Search Results (1,107)

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35${}^{\circ }$ on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

Apatite is a widespread accessory mineral, which can provide information on the geochemical characteristics of magma and the conditions of hydrothermal alteration of the rocks in magmatic–hydrothermal deposits. This study aims to understand the relationships between the geochemical and textural features of apatites from diorite porphyries that have undergone different degrees of hydrothermal alteration in the Sharlo Dere area, Chelopech epithermal Cu-Au deposit, Bulgaria. The apatites were characterized by laser ablation–inductively coupled plasma mass spectrometry, scanning electron microscopy with energy-dispersive X-ray spectroscopy, electron probe microanalysis with wave-dispersive spectroscopy, optical cathodoluminescence and multi-element mapping. Magmatic apatites from “hematitic”, propylitic and propylitic-sericitic zones of alteration are distinguished by euhedral crystals with oscillatory zoning and brown luminescence in CL images. In quartz-sericitic alteration zones, apatite has a yellow CL response. Hydrothermally altered apatites in the diorite porphyries overprinted by advanced argillic alteration have corroded, irregular forms and pink-green luminescence. Apatite crystals of magmatic origin reveal high contents of chlorine, strontium, light rare earth elements (LREE), negative Eu anomalies and high La_N/Sm_N and Ce_N/Yb_N ratios. Hydrothermally altered or hydrothermal apatites are distinguished by their higher contents of Na₂O, F, SO₃, Y and middle rare earth elements (MREEs) and their low La_N/Sm_N and Ce_N/Yb_N ratios. The intensity of hydrothermal alteration affects the luminescence and major and trace element contents, including the rare earth element patterns in the apatites, implying apatite can be used as a geochemical indicator to study magmatic–hydrothermal ore deposits. Full article

Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors. Full article

Genomic instability caused by defective DNA double-strand break (DSB) repair is a key determinant of cellular radiosensitivity. The Long–Evans cinnamon (LEC) rat is a rare naturally occurring model with marked radiosensitivity, and a major quantitative trait locus, X-ray hypersensitivity 1 (xhs1), has been mapped to rat chromosome 4; however, the causal mechanism has remained unclear. Here, we investigated the cellular and molecular basis of xhs1-associated radiosensitivity using LEA and LEC rat-derived cells and human cultured cells. Exploratory RNA-seq of pre-hepatitic liver tissue identified a sequence variant within the Hmces transcript in LEC rats. Consistently, HMCES protein levels were markedly reduced in multiple tissues and liver-derived cell lines from LEC rats. Functional analyses showed that reduced HMCES activity prolonged γH2AX signaling after X-ray irradiation, indicating delayed DSB resolution. Clonogenic survival assays demonstrated increased radiosensitivity in HMCES-deficient cells, which was partially rescued by restoring HMCES expression in stable LEA/LEC lines. Moreover, pimEJ5GFP reporter assays revealed significantly decreased end-joining repair activity in HMCES-knockout human cells. Together, these results establish HMCES as a critical mediator of DSB repair and cellular radioresistance, identify HMCES dysfunction as a core genetic determinant underlying xhs1-associated radiosensitivity, and provide mechanistic insight into radiation response architecture in a naturally occurring radiosensitive model. Full article

D-Tagatose is a rare sugar of interest as a low-calorie sweetener, and enzymatic isomerization of D-galactose is a practical production route. L-arabinose isomerase (L-AI; EC 5.3.1.4) is a promising catalyst for the above process, but many characterized L-AIs perform best at alkaline pH and high temperature and often require substantial divalent metal supplementation (e.g., Mn²⁺/Co²⁺), which complicates food-grade processing. Lactic acid bacteria (LAB) are attractive sources of food-compatible enzymes, yet structural information for LAB-derived L-AIs has been limited. Here, we report the 2.6 Å X-ray crystal structure of L-AI from Latilactobacillus sakei 23K (LsAI) and define its oligomeric assembly. Although the asymmetric unit contains a single monomer, crystallographic symmetry reconstructs a D₃-symmetric homohexamer composed of two face-to-face trimers, consistent with a higher-order assembly in solution. Interface analysis shows predominantly polar interaction networks, and normalized B-factor mapping reveals localized flexibility near active-site-proximal regions. These findings provide a structural basis for understanding LAB-derived L-AIs and support structure-guided engineering toward food-grade, low-metal biocatalysts for rare-sugar production. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article

