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Stat i s t i c s & M athemat ic s impr o ving A g r ic u lt u r e,the Envir onment, Food & Health
Biomathematics &Statistics ScotlandBiennial Report2011/2013
CONTENTS
Overview Director’s IntroductionResearch
Statistical Genomics & BioinformaticsProcess & Systems ModellingStatistical Methodology
Consultancy Advice & CollaborationPlant ScienceAnimal Health & WelfareEcology & Environmental ScienceHuman Health & Nutrition
Knowledge ExchangePostgraduate Research & TrainingTraining for ScientistsInformation Technology
Investing in our PeopleStaffStudentsManagement Group
Appendix 1 Selected Research Grants and Contracts
Appendix 2 Refereed Publications
Appendix 3 Conference Presentations, Lectures & Seminars
Appendix 4 External Committees
Glossary of Organisational AcronymsContact Points
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InsideCover
“to improve science and society through an understanding of variation, uncertainty and risk”
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 genomics and bioinformaticsprocess and systems modellingstatistical methodology
CONSULTANCY
KNOWLEDGE EXCHANGE
BioSS 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 welfareecology and environmental sciencehuman health and nutrition
BioSS 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 private sector. Key aspects of our programme of knowledge transfer include:
development of software productsdelivery of training courses for scientistssupervision of PhD students
Overview
1
in recognition of BioSS’s 25th Anniversary. This is a Latin square because every letter appears once in each row and once in each column. This particular Latin square has the unusual property that every letter is a horizontal neighbour to every other letter exactly twice, and every letter is a vertical neighbour to every other letter exactly twice. Latin squares with this property are called quasi-complete. This example is taken from Rosemary Bailey’s website http://www.maths.qmul.ac.uk/~rab/qcls.html: for more details see Freeman, G.H. (1981) Further results on quasi-complete Latin squares, JRSS B 43, 314-320 and Bailey,R.A. (1984) Quasi-complete Latin squares: construction and randomization. JRSS B 46, 323-334.
The composite image on the front cover follows a 5 by 5 Latin square pattern of the form below
Director’s Introduction
Dir
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Dear Reader,
It is my great pleasure to welcome you to the BioSS Biennial Report for 2011-13. Drawing this document together is always both a challenge and a privilege: the challenge is to do justice to my colleagues by describing the purpose and benefits of their work to a general readership; whilst the privilege is to portray just how much BioSS achieves, on so many different fronts. I do hope that, as you browse through this report, you will find something of particular interest to you. I have made a point of not attaching names to individual projects highlighted, as many of these projects have received direct input from multiple individuals and have been supported by many more: that BioSS is a collective endeavour can be seen from our re-accreditation with Investors in People Silver Status. Should you seek any further information, do feel free to contact me via [email protected].
This is the first Biennial Report that we have produced since the Scottish Crop Research Institute (SCRI) and the Macaulay Land Use Research Institute came together to form the James Hutton Institute on 1st April 2011. As a part of the former SCRI, BioSS is now a part of this new institute. Any uncertainties about how BioSS might relate to the new institute were quickly laid to rest by founding Director Professor Iain Gordon who regards the James Hutton Institute as guardian of BioSS for the benefit of all BioSS’s major stakeholders. The merging of two such long-established institutes, each with its own culture and working practices, has not been straightforward but substantial progress has been made, and I expect BioSS to continue to prosper under its new institutional arrangements.
The period covered by this report contains both organisational and professional milestones. Our organisational milestone is that, having been established on 1st April 1987, BioSS passed its 25th birthday, hence the five by five Latin square arrangement on the cover of this report. The professional milestone is that 2013 is the International Year of Statistics, which has received world wide support from over 2300 organisations. We took the strategic decision to celebrate both milestones in a single event, and were delighted that Professor Denise Lievesley from King’s College London accepted our invitation to give a guest lecture at the Royal Society of Edinburgh. Her lucid and stimulating lecture, entitled “Living statistics: the interplay between the development and application of statistical methods” was built around six general challenges faced by statisticians including the long-standing issue of the tension between pragmatism and purism.
There have been several changes in senior management which I would like to note. First, Dr Dirk Husmeier left his post as Leader of our Genomics and Statistical Bioinformatics Research Theme in October 2011 to take up the Chair of Statistics at the University of Glasgow vacated by Prof Mike Titterington’s retiral. Dirk is the third person to leave BioSS to take up a chair in a Scottish university, following in the footsteps of Profs Steve Buckland (St Andrews, 1993) and Gavin Gibson (Heriot Watt, 2000). We thank Dirk for his contributions to BioSS and wish him every success for the future. In his place we welcome his successor, Dr Ian Simpson, who joins BioSS on a shared appointment with the University of Edinburgh’s School of Informatics.
The second round of changes took place in August 2013, as Prof Chris Glasbey retired after working for 37 years in the A(F)RC Unit of Statistics and then BioSS. Chris has been Head of Research since the formation of BioSS, and has been highly influential in its development. Fortunately Chris is continuing to work with BioSS for a day a week: hence we both wish him well and look forward to continuing to draw on his expertise. Chris has been replaced as Head of Research by Dr Glenn Marion, and as Leader of our Statistical Methodology Research Theme on an Acting basis by Dr Adam Butler.
Looking ahead, 2014 will be an important year for BioSS on two counts. First, in February, we shall be hosting an external Review Panel, commissioned by the James Hutton Institute. Such reviews play an important role in the development of research organisations, providing a focus for internal reflection on past achievements and future directions. We look forward to welcoming Panel members, and to receiving their external perspectives on our activities and plans. Second, the financial year 2014/15 will be the fourth of our current five-year funding round from the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). During the year, RESAS will be conducting a high-level review of their research portfolio, in particular assessing progress made by two new types of initiatives: Centres of Expertise, to improve links between the research sector and public policy; and Strategic Partnerships, to boost research in sectors with high potential for commercial growth. The outcome of the RESAS review will be influential in determining the structures under which their Main Research Providers are funded from 2016/17 onwards.
As you read through this report, I hope you will appreciate that BioSS continues to make important quantitative contributions to a wide range of application areas, using established methods and both promoting and developing new approaches. Our achievements to date, allied with the increasing emphasis being placed on quantitative methods in all areas of science, leave BioSS well placed for the future.
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Development of more advanced statistical and computational methodologies is required to keep pace with new experimental technologies in molecular biology and genetics. Challenges to be met include dealing with increased volumes of data, improving the robustness of inference to noise in the data, and improving the models to make them biologically more realistic. Here we present five examples of how we have addressed these issues in BioSS.
Linkage analysis in tetraploid potato using dosage informationIn the past linkage maps have generally been constructed from binary data indicating the presence or absence of short sequences of DNA in parents and offspring. Modern technologies can give more quantitative measurements, for example the Illumina technology measures the ratio of fluorescence intensities for the two different alleles of a single nucleotide polymorphism site (SNP). In a tetraploid species, such as the cultivated potato, this ratio (referred to as the theta score) can be used to identify the five possible genotypes at a SNP: AAAA, AAAB, AABB, ABBB and BBBB. In a mapping population, the expected genotype frequencies among the offspring can be derived from the parental genotype dosages (the number of A or B alleles) for comparison with the observed frequencies. Many more SNPs provide useful information about genotype frequencies based on dosage than using presence/absence information alone. Likewise, recombination frequencies between pairs of SNPs can be estimated from their joint dosages with higher precision than just using presence/absence information, thus giving greater precision to the ordering of SNPs on a linkage map. By making use of the potential of dosage information we have increased the density and resolution of the potato linkage map, with more than 3800 SNP locations known. QTL mapping of the theta scores provided a confirmation of the location and dosage.
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 Genomics & BioinformaticsDevelopments in molecular genetics technologies are generating enormous quantities of data, often of new data types, allowing deeper studies of genomes and the relationship between genetics and biological function. Simultaneously, new computing technologies 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 & Systems ModellingMathematical modelling plays a key role in achieving many scientific objectives. BioSS aims to enhance this role 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 estimating risks in complex interacting systems. 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.
Discussing our research allows us to share experiences and helps generate new ideas.
The theta scores for SNP c1_10069 show five genotype classes, with the two parents (black and red stars) in the middle category. The offspring proportions are in the 1:8:18:8:1 ratio consistent with both parents having a double-duplex markers (AABB x AABB).
The top 20 cM of potato chromosome V. QTL mapping of the intensity ratio for SNP c2_23831 confirms its location at 6cM.
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Comparison of high throughput gene expression assays in barleyNext generation sequencing has revolutionized molecular biology in recent years by allowing scientists to sequence complete genomes far quicker and cheaper than previously. Applying this technology to RNA (RNAseq), usually reverse transcribed to cDNA, it also offers an interesting alternative to microarrays as a tool for transcriptomic analysis. As part of the International Barley Genome Sequencing Consortium (IBSC), the James Hutton Institute has conducted an RNAseq experiment comparing RNA sampled from the barley cultivar Morex at different parts and developmental stages of growing plants. BioSS has been involved in analysing these data and particularly in comparing them to microarray data obtained from the same samples.
One of the interesting issues here is which of the two technologies gives more reliable information when the expression of genes/transcripts is low. For this purpose we calculated the correlation between RNAseq and microarray measurements for each of 48000 genes that were measured in both datasets. We then subdivided the genes into 20 classes according to their overall intensity in either RNAseq or microarray samples and averaged the correlation values across all genes in a class. We found that correlations between the two technologies was higher when the classes were defined by the microarray data then when they were defined by the RNAseq data, suggesting that at least for this experiment measurements on the low end of the scale were more reliable for this new technology than they were for microarrays.
Rapid typing of E.coli isolates using proteomic spectra dataFoodborne pathogens, including E.coli O157 and related strains, remain a public health problem requiring typing of strains to investigate disease outbreaks. Conventional typing is slow, hence we have worked with bacteriologists at the Moredun Research Institute to explore the possibility of characterising strains within 24 hours using rapidly obtained proteomics spectra. Unfortunately, the raw spectra themselves cannot be used, being made up of a noisy background signal overlain with sharp but slightly misaligned peaks over a range of mass/charge ratios, these peaks representing the presence of individual proteins. High-quality pre-processing of the spectra is essential and involves a number of steps including smoothing, baseline correction, peak detection/alignment/binning, and normalisation. These processed spectra can be scored much more reliably as presence/absence of peaks. Relationships amongst
strains can then be derived by using the proteomic pairwise distances as data to produce a phylogenetic network. The relationships among strains in these networks are mostly hierarchical, but they do also allow for mosaic strains which can occur in bacterial populations. Initial application of our methods and software to study the relationships among 92 E.coli EHEC isolates indicates a good level of reproducibility, with six replicates taken on different days from a single biological sample all being correctly identified as similar to one another.
Agent based modeling of biological processesIn order to better understand biological processes we need to develop mathematical models of the underlying molecular systems that are able to integrate many different kinds of experimental data and handle the complexity and scale of their operating mechanisms. Dynamical models have traditionally been described using ordinary differential equations, but the size of many biological pathways and the combinatorial complexity of the interactions between members makes their use intractable. Recently a family of ‘rule-based’ agent languages have been developed that are sufficiently flexible to allow many different types of experimental data to be combined in a unified framework and make the modelling of large pathways possible.
In collaboration with researchers at the James Hutton Institute we have used one such agent based language, ‘Kappa’, to begin building generic models of plant pathogen interactions (PPIs). During infection, pathogen associated molecular patterns (PAMPs) are recognised by the host plant and lead to activation of the MAP kinase signaling pathway and expression of downstream immune resistance genes in the nucleus. By building models of PPIs we hope to identify potential targets for intervention to improve the ability of host plants to resist and respond to pathogens.
Sub-section of the conserved plant pathogen interaction pathway derived from KEGG illustrating the host response to bacterial pathogens mediated by the Microtubule associated protein kinase pathway (MEKK, MKK, MPK).
Genetic regulatory networks in the potato pathogen Dickeya The Dickeya genus of bacterial plant pathogens causes soft-rot on many crop plants including potatoes. There are currently no methods for preventing the Dickeya infection, which is becoming increasingly prevalent in Europe and causes considerable economic losses, hence it is important in to increase our understanding of the disease
In collaboration with the James Hutton Institute we are developing gene regulatory networks for Dickeya. We have used profile Hidden Markov Models (pHMMs) from the Superfamily and Pfam protein domain databases to identify the pool of transcription factors (TFs) present in Dickeya, and have screened Dickeya genomes using fast look-ahead filtration algorithms to identify possible transcription factor binding site locations for each available transcription factor. These TF to target gene associations have been used to construct the first global gene regulatory networks (GRNs) for Dickeya. We are currently using differential network analyses to explore the structural differences between the GRNs of different Dickeya isolates and using community based network
clustering methodologies and functional annotation to identify groups of genes that are associated with specific biological functions.
Comparison of condition specific network graphs can quantify the robustness and specificity of molecular interactions. Estimation of the relative contributions of biological pathways greatly improves the biological interpretation of the system.
Typical spectrum showing pre-processing options for baseline correction.
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Simulations of spatial meta-population models with density dependent dispersal illustrate a plausible mechanism for the perturbation effect:
(A) shows that for such a system with initially patchy distribution of infection between groups culling spreads infected individuals to new groups, which then infect additional individuals, especially once control measures are discontinued;
(B) shows that culling causes an increase in the rate of dispersal in the previously stable population, leading to an increase in the proportion of infected groups and in the rate of disease transmission.
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Mathematical models of biological and environmental processes both encapsulate and enhance our understanding of these systems. New modelling methodology is required to address the increasing complexity of models being considered, whilst new statistical methods are needed to fully integrate models and data in the face of a gathering pace of data acquisition. We are working closely with scientific collaborators to develop new methodology in the context of specific applications, as illustrated below.
Counter-intuitive increases in disease following cullingAlthough population reduction (often implemented by culling) is a recognised and widely used method of disease control in wildlife populations, counter intuitive increases in disease levels following culling (termed the perturbation effect) indicate that it may be less effective than current theory suggests. Evidence from studies of both badgers (Meles meles) and wild boar (Sus scrofa) suggest that culling disrupts social and demographic structures, leading to enhanced levels of disease transmission. Understanding the cause of this phenomenon will allow us to determine the levels of population reduction which may give rise to a substantial perturbation effect and hence to improve our ability to reduce disease incidence.
We find that epidemiological and demographic characteristics associated with the perturbation effect are common to many wildlife disease systems and reduce the efficiency of population reduction as a disease control strategy even when not leading to an increase in disease levels. Thus, cases where perturbation effects have been observed may be the “tip of the iceberg” and social and demographic mechanisms which enhance transmission as a result of population reduction should be considered routinely when designing control programmes.
Emergence of diversity-stability-productivity relationships The unprecedented rate of loss of the Earth’s biodiversity is a major concern, both in its own right and because of the potential for diversity to influence the stability of globally and locally important ecosystem functions and services. However, relationships between diversity, stability and ecosystems functions, like productivity, are still poorly understood. We have developed a generic resource competition model framework in which species assemblages co-evolve in the presence of a fluctuating environment. Changing the characteristics (variance and auto-correlation) of these environmental fluctuations leads to different levels of species diversity, biomass production and stability in the modelled communities. Analysis of simulation results for a range of different environments reveals emergent relationships between diversity, productivity and stability. Although the model is built on very simple and generic assumptions, our results agree with and unify a number of seemingly disparate aspects of the ecology literature such as the intermediate disturbance hypothesis, the hump-backed diversity-productivity curve, species-energy theory and biodiversity-ecosystem functioning theory.
Diversity (here, average species richness) is maximised at intermediate levels of biomass production (left) and stability (centre), consistent with the well-known hump-backed diversity-productivity relationship and the intermediate disturbance hypothesis respectively. In accordance with species-energy theory, diversity (species richness) increases as the resource supply rate increases.
Climate-induced tipping points in macro-parasite infection risk Macro-parasites present one of the most pervasive challenges to grazing livestock. Changing outbreak patterns in temperate regions have been attributed to climate change, as many stages of the macro-parasite life cycle are free-living (i.e. outside the host species) and are therefore sensitive to abiotic conditions. With potential for further climate driven increases in parasite prevalence and intensity, and consequent welfare and economic impacts, there is a need to foresee changes in risk and to develop control strategies. By developing a mechanistic model that incorporates the key elements of the transmission process, we have explored how changes in climate sensitive parameters will influence parasite outbreaks.
