Search Results (25)

Background/Objectives: Adverse neurological events caused by imipenem are usually more frequent in patients with epilepsy. This study investigates the effects of imipenem on hippocampal neurogenesis, astroglial activation, and behaviour in animals with pro-epileptogenic alterations. Methods: Five-month-old Wistar rats were divided into four groups: control (CT), pentylenetetrazol (PTZ), imipenem-treated group (IM), and imipenem after PTZ-kindling (IM-PTZ). Epileptogenesis was induced by PTZ-kindling for 28 days, followed by 10 days of imipenem (40 mg/kg) or saline injections. Rats underwent memory and anxiety tests followed by immunohistochemical analyses of doublecortin (DCX) and glial fibrillary acidic protein (GFAP) in the hippocampus. Results: Both kindled groups displayed comparable impairments in spatial learning and working memory, while imipenem alone did not alter behaviour. The IM-PTZ group failed to reach control-level performance only in the final Morris Water Maze acquisition trials, suggesting a deficit in memory consolidation rather than a total loss of retention, as the probe trial remained comparable across all groups. Although PTZ increased anxiety-like behaviour, no locomotor differences were observed in open-field test, confirming cognitive impairments were not due to motor deficits. These behavioural changes mirrored distinct cellular alterations: while PTZ alone triggered a compensatory increase in DCX neuronal density and astrocytic branching, imipenem co-administration suppressed this neurogenic response, bringing DCX to baseline. Furthermore, combined treatment induced an uncoupling of astrocytic response, marked by increased astrocyte density and reduced process complexity in dentate hilus and Cornus Ammonis 3 (CA3). Conclusions: These results suggest that imipenem modulates the cellular substrate of the pre-sensitized brain, interfering with hippocampal plasticity and glial compensatory mechanisms, rather than directly driving further behavioural deterioration. Full article

We investigate functional behavioural differentiation in genetically homogeneous animal collectives using the $ϵ$-machine and $ϵ$-transducer frameworks from symbolic dynamics. Long-term tracking of unmarked individuals in colonies of the clonally reproducing ant Pristomyrmex punctatus reveals two distinct movement modes—clustering within the group and solitary exploration outside it. Reconstructed individual $ϵ$-transducers expose a sharp asymmetry in computational structure between these modes: solitary explorers are described by a deterministic machine, whereas clustering ants require stochastic machines to capture their complex patterns of micro-movement. A population-level (universal) $ϵ$-transducer, inferred from pooled data, captures the shared behavioural repertoire across all individuals. Individual differences are parsimoniously explained as biased and partial traversals of a common state space rather than as distinct generative programs. We compare three predictive models: the $ϵ$-machine, which relies solely on an ant’s own output history; a memoryful $ϵ$-transducer, which additionally conditions on changes in the local neighbour count as social input; and a memoryless $ϵ$-transducer, which uses this social input alone. The memoryful transducer matches the $ϵ$-machine in prediction accuracy despite requiring ten times as many states, while the memoryless transducer performs substantially worse. This shows that an ant’s own behavioural history is the essential predictor of its future movement at the temporal resolution examined here. We argue, however, that this predictive redundancy does not entail the causal irrelevance of social input: the behavioural history itself accumulates the trace of past social encounters so that any role differentiation established through prior interactions is already inscribed in the output sequence that the $ϵ$-machine reads, and mode transitions—the moments at which social input most plausibly exerts causal influence—are rare events that contribute negligibly to aggregate one-step accuracy. Agent-based simulations driven by the universal $ϵ$-transducer reproduce basic motion statistics and transient aggregations but fail to generate the stable macroscopic clusters observed experimentally, pointing to the role of additional mechanisms such as longer-term memory or stigmergic coupling. Nevertheless, ants do respond to their social environment: an explorer encountering an increase in neighbours is absorbed into the cluster and ceases directed movement. Together, our results suggest a two-level organisation: within each behavioural mode, individual dynamics are self-sufficient for one-step prediction, while transitions between modes are environmentally triggered and represent switches between fundamentally different classes of dynamical organisation. Full article