This study presents a systematic investigation of laser surface hardening of 9CrSi tool steel with the aim of establishing the relationships between processing parameters, microstructural evolution, and resulting mechanical and tribological properties under the applied laser conditions. The influence of laser power, modulation frequency, and scanning speed on the hardened layer depth, microstructure, and surface properties was analyzed. Laser treatment produced a martensitic surface layer with varying fractions of retained austenite, while the transition zone consisted of martensite, granular pearlite, and carbide particles. X-ray diffraction identified the presence of α′-Fe, γ-Fe, and Fe₃C phases, with peak broadening associated with increased lattice microstrain induced by rapid self-quenching. The surface microhardness increased from approximately 220 HV_0.1 in the untreated state to 950–1000 HV_0.1 after laser hardening, with hardened layer thicknesses ranging from about 500 to 750 µm depending on the processing regime. Instrumented indentation showed higher elastic modulus values for all hardened conditions. Tribological tests under dry sliding conditions revealed reduced coefficients of friction and more than an order-of-magnitude decrease in wear rate compared with untreated steel. The results provide a parameter–microstructure–performance map for laser-hardened 9CrSi steel, demonstrating how variations in laser processing conditions affect hardened layer characteristics and functional performance. Full article

Determining RNA secondary structures is a fundamental challenge in computational biology and molecular sensing. Experimental techniques such as X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy can reveal RNA structures with atomic precision, but their high cost and time consuming nature limit large-scale applications. To address this issue, we introduce the Structure-Sensing Nucleotide Attention Learning framework (NTFold), a virtual sensing framework based on deep learning for accurate RNA secondary structure prediction. NTFold integrates a Nucleotide Attention Module (NAM) to explicitly model dependencies among nucleotides, thereby capturing fine-grained sequence correlations. The resulting correlation map is subsequently refined by a Structural Refinement Module (SRM), which preserves hierarchical spatial information and enforces structural consistency. Through this two stage learning paradigm, NTFold produces high-precision contact maps that enable reliable RNA secondary structure reconstruction. Extensive experiments demonstrate that NTFold outperforms existing deep learning-based predictors, highlighting its capability to learn both local and global nucleotide interactions in an sensor inspired manner. This study provides a new direction for integrating attention driven correlation modeling with structure-sensing refinement toward efficient and scalable RNA structural sensing. Full article

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amphissa, Greece, with validation based on ground samples. We applied the well-established Linear Spectral Unmixing (LSU) and Spectral Angle Mapping (SAM) classification techniques utilizing endmembers of two established spectral libraries and incorporated ground data through geochemical and mineralogical analyses, X-ray fluorescence (XRF), Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), and X-ray Diffraction (XRD), to assess classification performance. The main lithologies in this area are bauxites and limestones; therefore, aluminum oxyhydroxides, calcite, and iron oxide minerals were the dominant phases as indicated by the XRF/XRD results. Almost all target minerals were mapped with the three sensors and both methods. The performance of EnMap is affected by its coarser spatial resolution despite its higher spectral resolution using these methods. Sentinel-2 is most effective for mapping iron-bearing minerals, particularly hematite, due to its higher spatial resolution and the presence of diagnostic iron oxide absorption features in the VNIR. World View 3 Shortwave Infrared (WV3-SWIR) performs better when mapping calcite, benefiting from its eight SWIR spectral bands and very high spatial resolution (3.7 m). Hematite and calcite yield the highest accuracy, especially with SAM, indicating 0.80 for Sentinel-2 (10 m) for hematite and 0.87 for WV3-SWIR (3.7 m) for calcite. AlOOH shows higher accuracy with SAM, ranging from 0.57 to 0.80 across the sensors, while LSU shows lower accuracy, ranging from 0.20 to 0.73 across the sensors. This study showcases each sensor’s ability to map minerals while also demonstrating that spectral coverage and the spatial and spectral resolution, as well as the characteristics of the selected endmembers, exert a critical influence on the accuracy of mineral mapping in mine waste. Full article

