Restricted Covariance Priors with Applications in Spatial Statistics

We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance--covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.Comment: Published at http://dx.doi.org/10.1214/14-BA927 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/

"Fortunate are those who take the first steps"? The psychosocial impact of novel drug development.

Novel drug development offers people with cystic fibrosis exciting opportunities but is not without challenges. Currently, there is an understandable emphasis on protecting patients' physical health when developing treatments. However, there appears to be little consideration of how novel drug development impacts on psychosocial wellbeing, or the downstream consequences of this. Using an illustrative case and reviewing the literature we explore themes regarding the psychosocial impact of trial participation and novel drug development and identify areas requiring further research. Through this, we hope to prepare healthcare professionals to better understand the needs of their patients in this rapidly evolving landscape

New strategies for sustainable fisheries management: A case study of Atlantic salmon

This briefing paper considers the alarming declines in fish stocks in recent years, and how holistic, integrated approaches can help manage fish stocks within biologically sustainable limits. Using Atlantic salmon as a case study, the authors highlight the challenges facing fisheries management and conservation, and the implications for policy and management

Parametric Polyhedra with at least kk Lattice Points: Their Semigroup Structure and the k-Frobenius Problem

Given an integral $d \times n$ matrix $A$, the well-studied affine semigroup \mbox{ Sg} (A)=\{ b : Ax=b, \ x \in {\mathbb Z}^n, x \geq 0\} can be stratified by the number of lattice points inside the parametric polyhedra $P_A(b)=\{x: Ax=b, x\geq0\}$. Such families of parametric polyhedra appear in many areas of combinatorics, convex geometry, algebra and number theory. The key themes of this paper are: (1) A structure theory that characterizes precisely the subset \mbox{ Sg}_{\geq k}(A) of all vectors b \in \mbox{ Sg}(A) such that $P_A(b) \cap {\mathbb Z}^n$ has at least $k$ solutions. We demonstrate that this set is finitely generated, it is a union of translated copies of a semigroup which can be computed explicitly via Hilbert bases computations. Related results can be derived for those right-hand-side vectors $b$ for which $P_A(b) \cap {\mathbb Z}^n$ has exactly $k$ solutions or fewer than $k$ solutions. (2) A computational complexity theory. We show that, when $n$, $k$ are fixed natural numbers, one can compute in polynomial time an encoding of \mbox{ Sg}_{\geq k}(A) as a multivariate generating function, using a short sum of rational functions. As a consequence, one can identify all right-hand-side vectors of bounded norm that have at least $k$ solutions. (3) Applications and computation for the $k$-Frobenius numbers. Using Generating functions we prove that for fixed $n,k$ the $k$-Frobenius number can be computed in polynomial time. This generalizes a well-known result for $k=1$ by R. Kannan. Using some adaptation of dynamic programming we show some practical computations of $k$-Frobenius numbers and their relatives

Going the extra mile: why clinical research in cystic fibrosis must include children

This is an exciting time for research and novel drug development in cystic fibrosis. However, rarely has the adage, “Children are not just little adults” been more relevant. This article is divided into two main sections. In the first, we explore why it is important to involve children in research. We discuss the potential benefits of understanding a disease and its treatment in children, and we highlight that children have the same legal and ethical right to evidence-based therapy as adults. Additionally, we discuss why extrapolation from adults may be inappropriate, for example, medication pharmacokinetics may be different in children, and there may be unpredictable adverse effects. In the second part, we discuss how to involve children and their families in research. We outline the importance and the complexities of selecting appropriate outcome measures, and we discuss the role co-design may have in improving the involvement of children. We highlight the importance of appropriate staffing and resourcing, and we outline some of the common challenges and possible solutions, including practical tips on obtaining consent/assent in children and adolescents. We conclude that it is unethical to simply rely on extrapolation from adult studies because research in young children is challenging and that research should be seen as a normal part of the paediatric therapeutic journey

A Regression Tree Approach using Mathematical Programming

Regression analysis is a machine learning approach that aims to accurately predict the value of continuous output variables from certain independent input variables, via automatic estimation of their latent relationship from data. Tree-based regression models are popular in literature due to their flexibility to model higher order non-linearity and great interpretability. Conventionally, regression tree models are trained in a two-stage procedure, i.e. recursive binary partitioning is employed to produce a tree structure, followed by a pruning process of removing insignificant leaves, with the possibility of assigning multivariate functions to terminal leaves to improve generalisation. This work introduces a novel methodology of node partitioning which, in a single optimisation model, simultaneously performs the two tasks of identifying the break-point of a binary split and assignment of multivariate functions to either leaf, thus leading to an efficient regression tree model. Using six real world benchmark problems, we demonstrate that the proposed method consistently outperforms a number of state-of-the-art regression tree models and methods based on other techniques, with an average improvement of 7–60% on the mean absolute errors (MAE) of the predictions

