Search Results (12,873)

Leydig cells (LCs) represent a somatic testicular population responsible for testosterone synthesis, a hormone essential for spermatogenesis and male fertility. The obesity condition impairs LC steroidogenic activity, contributing to testicular oxidative stress and male reproductive dysfunctions. Using a high-fat-diet (HFD) murine model, we investigated the regulatory role of the nuclear factor of activated T cells 5 (NFAT5s) in the obesity-induced LC damage and the resulting alterations in intergenerationally inherited sperm circRNA cargo. Our findings reveal a significant upregulation of both circNFAT5 and NFAT5 protein levels in HFD testis. This molecular signature correlated with decreased antioxidant defense system, increased LC apoptosis, and impaired steroidogenesis. In vitro experiments, performed in TM3 cells, confirmed that NFAT5 nuclear shuttling drives proapoptotic gene activation, while NFAT5 silencing promotes LC survival. The analysis of HFD progeny (F1H) revealed a full recovery of testis oxidative status and LC apoptosis, linked with the recovery of NFAT5 expression. However, a steroidogenic deficiency persisted in F1H offspring. Notably, HFD and F1H epididymides exhibited NFAT5 overexpression concomitantly with impaired sperm morphology, motility, viability, and altered sperm circRNA profiles alongside a deregulated 4-hydroxy-2-nonenal (4HNE) profile, a marker of sperm oxidative stress. Lastly, an enhanced FUS-related amplification of circRNA perturbations was highlighted in F1H spermatozoa. Collectively, our findings reveal a dual functional role of NFAT5 as a testicular regulator of LC fate and an epididymal sentinel of metabolic stress, in turn linking paternal obesity to the persistent transmission of sperm epigenetic anomalies across the offspring. Full article

The emergence of vancomycin-intermediate Staphylococcus aureus (VISA) threatens the efficacy of this last-line antibiotic. The GraSR two-component system is frequently mutated in VISA strains. Here, we demonstrate that the GraS(T136I) point mutation, identified in the clinical VISA isolate XN108, is a key determinant of reduced vancomycin susceptibility. Introducing this mutation into the susceptible strain Newman increased the vancomycin MIC from 1.5 to 4 mg/L, while its reversion in XN108 decreased the MIC from 12 to 8 mg/L. The mutation conferred common phenotypes, including thickened cell wall, decreased autolysis, and reduced cell surface negative charge via upregulation of the dltABCD operon and mprF. Notably, the GraS(T136I) mutation also upregulated virulence genes (efb, hlb, sbi, hld) and enhanced hemolytic activity. Interestingly, despite this hypervirulent profile, the mutant showed impaired long-term survival within macrophages. Our study reveals that a single GraSR mutation can co-regulate vancomycin resistance and virulence, offering new insights into the adaptation of S. aureus to antibiotic pressure. Full article

Intrusion detection system (IDS) research relies on accurately labeled network traffic datasets; however, label quality in IDS datasets is seldom audited prior to modeling. Many publicly available IDS datasets assign ground-truth labels based on capture filenames or temporal session windows rather than per-flow inspection, a practice referred to as file-level labeling. This study identifies and corrects a systematic mislabeling instance in SDN-MG25, a CICFlowMeter-based dataset for software-defined networking (SDN)-enabled microgrid intrusion detection. IP composition analysis, which cross-references each attack-labeled flow with the documented attacker IP address, reveals that the BackgroundAttackTraffic (BAT) class, comprising 3167 flows (79.5% of all attack labels), contains no attacker-originated traffic. All BAT flows involve legitimate microgrid hosts communicating with external services during the attack capture window. Correcting this labeling error increases binary detection F1 from 0.578 to $0.956\pm 0.005$, an improvement of $+0.378$ that is 4.2 times greater than the best single modeling improvement (threshold tuning, $+0.090$). Furthermore, Confident Learning, a state-of-the-art automated label-noise detector, recovers only 8.4% of mislabeled BAT flows (recall $=0.084$, precision $=0.247$), indicating that domain-knowledge audits are essential for detecting systematic, class-level mislabeling that statistical methods cannot identify. The end-to-end pipeline Macro F1 improves from 0.749 to 0.862 after label correction. IP composition analysis is proposed as a mandatory prerequisite for IDS dataset evaluation, and a reproducible two-stage pipeline with feature-tier ablation for session confound diagnosis is provided. Full article