Background/Objectives: Trace metals, including copper (Cu) and zinc, are associated with the development and prognosis of hepatocellular carcinoma (HCC). However, their interference with magnetic resonance imaging (MRI) limits their use as potential biomarkers. This study investigated the usefulness of Synchrotron Radiation–excited X-ray Fluorescence (SR-XRF) imaging in studying the distribution of trace metals in HCC. Methods: This case–control study analyzed 33 specimens from 32 patients with HCC who underwent surgical resection (n = 29) or biopsy (n = 3) at Kobe University Hospital between December 1999 and November 2002. The findings of SR-XRF were compared with those of MRI and histopathology. Results: SR-XRF provided two-dimensional mapping of trace metal distribution with high spatial resolution (1.0 µm). The mean tumor-to-liver ratio (TLR) of Cu content was significantly higher in well-differentiated HCCs than in moderately and poorly differentiated HCCs (p < 0.05). Moreover, the mean TLRs of Cu content were significantly higher in high-intensity lesions than in iso- or low-intensity lesions on T1-weighted imaging (p < 0.05). Conclusions: This study supports previous evidence of the involvement of Cu in HCC development, suggesting its potential as a clinical biomarker for diagnosis and disease progression. Additionally, the results demonstrate that SR-XRF has potential for clinical application due to its ability to map trace metal distribution at high resolution. These findings suggest, rather than demonstrate, the association among Cu accumulation, tumor differentiation, and MRI signal characteristics. Full article

To address the limitations of traditional lithium battery defect detection—low efficiency, high missed detection rates for minute/composite defects, and inadequate multimodal fusion—this study develops an improved YOLOv8 model based on multimodal fusion and attention enhancement for unified full-lifecycle multi-type defect detection. Integrating visible-light and X-ray modalities, the model incorporates a Squeeze-and-Excitation (SE) module to dynamically weight channel features, suppressing redundancy and highlighting cross-modal complementarity. A Multi-Scale Fusion Module (MFM) is constructed to amplify subtle defect expression by fusing multi-scale features, building on established feature fusion principles. Experimental results show that the model achieves an mAP@0.5 of 87.5%, a minute defect recall rate (MRR) of 84.1%, and overall industrial recognition accuracy of 97.49%. It operates at 35.9 FPS (server) and 25.7 FPS (edge) with end-to-end latency of 30.9–38.9 ms, meeting high-speed production line requirements. Exhibiting strong robustness, the lightweight model outperforms YOLOv5/7/8/9-S in core metrics. Large-scale verification confirms stable performance across the battery lifecycle, providing a reliable solution for industrial defect detection and reducing production costs. Full article

Background: Chest X-ray (CXR) is widely used for the assessment of thoracic diseases, yet automated multi-label interpretation remains challenging due to subtle visual patterns, overlapping anatomical structures, and frequent co-occurrence of abnormalities. While recent deep learning models have shown strong performance, limitations in interpretability, anatomical awareness, and robustness continue to hinder their clinical adoption. Methods: The proposed framework employs a hybrid ConvNeXtV2–Vision Transformer (ViT) architecture that combines convolutional feature extraction for capturing fine-grained local patterns with transformer-based global reasoning to model long-range contextual dependencies. The model is trained exclusively using image-level annotations. In addition to classification, three complementary post hoc components are integrated to enhance model trust and interpretability. A segmentation-aware Gradient-weighted class activation mapping (Grad-CAM) module leverages CheXmask lung and heart segmentations to highlight anatomically relevant regions and quantify predictive evidence inside and outside the lungs. An ontology-driven neuro-symbolic reasoning layer translates Grad-CAM activations into structured, rule-based explanations aligned with clinical concepts such as “basal effusion” and “enlarged cardiac silhouette”. Furthermore, a lightweight out-of-distribution (OOD) detection module based on confidence scores, energy scores, and Mahalanobis distance scores is employed to identify inputs that deviate from the training distribution. Results: On the VinBigData test set, the model achieved a macro-AUROC of 0.9525 and a Micro AUROC of 0.9777 when trained solely with image-level annotations. External evaluation further demonstrated strong generalisation, yielding macro-AUROC scores of 0.9106 on NIH ChestXray14 and 0.8487 on CheXpert (frontal views). Both Grad-CAM visualisations and ontology-based reasoning remained coherent on unseen data, while the OOD module successfully flagged non-thoracic images. Conclusions: Overall, the proposed approach demonstrates that hybrid convolutional neural network (CNN)–vision transformer (ViT) architectures, combined with anatomy-aware explainability and symbolic reasoning, can support automated chest X-ray diagnosis in a manner that is accurate, transparent, and safety-aware. Full article

