Noncentral bimatrix variate generalised beta distributions

In this paper, we determine the density functions of nonsymmetrised doubly noncentral matrix variate beta type I and II distributions. The nonsymetrised density functions of doubly noncentral and noncentral bimatrix variate generalised beta type I and II distributions are also obtained.Comment: 14 page

Cluster analysis of protein array results via similarity of Gene Ontology annotation

BACKGROUND: With the advent of high-throughput proteomic experiments such as arrays of purified proteins comes the need to analyse sets of proteins as an ensemble, as opposed to the traditional one-protein-at-a-time approach. Although there are several publicly available tools that facilitate the analysis of protein sets, they do not display integrated results in an easily-interpreted image or do not allow the user to specify the proteins to be analysed. RESULTS: We developed a novel computational approach to analyse the annotation of sets of molecules. As proof of principle, we analysed two sets of proteins identified in published protein array screens. The distance between any two proteins was measured as the graph similarity between their Gene Ontology (GO) annotations. These distances were then clustered to highlight subsets of proteins sharing related GO annotation. In the first set of proteins found to bind small molecule inhibitors of rapamycin, we identified three subsets containing four or five proteins each that may help to elucidate how rapamycin affects cell growth whereas the original authors chose only one novel protein from the array results for further study. In a set of phosphoinositide-binding proteins, we identified subsets of proteins associated with different intracellular structures that were not highlighted by the analysis performed in the original publication. CONCLUSION: By determining the distances between annotations, our methodology reveals trends and enrichment of proteins of particular functions within high-throughput datasets at a higher sensitivity than perusal of end-point annotations. In an era of increasingly complex datasets, such tools will help in the formulation of new, testable hypotheses from high-throughput experimental data

The Influence of Markov Decision Process Structure on the Possible Strategic Use of Working Memory and Episodic Memory

Researchers use a variety of behavioral tasks to analyze the effect of biological manipulations on memory function. This research will benefit from a systematic mathematical method for analyzing memory demands in behavioral tasks. In the framework of reinforcement learning theory, these tasks can be mathematically described as partially-observable Markov decision processes. While a wealth of evidence collected over the past 15 years relates the basal ganglia to the reinforcement learning framework, only recently has much attention been paid to including psychological concepts such as working memory or episodic memory in these models. This paper presents an analysis that provides a quantitative description of memory states sufficient for correct choices at specific decision points. Using information from the mathematical structure of the task descriptions, we derive measures that indicate whether working memory (for one or more cues) or episodic memory can provide strategically useful information to an agent. In particular, the analysis determines which observed states must be maintained in or retrieved from memory to perform these specific tasks. We demonstrate the analysis on three simplified tasks as well as eight more complex memory tasks drawn from the animal and human literature (two alternation tasks, two sequence disambiguation tasks, two non-matching tasks, the 2-back task, and the 1-2-AX task). The results of these analyses agree with results from quantitative simulations of the task reported in previous publications and provide simple indications of the memory demands of the tasks which can require far less computation than a full simulation of the task. This may provide a basis for a quantitative behavioral stoichiometry of memory tasks

Exhaled carbon monoxide in asthmatics: a meta-analysis

<p>Abstract</p> <p>Background</p> <p>The non-invasive assessment of airway inflammation is potentially advantageous in asthma management. Exhaled carbon monoxide (eCO) measurement is cheap and has been proposed to reflect airway inflammation and oxidative stress but current data are conflicting. The purpose of this meta-analysis is to determine whether eCO is elevated in asthmatics, is regulated by steroid treatment and reflects disease severity and control.</p> <p>Methods</p> <p>A systematic search for English language articles published between 1997 and 2009 was performed using Medline, Embase and Cochrane databases. Observational studies comparing eCO in non-smoking asthmatics and healthy subjects or asthmatics before and after steroid treatment were included. Data were independently extracted by two investigators and analyzed to generate weighted mean differences using either a fixed or random effects meta-analysis depending upon the degree of heterogeneity.</p> <p>Results</p> <p>18 studies were included in the meta-analysis. The eCO level was significantly higher in asthmatics as compared to healthy subjects and in intermittent asthma as compared to persistent asthma. However, eCO could not distinguish between steroid-treated asthmatics and steroid-free patients nor separate controlled and partly-controlled asthma from uncontrolled asthma in cross-sectional studies. In contrast, eCO was significantly reduced following a course of corticosteroid treatment.</p> <p>Conclusions</p> <p>eCO is elevated in asthmatics but levels only partially reflect disease severity and control. eCO might be a potentially useful non-invasive biomarker of airway inflammation and oxidative stress in nonsmoking asthmatics.</p

Biological Convergence of Cancer Signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties