Search Results (12,178)

Background/Objectives: Preoperative characterization of salivary gland tumors remains challenging because conventional MRI and diffusion-weighted imaging may show overlapping features, particularly between Warthin tumors and malignant lesions. This study evaluated the diagnostic value of quantitative permeability MRI based on dynamic contrast-enhanced pharmacokinetic analysis for differentiating salivary gland tumors. Methods: This retrospective single-center study included 51 patients with 59 histologically confirmed salivary gland lesions examined between January 2022 and November 2024. MRI included diffusion-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI). Quantitative pharmacokinetic analysis based on the Extended Tofts model provided Ktrans, Kep, Ve, and Vp. Interobserver reproducibility was assessed using the intraclass correlation coefficient, and diagnostic performance was evaluated by receiver operating characteristic analysis. Results: Of 59 lesions, 44 were benign, and 15 were malignant. Warthin tumors showed significantly higher Kep values than malignant lesions (median, 998 vs. 480 × 10⁻³/min; p = 0.002) and lower Ve values (median, 121 vs. 245 × 10⁻³; p = 0.004). ADC values partially overlapped between Warthin tumors and malignant lesions. Interobserver agreement was good, with ICC values ranging from 0.78 to 0.86. Kep showed the highest diagnostic performance in this cohort, with an AUC of 0.96. A Kep threshold of 723 × 10⁻³/min yielded 92.3% sensitivity and 93.3% specificity. Conclusions: Quantitative DCE-MRI, particularly Kep, may provide additional information for differentiating Warthin tumors from malignant salivary gland lesions within a multiparametric MRI assessment. The threshold identified in this cohort is exploratory and requires external validation before routine clinical use. Full article

Convolutional neural network (CNN)-based methods have been widely adopted in hyperspectral image (HSI) classification tasks and have demonstrated superior performance due to their potent nonlinear fitting capabilities. However, as network depth increases, existing methods suffer from significant issues such as gradient vanishing and information loss, which limit further improvements in model performance. Meanwhile, current approaches primarily design entirely new network architectures to extract abstract features but fail to fully exploit the explicit gradient prior information inherent in the data, which affects model performance in complex scenarios. To address these issues, this paper proposes a Multiscale Dense HOG-driven Self-Attention Network (MDHANet) that incorporates the Histogram of Oriented Gradients (HOG) as prior knowledge. First, the model employs three densely connected branches at different scales to obtain diverse multi-level feature representations through multi-scale feature reuse. Furthermore, since HOG can explicitly encode the distribution of gradient orientations and magnitudes in local neighborhoods and accurately characterize the feature distribution, a dynamic HOG-driven self-attention module is designed. Within this module, a HOG prior-based feature rearrangement strategy together with a dual-branch self-attention mechanism enables the network to more effectively learn the spatial information of the data, enhancing the extraction of discriminative spatial–structure features and further improving classification performance. Extensive experimental results on multiple datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches. Extensive experimental results on four HSI datasets (IP, LK, HH, and HC) demonstrate that the proposed method outperforms existing state-of-the-art approaches, achieving OA of 99.13%, 97.32%, 99.02%, and 99.24%, respectively, with improvements of 0.83%, 0.41%, 2.69%, and 2.47% over the best competing methods. Full article

This paper proposes an improved multimodal fusion framework for 3D object detection, termed BEV-Nexus, which aims to address the issues of inaccurate depth estimation and inefficient fusion paradigms in existing image-point cloud fusion methods. We introduce a Point-Cloud-Guided Depth Prediction Network (PCGD-Net), which enhances the image branch’s depth prediction capability by embedding point cloud spatial prior, ground-truth loss constraint, and projected point cloud depth filling. Additionally, we design a Dynamic Self-adaptive Feature Fusion Module (DSF-Module), which computes multimodal feature similarity using window attention and performs weighted fusion based on self-adaptive weights, resolving alignment deviations in BEV features. Finally, we propose a Dilated Attention Enhancement Block (DAEB), which expands the receptive field through dilated convolution and integrates parameter-free attention mechanism (SimAM) for feature enhancement, ensuring efficiency while improving overall feature representation. Experimental results on nuScenes validation set show that BEV-Nexus outperforms it baseline (BEVFusion) by 1.8% mAP and 1.5% NDS. On the test set, BEV-Nexus improves mAP and NDS by 1.6% and 1.4%, respectively. Furthermore, the detection FPS remains nearly unchanged, demonstrating significant lightweight advantages. Full article

