Resolution and characterisation of multiple isoforms of bovine kappa-casein by 2-DE following a reversible cysteine-tagging enrichment strategy

Visualisation of multiple isoforms of kappa-casein on 2-D gels is restricted by the abundant alpha- and beta-caseins that not only limit gel loading but also migrate to similar regions as the more acidic kappa-casein isoforms. To overcome this problem, we took advantage of the absence of cysteine residues in alpha(S1)- and beta-casein by devising an affinity enrichment procedure based on reversible biotinylation of cysteine residues. Affinity capture of cysteine-containing proteins on avidin allowed the removal of the vast majority of alpha(S1)- and beta-casein, and on subsequent 2-D gel analysis 16 gel spots were identified as kappa-casein by PMF. Further analysis of the C-terminal tryptic peptide along with structural predictions based on mobility on the 2-D gel allowed us to assign identities to each spot in terms of genetic variant (A or B), phosphorylation status (1, 2 or 3) and glycosylation status (from 0 to 6). Eight isoforms of the A and B variants with the same PTMs were observed. When the casein fraction of milk from a single cow, homozygous for the B variant of kappa-casein, was used as the starting material, 17 isoforms from 13 gel spots were characterised. Analysis of isoforms of low abundance proved challenging due to the low amount of material that could be extracted from the gels as well as the lability of the PTMs during MS analysis. However, we were able to identify a previously unrecognised site, T-166, that could be phosphorylated or glycosylated. Despite many decades of analysis of milk proteins, the reasons for this high level of heterogeneity are still not clear.

Making it work: Identifying the challenges of collaborative international research

In this article, we explore the challenges - and benefits - of conducting collaborative research on an international scale. The authors - from Australia, Canada, and New Zealand - draw upon their experiences in designing and conducting a three-country study. The growing pressures on scholars to work in collaborative research teams are described, and key findings and reflections are presented. It is claimed that such work is a highly complex and demanding extension to the academic's role. The authors conclude that, despite the somewhat negative sense that this reflection may convey, the synergies gained and the valuable comparative learning that took place make overcoming these challenges a worthwhile process. The experiences as outlined in this paper suggest that developing understandings of the challenges inherent in undertaking international collaborative research might well be a required component of the professional development opportunities afforded to new scholars.

Paediatric research in emergency departments international collaborative (PREDICT): First steps towards the development of an Australian and New Zealand research network

Paediatric emergency research is hampered by a number of barriers that can be overcome by a multicentre approach. In 2004, an Australia and New Zealand-based paediatric emergency research network was formed, the Paediatric Research in Emergency Departments International Collaborative (PREDICT). The founding sites include all major tertiary children’s hospital EDs in Australia and New Zealand and a major mixed ED in Australia. PREDICT aims to provide leadership and infrastructure for multicentre research at the highest standard, facilitate collaboration between institutions, health-care providers and researchers and ultimately improve patient outcome. Initial network-wide projects have been determined. The present article describes the development of the network, its structure and future goals.

Eastern facade to lake, Collaborative Workshop, University of Queensland, St Lucia

View of eastern facade to lake, with Zelman Cowen building behind.

Moomba - A collaborative environment for supporting distributed extreme programming in global software development

Global Software Development (GSD) is an emerging distributive software engineering practice, in which a higher communication overhead due to temporal and geographical separation among developers is traded with gains in reduced development cost, improved flexibility and mobility for developers, increased access to skilled resource-pools and convenience of customer involvements. However, due to its distributive nature, GSD faces many fresh challenges in aspects relating to project coordination, awareness, collaborative coding and effective communication. New software engineering methodologies and processes are required to address these issues. Research has shown that, with adequate support tools, Distributed Extreme Programming (DXP) – a distributive variant of an agile methodology – Extreme Programming (XP) can be both efficient and beneficial to GDS projects. In this paper, we present the design and realization of a collaborative environment, called Moomba, which assists a distributed team in both instantiation and execution of a DXP process in GSD projects.

Collaborative decision-making processes for maintaining biodiversity: Two Australian case studies

There have been many models developed by scientists to assist decision-makers in making socio-economic and environmental decisions. It is now recognised that there is a shift in the dominant paradigm to making decisions with stakeholders, rather than making decisions for stakeholders. Our paper investigates two case studies where group model building has been undertaken for maintaining biodiversity in Australia. The first case study focuses on preservation and management of green spaces and biodiversity in metropolitan Melbourne under the umbrella of the Melbourne 2030 planning strategy. A geographical information system is used to collate a number of spatial datasets encompassing a range of cultural and natural assets data layers including: existing open spaces, waterways, threatened fauna and flora, ecological vegetation covers, registered cultural heritage sites, and existing land parcel zoning. Group model building is incorporated into the study through eliciting weightings and ratings of importance for each datasets from urban planners to formulate different urban green system scenarios. The second case study focuses on modelling ecoregions from spatial datasets for the state of Queensland. The modelling combines collaborative expert knowledge and a vast amount of environmental data to build biogeographical classifications of regions. An information elicitation process is used to capture expert knowledge of ecoregions as geographical descriptions, and to transform this into prior probability distributions that characterise regions in terms of environmental variables. This prior information is combined with measured data on the environmental variables within a Bayesian modelling technique to produce the final classified regions. We describe how linked views between descriptive information, mapping and statistical plots are used to decide upon representative regions that satisfy a number of criteria for biodiversity and conservation. This paper discusses the advantages and problems encountered when undertaking group model building. Future research will extend the group model building approach to include interested individuals and community groups.

Emergent temporal behaviour and collaborative work

Although collaboration manifestly takes place in time, the role of time in shaping the behaviour of collaborations, and collaborative systems, is not well understood. Time is more than clock-time or the subjective experience of time; its effects on systems include differential rates of change of system elements, temporally non-linear behaviour and phenomena such as entrainment and synchronization. As a system driver, it generates emergent effects shaping systems and their behaviour. In the paper we present a systems view of time, and consider the implications of such a view through the case of collaborative development of a new university timetabling system. Teasing out the key temporal phenomena using the notion of temporal trajectories helps us understand the emergent temporal behaviour and suggests a means for improving outcomes.