Research Cloud Data Communities

Big Data, big science, the data deluge, these are topics we are hearing about more and more in our research pursuits. Then, through media hype, comes cloud computing, the saviour that is going to resolve our Big Data issues. However, it is difficult to pinpoint exactly what researchers can actually do with data and with clouds, how they get to exactly solve their Big Data problems, and how they get help in using these relatively new tools and infrastructure. Since the beginning of 2012, the NeCTAR Research Cloud has been running at the University of Melbourne, attracting over 1,650 users from around the country. This has not only provided an unprecedented opportunity for researchers to employ clouds in their research, but it has also given us an opportunity to clearly understand how researchers can more easily solve their Big Data problems. The cloud is now used daily, from running web servers and blog sites, through to hosting virtual laboratories that can automatically create hundreds of servers depending on research demand. Of course, it has also helped us understand that infrastructure isn’t everything. There are many other skillsets needed to help researchers from the multitude of disciplines use the cloud effectively. How can we solve Big Data problems on cloud infrastructure? One of the key aspects are communities based on research platforms: Research is built on collaboration, connection and community, and researchers employ platforms daily, whether as bio-imaging platforms, computational platforms or cloud platforms (like DropBox). There are some important features which enabled this to work.. Firstly, the borders to collaboration are eased, allowing communities to access infrastructure that can be instantly built to be completely open, through to completely closed, all managed securely through (nationally) standardised interfaces. Secondly, it is free and easy to build servers and infrastructure, but it is also cheap to fail, allowing for experimentation not only at a code-level, but at a server or infrastructure level as well. Thirdly, this (virtual) infrastructure can be shared with collaborators, moving the practice of collaboration from sharing papers and code to sharing servers, pre-configured and ready to go. And finally, the underlying infrastructure is built with Big Data in mind, co-located with major data storage infrastructure and high-performance computers, and interconnected with high-speed networks nationally to research instruments. The research cloud is fundamentally new in that it easily allows communities of researchers, often connected by common geography (research precincts), discipline or long-term established collaborations, to build open, collaborative platforms. These open, sharable, and repeatable platforms encourage coordinated use and development, evolving to common community-oriented methods for Big Data access and data manipulation. In this paper we discuss in detail critical ingredients in successfully establishing these communities, as well as some outcomes as a result of these communities and their collaboration enabling platforms. We consider astronomy as an exemplar of a research field that has already looked to the cloud as a solution to the ensuing data tsunami

Pseudomonas aeruginosa AES-1 exhibits increased virulence gene expression during chronic infection of cystic fibrosis lung

Pseudomonas aeruginosa, the leading cause of morbidity and mortality in people with cystic fibrosis (CF), adapts for survival in the CF lung through both mutation and gene expression changes. Frequent clonal strains such as the Australian Epidemic Strain-1 (AES-1), have increased ability to establish infection in the CF lung and to superimpose and replace infrequent clonal strains. Little is known about the factors underpinning these properties. Analysis has been hampered by lack of expression array templates containing CF-strain specific genes. We sequenced the genome of an acute infection AES-1 isolate from a CF infant (AES-1R) and constructed a non-redundant micro-array (PANarray) comprising AES-1R and seven other sequenced P. aeruginosa genomes. The unclosed AES-1R genome comprised 6.254Mbp and contained 6957 putative genes, including 338 not found in the other seven genomes. The PANarray contained 12,543 gene probe spots; comprising 12,147 P. aeruginosa gene probes, 326 quality-control probes and 70 probes for non-P. aeruginosa genes, including phage and plant genes. We grew AES-1R and its isogenic pair AES-1M, taken from the same patient 10.5 years later and not eradicated in the intervening period, in our validated artificial sputum medium (ASMDM) and used the PANarray to compare gene expression of both in duplicate. 675 genes were differentially expressed between the isogenic pairs, including upregulation of alginate, biofilm, persistence genes and virulence-related genes such as dihydroorotase, uridylate kinase and cardiolipin synthase, in AES-1M. Non-PAO1 genes upregulated in AES-1M included pathogenesis-related (PAGI-5) genes present in strains PACS2 and PA7, and numerous phage genes. Elucidation of these genes' roles could lead to targeted treatment strategies for chronically infected CF patients. © 2011 Naughton et al

First report of generalized face processing difficulties in möbius sequence.