Our results show that changes in larval development and survival can result in non-linear responses in transmission dynamics, leading to distinct ‘tipping points’ in parasite burdens. Consequently, small changes in climatic conditions around the critical threshold could result in dramatic changes in outbreak patterns. The position of the tipping point is influenced by the initial level of larval contamination on pasture at the start of the grazing season, and once the tipping point has been surpassed, the magnitude of outbreaks will be determined by the host’s immune response. An understanding of this non-linear response to climate change will help inform management decisions and long term control strategies.
Plotting modelled parasite burden against larval development rate shows a non-linear threshold (or tipping point) above which disease burden increases rapidly.
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Plot of additive log-ratios for data from a three component mixture [Zn, V, Ba], in which the red dashed line indicates the transformed limit of detection and the red points indicate the imputed values for samples with measured concentrations of Zn below the limit of detection.
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Assessing the adequacy of models for spatio-temporal processes in epidemiology & ecologyStochastic spatio-temporal models play an increasingly important role in epidemiological and ecological studies of disease, invasive aliens and endemic species responses to climate changes. The dynamics of such systems can be extremely sensitive to the particular choice of model, with important implications for the design of control and risk management strategies.
Conventional Bayesian model assessment techniques (e.g. Bayes factor and Deviance Information Criterion) focus on relative model fit but do not readily offer insights in to which aspects of the model are at fault. Alternatively, posterior predictive checks can provide useful measures of model adequacy but are based on summary statistics which can be difficult to identify. We have therefore developed a novel approach for diagnosing misspecifications in a general spatio-temporal transmission model by embedding classical analysis of residuals within a Bayesian framework using latent residuals for each sub-process whose sampling properties are known given the specification of the fitted model. Application to simulated data suggests that our approach is more sensitive in detecting misspecification of the spatial transmission kernel than standard posterior predictive checks.
Inferred distributions of residuals related to a spatial dispersal process. In Scenario I a light tailed dispersal process (model II) is fitted to data generated from the heavy tailed model I, with the situation reversed
in Scenario II. Deviations from the known distribution (here uniform on the unit interval) of residuals related to the fitted spatial dispersal process indicate that extreme dispersal distances are under-represented in Scenario I and over represented in Scenario II.
Bayesian inference of genetic and epidemiological parameters from field disease data
Field level disease incidence data enable quantification of host genetic variation for disease resistance and are invaluable in identifying appropriate breeding strategies aimed at enhancing genetic resistance to disease. However current methods of analysis do not account for the often incomplete (e.g. due to lack of information
on exposure to disease) and noisy (due to imperfect diagnostic tests) character of such data, and this can lead to underestimation of the true extent of genetic resistance.
A Bayesian inferential framework has been developed to quantify host genetic variation in a manner which accounts for the complexities inherent in field disease data. The framework integrates genetic and epidemiological concepts with field disease data incorporating genetic relationships between animals, observed disease state, relative prevalence of the disease and sensitivity and specificity of the diagnostic test. Using the simulated data, we have found that the framework enables inference on both genetic (e.g. heritability of resistance to disease) and epidemiological (e.g. prevalence of disease) parameters that are of practical relevance to animal breeders.
Advances in science create demands for new statistical methodologies. Our research responds to the needs of our scientific collaborators, as they change the nature of the data they collect, the information they wish to extract from data, and the applications of this information. Here we illustrate some highlights from our research, demonstrating the breadth of motivating influences.
Dealing with concentrations below the limit of detectionA problem commonly faced by scientists working with chemical samples is the presence of trace elements at concentrations below the limit of detection. These limits usually refer to thresholds below which a particular analytical instrument is not capable of distinguishing a genuine chemical signal from background noise. They may, consequently, vary between laboratories and technologies – but may also depend on more mundane considerations, such as how clean an instrument is at the time of operation. The adequate handling of non-
detectable chemical concentrations within data analysis is of substantial practical concern.
Depending on the proportion of observations below the limits of detection, simple estimates derived from observed data may well not reflect the characteristics of the underlying distributions of concentrations. Unfortunately, the use of over-simplistic ad hoc methods has been widespread in the last decades. It has been shown that these procedures, which lack any rigorous statistical basis, are prone to producing biased estimates and obscuring genuine patterns. BioSS has been working to develop statistically sound methods
for analysing data on chemical concentrations that include values below a known limit of detection, with a particular emphasis on compositional analysis in which interest is focussed on the relative values of different concentrations rather than the concentrations themselves. Our preferred approach is based on (1) a modified
version of the Expectation-Maximization algorithm which exploits the available multivariate information to obtain unbiased parameter estimates, and (2) the log-ratio methodology which deals with the peculiarities of the compositional data. We have produced a package of computer routines for the R statistical language in order to facilitate the practical use of these methods.
Centered log-ratio biplot representation of chemical samples (points) and analytes (arrows). The red filled points represent samples which have required adjustments due to having values below the limit of detection.MnO
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Inferred probability surface for heritability of susceptibility to a disease (x-axis) and sensitivity of the diagnostic test (y-axis) are plotted, indicating accurate inference for a simulated data set with heritability of 0.25 and test sensitivity of 0.85.
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Observed speeds obtained from bird-based GPS records for an individual kittiwake (top), and the predicted behavioural states that are associated with fitting a two-state hidden Markov model to these data (bottom).
An example of a simulated marginal abatement cost curve for agricultural farming within Scotland. The height of each bar indicates the cost of implementing a measure per unit of abatement (negative costs representing savings), whilst the width of each bar indicates the total abatement which can by achieved if that measure were implemented.
12 13
The classification of spatio-temporal data on water quality in riversThe Scottish Environment Protection Agency (SEPA) monitors the quality of fresh waters in Scotland by conducting monthly sampling of various determinands at harmonised monitoring stations on 56 major rivers of Scotland. In these rivers, high concentrations of dissolved inorganic nitrogen (DIN) are indicative of poor water quality, because they can promote the eutrophication of surface or coastal waters and the contamination of groundwater. The DIN has three components (ammoniacal nitrogen, nitrite and nitrate) and can come from both agricultural and urban sources.
We have conducted a simultaneous analysis of all 56 trivariate time series of DIN over 10 years. The aim of this work has been to classify each month on each river into a small set of homogeneous groups, which represent different river states defined by the DIN
concentrations. Our analysis was performed by means of hidden Markov models (HMMs), because they can define the states from the data, controlling the rates of transitions between states and taking into account both spatial and the temporal correlations. The fitted model accounts for data heterogeneity by allowing the three forms of DIN to have separate means, variances and correlations for each state of each river. High probabilities of transitions between states in a river are indicative of change-points occurring in the dynamics of DIN concentrations.
Quantitative assessment of climate changeThe Scottish Government has made ambitious targets to reduce greenhouse gas emissions (mitigation), and is also committed to ensuring that our society and economy are sufficiently robust to withstand the adverse effects of climate change (adaptation). BioSS has been working within the ClimateXChange collaboration to use scientific expertise to advance both the mitigation and adaptation policy agendas.
One such project we have been involved with is an economic evaluation of mitigation options in the agricultural sector. Such options are usually presented via ‘marginal abatement cost curves’ (MACCs), which provide a graphical tool for comparing measures in terms of their total abatement potential and their cost effectiveness. The propagation of uncertainty within the MACCs has been evaluated by repeatedly drawing unknown parameters from statistical distributions, and the results have been used to identify the inputs that make the largest contribution to overall uncertainties.
Modelling the foraging distribution of seabirdsMovement is a vitally important aspect of animal behaviour. Whilst data on movement have traditionally been difficult to collect, recent technological developments – such as the increasing use of smartphones and the decreasing cost and weight of electronic tags – mean that automated data on movement are now becoming available in large quantities. Here we describe elements of three projects concerned with the analysis of automated data on seabird movement, all undertaken with the ultimate aim of protecting species for which Scotland holds important breeding populations.
The first project, undertaken in collaboration with RSPB, involved analysing the foraging behaviour of five seabird species – guillemot, razorbill, shag, kittiwake and fulmar – at colonies located throughout the British Isles utilising data obtained from GPS tags that had been attached to individual birds. Since the data do not distinguish unambiguously between foraging and non-foraging behaviour, a key element of our analysis involved classifying behaviour on the basis of time series of observed flight speeds, which we did using hidden Markov models. Subsequent analysis was primarily based on using logistic regression to compare the environmental characteristics of observed locations against the characteristics of ‘control’ points drawn from the study regions.
A second project, being carried out in collaboration with CEH, involves analysing automated GPS tag data to provide inputs to a mechanistic model of foraging behaviour and energy balance for birds. This model is then used to study the impacts of offshore wind farms on seabird behaviour in the Forth-Tay area (e.g. the increased flight distances that birds may need to travel in order to avoid wind farms, and the reduced energy intake that they may incur as a result of being excluded from areas with high prey densities), and thereby upon survival and productivity. The analysis of the GPS data is solely concerned with describing, rather than explaining, spatial variations in bird densities, and therefore makes use of a semi-parametric modelling approach (GAMs - generalised additive models).
The final project involved the analysis of flight paths of individual terns for JNCC, collected without the stress of capture by following birds in a boat as they leave their colonies to feed. This has the additional benefit of enabling the birds’ activity to be recorded alongside the location information from the on-board GPS. Previous work used a form of random effects modelling, but we found better results were obtained using a weighted regression analysis, as the observed flight paths were of very different lengths. The data sets were large, and in order to properly account for spatial correlation our regression analysis was conducted using the INLA (Integrated Nested Laplace Approximation) software. We found associations between spatial locations of foraging and variables such as levels of chlorophyll and seabed depth; importantly, the best predictive model depended on both colony location and tern species.
Map of the sea around Strangford Lough, coloured from blue (low) to red (high) according to the predicted relative usage by arctic terns.
Consultancy Advice & Collaboration
Plant Science
Plant
Sci
ence
14 15
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 Government'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.
Research in plant science is essential if we are to meet the challenge of feeding the growing global population in a period of climate change. Our collaborations with plant scientists range from studies of molecular processes within cells to the interactions between crops, pathogens and their environment. Alongside the need to make optimal use of traditional types of data, we must develop and apply new approaches to make efficient use of the increasingly large volumes of molecular marker, transcriptomic, metabolomic and proteomic data that are being generated by new technologies.
Uniformity of plant varietiesThe introduction of new varieties of plants is regulated by a process known as National Listing, and for many crop types only varieties on the National List may be marketed. As well as seeking inclusion in the National Lists, developers of new varieties may also apply for Plant Breeders’ Rights which if successful will secure intellectual property protection for these varieties. One requirement to qualify for these is that the new variety is sufficiently uniform in its relevant characteristics.
Where the characteristics are quantitative in nature, e.g. height, then the uniformity may be assessed by comparing variability with similar varieties. A widely-used statistical method for carrying out this comparison is known as the Combined-Over-Years Uniformity method (COYU) which uses a moving-average adjustment method to ensure that comparisons allow for relationships between variability and mean values. However it has been shown that this adjustment method leads to a bias, making COYU too strict.
In collaboration with the University of Aarhus, and supported by the Community Plant Variety Office, Defra and SASA, we have investigated approaches for improving the COYU method. We found that a cubic smoothing spline with low degrees of freedom fitted the relationships between variability and level of expression seen in practice. Using this alternative approach, we have proposed an alternative to the existing COYU method so that the bias is now minimal.
The process of gaining acceptance of the improved method by UPOV, the governing body, will take some time. To assist this, and to expedite uptake of the improved method following acceptance, we are now working with the Agri-Food and Biosciences Institute to produce software for worldwide circulation.
We have developed a new approach to allowing for relationships between variation and mean values in variety trials conducted to assess the uniformity of varieties.
Animal Health & Welfare
Plant
Sci
ence
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mal H
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16 17
We support research into the health and welfare of farm livestock which is of benefit to the animals, the Scottish public and the associated commercial sectors. BioSS contributions include helping to design efficient scientific experiments and national field surveys together with the undertaking of specialist data analysis tasks to extract maximum information from new and existing data.
Phylogenetic analysis of disease resistance genes in the potato genome Plants have a sophisticated defence system to protect against attacks by pathogens including bacteria, viruses, nematodes, insects, filamentous fungi and oocytes. The publication in 2011 of the first potato genome sequence by an international consortium, including scientists based at the James Hutton Institute, provided the opportunity to identify and classify all the R (resistance) genes belonging to the main type, NB-LRR, which contain a nucleotide-binding domain (NB-ARC) and a leucine-rich repeat (LRR). Working in collaboration with genomics scientists and bioinformaticians at the James Hutton Institute, BioSS produced a multiple alignment of these 438 NB-LRR genes using a hidden Markov model approach, and modelled their evolution from a single ancestral R gene, allowing for variation in the amino acid substitution rates along the sequences. The estimated phylogenetic tree shows the evolutionary history of duplication events and sequence divergence that has resulted in groups of highly similar genes with different functions. The model of protein evolution used in the Maximum Likelihood tree estimation was optimized using the model selection protocol in the TOPALi
package, developed by BioSS, which allows joint estimation of the tree and the evolutionary model. The locations, i.e. the physical map positions, were established for 370 of the predicted NB-LRR genes across the 12 potato chromosomes and the genes were found to be clustered spatially into 63 groups. The NB-LRR group functions can now be studied by plant pathologists to better understand their roles in plant defence.
Detail from the full phylogenetic tree which portrays the estimated relationships between the NB-LRR genes, using colour to indicate groups of particular interest.
Metabolomics of organic and conventionally grown potatoMetabolic profiling by mass spectrometry is one of the modern ‘omics technologies that can be used to compare crops grown under different production practices and environmental conditions. As part of an EU-funded project on comparative safety assessment methods (SAFEFOODS), potatoes were grown in a two-year experiment using conventional and organic fertiliser and conventional and organic crop protection (pesticide) regimes. In the second year, the potatoes were grown after two different pre-crops, winter barley and spring beans. Principal component analysis showed graphical evidence of differences between the two years (first principal component scores in figure) and also between the conventional and organic fertiliser treatment (second principal component scores). Formal over-years analyses of the metabolites confirmed that these were the major effects, and that there was little evidence that either the pre-crop or the choice of conventional or organic pesticide affected the levels of metabolites. Levels of twenty amino acids were consistently reduced by the organic fertiliser compared to the conventional, probably associated with the lower nitrogen content of the potatoes grown with organic fertiliser. By contrast, no differences were detected in the levels of poly-unsaturated fatty acids. This approach provides a useful tool to differentiate between growing practices and to identify the metabolites affected.
Electronic identification of individuals in the Scottish sheep population EU regulations introduced after the foot and mouth outbreak in 2001 resulted in the introduction of electronic tagging of sheep in Scotland in order that movements of individuals can be traced should another infectious disease outbreak occur. Batches of sheep pass through electronic scanners at critical control points (CCPs), mostly marts or abattoirs, and the individual identifiers on their tags are read and the data uploaded to a central data base (ScotEID), together with the batch size, date and holding numbers for their departure point and destination. Tags can be lost, or degrade so that scanners fail to read them, and there are increased difficulties in reading tags from large batches of sheep.
BioSS has been collaborating with SAOS Ltd. since 2009 to analyse data on read rates, using generalised linear mixed models (GLMMs) to investigate issues including effects of batch size, scanner type, type of CCP, and age and type of tag. The mean batch read rate in 2011 was 94.9%, and it is now widely accepted that 100% read
rates will not be consistently achieved. Since sheep tend to be moved in batches, it has been suggested that 100% read rates are not actually necessary for effective tracing. We have simulated the contact tracing process required in the event of a disease outbreak, and these simulations are allowing us to explore the impact of read rates on traceability.
Sheep passing through a fixed electronic scanner at St Boswells market in Scotland. Photo provided by SAOS Ltd.
Modelled percentage of sheep read during 2011 as a function of batch size, indicating the differential performance between fixed and handheld tag scanners at marts and abattoirs.
Principal component (PC) plot of metabolites identified by gas chromatography-mass spectrometry. The points indicate type of fertiliser application (filled = conventional, open = organic) as well as year of planting (shape of symbol).
