Precise Structural Analysis Research Articles

Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry.

Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creating high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.

Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.

Accurate landmark detection is essential for precise analysis of anatomical structures, supporting diagnosis, treatment planning, and monitoring in patients with spinal deformities. Conventional methods rely on laborious landmark identification by medical experts, which motivates automation. The proposed deep learning pipeline processes bi-planar radiographs to determine spinopelvic parameters and Cobb angles without manual supervision. The dataset used for training and evaluation consisted of 555 bi-planar radiographs from un-instrumented patients, which were manually annotated by medical professionals. The pipeline performed a pre-processing step to determine regions of interest, including the cervical spine, thoracolumbar spine, sacrum, and pelvis. For each ROI, a segmentation network was trained to identify vertebral bodies and pelvic landmarks. The U-Net architecture was trained on 455 bi-planar radiographs using binary cross-entropy loss. The post-processing algorithm determined spinal alignment and angular parameters based on the segmentation output. We evaluated the pipeline on a test set of 100 previously unseen bi-planar radiographs, using the mean absolute difference between annotated and predicted landmarks as the performance metric. The spinopelvic parameter predictions of the pipeline were compared to the measurements of two experienced medical professionals using intraclass correlation coefficient(ICC) and mean absolute deviation (MAD). The pipeline was able to successfully predict the Cobb angles in 61% of all test cases and achieved mean absolute differences of 3.3° (3.6°) and averaged ICC of 0.88. For thoracic kyphosis, lumbar lordosis, sagittal vertical axis, sacral slope, pelvic tilt, and pelvic incidence, the pipeline produced reasonable outputs in 69%, 58%, 86%, 85%, 84%, and 84% of the cases. The MAD was 5.6° (7.8°), 4.7° (4.3°), 2.8mm (3.0mm), 4.5° (7.2°), 1.8° (1.8°), and 5.3° (7.7°), while the ICC was measured at 0.69, 0.82, 0.99, 0.61, 0.96, and 0.70, respectively. Despite limitations in patients with severe pathologies and high BMI, the pipeline automatically predicted coronal and sagittal spinopelvic parameters, which has the potential to simplify clinical routines and large-scale retrospective data analysis.

Single-Molecule Identification and Quantification of Steviol Glycosides with a Deep Learning-Powered Nanopore Sensor.

Steviol glycosides (SGs) are a class of high-potency noncalorie natural sweeteners made up of a common diterpenoid core and varying glycans. Thus, the diversity of glycans in composition, linkage, and isomerism results in the tremendous structural complexity of the SG family, which poses challenges for the precise identification and leads to the fact that SGs are frequently used in mixtures and their variances in biological activity remain largely unexplored. Here we show that a wild-type aerolysin nanopore can detect and discriminate diverse SG species through the modulable electro-osmotic flow effect at varied applied voltages. At low voltages, the neutral SG molecule was drawn and stuck in the pore entrance due to an energy barrier around R220 sites. The ensuing binding events enable the identification of the majority of SG species. Increasing the voltage can break the barrier and cause translocation events, allowing for the unambiguous identification of several pairs of SGs differing by only one hydroxyl group through recognition accumulation from multiple sensing regions and sites. Based on nanopore data of 15 SGs, a deep learning-based artificial intelligence (AI) model was created to process the individual blockage events, achieving the rapid, automated, and precise single-molecule identification and quantification of SGs in real samples. This work highlights the value of nanopore sensing for precise structural analysis of complex glycans-containing glycosides, as well as the potential for sensitive and rapid quality assurance analysis of glycoside products with the use of AI.

Musculoskeletal Dimension and Brightness Reference Values in Lumbar Magnetic Resonance Imaging-A Radio-Anatomic Investigation in 80 Healthy Adult Individuals.

Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar spine of healthy young women and men is scarce. The current study was planned to investigate characteristic intersexual differences and to provide MRI-related musculoskeletal baseline values before the onset of biological aging. Methods: At a single medical center, lumbar MRI scans of 40 women and 40 men aged 20-40 years who presented with moderate nonspecific low back pain were retrospectively evaluated for sex-specific differences in cross-sectional sizes of the fifth lumbar vertebrae, psoas and posterior paravertebral muscles, and respective sex- and age-dependent average brightness alterations on T2-weighted axial sections in the L5-level. Results: In women (mean age 33.5 years ± 5.0 (standard deviation)), the investigated musculoskeletal cross-sectional area sizes were significantly smaller (p < 0.001) compared to those in men (mean age 33.0 years ± 5.7). Respective average musculoskeletal brightness values were higher in women compared to those in men, and most pronounced in posterior paravertebral muscles (p < 0.001). By correlating brightness results to those of subcutaneous fat tissue, all intersexual differences, including those between fifth lumbar vertebrae and psoas muscles, turned out to be statistically significant. This phenomenon was least pronounced in psoas muscles. Conclusions: Lumbar musculoskeletal parameters showed significantly larger dimensions of investigated anatomical structures in men compared to those in women aged 20-40 years, and an earlier onset and faster progress of bone loss and muscle degradation in women.

