Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses

Deconversion

Streib H. Deconversion. In: Rambo LR, Farhadian CE, eds. The Oxford Handbook on Religious Conversion. Oxford handbooks. Oxford: Oxford University Press; 2014: 271-296.To include a chapter on deconversion in a handbook on conversion is not only appropriate, but, as I argue, necessary for various reasons: It is no longer possible to ignore the fact that a growing number of contemporaries chose to convert more than once in their lifetime; multiple conversions are unavoidable in cultures in which religion occurs no longer as singular in a mono-religious environment, but as plural. Multiple conversions, however, involve deconversion(s). While some contributions use the term “conversion” for both the disaffiliation and the re-affiliation, I focus on “deconversion” in order to include disaffiliations without re-affiliation – which responds to the growing attention to atheists and apostates in the US (cf. Streib & Klein, 2011). Disaffiliation processes constitute an independent field of study that deserves special scientific attention. And here, the term “deconversion” may serve as a reminder of the depth and intensity of biographical change and the new orientation of one’s life that eventually is associated with disaffiliation and is not reserved to conversion. In this chapter, I will start with a conceptualization of deconversion, discuss recent quantitative and qualitative research, and finally draw conclusions and suggest directions for future research

Damping of magnetization dynamics by phonon pumping

We theoretically investigate pumping of phonons by the dynamics of a magnetic film into a non-magnetic contact. The enhanced damping due to the loss of energy and angular momentum shows interference patterns as a function of resonance frequency and magnetic film thickness that cannot be described by viscous ("Gilbert") damping. The phonon pumping depends on magnetization direction as well as geometrical and material parameters and is observable, e.g., in thin films of yttrium iron garnet on a thick dielectric substrate.Comment: 6 pages, 3 figures, 3 pages supplemental material with 3 additional figure

Dynamic Magnetoelastic Boundary Conditions and the Pumping of Phonons

We derive boundary conditions at the interfaces of magnetoelastic heterostructures under ferromagnetic resonance for arbitrary magnetization directions and interface shapes. We apply our formalism to magnet$\vert$nonmagnet bilayers and magnetic grains embedded in a nonmagnetic thin film, revealing a nontrivial magnetization angle dependence of acoustic phonon pumping.Comment: 17 pages, 5 figure

Connections between Classical and Parametric Network Entropies

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity

Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples

Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level