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Background: Environmental stresses, including high salinity and drought, severely diminish wheat yield and quality globally. The xyloglucan endotransglucosylase/hydrolase (XTH) family represents a class of cell wall-modifying enzymes and plays important roles in plants growth, development and stress adaptation. However, systematic analyses of XTH family genes and their functions under salt and drought stresses have not been undertaken in wheat. Results: In this study, we identified a total of 135 XTH genes in wheat, which were clustered into three evolutionary groups. These TaXTHs were unevenly distributed on 21 chromosomes of wheat with a majority of TaXTHs located on homelogous groups 2, 3 and 7. Gene duplication analysis revealed that segmental and tandem duplication were the main reasons for the expansion of XTH family in wheat. Interaction network predictions indicated that TaXTHs could interact with multiple proteins, including three kinases, one methyltransferase and one gibberellin-regulated protein. The promoters of the TaXTH genes harbored various cis-acting elements related to stress and hormone responses. RNA-seq data analyses showed that some TaXTH genes were induced by salt and drought stresses. Furthermore, we verified that TaXTH17 was induced by abiotic stresses and phytohormone treatments, and demonstrated that TaXTH17 was localized in the secretory pathway and cell wall. Functional analyses conducted in heterologous expression systems and in wheat established that TaXTH17 plays a negative role in plant resistance to salt and drought. Conclusions: We identified 135 XTH genes in wheat and conducted comprehensive analyses of their phylogenetic relationships, gene structures, conserved motifs, gene duplication events, chromosome locations, interaction networks, cis-acting elements and gene expression patterns. Furthermore, we provided solid evidence supporting the notion that TaXTH17 plays a negative role in plant resistance to salt and drought stresses. Collectively, our results provide valuable insights into understanding wheat XTHs, particularly their involvement in plant stress responses, and establish a foundation for further functional and mechanistic studies of TaXTHs. [ABSTRACT FROM AUTHOR]

Creating lightweight and impact-resistant box structures has been an enduring pursuit among researchers. A new energy-absorbing structure consisting of a bionic gradient lattice-enhanced thin-walled tube is presented in this article. The gradient lattice and thin-walled tube were prepared using selective laser melting (SLM) and wire-cutting techniques, respectively. To analyze the effects of gradient pattern, mass ratio, diameter range and impact speed on structural crashworthiness, low-speed impact at 4 m/s and finite element simulation experiments were conducted. The study demonstrates that the design of inward radial gradient lattice-reinforced thin-walled tubes can effectively enhance structure's energy-absorption efficiency and provide a more stable mode of deformation. It also shows a 17.44% specific energy-absorption advantage over the uniformly lattice-reinforced thin-walled tubes, with no significant overall gain in peak crushing force. A complex scale evaluation method was used to determine the optimum structure and the structure type with the best crashworthiness was found to be a gradient lattice-filled tube with a thickness of 0.9 mm and a slope index of 10. The gradient lattice-reinforced thin-walled tube suggested in this investigation offers guidance for designing a more efficient thin-walled energy-absorption structure. [ABSTRACT FROM AUTHOR]

The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling, which is an indispensable step to derive the process-structure-property relationship. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled data-sets (big data), which can be either obtained by experiments or first-principle simulations. Unfortunately, these labeled data-sets are expensive to obtain in AM due to the high expense of the AM experiments and prohibitive computational cost of high-fidelity simulations, hindering the direct applications of big-data based ML tools to metal AM problems. To fully exploit the power of machine learning for metal AM while alleviating the dependence on "big data", we put forth a physics-informed neural network (PINN) framework that fuses both data and first physical principles, including conservation laws of momentum, mass, and energy, into the neural network to inform the learning processes. To the best knowledge of the authors, this is the first application of physics-informed deep learning to three dimensional AM processes modeling. Besides, we propose a hard-type approach for Dirichlet boundary conditions (BCs) based on a Heaviside function, which can not only exactly enforce the BCs but also accelerate the learning process. The PINN framework is applied to two representative metal manufacturing problems, including the 2018 NIST AM-Benchmark test series. We carefully assess the performance of the PINN model by comparing the predictions with available experimental data and high-fidelity simulation results, using finite element based variational multi-scale formulation method. The investigations show that the PINN, owed to the additional physical knowledge, can accurately predict the temperature and melt pool dynamics during metal AM processes with only a moderate amount of labeled data-sets. The foray of PINN to metal AM shows the great potential of physics-informed deep learning for broader applications to advanced manufacturing. All the data-sets and the PINN code will be made open-sourced in https://yan.cee.illinois.edu/ once the paper is published. [ABSTRACT FROM AUTHOR]