Analytical Framework for Tension Characterization in Submerged Anchor Cables via Nonlinear In-Plane Free Vibrations

Submerged tensioned anchor cables (STACs) are pivotal components utilized extensively for anchoring and supporting offshore floating structures. Unlike tensioned cables in air, STACs exhibit distinctive nonlinear damping characteristics. Although existing studies on the free vibration response and tension identification of STACs often employ conventional Galerkin and average methods, the effect of the quadratic damping coefficient (QDC) on the vibration frequency remains unquantified. This paper re-examines the effect of bending stiffness on the static equilibrium configuration of STACs, and establishes the in-plane transverse free motion equations considering bending stiffness, sag, and hydrodynamic force. By introducing the bending stiffness influence coefficient and the Irvine parameter, the exact analytical solutions of symmetric and antisymmetric frequencies and modal shapes of STACs are derived. An improved Galerkin method is proposed to discretize the nonlinear free motion equations ensuring the accuracy and applicability of the analytical results. Additionally, this paper presents an analytical solution for the nonlinear free vibration response of the STACs using the improved averaging method, along with improved frequency formulas and tension identification methods considering the QDC. Through a case study, it is demonstrated that the improved methods introduced in this paper offer higher accuracy and wider applicability compared to the conventional approaches. These findings provide theoretical guidance and reference for the precise dynamic analysis, monitoring, and evaluation of marine anchor cable structures.

Discrete polyketones: synthesis, derivatization, and potential applications

Abstract The present account reviews recent progress in the synthesis, functionalization, and application of discrete polyketones. Whereas most polyketones are synthesized as polydisperse polymers with various molecular chain lengths and sizes, discrete polyketones are obtained in chemically pure forms. This allows precise structural analysis using nuclear magnetic resonance and X-ray diffraction. Discrete polyketones have been used to determine the critical chain length that distinguishes the crystallization behaviors of small molecules from those of macromolecules. Calix[3]pyrrole, which is a ring-contracted analogue of porphyrinogen, was first obtained from a cyclic hexaketone. The discovery of the strain-induced ring expansion reaction of calix[3]pyrroles has provided an important insight into solving a long-standing enigma in porphyrin synthesis. Chemical derivatization of discrete polyketones using ketone-derived transformations has resulted in the generation of various functional molecules for potential applications. These molecules have been used to develop several materials, including luminescent chromophores, ion adsorbents, drug–drug conjugates, and microfluidic devices for cancer diagnosis.

Total Third-Degree Variation for Noise Reduction in Atomic-Resolution STEM Images.

Scanning Transmission Electron Microscopy (STEM) enables direct determination of atomic arrangements in materials and devices. However, materials such as battery components are weak for electron beam irradiation and low electron doses are required to prevent beam-induced damages. Noise removal is thus essential for precise structural analysis of electron beam sensitive materials at atomic resolution. Total square variation (TSV) regularization is an algorithm that exhibits high noise removal performance. However, the use of the TSV regularization term leads to significant image blurring and intensity reduction. To address these problems, we here propose a new approach adopting L2 norm regularization based on higher-order total variation. An atomic-resolution STEM image can be approximated as a set of smooth curves represented by quadratic functions. Since the third-degree derivative of any quadratic function is 0, total third-degree variation (TTDV) is suitable for a regularization term. The application of TTDV for denoising the atomic-resolution STEM image of CaF2 observed along the [001] zone axis is shown, where we can clearly see the Ca and F atomic columns without compromising image quality.

