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Additional file 2. Supplementary Figure S1. Overall research design. Flow-process diagram presenting the process of comprehensive analysis. Supplementary Figure S2: Prognostic analysis of TMB and correlation with clinical characteristics. (A) Higher TMB levels correlated with better survival outcomes though P>0.05. (B–E) No significant difference of TMB levels was observed with clinical grade, AJCC stage, T status and M status. Supplementary Figure S3: Regression coefficient diagram based on LASSO algorithm. (A) LASSO coefficient profiles of 75 hub genes. A vertical line is drawn at the value chosen by 10‐fold cross‐validation. (B) Ten‐time cross‐validation for tuning parameter selection in the lasso regression. The vertical lines are plotted based on the optimal data according to the minimum criteria and 1-standard error criterion. The left vertical line represents the 3 hub genes finally identified. Supplementary Figure S4: Confirmation of risk score in the external validation group. (A) Heatmap presents the expression pattern of three hub genes in each patient. (B) Distribution of multi-genes signature risk score. (C) The survival status and interval of HCC patients. (D) Kaplan–Meier curve analysis presenting difference of overall survival between the high-risk and lowrisk groups. (E) ROC analysis was employed to estimate the prediction value of the prognostic signature. Supplementary Figure S5: Kaplan–Meier survival analysis for multiple HCC subgroups stratified by clinical variables. (A, B) Age. (C, D) Gender. (E, F) Tumor grade. (G,H) Stage. (I, J) T status. (K) N status. (L) M status. Supplementary Figure S6: (A-C) Areas under curves (AUCs) of the risk scores for predicting 1-, 2-, and 3-year overall survival time with other clinical characteristics. Supplementary Figure S7-S8: The representative results ofthe evaluation of tumor infiltrating immune cells with risk signature. Supplementary Figure S9: Correlation of prognostic risk score with mutation of genes. (A) The proportion of mutation of TTN between two risk score subgroups. (B) Kaplan-Meier curves for patients stratified by both mutation of TTN and risk score.

In this paper, we present a duo-graph Simultaneous Localization and Mapping (SLAM) method that enables visual-guided robot to efficiently and robustly generate consistent 3D map in a large-scale environment. Recently the conditional independent graph (CI-GRAPH) has been proved to be an efficient and robust method for solving the large-scale SLAM problem. However, it stems from extended kalman filter (EKF) SLAM that suffers from high computational load when number of the landmarks becomes large. Moreover, the big noise in the measurement can lead EKF SLAM far from convergence. These disadvantages are the cases in the visual-guided robot. To solve these problems, we propose a duo-graph structure that sustains the feasibility of a hierarchical graph-based SLAM framework. It implements two level graph-based SLAM: local and global SLAM. Utilizing the characteristic of the duo-graph structure, the local SLAM can efficiently localize itself while the global SLAM can accurately close loops in the large scale environment. In addition, our method can filter massive noisy visual features and eliminate mismatches in the global SLAM process. To demonstrate the superiority of our method, both simulation and real experiments are carried out.

In this paper, a graph-based hierarchical SLAM framework is proposed which ensures not only the high-speed operation of graph-based SLAM, but also feasibility of large-scale 3D map building. Both local and global level graph-based SLAM will be operated. They can be concurrently implemented in different computing units but must maintained communication which is called as a session. During each session, local level SLAM will create a pose-graph containing trajectory for mobile robot and information for local maps. To avoid communication congestion, raw density point cloud of each pose-node will be reduced into a sparser one by voxel grid filtering. When a session is closed, duo-graph strategy is executed to guarantee the consistency between successive local map. To associate massive local map information and closing loops in large-scale environment, graph-based algorithm will also be implemented in global level SLAM. Two experiments are carried out in the real indoor environment. In the first experiment, SLAM process is greatly accelerated in this framework by distributing the whole SLAM task into different level SLAM entities: local level SLAM operated on robot and global on laptop. It shows that this framework is scalable and it can be implemented in CS(Client-Server) model. Second experiment is conducted in our biggest work office around which we control the robot traverse for three times. It demonstrates that this framework can simultaneously keep fast graph-based SLAM in local-end and generate consistent and convergent 3D map in global-end SLAM process.