Shape Characterization Research Articles

Genome-wide association study of CMR image-based aortic stiffness in 45,789 individuals identifies loci linked with cardiovascular diseases

Abstract Background The use of artificial intelligence on large-scale cardiac magnetic resonance (CMR) imaging data has enabled detailed characterisation of cardiac shape and function for use in genome-wide association studies (GWAS), leading to improved understanding of genetic aetiology of cardiovascular diseases (CVDs). Aortic stiffness is associated with increased risk of cardiovascular events in observational studies. Here, we present a novel CMR-based pressure-independent measure of aortic stiffness, identify genetic associations and explore links with CVDs. Purpose Identify genetic contributions to pressure-independent aortic stiffness and links to CVDs using large-scale GWAS analysis on the UK Biobank cohort. Methods A pre-trained neural network was used to segment CMR images of participants in the UK Biobank to quantify ascending aorta diameter, which together with contemporaneous systolic and diastolic blood pressure measurements (SBP, DBP, respectively) were used to calculate a pressure-independent measure of aortic stiffness (β0). The measurement assumes an exponential relationship between pressure and aortic diameter. We then performed a GWAS on β0 in 45,789 participants of European ancestry without CVDs. A multi-trait-based conditional & joint analysis (mtCOJO) was performed to estimate genetic effects independent of SBP, DBP and aortic area. We mapped the independent lead variants associated with aortic stiffness at P < 5x10-8 to nearest gene, and characterised shared genetic association across CVDs using Open Targets Platform database. Results The GWAS identified 17 independent lead variants, including 6 that remained significant in the conditional analysis. Four of these lead variants, not previously reported in GWAS of other aortic traits, were located near CTD-2203A3.1, MCF2L, CFDP1 and CDH13 genes (red annotations in the Figure). MCF2L, CFDP1 and CDH13 have been associated with coronary artery disease, and CFDP1 with myocardial infarction and coronary atherosclerosis in previous studies. Conclusion A GWAS of CMR-derived aortic stiffness identified 4 loci distinct from other aortic traits that also associate with other CVDs, suggesting genetic links between aortic stiffness and CVDs. Further analyses are required to explore biological mechanisms underlying these genetic associations.Aortic Stiffness Manhattan Plot

Shape characterization of copper metallic particles recovered from jig separation of e-wastes

Shape characterization of copper metallic particles recovered from jig separation of e-wastes

Geometric descriptors for beta turns.

Beta turns, in which the protein backbone abruptly changes direction over four amino acid residues, are the most common type of protein secondary structure after alpha helices and beta sheets and play key structural and functional roles. Previous work has produced classification systems for turn geometry at multiple levels of precision, but these operate in backbone dihedral-angle (Ramachandran) space, and the absence of a local Euclidean-space coordinate system and structural alignment for turns, or of any systematic Euclidean-space characterization of turn backbone shape, presents challenges for the visualization, comparison and analysis of the wide range of turn conformations and the design of turns and the structures that incorporate them. This work derives a turn-local coordinate system that implicitly aligns turns, together with a set of geometric descriptors that characterize the bulk BB shapes of turns and describe modes of structural variation not explicitly captured by existing systems. These modes are shown to be meaningful by the demonstration of clear relationships between descriptor values and the electrostatic energy of the beta-turn H-bond, the overrepresentations of key side-chain motifs, and the structural contexts of turns. Geometric turn descriptors complement Ramachandran-space classifications, and they can be used to select turn structures for compatibility with particular side-chain interactions or contexts. Potential applications include protein design and other tasks in which an enhanced Euclidean-space characterization of turns may improve understanding or performance. The web-based tools ExploreTurns, MapTurns, and ProfileTurn, available at www.betaturn.com, incorporate turn-local coordinates and turn descriptors and demonstrate their utility.

