Authentication of feeding fats: Classification of animal fats,fish oils and recycled cooking oils

Classification of fats and oils involves the recognition of one/several markers typical of the product. The ideal marker(s) should be specific to the fat or oil. Not many chemical markers fulfill these criteria. Authenticity assessment is a difficult task, which in most cases requires the measurement of several markers and must take into account natural and technology-induced variation. The present study focuses on the identity prediction of three by-products of the fat industry (animal fats, fish oils, recycled cooking oils), which may be used for animal feeding. Their identities were predicted by their triacylglycerol fingerprints, their fatty acid fingerprints and their profiles of volatile organic compounds. Partial least square discriminant analysis allowed samples to be assigned successfully into their identity classes. Most successful were triacylglycerol and fatty acid fingerprints (both 96% correct classification). Proton transfer reaction mass spectra of the volatile compounds predicted the identity of the fats in 92% of the samples correctly.     

Gaussian Process Based Bayesian Semiparametric Quantitative Trait Loci Interval Mapping

Summary .  In linkage analysis, it is often necessary to include covariates such as age or weight to increase power or avoid spurious false positive findings. However, if a covariate term in the model is specified incorrectly (e.g., a quadratic term misspecified as a linear term), then the inclusion of the covariate may adversely affect power and accuracy of the identification of quantitative trait loci (QTL). Furthermore, some covariates may interact with each other in a complicated fashion. We implement semiparametric models for single and multiple QTL mapping. Both mapping methods include an unspecified function of any covariate found or suspected to have a more complex than linear but unknown relationship with the response variable. They also allow for interactions among different covariates. This analysis is performed in a Bayesian inference framework using Markov chain Monte Carlo. The advantages of our methods are demonstrated via extensive simulations and real data analysis.     

Biased Estimation of Adjusted Odds Ratios from Incomplete Covariate Data Due to Violation of the Missing at Random Assumption

We investigate the possible bias due to an erroneous missing at random assumption if adjusted odds ratios are estimated from incomplete covariate data using the maximum likelihood principle. A relation between complete case estimates and maximum likelihood estimates allows us to identify situations where the bias vanishes. Numerical computations demonstrate that the bias is most serious if the degree of the violation of the missing at random assumption depends on the value of the outcome variable or of the observed covariate. Implications for the analysis of prospective and retrospective studies are given.     

Simulated likelihood methods for complex double-platform line transect surveys

The conventional line transect approach of estimating effective search width from the perpendicular distance distribution is inappropriate in certain types of surveys, e.g., when an unknown fraction of the animals on the track line is detected, the animals can be observed only at discrete points in time, there are errors in positional measurements, and covariate heterogeneity exists in detectability. For such situations a hazard probability framework for independent observer surveys is developed. The likelihood of the data, including observed positions of both initial and subsequent observations of animals, is established under the assumption of no measurement errors. To account for measurement errors and possibly other complexities, this likelihood is modified by a function estimated from extensive simulations. This general method of simulated likelihood is explained and the methodology applied to data from a double-platform survey of minke whales in the northeastern Atlantic in 1995.     

Estimated Pseudopartial‐Likelihood Method for Correlated Failure Time Data with Auxiliary Covariates

Summary As biological studies become more expensive to conduct, statistical methods that take advantage of existing auxiliary information about an expensive exposure variable are desirable in practice. Such methods should improve the study efficiency and increase the statistical power for a given number of assays. In this article, we consider an inference procedure for multivariate failure time with auxiliary covariate information. We propose an estimated pseudopartial likelihood estimator under the marginal hazard model framework and develop the asymptotic properties for the proposed estimator. We conduct simulation studies to evaluate the performance of the proposed method in practical situations and demonstrate the proposed method with a data set from the studies of left ventricular dysfunction ( SOLVD Investigators, 1991 , New England Journal of Medicine 325 , 293–302).     

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

Summary .  In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach.     

Random sampling of constrained phylogenies: conducting phylogenetic analyses when the phylogeny is partially known

Statistical randomization tests in evolutionary biology often require a set of random, computer-generated trees. For example, earlier studies have shown how large numbers of computer-generated trees can be used to conduct phylogenetic comparative analyses even when the phylogeny is uncertain or unknown. These methods were limited, however, in that (in the absence of molecular sequence or other data) they allowed users to assume that no phylogenetic information was available or that all possible trees were known. Intermediate situations where only a taxonomy or other limited phylogenetic information (e.g., polytomies) are available are technically more difficult. The current study describes a procedure for generating random samples of phylogenies while incorporating limited phylogenetic information (e.g., four taxa belong together in a subclade). The procedure can be used to conduct comparative analyses when the phylogeny is only partially resolved or can be used in other randomization tests in which large numbers of possible phylogenies are needed.     

