赣榆县厉庄地埂利用型式试验

在赣榆县厉庄以大金鸡菊、紫穗槐和红柳三种经济植物进行了地埂利用型式试验,取得了比较明显的水土保持效果和改善农田小环境的生态效益。栽植后第三年植物覆盖率达90％或100％;地面蒸发量减少15.2～17.7％;截留降水量为0.5～0.7mm;大气相对湿度提高3.7～5.4％;土壤养分也有不同程度的增加。经济效益估测每亩地埂可收到300～600元,比种黄豆高5～9倍。效益最好的是红柳,大金鸡菊次之,紫穗槐较低。     

Hearing in honeybees: localization of the auditory sense organ

Airborne sound signals emitted by dancing honeybees (Apis mellifera) contain information about the locations of food sources. Honeybees can perceive these near field sounds and rely on them to decode the messages of the dance language. The dance sound is characterized by rhythmical air particle movement of high velocity amplitudes. The aim of the present study was to identify the sensory structures used to detect near field sound signals. In an operant conditioning experiment, bees were trained to respond to sound. Ablation experiments with these trained bees revealed that neither mechanosensory hairs on the antennae or head nor bristle fields at the joints of the antenna, but Johnston's organ, a chordotonal organ in the pedicel of the antenna, is used to detect near field sound in honeybees.     

Characterization of Borrelia Species Isolated from Ixodid Ticks,Ixodes ovatus

Thirty-two Borrelia isolates were obtained from the adult stage of ixodid ticks, Ixodes ovatus, collected in various localities in Japan. Borrelial isolates were cultivated and analyzed by polyacrylamide gel electrophoresis, with monoclonal antibodies, by pulsed field gel electrophoresis, and by genomic Southern hybridization. All borrelial isolates showed similar protein profiles and monoclonal antibody reactivities, while plasmid profiles were rather diverse. Genomic hybridization using rRNA gene probes demonstrated the genetic similarities of those isolates. We found no significant differences among the borrelial isolates tested, and the restriction fragment length polymorphism patterns of I. ovatus isolates were quite distinct from those of borrelial strains associated with Lyme disease. Therefore, the isolates of Borrelia obtained from I. ovatus were thought to fall into different genospecies.     

Longitudinal growth of head circumference in Punjabi infants in Chandigarh (India)

One hundred and fifty four (86 male and 68 female) Punjabi infants residing in Chandigarh (India) were longitudinally measured for head circumference at monthly age intervals during first year of life. The general pattern of growth of head circumference was characterised by initial sharp rise followed by slow gain during second half of infancy. Beyond birth male infants, possessed higher and statistically significant mean values than their female counterparts. The pattern-wise similarity between growth curves plotted for Punjabi and Western infants, may be attributed to protective effects of breast feeding. Head circumference velocity showed rapid deceleration immediately after birth up to about 4 months, thereafter, it declined slowly. Sex differences in monthly growth rates were found to be statistically significant (p<0.05) at a few of the age intervals during first year of life.     

Latent group detection in functional partially linear regression models

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.     

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.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

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.     

Elastic priors to dynamically borrow information from historical data in clinical trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.