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) measurements, which wastes energy when no paging arrivals occur. Meanwhile, heterogeneous Quality of Service (QoS) constraints make it difficult for fixed-parameter Idle Discontinuous Reception and Paging Early Indication mechanisms (IDRX/PEI) to balance energy, delay, and reliability. This paper develops a UAV-assisted 5G-A paging control framework that maps services into multiple QoS classes and models QoS violation risk and system energy consumption under unified accounting, including UE monitoring/reception energy and unmanned aerial vehicle (UAV) forwarding energy. We then propose a QoS-aware risk-driven paging strategy: an offline Long Short-Term Memory (LSTM) predictor is trained to estimate the time-to-next-arrival (TTNA) of paging events and produce a bounded urgency/risk signal to initialize class-dependent thresholds, while online triggering and QoS-feedback-based threshold adaptation regulate the empirical violation rate toward target constraints under varying loads, enabling a controllable energy–delay trade-off. A simulation-based evaluation is conducted to compare the proposed method with representative baselines (Enhanced Paging Monitoring (EPM), Split Paging Occasion (SPOP), and Predicted Paging Early Indication (PPEI)) and to examine the impact of SSB overhead and UAV relaying on the energy–delay–reliability trade-offs. Full article

Modern malware presents significant challenges to traditional detection methods, often leveraging fileless techniques, in-memory execution, and process injection to evade antivirus and signature-based systems. To address these challenges, alert-driven memory forensics has emerged as a critical capability for uncovering stealthy, persistent, and zero-day threats. This study presents a two-stage host-based malware detection framework, that integrates memory forensics, explainable machine learning, and ensemble classification, designed as a post-alert asynchronous SOC workflow balancing forensic depth and operational efficiency. Utilizing the MemMal-D2024 dataset—comprising rich memory forensic artifacts from Windows systems infected with malware samples whose creation metadata spans 2006–2021—the system performs malware detection, using features extracted from volatile memory. In the first stage, an Attentive and Interpretable Learning for structured Tabular data (TabNet) model is used for binary classification (benign vs. malware), leveraging its sequential attention mechanism and built-in explainability. In the second stage, a Voting Classifier ensemble, composed of Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models, is used to identify the specific malware family (Trojan, Ransomware, Spyware). To reduce memory dump extraction and analysis time without compromising detection performance, only a curated subset of 24 memory features—operationally selected to reduce acquisition/extraction time and validated via redundancy inspection, model explainability (SHAP/TabNet), and training data correlation analysis —was used during training and runtime, identifying the best trade-off between memory analysis and detection accuracy. The pipeline, which is triggered from host-based Wazuh Security Information and Event Management (SIEM) alerts, achieved 99.97% accuracy in binary detection and 70.17% multiclass accuracy, resulting in an overall performance of 87.02%, including both global and local explainability, ensuring operational transparency and forensic interpretability. This approach provides an efficient and interpretable detection solution used in combination with conventional security tools as an extra layer of defense suitable for modern threat landscapes. Full article

In this study, an adaptive event-driven coordination paradigm is proposed for achieving consensus in nonlinear multi-agent systems (MASs) over directed networks. First, a newly dynamic event-triggered mechanism with single-point historical information is introduced to minimize unnecessary network communication. And a more general form of an event triggering mechanism with moving window historical information is designed for further saving network resources. Considering that the use of historical information over a long period of time may cause deviations, an event-triggered mechanism that can adjust the maximum memory length is proposed in this work to minimize unnecessary network communication. Secondly, the unknown nonlinearities in the MAS model are addressed using the universal approximation capability of neural networks. Then, a methodology for distributed adaptive control under event-triggered mechanisms is introduced leveraging the memory-based command-filtered backstepping methodology, and the proposed scheme resolves the complexity explosion problem. Finally, a case study is conducted to validate the feasibility of the proposed method. Full article

To address the challenges faced by multiple autonomous underwater vehicles (AUVs) in formation control under complex marine environments—such as model uncertainties, external disturbances, dynamic communication topology variations, and limited communication resources—this paper proposes an integrated control framework that combines robust individual control, distributed cooperative formation, and dynamic event-triggered communication. At the individual control level, a robust control method based on a fractional-order sliding mode observer (FOSMO) and a fractional-order terminal sliding mode controller (FOTSMC) is developed. The observer exploits the memory and broadband characteristics of fractional calculus to achieve high-precision estimation of lumped disturbances, while the controller constructs a non-integer-order sliding surface with an adaptive gain law to guarantee finite-time convergence of tracking errors. At the formation coordination level, a distributed trajectory generation method based on dynamic consensus is proposed to achieve reference trajectory planning and formation maintenance in a cooperative manner. At the communication level, a dynamic-threshold event-triggered mechanism is designed, where the triggering condition is adaptively adjusted according to the state errors, thereby significantly reducing communication load and energy consumption. Theoretically, Lyapunov-based analysis rigorously proves the stability and convergence of the closed-loop system. Numerical simulations confirm that the proposed method outperforms several benchmark algorithms in terms of tracking accuracy and disturbance rejection. Moreover, the integrated framework maintains precise formation under communication topology variations, achieving a communication reduction rate exceeding 65% compared to periodic protocols while preserving coordination accuracy. Full article

