Varying coefficient model with unknown within-subject covariance for analysis of tumor growth curves

SUMMARY: In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback-Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors.     

Population size estimation in a two-list surveillance system with a discrete covariate

Summary .   Capture–recapture methods are widely adopted to estimate sizes of populations of public health interest using information from surveillance systems. For a two-list surveillance system with a discrete covariate, a population is divided into several subpopulations. A unified framework is proposed in which the logits of presence probabilities are decomposed into case effects and list effects. The estimators for the whole population and subpopulation sizes, their adjusted versions, and asymptotic standard errors admit closed-form expressions. Asymptotic and bootstrap individual and simultaneous confidence intervals are easily constructed. Conditional likelihood ratio tests are used to select one from three possible models. Real examples are investigated.     

A ten-year summary of reproductive parameters for ring-tailed lemurs at berenty, madagascar

From 1989 to 1998, 204 live births were recorded for ring-tailed lemurs (Lemur catta) at Berenty, Madagascar. Excluding unknown birth dates, the peak month of birth was September, with 82.0% (146/178) occurring during this period. The offspring sex ratio (1∶1.19) was not significantly different from 1∶1, and there was no association with the mother's age. The first births occurred at the ages of 2 to 4 yr. The annual birth rate was very low at the age of 2 yr (11.1%), but increased thereafter: to 50.0% at the age of 3 yr, and to 75–85% at the age of 4 or more years. Multiple births were very rare, since only three sets of twins and one set of triplets were recorded. As for the interbirth interval, a one-year interval was the most common (92.2%). Infant mortality within the first year was 37.7% (77/204). Neonatal mortality within the first month accounted for 31.2% of all infant dealths.     

Prediction Intervals in Nonlinear Regression

By treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two-sided nominally-q-prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a feasible value of the parameter vector is described. The quadratic approximation of the expected coverage is then obtained for a two-parameter nonlinear model. Finally we show how we may construct the prediction intervals when a certain type of nonlinear transformation of the parameter vector has been applied.     

The fossil record and estimating divergence times between lineages: Maximum divergene times and the importance of reliable phylogenies

Summary Bounded estimates on divergence times between lineaes are crucial to the calculation of absolute rates of molecular evolution. Upper (minimum) bounds on divergence times are easily estimated based on earliest fossil finds. Lower (maximum) bounds are more difficult to estimate; the age of putative ancestors may be used, though in practice it is virtually impossible to distinguish ancestors from primitive sister groups, which do not, of logical necessit, consitute lower bounds on divergence times. Two relatively new approaches to estimating lower bounds directly assess the incompleteness of the fossil record. The first uses taphonomic control groups to distinguish real absences from nonpreservation, while the second, and probably more powerful, uses the quality of the fossil recored to estimate confidence intervals on the bases of stratigraphic ranges. For some groups, especially vertebrates, the inclusion or exclusion of problematic fossils can dramaticaly affect estimated lower bounds on divergence times, often swamping the uncertainties due to the incompleteness of the fossil record and/or corelation and dating errors. When datable paleogeographic events reflect ancient divisions of faunas, a lower bound on the divergence time of speices within a fauna can be established based on the geologic, rather than fossil, record. The fossil records of hominids, eutherianmammals, echinoids, and geese are used as examples.This article was presented at the C.S.E.O.L. Conferrence on DNA-DNA Hybridization and Evolution, Lake Arrowhead, California, May 11–14, 1989     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

The spike trains generated by a neuron model are studied by the methods of nonlinear time series analysis. The results show that the spike trains are chaotic. To investigate effect of noise on transmission of chaotic spike trains, this chaotic spike trains are used as a discrete subthreshold input signal to the integrate-and-fire neuronal model and the FitzHugh-Nagumo(FHN) neuronal model working in noisy environment. The mutual information between the input spike trains and the output spike trains is calculated, the result shows that the transformation of information encoded by the chaotic spike trains is optimized by some level of noise, and stochastic resonance(SR) measured by mutual information is a property available for neurons to transmit chaotic spike trains.     

Sequential estimation for prescribed statistical accuracy in stochastic simulation of biological systems

Stochastic simulation of biological systems proceeds by repeatedly generating sample paths or trajectories of the underlying stochastic process, from which many relevant and important system properties can be obtained. While a great deal of research is targeted towards accelerated trajectory generation, issues concerned with the variability across trajectories are often neglected. Advanced methods for properly quantifying the statistical accuracy and determining a reasonable number of trajectories are hardly addressed formally in the context of biological system simulation, though mathematical statistics provides a large body of powerful theory. We invoke this theory and show how mathematically well-founded sequential estimation approaches serve for systematically generating enough but not too many trajectories for achieving a certain prescribed accuracy. The practical applicability is demonstrated and illustrated by numerical examples through simulation studies of an immigration-death process and a gene regulatory network.     

The structure of feeding behavior in a phytophagous insect (Hylobius abietis)

Analysis of the feeding behavior of animals using such a high temporal resolution that meals can be defined may improve our understanding of the mechanisms regulating feeding. Meals can be distinguished in an ethologically meaningful manner by using the ‘meal criterion’, the shortest non‐feeding interval between feeding bouts recognized as meals. However, such a criterion has only been determined for a few insect species. Applying a recent method developed for assessing meal criteria for vertebrates, we determined the meal criterion for Hylobius abietis (L.) (Coleoptera: Curculionidae) based on data from video recordings of single individuals feeding on seedlings of Norway spruce, Picea abies (L.) Karst. (Pinaceae). The pine weevil is an economically important pest insect, because it feeds on the stem bark of planted conifer seedlings. Weevils had 4–5 meals per day. Each meal lasted about 24 min during which about 13 mm² of bark per meal were removed. Females had longer total meal durations and longer non‐feeding intervals within meals than males. Girdling seedlings did not affect the weevils' feeding properties. The size of meals was significantly correlated with the duration of non‐feeding intervals before and after them. This study is one of few describing the feeding behavior of an insect at a temporal resolution that allows individual meals to be distinguished. With more meal‐related data from insects available, differences in meal properties may be interpreted based on phylogeny, ecology, and physiology. Our results may also assist in the setup and interpretation of studies of plant‐insect interactions, and facilitate the evaluation and development of methods to protect plants against herbivores.     

大鼠初级传入纤维与脊髓背角神经元间的动作电序列的突触传递

外周感觉神经元通过动作电位序列对信号进行编码,这些动作电位序列经过突触传递最终到达脑部。但是各种脉冲序列如何通过神经元之间的化学突触进行传递依然是一个悬而未决的问题。研究了初级传入A6纤维与背角神经元之间各种动作电位序列的突触传递过程。用于刺激的规则,周期、随机脉冲序列由短簇脉冲或单个脉冲构成。定义“事件”(event)为峰峰问期(intefspike interval)小于或等于规定阈值的最长动作电位串,然后从脉冲序列中提取事件间间期(interevent interval,IEI)。用时间,IEI图与回归映射的方法分析IEI序列,结果表明在突触后输出脉冲序列中可以检测到突触前脉冲序列的主要时间结构特征,特别是在短簇脉冲作为刺激单位时。通过计算输入与输出脉冲序列的互信息,发现短簇脉冲可以更可靠地跨突触传递由输入序列携带的神经信息。这些结果表明外周输入脉冲序列的主要时间结构特征可以跨突触传递,在突触传递神经信息的过程中短簇脉冲更为有效。这一研究在从突触传递角度探索神经信息编码方面迈出了一步。