Search Results (38,062)

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detec-tion of GM-derived ingredients to support traceability, regulatory compliance, and accu-rate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article

Diagnosis of primary hyperparathyroidism (PHPT) relies on the detection of hypercalcemia and increased circulating parathormone (PTH) levels. However, the disease induces a deep deregulation of phosphate metabolism. A total of 960 PHPT patients (848 females, 112 males) were retrospectively enrolled; biochemical and clinical data were collected at PHPT diagnosis. At variance with previous studies, hypophosphatemia was diagnosed using sex- and age-specific serum phosphate reference range. Reduced serum phosphate levels were detectable in 49% of PHPT males and 55% of PHPT females. Moderate hypophosphatemia (≤2.0 mg/dL) was more frequent in men than in women, and serum phosphate levels were lower in postmenopausal than premenopausal PHPT women. Vitamin D status did not alter the prevalence of hypophosphatemia. Serum phosphate levels negatively correlated with ionized calcium and PTH levels across PHPT premenopausal women, postmenopausal women, and men. Cluster analysis integrating the three interrelated parameters identified three distinct PHPT phenotypes: bone and kidney complications were more frequent in patients with more severe hypercalcemia and hypophosphatemia, though fractures were more abundant in the less severe phenotypes. Finally, considering the whole cohort, ionized calcium and PTH levels displayed a negative non-linear correlation with phosphate levels. In conclusion, hypophosphatemia in PHPT patients is common, and moderate hypophosphatemia is more frequent in males compared to females. Menopausal status is associated with less severe hypophosphatemia and PHPT disease. Hypophosphatemia is mainly determined by parathyroid tumor cells’ dysfunction. The non-linear negative relationships between phosphate, PTH and ionized calcium may suggest heterogeneous insensitivity of tumor parathyroid cells to extracellular phosphate. Full article

The Internet of Things (IoT) is increasingly embedded in critical infrastructures across healthcare, energy, transportation, and industrial automation, yet its pervasiveness introduces substantial security and resilience challenges. This paper presents a comprehensive review of recent advances in IoT resilience, focusing on developments reported between 2022 and 2025. A layered taxonomy is proposed to organize resilience strategies across hardware, network, learning, application, and governance layers, addressing adversarial, environmental, and hybrid stressors. The survey systematically classifies and compares more than forty representative studies encompassing deep learning under adversarial attack, generative and ensemble intrusion detection, hardware and protocol-level defenses, federated and distributed learning, and trust and governance-based approaches. A comparative analysis shows that while adversarial training, GAN-based augmentation, and decentralized learning improve robustness, their evidence is often confined to specific datasets or attack scenarios, with limited validation in large-scale deployments. The study highlights challenges in benchmarking adaptivity, cross-layer integration, and explainable resilience, concluding with future directions for creating antifragile IoT systems that can self-heal and adapt to evolving cyber–physical threats. Full article

The detection of deepfake speech has become a significant research area due to rapid advancements in generative AI for speech synthesis. These technologies pose significant security risks in applications such as biometric authentication, voice-controlled systems, and automatic speaker verification (ASV) systems. Therefore, enhancing the detection capabilities of such applications is essential to mitigate potential threats. This study investigates perceptual speech-pathological features, which are commonly used to evaluate the unnaturalness of voice disorders in clinical settings, as potential indicators for detecting deepfake speech. Specifically, the timbral attributes of hardness, depth, brightness, roughness, sharpness, warmth, boominess, and reverberation are examined. The analysis reveals that these attributes provide meaningful distinctions between genuine and synthetic speech. Furthermore, the detection performance is enhanced by extending the dimensional representation of timbral attributes, enabling a more comprehensive characterization of the speech signal. This paper proposes a method that combines two models: one utilizing the different dimensions of speech-pathological features with a deep neural network (DNN), and another employing a gammatone filterbank model that simulates the auditory processing mechanism of the human cochlea with ResNet-18 architecture, improving deepfake speech detection. The proposed method is evaluated on the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof) 2019 dataset. Experimental results demonstrate that the proposed approach outperforms baseline models in terms of Equal Error Rate (EER), achieving an EER of $5.93$%. Full article

