Elastic Model Research Articles

Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations

BackgroundCardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations.MethodsWe used summary statistics from a genome-wide association meta-analysis (n = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability.ResultsOur African-specific PGS displayed significant but variable within- and cross- trait prediction (max.R2 = 6.8%, p = 1.86 × 10−173). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p = 2.18 × 10−21) and low-density lipoprotein (logOR = − 0.141, SE = 0.022, p = 1.30 × 10−20); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10−4); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p = 2.22 × 10−5), hip circumference (logOR = 0.122, SE = 0.029, p = 2.28 × 10−5), waist circumference (logOR = 0.013, SE = 0.098, p = 8.13 × 10−4) and weight (logOR = 0.103, SE = 0.029, p = 4.89 × 10−5). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R2 increase = 2.6%, p = 4.45 × 10−12; hypertension: R2 increase = 2.6%, p = 2.37 × 10−13; obesity: R2 increase = 5.5%, 1.33 × 10−34).ConclusionsIn African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.

Static and dynamic stabilities of modified gradient elastic Kirchhoff–Love plates

The static and dynamic stabilities of modified gradient elastic Kirchhoff–Love plates (MGEKLPs), which incorporate two length-scale parameters related to strain gradient and rotation gradient effects, are comprehensively analyzed under various load forms and boundary conditions (BCs). The study of static stability employs static balance method and an improved energy method by introducing higher-order deformation gradients and corresponding energy terms. Utilizing the variational method, a sixth-order fundamental buckling differential equation for MGEKLPs under both transverse and in-plane loads is derived, serving as the foundation for the static balance method. The static stability analysis of MGEKLPs examines the combined effects of strain and rotation gradients on size-dependent critical buckling loads. Building on generalized strain energy with higher-order deformation energy, the energy method of classical elastic thin plate model is enhanced and applied to the static stability analysis of MGEKLPs. This approach enables the investigation of static stability without being constrained by the need to solve complex differential equations, making it applicable to various BCs and load scenarios. While static stability provides a description of stable state of an elastic system, dynamic stability offers a more scientific and rigorous analysis. The dynamic stability of simplified gradient elastic Kirchhoff–Love plates (SGEKLPs) with curved edges and different BCs is further investigated by combining the generalized strain energy with Lyapunov’s second stability method, presenting the dynamic stability criterion in the form of norms. A strict description of the dynamic stability of a SGEKLP over the entire time domain is provided for different supporting conditions, including case where all edges are supported and case with free edges. The analysis of size-dependent static and dynamic stabilities offers theoretical guidance for designing elastic thin plates with microstructures.

Applying a 2D Variational Rigid Block Modeling Method to Rubble Stone Masonry Walls Considering Uncertainty in Material Properties

ABSTRACT The computational cost of detailed micro-modeling of masonry structures can be minimized with rigid block analysis methods, where a variational formulation is particularly interesting as it allows for nonlinear pushover analysis. This work investigates the accuracy of this modeling method in predicting the response of rubble stone masonry walls in shear compression tests. To this end, we extend the mathematical programming problem by including cohesion, compressive strength, and tensile strength in the formulation and implement an improved pushover procedure, which guards compatibility of the displaced shape and crack patterns at the intersection of elastic and rigid contact models. Additionally, we reduce model uncertainty, introduced through sampling material properties with distributions estimated based on results from tests on mortar masonry joints and on mortar specimens, by correlating the predicted crack patterns with the observed ones. The findings show that (i) model falsification based on crack pattern is an effective method for reducing the parameter uncertainty of stone masonry walls; and that (ii) the improved pushover procedure predicts the pre- and post-peak response of stone masonry walls very well with analysis times of less than 20 minutes for 2D models where stones and mortar are modeled explicitly.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31–84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11–17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Elastic modulus prediction for high-temperature treated rock using multi-step hybrid ensemble model combined with coronavirus herd immunity optimizer

