Search Results (29,504)

Background: Edwardsiella piscicida is a significant intracellular pathogen of channel catfish (Ictalurus punctatus) and a major threat to U.S. aquaculture. A recently developed recombinant attenuated vaccine strain (χ16016) uses arabinose-regulated murA expression to trigger delayed cell wall lysis in vivo, ensuring biological containment while conferring strong protection against virulent challenge. Although its efficacy has been demonstrated, the host immune programs underlying protection remain incompletely defined. Methods: We used RNA sequencing to characterize tissue-specific transcriptomic responses in the intestines and kidneys of channel catfish at 7 days post-vaccination. Fish were vaccinated with χ16016 by either bath immersion or intracoelomic (IC) injection, and differentially expressed genes and enriched immune pathways were analyzed to determine how the vaccine delivery route shapes systemic and mucosal immune responses. Results: Across comparisons, 19,101 differentially expressed genes revealed pronounced route- and tissue-dependent immune remodeling. As aquaculture vaccination strategies increasingly prioritize scalability and practical deployment, understanding how the delivery route shapes immune outcomes is critical. Here, IC vaccination induced broader systemic transcriptional changes, particularly in the intestine, whereas bath immunization elicited a more focused yet coordinated mucosal response. Overall, intestinal tissue exhibited greater transcriptional responsiveness than kidney tissue, underscoring its central role in early vaccine-induced immunity. Functional enrichment analyses identified the activation of innate recognition pathways, MAPK and calcium signaling cascades, complement components, antigen processing machinery, and cell adhesion networks. Notably, bath immunization enriched the intestinal immune network for IgA production pathway, which represents an orthology-based mapping of conserved mucosal immune components, alongside the upregulation of IL-6, CXCL12–CXCR4, integrins (α4β7), MHC class II, complement C3, and polymeric immunoglobulin receptor (pIgR). Given that catfish rely primarily on IgM in mucosal immunity, these findings indicate the induction of IgM-mediated mucosal defense rather than classical mammalian IgA responses. Concurrent complement and scavenger receptor signatures suggest a transition toward efficient opsonophagocytic clearance with controlled inflammation at this subacute stage. Conclusions: This study provides the first systems-level view of host transcriptomic responses to a regulated-lysis E. piscicida vaccine in channel catfish. The findings demonstrate that immersion vaccination, although transcriptionally less expansive than injection, effectively activates coordinated mucosal innate and adaptive immune programs, supporting its practical use as a scalable vaccination strategy for aquaculture. Full article

The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately $125\pm 5$ ms, a task completion ratio (TCR) of about $92\pm 2$%, and a security success rate (SSR) near $95\pm 1.5$%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions. Full article

Flood disasters pose significant threats to human life and infrastructure, creating an urgent need for reliable vision-based environmental sensing technologies for rapid floodwater identification. Vision-based platforms such as unmanned surface vehicles (USVs) provide an effective solution for monitoring inland water environments; however, accurate floodwater segmentation remains challenging due to complex water boundaries, reflections, and background interference. To address these issues, we propose RFD-BiSeNet V2, a lightweight semantic segmentation network. Building upon BiSeNet V2, our model integrates an edge-aware learning strategy to track dynamic contours, a feature refinement module to suppress reflection noise, and a multi-scale feature fusion module to accommodate varying morphological scales. Evaluated on a comprehensive dataset incorporating USV data, UAV imagery, and diverse real-world scenes, RFD-BiSeNet V2 achieves an mIoU of 97.10%, outperforming the baseline by 6.68%. Crucially, the results demonstrate the practical implications of our architectural advancements: the edge-aware and feature refinement modules successfully sharpen ambiguous water boundaries and effectively filter out severe surface reflections, directly driving the segmentation accuracy. With a compact size of 5.95M parameters and real-time inference capabilities, the model offers a robust and highly efficient solution suitable for resource-constrained deployments across diverse intelligent environmental sensing systems. Full article

