Heterogeneous Specimens Research Articles

Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data

BackgroundSingle-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic heterogeneity but also possess a high level of noise, abundant missing entries and sometimes inadequate or no cell type annotations at all. Bulk-level gene expression data lack direct information of cell population composition but are more robust and complete and often better annotated. We propose a modeling framework to integrate bulk-level and single-cell RNASeq data to address the deficiencies and leverage the mutual strengths of each type of data and enable a more comprehensive inference of their transcriptomic heterogeneity. Contrary to the standard approaches of factorizing the bulk-level data with one algorithm and (for some methods) treating single-cell RNASeq data as references to decompose bulk-level data, we employed multiple deconvolution algorithms to factorize the bulk-level data, constructed the probabilistic graphical models of cell-level gene expressions from the decomposition outcomes, and compared the log-likelihood scores of these models in single-cell data. We term this framework backward deconvolution as inference operates from coarse-grained bulk-level data to fine-grained single-cell data. As the abundant missing entries in sc-RNASeq data have a significant effect on log-likelihood scores, we also developed a criterion for inclusion or exclusion of zero entries in log-likelihood score computation.ResultsWe selected nine deconvolution algorithms and validated backward deconvolution in five datasets. In the in-silico mixtures of mouse sc-RNASeq data, the log-likelihood scores of the deconvolution algorithms were strongly anticorrelated with their errors of mixture coefficients and cell type specific gene expression signatures. In the true bulk-level mouse data, the sample mixture coefficients were unknown but the log-likelihood scores were strongly correlated with accuracy rates of inferred cell types. In the data of autism spectrum disorder (ASD) and normal controls, we found that ASD brains possessed higher fractions of astrocytes and lower fractions of NRGN-expressing neurons than normal controls. In datasets of breast cancer and low-grade gliomas (LGG), we compared the log-likelihood scores of three simple hypotheses about the gene expression patterns of the cell types underlying the tumor subtypes. The model that tumors of each subtype were dominated by one cell type persistently outperformed an alternative model that each cell type had elevated expression in one gene group and tumors were mixtures of those cell types. Superiority of the former model is also supported by comparing the real breast cancer sc-RNASeq clusters with those generated by simulated sc-RNASeq data.ConclusionsThe results indicate that backward deconvolution serves as a sensible model selection tool for deconvolution algorithms and facilitates discerning hypotheses about cell type compositions underlying heterogeneous specimens such as tumors.

Monitoring damage growth and topographical changes in plate structures using sideband peak count-index and topological acoustic sensing techniques

Some topographies in plate structures can hide cracks and make it difficult to monitor damage growth. This is because topographical features convert homogeneous structures to heterogeneous one and complicate the wave propagation through such structures. At certain points destructive interference between incident, reflected and transmitted elastic waves can make those points insensitive to the damage growth when adopting acoustics based structural health monitoring (SHM) techniques. A newly developed nonlinear ultrasonic (NLU) technique called sideband peak count – index (or SPC-I) has shown its effectiveness and superiority compared to other techniques for nondestructive testing (NDT) and SHM applications and is adopted in this work for monitoring damage growth in plate structures with topographical features. The performance of SPC-I technique in heterogeneous specimens having different topographies is investigated using nonlocal peridynamics based peri-ultrasound modeling. Three types of topographies – “X” topography, “Y” topography and “XY” topography are investigated. It is observed that “X” and “XY” topographies can help to hide the crack growth, thus making cracks undetectable when the SPC-I based monitoring technique is adopted. In addition to the SPC-I technique, we also investigate the effectiveness of an emerging sensing technique based on topological acoustic sensing. This method monitors the changes in the geometric phase; a measure of the changes in the acoustic wave’s spatial behavior. The computed results show that changes in the geometric phase can be exploited to monitor the damage growth in plate structures for all three topographies considered here. The significant changes in geometric phase can be related to the crack growth even when these cracks remain hidden for some topographies during the SPC-I based single point inspection. Sensitivities of both the SPC-I and the topological acoustic sensing techniques are also investigated for sensing the topographical changes in the plate structures.

