Biomathematics &Statistics ScotlandBiennial Report2005/2007

Stati s t i c s & Mathematic s impr o ving A g r icul t ur e, the Envir onment , Food & Health

Overview

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BioSS undertakes research, consultancy and training in mathematics and statistics as applied to agriculture, the environment, food and health. Our Vision is:

We are one of the Main Research Providers (MRPs) for strategic research in environmental, agricultural and biological science funded by the Scottish Government’s Rural and Environment Research and Analysis Directorate (RERAD).

We have a distributed staff structure to allow close contact with scientists throughout Scotland. Whilst our main staff base is on the King’s Buildings science campus of the University of Edinburgh, we also have offices in Aberdeen, Dundee and Ayr. We have strong collaborations with many research organisations and universities, and provide high-level statistical, mathematical and computational skills to many client organisations in government and the private sector.

ResearchBioSS has an international reputation for its research in biomathematics and statistics. Our research is partitioned into three themes, each of which draws on the expertise and experience of staff:

statistical bioinformaticsprocess and systems modelling

statistical methodologyBioSS also has many active links with universities and research organisations in Scotland, the rest of the UK and beyond.

ConsultancyBioSS consultants add quantitative expertise to research throughout Scotland. Our staff have technical skills that are applicable to a wide range of scientific problems and the communication skills that allow them to interact effectively with scientists from other disciplines. Scientific areas in which we have particular expertise include:

plant scienceanimal health and welfare

ecology and environmental sciencehuman health and nutrition

Knowledge transferBioSS bridges the gap between the development and application of biomathematics and statistics, and we are strongly committed to the dissemination of modern quantitative methods to the scientific community, government and the bio-industries. Key aspects of our programme of knowledge transfer include:

development of software productsdelivery of training courses for scientists

supervision of PhD students

CONTENTS

Overview Director’s IntroductionResearch

Statistical BioinformaticsProcess & Systems ModellingStatistical Methodology

Consultancy Advice & CollaborationPlant ScienceAnimal Health & WelfareEcology & Environmental ScienceHuman Health & Nutrition

Knowledge TransferUser-friendly SoftwareTraining for ScientistsPostgraduate Research & Training

Information TechnologyStaffStudentsManagement Group

Appendix �: Selected Research Grants and ContractsAppendix 2: PublicationsAppendix 3: Conference Presentations, Lectures & SeminarsAppendix 4: External CommitteesGlossary of Organisational Acronyms

Contact points

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BioSS Staff, students and visitors demonstrate how international the research community has become.

Director’s Introduction

Director’sIntroduction

Director’sIntroduction

BIOLOGICAL & ENVIRONMENTAL

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Welcome to the BioSS biennial report for 2005/7. This report has been written to display the wide range of BioSS activities, giving details of a selection of recent and current projects. The variety of applications and methodologies used in our scientific work reflects the pivotal role that quantitative methods play in modern biological research. I hope that, among these highlights there is something of specific interest to every reader.

This report covers a period of substantial achievement. Our scientific output, in terms of the total number of refereed papers, has never been so high (72 for 2006 compared to a mean of close to 50 in past years). Our senior staff continue to achieve recognition, one spending three weeks as a McMaster Visiting Fellow at CSIRO in Australia and others now holding honorary chairs in five universities. At the other end of the career spectrum, we have been pleased to welcome many new recruits, all with promising careers ahead of them. Our main new initiative is the Centre of Excellence in Epidemiology, Population Health and Infectious Disease Control (EPIC), funded by the Scottish Executive. This collaborative centre includes five other internationally recognised research organisations, highlighting the strength of the Scottish research base in veterinary epidemiology. BioSS’s role in EPIC is to develop, evaluate and apply methods of statistical inference for models of animal infection dynamics. Many of the projects we are involved in with our long-term collaborators are making an impact at a national or a European scale. It is particularly satisfying to see our new relationship with the RSPB already bearing fruit, with a paper assessing the effectiveness of international conservation policy appearing in the prestigious journal Science.

We have summarised the ambitions of BioSS in a vision statement that encapsulates what we collectively set out to achieve:

"to improve science & society through an understanding of

variation, uncertainty and risk"

Dissecting this statement reveals many facets of our work. Our understanding is enhanced through process modelling and data analysis, as well as through the design of experiments and observational studies; the variation we study can be of a deterministic nature or partitioned into uncontrolled components that we often think of as being random; uncertainty refers to our state of knowledge, which is always clouded by both measurement error and natural variation; and at a probabilistic level risk refers to the integration of variation and uncertainty, with attention often focussing on the chance of extreme events. Much of our work is directed towards scientific objectives, but with a widening range of outcomes including providing an evidence base for improved policy making and contributing to economic activity through the levy boards and private sector organisations.

Simultaneously, we have developed a mission statement, setting out how we shall achieve our vision:

"to develop and apply quantitative methodologies with

a rigorous mathematical and statistical basis"

This makes clear the parallel strands of methodological development and application, with each benefiting from the presence of the other in a single organisation. Implicit but unstated in the mission statement is the enthusiasm with which BioSS has been embracing the massive potential of modern computing technologies, including cluster processors and web services.

Since the previous Biennial Report, there have been some fundamental changes in our chief sponsor, the Scottish Executive (SE), and the way it funds its programme of research on the environment and rural affairs. The publication of "Strategic Research for SEERAD: Environment, Biology and Agriculture 2005-2010" sets out a five-year science strategy, identifying outputs to be achieved in each of four broad scientific programmes. Associated with this strategy has been a desire from the SE to distance itself from the institutions it funds, taking on the role of customer rather than sponsor of its Main Research Providers (MRPs). Thus the previous situation, under which individuals organisations received core funding to carry out agreed programmes of research, has been replaced by a new model in which the MRPs each receive a base allocation to undertake research that delivers the requisite outputs.

The shift in SE funding, from organisation-based to programme-based, has motivated a change in the way BioSS manages its consultancy work. We have replaced our old system of Heads of Groups at the different BioSS centres, with four Principal Consultants, each taking responsibility for a scientific application area that encompasses one of the SE's four research programmes. These Principal Consultants have each organised a BioSS Integration Meeting with their respective SE programme managers to ensure we address the highest priority quantitative issues arising in the SE's research programmes. We have also created a post of External Development Manager in recognition of the need to continue to broaden the funding base on which BioSS draws.

Given the expertise and commitment of BioSS staff, I remain optimistic for the future. BioSS's excellent reputation, its blend of methodological skills and experience of important application areas, together with the increasingly quantitative nature of science, should all stand it in good stead. I do hope this report gives you an insight into the achievements and potential of BioSS as a distinctive and valuable contributor to science and society.

Research Statistical Bioinformatics

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Quantitative methodologies need constant development to meet the demands from science and opportunities from new computing technologies. BioSS's research is structured in three themes.

Statistical BioinformaticsDevelopments in molecular genetics technologies are generating enormous quantities of data, often of new data types. Simultaneously, new computing technologies, such as clusters and the GRID, are allowing easy access to rapidly increasing computer processing power and data storage capacity. BioSS aims to develop and automate methodology for analysing these data, harnessing the computing power to extract maximum information from the data.

Process and Systems ModellingMathematical modelling has a key role in achieving many scientific objectives. BioSS will ensure that the modelling is as effective as possible, by addressing generic issues including: simplification, analysis and approximation of models for complex systems; parameter estimation and model selection in stochastic process models; Bayesian methods for decision support; and methodologies for modelling risks to biodiversity, and complex interactions in epidemic processes. The strategy will be to develop methodology in the context of specific collaborative applications.

Statistical MethodologyStatistical methodology needs constant development, firstly to keep pace with the requirements of new technologies being used in the biological and environmental sciences, and secondly to address new questions that arise as science becomes ever more quantitative. In particular, there is a pressing need for new methodology to correctly interpret large, highly-structured data sets. BioSS will develop and adapt methodology in the key areas of image analysis and spatially-, temporally- and spatio-temporally-structured data.

New experimental technologies in molecular biology and genetics call for the development of more advanced statistical and machine learning methodologies. We need to deal with increased volumes of data, to improve the robustness of inference to noise in the data, and to improve the models themselves to make them biologically more realistic. The following four sections present examples of how we have addressed these issues in BioSS.

Association mapping in inbreeding plant speciesAssociation studies have been used to locate single genes for some diseases in humans. In plant studies, the analysis is complicated by issues of selection, self-fertilisation and especially population substructure. The latter varies from large scale structure, such as plants selected for different traits in different regions, to smaller scale differences in degrees of kinship. Different substructure models have been proposed, but when applied to experimental data often identify different regions as containing the quantitative trait loci (QTLs) that controlling important traits such as yield.

Simulation studies enable methods to be compared in a population where QTL locations, population structure and pedigrees are all known. We have modelled a simulation study on a real collection of barley germplasm from five regions. Ten genotyped landraces from each region were taken as founders. One thousand generations of landraces were generated with a selfing probability of 0.92. This was followed by 24 generations of selection on drought tolerance, yield or heading date, depending on region, to generate cultivars.

This simulated population is enabling us to compare methods for estimating kinship from various types of DNA marker data with the known pedigrees, and to explore the accuracy and limitations of models including population substructure and kinship to identify genuine QTLs. For example, we find many falsely significant associations (high values for -log 10 p-values) are estimated ignoring population structure that vanish when the structure is included in the model.

Assumptions about population structure have an important effect on the strength of evidence for associations of position on chromosome �H with heading date of barley in a dry environment in Spain.

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Comparison of methods to predict the Raf regulatory network, with (DGE) and without (UGE) taking the edge directions into account. We determined the number of true positive interactions for a fixed number, five, of false positives. Bayesian networks (BN) and graphical Gaussian models (GGM) use information from the expression data. Biological knowledge from KEGG is used either in isolation (OnlyPrior) or using our new Bayesian integration scheme (BN&Prior).

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Statistical analysis of molecular sequence alignments using web services and cluster computingA growing number of biological questions can be tackled by aligning homologous regions of DNA from different organisms or from related genes within the same organism. We have extended our TOPALi Java application to launch several statistical analyses of multiple alignment data from the user's desktop which run as “web services” on remote, powerful computer clusters, with monitoring of the remote job and results displayed locally. Some features of TOPALi v2 are described below.

Recombination breakpoint location estimation

DNA sequences can recombine during evolution. This can result in a recombinant sequence comprising regions, separated by recombination breakpoints, that have different evolutionary histories. Initial testing for breakpoints is crucial as many subsequent analyses assume no recombination. Our methods that use a parametric bootstrapping approach to assess statistical significance make optimal use of cluster computing resources.

Model selection, tree and ancestral sequence estimation

Model-based methods to construct phylogenetic trees require parameters in the evolutionary model to be optimised prior to tree estimation. TOPALi v2 has a model selection web service (ModelGenerator software) which ranks substitution models (88 models for proteins or 55 for DNA) according to statistical criteria.

Tree estimation web services include implementations of Maximum Likelihood (PhyML software) and Bayesian Inference (MrBayes software) methods. Ancestral sequences are predicted using a FASTML web service.

Positive selection analysis

TOPALi v2 has a “branch model” web service (using PAML software) to test for differences in evolutionary rates among groups of sequences (e.g. after a past gene duplication event) and also a “sites model” web service (also PAML) to test for sites evolving faster than the neutral model which may be functionally important.

Screenshot of our TOPALi software.

Combining multiple laser scans of microarraysThe first stage in the analysis of microarray data is estimation from laser scans of the level of expression of each gene. Typically, data are only used from a single scan, although, if multiple scans are available, sampling error can be reduced by combining them: a functional regression problem. Maximum likelihood estimation fails, but many alternative estimators exist, one of which is to maximise the likelihood of a Gaussian structural regression model. We have found by simulation that, surprisingly, this estimator is efficient for our particular application, even though the distribution of gene expressions is severely skewed and hence far from Gaussian.

Measured responses from laser scans 2, 3 and 4 plotted against those from laser scan � for each gene in a murine macrophage experiment (Division of Pathway Medicine, University of Edinburgh).

Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledgeThere have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited number of independent experimental conditions and the noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. We have developed a Bayesian approach to integrate expression data with multiple sources of prior knowledge, e.g. extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. We have evaluated the proposed scheme on the Raf signalling pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in mammalian immune system cells, demonstrating the benefits of combining biological knowledge with gene expression data.

The currently accepted Raf signalling network, showing proteins (nodes), the presence of interactions (lines) and the direction of signal transduction (arrows).

Process & Systems Modelling

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Mathematical modelling plays an important role in developing scientific understanding of complex biological processes. However, the gathering pace of data acquisition and consequent advances in knowledge require new mathematical methods, to cope with increasing complexity, and new statistical methods, to fully integrate data and models. Our strategy is to work closely with biological scientists to develop such methodology in the context of specific applications.

Modelling the risk of spread of alien speciesThe increasing presence of invasive alien species is widely recognised as a major threat to native biodiversity. In conjunction with Heriot-Watt University, models describing the spread of invasive species at the landscape scale were nd habitat type. The use of Markov chain Monte Carlo sampling within a Bayesian framework facilitated statistical assessments of relative suitability of different habitats, and enabled predictions of future spread to account both for uncertainty in model parameters and for stochastic variability in the spread of the species. The methods, widely applicable to data on spatio-temporal expansion, are illustrated (see figure) by application to data on the spread of giant hogweed across Britain in the 20th Century.

The figure shows a map of posterior predicted risk (the estimated probability, conditional on the model and data) of �0X�0km grid cells being colonised by giant hogweed between the end of 2000 (the last available data) and 20�0. The climate and land use are assumed to remain constant over this �0 year period.Data on existing sites from the National Biodiversity Network http://www.nbn.org.uk/

Estimating optimum seed rates for winter wheatAdvice to farmers on the amount of seed to sow is based on trials in which varieties of a crop are grown at different seed rates. Conventionally, the relationships between seed rate and crop yield are estimated separately for each trial and variety: information on the optima is then combined informally. With HGCA support, we have developed a method for combining results from seed-rate trials and choosing economically optimum seed rates for a variety. This Bayesian method incorporates information on the anticipated value of the harvest, seed and management costs, and expert knowledge of the crop and the soil. It can also take account of differences such as sowing date, latitude and management practices. The methodology is of more general applicability, particularly in areas where decisions on optimal dosing must be tailored to particular circumstances.