Generalizing Double and Triple Sampling for Repeated Surveys and Partial Verification

Population composition is often estimated by double sampling in which the value of a covariate is noted on each of a large number of randomly selected units and the value of the covariate and the exact class to which the unit belongs is noted for a smaller sample. The cross‐classified sample can be used to estimate the classification rates and these, in turn, can be used in conjunction with the estimated distribution of the covariate to obtain an improved estimate of the population composition over that obtained by direct observation of the identity of the individuals in a small sample. There are two approaches to this problem characterized by the way in which the classification rates are defined. The simplest approach uses estimates of the probability P(i | j) that the unit is actually in class i given that the covariate is in class j. The more complicated approach uses estimates of the probability Pi | j) that the covariate falls in class j given that the unit is actually in class i. The latter approach involves estimating more parameters than the former but avoids the necessity for the two samples to be drawn from the same population. We show the two approaches can be combined when there are multiple surveys. For example, one might conduct a disease survey for several years; in each year the accurate and/or error‐prone techniques may be applied to samples. The sensitivities and specificities of the error‐prone test are assumed constant across surveys. Generalizations allow for more than one error‐prone classifier and partial verification (estimation of misclassification rates by application of the accurate technique to fixed subsamples from each error‐prone category). The general approach is illustrated by considering a repeated survey for malaria.     

Semiparametric estimation exploiting covariate independence in two-phase randomized trials

Summary .  Recent results for case–control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment–biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton–Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment–biomarker interactions.     

Covariate-adjusted varying coefficient models

Covariate-adjusted regression was recently proposed for situations where both predictors and response in a regression model are not directly observed, but are observed after being contaminated by unknown functions of a common observable covariate. The method has been appealing because of its flexibility in targeting the regression coefficients under different forms of distortion. We extend this methodology proposed for regression into the framework of varying coefficient models, where the goal is to target the covariate-adjusted relationship between longitudinal variables. The proposed method of covariate-adjusted varying coefficient model (CAVCM) is illustrated with an analysis of a longitudinal data set containing calcium absorbtion and intake measurements on 188 subjects. We estimate the age-dependent relationship between these two variables adjusted for the covariate body surface area. Simulation studies demonstrate the flexibility of CAVCM in handling different forms of distortion in the longitudinal setting.     

A Bayesian approach to a logistic regression model with incomplete information

Summary .   We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes among subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities, e.g., a logistic regression model. In this article, estimation of the parameters is made from a Bayesian perspective by using a Markov chain Monte Carlo algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a dose–response study of a toxic chemical, perchlorate.     

Characteristics of postural sway in older adults standing on a soft surface

Postural responses to challenging situations were studied in older adults as they stood on a foam surface. The experiment was designed to assess the relative contributions made by visual and somatosensory information to the correction of postural sway. Twenty-four subjects, aged 56-83, stood for 20 s on a 1) firm or 2) foam surface with 1) the eyes open or 2) the eyes closed. Centre-of-pressure trajectories under the subjects' feet were measured by using a force platform. A repeated-measure two-way MANCOVA (two surfaces vs. two vision conditions) showed a significant main effect for the surface, but not for the vision. No covariate effect for age was found. Anterior-posterior sway increased in the subjects who were merely standing on the foam surface independent of the vision condition. Medial-lateral sway dramatically increased if the subjects stood on the foam surface with their eyes closed, but not if they stood with their eyes open. These results indicate that older adults rely more on visual information to correct mediolateral postural sway. It appears that the deterioration in visual acuity that occurs with aging may increase the risk of sideway falls, particularly in challenging situations, e.g., when standing on irregular or soft surfaces.     

Further comments on analysis of covariance in insect dietary studies

Raubenheimer & Simpson (1992) recently discussed the advantages of using analysis of covariance in insect dietary studies as an alternative to the more conventional ratio-based nutritional indices. We expand on some interpretations of Raubenheimer & Simpson and illustrate our points with examples from published and unpublished data sets. Specifically, we show that an ANCOVA on biomass gain incorporating initial biomass as a covariate provides information not immediately available using the analysis suggested by Raubenheimer & Simpson (ANCOVA on final biomass incorporating initial biomass as a covariate). Second, we show that dietary studies in which the covariate (food consumption) is affected by diet can provide information about the relative importance of preingestive effects (e.g., deterrence) and postingestive effects (e.g., antibiosis) on performance. Cautions about this latter use of ANCOVA are discussed.     

