Search Results (618)

Post-myocardial infarction (MI) remodeling is accompanied by metabolic stress, but the myocardial genes associated with copper handling are poorly defined. We sought to prioritize a tissue-derived candidate rather than establish a copper-dependent mechanism. Regional MI transcriptomes and an in-house left anterior descending coronary artery ligation mouse RNA-sequencing cohort were integrated with protein quantitative trait locus-based Mendelian randomization (MR). Follow-up comprised local and external tissue validation, cardiac single-cell RNA sequencing, Genotype-Tissue Expression co-expression, computational perturbation, and CRIP2 knockdown or overexpression in H9c2 cells exposed to hypoxia/reoxygenation (H/R). CRIP2 showed a nominal protective-direction MR association (odds ratio 0.831, 95% confidence interval 0.735–0.939; p = 0.0031), but did not pass the Bonferroni threshold. Crip2 was lower in the local MI model (p = 0.0168; n = 5 per group) and in an independent dataset. A prespecified lipoylated-tricarboxylic-acid module was negatively enriched after MI (normalized enrichment score −1.63; false discovery rate 0.012), whereas the broader copper-homeostasis set was not significant. Single-cell data localized Crip2 mainly to cardiomyocytes, but were not adequately replicated for condition-level inference. Under H/R, Atp7a was the only copper-handling transcript whose knockdown-by-oxygen interaction remained significant after adjustment (q = 0.0405). CRIP2 overexpression was associated with higher Cell Counting Kit-8 metabolic activity during H/R (interaction p = 0.00551), whereas the knockdown interaction was not significant. Copper abundance, mitochondrial function, and cuproptosis markers were not measured. The data prioritize CRIP2 for mechanistic study, but do not show that it regulates copper flux or post-MI remodeling. Full article

Agricultural nonpoint-source phosphorus (NPS-P) losses threaten receiving waters, but regional control is complicated by differences in source intensity, erosion sensitivity, and hydrologic connectivity. This study jointly applied an improved export coefficient model (IECM), the Revised Universal Soil Loss Equation (RUSLE), Global and Local Moran statistics, and Getis-Ord Gi* analysis to six contrasting basins. The outputs were cross-interpreted without a formal composite index. Average annual soil erosion ranged from 1.96 to 18.47 t ha⁻¹ yr⁻¹, with very slight and slight erosion dominating all basins. Annual NPS-P export ranged from 2169.55 to 12,028.32 t yr⁻¹ (1.26–2.74 kg ha⁻¹ yr⁻¹), and cultivated land contributed 48.60–69.32% of modeled export. Under 999 random permutations, Global Moran’s I ranged from 0.615 to 0.834 (pseudo p = 0.001), and Gi* hot spots occupied 26.18–33.59% of valid cells. The basin-level perturbation analysis indicated greater ranking robustness for clearly high- and low-load basins than for intermediate basins, while the cultivated-land sensitivity analysis quantified the influence of the dominant coefficient. The framework supports regional screening, monitoring prioritization, and subsequent field verification rather than calibrated event-scale prediction. Full article

