Search Results (399)

Background/Objectives: Bitter taste receptors (T2Rs), specifically T2R38, are present in the respiratory epithelium and react with bacterial quorum-sensing molecules to induce an innate immunity response. Although TAS2R38 polymorphisms have been correlated with susceptibility to chronic rhinosinusitis (CRS), they have not yet been explored in odontogenic rhinosinusitis (ORS), a distinct form of CRS with particular microbial and inflammatory features. We aim to establish a proof-of-concept methodology for investigating TAS2R38 genetic variants in ORS using direct maxillary sinus tissue analysis and demonstrate the feasibility of this translational approach. Methods: We conducted a prospective pilot case–control study of 36 ORS patients and 37 controls undergoing septoplasty without sinonasal disease. Maxillary sinus mucosal biopsies were obtained intraoperatively with informed consent. Genomic DNA was extracted using the PureLink Genomic DNA Mini Kit and quantified via NanoDrop spectrophotometry. TAS2R38 haplotypes were determined and classified as taster (PAV/PAV), non-taster (AVI/AVI), or intermediate (PAV/AVI) phenotype. Results: Among fully classifiable canonical TAS2R38 phenotypes (32 ORS patients, 28 controls), distributions were: tasters 12.5% vs. 25.0%, non-tasters 31.3% vs. 25.0%, and intermediate 56.3% vs. 50.0%. AVI/AVI non-taster status was not significantly associated with ORS susceptibility (OR = 1.36, 95% CI: 0.44–4.25; Fisher’s exact p = 0.775). Conclusions: This proof-of-concept study demonstrates that genotyping-grade genomic DNA can be recovered from acutely inflamed maxillary sinus mucosa, validating this substrate for future tissue-based expression, functional, and microbiome analyses not obtainable from peripheral samples; germline genotyping itself does not require sinus tissue. The observed difference in non-taster prevalence (31.3% vs. 25.0%) did not reach statistical significance and is reported descriptively. This directional trend is hypothesis-generating only and, given the limited statistical power, does not constitute evidence for an association. The demonstrated feasibility, together with the established biological rationale, supports an adequately powered confirmatory study and lays the foundation for future investigation of taste receptor genetics in ORS pathogenesis, and potentially personalized therapeutic strategies. Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Wearable pressure-sensing orthoses are increasingly used in neurorehabilitation to support gait recovery, monitor plantar pressure distribution, and improve patient mobility during repetitive therapy sessions. The performance of these devices depends strongly on the materials used in the skin-contact layer, since material properties influence comfort, flexibility, durability, and force transmission during daily use. This study proposes a hybrid multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) and the VIKOR method for material selection in sensor-integrated plantar orthoses. Five candidate materials, ethylene vinyl acetate (EVA), polyethylene (PE), polyurethane (PU), cobalt–chromium–molybdenum alloy (CoCrMo), and polypropylene (PP), were evaluated using five criteria: comfort and skin compatibility, elasticity, fatigue resistance, density, and energy dissipation. AHP was applied to determine the relative importance of the evaluation criteria using expert judgment, while VIKOR was used to rank the material alternatives and identify the compromise solution. The results showed that polyurethane achieved the best overall performance due to its balanced behavior in comfort, elasticity, and fatigue resistance, which are essential properties for long-term wearable neurorehabilitation devices. A sensitivity analysis confirmed that moderate variations in expert weighting did not modify the final ranking. Compared with conventional selection approaches based mainly on isolated material properties, the proposed framework offers a clear and reproducible method for integrating mechanical and user-related requirements into the material selection process for wearable orthoses. Full article

Change detection (CD) refers to the analysis of changes in the utilization of land, buildings, and other targets in the same surface environment using relevant technologies and remote sensing images. Although deep learning-based change detection methods have achieved excellent results, they remain highly dependent on extensive labeled data. High-resolution remote sensing imagery typically encompasses an abundance of details and a greater quantity of pixels compared to low-resolution datasets. Therefore, data annotation costs are significantly higher. Currently, within the context of semi-supervised change detection (SSCD) driven by consistency learning, pseudo-labels are usually selected only by threshold screening, but this ignores the spatial relationships among pixels and does not fully utilize unlabeled data, thereby affecting the model’s performance. Consequently, we propose a semi-supervised high-resolution remote sensing image change detection method based on label expansion. First, a “one weak, two strong” (OW-TS) consistency regularization (CR) framework is introduced to constrain the overall consistency between the prediction results of weak and strong augmentations, as well as between the two strong augmentations. At the same time, the location interaction map (LIM) is introduced to utilize the global–local relationship between pixels and mine the consistency of pseudo-labels, thereby improving the model’s accuracy. Empirical findings indicate that when the model is trained utilizing 20% labeled data and 80% unlabeled data on the LEVIR-CD dataset, the IoU^c index reaches 83.38%. The model performs well in smoothing the boundary between changed and unchanged areas and is comparable in performance to some fully supervised methods. Full article