Ecology & Environmental Science
Ani
mal H
ealth
& W
elfa
re
Ecol
ogy
& E
nvir
onm
enta
l Sci
ence
Cow wearing innovative methane sensor
APY−1
ASP−1CF−1
MEP−1
MIF−1
SAA−1
TGH−2
20ES
MIF-1
CF-120ES
ASP-1TGH-2
APY-1MEP-1
SAA-1
18 19
Uncovering the relationship between landscape features and well-beingGenerally speaking, people have an understandable tendency to want to be close to attractive landscapes. This manifests itself in a variety of contexts: for example, in the higher prices that are associated with scenic views from homes and hotels. But this general observation requires careful analysis to determine which landscape features are most important in a given situation, and to determine the relative effects of different kinds of landscapes.
In collaboration with BioSS, socio-economic scientists from the James Hutton Institute and the University of Aberdeen have been eliciting data from survey respondents, each presented with a carefully selected subset from a large number of available photographs. The data collected have been used to analyse how responses relating to perception of beauty or tranquillity (or similar concepts) can be explained by measurements of the landscape relating to water bodies, vegetation, geography, signs of human interference and so on, which can be regarded as summarising the content of the photographs. The majority of responses from the survey were recorded on seven-point Likert-type scales, which typically comprise ordered text ratings ranging, for example, from ‘Strongly dislike’ to ‘Strongly like’. We have implemented an analysis using ordinal mixed models: the mixed models aspect allows for variation between respondents over and above that due to each respondent only being presented with a subset of images, while the ordinal aspect allows the modelling to respect the ordered categorical nature of the response data, in contrast to simpler approaches which simply convert the Likert scales to numerical values subsequently treated as continuous data. The results from the study will improve our understanding of the role of different landscape characteristics in influencing development pressures in rural areas and the likely effects of landscape change on well-being.
BioSS’s interactions with ecologists and environmental scientists are centred on the need to develop and interpret a robust evidence base to underpin national policies. Our collaborative contributions for design and analysis range in scale from detailed experiments, which investigate particular processes under controlled conditions, through to national monitoring schemes, which provide broadly based information about our changing environment.
Assessing the effectiveness of a recombinant vaccine for Teladorsagia circumcincta Parasitic nematodes have devastating effects on animal health and production, affecting food security worldwide. In temperate regions such as Scotland, the principal cause of parasitic gastroenteritis in small ruminants is the nematode Teladorsagia circumcincta. This is commonly controlled by repeated applications of anthelmintics, but unfortunately resistance of T. circumcincta to anthelmintics is emerging rapidly. Since sheep can acquire immunity against this nematode after continual exposure, vaccination may be an alternative control strategy.
With scientists from MRI, BioSS analysed several trials of the effectiveness of a candidate vaccine based on recombinant proteins, using a range of statistical tools. Linear mixed modelling was applied to compare antigen-specific antibody responses over time; multivariate methods were used to explore antibody responses to recombinant proteins; negative binomial generalised linear models were fitted to model nematode burdens in the stomach. A novel generalised additive mixed modelling approach was formulated to explicitly model all the key
aspects of faecal worm egg counts, including nonlinearity over time. Overall, our analyses of MRI’s experimental data suggest that vaccination with recombinant proteins can protect sheep against infestation by T. circumcincta.
Biplot showing the mucosal IgG antibody responses to the recombinant proteins. Open circles represent immunized sheep; closed circles represent control, non-immunized sheep. The axes represent the different antigens, with arrows indicating directions of higher antigen-specific antibody responses. Immunized sheep show higher mean responses than control sheep.
Methane emissions from ruminants: information for the UK Greenhouse Gas Inventory Greenhouse gas emissions from agriculture are currently estimated for inclusion in the UK National Inventory using simple accounting approaches. In the livestock sector, no attempt is made at present to distinguish between different livestock breeds or methods of animal management. Thus many of the benefits of animal breeding or changes in management practices cannot contribute to meeting policy commitments to reduce greenhouse gas emissions: the only mitigation measure that will be picked up is a reduction in the numbers of animals.
The UK governments are therefore funding an extensive methodological exercise to improve the accounting, reporting and verification of emissions for inventory purposes, and to quantify the uncertainties in the resulting estimates. A co-ordinated series of experiments is being undertaken by a consortium comprising SRUC, the Agri-Food and Biosciences Institute, North Wyke Research, and the Universities of Reading, Nottingham and Aberystwyth, involving the measurement and analysis of methane emissions from cattle and sheep under a wide range of conditions. BioSS is acting as statistical advisor on the management group for this consortium project. As well as ensuring that the inventory-driven objectives of the studies are well reflected in experimental protocols, the statistical methods utilised and in generated outputs, we have facilitated workshop meetings between animal and data scientists to promote a shared understanding of the methodologies being used to quantify and propagate uncertainty across the inventory.
Analysis of ordinal assessment ratings from multiple respondents, each presented with a contrasting but balanced set of photographs, allows us to create an understanding of the basis for preferences of respondents for particular landscapes and landscape features.
Human Health & Nutrition
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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 processes can be large. Statistical and mathematical models have a pivotal role in enabling the interpretation of experimental and observational studies.
Estimating the changes in carbon stocks in Scottish soilsChanges in soil carbon have important consequences both for soil functioning and as part of the global cycling of carbon, with implications for climate change. Between 1978 and 1988, 721 soil samples were taken at points on a 10km grid throughout Scotland to form the National Soil Inventory of Scotland (NSIS). Between 2007 and 2009, 179 sites on a 20km grid were resampled and analysed for a number of properties, including carbon content. Working with scientists from the James Hutton Institute, BioSS contributed to the statistical analysis of these data, which aimed to quantify changes in soil carbon stock for different land-use types, in particular making allowance for soil bulk density not having been measured during the earlier sampling period. To address this, a model relating bulk soil density to near infrared (NIR) spectral data was developed for the later sampling period, and was used to predict bulk densities for the earlier period from the corresponding NIR data. We have conducted a simulation study to investigate the effect of the uncertainty in the bulk density estimation on the width of the confidence intervals for the change in carbon stock, and have adjusted the width of the confidence intervals accordingly.
The carbon content of forest soils has tended to increase, whereas the carbon content of agricultural soils has tended to decrease.
Estimation of red deer age from tooth wearKnowing the age of individual animals is crucial for almost any analysis involving populations, evolutionary ecology and conservation, since without controlling for age the estimated effects of associated variables (such as body size) on life history traits are likely to be misinterpreted. Accurate but slow estimation of age for most mammals can be achieved by studying layers of cement in teeth. A faster alternative, which is commonly used to estimate the ages of red deer, involves the inspection of the degree of tooth wear of dental facets of the molars. The standard approach involves making around 100 assessments spread over three molars per animal. Hence, in collaboration with the James Hutton Institute, BioSS has been investigating whether a reduced subset of assessments can perform as well as the full set. By fitting multiple regression models for (true) age estimation given a training data set using known ages, we have found that a streamlined process involving fewer than 30 assessments performed the best. We employed a Bayesian calibration model in order to assess the accuracy of the new method relative to a range of existing age estimation techniques, and showed the new method was the most accurate in addition to being faster to apply.
Identification of the effects of zinc deficiency on vascular health in ratsZinc is a necessary dietary component and deficiencies can lead to a range of developmental and health issues. BioSS has been closely involved in the design and analysis of a study by scientists at RINH of the impact of zinc deficiency on vascular health in rats by measuring changes in gene expression. RNA samples were obtained from a two stage experiment. Firstly, rats were fed diets resulting in different levels of zinc deficiency: a zinc deficient group at a zinc level of 9 µM (ZD9) and a zinc adequate group (ZA27) at 27 µM. As zinc deficiency leads to reduced food intake, a third group was fed the same amount of food as the ZD group but at an adequate zinc level (PF27). In the second stage, vascular smooth muscle cells (VSMCs) were treated with plasma from these animals, including a fourth treatment group (ZD27) where zinc was added back to the plasma of the zinc deficient group. This allowed us to separate direct and indirect effects of plasma zinc. RNA samples obtained from the VSMCs were allocated on Agilent 2-channel microarrays in a design that compared gene expression as precisely as possible between the different zinc treatment groups. In addition to an analysis to detect differential gene expression, we used existing biological information on gene pathways from databases such as Gene Ontology (GO), KeGG or Reactome to motivate joint analyses of the expression of sets of genes. This identified differences in the gene regulatory processes caused directly or indirectly by zinc deficiency.
A principal component score plot shows that PC1 separates the zinc deficient group from the others, whereas PC3 differentiates between the zinc adequate groups and the one where zinc was added back at stage two of the experiment. The loadings plot highlights in red the genes from the interferon alpha/beta signalling pathway and indicates that this immune response pathway is associated with the mechanism by which cells react to zinc deficiency.
Locations to be assessed for wear on occlusal (biting) surfaces of the molar teeth of red deer: our analysis was able to reduce the number of assessments needed to estimate deer age.
P24,25&30
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P4-13
P14-22
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Knowledge Exchange
In 2013 we celebrated our 25th anniversary and the International Year of Statistics with an event at the Royal Society of Edinburgh. At this, Prof Denise Lievesley, of King’s College London and a member of the steering committee of the International Year of Statistics, gave an invited lecture “Living statistics: the interplay between the development and application of statistical methods.” Her talk focussed on six key challenges facing the statistical profession, including the tension between relevance and autonomy, and the balancing act between pragmatism and purism.
BioSS organised the second Forum for Quantitative Science workshop during 2012. These workshops bring together scientists from the MRPs and CAMERAS partners to discuss issues of common concern. On this occasion the theme was networks – a concept that is useful in many applications – and our guest speaker was Brendan Murphy from University College Dublin. A characteristic of these workshops is that all participants are encouraged to participate, to share knowledge and arrive at mutual understanding.
Along with the Rowett Institute of Nutrition and Health, we contributed to the Channel 4 “Food Hospital” series. The programmes looked at the role of diet in helping with medical conditions. We assisted with design, analysis and interpretation of some of the demonstrative trials undertaken, in which viewers volunteered online to make dietary changes and record changes in outcomes such as blood pressure and cholesterol levels.
The work of BioSS is driven by the requirements of our collaborators and end-users, which include scientists, policy makers and industry, hence Knowledge Exchange is central to our activities. We work with the Scottish Government’s other Main Research Providers in a coordinated effort to improve the scope and impact of our Knowledge Exchange.
Knowledge exchange takes various forms, including:
Consultancy advice and collaboration
Publications and presentations of our research (also Appendices 2 & 3)
Training courses and workshops for scientists
Supervision of PhD students
Dietary influences on N-nitroso compounds in the gutN-nitroso compounds (NOCs) can be formed by chemical reactions in the gut, and are potentially carcinogenic. To identify dietary components that contribute to the formation of, or provide protection against, NOCs we analysed data from three controlled dietary intervention trials. During the trials, obese men were fed different weight loss diets, with varying intakes of red meat, protein, carbohydrate, fibre, vitamin C and nitrate. The amounts consumed were recorded and stool samples were collected and analysed for NOC concentrations. We
investigated which dietary intake variables were responsible for changes in faecal NOC by regressing the NOC data on one or more of the intake variables, accounting for differences between individuals, study groups and study duration using random effects models with various residual error structures. Our models identified that, in addition to red meat intake being an important contributor to the formation of NOCs, reduced total energy intake from low carbohydrate weight loss diets and increased nitrate intake are also associated with an increase in NOCs. Conversely, the intakes of dietary vitamin C and dietary fibre were found to play a potentially protective role. Our findings highlight the importance of balancing potentially problematic foods (red meat, nitrate) with protective ones such as vitamin C and dietary fibre.
Estimation of placental weight distributionExtreme birth weights, whether high or low, tend to be associated with pregnancy complications and subsequent health problems for the baby. It is therefore important to be able to identify women at risk at an early gestational age. We have collaborated with the Rowett Institute of Nutrition and Health on analyses of the Aberdeen Maternity and Neonatal Databank, with a particular focus on the risk of pregnancy complications and their links with maternal weight and placental characteristics. The data showed that an increase in body mass index (BMI) between pregnancies is associated with an increased risk of complications in the second or later pregnancies, independently of the initial or final BMI, and that a key factor in these relationships is the placenta size.
Knowledge of the distribution of placental weight at different gestational ages is important to identify extreme values, and to see if these are due to complications in the pregnancy. We derived and published a new set of centile curves to help identify low and high values for different gestational ages. These curves were estimated by fitting nonparametric smooth functions to the mean, variance and skewness of the observed distribution of placental weights at different gestational ages. It was found that placental weights during a first pregnancy tend to be lower than in later pregnancies, and that this had a greater effect than the gender of the baby. We also examined the centiles of the distribution of the fetal to placental weight ratio, and found that in this case parity did not have a significant effect.
The 3rd, 50th and 97th percentile curves showing the development of placental weight with gestational age, for first (dashed lines) and later (solid lines) pregnancies leading to the birth of baby girls.
BEATRICE SELL
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Postgraduate Research & Training
GUSTAF RYDEVIK
What are recent BioSS students doing now?
I am currently pursuing a PhD in statistical methods for disease surveillance. I am developing approaches for analysing multiple pathogen test data collected via the surveillance of wildlife. In particular, I am investigating how to estimate the historical development of epidemics in order to inform outbreak management, and how to visualise and summarise noisy data from multiplex assays to understand multi pathogen disease systems.
I previously worked as a biostatistician for the Swedish Center for Communicable Disease Control, and am motivated to develop methods that are both theoretically interesting as well as useful for the practice of disease surveillance. Studying for my PhD at BioSS, in addition to having supervisors in both SRUC and in the Environment Department at the University of York, provides opportunities to develop my research from a statistical as well as real-world perspective.
There is a fantastic depth of applied statistics knowledge within BioSS that I have the chance to draw on via seminars and individual discussions, and my supervisors at SRUC and York are exceptionally good at pointing out directions of research that can provide the most impact in the field of disease systems.
I am currently doing a PhD focussing on the bacteria that populate the human large intestine. In particular I am interested in the mechanisms that are influencing substrate preferences of different bacterial strains in the diverse and competitive environment that is the human large intestine. The models that I am developing are aimed to be computationally fast, transferable between different bacterial strains and hopefully also comprehensible to non mathematically trained scientists.
Being a student with BioSS has placed me in the lucky position of being located in a small group of statisticians within an institute of nutritional research, but at the same time finding myself part of a larger Scotland-wide statistics/mathematics research group as well as being registered with a university. Thus I am able to access experts in both mathematics and modelling as well as microbiology. This has helped me greatly in obtaining the necessary biological knowledge and I have also been able to enhance my understanding of mathematical modelling and statistics.
Naomi Foxis a Quantitative Ecologist in the Disease Systems team at SRUC working on a range of projects addressing issues including paraTB control, wildlife disease surveillance, and the potential impact of climate change on livestock health.
Frank Dondelinger is a Research Associate at the Netherlands Cancer Institute, applying statistical and machine learning approaches in systems biology and personalised medicine.
Joanne Hardstaffis a Post Doctoral Research Assistant in the Veterinary Epidemiology Economics and Public Health group at the Royal Veterinary College, London, conducting analyses for a range of projects including studying the effectiveness of surveillance and biosecurity.
During the period 2011-13 we supervised 13 PhD students, registered at the Universities of Aberdeen, Edinburgh, Glasgow, St Andrews, York, Venice, Heriot Watt University and Universiti Putra Malaysia. Two of these students write of their experiences below.
24 25
BioSS’s PhD programme plays an important role in training future generations of quantitative scientists. The research environment we provide in partnership with our co-supervisors allows students to develop their statistical, mathematical and computational skills in the face of challenging applications. Our students graduate with state-of-the-art methodological skills and experience in solving real-world problems, thus providing them with an excellent foundation for a successful career.
BioSS has a thriving PhD programme, jointly supervising students with several collaborating universities and research institutes (see P30). All projects combine
theory and application, developing new statistical/mathematical methodology to solve important, challenging problems in biological and environmental sciences.
We recruit students with strong mathematical/statistical/computing backgrounds, good communication skills and enthusiasm for addressing real-world problems. They graduate equipped with state-of-the-art methodological expertise and experience of collaborating with scientists in other disciplines, thus laying the foundations for successful careers.
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.
For further details of possible projects and funding, see http://www.bioss.ac.uk/postgrad.html
Jamie Prenticeis a Post Doctoral Research Assistant in the Institute of Biodiversity, Animal Health, and Comparative Medicine at the University of Glasgow, developing systems modelling approaches to the persistence and control of E. coli O157.
