Search Results (28,634)

A novel microwave biosensor operating in the C-band is developed and characterised for enhanced glucose sensing applications. The sensor is based on a single metamaterial asymmetric split-ring resonator (SASR) and has been investigated in two configurations: a single semi-circular design (SASR-S) and a double semi-circular design (SASR-D). The structural modifications were introduced to enlarge the sensing area by creating two high-field hotspots, thereby increasing the interaction between the electromagnetic (EM) field and the sample, which consequently enhances the overall sensor sensitivity. The sensor is fabricated on a Rogers AD350A substrate and is optimised to detect glucose levels in a 1 µL solution applied within each semi-circle sensing region. To characterise the sensor’s enhanced sensitivity, we performed a 3D electromagnetic simulation of a small droplet positioned within a semicircular sensing region, varying the relative permittivity of the droplet from 45 to 65. The resulting shifts in resonant frequency served as a primary indicator of dielectric sensitivity. The sensor’s response was experimentally validated using a vector network analyser to measure the transmission coefficient (S₂₁) of samples with no glucose and at glucose concentrations of 97 mg/dL to 286 mg/dL. The results demonstrate that the resonator configuration strongly influences the resonance frequency shift and sensitivity, with the SASR-D configuration being the most effective design. This has also been confirmed by measurements demonstrating a sensitivity of approximately 2.4 MHz/(mg/dL), representing an approximately two-fold improvement over the SASR-S sensor (sensitivity: 1.27 MHz/(mg/dL)) and a notable enhancement over previously reported sensors. These findings demonstrate the practical potential of the proposed sensor for blood glucose monitoring applications. Full article

Water pollution and its ecological consequences have become central concerns in watershed governance and aquatic ecosystem conservation. Conventional ecotoxicological research on water pollution has long relied on physicochemical monitoring, laboratory-based single-species exposure tests, and morphology-based biological surveys. Although these approaches have provided essential support for pollutant identification, toxicity characterization, and environmental standard setting, they remain insufficient for resolving community-level responses, food-web perturbations, and ecosystem degradation under multiple-stressor conditions. Environmental DNA (eDNA) has emerged as a promising molecular tool because it is non-invasive, highly sensitive, high-throughput, and capable of detecting multiple taxa simultaneously. In aquatic systems, eDNA applications have expanded from biodiversity detection to pollution diagnosis, ecological health assessment, restoration monitoring, and early warning of ecological risk, while increasingly being integrated with eRNA, multi-omics approaches, machine learning, hydrological modeling, and ecological network analysis. However, several challenges still constrain its broader application, including incomplete methodological standardization, false-positive and false-negative detections, insufficient reference databases, limited quantitative capacity, scale mismatches caused by transport and mixing, and difficulties in causal attribution. This review synthesizes recent progress in the use of eDNA for water-pollution research, with emphasis on its technical workflow, major application domains, integrative analytical frameworks, and methodological boundaries. More specifically, three main points are highlighted: (1) eDNA is shifting water-pollution research from single-species toxicity characterization toward community- and ecosystem-level ecological interpretation; (2) its greatest value lies in its integrative role at the interface of biodiversity monitoring, ecological risk assessment, and management-oriented decision support; and (3) future progress will depend on improvements in standardization, quantitative inference, regional reference databases, and multi-source data integration. Overall, this review clarifies how eDNA can contribute to more robust, ecologically meaningful, and management-relevant assessment of water pollution. Full article

