Search Results (5,488)

Background/Objectives: Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder. This retrospective study evaluated the demographic, genetic, and clinical characteristics of children diagnosed with DMD before age three to understand early clinical presentation profiles. Methods: The cohort included 198 boys diagnosed with DMD before three years of age between January 2020 and July 2025. Medical records, serum creatine kinase (CK) levels, language milestones via Denver II criteria, and multi-exon deletion maps were retrospectively evaluated. Results: Regarding the diagnostic entry pathways, the initial clinical trigger that led to medical investigation was incidental hyperCKemia in 91.4% of cases. Regardless of the presentation trigger, a definitive, confirmed diagnosis was established in all 198 cases: 196 patients (99.0%) were securely confirmed via genetic testing (MLPA or sequencing), while 2 patients (1.0%) with negative genetic panels were confirmed via muscle biopsy demonstrating a complete absence of dystrophin expression. Genetic analysis revealed deletions in 77.8% of patients, predominantly multi-exon deletions clustered in the distal hotspot region. Independent ambulation occurred at a median age of 16 months, and 14.6% achieved walking after 18 months. Delayed language development was observed in 29.8% of patients. Conclusions: Our findings indicate that early childhood DMD is characterized not only by early muscle involvement but also by prominent neurodevelopmental features. These findings underscore the value of early CK screening in young boys and support integrating standardized neurodevelopmental surveillance into early DMD care. Full article

Under the dwarf-rootstock dense planting method, existing apple detection models for intelligent harvesting suffer from excessive parameter counts that hinder deployment on resource-constrained devices, while lightweight alternatives often sacrifice detection accuracy. To address this dilemma, this paper proposes YOLO-SPM, a lightweight apple detection model based on the YOLOv12n architecture, specifically designed for complex orchard environments. The core innovation lies in a problem-driven, three-stage collaborative optimization strategy: first, PConv is introduced to replace standard convolutions in the A2C2f module, reducing computational redundancy by exploiting channel-wise feature similarity of apple targets; second, the parameter-free SimAM attention mechanism is embedded in the neck network to enhance the model’s focus on occluded fruit features without increasing model size, while MBConv is integrated into the detection head to further reduce computational cost; third, WIoU v3 is adopted as the loss function to compensate for the accuracy loss incurred by lightweight design through its dynamic focusing mechanism on difficult samples. This complementary design ensures that each module addresses a distinct bottleneck of the native YOLOv12n in orchard scenarios, achieving a balance between efficiency and accuracy rather than simple module stacking. Experimental results demonstrate that YOLO-SPM achieves a precision of 92.8% and mAP@0.5 of 93.1%, outperforming the baseline by 4.8 and 5.3 percentage points, respectively, while reducing parameter count, FLOPs, and memory footprint by 40.2%, 35.4%, and 41.8%. This study provides a feasible solution for high-precision apple identification in dwarf-rootstock dense planting orchard environments, with the potential for integration into automated harvesting systems upon future on-device validation. Full article

Heavy metal (HM) contamination in urban soils is a pressing global issue, particularly in rapidly industrializing regions like Kazakhstan, where anthropogenic activities such as transportation, energy production, and manufacturing exacerbate accumulation in ecosystems. In Almaty, the largest city in Kazakhstan, urban expansion and legacy pollution pose risks to soil functions, biodiversity, and public health through bioaccumulation and migration pathways. This study evaluates the spatial distribution and ecological impacts of total heavy metal concentrations (HMs) (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in Almaty’s soils to inform remediation strategies. Soil samples (n = 73) were collected using a systematic grid sampling method across urban, industrial, and peri-urban zones in Almaty. HM concentrations were determined via X-ray fluorescence spectrometry (XRF) following GOST 33850-2016 standards. Pollution indices (contamination factor K_c and integrated pollution index Z_c) were calculated relative to Kazakhstani permissible limits (PDK RK) and Russian approximate permissible concentrations (ODK RF). Statistical analyses included Spearman’s correlation, boxplots, and coefficient of variation. Morphological, physicochemical (pH, humus content), and biological assessments evaluated degradation. Spatial interpolation via GIS mapped the hotspots. HM distributions showed significant variability, with As, Zn, and Ni exceeding norms in >90% of samples (median K_c ≈ 5 for As). Z_c classified >70% of sites as hazardous or extremely hazardous (Z_c > 32), with hotspots in central-eastern districts (Z_c 90–145). Strong correlations (ρ ≥ 0.6) identified a technogenic group (Pb–Zn–Cu–Ni) from traffic and industry, contrasting predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution induced soil compaction, reduced humus/pH, and disrupting biogeochemical cycles. Local exceedances were noted near TECs, factories, and transport hubs. Almaty’s soils exhibit pervasive technogenic HM pollution, driven by urban sources, leading to ecosystem degradation and health risks. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Prioritized monitoring and phytoremediation in hotspots are recommended to enhance resilience, aligning with UN SDGs for sustainable cities and ecosystems. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Full article