Modeling continuous auxiliary covariate data in generalized linear mixed models using the kernel smoother

Auxiliary covariate data are often collected in biomedical studies when the primary exposure variable is only assessed on a subset of the study subjects. In this study, we investigate a semiparametric‐estimated likelihood estimation for the generalized linear mixed models (GLMM) in the presence of a continuous auxiliary variable. We use a kernel smoother to handle continuous auxiliary data. The method can be used to deal with missing or mismeasured covariate data problems in a variety of applications when an auxiliary variable is available and cluster sizes are not too small. Simulation study results show that the proposed method performs better than that which ignores the random effects in GLMM and that which only uses data in the validation data set. We illustrate the proposed method with a real data set from a recent environmental epidemiology study on the maternal serum 1,1‐dichloro‐2,2‐bis(p‐chlorophenyl) ethylene level in relationship to preterm births.     

Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models

Factor analysis models are widely used in health research to summarize hard-to-measure predictor or outcome variable constructs. For example, in the ELEMENT study, factor models are used to summarize lead exposure biomarkers which are thought to indirectly measure prenatal exposure to lead. Classic latent factor models are fitted assuming that factor loadings are constant across all covariate levels (e.g., maternal age in ELEMENT); that is, measurement invariance (MI) is assumed. When the MI is not met, measurement bias is introduced. Traditionally, MI is examined by defining subgroups of the data based on covariates, fitting multi-group factor analysis, and testing differences in factor loadings across covariate groups. In this paper, we develop novel tests of measurement invariance by modeling the factor loadings as varying coefficients, i.e., letting the factor loading vary across continuous covariate values instead of groups. These varying coefficients are estimated using penalized splines, where spline coefficients are penalized by treating them as random coefficients. The test of MI is then carried out by conducting a likelihood ratio test for the null hypothesis that the variance of the random spline coefficients equals zero. We use a Monte Carlo EM algorithm for estimation, and obtain the likelihood using Monte Carlo integration. Using simulations, we compare the Type I error and power of our testing approach and the multi-group testing method. We apply the proposed methods to summarize data on prenatal biomarkers of lead exposure from the ELEMENT study and find violations of MI due to maternal age.     

Speciation of tissue and cellular iron with on-line detection by inductively coupled plasma-mass spectrometry.

Iron accumulating to excess in tissues of humans and animal models occurs mainly as complexes with transferrin, ferritin, other hemoproteins, and insoluble hemosiderin particles. To determine the distribution of Fe amongst these molecular species, we have used inductively coupled plasma-mass spectrometry as a means of on-line, isotope-specific detection for their liquid chromatographic separation. The stable isotope 57Fe is a suitable isotope for monitoring the Fe content of each fraction, and its availability at high isotopic enrichment makes it an attractive choice for tracer studies when the use of a radioisotope is undesirable, e.g., in human subjects. The detection system offers the advantages of high sensitivity (detection limits in the parts per billion range), a wide dynamic range (linearity of the calibration curve over several orders of magnitude), and on-line analysis facilitating real-time evaluation of the chromatographic separation, in addition to isotope-specific information. The Fe distributions in healthy rat livers, liver and heart tissue from Fe-loaded human subjects, and human hepatocyte cultures are reported. The ferritin:hemosiderin ratio in these samples is shown to be an indicator of the degree of Fe loading and correlates well with that determined by Zeeman-corrected electrothermal atomic absorption as an alternative means of detection.     

Identifying variables responsible for clustering in discriminant analysis of data from infrared microspectroscopy of a biological sample.

In the biomedical field, infrared (IR) spectroscopic studies can involve the processing of data derived from many samples, divided into classes such as category of tissue (e.g., normal or cancerous) or patient identity. We require reliable methods to identify the class-specific information on which of the wavenumbers, representing various molecular groups, are responsible for observed class groupings. Employing a prostate tissue sample divided into three regions (transition zone, peripheral zone, and adjacent adenocarcinoma), and interrogated using synchrotron Fourier-transform IR microspectroscopy, we compared two statistical methods: (a) a new "cluster vector" version of principal component analysis (PCA) in which the dimensions of the dataset are reduced, followed by linear discriminant analysis (LDA) to reveal clusters, through each of which a vector is constructed that identifies the contributory wavenumbers; and (b) stepwise LDA, which exploits the fact that spectral peaks which identify certain chemical bonds extend over several wavenumbers, and which following classification via either one or two wavenumbers, checks whether the resulting predictions are stable across a range of nearby wavenumbers. Stepwise LDA is the simpler of the two methods; the cluster vector approach can indicate which of the different classes of spectra exhibit the significant differences in signal seen at the "prominent" wavenumbers identified. In situations where IR spectra are found to separate into classes, the excellent agreement between the two quite different methods points to what will prove to be a new and reliable approach to establishing which molecular groups are responsible for such separation.     

