Search Results (248)

Type 1 diabetes (T1D) is an immune-mediated disease characterized by progressive autoimmune destruction of pancreatic β cells. Although traditionally viewed as primarily T-cell-driven, B cells play essential roles in disease pathogenesis. In addition to producing islet autoantibodies, B cells contribute to immune activation through antigen presentation and cytokine secretion, thereby shaping autoreactive T-cell responses. The earliest clinical predictor of T1D is the appearance of islet autoantibodies in the blood, reflecting a breach in B-cell tolerance well before symptomatic disease onset. In individuals at high genetic risk, type I interferon (IFN) signatures are detectable in peripheral blood prior to seroconversion, suggesting that type I IFNs may act as upstream regulators of B-cell tolerance. Peripheral tolerance is enforced through layered checkpoints including transitional selection, maintenance of anergy, germinal center regulation, and regulatory B-cell differentiation. Studies in systemic autoimmunity demonstrate that type I IFN signaling lowers B-cell activation thresholds, enhances BCR and TLR responsiveness, promotes survival of autoreactive transitional clones via BAFF induction, destabilizes anergy, and skews differentiation toward inflammatory phenotypes such as T-bet+ age-associated B cells. Consistent with this model, single-cell transcriptomic and BCR repertoire analyses in T1D reveal clonal expansion and proinflammatory signatures in islet-reactive B cells during the preclinical stage. Together, these findings implicate the IFN–B-cell axis as a potential target for early disease modification. Full article

Network Intrusion Detection Systems (NIDS) face increasing challenges from sophisticated cyber threats, particularly zero-day attacks that evade signature-based methods. While supervised learning is effective for known attack classification, it struggles with novel threats, whereas anomaly-based approaches suffer from high false positive rates and unstable thresholds. To address these limitations, this paper proposes a decision-level adaptive intrusion-detection framework combining hierarchical CNN-based closed-set classification with autoencoder-based zero-day detection in a cascade architecture. The framework enables deployment-time adaptation by dynamically adjusting class-specific confidence thresholds and fusion parameters without model retraining. Experiments on the CSE-CIC-IDS2018 dataset demonstrate strong closed-set performance, achieving 98.98% accuracy and a macro-F1-score of 0.9342, with improved recall for minority attack classes under adaptive thresholding. Under a zero-day evaluation protocol in which Web_Attacks and Infiltration are excluded from training and validation, the proposed approach achieves an F1-score of 0.9319 while maintaining a low false positive rate of 0.0019. The framework is further evaluated on the Simulated University Network Environment (SUNE) dataset representing campus network traffic, achieving 96.18% closed-set accuracy and 97.54% accuracy in the integrated cascade setting. These results demonstrate that the proposed framework effectively balances minority attack detection, zero-day identification, and false-alarm control in dynamic and resource-constrained network environments. Full article

Despite the undisputable ecosystem importance of honeybees, human activities have a substantial impact on their health. Since foraging is directly linked to a wide range of crops and bee-attracting flowers, plant protection products are at the forefront of chemical scrutiny, along with contamination of pollen, nectar, beehive components and water by other xenobiotics. In this study, a non-targeted Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) screening was applied to 25 honeybee samples collected after reported death incidents in Greece. This approach led to the tentative annotation of over 50 compounds across various chemical classes, including pesticides, PFAS candidates not included in the EFSA “PFAS-4”, pharmaceuticals, antibiotics, industrial chemicals, and natural product constituents. In parallel, targeted pesticide residue analysis using liquid and gas chromatography coupled to tandem mass spectrometry (LC-MS/MS and GC-MS/MS) was performed, covering more than 250 active substances and providing direct quantitative results, revealing 11 active substances in concentrations ranging from <limit of quantification (LOQ) to 0.95 mg/kg, overlapping substantially with the HRMS detection. Overall, this study does not allow concrete causal attribution of mortality to specific chemicals; however, it documents complex co-occurrence patterns (pesticides together with other xenobiotics and plant bioactives), not excluding sublethal and mixture-toxicity effects. Quantified pesticide concentrations were below acute LD50-based thresholds, yet selected samples combined neonicotinoid/pyrethroid/fungicide signatures and other contaminants, supporting the need for mixture-toxicity frameworks and effect-based follow-ups. Full article

