Transdisciplinary learning: Transformative collaborations between students, industry, academia and communities.

Background and objectives of the case An analogy: Imagine you are invited to a dinner party, but instead of a stuffy sit-down affair, your host asks you to bring your favourite ingredient, and together you prepare a delicious feast of unique and distinct flavours. UTS’s transdisciplinary initiatives are changing the shape of higher education and forging innovative partnerships by bringing together diverse professional fields. With a focus on practice-based and problem-focused learning, UTS educational programs combine the strengths of multiple disciplines, industries, public sector organisations, and the community to turn real-world problems into rewarding opportunities for education and also “learning for a lifetime”. In place of the limitations of artificial disciplinary boundaries, transdisciplinary learning practices create synergistic and innovative approaches to grappling with complex applied challenges. Students, researchers, practitioners, community members and other stakeholders combine their knowledge, tools, techniques, methods, theories, concepts, as well as cultural and personal perspectives. By understanding problems holistically, the solutions that emerge are bold, innovative, and creative, as well as mutually beneficial. We view this as the future of education: good to work with, and good to think with — problem solving for (and with) industry and society. The Faculty of Transdisciplinary Innovation is re-imagining how education, research, and professional practice can work together to navigate today’s complex problems, and create commercially attractive and socially responsible futures. We also practice what we preach: for example, staff professional development to enact these models in our own teaching; educational programs to provide experiential learning around problem solving within a rapidly-changing environment involving students from across different disciplines and cultural backgrounds; as well as policy development and research on today’s pressing “wicked problems” with industry and government. Primary objectives of this next practice concept of transdisciplinary learning, include: - To promote a shift in industry-university engagement from producing “knowledge for society” to co-generating “knowledge with society”; - To build a resilient ecosystem for co-learning; - To create and sustain future-oriented degree programs with collaboration between industry, government, and community at the centre, geared to prepare our graduates for the complex challenges of a networked world; - To create an agile and responsive industry-university lab environment for generating and testing new experimental models; - To enable industry – by collaborating with our students and academics – to see their problems from a fresh perspective, often through different and revealing lenses, and to notice opportunities and spot challenges that may have otherwise been overlooked; - To prepare students to lead innovation in a rapidly-changing and challenging world; and - To graduate students who are ‘complexity-fluent’, systems thinkers, creative problem-posers and -solvers, and imaginative, ethical citizens

Expression of steroid receptor coactivator 3 in ovarian epithelial cancer is a poor prognostic factor and a marker for platinum resistance

BACKGROUND: Steroid receptor coactivator 3 (SRC3) is an important coactivator of a number of transcription factors and is associated with a poor outcome in numerous tumours. Steroid receptor coactivator 3 is amplified in 25% of epithelial ovarian cancers (EOCs) and its expression is higher in EOCs compared with non-malignant tissue. No data is currently available with regard to the expression of SRC-3 in EOC and its influence on outcome or the efficacy of treatment. METHODS: Immunohistochemistry was performed for SRC3, oestrogen receptor-α, HER2, PAX2 and PAR6, and protein expression was quantified using automated quantitative immunofluorescence (AQUA) in 471 EOCs treated between 1991 and 2006 with cytoreductive surgery followed by first-line treatment platinum-based therapy, with or without a taxane. RESULTS: Steroid receptor coactivator 3 expression was significantly associated with advanced stage and was an independent prognostic marker. High expression of SRC3 identified patients who have a significantly poorer survival with single-agent carboplatin chemotherapy, while with carboplatin/paclitaxel treatment such a difference was not seen. CONCLUSION: Steroid receptor coactivator 3 is a poor prognostic factor in EOCs and appears to identify a population of patients who would benefit from the addition of taxanes to their chemotherapy regimen, due to intrinsic resistance to platinum therapy

Fast automatic quantitative cell replication with fluorescent live cell imaging

Hoffmann N, Keck M, Neuweger H, et al. Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets. BMC Bioinformatics. 2012;13(1): 21.Background Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. Results In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CEMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CEMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). Conclusions We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CEMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net webcite. The evaluation scripts of the present study are available from the same source

