaluated the impact of culture conditions, extract storage, and spectral preprocessing on model performance, and validated results through nested cross-validation with statistical significance testing. Methods: A total of 282 clinical E. coli isolates were analyzed. Two MALDI-TOF MS datasets were generated from freshly cultured extracts (T₁) and recultured isolates one year later (T₃), yielding 4468 spectra. A third dataset from the T₁ extracts stored at −20 °C for one year (T₂) was evaluated for spectral stability but excluded from primary modeling likely due to storage-induced degradation. Protein spectra (m/z 2000–15,000) were preprocessed using an in-house developed MALDI-TOF preprocessing pipeline (MTPP) comprising variance stabilization, Savitzky–Golay smoothing, SNIP baseline correction, TIC normalization, LOWESS alignment, and MAD-based peak detection (SNR ≥ 3), yielding 121 m/z features. Four classifiers—Random Forest (RF), Logistic Regression, Support Vector Machine, and Gradient Boosting—were trained to predict resistance to 11 antibiotics using nested cross-validation: outer GroupShuffleSplit (5-fold, isolate-level) for evaluation and inner GroupKFold for recursive feature elimination (RFECV) and hyperparameter tuning (RandomizedSearchCV). Classification thresholds were optimized via the precision–recall curve. Model performance was assessed using AUROC, AUPRC, F1-score, Matthews Correlation Coefficient (MCC), and bootstrap 95% confidence intervals (1000 replicates). Pairwise model comparisons were tested with McNemar’s chi-squared test. Results: Among the 12 antibiotics included in the analysis (meropenem excluded for absence of resistance), resistance prevalence ranged from 1.1% (colistin) to 59.9% (amoxicillin). Colistin was subsequently also excluded from ML modeling due to insufficient resistant isolates (n = 3), leaving 11 antibiotics for prediction. The best predictive performance was observed for ciprofloxacin (AUROC 0.76 [95% CI 0.74–0.77]; F1 0.54; MCC 0.38) and ceftazidime (AUROC 0.68 [0.65–0.71]; F1 0.36; MCC 0.29), using 13 and 37 RFECV-selected features, respectively. Amoxicillin achieved the highest F1-score (0.76), driven by high recall (0.98) but modest AUROC (0.58). No meaningful predictive signal was detected for amikacin, cefepime, or tigecycline (AUROC ≤ 0.57, F1 ≤ 0.17), attributable to extreme class imbalance, and no robust multi-peak resistance signature was detected in this dataset. McNemar’s test confirmed that RF significantly outperformed Logistic Regression for all antibiotics (p < 0.01), while Gradient Boosting performed comparably to RF for ciprofloxacin (p = 0.17) and ceftazidime (p = 0.28). Frozen extracts (T₂) produced lower spectral similarity and were excluded from model training; the aligned T₁₊₃ dataset yielded the most stable performance across metrics. Conclusions: Machine learning analysis of MALDI-TOF spectra enables reproducible AMR prediction for selected antibiotics in E. coli, with ciprofloxacin and ceftazidime showing the strongest signal. Nested isolate-level cross-validation, multi-model comparison with statistical testing, and open-source code provide a transparent, reproducible foundation for integrating ML-assisted MALDI-TOF analysis into diagnostic AMR surveillance. Extract storage at −20 °C degrades spectral quality and should be avoided in ML training workflows. Full article

Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026^®T as a license-aware AI-QSP integration framework that connects the EHMN2026^® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. Methods: EHMN2026^®T integrates the SBML-encoded EHMN2026^® metabolic backbone with licensed TRANSFAC^® 2025.2, TRANSPATH^® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC^® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)–gene connections were mapped to EHMN genes, TRANSPATH^® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. Results: Promoter analysis of 1681 EHMN2026^® metabolic genes using 1147 mapped TRANSFAC^® matrices identified 291,387 ENSG-level TF–gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH^® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene–pathway or gene–chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. Conclusions: EHMN2026^®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF–gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC^®, TRANSPATH^® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms. Full article

