Cancer cells recruit monocytes, macrophages and other inflammatory cells by producing abundant chemoattractants and growth factors, such as macrophage colony-stimulating factor (M-CSF/CSF-1) and monocyte chemoattractant protein-1 (MCP-1/CCL2), to promote tumor growth and dissemination. An understanding of the mechanisms that target cancer cells and regulate tumor microenvironment is essential in designing anticancer therapies. Here, we showed that serum amyloid-A (SAA) and cathelicidin (LL-37) stimulated M-CSF and MCP-1 expression with or without lipopolysaccharide (LPS) administration; conversely, lipoxin-A 4 (LXA 4 ) and annexin-A1 (ANXA1) inhibited LPS-induced M-CSF and MCP-1 production by human (HepG2) and mouse (H22) hepatocellular carcinoma cells (HCCs). The effects of LXA 4 , ANXA1, SAA and LL-37 were dependent on the activation of their mutual cell-surface receptor formyl peptide receptor-2 (FPR2) and subsequent ROS-MAPK-NF-kB signalings. Furthermore, our results indicated that LPS switched macrophages into an IL-10 low IL-12 high M1 profile, whereas M-CSF þ MCP-1 and FPR2 agonists skewed them into M2 (IL-10 high IL-12 low ). In that respect, through modulating the phosphorylation of signal transducer and activator of transcription-3 (STAT3), LXA 4 and ANXA1 induced monocyte differentiation into M2a þ M2c-like cells and showed antitumorigenetic activities, whereas SAA, LL-37 and M-CSF þ MCP-1 led to M2b-or M2d-like polarization, which exacerbated HCC invasion in vitro and in vivo, respectively. Our results suggest that FPR2 has an appreciable pleiotropic regulator role in tumor immunoediting.

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Geometric errors directly affect the tool tip position, reduce machining accuracy, and are one of the most important errors of multi-axis machining tool. However, the geometric errors are intercoupling, and the measured values at different points vary and are stochastic. The identification of the most crucial geometric errors and the determination of a method to control them is a key problem to improve the machining accuracy of machine tool. To achieve this goal, a new analytical method, to identify crucial geometric errors for a multi-axis machine tool is proposed here based on multibody system (MBS) theory and global sensitivity analysis. The volumetric error modeling of multi-axis machine tool has been given by MBS theory, which describes the topological structure of multibody system simply and conveniently in a matrix. The stochastic characteristic of geometric errors is taken into consideration and Sobol global sensitivity analysis method is introduced to identify crucial geometric errors of machine tool. A vertical machining center is selected as an illustration example. The analysis results reveal that the analytical method presented in this paper can identify the crucial geometric errors and are helpful to improve the machining accuracy of multi-axis machine tool.

The machine tools are consisted of many parts and most of them are connected by the bolts. Accurate modeling of contact stiffness and damping for bolted joint is crucial in predicting the dynamic performance of machine tools. This paper presents a modified three-dimensional fractal contact model to obtain the stiffness and damping of bolted joint. Topography of the contact surface of bolted joint is fractal featured and determined by fractal parameters. Asperities in microscale are considered as elastic, elastic-plastic, and full plastic deformation. The expand coefficient is introduced to the size-distribution function of asperities. The real contact area, contact stiffness, and damping of the contact surface can be calculated by integrating the microasperities. The relationship of contact stiffness, damping, fractal dimension D, and fractal roughness parameter G can be obtained. Experiments are conducted to verify the efficiency of the proposed model. The results show that the theoretical mode shapes are in good agreement with the experimental mode shapes. The relative errors between the theoretical and experimental natural frequencies are less than 3.33%, which is less than those of the W-K model and L-L model. The presented model can be used to accurately predict the dynamic characteristic of bolted assembly in the machine tools.