Ethical decision-making in professional contexts requires integrating behavioral performance and neural processes, as it involves both deliberative and intuitive mechanisms. However, empirical evidence integrating these levels of analysis in professional ethics remains limited. To address this gap, this study examines the association between behavioral responses and electroencephalographic (EEG) activity during ethical–professional decision-making in a sample of Chilean university students. Thirty-two participants completed a computerized task involving personal and impersonal ethical–professional dilemmas related to psychological practice, making binary decisions while reaction times were recorded. EEG data were acquired using an 8-channel OpenBCI Cyton system. Mean EEG amplitude (0–800 ms post-stimulus) was computed for frontal (Fp1, Fp2, F7, F8) and parietal (Cz, Pz, P3, P4) regions of interest. Behavioral outcomes showed that impersonal dilemmas elicited significantly longer reaction times than personal dilemmas, consistent with greater deliberative demands. Trial-level mixed-effects models revealed a systematic frontal–parietal dissociation, where longer decision durations were associated with increased frontal EEG activity and concurrent parietal suppression. These findings support a systematic behavioral–neural association during ethical–professional decision-making, characterized by a frontal–parietal dissociation that reflects the dynamic competition between deliberative and integrative processes. Prolonged responses to impersonal dilemmas indicate greater deliberative demand, requiring extended integration of abstract professional norms. The observed neural pattern extends dual-process accounts of moral cognition and has implications for the design of ethics education programs that cultivate both deliberative and context-sensitive reasoning skills. Full article

The rapid proliferation of Internet of Things (IoT) devices has dramatically expanded the attack surface for cyber threats, exposing critical infrastructure to sophisticated intrusion attempts that traditional static intrusion detection systems (IDS) fail to counter effectively. This paper proposes an autonomous reinforcement learning (RL)-based IDS framework for dynamic IoT networks, capable of adaptive, real-time threat detection without human intervention. The proposed system integrates a Deep Q-Network (DQN) agent with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) feature extractor to identify and classify malicious network traffic across 33 attack categories. We evaluate the framework on two recent, publicly available benchmark datasets: CICIoT2023, comprising 8.94 GB of traffic from 105 real IoT devices, and CIC IoT-DIAD 2024, a flow-based dataset with diverse attack and benign scenarios. Experimental results demonstrate superior detection performance compared to baseline classifiers, including SVM, Random Forest, and standalone deep learning models, with improved F1-score, reduced false alarm rate (FAR), and lower detection latency. The reward-shaping strategy explicitly penalizes false positives, addressing a key limitation of prior RL-based IDS approaches. This work contributes a scalable, dataset-agnostic autonomous defense architecture suitable for real-world IoT deployment. Full article

The growth of Internet of Things (IoT) environments has expanded the attack surface of modern systems. Trojan attacks are a major challenge as they evade conventional detection mechanisms and operate silently within legitimate processes. This paper presents an automated Trojan detection framework for Windows-based IoT gateways. The framework combines custom dataset generation informative feature engineering and deep learning-driven analysis. A dataset of 3000 real world executable samples was created through controlled sandbox execution and forensic monitoring. The process captured behavioral static and network-level characteristics. An initial set contained 146 extracted features. A multi-stage feature selection process identified 33 informative attributes. This step allowed efficient learning and preserved discriminative power. A custom deep neural network model named TrDNN was developed using these features. The model captures complex nonlinear patterns linked to Trojan activity. The framework was evaluated against five classical machine learning models. It was also compared with five deep learning baselines. Results show that TrDNN achieves strong detection performance. The accuracy is 0.975. The precision is 0.972. The recall is 0.969. The F1 score is 0.970. The study also examines inference time and energy consumption. The model shows a balance between detection effectiveness, computational cost and energy efficiency. This makes it suitable for resource-constrained IoT gateway deployment. The detection model was integrated into an automated real-time monitoring pipeline. The system enables continuous process surveillance through Windows command line automation with minimal operational overhead. Statistical validation used paired t tests, Wilcoxon signed rank tests and McNemar chi-square test. The performance gains are statistically significant and do not indicate overfitting. The framework provides a reliable, efficient and deployable solution for Trojan detection in modern IoT systems. Full article