Acute and post-acute soft tissue reconstruction

The precise analysis of the injured structures requiring reconstruction in complex wound defects is aprerequisite for successful restoration. The fundamental reconstructive strategies for soft tissue defects of the extremities including injuries to vessels, nerves and burn wounds in the context of the acute and post-acute trauma phases are presented. The different phases of soft tissue reconstruction are described. Recommendations for action with respect to the reconstruction of the functional structures are described. Two clinical case examples illustrate the approach. The acute reconstruction phase is defined as the period 0-72 h after the traumatic event and includes surgical debridement, primary reconstruction of nerves and vessels using interpositional grafts if necessary and temporary soft tissue reconstruction. Combined thermomechanical trauma requires early debridement combined with internal fixation of open fractures. In the post-acute reconstruction phase, which is generally defined as aperiod of up to 6weeks after the traumatic event, definitive soft tissue reconstruction is performed. In the case of long reinnervation distances, nerve transfer or motor replacement plastic surgery is performed in the post-acute phase. The reconstruction of soft tissue after trauma necessitates a stage-dependent approach. In the acute phase procedures aim at the immediate preservation of the limb. In the post-acute phase, definitive soft tissue reconstruction is performed to enable maximum functional preservation. Combined thermomechanical injuries require early surgical treatment in order to prevent infections.

VGA‐Net: Vessel graph based attentional U‐Net for retinal vessel segmentation

AbstractSegmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net.

Mechanistic Insight into the Mechanical Unfolding of the Integral Membrane Diacylglycerol Kinase.

The essential forces stabilizing membrane proteins and governing their folding and unfolding are difficult to decipher. Single-molecule atomic force spectroscopy mechanically unfolds individual membrane proteins and quantifies their dynamics and energetics. However, it remains challenging to structurally assign unfolding intermediates precisely and to deduce dominant interactions between specific residues that facilitate either the localized stabilization of these intermediates or the global assembly of membrane proteins. Here, we performed force spectroscopy experiments and multiscale molecular dynamics simulations to study the unfolding pathway of diacylglycerol kinase (DGK), a small trimeric multispan transmembrane enzyme. The remarkable agreement between experiments and simulations allowed precise structural assignment and interaction analysis of unfolding intermediates, bypassing existing limitations on structural mapping, and thus provided mechanistic explanations for the formation of these states. DGK unfolding was found to proceed with structural segments varying in size that do not correlate with its secondary structure. We identified intermolecular side-chain packing interactions as one of the major contributions to the stability of unfolding intermediates. Mutagenesis creating packing defects induced a dramatic decrease in the mechano-stability of corresponding intermediates and also in the thermo-stability of DGK trimer, in good agreement with predictions from simulations. Hence, the molecular determinants of the mechano- and thermo-stability of a membrane protein can be identified at residue resolution. The accurate structural assignment established and microscopic mechanism revealed in this work may substantially expand the scope of single-molecule studies of membrane proteins.

Convenient pre-separation method depends on negative charges suitable for glycan sequencing analysis.

For precise structural analysis of complex glycans, it is necessary to fractionate glycans based on acidity prior to liquid chromatography-mass spectrometry analysis. Conventional fractionation using high-performance liquid chromatography is time consuming and not high throughput. Therefore, we developed a rapid method for the fractionation using a small cartridge column. This enables rapid multi-sample processing for glycan sequencing.

Artificial neural network for deciphering the structural transformation of condensed ZnO by extended x-ray absorption fine structure spectroscopy

Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypal B4 → B1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn–O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameter u and the anharmonic effect. We observed a clear decrease in u and an increased anharmonicity near the onset of the B4 → B1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complex in-situ conditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.

Fluorescence Super-Resolution Imaging Chip for Gene Silencing Exosomes.

Tumor cell-derived extracellular vesicles and their cargo of bioactive substances have gradually been recognized as novel biomarkers for cancer diagnosis. Meanwhile, the PD-L1 (Programmed Death-Ligand 1) protein, as an immune checkpoint molecule, is highly expressed on certain tumor cells and holds significant potential in immune therapy. In comparison to PD-L1 monoclonal antibodies, the inhibitory effect of PD-L1 siRNA (small interfering RNA) is more advantageous. In this article, we introduced a microfluidic chip integrating cell cultivation and exosome detection modules, which were intended for the investigation of the gene silencing effect of PD-L1 siRNA. Basically, cells were first cultured with PD-L1 siRNA in the chip. Then, the secreted exosomes were detected via super-resolution imaging, to validate the inhibitory effect of siRNA on PD-L1 expression. To be specific, a "sandwich" immunological structure was employed to detect exosomes secreted from HeLa cells. Immunofluorescence staining and DNA-PAINT (DNA Point Accumulation for Imaging in Nanoscale Topography) techniques were utilized to quantitatively analyze the PD-L1 proteins on HeLa exosomes, which enabled precise structural and content analysis of the exosomes. Compared with other existing PD-L1 detection methods, the advantages of our work include, first, the integration of microfluidic chips greatly simplifying the cell culture, gene silencing, and PD-L1 detection procedures. Second, the utilization of DNA-PAINT can provide an ultra-high spatial resolution, which is beneficial for exosomes due to their small sizes. Third, qPAINT could allow quantitative detection of PD-L1 with better precision. Hence, the combination of the microfluidic chip with DNA-PAINT could provide a more powerful integrated platform for the study of PD-L1-related tumor immunotherapy.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

