Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis

Background Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. Methodology/Principal Findings We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. Conclusions/Significance A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control

Optimal constraint-based decision tree induction from itemset lattices

International audienceIn this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction

Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Changes in deep-water CO2 concentrations over the last several decades determined from discrete pCO2 measurements

This paper is not subject to U.S. copyright. The definitive version was published in Deep Sea Research Part I: Oceanographic Research Papers 74 (2013): 48-63, doi:10.1016/j.dsr.2012.12.005.Detection and attribution of hydrographic and biogeochemical changes in the deep ocean are challenging due to the small magnitude of their signals and to limitations in the accuracy of available data. However, there are indications that anthropogenic and climate change signals are starting to manifest at depth. The deep ocean below 2000 m comprises about 50% of the total ocean volume, and changes in the deep ocean should be followed over time to accurately assess the partitioning of anthropogenic carbon dioxide (CO2) between the ocean, terrestrial biosphere, and atmosphere. Here we determine the changes in the interior deep-water inorganic carbon content by a novel means that uses the partial pressure of CO2 measured at 20 °C, pCO2(20), along three meridional transects in the Atlantic and Pacific oceans. These changes are measured on decadal time scales using observations from the World Ocean Circulation Experiment (WOCE)/World Hydrographic Program (WHP) of the 1980s and 1990s and the CLIVAR/CO2 Repeat Hydrography Program of the past decade. The pCO2(20) values show a consistent increase in deep water over the time period. Changes in total dissolved inorganic carbon (DIC) content in the deep interior are not significant or consistent, as most of the signal is below the level of analytical uncertainty. Using an approximate relationship between pCO2(20) and DIC change, we infer DIC changes that are at the margin of detectability. However, when integrated on the basin scale, the increases range from 8–40% of the total specific water column changes over the past several decades. Patterns in chlorofluorocarbons (CFCs), along with output from an ocean model, suggest that the changes in pCO2(20) and DIC are of anthropogenic origin.Rik Wanninkhof, Geun-Ha Park, John L. Bullister, and Richard A. Feely appreciate the support from the NOAA Office of Atmospheric and Oceanic Research and the Climate Observation Division. S.C.D. acknowledges support from NOAA Grant NA07OAR4310098. T.T. has been supported by grants from NSF and NOAA

Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram.

It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data

Altered Trabecular Bone Structure and Delayed Cartilage Degeneration in the Knees of Collagen VI Null Mice

Mutation or loss of collagen VI has been linked to a variety of musculoskeletal abnormalities, particularly muscular dystrophies, tissue ossification and/or fibrosis, and hip osteoarthritis. However, the role of collagen VI in bone and cartilage structure and function in the knee is unknown. In this study, we examined the role of collagen VI in the morphology and physical properties of bone and cartilage in the knee joint of Col6a1−/− mice by micro-computed tomography (microCT), histology, atomic force microscopy (AFM), and scanning microphotolysis (SCAMP). Col6a1−/− mice showed significant differences in trabecular bone structure, with lower bone volume, connectivity density, trabecular number, and trabecular thickness but higher structure model index and trabecular separation compared to Col6a1+/+ mice. Subchondral bone thickness and mineral content increased significantly with age in Col6a1+/+ mice, but not in Col6a1−/− mice. Col6a1−/− mice had lower cartilage degradation scores, but developed early, severe osteophytes compared to Col6a1+/+mice. In both groups, cartilage roughness increased with age, but neither the frictional coefficient nor compressive modulus of the cartilage changed with age or genotype, as measured by AFM. Cartilage diffusivity, measured via SCAMP, varied minimally with age or genotype. The absence of type VI collagen has profound effects on knee joint structure and morphometry, yet minimal influences on the physical properties of the cartilage. Together with previous studies showing accelerated hip osteoarthritis in Col6a1−/− mice, these findings suggest different roles for collagen VI at different sites in the body, consistent with clinical data