Accurate Structural Analysis Research Articles

System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring

Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed noise and linear shift-invariant point-spread functions are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic three-dimensional imaging. SSAI-3D enables robust axial deblurring by generating a diverse, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D is applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of three-dimensional heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.

Peptide-Scaffolded Detergents for Membrane Protein Studies.

Detergents are essential for preserving the structural integrity and functionality of membrane proteins (MPs) outside the biological membrane or in aqueous solution, and thus ensuring accurate biochemical and structural analyses. Here, we introduce peptide-scaffolded detergents, a novel class of hybrid molecules formed by preassembling detergent monomers with peptides of varying lengths, mediated via Click chemistry. These detergents are characterized by scalable, straightforward synthesis and enhanced solubility. Among the variants, A4B2 emerged as the optimal detergent, demonstrating superior thermal stabilization across a range of G protein-coupled receptors, including A2AAR, SMO and GLP-1R. Additionally, A4B2 exhibits a low critical micelle concentration and small micelle size, together making it particularly effective for electron microscopy studies of A2AAR. This innovative design leverages the benefits of peptide-based and traditional detergents, offering new insights for the development of advanced detergents in MP research.

Useful experimental aspects of small-wedge synchrotron crystallography for accurate structure analysis of protein molecules.

Recent advances in low-emittance synchrotron X-ray technology and highly sensitive photon-counting detectors have revolutionized protein micro-crystallography in structural biology. These developments and improvements to sample-exchange robots and beamline control have paved the way for automated and efficient unattended data collection. This study analyzed protein crystal structures such as type 2 angiotensin II receptor, CNNM/CorC membrane proteins and polyhedral protein crystals using small-wedge synchrotron crystallography (SWSX), which dramatically improves measurement efficiency through automated measurement. We evaluated the data quality using SWSX, focusing on `massive data collection'. In this context, `massive' refers to data sets with a multiplicity exceeding 100. The findings could potentially lead to the development of more efficient experimental conditions, such asobtaining high-resolution data using a smaller number of crystals. We have demonstrated that the application of machine learning, a modern key component of data science, to classify data groups is an integral part of the analysis process and may play a crucial role in improving data quality. These results indicate that SWSX is one of the essential candidates for crystal structure analysis methods for difficult-to-analyze samples: it can enable diverse and complex protein functional analysis.

Tree polynomials identify a link between co-transcriptional R-loops and nascent RNA folding.

R-loops are a class of non-canonical nucleic acid structures that typically form during transcription when the nascent RNA hybridizes the DNA template strand, leaving the non-template DNA strand unpaired. These structures are abundant in nature and play important physiological and pathological roles. Recent research shows that DNA sequence and topology affect R-loops, yet it remains unclear how these and other factors contribute to R-loop formation. In this work, we investigate the link between nascent RNA folding and the formation of R-loops. We introduce tree-polynomials, a new class of representations of RNA secondary structures. A tree-polynomial representation consists of a rooted tree associated with an RNA secondary structure together with a polynomial that is uniquely identified with the rooted tree. Tree-polynomials enable accurate, interpretable and efficient data analysis of RNA secondary structures without pseudoknots. We develop a computational pipeline for investigating and predicting R-loop formation from a genomic sequence. The pipeline obtains nascent RNA secondary structures from a co-transcriptional RNA folding software, and computes the tree-polynomial representations of the structures. By applying this pipeline to plasmid sequences that contain R-loop forming genes, we establish a strong correlation between the coefficient sums of tree-polynomials and the experimental probability of R-loop formation. Such strong correlation indicates that the pipeline can be used for accurate R-loop prediction. Furthermore, the interpretability of tree-polynomials allows us to characterize the features of RNA secondary structure associated with R-loop formation. In particular, we identify that branches with short stems separated by bulges and interior loops are associated with R-loops.

Extraction and characterization of rubberwood starch for accurate structural and physicochemical properties analysis

Rubberwood has high starch content and is susceptible to fungal and microbial attack. The modification of starch can prevent it from becoming a nutrient source of microorganisms and realize the purpose of anti-mildew and anti-corrosion of rubber wood. Understanding the structure and physicochemical properties of rubberwood starch aids to explore suitable methods for starch modification. This study investigates the distribution of starch in rubberwood and explores the feasibility of extracting starch by three different methods (water extraction, sodium metabisulfite extraction and ethanol extraction). The results showed that the distribution of rubberwood starch was consistent with the typical distribution of wood starch, mainly distributed in thin-walled cells. Three extraction methods proved the feasibility of extracting starch from rubberwood thin-walled cells. The microstructure, physical and chemical properties and structure of rubberwood starch were investigated by means of scanning electron microscope, X-ray diffraction, polarizing light microscope and nuclear magnetic resonance analysis. The results showed that compared with the internal starch of rubberwood, the shape of the starches extracted by the three methods did not change significantly, and they were all spherical and elliptical, with stable A-type crystal structure and obvious Malta cross diffraction pattern. However, it is worth noting that sodium metabisulfite and ethanol will increase the swelling power of rubberwood starch, which provides new insights into other uses of rubberwood starch. This work not only enriched our understanding of the characteristics of rubberwood starch, but also laid a theoretical basis and practical guidance for the development of efficient mildew prevention technology of rubberwood products.

Enhanced spectral reconstruction of ultrafast spatiotemporal encoded 2D NMR spectroscopy

BackgroundNuclear Magnetic Resonance (NMR) is extensively utilized in research as a non-invasive technique for investigating molecular structures and composite components. The spatiotemporal encoding (SPEN) technique effectively accelerates multi-dimensional NMR experiments. In ultrafast SPEN NMR, the acquired data are divided into odd and even segments corresponding to the positive and negative gradients during the decoding stage, respectively. However, the interlaced Fourier transform (FT) method used to reconstruct a full-width spectrum from these segments often suffers from severe noise contamination, necessitating the development of a more effective spectrum reconstruction method. ResultsIn this work, we analyze the noise amplification effect of the interlaced FT and find that the noise is most significant in two edge regions of the spectrum along the indirect dimension due to the relatively small time offset differences between odd and even segments in those regions. Consequently, we develop an iterative optimization method to obtain the full-width spectrum while mitigating the noise. The proposed method incorporates the odd and even data segments into an objective function with sparsity regularization to simplify the spectrum, which is then refined iteratively during the optimization. As a result, the reconstructed spectrum is significantly cleaner and maintains the full spectral width. Experimental results demonstrate a remarkable improvement in the readability and interpretability of SPEN data, evidenced by clearer signal peaks and reduced background noise. SignificanceThe proposed reconstruction method provides a reliable approach for processing SPEN 2D NMR data, effectively addressing the low sensitivity issue in the joint reconstruction on odd and even segments. Combining SPEN's ultrafast data acquisition with the proposed high-sensitivity spectrum reconstruction method enhances the utility of NMR for more accurate molecular structure analysis and component identification in composite samples, particularly promoting NMR research in rapid reaction systems.

A novel computer vision and point cloud-based approach for accurate structural analysis of a tall irregular timber structure

Wood material has been widely used in critical heritage structures such as pagodas, totem poles, and large-scale sculptures. Conducting rigorous structural analysis is crucial to protect these structures in high-seismic regions. Typically, these types of structures are modeled using an equivalent finite element (FE) model which used simple cylinder with a constant cross section equal to the average of the top and bottom cross section. However, simplified equivalent FE models may not accurately consider the irregularities and complexities of these structures. In this paper, advanced computer vision and point cloud techniques were adopted to accurately and rapidly construct a FE model of a 30-meter irregular timber sculpture. This was achieved using video scans, computer vision-aided 3D reconstruction, point cloud processing, and mesh to solid element conversion. The refined FE model was used to conduct capacity check, mesh sensitivity study, pushover analysis, and response spectrum analysis. The results of the refined FE model were compared to an equivalent FE model. The results show: 1) the proposed numerical modeling methodology for structural analysis can efficiently and accurately measure the dimension of the irregular sculpture up to 98.2 % accuracy; 2) the lateral stiffnesses of the 30-meter irregular sculpture vary significantly (up to 42.6 %) from one direction to the other; 3) the equivalent FE model overestimated the shear and moment capacities by 20.6 % and 13.2 %, respectively; 4) on average, the equivalent FE model overestimated the shear and moment demands by 8.9 % and 5.5 %, respectively. Overall, the proposed application has demonstrated a fast, economical and accurate method to conduct seismic evaluation and design for irregular structures.

