Search Results (143)

Background/Objectives: MB09 is a denosumab biosimilar to the reference products (RPs) Xgeva and Prolia. A population pharmacokinetic (popPK) meta-analysis was conducted to characterize the denosumab PK profile and to support MB09 biosimilarity. Methods: Pooled denosumab PK data from one phase I study [255 healthy adult men receiving a single 35 mg subcutaneous (SC) dose] and one phase III study (555 postmenopausal women with osteoporosis receiving two 60 mg SC doses, one every six months) were used. A one-compartment model with first-order absorption and elimination and parallel non-linear saturable clearance was used. Body weight was included on clearance as a structural covariate and treatment was tested as a covariate on all PK parameters. PK biosimilarity was assessed at 35 mg dose. Results: For a 70 kg subject, the apparent clearance and central volume of distribution for denosumab were 0.123 L/day [95% confidence interval (CI): 0.114, 0.132] and 9.33 L (95% CI: 9.11, 9.55), respectively. The Michaelis constant was 0.124 ng/mL and the maximum rate for the non-linear clearance was 0.139 ng/day. Model-based bioequivalence criteria were met for RP Xgeva, European and US-sourced, versus MB09 for a dose of 60 mg SC. The mean area under the plasma concentration curve (AUC) resultant from the simulation of MB09 120 mg SC was similar to the published mean AUC observed for Xgeva 120 mg SC every four weeks. Conclusions: This analysis provides a valuable assessment of denosumab PK characteristics and elucidates in more detail how the MB09 PK profile compares to the denosumab RPs, supporting the totality of evidence on MB09 biosimilarity. Full article

Food image recognition is a key research area in food computing, with applications in dietary assessment, menu analysis, and nutrition monitoring. However, imaging devices and environmental factors introduce noise, limiting classification performance. To address this, we propose a food image recognition method based on anti-noise learning and covariance feature enhancement. Specifically, we design a Noise Adaptive Recognition Module (NARM), which incorporates noisy images during training and treats denoising as an auxiliary task to enhance noise invariance and recognition accuracy. To mitigate the adverse effects of noise and strengthen the representation of small eigenvalues, we introduce Eigenvalue-Enhanced Global Covariance Pooling (EGCP) into NARM. Furthermore, we develop a Weighted Multi-Granularity Fusion (WMF) method to improve feature extraction. Combined with the Progressive Temperature-Aware Feature Distillation (PTAFD) strategy, our approach optimizes model efficiency without adding overhead to the backbone. Experimental results demonstrate that our model achieves state-of-the-art performance on the ETH Food-101 and Vireo Food-172 datasets. Specifically, it reaches a Top-1 accuracy of 92.57% on ETH Food-101, outperforming existing methods, and it also delivers strong results in Top-5 on ETH Food-101 and both Top-1 and Top-5 on Vireo Food-172. These findings confirmed the effectiveness and robustness of the proposed approach in real-world food image recognition. Full article

Finding out the dynamics of soil organic carbon and inorganic carbon is paramount for sustaining terrestrial carbon cycling and climate change mitigation. From the 1980s to 2010s, substantial changes in land use, climate, and agricultural practices have occurred across North China. This study systematically quantified the stratified dynamics of soil carbon stocks (0–100 cm with 20 cm intervals) and their compositional shifts by using the geographically weighted regression kriging model. The model integrated soil sample data from provincial surveys across North China with key environmental covariates (e.g., elevation, precipitation, air temperature, and the vegetation index) to spatially predict and analyze vertical carbon stock changes. The results indicated that soil carbon stocks decreased considerably by 5.86 Gt in the one-meter soil profile from the 1980s to the 2010s. Significant losses in soil inorganic carbon stocks directly contributed to net soil carbon sources. These significant soil inorganic carbon losses of 7.03 Gt, originating primarily from losses of 7.35 Gt in deeper soil layers (20–100 cm), effectively offset increases of 1.17 Gt in soil organic carbon. About two-thirds of regions in North China have been categorized as carbon source regions. These are distributed for the most part in arid and semi-arid areas and the Qinghai–Tibet Plateau. The remaining one-third of regions have been classified as carbon sink regions which are primarily found in the Loess Plateau, the Huang–Huai–Hai Plain, the Middle-lower Yangtze Plain, and the Northeast China Plain. Significant losses in soil inorganic carbon stocks caused by strong carbon sources may undermine global measures aimed at enhancing terrestrial ecosystem carbon sequestration and fixation. Our results highlight the urgent need to account for vulnerable subsurface inorganic carbon pools in regional carbon sequestration strategies and climate models. Full article

