Search Results (1,941)

The plasma membrane (PM) of eukaryotic cells plays a key role in the response to stress, acting as the first line of defense against environmental changes and protecting cells against intracellular perturbations. In this work, we explore how membrane-bound chaperones and membrane lipid domains work together to shape plasma membrane properties—a partnership we refer to as the “epichaperome–plasma membrane lipid axis.” This axis influences membrane fluidity, curvature, and domain organization, which in turn shapes the spatial and temporal modulation of signaling platforms and pathways essential for maintaining cellular integrity and homeostasis. Changes in PM fluidity can modulate the activity of ion channels, such as transient receptor potential (TRP) channels. These changes also affect processes such as endocytosis and mechanical signal transduction. The PM proteome undergoes rapid changes in response to membrane perturbations. Among these changes, the expression of heat shock proteins (HSPs) and their accumulation at the PM are essential mediators in regulating the physical state and functional properties of the membrane. Because of the pivotal role in stress adaptation, HSPs influence a wide range of cellular processes, which we grouped into three main categories: (i) mechanistic insights, differentiating in vitro (liposome, reconstituted membrane systems) and in vivo evidence for HSP-PM recruitment; (ii) functional outputs, spanning how ion channels are affected, changes in membrane fluidity, transcytosis, and the process of endocytosis and exosome release; and (iii) pathological effects, focusing on how rewired lipid–chaperone crosstalk in cancer drives resistance to drugs through altered membrane composition and signaling. Finally, we highlight Membrane Lipid Therapy (MLT) strategies, such as nanocarriers targeting specific PM compartments or small molecules that inhibit HSP recruitment, as promising approaches to modulate the functional stability of epichaperome assembly and membrane functionality, with profound implications for tumorigenesis. Full article

The accurate localization of radio frequency (RF) emitters plays a critical role in spectrum monitoring, public safety, and defense applications, particularly in environments where global navigation satellite systems are limited. This study investigates the feasibility of a single unmanned aerial vehicle (UAV) equipped with a Doppler-based software-defined radio sensor to localize modern RF sources without the need for external infrastructure or multiple UAVs. A custom-designed localization system was developed and tested using the L3Harris AN/PRC-152A tactical radio, which represents a class of real-world, dual-use emitters with lower frequency stability than laboratory signal generators. The approach was validated through both emulation studies and extensive field experiments under realistic conditions. The results show that the proposed system can localize RF emitters with an average error below 50 m in 80% of cases even when the transmitter is more than 600 m away. Performance was evaluated across different carrier frequencies and acquisition times, demonstrating the influence of signal parameters on localization accuracy. These findings confirm the practical applicability of Doppler-based single-UAV localization methods and provide a foundation for further development of lightweight, autonomous RF emitter tracking systems for critical infrastructure protection, spectrum analysis, and tactical operations. Full article

Linezolid, a synthetic antimicrobial agent, may induce oxidative damage in ocular tissues, particularly in the optic nerve. Adenosine triphosphate (ATP) is involved in the production of antioxidants that scavenge and neutralize reactive oxygen species. This study aims to evaluate the potential protective effect of exogenous ATP against linezolid-induced ocular damage in rats, in comparison with methylprednisolone. Wistar-type rats were divided into five groups as follows: healthy (HG), ATP-only (ATPG), linezolid-only (LZDG), ATP + linezolid (ATLDG), and methylprednisolone + linezolid groups (MPLDG). Oxidative stress markers, antioxidant biomarkers, and proinflammatory cytokines were analyzed in isolated ocular tissues. Optic nerve tissue was also evaluated histopathologically. Linezolid administration increased the oxidative stress marker MDA and the proinflammatory cytokine TNF-α, while decreasing antioxidant parameters such as tGSH, SOD and CAT in rat ocular tissues, compared to the healthy group. However, it did not significantly alter serum troponin I levels. Histopathological analysis revealed that linezolid induced oxidative damage and inflammation in optic nerve tissue, with marked glial alterations. ATP administration reduced linezolid-induced oxidative stress in ocular tissue, as indicated by decreased MDA levels. It also enhanced antioxidant defenses by increasing tGSH, SOD, and CAT levels. In addition, ATP lowered proinflammatory cytokine levels, thereby alleviating inflammation. These effects collectively contributed to the restoration of biochemical parameters toward normal levels. In addition, ATP mitigated linezolid-induced optic nerve damage and glial alterations. The critical role of ATP in reducing oxidative stress, restoring antioxidant balance, and suppressing inflammation may represent a promising therapeutic approach for linezolid-induced ocular toxicity. Full article

Antimicrobial resistance (AMR) is a growing global threat, exacerbated by the adaptive mechanisms of Gram-negative ESKAPE pathogens, which include biofilm formation and outer membrane vesicle (OMV) production. Biofilms create robust protective barriers that shield bacterial communities from immune responses and antibiotic treatments, while OMVs contribute to both defense and offense by carrying antibiotic-degrading enzymes and delivering virulence factors to host cells. These mechanisms not only enhance bacterial survival but also increase the virulence and persistence of infections, making them a significant concern in clinical settings. This review explores the molecular processes that drive biofilm and OMV formation, emphasizing their critical roles in the development of AMR. By understanding these mechanisms, new therapeutic strategies can be developed to disrupt these defenses, potentially improving the efficacy of existing antibiotics and slowing the spread of resistance. Additionally, the use of OMVs in vaccine development and drug delivery offers promising avenues for future research. Addressing these challenges requires a comprehensive approach, combining advanced research with innovative therapies to combat the escalating threat of AMR and improve patient outcomes. Full article

Acrylamide (ACR), a common dietary pro-oxidant generated in heat-processed foods, disrupts mitochondrial redox homeostasis. While its neurotoxic effects are recognized, the role of ACR in depression remains poorly understood. We hypothesized that dietary ACR exposure promotes depression via SIRT3-dependent mitochondrial oxidative injury. Through an integrative approach combining network toxicology (to prioritize candidate targets), transcriptomics, and Mendelian randomization (MR), we identified SIRT3 as the central mediator. Molecular dynamics simulations demonstrated that ACR’s primary metabolite glycidamide (GA) formed more stable and rigid complexes with key targets (including SIRT3, TP53, CASP3, JUN, PTGS2, and PTK2) than ACR itself, as evidenced by superior structural stability, reduced flexibility, and enhanced hydrogen bonding. Transcriptomic analysis of the human prefrontal cortex (datasets GSE54567 and GSE54568) revealed mitochondrial deacetylase sirtuin 3 (SIRT3) as the most significantly suppressed gene in depression (p < 0.01), suggesting an impairment in Superoxide dismutase 2 (SOD2)-mediated antioxidant defense. MR further established JUN and PTK2 as causal genetic risk factors for depression (JUN: Odds Ratio (OR) = 1.029, 95% CI = 1.002–1.057; PTK2: OR = 1.040, 95% CI = 1.005–1.076; JUN (OR) = 1.048, 95% CI = 1.021–1.076, PTK2: OR = 1.073, 95% CI = 1.039–1.109) of each MR estimates, while other candidates lacked genetic support. Our findings demonstrate that ACR induces depression primarily through SIRT3 suppression, activating JUN/PTK2 pathways, suggesting its potential role in environmental toxicant-induced redox imbalance. Full article

Defensins are a class of small cysteine-rich cationic antimicrobial peptides (AMPs) that play vital roles in immune-regulating insect–microbe interaction, offering great potential for developing pest control approaches using RNA interference (RNAi) and insect pathogens. However, the biocontrol potential of defensins from the destructive rice pest Nilaparvata lugens (brown planthopper, BPH) remains largely unexplored. Here, we identified and functionally characterized a defensin-encoding gene NldefB in BPH. The open reading frame (ORF) of NldefB is 315 bp in length, encoding 104 amino acids with a conserved Knot1 domain. The qRT-PCR results showed that the transcription level of NldefB went upward with the increasing developmental stages, with the highest expressions in the female adults and their fat body. The expression of NldefB was continuously induced by bacterial pathogens but exhibited a pattern of initial increase followed by a decrease when challenged by a fungal pathogen Metarhizium anisopliae. RNAi-mediated silencing of NldefB significantly decreased the host survival rate, egg production and hatchability, as well as the capability to resist fungal infection. Additionally, NldefB suppression resulted in a significant increase in microbial loads. Our findings underscored that NldefB plays essential roles in regulating host development, pathogen defense, and microbial maintenance, providing a potential target for RNAi- and microbe-mediated BPH biocontrol. Full article

