Search Results (315)

Background/Objectives: To Develop a CBCT-based transverse diagnostic method that establishes normative buccolingual inclination values for permanent first molars and objectively distinguishes between dentoalveolar transverse deficiency and skeletal maxillary deficiency. Methods: A total of 1120 initial CBCT scans were reviewed, and 40 subjects with normal occlusion met the inclusion criteria. Volumes were reoriented using a standardized three-plane protocol, and molar angulations were measured relative to reference planes parallel to the occlusal plane. Intra- and inter-examiner reliability were assessed using ICC. Descriptive, comparative, and correlation analyses were performed bilaterally and between arches. Results: No significant right–left differences were observed for upper molar angulation (URM vs. ULM: 99.5° vs. 99.1°; t(19) = 1.560, p = 0.135) or lower molar angulation (LRM vs. LLM: 78.9° vs. 78.9°; t(19) = 0.301, p = 0.767). Non-parametric analysis confirmed these findings (ULM vs. URM: Z = −1.203, p = 0.229; LLM vs. LRM: Z = −0.427, p = 0.669). Significant positive bilateral correlations were observed in both arches (upper: r_S = 0.784, p < 0.001; lower: r_S = 0.837, p < 0.001). A significant negative correlation was found between upper and lower molar angulations (left side: r_S = −0.626, p = 0.003; right side: r_S = −0.858, p < 0.001), demonstrating dentoalveolar compensation. Conclusions: CBCT enables the precise assessment of molar buccolingual inclination and the establishment of normative patterns essential for transverse diagnosis. The proposed method allows the quantification of the maxillary “basal defect” after virtual dental decompensation, providing an objective tool to differentiate dentoalveolar from skeletal transverse discrepancies and guide targeted treatment planning. Full article

Background: Among atmospheric-pressure mass spectrometry imaging (MSI) methods, desorption electrospray ionization (DESI) and desorption electro-flow focusing ionization (DEFFI) represent cost-effective, high-throughput approaches that utilize pneumatically assisted charged solvent droplets to directly desorb and ionize analytes from sample surfaces. Methods and Results: In this study, we systematically compare the performance of conventional DESI-MSI with previously reported DEFFI-MSI configurations on the Orbitrap mass spectrometer platform, focusing on evaluating the lateral spatial resolution, signal intensity, and imaging speed. By scanning a standard patterned sample which has sharp edges, DESI-MSI achieved a spatial resolution of 70 µm, while DEFFI-MSI achieved 15 µm (approximately 4.7-fold improvement). For the representative ion at m/z 782.5621, DEFFI-MSI demonstrated significantly higher signal intensity across solvent flow rates ranging from 0.5 to 1.5 µL min⁻¹. The enhanced ion yield directly translates to improved Orbitrap-based MSI efficiency: in both negative- and positive-ion modes, DEFFI generates rich full-scan mass spectra within the maximum 10 ms ion injection time, whereas DESI produces weaker mass spectra under the same conditions. Conclusions: Taken together, these results quantify the key performance metrics between DESI-MSI and DEFFI-MSI, demonstrating that DEFFI is the preferred method on Orbitrap-based MSI, because it simultaneously enhances spatial resolution, signal intensity, and imaging speed. Full article

Aiming at the challenges in accurately identifying complex pore-space types, significant scale variations, and overlapping log responses in carbonate reservoirs, this study takes the Jurassic Da’anzhai Member in the central Sichuan Basin as the research object. By integrating core observations, cast thin sections, scanning electron microscopy, and well log data, the genetic types and log response characteristics of pore spaces at different scales are systematically analyzed. Building on this, a multivariate distribution identification model for pore-space scales is established based on Bayesian discriminant theory. To enhance the model’s identification accuracy, Z-score normalization is introduced to eliminate dimensional differences. Nonlinear combined features, such as the ratio of the compensated acoustic log (AC) to the gamma ray log (GR) and the logarithmic difference between deep and shallow resistivity logs (RT and RI), are constructed to achieve a multidimensional coupling representation of reservoir physical properties; a class-balancing augmentation method based on Gaussian perturbation is adopted to mitigate decision bias caused by sample imbalance. The results show that the improved Bayesian model achieves F1 scores exceeding 0.80 for large-, small-, and micro-scale pore spaces, with an overall identification accuracy of 84.38%, significantly outperforming the conventional crossplot method’s accuracy of 59.38%. Validation through experiments and well log data demonstrates that the model’s identification results are consistent with core and thin-section observations, indicating that this method can effectively identify large-, small-, and micro-scale pore spaces in strongly heterogeneous carbonate reservoirs. This study provides a valuable approach for reservoir log interpretation and favorable reservoir prediction. Full article

