Search Results (329)

Accurate digital elevation models (DEMs) are essential for hydrological modelling and floodplain analysis, particularly in low-relief floodplains where small elevation errors can significantly affect flow routing and inundation extent. This study evaluated the vertical accuracy of six freely available global DEMs across the Flinders River catchment, North Queensland, Australia, using 30,916,100 quality-filtered ICESat-2 ATL06 elevation points for regression-based bias correction and airborne LiDAR datasets from five benchmark regions for independent validation. The evaluated DEMs included TANDEM-X, Copernicus DEM, ALOS AW3D30, SRTM, ASTER GDEM, and the Hydrological DEM. Vertical accuracy was assessed using mean error (ME), root mean square error (RMSE), and residual dispersion before and after calibration. Results showed substantial pre-calibration bias in the Hydrological DEM (ME = −2.93 m) and SRTM (ME = −2.66 m), whereas Copernicus DEM showed minimal initial bias (ME = −0.01 m). Regression-based correction reduced mean errors to within ±0.13 m across all DEMs. SRTM showed the largest improvement, with RMSE decreasing from 3.20 m to 0.55 m, while TANDEM-X achieved the highest post-calibration accuracy (RMSE = 0.14 m). Independent LiDAR validation confirmed improved vertical accuracy while preserving terrain morphology and river gradients. Full article

Background: The timing and efficacy of Achilles tendon lengthening (ATL) in Duchenne muscular dystrophy (DMD) remain controversial because indiscriminate surgery can accelerate ambulation loss. Case Description: This report presents a 5-year longitudinal comparative analysis (2021–2026) of monozygotic twins with identical genetic backgrounds (DMD exon 30–43 deletion) who received comparable rehabilitation and pharmacological management, including concurrent gene therapy in 2025. Twin A was managed conservatively with orthosis and subsequent serial casting for progressive equinus contracture, whereas Twin B underwent early bilateral ATL during transition to the non-ambulatory phase. Despite a temporary postoperative decline, Twin B demonstrated a more stable longitudinal motor trajectory, outperforming Twin A in gross motor function (Gross Motor Function Measure-88: 38.60% vs. 32.85% in 2026) by preserving residual standing and crawling dimensions. Longitudinal KIDSCREEN-52 assessments revealed a clinically meaningful improvement in psychological well-being in Twin B (+10.9 points) relative to Twin A. Conclusions: This single-pair twin case serves as a hypothesis-generating observation, highlighting a potential longitudinal association between early surgery and attenuated progressive motor decline within a multi-disciplinary care regimen. Given highly variable psychosocial outcomes, these trends cannot be directly attributed to surgery alone. When evaluating such orthopedic interventions, clinical decisions may benefit from looking beyond immediate gait metrics toward broader, long-term functional trends, though the relative contributions of concurrent therapies remain to be elucidated. Full article

ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation. These challenges include sparse sampling, signal saturation, terrain effects, and limited model generalization. To solve these challenges, this study proposes a new synergistic multi-source remote sensing framework for regional-scale AGB estimation by integrating terrain-corrected ICESat-2 canopy height and forest-type heterogeneity. The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types. In this paper, multi-source datasets were integrated, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission (SRTM), WorldClim, and a terrain-corrected canopy height model (CHM). Subsequently, candidate features were derived such as spectral, textural, topographic, and climatic variables. In terms of the terrain-corrected CHM, canopy structural parameters were extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data after terrain correction based on a high-resolution DEM. Footprint-level AGB samples were first generated using ICESat-2-derived canopy structural parameters through four regression approaches, including Multiple linear regression, Stepwise multiple regression, Ridge regression, and Lasso regression. These generated AGB samples were then used as response variables for subsequent regional-scale modeling. To build accurate AGB estimation model, key features were first identified using correlation analysis. To account for forest structural heterogeneity, three models including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed for regional AGB mapping. To evaluate the performance of the proposed AGB estimation model by integrating terrain-corrected canopy height and forest-type heterogeneity, this study conducted AGB estimation at the Harvard Forest (HARV) site in the United States. The experimental results show that forest-type-specific modeling improves model adaptability and robustness. Among the models (RF, XGBoost and SVM), RF achieved the best performance, with an average coefficient of determination of 0.694. The optimized model was applied to produce a 30 m resolution AGB map. The validation was conducted using airborne LiDAR-derived AGB referenced results. The validation shows that an overall coefficient of determination (R²) of 0.606 and a root mean square error (RMSE) of 16.53 Mg ha⁻¹. These results demonstrate that the proposed new synergistic AGB estimation framework, which integrates terrain-corrected ICESat-2 canopy height with forest-type-specific modeling, provides an accurate and reliable solution for regional-scale forest biomass mapping and carbon stock assessment. Full article