Extracting rural road networks from remote sensing images is crucial for data-driven precision agriculture planning. However, traditional semantic segmentation methods often struggle to achieve both high-precision boundary delineation and topological integrity, especially in heterogeneous rural landscapes. To address these issues, this study proposes MACER-UNet, a novel connectivity-aware road extraction model that integrates multi-scale perception and edge enhancement capabilities. Specifically, MACER-UNet employs ResNet-50 as the backbone network to extract robust deep semantic features. Within the encoder–decoder framework, an atrous spatial pyramid pooling module (ASPP) is embedded to capture rich multi-scale context cues, thereby enhancing robustness to varying road widths and inconsistent imaging conditions. During the decoding process, the convolutional block attention module (CBAM) recalibrates features to reduce noise from the agricultural background. The edge enhancement module (EEM) extracts high-frequency gradient cues for geometric correction and boundary sharpening. This architecture combines spatial attention and edge constraints to balance recognition accuracy and topological connectivity. On the public WHU-CR dataset, MACER-UNet achieved an intersection over union (IoU) of 50.37% and an F1 score of 67.02%, outperforming U-Net (44.27%), DeepLabv3+ (49.43%), and D-LinkNet (49.54%), and its connectivity was comparable to recent state-of-the-art road extraction methods such as C²Net (49.37%) and CGCNet (50.34%). On a self-built dataset with a 3 m resolution in Suihua, the model achieved an IoU of 42.56% and an F1 score of 59.71%. The evaluation results confirm that MACER-UNet provides a road network with geometric consistency and topological integrity for spatial analysis in rural environments. Full article

Coral reefs are among the most important and vulnerable marine ecosystems worldwide. AI-powered underwater visual monitoring has become essential for effective reef conservation, yet current methods still face severe limitations: spectral ambiguity caused by underwater turbidity, fine-grained confusion in early coral health assessment, and discrete forecasting models that cannot represent continuous ecological degradation dynamics. To address these issues, we propose Spectral-YOLOv13, a dual-domain vision-Mamba sensing framework for high-precision coral health evaluation and continuous ecological forecasting. The framework incorporates three novel components: a Wavelet-Integrated Omni-Neck (WIO-Neck) to perform multi-scale spectral filtering and suppress turbidity-induced noise; a Contrastive Prototype Head (CP-Head) to enhance discriminability between visually similar health states; and a Bio-Mamba Predictor based on state-space models to capture long-term continuous health trajectories. Extensive experiments on the CR-Mix++ dataset demonstrate that Spectral-YOLOv13 achieves 53.8% mAP with strong robustness in turbid underwater environments. It reduces four-week forecasting error by 26.8% and maintains real-time inference speed at 112 FPS. This work provides a reliable and high-performance vision framework for practical underwater coral reef monitoring and proactive conservation management. Full article

With the advancement of remote sensing technology, image acquisition has become more convenient and the amount of information captured has significantly increased, playing a vital role in numerous fields. However, cloud cover often results in missing image data, severely affecting data usability. In recent years, although deep learning methods have made progress in cloud removal tasks, the complexity of modeling multispectral band relationships and the scarcity of paired data remain major challenges. To address this, this paper proposes a two-stage codebook fusion vector-quantized generative adversarial network (TCF-VQ GAN) and a training framework. The first stage employs synthetic aperture radar (SAR), MODIS, and cloud-free data for unsupervised training; the second stage performs fusion fine-tuning using SAR and MODIS on paired cloudy/cloud-free data. The model incorporates a space-channel jointed gated convolution (SCGC) module to model irregular cloud cover and combines channel attention for band selection, while a dynamically weighted wavelet alignment loss function (DW²A) is designed to enhance multiscale feature representation. Experiments on the SEN12MS-CR and SMILE-CR datasets demonstrate that the proposed method outperforms existing methods across all metrics: on SEN12MS-CR, PSNR is 31.0397 and SAM is 4.7243; they are 33.5191 and 2.1663, respectively, on SMILE-CR. Furthermore, under fixed paired data conditions, simply adding auxiliary and cloud-free data further improves performance, validating the method’s effectiveness in data-scarce scenarios. Full article