Dongxiang wild rice (DXWR, Oryza rufipogon Griff.), the northernmost known wild rice species, exhibits exceptional tolerance to combined low-temperature and anaerobic stress during seed germination, providing a unique model for understanding plant adaptation to complex environmental constraints. Here, we employed an integrated multi-omics approach combining genomic, transcriptomic, and metabolomic analyses to unravel the synergistic regulatory mechanisms underlying this tolerance. Genomic comparative analysis categorized DXWR genes into three evolutionary groups: 18,480 core genes, 15,880 accessory genes, and 6822 unique genes. Transcriptomic profiling identified 10,593 differentially expressed genes (DEGs) relative to the control, with combined stress triggering the most profound changes, specifically inducing the upregulation of 5573 genes and downregulation of 5809 genes. Functional characterization revealed that core genes, including DREB transcription factors, coordinate energy metabolism and antioxidant pathways; accessory genes, such as glycoside hydrolase GH18 family members, optimize energy supply via adaptive evolution; and unique genes, including specific UDP-glycosyltransferases (UDPGTs), confer specialized stress resilience. Widely targeted metabolomics identified 889 differentially accumulated metabolites (DAMs), highlighting significant accumulations of oligosaccharides (e.g., raffinose) to support glycolytic energy production and a marked increase in flavonoids (153 compounds identified, e.g., procyanidins) enhancing antioxidant defense. Hormonal signals, including jasmonic acid and auxin, were reconfigured to balance growth and defense responses. We propose a multi-level regulatory network based on a “core-unique-adaptive” genetic framework, centered on ERF family transcriptional hubs and coordinated through a metabolic adaptation strategy of “energy optimization, redox homeostasis, and growth inhibition relief”. These findings offer innovative strategies for improving rice stress tolerance, particularly for enhancing germination of direct-seeded rice under early spring low-temperature and anaerobic conditions, by utilizing key genes such as GH18s and UDPGTs, thereby providing crucial theoretical and technological support for addressing food security challenges under climate change. Full article

Diketopiperazines (DKPs) are biologically important cyclic dipeptides widespread in nature, associated primarily with microorganisms. This is the case for the 2,5-DKP derivative cyclo(L-Leu-L-Pro) (cLP), also known as gancidin W or PPDHMP, identified from a variety of bacteria and fungi, and occasionally found in food products. The present review retraces the discovery of cLP, its identification in living species, its chemical syntheses, and its biochemical properties. In bacteria, cLP is often associated with other DKPs to serve as a defense element against other microorganisms and/or as a regulator of bacterial growth. cLP plays a role in quorum-sensing and functions as an anticariogenic and antifungal agent. The antimicrobial mechanism of action and molecular targets of cLP are evoked. The interest in cLP for combatting certain parasitic diseases, such as malaria, and cancers is discussed. The capacity of cLP to interact with CD151 and to down-regulate the expression of this tetraspanin can be exploited to reduce tumor dissemination and metastases. The review sheds light on the pharmacology and specific properties of cyclo(L-Leu-L-Pro), which can be useful for the development of a novel therapeutic approach for different human pathologies. It is also of interest to help define the bioactivity and mechanisms of action of closely related DKP-based natural products. Full article

Blockchain technology has rapidly evolved beyond cryptocurrencies, underpinning diverse applications such as supply chains, healthcare, and finances, yet its security vulnerabilities remain a critical barrier to safe adoption. However, attackers increasingly exploit weaknesses in consensus protocols, smart contracts, and network layers with threats such as Denial-of-Chain (DoC) and Black Bird attacks, posing serious challenges to blockchain ecosystems. We conducted anomaly detection using two independent datasets (A and B) generated from simulation attack scenarios including hash rate, Sybil, Eclipse, Finney, and Denial-of-Chain (DoC) attacks. Key blockchain metrics such as hash rate, transaction authorization status, and recorded attack consequences were collected for analysis. We compared both class-balanced and imbalanced datasets, applying Synthetic Minority Oversampling Technique (SMOTE) to improve representation of minority-class samples and enhance performance metrics. Supervised models such as Random Forest, Gradient Boosting, and Logistic Regression consistently outperformed unsupervised models, achieving high F1-scores (0.90), while balancing the training data had only a modest effect. The results are based on simulated environment and should be considered as preliminary until the experiment is performed in a real blockchain environment. Based on identified gaps, we recommend the exploration and development of multifaceted defense approaches that combine prevention, detection, and response to strengthen blockchain resilience. Full article

