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Residual Variance (residual + variance)

Distribution by Scientific Domains

Medical Sciences	40%
Life Sciences	25%
Mathematics and Statistics	15%
Earth and Environmental Science	10%
2 Other Domains	10%

Selected Abstracts

Probability models for pine twisting rust (Melampsora pinitorqua) damage in Scots pine (Pinus sylvestris) stands in Finland

FOREST PATHOLOGY, Issue 1 2005
U. Mattila
Summary Factors affecting the probability that pine twisting rust (Melampsora pinitorqua) damage occur in a Scots pine (Pinus sylvestris) stand were analysed using the 7th Finnish National Forest Inventory data (NFI7) from southern Finland in 1977,1983. The inventory was based on systematic sampling. The NFI7 data was measured in clusters, each of which consisted of 21 sample plots. In addition to the stand and site characteristics measured for forest management planning purposes, the data included records of damage by pine twisting rust and occurrence of aspens (Populus tremula, the other host plant of the pathogen) in the stands. Two multilevel logit models were developed for predicting the overall probability of pine twisting rust damage and the probability of severe pine twisting rust damage. Site and stand characteristics were used as explanatory variables in the models. Residual variance in the models was studied on the inventory crew, cluster and year levels. The occurrence of aspens and site fertility were the most important factors increasing the probability that pine twisting rust damage will occur in a stand. The damage probability also decreased with increasing effective temperature sum calculated for the location. The overall damage probability was equally high on peatlands and on mineral soil if there were aspens in the stand. If, however, there were no aspens in the stand, the probability of damage was higher on mineral soils than on peatlands. In addition, the overall probability was lower in naturally regenerated stands than in planted or sown stands, and it decreased with increasing mean age of pines. In both models, the residual variance was significant on the both the inventory crew and the cluster levels. Résumé Les facteurs de probabilité d'occurrence d'un dégât de rouille courbeuse (Melampsora pinitorqua) dans un peuplement de Pin sylvestre (Pinus sylvestris) ont été analysés en utilisant les données du 7 Inventaire Forestier National de Finlande (NF17) pour la Finlande du Sud et la période 1977,1983. L'inventaire est basé sur un échantillonnage systématique. Les données de NF17 sont mesurées dans des groupes constitués de 21 placettes. En plus des caractéristiques de la station et du peuplement mesurées à des fins de gestion forestière, les données comprennent des notations de dégâts par la rouille courbeuse et de présence des trembles (hôte alternant de 1'agent pathogène) dans les peuplements. Des modèles logit multiniveaux ont été développés pour prédire la probabilité globale de dégât de rouille courbeuse et la probabilité de dégât sévère. Les caractéristiques de la station et du peuplement ont été utilisées comme variables explicatives dans les modèles. La variance résiduelle des modèles a étéétudiée au niveau de 1'observateur, du groupe de placettes et de 1'année. La présence de trembles et la fertilité de la station sont les facteurs les plus importants d'augmentation de la probabilité de dégât de rouille dans un peuplement. D'autre part, la probabilité de dégât décroît avec la somme des températures effectives calculée pour le site. La probabilité globale de dégât est aussi élevée sur sols de tourbières que sur sols minéraux dans le cas où des trembles sont présents dans le peuplement. En 1'absence de trembles dans le peuplement, la probabilité de dégât est plus importante sur sols minéraux qu'en tourbières. Enfin, la probabilité de dégât est plus faible dans les peuplements régénérés naturellement que dans les peuplements semés ou plantés, et elle décroít avec 1'âge moyen des pins. Pour les deux modèles, la variance résiduelle est significative au niveau observateur et groupe de parcelles. Zusammenfassung Faktoren, die die Wahrscheinlichkeit einer Schädigung durch den Kieferndrehrost (Melampsora pinitorqua) in Beständen von Pinus sylvestris beeinflussen, wurden anhand der Daten der 7. Finnischen Nationalen Forstinventur (NF17) aus den Jahren 1977,1983 in Südfinnland untersucht. Die Datenerhebung basierte auf einer systematischen Probenahme. Die NF17 Daten wurden in Clustern erhoben, jedes Cluster bestand aus 21 Probeflächen. Neben den Bestandes- und Standortsmerkmalen, die für die forstliche Planung erhoben wurden, wurden Angaben zum Befall (schwach, stark) mit Kieferndrehrost und zum Vorkommen von Zitter-Pappel (Populus tremula, alternativer Wirt des Pathogens) berücksichtigt. Es wurden zwei Multi Logit - Modelle entwickelt zur Vorhersage der Gesamtwahrscheinlichkeit einer Kieferndrehrost-Schädigung sowie der Wahrscheinlichkeit einer schweren Schädigung durch den Pilz. Die Standorts- und Bestandesmerkmale wurden als erklärende Variablen verwendet. In den Modellen wurde die Restvarianz bezüglich Inventur-Erhebungsgruppe, Cluster und Jahr geprüft. Das Vorkommen von Zitter-Pappel und die Bodenfertilität waren die wichtigsten Faktoren für eine zunehmende Wahrscheinlichkeit einer Kieferndrehrost-Schädigung auf Bestandesebene. Die Schadenswahrscheinlichkeit verringerte sich mit zunehmender Temperatursumme, die für den Standort berechnet wurde. Die Gesamtschadenswahrscheinlichkeit war auf Torf- und Mineralböden gleich hoch, sofern Zitter-Pappeln im Bestand vorkamen. Ohne Zitter-Pappeln war die Schadenswahrscheinlichkeit auf Mineralböden höher. Zudem war die Gesamtschadenswahrscheinlichkeit in natürlich regenerierten Beständen niedriger als in gepflanzten oder gesäten Beständen, und sie nahm mit zunehmendem Durchschnittsalter der Kiefern ab. In beiden Modellen war die Restvarianz auf der Ebene der Inventur-Erhebungsgruppe und der Probecluster signifikant. [source]