Hybrid Wavelet‐Deep Learning Framework for Fluorescence Microscopy Images Enhancement

ABSTRACTFluorescence microscopy is a powerful tool for visualizing cellular structures, but it faces challenges such as noise, low contrast, and autofluorescence that can hinder accurate image analysis. To address these limitations, we propose a novel hybrid image enhancement method that combines wavelet‐based denoising, linear contrast enhancement, and convolutional neural network‐based autofluorescence correction. Our automated method employs Haar wavelet transform for noise reduction and a series of adaptive linear transformations for pixel value adjustment, effectively enhancing image quality while preserving crucial details. Furthermore, we introduce a semantic segmentation approach using CNNs to identify and correct autofluorescence in cellular aggregates, enabling targeted mitigation of unwanted background signals. We validate our method using quantitative metrics, such as signal‐to‐noise ratio (SNR) and peak signal‐to‐noise ratio (PSNR), demonstrating superior performance compared to both mathematical and deep learning‐based techniques. Our method achieves an average SNR improvement of 8.5 dB and a PSNR increase of 4.2 dB compared with the original images, outperforming state‐of‐the‐art methods such as BM3D and CLAHE. Extensive testing on diverse datasets, including publicly available human‐derived cardiosphere and fluorescence microscopy images of bovine endothelial cells stained for mitochondria and actin filaments, showcases the flexibility and robustness of our approach across various acquisition conditions and artifacts. The proposed method significantly improves fluorescence microscopy image quality, facilitating more accurate and reliable analysis of cellular structures and processes, with potential applications in biomedical research and clinical diagnostics.

Automated Finite Element Analysis of Stiffened Tanks Using Multifidelity Modeling

Accurate structural analysis of stiffened thin-walled pressure vessels enables critical design decisions to be made during the conceptual and baseline design phases. Furthermore, having multifidelity finite element models that couple shell and solid representations facilitates exploring many design iterations quickly with sufficient accuracy. However, multifidelity model creation and utilization can be cumbersome to perform interactively, such that automated model creation, mesh generation, analysis submission, and results postprocessing are highly desirable. Of particular difficulty is generating multifidelity meshes robustly. A framework for automatic multifidelity model development using parameterized scripting in commercial finite element packages is developed to address these critical modeling needs. Issues associated with automated meshing of highly complex assemblies are examined, and a volume decomposition scheme based upon face partitions is proposed. Application is made to a complex stiffened thin-walled pressure vessel with full shell and solid/shell multifidelity model accuracies assessed by comparison against all solid models. Results indicate the proposed technique’s potential for design iteration improvements with significant reductions in interactive model preparation and postprocessing costs, as well as overall computational effort. Potential novel applications and improvements to the presented framework that can enable its application to complex problems are discussed.

Enhancing Semantic Segmentation in High-Resolution TEM Images: A Comparative Study of Batch Normalization and Instance Normalization

Abstract Integrating deep learning into image analysis for transmission electron microscopy (TEM) holds significant promise for advancing materials science and nanotechnology. Deep learning is able to enhance image quality, to automate feature detection, and to accelerate data analysis, addressing the complex nature of TEM datasets. This capability is crucial for precise and efficient characterization of details on the nano—and microscale, e.g., facilitating more accurate and high-throughput analysis of nanoparticle structures. This study investigates the influence of batch normalization (BN) and instance normalization (IN) on the performance of deep learning models for semantic segmentation of high-resolution TEM images. Using U-Net and ResNet architectures, we trained models on two different datasets. Our results demonstrate that IN consistently outperforms BN, yielding higher Dice scores and Intersection over Union metrics. These findings underscore the necessity of selecting appropriate normalization methods to maximize the performance of deep learning models applied to TEM images.

An Adaptive Multi-Stage Fuzzy Logic Framework for Accurate Detection and Structural Analysis of Road Cracks

An Adaptive Multi-Stage Fuzzy Logic Framework for Accurate Detection and Structural Analysis of Road Cracks

Methodological foundations of somatoscopic research in automated decision support systems

Automated Decision Support Systems (ADSS) are widely applied in various fields of science and technology, particularly in medicine, where their role in diagnostic, prognostic, and therapeutic processes is undeniable. The use of computational technologies in medical practice has become a necessary stage in the development of the field; however, the increasing volume of data that requires processing, along with heightened demands for accuracy, speed, and reliability of recommendations, significantly complicates decision-making processes. The growing amount of data, combined with the need to minimize the risks of erroneous decisions, drives the scientific community to seek innovative information technologies that can ensure a high level of accuracy in computational operations while minimizing the time required for their execution. Somatoscopic measurements, which are a crucial element in assessing the condition of the musculoskeletal system and overall physical posture of a patient, require precise and systematic image analysis that allows the identification of critically important anatomical markers. This process involves the accurate determination of key points on images of anatomical structures, which serves as the foundation for making precise predictions about the patient's condition and subsequent treatment planning. In the context of continuously increasing volumes of medical data and stricter requirements for the quality of decisions, the implementation of innovative technologies, particularly deep learning, becomes increasingly relevant as it enhances the processes of analysis and diagnosis. The aim of this study is to conduct a comprehensive analysis of the methodological aspects of applying deep learning in the context of automating the diagnosis of the musculoskeletal system and posture assessment. The study emphasizes the use of specialized neural network architectures that enable the identification of key anatomical points on images, thereby facilitating a deeper and more accurate analysis of anatomical structures. This approach significantly improves the efficiency of diagnostic processes by minimizing the likelihood of errors in decision-making and optimizing the performance of medical systems. The results of the conducted research demonstrate the significant potential of deep learning algorithms in medical systems for the automated analysis of images, allowing for substantial improvements in the accuracy and speed of decision-making. Such automation contributes to reducing the risk of subjective errors associated with the human factor, which is particularly important in complex clinical cases. Therefore, the further development of research in this direction is of paramount importance for the medical field, as it opens new opportunities for solving complex diagnostic tasks at an innovative technological level. The integration of deep learning technologies into the processes of extracting somatoscopic data not only enhances diagnostic efficiency but also creates prerequisites for the development of new decision support systems that optimize medical practice.