Syndecan-1 Enhances Proliferation, Migration and Metastasis of HT-1080 Cells in Cooperation with Syndecan-2

Syndecans are transmembrane heparan sulphate proteoglycans. Their role in the development of the malignant phenotype is ambiguous and depends upon the particular type of cancer. Nevertheless, syndecans are promising targets in cancer therapy, and it is important to elucidate the mechanisms controlling their various cellular effects. According to earlier studies, both syndecan-1 and syndecan-2 promote malignancy of HT-1080 human fibrosarcoma cells, by increasing the proliferation rate and the metastatic potential and migratory ability, respectively. To better understand their tumour promoter role in this cell line, syndecan expression levels were modulated in HT-1080 cells and the growth rate, chemotaxis and invasion capacity were studied. For in vivo testing, syndecan-1 overexpressing cells were also inoculated into mice. Overexpression of full length or truncated syndecan-1 lacking the entire ectodomain but containing the four juxtamembrane amino acids promoted proliferation and chemotaxis. These effects were accompanied by a marked increase in syndecan-2 protein expression. The pro-migratory and pro-proliferative effects of truncated syndecan-1 were not observable when syndecan-2 was silenced. Antisense silencing of syndecan-2, but not that of syndecan-1, inhibited cell migration. In vivo, both full length and truncated syndecan-1 increased tumour growth and metastatic rate. Based on our in vitro results, we conclude that the tumour promoter role of syndecan-1 observed in HT-1080 cells is independent of its ectodomain; however, in vivo the presence of the ectodomain further increases tumour proliferation. The enhanced migratory ability induced by syndecan-1 overexpression is mediated by syndecan-2. Overexpression of syndecan-1 also leads to activation of IGF1R and increased expression of Ets-1. These changes were not evident when syndecan-2 was overexpressed. These findings suggest the involvement of IGF1R and Ets-1 in the induction of syndecan-2 synthesis and stimulation of proliferation by syndecan-1. This is the first report demonstrating that syndecan-1 enhances malignancy of a mesenchymal tumour cell line, via induction of syndecan-2 expression

Bias in random forest variable importance measures: Illustrations, sources and a solution

BACKGROUND: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. RESULTS: Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. CONCLUSION: We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research

Specific Syndecan-1 Domains Regulate Mesenchymal Tumor Cell Adhesion, Motility and Migration

Malignant mesothelioma is an asbestos induced cancer that is difficult to diagnose. Several studies have combined biomarkers to improve mesothelioma diagnosis, but with moderate success, and there is a need for new mesothelioma biomarkers. The tumour is often resistant to treatment and most patients will survive less than a year. An indicator of patient survival is the tumours growth pattern, which in turn is influenced by expressed proteoglycans. In this thesis work, we aim to improve the possibilities to diagnose malignant mesothelioma by combining biomarkers and by identifying new ones. We also investigate tumour driving mechanisms with focus on one of these suggested biomarkers, the cell-bound proteoglycan syndecan-1. We were able to construct a diagnostic two-step model based on biomarkers in patient material. By implementing a cut-off level and thereafter focusing on unresolved patients we combined hyaluronan and N-ERC/mesothelin (paper I), which significantly increased the diagnostic accuracy for malignant mesothelioma. To further improve diagnosis, we used mass spectrometry to find new biomarkers. We identified and validated galectin-1, which was excellent in discriminating mesotheliomas from adenocarcinomas (paper II). In the same study, we were also the first to describe aldo-keto reductase 1B10 as a novel prognostic mesothelioma biomarker. Syndecan-1 has been indicated as a marker for carcinomas. In paper I we describe how higher levels of syndecan-1 indicate the presence of a carcinoma over a mesothelioma. This was verified in paper II when syndecan-1 was identified as downregulated in fluids from mesothelioma patients compared to lung cancer patients. Paper III and paper IV focus on this proteoglycan. Malignant cell lines transfected with syndecan-1 and various truncated forms of syndecan-1 affected adhesion and migration, which are key features of cancer invasion (paper III). The results showed a domain- and cell type specific effect on the cells’ motility. Regulating syndecan-1 levels and analysing the global gene expression of mesothelioma cells made it evident that this proteoglycan has a strong influence on transforming growth factor β signalling and several growth factor pathways (paper IV). Links to cell migration and proliferation were furthermore identified, along with glycosaminoglycan modifying enzymes. These results can shed light on the complex role of syndecan-1 in invasion and growth of malignant mesenchymal cells. Taken together, this thesis work describes a complement to conventional mesothelioma diagnosis and identifies novel biomarkers. Furthermore, the potential biomarker syndecan-1 was shown to have an effect on cell motility and proliferation. These results increase our understanding of this aggressive malignancy

Effects of dependence in high-dimensional multiple testing problems

<p>Abstract</p> <p>Background</p> <p>We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices.</p> <p>Results</p> <p>We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable.</p> <p>Conclusion</p> <p>We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on <it>π</it>₀or FDR estimation in a dependency context.</p