Variable efficacy of repeated annual influenza vaccination

Conclusions have differed in studies that have compared vaccine efficacy in groups receiving influenza vaccine for the first time to efficacy in groups vaccinated more than once. For example, the Hoskins study [Hoskins, T. W., Davis, J. R., Smith, A. J., Miller, C. L. & Allchin, A. (1979) Lancet i, 33–35] concluded that repeat vaccination was not protective in the long term, whereas the Keitel study [Keitel, W. A., Cate, T. R., Couch, R. B., Huggins, L. L. & Hess, K. R. (1997) Vaccine 15, 1114–1122] concluded that repeat vaccination provided continual protection. We propose an explanation, the antigenic distance hypothesis, and test it by analyzing seven influenza outbreaks that occurred during the Hoskins and Keitel studies. The hypothesis is that variation in repeat vaccine efficacy is due to differences in antigenic distances among vaccine strains and between the vaccine strains and the epidemic strain in each outbreak. To test the hypothesis, antigenic distances were calculated from historical hemagglutination inhibition assay tables, and a computer model of the immune response was used to predict the vaccine efficacy of individuals given different vaccinations. The model accurately predicted the observed vaccine efficacies in repeat vaccinees relative to the efficacy in first-time vaccinees (correlation 0.87). Thus, the antigenic distance hypothesis offers a parsimonious explanation of the differences between and within the Hoskins and Keitel studies. These results have implications for the selection of influenza vaccine strains, and also for vaccination strategies for other antigenically variable pathogens that might require repeated vaccination.

The contagion indicator hypothesis for parasite-mediated sexual selection.

Hamilton and Zuk [Hamilton, W. D. & Zuk, M. (1982) Science 218, 384-387] proposed that females choosing mates based on the degree of expression of male characters obtain heritable parasite resistance for their offspring. Alternatively, the "contagion indicator" hypothesis posits that females choose mates based on the degree of expression of male characters because the latter indicate a male's degree of infestation of parasites and thus the risk that choosing females and their offspring will acquire these parasites. I examined whether parasite transmittability affects the probability that parasite intensity and male mating success are negatively correlated in intraspecific studies of parasite-mediated sexual selection. When females risk infection of themselves or their future offspring as a result of mating with a parasitized male, negative relationships between parasite intensity and male mating success are significantly more likely to occur than when females do not risk such infection. The direct benefit to females of avoiding parasitic infection is proposed to lead to the linkage between variable secondary sexual characters and the intensity of transmittable parasites. The direct benefits of avoiding associatively transmittable parasites should be considered in future studies of parasite-mediated sexual selection.

Preferential expansion and survival of B lymphocytes based on VH framework 1 and framework 3 expression: "positive" selection in appendix of normal and VH-mutant rabbits.

B cells with a rearranged heavy-chain variable region VHa allotype-encoding VH1 gene segment predominate throughout the life of normal rabbits and appear to be the source of the majority of serum immunoglobulins, which thus bear VHa allotypes. The functional role(s) of these VH framework region (FR) allotypic structures has not been defined. We show here that B cells expressing surface immunoglobulin with VHa2 allotypic specificities are preferentially expanded and positively selected in the appendix of young rabbits. By flow cytometry, a higher proportion of a2+ B cells were progressing through the cell cycle (S/G2/M) compared to a2- B cells, most of which were in the G1/G0 phase of the cell cycle. The majority of appendix B cells in dark zones of germinal centers of normal 6-week-old rabbits were proliferating and very little apoptosis were observed. In contrast, in 6-week-old VH-mutant ali/ali rabbits, little cell proliferation and extensive apoptosis were observed. Nonetheless even in the absence of VH1, B cells with a2-like surface immunoglobulin had developed and expanded in the appendix of 11-week-old mutants. The numbers and tissue localization of B cells undergoing apoptosis then appeared similar to those found in 6-week-old normal appendix. Thus, B cells with immunoglobulin receptors lacking the VHa2 allotypic structures were less likely to undergo clonal expansion and maturation. These data suggest that "positive" selection of B lymphocytes through FR1 and FR3 VHa allotypic structures occurs during their development in the appendix.

T-cell receptor (TCR) usage in Lewis rat experimental autoimmune encephalomyelitis: TCR beta-chain-variable-region V beta 8.2-positive T cells are not essential for induction and course of disease.

