CoMPLEX offers a number of research fellowships.
2020 Science is a collaborative research programme based at the University of Oxford, University College London, and Microsoft Research, Cambridge.
The programme is focused on fostering the creation of a new generation of future scientific leaders – new kinds of scientists with the ability to lead the way in tackling fundamental challenges in science in areas of societal importance. At the heart of the programme is the development and application of novel computational approaches, methods and tools to address some fundamental problems in natural science, and the scientific computing, scientific software development and software engineering that underpin the development of predictive models of complex, multi-scale biological systems.
• Address major research problems in natural systems modelling
• Create a new generation of scientific leaders
• Facilitate novel approaches in computational bioscience
Visit our website at 2020science.net
Keep up to date with all the latest news and events relating to the 2020 Science programme by joining our mailing list here.
The 2020 Science programme is funded through the EPSRC Cross-Discipline Interface Programme (grant number EP/I017909/1) and also supported by Microsoft Research Ltd.
Prof. Anne Warner Doctoral Fellow
The first recipient of the Prof Anne Warner Doctoral Fellowship is Dr. Thomas Blacker.
Two-photon microscopy and fluorescence lifetime image of NAD(P)H in the mammalian cochlea, the coiled structure of the inner ear responsible for converting sound waves into nerve impulses
Details of Tom's research during his PhD at CoMPLEX follow.
Monitoring cell metabolism with NAD(P)H fluorescence lifetime imaging
In live tissues, alterations in metabolism induce changes in the fluorescence decay of the spectrally identical redox carriers NADH and NADPH. The biochemical pathways and photophysical mechanisms that contribute to these changes are largely unknown. This work combined ultrafast laser spectroscopy and live-cell imaging to investigate these phenomena.
Time-resolved spectroscopy of NADH and NADPH was performed using single-photon and two-photon excitation. In solution, the fluorescence lifetimes of the two cofactors were identical. The anisotropy decay dynamics of both molecules indicated that distinct molecular configurations caused the presence of two emitting states, perhaps involving alternate cis/trans geometries of the amide group. Using a range of water/glycerol mixtures as solvents, the viscosity dependence of the non-radiative decay of NAD(P)H was shown to be well described by Kramers models of activated barrier crossing. This suggested that variations in the fluorescence lifetimes of the cofactors inside the cell result from differing levels of conformational restriction of the nicotinamide ring.
Despite identical fluorescence lifetimes in solution, fluorescence lifetime imaging studies on genetically modified cell lines indicated that NADPH possessed a different fluorescence lifetime inside the cell than intracellular NADH. This suggested that variations in the NAD(P)H fluorescence decay upon metabolic perturbation by pharmacological or pathological means, reported both in this work and in the literature, result from changes in the relative concentrations of NADH and NADPH. NAD(P)H FLIM was therefore used to observe elevated NADPH concentrations in the support cells of the mammalian cochlea, highlighting the potential of the technique as a label free method for monitoring the metabolic state of complex tissue preparations.
Blacker, T.S., Marsh, R.J., Duchen, M.R., and Bain, A.J. (2013). Activated barrier crossing dynamics in the non-radiative decay of NADH and NADPH. Chemical Physics: http://dx.doi.org/10.1016/j.chemphys.2013.02.019
Prof Rob Seymour Doctoral Fellow
The first recipient of the Prof Rob Seymour Doctoral Fellowship is Dr. Andrew Newell.
Andrew's research interests are centred on visual recognition. There are two strands to this. First, to understand how visual recognition might work in biological systems at the computational level. Second, using these ideas to develop computer vision systems to apply to problems in the life sciences.
For his PhD at CoMPLEX, Andrew developed a novel biologically inspired visual encoding scheme. This work used local feature detectors similar to those found in the early visual system, and looked at how combinations of such features can be used for different classification tasks. The resulting scheme demonstrated good performance on a wide range of problems including texture recognition, character recognition and classification of text extracted from natural images.
Recently, thanks to an EPSRC Doctoral Prize fellowship, Andrew extended the work to problems in forensic science such as writer identification, which involves establishing the author of a piece of handwriting, and grain identification, which is useful for determining the likely source of an unknown sample of material. He has now returned to CoMPLEX to extend the theoretical work and apply the techniques to problems in the life sciences.
Andrew is always interested in potential collaborations, both within academia and industry, for applications involving visual recognition.
Please contact him at firstname.lastname@example.org
A. J. Newell. What should we be comparing for writer identification? To appear in Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013
L. D. Griffin, H. M. Wahab & A. J. Newell Distributional learning of appearance. PLOS One 2013 8(2):e58074
A. J. Newell, R. M. Morgan, L. D. Griffin, P. A. Bull, J.R. Marshall, and G. Graham. Automated texture recognition of quartz sand grains for forensic applications. Journal of Forensic Sciences 2012 57(5):1285-1289
A. J. Newell and L.D. Griffin. Natural image character recognition using oriented Basic Image Features. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2011 p. 191-196
A. D. F. Clarke, F. Halley, A. J. Newell, L. D. Griffin, and M. J. Chantler. Perceptual similarity: A texture challenge. In Proceedings of the British Machine Vision Conference (BMVC) 2011 p. 52-58
A. J. Newell and L.D. Griffin. Multiscale histogram of oriented gradient descriptors for robust character recognition. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2011 p. 1085-1089
A. J. Newell, L. D. Griffin, R. M. Morgan, and P. A. Bull. Texture-based estimation of physical characteristics of sand grains. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2010, p. 504–509
Page last modified on 07 jun 13 15:19