Elastic modulus is a crucial mechanical parameter that measures the stiffness property of rock materials. In underground rock engineering, such as deep energy development and geological disposal of high-level nuclear waste, accurately determining the elastic modulus of engineering rock masses in high-temperature environments plays a vital role in understanding the instability-triggering conditions of hard and brittle rock masses in deep underground engineering and maintaining the safety and stability of deep underground engineering. Traditionally, conducting indoor experimental tests is time-consuming, labor-intensive, and costly. Therefore, developing a convenient and accurate rock elastic modulus prediction model based on machine learning technology holds significant importance. To this end, this study proposed a multi-step hybrid ensemble model (MHEM) for predicting high-temperature treated rock elastic modulus. The input parameters of the model include diameter, height, density, temperature, confining pressure, crack damage stress, and strength, while the output parameter is the elastic modulus. Subsequently, a coronavirus herd immunity optimizer (CHIO) intelligent optimization algorithm was employed to optimize the MHEM, and the CHIO-MHEM was established. Then, the performance of the developed models was compared and evaluated with eight other different prediction models. Finally, SHAP method and tree model feature analysis were used to quantify the feature importance of the prediction model. The research results indicate that, compared with other prediction models, the CHIO-MHEM exhibits superior performance, achieving accurate prediction of the elastic modulus of rocks treated at different high temperatures. Additionally, among all input parameters, rock density and temperature have the most significant influence on the rock elastic modulus.

Finite element analysis on non-contact lap splice connections in ultra-high-performance concrete

This study investigates the stress characteristics and failure modes of the lap splice region for non-contact lap-spliced rebar embedded in Ultra-High-Performance Concrete (UHPC), as well as the influence of lap parameters on these aspects. A series of refined finite element models of non-contact lap-spliced rebars embedded in UHPC were established and validated. The influences of UHPC mechanical properties, splice length, and clear spacing on lap region and the failure modes of specimens were studied. As the lap clear spacing increases from 0.5d to 4d, the ratio of principal compressive stress to principal tensile stress in the lap region of the elastic model decreases from 2.36 to 0.36. This trend indicates that with increasing clear spacing, the force transfer between rebars shifts from being dominated by compressive stress to tensile stress within the UHPC lap region. Consequently, the maximum load of the specimen transitions from being governed by compressive strength to being governed by tensile properties. With a clear spacing of 2d, the maximum load of the specimen increases with the splice length. The maximum load of 8d splice length specimen was increased by at least 27 % compared to the specimen with 0d splice length.

The Theories of Rubber Elasticity and the Goodness of Their Constitutive Stress–Strain Equations

One of the most important challenges in polymer science is a rigorous understanding of the molecular mechanisms of rubber elasticity by relating macroscopic deformation to molecular changes and deriving the constitutive stress–strain equation for the elastomeric network. The models developed from the last century to today describe many aspects of the physics of rubber elasticity; although these theories are successful, they are not complete. In this review we analyze the main theoretical and phenomenological models of rubber elasticity, including their assumptions, main characteristics, and stress–strain equations. Then, we compare the predictions of the theories to our experimental data of polydimethylsiloxane (PDMS) rubber, in order to highlight the goodness of the reviewed models. The nonaffine and phenomenological deformation models verify the experimental curves in tension and compression in the whole investigated deformation range λ≤2. On the contrary, the affine deformation hypothesis is rigorously verified only in the deformation range λ≤1.

Energy Evolution Characteristics and Hydraulic Fracturing Roof Cutting Technology for Hard Roof Working Face during Initial Mining: A Case Study

In the process of mining, a large area of hard roof will be exposed above a goaf and may suddenly break. This can easily induce rock burst and has a significant impact on production safety. In this study, based on the engineering background of the hard roof of the 2102 working face in the Balasu coal mine, the spatial and temporal characteristics of the strain energy of the roof during the initial mining process were explored in depth. Based on a theoretical calculation, it is proposed that hydraulic fracturing should be carried out in the medium-grained sandstone layer that is 4.8–22.43 m above the roof, and that the effective fracturing section in the horizontal direction should be within 30.8 m of the cutting hole of the working face. The elastic strain energy fish model was established in FLAC3D to analyze the strain energy accumulation of the roof during the initial mining process. The simulation and elastic strain energy results show that, as the working face advances to 70–80 m, the hard roof undergoes significant bending deformation. The energy gradient increases with the rapid accumulation of strain energy to a peak value of 140.54 kJ/m3. If the first weighting occurs at this moment in time, the sudden fracture of the roof will be accompanied by the release of elastic energy, which will induce rock burst. Therefore, it is necessary to implement roof cutting and pressure relief before reaching the critical step of 77 m. To this end, the comprehensive hydraulic fracturing technology of ‘conventional short drilling + directional long drilling’ is proposed. A field test shows that the hydraulic fracturing technology effectively weakens the integrity of the rock layer. The first weighting interval is 55 m, and it continues until the end of the pressure at the 70 m position. The roof collapses well, and the mining safety is improved. This study provides an important reference for hard roof control.