Orthotropic laminates are highly desirable in composite structures because they eliminate bending–twisting coupling, simplify structural behavior, improve analytical predictability, and facilitate structural design, optimization, and certification. However, achieving fully orthotropic behavior in laminated composites remains challenging because conventional laminate architectures generally retain stiffness couplings arising from anisotropic ply orientations and stacking-sequence effects. This work introduces the Tailored Antisymmetric Composite (TAC) concept, a laminate architecture that provides the closest practical approximation to full orthotropy while preserving broad stiffness-tailoring capability and manufacturability. TAC laminates are constructed from tailored antisymmetric sublaminates that enforce $D_{16}=D_{26}=0$ while maintaining extremely small extension–bending coupling terms $B_{16}$ and $B_{26}$. Representative TAC and symmetric Quad laminates were compared analytically, statistically, and experimentally. Monte Carlo simulations comprising 100,000 realizations with realistic ±0.1° AFP/ATL fiber-orientation deviations showed that the distributions of the extension–bending coupling terms $B_{16}^{*}$ and $B_{26 }^{*}$ remained nearly indistinguishable for both laminate architectures, with probability-density overlap coefficients between 0.87 and 0.98. In contrast, the bending–twisting coupling terms $D_{16}^{*}$ and $D_{26}^{*}$ were 140–600 times lower in TAC laminates than in the corresponding Quad laminates, and their statistical distributions exhibited complete separation. Experimental measurements of post-cure warpage confirmed that TAC laminates achieved dimensional stability comparable to symmetric Quad laminates while exhibiting lower variability. These results demonstrate that TAC laminates combine exact elimination of bending–twisting coupling with negligible extension–bending coupling, statistical robustness to realistic manufacturing variability, and excellent dimensional stability, establishing TAC as a practical and systematic route toward full orthotropy in laminated composite structures. Full article

ICESat-2 ATL08 is an important data source for global land surface elevation monitoring, while its accuracy has not been systematically evaluated in the complex terrain areas of central and southern China. Taking high-resolution digital surface models as reference data, this study carries out systematic verification with a total of 949 valid verification points covering 18 typical geomorphological areas in central and southern China. The verification sites cover various terrain types including plains, hills, mountains and alpine canyons. The results show that the average root mean square error of all sites is 3.318 m, ranging from 1.044 m to 5.120 m. Among them, plain areas have the highest accuracy (HS, RMSE = 1.044 m), followed by hilly areas with RMSE of approximately 1.610–3.871 m, and mountainous and alpine canyon areas show relatively poorer accuracy with RMSE of approximately 2.374–5.120 m. The overall mean error (ME) is −1.032 m, with ME values ranging from −4.575 m to +2.548 m across sites. The accuracy of ICESat-2 ATL08 in central-southern China is highly terrain-dependent: RMSE is 1.044 m in the plain site and ranges from 1.610 to 3.871 m in hilly areas and from 2.374 to 5.120 m in mountainous and alpine canyon areas. Therefore, users should consider terrain complexity when applying this product, and post-processing correction incorporating topographic information is recommended for alpine canyon areas where RMSE exceeds 5 m. Full article

Human T-cell leukemia virus-1 (HTLV-1) is the etiological agent of a series of chronic inflammatory diseases such as HTLV-associated myelopathy/Tropical spastic paraparesis (HAM/TSP), uveitis, dermatitis, and pneumonitis, and, importantly, of a T-cell lymphoproliferative neoplasm designed adult T-cell leukemia/lymphoma (ATL). Two viral proteins, Tax-1 and HBZ, are crucially involved in HTLV-1 infectivity and in ATL by altering key pathways of cell homeostasis. A fundamental distinction between the expression of the two oncoproteins exists, witnessed by the fact that Tax-1 is expressed in early phases of HTLV-1 infectivity and ATL onset but may be lost in a substantial number of established ATL, whereas HBZ is always expressed in all phases of HTLV-1 infection and in all ATL. Additionally, while Tax-1 can be localized both in the cytoplasm and nucleus in all cases of disease, recent evidence indicate that HBZ is localized solely in the cytoplasm in cells of HTLV-1-infected individuals, asymptomatic carriers (AC) and patients suffering from HAM/TSP. Importantly, ATL instead marks a progressive dislocation of HBZ in the nucleus. Thus, both the expression and the subcellular localization of HBZ represent distinctive elements in the process of HTLV-1-associated pathology. Within this frame, recent studies point to a very important involvement of HBZ in disarranging the homeostasis of the cell not only at the transcriptional but most importantly at the post-transcriptional level as a result of the interaction with crucial factors regulating RNA splicing and stability. These recent aspects of the HBZ biology will be discussed for their implication in HTLV-1-mediated oncogenesis. Full article

