Search Results (10,821)

Background: Fibrotic remodelling of the lamina cribrosa (LC) is a defining pathological feature of glaucomatous optic neuropathy and contributes to progressive optic nerve head deformation and axonal vulnerability. LC cells from glaucomatous donors exhibit a myofibroblast-like phenotype characterised by excessive extracellular matrix (ECM) production, a process associated with chronic cellular stress. cAMP responsive element-binding protein 3-like 1 (CREB3L1) is an endoplasmic reticulum–resident transcription factor implicated in stress-responsive regulation of collagen synthesis and matrix homeostasis. The role of CREB3L1 in glaucomatous LC cells, however, remains poorly defined. Methods: Primary human LC cells derived from donors with confirmed glaucoma (GLC; n = 3) and age-matched non-glaucomatous controls (NLC; n = 3) were examined. CREB3L1 expression was assessed at the mRNA and protein levels using quantitative RT-PCR and Western immunoblotting. The functional effects of CREB3L1 suppression were evaluated using siRNA-mediated knockdown in GLC cells, followed by analysis of ECM gene transcription (α-smooth muscle actin, collagen type I alpha 1, fibronectin) and cellular metabolic activity using an MTS assay. Results: CREB3L1 mRNA and protein expression were significantly elevated in GLC cells compared with NLC cells. siRNA-mediated knockdown of CREB3L1 effectively reduced its expression in GLC cells and was associated with significant suppression of profibrotic ECM gene transcription. In addition, CREB3L1 knockdown resulted in a marked reduction in cellular metabolic activity in glaucomatous LC cells. Conclusions: These findings identify CREB3L1 as a regulator of ECM-associated gene expression and cellular behaviour in glaucomatous lamina cribrosa cells. While preliminary, the data suggest that CREB3L1 may contribute to pathological fibrotic remodelling at the optic nerve head. Further mechanistic and in vivo studies will be required to determine whether modulation of CREB3L1-mediated pathways represents a viable therapeutic strategy in glaucoma. Full article

Accurate prediction of submitochondrial localization is fundamental to understanding mitochondrial biogenesis and cellular metabolic pathways. While deep representations from pre-trained protein language models (pLMs) have significantly advanced the field, traditional global average pooling methods often fail to capture critical, localized N-terminal targeting signals, particularly in long sequences where these motifs are mathematically diluted. To resolve this “signal dilution” bottleneck, we developed a multi-scale architecture that explicitly integrates high-resolution N-terminal features with global evolutionary context derived from ESM-2 embeddings. The proposed framework utilizes an orthogonal mixing strategy consisting of Token-mixing and Channel-mixing. Token-mixing is specifically designed to detect spatial rhythmic patterns across residue positions, while Channel-mixing refines the biochemical signatures within the latent feature space. Extensive benchmarking across diverse datasets demonstrates that our approach effectively maintains signal integrity. Compared to existing state-of-the-art methods, the model achieves a superior overall Generalized Correlation Coefficient (GCC) of 0.7443 on the SM424-18 dataset and 0.7878 on the SubMitoPred dataset, outperforming the latest benchmarks by 9.4% and 16.1%, respectively. Furthermore, on the independent M983 test set, our method maintained a high GCC of 0.6945, demonstrating a 9.9% improvement relative to the state-of-the-art methods. This robust and efficient framework provides a high-precision tool for large-scale mitochondrial proteomics. Full article

