Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions

In this paper we propose methodology for inference of binary-valued adjacency matrices from various measures of the strength of association between pairs of network nodes, or more generally pairs of variables. This strength of association can be quantified by sample covariance and correlation matrices, and more generally by test-statistics and hypothesis test p-values from arbitrary distributions. Community detection methods such as block modelling typically require binary-valued adjacency matrices as a starting point. Hence, a main motivation for the methodology we propose is to obtain binary-valued adjacency matrices from such pairwise measures of strength of association between variables. The proposed methodology is applicable to large high-dimensional data-sets and is based on computationally efficient algorithms. We illustrate its utility in a range of contexts and data-sets

Detection of Epigenomic Network Community Oncomarkers

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset

Particle-particle and quasiparticle random phase approximations: Connections to coupled cluster theory

We establish a formal connection between the particle-particle (pp) random phase approximation (RPA) and the ladder channel of the coupled cluster doubles (CCD) equations. The relationship between RPA and CCD is best understood within a Bogoliubov quasiparticle (qp) RPA formalism. This work is a follow-up to our previous formal proof on the connection between particle-hole (ph) RPA and ring-CCD. Whereas RPA is a quasibosonic approximation, CC theory is a correct bosonization in the sense that the wavefunction and Hilbert space are exactly fermionic. Coupled cluster theory achieves this goal by interacting the ph (ring) and pp (ladder) diagrams via a third channel that we here call "crossed-ring" whose presence allows for full fermionic antisymmetry. Additionally, coupled cluster incorporates what we call "mosaic" terms which can be absorbed into defining a new effective one-body Hamiltonian. The inclusion of these mosaic terms seems to be quite important. The pp-RPA an d qp-RPA equations are textbook material in nuclear structure physics but are largely unknown in quantum chemistry, where particle number fluctuations and Bogoliubov determinants are rarely used. We believe that the ideas and connections discussed in this paper may help design improved ways of incorporating RPA correlation into density functionals based on a CC perspective

A Power Variance Test for Nonstationarity in Complex-Valued Signals

We propose a novel algorithm for testing the hypothesis of nonstationarity in complex-valued signals. The implementation uses both the bootstrap and the Fast Fourier Transform such that the algorithm can be efficiently implemented in O(NlogN) time, where N is the length of the observed signal. The test procedure examines the second-order structure and contrasts the observed power variance - i.e. the variability of the instantaneous variance over time - with the expected characteristics of stationary signals generated via the bootstrap method. Our algorithmic procedure is capable of learning different types of nonstationarity, such as jumps or strong sinusoidal components. We illustrate the utility of our test and algorithm through application to turbulent flow data from fluid dynamics

Two-way sparsity for time-varying networks, with applications in genomics

We propose a novel way of modelling time-varying networks, by inducing two-way sparsity on local models of node connectivity. This two-way sparsity separately promotes sparsity across time and sparsity across variables (i.e., within time). Separation of these two types of sparsity is achieved with the introduction of a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling. We provide an efficient implementation of the proposed model via a Gibbs sampler, and we apply the model to data from neural development. In doing so, we demonstrate that the model we propose is able to infer changes in genomic network structure which match current biological knowledge. The novel network structures which are inferred by the proposed model identify potential targets for further experimental investigation by neuro-biologists

CD28 Ligation Increases Macrophage Suppression of T Cell Proliferation

When compared to spleen or lymph node cells, resident peritoneal cavity cells respond poorly to T cell activation in vitro. The greater proportional representation of macrophages in this cell source has been shown to actively suppress the T cell response. Peritoneal macrophages exhibit an immature phenotype $(MHC Class II^{lo}, B7^{lo})$ that reduces their efficacy as antigen presenting cells. Furthermore, these cells readily express inducible nitric oxide synthase (iNOS), an enzyme that promotes T cell tolerance by catabolism of the limiting amino acid arginine. Here, we investigate the ability of exogenous T cell costimulation to recover the peritoneal T cell response. We show that CD28 ligation failed to recover the peritoneal T cell response and actually suppressed responses that had been recovered by inhibiting iNOS. As indicated by cytokine ELISpot and neutralizing mAb treatment, this “co-suppression” response was due to CD28 ligation increasing the number of IFNγ-secreting cells. Our results illustrate that cellular composition and cytokine milieu influence T cell costimulation biology

Atheists on the Santiago way : examining motivations to go on pilgrimage

In the past 30 years, the camino to Santiago de Compostela has been recreated as an eclectic pilgrimage, open to both religious and atheist travelers. Following previous work on motivational orientations and religion, we conducted a study examining atheist versus religious pilgrims' motivations to walk the Santiago way. We assessed pilgrims (N = 360) at various parts of the northern Spanish camino using a questionnaire that measured motivations to go on pilgrimage. In addition, we measured levels of positive and negative affect, physical exertion, and emotional problems. Atheists scored significantly lower on community and religious types of motivations. However, in several measures no differences were found between groups. We suggest that both atheist and religious pilgrims are exploring forms of horizontal and vertical transcendence characterized by a desire to connect to nature and one's deeper sel