CLIMATIC AND TEMPORAL EFFECTS ON THE EXPRESSION OF SECONDARY SEXUAL CHARACTERS: GENETIC AND ENVIRONMENTAL COMPONENTS

EVOLUTION, Issue 3 2004
Dany Garant
Abstract Despite great interest in sexual selection, relatively little is known in detail about the genetic and environmental determinants of secondary sexual characters in natural populations. Such information is important for determining the way in which populations may respond to sexual selection. We report analyses of genetic and large-scale environmental components of phenotypic variation of two secondary sexual plumage characters (forehead and wing patch size) in the collared flycatcher Ficedula albicollis over a 22-year period. We found significant heritability for both characters but little genetic covariance between the two. We found a positive association between forehead patch size and a large-scale climatic index, the North Atlantic Oscillation (NAO) index, but not for wing patch. This pattern was observed in both cross-sectional and longitudinal data suggesting that the population response to NAO index can be explained as the result of phenotypic plasticity. Heritability of forehead patch size for old males, calculated under favorable conditions (NAO index median), was greater than that under unfavorable conditions (NAO index < median). These changes occurred because there were opposing changes in additive genetic variance (VA) and residual variance (VR) under favorable and unfavorable conditions, with VA increasing and VR decreasing in good environments. However, no such effect was detected for young birds, or for wing patch size in either age class. In addition to these environmental effects on both phenotypic and genetic variances, we found evidence for a significant decrease of forehead patch size over time in older birds. This change appears to be caused by a change in the sign of viability selection on forehead patch size, which is associated with a decline in the breeding value of multiple breeders. Our data thus reveal complex patterns of environmental influence on the expression of secondary sexual characters, which may have important implications for understanding selection and evolution of these characters. [source]

Probability models for pine twisting rust (Melampsora pinitorqua) damage in Scots pine (Pinus sylvestris) stands in Finland

Identifiability of parameters and behaviour of MCMC chains: a case study using the reaction norm model

