Phylogeny and historical biogeography of the Australian Camphorosmeae (Chenopodiaceae)

Diese Studie befasst sich mit der Phylogenie und Biogeographie der australischen Camphorosmeae, die ein wichtiges Element der Flora arider Gebiete Australiens sind. Die molekularen Phylogenien wurden mit Hilfe Bayes’scher Statistik und „maximum likelihood”berechnet. Um das Alter der Gruppe und interner Linien abzuschätzen, wurden die Methoden „Nonparametric rate smoothing” und “penalized likelihood” benutzt. Morphologische Merkmale wurden nach Kriterien der Parsimonie auf den molekularen Baum aufgetragen. „Brooks parsimony analysis”, „cladistic analysis of distributions and endemism”, „dispersal-vicariance analysis”,„ancestral area analysis” und „weighted ancestral area analysis” wurden angewandt, um Abfolge und Richtungen der Ausbreitung der Gruppe in Australien zu analysieren.Von sieben getesteten Markern hatten nur die nukleären ETS und ITS genügend Variation für die phylogenetische Analyse der Camphorosmeae. Die plastidären Marker trnL-trnF spacer,trnP-psaJ spacer, rpS16 intron, rpL16 intron und trnS-trnG spacer zeigten kein ausreichendes phylogenetisches Signal. Die gefundenen phylogenetischen Hypothesen widersprechen der jetzigen Taxonomie der Gruppe. Neobassia, Threlkeldia, Osteocarpum und Enchylaena sollten den Gattungen Sclerolaena bzw. Maireana zugeordnet werden. Die kladistische Analyse der Fruchtanhängsel unterstützt die taxonomischen Ergebnisse der auf DNA basierenden Phylogenie. Allerdings hat die Behaarung, die bei anderen Gruppen der Chenopodiaceae als wichtiges taxonomisches Merkmal herangezogen wird, die Phylogenie nicht unterstützt. Vorfahren der heutigen Camphorosmeen sind im Miozän, vor ca. 8-14 Millionen Jahren, durch Fernausbreitung vermutlich aus Asien in Australien eingewandert. Anfängliche Diversifizierung fand während des späten Miozäns bis in das frühe Pliozän vor ca. 4-7 Millionen Jahren statt. Am Ende des Pliozäns existierten schon 45% - 72% der Abstammungslinien der jetzigen Camphorosmeen. Dies weist auf eine schnelle Ausbreitung hin. Das Alter stimmt mit dem Einsetzen der Aridisierung Australiens überein, und deutet darauf hin, dass die Ausbreitung der ariden Gebiete eine große Rolle bei der Diversifizierung der Gruppe spielte. Die Vorfahren der australischen Camphorosmeae scheinen die Südküste Australiens zuerst besiedeln zu haben. Dies geschah vor dem Einsetzen der Aridisierung des Kontinents. Die anschließende Ausbreitung erfolgte in verschiedene Richtungen und folgte der fortschreitenden Austrocknung im späten Tertiär und im ganzen Quartär. Durch ihre Anpassung an Trockenheit ist der Erfolg der Camphorosmeae in den ariden Gebieten zu erklären.Die Abwesenheit von klaren phylogenetischen und artspezifischen Signalen zwischen Arten der australischen Camphorosmeae ist auf das junge Alter und die schnelle Diversifizierung der Gruppe zurückzuführen, welche die Häufung von Mutationen und eine starke morphologische Differenzierung nicht zugelassen haben.

Monotone Constrained Tensor-product B-spline with application to screening studies

When different markers are responsive to different aspects of a disease, combination of multiple markers could provide a better screening test for early detection. It is also resonable to assume that the risk of disease changes smoothly as the biomarker values change and the change in risk is monotone with respect to each biomarker. In this paper, we propose a boundary constrained tensor-product B-spline method to estimate the risk of disease by maximizing a penalized likelihood. To choose the optimal amount of smoothing, two scores are proposed which are extensions of the GCV score (O'Sullivan et al. (1986)) and the GACV score (Ziang and Wahba (1996)) to incorporate linear constraints. Simulation studies are carried out to investigate the performance of the proposed estimator and the selection scores. In addidtion, sensitivities and specificities based ona pproximate leave-one-out estimates are proposed to generate more realisitc ROC curves. Data from a pancreatic cancer study is used for illustration.