• A novel thin-walled filled structure based on a combination of a gradient lattice and a square thin-walled tube. • The gradient lattice-filled tube increases the maximum energy absorption by 36.65% compared with the empty tube. • The gradient pattern with gradually decreasing radius outwards is more favourable for energy absorption than the inwards pattern. • Multi-objective optimization of structural gradient parameters is beneficial to improve crashworthiness. Gradient structures have become a hot research topic because of their excellent crashworthiness. Inspired by the gradient distribution of human bone and the excellent energy absorption properties of thin-walled constructions, a novel biologically inspired functionally graded lattice-filled tube (FGLT) is proposed in this paper by combining a radial gradient lattice with a thin-walled structure. Lattice arrays with different gradient patterns were prepared by laser melting (SLM) technique and embedded in square thin-walled tubes for experimental crashworthiness study. It was found that there was a 25.47% increase in specific absorption energy of uniform lattice-filled tubes with respect to empty tubes, and a 36.65% increase in specific absorption energy of graded grating filled tubes with respect to empty tubes. It is noteworthy that the peak load of the structure is only improved by 3.95% compared to the empty tube for the gradient lattice as a filler embedded in the thin-walled tube. Finally, based on the response surface methodology (RSM) and the second-generation non-dominated ranking genetic algorithm (NSGA-II), the multi-objective optimization of the new bionic gradient structure with the maximum specific energy absorption and the minimum peak load is performed. The expression of the maximum standardized impact force is obtained by dimensionless processing, which can fully explore the influence of the structural parameters on the impact load. The functional gradient bionic structure provides new ideas for the design of more effective energy absorption structures and collision avoidance systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Mechanical and electronic properties of palladium dihydrides (PdH2) as a function of pressure were studied by ab initio calculations based on density functional theory (DFT). The ab initio random structure searching technique was employed for screening potential PdH2 crystal structures under high pressure. A hexagonal close packed (hcp) phase of PdH2 with space group P63mc was reported. The structure geometry and elastic constants were calculated as a function of pressure. It was found that H atoms are in the interstitial position of Pd atoms layer at 0 GPa. There is an electronic topology transition of hcp PdH2 at 15 GPa. When pressure exceeds above 15 GPa, one hydrogen atom occupies the tetrahedral site and another hydrogen atom locates in the interstitial position. When the c/a ratio is between 1.765 to 1.875, the hcp PdH2 is mechanically stable, and the Pd-H2b bond is the major factor that limits the mechanical stability. The elastic constant C44 is the first one that cannot satisfy the mechanical stability criteria under pressure. The anisotropy parameters are far from 1(one) shows that the hcp PdH2 is a highly anisotropic structure. The electronic structure study indicates that the bonding force between Pd and H atoms along the z-axis direction increases with the increasing pressure. Also, the phonon dispersion study shows that PdH2 is dynamic stability under pressure. The results suggest that hcp PdH2 can be metastable in van der Waals layered structure. [ABSTRACT FROM AUTHOR]

The elastic properties of random and ordered ZrO2 nanotube- (ZrNT-) reinforced HA and β-TCP biocomposites were carried out by a numerical investigation with finite element approach. The elastic modulus, shear modulus, and Poisson's ratio affected by various ZrNT volume fractions (5.0 vol.%, 5.5 vol.%, 6.5 vol.%, 7.5 vol.%, 8.5 vol.%, and 10.0 vol.%) and aspect ratios (3, 5, 10 and 20) for both random and ordered reinforced composites were obtained and analysed. The advantages of random and ordered reinforced composites were further discussed. The random reinforced composite is suggested to be a proper candidate for ZrO2 nanotube-reinforced biocomposite on the application of bone repair and the substitute. [ABSTRACT FROM AUTHOR]

Modern materials design requires reliable and consistent structure–property relationships. The paper addresses the need through transfer learning of deep material network (DMN). In the proposed learning strategy, we store the knowledge of a pre-trained network and reuse it to generate the initial structure for a new material via a naive approach. Significant improvements in the training accuracy and learning convergence are attained. Since all the databases share the same base network structure, their fitting parameters can be interpolated to seamlessly create intermediate databases. The new transferred models are shown to outperform the analytical micromechanics methods in predicting the volume fraction effects. We then apply the unified DMN databases to the design of failure properties, where the failure criteria are defined upon the distribution of microscale plastic strains. The Pareto frontier of toughness and ultimate tensile strength is extracted from a large-scale design space enabled by the efficiency of DMN extrapolation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new data-driven multiscale material modeling method, which we refer to as deep material network, is developed based on mechanistic homogenization theory of representative volume element (RVE) and advanced machine learning techniques. We propose to use a collection of connected mechanistic building blocks with analytical homogenization solutions to describe complex overall material responses which avoids the loss of essential physics in generic neural network. This concept is demonstrated for 2-dimensional RVE problems and network depth up to 7. Based on linear elastic RVE data from offline direct numerical simulations, the material network can be effectively trained using stochastic gradient descent with backpropagation algorithm, further enhanced by model compression methods. Importantly, the trained network is valid for any local material laws without the need for additional calibration or micromechanics assumption. Its extrapolations to unknown material and loading spaces for a wide range of problems are validated through numerical experiments, including linear elasticity with high contrast of phase properties, nonlinear history-dependent plasticity and finite-strain hyperelasticity under large deformations. By discovering a proper topological representation of RVE with fewer degrees of freedom, this intelligent material model is believed to open new possibilities of high-fidelity efficient concurrent simulations for a large-scale heterogeneous structure. It also provides a mechanistic understanding of structure–property relations across material length scales and enables the development of parameterized microstructural database for material design and manufacturing. [ABSTRACT FROM AUTHOR]

Multiscale modeling of heterogeneous material undergoing strain softening poses computational challenges for localization of the microstructure, material instability in the macrostructure, and the computational requirement for accurate and efficient concurrent calculation. In the paper, a stable micro-damage homogenization algorithm is presented which removes the material instability issues in the microstructure with representative volume elements (RVE) that are not sensitive to size when computing the homogenized stress–strain response. The proposed concurrent simulation framework allows the computation of the macroscopic response to explicitly consider the behavior of the separate constituents (material phases), as well as the complex microstructural morphology. A non-local material length parameter is introduced in the macroscale model, which will control the width of the damage bands and prevent material instability. The self-consistent clustering analysis (SCA) recently proposed by Liu et al. [ 1 ] provides an effective way of developing a microstructural database based on a clustering algorithm and the Lippmann–Schwinger integral equation, which enables an efficient and accurate prediction of nonlinear material response. The self-consistent clustering analysis is further generalized to consider complex loading paths through the projection of the effective stiffness tensor. In the concurrent simulation, the predicted macroscale strain localization is observed to be sensitive to the combination of microscale constituents, showing the unique capability of the SCA microstructural database for complex material simulations. [ABSTRACT FROM AUTHOR]

Mechanical stability is essential for solids and its stability criterion may date back to 75 years ago. Recently, the closed form necessary and sufficient conditions for elastic stability in all crystal classes have been investigated. Quasicrystals (QCs) are solids with long-range order and crystallographically forbidden rotational symmetries but without translational symmetry, attracting intense attentions in the last 30 years. In this work, we have explored the elastic constants and the elastic stability in detail for 1D, 2D and 3D QCs. All independent elastic constants and the closed form of necessary and sufficient conditions for elastic stability in all QCs classes are obtained, as a concise and pedagogical reference to stability criteria in aperiodic materials. Meanwhile, symmetry positions and stereographic projections of each QCs class are given as well. [ABSTRACT FROM AUTHOR]

Polymer nanocomposites have been envisioned as advanced materials for improving the mechanical performance of neat polymers used in aerospace, petrochemical, environment and energy industries. With the filler size approaching the nanoscale, composite materials tend to demonstrate remarkable thermomechanical properties, even with addition of a small amount of fillers. These observations confront the classical composite theories and are usually attributed to the high surface-area-to-volume-ratio of the fillers, which can introduce strong nanoscale interfacial effect and relevant long-range perturbation on polymer chain dynamics. Despite decades of research aimed at understanding interfacial effect and improving the mechanical performance of composite materials, it is not currently possible to accurately predict the mechanical properties of polymer nanocomposites directly from their molecular constituents. To overcome this challenge, different theoretical, experimental and computational schemes will be used to uncover the key physical mechanisms at the relevant spatial and temporal scales for predicting and tuning constitutive behaviors in silico, thereby establishing a bottom-up virtual design principle to achieve unprecedented mechanical performance of nanocomposites. A modular-based multiscale modeling approach for viscoelasticity of polymer nanocomposites has been proposed and discussed in this study, including four modules: (A) neat polymer toolbox; (B) interphase toolbox; (C) microstructural toolbox and (D) homogenization toolbox. Integrating these modules together, macroscopic viscoelasticity of polymer nanocomposites could be directly predicted from their molecular constituents. This will maximize the computational ability to design novel polymer composites with advanced performance. More importantly, elucidating the viscoelasticity of polymer nanocomposites through fundamental studies is a critical step to generate an integrated computational material engineering principle for discovering and manufacturing new composites with transformative impact on aerospace, automobile, petrochemical industries. [ABSTRACT FROM AUTHOR]