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2009
M.M. Shariati
Summary Markov chain Monte Carlo (MCMC) enables fitting complex hierarchical models that may adequately reflect the process of data generation. Some of these models may contain more parameters than can be uniquely inferred from the distribution of the data, causing non-identifiability. The reaction norm model with unknown covariates (RNUC) is a model in which unknown environmental effects can be inferred jointly with the remaining parameters. The problem of identifiability of parameters at the level of the likelihood and the associated behaviour of MCMC chains were discussed using the RNUC as an example. It was shown theoretically that when environmental effects (covariates) are considered as random effects, estimable functions of the fixed effects, (co)variance components and genetic effects are identifiable as well as the environmental effects. When the environmental effects are treated as fixed and there are other fixed factors in the model, the contrasts involving environmental effects, the variance of environmental sensitivities (genetic slopes) and the residual variance are the only identifiable parameters. These different identifiability scenarios were generated by changing the formulation of the model and the structure of the data and the models were then implemented via MCMC. The output of MCMC sampling schemes was interpreted in the light of the theoretical findings. The erratic behaviour of the MCMC chains was shown to be associated with identifiability problems in the likelihood, despite propriety of posterior distributions, achieved by arbitrarily chosen uniform (bounded) priors. In some cases, very long chains were needed before the pattern of behaviour of the chain may signal the existence of problems. The paper serves as a warning concerning the implementation of complex models where identifiability problems can be difficult to detect a priori. We conclude that it would be good practice to experiment with a proposed model and to understand its features before embarking on a full MCMC implementation. [source]

Bayesian comparison of test-day models under different assumptions of heterogeneity for the residual variance: the change point technique versus arbitrary intervals

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2004
P. López-Romero
Summary Test-day milk yields from Spanish Holstein cows were analysed with two random regression models based on Legendre polynomials under two different assumptions of heterogeneity of residual variance which aim to describe the variability of temporary measurement errors along days in milk with a reduced number of parameters, such as (i) the change point identification technique with two unknown change points and (ii) using 10 arbitrary intervals of residual variance. Both implementations were based on a previous study where the trajectory of the residual variance was estimated using 30 intervals. The change point technique has been previously implemented in the analysis of the heterogeneity of the residual variance in the Spanish population, yet no comparisons with other methods have been reported so far. This study aims to compare the change point technique identification versus the use of arbitrary intervals as two possible techniques to deal with the characterization of the residual variance in random regression test-day models. The Bayes factor and the cross-validation predictive densities were employed for the model assessment. The two model-selecting tools revealed a strong consistency between them. Both specifications for the residual variance were close to each other. The 10 intervals modelling showed a slightly better performance probably because the change point function overestimates the residual variance values at the very early lactation. Zusammenfassung Testtagsgemelke von Spanischen Holstein-Kühen wurden mittels zweier zufälliger Regressionsmodelle, basierend auf Legendre Polynomen, unter zwei unterschiedlichen Voraussetzungen von Heterogenität der Residualvarianz, untersucht, um die Variabilität der Restvarianz der Milchleistung der Testtage durch so wenig Parameter wie möglich beschreiben zu können: 1) dem Verfahren des Wechsel-Identifikationspunktes mit zwei unbekannten Änderungspunkten und 2) der Verwendung von 10 frei gewählten Intervallen der Residualvarianz. Beide Anwendungen beruhen auf einer vorherigen Untersuchung, in der der Verlauf der Residualvarianz durch die Verwendung von 30 Intervallen geschätzt wurde. Das Wechsel-Identifikationspunkt Verfahren wurde bereits bei der Untersuchung der Residualvarianz in der spanischen Population verwendet, aber das Verfahren wurde noch nicht mit anderen Methoden verglichen. Das Ziel dieser Studie war der Vergleich des Wechsel-Identifikationspunkt Verfahrens mit dem Gebrauch von frei wählbaren Intervallen als zwei Möglichkeiten zur Charakterisierung der Residualvarianz in zufälligen Testtags-Regressionsmodellen. Der Bayes'sche Faktor und die Vorhersage der Vergleichsprüfungsdichten wurden zur Bewertung der Modelle verwandt. Beide Verfahren zeigten eine überzeugende Konsistenz der Modelle und die Beschreibung der Residualvarianzen stimmte in beiden Fällen überein. Die Modellierung mit 10 Intervallen zeigte eine etwas bessere Leistung, möglicherweise weil die Wechsel-Identifikationspunkt Funktion die Residualvarianz in der sehr frühen Laktation überbewertet. [source]