Endocranial development in non-avian dinosaurs reveals an ontogenetic brain trajectory distinct from extant archosaurs

Modern birds possess highly encephalized brains that evolved from non-avian dinosaurs. Evolutionary shifts in developmental timing, namely juvenilization of adult phenotypes, have been proposed as a driver of head evolution along the dinosaur-bird transition, including brain morphology. Testing this hypothesis requires a sufficient developmental sampling of brain morphology in non-avian dinosaurs. In this study, we harness brain endocasts of a postnatal growth series of the ornithischian dinosaur Psittacosaurus and several other immature and mature non-avian dinosaurs to investigate how evolutionary changes to brain development are implicated in the origin of the avian brain. Using three-dimensional characterization of neuroanatomical shape across archosaurian reptiles, we demonstrate that (i) the brain of non-avian dinosaurs underwent a distinct developmental trajectory compared to alligators and crown birds; (ii) ornithischian and non-avialan theropod dinosaurs shared a similar developmental trajectory, suggesting that their derived trajectory evolved in their common ancestor; and (iii) the evolutionary shift in developmental trajectories is partly consistent with paedomorphosis underlying overall brain shape evolution along the dinosaur-bird transition; however, the heterochronic signal is not uniform across time and neuroanatomical region suggesting a highly mosaic acquisition of the avian brain form.

Preparation and Characterization of 1D, 2D, and 3D Shapes of Anisotropic Gold Nanoparticles

In recent times, the scientific community has become interested in gold nanoparticles, namely in the anisotropic class. Based on their shapes, they fall into three categories: one-, two-, and three-dimensional. The synthesis and properties of three different anisotropic gold nanoparticle preparations - gold nanobipyramids (one-dimension, 1D), gold nanoprisms (two-dimension, 2D), and gold stars (three-dimension, 3D) - are shown in this research. Here, our group describes a chemical reduction method mediated by seeds. X-ray powder diffraction, transmission electron microscopy, scanning electron microscopy, and ultraviolet-visible spectroscopy were used to analyze the anisotropic gold nanoparticles. The average dimensions of the nanobipyramids were 64.27 ± 7.31 nm for length and 25.87 ± 2.56 nm for diameter, according to the data; the average dimensions of the gold nanoprisms and goldstars were 43.54 ± 5.61 nm and 31.35 ± 7.01 nm, respectively. Furthermore, following centrifugation purification, the yields of triangle and bipyramidal particles rose from 40% to over 60% and from 60% to 90%, respectively. It was established what the ideal parameter concentration was to create anisotropic gold nanoparticles in three dimensions: one, two, and three.

Understanding Correlative Electron Microscopy Imaging with SEM, STEM-in-SEM and TEM for the Accurate Characterization of Size and Shape of Iron Oxide Nanoparticles

Understanding Correlative Electron Microscopy Imaging with SEM, STEM-in-SEM and TEM for the Accurate Characterization of Size and Shape of Iron Oxide Nanoparticles

Application of building prefabricated diaphragm walls: Mechanics-carbon dual-control parametric segmentation decision-making framework

As a crucial innovation in building below-grade envelope structures, prefabricated diaphragm walls (PDWs) require the integration of multi-layer information for effective segmentation. Existing decision-making approaches rely on a single condition, leading to significant disparities between decision outcomes and actual requirements. This study introduces the mechanic-carbon dual-control parametric segmentation decision-making (MCSD) framework for PDWs to integrate multi-layer information. MCSD framework firstly performs parametric characterization of PDW component shapes and local transport constraints. Refined numerical modeling is next employed to identify the dominant mechanical factors influencing segmentation decision-making. Optimization algorithm is developed to automatically generate feasible schemes. Finally, carbon assessment is utilized to identify optimal segmentation schemes. MCSD framework is verified by actual engineering and the following key findings are obtained. Compared to empirical methods, MCSD could quantitatively determine feasible segmentation positions across the entire surface of PDWs. Notably, MCSD reveals the competitive relationship between horizontal and longitudinal joints for the first time. By incorporating this mechanism for decision optimization, carbon dioxide equivalent could be reduced by up to 29.3%. Ultimately, MCSD transcends the traditional boundary between mechanics and carbon, enabling automated planning of PDW segmentation. This study provides scientific and quantitative guideline for the segmentation decisions of building prefabricated enclosure structures.

