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Hepatocellular carcinoma (HCC) was one of the most prevalent life-threatening cancers. Metastasis is the leading cause of cancer-related death in HCC. MiRNAs play essential roles in cancer metastasis.Expression of miR-652-3p in HCC was assessed. Function experiments of miR-652-3p and trinucleotide repeat-containing gene 6A protein (TNRC6A) were performed both in vitro and in vivo. mRNA sequencing, PCR, and western blot were performed to verify the target genes and pathway of miR-652-3p. The lung metastasis and xenograft cancer model in nude mice was established to investigate the effects of the miR-652-3p and TRNC6A on tumor metastasis in vivo. The relationship between the expression of the miR-652-3p, TNRC6A and the prognosis of HCC patients was analyzed.Upregulated miR-652-3p was found in the tumor tissues of HCC, especially in metastatic HCC patients. Overexpression of miR-652-3p promoted and knockdown of miR-652-3p suppressed HCC metastasis both in vitro and in vivo. MiR-652-3p promoted HCC metastasis via regulating the EMT pathway. TNRC6A was identified as a direct target of miR-652-3p, and the knockdown of TNRC6A restored repressed EMT and HCC metastasis caused by the inhibition of miR-652-3p. Clinical results revealed that high expression of miR-652-3p and low expression of TNRC6A were positively correlated to shortened overall survival and disease-free survival in HCC patients.MiR-652-3p promotes EMT and HCC metastasis by inhibiting TNRC6A expression in HCC. MiR-652-3p and TNRC6A may serve as potential biomarkers to predict prognosis in HCC patients with metastasis.

Spleen micro-perfusion level can be evaluated by DDVD (diffusion derived vessel density): DDVD (unit: au/pixel) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the spleen signal when b is 0 or 2 (s/mm

Aim: Liver vessel density can be evaluated by DDVD (diffusion derived vessel density): DDVD(b0b1) = Sb0/ROIarea0-Sb1/ROIarea1, where Sb0 and Sb1 refer to the liver signal when b is 0 or 1 (s/mm 2); ROIarea0 and ROIarea1 refer to the region-of-interest on b= 0 or 1 images. Sb1 and ROIarea1 may be replaced by Sb25 and ROIarea25 or Sb50 and ROIarea50 (i.e., by images of b=25 or 50 s/mm 2). With rat biliary duct ligation (BDL) model, this study assessed the usefulness of liver DDVD computed from a simplified IVIM imaging protocol. Materials and Methods: Male Sprague-Dawley rats were used, and 1, 2, 3, 4 weeks post BDL had rat number of 5, 5, 5,3 respectively. Partial recanalization (n=12) was performed with rats had BDL for 7 days and MRI was done mean 14 days after recanalization. Liver diffusion MR images were acquired using with a 3.0T magnet with a b-value distribution of 0, 30, 50, 75, 100, 150, 300, 700, 1000 s/mm 2. DDVDmean (control rats n=6) was the mean of DDVD(b0b25) and DDVD(b0b50). IVIM fitting started from b = 0 with segmented fitting and a threshold b of 50 s/mm 2 (n=5 for control rats). Three 3-D space was constructed using PF, D slow , and D fast ; PF, D slow , and DDVD mean ; or PF, D fast , and DDVD mean. Results: BDL week 1, 2, 3, 4 rats and partial recanalization rats had mean liver fibrosis METAVIR score of 1.5, 2.4, 3.25, 3.7 and 2.42 respectively. The control rats and BDL rats (n=18) had a liver DDVD mean of 84.0±26.2 and 44.7±14.4 au/pixel (p

New finite elastoplastic J2-flow equations are proposed for the purpose of explicitly simulating continuous transitions from plastic to pseudo-elastic effects of SMAs under multiple loading-unloading cycles. Novelties of such models are as follows: (i) New hardening effects are incorporated by introducing interplay between the changing of the yield surface radius and the moving of the yield surface center; (ii) extensive experimental data may be accurately simulated for any given number of loading-unloading cycles; (iii) the complicated task for simulating extensive data may be accomplished by independently fitting certain single-variable functions; and (iv) such single-variable functions may be presented in explicit forms for the purpose of automatically, accurately fitting extensive data, thus bypassing uncertainties and undue complexities usually involved in iteratively solving a coupled system of nonlinear constitutive rate equations toward identifying numerous unknown parameters. Numerical examples for model validation are presented and in good agreement with stress-strain data up to 20 loading-unloading cycles given in literature.

