Search Results (577)

6:2 chlorinated polyfluoroalkyl ether sulfonic acid (F-53B), a substitute for perfluorooctane sulfonate (PFOS), is widely used as a mist suppressant in the electroplating industry. With the implementation of PFOS regulations, the use of F-53B has correspondingly increased, and it is now detected in various environmental matrices. However, toxicological information on F-53B remains incomplete and insufficient for environmental risk assessment. In this study, we systematically investigated, for the first time, the toxicity and underlying mechanisms of action of F-53B to Escherichia coli. The results showed that the 24 h half-maximal growth inhibition concentration (IC₅₀) of F-53B was 23.56 mg/L, suggesting that F-53B may exhibit higher toxicity to E. coli than PFOS. Analyses of cell surface hydrophobicity, membrane permeability, membrane composition, and scanning electron microscopy (SEM) images showed that F-53B adsorbed onto the cell surface, altered membrane properties, and ultimately disrupted cell morphology. Increased intracellular levels of reactive oxygen species (ROS) and malondialdehyde (MDA), along with decreased activities of superoxide dismutase (SOD) and catalase (CAT), indicated enhanced oxidative stress induced by F-53B in E. coli. Furthermore, the alkaline comet assay demonstrated that F-53B exposure caused DNA damage. Taken together, the toxicity of F-53B to E. coli can be attributed to cell morphological disruption, oxidative stress, and DNA damage, ultimately leading to cellular inactivation or death. These findings advance our understanding of the cytotoxicity of F-53B in microorganisms. Full article

Capacitive ECG (cECG) technology offers significant potential for improving comfort and unobtrusiveness in long-term cardiovascular monitoring. Nevertheless, current research predominantly emphasizes basic heart rate monitoring by detecting only the R-wave, thereby restricting its clinical applicability. In this study, we proposed an advanced cECG mattress system and conducted a systematic evaluation. To enhance user comfort and achieve more accurate cECG morphological features, we developed a multi-layered cECG mattress incorporating flexible fabric active electrodes, signal acquisition circuits, and specialized signal processing algorithms. We conducted experimental validation to evaluate the performance of the proposed system. The system exhibited robust performance across various sleeping positions (supine, right lateral, left lateral and prone), achieving a high average true positive rate (TPR) of 0.99, ensuring reliable waveform detection. The mean absolute error (MAE) remains low at 1.12 ms for the R wave, 7.89 ms for the P wave, and 7.88 ms for the T wave, indicating accurate morphological feature extraction. Additionally, the system maintains a low MAE of 0.89 ms for the RR interval, 7.77 ms for the PR interval, and 7.85 ms for the RT interval, further underscoring its reliability in interval measurements. Compared with medical-grade devices, the signal quality obtained by the cECG mattress system is sufficient to accurately identify the crucial waveform morphology and interval durations. Moreover, the user experience evaluation and durability test demonstrated that the mattress system performed reliably and comfortably. This study provides essential information and establishes a foundation for the clinical application of cECG technology in future sleep monitoring research. Full article

Madagascar is one of the world’s most prominent biodiversity hotspots and is characterized by exceptionally high amphibian diversity, with 429 currently described, mostly endemic species. However, cytogenetic research on Malagasy amphibians has been conducted only intermittently over the years. Previous studies, mostly using conventional staining and banding methods and often confined to single taxa or isolated families, have provided only partial insights into the karyotype evolution and genome organization of the major Malagasy clades. In this contribution, we present the first comprehensive synthesis of all available cytogenetic data on Malagasy anurans, including chromosome number and morphology, heterochromatin distribution, and chromosomal markers across the major endemic Malagasy families. By integrating and comparing results from decades of scattered studies, this review reveals consistent patterns of chromosomal diversification and identifies evolutionary trends associated with speciation and adaptive radiation in Malagasy amphibians. Overall, native Malagasy amphibian species can be subdivided into two main karyotype groups: the first includes karyotypes with only biarmed chromosomes (Heterixalus, Ptychadena, Boophis, Mantella, and Guibemantis), while the second comprises karyotypes with one or more uniarmed elements (Gephyromantis, Mantidactylus, and Microhylidae). The localization of NORs follows a diverse pattern, often varying even among closely related species. Heterochromatin distribution and composition also appear to be species-specific and thus taxonomically informative. Beyond summarizing existing knowledge, this work establishes a unified framework for interpreting chromosome evolution within the unique biogeography and evolutionary history of Madagascar. Our synthesis provides essential baseline data for future molecular, genomic, and conservation studies, thereby enhancing our understanding of the mechanisms that have generated and maintained the island’s extraordinary amphibian diversity. Full article

Adenoviral vector vaccines were pivotal for COVID-19 control, but postmarketing safety surveillance has identified venous-predominant thrombotic risks not fully explained by platelet-centric mechanisms. We tested an RBC-associated hypothesis using an Ad5 vector-rAd/HA(PR8) rat model within a predefined sub-hemolytic window (<10% hemolysis). Ex vivo, we quantified RBC surface phosphatidylserine (PS) exposure, morphology remodeling by scanning electron microscopy, and microvesicle generation, all aligning with increased procoagulant activity. RBCs also exhibited dose-dependent increases in thrombin generation 4 h after intravenous exposure (10⁸–10⁹ OPU/Rat). In vivo, an inferior vena cava thrombosis model showed a pronounced, dose-responsive rise in thrombus burden, consistent with increased thrombogenic potential. Together, these integrated data provide experimental evidence consistent with RBC involvement under adenoviral exposure, supporting a biologically plausible link to the venous-predominant epidemiology observed during the COVID-19 vaccination era. Reported clinical adenoviral vaccine doses are of the same order of magnitude as the exposures tested here, supporting translational relevance while not implying inter-species or product equivalence. Incorporating RBC-focused endpoints, including PS exposure, morphology indices, microvesicle counts, and thrombin generation, into preclinical and early clinical assessments may enhance safety evaluation and inform vector design to mitigate venous thrombotic risk. Full article

Background/Objectives: Differentiating benign, premalignant, and early malignant vocal fold lesions is challenging. Perpendicular vascular changes (PVCs) per the European Laryngological Society (ELS) are key malignancy indicators. Enhanced contact endoscopy with narrow-band imaging (NBI-CE) visualizes intrapapillary capillary loops (IPCLs) at high magnification, independent of gross morphology. However, defining malignancy as any PVC increases sensitivity but lowers specificity—particularly in papillomas—whereas limiting malignancy to narrow-angle PVC improves specificity but risks false negatives and reduced reproducibility. Methods: We intraoperatively evaluated 146 histology-proven vocal fold lesions using NBI-CE. Six raters (three experienced otolaryngologists, three PhD students) classified vascular patterns. Two approaches were tested: (1) malignancy = narrow-angle PVC; (2) malignancy = any PVC. Outcomes were accuracy, sensitivity, specificity, and interrater agreement. Results: Approach (1) had higher specificity but lower sensitivity than (2) (~85% vs. ~70% specificity; ~50% vs. ~80% sensitivity). Accuracy did not differ significantly. Experienced raters showed higher interrater agreement and a more favorable sensitivity–specificity balance. Common errors were false positives in papillomas and false negatives in dysplasia/early carcinoma. Conclusions: PVC assessment with NBI-CE is feasible and informative. Choosing between “any PVC” and “narrow-angle only” entails a sensitivity–specificity trade-off and depends on lesion type and experience. Refined ELS descriptors and automated analysis may improve reproducibility and accuracy. Full article

Substation equipment operating in harsh environments is highly susceptible to corrosion, yet conventional image segmentation methods often fail to achieve precise delineation of corroded regions. Here, we propose an enhanced TransU-Net-based approach for corrosion segmentation. Deformable convolution is incorporated into the encoder to strengthen the model’s capacity to represent irregular corrosion morphologies. A composite color–texture fusion module is developed to jointly exploit color information from HSV and Lab spaces together with multi-scale texture features. In addition, a Shape-IoU loss function is introduced to refine boundary fitting and improve contour accuracy. Experimental evaluations demonstrate that the proposed method consistently outperforms state-of-the-art models across multiple metrics, achieving an Intersection over Union (IoU) of 75.42% and a Recall (PA) of 83.14%. These results confirm that the model substantially enhances corrosion recognition accuracy and edge integrity under complex background conditions, offering a promising strategy for intelligent maintenance of substation infrastructure. Full article

Background/Objectives: Rezum water vapor therapy for benign prostatic obstruction lacks standardized documentation, complicating data comparison. This study evaluates the completeness of non-standardized Rezum operative reports and validates a novel Rezum—Structured Reporting Protocol (SRP) to enhance documentation quality. Methods: Following the establishment of content validity, the SRP—which includes detailed diagrams for various prostatic urethral lengths (PUL) and intravesical prostatic protrusion (IPP) to document injection sites, along with a comprehensive 10-item checklist capturing factors that may influence outcomes—was retrospectively applied to 100 Rezum cases. Operative videos and non-standardized reports were analyzed and compared against the SRP. For criterion validity, inter-rater reliability was evaluated through a blinded review of 20 cases by three Rezum users and the protocol development panel, comparing checklist item ratings. Results: Median number of injections was 4.0 (IQR: 2–6), injection density was 12.7 (IQR: 10–16.7) mL (PVOL)/injection, and injection interval was 0.7 (IQR: 0.5–1) cm (PUL)/injection. Variations in injection techniques were noted, including non-standard locations in 10% of cases and alternating injection sequences between lobes in 22%. Only 30% of reports detailed injection sites accurately. The intraclass coefficient for the rating of PUL was 0.94 (95% CI: 0.89–0.97). The Fleiss Kappa for MLE and IPP was 0.84 (95% CI: 0.66–1.02) and 0.85 (95% CI: 0.67–1.03), respectively. The agreement rate was 93% for bladder neck/urethra morphology and 100% for injection sequence. Kendall’s W was 0.37 (p = 0.343) for the item of injection sites. Conclusions: Variability in Rezum surgical techniques was observed, particularly in injection density, injection intervals, and precise injection locations, as well as in the structured information of non-SRP-standardized operative reports. Content validity of the SRP was achieved, leading to high inter-rater reliability in its application. The SRP promotes the standardization and completeness of Rezum data, thereby supporting improved, consistent, and high-quality Rezum documentation. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

With the continuous deepening of Mars exploration missions, the Mars helicopter has become a key platform for acquiring high-resolution near-ground imagery. However, accurate semantic segmentation of the Martian surface remains challenging due to complex terrain morphology, sandstorm interference, and the limited onboard computational resources that restrict real-time processing. Existing models either introduce high computational overhead unsuitable for deployment on Mars aerial platforms or fail to jointly capture fine-grained local texture and global contextual structure information. To address these limitations, we propose LisseMars, a lightweight semantic segmentation network designed for efficient onboard perception. The model integrates a Window Movable Attention (WMA) module for enhanced global context extraction and a multi-convolutional feedforward module (CFFN) to strengthen local detail representation. A Dynamic Polygon Convolution (DPC) module is further introduced to improve segmentation performance on geometrically heterogeneous objects, while a Group Fusion Module (GFM) enables effective multi-scale semantic integration. Extensive experiments are conducted on both real Tianwen-1 Mars helicopter imagery and synthetic datasets. The results show that our method achieved a mean IoU of 78.56% with only 0.12 MB of model parameters, validating the effectiveness of the proposed framework. The real-time performance of proposed method on edge device deployment further demonstrate potential application for real Mars airborne missions. Full article