Multi-objective shape characterization of airside performance in sinusoidal wavy fin-and-tube heat exchangers with large-diameter tubes using CFD

Multi-objective shape characterization of airside performance in sinusoidal wavy fin-and-tube heat exchangers with large-diameter tubes using CFD

Classification of Ice Particle Shapes Using Machine Learning on Forward Light Scattering Images

Abstract Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes.

Mechanical Characterization and Production of Various Shapes Using Continuous Carbon Fiber-Reinforced Thermoset Resin-Based 3D Printing.

Continuous carbon fiber-reinforced (CCFR) thermoset composites have received significant attention due to their excellent mechanical and thermal properties. The implementation of 3D printing introduces cost-effectiveness and design flexibility into their manufacturing processes. The light-assisted 3D printing process shows promise for manufacturing CCFR composites using low-viscosity thermoset resin, which would otherwise be unprintable. Because of the lack of shape-retaining capability, 3D printing of various shapes is challenging with low-viscosity thermoset resin. This study demonstrated an overshoot-associated algorithm for 3D printing various shapes using low-viscosity thermoset resin and continuous carbon fiber. Additionally, 3D-printed unidirectional composites were mechanically characterized. The printed specimen exhibited tensile strength of 390 ± 22 MPa and an interlaminar strength of 38 ± 1.7 MPa, with a fiber volume fraction of 15.7 ± 0.43%. Void analysis revealed that the printed specimen contained 5.5% overall voids. Moreover, the analysis showed the presence of numerous irregular cylindrical-shaped intra-tow voids, which governed the tensile properties. However, the inter-tow voids were small and spherical-shaped, governing the interlaminar shear strength. Therefore, the printed specimens showed exceptional interlaminar shear strength, and the tensile strength had the potential to increase further by improving the impregnation of polymer resin within the fiber.

Three-dimensional characterization of particle size, shape, and internal porosity for Apollo 11 and Apollo 14 lunar regolith and JSC-1A lunar regolith soil simulant

Samples of soils collected by the Apollo 11 mission (10084,2036) and the Apollo 14 mission (14163,940) were obtained from the NASA Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) program. The particle size, shape, and internal porosity were characterized in three dimensions (3D) using a combination of X-ray computed tomography (XCT) and mathematical analysis, with various size and shape parameters measured and calculated for each particle. High-resolution scanning electron microscopy (SEM) was used to image the particles that were too small for the two XCT instruments used. Similar characterization was carried out on samples of JSC-1A lunar soil simulant, updating a previous analysis. Approximately 14,000 lunar regolith particles and 128,000 JSC1-A particles, covering a wide range of size and shape, were characterized for this paper and the results stored in a publicly accessible database. This large number of particles enabled, for the first time, statistically valid particle shape distributions to be generated. The 3D shape distributions of the two regoliths and JSC-1A were quantitatively compared and it was found that the way particle shape and porosity depends on particle size was different between regolith and simulant. The measured size distribution of particles in the lunar soils was applied to estimate the relative contributions of different sizes to the ensemble average particle single scattering albedo and phase function. By linking our particle counts to published sieve weight fractions for the lunar samples, we find that ∼80% of the total cross-section area is contributed by particles <20 μm diameter and ∼ 50% by particles <8 μm diameter. The orientation-averaged two-dimensional projected areas of the actual regolith particles were computed so that this estimate was also based on real particle shapes. Such small sizes dominating the total cross-section area suggest that calculations of the elements of the scattering matrix for individual particles may be possible with modest computing capabilities leading to the development of improved models for the quantitative interpretation of remote sensing spectrophotometry and polarimetry. This 3D characterization and database will enable other computational work to be done with real lunar regolith particle shapes, including discrete element method mechanical modeling, packing simulations, further light scattering calculations, dust contamination modeling, and modeling of lunar rover interactions with collected and packed regolith particles.

