Search Results (265)

Industrial Internet of Things (IIoT) and SCADA-connected networks are increasingly vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt time-sensitive industrial processes and compromise operational continuity. Effective mitigation requires accurate and low-latency attack detection at the network edge, where industrial gateways operate under strict constraints in computation, memory, and energy. This study investigates Deep Reinforcement Learning (DRL) for real-time binary DDoS detection and proposes a detector based on Proximal Policy Optimisation (PPO) for deployment in resource-constrained IIoT environments. Four DRL agents, namely Deep Q-Network (DQN), Double DQN, Dueling DQN, and PPO, are trained and evaluated within a unified experimental pipeline incorporating automatic label mapping, numerical feature selection, robust scaling, and class balancing. Experiments are conducted on three representative benchmark datasets: CIC-DDoS2019, Edge-IIoTset, and CICIoT23. Performance is assessed using accuracy, precision, recall, F1-score, false positive rate, false negative rate, and CPU inference latency. The reward function is asymmetric: +1 for correct classification, −1 for false positive, and −2 for false negative, penalising missed attacks more heavily for IIoT safety. The results show that PPO provides a competitive accuracy–latency tradeoff across all three datasets, achieving the highest mean accuracy of 97.65% and ranking first on CIC-DDoS2019 with a score of 95.92%, while remaining competitive on Edge-IIoTset (99.11%) and CICIoT23 (97.92%). PPO also converges faster than the value-based baselines. Inference latency is below 0.8 ms per sample on a standard CPU (Intel i7-11800H), confirming real-time feasibility. To support practical deployment, the trained PPO policies are exported to ONNX format (≈9 KB per model), enabling lightweight and PyTorch-independent inference on industrial edge gateways. Full article

RNA–protein interactions (RPIs), mediated primarily by RNA-binding proteins (RBPs), are central to post-transcriptional gene regulation and govern RNA splicing, transport, localization, translation, and decay. Dysregulation of RBPs and their associated RNA networks contributes to diverse pathologies, including cancer, neurodegenerative disorders, and viral infections. However, profiling RPIs remains a challenge due to their inherent transience, low binding affinity, and shifting spatial dynamics. This review provides a comprehensive and systematic overview of current methodologies for investigating RPIs. We discuss RNA-centric and protein-centric strategies. In addition, imaging-based approaches are evaluated for their capacity to resolve spatial and temporal dynamics of RBP–RNA interactions in situ. We compare these methodologies in terms of resolution, sensitivity, specificity, and biological applicability, emphasizing the importance of integrative strategies for constructing high-resolution, context-dependent RPI maps in physiological and disease settings. Full article

Infrared small-target detection (IRSTD) has benefited from deep learning, yet most existing methods still rely on dense pixel-level annotations, which are costly and often unreliable for tiny and weak targets. Point supervision offers a more practical alternative, but current point-supervised methods usually construct pseudo-labels based on the distance between pixels and annotated points or cluster centers, which introduces spatial bias and may miss genuine target pixels away from these reference points. To address this issue, we propose GMVG-DIR, a point-supervised IRSTD framework that combines Gradient-Guided Minimum Variance Growth (GMVG) with Deep Iterative Refinement (DIR). GMVG first estimates target likelihood from gradient-guided aggregation of contour closure and edge responses and then converts it into structurally coherent pseudo-labels via the Minimum Variance Growth filter, without relying on distance cues. DIR further improves the pseudo-labels by incorporating reliable semantic guidance into an iterative refinement process, thereby reducing error propagation. By emphasizing structural consistency rather than spatial proximity, the proposed framework better preserves irregular target shapes and remains robust to point-label deviation. Extensive experiments on NUDT-SIRST, IRSTD-1k, and NUAA-SIRST show that GMVG-DIR improves pseudo-label fidelity and achieves competitive point-supervised performance across multiple dataset-backbone settings, especially in IoU and $P_d$. Full article