This study investigated the effect of mechanical activation on the physicochemical properties of lepidolite and the leaching behavior of mechanically activated samples in sulfuric acid (H₂SO₄). Lepidolite was mechanically activated using a high-energy planetary ball mill (PBM) at 400 RPM with a 20:1 ball-to-feed weight ratio (BFR, g:g) and the samples activated for different durations were characterized for amorphous phase content, particle size, and morphology using various solid analyses. X-ray diffraction (XRD) revealed the progressive amorphization of lepidolite, with the amorphous fraction increased from 34.1% (unactivated) to 81.4% after 60 min of mechanical activation. Scanning electron microscopy (SEM) showed that mechanically activated particles became fluffy and rounded, whereas unactivated particles retained lamellar and angular shapes. The reactivity of minerals after mechanical activation was evaluated through a 2 M H₂SO₄ leaching test at different leaching temperatures (25–80 °C) and time periods (30–180 min). Although the leaching efficiencies of Li and Al slightly improved at higher leaching temperatures and longer leaching times, the leaching of these metals was primarily governed by the mechanical activation time. The highest Li and Al leaching efficiencies—87.0% for Li and 79.4% for Al—were obtained from lepidolite that was mechanically activated for 60 min under leaching conditions of 80 °C and a 10% (w/v) solid/liquid (S/L) ratio for 30 min. The elemental mapping images of leaching feed and residue produced via energy dispersive spectroscopy (EDS) indicated that unactivated particles in the leaching residue had much higher metal content than mechanically activated particles. Kinetic analysis further suggested that leaching predominantly occurs at mechanically activated sites and the apparent activation energies calculated in this study (<3.1 kJ·mol⁻¹) indicate diffusion-controlled behavior with weak temperature dependence. This result confirmed that mechanical activation significantly improves reactivity and that the residual unleached fraction can be attributed to unactivated particles. Full article

Accurate 3D reconstruction of guidewires is crucial in minimally invasive surgery and interventional procedures. Traditional biplanar X-ray–based reconstruction methods can achieve reasonable accuracy but involve high radiation doses, limiting their clinical applicability; meanwhile, single-view images inherently lack reliable depth cues. To address these issues, this paper proposes a multimodal guidewire 3D reconstruction approach that integrates magnetic field information. The method first employs the MiDaS v3 network to estimate an initial depth map from a single image and then incorporates tri-axial magnetic field measurements to enrich and refine the spatial information. To effectively fuse the two modalities, we design a multi-stage strategy combining nearest-neighbor matching (KNN) with a cross-modal attention mechanism (Cross-Attention), enabling accurate alignment and fusion of image and magnetic features. The fused representation is subsequently fed into a PointNet-based regressor to generate the final 3D coordinates of the guidewire. Experimental results demonstrate that our method achieves a root-mean-square error of 2.045 mm, a mean absolute error of 1.738 mm, and a z-axis MAE of 0.285 mm on the test set. These findings indicate that the proposed multimodal framework improves 3D reconstruction accuracy under single-view imaging and offers enhanced visualization support for interventional procedures. Full article

Pediatric pneumonia detection faces the challenge of pathological asymmetry, where immature lung tissues present blurred boundaries and lesions exhibit extreme scale variations (e.g., small viral nodules vs. large bacterial consolidations). Conventional detectors often fail to address these imbalances. In this study, we propose YOLO-SMD, a detection framework built upon a symmetrical design philosophy to enforce balanced feature representation. We introduce three architectural innovations: (1) DySample (Content-Aware Upsampling): To address the blurred boundaries of pediatric lesions, this module replaces static interpolation with dynamic point sampling, effectively sharpening edge details that are typically smoothed out by standard upsamplers; (2) SAC2f (Cross-Dimensional Attention): To counteract background interference, this module enforces a symmetrical interaction between spatial and channel dimensions, allowing the model to suppress structural noise (e.g., rib overlaps) in low-contrast X-rays; (3) SDFM (Adaptive Gated Fusion): To resolve the extreme scale disparity, this unit employs a gated mechanism that symmetrically balances deep semantic features (crucial for large bacterial shapes) and shallow textural features (crucial for viral textures). Extensive experiments on a curated subset of 2611 images derived from the Chest X-ray Pneumonia Dataset demonstrate that YOLO-SMD achieves competitive performance with a focus on high sensitivity, attaining a Recall of 86.1% and an mAP@0.5 of 84.3%, thereby outperforming the state-of-the-art YOLOv12n by 2.4% in Recall under identical experimental conditions. The results validate that incorporating symmetry principles into feature modulation significantly enhances detection robustness in primary healthcare settings. Full article