Fixed or random contemporary groups in genetic evaluation for litter size in pigs using a single trait repeatability animal model

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2003
D. Babot
Summary The importance of using fixed or random contemporary groups in the genetic evaluation of litter size in pigs was analysed by using farm and simulated data. Farm data were from four Spanish pig breeding populations, two Landrace (13 084 and 13 619 records) and two Large White (2762 and 8455 records). A simulated population (200 sows and 10 boars) selected for litter size, in which litter size was simulated using a repeatability animal model with random herd,year,season (HYS), was used to obtain simulated data. With farm data, the goodness-of-fit and the predictive ability of a repeatability animal model were calculated under several definitions of the HYS effect. A residual maximum likelihood estimator of the HYS variance in each population was obtained as well. In this sense, HYS was considered as either fixed or random with different number of individuals per level. Results from farm data showed that HYS variance was small in relation to the total variance (ranging from 0.01 to 0.04). The treatment of HYS effect as fixed, reduced the residual variance but the size of HYS levels does not explain by itself the goodness-of-fit of the model. The results obtained by simulation showed that the predictive ability of the model is better for random than for fixed HYS models. However, the improvement of predictive ability does not lead to a significant increase of the genetic response. Finally, results showed that random HYS models biased the estimates of genetic response when there is an environmental trend. Zusammenfassung Fixe oder zufällige Vergleichsgruppen bei der Zuchtwertschätzung für Wurfgröße beim Schwein mit einem Wiederholbarkeits-Tiermodell Der Einfluss von fixen oder zufälligen Vergleichgruppen bei der Zuchtwertschätzung für Wurfgröße beim Schwein wurde an realen Betriebsdaten und an simulierten Daten untersucht. Die Betriebsdaten stammen von vier spanischen Zuchtpopulationen, zwei Landrasse Populationen (13084 und 13619 Datensätze) und zwei Large White Populationen (2762 und 8455 Datensätze). Für die Simulation wurde eine Population (200 Sauen und 10 Eber), die auf Wurfgröße selektiert wurde, unter Berücksichtigung eines Wiederholbarkeitsmodelles und mit zufälligen Herden-Jahr-Saisonklassen simuliert. Anhand der Betriebsdaten wurde die Güte des Modells und die Vorhersagegenauigkeit des Wiederholbarkeitsmodelles mit verschiedenen Definitionen der Herden-Jahr-Saisonklassen geprüft. Mittels der REML-Methode wurden auch Varianzkomponenten für die Herden-Jahr-Saisonklassen geschätzt. Die Herden-Jahr-Saisonklassen wurden als fixer bzw. zufälliger Effekt mit unterschiedlicher Anzahl an Tieren pro Klasse im Modell berücksichtigt. Die Ergebnisse der Betriebsdaten ergaben, dass die Varianz für die Herden-Jahr-Saisonklassen nur einen kleinen Teil der Totalvarianz (von 0,01 bis 0,04) ausmachte. Mit den Herden-Jahr-Saisonklassen als fixer Effekt reduzierte sich die Restvarianz, aber die Größe der Herden-Jahr-Saisonklassen bestimmte nicht allein die Güte des Modells. Die Erhöhung der Vorhersagegenauigkeit ergab keinen signifikanten Anstieg des genetischen Fortschrittes. Abschließend bleibt festzustellen, dass Modelle mit zufälligen Herde-Jahr-Saisonklassen zu einem Bias des geschätzten genetischen Erfolges führten, wenn ein Umwelttrend vorhanden war. [source]

An explanatory model of medical practice variation: a physician resource demand perspective

JOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 2 2002
Michael J. Long MA PhD
Abstract Practice style variation, or variation in the manner in which physicians treat patients with a similar disease condition, has been the focus of attention for many years. The research agenda is further intensified by the unrealistic assumption that by reducing variation, quality will be improved, costs will be reduced, or both. There is a wealth of literature that identifies differences in health care use of many kinds, in apparently similar communities. Attempts have been made by many scholars to identify the determinants of variation in terms of differences in the population characteristics (e.g. age, sex, insurance, etc.) and geographical characteristics (e.g. distance to provider, number of physicians, number of hospital beds, etc.). When significant differences in use rates prevail after controlling for differences in population characteristics, it is often attributed to ,uncertainty', or the fact that there is no consensus on what constitutes the optimum treatment process. It is suggested by this literature that the greatest variation can be found in the circumstances where there is the most ,uncertainty'. In this work, a physician resource demand model is proposed in which it is suggested that, during the diagnosis and treatment process, physicians demand resources consistent with the clinical needs of the patients, modified by the intervening forces under which they practice. These intervening forces, or constraints, are categorized as patient agency constraints, organizational constraints and environmental constraints, which are characterized as ,induced variation'. It is suggested that when all of the variables that constitute these constraints are identified, the remaining variance represents ,innate variance', or practice style differences. It is further suggested that the more completely this model is specified, the more likely area differences will be attenuated and the smaller will be the residual variance. [source]

THE RELIABILITY OF NAÏVE ASSESSORS IN SENSORY EVALUATION VISUALIZED BY PRAGMATICAL MULTIVARIATE ANALYSIS

JOURNAL OF FOOD QUALITY, Issue 5 2002
M.G. O'SULLIVAN
The first part of this paper demonstrates a simple graphical way to visualize estimated variances, in terms of a plot of the total initial variance ("SIGNAL") versus residual variance ("NOISE"), as a pragmatic alternative to tables of F-tests. The recently developed Procrustes rotation in the bilinear "jack-knifing" form is then presented as a method for simplifying the comparison of PLS Regression models from different data sets. These methods are applied to sensory data in order to study if naïve (untrained) sensory panelists can produce reliable descriptions of systematic differences between various test meals. The results confirm that three panels of 15 naïve assessors each could give repeatable intersubjective description of the most dominant sensory variation dimensions. [source]

Measuring beta-diversity from taxonomic similarity

JOURNAL OF VEGETATION SCIENCE, Issue 6 2007
Giovanni Bacaro
Abstract Question: The utility of beta (,-) diversity measures that incorporate information about the degree of taxonomic (dis)similarity between species plots is becoming increasingly recognized. In this framework, the question for this study is: can we define an ecologically meaningful index of ,-diversity that, besides indicating simple species turnover, is able to account for taxonomic similarity amongst species in plots? Methods: First, the properties of existing measures of taxonomic similarity measures are briefly reviewed. Next, a new measure of plot-to-plot taxonomic similarity is presented that is based on the maximal common subgraph of two taxonomic trees. The proposed measure is computed from species presences and absences and include information about the degree of higher-level taxonomic similarity between species plots. The performance of the proposed measure with respect to existing coefficients of taxonomic similarity and the coefficient of Jaccard is discussed using a small data set of heath plant communities. Finally, a method to quantify ,-diversity from taxonomic dissimilarities is discussed. Results: The proposed measure of taxonomic ,-diversity incorporates not only species richness, but also information about the degree of higher-order taxonomic structure between species plots. In this view, it comes closer to a modern notion of biological diversity than more traditional measures of ,-di-versity. From regression analysis between the new coefficient and existing measures of taxonomic similarity it is shown that there is an evident nonlinearity between the coefficients. This nonlinearity demonstrates that the new coefficient measures similarity in a conceptually different way from previous indices. Also, in good agreement with the findings of previous authors, the regression between the new index and the Jaccard coefficient of similarity shows that more than 80% of the variance of the former is explained by the community structure at the species level, while only the residual variance is explained by differences in the higher-order taxonomic structure of the species plots. This means that a genuine taxonomic approach to the quantification of plot-to-plot similarity is only needed if we are interested in the residual system's variation that is related to the higher-order taxonomic structure of a pair of species plots. [source]

Hierarchical Bayesian modeling of random and residual variance,covariance matrices in bivariate mixed effects models