Background: Obesity among Saudi adolescents has risen sharply, yet whether school athletic participation is associated with students showing improved dietary habits and better anthropometric profiles compared to those of their non-athlete peers remains unclear. This study compared anthropometric indices and dietary habits between school athletes and non-athletes in Riyadh. Methods: A cross-sectional study was conducted with 124 male secondary school students (70 athletes and 54 non-athletes aged 16–17 years) in Riyadh, Saudi Arabia. Athletes were defined as students who reported engaging in vigorous-intensity sport for ≥3 days/week for ≥60 min/session. BMI, body weight, and waist circumference were measured objectively. Dietary habit frequencies across ten food categories were assessed using the validated Arab Teens Lifestyle Study (ATLS) questionnaire. Independent samples t-tests and chi-square tests were used; effect sizes were calculated as Cohen’s d. A Bonferroni-corrected threshold (p < 0.005) was applied for multiple dietary comparisons. Results: Athletes had significantly lower BMI (23.64 ± 5.39 vs. 30.28 ± 7.25 kg/m²; p < 0.001, d = 1.06), body weight (p < 0.001, d = 0.93), and waist circumference (85.46 ± 12.61 vs. 95.50 ± 17.89 cm; p < 0.001, d = 0.66). Obesity prevalence was 15.7% among athletes versus 51.9% among non-athletes. Of ten dietary variables, only fresh fruit consumption showed a between-group difference (62.9% vs. 40.7% high-frequency; p = 0.010), which did not survive Bonferroni correction. Conclusions: School athletes demonstrated substantially better anthropometric profiles than their non-athlete peers, but dietary habit frequencies were largely similar across both groups. The high obesity prevalence among non-athletes underscores the need for school-based programs that combine structured physical activity with targeted nutrition education. Full article

Forests worldwide are impacted by tropical cyclones which alter their structure and ecological functions. In this study, we investigated repeat track data from ICESat-2’s (Ice, Cloud and land Elevation Satellite-2’s) land and vegetation height product (ATL08) to quantify structural changes in forests, with a focus on coastal forests in Alabama and Florida affected by Hurricane Sally (2020). We evaluated pre-hurricane ATL08 along-track canopy estimates at the ATL08 100 m segment scale and 20 m sub-segment scale and quantified structural canopy changes using exact pre- and post-repeated tracks. Results demonstrated strong agreement between ATL08’s 98th percentile canopy height (RH98) and reference airborne LiDAR-derived RH98 at both spatial scales, with improved performance at the 20 m sub-segment scale (mean bias: −1.16 m; MAE: 2.28 m; RMSE: 3.44 m; r: 0.80). Samples over evergreen forests provided reduced bias (−2 m to −0.55 m), reduced RMSE (4.02 m to 2.96 m), and improved correlation (0.77 to 0.83) than woody wetlands for canopy height acquisition. Post-hurricane analyses revealed height reductions in tall canopy (20–30 m) of 1.51 m, while smaller trees (0–10 m) increased by 0.77 m, reflecting growth. Overall, findings highlight ICESat-2’s ability to monitor canopy height changes and offer prospects for integrating ICESat-2 data for damage assessments. Full article

High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this study by a 1.11 km² tidal flat near Dafeng Port—remains challenging, because ICESat-2 laser altimetry tracks across such areas are typically sparse and spatially clustered within narrow sub-regions, leaving extensive observation-blind zones without direct elevation labels. This label-clustering problem constrains the applicability of traditional empirical models and tends to cause deep learning models to generalize poorly beyond the spatial distribution of training samples. To address this issue, this study proposes a Residual Attention Physical-constraint Semi-supervised U-Net (RAPS-UNet) that fuses ICESat-2 ATL03/ATL08 elevation labels with Sentinel-1 SAR and Sentinel-2 optical features. The preprocessing pipeline comprises refined ICESat-2 photon filtering, adaptive inundation-frequency extraction, multi-source feature selection, and baseline DEM construction. RAPS-UNet integrates residual learning, attention-based multi-source fusion, physics-constrained loss, and confidence-weighted pseudo-label augmentation to improve extrapolation under clustered-label conditions. A four-level validation protocol—in-distribution validation, spatial holdout testing, and field-based assessment over both interpolation and extrapolation zones—was designed to evaluate spatial generalization. Against a field-surveyed DEM, RAPS-UNet achieved an overall RMSE of 0.20 m, an MAE of 0.16 m, and an R² of 0.91; the field-based interpolation and extrapolation zones yielded RMSEs of 0.17 m and 0.22 m, respectively, while the spatial holdout test reached an RMSE of 0.23 m and an R² of 0.81. Relative to the traditional inundation frequency–elevation linear model (RMSE = 0.35 m), RAPS-UNet reduced the field-validation RMSE by approximately 43%. The proposed framework therefore offers a practical approach for fine-scale coastal-zone topographic mapping under sparse and spatially clustered altimetry conditions. Full article

