Congruence of fatty acid methyl ester profiles and morphological characters of arbuscular mycorrhizal fungi in Gigasporaceae.

Arbuscular mycorrhizal (AM) fungi (Order Glomales, Class Zygomycetes) are a diverse group of soil fungi that form mutualistic associations with the roots of most species of higher plants. Despite intensive study over the past 25 years, the phylogenetic relationships among AM fungi, and thus many details of evolution of the symbiosis, remain unclear. Cladistic analysis was performed on fatty acid methyl ester (FAME) profiles of 15 species in Gigaspora and Scutellospora (family Gigasporaceae) by using a restricted maximum likelihood approach of continuous character data. Results were compared to a parsimony analysis of spore morphological characters of the same species. Only one tree was generated from each character set. Morphological and developmental data suggest that species with the simplest spore types are ancestral whereas those with complicated inner wall structures are derived. Spores of those species having a complex wall structure pass through stages of development identical to the mature stages of simpler spores, suggesting a pattern of classical Haeckelian recapitulation in evolution of spore characters. Analysis of FAME profiles supported this hypothesis when Glomus leptotichum was used as the outgroup. However, when Glomus etunicatum was chosen as the outgroup, the polarity of the entire tree was reversed. Our results suggest that FAME profiles contain useful information and provide independent criteria for generating phylogenetic hypotheses in AM fungi. The maximum likelihood approach to analyzing FAME profiles also may prove useful for many other groups of organisms in which profiles are empirically shown to be stable and heritable.

Calculating the probability of multitaxon evolutionary trees: bootstrappers Gambit.

The reconstruction of multitaxon trees from molecular sequences is confounded by the variety of algorithms and criteria used to evaluate trees, making it difficult to compare the results of different analyses. A global method of multitaxon phylogenetic reconstruction described here, Bootstrappers Gambit, can be used with any four-taxon algorithm, including distance, maximum likelihood, and parsimony methods. It incorporates a Bayesian-Jeffreys'-bootstrap analysis to provide a uniform probability-based criterion for comparing the results from diverse algorithms. To examine the usefulness of the method, the origin of the eukaryotes has been investigated by the analysis of ribosomal small subunit RNA sequences. Three common algorithms (paralinear distances, Jukes-Cantor distances, and Kimura distances) support the eocyte topology, whereas one (maximum parsimony) supports the archaebacterial topology, suggesting that the eocyte prokaryotes are the closest prokaryotic relatives of the eukaryotes.

Análise genética da produção in vitro de embriões em bovinos Guzerá

O objetivo dessa pesquisa foi avaliar aspectos genéticos que relacionados à produção in vitro de embriões na raça Guzerá. O primeiro estudo focou na estimação de (co) variâncias genéticas e fenotípicas em características relacionadas a produção de embriões e na detecção de possível associação com a idade ao primeiro parto (AFC). Foi detectada baixa e média herdabilidade para características relacionadas à produção de oócitos e embriões. Houve fraca associação genética entre características ligadas a reprodução artificial e a idade ao primeiro parto. O segundo estudo avaliou tendências genéticas e de endogamia em uma população Guzerá no Brasil. Doadoras e embriões produzidos in vitro foram considerados como duas subpopulações de forma a realizar comparações acerca das diferenças de variação anual genética e do coeficiente de endogamia. A tendência anual do coeficiente de endogamia (F) foi superior para a população geral, sendo detectado efeito quadrático. No entanto, a média de F para a sub- população de embriões foi maior do que na população geral e das doadoras. Foi observado ganho genético anual superior para a idade ao primeiro parto e para a produção de leite (305 dias) entre embriões produzidos in vitro do que entre doadoras ou entre a população geral. O terceiro estudo examinou os efeitos do coeficiente de endogamia da doadora, do reprodutor (usado na fertilização in vitro) e dos embriões sobre resultados de produção in vitro de embriões na raça Guzerá. Foi detectado efeito da endogamia da doadora e dos embriões sobre as características estudadas. O quarto (e último) estudo foi elaborado para comparar a adequação de modelos mistos lineares e generalizados sob método de Máxima Verossimilhança Restrita (REML) e sua adequação a variáveis discretas. Quatro modelos hierárquicos assumindo diferentes distribuições para dados de contagem encontrados no banco. Inferência foi realizada com base em diagnósticos de resíduo e comparação de razões entre componentes de variância para os modelos em cada variável. Modelos Poisson superaram tanto o modelo linear (com e sem transformação da variável) quanto binomial negativo à qualidade do ajuste e capacidade preditiva, apesar de claras diferenças observadas na distribuição das variáveis. Entre os modelos testados, a pior qualidade de ajuste foi obtida para o modelo linear mediante transformação logarítmica (Log10 X +1) da variável resposta.