Seeking the most informative design of test specimens for learning constitutive models

Accurate calibration of constitutive models is vital for predicting the mechanical behavior of engineering materials under various loading conditions. Traditionally, the calibration process involves a series of experiments on specimens with simple geometries to capture the complexities in the constitutive models. Each single test conveys a small amount of information that a well-trained human brain can handle, resulting in a large number of experiments needed for a complete calibration. Therefore, traditional calibration approaches are usually costly and time-consuming. With recent advancements in computational techniques, there is an emerging opportunity to leverage geometrically complex specimens in experiments to obtain a larger amount of information for computers to learn and calibrate the model. Despite some initial success, the most important question remains unsettled: How much information does a mechanical test convey? In this work, we answer this question by incorporating information entropy as a quantitative measure in the design of mechanical test specimens. We demonstrate the viability of the proposed approach by comparing the performance of selected test specimens for learning the plasticity model of sheet metal, e.g., the Hill48 anisotropic elastic-plastic model in this case. An optimal entropy criterion is proposed for selecting the appropriate heterogeneous test specimen for inverse calibration, depending on the cardinality of the stress state space considered in the model. Finally, Bayesian optimization is applied to uniaxial and biaxial tension specimens, using the stress state entropy as an objective function, to investigate the general principles of designing specimens with maximum information for learning constitutive models.

Laser speckle rheological microscopy reveals wideband viscoelastic spectra of biological tissues.

Viscoelastic transformation of tissue drives aberrant cellular functions and is an early biomarker of disease pathogenesis. Tissues scale a range of viscoelastic moduli, from biofluids to bone. Moreover, viscoelastic behavior is governed by the frequency at which tissue is probed, yielding distinct viscous and elastic responses modulated over a wide frequency band. Existing tools do not quantify wideband viscoelastic spectra in tissues, leaving a vast knowledge gap. We present wideband laser speckle rheological microscopy (WB-SHEAR) that reveals elastic and viscous response over sub-megahertz frequencies previously not investigated in tissue. WB-SHEAR uses an optical, noncontact approach to quantify wideband viscoelastic spectra in specimens spanning a range of moduli from low-viscosity fibrin to highly elastic bone. Via laser scanning, micromechanical imaging is enabled to access wideband viscoelastic spectra in heterogeneous tumor specimens with high spatial resolution (25 micrometers). The ability to interrogate the viscoelastic landscape of diverse biospecimens could transform our understanding of mechanobiological processes in various diseases.

An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method

Accurate material description is crucial to achieve high-quality results in computational analysis software. Phenomenological constitutive laws generalize the material behaviour observed in simple mechanical tests. The resulting empirical expressions contain parameters that need to be calibrated through an inverse optimization process. Advancements in Digital Image Correlation (DIC) techniques have enabled the extraction of non-uniform multi-axial displacement fields, facilitating the development of heterogeneous mechanical specimens and unlocking access to richer material behaviour data. Despite these advancements, constitutive models are still limited by mathematical formulations and biases due to simplifying assumptions. Artificial Neural Networks (ANNs), as universal function approximators, could potentially drive a paradigm shift in the field. ANNs do not require explicit pre-formulations, hence avoiding the need for identifying unknown parameters. Moreover, ANNs are able to implicitly learn patterns purely from data, allowing to circumvent the biases induced by the simplifying assumptions of first-order principles. However, the training of ANNs for implicit constitutive modelling is not straightforward, given the impossibility of obtaining direct measurements of stress. This paper explores the integration of Recurrent Neural Networks (RNNs) with the Virtual Fields Method (VFM) for material modelling. This approach uses global force and displacement data to indirectly train the neural network, with the equilibrium being evaluated globally through the VFM loss function. The proposed method is (i) computationally efficient by not requiring Finite Element Analysis (FEA), (ii) compatible with both full-field measurements and numerically generated data, and (iii) able to handle experimental boundary conditions.

A Microdissection Protocol for Proteogenomic Analysis of Histological Sections to Advance Drug Development.

