Non-small cell lung cancer (NSCLC) accounts for about 80–85% of total lung cancer cases. Identifying the molecular mechanisms of anti-tumor drugs is essential for improving therapeutic effects. ...Herein, we aim to investigate the role of thalidomide in the tumorigenicity of NSCLC.
The A549 xenograft nude mouse model was established to explore therapeutic effects of thalidomide. The expression of FGD5-AS1 was evaluated in carcinomatous and paracarcinomatous tissues from NSCLC patients as well as NSCLC cell lines. CCK-8 assay was performed to assess cell viability. The invasive capacity was examined using transwell assay. The tube formation assay was applied to determine cell angiogenesis. Flow cytometry was subjected to validate CD8+ T cell activity. The FGD5-AS1/miR-454–3p/ZEB1 regulatory network was analyzed using luciferase reporter, RIP and ChIP assays.
Thalidomide reduced tumor growth and angiogenesis and increased CD8+ T cell ratio in a mouse model. Enhanced expression of FGD5-AS1 was positively correlated with the poor survival of NSCLC patients. Knockdown of FGD5-AS1 notably suppressed the proliferation, invasion and angiogenesis of cancer cells as well as the apoptosis of CD8+ T cells. Thalidomide targeted FGD5-AS1 to exert its anti-tumor activity in NSCLC. FGD5-AS1 acted as a sponge of miR-454–3p to upregulate ZEB1, thus increasing the expression of PD-L1 and VEGFA. Simultaneous overexpression of FGD5-AS1 and silencing of miR-454–3p reversed thalidomide-mediated anti-tumor effects in NSCLC.
Thalidomide inhibits NSCLC angiogenesis and immune evasion via FGD5-AS1/miR-454–3p/ZEB1 axis-mediated regulation of VEGFA expression and PD-1/PD-L1 checkpoint.
•Thalidomide restrains angiogenesis and immune evasion of NSCLC in vivo.•Thalidomide regulated angiogenesis and the activity of CD8+ T cells via FGD5-AS1.•FGD5-AS1 serves as a sponge of miR-454-3p.•FGD5-AS up-regulates ZEB1 via targeting miR-454-3p to activate PD-L1 and VEGFA.•Thalidomide represses NSCLC progression through FGD5-AS1/miR-454-3p/ZEB1 axis.
Hydrogen peroxide (H2O2) is an environment‐friendly and efficient oxidant with a wide range of applications in different industries. Recently, the production of hydrogen peroxide through direct ...electrosynthesis has attracted widespread research attention, and has emerged as the most promising method to replace the traditional energy‐intensive multi‐step anthraquinone process. In ongoing efforts to achieve highly efficient large‐scale electrosynthesis of H2O2, carbon‐based materials have been developed as 2e− oxygen reduction reaction catalysts, with the benefits of low cost, abundant availability, and optimal performance. This review comprehensively introduces the strategies for optimizing carbon‐based materials toward H2O2 production, and the latest advances in carbon‐based hybrid catalysts. The active sites of the carbon‐based materials and the influence of coordination heteroatom doping on the selectivity of H2O2 are extensively analyzed. In particular, the appropriate design of functional groups and understanding the effect of the electrolyte pH are expected to further improve the selective efficiency of producing H2O2 via the oxygen reduction reaction. Methods for improving catalytic activity by interface engineering and reaction kinetics are summarized. Finally, the challenges carbon‐based catalysts face before they can be employed for commercial‐scale H2O2 production are identified, and prospects for designing novel electrochemical reactors are proposed.
The latest advances in carbon‐based hybrid catalysts toward hydrogen peroxide (H2O2) production are reviewed. In particular, the design of functional groups and the dependence of electrolyte pH play important roles to further improve the selectivity of H2O2 production via the oxygen reduction reaction.