Predominant usage of V beta 8.2 gene segments, encoding a T-cell receptor (TCR) beta chain variable region, has been reported for pathogenic Lewis rat T cells reactive to myelin basic protein (MBP). However, up to 75% of the alpha/beta T cells in a panel of MBP-specific T-cell lines did not display TCR V beta 8.2, V beta 8.5, V beta 10, or V beta 16 elements. To further investigate TCR usage, we sorted the T-cell lines for V beta 8.2- and V beta 10-positive T cells or depleted the lines of cells with these TCRs. V beta 8.2-positive T cells and one of the depleted T-cell lines strongly reacted against the MBP peptide MBP-(68-88). The depleted T-cell line caused marked experimental autoimmune encephalomyelitis (EAE) even in Lewis rats in which endogenous V beta 8.2-positive T cells had been eliminated by neonatal treatment with anti-V beta 8.2 monoclonal antibodies. T-cell hybridomas generated from this line predominantly used V beta 3 TCR genes coexpressed with TCR V alpha 2 transcripts, which were also used by V beta 8.2-positive T cells. Furthermore, V beta 10-positive T cells reactive to MBP-(44-67) were encephalitogenic when injected immediately after positive selection. After induction of EAE by sorted V beta 8.2- or V beta 10-positive T-cell lines, immunocytochemical analysis of the spinal cord tissue showed a predominance of the injected TCR or of nontypable alpha/beta T cells after injection of the depleted line. Our results demonstrate heterogeneity of TCR beta-chain usage even for a single autoantigen in an inbred strain. Moreover, V beta 8.2-positive T cells are not essential for the induction and progression of adoptive-transfer EAE.

Genetic analysis of complex demographic scenarios: Spatially expanding populations of the cane toad, Bufo marinus

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

Guideline to reference gene selection for quantitative real-time PCR

Today, quantitative real-time PCR is the method of choice for rapid and reliable quantification of mRNA transcription. However, for an exact comparison of mRNA transcription in different samples or tissues it is crucial to choose the appropriate reference gene. Recently glyceraldehyde 3-phosphate dehydrogenase and P-actin have been used for that purpose. However, it has been reported that these genes as well as alternatives, like rRNA genes, are unsuitable references, because their transcription is significantly regulated in various experimental settings and variable in different tissues. Therefore, quantitative real-time PCR was used to determine the mRNA transcription profiles of 13 putative reference genes, comparing their transcription in 16 different tissues and in CCRF-HSB-2 cells stimulated with 12-O-tetradecanoylphorbol-13-acetate and ionomycin. Our results show that Classical reference genes are indeed unsuitable, whereas the RNA polymerase II gene was the gene with the most constant expression in different tissues and following stimulation in CCRF-HSB-2 cells. (C) 2003 Elsevier Inc. All rights reserved.

Gene-flow between populations of cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae) is highly variable between years

Both large and small scale migrations of Helicoverpa armigera Hübner in Australia were investigated using AMOVA analysis and genetic assignment tests. Five microsatellite loci were screened across 3142 individuals from 16 localities in eight major cotton and grain growing regions within Australia, over a 38-month period (November 1999 to January 2003). From November 1999 to March 2001 relatively low levels of migration were characterized between growing regions. Substantially higher than average gene-flow rates and limited differentiation between cropping regions characterized the period from April 2001 to March 2002. A reduced migration rate in the year from April 2002 to March 2003 resulted in significant genetic structuring between cropping regions. This differentiation was established within two or three generations. Genetic drift alone is unlikely to drive genetic differentiation over such a small number of generations, unless it is accompanied by extreme bottlenecks and/or selection. Helicoverpa armigera in Australia demonstrated isolation by distance, so immigration into cropping regions is more likely to come from nearby regions than from afar. This effect was most pronounced in years with limited migration. However, there is evidence of long distance dispersal events in periods of high migration (April 2001-March 2002). The implications of highly variable migration patterns for resistance management are considered.

Estimating variable returns to scale production frontiers with alternative stochastic assumptions

Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.

A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

Bayesian regression filter and the issue of priors

We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.

Using Bayesian neural networks to classify segmented images

We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the Evidence Framework of MacKay (1992) and (ii) a Markov Chain Monte Carlo method due to Neal (1996) on a task of classifying segmented outdoor images. We also investigate the use of the Automatic Relevance Determination method for input feature selection.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.

Comparing methods to separate treatment from self-selection effects in an online banking setting

The literature discusses several methods to control for self-selection effects but provides little guidance on which method to use in a setting with a limited number of variables. The authors theoretically compare and empirically assess the performance of different matching methods and instrumental variable and control function methods in this type of setting by investigating the effect of online banking on product usage. Hybrid matching in combination with the Gaussian kernel algorithm outperforms the other methods with respect to predictive validity. The empirical finding of large self-selection effects indicates the importance of controlling for these effects when assessing the effectiveness of marketing activities.