Combining proteogenomics with laser capture microdissection (LCM) in cancer research offers a targeted way to explore the intricate interactions between tumor cells and the different microenvironment components. This is especially important for immuno-oncology (IO) research where improvements in the predictability of IO-based drugs are sorely needed, and depends on a better understanding of the spatial relationships involving the tumor, blood supply, and immune cell interactions, in the context of their associated microenvironments. LCM is used to isolate and obtain distinct histological cell types, which may be routinely performed on complex and heterogeneous solid tumor specimens. Once cells have been captured, nucleic acids and proteins may be extracted for in-depth multimodality molecular profiling assays. Optimizing the minute tissue quantities from LCM captured cells is challenging. Following the isolation of nucleic acids, RNA-seq may be performed for gene expression and DNA sequencing performed for the discovery and analysis of actionable mutations, copy number variation, methylation profiles, etc. However, there remains a need for highly sensitive proteomic methods targeting small-sized samples. A significant part of this protocol is an enhanced liquid chromatography mass spectrometry (LC-MS) analysis of micro-scale and/or nano-scale tissue sections. This is achieved with a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) approach developed for LC-MS analysis of fresh-frozen tissue specimens obtained via LCM. Included is a detailed in-gel digestion method adjusted and specifically designed to maximize the proteome coverage from amount-limited LCM samples to better facilitate in-depth molecular profiling. Described is a proteogenomic approach leveraged from microdissected fresh frozen tissue. The protocols may also be applicable to other types of specimens having limited nucleic acids, protein quantity, and/or sample volume.

Effect of Loading Direction on Deformation and Strength of Heterogeneous Paleo Clay Samples

Landslides result from weak surfaces with varying rock-soil properties, posing a significant concern for engineering and accurate deformation analysis. This study investigated the macroscopic physical and mechanical properties of paleo clay specimens during triaxial compression testing, aiming to elucidate the deformation mechanisms exhibited by these specimens under varying loading directions at both the loading and unloading ends, and numerical simulation methods were carried out to simulate actual engineering scenarios. The analysis encompasses deformation patterns, stress–strain relationships, Mohr stress circles, and numerical simulation failure cloud diagrams for soil samples under different loading directions. The results showed that the loading end of heterogeneous specimens exhibited noticeable deformations. Alteration of the loading direction induced variations in the failure mode. The position and size of the deformations for the only iron-manganese clay, loading end iron-manganese clay, and loading end reticulated clay samples changed with the clay layer at the loading end of the sample. Moreover, the stress–strain curves under different loading directions were different, with strain hardening and strain softening appearing in the two loading directions, respectively. The results of this study contribute to an in-depth understanding of the impact of the loading direction on the deformation and strength of paleo clay, thereby providing a foundation for landslide prevention and control measures.

Mechanical behavior of reservoirs with heterogeneous hydrate distribution: Analysis from the thickness and angle of the hydrate-bearing layer

The heterogeneity of natural hydrate-bearing sediments (HBSs) makes it of great engineering significance to investigate the mechanical properties of heterogeneous HBSs and develop an efficient method to determine their strength parameters. In this study, heterogeneous hydrate-bearing clayey sand specimens were prepared by controlling the water distribution in host specimens. The hydrate-bearing layers in the prepared specimens have similar hydrate saturation, different thicknesses and angles. Multistage triaxial experiments were conducted to investigate the effects of hydrate-bearing layer thickness (HBLT) and angle (HBLA) on the stress-strain behavior and strength parameters of the specimens under differing effective confining stresses. The results show that the specimens produce bulging deformation and strain-hardening failure mode. Within the study scope, the strain-hardening degree of the stress-strain curve decreases with increasing HBLT and decreasing HBLA. The failure strength of the specimen initially increases and then decreases with increasing HBLT, peaking at 6 cm HBLT; however, it is positively correlated with HBLA. As HBLT increases, the cohesion increases exponentially, while the internal friction angle initially increases and then decreases. HBLA contributes to a linear increase in both cohesion and internal friction angle. Empirical equations for the cohesion and internal friction angle of heterogeneous specimens were formulated by replacing the heterogeneity with homogeneous elements, and their validity was verified. Under high effective confining stress, in the initial deformation stage, the shear stress applied to specimens with HBLT larger than 6 cm gradually increases with the increasing HBLT, while the thickness of the hydrate-free layer, which is susceptible to deformation response under external load, decreases. Consequently, the number of broken and damaged hydrates in these specimens increases. In the following shear process, failed hydrates prevent the pores from being compressed and serve as lubrication, leading to a reduction in the strain-hardening degree of the stress-strain curve and a reduction in failure strength. HBLA changes the contact area between hydrate-bearing and hydrate-free layers, as well as the axial distribution of hydrates, thus influencing the mechanical behavior of the specimen.

