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Additional file 1: Fig. S1. Overview of experimental and research analysis methods. Fig. S2. Venn diagram of the distribution of genes regulated by different types of eQTLs (local eQTL and distant eQTL) at 20 DAF and 40 DAF. (a) Distribution number of genes which were regulated by different types of eQTLs at 20 DAF. (b) Distribution number of genes which were regulated by different types of eQTLs at 40 DAF. Fig. S3. Manhattan plot of BnaA05.FAD7 eGWAS at 40 DAF. Fig. S4. Study design on ATAC-seq of 6 representative accessions of B. napus. Fig. S5. Correlation analysis of 59 ATAC-seq samples. The samples are named according to “(22, 26, 34 or 40) DAF” + “accession_cellular ploidy (2C, 3C or 4C)” + “biological replicate” format naming. Fig. S6. Regional plot of ATAC-seq data and eGWAS results of BnaA08.TGD1. BnaA08.TGD1 is marked by a dashed line. The shaded area indicates the lead SNP of the local eQTL affecting BnaA08.TGD1. Fig. S7. Comparison of the explained variance (r2) of eQTLs for expression variation in or not in OCRs. Fig. S8. Expression correlation analysis of adjacent genes and randomly sampled gene pairs. Violin plot shows that the expression correlation of adjacent genes is significantly higher than that of randomly sampled gene pairs, *** indicates P < 0.001 in Kolmogorov-Smirnov test. Fig. S9. eQTL localization of B. napus. (a) Dot plot showing eQTL and their regulated genes in 19 chromosomes. x-axis indicates the physical position of each variant on the ZS11 genome. yaxis indicates the physical position of the localized gene on the ZS11 genome and each point indicates a detected eQTL locus. Points on the diagonal line indicate local eQTLs and points away from the diagonal line indicate distant eQTLs. (b) Average number of eQTL for different gene types. Fig. S10. The propensity of genome-wide homoeologous gene expression on different chromosomes of An and Cn at 40 DAF. Fig. S11. Effect of local eQTLs on asymmetric regulation (Cn gene regulating An gene) of subgenomes. Fig. S12. Comparison of gene correlations in different types of aHGPs at 40 DAF. “BG” represents all HGPs. “Local” represents aHGPs with local eQTLs and “No Local” represents aHGPs without local eQTLs. Fig. S13. The density map shows the enrichment of TFs in HGPs with feedback regulation. The blue inverted triangle represents 333 HGPs that are TFs. Fig. S14. The density plot shows the maximum number of eQTLs within 1 Mb. We defined the number of eQTLs within 1 Mb greater than 81 (P < 0.01) as a hotspot (red dot). Fig. S15. Characterization of SOC-related gene regulation in subgenomic imbalance. a Comparison of SOC-related gene correlations in different types of aHGPs at 20 DAF. “BG” represents all HGPs. “Local” represents aHGPs with local eQTLs and “No Local” represents aHGPs without local eQTLs. b Correlation between expression levels of BnaA02.PDAT1 and BnaC02.PDAT1 at 20 DAF. c Correlation between expression levels of BnaA01.AAD5 and BnaC01.AAD5 at 20 DAF. d Correlation between expression levels of BnaA07.WRI1 and BnaC06.WRI1 at 20 DAF. Fig. S16. The enrichment of hotspots regulating TWAS significant genes of SOC. The x-axis and y-axis represent enrichment of TWAS significant genes of SOC in hotspots at 20 DAF and 40 DAF. Fig. S17. Workflow of XGBoost module. The collected expression data of TFs were used to construct the XGBoost model, upstream TFs prediction is performed for each gene in a gene set, and finally the prediction results of the whole gene set are summarized. Fig. S18. Expression of BnaA09.SCL31 at 20 DAF is regulated by a distant hotspot eQTL, which in turn regulates downstream SOC-related genes and affects seed oil content. Box plots of SOC and SOC-related genes (BnaA07.NAC13, lysophosphatidic acid acyltransferase (BnaC07.LPAAT), GLYCOLIPID TRANSFER PROTEIN (BnaC06.GLPT), SHEWENELLA-LIKE PROTEIN