Search Results (135)

The evolving landscape of cybersecurity threats demands the modernization of Security Operations Centers (SOCs) to enhance threat detection, response, and mitigation. Security Orchestration, Automation, and Response (SOAR) platforms play a crucial role in addressing operational inefficiencies; however, traditional no-code SOAR solutions face significant limitations, including restricted flexibility, scalability challenges, inadequate support for advanced logic, and difficulties in managing large playbooks. These constraints hinder effective automation, reduce adaptability, and underutilize analysts’ technical expertise, underscoring the need for more sophisticated solutions. To address these challenges, we propose a hyper-automation SOAR platform powered by agentic-LLM, leveraging Large Language Models (LLMs) to optimize automation workflows. This approach shifts from rigid no-code playbooks to AI-generated code, providing a more flexible and scalable alternative while reducing operational complexity. Additionally, we introduce the IVAM framework, comprising three critical stages: (1) Investigation, structuring incident response into actionable steps based on tailored recommendations, (2) Validation, ensuring the accuracy and effectiveness of executed actions, (3) Active Monitoring, providing continuous oversight. By integrating AI-driven automation with the IVAM framework, our solution enhances investigation quality, improves response accuracy, and increases SOC efficiency in addressing modern cybersecurity threats. Full article

With the rapid development of research on Wireless Radar Sensor Networks (WRSNs), security issues have become a major challenge. Recent studies have highlighted numerous security threats in WRSNs. Given their widespread application value, the operational security of WRSNs needs to be ensured. This study focuses on the problem of malware propagation in WRSNs. In this study, the complex characteristics of WRSNs are considered to construct the epidemic VCISQ model. The model incorporates necessary factors such as node density, Rayleigh fading channels, and time delay, which were often overlooked in previous studies. This model achieves a breakthrough in accurately describing real-world scenarios of malware propagation in WRSNs. To control malware spread, a hybrid control strategy combining quarantine and patching measures are introduced. In addition, the optimal control method is used to minimize control costs. Considering the robustness and adaptability of the control method, two model-free reinforcement learning (RL) strategies are proposed: Proximal Policy Optimization (PPO) and Multi-Agent Proximal Policy Optimization (MAPPO). These strategies reformulate the original optimal control problem as a Markov decision process. To demonstrate the superiority of our approach, multi-dimensional ablation studies and numerical experiments are conducted. The results show that the hybrid control strategy outperforms single strategies in suppressing malware propagation and reducing costs. Furthermore, the experiments reveal the significant impact of time delays on the dynamics of the VCISQ model and control effectiveness. Finally, the PPO and MAPPO algorithms demonstrate superior performance in control costs and convergence compared to traditional RL algorithms. This highlights their effectiveness in addressing malware propagation in WRSNs. Full article

The citrus industry contributes to the cultivation of one of the most important fruit crops globally. However, citrus trees are susceptible to numerous Bisifusarium, Fusarium, and Neocosmospora-linked diseases, with dry root rot posing a serious threat to citrus orchards worldwide. These infections are exacerbated by biotic and abiotic stresses, leading to increased disease incidence. Healthy trees unexpectedly wilt and fall, exhibiting symptoms such as chlorosis, dieback, necrotic roots, root rot, wood discolouration, and eventual decline. Research indicates that the disease is caused by a complex of species from the Nectriaceae family, with Neocosmospora solani being the most prominent. To improve treatment and management strategies, further studies are needed to definitively identify these phytopathogens and understand the conditions and factors associated with Bisifusarium, Fusarium, and Neocosmospora-related diseases in citrus. This review focuses on the epidemiology and symptomatology of Fusarium and Neocosmospora species, recent advances in molecular techniques for accurate phytopathogen identification, and the molecular mechanisms of pathogenicity and resistance underlying Fusarium and Neocosmospora–citrus interactions. Additionally, the review highlights novel alternative methods, including biological control agents, for disease control to promote environmentally friendly and sustainable agricultural practices. Full article

Infection by Mycobacterium abscessus subsp. massiliense poses a growing public health threat, especially to immunocompromised individuals. The pathogenicity of this mycobacterium is directly linked to its ability to form biofilms, complex structures that confer resistance to antibiotics and the host immune response. The extracellular matrix of the biofilm acts as a physical barrier, hindering the penetration of drugs and the action of the immune system, while also inducing a slow-growth state that reduces susceptibility to antibiotics. Current therapies, which involve prolonged use of multiple antibiotics, are often ineffective and cause significant side effects. Therefore, it is essential to explore new strategies targeting bacterial resistance and biofilm destruction. This narrative review explores the biofilm-forming capacity of Mycobacterium abscessus subsp. massiliense and the potential of novel therapeutic strategies. Promising approaches include inhibiting biofilm formation, developing drugs with improved penetration of the extracellular matrix, combination therapies with agents that destabilize the biofilm structure, and modulating the host immune response. Investing in research and development of new therapeutic strategies is essential to combat this resistant bacterium and improve patient outcomes. Full article

Heavy-metal contamination of the environment is a serious worldwide issue, as it presents dangerous threats to both human health and aquatic ecosystems. This has led to a paradigm shift toward the development of simple, user-friendly, and economically viable remediation technologies that are essential for addressing heavy-metal soil pollution and for the global preservation of the environment. This review provides a comprehensive overview of environmental remediation strategies using cyclodextrin (CD) and its derivatives. Additionally, this study examines the effectiveness of methylated gamma-cyclodextrin (M-γ-CD) as a modified oligosaccharide for the elimination of toxic elements from impure soil matrices. M-γ-CD has emerged as a potent agent for treating soil impurities with noxious metals. As a modified form of cyclodextrin, M-γ-CD features hydrophobic cavities that are particularly adept at forming inclusion complexes with heavy-metal ions, thereby cumulating the aqueous solubility and efficiency of pollutants in environmental applications and improving soil bioremediation. This paper also reviews the unique structural configuration of M-γ-CD, which significantly enhances the solubility and mobility of cyclodextrins and facilitates the extraction of noxious metal particles such as Ni, Cu, and Pb from soil matrices. Furthermore, M-γ-CD is a promising soil remediation agent due to its capacity to boost contaminant solubility, improve environmental safety, offer cost-effectiveness, ensure adaptability, and minimize impact on soil parameters. Therefore, M-γ-CD is a desirable agent for the elimination of toxic metal impurities from soil. Full article

Forest trees significantly affect human life. The spread of pathogens, including bacterial ones, poses a serious threat to their health. Despite this, however, the species composition and distribution of pathogenic bacteria, as well as the etiology of common diseases affecting forest trees, remain virtually unstudied. In this study, we, for the first time, describe different species of Pseudomonas and Pantoea as new etiological agents associated with the symptoms of leaf spotting and wood darkening on Acer tataricum L., Fraxinus pennsylvanica L., Ulmus minor Mill. Ulmus laevis Pallas. and Populus tremula L. For the identification of bacteria species, we used an integrated approach based on the characterization of their morphology, biochemistry, physiology and genetics. Phylogenetic analysis was performed using multilocus typing for five genes for Pseudomonas and six genes for Pantoea. Leaf spotting on A. tataricum, F. pennsylvanica, U. minor and U. laevis was shown to be caused by Pseudomonas cerasi, Pseudomonas congelans, Pseudomonas graminis, Pseudomonas syringae and Pantoea agglomerans both in monoinfection and coinfection. Wood darkening in U. minor U. laevis and P. tremula was found to be associated with the presence of Pantoea sp. and P. agglomerans. The coinfection of forest trees with bacteria of the genera Pseudomonas and Pantoea indicates a complex mechanism of interaction between the two populations, which will be the subject of future studies. Full article

Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability. Full article

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

Cervical cancer poses a substantial threat to women’s health, underscoring the necessity for effective therapeutic agents with low toxicity that specifically target cancer cells. As cancer progresses, increased glucose consumption causes glucose scarcity in the tumor microenvironment (TME). Consequently, it is imperative to identify pharmacological agents capable of effectively killing cancer cells under conditions of low glucose availability within the TME. Previous studies showed that Gboxin, a small molecule, inhibited glioblastoma (GBM) growth by targeting ATP synthase without harming normal cells. However, its effects and mechanisms in cervical cancer cells in low-glucose environments are not clear. This study indicates that Gboxin notably enhanced autophagy, apoptosis, and ferroptosis in cervical cells under low-glucose conditions without significantly affecting cell survival under normal conditions. Further analysis revealed that Gboxin inhibited the activity of complex V and the production of ATP, concurrently leading to a reduction in mitochondrial membrane potential and the mtDNA copy number under low-glucose culture conditions. Moreover, Gboxin inhibited tumor growth under nutrient deprivation conditions in vivo. A mechanistic analysis revealed that Gboxin activated the AMPK signaling pathway by targeting mitochondrial complex V. Furthermore, increased AMPK activation subsequently promoted autophagy and reduced p62 protein levels. The decreased levels of p62 protein facilitated the degradation of Nrf2 by regulating the p62-Keap1-Nrf2 axis, thereby diminishing the antioxidant capacity of cervical cancer cells, ultimately leading to the induction of apoptosis and ferroptosis. This study provides a better theoretical basis for exploring Gboxin as a potential drug for cervical cancer treatment. Full article

As digitalization and artificial intelligence advance, cybersecurity threats intensify, making malware—a type of software installed without authorization to harm users—an increasingly urgent concern. Due to malware’s social and economic impacts, accurately modeling its spread has become essential. While diverse models exist for malware propagation, their selection tends to be intuitive, often overlooking the unique aspects of digital environments. Key model choices include deterministic vs. stochastic, planar vs. spatial, analytical vs. simulation-based, and compartment-based vs. individual state-tracking models. In this context, our study assesses fundamental infection spread models to determine those most applicable to malware propagation. It is organized in two parts: the first examines principles of deterministic and stochastic infection models, and the second provides a comparative analysis to evaluate model suitability. Key criteria include scalability, robustness, complexity, workload, transparency, and manageability. Using consistent initial conditions, control examples are analyzed through Python-based numerical methods and agent-based simulations in NetLogo. The findings yield practical insights and recommendations, offering valuable guidance for researchers and cybersecurity professionals in applying epidemiological models to malware spread. Full article

The growing demand for metal production promotes the search for alternative sources and novel modalities in metallurgy. Flotation tailings are an important secondary mineral resource; however, they might pose a potential environmental threat due to containing toxic metals. Therefore, proper leaching reagent selection is required. Ozone is an alternative oxidizing agent for metal leaching, as its use prevents contaminating product generation while increasing the noble metal extraction efficiency in the presence of complexing agents. In this study, the feasibility and efficiency of combining the use of thiosulfate and ozone for gold and silver extraction have been investigated as an eco-friendly alternative for recovery from flotation tailings. Two sets of samples from old flotation tailings of Copper Mine Bor (Serbia) were prepared and physico-chemically characterized, then treated in two experimental leaching procedures, followed by thorough XRD and SEM/EDS analyses of the products. It showed that after 1 h of leaching in a water medium at room temperature and a solid-to-liquid phase ratio of 1:4, 88.8% of Cu was obtained, while a high efficiency of Au extraction from solid residue (after Cu leaching) was attained (83.4%). The results suggest that ozone-assisted leaching mediated by Ca-thiosulfate can be an effective eco-friendly treatment for noble metals recovery from sulfide-oxide ores. Full article

Biological invasions pose a major environmental challenge, often facilitating the unregulated dissemination of pathogens and parasites associated with their hosts. These pathogens can severely impact native and cultivated species, with far-reaching ecological and economic consequences. Despite their importance, the mycobiota associated with invasive plant species remains relatively understudied, posing a complex challenge for researchers. The aim of this manuscript is to underscore the most significant threats posed by the uncontrolled transmission of fungal pathogens from invasive alien plants to native environments and agricultural systems, and to identify the factors influencing this phenomenon. We emphasize the role of pathogen spillback and spillover mechanisms in the domestication of invasive alien plants. The influence of environmental, host, and pathogen-related factors on the survival of fungal pathogens were also investigated. Finally, we explore the technical and legal feasibility of using plant pathogens as “green agents” to control invasive alien plants. Full article

