Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.

Investigation of the permeation of model formulations and a commercial ibuprofen formulation in Carbosil® and human skin using ATR-FTIR and multivariate spectral analysis

The purpose of the present study was to use attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and target factor analysis (TFA) to investigate the permeation of model drugs and formulation components through Carbosil® membrane and human skin. Diffusion studies of saturated solutions in 50:50 water/ethanol of methyl paraben (MP), ibuprofen (IBU) and caffeine (CF) were performed on Carbosil® membrane. The spectroscopic data were analysed by target factor analysis, and evolution profiles of the signal for each component (i.e. the drug, water, ethanol and membrane) over time were obtained. Results showed that the data were successfully deconvoluted as correlations between factors from the data and reference spectra of the components, were above 0.8 in all cases. Good reproducibility over three runs for the evolution profiles was obtained. From the evolution profiles it was observed that water diffused better through the Carbosil® membrane than ethanol, confirming the hydrophilic properties of the Carbosil® membrane used. IBU diffused slower compared with MP and CF. The evolution profile of CF was very similar to that of water, probably because of the high solubility of CF in water, indicating that both compounds are diffusing concurrently. The second part of the work involved a study of the evolution profiles of the components of a commercial topical gel containing 5% (w/w) of ibuprofen as it permeated through human skin. Although the system was much more complex, data were still successfully deconvoluted and the different components of the formulation identified except for benzyl alcohol which might be attributed to the low concentrations of benzyl alcohol used in topical formulations. (C) 2009 Elsevier B.V. All rights reserved.

Local examination of skin diffusion using FTIR spectroscopic imaging and multivariate target factor analysis

In the context of trans-dermal drug delivery it is very important to have mechanistic insight into the barrier function of the skin's stratum corneum and the diffusion mechanisms of topically applied drugs. Currently spectroscopic imaging techniques are evolving which enable a spatial examination of various types of samples in a dynamic way. ATR-FTIR imaging opens up the possibility to monitor spatial diffusion profiles across the stratum corneum of a skin sample. Multivariate data analyses methods based on factor analysis are able to provide insight into the large amount of spectroscopically complex and highly overlapping signals generated. Multivariate target factor analysis was used for spectral resolution and local diffusion profiles with time through stratum corneum. A model drug, 4-cyanophenol in polyethylene glycol 600 and water was studied. Results indicate that the average diffusion profiles between spatially different locations show similar profiles despite the heterogeneous nature of the biological sample and the challenging experimental set-up.

The role of space charge formation in the generation of thermally stimulated current (TSC) spectroscopy data for a model amorphous drug system

Thermally stimulated current (TSC) spectroscopy is attracting increasing attention as a means of materials characterization, particularly in terms of measuring slow relaxation processes in solid samples. However, wider use of the technique within the pharmaceutical field has been inhibited by difficulties associated with the interpretation of TSC data, particularly in terms of deconvoluting dipolar relaxation processes from charge distribution phenomena. Here, we present evidence that space charge and electrode contact effects may play a significant role in the generation of peaks that have thus far proved difficult to interpret. We also introduce the use of a stabilization temperature in order to control the space charge magnitude. We have studied amorphous indometacin as a model drug compound and have varied the measurement parameters (stabilization and polarization temperatures), interpreting the changes in spectral composition in terms of charge redistribution processes. More specifically, we suggested that charge drift and diffusion processes, charge injection from the electrodes and high activation energy charge redistribution processes may all contribute to the appearance of shoulders and 'spurious' peaks. We present recommendations for eliminating or reducing these effects that may allow more confident interpretation of TSC data.

Performance measurement and benchmarking of a major innovation programme

Purpose: The purpose of this paper is to describe the problems encountered and the solutions developed when using benchmarking and key performance indicators (KPIs) to monitor a major UK social house building innovation (change) programme. The innovation programme sought improvements to both the quality of the house product and the procurement process. Design/methodology/approach: Benchmarking and KPIs were used to quantify performance and in-depth case studies to identify underlying cause and effect relationships within the innovation programme. Findings: The inherent competition between consortium members; the complexity of the relationship between the consortium and its strategic partner; the lack of an authoritative management control structure; and the rapidly changing nature of the UK social housing market all proved problematic to the development of a reliable and robust monitoring system. These problems were overcome by the development of multi-dimensional benchmarking model that balanced the needs and aspirations of the individual organisations with the broader objectives of the consortium. Research limitations/implications: Whilst the research methodology provides insight into the factors that affected the performance of a major innovation programme its findings may not be representative of all projects. Practical implications: The lessons learnt should assist those developing benchmarking models for multi-client consortia. Originality/value: The work reported in this paper describes an inclusive approach to benchmarking in which a multiple client group and their strategic partner sought to work together for shared gain. Very few papers have addressed this issue.