The objects in high-resolution remote sensing images usually exhibit arbitrary orientations, multi-scale variations, dense distributions, and complex background interference, posing significant challenges to oriented object detection. Although existing DETR-style end-to-end detectors eliminate the need for anchor design and non-maximum suppression, they still suffer from insufficient orientation priors in object queries, limited orientation consistency in decoder feature interaction, and unstable set matching for oriented bounding boxes. To address these issues, this paper proposes an end-to-end Transformer framework, termed OPT-Net (Orientation-Preserving Transformer Network), for oriented object detection in remote sensing images. OPT-Net treats orientation information as a structured geometric prior and propagates it through query initialization, feature interaction, and matching optimization. Specifically, an Orientation-Aware Query Initialization (OAQI) module is designed to generate initial queries using center confidence and orientation priors. An Orientation-Consistent Cross-Attention (OCCA) mechanism is proposed to perform orientation-conditioned modulation on Value features while keeping the standard Query–Key attention computation unchanged. Furthermore, an Uncertainty-aware Matching Loss (UML) is introduced to incorporate instance-level geometric uncertainty into Hungarian matching and regression optimization. Experimental results on the DOTA-v1.0 and HRSC2016 datasets show that OPT-Net achieves 76.83% and 90.58% mAP, respectively, demonstrating competitive detection accuracy and adaptability to complex remote sensing scenarios. Ablation studies and visualization results further validate the effectiveness of each proposed module. Full article

Background: Herpes zoster (HZ), commonly known as shingles, and post-herpetic neuralgia (PHN) represent growing public health concerns, particularly among older adults. Despite the established efficacy of the herpes zoster vaccine (HZV), global uptake remains suboptimal. Objectives: This scoping review maps evidence from Saudi Arabia evaluating the baseline knowledge, awareness, attitudes, acceptance, hesitancy, and clinical uptake of the HZV among general adults, high-risk populations, and healthcare workers (HCWs). Methods: The JBI and PRISMA-ScR methodological frameworks were strictly adhered to during mapping. Eligible sources included peer-reviewed, observational cross-sectional studies conducted in Saudi Arabia and published in English between 2022 and 2026. The search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. Data were systematically extracted and charted using a standardized digital piloting framework to capture study characteristics (author, year, and region), sample sizes, target populations, knowledge percentages, actual vaccine uptake rates, and self-reported barriers. Results: Out of 25 retrieved records, 19 unique primary studies were mapped. Public knowledge of HZ complications and vaccine eligibility criteria was consistently low to moderate, falling below 50% across most cohorts. Conversely, while verbal willingness to receive the vaccine was highly favorable (ranging from 60% to 75%), a profound “intention–behavior gap” was observed, with actual clinical uptake being below 10%. Key barriers included a lack of public health campaigns, safety concerns regarding reactogenicity, online misinformation, and a lack of proactive provider communication. For HCWs, barriers included unclear local guidelines and a lack of workplace mandates. Ultimately, a proactive physician recommendation was identified as the single most powerful clinical facilitator, increasing vaccine acceptance by over 80% across all cohorts. Conclusions: While the shingles vaccine is now distributed completely free across Saudi Arabia, high public willingness has not translated into actual vaccination rates (10%) due to low public awareness of disease severity. Free vaccine availability alone is insufficient; primary care systems must shift from a passive delivery model to an active, provider-driven framework to successfully close this gap Full article