Background/Objectives: Hyperreflective foci (HRF) are supportive optical coherence tomography (OCT) imaging biomarkers that have been examined for their association with disease progression and severity in various retinal disorders. The accurate identification and segmentation of these tiny structures of lipid extravasation remain complicated because of their small size, class imbalance, similarity in the reflectivity patterns with the surrounding structures and imaging artifacts. While U-Net-based models have promised exceptional results for medical image segmentation, optimal architectural settings and suitable preprocessing methods for HRF detection remain unclear. Methods: This research assessed optimal settings for U-Net-based models for HRF segmentation by evaluating standard U-Net and attention U-Net under different preprocessing regimes. Attention U-Net employed Z-score normalization and contrast-limited adaptive histogram equalization (CLAHE) enhancement with soft dice loss. The standard U-Net was trained on OCT images with CLAHE using focal Tversky loss. A total of 435 fovea-centered OCT B scans with the corresponding, consensus-annotated HRF masks were utilized for this research. Results: The standard U-Net outperformed attention U-Net with a dice score of 0.5207, an AUC of 0.8411, and a recall of 0.6439 on raw OCT images. The attention U-Net with preprocessing (dice: 0.5033, AUC: 0.6987, recall: 0.5391) demonstrated satisfactory performance. The results showed that the U-Net model with CLAHE and focal Tversky loss improved recall by 19.4% relative to the attention U-Net, and this corresponds roughly to a 23% relative decline in false negatives. This indicates increased sensitivity in identifying HRF regions. Conclusions: The best-performing configuration using U-Net-based architectures for segmentation of HRFs combines the standard U-Net model with CLAHE and focal Tversky loss for handling class imbalance. This approach yields relatively higher sensitivity, indicating that the standard U-Net model delivers a simple and robust framework for automated HRF segmentation on the evaluated dataset, promising further validation in broader clinical datasets. Full article

Aim: Static guided computer-assisted apicoectomy has been shown to improve the precision of periapical surgery; however, limited data are available regarding its performance and accuracy in hard bone conditions. The primary aim of this study was to collect data on how this technique functions in hard bone and to evaluate the accuracy of different guided approaches under these conditions. Specifically, the accuracy of three surgical instruments—a commercially available bone drill, a bone trephine (partially guided), and an endo-trephine with a stopper (fully guided)—was compared in hard bone. Materials and methods: Sheep mandibles were scanned using cone-beam computed tomography (CBCT) and an intraoral scanner (STL). Digital planning was performed using commercially available dental implant surgical planning software. Guided apicoectomy procedures were carried out with the aid of 3D-printed surgical guides. Following the interventions, matching metal cylinders were inserted into the prepared osteotomies, and post-operative CBCT scans were acquired. Apical deviation from the digitally planned endpoint and angular deviation were analyzed to assess accuracy in hard bone. Results: The drill demonstrated a statistically significantly higher apical deviation compared to the endo-stop trephine (p < 0.001). No statistically significant difference in apical deviation was found between the bone trephine and the endo-stop trephine. Additionally, no significant differences were observed among the three approaches in the mesiodistal (x) and buccolingual (y) directions or in angular deviation; however, a statistically significant difference was detected in the vertical (z) dimension. Conclusions: Within the limitations of this study, static guided apicoectomy proved to be a reliable technique in hard bone conditions. The fully guided trephine approach demonstrated the highest drilling accuracy, while partially guided trephination and drilling showed greater deviations. These findings provide valuable data on the behavior and precision of different endosurgical guided instruments in hard bone and support the use of fully guided systems when high accuracy is required. Full article

Catalytic ozonation often suffers from a low ozone utilization rate and incomplete mineralization of organic pollutants. To address these challenges, we designed and prepared a novel catalyst via a one-step thermal polymerization method, anchoring single-atom manganese on a glucose-derived carbon network-modified C₃N₅ framework (Mn/C-C₃N₅). Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (AC-HAADF-STEM) on an FEI Titan Themis Z microscope confirmed the atomic dispersion of Mn sites, while Raman spectroscopy using a Renishaw inVia Reflex laser micro-Raman spectrometer verified the successful incorporation of a graphitic carbon network within the C₃N₅ matrix. Moreover, electrochemical analyses, including electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) performed on a Bio-Logic SP-150 electrochemical workstation, demonstrated that the integration of the conductive carbon matrix substantially enhanced the interfacial charge transfer capability. The optimized Mn/C-C₃N₅ catalyst demonstrated exceptional performance in phenol mineralization, achieving a 97% total organic carbon (TOC) removal within 60 min, a remarkable improvement compared to pristine C₃N₅ (30%). Furthermore, the catalyst exhibited excellent operational stability, preserving more than 95% of its original activity over five repeated runs. Mechanistic investigations, including electron paramagnetic resonance (EPR) spectroscopy and radical quenching experiments, revealed that the Mn/C-C₃N₅ system accelerated the generation of multiple oxidizing radicals (•O₂⁻, ¹O₂, and •OH), with •OH identified as the predominant reactive species responsible for complete mineralization. This work establishes an integrated catalytic platform and provides fundamental insights into electronic structure modulation for designing advanced oxidation catalysts. Full article

When snow is present, the snow surface is the interface between the atmosphere and the Earth’s surface. The snowpack energy balance is dictated in part by snow surface roughness, which can be quite dynamic. At the Sierra Nevada ski resort in Spain, we measured several snow surface forms: natural, with the presence of dust, with the presence of sun cups, and groomed snow (tracked and between tracks). The snow surface was assessed in 2-dimensions from snow roughness boards and in 3-dimensions from iPad surface scanning to measure across resolutions. Both data collection methods yielded similar roughness estimates via random roughness (RR) and variogram analysis (scale break, SB, and fractal dimension, D) for each distinct surface, yet the roughness differences between the surfaces were substantial. The geometry-based aerodynamic roughness length ($z_0$) was computed for the iPad-scanned surfaces, yielding an order-of-magnitude variability in $z_0$. This produced an order-of-magnitude difference in modelled sublimation. This work can inform snow management at ski areas and reflects some of the snow-surface conditions encountered in a natural snowpack. Full article

Through-plane electronic transport in porous membrane electrode assembly (MEA) electrodes is governed by the three-dimensional (3D) connectivity of the conducting phase. Here, we quantify the role of the spanning-cluster fraction $P_{\infty }$, defined as the fraction of conducting-phase voxels that belong to the $z$-spanning connected component in a finite reconstructed volume, on effective conductivity using scanning electron microscopy (SEM)-informed 3D reconstructions of four archetypal morphologies: a granular catalyst layer (CL), labeled CL1; a fibrous gas diffusion layer (GDL), labeled GDL1; an open-cell foam (OCF); and a micro-fibrous non-woven (MFM), labeled MFM1. Each morphology is reconstructed on a $150\times 150\times 150$ voxel grid, and $z$-spanning connectivity is identified with a 26-neighbor flood-fill algorithm. Steady-state conduction is solved by a finite-volume method (FVM) with an imposed potential difference between the z-faces and no-flux lateral boundaries. Although all samples exhibit through-thickness connectivity, the normalized conductivity ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}/{\sigma }_{\mathrm{b}\mathrm{u}\mathrm{l}\mathrm{k}}$ varies widely, from $\approx 0.134$ (MFM1) to $\approx 0.706$ (OCF). The corresponding ${(P}_{\infty },{\sigma }_{eff}/{\sigma }_{bulk})$ pairs are $\left(0.996,\approx 0.306\right)$ for CL1, $\left(0.999,\approx 0.303\right)$ for GDL1, $\left(0.997,\approx 0.706\right)$ for OCF, and $\left(0.901,\approx 0.134\right)$ for MFM1. OCF exhibits the highest response due to vertically coherent channels, whereas MFM1 underperforms due to laminated constrictions; CL1 and GDL1 lie in an intermediate regime with nearly isotropic skeletons. Overall, the results show that while a $z$-spanning connected component is required for measurable conduction, the magnitude of ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ is dictated by percolating-skeleton quality (bottlenecks, cross-sectional constrictions, and pathway alignment) rather than phase amount alone. The proposed descriptors therefore enable percolation-aware screening metrics for designing and comparing MEA-relevant GDL and CL microstructures. Full article