Copper is an indispensable trace element for maintaining metabolic homeostasis; however, the dysregulation and subsequent accumulation of Cu²⁺ are critically linked to neurodegenerative pathologies, including Alzheimer’s disease in humans. Consequently, the development of robust analytical tools for Cu²⁺ monitoring is of paramount importance. Here, we report a 2,2′-dipicolylamine porphyrin (DPAP)-based fluorescent sensor designed for the precise detection of metal cations. Photophysical investigations reveal that DPAP operates via a rapid turn-off fluorescence mechanism, achieving high-performance sensing in the parts-per-million range. Notably, the probe demonstrates exceptional sensitivity with detection limits of 26.3 nM for Cu²⁺ and 34.8 nM for Ni²⁺. Interference studies demonstrated the selectivity of DPAP for Cu²⁺ over a diverse range of competing metal ions such as Na⁺, Ag⁺, Ni²⁺, Cr³⁺, Pb²⁺, Al³⁺, Fe²⁺, Cd²⁺, and Zn²⁺. These results indicate that DPAP is a sensitive and selective probe suitable for copper ion detection. Full article

We present the development of an infrared sensor based on a meta surface utilizing Dolmen plasmonic nanostructures. This meta surface is engineered to enhance the absorption of infrared light at a specific wavelength. The sensor is optimized for high sensitivity and selectivity in the infrared spectrum. This straightforward meta surface sensor shows promise for various applications, including gas sensing, biosensing, and security. The design is compact and easy to fabricate with studied fabrication tolerance ensuring reliable performance. The sensor was tested for water-based sensing applications, and we tested its performance by using different materials such as ZrN, TiN, Cr, and Au on silicon dioxide. In a separate configuration, a gold nanostructure was fabricated on a silicon layer over a silicon dioxide base to examine the resulting plasmonic response. The results surpass those of other water quality sensors, underscoring the potential of this design for high-performance sensing. The sensor’s high sensitivity and low fabrication costs make it a promising technology for future sensing and monitoring applications. Full article

The development of cost-effective, eco-friendly, sensitive, and efficient analytical platforms for the monitoring of metal ions holds profound practical value. In this work, edible fungus carbon quantum dots (Ef-CQDs) are synthesized via a facile hydrothermal route using edible fungus as a green carbon precursor, and a novel fluorescence sensing strategy is established for the rapid and selective detection of Cr³⁺ in environmental water matrices. Systematic optical investigations revealed that the as-prepared Ef-CQDs displayed outstanding selectivity toward Cr³⁺ over other coexisting metal ions. Meanwhile, the Ef-CQDs exhibited considerable scavenging activity toward hydroxyl radicals and DPPH radicals, endowing them with favorable antioxidant performance. When applied for Cr³⁺ determination in real environmental water samples, the proposed Ef-CQDs achieved satisfactory spiked recoveries ranging from 95.2% to 100.6%. This study provided a promising and sustainable approach for the green, rapid, and reliable monitoring of Cr³⁺ in complex aqueous environments. Full article

This study investigated the role of microglia-derived insulin-like growth factor 1 (IGF-1) in modulating host defense and disease progression in a viral model of neuroinflammation and demyelination. Intracranial infection of susceptible mice with the glial-tropic JHM strain of mouse hepatitis virus (JHMV) induces acute encephalomyelitis, followed by an immune-mediated demyelinating disease that mimics many clinical and histologic features of multiple sclerosis (MS). Utilizing an inducible fractalkine receptor (Cx3cr1) promoter-driven Cre-loxP recombinant system, we performed timed ablation of Igf1 in microglia to assess its impact on the central nervous system (CNS) response to JHMV. While the loss of microglial IGF-1 did not impair the control of viral replication, it significantly exacerbated spinal cord demyelination. CyTOF and imaging mass cytometry analysis of spinal cords indicated increased myelin damage was associated with increased accumulation of CD8⁺Ly6C⁺ effector T cells and reduced expression of TREM2 that impaired transition into a disease-associated microglia (DAM) phenotype capable of sensing and potentially mitigating myelin damage. Collectively, these findings argue that microglial IGF-1 is a non-redundant coordinator of the CNS immune responses that occur in response to CNS viral infection. Full article

Background: Contemporary pediatric oncology confronts medical staff with challenges that are not only clinical but also ethical and existential in nature. The aim of this study was to identify the cognitive and affective factors associated with medical professionals’ attitudes toward fertility preservation procedures (oncofertility) in pediatric patients. In particular, the association of “existential vacuum” (lack of life goals, sense of meaninglessness), value systems, and religiosity on the level of competence and emotional acceptance of these procedures was examined. Methods: A cross-sectional observational study was conducted between January and September 2024 in pediatric oncology centers in Poland (Gdańsk, Lublin, Łódź, and Poznań). The study group consisted of 62 medical professionals (62.9% physicians and 37.1% nurses) selected using purposive sampling. The research protocol included an Author-Designed Questionnaire, the Scheler Value Scale (SVS), the Life Attitude Profile—Revised (LAP-R), and the Centrality of Religiosity Scale (CRS-15). Statistical analyses comprised Pearson’s r correlations, multiple regression analysis, and cluster analysis using the k-means method. Results: Participants demonstrated a moderate level of substantive competence in oncofertility (M = 2.31 on a 5-point scale). Regression analysis revealed that “existential vacuum” was the strongest negative predictor of competence (B = −0.34; p = 0.001), which was found to be a significant negative correlate of professional development in this area. In the affective domain, a pronounced normative conflict was observed: religiosity was negatively correlated with emotional acceptance of the procedures (r = −0.42; p < 0.001), indicating tension between medical imperatives and worldview-based beliefs. At the same time, the regression model showed that internalized religiosity and moral values might theoretically function as an “axiological buffer”; however, due to the severe psychometric limitations of the emotional acceptance measure (α = 0.268), these affective associations are highly tentative and unstable. Alternative measurement strategies are required to validate this hypothesis. Exploratory cluster analysis suggested the potential existence of two professional profiles: “Axiologically Integrated” staff members and a larger group of “Existential Skeptics”, who exhibited higher “existential vacuum” and lower psychosocial resources. Conclusions: Viewed through a dual-process interpretative lens, a theoretical phenomenon of cognitive–affective dissociation was explored. The highly tentative data suggest that “existential vacuum” might represent a hypothesized barrier to competence acquisition. Furthermore, findings regarding the affective domain—limited by the low reliability of the emotional measure—suggest religiosity could act as a potential source of normative tension. These exploratory profiles serve as hypotheses for future intervention designs rather than definitive clinical mechanisms. Full article

A homemade NiCr/NiSi thin-film thermocouple integrated with a PCBN turning tool was developed for real-time temperature monitoring during dry turning of AISI 1045 steel. The study addresses a practical limitation of existing cutting-temperature methods, namely the difficulty of combining local in situ sensing near the cutting edge with a transient thermal analysis framework that can interpret the measured signal under repeatable cutting conditions. The sensor was fabricated on an Al₂O₃ substrate by magnetron sputtering, protected by a SiO₂ layer, and tested at cutting speeds corresponding to spindle speeds of 1000, 1500 and 2000 rpm, with a cutting depth of 0.5 mm, a feed rate of 0.1 mm/rev and cutting times of 30–90 s. A three-dimensional transient heat-conduction model and inverse heat-flux reconstruction were then used to interpret the temperature history. The maximum measured temperature increased from 342 °C to 488 °C, and VB increased from 0.082 mm to 0.295 mm, showing a strong temperature–wear association within the investigated parameter window. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Chrome tanning of fish skins generates hazardous effluents and carcinogenic Cr(VI) residues; chromium-free routes to valorize collagen-rich by-products from aquaculture and coastal fisheries are therefore needed. We report a 12-stage ecological protocol employing acetic acid/NaCl pickling, Acacia mearnsii tannin, A. podalyriifolia retanning, mashed-papaya enzymatic bating, and cinnamon as antimicrobial/odor adjunct, scaled from bench to pilot using exclusively locally sourced inputs, for Nile tilapia (Oreochromis niloticus) and Patagonian flounder (Paralichthys patagonicus). Three trained operators evaluated macroscopic quality against five predefined criteria adapted from SATRA and ISO 3376 grading conventions, providing a structured feasibility baseline that does not substitute for the standardized instrumental testing designated as priority future work. Both species achieved satisfactory grain stability, complete tannin penetration, pliable handle, and cinnamon-dominant odor without residual amines; dark-brown coloration is a recognized practical limitation for fashion applications. In silico molecular docking (GNINA v1.0) was used to explore the mechanistic plausibility of each ecological substitution, generating testable hypotheses rather than definitive mechanistic conclusions: the multidentate polyphenol proxy (PGG) exhibited consistently superior collagen engagement over the flavanol monomer across both collagen constructs and all three scoring metrics (1CAG: Vina affinity −5.51 ± 0.13 vs. −3.54 ± 0.35 kcal/mol; CNNscore 0.874 ± 0.009 vs. 0.771 ± 0.010; 7CWK: Vina affinity −6.98 ± 1.43 vs. −4.37 ± 0.16 kcal/mol; CNNscore 0.858 ± 0.024 vs. 0.635 ± 0.094). Dipeptide probes were reproducibly accommodated in the papain catalytic cleft, with the closest configuration reaching 3.997 Å from the catalytic nucleophile (OCS25-SG). Trans-cinnamaldehyde occupied the quorum-sensing pocket with reproducible placement (CNNscore 0.718 ± 0.034) but without score-based selectivity over structural decoys, a result interpreted as hypothesis-generating for future microbiological validation. The protocol is reproducible from bench to pilot and generalizable across two species with distinct dermal architectures. Quantitative physical-mechanical testing (shrinkage temperature, tensile strength, elongation, tear load), CIELab colorimetric analysis, and effluent characterization (COD, BOD₅, total phenolics) are designated as priorities for future validation. Full article