Co-seismic and post-seismic slip associated with the 2021 Mw5.9 Arkalochori, Central Crete (Greece) earthquake constrained by geodetic data and aftershocks

The co-seismic and post-seismic deformation field associated with the Mw5.9 Arkalochori main shock that occurred in central Crete (Greece) on 27 September 2021 is analyzed using Copernicus Sentinel-1A & 1B images, GNSS measurements and seismological data. The fault geometry is constrained through the joint inversion of multiple datasets and the slip distribution for the co-seismic and post-seismic period is obtained using a homogeneous half-space elastic model and the Steepest Descent Method. The results indicate a blind normal fault striking 215° with a 55° dip to the northwest and the co-seismic slip model suggests a nearly circular main slip patch (8 × 6 km2) with a maximum slip of 0.98 m. Post-seismic displacements started rapidly after the main shock followed by a gradual decay as highlighted by the calculated InSAR time series. The temporal evolution of post-seismic slip is described by a simple logarithmic function, decaying faster at the southwest part of the fault. The cumulative afterslip model suggests that the maximum post-seismic slip of 0.23 m occurred within a similar depth range compared to the co-seismic one, yet with a shift towards the southwest. Post-seismic slip inside the main shock rupture area is sustained, highlighting the slow recovery of locking in the co-seismic slip region. Afterslip (seismic or aseismic) played a dominant role in the early post-seismic period acting complementarily to the main rupture. Indications suggest that the spatiotemporal evolution of the productive aftershock sequence may be driven afterslip, alongside other potential factors.

Study on fragmentation characteristics of carbon nanotube-enhanced concrete and a comparative evaluation of the ZWT and ottosen constitutive model

In order to study the difference between the crushing characteristics of carbon nanotubes (CNTs) concrete and ordinary concrete, this paper conducts a test between ordinary concrete and CNTs concrete with a volume content of 0.3 % at different impact speeds through the Hopkinson test, using fragmentation and particle size distribution as evaluation indexes. Crushed fragments are analyzed from the perspective of concrete pores. Finally, the nonlinear viscoelastic constitutive model and the nonlinear elastic constitutive model of CNTs concrete are then developed and compared. Based on the results, the average pores of the CNTs concrete decreased from 42.0 nm to 38.0 nm, but the pore area increased from 5.743 m2/g to 6.305 m2/g, indicating that CNTs can fill the internal pores. Meanwhile, the average particle size of broken CNTs concrete is significantly larger than that of ordinary concrete. However, the relationship between the crushing particle size and impact velocity is approximately y=A-Bln(x+C) for both concretes. According to the study of concrete constitutive models, compared to the Loand damage model, the combination of Loand model and Mazars model is more accurate in describing concrete damage characteristics, and the average correlation coefficient increases from 0.84 to 0.92. Meanwhile, compared to the Zhu-Wang-Tang (ZWT) model, Ottosen model can better capture the dynamic mechanical properties of CNT concrete, with a correlation coefficient over 0.98.

Multiscale Computational Analysis of the Effect of Taxol on Microtubule Mechanics.

Microtubules (MTs) are widely recognized as targets for cancer therapies. They are directly related to unique mechanical properties, closely dependent on MT architecture and tubulin molecular features. Taxol is known to affect tubulin interactions resulting in the stabilization of the MT lattice, and thus the hierarchical organization stability, mechanics, and function. A deeper understanding of the molecular mechanisms through which taxol modulates intertubulin interactions in the MT lattice, and consequently, its stability and mechanical response is crucial to characterize how MT properties are regulated by environmental factors, such as interacting ligands. In this study, a computational analysis of the effect of taxol on the MT was performed at different scales, combining molecular dynamics simulation, dynamical network analysis, and elastic network modeling. The results show that the taxol-induced conformational differences at the M-loop region increase the stability of the lateral interactions and the amount of surface in contact between laterally coupled tubulins. Moreover, the conformational rearrangements in the taxane binding site result in a different structural communication pattern. Finally, the different conformation of the tubulin heterodimers and the stabilized lateral interactions resulted in a tendency toward higher deformation of the vibrating MT in the presence of taxol. Overall, this work provides additional insights into taxol-induced stabilization and relates the conformational changes at the tubulin level to the MT mechanics. Besides providing useful insights into taxol effect on MT mechanics, a methodological framework that could be used to characterize the effects of other MT stabilizing agents is presented.