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

Background: When conservative therapies are insufficient for vasovagal syncope (VVS), procedural options such as permanent pacemakers or catheter ablation of ganglionated plexi (GP) may be considered. This meta-analysis aimed to evaluate the efficacy of GP catheter ablation in patients with VVS. Methods: A comprehensive literature search was performed in PubMed, Embase, and the Cochrane Library from 15 March 2024 to 10 May 2025. After duplicate removal, two reviewers independently screened studies and assessed full texts based on predefined criteria. A single-arm proportion meta-analysis was conducted. Results: Thirty-seven studies comprising 1585 participants were included. The pooled proportion of VVS recurrence after ablation was 8.9% (95% CI, 6.4–11.4%), but with substantial heterogeneity (I² = 74.4%, p < 0.001). Sensitivity and subgroup analyses confirmed the robustness of the pooled estimate. A meta-regression was performed to further explore potential effect modifiers, but no covariate reached statistical significance. Conclusions: This meta-analysis suggests that ganglionated plexi catheter ablation may be associated with a reduced recurrence of vasovagal syncope in selected populations. However, the findings are based predominantly on non-randomized observational studies, and the high between-study heterogeneity limits the strength of inference. Future randomized controlled trials with standardized methodologies are needed to confirm the long-term efficacy and safety of this intervention. Full article

Investigating the spatial distribution of chromium (Cr) in soil is essential for understanding Cr pollution and accurately assessing associated environmental risks. However, field sampling is challenging due to limited sampling points, and the spatial distribution of Cr is affected by multiple complex environmental covariates, thereby restricting model development and prediction accuracy. This study selected the Chizhou–Xuancheng border area in southern Anhui Province as the research region and collected 2035 data points. Machine learning models, including AdaBoost, GBDT, XGBoost, and MLP, were employed to predict Cr concentrations in conjunction with environmental covariates. To address the challenges of sparse sampling data and complex data relationships for Cr prediction, the PHMS-Transformer model is proposed. Featuring a shallow encoder design, configurable pooling strategies, and a lightweight structure, the model significantly reduces the number of parameters and alleviates overfitting under sparse sampling conditions, while the incorporation of multi-head self-attention mechanisms captures complex nonlinear relationships among multi-source environmental variables relevant to Cr. To further enhance model interpretability for Cr prediction, the SHAP model was applied to identify key factors influencing Cr distribution. Comprehensive comparisons indicate that the PHMS-Transformer model achieves superior performance in predicting Cr, demonstrating high accuracy and generalization capability, with clear advantages over traditional methods. These findings offer valuable insights for soil environmental protection and Cr pollution control and possess significant theoretical and practical implications. Soil Cr pollution represents a global environmental challenge, where achieving accurate predictions for Cr is particularly crucial yet difficult in regions with constrained data accessibility. The lightweight, high-precision, and interpretable PHMS-Transformer framework proposed in this study provides an effective technical solution to the widespread challenges of sample sparsity and model complexity inherent in predicting the spatial distribution of soil Cr globally. Therefore, this work offers significant reference value for advancing global soil environmental risk assessment and Cr pollution remediation efforts. Full article