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging (dMRI). Many analysis frameworks using dMRI data have been proposed to describe exchange, often using a double diffusion encoding (DDE) stimulated echo sequence. Techniques such as diffusion exchange weighted imaging (DEWI) and the filter exchange and rapid exchange models, use a specific subset of the full space DDE signal. In this work, a general representation of the DDE signal was employed with different sampling schemes (namely constant b1, diagonal and anti-diagonal) from the data reduction models to estimate exchange. A near-uniform sampling scheme was proposed and compared with the other sampling schemes. The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes. These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner. In synthetic data, the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth. In experimental data, the shifted diagonal and near-uniform sampling schemes outperformed the others, yielding the most consistent estimates with the full space estimation. The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes. In future studies, algorithms could be further developed for the optimization of sampling schemes, as well as incorporating additional properties, such as geometry and diffusion anisotropy, into exchange frameworks.

Aptamer-based regulation of DNA polymerase activity for the detection of protein-small molecule interactions

Herein, we developed a novel method for identifying protein-small molecule interactions (PSMIs) based on the switching of DNA polymerase activity. This strategy employs the small molecule-linked DNA probe (SMP) that could bind to and disrupt the DNA polymerase aptamer probe (DAP) capable of inhibiting DNA polymerase activity. Without the binding of the target protein, the SMP is readily degraded by the applied exonuclease I (exo I), preventing its binding to the DAP. The DAP then freely inhibits the polymerase activity through its specific binding to the polymerase. In the presence of the target protein, however, the binding between the target protein and the small molecule protects the SMP against the exo I-catalyzed degradation. The intact SMP now binds to the DAP and disrupts its active aptameric structure, preventing the DAP from inhibiting the polymerase activity. The uninhibited DNA polymerase catalyzes the primer extension reaction in conjugation with the TaqMan probe (TP), leading to a significant fluorescence enhancement. Taking advantage of this elaborate design principle, we successfully identified the two model PSMIs, streptavidin/biotin (SA/BTN) and anti-digoxigenin/digoxigenin (ADIG/DIG), as low as 4.01 nM and 6.72 nM, respectively, with excellent specificity. We further validated its practical applicability by reliably identifying the SA/BTN interaction in heterogeneous human serum specimens.

Design and validation of a heterogeneous interior notched specimen for inverse material parameter identification

Nowadays, virtual manufacturing, digitalisation and simulation predictions are essential for the design and the development of engineering parts and components, as well as companies’ productivity. The implemented constitutive model and the accuracy of the determined material parameters influence the reliability of these predictions. It is necessary to calibrate multiple parameters of a complex and robust constitutive model. This is a very time-consuming task and involves high costs when using a classical approach, in which several different homogeneous mechanical experiments are performed. The non-homogeneous mechanical tests can provide a larger variety of mechanical information in just one experiment, reducing the number of required tests for material characterisation. The aim of this work is to numerically design a specimen with an interior notch for a uniaxial loading test that presents strain heterogeneity. The shape of the interior notch is optimised to maximise the strain heterogeneity. The optimisation procedure is guided by a cost function established by several strain heterogeneity indicators. The best specimen shape exhibits numerically uniaxial tension and compression, pure shear and plane strain tension in the plastic region. The identifiability of Swift’s hardening law parameters was quantitatively investigated based on the partial derivative of the strain fields with respect to the sought parameters. Results show that the proposed design strategy enables to increase in strain heterogeneity. It is shown that the optimised specimen has significantly better identifiability compared to a tensile specimen with a circular hole. Finally, a comparison between a classical Finite Element Model Updating (FEMU) methodology and a FEMU-based approach that uses a DIC-levelling strategy was conducted in order to inversely identify the Swift hardening law parameters and validate the designed specimen. It was confirmed that the DIC-levelling method finds more accurate results than the classical approach, with the compromise of the computational time. It was also shown that the optimum designed specimen is adequate for inverse identification strategies. • Optimised heterogeneous specimen to test in standard tensile test machine. • Better hardening parameters’ identifiability than circular perforated specimen. • The strain states observed in FEA are exhibited in the virtual experiment. • Parameter identification using a DIC-levelled approach is more accurate than the classical. • On the reduction of time, resources and costs in the development of engineering parts.

