Stuart Barber

An in vitro screening cascade to identify neuroprotective antioxidants in ALS

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease, characterized by progressive dysfunction and death of motor neurons. Although evidence for oxidative stress in ALS pathogenesis is well described, antioxidants have generally shown poor efficacy in animal models and human clinical trials. We have developed an in vitro screening cascade to identify antioxidant molecules capable of rescuing NSC34 motor neuron cells expressing an ALS-associated mutation of superoxide dismutase 1. We have tested known antioxidants and screened a library of 2000 small molecules. The library screen identified 164 antioxidant molecules, which were refined to the 9 most promising molecules in subsequent experiments. Analysis of the in silico properties of hit compounds and a review of published literature on their in vivo effectiveness have enabled us to systematically identify molecules with antioxidant activity combined with chemical properties necessary to penetrate the central nervous system. The top-performing molecules identified include caffeic acid phenethyl ester, esculetin, and resveratrol. These compounds were tested for their ability to rescue primary motor neuron cultures after trophic factor withdrawal, and the mechanisms of action of their antioxidant effects were investigated. Subsequent in vivo studies can be targeted using molecules with the greatest probability of success.

Posterior probability intervals for wavelet thresholding

We use cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any given data set. Johnson transformations then yield the credible intervals themselves. Simulations show that these intervals have good coverage rates, even when the underlying function is inhomogeneous, where standard methods fail. In the case where the curve is smooth, the performance of our intervals remains competitive with established nonparametric regression methods. Copyright 2002 The Royal Statistical Society.

Predicting phenolic acid absorption in Caco-2 cells: a theoretical permeability model and mechanistic study.

There is a considerable need to rationalize the membrane permeability and mechanism of transport for potential nutraceuticals. The aim of this investigation was to develop a theoretical permeability equation, based on a reported descriptive absorption model, enabling calculation of the transcellular component of absorption across Caco-2 monolayers. Published data for Caco-2 permeability of 30 drugs transported by the transcellular route were correlated with the descriptors 1-octanol/water distribution coefficient (log D, pH 7.4) and size, based on molecular mass. Nonlinear regression analysis was used to derive a set of model parameters a′, β′, and b′ with an integrated molecular mass function. The new theoretical transcellular permeability (TTP) model obtained a good fit of the published data (R2 = 0.93) and predicted reasonably well (R2 = 0.86) the experimental apparent permeability coefficient (Papp) for nine non-training set compounds reportedly transported by the transcellular route. For the first time, the TTP model was used to predict the absorption characteristics of six phenolic acids, and this original investigation was supported by in vitro Caco-2 cell mechanistic studies, which suggested that deviation of the Papp value from the predicted transcellular permeability (Papptrans) may be attributed to involvement of active uptake, efflux transporters, or paracellular flux.

Participation Bias Assessment in Three High-Impact Journals

Studies into participation bias have examined participation trends, where it occurs, the factors affecting it, and methods to try to reduce it. However, some authors only discuss participation bias at the end of the study, some acknowledge it and apply a method to try to reduce it, while others ignore it or dismiss it as negligible. Issues of three high-impact epidemiology journals were examined; 81 articles were read and reviewed for potential participation bias. Categories were used to classify the approach taken to participation bias and the results recorded. Of the 81 articles considered, 42 (51%) were eligible and could have suffered from participation bias. It was found that 57% of these articles ignored the effects of participation bias, while 17% only considered it briefly in the discussion. Few articles (22%) attempted to reduce the participation bias, with over half of these using unsuitable methods (55%). This review highlights how participation bias is often not considered and hence the conclusions drawn from these studies may not be correct.

BAYESIAN ALIGNMENT OF SIMILARITY SHAPES.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.

Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology.

Abstract There is a need for a reliable statistical test which is appropriate for assessing cospeciation of more than two phylogenies. We have developed an algorithm using a permutation method that can be used to test for and infer tri-trophic evolutionary relationships of organisms given both their phylogenies and pairwise interactions. An overall statistic has been developed based on the dominant eigenvalue of a covariance matrix, and compared to values of the statistic computed when tree labels are permuted. The resulting overall p-value is used to test for the presence or absence of cospeciation in a tri-trophic system. If cospeciation is detected, we propose new test statistics based on partial correlations to uncover more details about the relationships between multiple phylogenies. One of the strengths of our method is that it allows more parasites than hosts or more hosts than parasites, with multiple associations and more than one parasite attached to a host (or one parasite attached to multiple hosts). The new method does not require any parametric assumptions of the distribution of the data, and unlike the old methods, which utilize several pairwise steps, the overall statistic used is obtained in one step. We have applied our method to two published datasets where we obtained detailed information about the strength of associations among species with calculated partial p-values and one overall p-value from the dominant eigenvalue test statistic. Our permutation method produces reliable results with a clear procedure and statistics applied in an intuitive manner. Our algorithm is useful in testing evidence for three-way cospeciation in multiple phylogenies with tri-trophic associations and determining which phylogenies are involved in cospeciation.

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

A general framework for regression modeling using localized frequency characteristics of explanatory variables is proposed. This novel framework can be used in any application where the aim is to model an evolving process sequentially based on multiple time series data. Furthermore, this framework allows time series to be transformed and combined to simultaneously boost important characteristics and reduce noise. A wavelet transform is used to isolate key frequency structure and perform data reduction. The method is highly adaptive, since wavelets are effective at extracting localized information from noisy data. This adaptivity allows rapid identification of changes in the evolving process. Finally, a regression model uses functions of the wavelet coefficients to classify the evolving process into one of a set of states which can then be used for automatic monitoring of the system. As motivation and illustration, industrial process monitoring using electrical tomography measurements is considered. This technique provides useful data without intruding into the industrial process. Statistics derived from the wavelet transform of the tomographic data can be enormously helpful in monitoring and controlling the process. The predictive power of the proposed approach is explored using real and simulated tomographic data. In both cases, the resulting models successfully classify different flow regimes and hence provide the basis for reliable online monitoring and control of industrial processes.

Integrated eicosanoid lipidomics and gene expression reveal decreased prostaglandin catabolism and increased 5-lipoxygenase expression in aggressive subtypes of endometrial cancer: Eicosanoid metabolism in endometrial cancer

Eicosanoids comprise a diverse group of bioactive lipids which orchestrate inflammation, immunity, and tissue homeostasis, and whose dysregulation has been implicated in carcinogenesis. Among the various eicosanoid metabolic pathways, studies of their role in endometrial cancer (EC) have very much been confined to the COX‐2 pathway. This study aimed to determine changes in epithelial eicosanoid metabolic gene expression in endometrial carcinogenesis; to integrate these with eicosanoid profiles in matched clinical specimens; and, finally, to investigate the prognostic value of candidate eicosanoid metabolic enzymes. Eicosanoids and related mediators were profiled using liquid chromatography–tandem mass spectrometry in fresh frozen normal, hyperplastic, and cancerous (types I and II) endometrial specimens (n = 192). Sample‐matched epithelia were isolated by laser capture microdissection and whole genome expression analysis was performed using microarrays. Integration of eicosanoid and gene expression data showed that the accepted paradigm of increased COX‐2‐mediated prostaglandin production does not apply in EC carcinogenesis. Instead, there was evidence for decreased PGE2/PGF2α inactivation via 15‐hydroxyprostaglandin dehydrogenase (HPGD) in type II ECs. Increased expression of 5‐lipoxygenase (ALOX5) mRNA was also identified in type II ECs, together with proportional increases in its product, 5‐hydroxyeicosatetraenoic acid (5‐HETE). Decreased HPGD and elevated ALOX5 mRNA expression were associated with adverse outcome, which was confirmed by immunohistochemical tissue microarray analysis of an independent series of EC specimens (n = 419). While neither COX‐1 nor COX‐2 protein expression had prognostic value, low HPGD combined with high ALOX5 expression was associated with the worst overall and progression‐free survival. These findings highlight HPGD and ALOX5 as potential therapeutic targets in aggressive EC subtypes. Copyright

Classification tree methods for panel data using wavelet-transformed time series

Wavelet-transformed variables can have better classification performance for panel data than using variables on their original scale. Examples are provided showing the types of data where using a wavelet-based representation is likely to improve classification accuracy. Results show that in most cases wavelet-transformed data have better or similar classification accuracy to the original data, and only select genuinely useful explanatory variables. Use of wavelet-transformed data provides localized mean and difference variables which can be more effective than the original variables, provide a means of separating “signal” from “noise”, and bring the opportunity for improved interpretation via the consideration of which resolution scales are the most informative. Panel data with multiple observations on each individual require some form of aggregation to classify at the individual level. Three different aggregation schemes are presented and compared using simulated data and real data gathered during liver transplantation. Methods based on aggregating individual level data before classification outperform methods which rely solely on the combining of time-point classifications.