Search Results (1,859)

Deep learning models for power quality (PQ) disturbance classification remain critically vulnerable to adversarial perturbations, with classification performance degrading severely under white-box attacks. Existing defenses address individual models in isolation and provide no mechanism for operators to assess whether the system is under attack or which classifier remains trustworthy. This paper proposes a two-stage framework that combines adversarial training with large language model (LLM) reasoning to improve both robustness and interpretability. In the first stage, four architecturally diverse classifiers, including a proposed Multi-Scale Temporal Attention Network (MSTAN), are evaluated under four adversarial attacks (FGSM, PGD, C&W, and UAP), and their failure patterns are recorded as structured vulnerability fingerprints. The ensemble is then retrained via adversarial training on mixed clean and perturbed signals. In the second stage, an LLM analyzes the ensemble predictions alongside the fingerprint knowledge base to perform attack detection, fingerprint-guided meta-classification, and operator-facing threat report generation. On a 17-class, 255,000-signal synthetic benchmark, adversarial training recovers FGSM and PGD accuracy from below 25% to the 53–78% range, with MSTAN achieving the highest post-training robustness (78.26% under FGSM, 65.41% under PGD). The LLM reasoning layer provides an additional 3.5–6.2 percentage point improvement over majority voting by selecting the most reliable ensemble member based on the inferred attack condition, and detects adversarial attacks with 87.6% overall accuracy. To our knowledge, this is the first integration of LLM-based ensemble reasoning into the PQ adversarial robustness pipeline and the first application of the C&W optimization attack to power quality signals. Full article

Toll-like receptors (TLRs) are essential components of the innate immune system that enable host cells to sense microbial and endogenous danger signals and to initiate inflammatory and antimicrobial responses. Activation of TLRs triggers complex intracellular signaling networks that culminate in the induction of pro-inflammatory cytokines, type I interferons, and co-stimulatory molecules. In addition to the well-characterized nuclear factor κB (NF-κB) and interferon regulatory factor (IRF) pathways, mitogen-activated protein kinases (MAPKs) play a critical modulatory role in TLR signaling. MAPK/ERK kinase (MEK) inhibitors were originally developed for the treatment of cancer and are widely used in clinical oncology. Accumulating evidence indicates that pharmacological inhibition of MEK/extracellular signal regulated kinase (ERK) signaling profoundly affects immune cell function and TLR-driven responses. Depending on timing, dose, and disease context, MEK inhibition can attenuate excessive inflammation but may also interfere with protective host defense mechanisms. This duality highlights the context-dependent role of MEK/ERK signaling in infection and inflammation. In this review, I summarize current knowledge on the integration of MEK/ERK signaling into TLR-mediated innate immune responses and discuss the immunological consequences of MEK inhibition in infectious and inflammatory settings. By synthesizing mechanistic and translational studies, I aim to provide a framework for understanding MEK inhibitors as immune modulators rather than as broadly acting anti-inflammatory agents. Full article

Marine algae are emerging as important biological resources for the discovery and development of bioactive compounds with applications across food, pharmaceutical, cosmetic, agricultural, aquaculture, environmental, and biotechnological systems. This review critically synthesizes current knowledge on macroalgae and microalgae as sources of sulfated polysaccharides, carotenoids, phenolic compounds, proteins, peptides, vitamins, mycosporine-like amino acids, and polyunsaturated fatty acids. Emphasis is placed on the relationship between algal source, cultivation conditions, compound structure, extraction strategy, formulation, and biological activity. Key mechanisms of action are discussed, including antioxidant defense, modulation of inflammatory signaling, inhibition of metabolic enzymes, antimicrobial and antiviral activity, interactions with the gut microbiota, and regulation of cell-cycle-related pathways. Recent progress in biotechnological production, green extraction, purification, analytical characterization, bioaccessibility, bioavailability, and delivery systems is evaluated in the context of real product development. The review further highlights the use of algal bioactives in functional foods, nutraceuticals, pharmaceuticals, cosmeceuticals, aquafeeds, crop biostimulants, and environmental remediation. Current limitations, including biomass variability, compound instability, limited human validation, regulatory complexity, safety concerns, and scale-up costs, are also addressed. Overall, marine algae provide a sustainable and multifunctional platform for developing bioactive products when discovery, processing, validation, and commercialization are integrated. Full article