Background/Objectives: Working memory (WM) performance relies on the coordination of updating and inhibition functions within the central executive system. However, their interaction under varying cognitive loads, particularly across sensory modalities, remains unclear. Methods: This study examined how sensory modality modulates flanker interference under increasing WM loads. Twenty-two participants performed a visual n-back task at three load levels (1-, 2-, and 3-back) while ignoring visual (within-modality) or auditory (cross-modality) flankers. Results: Behaviorally, increased WM load (2- and 3-back) led to reduced accuracy (AC) and prolonged reaction times (RTs) in both conditions. In addition, flanker interference was observed under the 2-back condition in both the visual within-modality (VM) and audiovisual cross-modality (AVM) tasks. However, performance impairment emerged at a lower load (2-back) in the VM condition, whereas in the AVM condition, it only emerged at the highest load (3-back). Significant performance impairment in the AVM condition occurred at higher WM loads, suggesting that greater WM load is required to trigger interference. Event-related potential (ERP) results showed that N200 amplitudes increased significantly for incongruent flankers under the highest WM load (3-back) in the visual within-modality condition, reflecting greater inhibitory demands. In the cross-modality condition, enhanced N200 was not observed across all loads and even reversed at low load (1-back). Moreover, the results also showed that P300 amplitude increased with load in the within-modality condition but decreased in the cross-modality condition. Conclusions: These results demonstrated that the interaction between updating and inhibition is shaped by both WM load and sensory modality, further supporting a sensory modality-specific resource allocation mechanism. The cross-modality configurations may enable more efficient distribution of cognitive resources under high load, reducing interference between concurrent executive demands. Full article

Febrile seizures (FS) are a common childhood neurological event associated with an increased risk of long-term cognitive and emotional deficits, though the precise mechanisms remain elusive. Using a rat model, we investigated the long-term effects of FS induced on postnatal day 10, assessing outcomes in young adulthood (P45-55). We report region-specific neuronal loss in the hippocampus, more extensive in the ventral segment. Molecular analysis revealed a broad downregulation of genes encoding ionotropic and metabotropic glutamate receptors and excitatory amino acid transporters. These alterations were most severe and persistent in the ventral hippocampus and medial prefrontal cortex. Behaviorally, rats with neonatal FS exhibited a hyperanxious phenotype, characterized by reduced locomotor and exploratory activity and impaired habituation to a novel environment. In contrast, spatial working memory and social behavior remained intact. Our results provide the first comprehensive evidence that neonatal FS trigger long-term, region-specific disruptions of the glutamatergic system within hippocampal–prefrontal circuits. These findings identify vulnerable molecular targets and precise neurobiological mechanisms that may underlie the heightened risk of anxiety-related disorders following early-life FS, suggesting new avenues for therapeutic intervention. Full article

Traumatic brain injury (TBI) results in a cascade of neuropathological events, which can significantly disrupt synaptic integrity. This review explores the acute, subacute and chronic phases of synaptic dysfunction and loss in trauma which commence post-TBI, and their contribution to the subsequent neurological sequelae. Central to these disruptions is the loss of dendritic spines and impaired synaptic plasticity, which compromise neuronal connectivity and signal transmission. During the acute phase of TBI, mechanical injury triggers presynaptic glutamate secretion and Ca²⁺ ion-mediated excitotoxic injury, accompanied by cerebral edema, mitochondrial dysfunction and the loss of the mushroom-shaped architecture of the dendritic spines. The subacute phase is marked by continued glutamate excitotoxicity and GABAergic disruption, along with neuroinflammatory pathology and autophagy. In the chronic phase, long-term structural remodeling and reduced synaptic densities are evident. These chronic alterations underlie persistent cognitive and memory deficits, mood disturbances and the development of post-traumatic epilepsy. Understanding the phase-specific progression of TBI-related synaptic dysfunction is essential for targeted interventions. Novel therapeutic strategies primarily focus on how to effectively counter acute excitotoxicity and neuroinflammatory cascades. Future approaches may benefit from boosting synaptic repair and modulating neurotransmitter systems in a phase-specific manner, thereby mitigating the long-term impact of TBI on neuronal function. Full article

Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive memory loss and cognitive decline. Its key pathological hallmarks include extracellular amyloid plaques composed of amyloid beta (Aβ) peptides and intracellular neurofibrillary tangles formed by hyperphosphorylated tau protein. Although numerous studies have investigated the complex pathology of AD, its underlying mechanisms remain incompletely understood. The amyloid cascade hypothesis continues to be the leading model of AD pathogenesis. It suggests that Aβ aggregation is the initial trigger of neurotoxicity, setting off a cascade of pathological events including inflammation, oxidative stress, tau hyperphosphorylation, synaptic dysfunction, and, ultimately, dementia. Molecular dynamics (MD) is a powerful tool in structure-based drug design (SBDD). By simulating biomolecular motions at the atomic level, MD provides unique insights into molecular properties, functions, and inhibition mechanisms—insights often inaccessible through other experimental or computational techniques. When integrated with experimental data, MD further deepens our understanding of molecular interactions and biological processes. Natural compounds, known for their pleiotropic pharmacological activities, favorable safety profiles, and general tolerability (despite occasional side effects), are increasingly explored for their potential in both the treatment and prevention of various diseases, including AD. In this review, we summarize current findings from MD simulations of natural compounds with anti-amyloidogenic potential. This work builds upon our previous publication, which focused on endogenous compounds and repurposed drugs. The review is structured as follows: an overview of the amyloid cascade hypothesis; a discussion of Aβ oligomeric structures and their stabilizing interactions; a section on molecular dynamics, including its challenges and future directions; and a comprehensive analysis of the inhibitory mechanisms of natural compounds, categorized by their shared structural features. Full article

Within the framework of a dynamic memory-event-triggered mechanism (DMETM), this paper proposes an estimate-based secure control algorithm for nonlinear networked control systems (NNCSs) that suffer from hybrid attacks. Firstly, a sampled-data observer is employed utilizing the output signals to estimate the states. Secondly, due to the limitation of data transmission capacity in NNCSs, a novel DMETM with auxiliary variable is proposed, which effectively leverages the benefits of historical sampled data. In the process of network data transmission, a hybrid attack model that simultaneously considers the impact of both deception and denial of service (DoS) attacks is introduced, which can undermine signal integrity and disrupt data transmission. Then, a memory-event-triggered controller is developed, and the mean square stability of the NNCSs can be ensured by selecting some appropriate values. Finally, a numerical simulation and a practical example are given to illustrate the meaning of the designed dynamic memory-event-triggered control (DMETC) algorithm. Full article

This paper studies the distributed robust predefined-time sliding mode control (DRPSC) problem for high-order heterogeneous multi-agent systems under input saturation while considering external disturbances and model uncertainties. Firstly, a distributed predefined-time state observer (PTSO) is designed for each agent to achieve individual estimation of the state information of the virtual leader within a predefined time, and the observer does not need to count on the global information of the system. Secondly, a predefined-time auxiliary dynamic system (PTADS) is developed to solve the actuator’s input saturation problem. Thirdly, a distributed predefined-time sliding mode controller (PTSMC) is proposed, which ensures that the error converges to a small region near zero within a predefined time and combines H_∞ control to deal with the lumped uncertainty disturbances in the system to improve the robustness of the system. In addition, a memory event-triggered mechanism (METM) is designed to reduce the communication frequency of the underactuated AUV-USV multi-agent system and reduce the consumption of communication resources. Finally, Lyapunov theory is employed to prove that the closed-loop system is predefined-time stable, and the simulation results demonstrate that the proposed method is effective. Full article