The utility of sowing seed is taken to be the value of the grain minus the cost of the seed: the optimum rate is the point where the curve reaches a maximum. The plots show the expected utility for two sowing dates: later sowing requires a higher seed rate in order to achieve maximum expected utility.

Genetic diversity of Scots pine treesMonoterpenes are a highly heritable group of phyto-chemicals that can be used as a surrogate measure of genetic diversity in Scots pine trees. Observed monoterpene diversity is surprisingly high at small as well as at large spatial scales. We have been simulating the patterns of monoterpene concentrations that arise from simple models of sexual recombination, dispersal, competition for space, and death. These simulated patterns allow us to assess whether observed levels of diversity are to be expected from the spatial population dynamics. The alternative hypothesis is that the diversity of monoterpenes, which are known to influence vulnerability of young trees to parasites, may be driven by the interaction between parasites and monoterpenes.

A simulated forest, with points representing the location of trees, and colour intensity for red green and blue indicating concentrations of three independent monoterpenes. Even with the small average dispersal of �0m for seeds and �00m for pollen, monoterpene diversity is high at small as well as at large spatial scales.

Statistical Methodology

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Statistics is the science of the collection, analysis and use of data. To meet demands and opportunities of our collaborators, BioSS's statistics research has focused on the analysis of digital images, spatial and temporal data. Here we illustrate some highlights.

Colour displays for categorical images “In a categorical image, each picture element (pixel) is assigned to a unique group. Such images arise in many contexts, including when a segmentation algorithm has been used to partition the pixels into groups according to some definition of connectivity. To produce a clear display of such an image we need as distinct a colour as possible to represent each group. We have developed a method for identifying such a set of colours. We use search algorithms and simulated annealing to maximise the minimum distance between any pair of colours in a set, as measured in a perceptual colour space.

Image analysis of plant varietiesThe shape of leaves is an important characteristic used by botanists and plant breeders in describing and classifying different species, subspecies and cultivars. It is not an easy thing to quantify. Many characteristics are used to describe the overall type of shape, and then a system of scores is used to specify the extent to which each characteristic is expressed. BioSS has been involved in developing tools whereby this may be done automatically and objectively, using methods of measurement in image analysis. An additional benefit of this is that it is often also possible to define and estimate an average shape (see figure). This has the benefit of allowing cultivar differences to be presented in a clear graphical format. We plan to continue developing these ideas, and to make the mathematical tools accessible through easy-to-use software.

Image of some pea stipules.

Electron scanning micrograph of cross-section of soil aggregate (black areas are pores and lighter areas are soil).

Connected pores in soil section displayed using optimal colour labels.

Understanding the effects of social hierarchy on disease dynamicsMany livestock diseases (e.g. paratuberculosis) also have wildlife hosts, and knowledge of disease dynamics in natural populations is often critical to the development of successful control strategies. A key challenge is to understand the epidemiological importance of interactions between individuals in natural populations. In conjunction with scientists at SAC, we have developed models to explore the effects of features of social hierarchy such as dominance on epidemiology. This work demonstrates that populations with a hierarchy have higher levels of disease than populations without a hierarchy. The biggest effects of hierarchy tend to be at

points where the disease is close to extinction, suggesting that such effects should be considered in models being used to examine the impact of control strategies.

The figure shows contours of the difference in prevalence between a model with hierarchy and one without. The disease is expected to become extinct above the lines for models with (red) and without (green) a social hierarchy respectively.

OPA survey and modellingOvine Pulmonary Adenocarcinoma (OPA) is a contagious lung cancer of sheep caused by a viral infection. BioSS have been working with MRI and SAC to inform the choice of potential control strategies for OPA in a project involving laboratory science, field epidemiology and mathematical modelling. We have designed a major field study of 125 holdings, sampling over 3000 sheep throughout Scotland, to provide the first ever snap-shot of OPA prevalence in the field. These data are being combined with knowledge of Scottish sheep husbandry systems and modes of OPA transmission to parameterise an agent-based model of sheep management and OPA infection on Scottish hill, upland and lowland farms. The results of this project will enable much better advice to be given to farmers and vets on how to control the disease.

The sheep management system on a Scottish hill farm used by our agent-based model, with females (breeding ewes and one year old hoggs), tups (breeding males) and lambs.

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Continuous monitoring of river water qualityLegislation such as the European Water Framework Directive has given rise to a need for closer monitoring of water quality in river catchments. The information gathered can be used both to influence long-term policy and to suggest short-term remedial actions. Of considerable importance to hydrologists is the issue of determining the relative contributions of water in rivers which is either of short-term residence in the catchment (soil water) or long-term residence (ground water). BioSS has been applying Bayesian models to alkalinity data from a study of the Feugh catchment by the Universities of Aberdeen and Glasgow, recorded

at 15-minute intervals over a period of one year. The high density of the data recorded means that successive observations are highly correlated, and our statistical models have been extended to deal with correlation at more than one time-scale. This enables us to obtain better estimates of the relationship between river flow rate on water quality.

The Water Framework Directive requires increased monitoring of Scotland’s rivers’ water quality.

Water quality, as indicated by alkalinity, is strongly affected by rates of river flow.

Discovering variation during pregnancy of the relationship between weather and birth weightSuppose we want to know how, at different times during pregnancy, weather influences birthweight. Such questions are of increased importance in the context of climate change. However they are difficult to answer from observational data, because small populations of animals are exposed to the same weather each year, hence the effective sample size in analyses relating birth weights to weather variables is the number of years. Also, as weather variables in successive intervals are positively correlated, use of standard multiple regression techniques will lead to successive regression coefficients being negatively correlated and having large associated confidence intervals (Figure on next page, left). More information can be extracted from the data by incorporating into the model a belief that adjacent time intervals should have similar regression coefficients. We have developed a technique for doing this using standard software for fitting linear mixed models, based on the assumption that differences between successive regression coefficients come from the same Gaussian distribution. The methods have been applied to demonstrate variation during pregnancy of the relationship between birth weights of red deer calves and temperature, this being generally positive during early to mid pregnancy and strongest in weeks 23 to 26 (Figure on next page, right).

Regression coefficients describing the relationship between birth weight of red deer calves and mean temperature in successive fortnights during pregnancy, together with 95% confidence intervals. Deer data collected on Rum during the period �97� to �998, and regression coefficients were estimated using standard multiple regression techniques (left) or by smoothing differences in successive regression coefficients (right).

Latent Gaussian models for multivariate and compositional food intake Data on the food eaten by consumers and on the nutritional contents of foods can help us to understand dietary risks to health. Such datasets are typically difficult to analyse because they contain many zeroes – one for each recorded food type not eaten during the observation period, or one for each component that is absent from a particular type of food. This means that, at face value, we cannot treat the data as coming from a standard distribution such as the Gaussian (or normal) distribution.

One solution is to assume that the data have been generated by a transformation of a standard underlying (latent Gaussian) distribution, with negative values of that distribution recorded as zeroes. We constructed a latent version of a factor analysis model to explore the multivariate relationships between the intake of different foods by British consumers. This model allows us to estimate the distribution of consumption of, for example, vitamins which are present in many food types. We have also developed a compositional model for describing the relative proportions of different nutrients within individual foods. Standard methods for estimating model parameters do not work in this context, so we have investigated the performance of inference using simulation-based approximations.

The latent Gaussian model used to analyse the nutritional composition of a range of fish products.Data: USDA Agricultural Research Service http://www.ars.usda.gov/ba/bhnrc/ndl

Relationship between the consumption of different food products by a sample of 2�97 adults consumers during one week.Data: Dietary & Nutritional Survey of British Adults http://www.data-archive.ac.uk/

Consultancy Advice & Collaboration

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Scottish raspberries are a high quality, high value product.

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Biological, environmental and social scientists benefit greatly from ready access to modern statistical and mathematical expertise to ensure that their research is

carried out in the most effective way and to help them analyse and interpret the increasingly complex data that are now routinely collected. BioSS is unique in the UK for combining in a single organisation a wide breadth both of methodological expertise and of experience in the application of quantitative methods to underpin scientific research.

BioSS staff are either permanently located at client organisations or run regular consultancy sessions on their sites. Thus local consultants are readily accessible and well known to their scientific colleagues. Local advice is supplemented by specialist expertise from BioSS staff at other sites when required. Scientific applications in which we have particular experience can be grouped into four broad areas:

Plant science

Animal health and welfare

Ecology and environmental science

Human health and nutrition

Long-term working relationships are particularly valuable as they allow us to develop a deeper understanding of specific areas of science and to become genuine research partners. The quality and impact of BioSS contributions to research programmes are reflected in our jointly authored outputs including many papers in refereed journals and conference presentations.

The expertise developed in our interactions with the Scottish Executive's Main Research Providers is of value to a much wider community. Consultancy agreements have been established with other research organisations, supporting their research and, by helping fund additional posts, allowing BioSS to strengthen its skill base. They also extend our range of scientific and organisational contacts and are an important way of raising the profile of BioSS.

Large volumes of molecular marker, transcriptomic, metabolomic and proteomic data have become commonplace in plant science. These are enhancing the discovery and understanding of key factors which control both food production and the relationship between plants and their environment. Our work now focuses on developing methods for new technologies and on adapting established methods to make optimal use of both traditional and novel types of data.

Analysis of ordinal disease scoresMany plant diseases, such as root rot in raspberries, are assessed visually at regular intervals using ordered categories such as the five point scale from 1 (healthy) to 5 (dead). These scores have a degree of subjectivity and are made by different assessors on different dates. Principal co-ordinates analysis can combine information across dates and assessors. Pairwise similarities, based on all the disease assessments, are derived for all plants. These similarities are then condensed into a small number of dimensions or co-ordinates.

In a raspberry root rot trial conducted by SCRI scientists, two cultivars and their offspring were scored on nine dates over a two-year period by three assessors. The first principal co-ordinate explained 43% of the similarity matrix and showed a strong spatial effect related to the slope of the field and consequent changes on soil moisture content. Heritabilities were calculated after removing this spatial effect and found to be significant for the first and third principal co-ordinates. By combining these principal co-ordinates with genetic marker data, quantitative trait loci for root rot resistance have been identified.

Variation in the first principal co-ordinate due to bed number.

Animal Health & Welfare

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6000

200 300 400 500 60010018001200

6000

200 300 400 500 60010018001200

6000

Amou

nt o

f tra

nscr

ipt

�6 �7

Research into animal disease and husbandry methods is driven by the need to produce high quality food products while promoting the health and welfare of animals in different production systems. Improved control of disease can reduce animal distress, provide economic benefits to farmers and reduce the impact of farming on the environment. BioSS supports a wide range of laboratory and field studies, helping design efficient experiments or surveys and providing specialist expertise for data analysis.

Distinguishing subtypes of E. coli O�57:H7 The bacterium Escherichia coli O157 presents a serious public health problem in Scotland: shed asymptomatically by infected cattle, it has the potential to cause serious illness, renal failure and even death in humans. Different populations of E. coli O157 present in field samples can be distinguished using Pulsed-Field Gel Electrophoresis (PFGE), a subtyping technique which uses a restriction enzyme to “chop-up” the DNA into pieces of different sizes. Together with collaborators at SAC, we have been studying a strain (EDL933) of E. coli O157:H7 whose PFGE pattern, obtained using the restriction enzyme Xba1, differs from the pattern predicted from its DNA sequence.

Having found no errors in the reported DNA sequence in the regions near the predicted restriction sites, we sought to to establish another explanation for the discrepancy. The distribution of dam methylation sites within the genome was investigated in silico and a new PFGE pattern was predicted by assuming that, whenever

the restriction enzyme Xba1 was expected to cut the DNA at a dam methylation site, no such cut occurred. The new predicted pattern agrees closely with that observed in practice. This work is important in enhancing our understanding of the relationship between PFGE banding patterns and the underlying DNA sequences of pathogens.

Digitised image of a PFGE image for strain EDL933 after Xba� digestion, showing only five clear bands between 200 and 300 kb; the arrow indicates where an additional band is sometimes present.

Identifying environmental influences on alternative splicingAlternative splicing (AS) is a post-transcriptional process that increases the proteomic and functional capacity of genomes through production of alternative messenger RNA (mRNA) transcripts from the same gene. The development of RT-PCR panels that measure the changes in ratio of different alternatively spliced mRNAs simultaneously is an exciting development in plant genetics, as it allows us to monitor AS. Traditional statistical methods such as analysis of variance are an essential means of identifying real differences in AS resulting from varied experimental conditions. We have found evidence that over a quarter of 90 AS events studied so far in Arabidopsis plants exhibit environmental responses. For example, when grown under long and short day conditions, 21 AS events showed significant changes in spliced transcript ratios (p<0.10).

Concentrations of transcripts of different length from parts of individual plants exposed to long and short day lengths.

Spatial effects in plant variety trialsIn plant variety trials, a complete replicate often occupies a large area of land and so is unlikely to be spatially homogeneous. Explicit inclusion of spatial effects in the analysis can complement the traditional design approach to handing this problem.

Figure 1 shows a variogram of the grain yield for a wheat trial after allowing for variety effects, indicating both a trend in yield across rows and a zigzag pattern across columns. A check on farm practice showed that the zigzag pattern was due to the direction of harvesting; east-west and west-east on odd and even numbered columns respectively. Two combine harvesters were also used. When these row and column effects were included in the analysis, residual variance was reduced by 40%. Accounting for the spatial effects increased the precision of the experiment and the power to detect true differences among the wheat varieties.

A variogram of the grain yield (t/ha) for a wheat trial, showing mean squared difference in residuals as a function of the number of rows and columns separating plots.

Summary PFGE banding patterns for strain EDL933 after Xba� digestion. The “Observed” column shows mean band lengths that were present on each of six separate gels; the remaining columns show the predicted band lengths depending on whether methylated restriction sites for Xba� were cut or not.