Leo Zijerveldis Post Doctoral Research Fellow in 3D seismic interpretation in the Department of Earth Science at the University of Bergen, Norway.
26 27
Information Technology
BioSS’s scientific achievements are made possible by excellent computing facilities designed to meet the needs of users, including a choice of desktop platforms for personal use and ready access to shared resources such as multi-core servers and multi-processor clusters.
Flexible personal provisionBioSS staff, students and selected collaborators have access to a dual platform managed desktop computing environment providing Windows and CentOS Linux. Our core server infrastructure is Linux-based virtualised and uses consolidated storage to ensure high energy efficiency and high availability. Leading statistical, mathematical and productivity software is packaged and made available by default on each platform.
High-performance computingFor computationally intensive tasks we have access to small scale multi-processor systems of our own as well as large-scale compute clusters at the University of Edinburgh and commercially. To assist our users in making use of the most recent advances in high-performance computing, we provide a dedicated compute server for prototyping massively-parallel code running on NVIDIA Telsa GPGPU cards in advance of scaled-up implementation on a cluster with GPGPU facilities.
Software developmentThe BioSS IT group also has significant expertise in end-user software development for both Windows and Linux. We provide software development advice and assistance to our scientists and their collaborators. This allows BioSS to deliver our innovative research in a form that is directly usable by our collaborators in other scientific research institutes or partners in commercial projects.
For example, we have developed and currently support: software for QTL analysis in tetraploid crops; interactive gut bacteria modelling software; image analysis tools for plant and animal breeding work, and data analysis systems for crop variety trials.
Travel-free meetingsBioSS has staff based at multiple sites throughout Scotland and collaborates with partners worldwide. We have therefore invested extensively in video-conferencing facilities, including both room-based and computer-based solutions including desktop sharing. This allows collaboration, training and project management activities to be
conducted at a moment’s notice, saving time and expense on unnecessary travel.
Training for Scientists
Train
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BioSS has a well established suite of training courses, each of which contain a mixture of expository and practical sessions.. These courses allowing participants to improve their understanding of quantitative methods, to extend their ability to implement these methods using computer packages, and to develop their knowledge of non-standard situations which require expert intervention.
Training programmeOur provision of training in statistics, bioinformatics and mathematical modelling has played a central role in developing our long-term collaborations with research
scientists 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 rooms equipped with the necessary computing facilities, usually at the participants’ site or nearby. Feedback obtained from our online evaluation is largely positive, indicating that these courses are well received by participants.
BioSS also provides in-house statistics courses for external organisations in the UK and abroad, selecting and mixing material if required from our ‘off-the-shelf ’ courses, to best serve the interests of these organisations. We also arrange workshop sessions to discuss specific interests of participants to avoid the overheads of preparing full courses from scratch. A particular feature of 2011-13 was being commissioned by the European Food Safety Authority to deliver training to its Panel on Dietetic Products, Nutrition and Allergies, enabling panel members to become more confident in their assessment of results derived from the use of a wide range of statistical methods.
The suite of BioSS courses ranges from basic statistics for scientific applications through to more specialist topics, with course titles as follows.
Basic Statistics courses
Getting Started in R
Experimental Design and Analysis of Variance
Regression and Curve Fitting
Graphical Methods for Multivariate Data
Statistical Methods for Repeated Measures Data
Introduction to Mixed Models and REML
Introduction to Mathematical Modelling
Phylogenetic Trees from Molecular Sequences
Statistical Design and Analysis of Microarray Experiments
Linkage analysis and QTL mapping in plants
Staff and Associates as at 1 April 2011
Investing in Our People
BioSS staff, students and associates meet regularly to discuss research ideas, consultancy projects and organisational matters.
Members of our IIP Committee guided BioSS to re-accreditation of its Silver Status.
INVESTOR IN PEOPLE
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Director: Prof. David Elston MSc
In BioSS, management and staff combine to create a supportive
working environment. We have been assessed as meriting Silver Status by the Investors in People scheme and have received a Silver Award under the Healthy Working Lives programme administered by NHS Scotland.
Investors in People: Silver Status retainedThe Investors in People (IIP) scheme aims to encourage organisations to improve how they engage with and develop their staff. This is done by publication of an evolving set of standards which are expressed in a way that makes them applicable to different sizes and types of organisations. Performance against the standard is periodically reviewed by an external assessor who interviews staff in confidence, then summarises the findings in a written report consisting of their assessment, together with pointers for future development.
BioSS had its most recent IIP assessment in March 2013, and was judged still to merit the Silver Status first awarded in March 2010. Our assessor held interviews with 15 people and reported on many positive findings, describing BioSS as “an empowering organisation” with: “a very strong team ethos”; “a universally accepted set of values”; and “a culture of continuous learning”. We have since begun to follow up on the recommendations for further improvement.
Healthy Working Lives: Silver Award obtainedThe NHS Scotland Healthy Working Lives scheme offers practical information to help improve health and safety and the wellbeing of everyone at work. The scheme includes the provision of advice to employers relevant to the working practices of their staff, and an assessment scheme for organisations to judge how well they are performing relative to a broad set of criteria.
Since obtaining a Bronze Award in August 2008 our AWARE team has led BioSS forwards in several ways. These have included: responding to specific issues raised in a survey of staff; delivering information to staff on the causes and possible methods to reduce stress; and establishing social activities such as ‘Biscuit Wednesdays’ and lunch time walks to encourage a higher proportion of staff in Edinburgh to take regular breaks. We were delighted therefore that these enhancements have led to receipt of a Silver Award in October 2011.
James Hutton Institute AberdeenPrincipal Consultant, Ecology & Environmental Science: Mark Brewer PhDBetty Duff BSc, Altea Lorenzo-Arribas MSc, Jackie Potts PhD, Luigi Spezia PhD
Rowett Institute of Nutrition & HealthPrincipal Consultant, Human Health & Nutrition: Graham Horgan PhDGrietje Holtrop PhD, Claus-Dieter Mayer PhD
SRUC AuchincruiveSarah Brocklehurst PhD
The King’s Buildings, EdinburghHead of Research & Leader of Statistical Methodology Research Theme: Prof. Chris Glasbey DSc, FRSELeader of Statistical Bioinformatics Research Theme: Ian Simpson PhDLeader of Process & Systems Modelling Research Theme: Glenn Marion PhDPrincipal Consultant, Animal Health & Welfare: Iain McKendrick PhDExternal Development Manager: Adrian Roberts MScIT Manager: David Nutter BScAdministrative Officer: Sarah Hirstwood
Adam Butler PhD, Stephen Catterall PhD, Zhou Fang PhD, Kokouvi Gamado PhD, Diane Glancy, Giles Innocent PhD, Helen Kettle PhD, Muriel Kirkwood DA, Jiayi Liu PhD, Alec Mann BSc, Mintu Nath PhD, Ian Nevison MA, Javier Palarea-Albaladejo PhD, Thanasis Vogogias MSc
Associates: Ross Davidson, Matthew Denwood, Naomi Fox, Andrew Harding, Tony Hunter, Dirk Husmeier, Jim McNicol, Helena Oakey, Chris Pooley, Jamie Prentice, Mike Talbot, Chris Theobald, Megan Towers
Staff leaving between 1 April 2011 and 1 April 2013: Dirk Husmeier, Yu Song, Chris Theobald
James Hutton Institute DundeePrincipal Consultant, Plant Science: Frank Wright PhDColin Alexander PhD, Christine Hackett PhD, Katrin MacKenzie PhD, Katharine Preedy PhD
to be experts at our work, carrying out excellent methodological research, skillfully combining our knowledge of quantitative methods and subject areas in our advisory activities, and providing effective support to enable our scientific achievements;
to be good collaborators, working openly and constructively with other organisations, maintaining an independent point of view based on objective analysis in adherence to the highest standards of scientific ethics;
to support each other, pooling experiences and abilities, fostering an ambitious working culture which respects individual needs, qualities and contributions;
to continually improve, by encouraging all individuals to develop their skills and profiles, including contributing to and benefiting from involvement with professional societies and the wider scientific community.
Our Core Values are:
Management GroupResearch Students
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Ricardo Alvarado BarrantesStatistical models in biogeography. Luigi Spezia with Prof C Gaetan, University of Padua, Italy. PhD awarded 2013.
Frank DondelingerA machine learning approach to reconstructing signalling pathways and interaction networks in biology. Dirk Husmeier with Dr A Storkey, University of Edinburgh. PhD awarded 2012.
Naomi FoxThe impacts of climate change on livestock parasites in the UK. Glenn Marion with Dr M Hutchings, SRUC, and Dr P White, University of York. PhD awarded 2013.
Joanne Hardstaff The role of wildlife in the epidemiology of TB in livestock in EU livestock systems. Glenn Marion with Dr M Hutchings, SRUC, and Dr P White, University of York. PhD awarded 2012.
Max Lau (Siu-yin Lau)Statistical inference in spatial epidemics. Glenn Marion with Prof G Gibson and Dr G Streftaris, Heriot Watt University.
Jamie Prentice Modelling the effects of population control on badger social systems and TB dynamics. Glenn Marion with Dr M Hutchings, SRUC, and P White, University of York. PhD awarded 2012.
Alastair RushworthSpatial regression for river networks. Mark Brewer with Dr S Langan & Dr S Dunn, James Hutton Institute, and Prof A Bowman, University of Glasgow.
Gustaf RydevikStatistical aspects of emerging wildlife disease surveillance. Giles Innocent and Glenn Marion with Dr M Hutchings, SRUC, and Dr P White, University of York.
Beatrice SellExploring and quantifying relationships between gut bacteria and their products. Grietje Holtrop with Dr I Stansfield, University of Aberdeen.
Chris SutherlandA quantitative investigation of metapopulation dynamics in a naturally fragmented population of water vole Arvicola amphibius. David Elston with Prof X Lambin, University of Aberdeen, and Dr L Thomas, University of St Andrews. PhD awarded 2013
Syarifah Nasrisya Syed Nor AzlanStochastic and spatial modelling of vector-borne disease: parameter estimation and risk assessment of Dengue fever in Peninsular Malaysia. Glenn Marion with Dr I Krishnarajah, Universiti Putra Malaysia.
Laura WaltonModelling wildlife disease emergence and surveillance. Glenn Marion with Dr M Hutchings, SRUC, and Dr P White, University of York.
Leo ZijerveldDeveloping models for TB transmission and control in heterogeneous wildlife populations. Glenn Marion with Dr R McDonald, FERA, and Dr M Hutchings, SRUC, and University of Edinburgh. PhD awarded 2012.
PhD students 2011-13 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 was Head of Research and
leader of the Statistical Methodology research
theme until his retirement in August 2013. 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.
Frank Wright is Principal Consultant for
Plant Science. He specialises in the analysis of genome sequence data, including estimating phylogenetic relationships from DNA
multiple alignments.
David Nutter is IT Manager and is
responsible for organisationand delivery of IT support
in BioSS. He chairs theComputer Liaison Groupwhich draws together IT
expertise in seven Scottishresearch organisations.
Sarah Hirstwoodis Administrative Officerand leads the delivery ofadministrative services inBioSS, including finance,
personnel and recordkeeping. She is the chief
point of contact onadministrative mattersbetween BioSS and our
parent organisation, James Hutton Institute.
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.
Ian Simpson joined BioSS in October
2012 and leads the Statistical Bioinformatics and Genomics research
theme. His main research interests lie in developing
statistical, machine learning and computational methods to analyse and integrate the many types of genome scale
data currently being generated.
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.
Glenn Marion leads the Process and
Systems Modelling research theme, and has been Head of Research from August
2013. His primary research area is stochastic process modelling, motivated by
collaborations with scientists from many
disciplines.
Appendix 1 Selected Research Grants, Contracts & Subcontracts 2011-2013
Appen
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EUROPEAN PROJECTSDevelop smart tools for predictions and improvement of crop yield, using pepper as the exemplar crop, with 7 European partners, funded by EU (C A Glasbey, Y Song, G W Horgan).
Strategies for the eradication of bovine tuberculosis, with SRUC and 11 European partners, funded by EU (G R Marion).
Novel technologies for surveillance of emerging and re-emerging infections of wildlife, with SRUC and 12 European partners, funded by EU (G R Marion, G T Innocent).
Future of the Atlantic marine environment, with RSPB and 5 European partners, funded by EU (A Butler).
Governance, infrastructure, lifestyle dynamics and energy demand: European post-carbon communities, with James Hutton Institute and 4 European partners, funded by EU (J M Potts, E I Duff ).
UK RESEARCH COUNCIL PROJECTSIntegrated genomic and proteomic characterisation of autotransporter proteins of obligate intracellular bacteria C. abortus and L. intracellularis, with MRI, funded by BBSRC (F Wright, J Sales, K Mackenzie).
GOVERNMENT PROJECTSEpidemiology, population health and infectious disease control, with SRUC, University ofEdinburgh, University of Glasgow, James Hutton Institute, and MRI, funded by Scottish Government (I J McKendrick, G R Marion, G T Innocent).
Integrating the use of climate impact projections in the MRPs, with James Hutton Institute and SRUC, funded by Scottish Government (D A Elston, A Butler, H Kettle, J M Potts).
Electronic identification of the Scottish sheep flock, with Scottish Agricultural Organisation Society, funded by the Scottish Government (C A Glasbey, S Brocklehurst, S Catterall).
The contribution of green and open space in public health and well-being, with James Hutton Institute, funded by Scottish Government (M J Brewer, E I Duff ).
European foulbrood: a risk-based approach to determining the optimum protocol for examining apiaries, funded by Scottish Government (I J McKendrick, G T Innocent, S Catterall).
Design and analysis of SG-funded variety trials and provision of advice, funded by Scottish Government (A M I Roberts, M A M Kirkwood, I M Nevison).
Development of high profile germplasm for UK production of blueberries with James Hutton Institute, funded by Scottish Government (C A Hackett).
Statistical analysis on transmissible spongiform encephalopathy (TSE) project, with MRI, funded by Defra (J Palarea-Albaladejo).
Research towards an integrated measurement of meat eating quality, funded by Scottish Government and Quality Meat Scotland (M Nath, C A Glasbey).
A study to investigate the management and welfare of continuously housed dairy cows, with SRUC, funded by Defra (I M Nevison).
Study to assess the subjective experience of broiler chickens with different gait scores, with SRUC funded by Defra (S Brocklehurst).
Wild bird indicator contract, with RSPB, funded by Defra (A Butler).
Measurements of methane emissions from livestock and their manures, with IBERS, funded by Defra (I J McKendrick).
Early environment effects on animal welfare, health and productivity, with SRUC, funded by Defra (S Brocklehurst).
Quantifying the subjective state of feed restricted broiler chickens using behavioural and neurochemical measures, with SRUC, funded by Defra (S Brocklehurst, M Nath).
To develop a cost effective and practical method to reduce E.coli O157 infection in cattle prior to slaughter, with SRUC, funded by Defra (I J McKendrick).
Study to provide scientific evidence on whether cage-based breeding for pheasants and partridges can fully meet birds’ needs, and if not to identify best practice for improving the breeding environment for gamebirds, with SRUC, funded by Defra (S Brocklehurst).
Welfare costs and benefits of existing and novel on-farm culling methods of poultry, with SRUC, funded by Defra (S Brocklehurst).
Epidemiological study to identify acceptable maximum journey lengths for pigs whilst maintaining welfare with SRUC, funded by Defra (I J McKendrick, M Nath, I Nevison).
Improvements to the national inventory: Methane Steering Group committee membership, with the University of Aberystwyth, funded by Defra (I J McKendrick).
Biological Impacts of Climate Change Observation-Net 2, with BTO and 6 other partners, funded by a Defra-led consortium of public bodies (M J Brewer, D A Elston).
Land-use intensity and ecological engineering – assessment tools for risks and opportunities in irrigated rice based production systems, with Helmholtz-Centre for Environmental Research, funded by German Federal Ministry of Education and Research (H Kettle, G R Marion).
GOVERNMENT AGENCY PROJECTSRiver Dee pearl mussel population: linking macrohabitat data with distribution, with James Hutton Institute, funded by Scottish Natural Heritage (M J Brewer, L Spezia).