Ulva prolifera (Chlorophyta), a pivotal species in green tide generation, is particularly vulnerable to abiotic stressors, including variations in temperature and light intensity, requiring specific regulatory frameworks for survival. Epigenetic modification is recognized as a molecular mechanism contributing to the flexible adaptability to environmental alterations. In this study, using DNA methylation pattern analysis, we investigated abiotic stress responsive methylation events, as well as gene and pathway expression patterns, in green macroalgae U. prolifera cultured under elevated temperature–light stress (30 °C and 300 µmol photons m⁻² s⁻¹⁾ and identified a negative correlation between CG methylation and gene expression patterns which indicated that abiotic stress caused CG demethylation and afterwards provoked the transcription response. CHG and CHH methylation exhibited an increased mutability and were preeminently found in transposable elements and intergenic regions, possibly contributing to genetic stability by restricting transposon activity. Furthermore, a rapid regeneration through spore ejection and the formation of new thalli was observed, which emphasized its tenacity capacity for stress memory. Our study also revealed an upregulation of genes associated with the glycolysis pathway and highlighted the critical roles of hexokinase, 6-phosphofructokinase-1, and fructose-6-phosphate in triggering glycolysis as a significant stress-adaptive pathway. Overall, these findings suggested that DNA methylation functions as a potential regulatory mechanism, maintaining environmental adaptability, genomic integrity, and underpinning regenerative capacity in U. prolifera. The findings elucidated the molecular resilience of U. prolifera, highlighting its feasibility for sustainable development and biotechnological applications. Full article

Drought stress negatively affects plant metabolism and growth, triggering complex defence mechanisms to limit damage. This study evaluated the effectiveness of a single foliar application of 1 mM L-proline (Pro) in winter wheat (Triticum aestivum L., cv. Bohemie) in two separate experiments differing in the time interval between application and drought—7 days (experiment 1) and 35 days (experiment 2). Net photosynthetic rate (A), transpiration rate (E), stomatal conductance (g_s), leaf water potential (Ψw), intrinsic water use efficiency (WUE_i), endogenous proline content (Pro), malondialdehyde content (MDA), and maximum quantum yield of photosystem II (F_v/F_m) were measured. In experiment 1, drought markedly reduced net photosynthetic rate, transpiration rate, stomatal conductance, and leaf water potential in both drought-stressed treatments, namely, without priming plants (S) and with Pro priming plants (SPro). Pro and MDA content increased under stress. Higher E and g_s in the SPro treatment indicated more effective stomatal regulation and a distinct water use strategy. Pro content was significantly lower in SPro compared to S, whereas differences in MDA levels between these treatments were not statistically significant. The second drought period (D2) led to more pronounced limitations in gas exchange in both S and SPro. Enhanced osmoregulation was reflected by lower Ψw (S < SPro) and higher Pro accumulation in S (S > SPro). The effect of exogenous Pro persisted in the form of reduced endogenous Pro synthesis and improved photosystem II protection. Rehydration of stressed plants restored all monitored physiological parameters, and Pro-treated plants exhibited a more efficient recovery of gas exchange. Experiment 2 demonstrated a long-lasting priming effect that improved the preparedness of plants for future drought events. In the SPro treatment, this stress memory supported more efficient osmoregulation, reduced lipid peroxidation, improved protection of photosystem II integrity, and a more effective restart of gas exchange following rehydration. Our findings highlight the potential of exogenous proline as a practical tool for enhancing crop resilience to climate-induced drought stress. Full article

This paper investigates the problem of robust fusion state estimation for multi-sensor systems under the influence of time-correlated fading channels, incorporating a dynamic event-triggered mechanism (DETM). The randomly occurring parameter uncertainties are characterized by a stochastic variable following a Bernoulli distribution, while sensor measurements are transmitted to the corresponding estimators through time-correlated fading channels and dynamic event-triggered mechanisms. The DETM dynamically adjusts the triggering threshold via regulation and memory factors, enhancing adaptability in data transmission while effectively reducing redundant communication overhead. Furthermore, an augmented state model is constructed by integrating system states, channel coefficients, and the event-triggering mechanism, thereby comprehensively capturing the impact of dynamic environments on state estimation. Based on this model, a local state estimation algorithm is designed to ensure the convergence of the upper bound of the local estimation error covariance, which is further minimized at each time step through adaptive adjustment of local estimator gains. Subsequently, the local estimates obtained from multiple estimators are fused using the covariance intersection fusion strategy, improving the overall estimation accuracy. Simulation experiments demonstrate that the proposed recursive fusion state estimation framework significantly reduces communication overhead and enhances estimation performance in the presence of both time-correlated fading channels and randomly occurring parameter uncertainties, while maintaining an acceptable computational cost. Compared to the traditional Kalman filtering method, the proposed recursive fusion state estimation algorithm improves estimation accuracy by 58% while increasing computational time by only 32.4%. Additionally, the DETM effectively reduces communication frequency by 36.7% Full article