Online learning has become increasingly prevalent in real-world applications, where data streams often comprise heterogeneous feature types—both nominal and numerical—and labels may not arrive synchronously with features. However, most existing online learning methods assume homogeneous data types and synchronous arrival of features and labels. In practice, data streams are typically heterogeneous and exhibit asynchronous label feedback, making these methods insufficient. To address these challenges, we propose a novel algorithm, termed Online Asynchronous Learning over Streaming Nominal Data (OALN), which maps heterogeneous data into a continuous latent space and leverages a model pool alongside a hint mechanism to effectively manage asynchronous labels. Specifically, OALN is grounded in three core principles: (1) It utilizes a Gaussian mixture copula in the latent space to preserve class structure and numerical relationships, thereby addressing the encoding and relational learning challenges posed by mixed feature types. (2) It performs adaptive imputation through conditional covariance matrices to seamlessly handle random missing values and feature drift, while incrementally updating copula parameters to accommodate dynamic changes in the feature space. (3) It incorporates a model pool and hint mechanism to efficiently process asynchronous label feedback. We evaluate OALN on twelve real-world datasets; the average cumulative error rates are 23.31% and 28.28% under the missing rates of 10% and 50%, respectively, and the average AUC scores are 0.7895 and 0.7433, which are the best results among the compared algorithms. And both theoretical analyses and extensive empirical studies confirm the effectiveness of the proposed method. Full article

Existing convolutional neural network (CNN) methods primarily depend on first-order feature modeling, which makes it challenging to effectively capture higher-order features in breast cancer histopathological images. Additionally, due to the limitations of the receptive field, CNNs have difficulty capturing long-range dependencies, thereby limiting the integration of global information. To address this, inspired by the strengths of high-order statistical features and extended receptive fields in visual tasks, this study proposes a novel high-order receptive field network (HoRFNet). Specifically, HoRFNet expands the receptive field and improves the model’s contextual awareness of pathological tissue structures by introducing a multi-branch convolutional structure with convolution kernels of varying sizes, along with dilated convolution layers. Additionally, HoRFNet integrates a matrix power normalization strategy in the covariance pooling module to model the global interactions between convolutional features, thereby improving the higher-order representation of complex textures and structural relationships in tissue images. The BreakHis dataset shows that HoRFNet achieves an image level classification accuracy of 99.50% and a patient level classification accuracy of 99.23%, significantly outperforming existing methods and demonstrating its effectiveness. Full article

Effectively capturing multi-scale object features is crucial for vision sensors used in road object detection tasks. Traditional spatial pyramid pooling methods fuse multi-scale feature information but lack adaptability in dynamically adjusting convolution operations based on their actual needs. This limitation prevents them from fully utilizing spatial hierarchies and contextual information. To address this challenge, we propose a Transformation Dilated Grouped Layer (TDGL) module, a fast multi-scale vision sensor based on deep learning, designed to enhance both efficiency and accuracy in road target feature extraction networks. The TDGL is built upon the Global Layer Normalization Convolution (GLConv) unit, which mitigates internal covariate shift by introducing scaling and offset parameters, modifying dilation strategies, and employing grouped convolution. These improvements enable the network to distinguish features at different scales effectively while optimizing spatial information processing and reducing computational costs. To validate its effectiveness, we integrate the TDGL module into the backbone of several YOLO models, forming the TDGL Net feature extractor. The experimental results obtained on the BDD100K dataset show that the mAP of the TDGL net reaches 40.3% with around 3.1M parameters. The inference speed of the TDGL net after transformation optimization reaches 58 FPS, which meets the requirement for the real-time detection of road obstacle targets by autonomous vehicles. Full article

This study aimed to investigate the PPK of EF in largemouth bass after oral and intravenous administration based on a nonlinear mixed effect model. Samples were collected using the sparse sampling method at pre-designed time points determined by high-performance liquid chromatography with a fluorescent detector. The initial PK parameters were estimated by reference search and the calculation of a naïve pooled approach. The covariate model included a variation in body weight. The oral dose data were best fitted by a one-compartment model. The injection dose data were best fitted by a two-compartment model. The results demonstrated that body weight had no marked effect on the parameters of PPK. Finally, the bioavailability was calculated to be 12.24%. The area under the concentration–time curve/minimum inhibitory concentration was estimated to be ≥408.16, indicating that EF at 20 mg/kg has high effectiveness for aquatic pathogens. Full article