Advice, committee work and analysis for crop trials and certification, funded by Fera (A M I Roberts, E A Hunter, M A M Kirkwood, A D Mann, I Nevison, D Nutter).
Forestry Commission remote sensing support contract, funded by UK Space Agency (G Horgan).
Government information from the space sector project support, funded by UK Space Agency (G Horgan).
Habitat association modelling of tern Sterna sp. tracking data including refinements, funded by the Joint Nature Conservation Committee (M J Brewer, J M Potts. E I Duff ).
Prediction of new colonies-seabird tracking data, funded by the Joint Nature Conservation Committee (J M Potts, M J Brewer, E I Duff ).
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GENERAL CONSULTANCY AGREEMENTSStatistical consultancy, advice and collaboration to Centre for Ecology and Hydrology, funded by Centre for Ecology and Hydrology (A Butler, J M Potts).
Statistical consultancy, advice and collaboration to Science and Advice for Scottish Agriculture, funded by SASA (A M I Roberts, G W Horgan, A D Mann, I M Nevison, J Palarea-Albaladejo).
Provision of statistical advice to Royal Society for the Protection of Birds research staff, funded by RSPB (A Butler, D A Elston).
Provision of statistical advice on the design and analysis of clinical trials for Provexis, funded by Provexis plc (G W Horgan).
Statistical consultancy for Slimming World, funded by Slimming World (G W Horgan).
Training services in biostatistics with focus on benefit assessment of foods, funded by EFSA (G W Horgan, C-D Mayer).
Statistical consultancy, advice and collaboration to the Royal Horticultural Society funded by Royal Horticultural Society (I Nevison).
Service level agreement to provision of statistical advice on the design and analysis to Triveritas, funded by Triveritas Ltd (G T Innocent, M Nath).
LEVY BOARD PROJECTSAdvice on the Home-Grown Cereals Authority recommended list variety trial processing system and fungicide performance trials, funded by Home-Grown Cereals Authority (A M I Roberts, E A Hunter, A D Mann, I M Nevison).
Appropriate fungicide doses on barley – production of dose response curves to advise growers on fungicide efficacy and potential resistance, with SRUC, funded by Home-Grown Cereals Authority (A M I Roberts, E A Hunter).
Management of clubroot (Plasmodiophora brassicae) in winter oilseed rape, with SRUC, funded by Home-Grown Cereals Authority (A M I Roberts and C M Theobald).
Rhynchosporium on barley: understanding the relationship between barley varietal resistance, fungicide resistance and the influence of seed-borne infection, with SRUC, funded by Home-Grown Cereals Authority (A M I Roberts, E A Hunter).
Advice for, and analysis of, independent variety trials, with SASA, SRUC & James Hutton Institute, funded by Potato Council (A M I Roberts, M A M Kirkwood, I M Nevison).
Protecting potatoes from free-living nematodes, with SRUC, funded by TSB and Potato Council (Z Fang and C M Theobald).
Variety trial processing software specifications, with NIAB & AFBI, funded by Fera (A M I Roberts, D Nutter, A D Mann, I Nevison).
OTHER PROJECTSDesign and analysis of variety trials, funded by British Society of Plant Breeders (A M I Roberts, M A M Kirkwood, I M Nevison).
Improving the conservation status of hen harriers in the English upland, with University of Aberdeen, funded by a consortium of stakeholders via Environment Council (D A Elston, L Spezia).
Ultrasound applications in broiler chickens, funded by Aviagen (C A Glasbey, D Nutter).
Analysis of seabird density data, with ESS Ecology, funded by Scottish and Southern Energy Renewables (D A Elston).
Modelling seabird collision risk for offshore wind turbines, with ESS Ecology, funded by Scottish and Southern Energy Renewables (D A Elston, S Catterall).
Consultancy for NOVUS, funded by NOVUS International, USA (G W Holtrop).
Why some hosts have high parasite burdens and the implications for the design of sustainable control strategies, with University of Glasgow, funded by Wellcome Trust (G R Marion)
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Appendix 2 Refereed Publications
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Grzegorczyk M., Husmeier D. & Rahnenfuehrer J. 2011. Modelling non-stationary dynamic gene regulatory processes with the BGM model. Computational Statistics 26, 199-218.
Horgan G.W. & Glasbey C.A. 2012. Image Analysis. In Encyclopedia of Environmetrics, 2nd edition, Eds. A.-H. El-Shaarawi and W. Piegorsch, 1329-1332. Wiley, Chichester. ISBN 9780470057339.
Horgan G.W. 2011. The behaviour of a neutral model of weight regulated only by body mass. Journal of Theoretical Biology 270, 1-6.
Husmeier D., Dondelinger F. & Lebre S. 2011. Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. In Proceedings of the twenty-fourth annual conference on Neural Information Processing Systems (NIPS), Eds. Lafferty, J et al., 901-909. Curran Associates. ISBN 9781617823800.
Lawson D.J., Holtrop G. & Flint H.J. 2011. Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem. Biometrical Journal 53, 543-556.
Lewis F.I., Butler A. & Gilbert L. 2011. A unified approach to model selection using the likelihood ratio test. Methods in Ecology and Evolution 2, 155-162.
Lin K., Husmeier D., Dondelinger F., Mayer C-D., Liu H., Pritchard L., Salmond G., Toth I.K. & Birch P.R.J. 2011. Reverse engineering gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum. In Computational Biology, Eds. David Fenyo, 253-281. Humana Press. ISBN 978-1-60761-841-6..
Liu J., Francis B. & Soothill K. 2011. A longitudinal study of escalation in crime seriousness. Journal of Quantitative Criminology 27, 175-196.
MacKenzie K. & Hackett C.A. 2012. Association mapping in a simulated barley population. Euphytica 183, 337-347.
Marion G., McInery G.J., Pagel J., Catterall S., Cook A.R., Hartig F. & O’Hara R.B. 2012. Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche. Journal of Biogeography 39, 2225-2239.
Martin-Fernandez J.A., Hron K., Templ M., Filzmoser P. & Palarea Albaladejo J. 2012. Model-based replacement of rounded zeros in CODA: classical and robust approaches. Computational Statistics and Data Analysis 56, 2688-2704.
Martin-Fernandez J.A., Palarea Albaladejo J. & Barcelo-Vidal C. 2011. Técnicas composicionales para concentraciones geoquímicas por debajo del límite de detección. Boletin Instituto Geologico y Minero de España 122, 459-468.
Martin-Fernandez J.A., Palarea Albaladejo J. & Olea R.A. 2011. Dealing with zeros. In Compositional data analysis. Theory and Applications, Eds. Pawlowsky-Glahn, V. & Buccianti, A, 43-58. Wiley, London, UK. ISBN 978-0470711354.
Mayer C-D., Lorent J. & Horgan G.W. 2011. Exploratory analysis of multiple omics datasets using the adjusted RV coefficient. Statistical Applications in Genetics and Molecular Biology 10.
Nevison I.M. & Roberts A.M.I. 2011. Predicting distinctness decisions after one growing cycle. Biuletyn Oceny Odmian 33, 35-47.
Palarea Albaladejo J. & Martin-Fernandez J.A. 2011. Examining distance-based grouping on the simplex sample space: the fuzzy clustering case. In 26th International Workshop on Statistical Modelling, IWSM 2011, Eds. Conesa, D., Forte, A., López-Quilez, A., Muñoz, F., 450-453. Copiformes S.L., Valencia, Spain. ISBN 978-84-694-5129-8.
Palarea Albaladejo J., Martin-Fernandez J.A. & Olea R.A. 2011. Non-detect bootstrap method for estimating distributional parameters of compositional samples revisited: a multivariate approach. In Proceedings of The Fourth Compositional Data Workshop, CODAWORK’11, Eds. Egozcue, J.J., Tolosana-Delgado, R., Ortego, M.I. Universitat de Girona, Girona, Spain. ISBN 978-84-87867-76-7.
Palarea Albaladejo J., Martin-Fernandez J.A. & Soto J.A. 2012. Dealing with distances and transformations for fuzzy C-means clustering of compositional data. Journal of Classification 29, 144-169.
1. METHODOLOGYAdams T., Ackland G., Marion G. & Edwards C. 2011. Effects of local interaction and dispersal in size-structured model populations. Ecological Modelling 222, 1414-1422.
Adams T., Ackland G., Marion G. & Edwards C. 2011. Understanding plantation transformation using a size-structured spatial population model. Forest Ecology and Management 261, 799-809.
Aderhold A., Husmeier D., Lennon J.J., Beale C.M. & Smith V.A. 2012. Hierarchical Bayesian models in ecology: reconstructing species interaction networks from non-homogeneous species abundance data. Ecological Informatics 11, 55-64.
Adriaens M.E., Jaillard M., Eijssen L.M.T., Mayer C-D. & Evelo C. 2012. An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies. BMC Genomics 13.
Bayer M., Milne I., Gordon S., Shaw P., Cardle L., Wright F. & Marshall D. 2011. Comparative visualization of genetic and physical maps with Strudel. Bioinformatics 27, 1307-1308.
Brewer M.J., O’Hara R.B., Anderson B.J. & Ohlemüller R. 2011. Climate Envelopes for Species Distribution Models. In 26th International Workshop on Statistical Modelling, Eds. Conesa, D., Forte, A., López-Quílez, A. and Muñoz, F., 93-98. IWSM 2011, Valencia, Spain. ISBN 9788469451298.
Brewer M.J., Sulkava M., Mäkinen H., Korpela M., Nöjd P. & Hollmén J. 2011. Logistic fitting method for detecting onset and cessation of tree stem radius increase. In Intelligent Data Engineering and Automated Learning - IDEAL 2011, Eds. H. Yin, W. Wang, V. Rayward-Smith, 204-211. Springer Verlag, Heidelberg. ISBN 978-3-642-23877-2.
Brewer M.J., Tetzlaff D., Malcolm I.A. & Soulsby C. 2011. Source distribution modelling for end-member mixing in hydrology. Environmetrics 22, 921-932.
Buckland S.T., Baillie S.R., Dick J.McP., Elston D.A., Magurran A.E., Scott E.M., Smith R.I., Somerfield P.J., Studney A.C. & Watt A.D. 2012. How should regional biodiversity be monitored? Environmental and Ecological Statistics 19, 601-626.
Catterall S., Cook A.R., Marion G., Butler A. & Hulme P.E. 2012. Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio-temporal dynamics of alien invasions using distribution data. Ecography 35, 901-911.
Davidson R.S., McKendrick I.J., Wood J.C., Marion G., Greig A., Stevenson K., Sharp J.M. & Hutchings M.R. 2012. Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds. BMC Veterinary Research 8, 159.
Denwood M.J., Love S., Innocent G.T., Matthews L., McKendrick I.J., Hillary N., Smith A. & Reid S.W.J. 2012. Quantifying the sources of variability in equine faecal egg counts: implications for improving the utility of the method. Veterinary Parasitology 188, 120-126.
Dondelinger F., Aderhold A., Lebre S., Grzegorczyk M. & Husmeier D. 2011. A Bayesian regression and multiple changepoint model for systems biology. In 26th International Workshop on Statistical Modelling, IWSM 2011, Eds. Conesa, D., Forte, A., López-Quilez, A., Muñoz, F., 189-194. Copiformes S.L., Valencia, Spain. ISBN 978-84-694-5129-8.
Elston D.A., Nevison I.M., Sier A.R.J., Scott A.W. & Morecroft M.D. 2011. Power calculations for monitoring studies: a case study with alternative models for random variation. Environmetrics 22, 618-625.
Grzegorczyk M. & Husmeier D. 2011. Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinformatics 27, 693-699.
Grzegorczyk M. & Husmeier D. 2011. Non-homogeneous dynamic Bayesian networks for continuous data. Machine Learning 83, 355-419.
Grzegorczyk M. & Husmeier D. 2012. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology. Statistical Applications in Genetics and Molecular Biology 11.
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Dieleman A., Magan J.J., Wubs M., Palloix A., Lenk S., Glasbey C.A. & van Eeuwijk F. 2012. Large scale phenotyping and data analysis of pepper genotypes in the EU-SPICY project. In XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on Genomics and Genetic Transformation of Horticultural Crops. Acta Horticulturae 929, Eds. R.E. Litz, K.M. Folta, M. Talon and F. Pliego Alfaro, 299-306. International Society for Horticultural Science. ISBN 9789066050488.
Dobson G., Vasukuttan V. & Alexander C.J. 2012. Evaluation of different protocols for the analysis of lipophilic plant metabolites by gas chromatography-mass spectrometry using potato as a model. Metabolomics 8, 880-893.
Dobson P., Graham J., Stewart D., Brennan R.M., Hackett C.A. & McDougall G. 2012. Over-seasons analysis of quantitative trait loci affecting phenolic content and antioxidant capacity in raspberry. Journal of Agricultural and Food Chemistry 60, 5360-5366.
Dolan A., MacFarlane S.A., McGavin W.J., Brennan R.M. & McNicol J.W. 2011. Blackcurrant reversion virus - validation of an improved diagnostic test, accelerating testing in breeding and certification of blackcurrants. Journal of Berry Research 1, 201-208.
Graham J., Hackett C.A., Smith K., Woodhead M., MacKenzie K., Tierney I., Cooke D.E.L., Bayer M.M. & Jennings S.N. 2011. Towards an understanding of the nature of resistance to Phytophthora root rot in red raspberry. Theoretical and Applied Genetics 123, 585-601.
Jiang N., Wang M., Jia T., Wang L., Leach L., Hackett C.A., Marshall D. & Luo Z.W. 2011. A robust statistical method for association-based eQTL analysis. PLoS ONE 6, e23192.
Jupe F., Pritchard L., Etherington G.J., MacKenzie K., Cock P.J.A., Wright F., Sharma S.K., Bolser D., Bryan G.J., Jones J.D.G. & Hein I. 2012. Identification and localisation of the NB-LRR gene family within the potato genome. BMC Genomics 13.
Kalyna M., Simpson C.G., Syad N., Lewandowska D., Marquez Y., Kuzenda B., Marshall J., Fuller J., Milne L., McNicol J.W., Dihn H., Barta A. & Brown D.J.F. 2012. Alternative splicing and nonsense-mediated decay modulate expression of important regulatory genes in Arabidopsis. Nucleic Acids Research 40, 2454-2469.
Lees A.K., Stewart J.A., Lynott J.S., Carnegie S.F., Campbell H. & Roberts A.M.I. 2012. The effect of a dominant Phytophthora infestans genotype (13_A2) in Great Britain on host resistance to foliar late blight in commercial potato cultivars. Potato Research 55, 125-134.
Liu H., McNicol J.W., Bayer M.M., Morris J.A., Cardle L., Marshall D., Schulte D., Stein N., Shi B-J., Taudien S., Waugh R. & Hedley P.E. 2011. Highly parallel gene-to-BAC addressing using microarrays. Biotechniques 50, 165-174
Ramsay L., Comadran J., Druka A., Marshall D.F., Thomas W.T.B., Macaulay M., MacKenzie K., Simpson C.G., Fuller J., Bonar N., Hayes P., Lundqvist U., Franckowiak J.D., Close T., Muehlbauer G. & Waugh R. 2011. Intermedium-C, a modifier of lateral spikelet fertility in barley, is an ortholog of the maize domestication gene TEOSINTE BRANCHED 2. Nature Genetics 43, 169-172.
Russell J.R., Bayer M., Booth C., Cardle L., Hackett C.A., Hedley P.E., Jorgenson L. & Brennan R.M. 2011. Identification, utilisation and mapping of novel transcriptome-based markers from blackcurrant (Ribes nigrum). BMC Plant Biology 11, doi:10.1186/1471-2229-11-147.
White P.J., Broadley M.R., Thompson J.A., McNicol J.W., Crawley M.J., Poulton P.R. & Johnston A.E. 2012. Testing the distinctness of shoot ionomes of angiosperm families using the Rothamsted Park grass continuous hay experiment. New Phytologist 196, 101-9.
3. ANIMAL HEALTH AND WELFAREBaker R.H., Buschbaum S., Matthews J.B., McKendrick I.J., Schnieder T., Strube C. & Nisbet A.J. 2011. GTP-Cyclohydrolase and development in Teladorsagia circumcincta and Dictyocaulus viviparus (Nematoda: Strongylida). Experimental Parasitology 128, 309-317.