Objectives: This study evaluated the test–retest repeatability of semi-quantitative and volumetric features derived from artificial intelligence (AI)-assisted lesion segmentation on ¹⁸F-DCFPyL Prostate Specific Membrane Antigen (PSMA)-PET/CT imaging of patients with prostate cancer (PCa). Specifically, we assessed the reliability of maximum, minimum and total standardized uptake values (SUV_max, SUV_mean, SUV_total) and lesion volume measurements across varying lesion sizes and explored the implications of variability for clinical decision-making. Methods: We analyzed ¹⁸F-DCFPyL PSMA-PET/CT images from 22 patients with metastatic PCa. Lesion segmentation was performed using the AI-guided TRAQinform IQ technology, followed by a manual review to eliminate potential false-positive sites of uptake. Lesion-level test–retest repeatability was evaluated using 95% limits of agreement (LOA), intra-class correlation coefficient (ICC), within-subject coefficient of variation (wCOV) and Bland–Altman analysis for SUV and volumetric parameters. Lesions were stratified by size (>1 cm³ and >1.5 cm³) to assess the impact of lesion volume cut-offs on measurement variability. Results: A total of 297 lesions were analyzed, including 191 lesions > 1 cm³ and 161 lesions > 1.5 cm³. Test–retest variability was higher in smaller lesions, with narrower LOA and lower wCOV for larger lesions. SUV_max and SUV_mean exhibited lower variability than SUV_total and lesion volume. The 95% LOA for SUV_max ranged from −33.81% to +38.02% for all lesions, improving to −31.82% to +31.01% for lesions > 1.5 cm³. Similar trends were observed for SUV_mean, SUV_total, and volume. Bland–Altman plots confirmed reduced variability in larger lesions, with no significant systematic bias. Conclusions: The test–retest repeatability of AI-assisted PSMA-PET/CT features varies by feature type, with semi-quantitative features demonstrating improved repeatability relative to volumetric features. Additionally, repeatability is influenced by lesion size, with larger lesions exhibiting greater reliability. These findings highlight the importance of lesion size-dependent thresholds in response assessment and variability-aware feature selection in prognostic models. Current algorithms may be better optimized for larger lesions and higher volumes of disease, with limitations remaining in the robust detection and segmentation of smaller/more subtle lesions. Full article

Purpose: Accurate preoperative risk stratification in endometrial cancer (EC) is essential for guiding surgical and therapeutic decisions. This study aimed to evaluate the discriminative performance of [18F]-FDG PET/CT-derived radiomic features combined with machine learning models for differentiating low-risk (LRH-EC) and high-risk histology (HRH-EC) subtypes. Methods: A total of 159 patients with histopathologically confirmed EC who underwent preoperative [18F]-FDG PET/CT were retrospectively analyzed. Radiomic features were extracted using LIFEx version 7.4.0 software following IBSI guidelines. After FDR correction and Pearson correlation–based redundancy reduction (|r| > 0.80), 16 radiomic features were retained for modeling. Three feature configurations (Conventional PET parameters, Radiomics16, and Combined) were evaluated. Machine learning models were developed using stratified 5-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, F1-score, Wilson confidence intervals, DeLong’s test, and McNemar’s test. Results: Artificial Neural Network (ANN) (AUC = 0.709) and Random Forest (RF) (AUC = 0.686) achieved the highest discriminative performance within the Radiomics16 feature set. No statistically significant superiority between algorithms or feature configurations was observed by DeLong analysis. However, McNemar’s test demonstrated significant patient-level classification differences for the Combined ANN model (p < 0.001). NGTDM_Coarseness and SUVmin emerged as the most influential features, reflecting tumor heterogeneity and metabolic activity. Conclusions: [18F]-FDG PET/CT-based radiomics combined with machine learning provides moderate yet consistent discrimination between LRH-EC and HRH-EC. While external validation is required, this approach may support noninvasive preoperative risk stratification in endometrial cancer. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, primarily employing the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) architecture, we integrated multi-source SAR imagery and DEM data to generate SR products that reveal distinct signatures in areas containing dense archeological remains and clearly delineate shallow, buried anthropogenic features. We further developed deep learning classification models that combine SR SAR and DEM inputs and trained them on known archeological site locations. This approach enabled the detection of previously undocumented structural features distributed along the coastal margin and throughout the Supe Valley. Our findings indicate that enhancing publicly available remote sensing datasets with advanced SR techniques can provide cost-effective and practical high-resolution archeological data, compared to data mining using aerial photography and high-resolution commercial satellite imagery, in terms of both cost and obstacle penetration. Full article