Metal–organic frameworks (MOFs) possess unique structural tunability, abundant coordination sites, and outstanding biosafety, rendering them highly advantageous for the development of high-performance magnetic resonance imaging (MRI) contrast agents. In light of the significant advancements in MOF-derived theranostic platforms, a comprehensive overview focusing on their classification and clinically oriented applications is urgently required. This review provides an in-depth examination of various categories of MOF-derived contrast agents, including T1, T2, dual-mode, ratiometric and 19F imaging systems, and analyzes the correlation between structural characteristics and imaging performance. Furthermore, it highlights typical MRI-guided therapeutic applications, such as those related to atherosclerosis, bacterial infections, and cancer immunotherapy. The review systematically addresses existing challenges, including issues related to biodegradability, metabolic behavior, and biosafety. It also summarizes the rational design principles for novel MOF contrast agents, aiming to facilitate their transition from fundamental research to clinical applications. Full article

Designing informative descriptors for time-varying complex networks is important for characterizing structural reconfiguration in evolving graph data. This paper introduces a thermodynamics-inspired framework for dynamic graph analysis, centered on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index. These quantities provide a compact representation of how graph organization changes over time. We apply this framework to resting-state fMRI data from autism spectrum disorder (ASD) and control subjects. At the event level, the temperature index shows a statistically significant but modest association with low-SSIM reconfiguration events, indicating that it serves as a weak yet reproducible marker of rapid network change. On controlled synthetic dynamic graphs, the framework exhibits regime-dependent sensitivity: spectral-core change is more informative under rewiring, whereas the temperature index is more informative under gain modulation. At the node level, node energy highlights regional differences between ASD and control groups, providing interpretable neuroscientific context for dynamic brain connectivity. Overall, the proposed framework provides a promising and computationally tractable approach for characterizing reconfiguration patterns in dynamic brain networks and other evolving complex systems. Full article

One of the major determinants of crop productivity and sustainable agricultural practices is soil fertility. Proper soil assessment helps farmers make informed decisions about nutrients and fertilizers. This study utilizes 16 machine learning classifiers for soil fertility prediction, including learner-based, ensemble-based, instance-based, and probabilistic-based models. The model’s performance is assessed using accuracy, precision, recall, and F1-score. This paper presents a machine learning model for predicting soil fertility based on soil physicochemical characteristics. The data used in the research comprise vital soil parameters: nitrogen, phosphorus, potassium, pH, organic carbon, electrical conductivity, and micronutrients. Missing-value imputation, label encoding, and feature standardization are among the data preprocessing methods used to enhance data quality. Correlation analysis, ANOVA F-score, and mutual information were used to assess feature importance and determine the most significant soil characteristics. The experimental observation reveals that the RF model achieves an accuracy of 90.91% compared to the other models. Additional assessment using multi-class Receiver Operating Characteristic (ROC) and Precision–Recall (PR) curves showed excellent discriminative ability across the dominant soil fertility, which was of high quality. The findings show that machine learning models, especially ensemble-based models, are effective at estimating soil fertility levels. The proposed framework provides a data-driven, reliable decision-support system to assess soil fertility, enabling farmers and agricultural experts to enhance nutrient management and crop production. Full article

Cell-based immunotherapies require noninvasive tools that can quantify the migration, biodistribution, and persistence of administered immune cells. This review focuses primarily on oncologic immune cell therapy, while also considering selected inflammatory disease models in which immune-cell trafficking is biologically relevant. We critically compare direct radionuclide labeling, sodium iodide symporter (NIS)-based reporter gene imaging, radionuclide-integrated nanoplatforms, and Cerenkov-based hybrid optical conversion strategies. Direct labeling with agents such as [⁸⁹Zr]Zr-oxine, [¹¹¹In]In-oxine, and [⁹⁹ᵐTc]Tc-HMPAO enables early positron emission tomography (PET)/single-photon emission computed tomography (SPECT) biodistribution assessment, usually within hours to several days after cell administration. NIS reporter imaging with [¹²⁴I]NaI, [¹²³I]NaI, [⁹⁹ᵐTc]TcO₄⁻, or [¹⁸F]TFB supports repeated viability-dependent imaging, because signal generation depends on active transporter expression in living engineered cells. Radionuclide-integrated gold nanoplatforms can improve intracellular retention and offer theranostic potential through combined imaging, photothermal, radiotherapeutic, or immunomodulatory functions. We further discuss PET/SPECT balance, radiopharmaceutical nomenclature, nanoparticle stabilization, ethical aspects of genetic modification, tumor-on-a-chip systems for preclinical testing, and limitations of narrative evidence synthesis. Together, these platforms provide complementary strategies for image-guided immune cell therapy, with translational relevance for patient selection, treatment optimization, safety monitoring, and oncology practice. In conclusion, NIS-centered nuclear imaging and radionuclide-integrated nanoplatforms represent complementary, clinically actionable tools for quantitative immune-cell tracking, therapeutic optimization, and safety monitoring in translational oncology and inflammatory disease research. Full article