Background/Objectives: Front-of-pack labelling systems such as Nutri-Score are promoted as public health tools to guide consumers towards healthier food choices. However, their capacity to accurately signal sugar content in child-targeted foods—a key determinant of dental caries risk—remains poorly characterised. This study aimed to evaluate whether Nutri-Score category reliably reflects sugar content in pre-packaged foods marketed to children, and to discuss the implications for paediatric oral health. Methods: A cross-sectional observational study analysed the nutritional labels of 100 pre-packaged foods directed at the paediatric population, all displaying a Nutri-Score label, selected from three major supermarket chains in Valencia, Spain. Products were grouped into eight predefined food categories. Sugar content (g/100 g or 100 mL), Nutri-Score category (A–E), and ordinal position of sugar in the ingredients list were recorded. Global association between Nutri-Score grade and sugar content was evaluated using Spearman’s rank correlation coefficient; category-level analyses used the Kruskal–Wallis or Mann–Whitney U test as appropriate. Results: A moderate statistically significant positive correlation was found between Nutri-Score grade and sugar content across all 100 products (ρ  =  0.515, p  <  0.001). In the exploratory category-level analyses, suggestive differences were observed in biscuits (H  =  8.72, p  =  0.033), milks and milk drinks (H  =  9.02, p  =  0.029), and desserts (H  =  8.31, p  =  0.016); none of these p-values survived Bonferroni correction for multiple comparisons (adjusted α  =  0.006). Notably, some products rated A and B contained high sugar levels: one A-rated breakfast cereal reached 22.4 g/100 g, and the highest B-rated product (a flavoured milk drink) contained 22.1 g/100 g. No significant association was detected in cereals, breads, dairy products, juices, or frozen foods. Conclusions: Nutri-Score demonstrated limited discriminatory ability to identify high-sugar child-targeted foods consistently across food categories. These findings support recommending that paediatric dental practitioners advise caregivers to evaluate sugar content and ingredients lists beyond front-of-pack grading. Further regulatory refinement of the algorithm to specifically weight added sugar exposure in child-targeted products may be warranted. Full article

Water-bearing faults pose critical hazards in tunnel excavation due to sudden water inrush, making reliable advance detection essential. This study presents a systematic forward modeling framework that integrates COMSOL Multiphysics and MATLAB to simulate three-dimensional direct current (DC) responses ahead of the tunnel face. A three-dimensional finite-element model was developed to investigate the influence of electrical anisotropy on apparent resistivity under controlled geological conditions. Electrical anisotropy of both surrounding rock and water-bearing faults is incorporated to evaluate its influence on apparent resistivity. Numerical experiments investigate the effects of surrounding-rock and fault anisotropy and reveal the mechanism behind hourglass-shaped low-resistivity anomalies. The results also reveal systematic biases when anisotropy is neglected, including forward-shifted anomaly positions, overestimated lateral extents, and more diffuse anomaly boundaries. When anisotropy is considered in both the surrounding rock and the fault, the simulated anomaly closely matches the preset fault location, demonstrating improved localization accuracy. The modeling results clarify the effects of key parameters on apparent resistivity responses and improve the interpretation of low-resistivity anomalies. They also provide a theoretical basis for enhancing the reliability of DC resistivity-based tunnel advance detection. Full article

Historical and cultural heritage parks are important spaces for heritage conservation, cultural transmission, and public recreation. However, conventional landscape perception research mainly relies on questionnaires and interviews, making it difficult to capture tourists’ visual preferences at scale. This study proposes a dual-task attention-enhanced ResNet framework based on social media user-generated content (UGC) images to investigate tourists’ landscape perception preferences in historical and cultural heritage parks. Using Yellow Crane Tower Park, Guqintai, and Guishan Scenic Area in Wuhan, China, as case studies, 6221 images were collected from Ctrip, Xiaohongshu, Weibo, and field surveys. The framework jointly performs landscape element detection and aesthetic attribute classification through shared feature representation and attention mechanisms. The proposed model achieved a composite Macro-F1 score of 0.7641, demonstrating robust classification performance. The results show that Buildings and Structures exhibited the highest average prediction probability (0.5824), while Spatial Legibility was the dominant aesthetic attribute (0.5322), indicating a perception pattern characterized by cultural-symbol prominence and enhanced spatial cognition. Vegetation and road networks were positively associated with spatial mystery, whereas excessive visual complexity reduced spatial legibility. These findings demonstrate the value of combining deep learning with social media image analytics for cultural landscape perception research and provide practical insights for landscape planning, heritage conservation, and tourism management. Full article