Revealing nanoscale structure and interfaces of protein and polymer condensates via cryo-electron microscopy.

Liquid-liquid phase separation (LLPS) is a ubiquitous demixing phenomenon observed in various molecular solutions, including in polymer and protein solutions. Demixing of solutions results in condensed, phase separated droplets which exhibit a range of liquid-like properties driven by transient intermolecular interactions. Understanding the organization within these condensates is crucial for deciphering their material properties and functions. This study explores the distinct nanoscale networks and interfaces in the condensate samples using a modified cryo-electron microscopy (cryo-EM) method. The method involves initiating condensate formation on electron microscopy grids to limit droplet growth as large droplet sizes are not ideal for cryo-EM imaging. The versatility of this method is demonstrated by imaging three different classes of condensates. We further investigate the condensate structures using cryo-electron tomography which provides 3D reconstructions, uncovering porous internal structures, unique core-shell morphologies, and inhomogeneities within the nanoscale organization of protein condensates. Comparison with dry-state transmission electron microscopy emphasizes the importance of preserving the hydrated structure of condensates for accurate structural analysis. We correlate the internal structure of protein condensates with their amino acid sequences and material properties by performing viscosity measurements that support that more viscous condensates exhibit denser internal assemblies. Our findings contribute to a comprehensive understanding of nanoscale condensate structure and its material properties. Our approach here provides a versatile tool for exploring various phase-separated systems and their nanoscale structures for future studies.

NeuroQuantify – An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning

BackgroundThe segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. New methodWe have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. ResultsNeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. Comparison with existing methodsNeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution. ConclusionsWe offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation.

3D electron diffraction for accurate structure analysis of nanoparticles

3D electron diffraction for accurate structure analysis of nanoparticles

Influence of soil base deformation methods on the formation of the stress-strain state of retaining walls

The results of numerical modeling of the interaction between a pile retaining wall and the soil base using the software complexes "Plaxis" and "LIRA-SAPR" are presented. A comparison of the stress-strain state of retaining walls using different calculation methods, taking into account the presence of rock soil, has been performed. In the first variant, the active pressure on the wall was determined manually in accordance with the current standards [3], and the subsequent calculation was carried out in the "LIRA-SAPR" software complex. The "pile-soil" system was modeled using FE 57, which are interconnected by FE 10 (rod), and the values of horizontal stiffness (Rx,y) were determined according to the requirements of [3]. In the second variant, the retaining wall calculation was performed in "Plaxis 2D". The soil behavior model is "Mohr-Coulomb", and for rock - "Hoek-Brown". It was considered that rock soils lie at the base of the retaining wall, which revealed significant differences in the distribution of bending moments along the length of the retaining wall. It was established that the stress-strain state in the first variant significantly differs from the second. The difference in maximum horizontal displacements after the calculation by the first and second methods was shown. Differences and variations in the values of bending moments occurring in the retaining wall were investigated. The importance of using modern geotechnical calculation software complexes for a more detailed and accurate analysis of structures and foundations was demonstrated. Additionally, an assessment of the impact of variations in the parameters of the soil and retaining wall models on the calculation results was conducted. The research results allow recommending the use of a comprehensive modeling approach to enhance the reliability and efficiency of retaining wall design. The analysis also shows that the application of different soil behavior models can significantly affect the final calculation results, highlighting the need for careful selection of modeling parameters. The obtained results have significant practical value for engineers and designers, as they allow for more accurate prediction of the behavior of retaining walls under various operating conditions. This contributes to improving the safety and cost-effectiveness of construction projects.