Analysis of regional wear failure of crankshaft pair of heavy duty engine

This study focuses on investigating the underlying causes of localized wear failure on one side of the oil hole in the third crankpin of a straight-six-cylinder internal combustion engine, constructed using 42CrMo material. A comprehensive analysis of this wear phenomenon was conducted through a series of meticulous experimental investigations and advanced finite element analysis. The experimental campaign encompassed macroscopic morphology observation, precise metallographic structure analysis, microscopic structure observation, iron spectrum abrasive particle detection, micro-hardness testing, and rigorous chemical composition analysis. Furthermore, finite element analysis was employed to simulate the stress and strain distribution of the crankshaft under realistic operating conditions, enabling a deeper understanding of the mechanisms driving the localized wear in the third crankpin region. The research results revealed that the primary driver behind the localized wear in the third crankpin region is the ingress of grinding debris and contaminated particles from the oil passage into the contact surface between the third crankpin and the bearing shell, leading to abrasive particle wear. Moreover, the synergistic effects of friction and elevated temperatures facilitate the migration of carbon elements from lubricants or other materials to the surface of the third crankpin, resulting in their accumulation. This carbon accumulation causes non-uniform stress distribution on the surface, exacerbating fatigue wear on the crankshaft crankpin. The finite element simulation outcomes demonstrated that the center of the oil hole on one side of the third crankpin experiences the most pronounced deformation. Under actual operating conditions, the deformation of the crankpin leads to an increase in clearance between the crankpin and the bearing shell, thereby impeding the formation of a complete oil film. Consequently, the occurrence of dry friction and elevated temperatures leads to the melting of the alloy on the bearing shell surface, giving rise to adhesive wear.

Picometer-precision few-tilt ptychotomography of 2D materials

From ripples to defects, edges and grain boundaries, the 3D atomic structure of 2D materials is critical to their properties. However the damage inflicted by conventional 3D analysis precludes its use with fragile 2D materials, particularly for the analysis of local defects. Here we dramatically increase the potential for precise local 3D atomic structure analysis of 2D materials, with both greatly improved dose efficiency and sensitivity to light elements. We demonstrate light atoms can now be located in complex 2D materials with picometer precision at doses 30 times lower than previously possible. Moreover we demonstrate this using WS2, in which the light atoms are practically invisible to conventional methods at low doses. The key advance is combining the concept of few tilt tomography with highly dose efficient ptychography in scanning transmission electron microscopy. We further demonstrate the method experimentally with the even more challenging and newly discovered 2D CuI, leveraging a new extremely high temporal resolution camera.

Ultra-high resolution CT imaging of interstitial lung disease: impact of photon-counting CT in 112 patients.

To compare lung parenchyma analysis on ultra-high resolution (UHR) images of a photon-counting CT (PCCT) scanner with that of high-resolution (HR) images of an energy-integrating detector CT (EID-CT). A total of 112 patients with stable interstitial lung disease (ILD) were investigated (a) at T0 with HRCT on a 3rd-generation dual-source CT scanner; (b) at T1 with UHR on a PCCT scanner; (c) with a comparison of 1-mm-thick lung images. Despite a higher level of objective noise at T1 (74.1 ± 14.1 UH vs 38.1 ± 8.7UH; p < 0.0001), higher qualitative scores were observed at T1 with (a) visualization of more distal bronchial divisions (median order; Q1-Q3) (T1: 10th division [9-10]; T0: 9th division [8-9]; p < 0.0001); (b) greater scores of sharpness of bronchial walls (p < 0.0001) and right major fissure (p < 0.0001). The scores of visualization of CT features of ILD were significantly superior at T1 (micronodules: p = 0.03; linear opacities, intralobular reticulation, bronchiectasis, bronchiolectasis, and honeycombing: p < 0.0001), leading to the reclassification of 4 patients with non-fibrotic ILD at T0, recognized with fibrotic ILD at T1. At T1, the mean (± SD) radiation dose (CTDI vol: 2.7 ± 0.5mGy; DLP: 88.5 ± 21mGy.cm) was significantly lower than that delivered at T0 (CTDI vol: 3.6 ± 0.9mGy; DLP: 129.8 ± 31.7mGy.cm) (p < 0.0001), corresponding to a mean reduction of 27% and 32% for the CTDIvol and DLP, respectively. The UHR scanning mode of PCCT allowed a more precise depiction of CT features of ILDs and reclassification of ILD patterns with significant radiation dose reduction. Evaluation of lung parenchymal structures with ultra-high-resolution makes subtle changes at the level of the secondary pulmonary lobules and lung microcirculation becoming visually accessible, opening new options for synergistic collaborations between highly-detailed morphology and artificial intelligence. • Photon-counting CT (PCCT) provides a more precise analysis of lung parenchymal structures and CT features of interstitial lung diseases (ILDs). • The UHR mode ensures a more precise delineation of fine fibrotic abnormalities with the potential of modifying the categorization of ILD patterns. • Better image quality at a lower radiation dose with PCCT opens new horizons for further dose reduction in noncontrast UHR examinations.