BIOMETRICAL JOURNAL, Issue 3 2010
Nora M. Bello
Abstract Bivariate mixed effects models are often used to jointly infer upon covariance matrices for both random effects (u) and residuals (e) between two different phenotypes in order to investigate the architecture of their relationship. However, these (co)variances themselves may additionally depend upon covariates as well as additional sets of exchangeable random effects that facilitate borrowing of strength across a large number of clusters. We propose a hierarchical Bayesian extension of the classical bivariate mixed effects model by embedding additional levels of mixed effects modeling of reparameterizations of u- level and e -level (co)variances between two traits. These parameters are based upon a recently popularized square-root-free Cholesky decomposition and are readily interpretable, each conveniently facilitating a generalized linear model characterization. Using Markov Chain Monte Carlo methods, we validate our model based on a simulation study and apply it to a joint analysis of milk yield and calving interval phenotypes in Michigan dairy cows. This analysis indicates that the e -level relationship between the two traits is highly heterogeneous across herds and depends upon systematic herd management factors. [source]

Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data

BIOMETRICS, Issue 1 2010
Yisheng Li
Summary We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a nonzero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a postprocessing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods. [source]

Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors

BIOMETRICS, Issue 2 2009
Benjamin R. Saville
Summary Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed-form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace's method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes. [source]

Strategies to improve efficacy and safety of a novel class of antiviral hyper-activation-limiting therapeutic agents: the VS411 model HIV/AIDS

BRITISH JOURNAL OF PHARMACOLOGY, Issue 4 2010
D De Forni
BACKGROUND AND PURPOSE Antiviral hyper-activation-limiting therapeutic agents (AV-HALTs) are a novel experimental drug class designed to both decrease viral replication and down-regulate excessive immune system activation for the treatment of chronic infections, including human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome. VS411, a first-in-class AV-HALT, is a single-dosage form combining didanosine (ddI, 400 mg), an antiviral (AV), and hydroxyurea (HU, 600 mg), a cytostatic agent, designed to provide a slow release of ddI to reduce its maximal plasma concentration (Cmax) to potentially reduce toxicity while maintaining total daily exposure (AUC) and the AV activity. EXPERIMENTAL APPROACH This was a pilot phase I, open-label, randomized, single-dose, four-way crossover trial to investigate the fasted and non-fasted residual variance of AUC, Cmax and the oral bioavailability of ddI and HU, co-formulated as VS411, and administered as two different fixed-dose combination formulations compared to commercially available ddI (Videx EC) and HU (Hydrea) when given simultaneously. KEY RESULTS Formulation VS411-2 had a favourable safety profile, displayed a clear trend for lower ddI Cmax (P= 0.0603) compared to Videx EC, and the 90% confidence intervals around the least square means ratio of Cmax did not include 100%. ddI AUC, was not significantly decreased compared to Videx EC. HU pharmacokinetic parameters were essentially identical to Hydrea, although there was a decrease in HU exposure under fed versus fasted conditions. CONCLUSIONS AND IMPLICATIONS A phase IIa trial utilizing VS411-2 formulation has been fielded to identify the optimal doses of HU plus ddI as an AV-HALT for the treatment of HIV disease. [source]

Disgust and eating disorder symptomatology in a non-clinical population: The role of trait anxiety and anxiety sensitivity

CLINICAL PSYCHOLOGY AND PSYCHOTHERAPY (AN INTERNATIONAL JOURNAL OF THEORY & PRACTICE), Issue 4 2009
Graham C. L. Davey
Abstract The present paper reports the results of a study investigating the relationship between a domains-independent measure of disgust (the Disgust Propensity and Sensitivity Scale-Revised) and measures of eating disorder symptomatology in a non-clinical population. Significant correlations between disgust sensitivity and disgust propensity and selected eating disorder symptomatology measures suggested that disgust is significantly correlated with measures of eating disorder symptomatology and is appraised more negatively. However, both measures of disgust propensity and sensitivity failed to predict any significant residual variance in scores on eating symptomatology measures when either trait anxiety or anxiety sensitivity was controlled for. This suggests that while the experience of disgust may be heightened in individuals with eating disorders, it may be linked to other relevant emotions such as anxiety and anxiety sensitivity rather than being an independent risk factor for symptoms.,Copyright © 2009 John Wiley & Sons, Ltd. Key Practitioner Message: The experience of disgust may be heightened in individuals with eating disorder symptomatology. Disgust levels may not be an independent predictor of eating disorder symptoms. In those with eating disorder symotomatology disgust may be linked to other emotions such as anxiety and anxiety sensitivity. [source]