A nova família de distribuições odd log-logística: teoria e aplicações

Neste trabalho, foi proposta uma nova família de distribuições, a qual permite modelar dados de sobrevivência quando a função de risco tem formas unimodal e U (banheira). Ainda, foram consideradas as modificações das distribuições Weibull, Fréchet, half-normal generalizada, log-logística e lognormal. Tomando dados não-censurados e censurados, considerou-se os estimadores de máxima verossimilhança para o modelo proposto, a fim de verificar a flexibilidade da nova família. Além disso, um modelo de regressão locação-escala foi utilizado para verificar a influência de covariáveis nos tempos de sobrevida. Adicionalmente, conduziu-se uma análise de resíduos baseada nos resíduos deviance modificada. Estudos de simulação, utilizando-se de diferentes atribuições dos parâmetros, porcentagens de censura e tamanhos amostrais, foram conduzidos com o objetivo de verificar a distribuição empírica dos resíduos tipo martingale e deviance modificada. Para detectar observações influentes, foram utilizadas medidas de influência local, que são medidas de diagnóstico baseadas em pequenas perturbações nos dados ou no modelo proposto. Podem ocorrer situações em que a suposição de independência entre os tempos de falha e censura não seja válida. Assim, outro objetivo desse trabalho é considerar o mecanismo de censura informativa, baseado na verossimilhança marginal, considerando a distribuição log-odd log-logística Weibull na modelagem. Por fim, as metodologias descritas são aplicadas a conjuntos de dados reais.

Alternativas de análise para experimentos G × E multiatributo

Geralmente, nos experimentos genótipo por ambiente (G × E) é comum observar o comportamento dos genótipos em relação a distintos atributos nos ambientes considerados. A análise deste tipo de experimentos tem sido abordada amplamente para o caso de um único atributo. Nesta tese são apresentadas algumas alternativas de análise considerando genótipos, ambientes e atributos simultaneamente. A primeira, é baseada no método de mistura de máxima verossimilhança de agrupamento - Mixclus e a análise de componentes principais de 3 modos - 3MPCA, que permitem a análise de tabelas de tripla entrada, estes dois métodos têm sido muito usados na área da psicologia e da química, mas pouco na agricultura. A segunda, é uma metodologia que combina, o modelo de efeitos aditivos com interação multiplicativa - AMMI, modelo eficiente para a análise de experimentos (G × E) com um atributo e a análise de procrustes generalizada, que permite comparar configurações de pontos e proporcionar uma medida numérica de quanto elas diferem. Finalmente, é apresentada uma alternativa para realizar imputação de dados nos experimentos (G × E), pois, uma situação muito frequente nestes experimentos, é a presença de dados faltantes. Conclui-se que as metodologias propostas constituem ferramentas úteis para a análise de experimentos (G × E) multiatributo.

Métodos de máxima verosimilitud en ecología y su aplicación en modelos de vecindad