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

Accurate radiation biodosimetry is urgently needed for medical management after large-scale radiation exposure. Partial-body irradiation with 5% bone marrow sparing (PBI/BM5) provides a realistic radiation model. The current study specifically focused on the high-dose lethality window (12–16 Gy), where survival transitioned from 100% to 0%, representing a clinically distinct and underserved scenario requiring dedicated biodosimetry tools. We defined the survival profile of male C57BL/6 mice after PBI/BM5 and found that doses of 13.5–14.0 Gy were nonlethal within 12 days, whereas 15.0–15.5 Gy caused 100% mortality within 12 days, with a calculated LD_50/12 of 14.68 Gy. A separate cohort of 14.0 Gy showed 100% survival up to 90 days post-radiation. To develop serum cytokine-based biodosimetry in high-dose radiation exposure, mice were exposed to 12.0–16.0 Gy PBI/BM5, and serum was collected on days 1, 3, and 7. A multiplex cytokine assay was used to quantify 70 total cytokines/chemokines. After the exclusion of 4 targets outside detection limits, 66 markers were utilized for downstream analysis. PCA, clustering and heatmaps, and LASSO classification revealed that cytokine signatures can classify radiation groups/status/doses. A 4-cytokine panel (IL-7, GDF-15, IL-16 and FLT3L) could distinguish naïve vs. irradiated mice on all study days. A 24-cytokine signature panel distinguished radiation survivors vs. non-survivors, and another 34-cytokine panel separated radiation doses (12–16 Gy); the prediction was better on day 7 compared to earlier time points. This exploratory study was specifically designed to define the systemic inflammatory response in a high-dose window where survival transitions from 100% to 0% (the ‘lethality threshold’) in a clinically relevant partial-body irradiation model. These findings show that serum cytokines have strong potential for high-dose triage, survival prediction, and dose discrimination within the near-lethal exposure range in a clinically relevant PBI/BM5 model. Extension to lower dose ranges is an important direction for future work. Full article

Niobium is a strategic critical mineral that supports emerging energy and high-end manufacturing. The geophysical parameters of carbonatite-alkaline rock-type niobium deposits constitute essential baseline data for regional geophysical exploration and prospecting target delineation. To clarify the geophysical response characteristics and exploration the significance of the Dagele niobium deposit in the Eastern Kunlun Region (western China). This study focuses on drill hole ZK3202. Samples from ore bodies, mineralized zones, and wall rocks of different lithologies were continuously measured. Combined with 1001.8 m of full-hole core digital logging data, statistical methods, including box plots, histograms, multi-parameter cross-plots, and correlation coefficient analysis, were applied to quantitatively investigate the physical property responses of lithologies such as calcite-biotite rock (ore body), calcite-bearing pyroxenite (mineralized zone) and amphibolite in the vertical profile. Lithological identification thresholds were established to divide the drill-hole into lithological and mineralized ore layers. The results show that the ore-bearing lithofacies exhibit a distinctive geophysical signature characterized by high density, strong magnetism, medium-low resistivity, high polarizability, and slightly elevated natural radioactivity, which clearly distinguishes them from surrounding from wall rocks. Based on five key parameters—density, magnetic susceptibility, resistivity, polarizability, and natural gamma—a lithological identification model for amphibolite and mineralized altered rock assemblages was established. This study also summarizes the multi-parameter coupling mechanism of ore-bearing lithofacies, which can effectively delineate favorable niobium-bearing horizons. This work fills a gap in the geophysical property characterization of carbonatite-alkaline complex-type niobium deposits in the Eastern Kunlun region and provides data support and regional reference for integrated gravity-magnetic-electrical-radioactive geophysical exploration, prospecting target delineation, and the exploration of similar niobium deposits in western China. Full article

This study presents a comprehensive multi-parameter analysis of seismo-ionospheric responses to the Mw 7.7 Myanmar earthquake on 28 March 2025, using GNSS-based Total Electron Content (TEC) data, seismic b-value trends, and acoustic gravity wave (AGW) signatures. A significant negative TEC anomaly (~30 TECU below the statistical threshold) was detected on 25 March, three days before the mainshock under geomagnetically quiet conditions, indicating a lithospheric origin. Concurrent variations in the Ionospheric Disturbance Index (IDI) and Rate of TEC Index (ROTI) indicate pronounced background departures and enhanced short-term variability during the preparation phase. Temporal b-value analysis shows a consistent decline from 1.12 to 0.58 across the 30-year to 6-month windows, with the lowest values clustering near the epicenter, indicating progressive stress accumulation. Spatial b-value mapping further reveals a low b-value zone overlapping the region of TEC depletion, while the Relative Seismic Hazard Index (RSHI) highlights high-hazard zones aligned with the epicentral area. Kernel density estimation (KDE) supports this coupling by showing a dominant low-b, low-vTEC cluster, consistent with linked lithospheric stress and ionospheric depletion. Overall, the integrated GNSS and seismic analyses demonstrate the value of multi-domain observations for characterizing earthquake preparation processes, highlighting a coherent physical linkage between crustal stress accumulation and ionospheric depletion that can enhance short-term seismic hazard assessment. Full article