Neonatal health requires precise lipid quantification in human milk to ensure proper nutritional development. Traditional manual methods, such as the creamatocrit, are limited by human-induced bias and significant measurement uncertainty. This study presents a low-cost Computer Vision System acting as an automated optical sensing modality for estimate the cream fraction (c) using advanced Machine Learning regression, which is subsequently used to derive fat and energy quantification through established analytical equations. The system is optimized for the Gold-LED spectrum, which enhances the dynamic range to 226 a.u. for robust feature extraction. We evaluated 28 distinct ML regression models across three feature spaces (Gray Scale, RGB, and Combined). The results, based on 6400 samples, demonstrate that the Rational Quadratic GPR model achieved the highest predictive stability with a coefficient of determination of $R^2=0.867$. This computational framework achieved a 57.5% reduction in relative error compared to manual benchmarks. SHAP analysis indicates that the model selectively attributes higher importance to Red channel intensities and Blue contrast gradients, which correspond to the optical scattering characteristics of lipid globules. These findings validate the system as a stable sensing modality for non-invasive quantification. The proposed architecture integrates cost-effective hardware with high-precision analytical modeling, offering a reagent-free and operationally feasible alternative for standardized nutritional assessment in neonatal intensive care units and milk banks. Full article

Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive continuous comfort scores across Macau’s street network. Four experiential street types are identified: historical–cultural districts, urban lifestyle areas, natural corridors, and leisure zones. SHAP analysis illustrates stable associations between predicted comfort scores and multi-layered spatial configurations, including cultural legibility and sequencing in historic cores, moderate greenery with functional anchoring in residential areas, and scene coherence in tourism zones. Semantic features serve as effective morphological proxies within the modeling framework. Methodologically, the framework demonstrates how explainable machine learning can be applied to dense Asian cities under observational conditions. Design implications emphasize interface continuity, microclimate adaptation, and functional enrichment, suggesting that pedestrian comfort is closely related to coherent spatial–experiential structures rather than isolated environmental upgrades. Full article

Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km² agricultural watershed in China’s Jianghuai region using unmanned aerial vehicle (UAV) -derived 0.1 m digital elevation models (DEMs) and coupled hydrological–erosion modeling. The Soil Conservation Service Curve Number (SCS-CN) and Modified Universal Soil Loss Equation (MUSLE) models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km²·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km²·a) and forested areas showing minimal export (<6.1 kg/km²·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution. Full article

An accurate prediction of how strongly a drug binds to its target (where the drug will have the desired effect) is very important for drug discovery. It helps select the most promising compounds and saves money by doing fewer experiments. We present DTBAffinity, a multi-modal regression framework that integrates chemically meaningful ligand descriptors with diverse protein sequence features in a unified gradient-boosting model. The representation of ligands includes physicochemical and topological descriptors (RDKit and Mordred), structural keys (MACCS and FP4), circular fingerprints (ECFP/Morgan), and SMILES-derived features from iFeatureOmega. For proteins, thousands of sequence-derived descriptors (composition, autocorrelations, physicochemical profiles, and evolutionary indices) from iFeatureOmega are used, together with contextual embeddings from large protein language models (ESM-1b, ESM-2). The feature matrices are cleaned up, variance filtered, z-score scaled, and univariate selected before being concatenated and modeled with regularized XGBoost ensembles. We evaluate DTBAffinity on two kinase-centric datasets that are commonly used: Davis (30,056 interactions: pKd values) and KIBA (118,254 interactions: integrated affinity scores). Various metrics are used to measure the performance, such as MSE, R², Pearson/Spearman correlations, Concordance Index (CI), r_m², and AUPR. On Davis, DTBAffinity yields MSE = 0.1885, CI = 0.9102, and AUPR = 0.8112, and on KIBA, it gives MSE = 0.1540, CI = 0.8686, and AUPR = 0.8361; thus, it is better than the state-of-the-art baselines such as KronRLS, SimBoost, DeepDTA, and GraphDTA. The findings here imply that the combination of interpretable descriptors and contextual embeddings in a robust boosting framework is a great way to realize accurate, interpretable, and generalizable DTBA prediction. Full article

This study investigates amplified hydraulic braking systems employed in high-performance motorsport applications, utilizing wedge mechanisms for self-energization. An analytical expression for the gain coefficient is derived from a simplified equilibrium analysis of the wedge-shaped pad, capturing the nonlinear dependency on both wedge angle and effective mean disc-pad friction. A previously validated coupled thermoelastic model for carbon-carbon (C/C) braking systems—developed in Dymola and Modelica using the finite volume method (FVM) and an analytical local friction formulation—is here adapted to wedge-amplified braking systems, with the aim of providing performance assessment during the design phase of new calipers at reduced computational cost compared to coupled thermoelastic finite element method (FEM) models. Several caliper configurations featuring different wedge angles are tested experimentally on a dynamometer. A reduction in the effective friction coefficient at high mean effective contact pressure—induced by pronounced wedge angles and reduced pad areas—is observed. To validate the thermoelastic model, simulated braking torque and disc surface temperature are compared against bench data. The model shows satisfactory predictive capability under various operating conditions and test cycles, with mean error indices on peak torque prediction below $5\%$ for the majority of the simulated cases. Finally, the validated model is used to virtually assess the performance of a new caliper prototype prior to its manufacturing and testing. Full article

