Cathepsin S expression: An independent prognostic factor in glioblastoma tumours - A pilot Study

Cysteine proteinases have been implicated in astrocytoma invasion. We recently demonstrated that cathepsin S (CatS) expression is up-regulated in astrocytomas and provided evidence for a potential role in astrocytoma invasion (Flannery et al., Am J Path 2003;163(1):175–82). We aimed to evaluate the significance of CatS in human astrocytoma progression and as a prognostic marker. Frozen tissue homogenates from 71 patients with astrocytomas and 3 normal brain specimens were subjected to ELISA analyses. Immunohistochemical analysis of CatS expression was performed on 126 paraffin-embedded tumour samples. Fifty-one astrocytoma cases were suitable for both frozen tissue and paraffin tissue analysis. ELISA revealed minimal expression of CatS in normal brain homogenates. CatS expression was increased in grade IV tumours whereas astrocytoma grades I–III exhibited lower values. Immunohistochemical analysis revealed a similar pattern of expression. Moreover, high-CatS immunohistochemical scores in glioblastomas were associated with significantly shorter survival (10 vs. 5 months, p = 0.014). With forced inclusion of patient age, radiation dose and Karnofsky score in the Cox multivariate model, CatS score was found to be an independent predictor of survival. CatS expression in astrocytomas is associated with tumour progression and poor outcome in glioblastomas. CatS may serve as a useful prognostic indicator and potential target for anti-invasive therapy.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

Model atmosphere and kinematical analyses of early-type stars from the Edinburgh-Cape Survey

We present high-resolution spectroscopic observations of 21 B- type stars, selected from the Edinburgh-Cape Blue Object Survey. Model atmosphere analyses confirm that 14 of these stars are young, main-sequence B-type objects with Population I chemical compositions. The remaining seven are found to be evolved objects, including subdwarfs, horizontal branch and post-AGB objects. A kinematical analysis shows that all 14 young main-sequence stars could have formed in the disc and subsequently been ejected into the halo. These results are combined with the analysis of a previous subsample of stars taken from the Survey. Of the complete sample, 31 have been found to be young, main-sequence objects, with formation in the disc, and subsequent ejection into the halo, again being found to be a plausible scenario.

The use of Raman microscopy to determine and localize vitamin E in biological samples

Alpha-tocopherol (aT), the predominant form of vitamin E in mammals, is thought to prevent oxidation of polyunsaturated fatty acids. In the lung, aT is perceived to be accumulated in alveolar type II cells and secreted together with surfactant into the epithelial lining fluid. Conventionally, determination of aT and related compounds requires extraction with organic solvents. This study describes a new method to determine and image the distribution of aT and related compounds within cells and tissue sections using the light-scattering technique of Raman microscopy to enable high spatial as well as spectral resolution. This study compared the nondestructive analysis by Raman microscopy of vitamin E, in particular aT, in biological samples with data obtained using conventional HPLC analysis. Raman spectra were acquired at spatial resolutions of 2-0.8 microm. Multivariate analysis techniques were used for analyses and construction of corresponding maps showing the distribution of aT, alpha-tocopherol quinone (aTQ), and other constituents (hemes, proteins, DNA, and surfactant lipids). A combination of images enabled identification of colocalized constituents (heme/aTQ and aT/surfactant lipids). Our data demonstrate the ability of Raman microscopy to discriminate between different tocopherols and oxidation products in biological specimens without sample destruction. By enabling the visualization of lipid-protein interactions, Raman microscopy offers a novel method of investigating biological characterization of lipid-soluble compounds, including those that may be embedded in biological membranes such as aT.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.