This study examines the hydro-sedimentological–radioecological controls governing the distribution of natural (K-40, Ra-226, Th-232) and anthropogenic (Cs-137) radionuclides in surface sediments of the western Black Sea shelf. Activity concentrations were determined by high-resolution gamma spectrometry, and radiological indices—including radium equivalent activity (Ra_eq), external hazard index (H_ex), and annual effective dose (AED)—were calculated to evaluate environmental safety. All indices remained well below internationally accepted thresholds, confirming the absence of radiological hazard in both coastal and offshore settings. Strong correlations between Ra-226 and Th-232 indicate dominant lithogenic control of natural radionuclides, whereas Cs-137 exhibits geochemical decoupling consistent with its behavior. A significant relationship between the fine-grained sediment fraction (<63 µm) and Cs-137 activity highlights the grain size effect, with offshore depositional zones acting as sediment-focusing areas where Cs-137 and excess Pb-210 co-accumulate under low-energy hydrodynamic conditions. Despite localized offshore enrichment, dose contribution analysis shows that natural radionuclides dominate the absorbed-dose budget, while Cs-137 contributes only marginally. Spatial predictive modeling using Artificial Neural Networks, validated under a Spatial Leave-One-Group-Out framework, yielded moderate generalization capacity (R² = 0.61 for Ra-226; R² = 0.41 for Cs-137), reflecting smoother spatial gradients of lithogenic radionuclides than heterogeneous radiocesium deposition. Furthermore, Machine Learning algorithms provided significant analytical value: a Random Forest (RF) model successfully classified environments (nearshore/shelf/depositional basin) based on distinct radionuclide signatures. At the same time, an optimized Artificial Neural Network (ANN-GA) enabled the nonlinear reconstruction of radiometric–granulometric patterns to identify local anomalies. The results show that radionuclide distributions are primarily structured by sediment provenance, grain size sorting, and hydrodynamic energy gradients rather than ongoing anthropogenic inputs. Full article

Systematically characterizing the time series deformation evolution of open-pit mine slopes is key to revealing their potential instability development and supporting subsequent deformation-level classification. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale approximately every ten days, may hold the key to those interactions. However, atmospheric propagation delays still have a significant impact on deformation calculations, and open-pit mine slopes monitored by InSAR often suffer from low coherence. This noise can obscure nonlinear and transient precursory signatures in deformation time series, reducing the identifiability of key temporal patterns required for automated interpretation. Here, we present a Coherence-conditioned Encoder–Decoder Long Short-Term Memory (CED-LSTM) denoising network for deformation time series. We generate a physics-aware synthetic dataset by modeling coherence-dependent measurement noise and temporally correlated atmospheric delays. The network jointly models deformation time series and coherence, using residual learning and adaptive gated composite loss to preserve deformation trends. It is designed to autonomously extract ground deformation signals from noise in InSAR time series without prior knowledge of where deformation occurs or how it evolves. On the synthetic validation set, the network achieved a root mean square error (RMSE) of 2.2 mm across the validation sequences. Applied to three InSAR datasets over an open-pit mine from March 2019 to March 2022, denoising suppresses noise and stabilizes deformation boundaries, enabling extraction of trend and transient indicators and a data-driven deformation-level score. Using quantile-based thresholds, these scores are then used to produce multi-year deformation-level classification maps. Full article