Melanoma is the most lethal form of skin cancer, and early detection is critical for improving patient outcomes. Although dermoscopy combined with deep learning has advanced automated skin-lesion analysis, progress is hindered by limited access to large, well-annotated datasets and by severe class imbalance, where melanoma images are substantially underrepresented. To address these challenges, we present the first systematic benchmarking study comparing four GAN architectures—DCGAN, StyleGAN2, and two StyleGAN3 variants (T and R)—for high-resolution ($512\times 512$) melanoma-specific synthesis. We train and optimize all models on two expert-annotated benchmarks (ISIC 2018 and ISIC 2020) under unified preprocessing and hyperparameter exploration, with particular attention to R1 regularization tuning. Image quality is assessed through a multi-faceted protocol combining distribution-level metrics (FID), sample-level representativeness (FMD), qualitative dermoscopic inspection, downstream classification with a frozen EfficientNet-based melanoma detector, and independent evaluation by two board-certified dermatologists. StyleGAN2 achieves the best balance of quantitative performance and perceptual quality, attaining FID scores of 24.8 (ISIC 2018) and 7.96 (ISIC 2020) at $\gamma =0.8$. The frozen classifier recognizes 83% of StyleGAN2-generated images as melanoma, while dermatologists distinguish synthetic from real images at only 66.5% accuracy (chance = 50%), with low inter-rater agreement ($\kappa =0.17$). In a controlled augmentation experiment, adding synthetic melanoma images to address class imbalance improved melanoma detection AUC from 0.925 to 0.945 on a held-out real-image test set. These findings demonstrate that StyleGAN2-generated melanoma images preserve diagnostically relevant features and can provide a measurable benefit for mitigating class imbalance in melanoma-focused machine learning pipelines. Full article

This study investigates the mitochondrial transcriptomic responses of Ramulus phyllodeus (Chen & He, 2008); Phasmatodea: Phasmatidae) to acute exposure to four widely used neurotoxic insecticides: chlorpyrifos, cyfluthrin, emamectin benzoate, and acetamiprid. Using quantitative real-time PCR (qRT-PCR), we quantified transcriptional changes in 10 mitochondrial protein-coding genes, which showed significant transcriptional changes (p < 0.05) when the insect was exposed to four commonly used pesticides (each at a concentration of 5 μg/L) for 24 h. Exposure to chlorpyrifos induced significant upregulation of ND2 (2.08 ± 0.048) and ND5 (1.38 ± 0.15). Cyfluthrin triggered coordinated upregulation across seven genes: ND1 (1.71 ± 0.07), ND2 (2.33 ± 0.38), ND3 (1.74 ± 0.25), ND5 (1.65 ± 0.38), COX1 (2.91 ± 0.40), COX3 (1.69 ± 0.18), and Cytb (2.81 ± 0.53). Emamectin benzoate induced the upregulation of ND1 (1.98 ± 0.21), ND2 (3.04 ± 0.41), ND3 (1.82 ± 0.26), ND4 (2.79 ± 0.64), COX1 (2.36 ± 0.34), ATP6 (3.26 ± 0.61), and Cytb (2.39 ± 0.81). Acetamiprid induced more selective upregulation, affecting only ND1 (1.67 ± 0.18), ND4 (1.43 ± 0.16), and ND5 (1.66 ± 0.10). Critically, each insecticide elicited a distinct, non-overlapping transcriptional signature, defined by both the identity and magnitude of responsive genes, indicating compound-specific modulation of mitochondrial gene expression. Notably, no gene exhibited significant downregulation under any single-compound treatment, and all differentially expressed genes were upregulated exclusively in response to individual pesticides. This absence of transcriptional suppression suggests that these neurotoxicants converge on shared upstream stress-response pathways that preferentially activate mitochondrial biogenesis or compensatory transcription, rather than inducing global transcriptional repression. Collectively, these findings establish mitochondrial protein-coding genes in R. phyllodeus as sensitive, mechanistically grounded molecular sentinels for neurotoxic pesticide exposure. The compound-specific transcriptional profiles further suggest potential utility in multiplex detection strategies for environmental monitoring, enabling discrimination among individual residues. Full article

Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. We evaluate four augmentation approaches—Synthetic Minority Over-sampling Technique (SMOTE), Gaussian Copula, Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN)—using the University of California Irvine (UCI) Bank Marketing dataset, which exhibits a class imbalance ratio of approximately 7.88:1. Our experimental framework assesses each method across three dimensions: statistical fidelity to the original data distribution evaluated through four complementary metrics (marginal numerical similarity, categorical distribution similarity, correlation structure preservation, and Kolmogorov–Smirnov test), machine learning utility measured through classification performance, and minority class detection capability. Results indicate that all augmentation methods achieved statistically significant improvements over the baseline ($p<0.05$). SMOTE achieved the highest recall (54.2%, a 117.6% relative improvement over the baseline) and F1-Score (0.437, +22.4% over the baseline) for minority class detection, while Gaussian Copula provided the highest composite fidelity score (0.930) with competitive predictive performance. A weak negative correlation ($\rho =-0.30$) between composite fidelity and classification performance was observed, suggesting that higher statistical fidelity does not necessarily translate to better downstream task performance. Deep learning-based methods (TVAE, CTGAN) showed statistically significant improvements over the baseline (recall: +58% to +63%) but underperformed compared to simpler methods under default configurations, suggesting the need for larger training samples or more extensive hyperparameter tuning. These findings offer reference points for practitioners working with moderately imbalanced tabular data with limited minority class samples, supporting the selection of generation strategies based on specific requirements regarding data fidelity and classification objectives. Full article

Objectives: This study aims to evaluate whether changes in perfusion index (PI) after the first deflation of the blood pressure cuff during remote ischemic conditioning (RIC) are associated with passive leg raising (PLR)-induced changes in stroke volume. In addition, we compared PI changes after cuff deflation during RIC between critically ill patients and healthy controls. Methods: This prospective, single-center study was conducted in a mixed ICU at a tertiary teaching hospital. Patients aged >18 years admitted to the ICU, monitored using calibrated pulse contour analysis, and scheduled for a PLR test as decided by the attending physicians were included. The PI was measured after blood pressure cuff deflations during RIC (3 cycles of brachial cuff inflation to 200 mmHg for 5 min, followed by instantaneous deflation to 0 mmHg for another 5 min) in the supine position after PLR. Preload responsiveness was defined as a ≥10% increase in the stroke volume index (SVI) during PLR. Data were compared with a healthy control group. Results: Thirty-three patients were included (median age 62; 45% in shock; 55% mechanically ventilated). When comparing critically ill patients with healthy volunteers, the maximum PI change (dPImax) and the time to reach it were higher in critically ill patients after the first and second cuff deflations (p < 0.05). However, after the third deflation, the difference was no longer significant. Following the first deflation, dPImax was significantly correlated with SVI changes during PLR (r = 0.63, p < 0.001). After the cuff was first deflated, we detected a PI cutoff with a positive SVI response (≥10%) during PLR, with a sensitivity of 64% and a specificity of 94% (area under the receiver operating characteristic curve 0.752; 95% CI, 0.564–0.940; p = 0.008). Conclusions: The maximum change in perfusion index following brachial blood pressure cuff deflation after five minutes of inflation may serve as a promising noninvasive bedside indicator of preload responsiveness in critically ill patients. Additionally, the observed normalization of PI kinetics during RIC suggests possible acute modulation of vascular reactivity, though further research is needed to confirm an association between PI changes and endothelial function. Full article

Obstructive sleep apnea (OSA), defined by repetitive airway obstruction during sleep, is significantly underdiagnosed, mainly due to the resource-intensive and time-consuming nature of sleep assessment technologies. Machine learning analysis of the tracheal breathing sounds (TBS) whilst awake offers an alternative approach for OSA quick screening. This study aimed to address the challenge of wakefulness OSA detection using TBS recorded with an inexpensive microphone in a noisy environment. Data of 247 individuals with various degrees of OSA severity were analyzed. Recorded data were segmented into inspiration and expiration phases, followed by acoustic features extraction, feature reduction, and classification. A two-level ensemble architecture was implemented. Nine sub-classifiers were stratified by anthropometric profiles. Each sub-classifier was constructed as an ensemble of bagged decision trees, with a final prediction via probability-based voting. The proposed algorithm achieved an accuracy of 77.1%, sensitivity of 84.3%, and specificity of 59.9%. Although these results have lower performance than those obtained previously using a high-quality microphone in a quiet room, they demonstrate that acoustic OSA detection whilst awake remains feasible, even in very noisy environments. Nevertheless, microphone quality emerged as a key determinant of classification performance. Full article