An X-ray computed μ-tomography analysis for the characterization of 3D-heart shape in a model of cardiac plasticity

An X-ray computed μ-tomography analysis for the characterization of 3D-heart shape in a model of cardiac plasticity

Resolving Nonequilibrium Shape Variations among Millions of Gold Nanoparticles.

Nanoparticles, exhibiting functionally relevant structural heterogeneity, are at the forefront of cutting-edge research. Now, high-throughput single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) creates opportunities for recovering the shape distributions of millions of particles that exhibit functionally relevant structural heterogeneity. To realize this potential, three challenges have to be overcome: (1) simultaneous parametrization of structural variability in real and reciprocal spaces; (2) efficiently inferring the latent parameters of each SPI measurement; (3) scaling up comparisons between 105 structural models and 106 XFEL-SPI measurements. Here, we describe how we overcame these three challenges to resolve the nonequilibrium shape distributions within millions of gold nanoparticles imaged at the European XFEL. These shape distributions allowed us to quantify the degree of asymmetry in these particles, discover a relatively stable "shape envelope" among nanoparticles, discern finite-size effects related to shape-controlling surfactants, and extrapolate nanoparticles' shapes to their idealized thermodynamic limit. Ultimately, these demonstrations show that XFEL SPI can help transform nanoparticle shape characterization from anecdotally interesting to statistically meaningful.

DEM simulation of powder phase movement in coke packed bed generated by scanning particles of a blast furnace

The rational movement and distribution of the powder phase play a significant role in promoting energy efficiency and low carbon emissions in blast furnace (BF) ironmaking. Precise description of the shape of ore is vital for simulating the solid phase motion in a BF. In this study, the shapes of actual particles were captured using a highly accurate 3D scanner, followed by a comparison of the geometric shape characterization of the regular and real particles. A detailed analysis was conducted on the characteristics of the packed bed formed by particles of various shapes and the movement process of the powder phase within the packed bed by using discrete element method (DEM). The distribution and hold-up of the powder phase in the polyhedral and spherical-polyhedral packed beds closely resembles that in the real packed bed. Given the computational time and simulation accuracy, polyhedral or spherical-polyhedral particles could potentially replace the spherical particles typically used in packed beds. This substitution is particularly relevant for applying the DEM to large-scale BF simulations.

Complex fracture description and quasi-elastic energy development mathematical model for shale oil reservoirs

Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is ​​constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.

A Study on the Design of BLDC Motor for Air Conditioning and Characterization of Slot Shape for Torque Ripple Reduction

A Study on the Design of BLDC Motor for Air Conditioning and Characterization of Slot Shape for Torque Ripple Reduction

Preparation of silymarin-loaded zein polysaccharide core–shell nanostructures and evaluation of their biological potentials

Abstract Silymarin-loaded zein polysaccharide core–shell nanoparticles (SZPCS-NPs) were synthesized where sodium alginate and pectin offer stability and controlled release qualities to zein, a maize protein, having excellent biocompatibility. The present study is an attempt to develop zein–silymarin polysaccharide core–shell nanostructures to enhance water solubility, thereby improving bioavailability and producing enhanced biological responses in living systems. SZPCS-NPs were prepared using pH-induced antisolvent precipitation method. Five different types of SZPCS-NPs were synthesized using different combinations of sodium alginate and pectin, namely P100–A00 (non-uniform size ranging from 20 to 100 nm), P70–A30 (spherical and uniform size measuring approximately 80 nm in diameter), P50–A50, P30–A70, and P00–A100 exhibited irregular shapes with the presence of some triangular and oval structures and non-uniform size ranging from 20 to 100 nm. The SZPCS-NPs P70–A30 possessed the best results in terms of shape, size, and other characterization studies. Furthermore, the SZPCS-NPs possessed a percent drug loading of 72.5% and entrapment efficiency of 51.7%, respectively. The resulting SZPCS-NPs exhibited an excellent relative bioavailability percentage of 97.4% in comparison to commercial silymarin, having 58.1%, and crude silymarin, having 46.97% bioavailability percentage, correspondingly. In addition, SZPCS-NPs possessed an almost two folds’ increase in antioxidant activity in comparison to crude and commercially available silymarin. Similarly, SZPCS-NPs also showed better stabilization in hepatic biomarker enzymes and possessed better hepatoprotective activity for a period of 6 weeks, in contrast to commercial and crude silymarin formulations.

