Search Results (4,507)

Background/Objectives: High-grade endometrial cancer (EC) is associated with poor outcomes, particularly in populations with a high burden of aggressive histologies. There is a critical need for accessible biomarkers to improve prognostic assessment and guide clinical management. Methods: In this study, we evaluated the feasibility and clinical relevance of monitoring circulating tumor DNA (ctDNA) by tracking somatic TP53 mutations using a routine next-generation sequencing (NGS) assay already implemented in diagnostic practice. Results: Among 21 patients with high-grade EC carrying TP53 mutations in the primary tumor, ctDNA was detectable in over 75% during follow-up. Baseline ctDNA detection strongly correlated with advanced disease: none of the FIGO I tumors were ctDNA-positive at diagnosis, whereas 73% of FIGO > I tumors showed detectable ctDNA. Patients with ctDNA detected at baseline had significantly poorer outcomes, with a 2-year recurrence-free survival (RFS) of 18% versus 60% and a 2-year overall survival (OS) of 40% versus 78%. Longitudinal monitoring revealed that postoperative persistence or reappearance of ctDNA was consistently associated with disease progression, often preceding radiological relapse. Conversely, early ctDNA clearance (at M4–M8) was associated with more favorable clinical trajectories. Conclusions: These findings highlight the potential role of ctDNA as a real-time molecular marker of minimal residual disease and tumor dynamics. Our results demonstrate that TP53-based ctDNA tracking using a standard NGS panel is feasible, sensitive, and clinically informative in high-grade EC. This approach may contribute to improving prognostic stratification and enabling more personalized, responsive clinical management, particularly in high-risk populations. Full article

Background/Objectives: Secondary prevention after a first acute myocardial infarction (AMI) is crucial to reduce complications and improve long-term outcomes. Persistent gender inequalities in cardiovascular care suggest differences in post-AMI healthcare pathways between men and women. Understanding these trajectories could guide post-discharge clinical management, secondary prevention, and follow-up after acute myocardial infarction. This study aimed to describe healthcare pathways following a first AMI and explore gender inequalities within these trajectories. Methods: We conducted an observational, population-based study using real-world data (RWD) from the CARhES cohort. A total of 4298 individuals discharged alive after a first AMI between 2017 and 2022 were included. Healthcare trajectories during the 90 days following discharge were reconstructed across primary and specialised care, emergency services, and hospital admissions, and stratified by sex and the occurrence of clinical outcomes. Results: Post-AMI care pathways were highly heterogeneous. Although general practitioners often served as the first point of contact, most trajectories began in emergency departments. Patients with clinical outcomes showed higher healthcare utilisation. Women accessed primary care more frequently, whereas men showed greater use of specialised services and higher readmission rates, patterns that may reflect differences in follow-up strategies and clinical management during the early post-discharge period. Despite this variability, overall trajectories showed no significant sex-based differences. Conclusions: Post-AMI care pathways are complex and variable, with gender differences in patterns of service use. Women more often accessed scheduled care, while men relied more on emergency services. These findings highlight the need for gender-sensitive post-discharge follow-up to guide clinicians in secondary prevention strategies for AMI. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

Mountain forests on the western edge of the Sichuan Basin are challenging to monitor at high resolution because rugged topography, cloud cover, and Landsat-7 SLC-off artifacts create data gaps, while the 2008 Wenchuan earthquake and subsequent restoration further alter vegetation dynamics. We fused Landsat 5/7/8/9 surface reflectance with MODIS MOD13Q1 using an index-then-fusion STARFM framework to reconstruct a continuous 30 m NDVI record for 2000–2024 and quantified forest fraction dynamics using annual forest/non-forest maps, transition analysis, and K-means clustering of pixel-wise NDVI trajectories. To identify dominant controls, we applied a multi-output random forest with spatial block cross-validation and SHAP attribution. The fused NDVI agrees well with MODIS across 100,000 samples (R² = 0.953; RMSE = 0.032), and the regional mean NDVI increased from 0.711 (2000) to 0.774 (2024), showing a post-2008 decline–stagnation–recovery pattern. Forest fraction rose from 48.2% to 72.9%, with accelerated gains after 2010 (+21.4%), and improving trajectories dominated (70.95%), concentrating near the Longmenshan fault zone. The driver model generalized well (micro-mean R² = 0.875), and SHAP ranked elevation (32.6%) and initial forest fraction (32.3%) above temperature and precipitation. These results provide high-resolution evidence of mountain forest change and its primary controls to support terrain-informed ecological management. Full article

Data-centric approaches are increasingly shaping wireless communication research, where the availability and quality of datasets directly influence the reliability of learning-based and model-driven methods. In this context, unmanned aerial vehicle (UAV) communication poses unique challenges, as it requires datasets that jointly capture geometric information, propagation conditions, and diverse link configurations. This work introduces a geometry-aware UAV communication dataset designed to support research on controlled UAV communication link directions and propagation scenarios. The dataset is generated using standardized 3GPP and ITU-R channel models across multiple urban, suburban, and rural regions, accounting for variations in altitude, carrier frequency, and node distribution. The dataset provides spatially resolved channel parameters along with geometry-rich files containing environmental features, which can be used to extract relevant parameters for UAV communication studies. These data support reproducible research in geometry-aware channel modelling, path-loss prediction, LOS/NLOS analysis, delay-related modelling, and trajectory-conditioned link-quality analysis. Full article

