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Additional file 2: Figure S1. [Kaplan-Meier curves of overall survival for the preoperative groups of CEA (a), CA19-9 (b), and CA125 (c) in colorectal cancer patients]. Figure S2. [Kaplan-Meier curves of overall survival for the first postoperative groups of CEA (a), CA1 -9 (b), and CA125 (c) in colorectal cancer patients]. Figure S3. [Longitudinal trajectories of CEA (a), CA19-9 (b) and CA125 (c) of survived and died patients]. Figure S4. [The eigenfunctions of FPCA for CEA (a), CA19-9 (b), and CA125 (c)]. Figure S5. [The first five eigenfunctions for CEA (a), CA19-9 (b), and CA125 (c) based on MFPCA of the three markers]. Figure S6. [Variable importance of preoperative CEA&CA19-9&CA125 model (a) and longitudinal CEA&CA19-9&CA125 model (b)]. Figure S7. [ROC curves of the preoperative, postoperative and longitudinal CEA&CA19-9&CA125 models at 60 months after surgery for internal validation (a) and external validation (b)]. Figure S8. [Personalized dynamic prediction for the survival probability of patient A (a), patient B (b) and patient C (c) based on longitudinal CEA&CA19-9&CA125 model]. Figure S9. [AUC (a) and BS (b) of the prediction models with MSI status at 24 to 60 months after surgery for external validation].

Additional file 1. Characteristics of the included and excluded patients in YNCH and SYSU6.

Background Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC. Methods Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3+ and CD8+ T-cells infiltration in the stroma region. Results Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24–0.63, P Conclusions We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3+ and CD8+ T-cells densities within the stroma region on WSIs of CRC and further predict survival.

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}., Accepted by KDD2023 (Applied Data Science Track)

China is well known for its wide and increasing commercial use of mobile social media for various purposes in different areas, ranging from online shopping to social networking. Such a popular commercial use was insightfully examined in relation to social relationship in the age of mobile internet, which enables people of either weak or strong connections to socialize anywhere anytime, leading to scenarios where mobile social media can be leveraged for profits. In what way can user experiences be guaranteed while platforms' value-added targets be achieved at the same time? In addressing that question, the authors of this chapter examined the commercial use of mobile social media in the context of complicated social networks. It is expected from the editor that further studies are to be carried out to comprehensively and comparatively examine the same topic in different countries or cultures.

Additional file 1: FigureS1. Association between preoperative CA19-9 status and overall survival. (a) overall population. (b) patients with normal preoperative CEA. (c) patientswith elevated preoperative CEA. Solid yellow lines are unadjustedhazard ratios, with dashed yellow lines showing 95% confidence intervalsderived from restricted cubic spline regressions. Reference lines for noassociation are indicated by the solid bold lines at a hazard ratio (HR) of 1.0. Dashed blue curves show the fraction of the population with different levels of preoperative CA19-9. Arrows indicate the concentration of preoperative CA19-9 with HR of 1.0. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidence interval; E, number of events; HR, hazard ratio; N, number of patients. FigureS2. Kaplan‐Meier curves for overall survival according to the preoperative CA19-9 group. (a) overall population. (b) patients with normal preoperative CEA. (c) patientswith elevated preoperative CEA. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen. FigureS3. Kaplan‐Meier curves according to the joint group of preoperative CEA and CA19-9 in colorectal cancer patients. (a) recurrence-free survival. (b) overall survival. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; OS, overall survival; RFS, recurrence-free survival. FigureS4. Forest plot for recurrence-free survival of preoperative CA 19-9 groups stratified by clinicopathological features based on the Cox models. P values for interaction were calculated using Cox regression model. HR and 95%CIs were given and visually represented by the squares and error bars. CA 19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidence interval; HR, hazard ratio. FigureS5. Forest plot for performance overallsurvival of preoperative CA19-9 groups stratified by clinicopathological features based on the Cox models. P values for interaction were calculated using Cox regression model. HR and 95%CIs were given and visually represented by the squares and error bars. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidenceinterval; HR, hazard ratio. FigureS6. Kaplan‐Meier curves according to the joint group of preoperative CEA and CA19-9 in patients with stage II colorectal cancer. (a) recurrence-free survival.(b) overall survival. CA 19-9, carbohydrate antigen 19-9;CEA, carcinoembryonic antigen; OS, overall survival; RFS, recurrence-freesurvival.

Additional file 1. SI. Inclusion and exclusion criteria. SII. Annotation of ultrasound images. SIII. Details of theautomated segmentation model for breast ultrasound images. SIV. Data Standardization and Feature Extraction. SV. Feature Selection and Model Construction. SVI. Consistency evaluation of features by Bland-Altman. Fig. S1. Inclusion and exclusion criteria. Fig. S2. U-net architecture. Fig. S3. Flowchart of feature assessment and SUAS construction. Fig. S4. Principal component analysis plot and linear model for evaluating batch effect correction in three institutions. Fig. S5. Relative variable contribution in Model2 and Model3. Fig. S6. Clinical usefulness evaluation of Model2 and Model3. Fig. S7. The changing trend of entropy-related features in three institutions. Table S1. The ultrasonographic images acquisition parameters of the multiple centers. Table S2. Similarity and consistency evaluation between automated segmentation and manual segmentation. Table S3. Key features selected for construction of phasal signatures based on ultrasound images at each single time point. Table S4. Multivariate analysis of clinicopathological model, Model 2 (the early-treatment model) and Model 3 (the posttreatment model). Table S5. Prediction performance of the signatures. Table S6. AUCs of early-pretreatment model (Model 2) and post-treatment model (Model 3) in breast cancer subtypes. Table S7. NRI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S8. IDI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S9. Prediction performance of the signatures and SUAS in randomly split datasets.