Data citation and the citation graph

The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data

Mechanisms of NADPH oxidase expression in cultured monocytes

The abstract regards the mechanisms of expression of different NADPH oxidase components in cultured monocyte

Biosynthesis and secretion of complement component C3 by activated human polymorphonuclear leukocytes

We tested the hypothesis that human polymorphonuclear leukocytes (PMN), bearing complement receptors CR1 and CR3, might also synthesize C3, particularly when activated by LPS or cytokines. Northern blot analysis of total RNA, obtained from purified PMN stimulated overnight with LPS or cytokines (IFN-gamma, TNF-alpha, and IL-1) showed the 5.3-kb RNA transcript reported for C3 in hepatocytes and monocytes. No transcripts for C4 and factor B were detected. Time course studies of C3 mRNA expression in PMN treated with LPS or TNF-alpha demonstrated a steady increase with a plateau at 24 h that correlated with secretion of C3, determined by ELISA. In contrast, IFN-gamma and IL-1 induced a transient increase in C3 transcript with a peak around 8 h after stimulation, which was not reflected in an increased rate of C3 secretion. The content of C3 protein in PMN culture media, measured by ELISA, was about 4 ng/ml/10(7) cells after overnight stimulation with LPS or TNF-alpha. A very small amount of C3 (about 0.7 ng/ml/10(7) cells) was detected in supernatants from unstimulated and IFN-gamma- or IL-1-induced PMN. Immunoprecipitation with a polyclonal anti-human C3, followed by SDS-PAGE analysis, from [35S]methionine labeled PMN, revealed the presence in culture supernatants of three major bands at 185, 115 and 70 kDa, corresponding to pro-C3, alpha and beta chains, respectively. Analysis of [14C]methylamine incorporation and of autolytic cleavage showed that the C3 produced in tissue culture by PMN contained an intact thiolester bond. The capacity of PMN to secrete functional C3 in response to LPS and TNF-alpha might be an important mechanism of host defense at sites of inflammation

Mining patterns in graphs with multiple weights

Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signifies importance. In real life, there are many graphs with weights on nodes and/or edges. For these graphs, it is fair that the importance (score) of a pattern is determined not only by the number of its appearances, but also by the weights on the nodes/edges of those appearances. Scoring functions based on the weights do not generally satisfy the apriori property, which guarantees that the number of appearances of a pattern cannot be larger than the frequency of any of its sub-patterns, and hence allows faster pruning. Therefore, existing approaches employ other, less efficient, pruning strategies. The problem becomes even more challenging in the case of multiple weighting functions that assign different weights to the same nodes/edges. In this work we propose a new family of scoring functions that respects the apriori property, and thus can rely on effective pruning strategies. We provide efficient and effective techniques for mining patterns in multi-weighted graphs, and we devise both an exact and an approximate solution. In addition, we propose a distributed version of our approach, which distributes the appearances of the patterns to examine among multiple workers. Extensive experiments on both real and synthetic datasets prove that the presence of edge weights and the choice of scoring function affect the patterns mined, and the quality of the results returned to the user. Moreover, we show that, even when the performance of the exact algorithm degrades because of an increasing number of weighting functions, the approximate algorithm performs well and with fairly good quality. Finally, the distributed algorithm proves to be the best choice for mining large and rich input graphs

Exemplar queries: a new way of searching

Modern search engines employ advanced techniques that go beyond the structures that strictly satisfy the query conditions in an effort to better capture the user intentions. In this work, we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries exemplar queries. We provide a formal specification of their semantics and show that they are fundamentally different from notions like queries by example, approximate queries and related queries. We provide an implementation of these semantics for knowledge graphs and present an exact solution with a number of optimizations that improve performance without compromising the result quality. We study two different congruence relations, isomorphism and strong simulation, for identifying the answers to an exemplar query. We also provide an approximate solution that prunes the search space and achieves considerably better time performance with minimal or no impact on effectiveness. The effectiveness and efficiency of these solutions with synthetic and real datasets are experimentally evaluated, and the importance of exemplar queries in practice is illustrated