In this study, we propose a structured valuation framework for non-fungible tokens (NFTs), a distinct class of digital assets whose pricing mechanisms remain insufficiently understood. Based on previous empirical studies and illustrative case analyses of three major NFT collections, we synthesize insights from non-cash-flow asset theory, market microstructure, and behavioral finance to construct a four-layer valuation framework consisting of the Asset, Market, Technology, and Ecosystem layers. We identify three NFT-specific mechanisms—verified digital scarcity, pseudonymous signaling, and on-chain herding—that modify or extend traditional valuation paradigms. Empirical evidence from the literature suggests that rarity-driven asset features and social-influence dynamics are dominant price determinants, while wash trading, fragmented liquidity, and platform incentive structures generate persistent distortions in price discovery. Case analyses of CryptoPunks, Bored Ape Yacht Club, and Pudgy Penguins demonstrate how differing risk exposures across the four layers translate into distinct valuation trajectories. With this framework, we obtain a basis for improved risk assessment, regulatory oversight, and business model design in NFT markets. Full article

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions. Full article

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. Full article

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss. Full article

We develop a local, patchwise geometric framework that embeds a broad class of potential Hamiltonian dynamical systems into a family of Riemannian Hamilton patches built over an underlying Gutzwiller manifold. We adopt a conformal (Jacobi) ansatz and a frame-adapted reconstruction procedure, through which we construct, on each patch, a pulled-back metric, along with a reduced (truncated) connection (not a metric-compatible connection) and a corresponding dynamical curvature tensor governing geodesic deviation in the Hamilton coordinates. Then, using the Poisson–Hodge reconstruction, we reconstruct coordinate potentials, enforcing harmonic obstructions, and along with exactness and Jacobian nondegeneracy conditions, we obtain explicit elliptic bounds that control the connection and curvature residuals. On the basis of this construction, we formalize the notion of a Hamilton manifold such that reparametrized geodesics approximate Newton trajectories with controlled acceleration and tolerances. As a generalized structural framework, to promote the local Jacobi reconstructions to a coherent dynamical evolution and provide a dynamical closure, we introduce a patchwise hyperbolic geometric flow for the pullback metric coupled to a kinetic (Vlasov) closure that controls reconstruction and curvature residuals. Under natural regularity, ellipticity, and overlap-tolerance assumptions, together with precise estimates that control the reconstruction and curvature errors, we establish short-time well-posedness of the coupled Vlasov–hyperbolic geometric flow that defines the patchwise Hamilton manifold. Motivated by this construction of the Hamilton manifold with atlas-dependent time, we propose convergence and stability conjectures for dissipative and conservative (non-dissipative) hyperbolic geometric flows. On a single patch, these conjectures characterize local orbital stability (in the sense of coercivity modulo symmetry) and identify local linear instability when unstable linear modes are present. On a finite atlas (the Hamilton manifold with atlas-dependent time), we state conjectures under which local stability propagates to global stability, provided that overlap residuals remain uniformly sufficiently small. The framework identifies the geometric origin of local instability diagnostics used in Hamiltonian mechanics and outlines a practical strategy for verifying stability or instability, numerically or analytically, on finite coverings of configuration space (the Hamilton manifold). Full article

The perception that hormonal contraception causes weight gain is a general belief that frequently hinders the initiation and continuation of effective family planning. This narrative review analyses data from Cochrane systematic reviews and recent pharmacogenomic studies to separate patient perception from metabolic reality. Analysis of high-quality data, including Cochrane systematic reviews, indicates that the association between Combined Hormonal Contraceptives (CHCs)—including oral pills, the transdermal patch, and the vaginal ring—and weight gain is not supported by consistent high-quality evidence. Placebo-controlled trials demonstrate that these methods are weight-neutral on average. Perceived weight increases in CHC users are likely mediated in part by fluid retention linked to the estrogenic stimulation of the Renin–Angiotensin–Aldosterone System (RAAS), rather than adipose tissue accumulation. Conversely, Depot Medroxyprogesterone Acetate (DMPA) represents a verified clinical risk for weight gain, showing a demonstrated clinical association with significant fat mass accumulation. Hypothesized biological mechanisms for this increase include hypothalamic appetite stimulation and glucocorticoid-like activity. The etonogestrel implant occupies a complex middle ground. While population-level data suggests weight neutrality, recent exploratory pharmacogenomic research has identified a specific variant in the Estrogen Receptor 1 (ESR1) gene. For the minority of women carrying this variant, the implant may trigger clinically significant weight gain, suggesting a biological basis for their subjective experience despite statistical evidence. Ultimately, the persistence of the weight gain concern is fueled by the nocebo effect and the misattribution of natural age-related weight trajectories to contraceptive use. Full article

In this paper, we study anomalous scattering and weak interactions of lumps for the $(2+1)$-dimensional generalized Kadomtsev-Petviashvili equation. Together with the long-wave-limit method, the normal scattering lump solutions are obtained. By adding perturbation terms to the normal scattering lump solutions, the anomalous scattering of two-lump and three-lump solutions is derived. The weak interactions among one soliton with two lumps, two solitons and two lumps as well as the interactions between one normal lump and two anomalous lumps are constructed. The dynamic properties of these anomalous scatterings and weak interactions are analyzed in detail. In these hybrid anomalous scattering solutions, from the long-time asymptotic behavior of the solutions, we find that the peak trajectories separate as $\sqrt{t}$ when $\left|t\right|\to \infty$. In particular, a head-on collision of two lumps leads to 135° scattering. Moreover, the anomalous lumps exhibit the same dynamical properties when they collide with solitons. The results described in this paper could also be generalized to the other (2 + 1)-dimensional integrable systems. Full article

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites. Full article