Chemo-mechanical-microstructural coupling in the tarsus exoskeleton of the scorpion Scorpio palmatus.

The multiscale structure of biomaterials enables their exceptional mechanical robustness, yet the impact of each constituent at their relevant length scale remains elusive. We used SAXD analysis to expose the intact chitin-fiber architecture within the exoskeleton on a scorpion's claw, revealing varying orientations, including Bouligand and unidirectional regions different from other arthropod species. We uncovered the contribution of individual components' constituent behavior to its mechanical properties from the micro- to the nanoscale. At the microscale, in-situ micromechanical experiments were used to determine site-specific stiffness, strength, and failure of the biocomposite due to fiber orientation, while metal-crosslinking of proteins is characterized via fluorescence maps. At the constituent level, combined with FEA simulations, we uncovered the behavior of fiber-matrix deformation with fiber diameter <53.7nm and protein modulus in the range 1.4-11MPa. The unveiled microstructure-mechanics relationship sheds light on the evolved structural functionalities and constituents' interactions within the scorpion cuticle. STATEMENT OF SIGNIFICANCE: The pincer exoskeleton is a fundamental part of the scorpion's body due to its multifunctionality. Precise structural and compositional analysis within the hierarchy is paramount to understand the fundamentals of the mechanical properties of the composite exoskeleton. Here, we expose the intact chitin-fiber architecture of the pincer exoskeleton using nondestructive analysis. In-situ mechanical characterization was performed at nanometer levels within the exoskeleton hierarchy, which complemented with simulations, uncovered the elastic modulus of the protein matrix. Our findings confirm the presence and distribution of metal ions and their role as reinforcements in the protein matrix via ligand coordinate bonds. In future work, these findings can be of great potential to inspire the design of composite materials.

A practical biaxial time-dependent material model of PVDF-coated polyester fabrics for structural applications

The utilization of air-supported fabric structures as formwork to construct concrete shells has attracted increasing attention in recent decades. An example of such an airform is the air-supported structure at Dahaize, which consists of a cylindrical part and a hemispherical part, and is 66 m in diameter and 65 m in height. The precise forming analysis of large-span air-supported structures requires the use of a reasonable material model that considers the biaxial stress condition and the creep behavior of the fabric. In this paper, a practical approach to modeling the biaxial time-dependent material behavior of composite fabrics is proposed based on the uniaxial linear viscoelastic theory and the plane stress orthotropic assumption. The multi-Kelvin physical model is developed to the two-dimensional form for the simulation of biaxial creep compliances. Biaxial tensile and biaxial creep tests are conducted on PVDF-coated polyester fabrics to calibrate the material parameters. The experimental stress conditions were determined according to the pre-estimated actual membrane stress distribution. The established material model is employed for the forming analysis of the Dahaize airform under conditions of varying creep times. The simulation results agree well with the on-site measured data, and this validates the accuracy and efficiency of the proposed material model.

Applications of various stereological tools for estimation of biological tissues.

To provide concise and brief important stereological application methods and techniques for estimating biological tissues. Stereology studies the quantity of biological tissue using little practice and the low price of counting and preparing tissue slices to obtain direct and accurate results. Since their establishment, the stereological techniques underwent much improvement, thus allowing more precise analysis of target structures using various approaches. Using stereological tools, advances in stereological techniques made the target tissues or organs represented by 2D instead of 3D dimensions. Process tools estimate volume, area and length. According to the exciting tissue and aims, the stereological tools perform differently. This review summarizes various stereological tools and techniques, providing brief information about the orientation method, slicer probe method, Delesse's principle, Cavalieri principle, disector, fractionator, nucleator, virtual cycloids and saucer, which are described in detail.