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An improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy.

The layout of measurement points is the key to the efficient inspection of free-form surfaces. Two algorithms are proposed for the layout of free-form surface measurement points: the free-form surface Gaussian curvature variation grid method and the isoparametric line curvature variation grid method. The former first divides the free-form surface into a uniform grid, determines the number of measurement points for each grid based on the change in the Gaussian curvature of each grid, and selects points within each grid based on a uniform distribution of the Gaussian curvature. The latter is achieved by first taking points from the curvature change on the initial U- and V-direction isoparametric lines, generating isoparametric lines from the points to divide the free-form surface into a grid, and selecting the grid intersections as measurement points. The effectiveness of the algorithm was verified by designing free-form surfaces, performing coordinate measuring machine (CMM) measurement experiments, and comparing the results with those of existing algorithms.

The present study examines the correlation between implicit self-esteem and the level of self-reported Behavioral Activation System (BAS) and Behavioral Inhibition System (BIS). Both name letter measure and implicit association test will be used to measure implicit self-esteem. Also, participants have to complete a self-report questionnaire to get their BAS and BIS score. Then, we sill correlate BAS, BIS with implicit self-esteem. We expect to have strong positive correlation between BAS and implicit self-esteem and strong negative correlation between BIS and implicit self-esteem. So, this study interpretates Implicit self-esteem in the biological view through BAS and BIS. And the finding may help us find a way to improve our implicit self-esteem.

In order to improve the inspection accuracy of free-form surface by CMM, this paper adopted the different sampling parameters to research the influence of the measurement accuracy of free-form surface. Through the combination of area uniform block random sampling and Latin hypercube random sampling, the minimum sampling grid block area and ball diameter were taken as the research parameters. Firstly, this research analysed theoretically the influence of measurement accuracy of free-form surface by different sampling parameters. Secondly, carrying out experiments verified the analytical results. Then, the influence of two groups of sampling parameters on the normal deviation of free-form surface could be acquired by analysing the experimental data. Finally, this research could obtain the result of normal deviation of free-form surface. The research results showed that the minimum block area of sampling and the diameter of measuring ball become smaller, and the profile error become larger when the number of measuring points were the same, the more it can reflect the actual contour of the free-form surface, which proves that the measurement accuracy is higher.

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation. [ABSTRACT FROM AUTHOR]

Objective: To study the differential gene expression and immune cell infiltration of gout patients, to find the key genes and immune cells of gout pathogenesis, and to explore the relationship between immune cells and gout. Methods: The gout chip GSE160170 was downloaded from the GEO database, and the differential gene expression analysis was carried out with the help of R language. Then, the STRING database was used to analyze the differential gene, and the Cytoscape software was used to screen the key genes, and then carry out enrichment analysis. At the same time, the infiltration of immune cells were analyzed. Results: The study found that IL-6, IL-1β, TNF, CCL3, CXCL8 and CXCL1 were key genes in the pathogenesis of gout, which were mainly exerted by IL-17, Toll-like receptor, NOD-like receptor, NF-κB and other signaling pathways. Processes such as cellular responses to lipo-polysaccharides, bacteria-derived molecules, and biological stimuli lead to disease; immune infiltration results indicate that memory B cells, activated NK cells, activated dendritic cells, activated mast cells and eosinophils were involved in the disease. It was significantly expressed in gout patients; the correlation analysis between immune cells showed that the expression of follicular helper T cells were positively correlated with the expression of activated mast cells, and the expression of unactivated NK cells and monocyte were negatively correlated. Conclusion: Key genes and differentially expressed immune cells are closely related to the pathogenesis of gout, providing new ideas for the study of the molecular mechanism of gout. [ABSTRACT FROM AUTHOR]