A comprehensive numerical approach to coil placement in cerebral aneurysms: mathematical modeling and in silico occlusion classification.

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well-established and minimally invasive method, it bears the risk of suboptimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted natural shape supporting the fixation within the aneurysm. For the spatial discretization, our mathematical coil model is based on the discrete elastic rod model which results in a dimension-reduced 1D system of differential equations. We include bending and twisting responses to account for the coils natural curvature and allow for the placement of several coils having different material parameters. Collisions between coil segments and the aneurysm wall are handled by an efficient contact algorithm that relies on an octree based collision detection. In time, we use a standard symplectic semi-implicit Euler time stepping method. Our model can be easily incorporated into blood flow simulations of embolized aneurysms. In order to differentiate optimal from suboptimal placements, we employ a suitable in silico Raymond-Roy-type occlusion classification and measure the local packing density in the aneurysm at its neck, wall region and core. We investigate the impact of uncertainties in the coil parameters and embolization procedure. To this end, we vary the position and the angle of insertion of the micro-catheter, and approximate the local packing density distributions by evaluating sample statistics.

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

Quantifying the effects of climate and environmental changes on evapotranspiration variability in the Sahel

Whilst considerable research has been carried out to understand the effects of reforestation on evapotranspiration (ET), such studies are generally absent in the Sahel even though the region is currently undergoing extensive reforestation to halt desertification and land degradation. The objective of this study is to identify and quantify the dominant climatic and environmental factors influencing ET variability in the Sahel. However, achieving this goal in the Sahel is hindered by a lack of in situ monitoring data. To overcome this challenge, this study adopts geospatial datasets along with analytical methods to assess the climatic and environmental factors affecting ET in 45 watersheds in the Sahel over a period of four decades (1982–2021). Analyses show significant increasing trends in annual ET in more than 90% of the watersheds. Precipitation and mean temperature show significant increasing trends in all watersheds while windspeed, vapour pressure, solar radiation, specific humidity, and atmospheric evaporative demand show mixed results with both increasing and decreasing trends in different watersheds. Environmental variables including soil moisture, and vegetation cover measured using the normalised difference vegetative index (NDVI) also show consistent increasing trends in all watersheds. Statistical analyses further show that climatic and environmental factors contribute about 80% of ET variance. The relative contribution of climatic variables on ET variance is 75.11% while that of environmental variables is 24.71%. This suggests that climatic factors have a higher influence on ET variability than environmental factors. Analyses using the double logarithm elasticity model show that vapour pressure has the highest positive elasticity coefficient (2.58) on ET while net radiation has the highest negative elasticity coefficient (−9.58). This is the first study to cover the whole Sahelian belt from west to east and the results may be crucial for adopting climate-smart reforestation policies, enhance regional water management and improve our knowledge of vegetation-hydrology-climate interactions in the Sahel.

A novel material elastic recovery model for Al6061 and oxygen free copper in micro-cutting

Material deformation error is an important factor affecting the surface forming accuracy of micro cutting. However, there is still a lack of an analytical model to predict the material elastic recovery (MER) error according to the basic machining parameters. In this paper, considering the basic machining parameters, a novel theoretical model for MER was proposed. Firstly, the location of the material dead zone (MDZ) was calculated by an analytical model considering the tool edge radius. Then the thrust force was predicted through a proposed theoretical model. Finally, MER was calculated based on elastic deformation theory. The reliability and robustness of the proposed theoretical model for MER was verified by numerical simulations and scratching experiments. And influences of the basic parameters on MER were also discussed. Research results indicated that average error between theoretical calculation and measurement data of MER is only 5.97 %. It indicates that the proposed model can improve the prediction accuracy of surface topography while micro cutting.

Membrane curvature sensing and symmetry breaking of the M2 proton channel from Influenza A.

