Planar projections of graphs

We introduce and study a new graph representation where vertices are embedded in three or more dimensions, and in which the edges are drawn on the projections onto the axis-parallel planes. We show that the complete graph on $n$ vertices has a representation in $\lceil \sqrt{n/2}+1 \rceil$ planes. In 3 dimensions, we show that there exist graphs with $6n-15$ edges that can be projected onto two orthogonal planes, and that this is best possible. Finally, we obtain bounds in terms of parameters such as geometric thickness and linear arboricity. Using such a bound, we show that every graph of maximum degree 5 has a plane-projectable representation in 3 dimensions.Comment: Accepted at CALDAM 202

Zinterhof Sequences in GRID-Based Numerical Integration

The appropriateness of Zinterhof sequences to be used in GRID-based QMC integration is discussed. Theoretical considerations as well as experimental investigations are conducted comparing and assessing different strategies for an efficient and reliable usage. The high robustness and ease of construction exhibited by those sequences qualifies them as excellent QMC point set candidates for heterogeneous environments like the GRID

"If only I had taken the other road...": Regret, risk and reinforced learning in informed route-choice

This paper presents a study of the effect of regret on route choice behavior when both descriptional information and experiential feedback on choice outcomes are provided. The relevance of Regret Theory in travel behavior has been well demonstrated in non-repeated choice environments involving decisions on the basis of descriptional information. The relation between regret and reinforced learning through experiential feedbacks is less understood. Using data obtained from a simple route-choice experiment involving different levels of travel time variability, discrete-choice models accounting for regret aversion effects are estimated. The results suggest that regret aversion is more evident when descriptional information is provided ex-ante compared to a pure learning from experience condition. Yet, the source of regret is related more strongly to experiential feedbacks rather than to the descriptional information itself. Payoff variability is negatively associated with regret. Regret aversion is more observable in choice situations that reveal risk-seeking, and less in the case of risk-aversion. These results are important for predicting the possible behavioral impacts of emerging information and communication technologies and intelligent transportation systems on travelers' behavior. © 2012 Springer Science+Business Media, LLC

Characterization of Schistosome Tegumental Alkaline Phosphatase (SmAP)

Schistosomes are parasitic platyhelminths that currently infect over 200 million people globally. The parasites can live for years in a putatively hostile environment - the blood of vertebrates. We have hypothesized that the unusual schistosome tegument (outer-covering) plays a role in protecting parasites in the blood; by impeding host immunological signaling pathways we suggest that tegumental molecules help create an immunologically privileged environment for schistosomes. In this work, we clone and characterize a schistosome alkaline phosphatase (SmAP), a predicted ∼60 kDa glycoprotein that has high sequence conservation with members of the alkaline phosphatase protein family. The SmAP gene is most highly expressed in intravascular parasite life stages. Using immunofluorescence and immuno-electron microscopy, we confirm that SmAP is expressed at the host/parasite interface and in internal tissues. The ability of living parasites to cleave exogenous adenosine monophosphate (AMP) and generate adenosine is very largely abolished when SmAP gene expression is suppressed following RNAi treatment targeting the gene. These results lend support to the hypothesis that schistosome surface enzymes such as SmAP could dampen host immune responses against the parasites by generating immunosuppressants such as adenosine to promote their survival. This notion does not rule out other potential functions for the adenosine generated e.g. in parasite nutrition

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

A Quasi-Monte Carlo Algorithm for the Global Illumination Problem in the Radiosity Setting

One of the main problems in computer graphics is to solve the global illumination problem, which is given by a Fredholm integral equation of the second kind, called the radiance equation (REQ). In order to achieve realistic images, a very complex kernel of the integral equation, modelling all physical effects of light, must be considered. Due to this complexity Monte Carlo methods seem to be an appropriate approach to solve the REQ approximately. We show that replacing Monte Carlo by quasi-Monte Carlo in some steps of the algorithm results in a faster convergence