Search Results (2,021)

The knowledge of comparative and developmental immunobiology has grown over the years and has been strengthened by the contributions of multi-omics research. High-performance microscopy, flow cytometry, scRNA sequencing, and the increased capacity to handle complex data introduced by machine learning have allowed the uncovering of aspects of great complexity and diversity in invertebrate immunocytes, i.e., immune-related circulating cells, which until a few years ago could only be described in terms of morphology and basic cellular functions, such as phagocytosis or enzymatic activity. Today, invertebrate immunocytes are recognized as sophisticated biological entities, involved in host defense, stress response, wound healing, organ regeneration, but also in numerous functional aspects of organismal life not directly related to host defense, such as embryonic development, metamorphosis, and tissue homeostasis. The multiple functions of immunocytes do not always fit the description of invertebrate organisms as simplified biological systems compared to those represented by vertebrates. However, precisely the increasing complexity revealed by immunocytes makes invertebrate organisms increasingly suitable models for addressing biologically significant and specific questions, while continuing to present the undeniable advantages associated with their ethical and economic sustainability. Full article

Polyphosphates are biopolymers composed of phosphate monomers linked by high-energy phosphoanhydride bonds. They are present across all life domains, serving as a source of energy, metal chelators, and playing a crucial role in stress defense. In Escherichia coli, polyphosphates also function as inorganic molecular chaperones. The present study aims to investigate whether polyphosphate serves a similar chaperone function in archaea, using Saccharolobus solfataricus as a model organism. To this end, polyphosphate was extracted and quantified, the ADP/ATP ratio was determined, insoluble protein extracts were analyzed at different time points after copper exposure, and qPCR was performed to measure the expression of stress-related genes. PolyP was extracted after exposing the archaeon S. solfataricus to different copper concentrations. We determined that polyP degradation is directly correlated with metal concentration. At the minimum inhibitory concentration (MIC) of 2 mM Cu²⁺, polyP degradation stabilized 2 h after exposure and showed no recovery even after 24 h. The ADP/ATP ratio was measured and showed differences in the presence or absence of polyP. The analysis of proteins precipitated under copper stress showed a higher proportion of insoluble proteins at an elevated metal concentration. On the other hand, increased protein precipitation was detected in the absence of polyP. Gene expression analysis via qPCR was conducted to assess the expression of genes involved in chaperone and chaperonin production, copper resistance, oxidative stress response, and phosphate metabolism under prolonged copper exposure, both in the presence and absence of polyP. The results indicated an upregulation of all the chaperonins measured in the presence of polyP. Interestingly, just some of these genes were upregulated in polyP’s absence. Despite copper stress, there was no upregulation of superoxide dismutase in our conditions. These results highlight the role of polyP in the copper stress response in S. solfataricus, particularly to prevent protein precipitation, likely due to its function as an inorganic chaperone. Additionally, the observed protein precipitation could be attributable to interactions between copper and some amino acids on the protein structures rather than oxidative stress induced by copper exposure, as previously described in E. coli. Our present findings provide new insights into the protective role of polyP as an inorganic chaperone in S. solfataricus and emphasize its importance in maintaining cellular homeostasis under metal stress conditions. Full article

In certain situations, romantic engagement with a partner can have detrimental effects on an individual’s well-being and overall health, exhibiting features attributable to addictive behaviors. Considering the clinical significance of this phenomenon and its prevalence among adolescents and young adults, the objective of this study was to investigate the potential associations between some risk factors for love addiction in a sample of university students, with a specific focus on adult attachment, separation anxiety, and defense mechanisms. A total of 332 participants (M_age = 23 years; SD = 2.462) completed a survey consisting of the Love Addiction Inventory—Short Form, Relationship Questionnaire, Seven Domains Addiction Scale (Separation Anxiety domain), and Forty Item Defense Style Questionnaire. The data were analyzed using Pearson’s correlation, and a multiple mediation model was also implemented. Results showed that fearful attachment was significantly and positively associated with love addiction. Furthermore, this relationship was mediated by separation anxiety and neurotic/immature defense mechanisms too. These findings contribute to the existing literature on love addiction and provide valuable insights for future research and clinical practice. Full article

