184 research outputs found
Finding community structure in networks using the eigenvectors of matrices
We consider the problem of detecting communities or modules in networks,
groups of vertices with a higher-than-average density of edges connecting them.
Previous work indicates that a robust approach to this problem is the
maximization of the benefit function known as "modularity" over possible
divisions of a network. Here we show that this maximization process can be
written in terms of the eigenspectrum of a matrix we call the modularity
matrix, which plays a role in community detection similar to that played by the
graph Laplacian in graph partitioning calculations. This result leads us to a
number of possible algorithms for detecting community structure, as well as
several other results, including a spectral measure of bipartite structure in
networks and a new centrality measure that identifies those vertices that
occupy central positions within the communities to which they belong. The
algorithms and measures proposed are illustrated with applications to a variety
of real-world complex networks.Comment: 22 pages, 8 figures, minor corrections in this versio
Expression and regulation of nonsteroidal anti-inflammatory drugâactivated gene (NAG-1) in human and mouse tissue
AbstractBackground & Aims: Nonsteroidal anti-inflammatory drugs (NSAIDs) induce NSAID-activated gene 1 (NAG-1), which has proapoptotic and antitumorigenic activities. However, NAG-1 expression and its relationship with apoptosis in human and mouse intestinal tract have not been determined. Methods: NAG-1 expression in human and mouse tissue was determined by immunohistochemistry, and apoptosis was estimated by in situ apoptosis detection. Apoptosis in NAG-1 overexpressing HCT-116 cells was examined with flow cytometry after cell sorting by green fluorescence protein. NAG-1 regulation in mouse cells was examined by Northern blot analysis, comparing sulindac-treated and nontreated mice. Results: Apoptosis was higher in NAG-1 overexpressing cells compared with controls. Human NAG-1 protein was localized to the colonic surface epithelium where cells undergo apoptosis, and higher expression was observed in the normal surface epithelium than in most of the tumors. This localization and lower expression in tumors was similar to that in the Min mouse, in which NSAIDs were also shown to regulate the expression of NAG-1 in mouse cells. Sulindac treatment of mice increased the NAG-1 expression in the colon and liver. Conclusions: Based on these results, we propose that NAG-1 acts as a mediator of apoptosis in intestinal cells and may contribute to cancer chemoprevention by NSAIDs.GASTROENTEROLOGY 2002;122:1388-139
Employing with conviction: The experiences of employers who actively recruit criminalised people
Atherton, P., & Buck, G. Employing with conviction: The experiences of employers who actively recruit criminalised people. Probation Journal, 68(2), pp. 186-205. Copyright © [2021] (Copyright Holder). Reprinted by permission of SAGE Publications.In England and Wales, criminal reoffending costs ÂŁ18 billion annually. Securing employment can support desistance from crime, but only 17% of ex-prisoners are employed a year after release. Understanding the motivations of employers who do recruit criminalised people therefore represents an important area of inquiry. This article draws upon qualitative interviews with twelve business leaders in England who proactively employ criminalised people. Findings reveal that inclusive recruitment can be (indirectly) encouraged by planning policies aimed to improve social and environmental well-being and that employers often work creatively to meet employeesâ additional needs, resulting in commercial benefits and (re)settlement opportunities
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
Minding impacting events in a model of stochastic variance
We introduce a generalisation of the well-known ARCH process, widely used for
generating uncorrelated stochastic time series with long-term non-Gaussian
distributions and long-lasting correlations in the (instantaneous) standard
deviation exhibiting a clustering profile. Specifically, inspired by the fact
that in a variety of systems impacting events are hardly forgot, we split the
process into two different regimes: a first one for regular periods where the
average volatility of the fluctuations within a certain period of time is below
a certain threshold and another one when the local standard deviation
outnumbers it. In the former situation we use standard rules for
heteroscedastic processes whereas in the latter case the system starts
recalling past values that surpassed the threshold. Our results show that for
appropriate parameter values the model is able to provide fat tailed
probability density functions and strong persistence of the instantaneous
variance characterised by large values of the Hurst exponent is greater than
0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on
Improving the Louvain Algorithm for Community Detection with Modularity Maximization
International audienc
Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique âfingerprintsâ by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. Background Glioblastoma are highly invasive brain tumors. Th
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