Fast VGG: An Advanced Pre-Trained Deep Learning Framework for Multi-Layered Composite NDE via Multifrequency Near-Field Microwave Imaging

ABSTRACT Microwave nondestructive testing (NDT) techniques show promise for composite inspection due to microwave signals’ ability to penetrate and interact with internal structures. However, current microwave imaging approaches have poor spatial resolution, struggling to distinguish defects from defect-free regions. This limits reliable subsurface analysis and widespread adoption. This paper introduces a novel multi-frequency microwave imaging fusion method using an open-ended waveguide and deep learning to enhance defect detection accuracy in carbon fiber-reinforced polymer (CFRP) composites. The proposed technique employs an optimized feature extraction strategy to improve differentiation between defective and sound areas for superior subsurface visualization. The method leverages VGG-19 for efficient feature extraction and parallel processing to merge information from multi-frequency images. Our method significantly improved detection accuracy and F1 score, surpassing non-deep learning image fusion techniques by at least 50%. The optimized fusion strategy enables clear visualization of various defect types across multiple subsurface layers. Our approach also reduces computation time versus standard VGG implementation by 3–4×, showing scalability. The results demonstrate the proposed method’s potential to overcome existing constraints and provide rapid, accurate subsurface analysis of complex CFRP structures through an optimized deep learning framework, holding significance for expanding microwave NDE applications.

Image2InChI: Automated Molecular Optical Image Recognition.

The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable representations. Therefore, in this paper, we propose an automated molecular optical image recognition model based on deep learning, called Image2InChI. Additionally, the proposed Image2InChI introduces a novel feature fusion network with attention to integrate image patch and InChI prediction. The improved SwinTransformer as an encoder and the Transformer Decoder as a decoder with patch embedding are applied to predict the image features for the corresponding InChI. The experimental results showed that the Image2InChI model achieves an accuracy of InChI (InChI acc) of 99.8%, a Morgan FP of 94.1%, an accuracy of maximum common structures (MCS acc) of 94.8%, and an accuracy of longest common subsequence (LCS acc) of 96.2%. The experiments demonstrated that the proposed Image2InChI model improves the accuracy and efficiency of molecular image recognition and provided a valuable reference about optical chemical structure recognition for InChI.

An Anisotropic Damage-Plasticity Constitutive Model of Continuous Fiber-Reinforced Polymers.

Accurate structural analyses of continuous fiber-reinforced polymers (FRPs) are imperative for diverse engineering applications, demanding efficient material constitutive models. Nonetheless, the constitutive modeling of FRPs is complicated by the nonlinear behavior resulting from internal damages and the inherent plasticity. Consequently, this study presents an innovative anisotropic constitutive model for FRPs, designed to adeptly capture both the damage evolution and plasticity. All requisite parameters can be easily obtained through fundamental mechanical tests, rendering the model practical and user-friendly. The model utilizes the three-dimensional Puck criteria to determine damages, initiating the evolution process through a combination of continuum damage mechanics and linear stiffness attenuation methods. This evolution is coupled with a one-parameter plastic model. Subsequently, the numerical implementation method, integrated into ANSYS, is detailed. This emphasizes the Cauchy stress and consistent tangent stiffness solution strategy. Finally, the effectiveness of the developed model is demonstrated through comprehensive verification, encompassing existing biaxial tension and open-hole-tension tests conducted on carbon and glass FRP laminates. The simulation results exhibit a remarkable correspondence with the experimental data, validating the reliability and accuracy of the proposed model.

Structural Assessment of a Masonry Arch Bridge before and after a traditional inner arch retrofitting technique

To assess the structural behaviour of masonry arch bridge and identify the occurrence of damage reliable numerical models to perform accurate nonlinear structural analyses are needed. The innovative strategy known as Discrete Macro-Element Model (DMEM) is here employed to simulate the performance of a masonry arch bridge before and after a traditional structural retrofitting. The adopted numerical strategy assures a geometrically consistent model characterized by a very low computational burden and reliable results. As case study, a masonry arch bridge located in Maletto (Italy), belonging to the rail network, around Etna volcano, called Ferrovia CircumEtnea (FCE). The bridge has been built in 19th century and was subjected to a structural retrofitting in 1984, because of a light settlement of the central pier due to landslide. The bridge was reinforced with the introduction of a supplemental thin inner reinforced concrete arch, at the intrados of the existing stone arches, externally confined by an external steel corrugated thick plate also used as formworks. In this paper the HiStrA bridge software, where the DMEM is implemented, is used for providing a structural assessment of the bridge in its original configuration and after the structural retrofitting. Both push-down and push-over analyses are performed clearly showing the efficacy of the adopted traditional retrofitting strategy.