This study was carried out to study the effects of selenylation on physicochemical and biological properties of polysaccharide (SPP) extracted from Sargassum pallidum. The selenized derivative of SPP (Se-SPP) with the selenium content of 2419 μg/g was synthesized by sodium selenite/dilute nitric acid method. Physicochemical characterization indicated that selenylation modification resulted in some changes in chemical composition, monosaccharide composition, molecular weight and surface morphology of polysaccharides. FT-IR spectroscopy showed that a new absorption peak appeared at 675 cm−1 in Se-SPP probably due to the substitution of selenyl groups. Bioactivity assay showed that Se-SPP exhibited higher scavenging radical activities and ferrous ion chelating activities than native SPP. Compared with SPP and acarbose, Se-SPP showed more significantly inhibitory effect on α-glucosidase activity in a noncompetitive inhibition type. The IC50 values of SPP, Se-SPP and acarbose were determined as 1.579, 0.896 and 2.742 mg/mL, respectively. These results suggest that Se-SPP can be used to develop a new selenium-complementary ingredient in functional foods.

The adsorption and lubrication of salivary pellicle is closely associated with saliva secretion, while the relationship between saliva flow rate and salivary pellicle lubrication is still unclear. In this study, the adsorption kinetics and lubrication behavior of salivary pellicle on human tooth enamel surface has been investigated in vitro at different salivary flow rates using quartz crystal microbalance with dissipation (QCM-D) and nano-indentation/scratch technique. Human whole saliva was collected from healthy donors. Regardless of flow rates, salivary protein adsorption on the enamel surface experiences initially rapid saturated adsorption to form a stable multi-layer structure of salivary pellicle and then slow oversaturated adsorption of some proteins with weak bonding. With the flow rate increasing, the saturated adsorption process is expedited, but the oversaturated adsorption is decelerated. Hence, the pellicle has decreased roughness, thickness, viscoelasticity, and normal load-bearing capability at high flow rates than at low flow rates. Its anti-wear properties are slightly weakened but its friction-reducing properties are enhanced. In sum, there exists a coupling effect of flow rate and adsorption time on the adsorption of salivary proteins. Saliva lubrication mainly depends on the saturated adsorption of salivary proteins on enamel surface.

The relationship between the microstructure and tribological behavior of mammalian tooth enamel has not been fully understood. In this paper, the microstructure, mechanical properties, and tribological behavior of canine (carnivore) and bovine (herbivore) enamel are studied using scanning electronic microscopy and nano-indentation/scratch technique, aiming to reveal the contribution of enamel microstructure to its mechanical and tribological properties. Canine enamel has a microstructure of hard keyhole-like rods embedded in soft inter-rod enamel, and its surface exhibits high resistance against both micro-crack initiation and crack-induced delamination during friction and wear process. Bovine enamel with the microstructure consisting of the hydroxyapatite (HAP) nano-fibers in decussation has higher surface hardness and better capabilities of resisting wear and encumbering crack propagation, as compared to canine enamel. In sum, the soft inter-rod enamel in the canine enamel contributes to high load tolerance and then protects enamel surface from brittle damage, while the staggered arrangement of HAP nano-fibers benefits hard bovine enamel in crack propagation resistance and then help resist wear and fatigue. The findings suggest that there exists a self-adaptation in enamel microstructure and tribological performance of mammals with their feeding habits, which will promote and assist the bionic design of high-performance materials.