Deep learning (DL)-based percussion methods in concrete-filled steel-tube (CFST) void detection have gained much attention. However, the detection signal contains a large amount of noise, which affects the accuracy of qualitative and quantitative analyses of the subsequent detection results. To improve the signal-to-noise ratio (SNR) during percussion detection, this study proposes a CFST void detection method using the good point set and vibrational snow ablation optimizer (GVSAO) algorithm and dual-channel parallel convolutional neural networks (CNNs). The proposed method employs the gram angle field (GAF) to transform percussive sound signals into images. It then constructs a dual-channel parallel CNN structure, where the GAF is decomposed into the following two maps: the gram angle sum field (GASF) and the gram angle difference field (GADF). These maps are simultaneously fed into the CNN for training. The outputs from the two channels are concatenated and fused. Finally, the GVSAO algorithm was used for model optimization to improve convergence speed and recognition accuracy. Both the temporal and spatial characteristics of the knocking sound signal are fully preserved, while the interference of different construction noises is effectively avoided. Validation experiments were conducted on CFST specimens with different heights of voids (0, 50, 100, and 150 mm) under different pressure loads. The original sample dataset and the signal-enhanced dataset were obtained by adding background noise with different SNRs. The test results show that the prediction accuracies on the original signal dataset are consistently above 98.74%. Among them, the accuracy achieves 100% at pressure loads of 0 and 50 tons. Additionally, the prediction accuracies on the signal-enhanced dataset are all above 97.2%, indicating that the model maintains a high level of classification performance. This suggests that the model can effectively suppress noise and exhibits excellent robustness. Full article

Triphenyl phosphate (TPhP) is a ubiquitous environmental contaminant and endocrine disruptor potentially associated with an increased risk of thyroid cancer (TC). However, whether TPhP directly contributes to TC remains unclear. This study integrated network toxicology and machine learning to investigate potential molecular associations between TPhP exposure and thyroid oncogenesis. By integrating multi-source databases and transcriptomic data, we constructed a TPhP–TC interaction network and established a TC risk prediction model using 127 machine learning algorithm combinations, identifying ten candidate hub genes. GO and KEGG enrichment analyses indicated that these genes are predominantly enriched in phosphorus metabolism, purine metabolism, and nuclear receptor signaling pathways, implying that TPhP may be linked to tumorigenesis through the disruption of metabolic reprogramming. SHAP analysis highlighted AHR and SLC20A2 as critical contributors to model performance. Molecular docking predicted stable binding between TPhP and all hub proteins in silico, with binding energies ranging from −9.2 to −6.6 kcal/mol. This study offers two computational contributions: (1) a quantifiable framework for predicting pollutant-associated TC risk and (2) systematic computational evidence for potential TPhP thyroid toxicity. These findings address a critical gap in understanding potential links between endocrine-disrupting chemical exposure and thyroid carcinogenesis, generating hypotheses for future experimental validation. Full article

Direction of arrival (DoA) estimation is a fundamental problem in modern communication systems, such as 5G/6G cellular systems, V2X, and radar. Modern DoA estimation techniques enhance signal reception, mitigate interference, enhance communication efficiency, improve capacity, and improve spatial selectivity. In this paper, a two-channel residual neural network (ResNet) CNN is designed and trained based on the covariance matrix for a realistic electromagnetic antenna array model by expanding the steering vector obtained from the embedded element radiations. The regression DoA estimation is parameterized for three scenarios: regression using a trigonometric angle process, regression directly in degrees, and regression in radians. Then, the proposed network is compared with the modified conventional multiple signal classification (MUSIC), minimum variance distortion-less response (MVDR), and a two-channel deep CNN. A microstrip antenna array is designed, operating at 28 GHz, using Ansys Electronic Desktop to obtain the 3D embedded element radiation, for both co-polarized and cross-polarized components, considering mutual coupling among the antenna array elements, finite-element spacing, and array geometry. The proposed degree-based ResNet CNN achieves sub-degree azimuth and elevation RMSE for angular separations greater than 10° at an SNR of 0 dB in our simulations, clearly outperforming modified MUSIC, MVDR, and deep CNN learning-based 2D DoA methods that require significantly higher SNR to reach comparable accuracy. Moreover, the network operating directly on the real and imaginary parts of the covariance matrix and predicting angles in degrees consistently yields lower RMSE than variants trained to predict radians or sine–cosine representations, while avoiding the steering vector knowledge and postprocessing steps, spatial spectra, peak search, or root-finding, used in existing approaches. Full article

