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To reveal the systematic response of neutrophils to sepsis and to study the hub lncRNAs in sepsis. Neutrophils taken from the femur and tibia of male C57 BL/6 mice were used in this study. And neutrophils were treated for 0 h, 0.5 h, 1 h, and 4 h with or without 1 µg/mL lipopolysaccharide (LPS) for further chip detection. In addition, cecal ligation and perforation were used to simulate sepsis. Here, we used different bioinformatics analyses, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), and gene regulatory network analysis, to analyze the systemic response of neutrophils to sepsis. We identified nine modules and found hub lncRNAs in each module. The blue and pink modules were closely related to the inflammatory state of sepsis. Some hub lncRNAs (NONMMUT005259, KnowTID_00004196, and NR_003507) may have functions related to the inflammatory state in sepsis. Based on a new biological approach, our research results revealed the systemic-level response of neutrophils to sepsis and identified several hub lncRNAs with potential regulatory effects on this condition.

Background Although the immune function of neutrophils in sepsis has been well described, the heterogeneity of neutrophils remains unclear during the process of sepsis. Methods In this study, we used a mouse CLP model to simulate the clinical scenario of patients with sepsis, neutrophil infiltration, abnormal distribution and dysfunction was analyzed. LPS was used to stimulate neutrophils in vitro to simulate sepsis; single-cell gene sequencing technology was used to explore the immunological typing. To explore the immunological function of immunosuppressive neutrophils, PD-L1 knockout neutrophils were cocultured with lymphocytes from wild-type mice. Results We found that neutrophils presented variant dysfunction at the late stage of sepsis, including inhibition of apoptosis, seriously damaged chemotaxis and extensive infiltration into the tissues. Single-cell RNA sequencing revealed that multiple subclusters of neutrophils were differentiated after LPS stimulation. The two-dimensional spatial distribution analysis showed that Foxp3+ T cells were much closer to Ly-6G than the CD4+ and CD8+ cells, indicating that infiltrated neutrophils may play immunomodulatory effect on surrounding T-regs. Further observations showed that LPS mediates PD-L1 over expression through p38α-MSK1/-MK2 pathway in neutrophils. The subsets of highly expressed PD-L1 exert immunosuppressive effect under direct contact mode, including inhibition of T cell activation and induction of T cell apoptosis and trans-differentiation. Conclusions Taken together, our data identify a previously unknown immunosuppressive subset of neutrophils as inhibitory neutrophil in order to more accurately describe the phenotype and characteristics of these cells in sepsis.

Machining accuracy is a very important index to measure the performance of NC machine tools, and how to improve the machining accuracy grabs the attention of both manufacturers and users. Since the geometric errors are intercoupling and are one of the main factors affecting machining accuracy of machine tools, in this paper, a new method for extracting key geometric errors based on the screw theory and extended Fourier amplitude sensitivity test (EFAST) method was proposed. First, screw theory was introduced, and the machining accuracy modeling of a three-axis vertical machining center (VMC) was established. Then, in order to analyze the coupling effects between geometric errors by high-dimensional nonlinear model, EFAST global sensitivity analysis method was adopted to help in extracting the main geometric errors. An illustration example on a three-axis VMC shows that the proposed method is effective and can be helpful to improve the geometric accuracy of machine tool.

Thermal errors adversely affect the precision of a machine tool operation. Therefore, reduction of thermal errors is essential for improving of machining accuracy. In this paper, in order to realize real-time compensation, taking a multi-axis machine tool as an example and a method for robust modeling and predicting thermally induced positional error is proposed. First of all, the number of temperature measuring points required in thermal error’s model was reduced based on the rough set theory, which greatly reduced variable searching and modeling time. Then through grey relation theory, systematic analysis of the similarity degree between thermal error and temperature data was carried out to select sensitive temperature measuring points, and the temperature variables in the thermal error’s model were reduced from 24 to 7 after optimization, which eliminated the coupling problems. For reducing the influence of unpredictable noises, radial basis function (RBF) and back propagation (BP) neural network modeling methods were adopted to predict thermally induced positional errors, and in comparison, the prediction accuracy of RBF neural network was found superior to that of traditional BP neural network. Finally, some measured data were selected to verify the validity of the proposed method, and the results showed that prediction accuracy of the proposed thermally induced positional error model was reliable.

Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its accuracy for quality control of products is of crucial importance to machine tool. In this paper, machining accuracy reliability is defined as the ability to perform its specified machining accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the machining accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the machining accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the machining accuracy reliability and sensitivity analysis of machine tools were developed. By taking the machining accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.