Uncertainty About the Incubation Period of AIDS and its Impact on Backcalculation

We analyze three sets of doubly-censored cohort data on incubation times, estimating incubation distributions using semi-parametric methods and assessing the comparability of the estimates. Weibull models appear to be inappropriate for at least one of the cohorts, and the estimates for the different cohorts are substantially different. We use these estimates as inputs for backcalculation, using a nonparametric method based on maximum penalized likelihood. The different incubations all produce fits to the reported AIDS counts that are as good as the fit from a nonstationary incubation distribution that models treatment effects, but the estimated infection curves are very different. We also develop a method for estimating nonstationarity as part of the backcalculation procedure and find that such estimates also depend very heavily on the assumed incubation distribution. We conclude that incubation distributions are so uncertain that meaningful error bounds are difficult to place on backcalculated estimates and that backcalculation may be too unreliable to be used without being supplemented by other sources of information in HIV prevalence and incidence.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses

Estimates of HIV prevalence are important for policy in order to establish the health status of a country's population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using conventional approaches to deal with missing data, such as imputation-based methods, will be biased. We develop a Heckman-type simultaneous equation approach which accounts for non-ignorable selection, but unlike previous implementations, allows for spatial dependence and does not impose a homogeneous selection process on all respondents. In addition, our framework addresses the issue of separation, where for instance some factors are severely unbalanced and highly predictive of the response, which would ordinarily prevent model convergence. Estimation is carried out within a penalized likelihood framework where smoothing is achieved using a parametrization of the smoothing criterion which makes estimation more stable and efficient. We provide the software for straightforward implementation of the proposed approach, and apply our methodology to estimating national and sub-national HIV prevalence in Swaziland, Zimbabwe and Zambia.

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Flexible strategies for association analysis with genomic phenotypes

Thesis (Ph.D.)--University of Washington, 2016-08

Normative reference values for the 20-meter shuttlerun test in a population-based sample of schoolchildren in Bogota, Colombia: The FUPRECOL Study

Our aim was to determine the normative reference values of cardiorespiratory fitness (CRF) and to establish the proportion of subjects with low CRF suggestive of future cardio-metabolic risk.

Triceps and subscapular skinfold thicknesses percentiles and cut-offs for overweight and obesity in a population-based sample of schoolchildren in Bogota, Colombia

OBJECTIVES: The aims of this study were to establish a Colombian smoothed centile charts and LMS tables for tríceps, subscapular and sum tríceps+subscapular skinfolds; appropriate cut-offs were selected using receiver operating characteristic analysis based in a populationbased sample of schoolchildren in Bogota, Colombia and to compare them with international studies. METHODS: A total of 9 618 children and adolescents attending public schools in Bogota, Colombia (55.7% girls; age range of 9–17.9 years). Height, weight, body mass index (BMI), waist circumference, triceps and subscapular skinfold measurements were obtained using standardized methods. We have calculated tríceps+subscapular skinfold (T+SS) sum. Smoothed percentile curves for triceps and subscapular skinfold thickness were derived by the LMS method. Receiver operating characteristics curve (ROC) analyses were used to evaluate the optimal cut-off point of tríceps, subscapular and sum tríceps+subscapular skinfolds for overweight and obesity based on the International Obesity Task Force (IOTF) definitions. Data were compared with international studies. RESULTS: Subscapular, triceps skinfolds and T+SS were significantly higher in girls than in boys (P <0.001). The median values for triceps, subscapular as well as T+SS skinfold thickness increased in a sex-specific pattern with age. The ROC analysis showed that subscapular, triceps skinfolds and T+SS have a high discrimination power in the identification of overweight and obesity in the sample population in this study. Based on the raw non-adjusted data, we found that Colombian boys and girls had high triceps and subscapular skinfolds values than their counterparts from Spain, UK, German and US. CONCLUSIONS: Our results provide sex- and age-specific normative reference standards for the triceps and subscapular skinfold thickness values in a large, population-based sample of 3 schoolchildren and adolescents from an Latin-American population. By providing LMS tables for Latin-American people based on Colombian reference data, we hope to provide quantitative tools for the study of obesity and its complications.

