Search Results (1,822)

Porcine group A rotavirus (PoRVA) is a significant cause of viral diarrhea in piglets, necessitating urgent global implementation of effective control strategies. This review assesses advancements in PoRVA in vitro cultivation and amplification, crucial for PoRVA vaccine development. Traditional PoRVA cultivation commonly employs primary porcine kidney cells or finite cell lines like MA-104, posing well-documented challenges in scalability, production cost, and their ability to recapitulate the natural intestinal microenvironment. Consequently, research has increasingly focused on adapting PoRVA to alternative systems, particularly immortalized porcine cell lines or physiologically relevant porcine intestinal organoids. This adaptation process, involving serial passaging, can induce genomic alterations and virulence attenuation in piglets, essential for generating live attenuated vaccine (LAV) candidates. Modern biotechnological tools, such as reverse genetics and synthetic genomics, have expedited the creation of recombinant PoRVA strains with defined antigenic profiles and enhanced in vitro growth characteristics. However, a significant concern regarding LAV candidates derived from cell culture adaptation is the risk of virulence reversion upon pig back-passage, necessitating thorough safety and genetic stability evaluations. Nevertheless, utilizing stable cell lines or organoid platforms presents a feasible and cost-effective approach for large-scale PoRVA vaccine production. Future research should focus on identifying vaccine candidates that provide broad protection and exceptional safety, with an emphasis on cross-protection against divergent epidemic genotypes, while ensuring the economic feasibility of innovative manufacturing approaches. Full article

The increasing demand for sustainable crop protection strategies has intensified interest in plant-beneficial bacteria as alternatives to synthetic agrochemicals. In this study, the soil-derived bacterium Pseudomonas putida V01 was isolated and characterized for its antifungal and plant growth-promoting potential through an integrated approach combining biological assays, untargeted metabolomics, and in vivo plant experiments. Cell-free culture filtrates exhibited strong antifungal activity against major phytopathogenic fungi, completely inhibiting the growth of Sclerotium rolfsii and significantly reducing mycelial development of Alternaria alternata and Fusarium proliferatum by 40% and 20%, respectively. Volatile organic compounds (VOCs) selectively inhibited Botrytis cinerea and A. alternata by 28% and 10%, respectively, and affected sporulation of F. proliferatum. Metabolomic profiling through LC-qTOF-MS and GC-MS analyses revealed a chemically diverse metabolome, including putatively annotated diketopiperazines, cyclic peptides, phenolic compounds, and fatty acids. VOC profiling indicated ketones and alcohols as the predominant volatile classes, with 2-undecanone and 2-undecanol among the most abundant compounds detected. In vivo assays on wheat seedlings showed significant increases in shoot growth, biomass accumulation, and chlorophyll content compared with untreated controls. These findings indicate that P. putida V01 combines complementary antifungal and plant growth-promoting activities associated with a diverse repertoire of diffusible and volatile metabolites. The integrated biological and metabolomic characterization highlights its potential as a multifunctional microbial inoculant for sustainable crop production and disease management. Full article

To address the issues of neglecting population heterogeneity and the difficulties in determining constraint parameters in residential load adjustable capacity forecasting, this paper proposes a data-driven forecasting method that considers profile heterogeneity. First, K-means++ is utilized to extract diverse user electricity consumption profiles. Second, to solve the problem of real response data scarcity, the difference-in-differences (DID) method is employed to empirically calibrate the true physical constraint boundaries of different clusters, and high-quality response samples are generated in batches based on an electricity cost minimization model. Finally, a Long Short-Term Memory (LSTM) time-series forecasting model is constructed to achieve the precise quantitative evaluation of adjustable capacity. Case studies demonstrate that after introducing user profile labels, the three accuracy metrics of the predictive model are improved by 16.29%, 24.52%, and 20.21%, respectively. Although the practical application of synthetic labels faces minor limitations caused by uncertain user behaviors, this scalable framework supports seamless incremental retraining using future empirical response data to realize continuous model evolution and persistent accuracy improvement, thereby providing technical support for load aggregators’ market bidding and the precise dispatch of power grid demand response. Full article