The Amazon Basin harbors a high diversity of phlebotomine sand flies, including several species that act as vectors of zoonotic pathogens such as Leishmania. Land-use changes, particularly forest conversion to agriculture, alter the sand fly diversity and community structure, with implications for the transmission of American Tegumentary Leishmaniasis (ATL). We evaluated the effects of forest-to-agriculture conversion on sand fly diversity and species composition in two rural areas, on opposite sides of the Amazonas River, in the Brazilian Amazon Region. Sand flies were collected using Center of Disease Control (CDC) light traps, installed in the forest and cropland environments, at the Presidente Figueiredo (North) and Urucurituba (South) municipalities, in Amazonas, Brazil. We collected a total of 1778 phlebotomine sand flies from 15 genera and 69 species. The most abundant species were Micropygomya rorotaensis (n = 436; 24.52%), Nyssomyia antunesi (n = 297; 16.70%), Sciopemyia sordellii (n = 101; 5.68%), Bichromomyia flaviscutellata (n = 84; 4.72%) and Evandromyia monstruosa (n = 72; 4.04%). In addition, four sand fly species were recorded for the first time in the Amazonas state: Brumptomyia mesai, Pressatia calcarata, Evandromyia aldafalcaoae and Lutzomyia carvalhoi. Sand fly richness, diversity, and community composition varied between riversides and environments, reflecting strong effects of anthropogenic disturbance. Although croplands supported reduced and more heterogeneous assemblages, several medically important vector species persisted across both environments. Species turnover was high, but patterns of species loss were weak, suggesting that community reorganization was driven by non-directional compositional change process. Our results indicate that land-use change reshapes sand fly communities without eliminating disease vectors, potentially increasing ATL transmission risk at the forest–anthropic interface. Full article

The proviral load of human T-cell lymphotropic virus type 1 (HTLV-1) is an important biomarker associated with the monitoring and risk stratification of adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). However, the lack of standardized quantification methods limits its broader application. This study evaluated a novel absolute quantification approach based on whole blood volume and compared its performance with established protocols. A total of 66 HTLV-1-infected individuals were analyzed using six qPCR-based methodologies, including volumetric quantification (copies/µL) by absolute quantification and the Tamegão-Lopes method, as well as normalization per 1000 cells (whole blood, buffy coat, PBMCs, and CD4+ T cells). Association and agreement were assessed using Pearson’s correlation, Bland–Altman analysis, concordance correlation coefficients (CCCs), and Deming regression. Absolute quantification showed strong correlation with both the Tamegão-Lopes method and CD4+-based quantification (r = 0.93 and 0.84, respectively; p < 0.001) and high agreement (CCC = 0.866 and 0.811, respectively), with modest systematic bias (−0.273 log₁₀ copies/µL and 0.115 log₁₀ copies/10³ cells, respectively). Leukocyte-normalized methods showed greater discrepancies, likely due to dilution by uninfected cells. These findings show that quantification based on total blood volume is a simplified, operationally feasible alternative for assessing HTLV-1 proviral load. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