DDA simulation study on fracturing failure reproducibility of heterogenous rock

The mesoscale heterogeneity of rock determines its macroscale mechanical performance to a large extent. In this paper, a sub-block element discontinuous deformation analysis (DDA) method coupled with a statistic heterogeneity model was used to simulate the fracturing failure of pre-notched rock specimens. The simulation recreated the macroscale whole-process nonlinear deformation and fracturing failure of pre-notched disc and rectangular specimens with some important features explored in experiments or other numerical simulations. Combing intact heterogeneous rock specimen simulations in previous work, the effect of the heterogeneity on the reproducibility of macroscale strength and failure patterns of the specimens were investigated. Results indicate that the influence degree of the heterogeneity on the macroscale equivalent strength and fracturing failure pattern reproducibility varies with the specimen particularities (disc or rectangular, pre-notched or intact, and inclination of the pre-existing flaw), and the reason behind was discussed from the point view of stress concentration. This work is meaningful for the understanding of the result discreteness of rock specimen experiments, and assures the importance to consider the rock heterogeneity in practical rock engineering.

Quantitative analysis of drug distribution in heterogeneous tissues using dual-stacking capillary electrophoresis-mass spectrometry.

Intratumour heterogeneity frequently leads to drug resistance, which is a major issue in drug discovery. Drug distribution is one of the key factors for elucidating the resistance mechanism; however, quantitative and regional drug measurement is challenging. Here, we developed a novel ultra-sensitive analytical method and applied it to HER3-targeting antibody-drug conjugate patritumab deruxtecan (HER3-DXd), aiming to explore its payload (DXd) distribution within heterogeneous tissues. The developed analytical method is named LDMS-CE-MS, a capillary electrophoresis-mass spectrometry (CE-MS) coupled with a novel sample preconcentration/separation method called "large-volume dual-sample stacking by micelle collapse and sweeping (LDMS)". First, the analytical performance of LDMS-CE-MS for DXd detection was evaluated. Subsequently, we evaluated the bystander effect of HER3-DXd, where tumour tissues were excised from xenograft models and clinical specimens after administration of HER3-DXd. HER3-high expression, adjacent, and HER3-low expression regions were then sampled by laser microdissection to quantify the released DXd. LDMS concentrated DXd by 1000-fold and separated it from the hydrophilic bio-matrix through continuous capture and release by the charged micelles, allowing quantification at sub-attomole-level. DXd concentrations decreased in the order of antigen-high expression > adjacent > antigen-low expression regions in the tumour xenograft model, whereas in clinical specimens, adjacent and antigen-high expression regions had approximately the same concentration. These distributions represent a bystander effect. Our LDMS-CE-MS successfully visualized the attomole-level drug distributions in heterogeneous clinical specimens. This new platform opens a new era of quantitative pharmacokinetic analysis, facilitating drug discovery and development.