Objective: To identify key ferroptosis-related biomarkers and investigate immune infiltration in meniscus injuries among individuals of different age groups. Methods: (1) We obtained our training dataset from the GEO database and analyzed gene expression matrices from the GSE191157 gene chip dataset. Simultaneously, we downloaded relevant genes associated with ferroptosis for further analysis. (2)Using the "limma" package, we conducted differential gene expression analysis between elderly and young patients with meniscus injuries and performed weighted gene co-expression network analysis (WGCNA) on all genes in the GSE191157 gene chip dataset. (3) We selected two color modules most correlated with meniscus injury and then intersected the genes in these modules with ferroptosis-related genes. (4)Subsequently, we conducted ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the intersected genes. (5) Using LASSO regression in machine learning, we analyzed the intersected genes related to ferroptosis in meniscus injuries among individuals of different ages and extracted core genes. (6) Finally, we conducted gene set enrichment analysis (GSEA) and single-sample gene set enrichment variation analysis (ssG-SEA) for immune infiltration and confirmed the correlation between core genes and ferroptosis in meniscus injuries. Results: A total of 2 517 differentially expressed genes were identified, and the intersection of these genes with ferroptosis-related genes yielded 61 genes. After WGCNA analysis, the blue and yellow modules were found to be most relevant to the disease, resulting in 46 intersected genes. Through LASSO regression analysis, four core genes (BEX1, MMP13, PTPN18, and SLC38A1) were selected. GSEA results indicated that ferroptosis in meniscus injuries is primarily associated with Toll-like receptor signaling, RNA transcription and splicing, mTOR signaling, and ECM-receptor interaction. Immune cell infiltration analysis revealed a significant correlation between core gene expression and immune cell infiltration. MMP13 exhibited a negative correlation with effector memory CD8 T cells, CD56dim natural killer cells, and central memory CD4 T cells, while SLC38A1, BEX1, and PTPN18 were positively correlated. Conclusion: This study identified four key genes associated with ferroptosis in meniscus injuries among individuals of different age groups, namely BEX1, MMP13, PTPN18, and SLC38A1. Immune cell infiltration analysis demonstrated a clear correlation between core gene expression and immune cell infiltration. [ABSTRACT FROM AUTHOR]

Avian pathogenic Escherichia coli (APEC) is a major bacterial infection that causes economic losses in the global poultry industry. Palygorskite (PAL) has been shown to enhance growth performance while improving antioxidative and anti-inflammatory properties of broilers. This study evaluated the protective effects of PAL on growth performance and liver function in broilers subjected to APEC challenge. A total of 320 one-day-old male Arbor Acres chicks were divided into 4 groups with 8 replicates of 10 birds each, based on a 2 × 2 factorial arrangement (basal diet or 5 g/kg PAL-supplemented diet) and inoculation (bacterial culture medium or APEC). PAL increased body weight gain (BWG) prior to APEC challenge (P  < 0.05). However, APEC caused losses in BWG, feed intake (FI), and feed efficiency, along with increased relative hepatic weight, hepatic pathology scores, and hepatic-cell apoptosis rate (P  < 0.05). Compared to normal birds, APEC increased interleukin (IL)-1β, interferon-γ (IFN- γ), tumor necrosis factor-α (TNF-α), malondialdehyde (MDA), and nitric oxide (NO) levels, as well as lysozyme (LZM) and myeloperoxidase (MPO) activities, while decreasing total antioxidant capacity (T-AOC) and IL-10 levels, and total superoxide dismutase (T-SOD) and catalase (CAT) activities in both serum and liver, APEC also raised alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities, but reduced total protein (TP), albumin (ALB), immunoglobulin (Ig) A, IgG, and IgM levels in serum (P  < 0.05). Moreover, APEC increased hepatic mRNA level of IL-1β , IFN-γ , TNF-α , nuclear factor kappa B, and inducible nitric oxide synthase (iNOS), while inhibited mRNA level of IL-10 (P  < 0.05). In contrast, PAL increased BWG and FI, and alleviated hepatic-cell apoptosis rate during the challenge period (P  < 0.05). Incorporation of PAL reduced triglyceride and NO contents, ALT, and AST activities, while increasing TP, ALB, IL-10, IgG, and IgM levels in serum, enhancing serum T-SOD and CAT activities, elevating hepatic T-AOC and CAT activities, inhibiting hepatic MDA accumulation, and reducing IL-1β levels and LZM activity in both liver and serum (P  < 0.05). An interactive effect was found for hepatic TNF-α and iNOS mRNA expression, in which PAL inhibited their mRNA expression in APEC-challenged birds (P  < 0.05). Overall, PAL addition partially mitigated the negative impact of the APEC challenge on the growth performance and liver function of broiler chicks at an early age. [ABSTRACT FROM AUTHOR]