Dracophane,a metacyclophane derivative from the resin of Dracaena cinnabari Balf

Dracophane, a novel structural derivative of metacyclophane, was isolated from the resin of Dracaena cinnabari Balf. The structure of this compound was determined by spectroscopic methods to be 3,12,21-trihydroxy-1,10,19-tris(4-hydroxyphenyl)-5,14,23-trimethoxy[3.3.3]metacyclophane.     

Growth dynamics of Dracaena cinnabari under controlled conditions as the most effective way to protect endangered species

Dracaena cinnabari Balf. fil. is an endangered endemic species growing on the Yemeni island of Soqotra. Dracaena woodlands are considered as one of the oldest forest communities on Earth. Uncontrolled grazing unfortunately caused a lack of naturally occurring regeneration. Our two-year research was focused on the growth dynamics of Dracaena seedlings from two separate populations. One hundred of germinated seeds from two different altitudes from the island were sown and planted under the same conditions. Average increment and difference between the growth dynamics of plants from the two localities were investigated. The observed data on this plant species revealed very interesting, hitherto unknown results. (1) The seedlings germinated within a time period from four to ten weeks. Germination rate was 90% on the Firmihin highland plateau and 78% on the Scand Mountain. (2) Average plant length from both localities was almost the same (24.9 cm) at the end of measurement. Differences in values between the two populations proved as non-significant. (3) A significant difference was found in the number of leaves and in the sum of lengths of all leaves on one plant. While the seedlings from Firmihin featured a wide spreading above-ground part with a large number of leaves, the plants from Scand invested more energy into faster leaves elongation rate. (4) Growth dynamics reflected seasonal changes. Increments were slower or ceased during the period of vegetative rest from autumn to spring. (5) Average mortality rate was 13%. Most of the plants died during the period of vegetative rest. Further study on germination and regeneration under artificial conditions seems like the only way to prevent species extinction.     

A computer program for stepwise logistic regression using maximum likelihood estimation

A computer program has been written which performs a stepwise selection of variables for logistic regression using maximum likelihood estimation. The selection procedure is based on likelihood ratio tests for the coefficients. These tests are used in a forward selection and a backward elimination at each step. The use of the program is illustrated by several examples.     

Bayesian logistic regression using a perfect phylogeny

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.     

基于边际回归的沈阳市绿化树种人工油松冠形模拟

构建城市典型绿化树种树冠外部轮廓预估模型,可以为城市绿化树种的空间配置优化奠定基础。本研究以辽宁省沈阳市典型绿化树种人工油松为对象,基于Crown Window装置获取60株样木树冠形状,利用幂函数、分段抛物线方程和修正Kozak方程选取基础模型,通过再参数化引入树冠结构因子(最大树冠半径)和相邻木竞争变量(相邻木平均树高、平均胸径、平均冠幅、相邻木株数,以及样木与其相邻木平均树冠接触高),构建耦合相邻木竞争及树冠结构因子的油松树冠外部轮廓预估模型。结果表明: 修正Kozak方程R_a²最大、均方根误差(RMSE)最小,模型稳定性良好,选取其为构建油松冠形模型的基础模型。通过再参数化在基础模型中引入最大树冠半径和相邻木平均胸径后,模型的R_a²提高0.0693,MSER为14.4%。分析最大树冠半径和相邻木竞争对油松树冠形状的影响发现,最大树冠半径对树冠形状影响较大,树冠半径随最大树冠半径增大而增大;相邻木平均胸径对树冠形状影响相比最大树冠半径较弱,树冠上半部分随相邻木竞争增强而增大,树冠下半部分随相邻木竞争增强而变小。本研究构建的耦合相邻木竞争及最大树冠半径的油松树冠外部轮廓边际回归模型具有良好的拟合优度,能够合理地模拟及预测沈阳市典型绿化树种人工油松的树冠形状。     

Modeling spatial distribution of plant species using autoregressive logistic regression method-based conjugate search direction

Plant Ecology - Modeling plant habitat range distributions is critical for monitoring and restoring species in their natural habitat. The classical logistic regression (LR) model for plant habitat...     

Variable selection for logistic regression using a prediction-focused information criterion

In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC measures the mean squared error of the estimator of the quantity of interest in the selected model. In this article, we propose more general versions of the FIC, allowing other risk measures such as the one based on L(p) error. When prediction of an event is important, as is often the case in medical applications, we construct an FIC using the error rate as a natural risk measure. The advantages of using an information criterion which depends on both the quantity of interest and the selected risk measure are illustrated by means of a simulation study and application to a study on diabetic retinopathy.     

