EC: Endometrial cancer; 3'-UTR: 3'-untranslated regions; TPX2: target protein for Xenopus kinesin-like protein 2; TCGA: the Cancer Genome Atlas; UCEC: uterine corpus endometrial carcinoma; CCK-8: cell counting kit-8; OD: optical density; FCM: flow cytometry; EMT: epithelial-mesenchymal transition.

Background Hepatitis B virus (HBV) infection is a major public health burden in China although it has steadily declined over the last two decades. A valid updated prevalence of HBV infection in China relies on a large sample size. Hence this study aimed to estimate HBV seroprevalence using a large inpatient population in Northeast China. Methods We consecutively enrolled 218,627 inpatients aged 1–70 years admitted to the First Hospital of Jilin University from January 2010 through December 2014. HBV serological markers were detected by chemiluminescence immunoassay (CLIA). Results Among the 218,627 collected samples, 16,254 (7.43%) were positive for HBsAg and 41.64% of patients were negative for all the HBV markers. The highest HBsAg prevalence was 10.05% in the 41–50 year age group and the lowest were 0.47% in the 1–10 and 2.35% in the 11–20 year age groups, respectively. HBsAg positivity was higher in males compared to females (8.94% vs. 5.80%). An HBsAg positivity of nearly 14% was found in middle-aged males, and positivity was 6.2% in females of childbearing age. One-third of this population only had a single HBsAb marker, which was also detected in 60% of patients aged under 20 years. Conclusion Though universal hepatitis B vaccination of infants has significantly reduced HBsAg prevalence in children, the number of most adults who have been infected with HBV remains steady. Extra care and resources should be provided to HBV-infected middle-aged males to stop the progression of chronic hepatitis B, and HBsAg positive females of childbearing age to block vertical HBV transmission.

ABSTRACT The evidence of long-term clinical dynamic on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) RNA re-positive case are less. We performed a 108 days follow-up on dynamic clinical presentations in a case, who hospitalized three times due to the positive recurrence of SARS-CoV-2 RNA after discharge, to understand the prognosis of the 2019-Coronavirus disease (COVID-19). In this case, positive SARS-CoV-2 recurred even after apparent recovery (normal CT imaging, no clinical symptoms, negative SARS-CoV-2 on stool sample and negative serum IgM test) from COVID-19, viral shedding duration lasted for 65 days, the time from symptom onset to disappearance was up to 95 days. Erythrocyte-associated indicators, liver function and serum lipid metabolism presented abnormal throughout during the observation period. Awareness of atypical presentations such as this one is important to prompt the improvement of the management of COVID-19.

Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model ( P = 0.0001) with an annual average percent change (AAPC) of − 3.3 (95% CI: − 4.3 to − 2.2, P < 0.001). A seasonality was observed across the 14 years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 which can be written as (1-B) (1-B 12 ) X t = (1–0.42349B) (1–0.43338B 12 ) ε t , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5–84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (> 70 cases per 100,000) were influenced by the autoregressive component for the past 14 years. Conclusion In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguishe...

Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. Conclusion In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Abstract Background The objective was to identify the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the China CDC. Joinpoint regression analysis was used to observe the trend. The monthly incidence was predicted by the Seasonal autoregressive integrated moving average (SARIMA) model. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission. Results We included 13,991,850 TB cases from 2004 to 2017. The final selected model was the 0 Joinpoint model with an annual average percent change of -3.3. A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences areas were mainly affected by the autoregressive component for the past fourteen years. Conclusion A significant decreasing trend was seen from 2004 to 2017. The seasonal peaks were in January and March every year. Obvious clusters were identified in Tibet and Xinjiang Province. A spatial heterogeneity in the component driving the transmission of TB was identified from the multivariate time series model. This suggested that targeted preventive efforts should be made in different provinces based on the main component contributing to the epidemics.

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In this paper, the design and optimization method of rocket parameters based on the surrogate model and the trajectory simulation system of the 3-DOF air-launched rockets were established. The Gaussian kernel width determination method based on the relationship between local density and width is used to ensure the efficiency and reliability of the optimization method, and at the same time greatly reduces the amount of calculation. An adaptive sampling point updating method was established, which includes three stages: location sampling, exploration sampling, and potential optimal sampling of the potential feasible region. The adaptive sampling is realized by the distance constraint. Based on the precision of the surrogate model, the convergence end criterion was established, which can achieve efficient and reliable probabilistic global optimization. The objective function of the optimization problem was deduced to determine the maximum load mass and reasonable constraints were set to ensure that the rocket could successfully enter orbit. For solid engine rockets with the same take-off mass as Launcherone, the launch altitude and target orbit were optimized and analyzed, and verified by 3-DOF trajectory simulation. The surrogate-based optimization algorithm solved the problem of the overall parameter design optimization of the air-launched rocket and it provides support for the design of air-launched solid rockets.