This paper presents an augmented computed—torque control (A-CTC) scheme for 6-DoF industrial manipulators operating under model uncertainty and external disturbances. The proposed controller combines a nominal computed torque law with an additional torque-domain residual damping term to compensate for modeling errors and unmodeled friction while preserving the baseline tracking structure. A Lyapunov-based analysis is developed for the resulting closed-loop system, and bounded output constraints are imposed on the residual term to support input-to-state stability and bounded tracking errors. The method is evaluated in simulation against PD feedforward (PD-ff) and sliding mode control (SMC) over multiple trajectories and disturbance scenarios. Across 30 randomized trials, A-CTC reduces joint-space RMSE by approximately 12.8%, end-effector RMSE by 13.0%, and disturbance-settling time by 32.4% compared with PD-ff, while increasing RMS torque by only 4.5%. Compared with SMC, A-CTC achieves lower joint-space and end-effector RMSE by 7.4% and 4.1%, respectively, while reducing RMS torque by 40.0% and saturation time by 54.5%. Additional simulation studies, including gain-sensitivity analysis, Jacobian conditioning assessment, and impulse response tests, further support a favorable accuracy–effort trade-off and improved disturbance rejection. The present results are based on simulation and will be validated experimentally in future work. Full article

The ability to identify emotions based on written text is one of the core areas of Natural Language Processing (NLP) and has many applications in areas such as mental health monitoring, sentiment analysis, and dialogue systems. This study proposes an explainable emotion recognition (EER) framework built on a fine-tuned RoBERTa-base model trained on the Emotions for NLP dataset with an accuracy of 92.4% and a weighted F1 score of 92.5%. To interpret the decision process of the EER model, we systematically applied four complementary explainable artificial intelligence (XAI) techniques to provide explanations and insights into how the model makes its predictions: SHAP for global token-level feature attribution, LIME for local instance-level explanations, multi-head attention visualization for structural interpretability, and integrated gradients via Captum for axiom-satisfying gradient-based attribution. Each of these four methods provides complementary multi-perspective views of EER model behavior, which can help increase model transparency, identify potential biases, and enable the responsible use of transformer-based models in critical environments (e.g., those requiring formal clinical documentation). Our experiments consistently show that the EER model identifies tokens as having the highest emotional expression level as the strongest predictive feature across methodological perspectives, with strong evidence of cross-methodological agreement regarding the semantic coherence of learned representations. Our findings have direct implications for the responsible implementation of AI-based emotion recognition systems in mental health support systems, where model user-interface transparency, bias mitigation, and clinical trust are necessary to ensure quality patient care. Full article

To address the insufficient frequency-support capability, the difficulty of multi-terminal power coordination, and the constraints on DC-voltage fluctuations in flexible DC transmission systems under weak-grid interconnection, this paper conducts a simulation-based control strategy study. First, based on the coupling relationship between AC frequency and DC voltage, an inertia-enhanced grid-forming/VSG control method is proposed, enabling converter stations to use DC-link capacitor energy to provide transient frequency support during the initial stage of a disturbance. Second, for multi-terminal flexible DC systems, an adaptive U-P-f dual-droop distributed control strategy is designed to coordinate unbalanced power sharing among multiple converter stations and to limit the DC-voltage deviation generated during frequency support. In this paper, a hybrid half-bridge/full-bridge MMC is adopted as a fixed-converter simulation platform, rather than being treated as an object of systematic topology optimization. Finally, a four-terminal MMC-HVDC simulation model is established in MATLAB/Simulink, and the proposed control strategy is evaluated under weak-grid step-load disturbances, different short-circuit-ratio conditions, and continuous pseudo-random load disturbance scenarios. Simulation results show that, under the tested operating conditions, the proposed method can reduce the maximum frequency deviation, suppress DC-voltage fluctuations, and improve the power-sharing process among multi-terminal converter stations compared with conventional VSG control and fixed-droop control. Full article

