Evaluation of live-attenuated Salmonella vaccines expressing Campylobacter antigens for control of C. jejuni in poultry

Campylobacter jejuni is a zoonotic bacterial pathogen of worldwide importance. It is estimated that 460,000 human infections occur in the United Kingdom per annum and these involve acute enteritis and may be complicated by severe systemic sequelae. Such infections are frequently associated with the consumption of contaminated poultry meat and strategies to control C. jejuni in poultry are expected to limit pathogen entry into the food chain and the incidence of human disease. Toward this aim, a total of 840 Light Sussex chickens were used to evaluate a Salmonella enterica serovar Typhimurium ΔaroA vaccine expressing the C. jejuni amino acid binding protein CjaA as a plasmid-borne fusion to the C-terminus of fragment C of tetanus toxin. Chickens were given the vaccine at 1-day-old and two weeks later by oral gavage, then challenged after a further two weeks with C. jejuni. Across six biological replicates, statistically significant reductions in caecal C. jejuni of c. 1.4 log10 colony-forming units/g were observed at three and four weeks post-challenge relative to age-matched unvaccinated birds. Protection was associated with the induction of CjaA-specific serum IgY and biliary IgA. Protection was not observed using a vaccine strain containing the empty plasmid. Vaccination with recombinant CjaA subcutaneously at the same intervals significantly reduced the caecal load of C. jejuni at three and four weeks post-challenge. Taken together these data imply that responses directed against CjaA, rather than competitive or cross-protective effects mediated by the carrier, confer protection. The impact of varying parameters on the efficacy of the S. Typhimurium ΔaroA vaccine expressing TetC-CjaA was also tested. Delaying the age at primary vaccination had little impact on protection or humoral responses to CjaA. The use of the parent strain as carrier or changing the attenuating mutation of the carrier to ΔspaS or ΔssaU enhanced the protective effect, consistent with increased invasion and persistence of the vaccine strains relative to the ΔaroA mutant. Expression in the ΔaroA strain of a TetC fusion to Peb1A, but not TetC fusions to GlnH or ChuA, elicited protection against intestinal colonisation by C. jejuni that was comparable to that observed with the TetC-CjaA fusion. Our data are rendered highly relevant by use of the target host in large numbers and support the potential of CjaA- and Peb1A-based vaccines for control of C. jejuni in poultry. © 2009 Elsevier Ltd. All rights reserved.

Scanning gate microscopy of current-annealed single layer graphene

We have used scanning gate microscopy to explore the local conductivity of a current-annealed graphene flake. A map of the local neutrality point (NP) after annealing at low current density exhibits micron-sized inhomogeneities. Broadening of the local e-h transition is also correlated with the inhomogeneity of the NP. Annealing at higher current density reduces the NP inhomogeneity, but we still observe some asymmetry in the e-h conduction. We attribute this to a hole-doped domain close to one of the metal contacts combined with underlying striations in the local NP. © 2010 American Institute of Physics.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

A key role for similarity in vicarious reward.

Humans appear to have an inherent prosocial tendency toward one another in that we often take pleasure in seeing others succeed. This fact is almost certainly exploited by game shows, yet why watching others win elicits a pleasurable vicarious rewarding feeling in the absence of personal economic gain is unclear. One explanation is that game shows use contestants who have similarities to the viewing population, thereby kindling kin-motivated responses (for example, prosocial behavior). Using a game show-inspired paradigm, we show that the interactions between the ventral striatum and anterior cingulate cortex subserve the modulation of vicarious reward by similarity, respectively. Our results support studies showing that similarity acts as a proximate neurobiological mechanism where prosocial behavior extends to unrelated strangers.