Background: Although genetic-environmental interactions are established in autism spectrum disorder (ASD), how environmental toxicants confer susceptibility remains unclear. This study aimed to investigate potential relationship between genetically predicted cerebrospinal fluid (CSF), metabolite levels and ASD liability, and to prioritize regulatory genes, key pathways, and candidate environmental toxicants. Methods: Using two ASD GWAS datasets (exploration data: 18,381 ASD cases/27,969 controls; validation data: 18,235 ASD cases/36,741 controls), we applied multi-omics approaches to prioritize ASD-associated CSF metabolites, regulatory SNPs, and genes. Enrichment analysis and protein–protein interaction (PPI) network analysis were performed on these metabolite-related genes to explore the potential mechanisms linking CSF metabolic disturbances to ASD. Finally, candidate environmental neurotoxicants were screened through protein-chemical interaction analysis, with binding relationships assessed via molecular docking prediction. Results: Two-sample Mendelian randomization (MR) analysis prioritized adenine and proline as candidate CSF metabolites with potential risk associations with ASD. Summary-data-based MR (SMR) prioritized 39 brain-specific quantitative trait loci (QTL) involving 35 candidate regulatory genes, including dual-metabolite modulator GRM8. Functional enrichment analyses suggested potential associations with mitochondrial dysfunction, Hippo signaling pathway, and microtubule dynamics impairment, with protein–protein interaction networks highlighting KATNA1/KATNAL2 as hubs. Protein-chemical interaction screening nominated 14 candidate environmental toxicants, including established chemicals (acetaminophen, valproic acid, estradiol) and novel candidates (SB-431542, K 7174, benzo[a]pyrene), with docking affinity assessed computationally. Conclusions: Our study provides suggestive evidence that elevated adenine and proline may be potential risk factors for ASD and suggests possible involvement of the mitochondrial–Hippo–microtubule pathway. We also propose benzo[a]pyrene as a candidate environmental toxicant that may perturb CSF metabolism. However, given the limited statistical significance, these findings require further validation. Full article

Background: Obesity is a major independent risk factor for atrial fibrillation (AF), contributing to adverse outcomes and complicating rhythm control. While lifestyle modification is recommended, catheter ablation remains central to management. Conventional thermal ablation techniques often yield suboptimal results in obese patients due to anatomical and biophysical challenges. Pulsed field ablation (PFA), a novel non-thermal modality, may overcome these limitations. Methods: A systematic search of PubMed, Scopus, and Embase identified observational cohort studies (2017–2026) evaluating PFA outcomes in obese AF patients or across BMI categories. Four cohort studies met the inclusion criteria. Results: Findings on AF recurrence were heterogeneous. Recurrence rates appeared lower with PFA compared to radiofrequency ablation (RFA), though differences were not statistically significant. In a PFA-only study, freedom from arrhythmia recurrence did not vary across BMI categories. Compared with cryoballoon ablation (CBA), one matched-cohort study demonstrated significantly higher one-year freedom from AF with PFA-PVI, whereas another reported no difference. Left atrial epicardial adipose tissue (LA EAT) emerged as the only independent predictor of recurrence in PFA patients, suggesting electrical field perturbation by fat tissue. Radiation exposure was lower with PFA than CBA, while fluoroscopy time and periprocedural complications were comparable across groups. Conclusions: Current observational evidence suggests that PFA may be a feasible and safe option for AF ablation in overweight and obese patients, with outcomes comparable to conventional ablation techniques. However, comparative efficacy and long-term safety remain uncertain. Obesity-related adipose hypertrophy, systemic inflammation, and atrial remodeling may alter electric field distribution, contributing to variable recurrence outcomes. Larger prospective and randomized trials are warranted to define the long-term role of PFA in this high-risk population. Full article

Explainable intrusion detection systems often provide feature attributions without indicating whether a security action should be released, downgraded, or deferred. This paper investigates whether action-governed explanations can provide bounded triage evidence under traffic feature feasibility constraints. We present TRACE, a framework that maps calibrated detector outputs to a finite action ladder, constructs conformal action sets, selects actions via a utility-minimax rule, and releases high-severity actions only when compact support contracts remain stable under feasible perturbations, where feasibility is a property of the processed benchmark features and not of packet-level realizability. Ablations isolate the conformal set and release gate as the primary drivers of system behavior. Across 11 gated dataset–model pairs, TRACE produces non-degenerate action sets with zero full-set collapse and defer/block rates from 0.603 to 1.000. Under held-out sample split tuning, it achieves higher average proxy utility than unconditional release and release rate-matched random release on all 11 pairs. Against the strongest simple selective gate, however, it matches on 6 of 11 pairs and trails on the remaining 5. Robustness sweeps confirm positive all-row utility on all pairs, though pass-only utility becomes fragile in ultra-low-release regimes. Unlike display-only attribution summaries, the TRACE contract records the plausible action set, feasibility checks, stability summaries, and an explicit release rationale. The results support TRACE as a bounded evidentiary framework for action-governed XAI in IDS, rather than claiming superiority over all IDS/XAI methods or general deployment readiness. Full article