Although global life expectancy has increased over the past 20 years due to advancements in managing infectious diseases, one-fifth of people still die from infections. In response to this ongoing threat, significant efforts are underway to develop vaccines and antimicrobial agents. However, pathogens evolve resistance mechanisms, complicating their control. The COVID-19 pandemic has underscored the limitations of focusing solely on the pathogen-killing strategies of immunology and microbiology to address complex, multisystemic infectious diseases. This highlights the urgent need for practical advancements, such as microbiome therapeutics, that address these limitations while complementing traditional approaches. Our review emphasizes key outcomes in the field, including evidence of probiotics reducing disease severity and insights into host-microbiome crosstalk that have informed novel therapeutic strategies. These findings underscore the potential of microbiome-based interventions to promote physiological function alongside existing strategies aimed at enhancing host immune responses and pathogen destruction. This narrative review explores microbiome therapeutics as next-generation treatments for infectious diseases, focusing on the application of probiotics and their role in host-microbiome interactions. While offering a novel perspective grounded in a cooperative defense system, this review also addresses the practical challenges and limitations in translating these advancements into clinical settings. Full article

GET3 is an ATPase protein that plays a pivotal role in the guided entry of the tail-anchored (GET) pathway. The protein facilitates the targeting and inserting of tail-anchored (TA) proteins into the endoplasmic reticulum (ER) by interacting with a receptor protein complex on the ER. The role of GET3 in various biological processes has been established in yeast, plants, and mammals but not in filamentous fungi. Fusarium graminearum is the major causal agent of Fusarium head blight (FHB), posing a threat to the yield and quality of wheat. In this study, we found that FgGET3 exhibits a high degree of sequence and structural conservation with its homologs across a wide range of organisms. Ectopic expression of FgGET3 in yeast restored the growth defects of the Saccharomyces cerevisiae ScGET3 knock-out mutant. Furthermore, FgGET3 was found to dimerize and localize to the cytoplasm, similar to its homologs in other species. Deletion of FgGET3 in F. graminearum results in decreased fungal growth, fragmented vacuoles, altered abiotic stress responses, reduced conidia production, delayed conidial germination, weakened virulence on wheat spikes and reduced DON production. Collectively, these findings underscore the critical role of FgGET3 in regulating diverse cellular and biological functions essential for the growth and virulence of F. graminearum. Full article

Organophosphate neuroactive agents represent severe security threats in various scenarios, including military conflicts, terrorist activities and industrial accidents. Addressing these threats necessitates effective protective measures, with a focus on decontamination strategies. Adsorbents such as bentonite have been explored as a preliminary method for chemical warfare agent immobilization, albeit lacking chemical destruction capabilities. Chemical decontamination, on the other hand, involves converting these agents into non-toxic or less toxic forms. In this study, we investigated the hydrolytic activity of a Cu(II) complex, previously studied for phosphate ester hydrolysis, as a potential agent for chemical warfare decontamination. Specifically, we focused on a ligand featuring a thiophene anchor bound through an aliphatic spacer, which exhibited high hydrolytic activity in its Cu(II) complex form in our previous studies. Paraoxon, an efficient insecticide, was selected as a model substrate for hydrolytic studies due to its structural resemblance to specific chemical warfare agents and due to the presence of a chromogenic 4-nitrophenolate moiety. Our findings clearly show the hydrolytic activity of the studied Cu(II) complexes. Additionally, we demonstrate the immobilization of the studied complex onto a solid substrate of Amberlite XAD4 via copolymerization of its thiophene side group with dithiophene. The hydrolytic activity of the resultant material towards paraoxon was studied, indicating its potential utilization in organophosphate neuroactive agent decontamination under mild conditions and the key importance of surface adsorption of paraoxon on the polymer surface. Full article