Nipah Virus Transmission in a Hamster Model

Based on epidemiological data, it is believed that human-to-human transmission plays an important role in Nipah virus outbreaks. No experimental data are currently available on the potential routes of human-to-human transmission of Nipah virus. In a first dose-finding experiment in Syrian hamsters, it was shown that Nipah virus was predominantly shed via the respiratory tract within nasal and oropharyngeal secretions. Although Nipah viral RNA was detected in urogenital and rectal swabs, no infectious virus was recovered from these samples, suggesting no viable virus was shed via these routes. In addition, hamsters inoculated with high doses shed significantly higher amounts of viable Nipah virus particles in comparison with hamsters infected with lower inoculum doses. Using the highest inoculum dose, three potential routes of Nipah virus transmission were investigated in the hamster model: transmission via fomites, transmission via direct contact and transmission via aerosols. It was demonstrated that Nipah virus is transmitted efficiently via direct contact and inefficiently via fomites, but not via aerosols. These findings are in line with epidemiological data which suggest that direct contact with nasal and oropharyngeal secretions of Nipah virus infected individuals resulted in greater risk of Nipah virus infection. The data provide new and much-needed insights into the modes and efficiency of Nipah virus transmission and have important public health implications with regards to the risk assessment and management of future Nipah virus outbreaks

Understanding communication networks in the emergency department

<p>Abstract</p> <p>Background</p> <p>Emergency departments (EDs) are high pressure health care settings involving complex interactions between staff members in providing and organising patient care. Without good communication and cooperation amongst members of the ED team, quality of care is at risk. This study examined the problem-solving, medication advice-seeking and socialising networks of staff working in an Australian hospital ED.</p> <p>Methods</p> <p>A social network survey (Response Rate = 94%) was administered to all ED staff (n = 109) including doctors, nurses, allied health professionals, administrative staff and ward assistants. Analysis of the network characteristics was carried out by applying measures of density (the extent participants are concentrated), connectedness (how related they are), isolates (how segregated), degree centrality (who has most connections measured in two ways, in-degree, the number of ties directed to an individual and out-degree, the number of ties directed from an individual), betweenness centrality (who is important or powerful), degree of separation (how many ties lie between people) and reciprocity (how bi-directional are interactions).</p> <p>Results</p> <p>In all three networks, individuals were more closely connected to colleagues from within their respective professional groups. The problem-solving network was the most densely connected network, followed by the medication advice network, and the loosely connected socialising network. ED staff relied on each other for help to solve work-related problems, but some senior doctors, some junior doctors and a senior nurse were important sources of medication advice for their ED colleagues.</p> <p>Conclusions</p> <p>Network analyses provide useful ways to assess social structures in clinical settings by allowing us to understand how ED staff relate within their social and professional structures. This can provide insights of potential benefit to ED staff, their leaders, policymakers and researchers.</p

A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

<p>Abstract</p> <p>Background</p> <p>Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.</p> <p>Results</p> <p>Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.</p> <p>Conclusion</p> <p>Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.</p

Graphs in molecular biology

Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network

The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae)

The study of traditional knowledge of medicinal plants has led to discoveries that have helped combat diseases and improve healthcare. However, the development of quantitative measures that can assist our quest for new medicinal plants has not greatly advanced in recent years. Phylogenetic tools have entered many scientific fields in the last two decades to provide explanatory power, but have been overlooked in ethnomedicinal studies. Several studies show that medicinal properties are not randomly distributed in plant phylogenies, suggesting that phylogeny shapes ethnobotanical use. Nevertheless, empirical studies that explicitly combine ethnobotanical and phylogenetic information are scarce.In this study, we borrowed tools from community ecology phylogenetics to quantify significance of phylogenetic signal in medicinal properties in plants and identify nodes on phylogenies with high bioscreening potential. To do this, we produced an ethnomedicinal review from extensive literature research and a multi-locus phylogenetic hypothesis for the pantropical genus Pterocarpus (Leguminosae: Papilionoideae). We demonstrate that species used to treat a certain conditions, such as malaria, are significantly phylogenetically clumped and we highlight nodes in the phylogeny that are significantly overabundant in species used to treat certain conditions. These cross-cultural patterns in ethnomedicinal usage in Pterocarpus are interpreted in the light of phylogenetic relationships.This study provides techniques that enable the application of phylogenies in bioscreening, but also sheds light on the processes that shape cross-cultural ethnomedicinal patterns. This community phylogenetic approach demonstrates that similar ethnobotanical uses can arise in parallel in different areas where related plants are available. With a vast amount of ethnomedicinal and phylogenetic information available, we predict that this field, after further refinement of the techniques, will expand into similar research areas, such as pest management or the search for bioactive plant-based compounds