Grid-based multi-agent path finding (MAPF) solvers scale to large teams, but their discrete schedules may not provide high-quality continuous finite-radius motions near the square-grid corner-passing threshold. We study endpoint-time-preserving geometric smoothing for disk agents at radius $0.35$. We establish an embedded-graph corner-passing threshold for synchronized finite-radius local passes and derive the square-grid radius $r_c=\sqrt{2}/4$. Finite-radius realizations are formulated as Lipschitz trajectories, and we prove that standard four-neighbor schedules without vertex conflicts or head-on edge swaps are pairwise continuously feasible up to this threshold. The smoother replaces windows by shortcuts only when speed, obstacle-clearance, pairwise continuous-collision detection, and length checks pass. Accepted shortcuts preserve endpoint times, schedule-level makespan, discrete arrival records, and discrete sum-of-costs while enforcing geometric length non-increase; the strict-decrease subset yields the reported geometric sum-of-costs reductions. Across six MovingAI map settings, LaCAM solves 575 benchmark instances; 570 smoothed trajectories pass finite-radius validation, with median geometric sum-of-costs reductions of 9.9% on the main slice and 11.2% on the five-map extension. A targeted 100-agent radius sweep further supports the threshold interpretation by showing a clean feasibility transition around the predicted corner-passing radius. The results support time-preserving smoothing as a validated geometric-quality layer for scalable grid planners. Full article

Accurate and up-to-date mapping of built-up areas is of great importance for sustainable urban planning, disaster management, and the monitoring of environmental changes. In this study, a residual U-Net-based deep learning architecture named FiveBandTTA is proposed for built-up area segmentation from Sentinel-2 multispectral satellite imagery. The proposed model aims to simultaneously learn spatial and spectral features by jointly processing RGB, NIR (B8), and SWIR (B11) bands within the same encoder–decoder structure. The model incorporates standard residual blocks following the conventional residual learning principle, multi-level skip connection mechanisms, and TTA-based inference strategies. Within the scope of the study, a multi-temporal built-up area dataset was constructed from Sentinel-2 imagery acquired over Kocaeli Province. The performance of the proposed model was comparatively evaluated against RGB Baseline, FiveBand Single, DeepLabV3+, and SegFormer models. Experimental results demonstrated that the proposed model achieved the highest segmentation performance among all compared approaches, obtaining 0.8447 IoU, 0.9124 Dice, and 0.9249 Precision scores. It was observed that the use of multispectral bands together with the residual encoder–decoder structure may contribute to improved representation of small-scale built-up regions and complex boundary structures. Furthermore, the comparative experiments indicated that the NIR and SWIR bands provide complementary spectral information for distinguishing built-up areas, while the TTA-based inference strategy may contribute to improved segmentation stability and prediction consistency. Overall, the obtained results demonstrate that the proposed approach is an effective and robust method for built-up area segmentation from medium-resolution Sentinel-2 imagery. Full article

Background: Accurate presurgical localization of eloquent cortex and subcortical pathways is essential in neurosurgery, guiding the balance between maximal safe resection and preservation of neurological function. This narrative review compares the clinical utility of task-based functional magnetic resonance imaging (tb-fMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) in neurosurgical populations, with emphasis on brain tumors and epilepsy. Methods: This narrative review was based on a non-systematic literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar from database inception to March 2026. The review focused on tb-fMRI and rs-fMRI for presurgical functional mapping in neurosurgical populations, including clinical utility, feasibility, validation, limitations, and workflow integration. Results: Tb-fMRI remains the most established noninvasive modality for motor and language mapping and language lateralization because of its task-specific activation maps and established role in clinical workflows. However, its use is limited by dependence on patient cooperation, task performance, and intact neurovascular coupling; thus, aphasia, cognitive impairment, fatigue, paresis, pediatric age, sedation, and tumor-related neurovascular uncoupling may render tb-fMRI inconclusive or misleading. Rs-fMRI offers a task-free alternative based on intrinsic functional connectivity, enabling simultaneous mapping of multiple resting-state networks from a single acquisition and providing particular value in non-cooperative, cognitively impaired, aphasic, pediatric, or sedated patients. Evidence indicates that rs-fMRI is most robust for sensorimotor mapping, with reported agreement with tb-fMRI and intraoperative direct electrical stimulation, whereas language mapping remains less consistent and more dependent on analytical methodology. Neither modality replaces intraoperative stimulation, which remains the reference standard. Conclusions: Current evidence supports a multimodal presurgical strategy in which tb-fMRI is used first-line in cooperative patients; rs-fMRI is added when task-based mapping is limited or infeasible, and both are interpreted alongside tractography, neuronavigation, and intraoperative mapping. Full article

