26,519 research outputs found
Polymicrobial oral biofilm models: simplifying the complex
Over the past century, numerous studies have used oral biofilm models to investigate growth kinetics, biofilm formation, structure and composition, antimicrobial susceptibility and host–pathogen interactions. In vivo animal models provide useful models of some oral diseases; however, these are expensive and carry vast ethical implications. Oral biofilms grown or maintained in vitro offer a useful platform for certain studies and have the advantages of being inexpensive to establish and easy to reproduce and manipulate. In addition, a wide range of variables can be monitored and adjusted to mimic the dynamic environmental changes at different sites in the oral cavity, such as pH, temperature, salivary and gingival crevicular fluid flow rates, or microbial composition. This review provides a detailed insight for early-career oral science researchers into how the biofilm models used in oral research have progressed and improved over the years, their advantages and disadvantages, and how such systems have contributed to our current understanding of oral disease pathogenesis and aetiology
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
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