On the design of mechanical heterogeneous specimens using multilevel topology optimization

Sheet metal forming processes are of upmost importance in many industrial applications. Their virtualization has been addressed by numerical simulation software. However, realistic results can only be obtained when an accurate reproduction of the material behavior is achieved. This is where material characterization and model calibration procedures play a key role. The conventional procedure is time-consuming and expensive and needs to be overcome. To improve the efficiency of this process, heterogeneous mechanical tests have been proposed. Due to their rich mechanical fields and the emergence of full-field measurement techniques, a high diversity of valuable information about the material behavior can be extracted with a single test. A systematic design approach has not been found yet since the majority of the newly proposed mechanical tests have been developed based on the empirical knowledge or trial and error. However, this work aims at filling this gap by proposing a new methodology to design optimal mechanical tests. A bi-level design optimization problem is established to find out the optimal geometry for the specimen that presents the highest heterogeneity of stress states. The specimen design process gathers the design by topology optimization and the compliant mechanisms’ theory, which are responsible for the highly heterogeneous displacement fields. The test setup is also optimized, considering the use of universal testing machines. Several numerical designs are analyzed based on their ability to induce several stress states and their feasibility. The most suitable test configuration is chosen and analyzed with Digital Image Correlation (DIC) using synthetic images. An evaluation of the test in an elastoplasticity framework is also performed. The effectiveness of the methodology in designing tests with valuable information for the material characterization process is validated.

Numerical Simulation of Failure Behavior of Brittle Heterogeneous Rock under Uniaxial Compression Test.

Rocks have formed heterogeneous characteristics after experiencing complex natural geological processes. Studying the heterogeneity of rocks is significant for rock mechanics. In this study, a linear parallel bond model with Weibull distribution in two-dimensional particle flow code (PFC2D) is adopted to study the mechanical characteristics and brittle failure mode of granite rock specimens with different heterogeneity. Firstly, we selected several combinations of key micro-parameters of the parallel bond model. Then, we subjected them to a Weibull distribution to satisfy heterogeneity, respectively. Finally, we chose one optimal combination plan after comparing the stress–strain curves of heterogeneous rock specimens. We analyzed the simulated results of heterogeneous rock specimens. The crack distribution of rock specimens under peak stress shows different characteristics: a diagonal shape in rock specimens with low heterogeneity indexes, or a rotated “y” shape in rock specimens with high heterogeneity indexes. As for failure mode, the numerical simulation results show high consistency with the laboratory experiment results. The rock specimen breaks down almost diagonally, and the whole specimen tends to form an x-shaped conjugate shear failure or the well-known “hour-glass” failure mode. With the increase of the homogeneity index of the rock specimen, the shear rupture angle becomes larger and larger. Generally, the crack number increases with time, and when the rock specimen reaches the peak failure point, the number of cracks increases sharply. The development of cracks in numerical rock specimens under compression test is a result of the coalescence of many microscopic cracks. Furthermore, tensile cracks formed initially, followed by shear behavior along the macroscopic crack plane. We also preliminarily study the mechanical characteristics of heterogeneous rock specimens with discontinuous structural planes. The discontinuous structural planes are simulated by the smooth-joint model. We can conclude that the discontinuous structural planes and the microscopic structural planes which contribute to the heterogeneity have a mutual influence on each other.

Direct material property determination: One‐dimensional formulation utilising full‐field deformation measurements

AbstractA direct approach is described to determine the elastic modulus distribution in a nominally heterogeneous material subject to tensile/compression loading and primarily experiencing deformations in the axial direction. The formulation is developed for uniaxial applications using basic theoretical constructs, resulting in a computational framework that has a matrix form [A] {E} = {R}, where the [A] matrix components are known functions of measured axial strains and axial positions, {R} components are known functions of axial body forces, applied loads and reactions and {E} components are the unknown elastic moduli at discrete locations along the length of the specimen. For a series of one‐dimensional (1D) material property identification procedure with known axial strains at discrete locations and various levels of random noise, results are presented to demonstrate the accuracy and noise sensitivity of the methodology. Finally, experimental measurements for a heterogeneous bone specimen are compared to our 1D model predictions, demonstrating that the predictions are in very good agreement with independent estimates at each load level of interest along the length of the bone specimen.

Atom Probe Analysis of Nanoparticles Through Pick and Coat Sample Preparation.

