Clinicopathological characteristics and prognosis of gastric signet ring cell carcinoma

BACKGROUND The clinicopathological features and prognosis of gastric signet ring cell carcinoma(GSRC)remain controversial,particularly with regard to sensitivity to postoperative adjuvant therapy.AIM To compare the pathological features of GSRC with those of gastric adenocarcinoma of different degrees of differentiation and the differences in survival prognosis between the different disease processes.METHODS By screening gastric cancer patients from 2010 to 2015 in the database of Surveillance,Epidemiology and End Results,and collecting the clinicopathological and prognostic data of gastric cancer patients who underwent surgery from January 2014 to December 2016 in the Second Affiliated Hospital of Nanchang University,we analyzed the general pathological characteristics of GSRC by the chi-square test.Univariate and multivariate analyses were conducted to compare the factors affecting the survival and prognosis of early and advanced gastric adenocarcinoma.The Kaplan-Meier curves were plotted to reveal the survival difference between early and advanced GSRC and different differentiated types of gastric adenocarcinoma.The prognosis model of advanced GSRC was established with R software,and the area under curve(AUC)and C-index were used to assess the accuracy of the model.RESULTS Analysis of pathological features revealed that signet ring-cell carcinoma(SRC)was more frequently seen in younger(<60 years),female,and White patients compared to non-SRC patients.SRC was less commonly associated with early gastric cancer(EGC)(23.60%vs 39.10%),lower N0(38.61%vs 61.03%),and larger tumour sizes>5 cm(31.15%vs 27.10%)compared to the differentiated type,while the opposite was true compared to the undifferentiated type.Survival prognostic analysis found no significant difference in the prognosis of SRC patients among EGC patients.In contrast,among advanced gastric cancer(AGC)patients,the prognosis of SRC patients was correlated with age,race,tumour size,AJCC stage,T-stage,and postoperative adjuvant therapy.The predictive model showed that the 3-year AUC was 0.787,5-year AUC was 0.806,and C-index was 0.766.Compared to non-SRC patients,patients with SRC had a better prognosis in EGC[hazard ratio(HR):0.626,95%confidence interval(CI):0.427-0.919,P<0.05]and a worse prognosis in AGC(HR:1.139,95%CI:1.030-1.258,P<0.05).When non-SRC was divided into differentiated and undifferentiated types for comparison,it was found that in EGC,SRC had a better prognosis than differentiated and undifferentiated types,while there was no significant difference between differentiated and undifferentiated types.In AGC,there was no significant difference in prognosis between SRC and undifferentiated types,both of which were worse than differentiated types.A prognostic analysis of postoperative adjuvant therapy for SRC in patients with AGC revealed that adjuvant postoperative radiotherapy or chemotherapy significantly improved patient survival(34.6%and 36.2%vs 18.6%,P<0.05).CONCLUSION The prognosis of SRC is better than that of undifferentiated type,especially in EGC,and its prognosis is even better than that of differentiated type.SRC patients can benefit from early detection,surgical resection,and aggressive adjuvant therapy.

Estimation of Incident-Induced Congestion on Signalized Arteries Using Traffic Sensor Data

This paper describes how to derive quantitative information about the effect of traffic incidents on urban traffic flow from the raw measurements detected by sectional loop sensors. 3-he two critical parame- ters of the travel delay and queue length, which reflect the temporal and spatial properties of inci- dent-induced congestion, cannot be directly determined from commonly used loop sensors. The modified queuing diagram is used here to quantify incident-induced queues and travel delays on signalized arteries using sensor data from the targeted and upstream links. The method is tested using data generated by the VISSIM simulation model, with results indicating its efficiency and accuracy with an average relative travel delay error of 18.67% for all samples which falls to 8.07% for high volume and very high volume conditions.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Virtual simulation testing of Autonomous Vehicles(AVs)is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs.Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets.However,the criticalities defined in their testing tasks are based on fixed assumptions,the obtained scenarios cannot pose a challenge to AVs with different strategies.To fill this gap,we propose an intelligent testing method based on operable testing tasks.We found that the driving behavior of Surrounding Vehicles(SVs)has a critical impact on AV,which can be used to adjust the testing task difficulty to find more challenging scenarios.To model different driving behaviors,we utilize behavioral utility functions with binary driving strategies.Further,we construct a vehicle interaction model,based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty.Finally,by adjusting SV’s strategies,we can generate more corner cases when testing different AVs in a finite number of simulations.