Ecology & Environmental Science

Ecology & Environmental Science

ConsultancyConsultancy

Ani

mal H

ealth

& W

elfa

re

Ecol

ogy

& E

nvir

onm

enta

l Sci

ence

0

20

40

60

80

100

Per

cen

tag

e o

f fa

rms

Sheep Scab: Southern Scotland

Sheep Scab: North

ern Scotland

Coccidiosis

Toxoplasmosis

Nutritional D

isorders CLA

Internal parasites

Lameness

Mastitis: N

orthern Scotla

nd

Mastitis: Southern Scotla

nd

Anthelmintic Resistance

Pasteurellosis

Enzootic Abortio

n Orf

1.0

0.5

0.0

-0.5

-1.0A B C D E F G H I J K

Dif

fere

nce

inp

rob

abili

ty

Deer Management Groups

�8 �9

Intervention studies to reduce the incidence of lameness in dairy cattle Lameness in dairy cattle causes distress to the animals and reduces their productivity. Intervention studies offer an opportunity to objectively assess strategies to reduce the incidence of lameness. However, the analysis of data from such studies is complicated by two issues. Firstly, the temporal and within-animal correlations

in data collected from different claws of many different animals. Secondly, the effects of variables such as time of year, time since calving and age of the animals are unavoidably confounded. As well as developing a range of analyses using both parametric and additive models, we have established a simulation approach to evaluating the statistical power and cost of different sampling strategies to improve the efficiency of future multi-factorial studies.

Sole and line lesions marked on a cow's hoof.

Study of perceived risks to sheep health To ensure that research priorities are aligned closely with the priorities of the industry, we have designed and analysed a survey of sheep farmers with the Moredun Research Institute and the Moredun Foundation. Our analysis identified the animal health issues that were perceived as the biggest threats to the farmers own holdings, and allowed differentiation between acute and non-acute risks. By making innovative use of generalised linear models, we identified differences in perceived risk between farmers in Scotland and England for a number of diseases including sheep scab and coccidiosis. We also found that the average reported perceptions of risk to farmers own holdings sometimes differed from the average perception of risks to the industry as a whole, with some diseases such as orf being perceived much more frequently as a threat at the farm level than at the national scale.

Percentage of Scottish farms in which each issue was reported as amongst the five most serious issues on a farmer’s own holding. Blue bars represent national estimates, while other colours identify issues (mastitis and sheep scab) with statistically significant geographical differences.

Environmental concerns are now felt more widely than ever before, and we are working with specialists in ecology, soil science and hydrology on projects of national importance. The role of BioSS is to assist in the provision of a sound, scientific evidence base to improve the state and management of Scotland’s environment and natural heritage.

Re-sampling the National Soils Inventory for ScotlandBetween 1978 and 1988, soil samples were taken at 721 locations throughout Scotland on a 10km grid and analysed for a range of properties, such as soil carbon content. A re-sampling programme is now taking place with the aim of detecting any significant changes in soil properties that may have taken place over the last 20 to 30 years. The available funding will only allow a subset of the original sites to be re-visited, and BioSS has been providing advice on the potential of stratified sampling schemes to increase the efficiency of the re-sampling scheme. Supplementary samples close to the main sampling points are also being taken to allow the uncertainty in estimates of change to be assessed.

Grazing impacts of sheep and deer on vegetation in upland ScotlandBecause of the spatial scales involved, there is little quantitative evidence on the magnitude of impacts different herbivores species have on rangeland habitats to inform management decisions. We have made extensive use of a Beowulf computing cluster to analyse large volumes of data collected by the Macaulay Institute over a number of years on the grazing and trampling impacts in substantial tracts of upland Scotland. Our results suggested that, in many Deer Management Group areas, the mean difference in impact level between vegetation units with sheep present and with sheep absent was greater than the equivalent difference for red deer. The relative levels of impacts on vegetation caused by sheep and red deer is politically sensitive as well as ecologically important, leading to high attendance at the stakeholder discussion meeting held at the Macaulay Institute in February 2007.

Estimated differences in MHI for dry heath vegetation between when sheep (green) and red deer (red) are present and when these herbivores are absent, with 95% credible intervals. MHI is the probability of impact being moderate, moderate-high or high, the other recorded classes being moderate-low and low.

Human Health & Nutrition

ConsultancyConsultancy

Ecol

ogy

& E

nvir

onm

enta

l Sci

ence

Hum

an

Hea

lth &

Nut

ritio

n

The River Dee flooding Aboyne in October 2002.

20 2�

Investigating the potential for providing soil forensic intelligenceSoil is a complex matrix of mineral and organic components. Clues as to the geographical origin of a soil sample and its surrounding vegetation can be gained by identifying and characterising these components. In the EPSRC-funded SoilFit project, staff from the

Macaulay Institute and BioSS are working with a large multi-disciplinary group of experts from a number of UK academic and law enforcement organisations to investigate the potential of advanced analytical methods in providing soil forensic intelligence to police investigations. This has involved the creation of a UK-wide soils database and the application of multivariate statistical methods to data of many different kinds, arising from a host of advanced laboratory techniques. In addition, the project involves software development for both comparing and locating soil samples. This work has led to wide media coverage, including a feature on the BBC television programme “Working Lunch”.

Changing flow rates of the River DeeThe River Dee in North-East Scotland has a world-renowned salmon fishery; concern has been expressed about the potential impact of low river flows on salmon populations. For example, in the summer of 2003 it was reported that many fish were dying due to a combination of low river levels and high temperatures. There are a number of potential influences on changes in flow patterns, including climate change and increased abstraction of water for public use. In collaboration with scientists at the Macaulay Institute, we have begun studying daily flow data for the River Dee dating back to 1929 to look for long-term changes in the distribution of flows, including changes in seasonal patterns and the size and frequency of extreme events.

The role of a balanced diet in promoting human health is being increasingly recognised. Research in this area must encompass a wide range of issues, from the quality of food available via the science of nutrition to the behaviour of individuals. Complex interactions abound, and the variation in observed process can be large. Statistical and mathematical models have a pivotal role in enabling the interpretation of experimental and observational studies.

Brain image analysisDiets with different composition in terms of protein, fat and carbohydrate are known both to have important physiological effects and to vary in how well they can be adhered to. Recent research at the Rowett Research Institute, in collaboration with Aberdeen University, has used PET (positron emission tomography) scanning to study the differences in brain activity in volunteers following diets with different carbohydrate content. The technology produces 3D images at a number of time points. BioSS has been involved in analysing the images to assess where there is evidence that glucose uptake might be related to appetite or diet.

PET scanner in action.

Brain scan slices of a volunteer on separate occasions when following dietsdiffering in carbohydrate content.

Knowledge Transfer

Consultancy

Hum

an

Hea

lth &

Nut

ritio

n

P24

P25

P26

P5-�3

P�4-22

5

10

0

-5

4 86 10 12 14 16

Higherin LD

Higherin SD

Ave

rag

e lo

g-r

atio

(lo

g-f

old

ch

ang

e)Average log-intensity

35

30

25

20

15

10

5

0

Bacterial composition %

bac fpr rum bif prop rrec erec-rrec

High CHOMedium CHOLow CHO

22 23

Microarray studies of weight regulationThe Siberian hamster, which is used as a model for weight regulation in humans, is known to adapt its body weight according to season, and shows big weight changes between in summer-like long day (LD) and winter-like short day (SD) photoperiods. Identification of genes contributing to the seasonal-appropriate body weight of the Siberian hamsters may provide an insight into how body weight is regulated in other mammals including humans. BioSS helped with normalisation of cDNA microarrays and analyses based on significance tests, controlling the false discovery rate, to find genes that were differentially expressed in hypothalamic samples from animals living under LD and SD conditions.

Plot of estimated expression ratios (LD to SD). Horizontal lines indicate 0 (solid) and 2-fold (dashed) expression ratios. Coloured spots indicate genes with strongest evidence for differential expression. Vertical line indicates cut-off for background noise.

Studying the bacterial composition of the digestive systemAtkins-type diets have been demonstrated to result in body weight loss. To investigate concerns about the impacts of such diets on other aspects of health, we have been involved in experiments on the effect of the low carbohydrate (CHO) content on microbial populations in the gut. The results show that the compositions of bacteria and their metabolic products have changed substantially. In particular, the butyrate producing bacterial group Roseburia rectale is reduced by a factor of three. We have begun developing mathematical models to further investigate the interactions between diet, bacteria, and their metabolic products such as butyrate which are thought to play a role in the prevention of colon cancer.

Average bacterial composition of faecal samples from �7 subjects. bac = Bacteroides

fpr = Faecalibacterium prausnitzii

rum = Ruminococcus

bif = Bifidobacteria

prop = Clostridial cluster IX

rrec = Roseburia rectale

erec - rrec = Clostridial clusters XIVa and XIVb excluding Roseburia rectale

Knowledge ExchangeKnowledge Exchange forms a key element of BioSS's purpose and acts as a driving force behind our activities. We aim to ensure that the best available quantitative methods are available to a broad array of end-users, including scientists, policy makers, research students and industry. In turn, BioSS’s research is motivated by the requirements of these end-users.Knowledge Exchange in BioSS takes various forms:

development of user-friendly software, making methodology developed by BioSS more widely and easily accessible;

training courses and workshops for scientists;

supervision of PhD students;

presentations of our research programme;

consultancy advice and collaboration.

Our consultancy work is above all else a Knowledge Exchange activity. Depending on the needs of the client, our contribution can be through advice on the appropriate methods to use or though a more hands-on role. Application of quantitative techniques to answer emerging questions frequently identifies gaps in existing methodology, and so motivates new lines of research.

As well as our consultancy work at the MRPs funded by the Scottish Executive, we have a widening portfolio of clients and funders. These include government departments and agencies, research organisations, the EU, levy boards, non-government organisations and private companies.

One of our newest clients is the RSPB, which funds us to work with their researchers on ecological projects. One piece of work, published in the prestigious journal Science in 2007, demonstrated that international conservation policy has delivered benefits for birds in Europe. The BioSS contribution to this work was to adapt a statistical technique not commonly used in ecological applications for the analysis of ordered categorical response data on the status of threatened species in different countries.

Red kite (Milvus milvus) is one of many European bird species under threat.Photograph reproduced with permission from RSPB Images

Training for ScientistsUser-friendly Software

Knowledge Transfer

Use

r-fr

iend

ly S

oftw

are

Train

ing f

or S

cien

tists

Knowledge Transfer

0

-50

-100

0 1000 2000 3000 4000 5000 6000 7000

Sequence Position

Cu

mu

lati

ve S

um

24 25

Providing training in statistics, bioinformatics and mathematical modelling has been a major part of BioSS activities for the past two decades. The core of our training effort has been a programme of intensive one or two day courses which have taken place in laboratories equipped with computer access, usually at the participants' site or nearby. In recent years we have extended our training provision to include other types of activity.

Presentations on specialist topicsMany specialised topics are not suitable for the format of a full training course, but can be better communicated in a seminar format. This allows the presenter to introduce the concepts, illustrate how appropriate software may be used, demonstrate some examples and interpret the results. Time can also be allowed for discussion with participants about issues relevant to their own work. We have made such presentations on the topics of false discovery rates and extreme value theory, and one on the use of wavelet methods is planned.

WorkshopsWhereas the seminar presentations are suitable for one-way knowledge transfer from BioSS specialists, workshops provide a forum in which scientists already working with particular concepts and methods can share their experiences. The role of BioSS staff on these occasions is to facilitate the discussions and contribute the specialised knowledge they have acquired. Successful workshops have already been held on Bayesian methodology and on species abundance modelling, and a workshop on pathways and network inference is being prepared.

Online training modulesThe Internet provides a powerful medium through which training information can be made available on a flexible basis, and BioSS took an early lead in this approach. The SMART training modules initially developed 10 years ago (www.bioss.ac.uk/smart) continue to attract worldwide interest and are very highly ranked by

Internet search engines. More recent developments have included the production of comprehensive online training in statistical methods for risk analysis, aimed at scientists working on large-scale environmental risk assessment. We anticipate that Internet-based training will play an increasing part in BioSS knowledge transfer.

Extract from web-based module in mathematical modelling, demonstrating the qualitatively different behaviour of stochastic and deterministic models of predator-prey systems.

Algorithms developed as part of our research and consultancy programme can be made available as stand-alone programs or as Web services, enabling them to be widely used.

ImaginMeasuring plant part characteristics by image analysis has several advantages over traditional manual methods: it is potentially quicker, more accurate and allows objective measurement of features which are more difficult to quantify. A permanent record of the appearance of each sample can also be kept for future reference. BioSS, in collaboration with SASA, has built an easy-to-use software tool, Imagin, to make the image analysis methods routinely available to scientists involved in plant breeding and crop variety registration.

Image of parsnip roots after annotation.

SEGS - segmentation of genomic sequencesThe successful annotation of a genomic sequence (consisting of the four bases A, C, G and T) requires a wide range of techniques and many methods exist to assist in this process. One feature of interest is the G+C content (the proportion of bases which are either C or G) which in many organisms is known to be higher in regions that code for proteins than in the rest of the sequence. We are developing a new program, SEGS, to assist in segmenting sequences of different lengths according to their G+C content. The program incorporates a flexible algorithm, based on the cumulative sums (cusums) of G+C content. SEGS produces graphical output of a sequence with appropriate segment boundaries marked. The statistical significance of observed changes in G+C content is assessed using a Kolmogorov-Smirnov type statistic.

Cusum plot showing segments of differing G+C content, based on moving windows of 5% of the sequence length.

Postgraduate Research & Training

Knowledge Transfer

Post

gra

dua

te R

esea

rch

& T

rain

ing

Post

gra

dua

te R

esea

rch

& T

rain

ing

Knowledge Transfer

ALEXANDER MANTZARIS

26 27

BioSS is one of the best places in Europe to do a PhD in applied statistics, biomathematics or statistical bioinformatics. We offer a research environment, access to real-world problems and university links that are second-to-none. Our students leave BioSS with modern methodological skills and experience of blending theory and application, so providing them with an excellent foundation for a successful career.

We seek to recruit students with strong mathematical, statistical, or computing backgrounds, good communication skills and enthusiasm for applying theoretical results. Our students all learn state-of-the-art methodological skills and gain valuable experience of collaborative interaction with scientists outside their own discipline. This contact with scientific experts ensures that new theory is brought to bear on problems of genuine importance.