Residual stress in selective laser melting (SLM) is one of the key challenges in terms of precision control, success rate and the performance of deposited components. Ti6Al4V belongs to α+β titanium ...alloy, the residual stress of selective laser melted (SLMed) Ti6Al4V component maybe affected by solid-state phase transformation result from complex thermal history of SLM. In the present study, effect of solid-state phase transformation on residual stress of SLM Ti6Al4V was investigated. A coupled modeling method of thermo-metallurgical-mechanical considering solid-state phase transition is provided and validated by microstructure observation and residual stress measurements. Then the solid-state phase transformation strain was neglected in the validated model, the computed residual stress is used to investigate the effect of solid-state phase transformation strain on residual stress by comparing with experimental measurements. In addition, the influence of the difference in yield strength and thermal expansion coefficient between the original phase and the transition phase on the residual stress is discussed. It has been found that the residual stress of SLMed Ti6Al4V is related to the direction, the longitudinal residual stress is about twice transverse, and they are all tensile stresses. The microstructure and residual stress predicted by the simulation are in good agreement with the experimental measurements. The microstructure of SLM Ti6Al4V is mainly composed of martensite α', and the average error between the predicted longitudinal stress and measurement is 2.1%. The solid-state phase transformation has a stress relaxation effect during the SLM Ti6Al4V, predicted longitudinal and transverse residual stress exceeds the experimental measurement by up to 80.7% and 53.9%, when neglecting the solid-state phase transformation strain. The influence of the solid-state phase transformation of SLM Ti6Al4V on the residual stress is mainly determined by the volume change between the solid-state phases.
As an emerging material, nanomaterials have attracted extensive attention due to their small size, surface effect and quantum tunneling effect, as well as potential applications in traditional ...materials, medical devices, electronic devices, coatings and other industries. Herein, the influence of nanoparticle selection, production process, grain size, and grain boundary structures on the mechanical properties of nanomaterials is introduced. The current research progress and application range of nano-materials are presented. The unique properties of nano-materials make them superior over traditional materials. Therefore, nanomaterials will have a broader application prospect in the future. Research on nanomaterials is significant for the development and application of materials science.
•Enable CNN-based physics-informed deep learning for PDEs on irregular domain.•The proposed network can be trained without any labeled data.•Boundary conditions are strictly encoded in a hard ...manner.•Investigated complex parametric PDEs, e.g., Naiver-Stokes with varying geometries.•Shows improvements of efficiency and accuracy over FC-NN formulations.
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural network (CNN) has attracted increasing attention in the scientific machine learning community, since the parameter-sharing feature in CNN enables efficient learning for problems with large-scale spatiotemporal fields. However, one of the biggest challenges is that CNN only can handle regular geometries with image-like format (i.e., rectangular domains with uniform grids). In this paper, we propose a novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data. In order to leverage powerful classic CNN backbones, elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of steady-state PDEs on irregular domains, including heat equations, Navier-Stokes equations, and Poisson equations with parameterized boundary conditions, varying geometries, and spatially-varying source fields. Moreover, the proposed method has also been compared against the state-of-the-art PINN with fully-connected neural network (FC-NN) formulation. The numerical results demonstrate the effectiveness of the proposed approach and exhibit notable superiority over the FC-NN based PINN in terms of efficiency and accuracy.
Catalysts are at the heart of the hydrogen evolution reaction (HER) for the production of pure and clean hydrogen. For practical applications, the scalable synthesis of efficient HER catalysts, which ...work in both acidic and alkaline media, is highly desired. In this work, the mechanochemically assisted synthesis of a Ru catalyst with HER performance surpassing Pt in both acidic and alkaline media is reported. Mass production of this Ru catalyst can be achieved via a two‐step procedure: the mechanochemical reaction between graphite and dry ice produces edge‐carboxylic‐acid‐functionalized graphene nanoplatelets (CGnP); mixing a Ru precursor and the CGnP in an aqueous medium introduces Ru ions, which coordinate on the CGnP. Subsequent annealing results in uniform Ru nanoparticles (≈2 nm) anchored on the GnP matrix (Ru@GnP). The efficient Ru@GnP catalyst can be easily powered by a single silicon solar cell using a wireless integration device. The self‐powered device exhibits robust hydrogen evolution under the irradiation of standard AM 1.5 solar light. This work provides a new opportunity for the low‐cost mass production of efficient and stable catalysts for practical applications.
The mechanochemically assisted synthesis of a ruthenium (Ru) on graphene nanoplatelet (GnP) catalyst is explored to demonstrate efficient and stable hydrogen evolution performance surpassing Pt in both acidic and alkaline media. The synthesis procedures start from uniform anchoring Ru ions on mechanochemically driven edge‐carboxylic‐acid‐functionalized graphene nanoplatelets (CGnP) to produce Ru@CGnP, which becomes Ru@GnP after reduction and annealing.