With the crude oil exploration activities in the Shanbei oilfield of China, the risk of soil contamination with crude oil spills has become a major concern. This study aimed at assessing the bioremediation potential of the petroleum polluted soils by investigating the expression of key functional genes decoding alkane and aromatic component degradation using an array of primers and real-time quantitative PCR (qPCR), and the functional microbiomes were determined using a combination of substrate-induced metabolic responses and high throughput sequencing. The results showed that the species that were more inclined to degrade aliphatic fraction of crude oil included Acinetobacter , Stenotrophomonas , Neorhizobium and Olivebacter. And Pseudomonas genus was a highly specific keystone species with the potential to degrade PAH fraction. Both aliphatic and PAH-degrading genes were upregulated when the soil petroleum contents were less than 10,000 mg/kg but downregulated when the oil contents were over 10,000 mg/kg. Bioremediation potential could be feasible for medium pollution with petroleum contents of less than 10,000 mg/kg. Optimization of the niche of Acinetobacter , Stenotrophomonas , Pseudomonas, Neorhizobium and Olivebacter species was beneficial to the biodegradation of refractory hydrocarbon components in the Shanbei plateau oilfield. [Display omitted] • Pseudomonas was the potential PAH degrader in the soils of Shanbei oilfield. • The dominant alkane-degrading species included Acinetobacter , Stenotrophomonas , Neorhizobium , and Olivebacter. • Hydrocarbon catabolic genes were abundant in the sites with oil contents less than 10000 mg/kg. • Bioremediation could be feasible for the soils with oil contents of less than 10,000 mg/kg. [ABSTRACT FROM AUTHOR]

Modeling nonlinear materials with arbitrary microstructures and loading paths is crucial in structural analyses with heterogeneous materials with uncertainty. However, it is computationally prohibitive because (1) fine meshes are required to resolve the microstructures for nonlinear simulations, (2) enormous model evaluations are expected to simulate stochastic microstructures and the loading variabilities, and (3) nonlinear material models are generally expensive to solve. In this paper, we present the microstructure-guided deep material network (MgDMN), a deep-learning-based approach to model multi-phase materials and efficiently predict their mechanical response under virtually any loading paths and microstructure definitions considering plasticity. This work is built upon the earlier development of the deep material network (DMN) method for fast predictions of nonlinear homogenized responses of multi-phase materials using linear elastic training data for a predefined microstructure. We extend DMN to MgDMN for any microstructure definition by parameterizing the microstructure space, mapping a small set of DMNs to their respective microstructure parameters, and interpolating the DMNs to generate new models in the microstructure space. The proposed method achieves superb efficiency and accuracy since the interpolated DMNs do not need additional training to predict nonlinear behavior of any new microstructures. We demonstrate our approach via modeling and uncertainty quantification of short fiber reinforced polymer (SFRP) composites. We show that for this dual-phase material, only four base DMNs are needed to predict the nonlinear stress responses for any loading path given any fiber volume fraction, orientation states, and constituent properties as long as the combination is within the microstructure space enclosed by the base models. The accelerated modeling process enables complex structural simulations of heterogeneous materials and nonlinear stochastic analyses with uncertain microstructures, which are essential in computational materials engineering and multiscale modeling. Our research also draws insights into the impact of microstructure uncertainties on linear and nonlinear material behaviors. [ABSTRACT FROM AUTHOR]

The discovery of efficient and accurate descriptions for the macroscopic behavior of materials with complex microstructure is an outstanding challenge in mechanics of materials. A mechanistic, data-driven, two-scale approach is developed for predicting the behavior of general heterogeneous materials under irreversible processes such as inelastic deformation. The proposed approach includes two major innovations: (1) the use of a data compression algorithm, k -means clustering, during the offline stage of the method to homogenize the local features of the material microstructure into a group of clusters; and (2) a new method called self-consistent clustering analysis used in the online stage that is valid for any local plasticity laws of each material phase without the need for additional calibration. A particularly important feature of the proposed approach is that the offline stage only uses the linear elastic properties of each material phase, making it efficient. This work is believed to open new avenues in parameter-free multi-scale modeling of complex materials, and perhaps in other fields that require homogenization of irreversible processes. [ABSTRACT FROM AUTHOR]

A continuing challenge in computational materials design is developing a model to link the microstructure of a material to its material properties in both an accurate and computationally efficient manner. In this paper, such a model is developed which uses image-based data from characterization studies combined with a newly developed self-consistent volume- integral micromechanics model (SVIM) for linear elastic material. It is observed that SVIM is able to capture the effective stress/strain distribution inside the inclusion, as well as effects of volume fraction and nearest inclusion distance on the effective properties of heterogeneous material. More importantly, SVIM can be applied to inclusions with arbitrary shape through discretizing the inclusion domain. For both 2-dimensional and 3-dimensional problems with circular and spherical inhomogeneities, SVIM's capability of predicting effective elastic properties is validated against experiments and direct numerical simulations using the finite element method. Finally, the effect of inclusion shape is predicted by SVIM. [ABSTRACT FROM AUTHOR]

