新疆猪毛菜属植物多样性地理分布格局及其环境解释

猪毛菜属(Salsola)是新疆干旱区分布最为丰富的被子植物属之一,是盐碱和荒漠区的先锋种和建群种,对西北干旱区植被恢复与建设具有巨大生态价值。基于新疆自然分布的33种猪毛菜属物种共741个分布数据,整合利用点格局法和物种分布模型法构建了物种丰富度(SR)、加权特有性指数(WE)和校正加权特有性指数(CWE)的分布格局。选取环境能量、水分可获得性、气候季节性、生境异质性、土壤条件和历史气候变化共6类19种生态因子,利用地理加权回归模型(GWR)探究了环境异质性对猪毛菜属物种丰富度的影响。结果显示：(1)基于现实点位模型和物种分布模型构建的物种丰富度具有一致性,均呈北高南低、西高东低的破碎化分布趋势,但物种分布模型的结果在空间上比点格局法更连续,物种丰富度的高值区主要分布于准噶尔盆地南缘、准噶尔西部山地、天山西端和天山南脉南缘;(2)加权特有性指数和校正加权特有性指数的分布格局与物种丰富度分布格局具有一定差异,其最大值集中分布于准噶尔盆地南缘、伊犁河谷和塔里木盆地西南缘;(3)GWR模型结果表明,海拔变幅、土壤酸碱度和最干月降水量是制约新疆分布的猪毛菜属丰富度和特有性分布的最重要因素。     

黄河流域生态安全屏障防风固沙时空变化及驱动因素

黄河流域是我国重要的生态屏障和经济区,防风固沙服务及其驱动机制的研究对于筑牢黄河流域生态安全屏障具有重要意义。基于修正土壤风蚀模型(RWEQ)定量评估黄河流域2000—2018年防风固沙服务时空变化,采用相关分析和地理加权回归模型(GWR)从社会、气候、土壤、植被、地形等角度探究各驱动因素对黄河流域防风固沙服务影响的空间异质性。结果表明：(1)2000—2018年黄河流域土壤风蚀模数与单位面积防风固沙量分别以0.04 t hm^-2 a^-1和0.14 t hm^-2 a^-1的速率下降,而防风固沙保有率和植被覆盖度则分别以0.05%·a^-1和0.26%·a^-1的速率上升。(2)黄河流域土壤风蚀以微度(<2 t hm^-2 a^-1)和轻度(2—25 t hm^-2 a^-1)为主,共占研究区面积的96.28%。土壤风蚀在空间分布呈现西北高东南低的特点,而防风固沙服务高值则主要分...     

海南岛热带山地雨林林分生物量估测方法比较分析

本文通过对海南岛热带山地雨林林分生物量估测方法的比较分析,表明材积转换法不适宜估算海南岛热带山地雨林林分生物量,其结果与皆伐法相比较一般偏高20％—40％;而用实测资料建立的生物量回归模型,对原始林林分有较好的估测结果,除树枝和树叶生物量外,树干、树皮及地上部分生物量的回归模型值,与皆伐法的结果比较,相对误差一般在±10％以内,为允许误差范围,而对热带山地雨林的更新林生物量的估测则效果较差,应建立相应的估测模型。平均木法有工作量小的优点,且误差也在16％以下,但要注意取样的树种多样性和取样强度,在实际中应当慎用。另外本文对测定热带山地雨林生物量(原始林)的所需面积大小问题作了研究,提出了生物量-面积曲线的概念,确定其最小调查面积为2500m~2以上。     

Variable selection in nonlinear function-on-scalar regression

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003–2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.     

Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.