Implicit bias, automatic attitudes or stereotypes outside conscious awareness, may influence clinicians’ communication, diagnosis, and treatment decisions, contributing to inequities in care. We conducted a scoping review to map measurement strategies used to assess implicit bias among health professionals and students in healthcare and training settings. Using Joanna Briggs Institute guidance and PRISMA-ScR, we searched PubMed, Embase, BVS, Google Scholar, and institutional repositories for studies to November 2025; two reviewers independently screened and charted data (protocol was developed a priori but submitted internal in organization, and then uploaded in OSF. Of 1864 records, 93 studies from 28 countries were included. We identified 57 bias domains, most often race/ethnicity, weight, and sexual orientation. Across studies, 42 unique instruments were reported; the Implicit Association Test was most common, while psychometric validation and administration details were frequently limited, constraining comparability and interpretation. Evidence gap mapping showed concentration in academic and hospital settings, with fewer studies in primary care or community contexts and limited attention to age, disability, and intersectionality-related biases. The evidence base is growing but fragmented; future work should prioritize standardized administration and reporting, stronger validation, and tools that better capture automatic responding across diverse identities and care settings to support education and equity-oriented interventions. Full article

Real-time kinematic (RTK) navigation, which enables centimeter-level positioning accuracy through carrier-phase differential correction, provides high-accuracy positioning for orchard robots, but short-term outages caused by canopy occlusion and signal interference may interrupt path guidance and increase lateral drift. To address this issue, this study proposes a vision-based takeover navigation method for orchard robots under short-term RTK failure conditions. First, an improved YOLOv11-based road segmentation and completion model, termed YOLOv11-VF, was developed. By introducing a Squeeze-and-Excitation (SE) channel attention mechanism, the model jointly perceives visible road regions and occluded road completion regions, thereby producing continuous and complete road semantic representations. Second, a structured geometric road representation was constructed from the segmentation results to extract the navigation reference line, and a joint direction-position constraint mechanism was established by integrating the reference line with the robot reference view axis. A hierarchical constraint strategy based on a travel corridor and a deadband region was further designed to jointly determine heading deviation and lateral drift. Finally, road segmentation, navigation-line extraction, parameter analysis, and vision-based takeover experiments were conducted in a standardized orchard environment. The results showed that YOLOv11-VF achieved Precision, Recall, AP₅₀, mAP_@0.5:0.95, and F1 values of 92.31%, 88.56%, 94.40%, 67.41%, and 90.40, respectively, showing the best overall segmentation performance among all compared models while maintaining good real-time performance. The proposed method also demonstrated high consistency in navigation-line extraction and maintained mean absolute deviations of 0.0176 ± 0.0041 m to 0.0718 ± 0.0138 m during RTK outage intervals over 10 repeated trials, indicating good path-following capability and operational stability. Full article