Los métodos de máxima verosimilitud (MMV) ofrecen un marco alternativo a la estadística frecuentista convencional, alejándose del uso del p-valor para el rechazo de una única hipótesis nula y optando por el uso de las verosimilitudes para evaluar el grado de apoyo en los datos a un conjunto de hipótesis alternativas (o modelos) de interés para el investigador. Estos métodos han sido ampliamente aplicados en ecología en el marco de los modelos de vecindad. Dichos modelos usan una aproximación espacialmente explícita para describir procesos demográficos de plantas o procesos ecosistémicos en función de los atributos de los individuos vecinos. Se trata por tanto de modelos fenomenológicos cuya principal utilidad radica en funcionar como herramientas de síntesis de los múltiples mecanismos por los que las especies pueden interactuar e influenciar su entorno, proporcionando una medida del efecto per cápita de individuos de distintas características (ej. tamaño, especie, rasgos fisiológicos) sobre los procesos de interés. La gran ventaja de aplicar los MMV en el marco de los modelos de vecindad es que permite ajustar y comparar múltiples modelos que usen distintos atributos de los vecinos y/o formas funcionales para seleccionar aquel con mayor soporte empírico. De esta manera, cada modelo funcionará como un “experimento virtual” para responder preguntas relacionadas con la magnitud y extensión espacial de los efectos de distintas especies coexistentes, y extraer conclusiones sobre posibles implicaciones para el funcionamiento de comunidades y ecosistemas. Este trabajo sintetiza las técnicas de implementación de los MMV y los modelos de vecindad en ecología terrestre, resumiendo su uso hasta la fecha y destacando nuevas líneas de aplicación.

A mitochondrial phylogeny of the rainforest skink genus Saproscincus, Wells and Wellington (1984)

The phylogenetic relationships and historical biogeography of 10 currently described rainforest skinks in the genus Saproscincus were investigated using mitochondrial protein-coding ND4 and ribosomal RNA 16S genes. A robust phylogeny is inferred using both maximum likelihood and Bayesian analysis, with all inter-specific nodes strongly supported when datasets are combined. The phylogeny supports the recognition of two major lineages (northern and southern), each of which comprises two divergent clades. Both northern and southern lineages have comparably divergent representatives in mid-east Queensland (MEQ), providing further molecular evidence for the importance of two major biogeographic breaks, the St. Lawrence gap and Burdekin gap separating MEQ from southern and northern counterparts respectively. Vicariance associated with the fragmentation and contraction of temperate rainforest during the mid-late Miocene epoch underpins the deep divergence between morphologically conservative lineages in at least three instances. In contrast, one species, Saproseincus oriarus, shows very low sequence divergence but distinct morphological and ecological differentiation from its allopatric sister clade within Saproseincus mustelinus. These results suggest that while vicariance has played a prominent role in diversification and historical biogeography of Saproscincus, divergent selection may also be important. (C) 2004 Elsevier Inc. All rights reserved.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

Statistical modeling and projections of lung cancer mortality in 4 industrialized countries

The purpose of this work was to model lung cancer mortality as a function of past exposure to tobacco and to forecast age-sex-specific lung cancer mortality rates. A 3-factor age-period-cohort (APC) model, in which the period variable is replaced by the product of average tar content and adult tobacco consumption per capita, was estimated for the US, UK, Canada and Australia by the maximum likelihood method. Age- and sex-specific tobacco consumption was estimated from historical data on smoking prevalence and total tobacco consumption. Lung cancer mortality was derived from vital registration records. Future tobacco consumption, tar content and the cohort parameter were projected by autoregressive moving average (ARIMA) estimation. The optimal exposure variable was found to be the product of average tar content and adult cigarette consumption per capita, lagged for 2530 years for both males and females in all 4 countries. The coefficient of the product of average tar content and tobacco consumption per capita differs by age and sex. In all models, there was a statistically significant difference in the coefficient of the period variable by sex. In all countries, male age-standardized lung cancer mortality rates peaked in the 1980s and declined thereafter. Female mortality rates are projected to peak in the first decade of this century. The multiplicative models of age, tobacco exposure and cohort fit the observed data between 1950 and 1999 reasonably well, and time-series models yield plausible past trends of relevant variables. Despite a significant reduction in tobacco consumption and average tar content of cigarettes sold over the past few decades, the effect on lung cancer mortality is affected by the time lag between exposure and established disease. As a result, the burden of lung cancer among females is only just reaching, or soon will reach, its peak but has been declining for I to 2 decades in men. Future sex differences in lung cancer mortality are likely to be greater in North America than Australia and the UK due to differences in exposure patterns between the sexes. (c) 2005 Wiley-Liss, Inc.