Meat flavor significantly influences consumer preference and market value, particularly for indigenous pig breeds renowned for distinctive sensory characteristics. While traditional research has focused on genetic factors and feeding regimens, emerging evidence suggests that gut microbiota plays a crucial role in meat quality attributes. However, the specific contribution of bacterial–fungal co-occurrence to meat flavor formation remains largely unexplored. This study aimed to characterize the associations between intestinal bacterial–fungal co-occurrence networks and the muscle flavor-related metabolite profiles of CX pigs, using an integrated multi-omics approach. Twenty male pigs (10 CX and 10 LAN, 12 months old) were subjected to comprehensive analyses, including meat quality evaluation, electronic nose analysis, 16S and 18S rRNA sequencing, and untargeted metabolomics. CX pigs exhibited significantly superior meat quality characteristics, including higher moisture content (p < 0.001), fat content (p = 0.008), and meat color scores (p < 0.001). Electronic nose analysis revealed significantly higher response values across all ten aroma sensors in CX pigs (p < 0.001), with the most pronounced differences observed in sensors detecting sulfur compounds and organic compounds. Untargeted metabolomics identified 40 differential metabolites, with 27 up-regulated in CX pigs, including key flavor compounds such as glycocholic acid, isorhamnetin, and pantothenic acid. Microbiome analysis demonstrated significantly higher bacterial alpha diversity in CX pigs (p < 0.05), with enrichment of beneficial bacteria, including Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, and Phascolarctobacterium, while fungal communities showed enrichment of Candida_Lodderomyces_clade. Correlation network analysis revealed that Rikenellaceae_RC9_gut_group demonstrated strong positive correlations with flavor compounds (r = 0.575 for isorhamnetin, r = 0.535 for pantothenic acid, p < 0.001) and all electronic nose responses (r = 0.434–0.691, p < 0.001). Bacterial–fungal co-occurrence networks showed synergistic relationships, with Rikenellaceae_RC9_gut_group positively correlated with Candida_Lodderomyces_clade (r = 0.711, p < 0.001) while exhibiting antagonistic relationships with Piromyces (r = −0.714, p < 0.001). These findings offer novel insights for developing microbiome-targeted strategies to enhance meat quality in pig production systems. Full article

Hepatitis B surface antigen (HBsAg) signal-to-cutoff (S/CO) values and isolated anti-HBc positivity represent two significant diagnostic challenges in hepatitis B virus (HBV) serology. This retrospective study, conducted in Karabük between 2021 and 2025, aimed to evaluate the correlation between these profiles and HBV DNA positivity to determine the optimal S/CO thresholds for confirmatory testing and clarify the clinical relevance of isolated anti-HBc reactivity. Among 17,356 patients screened for anti-HBc, isolated anti-HBc positivity was identified in 506 patients (2.9%), of whom 3.2% had detectable HBV DNA. Positivity was significantly higher in patients older than 50 years and in the Gastroenterology department. Separately, 213 specimens with HBsAg S/CO values between 1.0 and 10.0 underwent HBV DNA confirmation testing. ROC analysis yielded an excellent area under the curve of 0.937, with an S/CO threshold of ≥3.04, achieving 100% sensitivity for HBV DNA detection. All specimens with S/CO values between 1.0 and 3.0 were HBV DNA-negative. In this study population, no serum HBV DNA positivity was detected among samples with S/CO values below 3.04, suggesting that HBV DNA testing may help avoid unnecessary confirmatory work-up in this range. Anti-HBc screening should be prioritized in older patients. S/CO-based confirmatory algorithm using HBV DNA testing may improve diagnostic accuracy and reduce unnecessary clinical interventions in routine hepatitis B screening programs. Full article