Cybersecurity risk management is often complicated by fragmented solutions for threat identification and detection, vulnerability assessment, and control selection across multiple frameworks. This paper presents a unified, dynamically updated, threat-based cybersecurity control platform that addresses this challenge by integrating Information Technology (IT), Operational Technology (OT), and Internet of Things (IoT) standards, including ISO/IEC 27001:2022, National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) 2.0, and IEC 62443-3-3. The platform enables (1) querying a selected threat to identify associated vulnerabilities, (2) recommending applicable security controls across multiple frameworks, and (3) identifying overlapping or unique controls to avoid redundant implementation. Automated integration of Common Vulnerabilities and Exposures (CVEs) from the NIST National Vulnerability Database (NVD) links vulnerabilities to mapped threats and controls, supporting proactive risk management. A structured evaluation was conducted across 100 threat scenarios spanning IT, OT, and IoT domains, producing approximately 1000 threat–control relationships across 3 integrated frameworks. Performance evaluation demonstrates that the platform is scalable. While integrating additional frameworks, it maintains an average query latency of 0.40 s to 0.43 s, which implies an insignificant incremental latency increase of 0.03 s, while its web-based interface provides dynamic querying and visualization in a user-friendly manner for technical and non-technical users. By unifying threat, vulnerability, and control data, the platform streamlines compliance, reduces control retrieval time, and ensures traceable, consistent, and cross-framework mitigation strategies, enhancing informed cybersecurity decision making. Full article

To address the difficulty of road-constrained weed extraction and operational assessment in orchard road regions under weed encroachment, background interference, and complex illumination, this study developed a vision-based framework integrating road segmentation, in-road weed extraction, spatial quantification, and workload evaluation. A joint image enhancement strategy combining LAB-based luminance correction, HSV-based color gain adjustment, ExG enhancement, and morphological refinement was first applied to improve the separability of green vegetation targets. An improved YOLOv11 with an SE attention mechanism was then used for robust orchard road segmentation. On this basis, road-region constraints and a dual-threshold HSV–ExG strategy were combined to extract in-road weeds and calculate global weed coverage. Furthermore, a geometry-adaptive grid based on actual road boundaries was constructed to quantify grid-cell coverage, aggregation, spatial heterogeneity, and workload index. Results showed that the proposed enhancement method increased the mean and standard deviation of ExG by 21.30% and 19.22%, respectively. The improved YOLOv11 achieved 91.28% precision, 87.52% recall, 93.37% AP₅₀, 68.31% mAP_@0.5:0.95, and 89.36% F1-score. Across five sample groups, global weed coverage ranged from 0.6123 to 0.6471, and the workload index ranged from 0.6403 to 0.6859. Overall, the proposed method establishes an integrated image-based analytical pipeline that may support future variable-rate weeding and decision-making after further operational validation. Full article

We present a novel method for deriving radially resolved dynamical chronometers from galaxy rotation curves, allowing galaxy assembly histories to be reconstructed directly from kinematic data. In the Nexus Paradigm, the baryonic Tully–Fisher relation is used to estimate the dynamical mass profile. We compare this profile with independently derived intrinsic baryonic mass distributions obtained from stellar Sérsic fits and gas surface-density measurement yields. This yields a radial ratio that maps to formation redshift with radial resolution. Inverting this ratio within a standard cosmological framework produces a radial lookback-time profile, representing the time since each radial shell last experienced dynamical reconfiguration. Applying the method to a pilot sample of seven SPARC galaxies, including both high- and low-surface-brightness systems as well as the Milky Way, reveals diverse age structures: stratified profiles associated with inside-out growth and flatter profiles consistent with coherent disk assembly. The method requires no dark-matter halo fitting and offers a kinematic chronometer that complements stellar population and chemical evolution approaches. The NP rotation-curve parameters were determined by minimizing the chi-squared statistic between the observed and predicted velocities using a two-stage optimization consisting of a global differential-evolution search followed by nonlinear least-squares refinement. Observational uncertainties were taken from the published rotation-curve data, supplemented by a 5 km s⁻¹ systematic error floor added in quadrature to account for non-circular motions and other unresolved systematics. We also show that the governing dynamical equation admits a gravitoelectromagnetic interpretation, in which a velocity-dependent term generates disk-wide torques that regulate angular momentum transport. This leads to a unified stability framework in which galaxy morphology emerges from a single parameter regime: balanced conditions favor a coherent spiral structure, whereas dynamically hot regimes naturally produce diffuse and ultra-faint systems. The cosmological scaling of the effective gravitomagnetic field further suggests that the spiral structure is partly regulated by cosmic time. Although the inferred ages depend on the accuracy of the baryonic mass reconstruction and on the local validity of the evolving baryonic Tully–Fisher relation, our results show that rotation curves encode time-resolved dynamical information. This establishes the radial dynamical chronometer as a new observable for studying galaxy evolution and testing gravitational frameworks. Full article

