We have developed a pixellated high energy X-ray detector instrument to be used in a variety of imaging applications. The instrument consists of either a Cadmium Zinc Telluride or Cadmium Telluride (Cd(Zn)Te) detector bump-bonded to a large area ASIC and packaged with a high performance data acquisition system. The 80 by 80 pixels each of 250 μm by 250 μm give better than 1 keV FWHM energy resolution at 59.5 keV and 1.5 keV FWHM at 141 keV, at the same time providing a high speed imaging performance. This system uses a relatively simple wire-bonded interconnection scheme but this is being upgraded to allow multiple modules to be used with very small dead space. The readout system and the novel interconnect technology is described and how the system is performing in several target applications.

X-ray fluorescence techniques have proven beneficial for identifying and quantifying trace elements in biological tissues. A novel approach is being developed that employs x-ray fluorescence with an aim to locate heavy nanoparticles, such as gold, which are embedded into tissues. Such nanoparticles can be functionalized to act as markers for tumour characteristics to map the disease state, with the future aim of imaging them to inform cancer therapy regimes. The uptake of functionalized nanoparticles by cancer cells will also enable detection of small clusters of infiltrating cancer cells which are currently missed by commonly used imaging modalities. The novel system, consisting of an energy-resolving silicon drift detector with high spectral resolution, shows potential in both quantification of and sensitivity to nanoparticle concentrations typically found in tumours. A series of synchrotron measurements are presented; a linear relationship between fluorescence intensity and gold nanoparticle (GNP) concentration was found down to 0.005 mgAu ml(-1), the detection limit of the system. Successful use of a bench-top source, suitable for possible future clinical use, is also demonstrated, and found not to degrade the detection limit or accuracy of the GNP concentration measurement. The achieved system sensitivity suggests possible future clinical usefulness in measuring tumour uptake in vivo, particularly in shallow tumour sites and small animals, in ex vivo tissue and in 3D in vitro research samples.

A novel, pixellated, energy-resolving X-ray detector has been utilised to simultaneously combine angular and energy dispersive X-ray diffraction (XRD). This approach allows a system to measure XRD data using the benefits of both approaches. Data acquisition is fast, and the system contains no moving parts, making it ideal for applications in security, particularly for the detection of explosive materials hidden within packages or baggage. Explosive samples supplied by the Centre for Applied Science and Technology, U.K. were examined using the pixellated diffraction technique, and the XRD data were compared with those from inert materials typically found inside luggage. A method of processing the data was developed, which greatly reduces the amount of data outputted from the detector whilst preserving the angular and energy resolution. Using principal component and discriminant analyses, a model was developed which predicts the correct classification of an unknown sample as either "explosive" or "inert" for data with acquisition times as low as one second.

Huntington’s disease is a rare neurodegenerative disease caused by a cytosine–adenine–guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington’s disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-β) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca2+) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca2+, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

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