Fine-grained defects on rail surfaces are often inadequately detected by conventional vision-based object detection models in low-light environments. Although this problem can be mitigated by enhancing image brightness and contrast or employing deep learning-based object detectors, these methods frequently distort critical edge and texture information essential for accurate defect recognition. Herein, we propose a preprocessing framework that integrates two complementary modules, namely adaptive illumination enhancement (AIE) and EdgeSeal enhancement (ESE). AIE leverages contrast-limited adaptive histogram equalization and gamma correction to enhance local contrast while adjusting the global brightness distribution. ESE further refines defect visibility through morphological closing and sharpening, enhancing edge continuity and structural clarity. When integrated with the You Only Look Once v11 (YOLOv11) object detection model and evaluated on a rail defect dataset, the proposed framework achieves an ~7% improvement in mean average precision over baseline YOLOv11 and outperforms recent state-of-the-art detectors under diverse low-light and degraded-visibility conditions. The improved precision and recall across three defect classes (defects, dirt, and gaps) demonstrate the robustness of our approach. The proposed framework holds promise for real-time railway infrastructure monitoring and automation systems and is broadly applicable to low-light object detection tasks across other industrial domains. Full article

Objectives: Correctly identifying Central Nervous System (CNS) tumors through MRI is complicated by utilization of divergent MRI acquisition protocols, unequal tumor morphology, and a difficulty in systematically combining imaging with clinical information. This study presents the Adaptive Vision–Language Transformer (AVLT), a multimodal diagnostic infrastructure designed to integrate multi-sequence MRI with clinical descriptions while improving robustness and interpretability to domain shifts. Methods: AVLT integrates the MRI sequence ($T1$, $T1c$, $T2$, FLAIR) and clinical note text in a joint process using normalized cross-attention to establish association of visual patch embeddings with clinical token representations. An Adaptive Normalization Module (ANM) functions to mitigate distribution shift across datasets by adapting the statistics of domain-specific features. Auxiliary semantic and alignment losses were incorporated to enhance stability of multimodal fusion. Results: On all datasets, AVLT provided superior classification accuracy relative to CNN-, transformer-, radiogenomic-, and multimodal fusion-based models. The AVLT model accuracy was 84.6% on BraTS (OS), 92.4% on TCGA-GBM/LGG, 89.5% on REMBRANDT, and 90.8% on GLASS. AvLT AUC values are at least above 90 for all domains. Conclusions: AVLT provides a reliable, generalizable, and clinically interpretable method for accurate diagnosis of CNS tumors. Full article

This study proposes an enhanced deep learning framework for accurate detection of P-peaks in noisy photoplethysmographic (PPG) signals, utilizing a hybrid architecture that integrates wavelet-based analysis with neural network components. The P-peak detection task is formulated as a binary classification problem, where the model learns to identify the presence of a peak at each time step within fixed-length input windows. A temporal attention mechanism is incorporated to dynamically focus on the most informative regions of the signal, improving both localization and robustness. The proposed architecture combines Discrete Wavelet Transform (DWT) for multiscale signal decomposition, Convolutional Neural Networks (CNNs) for morphological feature extraction, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. A temporal attention layer is introduced after the recurrent layers to enhance focus on time steps with the highest predictive value. An evaluation was conducted on 30 model variants, exploring different combinations of input types, decomposition levels, and activation functions. The best-performing model—Type30, which includes DWT (3 levels), CNN, LSTM, and attention—achieves an accuracy of 0.918, precision of 0.932, recall of 0.957, and F1-score of 0.923. The findings demonstrate that attention-enhanced hybrid architectures are particularly effective in handling signal variability and noise, making them highly suitable for real-world applications in wearable PPG monitoring, digital twins for Heart Rate Variability (HRV), and intelligent health systems. Full article

The precise segmentation of thyroid nodules in ultrasound images is essential for computer-aided diagnosis and treatment. Although various deep learning methods have been proposed, similar intensity distributions and variable nodule morphology often lead to blurred segmentation boundaries and missed detection of small nodules. To address this problem, we propose a multiscale cross-level fusion U-net with combined wavelet convolutions (MCFU-net) for thyroid nodule segmentation. Firstly, the network designs a multi-branch wavelet convolution (MBWC) block, which decouples texture features through wavelet domain multiresolution analysis and reorganizes cross-channel features, thereby enhancing context extraction and aggregation capabilities during the encoding stage. Secondly, a scale-selective atrous pyramid (SSAP) module based on multi-level dynamic perception is constructed to achieve saliency enhancement for nodules of varying sizes, in order to improve the detection ability for small nodules. Thirdly, to decrease the loss of fine-grained information during upsampling, a cross-level fusion module (CLFM) with hierarchical refinement mechanisms is designed, which progressively reconstructs ambiguous boundary areas through multistage upsampling. Experiments conducted on two public ultrasound datasets, TN3K and DDTI, demonstrate the effectiveness and superiority of our method, achieving Dice coefficients of 85.22% and 78.21% and IoU values of 74.25% and 64.23%, respectively. Full article

Background: Early detection of recurrent brain tumors after malignant glioma surgery is a challenge in imaging-based assessment of glioma. Objective: The aim of this case series is to investigate whether there are signs for an improvement in the early detection of recurrent tumors using multiparametric magnetic resonance imaging (MRI) after glioma surgery. Methods: An MRI protocol was used with high-resolution fluid-attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), resting state functional MRI (rsfMRI), and contrast-enhanced high resolution T1-weighted (T1w). Longitudinal multiparametric MRI was performed in six patients with glioblastoma with one complete scan before surgery, one scan after surgery and at least two follow-up scans. A total of 27 complete multiparametric MRI data sets were available. Results: DTI analysis at the localizations of recurrent tumors showed early directionality loss in DTI by fractional anisotropy (FA) reduction accompanied by FLAIR hyperintensities before hyperintensities in contrast enhanced T1w were visible. One out of six patients showed a regional FA decrease at the localization of the recurrent tumor at a point of time even when the morphological T1w- and FLAIR images did not demonstrate any detectable changes. Functional connectivity alterations in a corresponding network could also be detected at the localizations of the recurrent tumor. Conclusions: In addition to routine T2w FLAIR and contrast enhanced T1w, DTI and rsfMRI might complement information for the early detection of recurrent malignant glioma. Prospective studies at larger scale are needed with respect to potential of DTI and rsfMRI for early recurrent tumor detection. Full article

It is increasingly recognized that urban planning is essential in promoting low-carbon urban development, and the research on urban carbon metabolism patterns informs the theoretical support for carbon neutrality through urban structural optimization. To investigate the correlation between carbon metabolism patterns and urban form, this study analyzes their spatiotemporal evolution, thereby informing low-carbon urban planning from a novel perspective. Using multi-temporal land use data from 2000 to 2025 in Haikou City, China, we calculated carbon emissions and sinks based on land use types, and applied GIS spatial analysis, landscape metrics and autocorrelation methods to reveal the dynamic relationship between urban form and carbon metabolism. The results indicate that, over the past 20 years, carbon emission areas in Haikou have continuously expanded, with high-emission zones clustering in the city center, while carbon sink areas have gradually contracted and become increasingly fragmented. In terms of response, there is a notable correlation between the evolution of carbon metabolism patterns and the changes in urban form. In High–High (HH) carbon emission clusters, the degree of aggregation is positively correlated with urban morphological complexity, which indicates that greater complexity leads to stronger clustering of carbon emissions. In contrast, for carbon sink areas, higher morphological complexity corresponds to lower aggregation and more pronounced fragmentation. This implies that urban morphological complexity and the aggregation of carbon metabolism clusters are particularly critical indicators in low-carbon urban planning. Balancing these indicators to optimize the urban layout serves as a strategy to enhance urban low-carbon resilience. Full article