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MAT01 - Mathematical methods to improve food safety and traceability

Summary of the impact

Recent food crises show the importance of having effective means of food identification and analysis. Many tests have been developed to monitor food, but analysis of the resulting data is highly problematic. Mathematical techniques developed by Dr Julie Wilson at the University of York allow complex mixtures to be analysed and interpreted. They have enabled the Food and Environment Research Agency (Fera) to maximize the information available from food testing, resulting in improved food safety and authentication worldwide, and underpin the analytical testing services delivered by Fera. The techniques have been incorporated into a bespoke Matlab based solution which is now routinely used by Fera's Chemical and Biochemical Profiling section in the specialist testing services which Fera provides across the food storage and retail, agri-environment and veterinary sectors to over 7,500 customers in over 100 countries. In addition, the techniques are used in Fera's research, supporting around £8M worth of work to develop a wide range of global applications including the determination of disease-related biomarkers, contaminant detection, food traceability and the development of drought- and disease-resistant crop varieties.

Submitting Institution

University of York

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Statistics
Biological Sciences: Biochemistry and Cell Biology
Medical and Health Sciences: Neurosciences

01_Phylogenetic analysis software BEAST informs public health responses to infection.

Summary of the impact

Impact: BEAST software has widespread applications with impacts on public health policy, service provision and awareness, and in other contexts such as commercial disputes and criminal cases.

Beneficiaries: Public agencies such as health bodies and criminal courts; ultimately, global and local populations subject to infectious disease epidemic and pandemic outbreaks in which BEAST is used to inform the response.

Significance and Reach: BEAST is critical software that has been used to understand the spread of and to inform the response to global pandemics such as H1N1 swine-flu. It is also used to determine disease origin and transmission issues in specific situations (e.g. in criminal cases). The reach of this software is therefore both global and local.

Attribution: Rambaut (UoE) co-led the phylogenetic research and developed BEAST with Drummond (Auckland, NZ). The subsequent epidemic and pandemic analyses were variously led by Rambaut and Pybus (Oxford) and by Ferguson (Imperial College London).

Submitting Institution

University of Edinburgh

Unit of Assessment

Biological Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Statistics
Biological Sciences: Genetics
Medical and Health Sciences: Medical Microbiology

UOA10-09: Driving clinical genetic testing and biotechnology development based on the International HapMap Project

Summary of the impact

The International HapMap project was a major international research collaboration to map the structure of common human genetic variation across populations from Europe, Asia and Africa. Mathematical Scientists from the University of Oxford played key roles in the development of statistical methods for the project, along with its overall design and management of the International HapMap Project.

Companies have used HapMap as the primary resource to design genome-wide microarrays to make novel discoveries in, for example, pharmacogenetic studies. The size of this market is estimated at $1.25 billion.

One novel discovery has led to a genetic test that is predictive of sustained viral suppression in patients treated for chronic hepatitis C. An estimated 2.7 to 3.9 million people are affected by HCV infection. This test is sold commercially by the company LabCorp and is a significant contributor to the company's testing volume. Finally, the project has been important in widening the public understanding of genetic variation.

Submitting Institution

University of Oxford

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Statistics
Biological Sciences: Genetics

Bayesian statistical methods applied to the quantification of forensic evidence

Summary of the impact

In a series of papers published from 1999 on, Aitken (Maxwell Institute) and collaborators applied Bayesian statistics to develop a methodology for the quantification of judicial evidence derived from forensic analyses. They proposed and implemented procedures for (i) determining the optimal size of samples that should be taken from potentially incriminating material (such as drugs seized); and (ii) the estimation of likelihood ratios characterising evidence provided by multivariate hierarchical data (such as the chemical composition of crime-scene samples). Their procedures have been recommended in international guideline documents (including a 2009 publication by the United Nations Office on Drugs and Crime) and have been routinely used by forensic science laboratories worldwide since 2008. The research has therefore had an impact on the administration of justice, leading to a better use of evidence and accompanying judicial and economic benefits. Examples are given from laboratories in Australia, Sweden and The Netherlands.

Submitting Institutions

University of Edinburgh,Heriot-Watt University

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Political

Research Subject Area(s)

Mathematical Sciences: Statistics
Economics: Applied Economics

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