Synthetic aperture radar (SAR) serves as a prominent remote sensing (RS) technology, permitting continuous maritime surveillance regardless of weather or time. Although deep learning-based detectors have achieved promising results in SAR imagery, the majority of current algorithms rely on axis-aligned bounding boxes, which are insufficient for accurately representing arbitrarily oriented ships, especially under speckle noise, complex coastal clutter, and real-time deployment constraints. To address this limitation, we propose a YOLO-based oriented ship detector (YOSDet). Specifically, a dynamic aggregation module (DAM) is incorporated into the backbone to enhance feature representation against non-stationary backscattering. An objective-guided detection head (OGDH) is developed to decouple classification and localization, complemented by a localization quality estimator (LQE) to calibrate classification confidence by mitigating the impact of scattering center shifts. Comparative evaluations conducted on three public SAR ship detection benchmarks validate the effectiveness of YOSDet. The proposed model outperforms existing detectors, achieving $mAP$ scores of 96.8%, 88.5%, and 67.3% on the SSDD+, HRSID, and SRSDD-v1.0 datasets, respectively. Furthermore, the consistency of our approach in both nearshore and offshore environments is confirmed through rigorous quantitative and qualitative assessments. Full article

Lilies (Lilium spp.) are highly valued in China for their edible and medicinal properties; however, bitterness in certain varieties limits consumer acceptance. Although historically attributed to colchicine, the presence of alkaloids in lilies remains a subject of debate. This research screened five lily species for colchicine and its 15 biosynthetic precursors, using Gloriosa superba and Colchicum autumnale as positive controls. While detected in the controls, none were detected in any tissues (bulbs, roots, stems, flowers, and leaves) of the five lilies. A comparative analysis of five lily varieties—Longyahong, Lanzhou, Lilium lancifolium, Longya, and Guiyanghong—revealed that Longyahong exhibited the strongest bitterness, which was localized exclusively in the bulb peels. Based on comparative LC-MS profiling between bitter and non-bitter varieties, three high-abundance compounds were selected for isolation and subsequent sensory evaluation. Two monomeric compounds were isolated and confirmed via chromatographic methods as the primary bitter components. This study provides compelling chemical and biochemical evidence of the presence of colchicine in the examined lilies. By identifying two specific bitter components in Longyahong bulb peels, these findings refute the long-standing misconception regarding colchicine in lilies and provide a chemical foundation for improving the palatability and commercial value of bitter lily varieties. Full article

Accurate and timely classification of skin lesions is critical for early cancer detection, yet current deep learning approaches suffer from high computational costs, limited interpretability, and poor transparency for clinical deployment. This study presents HCHS-Net, a lightweight and interpretable multimodal framework for six-class skin lesion classification on the PAD-UFES-20 dataset. The proposed framework extracts a 116-dimensional visual feature vector through three complementary handcrafted modules: a Color Module employing multi-channel histogram analysis to capture chromatic diagnostic patterns, a Haralick Module deriving texture descriptors from the gray-level co-occurrence matrix (GLCM) that quantify surface characteristics correlated with malignancy, and a Shape Module encoding morphological properties via Hu moment invariants aligned with the clinical ABCD rule. The architectural design of HCHS-Net adopts a biomimetic approach by emulating the hierarchical information processing of the human visual system and the cognitive diagnostic workflows of expert dermatologists. Unlike conventional black-box deep learning models, this framework employs parallel processing branches that simulate the selective attention mechanisms of the human eye by focusing on biologically significant visual cues such as chromatic variance, textural entropy, and morphological asymmetry. These visual features are concatenated with a 12-dimensional clinical metadata vector encompassing patient demographics and lesion characteristics, yielding a compact 128-dimensional multimodal representation. Classification is performed through an ensemble of three gradient boosting algorithms (XGBoost, LightGBM, CatBoost) with majority voting. HCHS-Net achieves 97.76% classification accuracy with only 0.25 M parameters, outperforming deep learning baselines, including VGG-16 (94.60%), ResNet-50 (94.80%), and EfficientNet-B2 (95.16%), which require 60–97× more parameters. The framework delivers an inference time of 0.11 ms per image, enabling real-time classification on standard CPUs without GPU acceleration. Ablation analysis confirms the complementary contribution of each feature module, with metadata integration providing a 2.53% accuracy gain. The model achieves perfect melanoma and nevus recall (100%) with 99.55% specificity, maintaining reliable discrimination at safety-critical diagnostic boundaries. Comprehensive benchmarking against 13 published methods demonstrates that domain-informed handcrafted features combined with clinical metadata can match or exceed deep learning fusion approaches while offering superior interpretability and computational efficiency for point-of-care deployment. Full article