Industrial Control Systems (ICSs), which are essential components of critical infrastructures, are inherently complex and vulnerable to cyberattacks. Advanced Persistent Threats (APTs) that target these systems are multi-stage, coordinated attacks that can lead not only to information loss but also to physical damage and loss of life. Traditional threat modeling approaches fall short in adapting to the dynamic nature of ICSs, necessitating new methodologies to predict and prevent such complex attacks. This work presents a digital twin-assisted dynamic threat modeling framework for ICS environments. The framework leverages a knowledge graph that integrates system data and cyber threat intelligence to predict potential attacks. In addition, the digital twin environment enables the validation of mitigation strategies before deployment in the physical system, while also supporting adaptive response and real-time mitigation. To predict the attacker’s next move, we propose a Relational Graph Convolutional Network (RGCN)-based model that utilizes enriched relational data such as tactics, campaigns, groups, techniques, and assets. The proposed RGCN model achieves a recall of 0.887, an F1-score of 0.893, and an AUC of 0.957 in predicting potential attack sequences. These results demonstrate that the model provides reliable and well-balanced predictive performance. Full article

Fluorosis, a systemic condition caused by chronic excessive fluoride intake, poses significant threats to livestock health and agricultural productivity worldwide. This systematic review synthesizes current evidence on the modulatory effects of exercise against fluorosis, integrating human studies, animal experiments, and methodological considerations. Human studies indicate negative associations between fluoride exposure and cognitive development, muscle function, and exercise capacity, with exercise influencing fluoride pharmacokinetics in an exercise-intensity-dependent manner. Animal experiments consistently demonstrate that regular moderate-intensity exercise attenuates fluoride-induced damage across multiple organ systems through activation of the Nrf2/ARE antioxidant pathway, modulation of BMP-2/Smads and OPG/RANKL/RANK signaling, suppression of inflammatory responses, and preservation of intestinal barrier integrity. Substantial heterogeneity exists among current fluorosis models regarding exposure dosages, durations, and exercise protocols, underscoring the need for standardization and consideration of genetic background. Overall, exercise shows promise for mitigating fluorosis-induced multi-organ damage, although human evidence remains limited. Future research should prioritize model optimization, elucidation of molecular targets, and exploration of synergistic interventions to provide a foundation for veterinary clinical management. Full article

In the context of intensified trade frictions and frequent financial market fluctuations, assessing the risk resilience of strategic emerging industries holds significant strategic value. Based on quarterly data from 2010 to 2025, this study empirically examines the time-varying and asymmetric shock effects of trade policy uncertainty and financial stress on the profitability of China’s strategic emerging industries using the TVP-VAR-SV model. The study finds that China’s strategic emerging industries exhibit significant asymmetric resilience differences when facing different external shocks, specifically demonstrating stronger trade resilience and weaker financial resilience. The shocks brought by trade uncertainty typically show short-term pain followed by rapid recovery, with the negative impact being largely eliminated within two quarters and subsequently turning into positive growth, reflecting outstanding recovery capability. In contrast, the impact of financial stress on corporate profitability has a profound long-tail effect, with negative disruptions often persisting for more than two years before gradually dissipating. This contrast indicates that trade policy uncertainty and financial stress affect industrial resilience through asymmetric response patterns in terms of impact intensity and persistence. Over time, as autonomy and controllability have improved, the industry’s defensive ability to cope with trade frictions has significantly strengthened, yet credit tightening and liquidity pressure in the financial sector remain the core threats to its profitability recovery. This study not only reveals the asymmetric resilience paths of strategic emerging industries under different external shocks but also provides empirical evidence and policy recommendations for the future improvement of the technology–finance system and the construction of a more resilient domestic industrial chain. Full article

Threat modelling is a methodology employed for identifying and analysing threats and applicable mitigations for web applications, mobile applications, infrastructure, and environments including smart home environments. Threat modelling starts with a tabletop exercise to identify threats. It provides extremely important insights into what can go wrong if certain events or a series of events take place. The identification of these events is critical to ensuring the right mitigation strategies are applied. Threat modelling also helps to identify security controls that may be assumed to provide required security, but, in reality, may not be addressing the existing and applicable threat(s). Existing literature, in the public domain and in academia, discusses threat materialisation for smart homes; however, entry points for a threat to materialise and exploit these vulnerabilities are not explored and a dedicated threat model for smart home environments is currently unavailable. Whilst threats can be mitigated by smart home device manufacturers, there are also mitigations that need to be applied by smart home owners who are both technology-aware and technology-unaware. In this paper, we propose a structured, domain-specific threat modelling methodology for smart home environments. The methodology models threats from a smart home owner’s perspective, identifies entry points and the mitigations that need to be implemented by a smart home owner. It also acknowledges that the attack surface expands and contracts and is not constant; which is addressed by applying zero-trust principles. Full article

Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical federated learning (VFL) enables such collaboration when data are split by features, but many existing methods focus on protecting raw data while exposing sensitive model information, such as gradients and Hessians—especially to the label-owning party. Techniques like Homomorphic Encryption and Secret Sharing help, but often rely on trusted or privileged parties and may still leak intermediate statistics. To address this, we propose MPC-XGB , a privacy-preserving framework for training XGBoost under VFL with an honest-but-curious threat model. It uses secure three-party computation with Replicated Secret Sharing, distributing data across non-colluding servers and performing all computations on shares. This ensures that raw data, labels, and model statistics remain hidden, while supporting both secure training and prediction. Experiments show that MPC-XGB achieves strong performance (0.93 accuracy, 0.82 AUC), comparable to that of existing methods, with improved privacy guarantees. Full article

Unsafe food poses a significant threat to global public health and the economy, making the early detection of food spoilage an ongoing and critical imperative. Herein, we report the design of a straightforward and highly effective fluorescence sensor for monitoring histamine (HI), a key biomarker of food deterioration, utilizing the direct interaction between the analyte and the sensor. We demonstrate that the inherently weak luminescent covalent organic framework (COF), TpPa-1, functions as a highly responsive “turn-on” luminescent switch in the presence of HI. Upon interaction with HI, the luminescence of TpPa-1 is significantly enhanced; this phenomenon is attributed to the generation of anionic N⁻ species via the deprotonation of the N−H unit, which effectively suppresses the electron transfer pathway from the nitrogen lone pair to the COF backbone. The TpPa-1 sensor exhibits excellent sensitivity and reproducibility for HI detection. Furthermore, we developed a reusable, fluorescent COF-based film that displays a distinct, naked-eye visible color transition from red to yellow-green upon exposure to histamine, establishing a robust platform for rapid, and preliminary food quality assessment. This work presents a novel, COF-based strategy for HI detection, offering substantial significance for public health and food safety monitoring. Full article

Functional antibody-dependent enhancement (ADE) represents a fundamental and context-dependent characteristic of antiviral antibody responses, reflecting the dual capacity of antibodies to mediate both the neutralization and Fc receptor-dependent enhancement of infection. In flavivirus research, this duality complicates the interpretation of conventional serological metrics and limits the reliability of single-parameter correlates of immunity, particularly in populations with complex exposure histories. Over the past decade, functional ADE assays have evolved from specialized mechanistic tools into integrated immune assessment platforms supporting translational immunology, vaccine evaluation, and population-level immune surveillance. These platforms incorporate Fcγ receptor-relevant target cell systems, standardized viral inputs, dilution series-based profiling, quantitative enhancement metrics, and structured quality control frameworks to enable reproducible, comparable, and context-aware functional measurements across cohorts and laboratories. A central concept emerging from these developments is that ADE reflects a dynamic functional immune state rather than an intrinsic property of antibodies or a direct indicator of pathological risk. Accordingly, functional ADE platforms support the contextual interpretation of antibody activity across physiologically relevant conditions, facilitating discrimination between transient functional enhancement and clinically meaningful immunological risk. By integrating functional ADE metrics with serological, cellular, and epidemiological data, these platforms provide a structured framework for interpreting immune profiles in vaccine evaluation, booster strategy design, and population-level risk stratification. This review synthesizes the development, standardization, and global dissemination of functional ADE platforms and discusses key principles governing biological relevance, analytical robustness, and inter-site transferability. Emerging directions integrating functional ADE profiling with systems immunology, immunogenomics, and computational modeling are highlighted as pathways toward predictive, decision-support-oriented frameworks. By positioning ADE platforms as immune assessment infrastructures rather than isolated assays, this review underscores their value for mechanistic inquiry, translational interpretation, and preparedness-oriented responses to emerging viral threats in the absence of definitive correlates of protection. Full article

Aim: Environmental sustainability has become a major global trend, drawing the attention of the global community due to the severe threats posed by climate change and environmental degradation. All forms of life are being affected. The planet itself seems to be falling apart. Hence, the call is to pay closer attention to environmental governance in order to conserve ecosystems and promote environmental sustainability. Botswana and Rwanda have received accolades and international recognition in Africa for their response to climate change and environmental challenges. Methods: This study examines good governance and environmental sustainability by assessing and comparing the governance framework used by these countries to respond to environmental challenges and the weaknesses experienced in implementing their policies. Key findings: A comparative analysis of the literature revealed that the quality of governance has a significant impact on environmental sustainability. The assessment also shows that similar governance approaches adopted by Botswana and Rwanda through the government elements of institutional framework, structures, and processes contributed to their success in environmental sustainability. Implications: In the same sense, both countries are also confronted with similar challenges, among which the lack of funding, infrastructural capacity, and variation of climate change impacts are the leading factors. Full article

Antivirus systems have evolved from static pattern matchers into complex algorithmic ecosystems that encapsulate the broader logic of modern cybersecurity. This review deconstructs their internal architecture, tracing the transition from deterministic string-matching automata to probabilistic, behavioral, and cloud-assisted paradigms. Foundational modules such as scanners, heuristic analyzers, behavioral monitors, and sandbox environments operate as interconnected computational strata, forming adaptive feedback loops that mirror principles of distributed intelligence. Signature-based methods, such as Aho-Corasick, Boyer-Moore, and Wu-Manber, remain core to real-time filtering, while probabilistic reasoning through Bayesian inference, Markov modeling, and Hidden Markov Models extends detection to polymorphic and metamorphic threats. Behavioral analysis, empowered by Support Vector Machines, deep neural architectures, and temporal models, enables semantic inference over system-call graphs and runtime telemetry. Moreover, cloud-assisted frameworks integrate federated learning and global reputation graphs, which transform detection into a collective intelligence process. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper proposes an efficient multi-stage intrusion detection framework based on an enhanced Extreme Gradient Boosting (XGBoost) model for IIoT environments. The proposed framework integrates data preprocessing, class imbalance handling, hyperparameter optimization, probability calibration, and class-specific decision thresholds within a unified pipeline. In addition, calibrated probability outputs are utilized as continuous indicators of prediction confidence, enabling more reliable and risk-aware decision-making. The hierarchical multi-stage design decomposes the detection task into progressively refined classification levels, improving discrimination among complex and overlapping attack categories. The framework is evaluated using the Edge-IIoTset benchmark dataset, which reflects realistic IIoT network traffic under both normal and malicious conditions. Experimental results demonstrate that the proposed approach achieved significant performance improvements, including up to 21% increase in recall and 15% improvement in macro F1 score compared to the baseline models. Furthermore, the model exhibits low inference latency and supports efficient deployment in time-sensitive IIoT monitoring scenarios. These results indicate that the proposed framework provides an effective and scalable solution for multi-class cyber threat detection in IIoT networks. Full article

Although RNA cytosine-5 methylation (m⁵C) is an important post-transcriptional regulatory mechanism, its contribution to plant antiviral immunity remains unclear. In this study, we identified Thiamine thiazole synthase 2 (TaTHI2) as a host mRNA target of the wheat m⁵C methyltransferase TaNSUN2 during infection by Chinese wheat mosaic virus (CWMV), a soil-borne virus that poses a major threat to wheat production. TaNSUN2 contributes to the m⁵C modification of TaTHI2 transcripts, enhancing mRNA stability and sustaining TaTHI2 accumulation. The disruption of a key m⁵C site markedly reduced methylation, weakened TaNSUN2–RNA binding, and accelerated transcript decay, leading to the compromised production of reactive oxygen species (ROS) and increased viral infection. Mechanistically, the TaNSUN2-dependent m⁵C modification stabilized TaTHI2 mRNA, thereby promoting ROS-mediated antiviral defense. Collectively, our results establish the m⁵C modification of TaTHI2 mRNA as a critical post-transcriptional control point in CWMV resistance and highlight TaNSUN2-dependent RNA methylation as an integral component of host antiviral immunity. Full article