The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial attack method called the Multi-Stage Dual-Perturbation Attack (MSDPA), specifically targeted at TSVMs. The MSDPA has two phases: initial adversarial samples are generated by an arbitrary range attack, and finer attacks are performed on critical features to induce the TSVM to generate false predictions. To improve the TSVM’s defense against MSDPAs, we incorporate adversarial training into the TSVM’s loss function to minimize the loss of both standard and adversarial samples during the training process. The improved TSVM loss function considers the adversarial samples’ effect and enhances the model’s adversarial robustness. Experimental results on several standard datasets show that our proposed adversarial defense-enhanced TSVM (adv-TSVM) performs better in classification accuracy and adversarial robustness than the native TSVM and other semi-supervised baseline algorithms, such as S³VM. This study provides a new solution to improve the defense capability of kernel methods in an adversarial setting. Full article

Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems. Full article

In recent years, federated learning (FL) has gained significant attention for its ability to protect data privacy during distributed training. However, it also introduces new privacy leakage risks. Membership inference attacks (MIAs), which aim to determine whether a specific sample is part of the training dataset, pose a significant threat to federated learning. Existing research on membership inference attacks in federated learning has primarily focused on leveraging intrinsic model parameters or manipulating the training process. However, the widespread adoption of privacy-preserving frameworks in federated learning has significantly diminished the effectiveness of traditional attack methods. To overcome this limitation, this paper aims to explore an efficient Membership Inference Attack algorithm tailored for encrypted federated learning scenarios, providing new perspectives for optimizing privacy-preserving technologies. Specifically, this paper proposes a novel Membership Inference Attack algorithm based on multiple adversarial perturbation distances (MAPD_MIA) by leveraging the asymmetry in adversarial perturbation distributions near decision boundaries between member and non-member samples. By analyzing these asymmetric perturbation characteristics, the algorithm achieves accurate membership identification. Experimental results demonstrate that the proposed algorithm achieves accuracy rates of 63.0%, 68.7%, and 59.5%, and precision rates of 59.0%, 65.9%, and 55.8% on CIFAR10, CIFAR100, and MNIST datasets, respectively, outperforming three mainstream Membership Inference Attack methods. Furthermore, the algorithm exhibits robust attack performance against two common defense mechanisms, MemGuard and DP-SGD. This study provides new benchmarks and methodologies for evaluating membership privacy leakage risks in federated learning scenarios. Full article

In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. Full article

(1) Background: Pasteurellosis is a global zoonotic bacterial disease, which has caused significant economic impacts in animal husbandry. Nevertheless, there is limited understanding of the immune response between goat peripheral blood lymphocytes (PBLs) and goat-derived Pasteurella multocida (P. multocida). (2) Methods: To investigate the immune response of host PBLs during infection with P. multocida type D, we established an in vitro cell model utilizing isolated primary goat PBLs. Utilizing this in vitro infection model, we employed an enzyme-linked immunosorbent assay (ELISA) to assess the cytokine profile variation in goat PBLs following infection. Meanwhile, RNA sequencing and quantitative PCR (qPCR) methods were employed to analyze the gene expression profile. (3) Results: The ELISA test results indicated that the expression levels of pro-inflammatory cytokines, such as IL-6, IFN-γ, CXCL10, and IL-17A, were significantly elevated within 12 h after infection with P. multocida. In contrast, the levels of the anti-inflammatory cytokine IL-10 were found to be reduced. RNA sequencing and functional enrichment analysis identified 2114 differentially expressed genes (DEGs) that were primarily associated with cytokine-cytokine receptor interactions, viral protein-cytokine interactions, and the IL-17 signaling pathway. Furthermore, protein-protein interaction (PPI) network analysis and qPCR highlighted CD86, CCL5, CD8A, CXCL8, CTLA4, TNF, CD274, IL-10, IL-6, CXCL10, IFNG, and IL-17A that were crucial for the response of PBLs to P. multocida infection. (4) Conclusions: This study systematically revealed the characteristics of PBLs in goats following infection with goat-derived P. multocida type D through the analysis of cytokines and gene expression, providing important theoretical insights for a deeper understanding of the defense mechanisms in goats against P. multocida. Full article

Centaurium erythraea Rafn. is a medicinal plant used as a model for studying plant developmental processes due to its developmental plasticity and ease of manipulation in vitro. Identifying the genes involved in its organogenesis and somatic embryogenesis (SE) is the first step toward unraveling the molecular mechanisms underlying its morphogenic plasticity. Although SE is the most common method of centaury regeneration, the genes involved in this have not yet been identified. The aim of this study was to identify the differentially expressed genes (DEGs) during key stages of SE and organogenesis using transcriptome data, with a focus on novel SE-related genes. The transcriptomic analysis revealed a total of 4040 DEGs during SE and 12,708 during organogenesis. Gene Ontology (GO) annotation showed that the highest number of SE-related genes was involved in defense responses. The expression of fifteen selected SE-related candidate genes was assessed by RT-qPCR across nine centaury developmental stages, including embryogenic tissues. Notably, a newly reported transcript, named CeNA1, was specifically activated during embryogenic callus (ec) induction, making it a potential novel marker for early SE. These findings provide, for the first time, insight into SE-related transcriptional patterns, representing a step closer to uncovering the molecular basis of centaury’s developmental plasticity. Full article