Students participate with staff in a dynamic programme of research meetings, reading groups and computing workshops, which give many opportunities for interaction. Also, students are provided with excellent computing facilities, including access to cluster processors.

Each PhD project combines development of new methodology with its application in the biological, environmental or social sciences. Our extensive contacts with research organisations and universities provide students with excellent opportunities to interact with scientists working in diverse areas of application.

ADRIANOWERHLI

What are recent BioSS students doing now?

After finishing an MSc in Computer Science in Brazil I joined BioSS and the University of Edinburgh in 2004 to pursue a PhD in Informatics. Staff and students at BioSS are lovely and they made me feel "at home" from the beginning of my studies. This quality is always very important but is yet more important for people who are experiencing a new country and culture. The supervision offered by BioSS is very competent and professional. The integration among students and staff provides a very good experience as a real-world research environment. Furthermore, the computing facilities and support provided are great. Throughout my PhD I was strongly encouraged to attend workshops and conferences and these opportunities have made an invaluable contribution to pursuing my PhD. Other than BioSS being an excellent place to study and research, Edinburgh is a lovely city to live in. It is a very cosmopolitan, beautiful and welcoming city, with many things to do and see making the hours out of office very enjoyable.

My PhD with BioSS officially began in December 2006, but I was involved in research here from before. I did an MSc at the University of Edinburgh in Bioinformatics, and my thesis for the course was done through the BioSS-Edinburgh University collaboration (the bachelor degree I built my studies on was in Software Engineering). The subject was to detect recombination in DNA sequence alignments, which I found fascinating as it was closer to any prospect of applicable research than other project proposals I had come across. Now in my PhD I am extending this work using cutting edge statistical methods. The position I have at BioSS allows me to have the most beneficial experience combining the university exposure and that of the institute producing research over all areas. There is a healthy international spirit here which was welcoming for me coming from Greece.

Isthri Krishnarajah is a lecturer in the Department of Mathematics, and an associate researcher in the Institute for Mathematical Research (INSPEM), Universiti Putra Mayalasia.

Michelle Sims is a Research Associate at Duke University Marine Laboratory using Bayesian spatial mapping techniques to model fisheries bycatch data.

Alex Cook is researching the optimal design of epidemic experiments, as a Research Associate in the School of Mathematical and Computer Sciences, Heriot-Watt University, and a Visiting Scholar in the Department of Plant Sciences at the University of Cambridge.

Ayona Chatterjee is an Assistant Professor at University of West Georgia, USA, and pursuing her research interests in risk assessment of dietary data in collaboration with the Department of Environmental and Occupational Health at Emory University, Georgia.

Mizanur Khondoker is a postdoctoral researcher in the Division of Pathway Medicine, University of Edinburgh, helping develop a BioSensing Platform to combine biochip technologies in molecular biology.

Stijn Bierman is a Biomathematician in BioSS, modelling a variety of types of ecological data including large-scale species distributions.

In 2007, we are supervising eleven students, registered at the Universities of Dundee, Edinburgh, Sheffield and York, Heriot Watt University and SAC. Two of this cohort of students write of their experiences below.

Staff as at � April 2007Information Technology

StaffInformation Technology

Staff and students at BioSS General Meeting at the Rowett Research Institute.

Staff and students based at The King’s Buildings in Edinburgh.

INVESTOR IN PEOPLE

The SCRI computing cluster.

28 29

Director: Professor David Elston MSc

The King’s Buildings, University of EdinburghHead of Research and Leader of Statistical Methodology Research Theme: Professor Chris Glasbey DScLeader of Statistical Bioinformatics Research Theme: Dirk Husmeier PhDLeader of Process & Systems Modelling Research Theme: Glenn Marion PhD,Principal Consultant, Animal Health & Welfare: Iain McKendrick PhDExternal Development Manager: Adrian Roberts MScIT Manager: Janet Dickson BScAdministrative Officer: Betty Heyburn MA

Stijn Bierman PhD, Adam Butler PhD, Stephen Catterall PhD, Diane Glancy, Muriel Kirkwood DA, Kuang Lin PhD, Alec Mann BSc, David Nutter BSc, Ian Nevison MA, Jitka Polechova PhD, Jill Sales MSc, Chris Theobald PhD

Scottish Crop Research InstituteHead of Consultancy and Principal Consultant, Plant Science: Jim McNicol MScColin Alexander BSc, Christine Hackett PhD, Dominik Lindner Dipl. Ing., Katrin MacKenzie PhD, Matthieu Vignes MSc, Frank Wright PhD

Rowett Research InstitutePrincipal Consultant, Human Health & Nutrition: Graham Horgan PhDGrietje Holtrop PhD, Claus-Dieter Mayer PhD

Macaulay InstitutePrincipal Consultant, Ecology & Environmental Science: Mark Brewer PhD Betty Duff BSc, Daniel Lawson PhD, Jackie Potts PhD,

SAC AuchincruiveSarah Brocklehurst PhD

Associates: Tony Hunter MPhil, Mike Talbot MPhilStaff leaving since 1 April 2005: Jon Coe, Nathalie Massat, Iain Milne, Dave Walker

BioSS staff, students and visitors enjoy a high-quality local computing environment with central multi-core servers, augmented by access to multi-processor clusters for

computer-intensive tasks. Access to BioSS facilities in Edinburgh is provided by a Secure Portal environment.

BioSS has a high-quality, resilient IT infrastructure in Edinburgh based on Linux workstations as central servers. All users have either a managed PC or Linux workstation. We also have access to clusters at Rowett Research Institute, SCRI and, more recently, Eddie, the Edinburgh Computer Data facility (ECDF) cluster.

IT support for BioSS has recently been augmented by employing an experienced Java Web programmer who also provides much needed support with Linux system administration. This will enable us to improve the security of our infrastructure and develop further tools to enable BioSS staff to work and collaborate more efficiently.

The BioSS Portal provides a Secure Meeting client that allows users to share their desktops and applications over a secure connection. This means for example that it is possible for a BioSS expert in a particular interactive package to demonstrate its use to remote collaborators by letting them see sequences of point-and-click operations on their own computer screen.

BioSS Portal Secure Meeting client.

Management GroupResearch Students

Research Students

Management Group

30 3�

Tom AdamsReconstructing Scotland's pine forests (Glenn Marion with Professor G J Ackland, University of Edinburgh and Dr C Edwards, Forest Research)

Mark BiltonThe influence of intraspecific genotype diversity on spatial vegetation dynamics (Glenn Marion with Professor R Pakeman, Macaulay Institute and Professor P Grime, University of Sheffield)

Ayona ChatterjeeProbabilistic risk assessment of dietary data (Graham Horgan and Chris Theobald, University of Edinburgh) PhD awarded 2005

Alex CookInference and prediction in plant communities using data augmentation within a Bayesian framework (Glenn Marion with Professor G Gibson, Heriot-Watt University) PhD awarded February 2006

Thorsten ForsterStatistical and algorithmic modelling for amalgamation of cross-domain data sources within an immunology framework (Chris Glasbey with Professor P Ghazal, University of Edinburgh)

John GustafssonUnwarping and analysing electrophoresis gels (Chris Glasbey with Professor M Rudemo, Chalmers University, Sweden) PhD awarded 2006

Lena HanssonDetecting RIDGES: regions of increased density of gene expression (Dirk Husmeier with Dr J D Armstrong and Professor P Ghazal, University of Edinburgh)

Mizanur KhondokerStatistical methods for preprocessing microarray gene expression data (Chris Glasbey with Dr B J Worton, University of Edinburgh) PhD awarded 2007

Isthri KrishnarajahNovel moment closure approximations to nonlinear stochastic models (Glenn Marion with Professor G Gibson, Heriot-Watt University) PhD awarded October 2005

Wolfgang LehrachBayesian machine learning methods forpredicting protein-peptide interactionsand detecting mosaic structures in DNA sequence alignments (Dirk Husmeier with Professor C Williams, University of Edinburgh)

Sandy MacDonaldRegulatory pathways involving iron acquisition in Pasteurella multocida A:3, their role in pathogenesis and relevance to disease mechanisms in Erwinia carotovora subspecies atroseptica (Frank Wright with Dr J C Hodgson, MRI, Dr J Liu, Durham University, and Professor I R Poxton, University of Edinburgh)

Alex MantzarisStatistical methods for analysing DNA sequence alignments in a phylogenetic context (Dirk Husmeier with Dr J D Armstrong, University of Edinburgh)

Alastair PoutModelling the movements of the Eurasian sparrowhawk Accipiter nisus from radio-tracking data (Mark Brewer with Dr J Ollason, University of Aberdeen and Dr J Yearsley, Macaulay Institute) PhD awarded 2005

Christelle RobertElucidation of regulatory and signalling networks that control bacterial disease development (Frank Wright with Dr L Pritchard, SCRI and Professor G Barton, University of Dundee)

Michelle SimsApplications of linear mixed models in ecology (David Elston with Dr I Wilson and Professor X Lambin, University of Aberdeen) PhD awarded 2005

Lesley SmithThe effect of farming systems on disease risk to grazing animals (Glenn Marion with Dr M Hutchings, SAC, and Dr P White, University of York)

Adriano WerhliReconstruction of gene regulatory networks from postgenomic data (Dirk Husmeier with Dr J D Armstrong, University of Edinburgh)

2005 - 2007 with project title and supervisors

David Elston is Director of BioSS. His research interests include multilevel models, with

environmental and ecological applications, and

population dynamics modelling.

Chris Glasbey is Head of Research and leader of the Statistical Methodology research

theme. His expertise is in spatial and temporal

modelling, applied to image analysis, bioinformatics and

meteorology.

Graham Horgan is Principal Consultant for

Human Health and Nutrition and co-ordinates the BioSS programme of

training courses for scientists. His main

research interests lie in statistical methods in

biomedical and animal sciences, image analysis and spatial data interpretation.

Jim McNicol is Head of Consultancy and

Principal Consultant for Plant Science. His

statistical interests include multivariate methods,

expert systems and Bayesian modelling.

Janet Dickson is IT Manager and is

responsible for organisation and delivery of IT support in BioSS. She chairs the Computer Liaison Group

that draws together IT expertise in seven Scottish

research organisations.

Betty Heyburnis Administrative Officer and leads the delivery of administrative services in BioSS, including finance,

personnel and record keeping. She is the chief

point of contact on administrative matters between BioSS and our

parent organisation, SCRI.

Iain McKendrick is Principal Consultant for

Animal Health and Welfare. His research interests are in

the development of statistical multilevel

modelling and mathematical modelling techniques to make them

more applicable to problems in veterinary

epidemiology.

Adrian Roberts is External Development Manager. He is leader of

BioSS's many inputs to the assessment of new plant varieties, and undertakes

research on the relationship between phenology and the

weather.

Mark Brewer is Principal Consultant for

Ecology and Environmental Science. His personal research interests lie in

modelling spatially- and temporally-correlated data

in a Bayesian setting.

Dirk Husmeier leads the Statistical

Bioinformatics research theme. His particular

expertise is in the application of machine learning techniques to

systems biology, phylogenetics and postgenomic data

integration.

Glenn Marion leads the Process and

Systems Modelling research theme. His primary

research area is stochastic process modelling,

motivated by collaborations with scientists from many

discipline

Appendix � Selected Research Grants and Contracts 2005 - 2007

Res

earc

h G

rant

s &

Con

tract

s

Res

earc

h G

rant

s &

Con

tract

s

Appendix � Appendix 1

32 33

A functional genomic approach to understanding the molecular pathogenesis of sheep scrapie, with University of Edinburgh, funded by BBSRC (J Sales)

A study to compare the health and welfare of laying hens in different types of enriched cage, with Scottish Agricultural College, funded by Defra (S Brocklehurst)

Advice on statistical matters related to plant variety registration and seed testing, funded by Defra (A M I Roberts, E A Hunter, A D Mann)

Advice on the HGCA recommended list variety trial processing system, funded by Crop Evaluation Ltd (A M I Roberts, E A Hunter, A D Mann, I M Nevison)

Advice, design and analysis of variety trials, funded by British Society of Plant Breeders (A M I Roberts, M A M Kirkwood, D Glancy)

Appropriate fungicide doses on barley – production of dose response curves to advise growers on fungicide efficacy and potential resistance, with Scottish Agricultural College, funded by Home Grown Cereals Authority (A M I Roberts, E A Hunter)

Assessing large-scale environmental risks with tested methods, with 67 EU partners, funded by EU (G R Marion, S M Bierman, A Butler)

Assessment of herbivore impacts on capercaillie habitats in Upper Deeside, with Macaulay Land Use Research Institute, funded by Scottish Natural Heritage (M J Brewer)

Assessment of Phytophthora idaei, with Scottish Crop Research Institute, funded by the Scottish Executive (J W McNicol)

Clubroot control using novel and sustainable methods, with ADAS UK Ltd, Scottish Agricultural College, funded by Defra (C M Theobald)

Consumption of wholegrain foods and markers of cardiovascular disease risk, with Rowett Research Institute, funded by FSA (G W Horgan)

Control of pulmonary adenocarcinoma (Jaagsiekte) in the Scottish sheep flock, with Moredun Research Institute, Scottish Agricultural College, funded by SEERAD (I J McKendrick, S P Catterall)

Design and operation of a UK soil monitoring network, with Macaulay Land Use Research Institute, Institute of Arable Crops Research, Centre for Ecology and Hydrology, funded by Environment Agency (J M Potts, D A Elston)

Determining the prevalence of BVD in Scottish cattle to effect improved disease control, with Scottish Agricultural College, funded by SEERAD (I J McKendrick)

Development of a soil monitoring scheme for Scotland, with the Macaulay Land Use Research Institute, funded by the Scottish Executive (J M Potts)

Development of a vaccine to control the poultry red mite and improve laying hen welfare, with Scottish Agricultural College, funded by Defra (S Brocklehurst)

Effect of TM-QTL on meat yield in sheep, with Scottish Agricultural College, LINK project co-funded by Defra and sheep industry (C A Glasbey, A D Mann)

Elucidation of regulatory and signalling networks that control bacterial disease development, with Scottish Crop Research Institute, Moredun Research Institute, funded by SEERAD (F Wright, D Husmeier, C Dieter-Mayer, K Lin)

Epidemiology, population health and infectious disease control (EPIC), with Scottish Agricultural College, University of Glasgow, Moredun Research Institute, University of Edinburgh, Macaulay Land Use Research Institute, funded by the Scottish Executive (I J McKendrick, G R Marion)

Escherichia coli 0157 interventions and control, with Scottish Agricultural College, funded by Defra (I J McKendrick)

Evaluation of video image analysis of sheep carcasses, funded by MLC (C A Glasbey)

Further development of a force plate method for objective and reliable assessment of broiler leg health under commercial conditions, with Scottish Agricultural College, funded by Defra (S Brocklehurst)

Genetic reduction in energy use and emissions of nitrogen through cereal production, with Scottish Crop Research Institute, funded by the Scottish Executive and Defra (C A Hackett)

Green Grain: Genetic Reduction in Energy use and Emissions of Nitrogen through cereal production, with Scottish Crop Research Institute, funded by the Scottish Executive, Defra (C A Hackett)

IMAGIN: a system for automatic measurement of crop characteristics, funded by Scottish Agricultural Science Agency (G W Horgan, A M I Roberts, A D Mann, I M Nevison)

Improving carcass quality of UK hill sheep using computed tomography, with Scottish Agricultural College, funded by Defra (C A Glasbey, A D Mann)

Independent variety trials programme, with Scottish Agricultural Science Agency, Scottish Agricultural College Commercial Ltd, Scottish Crop Research Institute, funded by British Potato Council (A M I Roberts, M A M Kirkwood)

Integration of soil fingerprinting techniques, with Macaulay Land Use Research Institute, funded by EPSRC (M J Brewer)

Investigation on the impacts of changing flows on the River Dee in relation to potential impacts on aquatic ecology, with Macaulay Land Use Research Institute, funded by Scottish Natural Heritage (M J Brewer, J M Potts)

Metabolome variability in crop plants, with Scottish Crop Research Institute, funded by the Food Standards Agency (J W McNicol, M Vignes)

National soil monitoring network: review & assessment study, funded by SNIFFER (D A Elston, J M Potts)

Novel approaches to networks of interacting autonomes, with University of Edinburgh, University of Manchester, Manchester Metropolitan University, funded by EPSRC (G R Marion, J B Coe, J Polechova)

Prevalence and concentration of Escherichia coli serotype O157:H7 and other VTEC in sheep presented for slaughter in Scotland, with Scottish Agricultural College, funded by FSA Scotland (I J McKendrick)

Promoting food safety through a new integrated risk analysis approach for foods, with 32 EU partners, funded by the EU (J W McNicol, N Massat, C J Alexander)

Provision of statistical advice to RSPB staff, funded by RSPB (S M Bierman, D A Elston)

Provision of statistical services for VCU work in Scotland, funded by the Scottish Executive (A M I Roberts, M A M Kirkwood, I M Nevison)

Appendix 3 Conference Presentations, Lectures & Seminars

Appendix 2 Publications

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�. METHODOLOGyBierman, S., Elston, D.A., Lambin, X., Fairbairn, J.P., Tidhar, D. & Petty, S.J. 2006. Changes over time in the spatiotemporal dynamics of field voles (Microtus agrestis L.). American Naturalist 167, 583-590.

Brewer, M.J., Filipe, J.A.N., Elston, D.A., Dawson, L.A., Mayes, R.W., Soulsby, C. & Dunn, S.M. 2005. A hierarchical model for compositional data analysis. Journal of Agricultural, Biological, and Environmental Statistics 10, 19-34.

Coe, J.B. & Mao, X. 2005. Gompertz mortality law and scaling behavior of the Penna model. Physical Review E 72, 051925.

Durban, J.W. & Elston, D.A. 2005. Mark-recapture with occasion and individual effects: abundance estimation through Bayesian model selection in a fixed dimensional parameter space. Journal of Agricultural, Biological, and Environmental Statistics 10, 291-305.

Durban, J.W., Elston, D.A., Ellifrit, D.K., Dickson, E., Hammond, P.S. & Thompson, P.M. 2005. Multisite mark-recapture for cetaceans: population estimates with Bayesian model averaging. Marine Mammal Science 21, 80-92.

Gibson, G., Otten, W., Filipe, J.A.N., Cook, A., Marion, G. & Gilligan, C.A. 2006. Bayesian estimation for percolation models of disease spread in plant populations. Statistics and Computing 16, 391-402.

Glasbey, C.A. 2006. Warping of electrophoresis gels using generalisations of dynamic programming. In Interdisciplinary Statistics and Bioinformatics, Eds. S. Barber, P.D. Baxter, K.V. Mardia and R.E. Walls, 71-74. Leeds University Press, Leeds.

Glasbey, C.A. & Khondoker, M.R. 2005. Correction for pixel censoring in cDNA microarrays. In Statistical Solutions to Modern Problems: Proceedings of the 20th International Workshop on Statistical Modelling, Eds. A.R. Francis, K.M. Matawie, A. Oshlack, G.K. Smyth, 17-31. University of Western Australia, Sydney, Australia.

Glasbey, C.A., Vali, L. & Gustafsson, J.S. 2005. A statistical model for unwarping of 1-D electrophoresis gels. Electrophoresis 26, 4237-4242.

Holtrop, G., Petrie, J., McElhiney, J. & Dennison, N. 2006. Can general anaesthesia be used for the Paralytic Shellfish Poison bioassay? Toxicon 47, 336-347.

Horgan, G.W. 2005. Interpretation of two-stage experiments. Laboratory Animals 39, 75-79.

Horgan, G.W. 2005. Optimising two-dye microarray designs for estimation. Journal of Biotechnology 118, 1-8.

Horgan, G.W. & Ball, B.C. 2005. Modelling the effect of water distribution and hysteresis on air-filled pore space. European Journal of Soil Science 56, 647-654.

Horgan, G.W., Travis, A.J. & Liang, J. 2005. Automatic recognition of maize cell types using context information. Micron 36, 163-167.

Hudson, I., Keatley, M. & Roberts, A.M.I. 2005. Statistical methods in phenological research. In Statistical Solutions to Modern Problems: Proceedings of the 20th International Workshop on Statistical Modelling, Eds. A.R. Francis, K.M. Matawie, A. Oshlack, G.K. Smyth, 259-270. University of Western Sydney, Sydney, Australia.

Husmeier, D. 2005. Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. Bioinformatics 21, ii166-ii172.

Husmeier, D. 2006. Detecting mosaic structures in DNA sequence alignments. In Biomathematics: Modelling and Simulation, Eds. Misra, JC, ISBN 981-238-110-4, 1-35. World Scientific, London.

Husmeier, D. & Wright, F. 2005. Detecting recombination in DNA sequence alignments. In Probabilistic Modeling in Bioinformatics and Medical Informatics, Eds. Husmeier, D., Dybowski, R. & Roberts, S., 147-190. Springer Verlag.

Husmeier, D., Wright, F. & Milne, I. 2005. Detecting interspecific recombination with a pruned probabilistic divergence measure. Bioinformatics 21, 1797-1806.

Scoping study for otter surveillance, funded by Natural England and the Environment Agency (M J Brewer)

Scottish Badger Survey – design advice, funded by the Scottish Executive (S M Bierman, A M I Roberts)

Service level agreement for statistical consultancy advice and collaboration to Centre of Ecology and Hydrology Banchory, funded by Centre of Ecology and Hydrology (D A Elston, E I Duff, J M Potts)

Service level agreement for statistical consultancy advice and collaboration to the Scottish Agricultural Science Agency, funded by the Scottish Agricultural Science Agency (A M I Roberts)

Shellfish toxin testing method assessment, funded by Food Standards Agency (G Holtrop)

Sustainable systems for pig weaner management (AGEWEAN), with University of Newcastle, ADAS Consulting Ltd, Scottish Agricultural College, Harper Adams University College, Meat and Livestock Commission, funded by Defra. (C M Theobald)

The welfare effects of different methods of depopulation on laying hens, with Scottish Agricultural College, Roslin Institute, Silsoe Research Institutes, funded by Defra (I M Nevison)

UK Population Biology Network Project 2a - predicting population dynamics in a changing environment, with University of Aberdeen, University of East Anglia and University of Leeds, funded by NERC (D A Elston)

Understanding and improving flavour characteristics of potato, with Scottish Crop Research Institute, funded by Defra Link (I M Nevison)

Understanding fungicide mixtures to control Rhynchosporium in barley and minimise resistance shifts, with Scottish Agricultural College, funded by Home Grown Cereals Authority (E A Hunter, A M I Roberts)

Visualisation and analysis of biological sequences, alignments and structures, with Scottish Crop Research Institute, funded by BBSRC (F Wright, D Lindner, I Milne)

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Kedzierska, A. & Husmeier, D. 2006. A heuristic Bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene conversion. Statistical Applications in Genetics and Molecular Biology 5, Article 27.

Khondoker, M.R., Glasbey, C.A. & Worton, B.J. 2006. Statistical estimation of gene expression using multiple laser scans of microarrays. Bioinformatics 22, 215-219.

Krishnarajah, I., Cook, A., Marion, G. & Gibson, G. 2005. Novel moment closure approximations in stochastic epidemics. Bulletin of Mathematical Biology 67, 855-873.

Kühn, I., Bierman, S., Durka, W. & Klotz, S. 2006. Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods. New Phytologist 72, 127 - 139.

Lehrach, W.P., Husmeier, D. & Williams, C.K.I. 2006. A regularized discriminative model for the prediction of protein-peptide interactions. Bioinformatics 22, 532-540.

Lehrach, W.P., Husmeier, D. & Williams, C.K.I. 2006. Probabilistic in silico prediction of protein-peptide interactions. In Systems Biology and Regulatory Genomics, Eds. Eskin E., Ideker T., Raphael B. and Workman C, 188-197. Springer Verlag, San Diego, USA.

Marion, G., Swain, D.L. & Hutchings, M.R. 2005. Understanding foraging behaviour in spatially heterogenous environments. Journal of Theoretical Biology 232, 127-142.

Mayer, C-D. & Glasbey, C.A. 2005. Statistical methods in microarray data analysis. In Probabilistic Modelling in Bioinformatics and Medical Informatics, Eds. D. Husmeier, R. Dybowski and S. Roberts, 211-238. Springer Verlag, London.

Paulo, M.J., van der Voet, H., Wood, J.C., Marion, G. & van Klaveren, J.D. 2006. Analysis of multivariate extreme intakes of food chemicals. Food and Chemical Toxicology 44, 994-1005.

Sims, M., Wanless, S., Harris, M.P., Mitchell, I. & Elston, D.A. 2006. Evaluating the power of monitoring plot designs for detecting long-term trends in the numbers of common guillemots. Journal of Applied Ecology 43, 537-546.

Small, M., Tse, C.K. & Walker, D.M. 2006. Super-spreaders and the rate of transmission of the SARS virus. Physica D 215, 146-158.

Theobald, C.M., Roberts, A.M.I., Talbot, M. & Spink, J.H. 2006. Estimation of economically optimum seed rates for winter wheat from series of trials. Journal of Agricultural Science 144, 303-316.

Walker, D.M. 2006. Parameter estimation using Kalman filters with constraints. International Journal of Bifurcation and Chaos 16, 1067-1078.

Walker, D.M. & Marion, G. 2006. Selecting nonlinear stochastic process rate models using information criteria. Physica D 213, 190-196.

Walker, D.M., Perez-Barberia, F.J. & Marion, G. 2006. Stochastic modelling of ecological processes using hybrid Gibbs samplers. Ecological Modelling 198, 40-52.

Walker, D.M. & Small, M. 2006. Detecting unstable fixed points using Kalman filters with constraints. IEEE Transactions on Circuits and Systems I 53, 2818-2827.

Webster, R., Welham, S.J., Potts, J.M. & Oliver, M.A. 2006. Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Computers and Geosciences 32, 1320-1333.

Werhli, A.V., Grzegorczyk, M., Chiang, M.T. & Husmeier, D. 2006. Improved Gibbs sampling for detecting mosaic structures in DNA sequence alignments. In Statistics in Genomics and Proteomics, Eds. Urfer W. and Turkman M. A, 23-34. Centro Internacional de Matematica, Coimbra, Portugal.

Werhli, A.V., Grzegorczyk, M. & Husmeier, D. 2006. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22, 2523-2531.

Wood, J.C., McKendrick, I.J. & Gettinby, G. 2006. A simulation model for the study of the within-animal infection dynamics of E. coli O157. Preventive Veterinary Medicine 74, 180-193.

Wood, J.C., McKendrick, I.J. & Gettinby, G. 2006. Assessing the efficacy of within-animal control strategies against E. coli O157:a simulation study. Preventive Veterinary Medicine 74, 194-211.

Wood, J.C., Spiers, D.C., Naylor, S.W., Gettinby, G. & McKendrick, I.J. 2006. A continuum model of the within-animal population dynamics of E. coli O157. Journal of Biological Systems 14, 425-443.

Wood, M., Jolliffe, I. & Horgan, G.W. 2005. Variable selection for discriminant analysis of fish sounds. Journal of Agricultural, Biological, and Environmental Statistics 10, 321-336.

2. PLANT SCIENCEBegg, G.S., Hockaday, S., McNicol, J.W., Askew, M. & Squire, G.R. 2006. Modelling the persistence of volunteer oilseed rape (Brassica napus). Ecological Modelling 198, 195-207.

Bradshaw, J.E., Hackett, C.A., Lowe, R., McLean, K., Stewart, H.E., Tierney, I., Vilaro, M. & Bryan, G.J. 2006. Detection of a quantitative trait locus for both foliage and tuber resistance to late blight [Phytophthora infestans (Mont.) de Bary] on chromosome 4 of a dihaploid potato clone (Solanum tuberosum subsp. tuberosum). Theoretical and Applied Genetics 113, 943-951.

Druka, A., Muehlbauer, G., Druka, I., Caldo, R., Baumann, U., Rostoks, N., Schreiber, A., Wise, R., Close, T., Kleinhofs, A., Graner, A., Schulman, A., Langridge, P., Sato, K., Hayes, P., McNicol, J.W., Marshall, D. & Waugh, R. 2006. An atlas of gene expression from seed to seed through barley development. Functional Integrated Genomics 6, 202-211.

Graham, J., Smith, K., Tierney, I., MacKenzie, K. & Hackett, C.A. 2006. Mapping gene H controlling cane pubescence in raspberry and its association with resistance to cane botrytis and spur blight, rust and cane spot. Theoretical and Applied Genetics 112, 818-831.

Griffiths, B.S., Caul, S., Thompson, J., Birch, A.N.E., Scrimgeour, C.M., Cortet, J., Foggo, A., Hackett, C.A. & Krogh, P.H. 2006. Soil microbial and faunal community responses to Bt maize and insecticide in two soils. Journal of Environmental Quality 35, 734-741.

Lehesranta, S.J., Davies, H.V., Shepherd, L.V.T., Koistinen, K.M., Massat, N., Nunan, N.M., McNicol, J.W. & Kärenlampi, S.O. 2006. Proteomic analysis of the potato tuber life cycle. Proteomics 6, 6042-6052.

Lehesranta, S.J., Davies, H.V., Shepherd, L.V.T., Nunan, N.M., McNicol, J.W. & Koistinen, K.M. 2005. Comparison of tuber proteomes of potato (Solanum sp.) varieties, landraces and genetically modified lines. Plant Physiology 138, 1690-1699.

Liu, Z., Bos, J.I.B., Armstrong, M., Whisson, S.C., da Cunha, L., Torto-Alalibo, T., Win, J., Avrova, A.V., Wright, F., Birch, P.R.J. & Kamoun, S. 2005. Patterns of diversifying selection in the phytotoxin-like scr74 gene family of Phytophthora infestans. Molecular Biology and Evolution 22, 659-672.

Nevo, E., Beharav, A., Meyer, R.C., Hackett, C.A., Forster, B.P., Russell, J.R. & Powell, W. 2005. Genomic microsatellite adaptive divergence of wild barley by microclimatic stress in 'Evolution canyon', Israel. Biological Journal of the Linnean Society 84, 205-224.

Shepherd, L.V.T., McNicol, J.W., Razzo, R., Taylor, M.A. & Davies, H.V. 2006. Assessing the potential for unintended effects in genetically modified potatoes perturbed in metabolic and developmental processes. Transgenic Research 15, 409-425.

Tuomainen, M.H., Nunan, N.M., Lehesranta, S.J., Tervahauta, A.I., Hassinen, V.H., Schat, H., Auriola, S., McNicol, J.W. & Kärenlampi, S.O. 2006. Multivariate analysis of protein profiles of metal hyperaccumulator Thlaspi caerulescens accessions. Proteomics 6, 3696-3706.

Waugh, R., Rostoks, N., MacKenzie, K., Milne, L., Svensson, J.T., Bhat, P., Stein, N., Varshney, R.K., Marshall, D., Graner, A. & Close, T.J. 2006. Recent history of artificial outcrossing facilitates whole-genome association mapping in elite inbred crop varieties. Proceedings of the National Academy of Sciences USA 103, 18656-18661.

Winfield, M., Lloyd, D., Griffiths, W., Bradshaw, J.E., Muir, D., Nevison, I.M. & Bryan, G.J. 2005. Assessing organoleptic attributes of Solanum tuberosum and S. phureja potatoes. Aspects of Applied Biology 76, 127-135.

Woodhead, M., Russell, J.R., Squirrell, J., Habeshaw, D., MacKenzie, K., Gale, M.D. & Powell, W. 2005. Comparative analysis of population genetic structure in Athyrium distentifolium (Pteridophyta) using AFLPs and SSRs from anonymous and transcribed gene regions. Molecular Ecology 14, 1681-1695.

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Wright, K.M., Wood, N., Roberts, A.G., Chapman, S., Boevink, P., MacKenzie, K. & Oparka, K.J. 2006. Targeting of the TMV movement protein to plasmodesmata requires the actin/ER network - evidence from FRAP. Traffic 8, 21-31.

3. ANIMAL HEALTH AND WELFARE Bailey, T.C., Carvalho, M.S., Lapa, T.M., Souza, W.V. & Brewer, M.J. 2005. Modeling of under-detection of cases in disease surveillance. Annals of Epidemiology 15, 335-343.

Bartley, P.M., Wright, S.E., Sales, J., Chianini, F., Buxton, D.A. & Innes, E.A. 2006. Long-term passage of tachyzoites in tissue culture can attenuate virulence of Neospora caninum in vivo. Parasitology 133, 421-432.

Fontaine, M.C., Baird, G.D., Connor, K.M., Rudge, K., Sales, J. & Donachie, W. 2006. Vaccination confers significant protection of sheep against infection with a virulent United Kingdom strain of Corynebacterium pseudotuberculosis. Vaccine 24, 5986-5996.

Halliday, J., Chase-Topping, M.E., Pearce, M.C., McKendrick, I.J., Allison, L., Fenlon, D., Low, J.C., Mellor, D.J., Gunn, G.J. & Woolhouse, M.E.J. 2006. Herd-level risk factors associated with the presence of Phage type 21/28 E.coli O157 on Scottish cattle farms. BioMed Central Microbiology 6, 99.

Johnson, D.R., Sales, J. & Matthews, J.B. 2005. Local cytokine responses in Dictyocaulus viviparus infection. Veterinary Parasitology 128, 309-318.

Judge, J., Kyriazakis, I., Greig, A., Allcroft, D.J. & Hutchings, M.R. 2005. Clustering of Mycobacterium avium subsp paratuberculosis in rabbits and the environment: how hot is a hot spot? Applied and Environmental Microbiology 71, 6033-6038.

Livingstone, M., Entrican, G., Wattegedera, S., Buxton, D.A., McKendrick, I.J. & Longbottom, D. 2005. Antibody responses to recombinant protein fragments of the Major Outer Membrane Protein and Polymorphic Outer Membrane Protein POMP90 in Chlamydophila abortus infected pregnant sheep. Clinical and Diagnostic Laboratory Immunology 12, 770-777.

Logue, D.N., Offer, J.E. & Brocklehurst, S. 2006. Animal health impacts of early lactation management: the effect on lameness. UK Vet Livestock 11, 30-37.

Low, A.S., Dziva, F., Torres, A.G., Martinez, J.L., Rosser, T., Naylor, S.W., Spears, K.J., Holden, N., Mahajan, A., Findlay, J., Sales, J., Smith, D.G.E., Low, J.C., Stevens, M.P. & Gally, D.L. 2006. Cloning, expression and characterisation of a fimbrial operon, F9, from enterohaemorrhagic Escherichia coli O157:H7. Infection and Immunity 74, 2233-44.

Low, J.C., McKendrick, I.J., McKechnie, C., Fenlon, D., Naylor, S.W., Currie, C., Smith, D.G.E., Allison, L. & Gally, D.L. 2005. Rectal carriage of enterohemorrhagic Escherichia coli O157 in slaughtered cattle. Applied and Environmental Microbiology 71, 93-97.

Matthews, L., McKendrick, I.J., Ternent, H., Gunn, G.J. & Woolhouse, M.E.J. 2006. Super-shedding cattle and the transmission dynamics of Escherichia coli O157. Epidemiology and Infection 134, 131-142.

Milne, E., Crawshaw, W.M., Brocklehurst, S., Wright, S.E., Maley, S.W. & Innes, E.A. 2006. Associations between Neospora caninum specific antibodies in serum and milk in two dairy herds in Scotland. Preventive Veterinary Medicine 77, 31-47.

Navajas, E., Glasbey, C.A., McLean, K.A., Fisher, A.V., Charteris, A.J.L., Lambe, N.R., Bunger, L. & Simm, G. 2006. In vivo measurements of muscle volume by automatic image analysis of spiral computed tomography scans. Animal Science 82, 545-553.

Pearce, M.C., Evans, J., McKendrick, I.J., Smith, A.W., Knight, H.I., Mellor, D.J., Woolhouse, M.E.J., Gunn, G.J. & Low, J.C. 2006. Prevalence and virulence factors of E. coli serogroups O26, O103, O111 and O145 shed by store and finishing cattle in Scotland. Applied and Environmental Microbiology 72, 653-659.

Rhind, S.M., Kyle, C.E., Riach, D.J. & Duff, E.I. 2006. Effects of nutrition on hormone profiles and patterns of deiodinase activity in the skin and associated patterns of hair follicle activity and moult in cashmere goats. Animal Science 82, 723-730.

Rhind, S.M., Kyle, C.E., Telfer, G., Duff, E.I. & Smith, A. 2005. Alkyl phenols and diethylhexyl phthalate in tissues of sheep grazing pastures fertilised with sewage sludge or inorganic fertiliser. Environmental Health Perspectives 113, 447-453.

Sibbald, A.M., Elston, D.A., Smith, D.J.F. & Erhard, H.W. 2005. A method for assessing the relative sociability of individuals within groups: an example with grazing sheep. Applied Animal Behaviour Science 91, 57-73.

Rodger, S.M., Maley, S.W., Wright, S.E., Mackellar, A., Wesley, F., Sales, J. & Buxton, D.A. 2006. Role of endogenous transplacental transmission in toxoplasmosis in sheep. Veterinary Record 159, 768-772.

Rutherford, K.M.D., Haskell, M.J., Glasbey, C.A. & Lawrence, A.B. 2006. The responses of growing pigs to a chronic-intermittent stress treatment. Physiology and Behaviour 89, 670-680.

Savory, C.J., Kostal, L. & Nevison, I.M. 2006. Circadian variation in heart rate, blood pressure, body temperature and EEG of immature broiler breeder chickens in restricted-fed and ad-libitum-fed stakes. British Poultry Science 47, 599-606.

Scantlebury, M., Harris, S., Allcroft, D.J. & Hutchings, M.R. 2006. Individual trade-offs between nutrition and risk of interspecific transmission of disease by grazing: cows, badger latrines and bovine tuberculosis. Behaviour 143, 141-158.

Toft, N., Innocent, G.T., McKendrick, I.J., Ternent, H., Gunn, G.J. & Reid, S. 2005. Spatial distribution of Escherichia coli O157 positive farms in Scotland. Preventive Veterinary Medicine 71, 45-56.

Urquhart, K.A. & McKendrick, I.J. 2006. Prevalence of "head shooting" and the properties of the wounds in culled wild Scottish red deer. Veterinary Record 159, 75-79.

4. ECOLOGy AND ENVIRONMENTAL SCIENCEAugustin, N.H., McNicol, J.W. & Marriott, C.A. 2006. Using the truncated Auto-Poisson model for spatially correlated counts of vegetation. Journal of Agricultural, Biological, and Environmental Statistics 11, 1-23.

Barthram, G.T., Duff, E.I., Elston, D.A., Griffiths, J.H., Common, T.G. & Marriott, C.A. 2005. Frequency distributions of sward height under sheep grazing. Grass and Forage Science 60, 4-16.

Barthram, G.T., Elston, D.A., Griffiths, J.H., Bolton, G.R. & Wright, G.G. 2006. Within-year variation in the vegetative spread of five temperate grasses. Journal of Vegetation Science 17, 315-322.

Barthram, G.T., Elston, D.A. & Mullins, C.E. 2005. The physical resistance of grass patches to invasion. Plant Ecology 176, 79-85.

Bayfield, N.G., Conroy, J., Birnie, R.V., Geddes, A., Midgley, J.L., Shucksmith, M.D. & Elston, D.A. 2005. Current awareness, use and perceived priorities for rural databases in Scotland. Land Use Policy 22, 153-162.

Birnie, R.V., Geddes, A., Bayfield, N.G., Midgley, J.L., Shucksmith, M.D. & Elston, D.A. 2005. Improving the rural data infrastructure of Scotland: an overview. Land Use Policy 22, 145-152.

Brown, L.E., Hannah, D.M., Milner, A.M., Soulsby, C., Hodson, A.J. & Brewer, M.J. 2006. Water source dynamics in a glacierized alpine catchment (Taillon-Gabiétous, French Pyrénées). Water Resources Research 42, W08404.

Bruneau, P.M.C., Davidson, D.A., Grieve, I.C., Young, I.M. & Nunan, N.M. 2005. The effects of soil horizons and faunal excrement on bacterial distribution in the surface horizons of an upland grassland soil. FEMS Microbiology Ecology.

Bruneau, P.M.C., Davidson, D.A., Grieve, I.C., Young, I.M. & Nunan, N.M. 2005. The effects of soil horizons and faunal excrement on bacterial distribution in an upland grassland soil. FEMS Microbiology Ecology 52, 139-144.

Duncan, A.J., Ginane, C., Elston, D.A., Kunaver, A. & Gordon, I.J. 2006. How do herbivores trade-off the positive and negative consequences of diet selection decisions? Animal Behaviour 71, 93-99.

Duncan, A.J., Reid, S.A., Thoss, V. & Elston, D.A. 2005. Browse selection in response to simulated seasonal change in diet quality through post-ingestive effects. Journal of Chemical Ecology 31, 729-744.

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Erhard, H.W., Elston, D.A. & Davidson, G.C. 2006. Habituation and extinction in an approach-avoidance test: an example with sheep. Applied Animal Behaviour Science 99, 132-144.

Evans, D.M., Redpath, S., Elston, D.A., Evans, S.A., Mitchell, R.J. & Dennis, P. 2006. To graze or not to graze? Sheep, voles, forestry and nature conservation in the British uplands. Journal of Applied Ecology 43, 499-505.

Evans, D.M., Redpath, S., Evans, S.A., Elston, D.A. & Dennis, P. 2005. Livestock grazing affects the egg size of an insectivorous passerine. Biology Letters 1, 322-325.

Evans, D.M., Redpath, S., Evans, S.A., Gardner, C.J., Elston, D.A., Dennis, P. & Pakeman, R.J. 2006. Low intensity, mixed livestock grazing improves the breeding abundance of a common insectivorous passerine. Biology Letters 2, 636-638.

Gimona, A. & Brewer, M.J. 2006. Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in Scotland. Journal of Animal Ecology 75, 1140-1146.

Ginane, C., Duncan, A.J., Young, S.A., Elston, D.A. & Gordon, I.J. 2005. Herbivore diet selection in response to simulated variation in nutrient rewards and plant secondary compounds. Animal Behaviour 69, 541-550.

Green, J.J., Dawson, L.A., Proctor, J., Duff, E.I. & Elston, D.A. 2005. Fine root dynamics in a tropical rain forest is influenced by rainfall. Plant and Soil 276, 23-32.

Hendry, A.P., Grant, P.R., Grant, B.R., Ford, H.A., Brewer, M.J. & Podos, J. 2006. Possible human impacts on adaptive radiation: beak size bimodality in Darwin's finches. Proceedings of the Royal Society B: Biological Sciences 273, 1887-1894.

Iason, G.R., Lennon, J.J., Pakeman, R.J., Thoss, V., Beaton, J.K., Sim, D.A. & Elston, D.A. 2005. Does chemical composition of individual Scots pine trees determine the biodiversity of their associated ground vegetation? Ecology Letters 8, 364-369.

Lewis, S., Wanless, S., Elston, D.A., Schultz, M.D., Mackley, E., du Toit, M., Underhill, J.G. & Harris, M.P. (2006). Determinants of quality in a long lived colonial species. Journal of Animal Ecology 75, 1304-1312.

MacMillan, D., Leinhoop, N., Potts, J.M. & Philip, L. 2005. New approaches to valuing environmental benefits using contingent valuation. In Environment, Information and Consumer Behaviour, Eds. C.S. Russell and S. Krarup, 249-262. Edward Elgar, Cheltenham.

Midgley, J.L., Shucksmith, M.D., Birnie, R.V., Geddes, A., Bayfield, N.G. & Elston, D.A. 2005. Rural development policy and community data needs in Scotland. Land Use Policy 22, 163-174.

Nunan, N.M., Daniell, T.J., Singh, B.K., Papert, A., McNicol, J.W. & Prosser, J.I. 2005. Links between plant and rhizoplane bacterial communities in grassland soils, characterized using molecular techniques. Applied and Environmental Microbiology 71, 6748-6792.

Nunan, N.M., Ritz, K., Rivers, M., Feeney, D.S. & Young, I.M. 2006. Investigating microbial micro-habitat structure using X-ray computed tomography. Geoderma 133, 398-407.

Nunan, N.M., Singh, B.K., Reid, E.J., Ord, B.G., Papert, A., Squires, J., Prosser, J.I., Wheatley, R.E., McNicol, J.W. & Millard, P. 2006. Sheep-urine-induced changes in soil microbial community structure. FEMS Microbiology Ecology 56, 310-320.

Nussey, D.H., Clutton-Brock, T.H., Elston, D.A., Albon, S.D. & Kruuk, L.E.B. 2005. Phenotypic plasticity in a maternal trait in red deer. Journal of Animal Ecology 74, 387-396.

Redpath, S., Mougeot, F., Leckie, F.M., Elston, D.A. & Hudson, P.J. 2006. Testing the role of parasites in driving the cyclic population dynamics of a gamebird. Ecology Letters 9, 410-418.

Roumet, C., Picon-Cochard, C., Dawson, L.A., Joffre, R., Mayes, R.W., Blanchard, A. & Brewer, M.J. 2006. Quantifying species composition in root mixtures using two methods: NIR spectroscopy and plant wax markers. New Phytologist 170, 631-638.

Sang, N.S., Birnie, R.V., Geddes, A., Bayfield, N.G., Midgley, J.L., Shucksmith, M.D. & Elston, D.A. 2005. Improving the rural data infrastructure: the problem of addressable spatial units in a rural context. Land Use Policy 22, 175-186.

Settele, J., Hammen, V., Hulme, P., Karlson, U., Klotz, S., Kotarac, M., Kunin, W., Marion, G., O'Connor, M., Petanidou, T., Peterson, K., Potts, S., Pritchard, H., Pysek, P., Rounsevell, M., Spangenberg, J., Steffan-Dewenter, I., Sykes, M., Vighi, M., Zobel, M. & Kuhn, I. 2005. ALARM:Assessing LArge-scale environmental Risks for biodiversity with tested Methods. GAIA - Ecological Perspectives in Science, Humanities, and Economics 14, 69-72.

Singh, B.K., Munro, S., Reid, E.J., Ord, B.G., Potts, J.M., Paterson, E. & Millard, P. 2006. Investigating microbial community structure in soils by physiological, biochemical and molecular fingerprinting methods. European Journal of Soil Science 57, 72-82.

Stien, A., Bjorn, P.A., Heuch, P.A. & Elston, D.A. 2005. Population dynamics of salmon lice Lepeophtheirus salmonis on Atlantic salmon and sea trout. Marine Ecology Progress Series 290, 263-275.

5. HUMAN HEALTH AND NUTRITIONAndersen, H.S., Gambling, L., Holtrop, G. & McArdle, H.J. 2006. Maternal iron deficiency identifies critical windows for growth and cardiovascular development in the rat postimplantation embryo. Journal of Nutrition 136, 1171-1177.

Belenguer, A., Duncan, S.H., Calder, A.G., Holtrop, G., Louis, P., Lobley, G.E. & Flint, H.J. 2006. Two routes of metabolic cross-feeding between bifidobacteria and butyrate producing anaerobes from the human gut. Applied and Environmental Microbiology 72, 3593-3599.

Drew, J.E., Padidar, S., Horgan, G.W., Duthie, G.G., Russell, W., Reid, M., Duncan, G. & Rucklidge, G. 2006. Salicylate modulates oxidative stress in the rat colon: a proteomic approach. Biochemical Pharmacology 72, 204-216.

Krol, E., Redman, P., Thompson, P.J., Williams, R., Mayer, C-D., Mercer, J. & Speakman, J. 2005. Effect of photoperiod on body mass, food intake and body composition in the field vole, Microtus agrestis. Journal of Experimental Biology 208, 571-84.

Lapierre, H., Pacheco, D., Berthiaume, R., Ouellet, D.R., Schwab, C., Dubreuil, P., Holtrop, G. & Lobley, G.E. 2006. What is the true supply of amino acids for a dairy cow? Journal of Dairy Science 89 (E. Suppl.), E1-E14.

Lobley, G.E., Wester, T.J., Holtrop, G., Parker, D., Dibner, J.J. & Vazquez-Anon, M. 2006. Absorption and digestive tract metabolism of 2-hydroxy-4-thiomethylbutyrate (HMTBA) in lambs. Journal of Dairy Science 89, 3508-3521.

Mazlan, N., Horgan, G.W. & Stubbs, J. 2006. Energy density and weight of food effect short-term caloric compensation in men. Physiology and Behaviour 87, 679-686.

O'Kennedy, N., Crosbie, L.C., Whelan, S., Luther, V., Horgan, G.W., Broom, J.I., Webb, D.J. & Dutta-Roy, A. 2006. Effects of tomato extract on platelet function: a double-blinded crossover study in healthy humans. American Journal of Clinical Nutrition 84, 561-569.

Özbilgin, H., Ferro, R.S.T., Robertson, J.H.B., Holtrop, G. & Kynoch, R.J. 2006. The seasonal variation in trawl cod-end selection of haddock in the northern North Sea. ICES Journal of Marine Science 63, 737-748.

Scott, K.P., Martin, J.C., Campbell, G., Mayer, C-D. & Flint, H.J. 2006. Whole-genome transcription profiling reveals genes up-regulated by growth on fucose in the human gut bacterium. Journal of Bacteriology 188, 4340-4349.

Taylor, B.R., Younie, D., Matheson, S., Coutts, M., Mayer, C-D., Watson, C.A. & Walker, R.L. 2006. Output and sustainability of organic ley/arable crop rotations at two sites in northern Scotland. Journal of Agricultural Science 435-447.

Whybrow, S., Harrison, C.L.S., Mayer, C-D. & Stubbs, J. 2006. Effects of added fruits and vegetables on dietary intakes and body weight in Scottish adults. British Journal of Nutrition 95, 496-503.

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Bierman, S. 2005. Incorporating probabilities of non-detection in species presence/absence models. Invited seminar, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews.

Bierman, S., Kühn, I., Durka, W., Klotz, S. & Lambin, X. 2006. The uses of Bayesian image restoration techniques in the analysis of species atlas survey data with spatially varying non-detection probabilities. Invited seminar, School of Mathematics, University of Edinburgh.

Bierman, S., Sims, M. & Roberts, A.M.I. 2005. Recently developed statistical tools to investigate the relationship between the climate and species performance. Poster presentation, Scottish Biodiversity Forum Meeting, Royal Botanic Garden Edinburgh.

Brewer, M.J. 2005. Variable smoothing in Bayesian spatial modelling. Contributed talk, 25th European Meeting of Statisticians, Oslo University, Oslo, Norway.

Brewer, M.J. 2006. Variable smoothing in Bayesian intrinsic autoregressions. Poster presentation, Bayesian Inference in Complex Stochastic Systems, University of Warwick.

Brewer, M.J. 2006. Variable smoothing in Bayesian spatial modelling. Contributed talk, International Workshop on Spatio-temporal Modelling (METMA3), Pamplona, Spain.

Brewer, M.J. 2006. Variable smoothing in Bayesian spatial modelling. Contributed talk, IceBUGS 2006, Tvärminne Zoological Station, Hanko, Finland.

Brewer, M.J. 2006. Variable smoothing in Bayesian spatial modelling. Invited seminar, Department of Statistics, University of Leeds.

Butler, A. & Glasbey, C.A. 2006. A latent Gaussian model for compositional data with structural zeroes. Contributed talk, Royal Statistical Society International Conference, Belfast.

Butler, A. & Glasbey, C.A. 2006. Approximate inference for a latent Gaussian model of compositional data with structural zeroes. Poster presentation, Recent Advances in Monte Carlo Based Inference, University of Cambridge.

Butler, A. & Tawn, J.A. 2005. Conditional extremes of a Markov chain. Invited seminar, School of Mathematics, Edinburgh University.

Butler, A., Bierman, S. & Marion, G. 2005. The ALARM project: ecological risk assessment at the pan-European scale. Poster presentation, Bayesian Methods for Population Ecology Workshop, University of Cambridge.

Butler, A., Heffernan, J.E., Tawn, J.A. & Flather, R.A. 2005. Extreme value theory, climate change and coastal flood risk. Invited seminar, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews.

Butler, A., Heffernan, J.E., Tawn, J.A. & Flather, R.A. 2005. Spatial and temporal models for synthetic oceanographic extremes. Contributed talk, 25th European Meeting of Statisticians, Oslo, Norway.

Butler, A., Heffernan, J.E., Tawn, J.A. & Flather, R.A. 2006. Analysing trends in the magnitude & frequency of extremes events. Invited seminar, School of Mathematical and Computer Sciences, Heriot-Watt University.

Butler, A., Heffernan, J.E., Tawn, J.A., Flather, R.A. & Horsburgh, K.J. 2005. Extreme value theory, climate change and coastal flood risk. Invited talk, Royal Statistical Society Highlands Local Group, Aberdeen.

Elston, D.A. 2006. Challenges and advances in ecology and the environment. Invited discussant, Bath Institute for Complex Systems Conference: Stochastic Complex Systems, University of Bath.

Elston, D.A. 2006. Monitoring, estimating trends and modelling species-environment interactions: essential activities for understanding and predicting changes in components of biodiversity. Invited talk, The International Environmetrics Society Conference 2006, Kalmar, Sweden.

Appendix 3 Conference Presentations, Lectures & Seminars

Elston, D.A. 2006. Some examples in which random effect modelling benefits ecological research. Invited seminar, School of Biological Sciences, University of Aberdeen.

Glasbey, C.A. & Allcroft, D.J. 2005. A STARMA model for solar radiation in a microclimate. Invited talk, Workshop on Recent Advances in Modelling Spatio-Temporal Data, Southampton.

Glasbey, C.A. & Khondoker, M.R. 2005. Statistical models to correct for saturation effects in cDNA microarrays. Invited talk, Biometrics Multi-Region Conference, Leicester.

Glasbey, C.A. & Khondoker, M.R. 2005. Statistical models to correct for saturation effects in cDNA microarrays. Invited seminar, Stochastic Centre, Chalmers University, Gothenburg, Sweden.

Glasbey, C.A. 2005. How to segment 3-D images and analyse 1-D electrophoresis gels. Invited seminar, Department of Mathematics, University of Bristol.

Glasbey, C.A. 2005. How to segment 3-D images and analyse 1-D electrophoresis gels. Invited talk, Royal Statistical Society Medical Section Meeting, London.

Glasbey, C.A. 2005. Image warping and segmentation using generalisations of dynamic programming. Invited talk, Australian Mathematical Sciences Institute Symposium on Recent Advances in Biostatistics, Bioinformatics and MCMC, Sydney, Australia.

Glasbey, C.A. 2005. Image warping and segmentation using generalisations of dynamic programming. Invited talk, 25th European Meeting of Statisticians, Oslo, Norway.

Glasbey, C.A. 2005. Image warping and segmentation using generalisations of dynamic programming. Invited talk, Statistical Society of Australia: Queensland Branch, Brisbane, Australia.

Glasbey, C.A. 2005. Problems at the interface between statistics, biomathematics and bioinformatics. Invited talk, Workshop on The Mathematics of Complex Systems, Bath Institute for Complex Systems, University of Bath.

Glasbey, C.A. 2005. Seeing is believing? Inaugural Professorial Lecture, SAC, Edinburgh.

Glasbey, C.A. 2005. Seeing is believing? Invited talk, Norwegian Statistical Society, Oslo, Norway.

Glasbey, C.A. 2006. A statistical approach to image warping. Invited talk, 26th European Meeting of Statisticians, 49-50, Torun, Poland.

Glasbey, C.A. 2006. Functional regression to combine multiple laser scans of cDNA microarrays. Contributed talk, Royal Statistical Society International Conference, Belfast.

Glasbey, C.A. 2006. Image restoration and segmentation using generalisations of dynamic programming. Invited talk, French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, Skagen, Denmark.

Glasbey, C.A. 2006. Image restoration and segmentation using generalisations of dynamic programming. Invited seminar, Stochastic Centre, Chalmers University, Gothenburg, Sweden.

Glasbey, C.A. 2006. Image restoration, segmentation and warping using generalisations of dynamic programming. Invited talk, Gregynog Statistical Conference.

Glasbey, C.A. 2006. The challenge of bioinformatics. Invited talk, Joint Meeting of Rothamsted Research and the British and Irish meeting of the International Biometric Society, Rothamsted.

Glasbey, C.A. 2006. Warping of electrophoresis gels using generalisations of dynamic programming. Invited talk, Workshop on Statistics for Gene and Protein Expression, 20, Gothenburg, Sweden.

Hackett, C.A. & Bradshaw, J.E. 2005. QTL mapping in autotetraploid populations. Poster presentation, International Biometric Society Multiregional Conference, Leicester.

Hackett, C.A. & Brennan, R.M. 2006. Linkage analysis in a mixed population of blackcurrant. Invited seminar, Statistical Society of Australia, Queensland Branch, Queensland Bioscience Precinct, Brisbane, Australia.

Hackett, C.A. 2006. Linkage analysis and QTL mapping in autotetraploid species. Invited talk, Polyploid QTL Brainstorming Workshop, Queensland Bioscience Precinct, Brisbane, Australia.

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Hackett, C.A. 2006. Some experiences of linkage analysis and QTL mapping with outbreeding plant species. Invited Seminar, School of Biosciences, University of Birmingham.

Hackett, C.A., Brennan, R.M., Jorgenson, L. & Russell, J.R. 2006. Linkage analysis and QTL mapping in blackcurrant (Ribes nigrum L.) using a population of full sib and selfed offspring. Contributed talk, Eucarpia Biometrics in Plant Breeding, Zagreb, Croatia.

Holtrop, G. & Horgan, G.W. 2006. Risk assessment of shellfish toxin testing schemes 2001-2004 and development of improved schemes. Invited presentation, Food Standards Agency Scotland, Fish & Shellfish Research Programme Review, Edinburgh.

Holtrop, G., Duncan, S.H., Belenguer, A. & Johnstone, A.M. 2006. Bayesian networks for exploring relationships between faecal bacteria and their metabolites. Poster presentation, Bayesian Inference in Complex Stochastic Systems, Warwick.

Horgan, G.W. 2005. Digital image analysis in plant research. Invited talk, Workshop on Optimised Usage of Experimental Information for Plant Research and Breeding Decisions, Poznań, Poland.

Horgan, G.W. 2006. Digital image analysis in biological research. Invited talk, 3rd Workshop on Lasers and Optics in Agricultural Research, University of Lavras, Brazil.

Horgan, G.W., Roberts, A.M.I. & Green, F.N. 2005. Landmark and other methods for assessing plant part shape. Contributed talk, 24th Leeds Annual Statistical Research Workshop (Quantitative Biology, Shape Analysis, and Wavelets), University of Leeds.

Husmeier, D. 2005. Detecting mosaic structures in DNA sequence alignments. Invited talk, Workshop on Statistics in Genomics and Proteomics, Estoril, Portugal.

Husmeier, D. 2005. Detecting mosaic structures in DNA sequence alignments. Contributed talk, Workshop on Statistics in Genomics and Proteomics, Monte Estoril, Portugal.

Husmeier, D. 2005. Learning local gene interaction networks from noisy gene expression data with probabilistic graphical models. Invited talk, Computational Methods in Systems Biology, Edinburgh.CHECK

Husmeier, D. 2005. Phylogenetic factorial hidden Markov models for detecting mosaic structures in DNA sequence alignments. Poster presentation, 13th International Conference on Intelligent Systems for Molecular Biology, Detroit, USA.

Husmeier, D. 2005. Predicting mosaic structures in DNA sequence alignments. Invited talk, Royal Statistical Society North Eastern Local Group, Newcastle.

Husmeier, D. 2006. Bayesian methods for detecting recombination in DNA sequence alignments. Invited seminar, Warwick Systems Biology Centre, University of Warwick.

Husmeier, D. 2006. Segmenting DNA sequence alignments with Bayesian factorial hidden Markov models. Invited talk, Workshop on Statistical Modelling of Complex Systems, Munich, Germany.

Khondoker, M.R., Glasbey, C.A. & Worton, B.J. 2006. A comparison of parametric and nonparametric methods for normalising microarray data. Contributed talk, 23rd International Biometric Conference, Montreal, Canada.

Lehrach, W.P., Husmeier, D. & Williams, C.K.I. 2005. A regularised discriminative model for the prediction of protein-peptide interactions. Poster presentation, 4th European Conference on Computational Biology, Madrid, Spain.

Lehrach, W.P., Husmeier, D. & Williams, C.K.I. 2006. A regularised discrimative model for the prediction of protein-peptide interactions. Contributed talk, Mathematical and Statistical Aspects of Molecular Biology, Dublin, Ireland.

Marion, G. 2006. Linking movement and vegetation data via parameter estimation in a model of foraging behaviour. Invited talk, Where are we with GPS?, Scottish Agricultural College, Auchincruive.

Marion, G. 2006. Spatio-temporal stochastic models for the spread of invasive species & infectious disease. Invited talk, UK PopNet-sponsored Workshop "Towards an Integrated Assessment of the Environmental Risks Posed by Non-native Species, GMOs and Wildlife Diseases", Douneside House, Tarland, Aberdeenshire.

Marion, G. 2006. Statistical methods for process-based models. Invited seminar, School of Mathematics and Statistics, University of Newcastle.

Marion, G. (2005) Mathematical and statistical techniques for understanding complex systems. Invited talk, Horizons in Livestock Sciences: Redesigning Animal Agriculture. Gold Coast, Queensland, Australia, 2005.

Marion, G. (2005) Mathematical and statistical methods for assessing risks to biodiversity using large-scale data. Invited seminar, School of Geosciences, University of Edinburgh, 2005.

Mayer, C-D. 2005. Detecting heterogenous variance-covariance structures in gene expression data. Contributed talk, 15th Annual meeting of Mathematical and Statistical Aspects of Molecular Biology, Rothamsted Research, Harpenden.

Mayer, C-D. 2006. Detecting heterogeneous variance-covariance structures in gene expression data. Contributed talk, Statistics for Gene and Protein Expression, Stochastic Centre, Gothenburg, Sweden.

McKendrick, I.J. 2006. E. coli O157: Statistics and mathematical modelling to integrate epidemiology and clinical trials. Invited talk, Joint Meeting of the Royal Statistical Society Highlands Local Group and Highlands Biostatistics Group, SAC Inverness.

McKendrick, I.J., Bennett, M. & Fitzpatrick, J.L. 2006. Analysis of censored, ordered responses from a U.K. sheep health study. Poster presentation, Society for Veterinary Epidemiology and Preventive Medicine Annual Conference, University of Exeter.

Nevison, I.M., Horgan, G.W., Green, F.N., Roberts, A.M.I., Campbell, G.D. & Mann, A.D. 2006. The use of image analysis in parsnip DUS testing. Contributed talk, 8th Working Seminar on Statistical Methods, Poznan, Poland.

Roberts, A.M.I. 2006. Smoothing methods for phenology. Poster presentation, Royal Statistical Society International Conference, Belfast.

Roberts, A.M.I. 2006. Statistical methods for analysing phenological data. Invited talk, Association of Applied Biologists Meeting on Phenological Change, Causes and Consequences, Royal Botanic Garden Edinburgh.

Roberts, A.M.I., Theobald, C.M. & McNeil, M. 2005. A Bayesian calibration model to predict fungal contamination levels in wheat seed based on a PCR assay. Poster presentation, International Biometric Society Multi-Region Conference, University of Leicester.

Sales, J. 2006. Microarray design. Contributed talk, Bioconductor workshop, Bressanone, Italy.

Sales, J., Zahra, R. & Leach, D. 2005. Estimation of mutation rates of tri-nucleotide repeats in Escherichia coli. Poster presentation, Workshop on Statistics in Genomics and Proteomics, Monte Estoril, Portugal.

Theobald, C.M. & Roberts, A.M.I. 2006. Bayesian calibration for predicting fungal contamination levels in seed based on a quantitative PCR assay. Invited seminar, Department of Agronomy, Iowa State University, USA.

Theobald, C.M. 2006. Bayesian solutions to some decision problems in crop management and variety choice. Invited Lecture, Plant Breeding Lecture Series, Iowa State University, USA.

Theobald, C.M., Roberts, A.M.I. & Talbot, M. 2005. Bayesian solutions to some decision problems in agriculture. Contributed talk, 3rd International Conference of the Eastern Mediterranean Region of the International Biometric Society, Corfu, Greece.

Walker, D.M. 2005. System identification using constrained Kalman filters. Contributed talk, International Symposium on Nonlinear Theory and its Application, Bruges, Belgium.

Werhli, A.V., Grzegorczyk, M., Husmeier, D. & Urfer, W. 2006. Comparative evaluation of the accuracy of reverse engineering. Contributed talk, Mathematical and Statistical Aspects of Molecular Biology, Dublin, Ireland.

Werhli, A.V., Grzegorczyk, M., Husmeier, D. & Urfer, W. 2006. Comparative evaluation of the accuracy of reverse engineering gene regulatory networks with various machine learning methods. Poster presentation, 14th International Conference on Intelligent Systems for Molecular Biology, Fortaleza, Brazil.

Appendix 4External Committees 2005-2007

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M.J. Brewer Associate Editor, Journal of Statistical Computation and Simulation

Associate Editor, Biometrics

A. Butler Member, Royal Statistical Society Edinburgh Local Group Committee

S. Brocklehurst HRI Ethical Review Committee

SAC Auchincruive Ethical Review Committee

D.A. Elston Chair, Royal Statistical Society Highlands Local Group Committee

Associate Editor, Applied Statistics

Scientific and Technical Advisory Group, UK Environmental Change Network

Advisory Committee for University of York MRes Course ‘Mathematics in the Living Environment’

C.A. Glasbey Editor, Applied Statistics.

Council of International Biometric Society

Committee of British and Irish Region of International Biometric Society

Review Committee for INRA-MIA, Paris

EPSRC Peer Review College

Committee of Professors of Statistics (COPS)

International Program Committee for IBC2006 - International Biometric Conference

Scientific Programme Committee for RSS2006 - Royal Statistical Society Conference

Programme Committee for French-Danish Workshops on Spatial Statistics and Image Analysis in Biology

Scientific Committee for Workshop on Statistics for Gene and Protein Expression, Gothenburg, Sweden

Conference Advisory Committee of International Biometric Society (IBS)

Royal Statistical Society Publications Network of Advisors

SABRI Postgraduate Liaison Officers Committee

C.A. Hackett Associate Editor, Theoretical and Applied Genetics

Scientific Committee, Eucarpia Section Biometrics in Plant Breeding XIIIth Meeting, Zagreb

Guest Editor, Euphytica for proceedings of above meeting

RSS Study Group on Statistical Bioinformatics and Genetics

G. Holtrop Editorial Board of British Journal of Nutrition

RRI Ethical Review Committee

G.W. Horgan Member, Royal Statistical Society Highlands Local Group Committee

Expert Panel for Food Standards Agency Low Income Diet and Nutrition Survey

RRI Ethical Review Committee

G.R. Marion Module Head, Project Coordination Committee EU FP6 Project: ALARM

C-D. Mayer RRI Ethical Review Committee

Secretary, Royal Statistical Society Highlands Local Group Committee

I.J. McKendrick MRI Ethical Review Committee

J.W. McNicol Senior Statistical Editor, Annals of Applied Biology

I.M. Nevison SAC Edinburgh Ethical Review Committee

J.M. Potts Member, Royal Statistical Society Highlands Local Group Committee

A.M.I. Roberts Chairman, Inter-departmental Statisticians’ Group for National List and Seeds Committee

Potato Experts Group, UK Plant Varieties and Seeds Committee

Vegetable DUS Group, UK Plant Varieties and Seeds Committee

UPOV Technical Working Party on Automation and Computer Programs

SASA Ethical Review Committee

J. Sales Institute of Animal Health Neuropathogenesis Unit Ethical Review Committee

F. Wright Program Committee Member, Intelligent Systems for Molecular Biology 2004- 2005

BBSRC / MRC Collaborative Computational Project 11 (CCP11) Bioinformatics Committee

Glossary of Organisational Acronyms

EDINBURGH

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ABERDEEN

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BBSRC Biotechnology and Biological Sciences Research Council

BioSS Biomathematics and Statistics Scotland

CEH Centre for Ecology and Hydrology

Defra Department of Environment, Food and Rural Affairs

EPSRC Engineering and Physical Sciences Research Council

EU European Union

FSA Food Standards Agency

HGCA Home-Grown Cereals Authority

HRI Hannah Research Institute

MLURI Macaulay Land Use Research Institute

MRC Medical Research Council

MRI Moredun Research Institute

MRP Main Research Provider (of SEERAD/RERAD)

NERC Natural Environment Research Council

INRA French National Institute for Agricultural Research

INRA-MIA Applied Mathematics and Computer Science Department of INRA

RBGE Royal Botanic Garden Edinburgh

RERAD The Scottish Government’s Rural and Environment Research and Analysis Directorate

RRI Rowett Research Institute

RSPB Royal Society for the Protection of Birds

SABRI Scottish Agricultural and Biological Research Institute

SAC Scottish Agricultural College

SASA Scottish Agricultural Science Agency

SCRI Scottish Crop Research Institute

SEERAD Scottish Executive Environment and Rural Affairs Department

SEPA Scottish Environment Protection Agency

SNIFFER Scotland and Northern Ireland Forum for Environmental Research

UPOV International Union for the Protection of New Varieties of Plants

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Acknowledgements

Design: John McNeill, Media Unlimited

Production Co-ordination: David Elston and Muriel Kirkwood

Photography: We thank, collectively, our collaborators in CEH, HRI, MRI, MLURI, RRI, SAC, SASA & SCRI Gary Baker, GB Photography

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