Background: Tauopathies are neurodegenerative disorders characterized by the abnormal hyperphosphorylation and aggregation of the microtubule-associated protein tau, leading to disrupted neuronal connectivity and progressive brain dysfunction. Despite their clinical relevance, most in vitro models have focused primarily on molecular and cellular aspects, with limited emphasis on alterations in network dynamics and functional connectivity. Methods: We developed an in vitro tauopathy model based on mouse primary neuronal cultures, enabling the investigation of network-level alterations under controlled conditions. We compared three experimental groups: untreated control cultures, cultures exposed to extracellular wild-type tau, and cultures treated with pathological tau (pTau) isolated from the sarkosyl-insoluble fraction of P301S (+/−) transgenic mice. To increase susceptibility to tau-induced pathology, all conditions were additionally transduced with adeno-associated viral vectors encoding human P301L tau. To quantify for damage, spontaneous neuronal activity was monitored throughout network maturation—from day in vitro (DIV) 7 to 16—using fluorescence calcium imaging, and multiple metrics describing network dynamics and functional organization were compared at DIV 12. Results: We observed that exposure to pTau did not induce overt cytotoxicity or major disruptions in global network dynamics, although a mild increase in network bursting activity was observed. Longitudinal analysis of network maturation further revealed largely similar developmental trajectories across experimental groups, with only subtle and persistent differences in bursting-related activity in pTau-treated cultures. Conclusions: We propose that the early developmental stage of the cultures, together with the intrinsic bursting and ongoing synaptic plasticity of primary neuronal networks, masks subtle pathological effects. This may limit the sensitivity of two-dimensional in vitro systems to detect network-level dysfunction, suggesting that more mature or structurally complex models, such as brain organoids, may be required to reveal robust functional deficits. Full article

Background/Objectives: Spinopelvic alignment represents the sagittal relationship between the lumbar spine and pelvis and is closely related to compensatory posture and disability in degenerative lumbar diseases. Previous studies have mainly focused on magnetic resonance imaging (MRI)-derived paraspinal muscle morphology, whereas evidence linking objective dynamic trunk strength to spinopelvic alignment remains limited. The present study aims to investigate the associations between spinopelvic alignment and muscle degeneration as assessed by MRI-based morphology and trunk isokinetic dynamometry. Methods: In this prospective cross-sectional study, patients undergoing lumbar fusion surgery for degenerative lumbar diseases at a tertiary center were consecutively enrolled. Peak trunk isokinetic torques were measured using the BioniX Sim3 Pro system. Preoperative paraspinal muscle degeneration and spinopelvic alignment were measured. Results: A total of 119 patients were analyzed (52.1% female). In the total sample, paraspinal muscle degeneration, isokinetic torques, and spinopelvic alignment showed statistically significant but generally weak associations. After adjustment for age, sex, and body mass index (BMI), rotation and flexion torques were associated with pelvic tilt, and multifidus functional cross-sectional area (MF-fCSA) was associated with lumbar lordosis (LL), pelvic tilt (PT), pelvic incidence-lumbar lordosis (PI-LL), and PI-LL mismatch. In the hypothesis-generating sex-stratified analyses, trunk isokinetic torques were associated with spinopelvic alignment in males but not females, whereas MF-fCSA was associated with spinopelvic alignment in both sexes. Conclusions: Paraspinal muscle morphology and trunk isokinetic strength were significantly associated with spinopelvic alignment, although the magnitudes of these associations were weak. These findings suggest that imaging-based muscle morphology and dynamic trunk muscle performance are associated with spinopelvic alignment but their implications should be interpreted with caution. Paraspinal muscle imaging and dynamic isokinetic strengths may provide complementary information in the assessment of patients with degenerative lumbar disease. Longitudinal studies are needed to determine their prognostic and clinical value. Full article