Based on the advantages of thin-walled tubes and lattice structures in energy absorption and improved crashworthiness, a hybrid structure of lattice-reinforced thin-walled tubes with different cross-sectional cell numbers and gradient densities was constructed, and a high crashworthiness absorber with adjustable energy absorption was proposed. The experimental and finite element characterization of the impact resistance of uniform density and gradient density hybrid tubes with different lattice arrangements to withstand axial compression was carried out to investigate the interaction mechanism between the lattice packing and the metal shell, and the energy absorption of the hybrid structure was increased by 43.40% relative to the sum of its individual components. The effect of transverse cell number configuration and gradient configuration on the impact resistance of the hybrid structure was investigated, and the results showed that the hybrid structure showed higher energy absorption than the empty tube, and the best specific energy absorption was increased by 83.02%; the transverse cell number configuration had a greater effect on the specific energy absorption of the hybrid structure with uniform density, and the maximum specific energy absorption of the hybrid structure with different configurations was increased by 48.21%. The gradient density configuration had a significant effect on the peak crushing force of the gradient structure. In addition, the effects of wall thickness, density and gradient configuration on energy absorption were quantitatively analyzed. This study provides a new idea to optimize the impact resistance of lattice-structure-filled thin-walled square tube hybrid structures under compressive loading through a combination of experiments and numerical simulations. [ABSTRACT FROM AUTHOR]

Despite the increasing importance of strain localization modeling (e.g., failure analysis) in computer-aided engineering, there is a lack of effective approaches to capturing relevant material behaviors consistently across multiple length scales. We aim to address this gap within the framework of deep material networks (DMN) — a machine learning model with embedded mechanics in the building blocks. A new cell-division scheme is proposed to track the scale transition through the network, and its consistency is ensured by the physics of fitting parameters. Essentially, each microscale node in the bottom layer is described by an ellipsoidal cell with its dimensions back-propagated from the macroscale material point. New crack surfaces in the cell are modeled by enriching cohesive layers, and failure algorithms are developed for crack initiation and evolution in the implicit DMN analysis. Besides studies on a single material point, we apply the multiscale model to concurrent multiscale simulations for the dynamic crush of a particle-reinforced composite tube and various tests on carbon fiber reinforced polymer composites. For the latter, experimental validations on an off-axis tensile test specimen are also provided. • We propose a novel cell-division scheme for tracking scale transition inside DMN. • We derive the analytical solutions of cell division in a physics-based building block. • We enrich the network with cohesive layers to model crack initiation and evolution. • We validate DMN-based concurrent multiscale simulations with composite experiments. [ABSTRACT FROM AUTHOR]

A category of metamaterials consisting of chiral cytosolic elements assembled periodically, in which the introduction of a rotatable annular structure gives metamaterials the ability to deform in compression–shear, has been a focus of research in recent years. In this paper, a compression–shear coupling model is developed to predict the compressive deformation behaviour of chiral metamaterials. This behaviour will be analysed by coupling the rotation of the annular node and the bending characteristics of ligament beam, which are obtained as a function of the length of ligament beam and the angle of rotation at the end of the beam. The shape function of the ligament beam under large deformation is obtained based on the elliptic integral theory; the function characterises the potential relationship between key parameters such as displacement and rotation angle at any point on the ligament beam. By simulating the deformation of cells under uniaxial compression, the reasonableness of the large deformation model of the ligament beam is verified. On this basis, a chiral cell-compression mechanical model considering the ductile deformation of the annular node is established. The compression–shear deformation of two-dimensional planar chiral metamaterials and three-dimensional cylindrical-shell chiral metamaterials was predicted; the offset displacements and torsion angles agreed with the experimental and finite element simulation results with an error of less than 10%. The developed compression–shear coupling model provides a theoretical basis for the design of chiral metamaterials, which meet the need for the precise control of shapes and properties. [ABSTRACT FROM AUTHOR]