Valores de referencia de la fuerza prensil en escolares colombianos. Estudio FUPRECOL

The primary aim of this study was to generate normative handgrip strength (HG) data for 10- to 17.9-year-olds. The secondary aim was to determine the relative proportion of Colombian children and adolescents that fall into established Health Benefit Zones (HBZ). This cross-sectional study is enrolling 7268 schoolchildren (boys n=3129 and girls n=4139, age 12.7 (2.4) years old. HG was measured using a hand dynamometer with an adjustable grip. Five HBZs (Needs Improvement, Fair, Good, Very Good, and Excellent) have been established that correspond to combined-HG. Centile smoothed curves, percentile and tables for the 3rd, 10th, 25th, 50th, 75th, 90th and 97th percentile were calculated using Cole’s LMS method. HG peaked in the sample at 22.2 (8.9) kg in boys and 18.5 (5.5) kg in girls. The increase in HG was greater for boys than for girls, but the peak HG was lower in girls than in boys. The HBZ data indicated that a higher overall percentage of boys than girls at each age group fell into the “Needs Improvement” zone, with differences particularly pronounced during adolescence. Our results provide, for the first time, sex- and age-specific HG reference standards for Colombian schoolchildren aged 9-17.9 years.

Normative reference of standing long jump for Colombian schoolchildren aged 9-17.9 years: The FUPRECOL study

El propósito del presente estudio era generar los valores normativos de salto largo para niños de 9-17.9 años, e investigar las diferencias de sexo y grupo de edad

FAST ADAPTIVE PENALIZED SPLINES

This paper proposes a numerically simple routine for locally adaptive smoothing. The locally heterogeneous regression function is modelled as a penalized spline with a smoothly varying smoothing parameter modelled as another penalized spline. This is being formulated as hierarchical mixed model, with spline coe±cients following a normal distribution, which by itself has a smooth structure over the variances. The modelling exercise is in line with Baladandayuthapani, Mallick & Carroll (2005) or Crainiceanu, Ruppert & Carroll (2006). But in contrast to these papers Laplace's method is used for estimation based on the marginal likelihood. This is numerically simple and fast and provides satisfactory results quickly. We also extend the idea to spatial smoothing and smoothing in the presence of non normal response.

Empirical likelihood based longitudinal studies

In longitudinal data analysis, our primary interest is in the regression parameters for the marginal expectations of the longitudinal responses; the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly for correlated discrete outcome data. Marginal modeling approaches such as generalized estimating equations (GEEs) have received much attention in the context of longitudinal regression. These methods are based on the estimates of the first two moments of the data and the working correlation structure. The confidence regions and hypothesis tests are based on the asymptotic normality. The methods are sensitive to misspecification of the variance function and the working correlation structure. Because of such misspecifications, the estimates can be inefficient and inconsistent, and inference may give incorrect results. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its characteristics and asymptotic properties. We also provide an algorithm based on EL principles for the estimation of the regression parameters and the construction of a confidence region for the parameter of interest. We extend our approach to variable selection for highdimensional longitudinal data with many covariates. In this situation it is necessary to identify a submodel that adequately represents the data. Including redundant variables may impact the model’s accuracy and efficiency for inference. We propose a penalized empirical likelihood (PEL) variable selection based on GEEs; the variable selection and the estimation of the coefficients are carried out simultaneously. We discuss its characteristics and asymptotic properties, and present an algorithm for optimizing PEL. Simulation studies show that when the model assumptions are correct, our method performs as well as existing methods, and when the model is misspecified, it has clear advantages. We have applied the method to two case examples.