广东省中老年人群睡眠状况与高血压患病的关联性研究

目的 研究广东省中老年人群睡眠状况与高血压患病的关联性,为高血压防控“关口前移”提供科学依据。方法 采用“2018年广东省慢性病及危险因素监测数据”,按照多阶段整群随机抽样方法,纳入5 636名≥45岁中老年人为研究对象。运用SAS 9.4软件进行Rao-Scott 检验和基于复杂抽样构建logistic回归模型分析睡眠状况与高血压患病的关系。运用R 4.0.5软件绘制限制性立方样条曲线分析睡眠时间与高血压患病风险的量效关系。结果 广东省中老年人高血压患病率是37.93%,平均睡眠时间是(7.36±1.61)h; 其中男性平均睡眠时间多于女性,而女性睡眠问题比例显著高于男性。经多因素回归模型控制后,睡眠时间不足或过多(OR=1.280,95%CI:1.128～1.452; OR=1.300,95%CI:1.038～1.627)、入睡困难(OR=1.286,95%CI:1.018～1.626)、中间觉醒≥2次(OR=1.239,95%CI:1.036～1.483)和服用安眠药(OR=1.567,95%CI:1.086～2.261)均使高血压患病风险升高。结论 广东省中老年人群的睡眠时间不足或过多、入睡困难、中间觉醒≥2次和服用安眠药均与高血压患病显著正相关,应对该特征的人群进行干预,旨在降低高血压的患病率。     

Interindividual functional mapping: a nonlinear local approach

Within the scope of three-dimensional brain imaging, we propose an interindividual fusion scheme to register functional activations according to anatomical cortical structures, the sulci. This paper is based on the assumption that an important part of functional intersubject variability is encoded in anatomical variability. The aim of this paper is therefore to propose a generic framework to register functional activations according to the relevant anatomical landmarks. Compared to "classical" interindividual fusion schemes, this approach is local. It relies on a statistical sulci shape model accounting for the interindividual variability of a population of subjects and providing deformation modes relative to a reference shape (a mean sulcus). The deformation field obtained between a given sulcus and the reference sulcus is extended to a neighborhood of the given sulcus by using the thin-plate spline interpolation. It is then applied to functional activations located in the vicinity of this sulcus. This approach is compared with rigid and nonrigid registration methods. In this paper, we present results on MEG somatosensory data acquired on 18 subjects. We show that the nonlinear local fusion scheme significantly reduces the observed functional variability.     

U-shaped association of serum uric acid with cardiovascular disease risk scores and the modifying role of sex among Chinese adults

Background and aimsSerum uric acid (SUA) is involved in the development of cardiovascular disease (CVD). However, information on the dose-response relationship between SUA and CVD is limited in the Chinese population. This study aimed to investigate the potential nonlinear dose-response association of SUA with CVD risk in a Chinese population and to explore the effect of sex on these associations.Methods and resultsCross-sectional data, from 6252 Chinese adults aged 30–74 years who participated in the China Health and Nutrition Survey 2009, were stratified by SUA deciles. The 10-year risk of CVD was determined using the Framingham risk score. A restricted cubic spline (RCS) was incorporated into the logistic models to assess the nonlinear relationship between SUA and CVD. Among the participants, 65%, 20%, and 15% had low, moderate, and high 10-year CVD risks, respectively. Compared with the reference SUA strata of 225 to <249 μmol/L, CVD risk was significantly increased at SUA ≥294 μmol/L, with adjusted ORs ranging from 2.39 (1.33–4.33) to 4.25 (2.37–7.65). An increasingly higher nonsignificant CVD risk was found at SUA <225 μmol/L and showed a nonlinear U-shaped association. In the fitted RCS model, an approximate U-shaped association between SUA and CVD risk scores was found in women, but this significant nonlinear relationship was not found in men.ConclusionThis study showed that both lower and higher SUA levels were associated with a higher 10-year CVD risk among Chinese adults, forming a U-shaped relationship, and this pattern was particularly pronounced for women.     

Solving the Faddeev-Merkuriev Equations in Total Orbital Momentum Representation via Spline Collocation and Tensor Product Preconditioning

The computational approach for solving the Faddeev-Merkuriev equationsin total orbital momentum representation is presented. These equations describe a system of three quantum charged particles and are widely used in bound state and scattering calculations. The approach is based on the spline collocation method and exploitsintensively the tensor product form of discretized operators and preconditioner, whichleads to a drastic economy in both computer resources and time.     

基于组合不变量配准的数字减影算法

在现代医学中，DSA是X光片血管可视化技术的重要组成部分之一，在DSA中图像配准对消除运动伪影起着关键作用。运用一种基于组合不变量的相似度测量的模板匹配和弹性TPS算法配准减影方法，并根据实际情况有所改进。经过试验证明这种方法可以自动并有效地去除运动伪影，其速度和血管图像质量均比较理想。     

Estimating lead‐time bias in lung cancer diagnosis of patients with previous cancers

Surprisingly, survival from a diagnosis of lung cancer has been found to be longer for those who experienced a previous cancer than for those with no previous cancer. A possible explanation is lead‐time bias, which, by advancing the time of diagnosis, apparently extends survival among those with a previous cancer even when they enjoy no real clinical advantage. We propose a discrete parametric model to jointly describe survival in a no‐previous‐cancer group (where, by definition, lead‐time bias cannot exist) and in a previous‐cancer group (where lead‐time bias is possible). We model the lead time with a negative binomial distribution and the post–lead‐time survival with a linear spline on the logit hazard scale, which allows for survival to differ between groups even in the absence of bias; we denote our model Logit‐Spline/Negative Binomial. We fit Logit‐Spline/Negative Binomial to a propensity‐score matched subset of the Surveillance, Epidemiology, and End Results–Medicare linked data set, conducting sensitivity analyses to assess the effects of key assumptions. With lung cancer–specific death as the end point, the estimated mean lead time is roughly 11 months for stage I&II patients; with overall survival, it is roughly 3.4 months in stage I&II. For patients with higher‐stage lung cancers, the mean lead time is 1 month or less for both outcomes. Accounting for lead‐time bias reduces the survival advantage of the previous‐cancer group when one exists, but it does not nullify it in all cases.     

Regression B-spline smoothing in Bayesian disease mapping: with an application to patient safety surveillance

In the context of Bayesian disease mapping, recent literature presents generalized linear mixed models that engender spatial smoothing. The methods assume spatially varying random effects as a route to partially pooling data and 'borrowing strength' in small-area estimation. When spatiotemporal disease rates are available for sequential risk mapping of several time periods, the 'smoothing' issue may be explored by considering spatial smoothing, temporal smoothing and spatiotemporal interaction. In this paper, these considerations are motivated and explored through development of a Bayesian semiparametric disease mapping model framework which facilitates temporal smoothing of rates and relative risks via regression B-splines with mixed-effect representation of coefficients. Specifically, we develop spatial priors such as multivariate Gaussian Markov random fields and non-spatial priors such as unstructured multivariate Gaussian distributions and illustrate how time trends in small-area relative risks may be explored by splines which vary in either a spatially structured or unstructured manner. In particular, we show that with suitable prior specifications for the random effects ensemble, small-area relative risk trends may be fit by 'spatially varying' or randomly varying B-splines. A recently developed Bayesian hierarchical model selection criterion, the deviance information criterion, is used to assess the trade-off between goodness-of-fit and smoothness and to select the number of knots. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risks and to quantify uncertainty. The study offers a disease and health outcome surveillance methodology for flexible and efficient exploration and assessment of emerging risk trends and clustering. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (also known as iatrogenic injuries) among hospitalized elderly patients in British Columbia, Canada.     

基于限制性立方样条模型分析中老年人群空腹血糖受损与体质指数的关系

目的 :调查重庆地区中老年人群空腹血糖(fasting blood-glucose,FBG)水平,分析空腹血糖受损(impaired fasting glucose,IFG)相关的危险因素,为糖尿病的防控提供依据。方法:回顾性分析2019年1至12月在重庆医科大学附属第一医院进行体检的中老年人群,收集空腹血糖FBG及相关生理生化指标,应用logistic回归模型结合限制性立方样条模型(restricted cubic splines,RCS)分析IFG与体质指数(body mass index,BMI)的剂量-反应关系。结果:44 525名体检者共检出3 913例IFG,检出率为8.79%,其中男性检出率为10.71%;女性检出率为6.84%(P<0.001)。将研究对象分为正常血糖(normal glucose tolerance,NGT)组和IFG组,2组在性别、年龄、BMI、腰围(waist circumference,WC)、收缩压(systolic blood pressure,SBP)、舒张压(diastolic blood pressure,DBP)、高密度脂蛋白(...     

Signal extraction and the formulation of unobserved components models

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are four main themes. The first concerns the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is about the way in which ARIMA‐based methods for trend extraction relate to those based on unobserved components. The third explores the impact of heteroscedasticity and irregular spacing and shows how setting up models with t‐distributed disturbances leads to weighting patterns which are robust to outliers and breaks. Finally, a comparison is made between implied weighting patterns with kernels used in non‐parametric trend estimation and equivalent kernels used in spline smoothing. It is demonstrated that with irregularly spaced data, the weighting used by conventional spline smoothing techniques is not the same as that obtained from the time series model based approach.     

基于微区域PM2.5浓度卡尔曼插值预测模型的研究

目前颗粒物(尤其是PM2.5)污染问题日趋严重,人们对其关注度越来越高。本文提出一种结合三次样条插值方法的卡尔曼预测模型并将其应用于微区域校园环境PM2.5浓度的预测,以及实现PM2.5浓度的插值模拟图,模拟PM2.5的空间分布。本文实验基于实验室已搭建的环境信息监测系统服务器数据,其PM2.5浓度数据预测值和实际值通过Wilcoxon带符号秩检验后,双侧渐进显著性概率为0.527,远大于显著性水平α=0.05。同时,与神经网络模型预测方法(BP预测)和支持向量机预测方法(SVM预测)对比,卡尔曼预测模型的结果更理想,其日均值PM2.5浓度数据预测值和监测值的平均绝对误差(MEA)为1.8μg/m3,平均相对误差(MER)为6%,相关系数R为0.87。实验结果表明:卡尔曼预测模型能有效地用于PM2.5浓度预测,结合样条插值方法可以较好地模拟PM2.5的空间分布及局部污染特征。