Announcement of an approaching death: Glaciar Norte, Citlaltépetl volcano, Mexico

This study shows the dramatic but probable future evolution of Mexico's largest glacier. Mass and energy balances of this glacier have been previously studied, as well as reconstructions of its evolution up to the present time. In this work, for the first time, the real evolution of the glacier is compared with the first years of models of its future evolution.Two dynamic models of glacier thickness evolution have been developed based on a network of stakes and ground penetrating radar complemented by geometrical characterizations of the glacier bed shape. Calculated mean annual mass balance leads the equilibrium line altitude to be increasingly higher and even higher than that of the mountain summit.Two models that complement each other to describe the how and when of the different states and stages in which the glacier front retracts, the thickness decreases causing the separation of the glacier to finally disappear in the middle of the 21st century. This study provides the first view of a glacier that is about to disappear in Mexico, where the cryosphere represents the best climate proxy for future scenarios of related ecosystems, water availability.

Enhanced mesoscale and macroscale parameters for accurate 3D shape characterization

This paper investigates the limitations of widely used 3D Wadell's roundness and true sphericity metrics for characterizing granular materials. It introduces two improved indices: the mesoscale interlocking index and the radial distance factor. The study involves diverse particles scanned using optical interferometry and X-ray computed tomography. A mean curvature method is employed for surface segmentation, and a computationally efficient 3D mesh simplification procedure is proposed. Python scripts are developed for surface segmentation, mesh simplification, and 3D shape characterization. The new indices are validated by assessing angular and rounded 3D particles and changes in the shape of aggregates through the Los Angeles abrasion test. The results demonstrate the ability of the proposed indices to differentiate visually similar particles with identical Wadell's roundness and true sphericity, showcasing their effectiveness in accurately describing particle shape. The statistical distribution of 3D particle curvatures offers an alternative method for understanding particles' shape and abrasion.

3D shape and size characterization of micron-sized coal particle with XRCT and SH

The shape and size of solid fuel particles are significant parameters in combustion process, and have attracted broad attentions in simulation and experiment studies. In this study, X-ray computer tomography (XRCT) and spherical harmonics (SH) were introduced and combined to conduct accurate morphology characterization of micron-sized coal powder in three dimensions (3D) with purpose of providing references for morphology characterization, size measurement, and modeling of solid fuel particles. The shape parameters including volume, surface area, sphericity index, specific surface area, enlongation index, and flatness index, and the size parameters including diameter of volume equivalent sphere, three principal dimensions, and sieve size, were obtained with SH expansion. Validation on standard geometries demonstrated the accuracy of the method under the SH degree N ≥ 20, with errors for volume, surface area, and sieve size within 1 %, 3 %, and 2.5 %, respectively. These parameters were analyzed statistically to reveal the morphological characteristics of micron-sized coal powder. The Zingg diagram indicated that nearly 60 % of the coal powder particles are nearly spherical. Comparison among 3D size, 2D size, and sieve size implied that characterizing with 2D size may overestimate the fineness of coal powder. Although there was a variance in the particle size distribution, it was demonstrated that there exists a strong linear relationship between the 3D size, 2D size, and sieve size. The method could serve as a functional auxiliary tool for fuel particle research.