Argillaceous siltstone is prone to deformation and softening when exposed to water, which poses a great threat to practical engineering. There are significant differences in the degrees of damage to this type of rock caused by solutions with different water temperatures. This study aimed to better understand the effect of temperature on argillaceous siltstone by designing immersion tests at water temperatures of 5, 15, 25, and 35 °C, analyzing the mechanical properties and cation concentration shifts under each condition. A water temperature–force coupled geometric damage model for argillaceous siltstone was developed, incorporating a Weibull distribution function and composite damage factors to derive a statistical damage constitutive model. The findings reveal that, with increasing water temperature, the peak strength and elastic modulus of argillaceous siltstone display a concave trend, initially decreasing and then increasing, while the cation concentration follows a convex trend, first increasing and then decreasing. Between 15 and 25 °C, the stress–strain behavior transitions from a four-phase to a five-phase pattern, with pronounced plasticity. The model’s theoretical curves align closely with experimental data, with the Weibull parameters m and λ effectively capturing the rock’s strength and plastic characteristics. Changes in water temperature notably influence the damage variable D₁₂ in the context of water temperature–peak stress coupling, with D₁₂ initially increasing and then decreasing with higher temperatures. These research results can provide new methods for exploring the paths of soft rock disasters and provide guidance for designing defenses in geotechnical engineering. Full article

Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based intrusion detection systems (IDSs) are mainly designed to detect malicious network traffic. Unfortunately, ML models have recently been demonstrated to be vulnerable to adversarial perturbation, and therefore enable potential attackers to crash the system during normal operation. Among different attacks, generative adversarial networks (GANs) have been known as one of the most powerful threats to cybersecurity systems. To address these concerns, it is important to explore new defense methods and understand the nature of different types of attacks. In this paper, we investigate four serious attacks, GAN, Zeroth-Order Optimization (ZOO), kernel density estimation (KDE), and DeepFool attacks, on cybersecurity. Deep analysis was conducted on these attacks using three different cybersecurity datasets, ADFA-LD, CSE-CICIDS2018, and CSE-CICIDS2019. Our results have shown that KDE and DeepFool attacks are stronger than GANs in terms of attack success rate and impact on system performance. To demonstrate the effectiveness of our approach, we develop a defensive model using adversarial training where the DeepFool method is used to generate adversarial examples. The model is evaluated against GAN, ZOO, KDE, and DeepFool attacks to assess the level of system protection against adversarial perturbations. The experiment was conducted by leveraging a deep learning model as a classifier with the three aforementioned datasets. The results indicate that the proposed defensive model refines the resilience of the system and mitigates the presented serious attacks. Full article

Background: Inflammation is a natural body’s defense mechanism against harmful stimuli such as pathogens, chemicals, or irradiation. But when the inflammatory response becomes permanent, it can lead to serious health problems. In the present study, the antioxidant and anti-inflammatory potentials of the Eriosema montanum methanolic extract (EMME), as well as its isolated fractions (FA-FJ) and compounds (1–7), were evaluated by using in vitro and cellular models. Methods: The total phenolic and flavonoid contents were determined using, respectively, Folin–Ciocalteu and aluminum chloride colorimetric methods, while 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), 2,2′-diphenyl-1-picrylhy-drazyl (DPPH), and ferric ion reducing antioxidant power (FRAP) were used to determine the antioxidant activity. Thin Layer Chromatography (TLC) and column chromatography (CC) were used to isolate and purify the compounds and their elucidation using their NMR spectroscopic data. Results: EMME had moderate antioxidant and anti-inflammatory activities, while fraction FF showed much higher efficacy with IC₅₀ values of 34.64, 30.60, 16.43, and 77.29 μg/mL against DPPH, ABTS, NO, and 15-LOX inhibitory activities, respectively. The EMME fraction was found to be very rich in flavonoids and phenolic compounds, with 82.11 mgQE/g and 86.77 mgGAE/g of dry extract, respectively. Its LC-MS profiling allowed us to identify genistin (5) as the most concentrated constituent in this plant species, which was further isolated together with six other known compounds, namely, n-hexadecane (1), heptacosanoic acid (2), tricosan-1-ol (3), lupinalbin A (4), d-pinitol (6), and stigmasterol glucoside (7). Given these compounds, genistin (5) showed moderate activity against reactive oxygen species (ROS) and NO production in LPS-stimulated RAW264.7 cells compared to EMME, which suggested a synergy of (5) with other compounds. To the best of our knowledge, compounds (1), (2), and (3) were isolated for the first time from this plant species. Full article