A fundamental issue in multiscale materials modeling and design is the consideration of traction–separation behavior at the interface. By enriching the deep material network (DMN) with cohesive layers, the paper presents a novel data-driven material model which enables accurate and efficient prediction of multiscale responses for heterogeneous materials with interfacial effect. In the newly invoked cohesive building block, the fitting parameters have physical meanings related to the length scale and orientation of the cohesive layer. It is shown that the enriched material network can be effectively optimized via a multi-stage training strategy, with training data generated only from linear elastic direct numerical simulation (DNS). The extrapolation capability of the method to unknown material and loading spaces is demonstrated through the debonding analysis of a unidirectional fiber-reinforced composite, where the interface behavior is governed by an irreversible softening mixed-mode cohesive law. Its predictive accuracy is validated against the nonlinear path-dependent DNS results, and the reduction in computational time is particularly significant. • New architecture of material network enriched by adaptable cohesive networks. • Physics-based building block with a length parameter to describe the size effect. • Multi-stage training strategy to reduce the number of fitting parameters at each stage. • Extrapolation of enriched DMN from linear elasticity to nonlinear failure analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] Breast cancer is the most commonly diagnosed cancer in women. Among all types, triple-negative breast cancer is particularly challenging to cure because of its high recurrence rates and invasive and metastatic capacity. Although numerous studies have explored the role of TP53 mutations in cancer, there is a dearth of research regarding the correlation between TP53 mutations and breast cancer cell proliferation. In this study, our aim was to examine the impact of TP53 mutations on the prognosis of patients with breast cancer bioinformatics techniques. To detect cell proliferation, a CCK8 assay was performed, and western blotting was used to identify the expression of p53, p38, and p-p38 proteins. Cellular mRNA sequencing was used to screen target genes of TP53 mutations, and molecular docking was performed to identify the drugs that could hinder the proliferation of breast cancer cells.The results showed that the TP53 mutation rate is higher in patients with triple-negative breast cancer than non-triple-negative breast cancer, and those with TP53 mutations tended to have a poorer prognosis than those without. Patients with R175H site mutations also had shorter survival times than those without. Cytological experiments revealed that the TP53 R175H mutation increases the rate of breast cancer cell proliferation. In conjunction with this, CORO1A was found to be a downstream target of TP53 mutations, and it was determined to promote breast cancer cell proliferation. Moreover, CORO1A overexpression resulted in the downregulation of p-p38 levels. Molecular docking studies further revealed that tea polyphenols can inhibit breast cancer proliferation by binding to p53. [ABSTRACT FROM AUTHOR]

• This study explores the feasibility of using laser generated ultrasonic wave to detect human skin tumors. • The pearson correlation coefficient method is used to evaluate the skin tumor-induced waveform variation and thus position the skin tumor accurately. • The correlation coefficient method is a feasible tool detecting and characterizing tumor in human skin. Tumor as a kind of skin cancer is a serious threat to human health, In order to diagnose and treat skin diseases, many organizations are studying methods to confirm and monitor skin diseases by detecting the characteristics of human skin. This study explores the feasibility of using laser generated ultrasonic wave to detect human skin tumors, the pearson correlation coefficient method is used to evaluate the skin tumor-induced waveform variation and thus position the skin tumor accurately. The simulation results show that the correlation coefficient method is a feasible tool detecting and characterizing tumor in human skin. [ABSTRACT FROM AUTHOR]

5-Oxo-6,8,11,14-eicosatetraenoic acid (5-oxo-ETE) is an arachidonic acid metabolite produced along with leukotrienes via the 5-lipoxygenase pathway. Metabolomics studies have shown that 5-oxo-ETE level is elevated in the serum in acute myocardial infarction (AMI). The actions of 5-oxo-ETE are mediated by the highly selective oxoeicosanoid receptor (OXE-R). Moreover, increased OXE-R content was verified in AMI patients and mice. However, the precise role of OXE-R in AMI is unclear. In the present study, we demonstrate that 5-oxo-ETE triggered myocardial injury in mice. Pathway enrichment analysis identified branched chain amino acid transaminase 1/2 (BCAT1/2) as potential mediators of this effect. Western blot and immunohistochemical analyses showed that BCAT1/BCAT2 expression was significantly reduced by AMI in vitro and in vivo, while pharmacologic inhibition of BCAT1/BCAT2 accelerated myocardial injury. Conversely, heart-specific overexpression of BCAT1/BCAT2 in mice protected against ischemic myocardial injury. Treatment with the selective OXE-R inhibitor Gue1654 alleviated coronary artery ligation-induced ischemic myocardial injury in mice and oxygen/glucose deprivation-induced injury in cardiomyocytes through activation of BCAT1, while inhibiting OXE-R suppressed protein kinase C-ε (PKC-ε)/nuclear factor κB (NF-κB) signaling and cardiomyocyte apoptosis. Overall, our study confirmed a novel target OXE-R for the treatment of AMI based on metabolomics, and targeting OXE-R may represent unrecognized therapeutic intervention for cardiovascular diseases through activation of BCAT1. [ABSTRACT FROM AUTHOR]