PHOSPHATASE 1 (BnaC03.SLP1)) based on haplotypes of distant eQTL. Fig. S19. The population information of BnaA09.SCL31 expression level at 20 DAF. a Box plots for expression levels at 20 DAF based on the haplotypes of variants in the gene region and the upstream 2 kb region of BnaA09.SCL31. b Correlation between SOC and expression levels of BnaA09.SCL31 at 20 DAF. Fig. S20. Identification of Arabidopsis T-DNA Mutants of SCL31. (a) Schematic diagram of TDNA insertion sites in SCL31 mutants (scl31-1, scl31-2). (b) Identification of homozygous TDNA mutants by PCR with a pair of gene specific primers (LP+RP) or combination of T-DNA border primer (LB1.3) and gene specific primers (RP). (c) Expression of SCL31 in WT, scl31-1, scl31-2 analyzed by quantitative RT-PCR using RNA extracted from leaves. Values are means(SD) (n = 3 biological repeats). ** indicates P < 0.01 and *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S21. Fatty acid composition of SCL31 mutant seeds (scl31-1, scl31-2). Values are means(SD) (n = 5 biological repeats). * indicates P < 0.05 and ** indicates P < 0.01 compared with WT in Student’s t test. Fig. S22. Silique and seed of SCL31 mutant lines (scl31-1, scl31-2). (a) Silique of scl31-1 and scl31-2. Bar = 2mm. (b) Seed of scl31-1 and scl31-2. Bar = 500𝜇m. (c) Seed length of scl31-1 and scl31-2. Values are means(SD) (n = 5 biological repeats) and different letters indicate differences at P < 0.05 using Student’s t test. (d) Seed width of scl31-1 and scl31-2. Values are means(SD) (n = 5 biological repeats) and different letters indicate differences at P < 0.05 using Student’s t test. Fig. S23. Workflow of Basenji module. ATAC-seq data are used to identify OCRs and construct basenji models. Subsequently, 2 kb promoter sequences of genes in the gene set are collected for key motif identification. Fig. S24. The performance metrics of the Basenji deep learning model trained with the ZS11 reference genome and 59 rapeseed ATAC-seq samples. The horizontal axis represents 59 different samples and the vertical axis represents the performance data of the corresponding fold (where r2 represents the square of the correlation. AUPRC is the accuracy between the predicted regression value and the true value using the precision score function in scikit-learn package to calculate the accuracy between the predicted regression value and the true value; AUROC is the area under the precision-recall curve of the predicted regression value and the true value using the roc_auc_score function in the in scikit-learn package). Fig. S25. The correlation between BnaA07.NAC13 and the expression of the downstream genes potentially regulated by BnaA07.NAC13. Fig. S26. Transcriptional regulation of BnaC04.MYC70 and BnaC02G0181500ZS are activated by BnaA07.NAC13. a Schematic representation of the constructs used for the dual-luciferase assay. The effector constructs contain BnaA07.NAC13 driven by the CaMV35S promoter. The reporter construct contains the firefly luciferase driven by BnaC04.MYC70 and BnaC02G0181500ZS promoter, and the Renilla luciferase (REN) driven by the CaMV35S promoter. And the black square is the terminator. b,c Bar graph showing the relative LUC/REN ratio in the dual-luciferase assay. Values are means(SD) (n = 3 biological repeats). Fig. S27. Expression of BnaA07.NAC13 and BnaC07.NAC13 in WT and OE lines analyzed by quantitative RT-PCR using RNA extracted from leaves. (a, b) Values are means(SD) (n = 3 biological repeats). *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S28. Fatty acid composition phenotype of OE lines (BnaA07.NAC13, BnaC07.NAC13). (a, b) Values are means(SD) (n = 5 biological repeats). * indicates P < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S29. Manhattan plot of BnaA07.NAC13 eGWAS at 20 DAF and 40 DAF. Fig. S30. Correlation between expression levels of BnaA07.NAC13 and TFs (such as BnaA09.SCL31 , WIP DOMAIN