Background: Physical inactivity among Saudi Arabian adolescents is a critical public health concern due to its contribution to the rising prevalence of overweight, obesity, and non-communicable diseases. Despite this, the motivational profiles and perceived barriers that differentiate athletic from non-athletic adolescents remain understudied in the Saudi literature, particularly within the school Physical Education (PE) context. Grounded in Self-Determination Theory (SDT), the present study examined the factors preventing and motivating Saudi adolescents to engage in physical activity (PA) and discusses findings in terms of their implications for PE teachers and school-based intervention. Method: A cross-sectional study was conducted with 124 male high school students in Riyadh (mean age: 16.79 ± 0.66 years). Participants were categorized as either athletes (n = 70) or non-athletes (n = 54) based on pre-defined engagement criteria: athletes were required to report vigorous-intensity sport participation on three or more days per week for a minimum of 60 min per session. Anthropometric measurements, lifestyle behaviors (diet, screen time, sleep), motivations, and barriers were assessed using the validated Arab Teens Lifestyle Study (ATLS) questionnaire. Independent samples t-tests and chi-square tests were used to compare between-group differences; effect sizes are reported. Result: Non-athletes had a significantly higher mean BMI (29.40 ± 6.77 kg/m²) and waist circumference (98.65 ± 21.63 cm) compared to athletes (BMI: 22.19 ± 4.44 kg/m²; waist: 78.84 ± 9.51 cm; both p < 0.001). No significant differences were observed in screen time, sleep duration, or dietary habits. The primary motivations for PA among athletes were health benefits (27.1%), recreation (25.7%), and competition (20.0%), reflecting an autonomous motivational profile consistent with SDT. Among non-athletes, the predominant barriers were the lack of suitable facilities (25.9%) and the absence of an exercise partner (22.2%); reflecting unmet SDT needs for competence and relatedness respectively, while only 9.3% cited having a lack of time. Conclusions: Non-athletic participants face a significant health disadvantage characterized by higher rates of overweight and central obesity. In contrast to global trends, where academic commitments dominate barriers to PA, the principal barriers in this population are environmental and social, reflecting unmet psychological needs that PE teachers are uniquely positioned to address. Rather than focusing solely on infrastructure, PE promoters should implement need-supportive teaching practices, including competence-building tasks and cooperative peer structures, to foster the intrinsic motivational profile observed in the athletes and promote long-term PA adherence among non-athletic students, in alignment with the health objectives of Saudi Vision 2030. Full article

Driven by climate change and population growth, coastal flood risk is rising, making high-precision Digital Elevation Models (DEMs) essential for inundation simulation and risk assessment. Although global open-source DEMs are increasingly available, their regional applicability and uncertainty still require quantitative evaluation. Taking Lianyungang, a coastal city in eastern China, as the study area, this study used ICESat-2 ATL08 laser altimetry as the reference to assess the vertical accuracy of eight mainstream open-source DEMs: the ASTER GDEM, FABDEM, AW3D30 DEM, SRTM DEM, MERIT DEM, NASA DEM, Copernicus DEM, and TanDEM-X DEM. The effects of slope, aspect, and land cover on DEM errors were analyzed, and the Height Above Nearest Drainage (HAND) model was used to evaluate how DEM vertical accuracy and spatial resolution affect flood inundation simulation. The results show that the FABDEM has the highest accuracy (RMSE = 1.24 m; NMAD = 0.49 m), followed by the Copernicus DEM GLO-30 (RMSE = 1.56 m; NMAD = 0.65 m), whereas the ASTER GDEM performs worst (RMSE = 5.36 m; NMAD = 3.69 m). The SRTM DEM systematically underestimates ICESat-2 elevations, with mean and median errors of −1.85 m and −1.80 m, mainly due to acquisition time differences and land-use changes in Lianyungang. DEM errors generally increase with slope, are higher on west-facing slopes, and are larger over water bodies than over cropland and impervious surfaces. HAND simulations show that DEM-derived inundation differences are greatest under low-threshold conditions. At the 1 m HAND threshold, the MERIT DEM produces the largest inundation area (4370.28 km²), while the ASTER GDEM produces the smallest area (3330.53 km²); these differences decrease as the threshold increases. Overall, the FABDEM provides the most accurate flood inundation representation in Lianyungang, while the Copernicus DEM GLO-30 is a reliable alternative. Full article

High-latitude coastal regions are highly sensitive to climate change, yet their geomorphology is obscured by sea ice, landfast ice and seasonal snow, restricting the applicability of optical remote sensing for fine coastal classification. To address this limitation, we develop an interpretable coastal classification framework integrating ICESat-2 photon-counting LiDAR and explainable machine learning. Multi-dimensional morphometric features describing cross-shore geometry, vertical relief and local slope variability are extracted from ICESat-2 ATL03 along-track profiles to train a CatBoost classifier, with five-fold cross-validation and sample weighting to mitigate class imbalance. Introducing SHAP-based interpretability into ICESat-2-driven coastal geomorphic classification enables the identification of morphometric controls on coastal-type differentiation. Validated in the Bering Sea with 447 profiles and a 75%/25% stratified split, the framework achieved an overall accuracy of 86.6%, a macro-average recall of 89.4% and a Kappa coefficient of 0.84. SHAP analysis identifies that coastal width is the most influential feature for model-based classification of coastal geomorphic types, while slope and local steepness variability serve as important predictive indicators for distinguishing rocky and sedimentary coasts. This framework links data-driven classification to geomorphic processes and provides a potentially generalisable approach for fine-scale coastal mapping in high-latitude environments. Full article