Expanding the research area of behavior change support systems

The First International Workshop on Behavior Change Support Systems attracted a great research interest. The selected papers focused on abstraction, implementation and evaluation of Behavior Change Support Systems. The workshop is an evidence of how researchers from around the globe have their own perspective of behavior change interventions. In this abstract, we have attempted to outline core issues that can enhance persuasiveness of such support systems. Finally, we highlight important research questions relating to the development of effective Behavior Change Support Systems.

Distinct subpopulations of gamma delta T cells are present in normal and tumor-bearing human liver

Gamma delta T cells are thought to mediate immune responses at epithelial surfaces. We have quantified and characterized hepatic and peripheral blood gamma delta T cells from 11 normal and 13 unresolved tumor-bearing human liver specimens. gamma delta T cells are enriched in normal liver (6.6% of T cells) relative to matched blood (0.9%; P = 0.008). The majority express CD4(-)CD8(-) phenotypes and many express CD56 and/or CD161. In vitro, hepatic gamma delta T cells can be induced to kill tumor cell lines and release interferon-gamma, tumor necrosis factor-alpha, interleukin-2 and interleukin-4. Analysis of V gamma and V delta chain usage indicated that V delta 3(+) cells are expanded in normal livers (21.2% of gamma delta T cells) compared to blood (0.5%; P = 0.001). Tumor-bearing livers had significant expansions and depletions of gamma delta T cell subsets but normal cytolytic activity. This study identifies novel populations of liver T cells that may play a role in immunity against tumors.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.