Background: Breast cancer is a commonplace carcinoma in females. Recurrence and metastasis are the main problems affecting the survival rate of patients. The fundamental reason is the lack of understanding of the mechanism of breast cancer metastasis. This study aims to deliberate on the efficaciousness of Nuclear protein 1 (NUPR1)-mediated autophagy on breast cancer metastasis.Methods: The proliferation, migration and invasion ability of breast cancer cells were appraised by CCK-8, wound healing, and colony formation, as well as transwell assay. The relationship between NUPR1 and Translocation factor E3 (TFE3) was appraised by qPCR, western blot and ChIP. Migration-invasion-related proteins and autophagy-related proteins were appraised by western blot. The effects of NUPR1 on malignancy formation and metastasis were studied in vivo.Results: NUPR1 was upregulated in breast cancer cells and tissues. NUPR1 knockdown restrained the proliferation, migration and invasion of ZR-75-30 cells. Moreover, NUPR1 knockdown restrained malignancy formation and metastasis in vivo. Mechanically, NUPR1 promoted autophagy through activation of TFE3 transcription, thereby regulating the process of breast cancer metastasis.Conclusion: This paper elucidates the molecular mechanism of NUPR1 promoting breast cancer metastasis by activating autophagy through TFE3 signaling pathway, which provided biological basis for intervention of blocking distant metastasis.

Human tooth enamel is composed of enamel rods and surrounding inter-rod enamel. As the fundamental block of enamel, hydroxyapatite (HAP) nanofibers are mostly longitudinally aligned in the rods but inclined in the inter-rod enamel. The surface hardening of enamel by occlusal loading is reportedly a result of hydroxyapatite nanofiber fragmentation and rearrangement and plays an important role in the anti-wear performance of enamel, but little is known about the effect of HAP nanofiber orientation on enamel surface hardening. In this study, the occlusal loading-induced surface hardening behaviors of enamel at different zones (rod and inter-rod) and different orientations (occlusal and axial) were investigated in vitro using impact treatment and a nanoindentation technique, aiming to reveal the effect of nanofiber orientation on enamel surface hardening. It was found that surface hardening by occlusal loading occurs in the rod and inter-rod areas, but the former shows a greater hardening degree than the latter, leading to an increase in the mechanical heterogeneity of enamel surface. Under the same loading condition, the HAP nanofibers in the inter-rod enamel are more likely to tilt into transverse nanofibers than those in the rods. Compared with longitudinal nanofibers, transverse nanofibers fragment into more transverse crystal particles, but the transverse particles impair the compactness of the hardening layer and decrease its hardening degree. In sum, inherent non-uniform nanofiber orientation endows the enamel with the ability to undergo heterogeneous surface hardening upon occlusal loading, which is critical for providing and maintaining its surface mechanical heterogeneity. These findings extend the understanding of the relationship between microstructure and mechanical properties of dental enamel and provide valuable insights into the bionic design of engineering materials.

In this study, the ancient ivories from Jinsha ruins site have been consolidated through impregnation in aqueous hydroxyapatite (HAP) colloid prepared by the hydrothermal method. Microscopic examinations and nano-indentation/scratch tests were conducted to characterize the morphology and mechanical properties of ivory's surface. After the consolidation treatment, a compact HAP nano-particle layer is formed on the original ivory surface, and the loose and porous surface is repaired. The hardness, elastic modulus, and anti-scratch performance of the repaired ivory surface are significantly improved. Also, the consolidated surface acts as a protective layer, which isolates the ivory from the ambient environment and avoids further deterioration. The consolidation has compatible aesthetics, and does not introduce extraneous substances into the ivory. The results have shown nano-HAP is a promising consolidant for ancient ivories.

Graphical abstract, Loop-mediated isothermal amplification (LAMP) is widely used in detection of pathogenic microorganisms including SARS-CoV-2. However, the performance of LAMP assay needs further exploration in the emerging SARS-CoV-2 variants test. Here, we design serials of primers and select an optimal set for LAMP-based on SARS-CoV-2 N gene for a robust and visual assay in SARS-CoV-2 diagnosis. The limit of detectable template reaches 10 copies of N gene per 25 μL reaction at isothermal 58℃ within 40 min. Importantly, the primers for LAMP assay locate at 12 to 213 nt of N gene, a highly conservative region, which serves as a compatible test in emerging SARS-CoV-2 variants. Comparison to a commercial qPCR assay, this LAMP assay exerts the high viability in diagnosis of 41 clinical samples. Our study optimizes an advantageous LAMP assay for colorimetric detection of SARS-CoV-2 and emerging variants, which is hopeful to be a promising test in COVID-19 surveillance.