Maximum entropy,logistic regression,and species abundance

There is considerable debate about the utility of statistical mechanics in predicting diversity patterns in terms of life history traits. Here, I reflect on this debate and show that a community is controlled by the balance of two opposite forces: the entropic part (the natural tendency of the system to be in the configuration with the highest possible entropy) and environmental, ecological and evolutionary constraints maintaining order (reducing entropy). The Boltzmann distribution law that can be derived from the maximum entropy formalism provides a fundamental model for linking species abundance to life history traits and environmental constraining factors. This model predicts a global pattern of diversity evenness along a latitudinal gradient. Although the Boltzmann distribution and the logistic regression models represent two fundamentally different approaches, the two models have an identical mathematical form. Their identical formalisms facilitate the interpretation of logistic regression models with statistical mechanics, and reveal several limitations of the maximum entropy formalism. I argued that although maximum entropy formalism is a promising tool for modeling species abundances and for linking microscopic quantities of individual life history traits to macroscopic patterns of diversity, it is necessary to revise the Boltzmann distribution law for successful prediction of species abundance.     

Attributable risk estimation from case-control data via logistic regression.

By fitting an unconditional logistic regression model to unmatched case-control data, an estimate of the joint population attributable risk for the factor included is obtained. This estimate and its asymptotic variance can easily be computed from the intercept parameter and its asymptotic variance. A generalization to the analysis of stratified data with large strata enables the calculation of stratum-specific attributable risks and their variances via stratum-specific intercept parameters. If sampling of cases is independent of strata, an estimate of the summary attributable risk and its asymptotic variance may be obtained as a weighted sum of the stratum-specific attributable risks.     

Roystonea regia a monocotyledonous tree, bears rhizobial root nodules

The first instance of a monocotyledonous treeRoystonea regia (royal palm) bearing root nodules is reported. They are formed by a Gram-negative bacterium which was tentatively identified asRhizobium sp. The nodules are formed on younger roots of the tree mostly in the rainy season. Younger plants also become nodulated. Mature nodules are oval to cylindrical in shape, unbranched and loosely attached to the roots which show ability to fix nitrogen.     

Approximate maximum likelihood estimation for logistic regression with covariate measurement error

In nutritional epidemiology, dietary intake assessed with a food frequency questionnaire is prone to measurement error. Ignoring the measurement error in covariates causes estimates to be biased and leads to a loss of power. In this paper, we consider an additive error model according to the characteristics of the European Prospective Investigation into Cancer and Nutrition (EPIC)‐InterAct Study data, and derive an approximate maximum likelihood estimation (AMLE) for covariates with measurement error under logistic regression. This method can be regarded as an adjusted version of regression calibration and can provide an approximate consistent estimator. Asymptotic normality of this estimator is established under regularity conditions, and simulation studies are conducted to empirically examine the finite sample performance of the proposed method. We apply AMLE to deal with measurement errors in some interested nutrients of the EPIC‐InterAct Study under a sensitivity analysis framework.     

Crown exposure to light and tree allometry of 11 tree species in a snowy cool-temperate forest in Japan

Crown exposure to light (CE) and tree allometry were investigated for 11 species in a snowy cool-temperate secondary forest dominated by Fagus crenata and Betula ermanii in Japan. The 11 species differentiated horizontal and vertical light gradients for regeneration. CE was highly variable across species in small trees, but variation in CE decreased with increasing height. The 11 species were classified into three patterns of height-dependent change in CE in comparison to community-level trends, and rank reversal of CE with increasing height was not apparent. Allometric relationships between trunk diameter (D) and height (H) and between D and trunk length (L) differed little between trees of high and low CE within species. In contrast, slopes of the allometric relationships between D and H differed across species; species with larger maximum height (H _max) were taller at a given D, as was noted in previous studies of warm-temperate and tropical forest trees. Differences in trunk angle among the species of different H _max were the main factor generating the differences in allometric relationships between D and H in this forest. Trunk angle increased with increasing height in the species of large H _max but decreased in those of small H _max. Hence, allometric relationships between D and L were not related to H _max. Since the species of small H _max grow laterally and are easily covered in snow during winter while those of large H _max grow vertically above snow cover, differences in trunk angle may reflect species mechanical properties.     

Classification using partial least squares with penalized logistic regression

MOTIVATION: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. RESULTS: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.