The M2 proton channel aids in the exit of mature influenza viral particles from the host plasma membrane through its ability to stabilize regions of high negative Gaussian curvature (NGC) that occur at the neck of budding virions. The channels are homo-tetramers that contain a cytoplasm-facing amphipathic helix (AH) that is necessary and sufficient for NGC generation; however, constructs containing the transmembrane spanning helix, which facilitates tetramerization, exhibit enhanced curvature generation. Here, we used all-atom molecular dynamics (MD) simulations to explore the conformational dynamics of M2 channels in lipid bilayers revealing that the AH is dynamic, quickly breaking the fourfold symmetry observed in most structures. Next, we carried out MD simulations with the protein restrained in four- and twofold symmetric conformations to determine the impact on the membrane shape. While each pattern was distinct, all configurations induced pronounced curvature in the outer leaflet, while conversely, the inner leaflets showed minimal curvature and significant lipid tilt around the AHs. The MD-generated profiles at the protein-membrane interface were then extracted and used as boundary conditions in a continuum elastic membrane model to calculate the membrane-bending energy of each conformation embedded in different membrane surfaces characteristic of a budding virus. The calculations show that all three M2 conformations are stabilized in inward-budding, concave spherical caps and destabilized in outward-budding, convex spherical caps, the latter reminiscent of a budding virus. One of the C2-broken symmetry conformations is stabilized by 4 kT in NGC surfaces with the minimum energy conformation occurring at a curvature corresponding to 33 nm radii. In total, our work provides atomistic insight into the curvature sensing capabilities of M2 channels and how enrichment in the nascent viral particle depends on protein shape and membrane geometry.

Flat-Bottom Elastic Network Model for Generating Improved Plausible Reaction Paths.

Rapid generation of a plausible reaction path connecting a given reactant and product in advance is crucial for the efficient computation of precise reaction paths or transition states. We propose a computationally efficient potential energy based on the molecular structure to generate such paths. This potential energy has a flat bottom consisting of structures without atomic collisions while preserving nonreactive chemical bonds, bond angles, and partial planar structures. By combining this potential energy with the direct MaxFlux method, a recently developed reaction-path/transition-state search method, we can find the shortest plausible path passing within the bottom. Numerical results show that this combination yields lower energy paths compared to the paths obtained by the well-known image-dependent pair potential. We also theoretically investigate the differences between these two potential energies. The proposed potential energy and path generation routine are implemented in our Python version of the direct MaxFlux method, available on GitHub.

The discontinuous strain method: accurately representing fatigue and failure

AbstractFatigue simulation requires accurate modeling of unloading and reloading. However, classical ductile damage models treat deformations after complete failure as irrecoverable—which leads to unphysical behavior during unloading. This unphysical behavior stems from the continued accumulation of plastic strains after failure, resulting in an incorrect stress state at crack closure. As a remedy, we introduce a discontinuous strain in the additive elasto-plastic strain decomposition, which absorbs the excess strain after failure. This allows representing pre- and post-cracking regimes in a fully continuous setting, wherein the transition from the elasto-plastic response to cracking can be triggered at any arbitrary stage in a completely smooth manner. Moreover, the presented methodology does not exhibit the spurious energy release observed in hybrid approaches. In addition, our approach guarantees mesh-independent results by relying on a characteristic length scale—based on the discretization’s resolution. We name this new methodology the discontinuous strain method. The proposed approach requires only minor modifications of conventional plastic-damage routines. To convey the method in a didactic manner, the algorithmic modifications are first discussed for one- and subsequently for two-/three-dimensional implementations. Using a simple ductile constitutive model, the discontinuous strain method is validated against established two-dimensional benchmarks. The method is, however, independent of the employed constitutive model. Elastic, plastic, and damage models may thus be chosen arbitrarily. Furthermore, computational efforts associated with the method are minimal, rendering it advantageous for accurately representing low-cycle fatigue but potentially also for other scenarios requiring a discontinuity representation within a plastic-damage framework. An open-source implementation is provided to make the proposed method accessible.

