Influence Factors and Prediction Model of Bond Strength of Tunnel Fireproof Coating under Freeze–Thaw Cycles

The freeze–thaw resistant performance of a tunnel fireproof coating (TFC) has an important impact on bonding property and durability. The influence of redispersible emulsion powder, polypropylene fiber and air-entraining agent on TFCs was studied. Transverse fundamental frequency and ultrasonic sound velocity were used to evaluate the damage degree of TFC, and the mechanism was revealed by SEM and pore structure. The results show that the most beneficial effect on bond strength of TFC is redispersible emulsion powder, followed by air-entraining agent, and then polypropylene fiber. After freeze–thaw cycles, the cumulative pore volume of micropores in the TFC increases obviously, while the porosity of macropores does not change significantly. A prediction model was proposed, which can calculate the bond strength from the damage degree of TFC under freeze–thaw cycles. The achievement can promote the application of TFC in cold regions.     

Association of genetic polymorphisms and autoimmune Addison’s disease

Autoimmune Addison’s disease (AAD) is a complex genetic disease that results from the interaction of a predisposing genetic background with as yet unknown environmental factors. The disease is marked by the appearance of circulating autoantibodies against steroid 21-hydroxylase. Mutations of the autoimmune regulator gene are responsible for the so-called autoimmune polyendocrine syndrome type I (APS I), of which AAD is a major disease component. Among genetic factors for isolated AAD and APS II, a major role is played by HLA class II genes: HLA-DRB1^*0301-DQA1^*0501-DQB1^*0201 and DRB1^*04-DQA1^*0301-DQB1^*0302 are positively, and RB1^*0403 is negatively, associated with a genetic risk for AAD. The MHC class I chain-related gene A allele 5.1 is strongly and positively associated with AAD. Other gene polymorphisms contributing to genetic risk for AAD are MHC2TA, the gene coding for class II transactivator, the master regulator of class II expression, cytotoxic T lymphocyte antigen-4, PTPN22 and the vitamin D receptor.     

SIR型传染病的预测方法研究

首先利用疾病传播的规律建立了SIR型传染病传播机理的微分方程模型,根据历史资料对模型中的参数进行多项式回归,得出回归曲线。基于此模型建立了甲型H1N1的动态预测模型,并以2009年全国的甲型H1N1数据为例进行了预测和分析,预测结果显示该模型具有一定的精度和可行性,可以给相关部门提供切实可行的参考建议。     

基于ARIMA乘积季节模型的科室级常规耗材需求量预测研究

目的探讨季节性因素的时间序列分析方法在科室常规耗材库管理中的应用,分析和预测未来一段时间内医用常规耗材的使用需求。方法采用整合移动平均自回归(Autoregressive Integrated Moving Average,ARIMA)乘积季节模型对北京市某医院某科室某品牌注射器2014年1月至2018年12月的逐月使用量进行预测。结果ARIMA(0,1,2)(0,1,1)12模型的平均绝对百分比误差为5.308,在实际业务可接受范围之内,因此模型拟合效果较好,预测结果接近实际产生值。结论ARIMA(0,1,2)(0,1,1)12模型能够准确的进行该类医用常规耗材的短期预测,可应用于医院物资管理信息系统中,实现对医院耗材的合理管控,并为科室制定医用耗材的资金支出预算提供可靠依据。     

Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms:sourced from retrospective studies

Although intracranial hemorrhage in moyamoya disease can occur repeatedly, predicting the disease is difficult. Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors, evaluating the weight of different factors, and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease. To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes, we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling, including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes. Another 500 hemispheres with normal vessel appearance were selected as negative samples. We used deep residual neural network(Res Net-152) algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery, then trained and validated the model. The accuracy, sensitivity, and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64 ± 0.87%, 96.55 ± 3.44%, and 98.29 ± 0.98%, respectively. The area under the receiver operating characteristic curve was 0.990. We used a combined multi-view conventional neural network algorithm to integrate age, sex, and hemorrhagic factors with features of the digital subtraction angiography. The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69 ± 1.58% and the sensitivity and specificity were 94.12 ± 2.75% and 89.86 ± 3.64%, respectively. The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, China(approved No. 2014-278) on January 12, 2015.     

Trends in adolescent alcohol use in the Netherlands, 1992–2015: Differences across sociodemographic groups and links with strict parental rule-setting

BackgroundFrom an international perspective, studying trends in adolescent alcohol use in the Netherlands is an important case study. Whereas Dutch adolescents topped the international rankings of alcohol consumption in the beginning of this century, they are nowadays found more toward the bottom of these rankings. This study examines time trends in adolescent alcohol use between 1992 and 2015, and tests whether these trends differ according to gender, age group, and educational track. Moreover, it examines to what extent the strictness of parental rule-setting can explain the identified trends.MethodsUsing data from ten waves of two nationally representative studies with a repeated cross-sectional design, trends were examined for eight different alcohol measures. Interaction analyses were conducted to test for subgroup differences. All analyses were controlled for educational track, family structure, and ethnicity. For the period 2007–2015, trends in parental alcohol-specific rule-setting were included as a predictor of the trends in adolescent alcohol use.ResultsAdolescent alcohol use increased substantially between 1992 and 2003, and decreased sharply thereafter. Trends were stronger for 12- to 15-year olds, compared to the 16-year olds, and for adolescents attending higher educational tracks, compared to adolescents attending lower educational tracks. Overall, gender differences remained constant over time. Between 2007 and 2015, strict parental alcohol-specific rule-setting increased substantially, and this (partly) explained the strong decline in adolescent alcohol use during this period.ConclusionThis study shows clear time trend changes in alcohol use among Dutch adolescents. The phenomenal decrease in adolescent alcohol use since 2003 appears to be closely related to a radical change in parenting behaviours surrounding the alcohol use of their children. While national prevention programs may have encouraged stricter parenting behaviours, the decline in alcohol use should be interpreted in a broader context of internationally changing sociocultural norms regarding adolescent alcohol use.