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Project title 1:  Production of novel molecular binders for multiplex analysis of toxic contaminants to enhance food security
Supervisors:  Dr Katrina Campbell and Professor Chris Elliott
Institute for Global Food Security, Queen's University Belfast

Background:
Toxic contaminants in food can originate from man-made chemicals eg drugs, PCBs or natural sources eg mycotoxins and plant toxins.  As consumer food safety awareness and the requirement for global food sustainability is increasing there is a greater concern for food safety authorities to improve monitoring of our food supply chain for toxic contaminants.  This strategy is not only immediate to protect consumer health but to future proof the industry to determine the risk of exposure under different environmental conditions for the implementation of certain measures based on the perceived risk of exposure. In order to measure and control exposure inexpensive rapid detection methods must be available for extensive cost effective use that can detect multiple toxic contaminants simultaneously.  The progression in biosensor platforms offers this capability as both a laboratory based tool and remotely applied application for "in situ" monitoring.  The ASSET Technology Centre at Queen's University Belfast has developed over recent years a world class centre for bioanalytical analysis using various biosensor technologies.  This technology works on the measurement of a competitive interaction of a binding molecule eg antibody with a target antigen eg drug or toxin.  Antibodies are no longer considered cost effective or ethical binding molecules due to the use of animals in their production and in vitro techniques are improving to generate molecular binders for this technology.

This project will explore the synthesis of novel binders for use in sensor technology platforms as both toxin mimics and binding molecules.  Important regulated and emerging toxin contaminants will be selected.  Novel binders e.g. aptamers / phage will be generated to these targets.  The binders will be characterised for sensitivity and specificity using various sensor platforms suitable for multiplex analysis.  The compatibility of the binders for detecting toxic contaminants in food produce and animal feeds using a QuEChERS type approach will be determined.

For informal discussions about the project and suitability contact Dr Katrina Campbell (katrina.campbell@qub.ac.uk).

Requirements:  Applicants should have a primary degree (2i or 1st) and/or an M.Sc. in an appropriate discipline (e.g., Molecular biology, Biochemistry).

Funding:  The position is fully funded and open to applicants from the UK and EU.

Starting date:  The project will start in September 2013.  The funding is for a three year PhD project, to be completed by end of August 2016.

Location:  The project will be based within the ASSET Technology Centre within the Institute for Global Food Security at Queen's University.

Application procedure:  Apply using the online facility.

Closing date for applications:  March 1st, 2013.  Interviews for shortlisted candidates will be held early in April or May 2013 at Queens University Belfast.

Project title 2:  Omics approaches to the study of toxin producing algae
Supervisors:  Dr Caroline Meharg and Professor Chris Elliott
Institute for Global Food Security, Queen’s University Belfast

Background:
Dinoflaggelates Alexandrium spp. are widely distributed bloom forming marine micro-algae.  These organisms can produce potent neurotoxins which cause paralytic Shellfish poisoning (PSP).  Human exposure to this toxin is potentially deadly and occurs via consumption of toxin exposed filter-feeding shellfish.  Understanding the factors which contribute to the formation of algal blooms and PSP production and identification of the environmental and genetic key drivers is of environmental, economic and clinical importance.  The dinoflaggelate Alexandrium spp. contains a number of toxic as well as non-toxic species.  The environmental factors and genes inducing toxin production are as yet not well understood.  Due to the unusual complexity and large size of dinoflaggelate genomes, there are only a small number of Genome sequencing projects for these organisms and Transcriptome sequencing has become the major focus in this field.  Transcriptome data of a number of dinoflaggelates including Alexandrium spp. is available in Genbank and can be used as a reference dataset for further gene assembly and gene expression studies of these organisms.  The same applies to the limited number of sequenced dinoflaggelate Genomes.

Based on existing Transcriptome and Genome data in Genbank, with integration of new Transcript and Metabolite data, this project aims to identify Transcripts, Metabolites and inducers involved in the induction of processes associated with PSP toxin production and/or environmental stress responses associated with algal bloom and production of PSP.  Illumina and 454 Sequencing of toxic and non-toxic Alexandrium strains under various environmental conditions can be employed for both de novo transcriptome assembly as well as gene expression analysis/identification of fold changes of known and novel genes relevant to environmental stress responses and PSP production.  Metabolomics Results can be integrated with High throughput Gene expression and targeted PCR Gene level Results.  In the absence of a sequenced Genome, Genome walking approaches can be employed for de novo sequencing and analysis of the region upstream and downstream from a gene of interest for further characterisation of the organism.  Both toxic and non-toxic algal species will be studied under various environmental conditions.  The combination of metabolite and gene responses as well as identification of genomic regions upstream and downstream of identified genes of interest may help elucidate specific drivers of toxin production in these organisms and identification of corresponding genes and metabolites.

For informal discussions about the project and your suitability contact Dr Caroline Meharg (caroline.meharg@qub.ac.uk).