Liquid biopsy has emerged as a transformative approach in modern oncology, offering minimally invasive access to tumor-derived biomarkers through the analysis of circulating tumor DNA, cell-free DNA, and extracellular vesicles such as exosomes. Unlike conventional tissue biopsies, liquid biopsy enables real-time monitoring of tumor dynamics, molecular heterogeneity, treatment response, and the development of therapeutic resistance. Recent advances in ultra-sensitive molecular technologies, including digital droplet polymerase chain reaction, next-generation sequencing, methylation profiling, and fragmentomic analysis, have substantially improved the sensitivity and specificity of circulating nucleic acid detection, facilitating their integration into precision cancer medicine. ctDNA analysis has demonstrated significant clinical utility across multiple malignancies, including lung, breast, colorectal, pancreatic, and prostate cancers, particularly in the identification of actionable genomic alterations, minimal residual disease, and mechanisms of acquired resistance. In parallel, cell-free DNA provides broader insights into tumor biology and systemic genomic alterations, while exosomes contribute additional layers of molecular information through the transport of nucleic acids, proteins, and signaling molecules involved in intercellular communication and tumor microenvironment modulation. The integration of artificial intelligence and machine learning approaches further enhances the interpretative power of liquid biopsy-derived datasets and supports the development of personalized therapeutic strategies. Despite these advances, important challenges remain, including low tumor fraction in early-stage disease, biological and technical variability, clonal hematopoiesis-associated false positives, assay standardization, and cost-effectiveness considerations. Nevertheless, the expanding clinical applicability of liquid biopsy technologies positions them as essential components of contemporary precision oncology. This review summarizes the biological foundations, analytical methodologies, current clinical applications, technological innovations, and future perspectives of circulating tumor DNA, cell-free DNA, and exosome-based liquid biopsies in cancer diagnosis, monitoring, and personalized treatment strategies. Full article

Automating activities in unstructured environments, such as construction sites, has been challenging due to unpredictable events, limiting robot adoption compared to structured settings. Recently, pre-trained foundation models, particularly Large Language Models (LLMs), have shown promise in addressing this challenge through superior generalization capabilities. This study employed a multi-dimensional method that systematically reviews the field from different perspectives of foundation models in robotics and unstructured environments, and synthesizes them with deliberative acting theory. The findings revealed that LLMs’ linguistic capabilities are primarily used to improve perception and human–robot interactions in robotic tasks, while applications in project management, safety, and natural hazard detection are the most utilized applications of foundation models in unstructured environments. Our synthesis shows an empirical gap in the field where fewer identified studies within unstructured environments validated their foundation model applications using physically deployed robots. We positioned the current state-of-the-art on a five-level automation scale of conditional automation. These findings inform future scenarios, challenges, and solutions toward autonomous safe unstructured environments. Our study serves as a benchmark to track our progress toward that future. Full article

Acute bronchiolitis is the leading cause of infant hospitalization worldwide, with respiratory syncytial virus (RSV) historically predominating. Prospective virological data for the 2025–2026 epidemic season in Morocco were lacking. A prospective observational cohort study was conducted at the Department of Pediatric Infectious Diseases and Clinical Immunology, Casablanca Mother-Child Hospital, from August 2025 through March 2026. Consecutive infants aged 1–24 months hospitalized with acute viral bronchiolitis underwent nasopharyngeal sampling and multiplex rapid antigen testing for RSV, influenza A, influenza B, and SARS-CoV-2. A total of 131 infants were enrolled (median age 4.8 months; male-to-female ratio 0.84:1). At least one virus was identified in 63 patients (48.1%; 95% CI 39.1–57.3%). RSV predominated: 49 sole infections and 2 co-infections with influenza A, totaling 51 positives (80.9% of virus-positive cases; 38.9% of the cohort). Influenza A totaled 8 cases (12.7%), including the 2 co-infections; influenza B accounted for 2 further cases (3.2%). SARS-CoV-2 was not detected. Epidemic activity peaked in January 2026 (65 admissions), declining through February (34) and March (12). All detected pathogens have licensed preventive options, supporting the introduction of nirsevimab, maternal RSV vaccination, and seasonal influenza vaccination as public health priorities in Morocco. Full article