Point cloud perception is important in autonomous driving, robotics, and other security-critical 3D systems, yet learned point cloud classifiers remain vulnerable to transferable adversarial perturbations. A central difficulty in transfer-based black-box attacks is surrogate overfitting: an update that is highly effective on an accessible source model may not generalize to an unknown target architecture. We introduce SpecEOT, a source-agnostic and graph-spectral expectation-over-transformation attack. A fixed graph Fourier transform (GFT) basis is constructed from each clean point cloud. At every optimization iteration, each non-identity view independently samples a frequency band and a perturbation sign from uniform distributions; the resulting view gradients are averaged with equal weights and used to update the adversarial point cloud through projected Adam ascent. We evaluate the stochastic method over repeated seeds, extend the ablation to two source architectures, and analyze the interaction between band count and randomization strength while reporting computational cost and assessing robustness to Gaussian jitter and point dropout. SpecEOT achieves strong transferability on ModelNet40 and ShapeNet. Full article

Emergency communication support is a critical prerequisite for effective disaster response. To address the problems of one-sided indicator weighting and the uncertain quantification of qualitative indicators in emergency communication network evaluation, this study proposes a combined weighting–cloud model–TOPSIS framework. First, an evaluation indicator system is constructed from four dimensions: transmission performance, coverage capability, service quality, and survivability. Second, an FAHP–entropy combined weighting strategy is used to integrate expert knowledge and data-driven information. Third, a backward cloud generator is introduced to quantify qualitative indicators while preserving both fuzziness and randomness; triangular fuzzy numbers and relative preference relations are further used to convert cloud outputs into comparable scalar values. Finally, TOPSIS is applied to rank alternative emergency communication plans. A flood rescue case study and additional sensitivity tests show that the ranking of the three plans remains stable under perturbations of weights, input data, and indicator inclusion. This paper also provides the raw input data, expert scoring details, simulation assumptions, and a comparison with other MCDA methods to improve reproducibility. Full article

Landslide susceptibility mapping (LSM) serves as a fundamental technical support for geohazard prevention and mitigation across mountainous terrains. This research constructs a multi-scale terrain unit integrated modeling framework targeting complex mountainous geomorphic settings, taking Zhenping County as the research object. Multi-resolution digital elevation model (DEM) datasets, multi-source satellite remote sensing imagery (GF-2), geological vector datasets and hydrological survey data are jointly adopted as the basic data source. The r.slopeunits module embedded in GRASS GIS is utilized to automatically segment slope units, and a comprehensive composite index S, coupling slope partition quality indicator F and model prediction accuracy metric R, is proposed to adaptively optimize two critical slope-unit hyperparameters: circular variance (c) and minimum unit area (a). Four DEM spatial resolutions (15 m, 25 m, 50 m, 100 m) are systematically calibrated with 42 groups of c–a parameter combinations to screen out the optimal slope-unit segmentation scheme (c = 0.1, a = 200,000 m²). Twelve landslide predisposing covariates covering topography, hydrology, lithology, human engineering activities and land cover are selected after multicollinearity diagnosis via Variance Inflation Factor and mean utility factor contribution evaluation. Logistic regression tree (LMT), LMT-Adaboost and LMT-Random Subspace are compared by random cross-validation and spatial block cross-validation. Parameter sensitivity analysis is further carried out to quantify the stability of model outputs against DEM resolution and slope-unit parameter perturbations. The LMT-RSM ensemble achieved the highest spatial cross-validation AUC (0.954 ± 0.019), outperforming LMT (0.925 ± 0.023) and AdaBoost-LMT (0.934 ± 0.021). The DeLong test confirmed that LMT-RSM’s superiority over LMT is statistically significant (p < 0.0001). The proportion of landslides in the very high and high susceptibility zones under the LMT-RSM model reached 95.98%, demonstrating relatively excellent spatial discrimination. This study provides an operational framework combining optimized slope units, ensemble learning, and spatially explicit validation for robust LSM in complex terrain, and offers a reproducible technical pathway for landslide risk prevention in mountainous regions. Full article

