Expression profiling with RNA from formalin-fixed, paraffin-embeeded material

Background Molecular characterization of breast and other cancers by gene expression profiling has corroborated existing classifications and revealed novel subtypes. Most profiling studies are based on fresh frozen (FF) tumor material which is available only for a limited number of samples while thousands of tumor samples exist as formalin-fixed, paraffin-embedded (FFPE) blocks. Unfortunately, RNA derived of FFPE material is fragmented and chemically modified impairing expression measurements by standard procedures. Robust protocols for isolation of RNA from FFPE material suitable for stable and reproducible measurement of gene expression (e.g. by quantitative reverse transcriptase PCR, QPCR) remain a major challenge. Results We present a simple procedure for RNA isolation from FFPE material of diagnostic samples. The RNA is suitable for expression measurement by QPCR when used in combination with an optimized cDNA synthesis protocol and TaqMan assays specific for short amplicons. The FFPE derived RNA was compared to intact RNA isolated from the same tumors. Preliminary scores were computed from genes related to the ER response, HER2 signaling and proliferation. Correlation coefficients between intact and partially fragmented RNA from FFPE material were 0.83 to 0.97. Conclusion We developed a simple and robust method for isolating RNA from FFPE material. The RNA can be used for gene expression profiling. Expression measurements from several genes can be combined to robust scores representing the hormonal or the proliferation status of the tumor.

Plant-wax, alkenone and major element analyses of ODP Site 659 for the Last Glacial cycle and associated coretop data

We present a hydrologic reconstruction of the Sahara-Sahel transition, covering the complete last glacial cycle (130 ka), based on a combination of plant-wax-specific hydrogen (dD) and carbon isotopes (d13C). The dD and d13C signatures of long-chain n-alkanes from ODP Site 659 off NW Africa reveal a significant anti-correlation. Complementary to published pollen data, we infer that this plant-wax signal reflects sensitive responses of the vegetation cover to precipitation changes in the Sahel region, as well as varying contributions from biomes north of the Sahara (C3 domain) by North-East Trade Winds (NETW). During arid phases, especially the northern parts of the Sahel likely experienced crucial water stress, which resulted in a pronounced contraction of the vegetation cover, thus reducing the amount of C4 plant waxes from the region. The increase in NETW strength during dry periods further promoted a more pronounced C3-plant-wax signal derived from the North African C3 plant domain. During humid periods, the C4-dominated Sahelian environments spread northward into the Saharan realm, in association with lower NETW inputs of C3 plant waxes. Arid-humid cycles deduced from plant-wax dD are in accordance with concomitant changes in weathering intensity reflected in varying major element distributions. Environmental shifts are generally linked to periods with large fluctuations in Northern Hemisphere summer insolation. During Marine Isotope Stages 2 and 3, when insolation variability was low, coupling of the hydrologic regime to alkenone-based estimates of NE Atlantic sea-surface temperatures becomes apparent.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

A bridging transition technique for the combination of meshfree method with finite element method in 2D solids and structures

For certain continuum problems, it is desirable and beneficial to combine two different methods together in order to exploit their advantages while evading their disadvantages. In this paper, a bridging transition algorithm is developed for the combination of the meshfree method (MM) with the finite element method (FEM). In this coupled method, the meshfree method is used in the sub-domain where the MM is required to obtain high accuracy, and the finite element method is employed in other sub-domains where FEM is required to improve the computational efficiency. The MM domain and the FEM domain are connected by a transition (bridging) region. A modified variational formulation and the Lagrange multiplier method are used to ensure the compatibility of displacements and their gradients. To improve the computational efficiency and reduce the meshing cost in the transition region, regularly distributed transition particles, which are independent of either the meshfree nodes or the FE nodes, can be inserted into the transition region. The newly developed coupled method is applied to the stress analysis of 2D solids and structures in order to investigate its’ performance and study parameters. Numerical results show that the present coupled method is convergent, accurate and stable. The coupled method has a promising potential for practical applications, because it can take advantages of both the meshfree method and FEM when overcome their shortcomings.

Combination strategies for a factor analysis phone-conditioned speaker verification system

This work aims to take advantage of recent developments in joint factor analysis (JFA) in the context of a phonetically conditioned GMM speaker verification system. Previous work has shown performance advantages through phonetic conditioning, but this has not been shown to date with the JFA framework. Our focus is particularly on strategies for combining the phone-conditioned systems. We show that the classic fusion of the scores is suboptimal when using multiple GMM systems. We investigate several combination strategies in the model space, and demonstrate improvement over score-level combination as well as over a non-phonetic baseline system. This work was conducted during the 2008 CLSP Workshop at Johns Hopkins University.

From surfboard wax to salient health inequity : School programs to develop globally aware and active citizens

During the past century, significant improvements in the prevention, detection and treatment of infectious disease have positively impacted upon quality and quantity of life for many people worldwide. Despite this progress, there are large numbers of people currently living in developing regions of the world where infectious disease continues unabated. SurfAid International is a humanitarian organisation that has brought significant health improvements to the people living on the Mentawai and Nias islands of Indonesia. The SurfAid International Schools Program aims to develop global citizenship and social responsibility by providing a bridge between school settings and the critical work of SurfAid International. This paper provides a rationale for the development of contextualised school based programs and identifies potential impact upon the thoughts and actions of young people in schools.

Are combination forecasts of S&P 500 volatility statistically superior?

Forecasting volatility has received a great deal of research attention, with the relative performances of econometric model based and option implied volatility forecasts often being considered. While many studies find that implied volatility is the pre-ferred approach, a number of issues remain unresolved, including the relative merit of combining forecasts and whether the relative performances of various forecasts are statistically different. By utilising recent econometric advances, this paper considers whether combination forecasts of S&P 500 volatility are statistically superior to a wide range of model based forecasts and implied volatility. It is found that a combination of model based forecasts is the dominant approach, indicating that the implied volatility cannot simply be viewed as a combination of various model based forecasts. Therefore, while often viewed as a superior volatility forecast, the implied volatility is in fact an inferior forecast of S&P 500 volatility relative to model-based forecasts.