Abstract
The one-step electrochemical synthesis of H
2
O
2
is an on-site method that reduces dependence on the energy-intensive anthraquinone process. Oxidized carbon materials have proven to be ...promising catalysts due to their low cost and facile synthetic procedures. However, the nature of the active sites is still controversial, and direct experimental evidence is presently lacking. Here, we activate a carbon material with dangling edge sites and then decorate them with targeted functional groups. We show that quinone-enriched samples exhibit high selectivity and activity with a H
2
O
2
yield ratio of up to 97.8 % at 0.75 V vs. RHE. Using density functional theory calculations, we identify the activity trends of different possible quinone functional groups in the edge and basal plane of the carbon nanostructure and determine the most active motif. Our findings provide guidelines for designing carbon-based catalysts, which have simultaneous high selectivity and activity for H
2
O
2
synthesis.
Low-volume fraction particle-reinforced aluminum matrix composites (PRAMCs) are commonly prepared using powder metallurgy (PM) technology, followed by heat treatment processes to improve the ...performance of PRAMCs. PRAMCs are prone to produce significant uneven residual stresses during the quenching process. It is difficult to eliminate the quenching residual stress of PRAMCs via the heat treatment process of the matrix aluminum alloy. Therefore, this study systematically investigates the effects of solid solution and artificial aging treatment processes on residual stress, mechanical properties, and microstructure of SiCp/Al–Cu–Mg composites through orthogonal and single-factor experimental methods. After optimizing the heat treatment process, minor residual stress with a 71.6% von Mises residual stress relief rate and good comprehensive mechanical properties are achieved. Based on the experimental results, macro and micro mechanisms of residual stress evolution and strengthening of SiCp/Al–Cu–Mg composites during heat treatment are revealed, providing valuable insights into the process of heat treatment of PRAMCs.
Hydrogen adsorption/desorption behavior plays a key role in hydrogen evolution reaction (HER) catalysis. The HER reaction rate is a trade-off between hydrogen adsorption and desorption on the ...catalyst surface. Herein, we report the rational balancing of hydrogen adsorption/desorption by orbital modulation using introduced environmental electronegative carbon/nitrogen (C/N) atoms. Theoretical calculations reveal that the empty d orbitals of iridium (Ir) sites can be reduced by interactions between the environmental electronegative C/N and Ir atoms. This balances the hydrogen adsorption/desorption around the Ir sites, accelerating the related HER process. Remarkably, by anchoring a small amount of Ir nanoparticles (7.16 wt%) in nitrogenated carbon matrixes, the resulting catalyst exhibits significantly enhanced HER performance. This includs the smallest reported overpotential at 10 mA cm
(4.5 mV), the highest mass activity at 10 mV (1.12 A mg
) and turnover frequency at 25 mV (4.21 H
s
) by far, outperforming Ir nanoparticles and commercial Pt/C.
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system. ...Due to the multi-scale nature of the physics and sensitivity from meshing a complicated geometry, such process can be computational prohibitive for most real-time applications (e.g., clinical diagnosis and surgery planning) and many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing a cost-effective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling strong nonlinearity and high dimensionality. However, the off-the-shelf DL architectures, success of which heavily relies on the large amount of training data and interpolatory nature of the problem, fail to operate when the data becomes sparse. Unfortunately, data is often insufficient in most parametric fluid dynamics problems since each data point in the parameter space requires an expensive numerical simulation based on the first principle, e.g., Navier–Stokes equations. In this paper, we provide a physics-constrained DL approach for surrogate modeling of fluid flows without relying on any simulation data. Specifically, a structured deep neural network (DNN) architecture is devised to enforce the initial and boundary conditions, and the governing partial differential equations (i.e., Navier–Stokes equations) are incorporated into the loss of the DNN to drive the training. Numerical experiments are conducted on a number of internal flows relevant to hemodynamics applications, and the forward propagation of uncertainties in fluid properties and domain geometry is studied as well. The results show excellent agreement on the flow field and forward-propagated uncertainties between the DL surrogate approximations and the first-principle numerical simulations.
•Proposed a simulation-free, physics-constrained deep learning for surrogate CFD model.•Boundary-encoded neural network outperforms the one with soft boundary constraints.•Demonstrated effectiveness of the label-free learning on a few vascular flows.
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