Early detection of dental caries in children is critical to prevent irreversible tooth damage and guarantee optimal oral health outcomes. However, interpreting pediatric panoramic radiographs throughout the mixed dentition stage remains a very challenging task due to overlap in anatomical structures and developmental variability. This complexity underscores the need for well curated, representative datasets that enable the development of reliable computer-aided diagnostic models. Herein, this study introduces the Mixed Dentition Orthopantomogram Dataset, a newly developed, publicly available dataset of children that was carefully labeled by dental specialists to identify proximal and occlusal caries regions in the range of 3–12 years. To evaluate the dataset’s applicability for artificial intelligence research, we benchmarked it using both classification and segmentation models. A patch-based classifier achieved an average AUC of 0.89 and Recall 0.85 in distinguishing healthy and carious regions. For segmentation, we evaluated U-Net and Attention U-Net with multiple loss functions, and the Attention U-Net trained with Focal loss achieved the best Dice score of 0.94. Collectively, these findings support the dataset’s utility for pediatric caries analysis and demonstrate the viability of deep learning approaches for mixed dentition panoramic imaging. Full article

Background: Fascicular turnover flap (FTF) is a donor-sparing option for segmental facial nerve repair. This study compared autologous nerve grafting with proximally based and distally based FTF in a rat facial nerve model. Methods: Adult male Wistar rats were randomized to autograft, proximal FTF, or distal FTF (n = 8 per group). A single additional animal with an untreated defect served as a qualitative histological reference. The prespecified primary endpoint was whisker motion amplitude at week 8; the secondary endpoints were central section histomorphometry (nerve tissue area, µm²) and variability metrics (IQR, SD, and coefficient of variation) as measures of reproducibility. Non-parametric tests (Kruskal–Wallis; Mann–Whitney U) were used; pairwise functional comparisons were Holm-corrected; and effect sizes were expressed as Cliff’s δ. Results: At week 8, the overall functional comparison was significant (Kruskal–Wallis p = 0.047), but no pairwise contrast remained significant after Holm correction. Functional recovery was highest in the autograft group, followed by proximal FTF and distal FTF. Both FTF groups showed lower inter-animal variability than autograft for the week-8 functional endpoint, with the distal FTF showing the lowest dispersion. Central section nerve area comparisons did not reach global significance; effect sizes and descriptive statistics favored autograft, and a single unadjusted pairwise contrast (autograft > proximal FTF) should be interpreted cautiously. Conclusions: Both FTF configurations achieved measurable functional and structural regeneration while avoiding an additional free donor nerve graft. Within an 8-week window, autograft remained the benchmark. Between FTF variants, distal FTF produced more stable functional outcomes, but this did not translate into superior functional recovery. Confirmation in larger, balanced cohorts with longer follow-up and vascular/neural labeling is warranted. Full article

Physical integration between endosymbiotic algae and host mitochondria is a recurring feature across photosynthetic symbioses, yet the structural nature of this association has remained unresolved. In the ciliate Paramecium bursaria, each endosymbiotic Chlorella cell is enclosed by a perialgal vacuole (PV) membrane consistently surrounded by host mitochondria, suggesting a conserved architecture for metabolic interaction. Although transmission electron microscopy has shown close membrane apposition, it has remained unclear whether this reflects incidental proximity or a reinforced adhesion. Here, we provide direct evidence that the PV membrane and host mitochondrial membrane form a stable physical association. Using discontinuous Percoll centrifugation, we isolated intact units in which Chlorella and mitochondria co-sedimented, indicating that their association withstands mechanical disruption. By fluorescently labeling the PV and mitochondrial membranes with BODIPY FL C₅-ceramide (BC₅C), together with a mitochondria-specific monoclonal antibody and DAPI, we visualized the PV membrane under light microscopy and demonstrated that the mitochondrial–PV membrane complex persists after homogenization and centrifugation. As expected from the membrane-insertion behavior of BC₅C, this fluorescent labeling revealed that the PV–mitochondrial membrane association is structurally reinforced rather than incidental, providing a mechanistic framework for understanding how Chlorella cells are stably positioned beneath the host cortex. Full article

NANOG overexpression has been reported to reverse aging-associated decline in mesenchymal stem/stromal cell (MSC) function, but the molecular machinery engaged by NANOG in MSCs remains incompletely defined. Here, we applied APEX proximity labeling coupled with quantitative mass spectrometry to define the NANOG proximity interactome (proxeome) in human MSCs. Of 1040 quantified proteins, 828 were significantly enriched in the APEX-NANOG (H₂O₂ labeling) samples, consistent with a broad NANOG-centered neighborhood rather than a single stoichiometric complex. Enriched proteins encompass RNA-processing pathways (including splicing/RNP factors and selected m6A-related proteins), transcriptional coactivation and elongation control (Mediator and 7SK/P-TEFb regulators), chromatin repression/poising modules (Polycomb and HDAC/NuRD/CoREST/SIN3), ATP-dependent chromatin remodeling (BAF/SWI-SNF), three-dimensional genome organization and replication-coupled chromatin maintenance (CTCF/cohesin, CHAF1A, RIF1, UHRF1), and regulators of MSC identity and signal integration (Hippo/mechanotransduction and TGFβ-linked transcriptional circuits). Together, these data provide a spatial proteomic map of NANOG-associated nuclear neighborhoods in MSCs and a foundation for mechanistic hypotheses for how NANOG may stabilize stem-like programs. Full article

Classic machine learning and regime identification methods applied to financial time series lack theoretical guarantees and exhibit systematic failure modes: heavy-tails invalidate moment-based geometry, rendering distances and centroids dominated by extremes or unstable; jumps violate smoothness, destabilizing local regressions, kernel methods, and gradient-based learning; and non-stationarity disrupts neighborhood relations, so distances in classical feature spaces no longer reflect meaningful proximity. To address these challenges, we propose a topology-based machine-learning framework grounded on probabilistic reconstruction of state-space geometry, which replaces moment- and smoothness-dependent representations with deformation-stable summaries of state-space geometry, preserving neighborhoods, adjacency, and topology. The finite-sample validity of homeomorphic state-space reconstruction, required for topology-based machine learning, is assessed through numerical studies on synthetic data with heavy tails, jumps, and known ground-truth regimes. Further diagnostics of local invertibility and bounded geometric distortion quantify when embedding windows are consistent with local diffeomorphic behavior, enabling metric-sensitive, geometry-aware learning. Clustering of Hilbert-space summaries accurately recovers underlying market tail-risk regimes with robust results across selected filtrations. Temporal, feature-space, and cluster-label null tests confirm that topology-based clustering captures genuine topological structure rather than noise or artifacts, and encodes temporal dependencies at local, mesoscopic, and network levels associated with market regimes. Full article

This paper presents the synthesis, physicochemical characterisation, and analytical applications of chemiluminescent (CL) labels based on acridinium salts (ALs) for biomedical diagnostics. These compounds emit light as a result of oxidative reactions and represent an established class of reagents widely employed in chemiluminescence immunochemical assays (CLIAs) today. A series of structurally differentiated acridinium labels (AL1–AL5) was synthesised applying mostly original synthetic routes and purified to chromatographic purity (>90%, RP-HPLC). The compounds, including a commercial product treated as a reference, were successfully conjugated to anti-human IgG, yielding stable immunochemical reagents suitable for immunoassays with CL detection. The chemiluminescence properties of the obtained labels and their protein conjugates were investigated in aqueous buffers and in the presence of surfactants. The emission profiles exhibited characteristic flash-type kinetics with emission maxima occurring within 0.15–0.25 s after reaction initiation. The presence of surfactants more or less significantly enhanced the emission intensity, with signal increases of up to approx. 2-fold compared to surfactant-free systems. Analytical calibration demonstrated a linear response of signal derived from native labels over at least one order of magnitude of concentration, with detection limits falling in the range of 10⁻⁹–10⁻¹⁰ M, confirming the high sensitivity of the developed compounds. The experimental results were supported by theoretical studies using density functional theory (DFT), which confirmed the energetic feasibility of the CL reaction pathway and identified structural factors influencing activation barriers. Additional semiempirical calculations (PM7) indicated that the dielectric environment and proximity of ionic species can influence the reaction energetics, providing mechanistic support for the experimentally observed effects of surfactants. The results demonstrate that both molecular structure and microenvironment influence CL efficiency and kinetics of the investigated systems. The developed acridinium labels exhibit analytical performance better or comparable to commercial reagents and are fully compatible with standard immunodiagnostic conjugation protocols, confirming their suitability for use in modern chemiluminescent immunoassays. Full article

Ochratoxin A (OTA) is a highly toxic mycotoxin commonly detected in food and agricultural products, requiring sensitive analytical methods for reliable monitoring. Herein, we report an ultrasensitive turn-on electrochemical aptasensor for OTA detection based on a target-induced displacement of an aptamer–complementary DNA (cDNA) duplex assembled on an electrochemically reduced graphene oxide (ERGO)-modified glassy carbon electrode (GCE). In the absence of OTA, a methylene blue (MB)-labeled aptamer hybridized with cDNA is immobilized on the ERGO surface via π–π stacking interactions, forming a rigid duplex that suppresses electron transfer and yields a low electrochemical signal. Upon OTA binding, the aptamer undergoes a conformational transition into a G-quadruplex structure, leading to dissociation of the cDNA strand. This target-induced folding brings the MB redox tag into close proximity to the ERGO surface, markedly accelerating electron transfer and enhancing the cathodic reduction current of MB, thereby producing a pronounced signal-on response in square-wave voltammetry (SWV). The ERGO-modified electrode provides a conductive and stable interface without chemical linkers. Under optimized conditions, the aptasensor shows a linear response to OTA from 10 fM to 100 pM with an ultralow LOD of 0.67 fM, together with high selectivity, good reproducibility, and satisfactory stability. This work demonstrates a simple and effective turn-on aptasensing strategy for sensitive electrochemical detection of OTA. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

Mangrove extent has increased in many regions under strengthened conservation policies and large-scale restoration programs. Nevertheless, mangrove ecosystems continue to face multiple pressures, including limited total area, habitat degradation, biodiversity decline, and biological invasion, and localized deterioration in ecosystem structure and function has been increasingly reported. Despite extensive mapping efforts, the spatiotemporal dynamics of mangrove degradation—particularly in tidally influenced environments—remain insufficiently understood. Focusing on the Nanliu River Delta, the largest deltaic mangrove system in the Northern Beibu Gulf of China, this study integrates long-term Landsat time-series imagery (1990–2025) with deep learning to quantify both mangrove extent change and canopy degradation. To mitigate tidal inundation effects, a NDVI Pseudo-P75 compositing strategy was applied using Google Earth Engine (GEE), enabling consistent observation of mangrove canopies across tidal stages. Global Mangrove Watch v4 (GMW-V4) and HGMF2020 mangrove dataset for China were used as reference labels to train a ResNet34–UNet segmentation framework incorporating Digital Elevation Model (DEM) constraints. The model achieved high classification performance, with an IoU of 0.822 for mangroves and 0.981 for background, yielding a mean IoU of 0.902. The resulting maps, following manual verification, provided a robust basis for spatiotemporal and degradation analyses. Canopy condition was further assessed using the Enhanced Vegetation Index (EVI), which is less prone to saturation in high-biomass mangrove stands. Results show that mangrove area in the Nanliu River Delta expanded from 266 ha in 1990 to 1414 ha in 2025, with the annual expansion rate after 2005 being nearly seven times higher than that before 2005. Despite this net gain, a cumulative loss of 347.45 ha was recorded, primarily during 1990–2000, with approximately 70% converted to aquaculture and coastal engineering. Spatial analysis revealed that mangrove expansion occurred predominantly seaward, whereas both mangrove loss and canopy degradation exhibited an inverse J-shaped relationship with seawall proximity. More than 80% of mangrove loss occurred within 200 m of seawalls, indicating concentrated anthropogenic encroachment, while 75.6% of canopy degradation was observed within 350 m, potentially reflecting landward forest senescence. These results indicate a transition in dominant threats from permanent land conversion in the late 20th century to more subtle, internal functional degradation in recent decades, underscoring the need to complement extent-based assessments with canopy condition monitoring in mangrove conservation and management. Full article