Mature waterflood reservoirs contain long injection–production histories, yet routine surveillance remains challenged by delayed producer responses and interference from multiple injectors. Existing diagnostic plots, CRM-type analyses, and data-driven models are useful, but they often require preselected patterns, aggregate long-period behavior, or provide limited event-level traceability. This study presents an event-window evidence-fusion workflow (EWEF) for screening candidate injection–production responses without treating the screened objects as causal proof. EWEF uses material injection-rate perturbations as observational triggers and constructs auditable event-window evidence for candidate injector–producer objects. The workflow includes dynamic-record checking, injection-event extraction, candidate-pair construction, pre/post response-window quantification, lag scanning of oil-rate and water-cut responses, static-prior scoring from geometry and transmissibility proxies, relative multi-injector attribution, and rule-model disagreement routing. A transparent rule layer assigns engineering review classes, and a random-forest model trained on rule-derived weak labels serves only as a consistency-review gate. The workflow was applied to three anonymized field waterflood samples comprising 54 dynamic wells, 153 injection perturbation events, 297 candidate injector–producer evidence objects, and 30 final review-list objects. In the weak-label consistency model, lagged oil-rate correlation, water-cut shift, oil-rate shift, static prior, and water-cut lag correlation were recurrent influential input variables. In one representative event, the six-month post-event window showed that oil rate increased by 5.49 m³/d, whereas water cut decreased by 0.52 percentage points relative to the pre-event baseline. Lag scanning yielded modest correlations, with maximum absolute values of approximately 0.34; these correlations are treated as descriptive screening evidence rather than as statistically confirmed connectivity indicators. EWEF therefore provides a traceable workflow for screening and prioritizing field-review candidates. It does not establish causal interwell connectivity. Confirmation requires independent tracer, pressure, intervention, logging, or simulation evidence, and field-specific calibration remains necessary. Full article

Microgrid integration introduces fast, stochastic disturbances that challenge frequency stability. This paper presents a two-degree-of-freedom fractional-order proportional tilt integral derivative plus one controller (2DOF-FOPTID+1) tuned with a Modified Walrus Optimization Algorithm (MWA) to mitigate frequency deviations while preserving tracking performance. The novelty lies in jointly deploying a 2DOF-FOPTID+1 structure for decoupled tracking and regulation, an MWA-based tuning strategy tailored for resilient frequency control, and the explicit use of aggregated electric vehicles as fast distributed storage to damp frequency and tie-line power excursions; hardware-in-the-loop validation using an OPAL-RT platform is included to demonstrate practical feasibility. The controller is evaluated under step and random load variations, and robustness is examined for $\pm 25\%$ parameter perturbations and stochastic renewable inputs. Compared with the strong baselines PID, FOPID, 2DOF-PID, and FOPTID, the proposed approach reduces settling time by up to 39.27% and lowers peak-to-peak frequency deviation by about 20.88% under these operating scenarios, indicating a practical and effective solution for enhancing frequency resilience in microgrid-integrated power systems. Full article