Potts J.M. 2011. Basic Concepts. In Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd Edition, Eds. Jolliffe, I.T. and Stephenson, D.B., 11-28. Wiley, Chichester. ISBN 978-0-470-66071-3.
Roberts A.M.I. 2012. Comparison of regression methods for phenology. International Journal of Biometeorology 56, 707-717.
Rushworth A.M., Bowman A.W., Brewer M.J. & Langan S.J. 2011. Distributed lag models for hydrological data. In 26th International Workshop on Statistical Modelling, Eds. Conesa, D., Forte, A., López-Quílez, A. and Muñoz, F, 529-533. IWSM 2011, Valencia, Spain. ISBN 9788469451298.
Song Y., Glasbey C.A., van der Heijden G.W.A.M., Polder G. & Dieleman A. 2011. Combining stereo and Time-of-Flight images with application to automatic plant phenotyping. In Proceedings of the 17th Scandinavian Conference on Image Analysis (SCIA 2011), Eds. Heyden, Anders and Kahl, Fredrik, 467-478. Springer Verlag, Berlin. ISBN 9783642212260.
Spezia L., Futter M.N. & Brewer M.J. 2011. Periodic multivariate Normal hidden Markov models for the analysis of water quality time series. Environmetrics 22, 304-317.
Theobald C.M., Chatterjee A. & Horgan G.W. 2012. A hierarchical Bayesian mixture model for repeated dietary records. Food and Chemical Toxicology 50, 320-327.
van der Heijden G.W.A.M., Song Y., Horgan G.W., Polder G., Dieleman A., Bink M., Palloix A., van Eeuwijk F. & Glasbey C.A. 2012. SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Functional Plant Biology 39, 870-877.
2. PLANT SCIENCEClark K.E., Hartley S.E., Brennan R.M., Jennings S.N., McMenemy L.S., McNicol J.W., Mitchell C. & Johnson S.N. 2012. Effects of cultivar and egg density on a colonising vine weevil (Otiorhynchus sulcatus) population and its impacts on red raspberry growth and yield. Crop Protection 32, 76-82.
Clark K.E., Hartley S.E., Brennan R.M., MacKenzie K. & Johnson S.N. 2012. Oviposition and feeding behaviour by the vine weevil Otiorhynchus sulcatus on red raspberry: effects of cultivars and plant nutritional status. Agricultural and Forest Entomology 14, 157-163.
Clark K.E., Hartley S.E., Brennan R.M., MacKenzie K. & Johnson S.N. 2012. Investigating preference-performance relationships in aboveground-belowground life cycles: a laboratory and field study with the vine weevil (Otiorhynchus sulcatus). Bulletin of Entomological Research 102, 63-70.
Comadran J., Ramsay L., MacKenzie K., Hayes P., Close T.J., Muehlbauer G., Stein N. & Waugh R. 2011. Patterns of polymorphism and linkage disequilibrium in cultivated barley. Theoretical and Applied Genetics 122, 523-531.
Cooke D.E.L., Cano L.M., Raffaele S., Bain R.A., Cooke L.R., Etherington G.J., Deahl K.L., Farrer R.A., Gilroy E.M., Goss E.M., Gruenwald N.J., Hein I., MacLean D., McNicol J.W., Randall E., Oliva R.F., Pel M.A., Shaw D.S., Squires J.N., Taylor M.C., Vleeshouwers V.G.A.A., Birch P.R.J., Lees A.K. & Kamoun S. 2012. Genome analyses of an aggressive and invasive lineage of the Irish potato famine pathogen. PLoS Pathogens 8, e1002940.
Daniell T.J., Davidson J., Alexander C.J., Caul S. & Roberts D.M. 2012. Improved real-time PCR estimation of gene copy number in soil extracts using an artificial reference. Journal of Microbiological Methods 91, 38-44.
Deflorio G., Horgan G.W., Jaspars M. & Woodward S. 2012. Defence response of Sitka spruce before and after inoculation with Heterobasidion annosum: 1H NMR fingerprinting of bark and sapwood metabolites. Analytical and Bioanalytical Chemistry 402, 3333-3344.
Deflorio G., Horgan G.W., Woodward S. & Fossdal C.G. 2011. Gene expression profiles, phenolics and lignin of Sitka spruce bark and sapwood before and after wounding and inoculation with Heterobasidion annosum. Physiological and Molecular Plant Pathology 75, 180-187.
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Getachew A.M., Innocent G.T., Trawford, A., Reid S.W.J. & Love S. 2012. Gasterophilosis: a major cause of rectal prolapse in working donkeys in Ethiopia. Tropical Animal Health and Production 44, 757-762.
Halliday A.M., Lainson F.A., Yaga R., Inglis N.F., Bridgett S., Nath M. & Knox D.P. 2012. Transcriptional changes in Teladorsagia circumcincta upon encountering host tissue of differing immune status. Parasitology 139, 387-405.
Hardstaff J.L., Bulling M.T., Marion G., Hutchings M.R. & White P.C.L. 2012. Impact of external sources of infection on the dynamics of bovine tuberculosis in badger populations. BMC Veterinary Research 8, DOI: 10.1186/1746-6148-8-92.
Heller J., Innocent G.T., Denwood M.J., Reid S.W.J., Kelly L. & Mellor D.J. 2011. Assessing the probability of acquisition of meticillin-resistant Staphylococcus aureus (MRSA) in a dog using a nested stochastic simulation model and logistic regression sensitivity analysis. Preventive Veterinary Medicine 9, 211-224.
Hughes V.M., Garcia-Sanchez A., Smith S., McLean K., Lainson F.A., Nath M. & Stevenson K. 2012. Proteome-determined type-specific proteins of Mycobacterium avium subspecies paratuberculosis. Veterinary Microbiology 158, 153-162.
Humphry R.W., Bruelisauer F., McKendrick I.J., Nettleton P. & Gunn G.J. 2012. Prevalence of antibodies to bovine viral diarrhoea virus in bulk tank milk and associated risk factors in Scottish dairy herds. Veterinary Record 171, 445.
Lambe N.R., Richardson R.I., Macfarlane J.M., Nevison I.M., Haresign W., Matika O. & Bunger L. 2011. Genotypic effects of the Texel Muscling QTL (TM-QTL) on meat quality in purebred Texel lambs. Meat Science 89, 125-132.
Lewis F.I., Brulisauer F., Cousens C., McKendrick I.J. & Gunn G.J. 2011. Diagnostic accuracy of PCR for Jaagsiekte sheep retrovirus using field data from 125 Scottish sheep flocks. Veterinary Journal 187, 104-108.
Nunn F.G., Burgess S.T.G., Innocent G.T., Nisbet A.J., Bates P. & Huntley J.F. 2011. Development of a serodiagnostic test for sheep scab using recombinant protein, Pso o 2. Molecular and Cellular Probes 25, 212-218.
Russell G.C., Benavides J., Grant D.M., Todd H., Thomson J., Puri V., Nath M. & Haig D. 2012. Host gene expression changes in cattle infected with Alcelaphine herpesvirus 1. Virus Research 169, 246-254.
Sait M., Clark E.M., Wheelhouse N., Spalding L., Livingstone M., Sachseh K., Markey B.K., Magnino S., Siarkou V.I., Vretou E., Caro M.R., Yaga R., Lainson F.A., Smith D.G.E., Wright F. & Longbottom D. 2011. Genetic variability of Chlamydophila abortus strains assessed by PCR-RFLP analysis of polymorphic membrane protein-encoding genes. Veterinary Microbiology 151, 284-290.
Sandilands V., Brocklehurst S., Sparks N., Baker L., McGovern R., Thorp B. & Pearson D. 2011. Assessing leg health in chickens using a force plate and gait scoring: how many birds is enough? Veterinary Record 168, 77.
Thonur L., Maley M., Gilray J.A., Crook T., Laming E., Turnbull D., Nath M. & Willoughby K. 2012. One-step multiplex real time RT-PCR for the detection of bovine respiratory syncytial virus, bovine herpesvirus 1 and bovine parainfluenza virus 3. BMC Veterinary Research 8, 37.
Tolkamp B.J., Allcroft D.J., Barrio J.P., Bley T.A.G., Howie J.A., Jacobsen T.B., Morgan C.A., Schweitzer D.P.N., Wilkinson S., Yeates M.P. & Kyriazakis I. 2011. The temporal structure of feeding behaviour. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology 301, R378-R393.
Vinuela-Fernandez I., Jones E., McKendrick I.J. & Molony V. 2011. Quantitative assessment of increased sensitivity of chronic laminitic horses to hoof-tester evoked pain. Equine Veterinary Journal 43, 62-68.
Wemelsfelder F., Hunter E.A., Paul E.S. & Lawrence A.B. 2012. Assessing pig body language: agreement and consistency between pig farmers, veterinarians, and animal activists. Journal of Animal Science 90, 3652-3665.
Ballingall K.T., Nath M., Holliman A., Laming E., Steele P.J. & Willoughby K. 2011. Lack of evidence for an association between MHC diversity and the development of bovine neonatal pancytopenia in Holstein dairy cattle. Veterinary Immunology and Immunopathology 141, 128-132.
Bartley K., Huntley J.F., Wright H., Nath M. & Nisbet A.J. 2012. Assessment of cathepsin D and L-like proteinases of poultry red mite, Dermanyssus gallinae (De Geer), as potential vaccine antigens. Parasitology 139, 755-765.
Bartley P.M., Wright S.E., Maley S.W., Macaldowie C.N., Nath M., Hamilton C.M., Katzer F., Buxton D.A. & Innes E.A. 2012. Maternal and foetal immune responses of cattle following an experimental challenge with Neospora caninum at day 70 of gestation. Veterinary Research 43, 38.
Baxter E.M., Jarvis S., Palarea Albaladejo J. & Edwards S.A. 2012. The weaker sex? The propensity for male-biased piglet mortality. PLoS ONE 7, e30318, doi:10.1371/journal.pone.
Benavides J., Katzer F., Maley S.W., Bartley P.M., Cantón G.J., Palarea Albaladejo J., Purslow C., Pang Y.P., Rocchi M.S., Chianini F., Buxton D.A. & Innes E.A. 2012. High rate of transplacental infection and transmission of Neospora caninum following experimental challenge of cattle at day 210 of gestation. Veterinary Research 43.
Benavides J., Maley S.M., Pang Y.P., Palarea Albaladejo J., Eaton S.L., Katzer F., Innes E.A., Buxton D.A. & Chianini F. 2011. Development of lesions and tissue distribution of parasite in lambs orally infected with sporulated oocysts of Toxoplasma gondii. Veterinary Parasitology 179, 209-215.
Bunger L., Macfarlane J.M., Lambe N.R., Conington J., McLean K.A., Moore K., Glasbey C.A. & Simm G. 2011. Use of X-ray computed tomography (CT) in UK sheep production and breeding. In CT Scanning, Eds. K. Subburaj. InTech - an Open Access publisher. ISBN 978-953-307-305-7.
Burgess C.G.S., Bartley Y., Redman E., Skuce P.J., Nath M., Whitelaw F., Tait A., Gilleard J.S. & Jackson F. 2012. A survey of the trichostrongylid nematode species present on UK sheep farms and associated anthelmintic control practices. Veterinary Parasitology 189, 299-307.
Burgess S., Innocent G.T., Nunn F., Frew D., Kenyon F., Nisbet A.J. & Huntley J.F. 2012. The use of a Psoroptes ovis serodiagnostic test for the analysis of a natural outbreak of sheep scab. Parasites and Vectors 5.
Chase-Topping M.E., Rosser T., Allison L.J., Courcier E., Evans J., McKendrick I.J., Pearce M.C., Handel I., Caprioli A., Karch H., Hanson M.F., Pollock K.G.J., Locking M., Woolhouse M.E.J., Matthews L., Low J.C. & Gally D.L. 2012. Pathogenic potential to humans of bovine Escherichia coli O26 in Scotland. Emerging Infectious Diseases 18, 439-448.
Dicker A., Nath M., Yaga R., Nisbet A.J., Lainson F.A., Gilleard J.S. & Skuce P.J. 2011. Teladorsagia circumcincta: the transcriptomic response of a multi-drug-resistant isolate to ivermectin exposure in vitro. Experimental Parasitology 127, 351-356.
Evans J., Knight H.I., McKendrick I.J., Stevenson H., Barbudo A.V., Gunn G.J. & Low J.C. 2011. Prevalence of Escherichia coli O157:H7 and serogroups O26, O103, O111 and O145 in sheep presented for slaughter in Scotland. Journal of Medical Microbiology 60, 653-660.
Forbes A.B., Ramage C., Sales J., Baggott D. & Donachie W. 2011. Determination of the duration of antibacterial efficacy following administration of gamithromycin using a bovine Mannheimia haemolytica challenge model. Antimicrobial Agents and Chemotherapy 55, 831-835.
Fox N., Marion G., Davidson R.S., White P.C.L. & Hutchings M.R. 2012. Livestock helminths in a changing climate: approaches and restrictions to meaningful predictions. Animals 2, 93-107.
Fox N., White P.C.L., McClean C.J., Marion G., Evans A. & Hutchings M.R. 2011. Predicting impacts of climate change on Fasciola hepatica risk. PLoS ONE 6, e16126.
Getachew A.M., Innocent G.T., Proudman C.J., Trawford, A., Feseha G., Reid S.W.J., Faith B. & Love S. 2012. Equine cestodosis: a sero-epidemiological study of Anoplocephala perfoliata infection in Ethiopia. Veterinary Research Communications 36, 93-98.
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Mayes R.W., Brewer M.J., Dawson L.A. & Ross J.M. 2012. An objective method for comparing unresolved complex mixture (UCM) ‘humps’ in gas chromatograms. In Environmental Forensics: Proceedings of the 2011 INEF Conference, Eds. Robert D. Morrison, Gwen O’Sullivan. Royal Society of Chemistry, Cambridge. ISBN 978-1849733724.
McLaggan D., Amezaga M.R., Petra E., Frost A., Duff E.I., Rhind S.M., Fowler P.A., Glover L.A. & Lagido C. 2012. Impact of sublethal levels of environmental pollutants found in sewage sludge on a novel Caenorhabditis elegans model biosensor. PLoS ONE 7, e46503.
Mitchell R.J., Hester A.J., Campbell C.D., Chapman S.J., Cameron C.M., Hewison R.L. & Potts J.M. 2012. Explaining the variation in the soil microbial community: do vegetation composition and soil chemistry explain the same or different parts of the microbial variation? Plant and Soil 351, 355-362.
Mitchell R.J., Keith A.M., Potts J.M., Ross J.M., Reid E.J. & Dawson L.A. 2012. Overstory and understory vegetation interact to alter soil community composition and activity. Plant and Soil 352, 65-84.
O’Brien S., Webb A., Brewer M.J. & Reid J.B. 2012. Use of kernel density estimation and maximum curvature to set Marine Protected Area boundaries: identifying a Special Protection Area for wintering red-throated divers in the UK. Biological Conservation 156, 15-21.
Rhind S.M., Kyle C.E., Mackie C., Yates K. & Duff E.I. 2011. Geographic variation in tissue accumulation of endocrine disrupting compounds (EDCs) in grazing sheep. Environmental Pollution 159, 416-422.
Scott V., Kettle H. & Merchant C.J. 2011. Sensitivity analysis of an ocean carbon cycle model in the North Atlantic: an investigation of parameters affecting the air-sea CO2 flux, primary production and export of detritus. Ocean Science 7, 405-419.
Sutherland C., Elston D.A. & Lambin X. 2012. Multi-scale processes in metapopulations: contributions of stage structure, rescue effect and correlated extinctions. Ecology 93, 2465-2473.
Vinten A.J.A., Artz R.R.E., Thomas N., Potts J.M., Avery L., Langan S.J., Watson H., Cook Y., Taylor C., Abel C., Reid E.J. & Singh B.K. 2011. Comparison of microbial community assays for the assessment of stream biofilm ecology. Journal of Microbiological Methods 85, 190-198.
5. HUMAN HEALTH AND NUTRITIONBachmair E-M., Bots M.L., Mennen L.I., Kelder T., Evelo C., Horgan G.W., Ford I. & de Roos B. 2012. Effect of supplementation with an 80:20 cis9, trans11 conjugated linoleic acid blend on the human platelet proteome. Molecular Nutrition & Food Research 56, 1148-1159.