Little is so far known about genetic diversity and phylogeographic structure of native cattle breeds from Yunnan in Southwestern China, a frontier zone that connects China with South and Southeast Asia. Herein we analyzed a 910-bp fragment of mitochondrial DNA (mtDNA) D-loop region sequences of 280 individuals from 6 Yunnan native cattle breeds, of which 251 sequences were newly determined. There were 93 variable sites that defined 117 haplotypes among all sequences. Phylogenetic analysis of all haplotypes revealed that Yunnan native cattle had two distinct mtDNA lineages - taurine and zebu. The taurine sequences fell into four haplogroups T1a, T2, T3 and T4, whereas the zebu sequences grouped into two haplogroups I1 and I2. In addition, our results revealed patterns of gradient changes in frequencies of taurine and zebu mtDNA lineages across different geographic regions of Yunnan. Furthermore, three kinds of methods (haplotype network, analysis of molecular variance, and Mantel tests) consistently showed no significant phylogeographic structuring among Yunnan native cattle breeds. This might be attributed to historical strong gene flow and genetic admixture among native cattle breeds in different geographic regions.

Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations . The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.

Due to the complexity of the oral environment, understanding the relationship between the enamel microstructure and corrosion behavior of mammalian teeth is important for the development and bionic design of novel dental materials with corrosion resistance. In this paper, enamel microstructure and its influence on corrosion and tribocorrosion behavior of bovine and human molars are studied using focused ion beam & scanning electronic microscopy (FIB-SEM), transmission electron microscope (TEM), nanoindentation, and wear testing. It was found that the regions with transversal HAP nanocrystallites have better corrosion resistance than those with longitudinal crystallites. Apart from the chemical composition, the microstructure of enamel also involves in its surface corrosion. HAP nanocrystallite orientation changes the acid permeability into the enamel surface. Transversal nanocrystallites result in zigzagged acid permeation channels to increase the difficulty of acid penetration and then improve the corrosion resistance of enamel surface. The higher the fraction of transversal nanocrystallites on a surface, the better is the corrosion resistance. These findings extend our understanding of the corrosion/wear mechanisms of dental enamel with different microstructures.

Innovative elastoplastic J 2 − flow models with nonlinear combined hardening are proposed for the purpose of simulating multi-axial thermo-coupled fatigue failure for metals and alloys, in a direct sense without involving any usual damage-like variables and any ad hoc failure criteria. As contrasted with usual models, the yield condition and the loading-unloading conditions need not be imposed as extrinsic coercive conditions but may be automatically incorporated into a free, smooth flow rule in a more realistic sense. Novel results in three respects are available directly from model predictions, namely, (i) complex features of thermo-coupled fatigue failure may be automatically represented by simple asymptotic properties of the hardening functions introduced, (ii) critical failure states may be derived with a criterion in unified form, and, in particular, (iii) direct procedures may be established for determining fatigue lives under either stress cycles or thermal cycles. Simulation results in the simplest cases of the proposed model are in good agreement with experimental data.

A hyperelastic model is proposed to characterize large elastic deformation behaviors of soft solids. Novelties in four respects are incorporated in this model, namely, (i) general compressible deformations may be treated and, accordingly, the usually-assumed incompressibility constraint and associated issues may be bypassed; (ii) applicability for all kinds of material samples may be automatically ensured, in the sense that any given test data for three deformation modes may be exactly fitted, including uniaxial, equi-biaxial and plane-strain extension; (iii) model parameters of direct physical meanings may be provided to characterize both strain-stiffening and softening effects; and (iv) breaking behavior may be simulated. Numerical results for model validation are in good agreement with Treloar’s classic data for rubbers and with extensive data for various kinds of polymer gels.

Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited observations. Existing works mainly focus on using only one observation data source, e.g., velocity, wall pressure, lift or drag force, to reconstruct the flow. In practical applications observations are disparate data sources that often vary in dimension and quality. Simultaneously incorporating these disparate data is worth investigation to improve the flow reconstruction. In this work, we investigate the disparate data assimilation with ensemble methods to enhance the reconstruction of turbulent mean flows. Specifically, a regularized ensemble Kalman method is employed to incorporate the observation of velocity and different sources of wall quantities (e.g., wall shear stress, wall pressure distribution, lift and drag force). Three numerical examples are used to demonstrate the capability of the proposed framework for assimilating disparate observation data. The first two cases, i.e., a one-dimensional planar channel flow and a two-dimensional transitional flow over plate, are used to incorporate both the sparse velocity and wall friction. In the third case of the flow over periodic hills, the wall pressure distribution and the lift and drag force are regarded as observation in addition to velocity, to recover the flow fields. The results demonstrate the merits of incorporating various disparate data sources to improve the accuracy of the flow-field estimation. The ensemble-based method can assimilate disparate data non-intrusively and robustly without requiring significant changes to the model simulation codes. The method demonstrated here opens up possibilities for assimilating realistic experimental data, which are often disparate.

The emerging push of the differentiable programming paradigm in scientific computing is conducive to training deep learning turbulence models using indirect observations. This paper demonstrates the viability of this approach and presents an end-to-end differentiable framework for training deep neural networks to learn eddy viscosity models from indirect observations derived from the velocity and pressure fields. The framework consists of a Reynolds-averaged Navier–Stokes (RANS) solver and a neural-network-represented turbulence model, each accompanied by its derivative computations. For computing the sensitivities of the indirect observations to the Reynolds stress field, we use the continuous adjoint equations for the RANS equations, while the gradient of the neural network is obtained via its built-in automatic differentiation capability. We demonstrate the ability of this approach to learn the true underlying turbulence closure when one exists by training models using synthetic velocity data from linear and nonlinear closures. We also train a linear eddy viscosity model using synthetic velocity measurements from direct numerical simulations of the Navier–Stokes equations for which no true underlying linear closure exists. The trained deep-neural-network turbulence model showed predictive capability on similar flows.

The transcriptional coactivator with PDZ binding motif (TAZ) is reported as one of the nuclear effectors of Hippo-related pathways. TAZ is found overexpressed in many primary tumors and could regulate many biological processes. However, little is known about the role of TAZ in Intrahepatic Cholangiocarcinoma (ICC). In this study, we found that TAZ is expressed more in ICC tissues than in peritumoral tissue, and a robust expression of TAZ is correlated with a lower overall survival rate of ICC patients after hepatectomy. TAZ knockdown results in an increase in cell apoptosis, a promotion of cell-cycle arrest and a decrease in tumor size and weight in vivo through an increased expression of p53. Vitamin D3 can also inhibit cell proliferation by promoting p53 expression in ICC cells. A reduction in TAZ can also enhance the sensitivity of tumor cells to vitamin D by regulating the p53/CYP24A1 pathway. In conclusion, TAZ is associated with the proliferation and drug-resistance of ICC cells, and could be a novel therapeutic target for the treatment of ICC.

A new elastoplastic J 2 − flow model with nonlinear combined hardening is proposed for the purpose of achieving an explicit, comprehensive simulation of pseudo-elastic SMAs over the whole range of strain up to failure. To this goal, a new, explicit methodology based on any given uniaxial data is introduced to obtain multi-axial expressions for the constitutive quantities incorporated in this model, in such a unified sense that all three successive deformation stages may be simultaneously represented, including a pseudo-elastic stage with perfect strain recovery, a hardening stage with partial strain recovery, and a softening stage up to failure. As such, any given shapes of uniaxial stress–strain curves from both loading and unloading in these three stages may be automatically reproduced from the proposed model, without involving usual complicated numerical procedures in treating nonlinear rate constitutive equations with a number of switching conditions. Numerical examples with test data are supplied to simulate a complete process including all three stages.