The 2dF-SDSS LRG and QSO (2SLAQ) survey: the z < 2.1 quasar luminosity function from 5645 quasars to g=21.85

We have used the Two-Degree Field (2dF) instrument on the Anglo-Australian Telescope (AAT) to obtain redshifts of a sample of z < 3 and 18.0 < g < 21.85 quasars selected from Sloan Digital Sky Survey (SDSS) imaging. These data are part of a larger joint programme between the SDSS and 2dF communities to obtain spectra of faint quasars and luminous red galaxies, namely the 2dF-SDSS LRG and QSO (2SLAQ) Survey. We describe the quasar selection algorithm and present the resulting number counts and luminosity function of 5645 quasars in 105.7 deg(2). The bright-end number counts and luminosity functions agree well with determinations from the 2dF QSO Redshift Survey (2QZ) data to g similar to 20.2. However, at the faint end, the 2SLAQ number counts and luminosity functions are steeper (i.e. require more faint quasars) than the final 2QZ results from Croom et al., but are consistent with the preliminary 2QZ results from Boyle et al. Using the functional form adopted for the 2QZ analysis ( a double power law with pure luminosity evolution characterized by a second-order polynomial in redshift), we find a faint-end slope of beta =-1.78 +/- 0.03 if we allow all of the parameters to vary, and beta =-1.45 +/- 0.03 if we allow only the faint-end slope and normalization to vary (holding all other parameters equal to the final 2QZ values). Over the magnitude range covered by the 2SLAQ survey, our maximum-likelihood fit to the data yields 32 per cent more quasars than the final 2QZ parametrization, but is not inconsistent with other g > 21 deep surveys for quasars. The 2SLAQ data exhibit no well-defined 'break' in the number counts or luminosity function, but do clearly flatten with increasing magnitude. Finally, we find that the shape of the quasar luminosity function derived from 2SLAQ is in good agreement with that derived from Type I quasars found in hard X-ray surveys.

Australia's National Mental Health Strategy and deinstitutionalization: some empirical results

Objective: To determine the role of the National Mental Health Strategy in the deinstitutionalization of patients in psychiatric hospitals in Queensland. Method: Regression analysis (using the maximum likelihood method) has been applied to relevant time-series datasets on public psychiatric institutions in Queensland. In particular, data on both patients and admissions per 10 000 population are analysed in detail from 1953-54 to the present, although data are presented from 1883-84. Results: These Queensland data indicate that deinstitutionalization was a continuing process from the 1950s to the present. However, it is clear that the experience varied from period to period. For example, the fastest change (in both patients and admissions) took place in the period 1953-54 to 1973-74, followed by the period 1974-75 to 1984-85. Conclusions: In large part, the two policies associated with deinstitutionalization, namely a discharge policy ('opening the back door') and an admission policy ('closing the front door') had been implemented before the advent of the National Mental Health Strategy in January 1993. Deinstitutionalization was most rapid in the 30-year period to the early 1980s: the process continued in the 1990s, but at a much slower rate. Deinstitutionalization was, in large part, over before the Strategy was developed and implemented.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Molecular characterization of Thelastomatoidea (Nematoda : Oxyurida) from cockroaches in Australia

A molecular approach was used to genetically characterize 5 species (Aoruroides queenslandensis. Blattophila sphaerolaima, Cordonicola gibsoni, Desmicola ornato and Leidynemella fusiformis) belonging to the superfamily. Thelastomatoidea fi (Nematoda: Oxyurida), a group of pinworms that parasitizes terrestrial arthropods. The D3 domain of the large subunit Of nuclear ribosomal RNA (LSU) was sequenced for individual specimens, and the analysis of the sequence data allowed the genetic relationships of the 5 species to be studied dagger. The sequence variation in the D3 domain within individual species (0-1-8%) was significantly less than the differences among species (4(.)3-12(.)4%). Phylogenetic analyses, Using maximum parsimony, maximum likelihood, and neighbour-joining, tree-building methods, established relationships among the 5 species of Thelastomatoidea and Oxyuris equi (a species of the order Oxyurida). The molecular approach employed provides the prospect for developing DNA tools for the specific identification of the Thelastomatoidea, irrespective of developmental stage and sex, as a basis for systematic, ecological and/or population genetic investigations of members within this superfamily.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.