Genetic cardiomyopathies (GCMs) are chronic heart muscle disorders requiring lifelong monitoring and treatment. Although quality of life (QoL) and health-related quality of life (HRQoL) are increasingly recognized as important outcomes in cardiomyopathy care, their conceptualization and measurement remain inconsistent. This scoping review aims to (a) identify the tools most commonly used to assess QoL and HRQoL in adults with genetic cardiomyopathies and (b) map the thematic areas of existing studies, including symptom burden, psychological distress, diagnostic challenges, and the impact of medical and psychological interventions. PubMed, Scopus, and PsycINFO were systematically searched, and the final search was completed in November 2025. Seventeen peer-reviewed studies met the inclusion criteria and were included in this scoping review. The review followed the PRISMA extension for Scoping Reviews and included both quantitative, qualitative and mixed-methods designs. Most studies employed standardized tools such as EQ-5D (N = 5), SF-36/SF36v2 (N = 5), and the Kansas City Cardiomyopathy Questionnaire (N = 3), while others included the Minnesota Living with Heart Failure Questionnaire (N = 2) and disease-specific or ad hoc measures. The most frequently investigated themes included impairments in physical functioning, emotional well-being, symptom burden, psychological distress, and social participation. Several studies showed that patients’ perceived QoL was more closely associated with symptom burden and psychological adjustment than with objective clinical indicators alone. Clinical interventions showed mixed or limited effects on QoL and HRQoL outcomes, even when clinical parameters improved. Qualitative research further emphasized the lived experiences of patients and families, highlighting unmet needs in care. Less commonly addressed findings concerned caregiver perspectives, patient–provider communication, treatment adherence, socioeconomic disadvantage, healthcare costs, productivity loss, and the experiences of patients with rarer cardiomyopathy-related conditions. The results highlight how QoL and HRQoL are central but still inconsistently assessed outcomes in cardiomyopathy research. This review calls for greater conceptual clarity between QoL and HRQoL, greater standardization in measurement tools, broader inclusion of psychosocial variables, and more patient-centred research approaches to better support individuals living with cardiomyopathies. Full article

Background: Intraductal carcinoma (IDC) and invasive cribriform (Cr) histologic patterns are important adverse morphologies in prostate cancer (PCa) and may influence pretreatment risk stratification. This study evaluated the feasibility of time-dependent diffusion magnetic resonance imaging (t_d-dMRI)-based microstructural mapping for preoperative characterization of these aggressive morphologies. Methods: This retrospective study included 95 men with pathologically confirmed PCa on radical prostatectomy specimens from March 2023 to March 2025. T_d-dMRI was performed using pulsed and oscillating gradient diffusion sequences. Microstructural parameters, including extracellular diffusivity (D_ex), cell diameter (d), intracellular volume fraction (f_in), cellularity, and diffusivities at 0, 17, and 33 Hz (ADC_0Hz, ADC_17Hz, and ADC_33Hz), were estimated using a two-compartment model. Conventional apparent diffusion coefficient (ADC_DWI) values were obtained from standard diffusion-weighted imaging. Parameters were compared between tumors with and without Cr/IDC patterns, and diagnostic performance was assessed using receiver operating characteristic analysis. Pairwise comparisons of AUCs were performed using the DeLong test. Results: Among 95 participants, 62 (65.3%) had Cr/IDC patterns. Compared with Cr/IDC-negative tumors, Cr/IDC-positive tumors showed higher f_in and cellularity (both p < 0.001) and lower ADC_DWI, ADC_0Hz, ADC_17Hz, and ADC_33Hz values (all p < 0.05). D_ex and d did not differ significantly between groups. Among t_d-dMRI-derived parameters, f_in showed the highest diagnostic performance (AUC = 0.757; 95% CI, 0.654–0.860). Conclusions: T_d-dMRI-based microstructural mapping demonstrates promise for characterizing the Cr/IDC morphologies in PCa. Full article