In this study, a resorufin derivative RTP-1, which is a novel fluorescent “turn-on” probe for sensitive detection of hydrazine within 30 min, is designed and synthesized. The selective deprotection of the ester group of the probe by hydrazine led to a prominent enhancement of fluorescent intensity, as well as a remarkable color change from colorless to pink, which could be distinguished by naked eye. The fluorescence enhancement showed decent linear relationship with hydrazine concentration ranging from 0 to 50 μmol/L, with a detection limit of 0.84 μmol/L. The specificity of RTP-1 for hydrazine to a number of metal ions, anions and amines is satisfactory. The sensing mechanism of RTP-1 and hydrazine was evaluated by HPLC, ESI mass spectrometry and density functional theory (DFT). Moreover, we have utilized this fluorescent probe for imaging hydrazine in living cells, and the fluorescence was clearly observed when the cells were incubated with hydrazine (100 μmol/L) for 30 min.

Bayesian uncertainty quantification (UQ) is of interest to industry and academia as it provides a framework for quantifying and reducing the uncertainty in computational models by incorporating available data. For systems with very high computational costs, for instance, the computational fluid dynamics (CFD) problem, the conventional, exact Bayesian approach such as Markov chain Monte Carlo is intractable. To this end, the ensemble-based Bayesian methods have been used for CFD applications. However, their applicability for UQ has not been fully analyzed and understood thus far. Here, we evaluate the performance of three widely used iterative ensemble-based data assimilation methods, namely ensemble Kalman filter, ensemble randomized maximum likelihood method, and ensemble Kalman filter with multiple data assimilation for UQ problems. We present the derivations of the three ensemble methods from an optimization viewpoint. Further, a scalar case is used to demonstrate the performance of the three different approaches with emphasis on the effects of small ensemble sizes. Finally, we assess the three ensemble methods for quantifying uncertainties in steady-state CFD problems involving turbulent mean flows. Specifically, the Reynolds averaged Navier–Stokes (RANS) equation is considered the forward model, and the uncertainties in the propagated velocity are quantified and reduced by incorporating observation data. The results show that the ensemble methods cannot accurately capture the true posterior distribution, but they can provide a good estimation of the uncertainties even when very limited ensemble sizes are used. Based on the overall performance and efficiency from the comparison, the ensemble randomized maximum likelihood method is identified as the best choice of approximate Bayesian UQ approach among the three ensemble methods evaluated here.

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Although the advancement of high performance computing has made resolving small-scale physics possible, such simulations are still very expensive. Therefore, reliable and accurate closure models for the unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Benard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate.

In contrast with the widely known elastoplastic behavior of hard solids with very small elastic deformations, we study elastoplastic behavior of soft solids dominated by very large elastic deformations. Toward this goal, thermo-coupled rate-independent and rate-dependent elastoplastic J 2 -flow models with evolving rubberlike elasticity are established for the first time in a sense of identically meeting the Clausius–Duhem inequality. Novel results are presented for coupling effects in three respects: (i) how finite strain elastic behavior may evolve with development of plastic flow; (ii) how plastic flow may be induced in a process of pure heating; and (iii) how strain rate effects may be characterized to ensure smooth transitions to the rate-independent case. It is demonstrated that complex inelastic deformation features observed in soft solids such as elastomers, including the Mullins effect, the permanent set, the induced anisotropy, the thermal recovery and the rate effect etc., may be derived as direct, natural consequences of the proposed model. In particular, explicit expressions for the constitutive functions incorporated are derivable from the uniaxial data for the purpose of achieving an explicit, exact simulation of the foregoing inelastic features, thus bypassing usual complexities both in choosing suitable forms of constitutive functions and in identifying unknown parameters in an approximate sense.