Over the past decade, the incidence of infectious syphilis has been on the rise in the general Chinese population. Consequently, Treponema Pallidum (TP) testing has been proposed as a surrogate marker for sexually transmitted pathogens and for monitoring risky sexual behaviors among blood donors globally. In addition, sexual contact with individuals chronically infected with hepatitis B virus (HBV) is recognized as one of the primary routes of HBV transmission. Blood donors may acquire HBV infection through sexual contact with chronically infected partners, particularly with occult hepatitis B infections (OBIs), which are characterized by intermittent and extremely low viral loads. Therefore, the prevalence of OBIs among syphilis-positive blood donations and the corresponding risks to blood safety require further investigation. This study aimed to investigate the prevalence of OBIs among syphilis-positive blood donors and assess the surrogate value of TP testing for evaluating OBI-related risks to blood supply. After routine screening using serological assays and nucleic acid testing (NAT), blood donation samples with positive anti-TP enzyme-linked immunosorbent assay (ELISA) results were collected and further confirmed by the Treponema Pallidum Particle Agglutination Assay (TPPA). For blood donations confirmed positive for syphilis, further tests were performed to characterize whether the donations had HBV co-infection, including electrochemiluminescence immunoassay (ECLI) for the detection of hepatitis B surface antigen (HBsAg), anti-hepatitis B surface antibody (anti-HBs), hepatitis B e antigen (HBeAg), anti-hepatitis B e antibody (anti-HBe), and anti-hepatitis B core antibody (anti-HBc). Additionally, quantitative real-time polymerase chain reaction (qPCR) was used for HBV DNA quantification, and nested PCRs for the S and basal core promoter/precore (BCP/PC) region were conducted in combination with high-volume nucleic acid extraction. Subsequently, molecular characterization of HBV DNA in these co-infected samples was carried out by DNA sequencing to analyze the viral genetic features. Of 252 anti-TP ELISA+ donations screened from 64,871 blood samples, 138 (138/250, 55.2%) donations were confirmed syphilis-positive but NAT−, among which 78 (78/138, 56.5%) were anti-HBc-positive, and 88 (88/138, 63.7%) had anti-HBs. Notably, seven donations (7/138, 5.1%) were diagnosed as OBI co-infections, and available sequence analysis revealed that three cases were genotype B and one case was genotype C. In addition, several mutations in the S region of the HBV genome were identified, including Q101R, K122R, Q129H, T131N, M133T, G145R, and Y161F mutations. Furthermore, nucleotide mutations such as T1719G, A1752T, G1896A, and A1762T/G1764A in the BCP/PC regions were also detected in these OBI donations. These mutations may contribute to the extremely low HBV viral loads and/or failure in HBsAg detection, collectively leading to OBIs. These data indicate that syphilis screening of blood donors has potential to serve as an additional safeguard measure for excluding donations co-infected with OBIs. The high prevalence of undetected OBIs in syphilis-positive blood donors further supports that syphilis screening has the potential to serve as a surrogate marker for HBV-related risks in the blood supply. Full article

Intrusion detection systems for 5G networks are typically reported at a single operating point, obscuring the trade-off between missed attacks and false alarms that governs real deployments. Building on a lightweight conditional counter-inspection pipeline, in which a global classifier is selectively validated by curriculum-biased experts under a unanimous dissent rule, we remove the constraint that all components share one learning algorithm, assigning decision trees, random forests, extremely randomized trees, and histogram-based gradient boosting independently to the global (G), malicious-biased (EM), and benign-biased (EB) roles. Across two datasets of contrasting difficulty, 5G-NIDD and UNSW-NB15, all 14 evaluated tree-based configurations reduce missed attacks, by 36.5–79.6% on 5G-NIDD, confirming that the recovery effect is a property of the architecture rather than of decision trees. The expert assignment also selects which error the system controls: the same pipeline can be steered toward fewer false alarms, fewer missed attacks, or higher aggregate F1 without retraining the first stage. The mechanism also rescues a weak linear filter: on 5G-NIDD it cuts false positives and false negatives by 92.8% and 95.8%, and on UNSW-NB15 it raises F1 from 0.903 to 0.934 while reducing missed attacks by 35.5%. These results reframe the pipeline as a configurable validation layer matched to a deployment’s cost structure. We further show, through direct measurement on both datasets, that the conditional routing evaluates at most four of seven models per record, keeping classifier inference below 0.1 ms per record and leaving the detection stage a small contributor to overall processing cost. Full article