Beyond frequencies: Graph Pattern mining in multi-weighted graphs

Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signies importance. Several measures of frequency have been proposed that respect the apriori property, essential for an e-cient search of the patterns. This property states that the number of appearances of a pattern in a graph cannot be larger than the frequency of any of its sub-patterns. In real life, there are many graphs with weights on nodes and/or edges. For these graphs, it is fair that the importance (score) of a pattern is determined not only by the number of its appearances, but also by the weights on the nodes/edges of those appearances. Scoring functions based on the weights do not generally satisfy the apriori property, thus forcing many approaches to employ other, less ecient, pruning strategies to speed up the computation. The problem becomes even more challenging in the case of multiple weighting functions that assign dierent weights to the same nodes/edges. In this work, we provide ecient and eective techniques for mining patterns in multi-weight graphs. We devise both an exact and an approximate solution. The rst is characterized by intelligent storage and computation of the pattern scores, while the second is based on the aggregation of similar weighting functions to allow scalability and avoid redundant computations. Both methods adopt a scoring function that respects the apriori property, and thus they can rely on eective pruning strategies. Extensive experiments under dierent parameter settings prove that the presence of edge weights and the choice of scoring function aect the patterns mined, and hence the quality of the results returned to the user. Finally, experiments on datasets of dierent sizes and increasing numbers of weighting functions show that, even when the performance of the exact algorithm degrades, the approximate algorithm performs well and with quite good quality

Multi-example search in rich information graphs

In rich information spaces, it is often hard for users to formally specify the characteristics of the desired answers, either due to the complexity of the schema or of the query language, or even because they do not know exactly what they are looking for. Exemplar queries constitute a query paradigm that overcomes those problems, by allowing users to provide examples of the elements of interest in place of the query specification. In this paper, we propose a general approach where the user-provided example can comprise several partial specification fragments, where each fragment describes only one part of the desired result. We provide a formal definition of the problem, which generalizes existing formulations for both the relational and the graph model. We then describe exact algorithms for its solution for the case of information graphs, as well as top-k algorithms. Experiments on large real datasets demonstrate the effectiveness and efficiency of the proposed approach

Production of oxygen radicals in pathogen-activated dendritic cells

Activation of NADPH oxidase represents an essential mechanism of defense against pathogens. Dendritic cells (DC) are phagocytic cells specialized in Ag presentation rather than in bacteria killing. Human monocyte-derived DC were found to express the NADPH oxidase components and to release superoxide anions in response to phorbol esters and phagocytic agonists. The NADPH oxidase components p47phox and gp91phox were down-regulated during monocyte differentiation to DC, and maturation of DC with pathogen-derived molecules, known to activate TLRs, increased p47phox and gp91phox expression and enhanced superoxide anions release. Similar results were obtained with plasmacytoid DC following maturation with influenza virus. In contrast, activation of DC by immune stimuli (CD40 ligand) did not regulate NADPH oxidase components or respiratory burst. NADPH oxidase-derived oxygen radicals did not play any role in DC differentiation, maturation, cytokine production, and induction of T cell proliferation, as based on the normal function of DC generated from chronic granulomatous disease patients and the use of an oxygen radical scavenger. However, NADPH oxidase activation was required for DC killing of intracellular Escherichia coli. It is likely that the selective regulation of oxygen radicals production by pathogen-activated DC may function to limit pathogen dissemination during DC trafficking to secondary lymphoid tissues

Receptor type tyrosine phosphatase gamma (PTP gamma) is a novel dendritic cell marker

In the present study we evaluated PTPgamma expression in the human hematopoietic system. PTPgamma mRNA expression is highest in spleen and, to a lesser extent, thymus. PTPgamma expression was detected in large, dendritic elements in these and all the other hemopoietic and non-hemopoietic tissues analyzed. Their phenotypic characterization was in agreement with immature, tissue-localized myeloid-derived dendritic cells (CD11c(+), Mannose Receptor(+), DC-SIGN(+), CD1a(-)). Confocal microscopy analysis demonstrate its cell membrane localization in in vitro cultured dendritic cells. In the same cellular model other receptor type tyrosine phosphatases (CD45, CD 148 and PTPepsilon) were differentially regulated upon lipopolisaccaride treatment. Taken together these results point to a highly selective expression and modulation of this receptor-type tyrosine phosphatase in a myeloid DC subset and a potential role in the regulation of immune response, providing a novel too] to unravel the complex biology of these cells