Mucosal immunity in the respiratory tract provides the first line of defense against airborne pathogens, yet most current vaccines fail to induce strong and durable immune responses at these sites. Respiratory viruses, including respiratory syncytial virus (RSV), influenza viruses, and coronaviruses, remain major global health threats, in part due to their ability to evade long-term mucosal protection. Although systemic vaccination generates robust circulating immunity, it induces limited local responses, particularly secretory immunoglobulin A (IgA), which is critical for preventing viral entry and transmission at the airway surface. The mechanisms regulating B cell responses within the airway mucosa are not fully understood. B cell–activating factor (BAFF), a member of the tumor necrosis factor (TNF) superfamily, has emerged as an important context-dependent regulator of mucosal B cell immunity. BAFF is produced by airway epithelial cells and multiple myeloid populations, including dendritic cells and neutrophils, and is rapidly induced during respiratory viral infection through type I interferon–dependent pathways. Functionally, BAFF supports B cell survival, differentiation, and class-switch recombination, promoting the generation of antibody-secreting plasma cells and enhancing IgA production. In the lung, these effects align with early, intermediate, and late stages of the response, supporting initial local antibody production, the formation of inducible bronchus-associated lymphoid tissue (iBALT), and the development of tissue-resident memory B cells that sustain long-term immunity. Although BAFF plays an essential role in mucosal immunity, its activity requires tight regulation to maintain immune balance. Current evidence supports BAFF as a promising immunomodulatory component and highlights its potential as an adjuvant platform for enhancing mucosal vaccine efficacy, warranting further investigation as a potential adjuvant in this context. Full article

Persistent high-risk human papillomavirus infection is a critical prerequisite for cervical intraepithelial neoplasia and cervical cancer, yet viral factors alone do not fully explain why most infections clear while a subset persists and progresses. Emerging longitudinal, multi-omics, and mechanistic evidence supports a plausible model in which the cervicovaginal microbiota is not a passive bystander but a functional determinant of mucosal immunity, epithelial barrier integrity, and local metabolic tone. Lactobacillus-dominant community states, particularly those enriched in Lactobacillus crispatus, are generally associated with lower pH, regulated inflammatory signaling, stronger barrier function, and a higher likelihood of HPV clearance. In contrast, anaerobe-enriched dysbiosis is linked to elevated pro-inflammatory cytokines, altered antigen presentation, immune checkpoint signatures consistent with T-cell dysfunction, and metabolic shifts involving lactate depletion and accumulation of short-chain fatty acids and other metabolites that can influence epithelial and immune-cell programs. Importantly, the interaction is bidirectional: hrHPV can remodel the microenvironment by suppressing host defense peptides and perturbing mucosal barriers, thereby reducing Lactobacillus fitness and reinforcing dysbiosis in a feed-forward loop that favors persistence and oncogenic progression. This review integrates functional ecology, longitudinal clinical evidence, immunological and metabolic mechanisms, and translational implications, highlighting opportunities for microbiome-informed risk stratification and adjunctive interventions, as well as key gaps requiring standardized longitudinal multi-omics and rigorously designed clinical trials. Full article