Endothelial dysfunction (ED) is an early event in cardiovascular and metabolic diseases, including atherosclerosis, diabetes, and hypertension. Emerging evidence highlights the interplay between chronic inflammation and oxidative stress, collectively termed OxInflammation, as a major driver of vascular injury and impaired tissue repair. Among the key mediators of this response is the Nod like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a multiprotein complex that promotes the release of inflammatory cytokines, including Interleukin 1β (IL-1β) and Interleukin-18 (IL-18), and induces gasdermin D-mediated pyroptotic cell death. Activation of NLRP3 disrupts endothelial function, reduces nitric oxide availability, and accelerates vascular inflammation and injury. This review discusses current evidence on pharmacological strategies targeting NLRP3 inflammasome signaling using both natural and synthetic inhibitors. Studies have shown that inhibiting NLRP3 can reduce inflammation and oxidative stress, preserve endothelial integrity, improve vascular function, and support tissue repair. Several NLRP3-targeting compounds have advanced into early-phase clinical trials, showing encouraging safety profiles and efficacy in individuals with cardiovascular risk factors. By integrating the emerging concept of OxInflammation with endothelial dysfunction, this review critically evaluates the therapeutic and translational potential of NLRP3 inflammasome inhibition in cardiovascular and metabolic disorders. Collectively, the available evidence supports NLRP3 as a promising therapeutic target for restoring endothelial homeostasis and promoting vascular repair. However, further clinical studies are needed to establish long-term efficacy, optimal dosing strategies, and appropriate patient selection criteria. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Acute inflammatory disorders, including acute lung injury, acute pancreatitis, ischaemia–reperfusion injury, and sepsis, are major clinical challenges characterised by rapid progression, a characteristic cytokine storm, and high mortality rates. The receptor for advanced glycation end-products (RAGE) serves as a pivotal multi-ligand pattern recognition receptor that integrates PAMPs and DAMPs. Excessive RAGE engagement triggers detrimental signalling cascades, notably NF-κB and MAPKs, which exacerbate hyperinflammation and lead to progressive organ dysfunction. Consequently, the RAGE axis represents a potent therapeutic target for mitigating hyperinflammation and improving clinical outcomes in acute inflammatory disorders. While initial pharmacological efforts focused on synthetic inhibitors and biologics, there is a shifting focus toward bioactive alternatives with high safety profiles. Here, we present recent molecular insights into RAGE-mediated pathogenesis in acute inflammatory disorders and evaluate current therapeutic strategies. Furthermore, we emphatically summarise the bioactive natural products, including terpenoids, flavonoids, alkaloids, and a xanthone, that prevent and treat acute inflammatory disorders by disrupting RAGE–ligand interactions and suppressing downstream oxidative stress and cytokine release. Integrating these molecular mechanisms with the pharmacological profiling of natural RAGE modulators provides a robust foundation for the development of next-generation therapeutic strategies to improve clinical outcomes in acute inflammatory disorders. Full article