This paper introduces a hybrid learning framework that synergistically combines Reinforcement Learning (RL) and Supervised Learning (SL) to train autonomous cyber-defense agents capable of operating effectively in dynamic and adversarial environments. The proposed approach leverages RL for strategic exploration and policy development, while incorporating SL to distill high-reward trajectories into refined policy updates, enhancing sample efficiency, learning stability, and robustness. The framework first targets specialized agent training, where each agent is optimized against a specific adversarial behavior. Subsequently, it is extended to enable the training of a generalized agent that learns to counter multiple, diverse attack strategies through multi-task and curriculum learning techniques. Comprehensive experiments conducted in the CybORG simulation environment demonstrate that the hybrid RL–SL framework consistently outperforms pure RL baselines across both specialized and generalized settings, achieving higher cumulative rewards. Specifically, hybrid-trained agents achieve up to 23% higher cumulative rewards in specialized defense tasks and approximately 18% improvements in generalized defense scenarios compared to RL-only agents. Moreover, incorporating temporal context into the observation space yields a further 4–6% performance gain in policy robustness. Furthermore, we investigate the impact of augmenting the observation space with historical actions and rewards, revealing consistent, albeit incremental, gains in SL-based learning performance. Key contributions of this work include: $\left(i\right)$ a novel hybrid learning paradigm that integrates RL and SL for effective cyber-defense policy learning, $\left(ii\right)$ a scalable extension for training generalized agents across heterogeneous threat models, and $\left(iii\right)$ empirical analysis on the role of temporal context in agent observability and decision-making. Collectively, the results highlight the promise of hybrid learning strategies for building intelligent, resilient, and adaptable cyber-defense systems in evolving threat landscapes. Full article

We here delve into the intricate and evolving concepts of brain death and consciousness, particularly at the end of life. We examine the historical and technological advancements that have influenced our understanding of death, such as mechanical ventilation and resuscitation techniques. These developments have challenged traditional definitions of death, leading to the concept of brain death, defined as the irreversible loss of all brain functions, including the brainstem. We emphasize that consciousness exists on a continuum, ranging from full alertness to deep coma and complete cessation of brain activity. It explores various disorders of consciousness, including coma, vegetative state, minimally conscious state, and locked-in syndrome, each with distinct characteristics and levels of awareness. Neuroimaging techniques, such as EEG, fMRI, and DTI, are highlighted for their crucial role in diagnosing and understanding disorders of consciousness. These techniques help to detect covert consciousness, assess brain activity, and predict recovery potential. The phenomenon of the “wave of death,” which includes a paradoxical surge in brain activity at the point of death, is also discussed. We address the challenges in defining and understanding both death and consciousness, calling for biologically grounded, ethically defensible, and culturally sensitive definitions. We advocate for standardized neuroimaging protocols, longitudinal studies, and the integration of artificial intelligence to improve diagnosis and treatment. In conclusion, the document underscores the importance of an integrated, evidence-based approach to understanding the gray zones between life and death, recognizing that consciousness and death are dynamic processes with both biological and experiential dimensions. Full article

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of Things (AIoT). We illustrate the operating procedures of IoHT systems in detail. We highlight their applications in disease management, health promotion, and active ageing. Key enabling technologies, including cloud computing, edge computing architectures, machine learning, and smart sensors, are examined in relation to continuous health monitoring, personalized interventions, and predictive decision support. This paper also indicates potential challenges that IoHT systems face, including data privacy, ethical concerns, and technology transition and aversion, and it reviews corresponding defense mechanisms from perception, policy, and technology levels. Future research directions are discussed, including explainable AI, digital twins, metaverse applications, and multimodal sensor fusion. By integrating IoT and AI, these systems offer the potential to support more adaptive and human-centered healthcare delivery, ultimately improving treatment outcomes and supporting healthy ageing. Full article