Higher education policy on generative artificial intelligence has developed rapidly, yet much of this development remains stronger on governance, permission, disclosure, and assurance than on pedagogy. Universities increasingly move beyond blanket prohibition by distinguishing between restricted and permitted contexts, requiring acknowledgement of tool use, and introducing verification mechanisms to protect authorship and understanding. However, publicly visible institutional approaches appear less developed in providing structured, student-facing workflows that guide responsible AI engagement during assessment completion. This article, informed by a bounded qualitative document analysis, uses the term pedagogical middle layer to describe the process guidance needed between institutional permission settings and academic-integrity or misconduct procedures. Drawing on recent literature and a purposive scan of selected publicly available university policy and guidance documents, the paper argues that current public-facing models are often effective at defining boundaries but less explicit in guiding disciplined, transparent, and defensible forms of human–AI collaboration. In response, the paper presents the Structured AI-Guided Education Assessment Policy (SAGE-AP) as a theoretically grounded policy proposal for AI-assisted assessment, rather than as an empirically validated policy intervention. SAGE-AP frames assessment as a staged process in which students begin from their own understanding, engage with AI critically, document evaluative decisions, refine outputs responsibly, and defend the reasoning represented in the final submission. The paper contributes to institutional policy development by clarifying how permission settings may be complemented by pedagogical process guidance in the generative AI era. Full article

The accumulation of polylactic acid nano-plastics (PLA-NPs) in saline–alkali soils poses a potential threat to crop growth; however, the underlying toxicological mechanisms remain poorly understood. We conducted a hydroponic experiment to investigate the effects of polylactic acid (PLA) NPs (100 and 500 mg L⁻¹) under conditions both in the presence (50 mmol L⁻¹ NaCl) and absence of salt stress on maize seed germination, seedling growth, physiological characteristics, and transcriptomic responses. The results showed that exposure to PLA-NPs, particularly at a high concentration (500 mg L⁻¹), significantly inhibited seed germination and seedling growth. Compared to the low concentration (100 mg L⁻¹) of PLA-NPs, the high concentrations (500 mg L⁻¹) reduced the germination percentage by 25.0% and fresh weight by 25.8% and increased root MDA (6.7%), SOD (30.0%), POD (6.3%), ASA (13.4%), and GSH (13.1%). Under the same concentration of the PLA, PLA + NaCl treatments exerted stronger inhibitory effects than PLA-NPs alone, with the seed germination percentage and fresh weight reduced by an average of 52.7% and 6.6%, respectively. Notably, the inhibitory effects and integrated biomarker response (IBR) index of the PLA 500 + NaCl treatment were the highest. The presence of PLA-NPs in roots was confirmed using confocal laser scanning microscopy. GO enrichment analysis showed that pathways related to nutrient reservoir activity, oxidoreductase activity, hydrogen peroxide catabolic process, and hydrogen peroxide metabolic process were enriched under PLA-NP and PLA + NaCl treatments. KEGG analysis further indicated enrichment in phenylpropanoid biosynthesis, ABC transporters, and alpha-linolenic acid metabolism. The PLA-NP and PLA + NaCl treatments upregulated genes associated with oxidoreductase activity (Zm00001eb238800, Zm00001eb128620, and Zm00001eb020790). These findings suggest that synergistic toxicity of PLA-NPs and salinity stress in maize is primarily driven by the internalization of PLA-NPs and Na⁺ within maize roots, which negatively impacts maize seed germination and seedling growth by disrupting redox homeostasis and metabolic balance, thereby forcing plants to reallocate resources from growth toward oxidative stress defense. This study provides critical insights into the environmental risks of biodegradable nano-plastics in saline–alkali soil environments. Full article

Plants must continuously balance the trade-offs between growth and defense, a constraint that is exacerbated by biotic and abiotic stresses, particularly when they occur together. Ethylene (ET) serves as a central, integrative regulatory node controlling this by linking developmental programs to stress-responsive signaling networks. Advances at the molecular and systems levels have revealed that ET mediates the redistribution of metabolic resources via coordinated regulation of its synthesis, perception, and downstream signaling. The ETR (Ethylene Receptor)-CTR1 (Constitutive Triple Response 1)-EIN2 (Ethylene Insensitive 2)-EIN3(Ethylene Insensitive 3) signaling module lies at the core of this network, integrating multiple hormonal pathways. Through dynamic crosstalk with jasmonic acid (JA), salicylic acid (SA), abscisic acid (ABA), auxin (AUX), and gibberellins (GA), ET enables the fine-tuned coordination of growth inhibition, immune activation, and stress acclimation in response to environmental fluctuations. Processes such as induced systemic resistance, programmed cell death, and architectural plasticity further reinforce this regulatory framework, with ethylene-responsive transcription factors, including ERFs (ethylene responsive factor gene family) and WRKYs, acting as critical convergence points. Emerging insights into ACC (1-aminocyclopropane-1-carboxylic acid)-dependent signaling, chromatin remodeling, and tissue-specific regulation expand the functional scope of ET beyond traditional hormone paradigms. At the same time, the ability of pathogens to manipulate ET signaling underscores its dual role in both promoting immunity and facilitating susceptibility. By integrating molecular, physiological, and ecological perspectives, this review highlights ET as a central coordinator of plant stress resilience and growth optimization, providing a unifying framework for understanding how plants adapt to complex and dynamic environments. Full article

The skin serves as the body’s first line of defense against environmental threats, acting as a barrier between external aggressors and internal systems. Current evidence regarding the roles of sulfur dioxide (SO₂) in biology and medicine is limited. Environmental pollutants, including SO₂, can increase the production of reactive oxygen species in the skin, leading to oxidative damage that may worsen various dermatological conditions. Endogenous SO₂, proposed as the fourth member of the gasotransmitter family, functions as a biological signaling molecule. It is generated in various human skin cells, including vascular smooth muscle cells, endothelial cells, mast cells, keratinocytes, macrophages, adipocytes, fibroblasts, dermal immune cell population, etc, where it performs multiple functions at physiologically relevant concentrations. Endogenous SO₂ plays a crucial role in regulating cell signaling and maintaining skin homeostasis through its antioxidant, anti-inflammatory, and cytoprotective effects. Abnormal generation and metabolism of SO₂ are linked to several critical processes in the skin, including vascular biology, immune response, cell proliferation, pigmentation, malignancy, protective barriers, senescence, and resistance to stress. This paper provides a narrative review of the significant roles of SO₂ in skin health and disease. A comprehensive understanding of the complex molecular effects and mechanisms mediated by SO₂ in human skin, along with the development of gas therapy, will be essential for translating fundamental research into clinical applications. Full article

Spatially explicit water-quality information is critical for precision management in pond aquaculture but point sampling alone cannot capture pond-to-pond heterogeneity in multi-unit farms. This single-date, single-farm study re-evaluated the potential of UAV hyperspectral imagery for water-quality screening in inland aquaculture ponds in Shanghai, China, using site-matched extraction from a 138-band orthomosaic (450–998 nm, Cubert S185) acquired during a single UAV survey on 24 August 2023 and matched with 23 GPS-registered sampling sites. Eight water-quality parameters were analyzed: chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), ammonium (${\mathrm{NH}}_4^{+}$ ), nitrite (${\mathrm{NO}}_2^{-}$), nephelometric turbidity unit (NTU), chlorophyll-a (Chla), and total suspended solids (TSS). Raw single-band correlations were modest ($\left|\mathrm{r}\right|$= 0.236–0.417), but two-band difference spectral indices (DSI), normalized spectral indices (NSI), and ratio spectral indices (RSI) substantially improved sensitivity, with $\left|\mathrm{r}\right|$ reaching 0.558–0.928. Quadratic inversion models were calibrated on the full dataset and assessed using three validation layers: two-fold cross-validation, nested leave-one-pond-out (LOPO) validation with within-fold predictor reselection, and extraction-window sensitivity tests. Bootstrap 95% confidence intervals for calibration (Cal) R² characterize small-sample uncertainty (n = 23). Three parameters satisfied all three defensibility criteria (Cal R² > 0.5, CV R² > 0.2, and LOPO R² > 0.2): ${\mathrm{NH}}_4^{+}$ (Cal R² = 0.836 [0.61, 0.94]; LOPO R² = 0.420), COD (0.607 [0.34, 0.82]; 0.328), and NTU (0.862 [0.77, 0.96]; 0.204). TP, TN, ${\mathrm{NO}}_2^{-}$, TSS, and Chla showed overfit behavior under nested holdout and were demoted to exploratory products. A TreeSHAP analysis confirmed that band-to-band contrast carried more explanatory power than raw reflectance magnitude. Extraction-sensitivity tests further demonstrated that positional uncertainty (±2-pixel offset: ΔCV $R^2$= 0.23–0.41) exceeded averaging-window sensitivity (3 × 3→10 × 10: ΔCV $R^2$ ≤ 0.11), identifying geolocation control as the dominant robustness constraint. This single-date, single-farm reanalysis suggests that UAV hyperspectral imagery may support exploratory pond-scale screening of ${\mathrm{NH}}_4^{+}$, COD, and NTU. However, robust quantitative inversion and broader transferability remain unverified and will require denser sampling, improved geolocation control, pond-edge masking, multi-site observations, and multi-temporal calibration. Full article