Background/Objectives: Lifestyle behaviors such as smoking, vaping, and physical activity can induce epigenetic modifications that influence health trajectories and may provide forensic value. DNA methylation signatures linked to these behaviors offer potential for behavioral inference, personalized health assessment, and improved investigative practices. This study aimed to characterize methylation patterns associated with nicotine exposure and exercise using buccal cell DNA profiling, and to evaluate the extent to which these patterns differentiate harmful and protective lifestyle habits. Methods: Buccal epithelial DNA was analyzed using the Illumina Infinium MethylationEPIC v2 BeadChip to assess genome-wide methylation. Participants were categorized by smoking status, vaping behavior, and exercise activity. Differentially methylated regions (DMRs) and CpG sites were identified through pairwise comparisons among smokers, vapers, non-smokers/non-vapers, athletes, and sedentary individuals. A threshold of p < 1 × 10⁻⁴ was applied for significant differentially methylated CpG sites. Results: Distinct epigenetic profiles were associated with smoking/vaping and physical activity. Five DMRs differentiated smokers from non-smokers/non vapers, while 11 DMRs distinguished vapers from the same reference group. Twenty-eight DMRs displayed divergent methylation patterns between smokers and vapers. Exercise also showed measurable epigenetic influence: control athletes exhibited 26 significantly differentially methylated CpG sites relative to non-athletes, and smoker athletes demonstrated 126 suggestive differential sites compared to sedentary smokers. Additionally, 63 sites differentiated smoker athletes from non-smoker/non-vaper non-athletes, indicating interactions between risk-associated and health-promoting behaviors. Conclusions: Buccal cell DNA methylation profiling effectively captured signatures associated with smoking, vaping, and physical activity. These findings underscore the potential of epigenetic markers for lifestyle assessment in both personalized medicine and forensic investigations. Full article

Background and Objectives: Serum biomarker discovery in resectable pancreatic ductal adenocarcinoma (PDAC) remains a critical unmet need, as over 80% of patients present with unresectable disease. Serum proteomics offers a promising approach for identifying circulating biomarkers associated with early-stage disease; however, clinical translation has been limited by inconsistent validation and the absence of clinically relevant comparator populations. Materials and Methods: We performed a discovery-phase study using data-independent acquisition mass spectrometry-based serum proteomics in 35 patients with resectable, non-metastatic PDAC and 34 non-cancer controls without hepato-biliary-pancreatic disease. Following quality filtering (≥80% detection threshold), 407 proteins were retained for analysis. Differential abundance was assessed using Welch’s t-test with Benjamini–Hochberg correction (FDR < 0.01, |FC| ≥ 1.5). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis and logistic regression with repeated stratified 5-fold cross-validation (100 repetitions) and bootstrap resampling (1000 iterations). Functional enrichment analysis was performed using g:Profiler. Results: Ninety proteins were significantly altered in PDAC (50 increased, 40 decreased). Inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) demonstrated the highest individual diagnostic performance (AUC = 0.90), followed by coagulation factor XIII A chain (F13A1; AUC = 0.89) and ferritin light chain (FTL; AUC = 0.86). Functional enrichment revealed significant overrepresentation of secretory granule lumen components (adjusted p = 0.001) and complement/coagulation pathways (adjusted p < 0.001). An enrichment-guided three-protein panel (ITIH3, F13A1, and FTL) achieved an AUC of 0.98 (95% CI: 0.95–1.00), with a cross-validated mean AUC of 0.96, sensitivity of 83% (95% CI: 66.4–93.4%), and specificity of 100% (95% CI: 89.7–100%) within the discovery cohort. Conclusions: This discovery-phase study identifies a biologically coherent serum protein signature enriched for secretory granule lumen components in resectable PDAC. The three-protein panel demonstrates strong internal validation performance; however, these estimates may be optimistic due to feature selection performed prior to cross-validation. External validation in independent cohorts—including chronic pancreatitis controls and parallel CA19-9 assessment—will be essential to determine clinical applicability. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

The decentralisation of autonomous Unmanned Aircraft Systems (UASs) introduces significant challenges in terms of establishing secure communication and consensus in contested, resource-constrained environments. This research addresses these challenges by conducting a comprehensive performance evaluation of two cryptographic technologies: Messaging Layer Security (MLS) for group key exchange, and threshold signatures (FROST and BLS) for decentralised consensus. Seven leading open-source libraries were methodically assessed through a series of static, network-simulated, and novel bulk-signing benchmarks to measure their computational efficiency and practical resilience. This paper confirms that MLS is a viable solution, capable of supporting the group sizes and throughput requirements of a UAS swarm. It corroborates prior work by identifying the Cisco MLSpp library as unsuitable for dynamic environments due to poorly scaling group management functions, while demonstrating that OpenMLS is a highly performant and scalable alternative. Furthermore, the findings show that operating MLS in a ‘key management’ mode offers a dramatic increase in performance and resilience, a critical trade-off for UAS operations. For consensus, the benchmarks reveal a range of compromises for developers to consider, while identifying the Zcash FROST implementation as the most effective all-around performer for sustained, high-volume use cases due to its balance of security features and efficient verification. Full article

Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article