An epigenome-wide study of selenium status and DNA methylation in the Strong Heart Study

BackgroundSelenium (Se) is an essential nutrient linked to adverse health endpoints at low and high levels. The mechanisms behind these relationships remain unclear and there is a need to further understand the epigenetic impacts of Se and their relationship to disease. We investigated the association between urinary Se levels and DNA methylation (DNAm) in the Strong Heart Study (SHS), a prospective study of cardiovascular disease (CVD) among American Indians adults. MethodsSelenium concentrations were measured in urine (collected in 1989–1991) using inductively coupled plasma mass spectrometry among 1,357 participants free of CVD and diabetes. DNAm in whole blood was measured cross-sectionally using the Illumina MethylationEPIC BeadChip (850 K) Array. We used epigenome-wide robust linear regressions and elastic net to identify differentially methylated cytosine-guanine dinucleotide (CpG) sites associated with urinary Se levels. ResultsThe mean (standard deviation) urinary Se concentration was 51.8 (25.1) μg/g creatinine. Across 788,368 CpG sites, five differentially methylated positions (DMP) (hypermethylated: cg00163554, cg18212762, cg11270656, and hypomethylated: cg25194720, cg00886293) were significantly associated with Se in linear regressions after accounting for multiple comparisons (false discovery rate p-value: 0.10). The top hypermethylated DMP (cg00163554) was annotated to the Disco Interacting Protein 2 Homolog C (DIP2C) gene, which relates to transcription factor binding. Elastic net models selected 425 hypo- and hyper-methylated DMPs associated with urinary Se, including three sites (cg00163554 [DIP2C], cg18212762 [MAP4K2], cg11270656 [GPIHBP1]) identified in linear regressions. ConclusionsUrinary Se was associated with minimal changes in DNAm in adults from American Indian communities across the Southwest and the Great Plains in the United States, suggesting that other mechanisms may be driving health impacts. Future analyses should explore other mechanistic biomarkers in human populations, determine these relationships prospectively, and investigate the potential role of differentially methylated sites with disease endpoints.

Explaining the correlation between subharmonic amplitude and ambient pressure for subharmonic-aided pressure estimation (SHAPE)

BackgroundSubharmonic aided pressure estimation (SHAPE) is an innovative non-invasive technique that leverages ultrasound subharmonic imaging to estimate pressure. This method exploits the “negative correlation between subharmonic amplitude and ambient pressure” observed in experimental settings. Despite extensive experimental validation and some promising results in clinical studies, the underlying mechanism of SHAPE remains incompletely understood. Although some studies have attempted to provide theoretical explanations, definitive conclusions have yet to be reached. In addition, theoretical investigations have mainly focused on the steady oscillation of bubbles under long pulse excitation, which contrasts with the short pulse excitation required for clinical SHAPE applications. An understanding of the SHAPE principle under short pulse excitation is needed. MethodsThe exponential elasticity model (EEM) was used to simulate Sonazoid bubbles, and a probe-to-probe acoustic propagation model was introduced to mimic a practical SHAPE scenario. The simulated acoustic signals in response to three-cycle sinusoidal pulse excitations were analyzed for spectral composition. The relationship between microbubble oscillation patterns and subharmonic characteristics was identified through detailed investigation. ResultsFor the excitation pulse of 2.5 MHz frequency and 350 kPa magnitude, bubbles larger than the resonance radius (2.29 μm) exhibited significant subharmonics in the magnitude spectrum, while bubbles smaller than the subharmonic resonance radius (3.85 μm) showed the activity of scattering subharmonic energy and the sensitivity to ambient pressure. The emergence of subharmonics when increasing excitation power was related to the increasing amplification of the bubble self-oscillation and the period-doubling features in the acoustically forced oscillation. The negative correlation between subharmonic amplitude and ambient pressure was attributed to the reduced self-oscillation caused by increasing ambient pressure and hence bubble size reduction. Microbubbles falling between 2 and 3 μm showed the desired subharmonic sensitivity to ambient pressure under the specified excitation conditions. ConclusionThe transient oscillatory behavior of microbubbles in response to short pulse excitation, characterized by a ringing down self-oscillation after the acoustic forcing effect has ceased, is crucial for understanding the subharmonic emergence and the observed negative correlation between subharmonic amplitude and ambient pressure. The proposed concepts of subharmonic resonance radius, subharmonic-significant bubbles, and subharmonic-active bubbles provide valuable insights into the diverse subharmonic behavior of microbubbles. The theoretical explanation of this negative correlation highlights the importance of using subharmonic-significant-and-active bubbles for SHAPE applications.