Requirements:  Applicants should have a primary degree (2i or 1st) and/or an MSc. in an appropriate discipline (e.g., Microbiology, Botany, Biology, Pharmacology, Food Science, Biochemistry, Bioinformatics/Computer Science etc.).

Funding:  The position is fully funded and open to applicants from the UK and EU.

Starting date:  The project will start 1 October 2013.  The funding is for a three year Ph.D. project, to be completed by end of August 2016.

Location:  The project will be based within the world famous ASSET Technology Centre within the Institute for Global Food Security at Queen's University.

Application procedure:  Apply using the online facility.

Closing date for applications:  March 1st, 2013.  Interviews for shortlisted candidates will be held early in April or May 2013 at Queens University Belfast.

Project title 3:  Project title Biomarkers for mycotoxins exposure: development and application
Supervisors:  Dr Yun Yun Gong, Professor Chris Elliott and Dr Campbell (Queen's University Belfast)
Institute for Global Food Security, Queen's University Belfast

Background:
Biomarkers for mycotoxin exposure enable understanding of the role of mycotoxins in adverse human health effects.  Biomarkers for several key mycotoxins, aflatoxin, fumonisin, ochratoxin, and deoxynivalenol have been developed and applied in epidemiology studies.  The most successful examples, the aflatoxin albumin and DNA adduct biomarkers, significantly advanced our knowledge of the toxins' carcinogenic and child growth faltering effects, and contribute to evaluation of intervention effectiveness.  Methods for aflatoxin biomarker detection generally have two types, solid phase cartridge purification followed by either ELISA quantification where a high affinity antibody is available, or LC-MS detection where suitable equipment and pure standards are available.  Currently available ELISA detection method for long-term aflatoxin biomarkers has limited capacity and so do not meet the increasing demand of application in field studies.  Modern antibody techniques and advances in LC-MS/MS make it possible to improve the efficiency and capacity of biomarker detection to promote a wider range of health research activities.

Based on existing biomarker methods, this project aims to develop more efficient and higher throughput detection methods using both high affinity antibody and LC-MS technology to serve future large scale human biomarker studies, with a focus on aflatoxin biomarkers.  Building on the team's combined expertise in this field the outcome of the research is expected to develop high-throughput reliable detection methods for aflatoxin and other mycotoxin biomarkers.  Small scale application of the traditional and new biomarker method will also be conducted.

The project will be based within the world-famous ASSET Technology Centre within the Institute for Global Food Security at Queen's University (School of Biological Sciences).

The project will start in September 2013. The funding is for a three year PhD project, to be completed by the end of August 2016.

The closing date for applications is 15 March 2013.  Interviews for shortlisted candidates will be held early in April or May 2013 at Queen's University Belfast.

For informal discussions about the project and your suitability contact Dr Yun Yun Gong (y.gong@qub.ac.uk) and Professor Chris Elliott (chris.elliott@qub.ac.uk).

Requirements:  Applicants should have a primary degree (2i or 1st) and/or an M.Sc. in an appropriate discipline (e.g., Environmental health, Biomedical Science, Biology, Pharmacology, Food Science, Biochemistry etc.).

Funding Notes::  The position is fully funded and open to applicants from the UK and EU.

Application procedure:  Apply using the online facility.

Project title 4:  Software and hardware integration in tackling global food fraud and food processing problems
Supervisors:  Dr Tassos Koidis and Dr Jesus Martinez del Rincon.

Project Description:
In the current scene of the global food supply and production, it is becoming increasingly challenging to detect sophisticated fraud, to tackle food processing problems and to meet consumer's elevated requirements of quality and safety.  There is therefore a need of developing intelligent information systems, based on machine learning, artificial intelligence and pattern recognition, as well as integrating software and hardware in solving some key food science related problems such as detecting adulteration and geographical origin (e.g. oils) and automating food process control.

Current methods to authenticate foods are based on laboratory analyses or processing control requires heavy and expensive instrument or manual labour.  By applying pattern recognition methods, food properties can be estimated accurately under complex scenarios, which would otherwise require time-consuming and often expensive analytical testing.  In this project we aim to explore the applicability of signal processing and pattern recognition techniques in order to develop software useful in tackling food authenticity, safety and security problems.  Moreover, mobile technology has yet to find its way in areas like food processing and control, which creates even more opportunities if appropriate software is embedded into low-cost mobile devices.

Some outcomes to be explored in this PhD are: a) software tools that help with calibration and analysis of complex chromatography, spectroscopic and other signals to verify authenticity of specific foods, based on machine learning and pattern recognition techniques, b) development of next generation searchable databases with food authenticity data to fight global food fraud, c) the proof-of-concept use of low-cost mobile computer in food processing and processing control including decision making.

The closing date for applications is 25 February 2013.

Start Date:  October 2013.
End Date:  October 2016.

Funding Notes:
The position is fully funded by a DEL studentship.  PLEASE NOTE THAT ONLY UK APPLICANTS ARE ELIGIBLE FOR A FULL DEL AWARD.

Application procedure:  Apply using the online facility.

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