PROTEIN 3 (BnaA09.WIP3), CRY2-INTERACTING BHLH 4 (BnaA09.CIB4), BLUEJAY (BnaA09.BLJ)) in Hotspot87-88 at 20 DAF and 40 DAF. Fig. S31. Correlation between expression levels of BnaA09.SCL31 and BnaA07.NAC13 at 20 DAF and 40 DAF. Fig. S32. Size and number of LDs expressing BnaA09.SCL31 and BnaA07.NAC13s in tobacco leaves. a LD count expressing BnaA09.SCL31 and BnaA07.NAC13s by size (average diameter). Values are means(SD) (n = 3 biological repeats). Different letters indicate significant difference at P < 0.05, as determined by one-way ANOVA with Tukey’s post-test. b Quantification of LD sizes in leaf mesophyll cells. Circles represent size of LDs. Fig. S33. Expression of NAC13, SUD1, LPAAT, SCL30, SLP1 in WT, scl31-1, scl31-2 analyzed by quantitative RT-PCR using RNA extracted from leaves. (a-e) Values are means(SD) (n = 3 biological repeats). “NS” indicates P > 0.05, * indicates P < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S34. Transcriptional regulation of genes are activated by BnaA09.SCL31 or BnaA07.NAC13. a Schematic representation of the constructs used for the dual-luciferase assay. The effector constructs contain BnaA09.SCL31 and BnaA07.NAC13 driven by the CaMV35S promoter, respectively. The reporter construct contains the firefly luciferase driven by BnaA07.NAC13, BnaC07.LPAAT, BnaC03.SLP1 and BnaA07.SRO3 promoter, and the Renilla luciferase (REN) driven by the CaMV35S promoter. And the black square is the terminator. b Bar graph showing the relative LUC/REN ratio in the dual-luciferase assay. Values are means(SD) (n = 3 biological repeats). BnaA07.SRO3 (SIMILAR TO RCD ONE 3).

Additional file 3. Review history.

Thrombolysis with recombinant tissue plasminogen activator (rt-PA) has gained international recognition, clinical outcomes following this thrombolytic therapy varied from patient to patient. Factors affecting clinical outcomes have not been well understood yet, so this retrospective case-control study aimed to investigate factors that may influence clinical outcomes of acute ischemic stroke treated with intravenous rt-PA.One hundred and one patients with acute ischemic stroke who received intravenous rt-PA thrombolysis within 4.5 hours from disease onset were included. Patients were divided into good or poor outcome group according to modified Rankin Scale (mRS) score, good outcome group: mRS score of 0-1; poor outcome group: mRS of 2-6. Stroke characteristics were compared between the two groups. Factors for stroke outcomes were analyzed via univariate analysis and Logistic regression.Of the 101 patients studied, patients in good outcome group (n = 55) were significantly younger than patients in poor outcome group (n = 46, (62.82 ± 14.25) vs. (68.81 ± 9.85) years, P = 0.029). Good outcome group had fewer patients with diabetic history (9.09% vs. 28.26%, P = 0.012), fewer patients with leukoaraiosis (7.27% vs. 28.26%, P = 0.005) and presented with lower blood glucose level ((5.72 ± 1.76) vs. (6.72 ± 1.32) mmol/L, P = 0.012), lower systolic blood pressure level ((135.45 ± 19.36) vs. (148.78 ± 19.39) mmHg, P = 0.003), lower baseline NIHSS score (12.02 ± 5.26 vs. 15.78 ± 4.98, P = 0.002) and shorter onset-to-treatment time (OTT) ((2.38 ± 1.21) vs. (2.57 ± 1.03) hours, P = 0.044) than poor outcome group. Logistic regression analysis showed that absence of diabetic history (odds ratio (OR) 0.968 (95% CI 0.941-0.996)), absence of leukoaraiosis (OR 0.835 (95% CI 0.712-0.980)), lower baseline NIHSS score (OR 0.885 (95% CI 0.793-0.989)), lower pre-thrombolysis systolic blood pressure (OR 0.962 (95% CI 0.929-0.997)), and lower blood glucose level (OR 0.699 (95% CI 0.491-0.994)) before thrombolysis were significantly associated with better outcome.Patients with no history of diabetes, no leukoaraiosis, low blood glucose level, low systolic blood pressure level and low baseline NIHSS score before thrombolysis have a better outcome.