Multiplicative random regression model for heterogeneous variance adjustment in genetic evaluation for milk yield in Simmental

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2008
M.H. Lidauer
Summary A multiplicative random regression (M-RRM) test-day (TD) model was used to analyse daily milk yields from all available parities of German and Austrian Simmental dairy cattle. The method to account for heterogeneous variance (HV) was based on the multiplicative mixed model approach of Meuwissen. The variance model for the heterogeneity parameters included a fixed region × year × month × parity effect and a random herd × test-month effect with a within-herd first-order autocorrelation between test-months. Acceleration of variance model solutions after each multiplicative model cycle enabled fast convergence of adjustment factors and reduced total computing time significantly. Maximum Likelihood estimation of within-strata residual variances was enhanced by inclusion of approximated information on loss in degrees of freedom due to estimation of location parameters. This improved heterogeneity estimates for very small herds. The multiplicative model was compared with a model that assumed homogeneous variance. Re-estimated genetic variances, based on Mendelian sampling deviations, were homogeneous for the M-RRM TD model but heterogeneous for the homogeneous random regression TD model. Accounting for HV had large effect on cow ranking but moderate effect on bull ranking. [source]

Stability of genetic parameter estimates for production traits in pigs

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2001
J. Wolf
Changes in variance component estimates in growing sets of performance data in two pig breeds were investigated. Data was used from the field and station test of Czech Landrace (LA: 75 099 observations) and the Slovakian breed, White Meaty swine (WM: 32 203 observations). In LA the traits analysed were estimated lean meat content (LM) and average daily gain (ADGF) on field test and average daily gain (ADGS) and weight of valuable cuts (VCW) on station test. In WM the traits analysed were backfat thickness on field and station test (BFF, BFS, respectively), proportion of valuable cuts (VCP) on station test, ADGF and ADGS. Covariance components were estimated from four- and five-trait animal models using the VCE software. Omitting data from factor levels with a low number of records led to 4.2% of LA records and 21.7% of WM records being deleted. Changes in genetic and residual variance estimates were less than 5% for all traits in LA and less than 12% for all traits except ADGS in WM. The changes in estimated genetic variances caused by 18 months (LA) or 24 months (WM) of new data were 2,25% and the changes in estimated residual variances were less than 5% in LA and less than 20% in WM. In both breeds, changes in heritability estimates did not exceed 0.06 in absolute value. In LA, it is reasonable to use genetic parameter estimates for 3 years before re-estimation. In WM the time interval should be shorter because of changes in the estimates caused by their lower accuracy arising from the smaller size of the data-set and smaller frequency of station testing. Stabilität der Schätzwerte genetischer Parameter für Produktionsmarkmale beim Schwein Für zwei Schweinerassen wurden Änderungen der Varianzkomponentenschätzwerte in wachsenden Leistungsprüfungsdatensätzen untersucht. Die beiden Ausgangsdatensätze bestanden aus Feld- und Stationsprüfdaten der Tschechischen Landrasse (LA , 75 099 Beobachtungen) bzw. der Slowakischen Rasse White Meaty (WM , 32 203 Beobachtungen). Folgende Merkmale wurden ausgewertet: Magerfleischanteil (LM) und Lebenstagszunahme (ADGF) aus der Feldprüfung sowie Prüftagszunahme (ADGS) und Gewicht wertvoller Teilstücke (VCW) aus der Stationsprüfung bei der LA; Rückenspeckdicke aus der Feld- und Stationsprüfung (BFF bzw. BFS), Anteil wertvoller Teilstücke (VCP) aus der Stationsprüfung sowie ADGF und ADGS bei der Rasse WM. Die Kovarianzkomponenten wurden für Vier- bzw. Fünf-Merkmals-Tiermodelle mit dem Programm VCE berechnet. Das Auslassen von Daten von Klassen mit geringer Besetzung führte dazu, daß in der LA 4,2% und in WM 21,7% der Daten gelöscht wurden. Die Änderungen in den genetischen und den Rest-Varianzen waren in der LA bei allen Merkmalen kleiner als 5% und in WM bei allen Merkmalen mit Ausnahme von ADGS kleiner als 12%. Durch Hinzufügen von Daten aus einem Zeitraum von 18 (LA) bzw. 24 (WM) Monaten änderten sich die genetischen Varianzen um 2 bis 25%. Die Änderungen in den Restvarianzen lagen unter 5% bei der LA und unter 20% bei WM. Die maximale Änderung der Heritabilitätskoeffizienten überstieg in beiden Rassen nicht 0,06. Bei der LA sollte ein Zeitintervall von drei Jahren zu einer Neuschätzung der genetischen Parameter ausreichen, bei WM sollte wegen der beobachteten Änderungen der Schätzwerte, der kleineren Datenmenge und des geringeren Anteils stationsgeprüfter Tiere das Zeitintervall kürzer sein. [source]

