The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Bursty egocentric network evolution in Skype

In this study we analyze the dynamics of the contact list evolution of millions of users of the Skype communication network. We find that egocentric networks evolve heterogeneously in time as events of edge additions and deletions of individuals are grouped in long bursty clusters, which are separated by long inactive periods. We classify users by their link creation dynamics and show that bursty peaks of contact additions are likely to appear shortly after user account creation. We also study possible relations between bursty contact addition activity and other user-initiated actions like free and paid service adoption events. We show that bursts of contact additions are associated with increases in activity and adoption - an observation that can inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013

A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

<p>Abstract</p> <p>Background</p> <p>Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.</p> <p>Results</p> <p>Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.</p> <p>Conclusion</p> <p>Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.</p

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table

Dynamics of conflicts in Wikipedia

In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.Comment: Supporting information adde

NS1 Specific CD8(+) T-Cells with Effector Function and TRBV11 Dominance in a Patient with Parvovirus B19 Associated Inflammatory Cardiomyopathy

Background: Parvovirus B19 (B19V) is the most commonly detected virus in endomyocardial biopsies (EMBs) from patients with inflammatory cardiomyopathy (DCMi). Despite the importance of T-cells in antiviral defense, little is known about the role of B19V specific T-cells in this entity. Methodology and Principal Findings: An exceptionally high B19V viral load in EMBs (115,091 viral copies/mg nucleic acids), peripheral blood mononuclear cells (PBMCs) and serum was measured in a DCMi patient at initial presentation, suggesting B19V viremia. The B19V viral load in EMBs had decreased substantially 6 and 12 months afterwards, and was not traceable in PBMCs and the serum at these times. Using pools of overlapping peptides spanning the whole B19V proteome, strong CD8(+) T-cell responses were elicited to the 10-amico-acid peptides SALKLAIYKA (19.7% of all CD8(+) cells) and QSALKLAIYK (10%) and additional weaker responses to GLCPHCINVG (0.71%) and LLHTDFEQVM (0.06%). Real-time RT-PCR of IFN gamma secretion-assay-enriched T-cells responding to the peptides, SALKLAIYKA and GLCPHCINVG, revealed a disproportionately high T-cell receptor Vbeta (TRBV) 11 expression in this population. Furthermore, dominant expression of type-1 (IFN gamma, IL2, IL27 and Tbet) and of cytotoxic T-cell markers (Perforin and Granzyme B) was found, whereas gene expression indicating type-2 (IL4, GATA3) and regulatory T-cells (FoxP3) was low. Conclusions: Our results indicate that B19V Ag-specific CD8(+) T-cells with effector function are involved in B19V associated DCMi. In particular, a dominant role of TRBV11 and type-1/CTL effector cells in the T-cell mediated antiviral immune response is suggested. The persistence of B19V in the endomyocardium is a likely antigen source for the maintenance of CD8(+) T-cell responses to the identified epitopes

SPARC 2017 retrospect & prospects : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2017 SPARC conference. This year we not only celebrate the work of our PGRs but also the 50th anniversary of Salford as a University, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 130 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to exploit this great opportunity to engage with researchers working in different subject areas to your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Postpartum depression in the Occupied Palestinian Territory:a longitudinal study in Bethlehem

BACKGROUND: Postpartum depression (PPD) affects women from different cultures around the world. No previous studies have investigated PPD among women in Palestine. Fertility rates in Palestine are among the highest in the world, hence even low rates of PPD could have considerable national impact. The aim of this study was to determine the prevalence of, and risk factors for, PPD among Palestinian mothers. METHODS: 101 mothers were recruited during the registration of their child’s birth (within 1 week) at the Bethlehem branch of the Ministry of Interior. Participants were assessed via a face to face interview, and were followed up 1 week, 2 weeks, 6 weeks, 3 months, and 6 months later by telephone interview. Interviews included the Arabic Edinburgh Postnatal Depression Scale (EPDS), with PPD indicated by depressive symptoms (EPDS score ≥11) at ≥2 follow-up time points. Pearson’s correlation was calculated between repeated EPDS scores, and multivariable logistic regression was used to investigate risk factors for PPD. RESULTS: The prevalence of depressive symptoms was fairly constant (14–19%) over the follow-up period. Most depressive symptoms developed within 1 month of delivery; mothers with depressive symptoms at 3 months postpartum were highly likely to still have symptoms at 6 months. 27.7% (28/101) of women met our criteria for PPD. High parity (odds ratio (OR) 4.52 (95% CI 0.90, 22.8) parity 3+ versus primiparous), unplanned pregnancy (OR 2.44 (0.99, 6.01)) and sex of child not being the one desired (OR 5.07 (1.12, 22.9)) were associated with PPD, but these associations were attenuated in multivariable analysis. CONCLUSIONS: The prevalence of PPD in Palestine appears to be higher than in high income countries, but similar to the prevalence in other Middle Eastern countries. High parity and unplanned pregnancy were identified as risk factors for PPD, suggesting that fully meeting the need for family planning could reduce the incidence of PPD in the Palestinian population

Structure of Protein Interaction Networks and Their Implications on Drug Design

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects