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Additional file 3: Figure S1. Overview of Luchuan genome assembly. (a) The flowchart of the contig, scaffold, and chromosome assembly in this study; and (b) Hi-C contact heatmap. Genome-wide analysis of chromatin interactions in Luchuan genome. The numbering of a chromosome is based on the chromosome length from the longest to the shortest. Figure S2. Circos plot shows the genome characterization of the Luchuan and Duroc pigs. (I) Syntenic alignments between the Luchuan and Duroc genome based on one-to-one orthologous genes; (II) GC content in non-overlapping 1 Mb windows; (III) Percent coverage of TEs in non-overlapping 1 Mb windows; (IV) Gene density calculated on the basis of the number of genes in non-overlapping 1 Mb windows; and (V) The length of pseudo-chromosome, light red, and light purple ideograms represent the chromosomes of Luchuan and Duroc pigs, respectively. Each ticket above the ideograms is on a scale of 50 Mb. Figure S3. Comparative genomic and phylogenetic analyses. (a) Venn diagram showing shared orthologous groups among genomes of Luchuan, Duroc, goat, and human; and (b) Phylogenetic tree with divergence times and history of orthologous gene families. Numbers on the nodes represent divergence times, with the error range shown in parentheses. The numbers of gene families that expanded (red) or contracted (blue) in each lineage after speciation are shown on the corresponding branch. MRCA is the most recent common ancestor. Figure S4. PCA plots. (a) PCA of the first three principal components. BAMEI, Bamei; BER, Berkshire; BMX, Bamaxiang; CWB, Chinese wild boar; DUR, Duroc; EHL, Erhualian; GST, Tibetan (Gansu); HAM, Hampshire; HTDE, Hetao; JH, Jinhua; KBP, Korean black pig; KWB, Korean wild boar; LD, Landrace; LUC, Luchuan; LW, Large White; LWH, Laiwu; MEI, Meishan; MIN, Min; PTL, Pietrain; RC, Rongchang; SCT, Tibetan (Sichuan); TC, Tongcheng; TT, Tibetan (Tibet); WZS, Wuzhishan; YM, Yucatan Miniature; YNT, Tibetan (Yunnan); and (b) PCA plots of the first two principal components by classifying them into seven groups: Chinese wild boar (CWB), Korean wild boar (BWB), Luchuan pig (LUC), Duroc (DUR), Korean black pig (KBP), other eastern pigs (EST) and western pigs (WST). Figure S5. (a) LD decay determined by squared correlations (r2) of allele frequencies against the distance between polymorphic sites in eastern (EST; orange) and western (WST; green) pig groups. (b) LD decay in DUR (Duroc; red) and LUC (Luchuan; blue) pig groups. Figure S6. Enrichment pattern of genes under potential selection in GWAS regions associated with some economic traits. Red represents genes located in selective regions of eastern pigs while blue represents those of western pigs. * represents FDR < 0.05. ** represents FDR < 0.01. Figure S7. GO enrichment of differentially expressed genes between Luchuan and Duroc pigs in 10 tissues. GO biological process analysis of the differentially expressed mRNAs between Luchuan and Duroc pigs in each tissue. Up indicates the genes with expression levels that are higher in Luchuan pigs compared to Duroc pigs. Down indicates the genes with expression levels that are lower in Luchuan pigs compared to Duroc pigs. Figure S8. Analysis of DEG in skeletal muscle tissues. (a) The number of DEG in different tissues. Duroc: the expression level is higher in Duroc pigs compared to Luchuan pigs. Luchuan: the expression level is higher in Luchuan pigs compared to Duroc pigs; (b) Heatmap showing the expression of DEG related to “muscle contraction” in skeletal muscle tissues; and (c) Heatmap showing the expression of DEG related to “cell cycle” in skeletal muscle tissues. Figure S9. Frequencies of the Luchuan promoter allele across different pig breeds. DUR: Duroc pig; LW: Large White pig; LD: Landrace pig; YM: Yucatan miniature pig; YNT: Tibetan pig (Yunnan); TC: Tongcheng pig; TT: Tibetan pig (Qinghai-Tibetan); HTDE: Hetaodaer pig; SCT: Tibetan pig (Sichuan); WZS: Wuzhishan pig; BMX: Bamaxiang pig; EHL: Erhualian pig; LUC: Luchuan pig; LWH: Laiwu black pig; MIN: Min pig (Shandong). Figure S10. QC of ATAC-seq peaks. Heatmap of reads density around TSS for five tissues. Figure S11. Muscle fiber differences between Duroc and Luchuan pigs. Proportions of slow muscle fiber. Figure S12. Knockdown and overexpression efficiency of Tnnc1 in C2C12 cells and in vivo. (a) Tnnc1 expression after siRNA knockdown in C2C12 cells; (b) Tnnc1 expression after overexpression in C2C12 cells; and (c) Tnnc1 expression after in vivo siRNA knockdown in mouse skeletal muscle. Figure S13. (a) Expression of Ho-1 after overexpression of Tnnc1; (b) Expression of Ho-1 after siRNA knockdown of Tnnc1; (c) Expression of Ogg1 after overexpression of Tnnc1; and (d) Expression of Ogg1 after siRNA knockdown of Tnnc1. Figure S14. (a) Expression of slow muscle (Myh7) and fast muscle (Myh2, Myh4, Myh1) markers after siRNA knockdown of Sema3g in C2C12 cells; (b) Measuring the same markers as (a) after overexpression of Sema3g in C2C12 cells; (c) Choosing siRNAs with the best knockdown efficiency; (d) Evaluation of the overexpression efficiency; and (e) Comparing the expression level of Sema3g before and after knocking down Cebpa. * represents p-value < 0.05. ** represents p-value < 0.01.

Additional file 1. Additional materials and methods [20, 21, 24–52, 54–57, 59–66, 118–130]

Purpose Massive perioperative blood loss in complex revision total joint arthroplasty (TJA) often requires blood transfusions. Tranexamic acid (TXA) has been used in elective primary TJA to minimize blood loss and transfusions. The purpose of this meta-analysis was to evaluate the safety and efficacy of intravenous TXA in revision TJA. Methods A literature search of PubMed, Scopus, and the Cochrane Controlled Trials Register was performed to identify studies published between January 2000 and May 2017. All randomized controlled trials (RCTs) and retrospective cohort observational studies evaluating the efficacy of intravenous TXA during revision total knee arthroplasty (TKA) and total hip arthroplasty (THA) were included. The mean differences (MDs) of blood loss, hemoglobin (Hb) change, and red blood cell (RBC) units transfused were compiled, and ORs of transfusion and venous thromboembolism (VTE) events in TXA and control groups were calculated. Results Seven studies involving 930 patients were included (501 TXA vs 429 control). Intravenous TXA use had a significantly less blood transfusion (OR=0.20, 95% CI=0.11–0.34, P

Background: DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions. Research question: would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis? Study design and methods: biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated. Results: twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10-17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10-18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively. Interpretation: the integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.