Background/Objectives: Thrombus composition influences the success of endovascular therapy in stroke, but conventional CT is limited in determining it. Spectral-detector-CT (SDCT) can apply material-decomposition and virtual monoenergetic (MonoE) imaging, which may provide a way to gain information on thrombus composition. This experimental study aimed to evaluate the differentiability of heterogeneous thrombi with variable red blood cell (RBC) content using SDCT. Methods: Ten thrombus entities with different compositions on RBC and plasma, thus fibrin content, were manufactured (volumetric RBC%/Plasma% = 90/10; 80/20; 70/30; 60/40; 50/50; 40/60; 30/70; 20/80; 10/90; 5/95) and scanned in an SDCT. Conventional Hounsfield-unit (HU) values, spectral electron density (ED), effective atomic number (Z-effective) and HU in MonoE maps ranging from 40– to 200 keV were evaluated for thrombus differentiation. Results: Conventional HU increased with RBC content, allowing us to differentiate the entities (p < 0.001). ED values also increased with RBC content and allowed for differentiation too (p < 0.001). Z-effective values showed no differences among the different entities (p > 0.05). Regarding the mass-attenuation curves from 40 to 200 keV the different thrombi showed a similar curve progression with highest HU values at 40 and lowest at 200 keV. The thrombi could be distinguished overall at each monoenergetic level by HU (p < 0.001 for each level). The absolute decrease in HU between 40 and 200 keV was thereby not significantly different between the different entities, but the relative decrease was, as it was more pronounced in thrombi with lower RBC content (p < 0.001). Conclusions: Spectral CT enables differentiation between thrombi with different RBC and fibrin contents by means of ED or analysis of the mass-attenuation curve. This offers alternative possibilities that go beyond characterisation based on CT-density alone. The additional inclusion of spectral parameters in thrombus diagnostics could therefore improve diagnosis and treatment. Full article

Correct focal positioning is essential for microscopy imaging of live moving subjects such as Caenorhabditis elegans. However, many methods can be too slow to perform real-time control to keep the subject in focus. In this work, we propose a convolutional neural network-based method to perform one-shot prediction of the optimal focusing distance, without the need to scan iteratively the optical axis to find the optimal position. A new data augmentation technique is proposed, and its effectiveness is validated through statistical analysis. This technique is shown to improve results without the need for additional data collection. Several architectures are trained in z-stacks of images, using the proposed data augmentation technique, and compared on a validation set. Through this comparison, we find that the ConvNext V2, a novel architecture in this context, outperforms other models proposed in previous works. Furthermore, the impact of the Field of View used for the model’s prediction is studied, with the aim of further understanding the influence of spatial resolution and spatial compression on the performance of the model. Full article