Leishmaniasis remains a major neglected tropical disease with limited therapeutic options, challenged by drug toxicity and emerging resistance to current treatments like miltefosine. In this study, a virtual library of approximately 150 azole-derived compounds was screened in silico to identify promising thiazole and imidazole scaffolds, leading to the rational design of novel hybrid molecules. Molecular docking against thioredoxin reductase (PDB ID: 4CBQ), a key enzyme in the redox metabolism of Leishmania mexicana, showed improved binding affinity compared to miltefosine, with compound 3f showing the most favourable interaction profile. Among the synthesized series 3a–f, compound 3f (4-NO₂Ph) exhibited the most favourable predicted binding parameters within the series (∆G = −16.08, Ki = 0.0019 nM). Biological evaluation was performed against L. mexicana promastigotes as an early-stage phenotypic screening model to identify active compounds with potential relevance during the initial infective phase, and a markedly improved in vitro inhibitory effect (IC₅₀ = 22.41 µM) compared to miltefosine (IC₅₀ = 132.42 µM), representing a six-fold increase in molar potency. Furthermore, hybrid thiazolyl–imidazole systems (series 3) consistently outperformed single-core analogues, likely due to enhanced molecular planarity and lipophilicity provided by the imine linkage. Cytotoxicity assays in Vero cells revealed a high safety margin for the lead compounds, with compound 3f achieving a Selectivity Index (SI) of around 89, significantly outperforming the reference drug. Acute toxicity studies (LD₅₀) in murine models further confirmed the safety profile, with values exceeding 2000 mg/kg for the most active derivatives. These findings identify thiazolyl–imidazole hybrids as promising early-stage scaffolds for antileishmanial drug discovery, particularly for early infection/prophylactic screening. Full article

Background/Objectives: Sarcopenia—the progressive loss of skeletal muscle mass and function—is a growing public health challenge in ageing populations. Island territories face compounded vulnerabilities due to distinct epidemiological and socio-economic profiles. This study examines sarcopenia risk prevalence among community-dwelling older adults in Tenerife (Canary Islands, Spain) and estimates the economic burden alongside the cost-effectiveness of evidence-based interventions. Methods: A cross-sectional study was conducted among 374 community-dwelling older adults (mean age 80.4 years, SD 4.8; 51.1% female) recruited from primary care health centres across three health zones in Tenerife. Participants were stratified into a control group without established chronic disease-related functional decline (Group 1; n = 274) and a case group with multimorbidity and functional limitations (Group 3; n = 100). Sarcopenia risk was assessed using the SARC-F questionnaire (threshold ≥ 4). A comprehensive geriatric battery—including the Barthel Index, FRAIL scale, MNA-SF, Pfeiffer test, SPPB, handgrip dynamometry, and IPAQ—characterised multidimensional vulnerability. Annual direct and indirect costs were estimated using unit costs from Spanish national health accounts, and intervention cost-effectiveness was modelled using published meta-analytic data. Results: Overall sarcopenia risk prevalence was 36.4% (n = 136; SARC-F ≥ 4), rising to 83.0% in the case group versus 19.3% in controls (OR ≈ 21.5, p < 0.001). Prevalence was 42.1% in males and 30.9% in females. Diabetes was independently associated with elevated risk (44.8% vs. 29.9%; OR 1.90, 95% CI 1.23–2.92; p = 0.003). Health Zone 1 exhibited the highest prevalence (63.0%) versus Zones 2 (23.5%) and 3 (32.8%). Multidimensional vulnerability was pervasive: 28.6% of participants were frail, 75.7% had nutritional compromise, 11.5% showed moderate cognitive impairment, and 89.8% reported low or no physical activity. The estimated annual socio-economic cost of sarcopenia in Tenerife is approximately EUR 88.9 million (Spain nationally: EUR 12.1 billion). Combined exercise–nutrition interventions yield cost-per-QALY ratios of EUR 3800–7000, far below Spain’s EUR 25,000/QALY threshold. Conclusions: Sarcopenia constitutes a major, multidimensionally compounded health burden in Tenerife’s older population, concentrated among frail, diabetic, nutritionally compromised, and physically inactive individuals. The economic case for universal SARC-F screening and multicomponent intervention is compelling, exceeding cost-effectiveness thresholds by a wide margin. Territorial disparities in burden call for equity-oriented, place-based resource allocation within the Canarian health system. Full article