An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Correction of a bootstrap approach to testing for evolution along lines of least resistance

Testing for an association between the leading vectors of multivariate trait (co)variation within populations (the ‘line of least resistance’) and among populations is an important tool for exploring variational bias in evolution. In a recent study of stickleback fish populations, a bootstrap‐based test was introduced that takes into account estimation error in both vectors and hence improves the previously available bootstrap method. Because this test was implemented incorrectly, however, I here describe the correct test protocol and provide a reanalysis of the original data set. The application of this new test protocol should improve future investigations of evolution along lines of least resistance and other vector comparisons.     

Conditional Maximum Likelihood Estimation Following a Group Sequential Test

We consider estimation after a group sequential test. An estimator that is unbiased or has small bias may have substantial conditional bias (Troendle and Yu, 1999, Coburger and Wassmer, 2001). In this paper we derive the conditional maximum likelihood estimators of both the primary parameter and a secondary parameter, and investigate their properties within a conditional inference framework. The method applies to both the usual and adaptive group sequential test designs. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

植物KCO基因密码子使用特性

以植物钾离子外排通道（K’channeloutward．rectifier,KCO）基因为研究对象,运用CodonW软件分析了75个植物KCO基因密码子的使用模式,探讨密码子的使用模式和影响密码子使用的各种可能因素。结果表明：碱基组成差异（r=0．961,P〈0．01）和自然选择（r=0．568,P〈0．01）是影响密码子使用的主要因素,并且高表达的基因强烈偏爱使用以G或C结尾的密码子。确定了UUC、CUC等26个均以G／C结尾的密码子为植物KcD基因的高表达优越密码子。     

A Multiphase Design Strategy for Dealing with Participation Bias

Summary A recently funded study of the impact of oral contraceptive use on the risk of bone fracture employed the randomized recruitment scheme of Weinberg and Wacholder (1990, Biometrics 46, 963–975). One potential complication in the bone fracture study is the potential for differential response rates between cases and controls; participation rates in previous, related studies have been around 70%. Although data from randomized recruitment schemes may be analyzed within the two‐phase study framework, ignoring potential differential participation may lead to biased estimates of association. To overcome this, we build on the two‐phase framework and propose an extension by introducing an additional stage of data collection aimed specifically at addressing potential differential participation. Four estimators that correct for both sampling and participation bias are proposed; two are general purpose and two are for the special case where covariates underlying the participation mechanism are discrete. Because the fracture study is ongoing, we illustrate the methods using infant mortality data from North Carolina.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

Projecting future grassland productivity to assess the sustainability of potential biofuel feedstock areas in the Greater Platte River Basin

This study projects future (e.g., 2050 and 2099) grassland productivities in the Greater Platte River Basin (GPRB) using ecosystem performance (EP, a surrogate for measuring ecosystem productivity) models and future climate projections. The EP models developed from a previous study were based on the satellite vegetation index, site geophysical and biophysical features, and weather and climate drivers. The future climate data used in this study were derived from the National Center for Atmospheric Research Community Climate System Model 3.0 ‘SRES A1B’ (a ‘middle’ emissions path). The main objective of this study is to assess the future sustainability of the potential biofuel feedstock areas identified in a previous study. Results show that the potential biofuel feedstock areas (the more mesic eastern part of the GPRB) will remain productive (i.e., aboveground grassland biomass productivity >2750 kg ha^?1 year^?1) with a slight increasing trend in the future. The spatially averaged EPs for these areas are 3519, 3432, 3557, 3605, 3752, and 3583 kg ha^?1 year^?1 for current site potential (2000–2008 average), 2020, 2030, 2040, 2050, and 2099, respectively. Therefore, the identified potential biofuel feedstock areas will likely continue to be sustainable for future biofuel development. On the other hand, grasslands identified as having no biofuel potential in the drier western part of the GPRB would be expected to stay unproductive in the future (spatially averaged EPs are 1822, 1691, 1896, 2306, 1994, and 2169 kg ha^?1 year^?1 for site potential, 2020, 2030, 2040, 2050, and 2099). These areas should continue to be unsuitable for biofuel feedstock development in the future. These future grassland productivity estimation maps can help land managers to understand and adapt to the expected changes in future EP in the GPRB and to assess the future sustainability and feasibility of potential biofuel feedstock areas.     