The ability to analyze nanoparticles in the atom probe has often been limited by the complexity of the sample preparation. In this work, we present a method to lift–out single nanoparticles in the scanning electron microscope. First, nanoparticles are dispersed on a lacey carbon grid, then positioned on a sharp substrate tip and coated on all sides with a metallic matrix by physical vapor deposition. Compositional and structural insights are provided for spherical gold nanoparticles and a segregation of silver and copper in silver copper oxide nanorods is shown in 3D atom maps. Using the standard atom probe reconstruction algorithm, data quality is limited by typical standard reconstruction artifacts for heterogeneous specimens (trajectory aberrations) and the choice of suitable coatings for the particles. This approach can be applied to various unsupported free-standing nanoparticles, enables preselection of particles via correlative techniques, and reliably produces well-defined structured samples. The only prerequisite is that the nanoparticles must be large enough to be manipulated, which was done for sizes down to ~50 nm.

Identification of Swift Law Parameters Using FEMU by a Synthetic Image DIC-Based Approach

Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The calibration of more elaborated material models with a larger number of parameters becomes very time and cost consuming. The development of image-based technology has enhanced the interest in inverse identification methods, which, when coupled with full-field measurements, have the potential to reduce the number of experimental tests required to accurately identify material properties. This work aims to identify the Swift hardening law parameters of a dual-phase steel using a tensile test on a heterogeneous dogbone specimen under uniaxial and quasi-static loading conditions using the finite element model updating (FEMU) technique. The numerical results were used to generate synthetic images, which were then processed by digital image correlation (DIC) and used as the reference in the identification procedure. Two different approaches were tested: (i) directly comparing the numerical results to the reference; (ii) using DIC-levelled numerical data by iteratively generating synthetic images and using the DIC filter with the same settings as were used on the reference (virtual experiment). The identification results obtained from both approaches are compared and discussed.

Reconstruction of 3D Shapes of Granite Minerals and Generation of Random Numerical Specimens

Abstract The existing methods of generating random mineral grains in numerical rock specimens mostly adopt random polygons (in 2D) or random polyhedrons (in 3D) to represent mineral grains. Although this simplification is effective and reasonable, the complex three-dimensional (3D) shape of mineral grains can be reconstructed by computed tomography (CT) scan and image processing techniques, and then, random grains with real shape can be generated in a numerical specimen. In this paper, we proposed an improved grain-based model to construct random mineral grains with real 3D shape in numerical specimens. The granite specimens with granular minerals are scanned by CT. After the CT slices are denoised and corrected, the minerals, including biotite, quartz, and feldspar, are segmented from the CT slices to reconstruct the 3D mineral shapes; these shapes are stored in grain library. By importing the grain library in PFC (Particle Follow Code, a simulation software), heterogeneous specimens with random mineral grains are constructed, and uniaxial compression tests are carried out on them. Results show that the ranges of Young’s modulus, Poisson’s ratio, and uniaxial compressive strength of these numerical specimens are similar to those of real specimens measured in the laboratory. Therefore, the proposed method is feasible and reasonable. This work can provide a reference for the study of constructing heterogeneous numerical rock specimens in rock mechanics.

Evaluation of image registration for measuring deformation fields in soft tissue mechanics

AbstractHigh‐fidelity biomechanical models usually involve the mechanical characterisation of biological tissues using experimental methods based on optical measurements. In most experiments, strains are evaluated based on displacements of a few markers and represents an average within the region of interest (ROI). Full‐field measurements may improve description of non‐homogeneous materials such as soft tissues. The approach based on non‐rigid image registration is proposed and compared with standard digital image correlation (DIC) on a set of samples, including (i) complex heterogeneous deformations with sub‐pixel displacement, (ii) a typical uniaxial tension test of aorta, and (iii) an indentation test on skin. The possibility to extend the ROI to the whole sample and the exploitation of a natural tissue pattern represents the main assets of the proposed method whereas the results show similar accuracy as standard DIC when analysing sub‐pixel deformations. Therefore, displacement and strain fields measurement based on image registration is very promising to characterise heterogeneous specimens with irregular shapes and/or small dimensions, which are typical features of soft biological tissues.