In vertebrates, circulating and tissue-resident immune cells contribute to host defense and tissue homeostasis. Their balance is maintained through hematopoiesis and anatomical reservoirs. In mollusks, this balancing is poorly investigated. Thus, the dynamics of circulating and tissue-resident hemocytes after a hemolymph withdrawal were analyzed in Pomacea canaliculata, a snail that allows repeated hemolymph withdrawals and possesses posterior kidney (PK) cell aggregates known as hemocyte islets. In the absence of specific hemocyte markers, flow cytometry data revealed dynamic changes in circulating hemocyte number and populations within 48 h following hemolymph withdrawal. In addition, computer-assisted image analysis of histological sections showed a significant transient reduction in PK hemocyte-islet area. Analyses of publicly available P. canaliculata transcriptomes revealed predominant and highly variable expression of two hematopoiesis-related genes, namely Transglutaminase (PcTGase) and Hematopoietically expressed homeobox protein (PcHhex), in circulating hemocytes and PK. Fluorescence in situ hybridization highlighted constitutive PcTGase and PcHhex expression within PK hemocyte islets. RT-qPCR experiments confirmed transcriptomic data on PcTGase and PcHhex constitutive expression, without significant modulation following hemolymph withdrawal. Collectively, these findings provide the first evidence of parallel recovery dynamics involving circulating hemocytes and PK hemocyte islets in P. canaliculata. Full article

Soil moisture content (SMC) is critical for crop growth and precision irrigation. Multi-angular remote sensing can capture directional reflectance variations that may provide additional information related to crop water status. However, the potential of hotspot-sensitive multi-angular observations for root-zone SMC estimation remains insufficiently explored, particularly in winter wheat. This study evaluated the potential of hotspot-sensitive multi-angular observations for root-zone SMC estimation using UAV observations and the LESS (LargE-Scale remote sensing data and image Simulation framework) radiative transfer model. Multispectral imagery of winter wheat was acquired under four irrigation treatments (T1–T4). The LESS model was parameterized using field-measured canopy characteristics to simulate bidirectional reflectance factors (BRFs) across the observation hemisphere. The simulated and observed datasets were used to analyze hotspot dynamics and evaluate the contribution of hotspot-sensitive observations to SMC estimation. Among the investigated bands, hotspot characteristics at 560 nm exhibited the strongest response to water stress. LESS-simulated BRFs showed greater angular variability (CV ≈ 0.30) than UAV-observed BRFs (CV ≈ 0.18). The highest SMC estimation performance was obtained in hotspot-sensitive backscattering directions, with an R² ≈ 0.60 and an RMSE ≈ 0.022 m³ m⁻³. These findings indicate that hotspot-sensitive multi-angular observations provide useful complementary information for SMC estimation. Full article

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

Alcohol dependence or alcohol use disorder is a chronic mental disorder that imposes a heavy global burden. Small extracellular vesicles (EVs) are emerging as a promising source of real-time information about brain status, yet existing studies are predominantly cross-sectional. We aimed to characterize longitudinal changes in EV properties and immunophenotype during alcohol withdrawal treatment. In our study plasma-derived EVs were analyzed and immunophenotyping via flow cytometry was performed to measure the concentrations of vesicle fractions carrying erythrocyte-, platelet-, endothelial-, and neuronal-associated markers in patients undergoing alcohol withdrawal and in healthy volunteers. Patient-derived EVs exhibited greater size heterogeneity, and microscopy images revealed vesicles with altered morphology. Concentrations of EVs bearing NLGN3 were lower in patients. CD62P+ and CD146+ EVs were also less represented in patients. Notably, CD31+ EVs separated into two distinct clusters; their concentrations were higher in patients with a binge drinking pattern but markedly lower in those with a steady pattern. The findings reveal dynamic phenotypic shifts in circulating EVs during alcohol withdrawal. The divergent patterns according to alcohol use history highlight the potential of EV profiling for personalized monitoring and underscore the necessity of longitudinal designs in vesicle-based biomarker research. Full article

The tourism sector in the Sultanate of Oman is central to “Oman Vision 2040”, with a strategic focus of the government on its dynamic transformation. Coastal regions, vital to tourism, are affected by changes to the coastline due to flash floods, sea-water flooding, and erosion. Despite its implications for tourism and the economy, this topic remains relatively under-explored, especially as to use of Sentinel-1 satellite images. This study assesses water-level changes due to erosion in the urban coastal region of the Al-Batinah governorate via land cover classification. Using the Support Vector Machine (SVM) classification technique, the overall accuracy is found to be 97.7% and the Kappa coefficient value for the year 2018 is 1.0. Although, when using the Random Forest (RF) classification technique, the accuracy is nearly identical, there is varying precision for the water area. A critical observation is made, showing significant increase of the water area from 2.99% in the year 2017 to 12.36% in the year 2025, suggesting water encroachment. With fixed-effect and combined-effect size meta-analysis models, the confidence levels were identified as 95.0% and 0.37, respectively, indicating a consistent variation in water area that supports the outcomes of image classification. This study offers a valuable insight for policymakers as to managing coastal regions, along with providing assistance to vulnerable coastal communities. The study focuses on a particular governorate, given the satellite images, whereas a broader regional comparison would address the limitation of the generalizability of results. In the future, the research could integrate surveys from coastal communities and businesses for a comprehensive qualitative data perspective on the region’s tourism sector. Full article

Most studies of Wang Bi’s “Mingxiang” 明象 (“Clarifying the Images”) treat its “grasping the meaning lies in forgetting the words” as a methodological rule for interpreting the Yijing, overlooking the deeper philosophical position behind it. We argue that this position shifts the inquiry from objective entities assumed by other Wei-Jin thinkers to the mind’s own activity, offering an epistemological and religious perspective on how the Yijing conveys the sages’ insight. On this basis, we reinterpret two key terms in Wang Bi’s reading: jin 盡 should be understood not as “exhaust” but as “express properly”, capturing the dialectical interplay of “loss” and “gain” in the presentation of meaning; wang 忘 should be understood in the sense of Aufhebung (sublation), which negates, preserves, and transcends images and words. Wang Bi’s insight is that the “words–images–meaning” triad forms a living unity that emerges only within the concrete activity of the mind. Through imagination, images are not fixed signs but self-transcending movements toward meaning. A concrete reading of the Mingyi 明夷 hexagram illustrates this dynamic, and an analysis of the Chinese script (revealing the etymological link between yan 言 and yin 音 and the internality of yi 意) further supports the argument that meaning is inseparable from the mind’s activity. The comparison with Augustine’s distinction between vox and verbum then brings to light the universal religious significance of this hermeneutics framework: the “words–images–meaning” triad offers a model of how the transcendent becomes accessible through symbols, and a vision of scriptural reading as spiritual cultivation. “Clarifying the Images” thus advances not merely a hermeneutic method within the Yijing tradition, but opens an epistemological path that takes the mind’s own activity as its starting point. This, we hold, is the universal philosophical and religious value of Wang Bi’s text, and a contribution deserving attention in the study of how the Yijing informs the mind’s grasp of meaning. Full article

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes wavefields generated by a large transmission tower collapse and sledgehammer impacts used for subsurface imaging. The wavefields were recorded simultaneously by a 1.4 km DAS array and 71 collocated nodal stations (NSs). Using 63 DAS–NS pairs, we quantify the strain-to-velocity conversion method performance using amplitude and phase transfer functions (TFs) between DAS-derived and NS particle velocity records, with the root-mean-square error (RMSE) evaluated across three frequency bands: 0.5–100 Hz, 1–10 Hz, and 10–100 Hz. The results show that fk-rescaling provides the most stable amplitude response across both source types, while both the fk-rescaling and slant-stack methods generally yield the best phase agreement. Curvelet-based conversion shows a greater variability and larger RMSE values. All methods yield a poorer amplitude reconstruction at higher frequencies, while the phase content is generally preserved more reliably than amplitudes. Differences between the tower collapse and sledgehammer sources demonstrate the influence of the source characteristics and spatial processing window length on the conversion performance. The findings provide practical guidance for selecting suitable strain-to-velocity conversion methods for active-source DAS applications, particularly where collocated reference sensors are unavailable. Full article

Introduction: Chronic stroke frequently causes structural and functional impairments in the tibialis anterior (TA) muscle, leading to foot-drop and altered gait kinematics. We investigated the relationship between ultrasonographic parameters of the TA muscle and functional gait performance in chronic stroke survivors. Methods: This cross-sectional, observational pilot study evaluated eight consecutive chronic stroke patients (>6 months post-stroke). Structural parameters of TA (rest and contraction thickness, pennation angle) were documented by ultrasound imaging. A dynamic feature calculated was the Contraction Index (CI = effort/resting thickness). Functional metrics included the Medical Research Council (MRC) muscle strength scale and the 10-Meter Walk Test (10MWT) for gait velocity. Results: Statistical analysis revealed a moderate negative correlation trend between resting TA muscle thickness and functional gait speed (r = −0.618, p = 0.102). Conversely, a moderate positive correlation trend was found (r = 0.548, p = 0.160). Ultrasound imaging successfully differentiated three distinct pathological phenotypes: an atrophic phenotype (low pennation angle, flaccid muscle failure), a severely shortened spastic phenotype (increased resting thickness, high pennation angle, pathological CI < 1.0), and a spastic co-contraction loop. Patients with the atrophic phenotype achieved high mechanical efficiency with an ankle–foot orthosis (AFO), whereas the spastic phenotype exhibited resistance against the orthotic device. Conclusions: Musculoskeletal ultrasound provides objective parameters for post-stroke TA muscle remodeling and contributes to completing the assessment and therapy. Identifying specific structural muscle phenotypes allows clinicians to optimize target-specific neurorehabilitation strategies, predict AFO efficiency, and guide antispastic interventions such as botulinum toxin injections. Full article

People counting is one of the key tasks in intelligent monitoring systems. However, accurately counting people in dynamic environments can be extremely challenging. This is especially true in mobile robot applications, where challenges such as a moving camera, varying conditions, and a limited field of view due to environmental obstacles arise. In such scenarios, the people counting task primarily involves visually detecting and grouping individuals to determine the total number of unique people. This paper presents a people counting algorithm based on visual people detection and clustering. The method utilizes the You Only Look Once (YOLO) detector to identify the bounding boxes of detected individuals and extract features from their corresponding regions of interest (ROIs). Additionally, the dimension of the extracted features is reduced using an encoder and clustered to distinguish individuals, with the number of clusters serving as an estimate of the number of people. The method was tested on a dataset containing 45 independent sequences with a total of 19,350 RGB images, complete with metadata for people detection and re-identification. This dataset encompasses various settings, particularly scenarios featuring mobile robots moving and capturing frames in indoor environments. The experiments demonstrate the effectiveness of the proposed method in different configurations. The best results were achieved using the YOLOv10n detector combined with K-means or SK-means clustering, yielding a Mean Absolute Error ($MAE$) of 1.11. The proposed encoder-based method significantly reduces clustering time and operates effectively within the limited resources available on mobile robotic platforms such as the Jetson Nano. Full article

Robotic percutaneous coronary intervention (PCI) remains predominantly teleoperated, while the most demanding part of the procedure, guidewire navigation through a moving coronary tree under fluoroscopy, still depends on the continuous human interpretation of vessel anatomy, cardiac phase, guidewire position, and target location. We present a preclinical perception-to-action framework for AI-driven robotic PCI that integrates fluoroscopic perception, dynamic coronary vessel memory, vessel-coordinate state estimation, and robot-executable guidewire command generation on a robotic PCI platform. During contrast angiography, phase-indexed vessel–catheter templates and a dynamic vessel-coordinate coronary map are generated. During guidewire manipulation without contrast injection, live fluoroscopy is segmented into catheter and guidewire structures, cardiac phase is estimated by overlap between the live catheter–guidewire skeleton and stored vessel–catheter templates, the guidewire is assigned to the most likely vessel branch, and the distal tip is projected to a vessel-coordinate target representation for robotic action generation. The segmentation dataset contained 6269/1567/957 vessel images, 3857/964/537 guidewire images, and 4796/1199/667 catheter images for training/validation/test splits, respectively. Test-set Dice scores were 90.7% for vessels, 92.6% for catheters, and 89.0% for guidewires. In 623 real-time fluoroscopy frames from physician-supervised animal experiments, phase selection accuracy was 589/623 (94.6%; 95% CI, 92.5–96.1%) and vessel assignment accuracy was 575/623 (92.3%; 95% CI, 89.9–94.1%). Across 34 scenario-level episodes and their associated command-level decisions, correct-command rates ranged from 83.6% to 95.6% across target navigation and safety scenarios. These results provide preclinical evidence that live fluoroscopic perception can be converted into robot-executable coronary guidewire actions within an integrated AI-robotic PCI workflow. The study was designed to establish early system feasibility rather than to prove clinical superiority, large-scale generalization, or comparative advantages over manual or teleoperated robotic PCI. Full article