Plastid Sequence Evolution: A New Pattern of Nucleotide Substitutions in the Cucurbitaceae

Nucleotide substitutions (i.e., point mutations) are the primary driving force in generating DNA variation upon which selection can act. Substitutions called transitions, which entail exchanges between purines (A=adenine, G=guanine) or pyrimidines (C=cytosine, T=thymine), typically outnumber transversions (e.g., exchanges between a purine and a pyrimidine) in a DNA strand. With an increasing number of plant studies revealing a transversion rather than transition bias, we chose to perform a detailed substitution analysis for the plant family Cucurbitaceae using data from several short plastid DNA sequences. We generated a phylogenetic tree for 19 taxa of the tribe Benincaseae and related genera and then scored conservative substitution changes (e.g., those not exhibiting homoplasy or reversals) from the unambiguous branches of the tree. Neither the transition nor (A+T)/(G+C) biases found in previous studies were supported by our overall data. More importantly, we found a novel and symmetrical substitution bias in which Gs had been preferentially replaced by A, As by C, Cs by T, and Ts by G, resulting in the GACTG substitution series. Understanding this pattern will lead to new hypotheses concerning plastid evolution, which in turn will affect the choices of substitution models and other tree-building algorithms for phylogenetic analyses based on nucleotide data.     

The mutational structure of metabolism in Caenorhabditis elegans

A properly functioning organism must maintain metabolic homeostasis. Deleterious mutations degrade organismal function, presumably at least in part via effects on metabolic function. Here we present an initial investigation into the mutational structure of the Caenorhabditis elegans metabolome by means of a mutation accumulation experiment. We find that pool sizes of 29 metabolites vary greatly in their vulnerability to mutation, both in terms of the rate of accumulation of genetic variance (the mutational variance, VM) and the rate of change of the trait mean (the mutational bias, ΔM). Strikingly, some metabolites are much more vulnerable to mutation than any other trait previously studied in the same way. Although we cannot statistically assess the strength of mutational correlations between individual metabolites, principal component analysis provides strong evidence that some metabolite pools are genetically correlated, but also that there is substantial scope for independent evolution of different groups of metabolites. Averaged over mutation accumulation lines, PC3 is positively correlated with relative fitness, but a model in which metabolites are uncorrelated with fitness is nearly as good by Akaike's Information Criterion.     

Quantifying agonist activity at G protein-coupled receptors

When an agonist activates a population of G protein-coupled receptors (GPCRs), it elicits a signaling pathway that culminates in the response of the cell or tissue. This process can be analyzed at the level of a single receptor, a population of receptors, or a downstream response. Here we describe how to analyze the downstream response to obtain an estimate of the agonist affinity constant for the active state of single receptors. Receptors behave as quantal switches that alternate between active and inactive states (Figure 1). The active state interacts with specific G proteins or other signaling partners. In the absence of ligands, the inactive state predominates. The binding of agonist increases the probability that the receptor will switch into the active state because its affinity constant for the active state (K(b)) is much greater than that for the inactive state (K(a)). The summation of the random outputs of all of the receptors in the population yields a constant level of receptor activation in time. The reciprocal of the concentration of agonist eliciting half-maximal receptor activation is equivalent to the observed affinity constant (K(obs)), and the fraction of agonist-receptor complexes in the active state is defined as efficacy (ε) (Figure 2). Methods for analyzing the downstream responses of GPCRs have been developed that enable the estimation of the K(obs) and relative efficacy of an agonist. In this report, we show how to modify this analysis to estimate the agonist K(b) value relative to that of another agonist. For assays that exhibit constitutive activity, we show how to estimate K(b) in absolute units of M(-1). Our method of analyzing agonist concentration-response curves consists of global nonlinear regression using the operational model. We describe a procedure using the software application, Prism (GraphPad Software, Inc., San Diego, CA). The analysis yields an estimate of the product of K(obs) and a parameter proportional to efficacy (τ). The estimate of τK(obs) of one agonist, divided by that of another, is a relative measure of K(b) (RA(i)). For any receptor exhibiting constitutive activity, it is possible to estimate a parameter proportional to the efficacy of the free receptor complex (τ(sys)). In this case, the K(b) value of an agonist is equivalent to τK(obs)/τ(sys). Our method is useful for determining the selectivity of an agonist for receptor subtypes and for quantifying agonist-receptor signaling through different G proteins.