Reverse simulation models of facial expression recognition suggest that we recognize the emotions of others by running implicit motor programmes responsible for the production of that expression. Previous work has tested this theory by examining facial expression recognition in participants with Möbius sequence, a condition characterized by congenital bilateral facial paralysis. However, a mixed pattern of findings has emerged, and it has not yet been tested whether these individuals can imagine facial expressions, a process also hypothesized to be underpinned by proprioceptive feedback from the face. We investigated this issue by examining expression recognition and imagery in six participants with Möbius sequence, and also carried out tests assessing facial identity and object recognition, as well as basic visual processing. While five of the six participants presented with expression recognition impairments, only one was impaired at the imagery of facial expressions. Further, five participants presented with other difficulties in the recognition of facial identity or objects, or in lower-level visual processing. We discuss the implications of our findings for the reverse simulation model, and suggest that facial identity recognition impairments may be more severe in the condition than has previously been noted

Performance of the QWIP Focal Plane Array for NASA's Landsat 9 Mission

The flight focal plane array (FPA) for the Thermal Infrared Sensor 2 (TIRS-2) instrument, to be flown on Landsat 9, was built and characterized at NASA Goddard Space Flight Center (GSFC). The FPA was assembled using GaAs quantum well infrared photodetector (QWIP) arrays from the same lot as the TIRS instrument on Landsat 8. Each QWIP array is hybridized to an Indigo ISC9803 readout integrated circuit (ROIC) with 640 x 512, 25m by 25m pixels. Each QWIP hybrid was tested at the NASA/GSFC Detector Characterization Laboratory (DCL) as a single sensor chip assembly (SCA). The best SCAs in terms of performance were then built up into an FPA consisting of three SCAs, required to provide the necessary 15-degree field of view of the instrument. The FPA was tested to determine if project requirements were being met as a fully assembled unit. The performance of the QWIP SCAs and the fully assembled, NASA flight-qualified FPA will be reviewed

Owning an overweight or underweight body: distinguishing the physical, experienced and virtual body

Our bodies are the most intimately familiar objects we encounter in our perceptual environment. Virtual reality provides a unique method to allow us to experience having a very different body from our own, thereby providing a valuable method to explore the plasticity of body representation. In this paper, we show that women can experience ownership over a whole virtual body that is considerably smaller or larger than their physical body. In order to gain a better understanding of the mechanisms underlying body ownership, we use an embodiment questionnaire, and introduce two new behavioral response measures: an affordance estimation task (indirect measure of body size) and a body size estimation task (direct measure of body size). Interestingly, after viewing the virtual body from first person perspective, both the affordance and the body size estimation tasks indicate a change in the perception of the size of the participant’s experienced body. The change is biased by the size of the virtual body (overweight or underweight). Another novel aspect of our study is that we distinguish between the physical, experienced and virtual bodies, by asking participants to provide affordance and body size estimations for each of the three bodies separately. This methodological point is important for virtual reality experiments investigating body ownership of a virtual body, because it offers a better understanding of which cues (e.g. visual, proprioceptive, memory, or a combination thereof) influence body perception, and whether the impact of these cues can vary between different setups

Do you hear what I see? An audio-visual paradigm to assess emotional egocentricity bias

We often use our own emotions to understand other people’s emotions. However, emotional egocentric biases (EEB), namely the tendency to use one’s own emotional state when relating to others’ emotions may hinder this process, especially when emotions are incongruent. We capitalized on the classic EEB task to develop a new version that is easier to implement and control. Unlike the original EEB that relies on a combination of private (e.g. touch) and public (e.g. vision) sensory information, our EEB task (AV-EEB) used audio-visual stimuli to evoke (in)congruent emotions among participants. Auditory and visual signals are both public and make the task easier to implement and control. We provide lab-based and online validations of the AV-EEB, and demonstrate a positive relationship between EEB and social negative potency. This new easily implemented version of the EEB task can accelerate the investigation of egocentricity biases in several research areas

A conceptual framework for supply chain collaboration:empirical evidence from the agri-food industry

Purpose - The purpose of this paper is to analyse the concept of supply chain collaboration and to provide an overall framework that can be used as a conceptual landmark for further empirical research. In addition, the concept is explored in the context of agri-food industry and particularities are identified. Finally, the paper submits empirical evidence from an exploratory case study in the agri-food industry, at the grower-processor interface, and information regarding the way the concept is actually applied in small medium-sized enterprises (SMEs) is presented. Design/methodology/approach - The paper employed case study research by conducting in-depth interviews in the two companies. Findings - Supply chain collaboration concept is of significant importance for the agri-food industry however, some constraints arise due to the nature of industry's products, and the specific structure of the sector. Subsequently, collaboration in the supply chain is often limited to operational issues and to logistics-related activities. Research limitations/implications - Research is limited to a single case study and further qualitative testing of the conceptual model is needed in order to adjust the model before large scale testing. Practical implications - Case study findings may be transferable to other similar dual relationships at the grower-processor interface. Weaker parts in asymmetric relationships have opportunities to improve their position, altering the dependence balance, by achieving product/process excellence. Originality/value - The paper provides evidence regarding the applicability of the supply chain collaboration concept in the agri-food industry. It takes into consideration not relationships between big multinational companies, but SMEs