The rapid expansion of distributed cloud platforms introduces critical security challenges, specifically non-deterministic race conditions like Time-of-Check to Time-of-Use (TOCTOU) vulnerabilities. Traditional passive detection methods often fail to identify these transient “Heisenbugs” due to the asynchronous nature of multi-threaded control planes. To address this, we propose a novel DAG-Guided Active Fuzzing framework. Our approach constructs a Directed Acyclic Graph (DAG) to map causal dependencies of API operations and implements deterministic proactive scheduling. By injecting microsecond-level delays into identified race windows, the system enforces adversarial interleavings to expose hidden order and atomicity violations. Validated on 32 verified vulnerabilities across six distributed systems (including Hadoop and OpenStack), our method achieves an overall Recall (Detection Rate) of 68.8% across the entire dataset and a peak Precision of 92% in reproducibility tests, significantly outperforming random fuzzing baselines ($p<0.01$). Furthermore, the framework maintains a low runtime overhead of 11.5%. These findings demonstrate a favorable trade-off between detection depth and system efficiency, establishing the approach as a robust toolchain for transforming theoretical concurrency risks into reproducible security findings in large-scale cloud infrastructure. Full article

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vulnerabilities, particularly to Distributed Denial-of-Service (DDoS) attacks that can severely disrupt network availability and performance. To address these challenges, machine-learning (ML) techniques have been increasingly adopted to enable intelligent, adaptive, and data-driven DDoS detection mechanisms within SDN environments. This study presents a PRISMA-guided systematic literature review of recent ML-based approaches for DDoS detection in SDN-based networks. A comprehensive search of IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar identified 38 primary studies published between 2021 and 2025. The selected studies were systematically analyzed to examine learning paradigms, experimental environments, evaluation metrics, datasets, and emerging architectural trends. The synthesis reveals that while single machine-learning classifiers remain dominant in the literature, hybrid and ensemble-based approaches are increasingly adopted to improve detection robustness under dynamic and high-volume traffic conditions. Experimental evaluations are predominantly conducted using SDN emulation platforms such as Mininet integrated with controllers, including Ryu and OpenDaylight, with performance commonly measured using accuracy, precision, recall, and F1 score, alongside emerging system-level metrics such as detection latency and controller resource utilization. Public datasets, including CICIDS2017, CICDDoS2019, and InSDN, are widely used, although a significant portion of studies rely on custom SDN-generated datasets to capture control-plane-specific behaviors. Despite notable advances in detection accuracy, several challenges persist, including limited generalization to low-rate and unknown attacks, dependency on synthetic traffic, and insufficient validation under real-time operational conditions. Based on the synthesized findings, this review highlights key research directions toward intelligent, scalable, and resilient DDoS defense mechanisms for future Internet architectures, emphasizing adaptive learning, lightweight deployment, and integration with programmable networking infrastructures. Full article

So-called ‘war rugs’ started being produced in Afghanistan after the Soviet invasion in 1979. These textiles have sparked debate as symbols of resilience and political commentary but also as controversial commodification of human suffering. However, their manufacture and materiality have not been studied so far. In the framework of the British Museum exhibition “War rugs: Afghanistan’s knotted history”, a scientific investigation was conducted on nine rugs from the collection. Approximately 65 samples were analysed by optical microscopy (OM), scanning electron microscopy coupled to energy dispersive X-ray spectroscopy (SEM-EDX) and high-pressure liquid chromatography coupled to diode array detector and tandem mass spectrometry (HPLC-DAD-MS/MS) to study the fibres, mordants and dyes used in the production of the rugs. Scanning X-ray fluorescence (MA-XRF) and multiband imaging (MBI) were also used directly on the rugs to map the distribution of specific mordants and dyes, respectively. The results revealed the intentional use of white or dark wool as the substrate for dyeing, to obtain specific colour shades. A wide range of synthetic dyes was detected, including Acid Orange 7, Acid Red 88, Basic Green 4, Acid Blue 92, Acid Black 1 and Direct Black 38 in the earlier rugs, whereas Direct Yellow 1, Direct Brown 1, Direct Yellow 12, Acid Green 25, Acid Blue 113 and Direct Blue 15 were identified in the later rugs. Some synthetic dyes remained unidentified. Additionally, natural dyes were used in three rugs. An emodin-based colourant, possibly obtained from dock or sorrel (Rumex spp.), was detected in two light brown areas. A berberine-based colourant consistent with barberry (Berberis spp.) was detected in a yellow area. These results represent the first scientific study of these objects and enable preliminary insights into the details of this complex craft that has evolved from centuries of carpet making in this area. Full article