Transcriptomic Data Analysis Using Graph-Based Out-of-Core Methods

Biological data derived from high-throughput microarrays can be transformed into finite, simple, undirected graphs and analyzed using tools first introduced by the Langston Lab at the University of Tennessee. Transforming raw data can be broken down into three main tasks: data normalization, generation of similarity metrics, and threshold selection. The choice of methods used in each of these steps effect the final outcome of the graph, with respect to size, density, and structure. A number of different algorithms are examined and analyzed to illustrate the magnitude of the effects. Graph-based tools are then used to extract putative gene networks. These tools are loosely based on the concept of clique, which generates clusters optimized for density. Innovative additions to the paraclique algorithm, developed at the Langston Lab, are introduced to generate results that have highest average correlation or highest density. A new suite of algorithms is then presented that exploits the use of a priori gene interactions. Aptly named the anchored analysis toolkit, these algorithms use known interactions as anchor points for generating subgraphs, which are then analyzed for their graph structure. This results in clusters that might have otherwise been lost in noise. A main product of this thesis is a novel collection of algorithms to generate exact solutions to the maximum clique problem for graphs that are too large to fit within core memory. No other algorithms are currently known that produce exact solutions to this problem for extremely large graphs. A combination of in-core and out-of-core techniques is used in conjunction with a distributed-memory programming model. These algorithms take into consideration such pitfalls as external disk I/O and hardware failure and recovery. Finally, a web-based tool is described that provides researchers access the aforementioned algorithms. The Graph Algorithms Pipeline for Pathway Analysis tool, GrAPPA, was previously developed by the Langston Lab and provides the software needed to take raw microarray data as input and preprocess, analyze, and post-process it in a single package. GrAPPA also provides access to high-performance computing resources, via the TeraGrid

Price Level Convergence and Inflation in Europe

Consumer price inflation in the euro area declined steadily during most of the 1990s. However, in the last two years, both headline and core inflation have risen throughout the area, and sizable cross-country differences in inflation have re-emerged. This is illustrated by Figure 1, which shows the headline consumer price inflation rate for the euro area as a whole and for select member countries. As of October 2000, all euro area countries had headline inflation rates above the European Central Bank's 2 percent medium-term ceiling, with rates ranging from 2.1 percent in France and Austria to 6 percent in Ireland. In Greece, which will join the euro area on 1 January 2001, inflation was 3.8 percent. One factor, discussed prominently in policymaking circles, that may be contributing to cross-country differences in inflation is price level convergence or "inflation catch-up". According to the argument, if prices expressed in a common currency are initially different across countries, convergence to a common level of prices implies higher inflation in countries where prices are initially low. There are several reasons to expect at least some price convergence in Europe. Progress toward a single market, including already completed trade liberalization and adoption of the single currency, should narrow differences in common-currency prices across countries, at least for traded goods. To the extent that the currency conversion rates chosen at the launch of the euro did not equate price levels across the euro area, scope remained for further price convergence after January 1999. The Balassa-Samuelson hypothesis provides another explanation why prices of nontraded goods might rise faster in poorer European countries. Suppose that poor countries are initially low-price countries, and that economic integration creates pressure for European-wide convergence of productivity levels in making traded goods. In addition, suppose that productivity levels in making nontraded goods converge at a much slower rate, if at all. Under these assumptions, poor countries will find that their productivity growth is concentrated in the traded goods sector. The rise in output and wages in the traded goods sector that would result from a European-wide convergence of productivity, would then push up wages and hence prices in the nontraded goods sector of the poor countries, compared to the wealthier, high-price countries.

Curriculum renewal for interprofessional education in health

In this preface we comment on four matters that we think bode well for the future of interprofessional education in Australia. First, there is a growing articulation, nationally and globally, as to the importance of interprofessional education and its contribution to the development of interprofessional and collaborative health practices. These practices are increasingly recognised as central to delivering effective, efficient, safe and sustainable health services. Second, there is a rapidly growing interest and institutional engagement with interprofessional education as part of pre-registration health professional education. This has changed substantially in recent years. Whilst beyond the scope of our current studies, the need for similar developments in continuing professional development (CPD) for health professionals was a consistent topic in our stakeholder consultations. Third, we observe what might be termed a threshold effect occurring in the area of interprofessional education. Projects that address matters relating to IPE are now far more numerous, visible and discussed in terms of their aggregate outcomes. The impact of this momentum is visible across the higher education sector. Finally, we believe that effective collaboration is a critical mediating process through which the rich resources of disciplinary knowledge and capability are joined to add value to existing health service provision. We trust the conceptual and practical contributions and resources presented and discussed in this report contribute to these developments.Office of Learning and Teaching Australi

Geraldton region land resources survey

This report presents results from soil and landform mapping at a scale of 1:250,000 for approximately 2.1 million hectares of the wheat-sheep belt of Western Australia. The study area corresponds with the Geraldton advisory district for Agriculture Western Australia and is the northern extent of the wheatbelt. The area covers parts of three major geological regions of Western Australia: the Perth Basin, Carnarvon Basin and the Yilgarn Craton. Fifty-one soil-landscape systems have been identified, some of which have been divided into subsystems, and are illustrated on the accompanying maps. The landform, parent material, rainfall, land use, native vegetation and soils are described within the text. Forty-eight soil series are described. Five soil properties and six degradation risks are described for each soil series. The areas for each system are indicated with approximate proportions for soils. The report provides an inventory of the soil and land resources of the Geraldton agricultural region for use in regional land use planning and interpretation

Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data

Background The continuing proliferation of high-throughput biological data promises to revolutionize personalized medicine. Confirming the presence or absence of disease is an important goal. In this study, we seek to identify genes, gene products and biological pathways that are crucial to human health, with prostate cancer chosen as the target disease. Materials and methods Using case-control transcriptomic data, we devise a graph theoretical toolkit for this task. It employs both innovative algorithms and novel two-way correlations to pinpoint putative biomarkers that classify unknown samples as cancerous or normal. Results and conclusion Observed accuracy on real data suggests that we are able to achieve sensitivity of 92% and specificity of 91%

Irrigation system economics as affected by field size

Presented at the Central Plains irrigation short course and exposition on February 4, 1997 at the Colby Community Building in Colby, Kansas

MF2242

Daniel O'Brien et al., Economic comparison of SDI and center pivots for various field sizes, Kansas State University, October 1997

Gravity Waves in the Sun

We present numerical simulations of penetrative convection and gravity wave excitation in the Sun. Gravity waves are self-consistently generated by a convective zone overlying a radiative interior. We produce power spectra for gravity waves in the radiative region as well as estimates for the energy flux of gravity waves below the convection zone. We calculate a peak energy flux in waves below the convection zone to be three orders of magnitude smaller than previous estimates for m=1. The simulations show that the linear dispersion relation is a good approximation only deep below the convective-radiative boundary. Both low frequency propagating gravity waves as well as higher frequency standing modes are generated; although we find that convection does not continually drive the standing g-mode frequencies.Comment: 22 pages, 14 figures, submitted to MNRA