Fish condition analysis by a weighted least squares procedure: testing geographical differences of an endangered Iberian cyprinodontid

JOURNAL OF FISH BIOLOGY, Issue 6 2001
A. Vila-Gispert
A weighted procedure for analysing fish condition, when conventional ANCOVA assumptions cannot be assumed because the coefficients of regression and residual variances were not equal among groups, proved adequate for most data sets. Data for an Iberian cyprinodontid fish were used to demonstrate the proposed method. [source]

Origin of Fueguian-Patagonians: An approach to population history and structure using R matrix and matrix permutation methods

AMERICAN JOURNAL OF HUMAN BIOLOGY, Issue 3 2002
Rolando González José
A complicated history of isolation between Fueguian and Patagonian groups (originated by the appearance of the Straits of Magellan) as much as differences in population structure and life strategies constitute important factors in the clustering pattern of those groups. The aim of this work was to test several hypotheses about population structure and history of Fueguian-Patagonians to propose a model that incorporates predictions for future studies. R matrix methods and matrix permutation analyses were performed upon a data matrix of craniofacial measurements of 441 skulls divided into nine samples pertaining to six Patagonian and three Fueguian populations. Association of biological distances with three matrices representing several settlement patterns was tested using matrix permutation tests. Results of R matrix study show that the minimum genetic distance obtained confirms separation between Fueguians and Patagonians. Moreover, an analysis of residual variances from the expected regression line confirms admixture between Andean and Pampean populations and Araucanian groups, consistent with ethnohistorical observations. A model representing a long history of isolation between Fueguian and Patagonians, rather than a model emphasizing differences in life-strategies, presented the best correlation with the biological distance matrix. Because similar results were already obtained in archaeological, molecular, and morphological studies, a model for the settlement of Tierra del Fuego is proposed. It is summarized by four main hypotheses that can be tested independently by different disciplines in the future. Am. J. Hum. Biol. 14:308,320, 2002. © 2002 Wiley-Liss, Inc. [source]

Investigation of Gibbs sampling conditions to estimate variance components from Japanese Black carcass field data

ANIMAL SCIENCE JOURNAL, Issue 5 2009
Aisaku ARAKAWA
ABSTRACT The genetic evaluation using the carcass field data in Japanese Black cattle has been carried out employing an animal model, implementing the restricted maximum likelihood (REML) estimation of additive genetic and residual variances. Because of rapidly increasing volumes of the official data sets and therefore larger memory spaces required, an alternative approach like the REML estimation could be useful. The purpose of this study was to investigate Gibbs sampling conditions for the single-trait variance component estimation using the carcass field data. As prior distributions, uniform and normal distributions and independent scaled inverted chi-square distributions were used for macro-environmental effects, breeding values, and the variance components, respectively. Using the data sets of different sizes, the influences of Gibbs chain length and thinning interval were investigated, after the burn-in period was determined using the coupling method. As would be expected, the chain lengths had obviously larger effects on the posterior means than those of thinning intervals. The posterior means calculated using every 10th sample from 90 000 of samples after 10 000 samples discarded as burn-in period were all considered to be reasonably comparable to the corresponding estimates by REML. [source]