Manual data storage methods on various mobile devices, IoT devices, and traditional computing platforms still lack sufficient security governance due to the absence of a unified security framework. Unlike application controlled environments, manual storage locations such as file systems, removable media, and IoT devices are highly susceptible to unauthorized access, misuse, and exfiltration. To address this problem, the paper proposes a security framework for manual storage systems using metadata, and the proposed framework includes three different algorithms, namely MARShield, ThreshGuard, and SentinelScheduler. These three algorithms operate together to ensure security for manual storage systems. MARShield is used for enforcing immutable metadata, multi-access rights based on tokens, and persistent source tracking by cryptographically securing provenance logs. ThreshGuard, on the other hand, enables the use of adaptive threshold-based misuse regulation and bottleneck-controlled serialized execution. SentinelScheduler optimizes the use of cryptography by incorporating trust-based application profiling and idle-time scheduling for heavy security operations. The proposed methodology is evaluated using a hybrid approach combining real-world datasets (CIC-IoT2023, TON-IoT, Bot-IoT and ISCX VPN non-VPN) and dataset-driven synthetic access pattern generation. Real datasets are used to model realistic IoT traffic behaviors, while additional synthetic scenarios are introduced to evaluate adaptability against evolving and previously unseen attack patterns. Network level features from these datasets are systematically transformed into storage-level access behaviors to evaluate metadata-driven access control. The experimental results indicate improved detection accuracy (94.6%), reduced false positive rate (4.3%), improved misuse control efficiency (92%) and scalability (94%). The proposed methodology for securing manual storage domains is scalable, adaptive, and portable, extending the security of applications and their associated domains. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient ($\lambda$), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Polymer coatings are integral to nearly every modern cardiovascular and endovascular device, including drug-eluting stents (DESs) and drug-coated balloons (DCBs), bioabsorbable vascular scaffolds (BVSs), occluders, grafts, and catheter and guidewire hydrophilic surfaces. Persistent complications, including late stent thrombosis, delayed endothelialization, hypersensitivity, and restenosis, show that coatings actively shape biological responses rather than acting as inert drug carriers. Their surface chemistry, drug release kinetics, and degradation behavior are upstream determinants of blood– and tissue–material responses that govern healing and failure. This review frames coating selection as a structure–property–biological response problem. It surveys the major classes of synthetic polymer coatings and the defining surface and bulk properties. This review also examines how composition and architecture control drug release, and traces the interfacial cascade of protein adsorption, coagulation and complement activation, platelet and leukocyte responses, and neutrophil extracellular trap (NET) formation. These mechanisms are linked to contemporary design strategies that improve hemocompatibility, limit thrombosis, promote endothelial recovery, and tune degradation, and to the standardization and translation gaps that remain. The central message is that polymer coatings are not biologically equivalent. Their surface chemistries and degradation profiles determine the thrombo-inflammatory outcomes. Therefore, coating design should be guided by intended biological response, not drug release alone. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Bio-based coatings enriched with essential oils (EOs) represent a promising alternative to synthetic preservatives to extend strawberries’ shelf-life. This study evaluated the effects of chitosan (CHT) formulations containing three selected EOs (Illicium verum, Citrus sinensis, and Citrus limon) on the volatile profile, sensory quality, and antifungal activity of strawberry fruits. Volatile emissions were characterized by Headspace Solid Phase Micro-Extraction/Gas Chromatography-Mass Spectrometry, while sensory properties were assessed using Quantitative Descriptive Analysis. Antifungal activity was evaluated both in vitro and in vivo against Botrytis cinerea. Chitosan alone slightly modified the volatile profile, while EO-enriched coatings induced marked and concentration-dependent changes, reflecting the chemical composition of the incorporated EOs. Among the tested formulations, CHT combined with 1% C. sinensis EO provided the best balance between preservation of the characteristic strawberry aroma and overall sensory acceptance. In vitro assays showed that EO volatiles, particularly from C. sinensis and I. verum, significantly inhibited fungal growth, while diffusible compounds were less effective. In vivo, EO-containing coatings reduced disease incidence and severity by approximately 50%. These findings highlight the potential of CHT–EO coatings as sustainable options for postharvest preservation, although optimization of EO type and concentration is crucial to balance sensory quality and antimicrobial efficacy. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Introduction: Fish and humans share evolutionarily conserved pathways regulating lipid metabolism. However, the effects of pharmaceuticals on lipid homeostasis in fish remain poorly understood, particularly regarding mechanistic lipid dysregulation and its implications for fish physiology and environmental toxicology. While hypolipidemic drugs such as statins have been shown to modulate lipid metabolism in teleosts, other lipid-lowering agents, including cholesterol absorption inhibitors, remain largely unexplored. Additionally, synthetic hormones have been shown to interfere with lipid regulation, although their effects—particularly those of progestins—remain poorly characterized. Objective: This study aimed to explore the mechanistic lipid disruptions induced by potential hypo- and hyperlipidemic modulating pharmaceuticals in brown trout juveniles exposed to subchronic pharmacological conditions. Methodology: Juvenile brown trout were exposed via intramuscular injection every 72 h for 28 days and allocated into six experimental groups (n = 12 per group): control (C; 0.7% NaCl), solvent control (SC; 0.7% NaCl, 0.9% ethanol, 0.1% dimethyl sulfoxide), atorvastatin (ATV; 0.3 µg·g⁻¹), ezetimibe (EZB; 0.3 µg·g⁻¹), 17α-ethinylestradiol (EE2; 2 µg·g⁻¹), and levonorgestrel (LNG; 0.1 µg·g⁻¹). All concentrations represented pharmacological doses. On day 28, the fish were euthanized and sampled. Endpoints included biometric measurements, blood lipid profiling, serum biochemistry, and hepatic lipid accumulation. Results: ATV fish displayed greater body length, whereas EE2 increased liver weight and hepatosomatic index. EE2 reduced high-density lipoproteins and increased low-density lipoproteins, while atorvastatin reduced low-density lipoproteins. EE2 exposure also increased albumin levels and decreased glucose concentrations. Furthermore, EE2 significantly enhanced hepatic lipid deposition. Conclusions: The hyperlipidemic effects of EE2 were the most pronounced, whereas ATV produced the strongest hypolipidemic responses, consistent with its known effects in humans, and also influenced biometry. These findings provide a robust foundation for understanding how pharmaceuticals influence lipid metabolism and related physiological processes such as growth in fish, with relevance for both fish physiology research and environmental toxicology. Full article

Surface seismic data often suffer from limited bandwidth and uneven illumination, which degrade PSDM (prestack depth migration) in deep and structurally complex settings. VSP (vertical seismic profiling), particularly DAS-VSP, provides a higher signal-to-noise ratio and richer high-frequency content near the wellbore but has a limited lateral imaging aperture. To exploit the complementary strengths of these two observation systems, we propose an OVT domain (offset vector tile) joint Kirchhoff prestack depth migration workflow that integrates surface seismic and VSP data within a unified depth domain framework. The workflow includes wavelet (amplitude–phase) matching, consistent datuming, joint well–surface tomographic velocity model building using both surface CIG (common image gather) residual moveout and VSP first-arrival constraints, efficient travel time table construction based on 3D eikonal solvers, OVT domain joint migration, azimuth-dependent CIG depth correction for anisotropy, and ray-based illumination compensation for amplitude balancing. Synthetic tests demonstrate that the proposed method improves reflector continuity and increases the effective bandwidth of the joint image relative to surface-only PSDM. A field application in the northwest Sichuan Basin further shows that the joint imaging better matches well synthetics in the target interval, increasing the correlation coefficient from 0.753 (surface-only) and 0.738 (VSP-only) to 0.787 (joint) while reducing inter-azimuth CIG depth residuals to within 3 m after anisotropy correction. These results indicate that OVT domain joint imaging can enhance thin-bed resolution and near-well structural delineation, providing a practical multi-source data fusion solution for high-fidelity depth imaging in complex reservoirs. Full article