Background: Early and objective identification of cognitive decline in aging populations remains a clinical challenge. Pupillary light reflex (PLR) and eye movement parameters represent non-invasive, quantitative biomarkers of autonomic and central nervous system integrity, yet their diagnostic utility for cognitive impairment in community-dwelling older adults, particularly in those with mild or borderline impairment (predominantly GDS-Stage 2), remains underexplored. Methods: This cross-sectional study analyzed 383 community-dwelling older adults (mean age 69.78 ± 6.29 years; 68.7% female). Ten PLR parameters (n = 202 with complete PLR measurements) and ten eye movement parameters were measured. Associations with cognitive decline (deterioration grade, GDS 2–4) were evaluated using Spearman correlation analysis and multivariate linear regression (adjusted for age, sex, BMI, and hypertension). Stratified analyses and ordinal logistic regression sensitivity analysis were performed to assess robustness. FDR correction (Benjamini–Hochberg) was applied for multiple comparisons. Predictive modeling was conducted using ElasticNet regression with 5-fold cross-validation. Results: After FDR correction, resting pupil diameter (ρ = −0.47, q < 0.001), constriction amplitude (ρ = −0.40, q < 0.001), mean constriction velocity (ρ = −0.36, q < 0.001), mean dilation velocity (ρ = −0.36, q < 0.001), and all eye movement velocity parameters (ρ = −0.22 to −0.41, q < 0.001) demonstrated significant negative correlations with cognitive decline. Multivariate regression confirmed resting pupil diameter (β = −0.286, q < 0.001) and constriction amplitude (β = −0.223, q < 0.001) as independent predictors. Sensitivity analysis using ordinal logistic regression yielded consistent results. Predictive modeling yielded modest performance for the primary outcome (PLR-only cross-validated R² = 0.184), whereas models using eye movement features alone or in combination with PLR features performed near chance (R² ≤ 0.04) or showed instability, indicating that these parameters are not yet suitable as standalone diagnostic tools. Exploratory analyses of depression and anxiety were limited by floor effects (≥89% zero scores). Conclusions: PLR and eye movement parameters show significant negative associations with cognitive decline in older adults, particularly in a sample skewed toward mild impairment (predominantly GDS-Stage 2). These findings provide preliminary, hypothesis-generating signals that warrant validation in clinical samples with broader cognitive impairment distributions, and these parameters should not yet be considered standalone diagnostic biomarkers. Full article

Vibration-based fault diagnosis is widely used for rotating machinery health monitoring, but practical diagnosis is often limited by scarce fault labels and uncertain measurement length. Longer vibration records can improve decision reliability but increase sensing and computational cost, whereas overly short records may yield unreliable predictions under noise and measurement corruptions. This paper studies few-shot fault diagnosis as a measurement-constrained decision task, in which the model identifies the fault class and determines when sufficient vibration evidence has been acquired. We propose a measurement-efficient diagnosis framework that combines prior knowledge from unlabeled healthy signals, physically constrained augmentation of scarce labeled samples, and adaptive early stopping in a shared one-dimensional feature extractor. The framework is evaluated on the UORED-VAFCLS and Paderborn University bearing datasets under 6-, 8-, and 10-shot settings with controlled corruption levels. Results show robust diagnostic performance with fewer acquired vibration windows than with fixed-length inference. In the representative PU-Hard 8-shot setting, the proposed method achieves 80.26% accuracy with an average of 1.2432 acquired windows and reduces the evaluation cost J from 0.3929 to 0.2596 compared with fixed four-window inference. These results indicate that adaptive measurement improves the accuracy–cost trade-off in few-shot vibration diagnosis. Full article