Femtosecond laser processing has emerged as a promising post-deposition method for tailoring the properties of dielectric thin films, offering localized modification without thermal damage. This study investigates the effect of sub-ablative femtosecond laser irradiation on the nonlinear optical response of a TiO₂ single-layer coating deposited on soda-lime glass by electron-beam evaporation. The coating was modified using 35 fs pulses at 800 nm delivered at a repetition rate of 1 kHz and a fluence of 0.083 J/cm² while varying the number of pulses per spot. The effective nonlinear refractive index (n_2,eff) and effective nonlinear absorption coefficient (β_eff) were measured using the z-scan technique with femtosecond excitation. The as-deposited TiO₂ coating exhibited a negative effective nonlinear refractive index, signifying a self-defocusing nonlinear response, while femtosecond laser irradiation leads to pronounced changes in the effective nonlinear parameters. An increase in the magnitude of both effective nonlinear coefficients and a reversal of the sign of the effective nonlinear refractive index are experimentally observed after irradiation with higher pulse numbers. These findings provide experimental evidence that sub-ablative femtosecond laser processing can be used as a post-deposition tool to control the nonlinear optical response of TiO₂ thin films. Full article

Objectives: Systemic low-grade inflammation is associated with steatohepatitis in adults. We aim to explore if systemic low-grade inflammation, measured by plasma high-sensitivity C-reactive protein (hs-CRP), is also linked to steatotic liver disease in adolescents. Methods: In the cross-sectional Early Vascular Ageing in the YOUth study, systemic low-grade inflammation was measured by hs-CRP and liver fat content was quantified by the controlled attenuation parameters (CAP) derived from FibroScan^® (Echosense, Paris, France) measurements in 14- to 19-year-old Austrian adolescents. Cardiovascular risk factors and anthropometric data were collected through face-to-face interviews, physical examinations, and comprehensive fasting blood analyses. Linear regression models were performed to analyze the association between hs-CRP and CAP values. Results: A total of 1300 adolescents (64.6% female) with a mean age of 17.2 ± 1.3 years were included in this analysis. hs-CRP was significantly associated with CAP values in the simple linear regression model (b = 1.35, p = 0.044) and after adjustment for sex and age (b = 1.84, p = 0.006), suggesting an increase in systemic low-grade inflammation with increasing liver fat content. However, further adjustment for major factors of the metabolic syndrome (Homeostatic Model Assessment for Insulin Resistance, non-high-density lipoprotein cholesterol, body mass index z-score, systolic blood pressure z-score) led to a loss of significance of the mentioned association (b = −0.55, p = 0.419). Conclusions: Systemic low-grade inflammation measured by hs-CRP is linked to higher liver fat content in our adolescent cohort. However, this association is largely driven by components of the metabolic syndrome and the overall metabolic milieu, rather than reflecting liver-specific inflammation. Full article

Conducting regular verticality inspections for thin tower structures is essential for ensuring structural safety, extending service life, and optimizing operation and maintenance strategies. However, the traditional theodolite inspection method, as a commonly used technique for verticality assessment, still has certain limitations, including strict requirements for station setup, the need for high-altitude contact-based operations, and difficulty in accurately resolving the tilt azimuth of the central axis. More importantly, the conventional method provides insufficient understanding of the overall verticality geometric characteristics of thin tower structures, particularly lacking in systematic approaches for characterizing the axis morphology under non-contact, full three-dimensional (3D) perception conditions. Therefore, this study proposes a refined method for inspecting the verticality of thin tower structures using the Marching Square algorithm. The tower body of a tower crane was selected as the experimental subject. Firstly, ground-based LiDAR was employed to scan and acquire the raw point cloud data of the tower crane. After point cloud registration and denoising, high-precision and valid point cloud data of the tower body were obtained. Secondly, a cross-sectional slicing segmentation strategy was designed for the point cloud of the tower body standard sections, and a slice-polygon-contour extraction method based on the Marching Square algorithm was proposed to extract the contour vertices and compute the coordinates of the contour centroids. Finally, a spatial line-fitting algorithm based on the least squares method was proposed to fit a 3D line to the coordinates of the contour centroids, thereby determining the direction vector of the central axis. The direction vector was then subjected to vector operations with the x-axis and z-axis in the station-center space coordinate system to derive the tilt azimuth and tilt angle of the central axis, thereby providing the verticality inspection results of the tower crane. The experimental results indicate that the four cross-section slicing segmentation schemes designed using the proposed method in this study yielded tower crane verticality values of 2.45‰, 2.35‰, 2.20‰, and 2.18‰. All verticality values meet the verticality requirement of no more than 4‰ specified in GB/T 5031-2019 (Tower Cranes). This verifies that the proposed method is feasible and effective, providing a novel, high-precision, and non-contact inspection method for inspecting the anti-overturning stability of thin tower structures. Full article

Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We conducted a feasibility study to compare the effectiveness of different feedback modalities, and to evaluate the technical performance of NFB across two scanner field strengths. Methods: In a single-session study, nine healthy adults (6 men, 3 women) voluntarily adapted the activation level of the primary sensorimotor cortex (SMC) to reach three predefined activation levels. We contrasted a continuous, signal-proportional feedback (cFB; a thermometer-style bar) with an affect-based, categorical feedback (aFB; a smiling face). A no-feedback transfer condition (noFB) was included to probe regulation based on internal representations alone. To assess technical feasibility, three participants were scanned at 7T and six at 3T. Results: Participants achieved successful regulation in 44.4% of trials overall (cFB 46.9%, aFB 43.8%, noFB 42.6%). Overall success rates did not differ significantly between modalities and field strengths when averaged across the session; given the small feasibility sample, this null result is inconclusive and does not establish equivalence. Learning effects were modality-specific. Only cFB showed a significant within-session improvement (+14.8 percentage points from RUN1 to RUN2; p = 0.031; d_z = 0.94), whereas aFB and noFB showed no evidence of learning. Exploratory whole-brain contrasts (uncorrected) suggested increased recruitment of ipsilateral motor regions during noFB. The real-time pipeline demonstrated robust technical performance: transfer/reconstruction latency averaged 497.8 ms and workstation processing averaged 296.8 ms (≈795 ms end-to-end), with rare stochastic outliers occurring predominantly during 7T sessions. Conclusions: In this single-session motor rt-fMRI NFB paradigm, continuous signal-proportional feedback supported rapid within-session learning, whereas affect-based categorical cues did not yield comparable learning benefits. Stable low-latency operation was achievable at both 3T and 7T. Larger, balanced studies are warranted to confirm modality-by-learning effects and to better characterize transfer to feedback-free self-regulation. Full article

Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. Here, we tested if AI-based 3D nuclear segmentation can improve the accuracy, reproducibility, and diagnostic reliability of FISH reading in critical situations. Materials and Methods: Formalin-fixed follicular lymphoma sections were FISH-labeled for BCL2 gene rearrangements and digitally scanned in multilayer Z-stacks. The analytic performance in nuclear segmentation of the adaptive thresholding-based FISHQuant, and the freely accessible AI-based NucleAIzer, StarDist, and Cellpose algorithms, were compared to the eye control-based traditional FISH testing, primarily focusing on nuclear segmentation. Results: We revealed that the Cellpose algorithm showed limited sensitivity to low-intensity signals and the adaptive thresholding 2D segmentation, and FISHQuant struggled to resolve densely packed nuclei, occasionally underestimating their counts. In contrast, 3D segmentation across focal planes significantly improved the nuclear separation and signal localization. AI-driven 3D models, especially NucleAIzer and StarDist, showed improved precision, lower variance (VP/VS ≈ 0.96), and improved gene spot correlation (r > 0.82) across multiple focal planes. The similar average number of gene spots per cell nuclei in the AI-based analyses as the eye control counting, despite the elevated number of cell nuclei found with AI, validated the AI nuclear segmentation results. Conclusions: Inaccurate segmentation limits automated diagnostic FISH signal evaluation. Deep learning 3D approaches, particularly NucleAIzer and StarDist, may overcome thresholding and 2D constraints and improve the consistency of nuclear detection, resulting in better classification of pathogenetic gene aberrations with automated workflows in digital pathology. Full article