The Ecological Security Pattern (ESP), composed of ecological sources, resistance surfaces, and corridors, provides a spatial basis for mitigating urban landscape fragmentation and sustaining ecological security. However, most urban ESP studies have focused on its spatial delimitation, while the assessment of network vulnerability under disturbance remains limited. This study applies an integrated, exploratory, and model-based methodological framework that combines ESP mapping, ecological network analysis, attack simulation, and load–capacity cascading failure modelling to generate simulated indications of the potential vulnerability of the urban ecological network of Bogota. The results identified 58 ecological sources with a combined area of 123.02 km² (19.58% of the study area) and 107 active corridors. In the simulations, sources N540, N433, and N847 showed the highest topological relevance, whereas sources N933, N337, and N847 concentrated the greatest functional importance. In the disturbance simulations, the network showed greater relative robustness to random removals; in contrast, degree- and betweenness-targeted removals produced a more accelerated loss of the connected component, whereas degree- and PageRank-based perturbations accelerated simulated functional degradation. In the dynamic scenario analyzed, the model organized the network into four risk levels and suggested indirect and multi-stage trajectories of simulated potential failure propagation. These findings contribute to the exploratory diagnosis of ESP functional vulnerability and provide preliminary, simulation-based spatial criteria to guide exploratory ecological prioritization analyses and scenario assessment in Bogotá and dense Global South metropolises. Full article

Industrial Internet of Things (IIoT) deployments face a security challenge that neither physics-based nor AI-based anomaly detection addresses alone: physics models are adversarially robust but miss behavioral attacks that remain within physical bounds, while AI models detect behavioral anomalies but are vulnerable to adversarial evasion and blind to physical sensor spoofing. This paper proposes PhySec-Edge, a hybrid framework integrating a Physics Validation Engine (PVE) with a multi-model Edge AI Detection Engine (EADE) in a layered, residual-sharing architecture. The PVE applies process model residuals, Kalman filter state estimation, cross-sensor consistency checks, and temporal gradient validation to generate physics-grounded anomaly signals. The EADE is designed around LSTM temporal detection, variational autoencoder reconstruction analysis, and graph neural network process monitoring augmented with PVE residuals; the current evaluation uses computationally tractable proxy implementations to provide a conservative lower bound on the benefits of residual sharing. Randomized smoothing is applied under bounded perturbation assumptions to improve adversarial robustness. PhySec-Edge is evaluated in a controlled synthetic IIoT setting parameterized using SWaT-inspired structural and statistical assumptions, comprising 9875 samples across seven attack classes. Across five random seeds, the hybrid framework achieves mean precision = 0.789 ± 0.004, recall = 0.808 ± 0.003, F1 = 0.798 ± 0.003, and FPR = 5.0% ± 0.0%, compared to F1 = 0.654 ± 0.006/FPR = 24.0% for the physics-only baseline and F1 = 0.774 ± 0.003/FPR = 5.0% for the AI-only baseline. An ablation study identifies residual augmentation as the primary individual improvement mechanism (ΔF1 = +0.017), while the full hybrid configuration achieves a combined gain of ΔF1 = +0.025 over the AI-only baseline. Critical hybrid advantages appear on adversarial evasion (+0.15 F1) and firmware implant (+0.17 F1), the two attack classes where neither layer alone is sufficient. A preliminary feasibility check on an Edge-IIoTset-inspired benchmark confirms that the architectural advantage pattern generalizes across dataset structures. Gateway latency analysis confirms compatibility with soft real-time industrial monitoring constraints. Full article

Two-dimensional (2D) intrinsic ferromagnets with perpendicular magnetic anisotropy (PMA) have been experimentally verified as promising candidates for nanoscale spintronic devices and magnetic random-access memories. In this work, we systematically investigate the stability, electronic structure, and magnetic properties of monolayer MOCl (M = Ti, V, Cr, Mo) via first-principles calculations. The results demonstrate that allshi ciju monolayers MOCl (M = Ti, V, Cr, Mo) are intrinsic ferromagnetic semiconductors, with magnetic moments of 1.0 μ_B/Ti atom, 2.0 μ_B/V atom, 2.5 μ_B/Cr atom and 3.0 μ_B/Mo atom, respectively. Notably, both monolayers TiOCl and CrOCl exhibit perpendicular magnetic anisotropic energy (MAE), which is mainly contributed by metal atoms Ti and Cr, respectively. Drawing on the second-order perturbation theory, we conduct an analysis of the density of states and the magnetic anisotropy energy (MAE) resolved by d orbitals for Ti and Cr atoms. Our analysis shows that in monolayer TiOCl, the MAE of Ti atoms mainly stems from the disparities in matrix elements between the d_yz and d_x²−y² (d_xz) orbitals. Conversely, in monolayer CrOCl, the MAE of Cr atoms is largely due to the differences in matrix elements between the d_xy (d_yz) and d_x²−y² (d_z²) orbitals. Biaxial strain can efficiently regulate the MAE of monolayer CrOCl. Specifically, when under tensile strain, the MAE of monolayer CrOCl experiences a substantial increase. Our research results indicate that both monolayers TiOCl and CrOCl have significant potential for use in spintronic devices and high-density data storage systems. Full article