Beattie J.H., Gordon M-J., Duthie S.J., McNeil C.J., Horgan G.W., Nixon G.F., Feldmann J. & Kwun I.S. 2012. Suboptimal dietary zinc intake promotes vascular inflammation and atherogenesis in a mouse model of atherosclerosis. Molecular Nutrition & Food Research 56, 1097-1105.
Beattie J.H., Nicol F., Gordon M-J., Reid MD., Cantlay L., Horgan G.W., Kwun I.S., Ahn J.Y. & Ha T.Y. 2011. Ginger phytochemicals mitigate the obesogenic effects of a high-fat diet in mice: A proteomic and biomarker network analysis. Molecular Nutrition & Food Research 55, S203-13.
Belanche A.B., Abecia L., Holtrop G., Guada J.A., Castrillo C., de la Fuente G. & Balcells J. 2011. Study of the presence or absence of protoza on rumen fermentation and microbial protein contribution to the chyme. Journal of Animal Science 89, 4163-4174.
Belenguer A., Holtrop G., Duncan S.H., Anderson S.E., Calder A.G., Flint H.J. & Lobley G.E. 2011. Rates of production and utilisation of lactate by microbial communities from the human colon. FEMS Microbiology Ecology 77, 107-119.
Bosch M., Mayer C-D., Cookson A. & Donnison I. 2011. Identification of genes involved in cell wall biogenesis in grasses by differential gene expression profiling of elongating and non-elongating maize internodes. Journal of Experimental Botany 62, 3545-3561.
Wheelhouse N.M., Sait M., Aitchison K., Livingstone M., Wright F., McLean K., Inglis N.F., Smith D.G.E. & Longbottom D. 2012. Processing of Chlamydia abortus polymorphic membrane protein 18D during the chlamydial developmental cycle. PLoS ONE 7.
Wilson K., Sammin D., Harmeyer S., Nath M., Livingstone M. & Longbottom D. 2012. Seroprevalence of chlamydial infection in cattle in Ireland. Veterinary Journal 193, 583-585.
4. ECOLOGY AND ENVIRONMENTAL SCIENCEBirkel C., Paroli R., Spezia L., Dunn S.M., Tetzlaff D. & Soulsby C. 2012. A new approach to simulating stream isotope dynamics using Markov switching autoregressive models. Advances in Water Resources 46, 20-30.
Birkel C., Soulsby C., Tetzlaff D., Dunn S.M. & Spezia L. 2012. High-frequency storm event isotope sampling reveals time-variant transit time distributions and influence of diurnal cycles. Hydrological Processes 26, 308-316.
Burthe S., Butler A., Searle K.R., Hall S.J.G., Thackeray S.J. & Wanless S. 2011. Demographic consequences of increased winter births in a large aseasonally breeding mammal (Bos taurus) in response to climate change. Journal of Animal Ecology 80, 1134-1144.
Burthe S., Daunt F., Butler A., Elston D.A., Frederiksen M., Johns D., Newell M., Thackeray S.J. & Wanless S. 2012. Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web. Marine Ecology Progress Series 454, 119-133.
Cole L., Brocklehurst S., Elston D.A. & McCracken D. 2012. Riparian field margins: can they enhance the functional structure of ground beetle (Coleoptera: Carabidae) assemblages in intensively managed grassland landscapes? Journal of Applied Ecology 49, 1384-1395.
Cole L., Brocklehurst S., McCracken D., Harrison W. & Robertson D. 2012. Riparian field margins: their potential to enhance biodiversity in intensively managed grasslands. Insect Conservation and Diversity 5, 86-94.
Cooksley S., Brewer M.J., Donnelly D., Spezia L. & Tree A. 2012. Impacts of artificial structures on the freshwater pearl mussel Margaritifera margaritifera in the River Dee, Scotland. Aquatic Conservation: Marine and Freshwater Ecosystems 22, 318-330.
Cornulier T., Robinson R.A., Elston D.A., Lambin X., Sutherland W.J. & Benton T.G. 2011. Bayesian reconstitution of environmental change from disparate historical records: hedgerow loss and farmland bird declines. Methods in Ecology and Evolution 2, 86-94.
Demars B.O.L., Potts J.M., Trémolières M., Thiébaut G., Gougelin N. & Nordmann V. 2012. River macrophyte indices: not the Holy Grail! Freshwater Biology 57, 1745-1759.
Frederiksen M., Elston D.A., Edwards M., Mann A.D. & Wanless S. 2011. Mechanisms of long-term decline in size of lesser sandeels in the North Sea explored using a growth and phenology model. Marine Ecology Progress Series 432, 137-147.
Iason G.R., O’Reilly-Wapstra J.M., Brewer M.J., Summers R.W. & Moore B.D. 2011. Do multiple herbivores maintain chemical diversity of Scots pine monoterpenes? Philosophical Transactions of the Royal Society B: Biological Sciences 366, 1337-1345.
Kuester E., Bierman S., Klotz S. & Kühn I. 2011. Modelling the impact of climate and land use change on the geographical distribution of leaf anatomy in a temperate flora. Ecography 34, 507-518.
Last F. & Roberts A.M.I. 2012. Onset of flowering in biennial and perennial garden plant: association with variable weather and changing climate between 1978 and 2007. Sibbaldia 10, 85-132.
Linder H.P., Bykova O., Dyke J., Etienne R., Hickler T., Kühn I., Marion G., Ohlemüller R., Schymanski S.J. & Singer A. 2012. Biotic modifiers, environmental modulation and species distribution models. Journal of Biogeography 39, 2179-2190.
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Bouwman F.G., de Roos B., Rubio-Aliaga I., Crosley K., Duthie S.J., Mayer C-D., Horgan G.W., Polley A.C., Heim C., Coort S.L.M., Evelo C.T., Mulholland F., Johnson I.T., Elliott R., Daniel H. & Mariman E.C.M. 2011. 2D-electrophoresis and multiplex immunoassay proteomic analysis of different body fluids and cellular components reveal known and novel markers for extended fasting. BMC Medical Genomics 4.
de Roos B., Wanders A.J., Wood S., Horgan G.W., Rucklidge G., Reid M., Siebelink E. & Brouwer I.A. 2011. A high intake of industrial or ruminant trans fatty acids does not affect the plasma proteome in healthy men. Proteomics 11, 3928-3934.
Drew J.E., Mayer C-D., Farquharson A.J., Young P. & Barrera L.N. 2011. Custom design of a GeXP multiplexed assay used to assess expression profiles of inflammatory gene targets in normal colon, polyp, and tumor tissue. Journal of Molecular Diagnostics 13, 233-242.
Hoggard N., Cruickshank M., Moar K.M., Bashir S. & Mayer C-D. 2012. Using gene expression to predict differences in the secretome of human omental vs subcutaneous adipose tissue. Obesity 20, 1158-67.
Holtrop G., Johnstone A.M., Fyfe C. & Gratz S.W. 2012. Diet composition is associated with endogenous formation of N-nitroso compounds in obese men. Journal of Nutrition 142, 1652-1658.
Horgan G.W. & Whybrow S. 2012. Associations between fat, sugar and other macronutrient intakes in the National Diet and Nutrition Survey. Nutrition Bulletin 37, 213-223.
Johnstone A.M., Lobley G.E., Horgan G.W., Bremner D.M., Fyfe C., Morrice P.C. & Duthie G.G. 2011. Effects of a high-protein, low-carbohydrate v. high-protein, moderate-carbohydrate weight-loss diet on antioxidant status, endothelial markers and plasma indices of the cardiometabolic profile. British Journal of Nutrition 106, 282-291.
Lapierre H., Holtrop G., Calder A.G., Renaud J. & Lobley G.E. 2012. Is D-methionine bio-available to the dairy cow? Journal of Dairy Science 95, 353-362.
Lapierre H., Vazquez-Anon M., Parker D., Dubreuil P., Holtrop G. & Lobley G.E. 2011. Metabolism of 2-hydroxy-4-(methylthio)butanoate (HMTBA) in lactating dairy cows. Journal of Dairy Science 94, 1526-1535.
Lockyer A.E., Emery A.M., Kane R.A., Walker A.J., Mayer C-D., Mitta G., Costeau C., Coen M.A., Hanelt B., Rollinson D., Noble L.R. & Jones C.S. 2012. Early differential gene expression in haemocytes from resistant and susceptible biomphalaria glabrata strains in response to Schistosoma mansoni. PLoS ONE 7, e511020.
Macdiarmid J., Kyle J., Horgan G.W., Loe J., Fyfe C., Johnstone A.M. & McNeill G. 2012. Sustainable diets for the future: can we contribute to reducing greenhouse gas emissions by eating a healthy diet? American Journal of Clinical Nutrition 96, 632-639.
Marcon M., Mariano K., Braga R., Paglis C., Scalco M. & Horgan G.W. 2011. Estimation of total leaf area in perennial plants using image analysis. Revista Brasileira de Engenharia Agrícola e Ambiental 15, 96-101.
McGeoch S.C., Holtrop G., Fyfe C., Lobley G.E., Pearson D.W.M., Abraham P., Megson I.L., MacRury S.M. & Johnstone A.M. 2011. Food intake and dietary glycaemic index in free-living adults with and without type 2 diabetes mellitus. Nutrients 3, 683-693.
McIntosh F.M., Maison N., Holtrop G., Young P., Stevens V.J., Ince J., Johnstone A.M., Lobley G.E., Flint H.J. & Louis P. 2012. Phylogenetic distribution of genes encoding beta-glucuronidase activity in human colonic bacteria and the impact of diet on faecal glycosidase activities. Environmental Microbiology 14, 1876-1887.
McLean M.H., Murray G.I., Stewart K.N., Norrie G., Mayer C-D., Hold G.L., Thompson J., Fyfe N., Hope M., Mowat N.A., Drew J.E. & El-Omar E. 2011. The inflammatory microenvironment in colorectal neoplasia. PLoS ONE 6, e15366.
Mulder I.E., Schmidt B., Lewis M., Delday M.I., Stokes C.R., Bailey M., Aminov R.I., Gill B.P., Pluske J.R., Mayer C-D. & Kelly D. 2011. Restricting microbial exposure in early life negates the immune benefits associated with gut colonization in environments of high microbial diversity. PLoS ONE 6, e28279.
Nilaweera K.N., Herwig A., Bolborea M., Campbell G., Mayer C-D., Morgan P.J., Ebling F.J.P. & Barrett P. 2011. Photoperiodic regulation of glycogen metabolism, glycolysis, and glutamine synthesis in tanycytes of the Siberian hamster suggests novel roles of tanycytes in hypothalamic function. GLIA 59, 1695-1705.
Ostertag L.M., O’Kennedy N., Horgan G.W., Kroon P.A., Duthie G.G. & de Roos B. 2011. In vitro anti-platelet effects of simple plant-derived phenolic compounds are only found at high, non-physiological concentrations. Molecular Nutrition & Food Research 55, 1624-1636.
Rodríguez-Gutiérrez G., Duthie G.G., Wood S., Morrice P.C., Nicol F., Reid M., Cantlay L., Kelder T., Horgan G.W., Guzman J.F-B. & de Roos B. 2012. Alperujo extract, hydroxytyrosol, and 3,4-dihydroxyphenylglycol are bioavailable and have antioxidant properties in vitamin E-deficient rats:a proteomics and network analysis approach. Molecular Nutrition & Food Research 56, 1131-1147.
Rubio-Aliga I., de Roos B., Duthie S.J., Crosley K., Mayer C-D., Horgan G.W., Colquhoun I., Le Gall G., Huber F., Kremer W., Rychlik M., Wopereis S., van Ommen B., Schmidt G., Heim C., Bouwman F.G., Mariman E.C., Mulholland F., Johnson I.T., Polley A.C., Elliot R.M. & Daniel H. 2011. Metabolomics of prolonged fasting in humans reveals new catabolic markers. Metabolomics 7, 375-87.
Rungapamestry V., McMonagle J., Reynolds C., Rucklidge G., Reid M., Duncan G., Ross K., Horgan G.W., Toomey S., Moloney A.P., de Roos B. & Roche H.M. 2012. Inter-organ proteomic analysis reveals insights into the molecular mechanisms underlying the anti-diabetic effects of cis-9, trans-11-conjugated linoleic acid in ob/ob mice. Proteomics 12, 461-476.
Russell W., Gratz S.W., Duncan S.H., Holtrop G., Ince J., Scobbie L., Duncan G., Johnstone A.M., Lobley G.E., Wallace R.J., Duthie G.G. & Flint H.J. 2011. High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. American Journal of Clinical Nutrition 93, 1062-2072.
Scott K.P., Martin J.C., Chassard C., Clerget M., Potrykus J., Campbell G., Mayer C-D., Young P., Rucklidge G., Ramsay A.G. & Flint H.J. 2011. Substrate-driven gene expression in Roseburia inulinivorans: importance of inducible enzymes in the utilization of inulin and starch. Proceedings of the National Academy of Sciences USA 108, 4672-4679.
Tannock G.W., Wilson C., Loach D., Cook G.M., Eason J., Holtrop G. & Lawley B. 2012. Nutritional adaptations provide niche differentiation that enables cohabitation of Lactobacillus Reuteri 100-23 and Lactobacillus Johnsonii 100-33 through resource partitioning in the mouse forestomach. International Society for Microbial Ecology Journal 6, 927-938.
Thies F., Masson L., Rudd A., Vaughan N., Tsang C., Brittenden J., Simpson W.G., Duthie S.J., Horgan G.W. & Duthie G.G. 2012. Effect of a tomato-rich diet on markers of cardiovascular disease risk in moderately overweight, disease-free, middle-aged adults: a randomized controlled trial. American Journal of Clinical Nutrition 95, 1013-1022.
Walker A.W., Ince J., Duncan S.H., Webster L.M., Holtrop G., Ze X.L., Brown D.S., Stares M.D., Scott P., Bergerat A., Louis P., McIntosh F., Johnstone A.M., Lobley G.E., Parkhill J. & Flint H.J. 2011. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. International Society for Microbial Ecology Journal 5, 220-230.
Wallace J.M., Horgan G.W. & Bhattacharya S. 2012. Placental weight and efficiency in relation to maternal body mass index and the risk of pregnancy complications in women delivering singleton babies. Placenta 33, 611-618.
Wilson F.A., Holtrop G., Calder C.A., Anderson S.E., Lobley G.E. & Rees W.D. 2012. Effects of methyl-deficient diets on methionine and homocysteine metabolism in the pregnant rat. American Journal of Physiology - Endocrinology and Metabolism 302, E1531-E1540.
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Alexander C.J., Shepherd L.V.T., Sungurtas J., McNicol J.W., Stewart D. & Davies H.V. 2012. Statistical analysis of combined metabolomics datasets from the potato tuber life cycle. Poster presentation, Metabomeeting 2012, Manchester Conference Centre.
Brewer M.J. 2011. Bayesian modelling. Lecture, Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.
Brewer M.J. 2011. Random effects modelling. Lecture, Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.
Brewer M.J. 2012. Bayesian modelling. Lecture, Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.
Brewer M.J. 2012. Random effects modelling. Lecture, Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.
Brewer M.J. 2012. Climate envelopes and species distribution - reaching the plateau. Poster presentation, VIth International Workshop on Spatio-Temporal Modelling (METMAVI), University of Minho, Portugal.
Brewer M.J. 2012. Source distribution modelling for compositional analysis in hydrology. Invited talk, St Andrews/Highlands Royal Statistical Society Local Group Meeting, University of St Andrews.
Brewer M.J., O’Hara R.B., Anderson B.J. & Ohlemüller R. 2011. Climate envelopes for species distribution models. Contributed talk, Spatial Statistics 2011: Mapping Global Change, University of Twente, Enschede, The Netherlands.
Brewer M.J., O’Hara R.B., Anderson B.J. & Ohlemüller R. 2012. Climate envelopes for species distribution models. Contributed talk, International Statistical Ecology Conference 2012, Krokkleiva, Norway.
Butler A. & Owen E. 2011. The use of GPS tracking data to infer foraging behaviour in seabirds. Contributed talk, International Classification Conference, University of St. Andrews.
Butler A. & Owen E. 2011. The use of GPS tracking data to infer foraging behaviour in seabirds. Invited talk, Advances in the Analysis of Animal Movement, International Biometric Society / British Ecological Society joint meeting, London.
