Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Gas Biosensor Arrays Based on Single-Stranded DNA-Functionalized Single-Walled Carbon Nanotubes for the Detection of Volatile Organic Compound Biomarkers Released by Huanglongbing Disease-Infected Citrus Trees.

Volatile organic compounds (VOCs) released by plants are closely associated with plant metabolism and can serve as biomarkers for disease diagnosis. Huanglongbing (HLB), also known as citrus greening or yellow shoot disease, is a lethal threat to the multi-billion-dollar citrus industry. Early detection of HLB is vital for removal of susceptible citrus trees and containment of the disease. Gas sensors are applied to monitor the air quality or toxic gases owing to their low-cost fabrication, smooth operation, and possible miniaturization. Here, we report on the development, characterization, and application of electrical biosensor arrays based on single-walled carbon nanotubes (SWNTs) decorated with single-stranded DNA (ssDNA) for the detection of four VOCs-ethylhexanol, linalool, tetradecene, and phenylacetaldehyde-that serve as secondary biomarkers for detection of infected citrus trees during the asymptomatic stage. SWNTs were noncovalently functionalized with ssDNA using π-π interaction between the nucleotide and sidewall of SWNTs. The resulting ssDNA-SWNT hybrid structure and device properties were investigated using Raman spectroscopy, ultraviolet (UV) spectroscopy, and electrical measurements. To monitor changes in the four VOCs, gas biosensor arrays consisting of bare SWNTs before and after being decorated with different ssDNA were employed to determine the different concentrations of the four VOCs. The data was processed using principal component analysis (PCA) and neural net fitting (NNF)

Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns

Criteria for the diploma qualifications in science at advanced level: principal learning

"The purpose of this document is to record a full set of criteria for level 3 principal learning qualifications for the Advanced Diploma in science. It also sets out the overall aims of the Diplomas in science." - purpose

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Report by the ESA-ESO Working Group on Fundamental Cosmology

ESO and ESA agreed to establish a number of Working Groups to explore possible synergies between these two major European astronomical institutions. This Working Group's mandate was to concentrate on fundamental questions in cosmology, and the scope for tackling these in Europe over the next ~15 years. One major resulting recommendation concerns the provision of new generations of imaging survey, where the image quality and near-IR sensitivity that can be attained only in space are naturally matched by ground-based imaging and spectroscopy to yield massive datasets with well-understood photometric redshifts (photo-z's). Such information is essential for a range of new cosmological tests using gravitational lensing, large-scale structure, clusters of galaxies, and supernovae. Great scope in future cosmology also exists for ELT studies of the intergalactic medium and space-based studies of the CMB and gravitational waves; here the synergy is less direct, but these areas will remain of the highest mutual interest to the agencies. All these recommended facilities will produce vast datasets of general applicability, which will have a tremendous impact on broad areas of astronomy.Comment: ESA-ESO Working Groups Report No. 3, 125 pages, 28 figures. A PDF version including the cover is available from http://www.stecf.org/coordination/esa_eso/cosmology/report_cover.pdf and a printed version (A5 booklet) is available in limited numbers from the Space Telescope-European Coordinating Facility (ST-ECF): [email protected]