Background: Cerliponase alfa is currently the only approved disease-modifying therapy for neuronal ceroid lipofuscinosis type 2 (CLN2) disease and requires lifelong intracerebroventricular (ICV) infusion, traditionally via a scalp-sited ventricular access device (VAD). Chest-sited port (chest port) for intracerebroventricular access using a tunneled central venous access device is described as an alternative, though data remain limited. Methods: We present an anonymized caregiver narrative perspective describing two pediatric patients with CLN2 disease receiving cerliponase alfa infusions via different ICV access strategies: one patient who transitioned from a scalp-based ventricular access device to a chest port and one patient who initiated therapy with a chest port. A semi-structured caregiver interview was used to capture experiential insights related to decision-making, procedural burden, safety considerations, and psychosocial adaptation. Results: The caregiver identified key advantages of chest ports for ICV infusion, including durability of the device, enhanced securement, and smooth long-term routine integration. The transition from a scalp VAD to a chest port was described as proactive, well-coordinated, and associated with high caregiver satisfaction. Noted considerations included increased visibility of the access needle to the child, proximity to oral secretions, and potential misidentification of the port by emergency medical services. Families implemented mitigation strategies through labeling, education, and coordination with the care team. Conclusions: This caregiver-centered case report highlights how access device choice meaningfully shapes treatment burden, safety planning, and daily life for families managing CLN2 disease. As chest-port methodologies become adopted, incorporating caregiver and patient perspectives is essential to developing patient-centered treatment options for long-term intracerebroventricular therapy. Full article

Scientific collaboration is increasingly needed to address complex research challenges, yet identifying promising partners in the absence of prior co-authorship remains difficult. We present a decision-support pipeline for discovering researchers who have not previously worked together and whose collaboration is unlikely to emerge without deliberate intervention or institutional incentives. The approach leverages document-level semantic representations to estimate proximity between publications, aggregates these similarities at the author level, and surfaces collaboration opportunities that are not evident from the co-authorship graph. To support interpretation by decision makers, a separate LLM module proposes potential joint research directions, which are subsequently annotated with multi-label fields of study. We evaluate the pipeline through an institutional case study, analyzing 7531 publications from 2009 to 2024 using retrospective, temporally shifted windows. While only a small fraction of suggested pairs materialized spontaneously in subsequent periods, the collaborations that do emerge exhibit strong semantic alignment with the computed recommendations (high cosine similarity) and substantial thematic overlap. These results indicate that semantic proximity can act as an early indicator of latent complementarity between researchers without prior ties, supporting intentional institutional mediation and complementing topology-driven approaches that predict links under passive evolution. Full article

Ground incidents on airport aprons can cause substantial operational disruption and economic loss, while conventional surveillance (e.g., Surface Movement Radar (SMR), Closed-Circuit Television (CCTV)) often lacks the resolution and proactive decision support required for close-proximity operations. This study proposes a UAV-deployable, camera-agnostic Computer Vision (CV) framework for collision-risk warning from elevated viewpoints. An optimised YOLOv8-Seg backbone performs multi-class aircraft segmentation (airplane, wing, nose, tail, and fuselage) and is integrated with four MOT algorithms under identical evaluation settings. For quantitative tracker benchmarking, DeepSORT provides the strongest overall performance on the airplane-only MOTChallenge-format ground truth (MOTA 92.77%, recall 93.27%). To mitigate the scarcity of annotated apron-incident data, a labelled 997-frame MOT dataset is created via an MSFS simulation-based reenactment inspired by the 2018 Asiana–Turkish Airlines wing-to-tail event at Istanbul Ataturk Airport. The framework further introduces a dual-module warning mechanism that can operate independently: (i) a reactive module using image-plane proximity derived from segmentation masks, and (ii) a proactive module that predicts short-horizon conflicts via trajectory extrapolation and IoU-based future overlap analysis. The approach is evaluated on multiple simulated incident scenarios and assessed on a real apron video from Hong Kong International Airport; additionally, laboratory-scale UAV experiments using diecast aircraft models provide end-to-end feasibility evidence on unmanned-platform imagery. Overall, the results indicate timely warnings and practical feasibility for low-overhead UAV-enabled apron monitoring. Full article