Protection of mice from group A streptococcal infection by intranasal immunisation with a peptide vaccine that contains a conserved M protein B cell epitope and lacks a T cell autoepitope

Infection with group A streptococci (GAS) can lead to rheumatic fever (RF) and rheumatic heart disease (RHD) which are a major health concern particularly in indigenous populations worldwide, and especially in Australian Aboriginals. A primary route of GAS infection is via the upper respiratory tract, and therefore, a major goal of research is the development of a mucosal-based GAS vaccine, The majority of the research to date has focused on the GAS M protein since immunity to GAS is mediated by M protein type-specific opsonic antibodies. There are two major impediments to the development of a vaccine-the variability in M proteins and the potential for the induction of an autoimmune response. To develop a safe and broad-based vaccine, we have therefore focused on the GAS M protein conserved C-region, and have identified peptides, J8 and the closely related J8 peptide (J14), which may be important in protective immunity to GAS infection. Using a mucosal animal model system, our data have shown a high degree of throat GAS colonisation in B10.BR mice 24 h following intranasal immunisation with the mucosal adjuvant, cholera toxin B subunit (CTB), and/or diptheria toxoid (dT) carrier, or PBS alone, and challenge with the M1 GAS strain. However, GAS colonisation of the throat was significantly reduced following intranasal immunisation of mice with the vaccine candidate J8 conjugated to dT or J14-dT when administered with CTB. Moreover, J8-dT/CTB and J14-dT/CTB-immunised mice had a significantly higher survival when compared to CTB and PBS-immunised control mice. These data indicate that immunity to GAS infection can be evoked by intranasal immunisation with a GAS M protein C-region peptide vaccine that contains a protective B cell epitope and lacks a T cell autoepitope. (C) 2002 Published by Elsevier Science Ltd.

Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 'transcriptional units', contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

Product form approximations for highly linear loss networks with trunk reservation

Admission controls, such as trunk reservation, are often used in loss networks to optimise their performance. Since the numerical evaluation of performance measures is complex, much attention has been given to finding approximation methods. The Erlang Fixed-Point (EFP) approximation, which is based on an independent blocking assumption, has been used for networks both with and without controls. Several more elaborate approximation methods which account for dependencies in blocking behaviour have been developed for the uncontrolled setting. This paper is an exploratory investigation of extensions and synthesis of these methods to systems with controls, in particular, trunk reservation. In order to isolate the dependency factor, we restrict our attention to a highly linear network. We will compare the performance of the resulting approximations against the benchmark of the EFP approximation extended to the trunk reservation setting. By doing this, we seek to gain insight into the critical factors in constructing an effective approximation. (C) 2003 Elsevier Ltd. All rights reserved.

An augmented Lanczos algorithm for the efficient computation of a dot-product of a function of a large sparse symmetric matrix

The Lanczos algorithm is appreciated in many situations due to its speed. and economy of storage. However, the advantage that the Lanczos basis vectors need not be kept is lost when the algorithm is used to compute the action of a matrix function on a vector. Either the basis vectors need to be kept, or the Lanczos process needs to be applied twice. In this study we describe an augmented Lanczos algorithm to compute a dot product relative to a function of a large sparse symmetric matrix, without keeping the basis vectors.