In this work, a coarse-graining method previously proposed by the authors in a companion paper based on solving diffusion equations is applied to CFD–DEM simulations, where coarse graining is used to obtain solid volume fraction, particle phase velocity, and fluid–particle interaction forces. By examining the conservation requirements, the variables to solve diffusion equations for in CFD–DEM simulations are identified. The algorithm is then implemented into a CFD–DEM solver based on OpenFOAM and LAMMPS, the former being a general-purpose, three-dimensional CFD solver based on unstructured meshes. Numerical simulations are performed for a fluidized bed by using the CFD–DEM solver with the diffusion-based coarse-graining algorithm. Converged results are obtained on successively refined meshes, even for meshes with cell sizes comparable to or smaller than the particle diameter. This is a critical advantage of the proposed method over many existing coarse-graining methods, and would be particularly valuable when small cells are required in part of the CFD mesh to resolve certain flow features such as boundary layers in wall bounded flows and shear layers in jets and wakes. Moreover, we demonstrate that the overhead computational costs incurred by the proposed coarse-graining procedure are a small portion of the total computational costs in typical CFD–DEM simulations as long as the number of particles per cell is reasonably large, although admittedly the computational overhead of the coarse-graining procedure often exceeds that of the CFD solver. Other advantages of the diffusion-based algorithm include more robust and physically realistic results, flexibility and easy implementation in almost any CFD solvers, and clear physical interpretation of the computational parameter needed in the algorithm. In summary, the diffusion-based method is a theoretically elegant and practically viable option for practical CFD–DEM simulations.

Members of the Toll-like receptor (TLR) gene family have central roles in initiating innate immune responses after the detection of invading pathogens. In this study, we evaluated the genetic diversity of TLR3 and TLR8 genes among five Chinese native cattle breeds - Yunnan humped (DHMS), Wenshan (WSGN), Dianzhong (CXSB), Zhaotong (ZTGN), and Dengchuan (DLEY) cattle in Yunnan, southwest China. Our results showed that both genes had lower genetic diversity and haplotype diversity in DHMS cattle compared to those of other breeds. Additionally, population genetic analyses of TLR8 suggested that WSGN, DLEY, and ZTLD cattle had experienced a historic population contraction. Analyses of both TLR3 and TLR8 indicated significant negative Fay and Wu's H values (-4.19, p = 0.043 and -6.81, p = 0.004) for Yunnan humped cattle, suggesting recent adaptive evolution. In particular, the distribution of non-synonymous mutations differed among the five Chinese native cattle breeds in Yunnan, implying substantial genetic differences. Furthermore, the predicted functional effects of amino acid substitutions showed that the frequency of damage was lower in DHMS compared to the other four breeds. Thus, our findings suggest that the genetic diversity of TLR3 and TLR8 varied among the five cattle breeds studied, providing important genetic insights into disease resistance in these breeds.

Rate-independent finite elastoplastic equations with thermo-coupled effects are proposed to bypass the yield condition and loading–unloading conditions. These new equations are shown to be more realistic and of much simpler structure than classical equations. In a sense of thermodynamic consistency the strength property of elastoplastic solids is then studied from the standpoint of stress-bearing capacity. It is shown that asymptotic loss of the strength may be derived directly from elastoplastic equations, thus leading to the finding of a noticeable phenomenon, namely, asymptotic vanishing of the stress concurrent with developing elastoplastic flow. It is indicated that this finding may suggest a natural constitutive characterization of fatigue, fracture and failure as certain limiting cases of elastoplastic behavior. Simple models with asymptotic loss of the strength are constructed as examples of potential practical applications.

Bcl2-associated athanogene 3 (BAG3) protein is a co-chaperone of heat-shock protein (Hsp) 70 and may regulate major physiological and pathophysiological processes. However, few reports have examined the role of BAG3 in human hepatocellular carcinoma (HCC). In this study, we show that BAG3 regulates epithelial-mesenchymal transition (EMT) and angiogenesis in HCC. BAG3 was overexpressed in HCC tissues and cell lines. BAG3 knockdown resulted in reduction in migration and invasion of HCC cells, which was linked to reversion of EMT by increasing E-cadherin expression and decreasing N-cadherin, vimentin and slug expression, as well as suppressing matrix metalloproteinase 2 (MMP-2) expression. In a xenograft tumorigenicity model, BAG3 knockdown effectively inhibited tumor growth and metastasis through reduction in CD34 and VEGF expression and reversal of the EMT pathway. In conclusion, BAG3 is associated with the invasiveness and angiogenesis in HCC, and the BAG3 gene may be a novel therapeutic approach against HCC.