Butler A. & Owen E. 2011. Understanding seabirds: a statistical perspective. Seminar, School of Mathematics, Statistics & Actuarial Science, University of Kent at Canterbury.
Butler A. 2011. Extremal dependence. Seminar, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews.
Butler A. 2011. Some statistical challenges in studying wildlife populations. Seminar, School of Mathematics, University of Edinburgh.
Butler A. 2012. Understanding seabirds: a statistical perspective. Invited talk, Royal Statistical Society Manchester Local Group, University of Manchester.
Elston D.A. 2011. Variation, uncertainty, risk and confidence: a statistician’s perspective. Invited talk, CAMERAS Conference, Volcanoes and Snowstorms – Effective Decision Making and Public Communication in a Risky and Uncertain World, Dynamic Earth, Edinburgh.
Elston D.A., Butler A., Kettle H., Potts J.M., Baggaley N.J., Matthews R., Muhammed S., Rivington M., Topp K& Rees R. 2012. Sources of variation and uncertainty in models of the impacts of climate change. Invited talk, Royal Statistical Society Edinburgh Local Group Meeting on Climate Change, University of Edinburgh.
Appendix 3 Conference Presentations, Lectures & Seminars
Elston D.A., Butler A., Kettle H., Potts J.M., Baggaley N.J., Matthews R., Muhammed S., Rivington M., Topp K& Rees R. 2012. Stratification of climate projections for efficient model-based assessment of uncertainty and variation in climate impact projections. Contributed talk, International Biometrics Conference, Kobe, Japan.
Glasbey C.A. & Allcroft D.J. 2011. Spatio-temporal weather models. Seminar, School of Mathematical Sciences, University of Nottingham.
Glasbey C.A. & Allcroft D.J. 2011. Spatio-temporal weather models. Invited talk, Modern Spatial Statistics Conference - In Honour of Julian Besag FRS, Queensland University of Technology, Brisbane, Australia.
Glasbey C.A. & Allcroft D.J. 2012. Spatio-temporal weather models. Invited talk, Royal Statistics Society Lancashire & East Cumbria Local Group, Lancaster.
Glasbey C.A. 2011. Algorithms for fitting non-parametric models to image data. Invited talk, Queensland University of Technology, Brisbane, Australia.
Glasbey C.A. 2011. Dynamic programming versus graph cut algorithms for fitting non-parametric models to image data. Seminar, Stochastic Centre, Chalmers University, Gothenburg, Sweden.
Glasbey C.A. 2011. Dynamic programming versus graph cut algorithms for fitting non-parametric models to image data. Seminar, School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland.
Glasbey C.A. 2012. Dynamic programming versus graph cut algorithms for phenotyping by image analysis. Seminar, Department of Mathematics, University of York.
Glasbey C.A. 2012. Semi-automatic 3D segmentation. Invited talk, First Annual Conference on Carcass Evaluation, Meat Quality, Software and Traceability (FAIMI1), Dublin, Ireland.
Glasbey C.A., Allcroft D.J. & Butler A. 2011. Tobit models for multivariate, spatio-temporal and compositional data. Seminar, School of Mathematics, University of Birmingham.
Glasbey C.A., Allcroft D.J. & Butler A. 2011. Tobit models for multivariate, spatio-temporal and compositional data. Invited talk, Danish Society of Theoretical Statistics, Copenhagen, Denmark.
Glasbey C.A. 2012. Plant phenotyping. Invited talk, Eucarpia satellite meeting on SPICY, Hohenheim, Germany.
Glasbey C.A., Horgan G.W., Song Y., van der Heijden G.W.A.M. & Polder G. 2012. Image analysis for automatic phenotyping. Invited talk, Visionday, Lyngby, Denmark.
Glasbey C.A., Lambe N.R., Ross D., Richardson R.I., Navajas E., Hyslop J.J. & Roehe R. 2012. Prediction of intramuscular fat from beef ultrasound scans. Contributed talk, British Society of Animal Science Annual Meeting, Nottingham.
Hackett C.A., McLean K. & Bryan G.J. 2012. Linkage analysis and QTL mapping in autotetraploids using SNP dosage data. Contributed talk, Eucarpia section Biometrics in Plant Breeding XVth meeting, Hohenheim, Germany.
Hackett C.A., McLean K. & Bryan G.J. 2012. Using SNP dosage data for linkage analysis and QTL mapping in autotetraploid species. Poster presentation, 4th International Conference on Quantitative Genetics, Edinburgh.
Holtrop G. 2012. Risk assessment for the Scottish biotoxin monitoring programme for shellfish. Invited talk (with C. Gay, FSA), British Science Festival, Aberdeen.
Husmeier D. 2011. Bayesian modelling in systems genetics. Invited talk, Theo Murphy International Scientific Meeting on Modelling Networks from Sequence to Consequence in Eukaryotes, The Kavli Royal Society International Centre, Chicheley, Buckinghamshire.
Husmeier D. 2011. Modelling time-varying gene regulatory processes with probabilistic graphical models. Invited talk, Third Biennial Newcastle Workshop on Statistical Bioinformatics and Stochastic Systems Biology, Newcastle.
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Innocent G.T., Marion G. & McKendrick I.J. 2012. The use of Chapman-Kolmogorov equations and MCMC to analyse S-I-S data. Seminar, School of Mathematics, University of Edinburgh.
Innocent G.T., Marion G., Smith L., Hutchings M.R. & McKendrick I.J. 2012. Heterogeneity in animal contact networks - its measurement, modelling and consequences. Contributed talk, 13th International Symposium on Veterinary Epidemiology and Economics, Maastricht, The Netherlands.
Innocent G.T., McKendrick I.J., Smith L., Hutchings M.R. & Marion G. 2011. Dynamic social networks: analysis and consequences: an example from cattle within group contact data. Seminar, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow.
Kettle H., Louis P., Flint H.J. & Holtrop G. 2011. Modelling the emergent dynamics of microbial communities in the human colon. Contributed talk, 8th European Conference on Mathematical and Theoretical Biology, Krakow, Poland.
Marion G., Catterall S., Cook A. & Hulme P. 2011. Modelling the spatial spread of invasive aliens: process-based models and Bayesian inference. Contributed talk, 8th European Conference on Mathematical and Theoretical Biology, Kraków, Poland.
Marion G., Catterall S., Cook A., Butler A. & Hulme P. 2011. Inferring the spatial spread of invasive aliens using epidemiological models. Contributed talk, Inference For Epidemic-related Risk (InFER2011), University of Warwick.
Marion G., Catterall S., Cook A.R., Butler A. & Hulme P. 2012. Modelling the spatial spread of invasive aliens: process-based models and Bayesian inference. Contributed talk, International Statistical Ecology Conference, Krokkleiva, Norway.
Mayer C-D. 2012. Combined analysis of high-dimensional omics data sets. Seminar, School of Mathematical Sciences, University of St. Andrews.
Mayer C-D. 2012. Statistical analysis of omics data. Invited talk, Workshop in Advanced Methods in Crop Breeding, Newcastle University of Newcastle.
Nath M., Innocent G.T., Gunn G.J. & McKendrick I.J. 2011. Bayesian modelling to estimate the sensitivity of the Immuno-Magnetic Separation (IMS) method of detecting Escherichia coli O157 in bovine faecal samples. Contributed talk, British Society of Animal Science Annual Conference 2011, University of Nottingham.
Nevison I.M., Bradshaw J.E., Shankland C.E., Bryan G.J. & Winfield M. 2012. Observations on the design of sensory profiling trials for a back-cross population of potato varieties. Contributed talk, Royal Statistical Society 2012 International Conference, Telford.
Palarea Albaladejo J. & Martin-Fernandez J.A. 2011. Practical issues in statistical modelling of CODA. Invited talk, Centennial Congress of the Spanish Royal Mathematical Society, Avila, Spain.
Palarea Albaladejo J. 2012. Recent advances in dealing with values below detection limit in compositional data sets. Seminar, School of Mathematics & Statistics, University of Glasgow.
Potts J.M., Elston D.A., Butler A., Muhammed S., Rivington M., Topp K., Kettle H., Baggaley N.J., Rees R. & Matthews R. 2012. Using the UKCP09 climate projections to assess uncertainty and variation in projected impacts of climate change on crop yields. Contributed talk, Royal Statistical Society 2012 International Conference, Telford.
Potts J.M., Rivington M., Muhammed S., Topp K., Butler A., Elston D.A., Kettle H., Matthews R. & Rees R. 2011. Using UKCP09 to assess impacts of climate change upon agriculture within Scotland. Poster presentation, Equipping society for climate change through improved treatments of uncertainty, Leeds.
Roberts A.M.I. 2012. Improving penalised regression models for phenology by using thermal time. Contributed talk, Edinburgh Phenology Meeting 2012, University of Edinburgh.
Roberts A.M.I. 2012. Improving penalised regression models for phenology by using thermal time. Contributed talk, Royal Statistical Society 2012 International Conference, Telford.
Song Y. 2011. Image analysis approaches for plant phenotyping. Seminar, Computational Biology and Bioimaging Group, University of Warwick.
Spezia L. 2011. Markov mixture models in Bayesian variable selection. Invited talk, Bayes 250 Workshop, University of Edinburgh.
Spezia L. 2012. Bayesian variable selection in Markov mixture models. Seminar, University of Perugia, Italy.
Spezia L. 2012. Modelling the states of Scotland’s rivers. Seminar, University of Venice, Italy.
Spezia L., Cooksley S., Brewer M.J., Donnelly D. & Tree A. 2012. Modelling species abundance in a river by negative binomial hidden Markov models. Contributed talk, International Statistical Ecology Conference 2012, Krokkleiva , Norway.
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Appendix 4External Committees 2011-2013
50 51
D. Husmeier* Editorial Board, Statistical Applications in Genetics and Molecular Biology
Editorial Board, IEEE Transactions on Bioinformatics and Computational Biology
Associate Editor, Statistics and Computing
J. Liu RSS Edinburgh Local Group Committee
G. R. Marion Management board, Strategic Partnership in Animal Science Excellence (SPASE)
Editorial Board, BioRisk
Associate Editor, Wildlife Research
C-D. Mayer RINH Scientific Review Committee
RSS Highlands Local Group Committee
Editorial Board, British Journal of Nutrition
I. J. McKendrick MRI Ethical Review Committee
Project Management Committee, Defra Methane Inventory Project ACO115
I. M. Nevison SRUC Edinburgh Ethical Review Committee
Potato Experts Group which advises the UK National List and Seeds Committee
J. M. Potts RSS Highlands Local Group Committee
GenStat Procedure Library Editorial Committee
Statistics Advisory Panel, European Journal of Soil Science
A. M. I. Roberts Secretary, Inter-departmental Statisticians’ Group for National List and Seeds Committee
Potato Experts Group which advises the UK National List and Seeds Committee
Vegetable DUS Group which advise the UK National List and Seeds Committee
International Union for the Protection of Plant Varieties Technical Working Party on Automation and Computer Programs
SASA Ethical Review Committee
T. I. Simpson Editorial Board, American Journal of Bioinformatics
*To October 2011
M. J. Brewer Associate Editor, Biometrics
Treasurer, International Biometric Society (IBS), British and Irish Region
Scottish Government Land Use Consultative Group
Royal Statistical Society (RSS) Highlands Local Group Committee
Local Organising Committee, IBS Channel Network Meeting 2013
A. Butler Secretary, RSS Edinburgh Local Group
RSS Environmental Statistics Section Committee
S. Brocklehurst SRUC Auchincruive Ethical Review Committee
D. A. Elston Dutch Biometry Award Panel 2012
RSS Honours Committee
RSS Panel on Statistics for Ecosystem Change
RSS Highlands Local Group Committee
Academy for PhD Training in Statistics Advisory Committee
UK Environmental Change Network Scientific and Technical Advisory Group
University of York MRes Course Mathematics in the Living Environment Advisory Group
C. A. Glasbey Representative Council of IBS
British and Irish Region Committee, IBS
RSS Statistical Computing Section Committee
RSS Publications Network of Advisors.
EPSRC Peer Review College
Fisher Memorial Committee
Committee of UK Professors of Statistics (COPS)
Scottish Mathematical Sciences Training Centre Management Group
Programme Committee IBC 2014
Royal Society of Edinburgh Sectional Committee B4
C. A. Hackett Associate Editor, Theoretical and Applied Genetics
G. Holtrop Editorial Board, Public Health Nutrition
Editorial Board, Journal of Nutrition
G. Horgan RSS Highlands Local Group Committee
RINH Scientific Review Committee
Glossary of Organisational Acronyms and Abbreviations
Glo
ssary
CONTACT POINTS FOR BioSS and its principal collaborators
52
Biomathematics & Statistics Scotland James Clerk Maxwell Building The King’s BuildingsEdinburgh EH9 3JZ
Web address: www.bioss.ac.uk Email: [email protected]
Registered Office: James Hutton Institute, Invergowrie, Dundee, DD2 5DA.
A Company Limited by Guarantee and having charitable status.
Registered in Scotland No. SC374851
Scottish charity No. SC041796
Acknowledgements
Design: John McNeill, Media Unlimited
Production Co-ordination: David Elston, Muriel Kirkwood & Christine Hackett
Photography & Images: We thank, collectively, our own staff, collaborators in Hutton, MRI, RBGE, RINH, SRUC and SASA, specifically P15 carrots SASA P17 sheep SAOS P18 cow SRUC P20 tooth Hutton This page, each institution photographed
Front cover, badger http://en.wikipedia.org/wiki/ File: Badger-badger.jpg
Gary Baker, GB Photography
Additional pictures purchased from www.istockphoto.com
James Hutton InstituteCraigiebucklerAberdeen, AB15 8QHwww.hutton.ac.uk
James Hutton InstituteInvergowrieDundee, DD2 5DAwww.hutton.ac.uk
Moredun Research InstitutePentlands Science ParkBush LoanPenicuik, EH26 0PZwww.moredun.org.uk
Rowett Institute of Nutrition and HealthUniversity of AberdeenBucksburnAberdeen, AB21 9SBwww.abdn.ac.uk/rowett
Royal Botanic Garden Edinburgh20A Inverleith RowEdinburgh, EH3 5LRwww.rbge.org.uk
Science and Advice for Scottish Agriculture Roddinglaw RoadEdinburgh, EH12 9FJwww.sasa.gov.uk
Scotland’s Rural CollegeThe King’s BuildingsWest Mains Road Edinburgh EH9 3JGwww.sruc.ac.uk
ARC The former Agricultural Research Council
AFBI Agri-Food and Biosciences Institute
AFRC The former Agricultural and Food Research Council
BBSRC Biotechnology and Biological Sciences Research Council
BioSS Biomathematics and Statistics Scotland
BTO British Trust for Ornithology
CAMERAS Coordinated Agenda for Marine, Environment and Rural Affairs Science (partnership of public bodies in Scotland)
CEH Centre for Ecology and Hydrology
Defra Department of Environment, Food and Rural Affairs
EFSA European Food Safety Authority
EPSRC Engineering and Physical Sciences Research Council
EU European Union
Fera Food and Environment Research Agency
Hutton James Hutton Institute
IBERS University of Aberystwyth Institute of Biological, Environmental and Rural Sciences
IBS International Biometric Society
JNCC Joint Nature Conservation Committee
MLURI Macaulay Land Use Research Institute, now part of James Hutton Institute
MRI Moredun Research Institute
MRP Main Research Provider (of RESAS)
NIAB National Institute of Agricultural Botany
RBGE Royal Botanic Garden Edinburgh
RESAS Scottish Government Rural and Environment Science and Analytical Services Division
RINH Rowett Institute of Nutrition and Health
RSPB Royal Society for the Protection of Birds
RSS Royal Statistical Society
SAOS Scottish Agricultural Organisation Society
SASA Science and Advice for Scottish Agriculture
SCRI Scottish Crop Research Institute, now part of James Hutton Institute
SEPA Scottish Environment Protection Agency
SNH Scottish Natural Heritage
SRUC Scotland’s Rural College
Stat i s t i c s & M athemat ic s impr o ving A g r ic u lt u r e,the Envir onment, Food & Health
Biomathematics &Statistics ScotlandBiennial Report2011/2013