Hyperuricemia (HUA) is a common immunometabolic disorder associated with gout, renal dysfunction, and systemic inflammation, yet the molecular targets through which non-starch polysaccharides (NSPs) may modulate HUA-related inflammation remain unclear. Here, we applied an integrative bioinformatics and computational workflow combining public transcriptomic datasets, curated NSP-related targets, protein–protein interaction analysis, enrichment analysis, single-cell RNA sequencing, and Mendelian randomization. We further included GutMGene-based orthogonal support analysis, guided docking, structural dynamics analysis, exploratory ADMET profiling, and in silico TLR4 knockout to extend target prioritization. This approach prioritized a TLR4-centered inflammatory module, with TLR4, MSR1, TIRAP, and CXCL8 emerging as candidate genes. Enrichment analyses linked these genes to innate immune and NF-$\kappa$B-related pathways, whereas single-cell analyses localized the prioritized signals mainly to myeloid compartments during gout flares. Mendelian randomization suggested positive associations between genetically predicted expression of TLR4-axis genes and serum uric acid levels. Under electrostatic-guided docking conditions, fucoidan and alginate yielded plausible interaction models with TLR4, and normal mode and RMSF analyses suggested altered flexibility in the MD-2 region. In silico Tlr4 knockout further perturbed urate-handling programs in renal proximal tubule-enriched cells. Together, these findings do not establish TLR4 as a newly discovered hyperuricemia gene or confirm direct receptor antagonism by NSPs, but they provide an NSP-oriented integrative framework that prioritizes the TLR4 axis, highlights myeloid-cell relevance, and nominates fucoidan and alginate for experimental follow-up. Full article

This paper proposes an uncertainty-quantified surrogate framework for ordinary differential equations (ODEs) based on ensembles of extreme learning machines (ELMs). The method constructs $M=100$ independently randomized ELM surrogates for a given ODE trajectory, uses the ensemble mean as the predictor, and defines a pointwise ensemble spread as a preliminary uncertainty measure. To obtain statistically valid prediction intervals, a split-conformal calibration procedure is applied to the ensemble spread, yielding finite-sample marginal coverage under exchangeability while preserving computational efficiency, since each ELM is trained via a single ridge-regression solve. Theoretical results establish exact satisfaction of the prescribed initial condition, stability of the ensemble mean and variance with respect to perturbations in the training data, and almost-sure convergence of the empirical ensemble variance to the corresponding random-feature prediction variance. The hidden-layer sampling hypothesis is made explicit: the experiments use bounded hyperbolic-tangent features with independent uniform draws as the default and independent normal draws in sensitivity tests, both of which satisfy the finite-moment assumptions required by the convergence theorem. Comparisons with capacity-matched Bayesian random-feature neural surrogates and Monte Carlo dropout clarify differences in uncertainty representation. Numerical experiments on exponential, logistic, and damped oscillator dynamics demonstrate accurate reconstruction and calibrated uncertainty quantification in sparse and noisy regimes. Additional ablation studies quantify the effect of the denominator safeguard, calibration-sample size, ensemble size, training time, noise level, and hidden-parameter distribution. Full article