Multiple trade‐offs regulate the effects of woody plant removal on biodiversity and ecosystem functions in global rangelands

Woody plant encroachment is a major land management issue. Woody removal often aims to restore the original grassy ecosystem, but few studies have assessed the role of woody removal on ecosystem functions and biodiversity at global scales. We collected data from 140 global studies and evaluated how different woody plant removal methods affected biodiversity (plant and animal diversity) and ecosystem functions (plant production, hydrological function, soil carbon) across global rangelands. Our results indicate that the impact of removal is strongly context dependent, varying with the specific response variable, removal method, and traits of the target species. Over all treatments, woody plant removal increased grass biomass and total groundstorey diversity. Physical and chemical removal methods increased grass biomass and total groundstorey biomass (i.e., non‐woody plants, including grass biomass), but burning reduced animal diversity. The impact of different treatment methods declined with time since removal, particularly for total groundstorey biomass. Removing pyramid‐shaped woody plants increased total groundstorey biomass and hydrological function but reduced total groundstorey diversity. Environmental context (e.g., aridity and soil texture) indirectly controlled the effect of removal on biomass and biodiversity by influencing plant traits such as plant shape, allelopathic, or roots types. Our study demonstrates that a one‐size‐fits‐all approach to woody plant removal is not appropriate, and that consideration of woody plant identity, removal method, and environmental context is critical for optimizing removal outcomes. Applying this knowledge is fundamental for maintaining diverse and functional rangeland ecosystems as we move toward a drier and more variable climate.     

Population Dynamics of <Emphasis Type="Italic">Gluconacetobacter diazotrophicus</Emphasis> in Sugarcane Cultivars and Its Effect on Plant Growth

Different experiments have estimated that the contribution of biological nitrogen fixation (BNF) is largely variable among sugarcane cultivars. Which bacteria are the most important in sugarcane-associated BNF is unknown. However, Gluconacetobacter diazotrophicus has been suggested as a strong candidate responsible for the BNF observed. In the present study, bacteria-free micropropagated plantlets of five sugarcane cultivars were inoculated with three G. diazotrophicus strains belonging to different genotypes. Bacterial colonization was monitored under different nitrogen fertilization levels and at different stages of plant growth. Analysis of the population dynamics of G. diazotrophicus strains in the different sugarcane varieties showed that the bacterial populations decreased drastically in relation to plant age, regardless of the nitrogen fertilization level, bacterial genotype or sugarcane cultivars. However, the persistence of the three strains was significantly longer in some cultivars (e.g., MEX 57-473) than in others (e.g., MY 55-14). In addition, some strains (e.g., PAl 5^T) persisted for longer periods in higher numbers than other strains (e.g., PAl 3) inside plants of all the cultivars tested. Indeed, the study showed that the inoculation of G. diazotrophicus may be beneficial for sugarcane plant growth, but this response is dependent both on the G. diazotrophicus genotype and the sugarcane variety. The most positive response to inoculation was observed with the combination of strain PAl 5^T and the variety MEX 57-473. Although the positive effect on sugarcane growth apparently occurred by mechanisms other than nitrogen fixation, the results show the importance of the sugarcane variety for the persistence of the plant–bacteria interaction, and it could explain the different rates of BNF estimated among sugarcane cultivars.     

Diagnosis and prognosis potential of four gene promoter hypermethylation in prostate cancer

The current prostate special antigen (PSA) test causes the overtreatment of indolent prostate cancer (PCa). It also increases the risk of delayed treatment of aggressive PCa. DNA methylation aberrations are important events for gene expression dysregulation during tumorigenesis and have been suggested as novel candidate biomarkers for PCa. This may improve the diagnosis and prognosis of PCa. This study assessed the differential methylation and messenger RNA (mRNA) expression between normal and PCa samples. Correlation between promoter methylation and mRNA expression was estimated using Pearson's correlation coefficients. Moreover, the diagnostic potential of candidate methylation markers was estimated by the receiver operating characteristic (ROC) curve using continuous beta values. Survival and Cox analysis was performed to evaluate the prognostic potential of the candidate methylation markers. A total of 359 hypermethylated sites 3435 hypomethylation sites, 483 upregulated genes, and 1341 downregulated genes were identified from The Cancer Genome Atlas database. Furthermore, 17 hypermethylated sites (covering 13 genes), including known genes associated with hypermethylation in PCa (e.g., AOX1 and C1orf114), showed high discrimination between adjacent normal tissues and PCa samples with the area under the ROC curve from 0.88 to 0.94. Notably, ANXA2, FGFR2, HAAO, and KCNE3 were identified as valuable prognostic markers of PCa through the Kaplan–Meier analysis. Using gene methylation as a continuous variable, four promoter hypermethylation was significantly associated with disease-free survival in univariate Cox regression and multivariate Cox regression. This study identified four novel diagnostic and prognostic markers for PCa. The markers provide important strategies for improving the timely diagnosis and prognosis of PCa.