One goal of modeling for metal Additive Manufacturing (AM) is to predict the resultant mechanical properties from given manufacturing process parameters and intrinsic material properties, thereby reducing uncertainty in the material built. This can dramatically reduce the time and cost for the development of new products using AM. We have realized the seamless linking of models for the manufacturing process, material structure formation, and mechanical response through an integrated multi-physics modeling framework. The sequentially coupled modeling framework relies on the concept that the results from each model used in the framework are contained in space-filling volume elements using a prescribed structure. This framework is implemented to show a prediction of the decrease in fatigue life caused by insufficient fusion resulting from low laser power relative to the hatch spacing. In this demonstration, powder spreading and thermal-fluid flow models are used to predict the thermal history and void formation in a multilayer, multi-track build with different processing conditions. The results of these predictions are passed to a cellular automaton-based prediction of grain structure. Finally, the predicted grain and void structure is passed to a reduced-order micromechanics-based model to predict mechanical properties and fatigue life arising from the different processing conditions used in the process model. The simulation results from this combination of models demonstrate qualitative agreement with experimental observations from literature, showing the appealing potential of an integrated framework. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) possesses appealing potential for manipulating material compositions, structures and properties in end-use products with arbitrary shapes without the need for specialized tooling. Since the physical process is difficult to experimentally measure, numerical modeling is a powerful tool to understand the underlying physical mechanisms. This paper presents our latest work in this regard based on comprehensive material modeling of process-structure-property relationships for AM materials. The numerous influencing factors that emerge from the AM process motivate the need for novel rapid design and optimization approaches. For this, we propose data-mining as an effective solution. Such methods—used in the process-structure, structure-properties and the design phase that connects them—would allow for a design loop for AM processing and materials. We hope this article will provide a road map to enable AM fundamental understanding for the monitoring and advanced diagnostics of AM processing. [ABSTRACT FROM AUTHOR]

A method to obtain the equivalent Poisson's ratio in chemical bonds as classical beams with finite element method was proposed from experimental data. The UFF (Universal Force Field) method was employed to calculate the elastic force constants of Zr O bonds. By applying the equivalent Poisson's ratio, the mechanical properties of single-wall ZrNTs ( ZrO 2 nanotubes) were investigated by finite element analysis. The nanotubes' Young's modulus ( Y ), Poisson's ratio ( ν ) of ZrNTs as function of diameters, length and chirality have been discussed, respectively. We found that the Young's modulus of single-wall ZrNTs is calculated to be between 350 and 420 GPa. [ABSTRACT FROM AUTHOR]

• The dynamic response laws of the central diaphragm structure after the explosion were studied according to the field monitoring. • The damage characteristics of the central diaphragm near the blasting source were investigated and the four stages of the failure evolution were summarized. • The vibration response and the failure modes of the central diaphragm structure under the condition of different charge quantities and blasting distances were simulated. • The relation function containing the blasting tensile stress index was obtained through dimensional analysis. The blasting impact poses a serious threat to the safety of temporary support structures in tunnel blasting construction, such as a temporary middle wall. Thus, this study investigated and explored these problems. First, the dynamic response results of the temporary middle wall after the explosion were studied according to the field monitoring data. Subsequently, the damage characteristics of the temporary middle wall near the blasting source were investigated on field, and the development stages of the failure evolution were summarized. Furthermore, combined with the explicit finite element software LS-DYNA, the fluid–solid coupling algorithm was used to simulate the vibration response and the failure modes of the temporary middle wall under the condition of different charge weight and blasting distances. According to the field investigation, the closest blasting holes away from the temporary middle wall had a much more significant impact on its damage than other holes. The failure reason of the sprayed concrete of the supporting structure is caused by the excess high peak tension or compression stress. The failure process of supporting structure can be divided into four development stages with the reduced distance away from the blasting source: the appearance of the cracks, the cracks interconnect to form large X-shaped cracks, structural bending-shear coupling failure, and instability failure. The displacement values of the steel arch in the temporary middle wall continuously exceed 7 cm, 20 cm, 28 cm, and 34 cm, respectively, corresponding to the above four failure stages. The relation function containing the blasting tensile stress (BTS) parameter is obtained through dimensional analysis, which can be directly used to design and the damage evaluated of temporary support structures subjected to blasting at close range. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vector deformation, adjustable over a wide area under uniaxial loads, was controlled by the chiral cells' geometric parameters. The chiral cells' deformations were summed as vector additions. • Structures with various coupling deformation properties were designed, such as compression to dislocation and compression to bending. • A variable-geometry wing was designed. • Based on a genetic algorithm, an active design method was used to encode target deformations into the structures. Chiral cell structures have the potential to transform the robot design field because of the structures' ability to adopt various deformations under uniaxial load, such as twisting and a negative Poisson's ratio. The rationale that underpins this potential is asymmetric deformation that is similar to shearing; such a deformation has vector characteristics. In this paper, several cellular structures were designed that deformed the cell structure in many ways under uniaxial load, in accordance with the deformation vector addition principle of chiral cells (such as compression–dislocation, compression–bending coupling deformation, and variable wings with forward and backward sweep functions)—which were verified by finite-element analyses and experiments. With a given objective deformation, a genetic algorithm was introduced to encode structures with various chiral cells, and the feasibility of the method was verified through several examples. The research herein shows that structures with various deformation demands can be designed through reasonable assembly of chiral cells, which has potential applications to the field of variant structures design. [ABSTRACT FROM AUTHOR]