Cadmium (Cd), a pervasive and highly phytotoxic metal pollutant, poses severe threats to agricultural productivity, ecosystem stability, and human health through its entry into the food chain. Plants have evolved intricate defense mechanisms, among which the strategic manipulation of nutrient elements emerges as a critical physiological and biochemical strategy for mitigating Cd stress. This comprehensive review delves deeply into the multifaceted roles of essential macronutrient elements (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur), essential micronutrient elements (zinc, iron, manganese, copper) and non-essential beneficial elements (silicon, selenium) in modulating plant responses to Cd toxicity. We meticulously dissect the physiological, biochemical, and molecular underpinnings of how these nutrients influence Cd bioavailability in the rhizosphere, Cd uptake and translocation pathways, sequestration and compartmentalization within plant tissues, and the activation of antioxidant defense systems. Nutrient elements exert their influence through diverse mechanisms: competing with Cd for root uptake transporters, promoting the synthesis of complexes that reduce Cd mobility, stabilizing cell walls and plasma membranes to restrict apoplastic flow and symplastic influx, modulating redox homeostasis by enhancing antioxidant enzyme activities and non-enzymatic antioxidant pools, regulating signal transduction pathways, and influencing gene expression profiles related to metal transport, chelation, and detoxification. The complex interactions between nutrients themselves further shape the plant’s capacity to withstand Cd stress. Recent advances elucidating nutrient-mediated epigenetic regulation, microRNA involvement, and the role of nutrient-sensing signaling hubs in Cd responses are critically evaluated. Furthermore, we synthesize the practical implications of nutrient management strategies, including optimized fertilization regimes, selection of nutrient-efficient genotypes, and utilization of nutrient-enriched amendments, for enhancing phytoremediation efficiency and developing low-Cd-accumulating crops, thereby contributing to safer food production and environmental restoration in Cd-contaminated soils. The intricate interplay between plant nutritional status and Cd stress resilience underscores the necessity for a holistic, nutrient-centric approach in managing Cd toxicity in agroecosystems. Full article

Conventional plant disease management primarily depends on chemical pesticides. However, with the rising concerns related to human health, environmental sustainability, and the emergence of resistant pathogens, biocontrol agents (BCAs) have gained more attention as eco-friendly alternatives. Among the potential biocontrol agents, yeasts stand out due to their safety, adaptability, and diverse antagonistic mechanisms, ranging from competition and enzyme secretion to volatile compound production and immunity induction. Despite their potential, yeast-based BCAs face limitations in field efficacy, regulation, and an incomplete understanding of their molecular interactions. Most current studies focus on simple, pairwise interactions, overlooking the complexity of agroecosystems, where plants, pathogens, and BCAs interact within broader microbial communities. This review addresses the importance of understanding tripartite interactions among plants, pathogens, and yeasts, supported by integrated transcriptomic and comparative genomic approaches, as well as meticulous observations of phenotypic expressions to uncover strain-specific defense mechanisms and mode of action. By referring to well-studied models like Blumeria graminis f.sp. hordei–Hordeum vulgare–Pseudozyma flocculosa and Trichoderma tripartite systems, we highlight the underexplored potential of yeasts to modulate plant immunity and influence pathogen behavior through complex molecular crosstalk. Bridging these knowledge gaps through integrating proteomic, metabolomic, and transcriptomic analyses, we can better harness yeasts in sustainable and targeted biocontrol strategies. Full article

Investment decision-making is often characterized by uncertainty and the subjective weighting of criteria. This study aims to develop a more robust decision support framework by integrating the Generalized Interval-Valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS) with the Analytic Hierarchy Process (AHP) to objectively weight criteria and handle multi-evaluator hesitancy. In the proposed GIVHIFSS-AHP model, the AHP is employed to derive mathematically consistent criterion weights, which are subsequently embedded into the GIVHIFSS structure to accommodate interval-valued and hesitant evaluations from multiple decision-makers. The model is applied to a numerical case study evaluating five investment alternatives. Its performance is assessed through a comparative analysis with standard GIVHIFSS and GIFSS models, as well as a sensitivity analysis. The results indicate that the model produces financially rational rankings, identifying blue-chip technology stocks as the optimal choice (score: +2.4). The comparative analysis confirms its superiority over existing models, which yielded less-stable rankings. Moreover, the sensitivity analysis demonstrates the robustness of the results against minor perturbations in criterion weights. This research introduces a novel and synergistic integration of the AHP and GIVHIFSS. The key advantage of this approach lies in its ability to address the long-standing issue of arbitrary criterion weighting in Fuzzy Soft Set models by embedding the AHP as a foundational mechanism for ensuring validation and objectivity. This integration results in mathematically derived, consistent weights, thereby yielding empirically validated, more reliable, and defensible decision outcomes compared with existing models. Full article