As online shopping has increased, the business models of online stores have diversified. When consumers cannot experience an actual product, merchants will promote products through a display to attract customers. Virtual reality (VR) provides an immersive platform for consumers to interact with virtual scenarios. Unfortunately, cybersickness remains a problem in VR. The uncomfortable effects of VR hinder its commercial expansion and the broader adoption of 3D virtual stores. Cybersickness has many causes, including personal characteristics, hardware interfaces, and operation behavior. This study develops a fuzzy-control anti-cybersickness intelligent system (FCACIS) with these factors dynamically and actively. The system retrieves the operation value and inferences the cybersickness symptom value (CSSV). When the CSSV exceeds the alarm value, a dialog mode is introduced to remind users to be aware of possible cybersickness. If the CSSV continues to increase, a cybersickness defense mechanism is activated, such as decreasing the field of view and freezing the screen. The experimental results revealed a significant difference in SSQ scores between subjects who navigated a 3D virtual store with and without the FCACIS. The SSQ scores of subjects with the FCACIS (SSQ = 20.570) were significantly lower than those of subjects without the FCACIS (SSQ = 32.880). The FCACIS effectively alleviated cybersickness for subjects over 40 years old. Additionally, the FCACIS effectively slowed the onset of cybersickness in men and women. The anti-cybersickness effect of the FCACIS on flat-panel displays was greater than that on HMDs. The symptoms of cybersickness for a 3DOF controller were also reduced. Full article

In the domain of cybersecurity, cyber threats targeting network devices are very crucial. Because of the exponential growth of wireless devices, such as smartphones and portable devices, cyber risks are becoming increasingly frequent and common with the emergence of new types of threats. This makes the automatic and accurate detection of network-based intrusion very essential. In this work, we propose a network-based intrusion detection system utilizing the comprehensive feature engineering approach combined with boosting machine-learning (ML) models. A TCP/IP-based dataset with 25,192 data samples from different protocols has been utilized in our work. To improve the dataset, we used preprocessing methods such as label encoding, correlation analysis, custom label encoding, and iterative label encoding. To improve the model’s accuracy for prediction, we then used a unique feature engineering methodology that included novel feature scaling and random forest-based feature selection techniques. We used three conventional models (NB, LR, and SVC) and four boosting classifiers (CatBoostGBM, LightGBM, HistGradientBoosting, and XGBoost) for classification. The 10-fold cross-validation methods were employed to train each model. After an assessment using numerous metrics, the best-performing model emerged as XGBoost. With mean metric values of 99.54 ± 0.0007 for accuracy, 99.53 ± 0.0013 for precision, 99.54 ± 0.001 for recall, and an F1-score of 99.53 ± 0.0014, the XGBoost model produced the best performance overall. Additionally, we showed the ROC curve for evaluating the model, which demonstrated that all boosting classifiers obtained a perfect AUC value of one. Our suggested methodologies show effectiveness and accuracy in detecting network intrusions, setting the stage for the model to be used in real time. Our method provides a strong defensive measure against malicious intrusions into network infrastructures while cyber threats keep varying. Full article

With the development of technology, the need for software and software products to manage, control, and develop activities in many sectors is increasing daily. In order to create suitable software that will meet the needs of businesses and customers, the software application must be tested in detail before reaching the end user. For this reason, software testing processes are gaining importance in software development activities. This article discusses which errors are critical to solve in complex situations for the reliability and quality of the software product and the relationship between errors. In this study, the classical FMEA method was primarily used to identify and prioritize errors in an ongoing project of a company that provides software services in the defense industry. Later, to eliminate the shortcomings of the classical FMEA method, a new model, the weighted FMEA method (which calculates the risk priority score with five sub-severity components), was developed and applied. In the newly developed weighted FMEA method, the weights were determined by the fuzzy SWARA (Step-Wise Weight Assessment Ratio Analysis) method since the weights of the severity subcomponents were not the same. The risk priority number (RPN) of error types was calculated using classical FMEA and weighted FMEA. Since the RPNs calculated with weighted FMEA are calculated with more subcomponents, the chances of the RPNs’ errors appearing the same are much less than the RPNs calculated with classical FMEA. This situation indicates that the RPNs calculated with weighted FMEA are obtained from a more profound analysis. Full article