Cemented particulate composite is a kind of composite material with high strength, high energy absorption, and multifunctional characteristics, which is widely used in engineering practice. The calculation of the mechanical properties of granular composites based on theoretical methods has always been a topic of discussion. A micromechanical model with a three-dimensional rigid beam-spring network (3D-RBSN) is proposed here. The stiffness matrix of the model was calculated theoretically. The model was applied to the analysis of the mechanical properties of composites material with glass beads and epoxy resin. The results indicate that the 3D-RBSN model can effectively predict the mechanical properties of composite materials, such as Young's modulus and Poisson's ratio. Furthermore, the damage evolution process of cemented particulate composite with initial defects was analyzed based on the 3D-RBSN model. [ABSTRACT FROM AUTHOR]

The high-pressure behaviour of the ternary sulphides, RbXS 2 (X = Y and La), has been investigated by using first-principle calculations based on density functional theory. Upon applying hydrostatic pressure, the unit-cell parameters (a, c) decrease with different rates, indicating an anisotropic axial compression. The most of RbYS 2 and RbLaS 2 crystals compressibility comes from Rb+1–S−2 bonds. Elastic constants and their dependence on pressure and related mechanical properties have been reported and analysed. From Pugh's criterion, RbYS 2 and RbLaS 2 turn from brittle to ductile material for applied pressures beyond 3.1 GPa and 2.9 GPa, respectively. Stability criteria show that RbYS 2 and RbLaS 2 are not mechanically stable in α-NaFeO 2 crystal structure above 20.63 GPa and 16.24 GPa, respectively. Both RbYS 2 and RbLaS 2 have indirect band gap, which decreases with increasing pressure. However, no indirect-direct band gap transition is observed for both materials. Finally, the calculated optical spectrum of both compounds exhibits an anisotropy and a broadening at high pressures. • Structural, elastic, electronic and optical properties of RbYS 2 and RbLaS 2 , have been investigated at zero and high pressure. • Both RbYS 2 and RbLaS 2 brittle at zero pressure and ductile at high pressure. • RbYS 2 more resistance to the pressure than RbLaS 2 in α-NaFeO 2 structure. • RbYS 2 and RbLaS 2 become mechanically unstable at pressures beyond 20.63 GPa and 16.24 GPa, respectively. [ABSTRACT FROM AUTHOR]

Metoprolol (Met) is widely applied in the treatment of myocardial infarction and coronary heart disease in clinic. However, the metabolic network in vivo affected by Met manipulation is still unclear and it's therapeutic molecular mechanisms were remained to be furthered elucidated except β1 adrenergic receptor. Myocardial infarction (MI) was induced by permanent CAL for 24 h in ICR mice. Myocardial infarct size, biochemical indicators such as creatine kinase (CK), lactate dehydrogenase (LDH), C-reactive Protein (CRP), tumor necrosis factor-α (TNF-α) and cardiac troponin I(cTn-I), cardiac function and myocardial pathological changes were detected to ensure the improvement of Met on MI. Subsequently, the significantly changed endogenous metabolites and the network in both serum and urine were screened and constructed through metabolomics by using HPLC-Q-TOF/MS. Finally, the potential regulatory enzymes that could be the possible new therapeutic targets of Met were selected and validated by western blotting and immunohistochemistry based on the screened differential metabolites and the enrichment analysis. Met effectively reduced the infarct size of myocardial infarction mice, improved the biochemical indicators, and ameliorated the cardiac function and pathological conditions. Our study further found that Met could regulate the pathways of glycine, serine and threonine metabolism, cysteine and methionine metabolism, purine and pyrimidine metabolism under the pathological conditions of MI. Moreover, several regulatory enzymes involved GATM, CSE and NT5E were demonstrated to be regulated by Met. This study constructed the regulatory metabolic network map of Met, elucidated the endogenous metabolic pathway regulated by Met, and validated the new potential therapeutic targets of Met in MI, which might provide a further reference for the clinical application of Met. [ABSTRACT FROM AUTHOR]