Recent evidence suggests that sex-related differences in cardiovascular health extend beyond traditional risk factors, affecting vascular structure and function. This study aimed to examine sex differences in vascular parameters, including central and peripheral blood pressure, pulse wave velocity (PWv), augmentation index at 75 bpm (AIx75), cardiac output, stroke volume, and peripheral vascular resistance, using harmonized data from three population-based cohorts (EVasCu, VascuNET, and ExIC-FEp) as part of the MUJER-EVA project. A total of 669 adult participants were included in this pooled cross-sectional analysis. Sex-stratified comparisons were conducted using multiple linear regression models adjusted for anthropometric, sociodemographic, and clinical covariates. The results showed that men had significantly higher values for central and peripheral blood pressure (p < 0.001), PWv (p = 0.003), cardiac output (p < 0.001), and stroke volume (p < 0.001), whereas women presented higher values of AIx75 (p < 0.001) and peripheral vascular resistance (p = 0.002). These differences remained statistically significant after full adjustment for potential confounders. These findings highlight the need to consider sex as a key biological variable in cardiovascular research and clinical decision-making. Incorporating sex-specific reference values and personalized treatment strategies could improve vascular health assessment and the effectiveness of cardiovascular disease prevention. Full article

This study aimed to investigate the association between protective factors, marijuana use, and suicidal behavior among Black LGBQ U.S. adolescents. Methods: A subsample of 991 Black LGBQ adolescents was derived from the 2019 Combined High School YRBSS dataset. Suicidal behavior was measured as suicidal planning and/or previous suicide attempts. Marijuana usage gauged lifetime consumption. The protective factors included sports team participation, physical activity, eating breakfast, hours of sleep, and academic performance. Age and sex were entered as covariates. Multiple imputation by chained equations (MICE) was used to address missing data, and pooled binary logistic regression analyses were conducted. Results: Academic performance and hours of sleep were significantly associated with lower odds of suicidal behavior and lifetime marijuana use. Sports team participation was associated with higher odds of lifetime marijuana use. Being female was linked to higher odds of marijuana use, while older age was associated with lower odds. Discussion: For Black LGBQ youth, academic performance and sufficient sleep may function as protective factors. Participating in sports was associated with greater odds of risk behaviors, highlighting the need to assess the experiences of Black LGBQ youth in sports. Implications and Contributions: Our findings inform school programming, policy, and practice by identifying academic support and sleep health as intervention areas. Full article

Historical forestry practices (e.g., fire suppression, heavy timber logging) have contributed to a discernable change in stand composition of western forests in the U.S., which now comprise a tinderbox mixture of increased surface and ladder fuels, dense stands, and fire-intolerant species. Forest managers are mitigating this concern by implementing silviculture practices (e.g., selective logging, thinning, prescribed burning) to reduce fuel loads and improve stand resiliency. Concern for habitat specialists, such as the fisher (Pekania pennanti), have arisen as they may be negatively influenced in the short-term by modifications to their environment that are needed to ensure long-term habitat persistence. To address this issue, we initiated an 8-year study in 2010 in Ashland, Oregon, to determine the behavioral response of fishers to fuel reduction treatments applied in forested stands. We measured the distance of each location from eight GPS-collared fishers to all treatments before and after they were treated within each home range, and performed three statistical tests for robustness, including a multi-response permutation procedure, chi-squared test of independence, and a Kolmogorov–Smirnov assessment. We found high variation among individuals to the tolerance of habitat manipulation. Using effect size to interpret the magnitude of fisher response to pre- and post-treatment effects, 1 fisher showed a moderate negative relationship to fuel reduction treatments, 5 exhibited a weak negative response, and 2 had a weak positive association with treatments. We used analysis of variance on the three fishers exhibiting the largest effect sizes to treatment disturbance, and used treatment, temporal, and habitat covariates to explore whether these factors influenced behavioral differences. Treatment season and vegetation class were important factors influencing response distance in the pre-treatment period. Post-treatment variables eliciting a negative treatment response were treatment season and treatment size, and results were slightly different when parsing out individual effects compared to a pooled sample set. Our findings suggested that seasonal timing and the location of management activities could influence fisher movement throughout their home range, but it was largely context-dependent based on the perceived risks or benefits to individuals. Full article

The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed. Full article

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios. Full article