Automatic sign language generation has the potential to support information accessibility for deaf and hard-of-hearing individuals. Generating sign language pose sequences from natural language text can serve as an intermediate representation for avatar-based sign language expression and sign language video synthesis. However, text-to-sign pose generation is challenging because sign language conveys meaning through both manual movements and non-manual signals, while requiring temporally coherent motion over local and sentence-level contexts. In addition, text length does not directly correspond to the number of pose frames required for sign language expression. To address these issues, this study proposes a text-to-Korean Sign Language (KSL) pose generation model based on non-manual signal conditioning and multi-scale temporal refinement. The proposed framework integrates a text encoder, pose decoder, non-manual signal conditioning, multi-scale temporal refinement, and length prediction/blending. The model generates normalized 58-joint KSL keypoint sequences from morpheme-level text inputs and jointly optimizes pose reconstruction, motion continuity, bone consistency, PCK-aware precision, non-manual signal prediction, and length consistency. Experimental results on a KSL text–pose dataset show that the proposed model outperforms text-only and Transformer-based baselines. Compared with the Transformer text-to-pose baseline, the proposed model reduced MPJPE from 0.408236 to 0.316366 and Pose MAE from 0.165473 to 0.128570. It also improved PCK@0.05 from 0.136090 to 0.163928 and reduced the length relative error from 0.221455 to 0.127152. In particular, the best-threshold non-manual F1 substantially increased from 0.010859 to 0.494566. These results suggest that text-based KSL pose generation should jointly consider non-manual expressions, length consistency, and long-term temporal motion structure rather than relying only on frame-wise keypoint prediction. However, the reported improvements should be interpreted as coordinate- and label-level evidence, not as a complete validation of linguistic meaningfulness or real-world accessibility. Full article

An 8-week feeding trial was conducted to investigate the regulatory effects of dietary chitosan oligosaccharide (COS), chlorogenic acid (CGA), and thymopeptide (TH) on the growth performance, antioxidant capacity, and metabolism of juvenile Rhinogobio ventralis. A control group (CON) and three treatment groups (COS, CGA, TH) were established, and the underlying molecular mechanisms were analyzed by transcriptomics and metabolomics. The results showed that dietary supplementation with COS, CGA, and TH significantly improved weight gain rate, specific growth rate, and feed utilization efficiency, with COS exhibiting the strongest growth-promoting effect. COS also significantly increased hepatic catalase and superoxide dismutase activities and decreased malondialdehyde content, indicating a marked antioxidant effect, whereas CGA and TH showed no significant effects on these parameters. Transcriptomic analysis revealed significant differences in gene expression profiles among the groups, with commonly enriched pathways including the FoxO signaling pathway, fatty acid biosynthesis, and nicotinate and nicotinamide metabolism. Metabolomic analysis showed that the three additives significantly reshaped the hepatic metabolic profiles: COS mainly activated anabolic metabolism, CGA predominantly suppressed metabolism, and TH exhibited bidirectional regulation, with bile secretion, neuroactive ligand–receptor interaction, and the Fc epsilon RI signaling pathway commonly enriched in all three groups. Integrative KEGG pathway analysis based on transcriptomic and metabolomic predictions further identified the FoxO signaling pathway and neuroactive ligand–receptor interaction as common targets shared by all three additives. In conclusion, COS, CGA, and TH improve the health status of R. ventralis through differential regulation of immune–metabolic networks. Among them, COS is the most suitable feed additive for growth promotion and antioxidant protection in this species. Full article

Detecting and localizing leaks in buried pipelines typically requires direct access to the pipe, which is often impractical in real-world conditions. Although ground-surface vibration measurements offer a non-intrusive alternative, their potential for spatial leak localization remains underexplored, particularly in relation to frequency-dependent attenuation effects. This study investigates how frequency-dependent energy decay encodes spatial information in leak-induced ground vibrations. Experimental wok was conducted using an outdoor buried pipeline testbed, where surface acceleration data were collected with a movable array of piezoelectric sensors. The measurements were reorganized into L-shaped sensor trios to enable directional analysis and increase the number of spatial configurations. Energy-based features extracted from discrete frequency bands were used to represent the leak signatures, capturing both attenuation behavior and soil–pipe interaction effects. Artificial Neural Network and Random Forest models were trained to estimate leak coordinates in a local reference frame. The results demonstrate high localization accuracy at the centimeter scale and reveal consistent relationships between prediction error, distance, and signal-to-noise ratio. These findings show that frequency-dependent attenuation provides a robust basis for spatial inference, and that combining ground surface vibration measurements with lightweight machine learning models offers an effective and non-intrusive solution for leak localization in buried pipelines. Full article