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Additional file 3: Table S1. Clinicopathological characteristics of the enrolled patients. Table S2. Primer sequences for real-time PCR or quantitative real-time PCR. Table S3. The miRNA target prediction software circInteratome and circbank predicted miRNAs which could bind hsa_circ_0003972. Table S4. The miRNA target prediction software circInteratome and circbank predicted miRNAs which could bind hsa_circ_0007288. Table S5. The expression profile of the miRNAs that were upregulated in OC tissues as compared with normal ovarian tissues downloaded from GSE47841. Table S6. The downregulated mRNAs in OC tissues in TCGA database compared with normal ovarian tissues in GTEx database. Table S7. The 137 candidate mRNAs expression pattern in OC tissues as compared with normal ovarian tissues. Table S8. The microarray results of the 46 upregulated circRNAs and 595 downregulated circRNAs in OC patients compared with the benign control (fold change > 2 and P-value

Geometric characteristics provide an important means for characterization of the quality of direct laser deposition. Therefore, improving the accuracy of a prediction model is helpful for improving deposition efficiency and quality. The three main input variables are laser power, scanning speed, and powder-feeding rate, while the width and height of the melt track are used as outputs. By applying a multi-output support vector regression (M-SVR) model based on a radial basis function (RBF), a non-linear model for predicting the geometric features of the melt track is developed. An orthogonal experimental design is used to conduct the experiments, the results of which are chosen randomly as training and testing data sets. On the one hand, compared with single-output support vector regression (S-SVR) modeling, this method reduces the root mean square error of height prediction by 22%, with faster training speed and higher prediction accuracy. On the other hand, compared with a backpropagation (BP) neural network, the average absolute error in width is reduced by 5.5%, with smaller average absolute error and better generalization performance. Therefore, the established model can provide a reference to select direct laser deposition parameters precisely and can improve the deposition efficiency and quality.