The increasing integration of photovoltaic systems into modern power grids requires forecasting models that not only provide accurate predictions but also reliable uncertainty quantification under evolving operating conditions. In this paper, we propose an Out-of-distribution-aware time series conformal prediction framework with adaptive retraining, designed to address key limitations of standard conformal prediction methods in temporally dependent and dynamically changing environments. The framework is built upon the Ensemble batch prediction intervals method, which enables distribution-free uncertainty quantification without relying on a fixed calibration set, making it particularly suitable for time series applications. To ensure robustness to distribution shifts, a conformal out-of-distribution detection module is incorporated, where out-of-distribution detection is formulated as a hypothesis testing problem and enhanced through calibration-conditional p-values obtained via the Simes correction, providing conservative false-positive control intended to limit unnecessary model retraining. The proposed framework demonstrates superior performance compared to state-of-the-art approaches in uncertainty quantification, while conformal out-of-distribution detection reduces false positives and the adaptive retraining mechanism ensures effective adaptation to evolving data distributions in real-world scenarios. Full article

Respiratory sound monitoring is a promising non-invasive tool for precision pig farming, but practical evidence from calibrated room-level deployment under degraded air-quality conditions remains limited. This study reports a 28-day exploratory room-level case study in which 52 growing pigs were housed in two rooms: one standard-ventilation room and one ventilation-restricted room, and monitored with one microphone per room emphasizing mixed room-level soundscape monitoring rather than individual pig cough counts or replicated treatment inference. Because the design lacked independent room-level replication, all room contrasts and p-values were interpreted as exploratory descriptive screening summaries rather than causal treatment effects. Airflow verification, playback calibration at multiple pen positions, and background-noise spectral analysis were performed to address measurement bias. Signal inspection showed that biologically relevant vocal energy was retained after 16 kHz resampling, while class imbalance was handled by inverse-frequency weighting and macro-F1-based model selection. The Audio Spectrogram Transformer (AST) pipeline was subjected to five-fold group-blocked cross-validation, and temporal validation. The model achieved a test macro-F1 of 0.937, five-fold macro-F1 of 0.928 ± 0.019, and three-day deployment validation macro-F1 of 0.914. In this two-room dataset, the ventilation-restricted room displayed higher room-level cough-like detections, aggressive vocalizations, normal vocalizations, lower silence, reduced growth, and poorer air quality. Cough-like detections showed recurring clock-time clustering, with the most sustained elevation during 19:00–22:00 and a smaller peak around 10:00 with the highest occurrences at 20.00 (2.84 room-level cough-like detections standardized to group size). Audio-only early-warning analysis flagged deteriorated air-quality windows with AUROC = 0.91 and AUPRC = 0.88 and provided a median 34 min lead time before environmental threshold exceedance, highlighting practical utility as an early inspection cue for farmers before air-quality deterioration becomes more pronounced. Cough-like events descriptively co-varied positively with NH₃, temperature, and CO₂. Overall, calibrated AST-based monitoring can summarize group-level acoustic changes associated with degraded room environments, while multi-room and multi-farm replication remains necessary for causal inference and generalization. Full article

Background: Accurate preoperative localization of hyperfunctioning parathyroid glands remains challenging, particularly in secondary hyperparathyroidism where multiglandular disease may compromise scintigraphic performance. Methods: Our study evaluated biochemical, morphometric, and histopathological predictors of ^99mTc-sestamibi scintigraphy detectability in both primary and secondary hyperparathyroidism. The study group included a total of 162 patients with primary and secondary hyperparathyroidism who underwent dual-phase ^99mTc-sestamibi scintigraphy followed by parathyroidectomy. Demographics, biochemical parameters, histopathological features, lesion volume and scintigraphic findings were assessed at patient and lesion level. Results: In primary hyperparathyroidism, adenomas were larger and more frequently detected than hyperplastic glands. Lesion volume and solid growth pattern were found as positive predictors of sestamibi uptake. In secondary hyperparathyroidism, nodular hyperplasia was associated with larger volume, higher cellularity, and more frequent localizing studies. Upper quadrant position and diffusely hyperplastic lesions were associated with higher lesion miss rates, while lesion volume increased the likelihood of detection. Conclusions: Our findings highlight that ^99mTc-sestamibi scintigraphy performance is strongly influenced by lesion volume, histopathological architecture and anatomical position, underscoring the needs for cautious interpretation of negative or incomplete scans, especially in secondary hyperparathyroidism. Full article