Owing to their distinctive physicochemical features, their structural analogues of benzene ring bioisosteres, and their strong affinity for biomacromolecules, pyridine derivatives function both as core structural scaffolds in pharmacologically active compounds and as versatile elements for optimizing key drug-like properties, such as water solubility, membrane permeability, and metabolic stability. In this study, we synthesized five pyridine-fused heterocyclic compounds using common synthetic intermediates as precursors. Full article

Theoretical studies of transonic accretion onto black holes reveal a wide range of possible solutions, broadly classified into smooth flows and flows featuring shocks. Accretion solutions that involve the formation of shocks are particularly intriguing, as they are expected to naturally produce observable variability features. However, despite their theoretical significance, time-dependent studies exploring the stability and evolution of such shocked solutions remain relatively scarce. To address this gap, we perform simulations of transonic accretion flows around a black hole in an ideal magnetohydrodynamic framework. Our simulations are initialized using boundary conditions derived from semi-analytical hydrodynamical models, allowing us to explore the stability of these flows under varying magnetic field strengths. Our results indicate that mildly magnetized flows in a uniform vertical magnetic field alter the accretion dynamics through magnetic pressure, with the resulting force imbalance driving oscillations in the shock front. Variations in the emitted luminosity arising from shock oscillations appear as quasi-periodic oscillations (QPOs), a characteristic feature commonly observed in accreting black holes. We find that the QPO frequency is determined by the radial position of the shock front: oscillations occurring closer to the black hole produce frequencies of tens of hertz, whereas shocks located farther out yield sub-hertz frequencies. Full article

Background and Objective: Axial spondyloarthritis (axSpA) is a group of chronic inflammatory diseases that primarily affect the sacroiliac joints. Early diagnosis is crucial for preventing irreversible structural damage. Magnetic Resonance Imaging (MRI) is the gold standard for detecting early inflammatory changes such as sacroiliitis. However, conventional MRI interpretation is inherently subjective and susceptible to both intra- and inter-observer variability. Therefore, artificial intelligence (AI)-driven diagnostic solutions are increasingly being explored. Among them, the Gated Attention Multiple Instance Learning (MIL) framework holds strong potential in modeling heterogeneous inflammatory distributions, thanks to its slice-level attention mechanism. This study aims to evaluate the diagnostic performance of a deep learning model based on Gated Attention MIL for automated sacroiliitis detection. Furthermore, its results are compared with a baseline deep learning architecture (standard ResNet-18), and its consistency with radiologist annotations is analyzed. Materials and Methods: The dataset included 554 subjects, comprising 276 patients diagnosed with axSpA and 278 healthy controls. All MRI data were derived from axial T2-weighted fat-suppressed (T2_TSE_TRA_FS) sequences. Patient-wise data splitting was employed to construct training, validation, and independent test sets. The proposed model architecture integrates ResNet-18-based feature extraction, a gated attention mechanism for instance-level weighting, and bag-level classification. Additionally, Test-Time Augmentation (TTA) was implemented to enhance robustness during inference. Results: On the independent test set, the model achieved an accuracy of 85.88%, sensitivity of 92.86%, specificity of 79.07%, and an F1-score of 86.67%. Attention heatmaps generated by the MIL module showed strong spatial overlap with bone marrow edema regions annotated by expert radiologists. Implementation of TTA led to an approximate 10% improvement in overall classification accuracy. Conclusions: The Gated Attention MIL framework demonstrated high diagnostic performance for sacroiliitis detection, indicating its value as a reliable decision support tool for early axSpA diagnosis. Validation on larger, multi-center datasets is warranted to ensure generalizability and to support clinical integration in routine radiology workflows. Full article

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article