The frequency of epstein-barr virus infection and associated lymphoproliferative syndrome after transplantation and its manifestations in children

Twenty cases of Epstein-Barr virus (EBV)-associated lymphoproliferative syndrome (LPS), defined by the presence of EBV nuclear antigen and/or EBV DNA in tissues, were diagnosed in 1467 transplant recipients in Pittsburgh from 1981—1985. The frequency of occurrence in pediatric transplant recipients was 4% (10/ 253), while in adults it was 0.8% (10/1214) (P < .0005). The frequency of LPS in adults declined after 1983 coincidental with the introduction of cyclosporine monitoring. However there was no apparent decline of LPS in children. We describe these ten pediatric cases and one additional case of LPS in a child who received her transplant before 1981. The frequency of EBV infection in 92 pediatric liver recipients was 63%. Of these subjects, 49% were sero-negative and 77% of those acquired primary infection. Of 11 cases of pediatric EBV-associated LPS, 10 were in children who had primary infection shortly before or after transplantation. These results reinforce the impor-tance of primary EBV infection in producing LPS, which was previously shown in adults. Children are at greater risk because they are more likely to be seronegative for EBV and to acquire primary infection. Three clinical types of LPS were recognized in children. The first (5 cases) was a self-limited mononucleo-sislike syndrome. The second syndrome (4 cases) began similarly, but then progressed over the next two months to widespread lymphoproliferation in internal organs and death. The third type (2 cases) was an extranodal intestinal monoclonal B cell lymphoma, occurring late after primary infection. © 1988 by The Williams and Wilkins Co

A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks

This paper presents a novel spectral algorithm with additive clustering designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of hidden communities, is proved to be consistent under the SBMO when the degrees in the graph are (slightly more than) logarithmic. The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Percolation in the classical blockmodel

Classical blockmodel is known as the simplest among models of networks with community structure. The model can be also seen as an extremely simply example of interconnected networks. For this reason, it is surprising that the percolation transition in the classical blockmodel has not been examined so far, although the phenomenon has been studied in a variety of much more complicated models of interconnected and multiplex networks. In this paper we derive the self-consistent equation for the size the global percolation cluster in the classical blockmodel. We also find the condition for percolation threshold which characterizes the emergence of the giant component. We show that the discussed percolation phenomenon may cause unexpected problems in a simple optimization process of the multilevel network construction. Numerical simulations confirm the correctness of our theoretical derivations.Comment: 7 pages, 6 figure

Biological weed control to relieve millions from ambrosia allergies in Europe

Invasive alien species (IAS) can substantially affect ecosystem services and human well-being. However, quantitative assessments of their impact on human health are rare, and the benefits of implementing sustainable IAS management likely to be underestimated. Here we report the effects of the allergenic plant Ambrosia artemisiifolia on public health in Europe and the potential impact of the accidentally introduced leaf beetle Ophraella communa on the number of patients and healthcare costs. We find that, prior to the establishment of O. communa, some 13.5 million persons suffered from Ambrosia-induced allergies in Europe, causing costs of Euro 7.4 billion annually. Our projections reveal that biological control of A. artemisiifolia will reduce the number of patients by approximately 2.3 million and the health costs by Euro 1.1 billion per year. Our conservative calculations indicate that the currently discussed economic costs of IAS underestimate the real costs and thus also the benefits from biological control

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure

A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure

Community detection is the process of assigning nodes and links in significant communities (e.g. clusters, function modules) and its development has led to a better understanding of complex networks. When applied to sizable networks, we argue that most detection algorithms correctly identify prominent communities, but fail to do so across multiple scales. As a result, a significant fraction of the network is left uncharted. We show that this problem stems from larger or denser communities overshadowing smaller or sparser ones, and that this effect accounts for most of the undetected communities and unassigned links. We propose a generic cascading approach to community detection that circumvents the problem. Using real and artificial network datasets with three widely used community detection algorithms, we show how a simple cascading procedure allows for the detection of the missing communities. This work highlights a new detection limit of community structure, and we hope that our approach can inspire better community detection algorithms.Comment: 14 pages, 12 figures + supporting information (5 pages, 6 tables, 3 figures

Interference with oxidative phosphorylation enhances anoxic expression of rice α-amylase genes through abolishing sugar regulation

Rice has the unique ability to express α-amylase under anoxic conditions, a feature that is critical for successful anaerobic germination and growth. Previously, anaerobic conditions were shown to up-regulate the expression of Amy3 subfamily genes (Amy3B/C, 3D, and 3E) in rice embryos. These genes are known to be feedback regulated by the hydrolytic products of starchy endosperm such as the simple sugar glucose. It was found that oxygen deficiency interferes with the repression of Amy3D gene expression imposed by low concentrations of glucose but not with that imposed by higher amounts. This differential anoxic de-repression depending on sugar concentration suggests the presence of two distinct pathways for sugar regulation of Amy3D gene expression. Anoxic de-repression can be mimicked by treating rice embryos with inhibitors of ATP synthesis during respiration. Other sugar-regulated rice α-amylase genes, Amy3B/C and 3E, behave similarly to Amy3D. Treatment with a respiratory inhibitor or anoxia also relieved the sugar repression of the rice CIPK15 gene, a main upstream positive regulator of SnRK1A that is critical for Amy3D expression in response to sugar starvation. SnRK1A accumulation was previously shown to be required for MYBS1 expression, which transactivates Amy3D by binding to a cis-acting element found in the proximal region of all Amy3 subfamily gene promoters (the TA box). Taken together, these results suggest that prevention of oxidative phosphorylation by oxygen deficiency interferes with the sugar repression of Amy3 subfamily gene expression, leading to their enhanced expression in rice embryos during anaerobic germination