The Robot Operating System (ROS) is widely used in modern robotics, but its open architecture makes it vulnerable to numerous cyber threats. Although machine learning (ML)-based intrusion detection systems (IDSs) demonstrate strong classification performance on ROS-specific datasets, reliance on topology-dependent identifiers such as source and destination IP addresses, port numbers, and Flow IDs remains a critical limitation in current research. This reliance may encourage algorithms to exploit scenario-specific endpoint signatures instead of relying primarily on transferable behavioral patterns. Consequently, classification scores may be artificially inflated due to data leakage. This study addresses this issue by quantitatively measuring the impact of data leakage and introducing a topology-independent, explainable ROS framework that provides a more realistic, leakage-aware, and topology-independent evaluation framework. The evaluation involved testing the LightGBM, XGBoost, and CatBoost algorithms on ROSIDS23. Additionally, Random Forest and Gradient Boosting were included to verify the presence of data leakage. In our ablation study, models that included topology features achieved near-perfect Macro-F1 values of 0.999 to 1.000. In contrast, removing topology-dependent features reduced the Macro-F1 score to about 0.66. This finding shows that topology descriptors, rather than just transferable attack behaviors, can significantly influence the near-perfect scores seen with topology-preserving protocols. Even without topology data, ML models effectively captured temporal behavioral patterns and detected DoS attacks with nearly perfect performance, reaching F1 scores of 0.99 or higher. However, semantic attacks like Unauthorized Subscribe remained tough to classify, with F1 scores of 0.43 or lower. Additionally, SHapley Additive exPlanations (SHAP) analysis improves the interpretability of IDSs by identifying the main behavioral features that drive model decisions and suggesting feature-level directions for rule-based defense configurations in ROS environments. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Mitochondrial dysfunction has been increasingly implicated in the pathobiology of neurodevelopmental conditions, particularly autism and attention-deficit/hyperactivity disorder (ADHD). Because the developing brain is critically dependent on sustained ATP production, impairments in oxidative phosphorylation, mitochondrial dynamics, and redox balance may disrupt neuronal maturation, synaptic development, and neural circuit refinement during sensitive developmental periods. This review examines evidence from postmortem neurochemistry, genomics, magnetic resonance spectroscopy, and biomarker research to characterize mitochondrial impairment across autism and ADHD. Studies in autism report an elevated burden of heteroplasmic mitochondrial DNA (mtDNA) variants, along with alterations in mtDNA copy number, respiratory chain capacity, fission–fusion dynamics, and antioxidant defenses. Postmortem data demonstrate reduced activity of electron transport chain Complexes I, III, and V in the frontal cortex, temporal lobe, and cerebellum. These bioenergetic abnormalities are accompanied by elevated oxidative stress markers alongside mitochondria-mediated immune activation. In vivo neuroimaging corroborates these findings through elevated cerebral lactate and reduced phosphocreatine-to-ATP ratios. Evidence in ADHD is limited, but similarly implicates mitochondrial dysfunction, consistent with the frequent co-occurrence of these conditions and their partially shared architecture. The available literature supports mitochondrial dysfunction as a transdiagnostic biological feature of neurodevelopmental conditions, with relevance to mechanistic biomarker identification and targeted therapeutic development. Full article

Hierarchical Public Sector Organizations (PSOs), particularly military organizations, face persistent challenges in sustaining innovation due to structural rigidity, hierarchical control, and embedded resistance to change. While existing literature explains why innovation emerges and why it is resisted, significantly less attention has been devoted to understanding how innovation becomes institutionalized as a sustained organizational capability. This study addresses this gap by introducing the Bi-focal Innovation Contagion Model (BICM), an agent-based framework that conceptualizes innovation diffusion and resistance as a co-evolutionary “tug-of-war” between competing organizational forces. The model integrates top-down governance mechanisms and bottom-up innovation processes, capturing how heterogeneous actors interact within hierarchical systems to shape the diffusion, assimilation, and stabilization of innovation over time. Using the Israel Defense Forces (IDF) as an empirical source case, the model explores how Innovation Management Systems (IMS) may be designed to support the institutionalization of innovation as a self-sustaining organizational capability within hierarchical PSOs. Simulation results suggest that hybrid innovation architectures may better sustain innovation across varying leadership conditions. This occurs when centralized strategic coordination is combined with decentralized innovation activity and supported by mature innovation agents with sufficient centrality and hierarchical reinforcement. The findings highlight the critical role of IMS as an organizational architecture for achieving anti-fragility, enabling innovation dynamics to persist, adapt, and strengthen in the face of uncertainty, leadership turnover, and shifting strategic priorities. By integrating agent-based modeling with organizational theory, this study contributes a dynamic framework for understanding and designing sustainable innovation systems in hierarchical PSOs. Full article

The rhizosphere microbiome and phytohormone signaling are critical determinants of plant growth and stress resilience. This study evaluated the combined effects of Streptomyces sp. HU2014 and coronatine (COR) on maize (Zea mays L.) seedlings. Four treatments were established: control (CK), COR seed soaking (Cor), HU2014 soil inoculation (S), and combined S + Cor (SCor). Growth parameters, chlorophyll content, and antioxidant/oxidative stress markers were measured, and root and leaf transcriptomes, together with root metabolomes, were compared between SCor and CK, followed by qRT-PCR validation. Compared with CK, SCor treatment significantly increased stem diameter (~60%), plant height (~20%), and relative chlorophyll content (SPAD, ~50%). Soluble sugar levels were elevated by over 40% in both leaves and roots, accompanied by tissue-specific modulation of antioxidant enzymes. Transcriptomic analysis of SCor vs. CK revealed 2459 differentially expressed genes (DEGs) in leaves and 3444 DEGs in roots; leaves exhibited upregulation of photosynthetic pigment metabolism (porphyrin and carotenoid pathways) and volatile defense compounds (alkaloids and monoterpenoids), whereas roots showed enrichment in phenylpropanoid/flavonoid biosynthesis, benzoxazinoid synthesis, and starch/sucrose metabolism. Metabolomics of SCor vs. CK identified 526 differentially accumulated metabolites (DAMs) in roots, with significant enrichment in aminoacyl-tRNA biosynthesis, phenylalanine metabolism, and linoleic acid metabolism. Integrative multi-omics analysis further revealed that the JA precursor 13-epi-12-oxo-phytodienoic acid co-clustered with stress-responsive transcription factors (e.g., DREB1C), while tricarboxylic acid (TCA) intermediates and phenylpropanoid metabolites were linked to energy and lignin biosynthesis genes. qRT-PCR confirmed the expression trends of 14 out of 15 tested genes. Collectively, combined HU2014 and COR application triggers tissue-specific transcriptional and metabolic reprogramming in maize, coupling JA-mediated stress signaling with enhanced carbon metabolism and secondary defense compound synthesis to promote rhizosphere adaptation and seedling vigor. Full article