Screening by coral green fluorescent protein (GFP)-like chromoproteins supports a role in photoprotection of zooxanthellae

Green fluorescent protein (GFP)-like pigments are responsible for the vivid colouration of many reef-building corals and have been proposed to act as photoprotectants. Their role remains controversial because the functional mechanism has not been elucidated. We provide direct evidence to support a photoprotective role of the non-fluorescent chromoproteins (CPs) that form a biochemically and photophysically distinct group of GFP-like proteins. Based on observations of Acropora nobilis from the Great Barrier Reef, we explored the photoprotective role of CPs by analysing five coral species under controlled conditions. In vitro and in hospite analyses of chlorophyll excitation demonstrate that screening by CPs leads to a reduction in chlorophyll excitation corresponding to the spectral properties of the specific CPs present in the coral tissues. Between 562 and 586 nm, the CPs maximal absorption range, there was an up to 50 % reduction of chlorophyll excitation. The screening was consistent for established and regenerating tissue and amongst symbiont clades A, C and D. Moreover, among two differently pigmented morphs of Acropora valida grown under identical light conditions and hosting subclade type C3 symbionts, high CP expression correlated with reduced photodamage under acute light stress

Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

Targeted Photodynamic Therapy and Photochemical Internalization of Human Head and Neck Cancer:a preclinical study in vitro and in vivo

Photodynamic therapy (PDT) is a treatment modality based on a tumour-localising photosensitizer and light exposure to induce necrosis and apoptosis of tumour cells. It is used to treat head and neck cancer, but its inadequate selectivity and specificity lead to phototoxicity of normal tissues. Targeted PDT employs a conjugate, a dye and an antibody (against a tumour-overexpressing molecule), to enhance selectivity and specificity of PDT. Photochemical internalisation (PCI) uses the principle of PDT for light-enhanced cytosolic release of anti-cancer drugs that are entrapped in the endo/lysosomal vesicles of cancer cells. The aims of this thesis were to improve selectivity and specificity of PDT and PCI with cetuximab-IR700DX conjugate. The thesis started with studying killing effects of targeted PDT in human head and neck tumour cell lines. Such therapeutic effects were then confirmed in a xenografted human head and neck tumour in a mouse skin-fold window-chamber model in vivo. A low light fluence rate enhanced such targeted PDT effects. The thesis was ended with investigating bleomycin-based PCI with temoporfin and gelonin-based PCI with targeted PDT in the human tumour cell lines in vitro

Colour constancy beyond the classical receptive field

The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results suggest a dynamical adaptation mechanisms contribute to achieving higher accuracy in computational colour constancy

Spatial analysis and modelling of fire severity and vegetation recovery on and around Mt Cooke, south-western Australia

The South Western Australian Floristic Region (SWAFR) is an area with high biodiversity and species endemism. Numerous granite outcrops within the area provide specialised ecosystems for these endemic plants that are under threat by changes to the fire regime. This study reviews a fire on Mt Cooke in 2003. Using remote sensing and GIS, the fire is studied in relation to vegetation and fire indices to assess the fire severity and studies if the topography affected the fire severity. The vegetation recovery is monitored for ten years post-fire to assess recovery rates

Development of instrumentation for autofluorescence spectroscopy and its application to tissue autofluorescence studies and biomedical research

Autofluorescence spectroscopy is a promising non-invasive label-free approach to characterise biological samples and has shown potential to report structural and biochemical changes occurring in tissue owing to pathological transformations. This thesis discusses the development of compact and portable single point fibre-optic probe-based instrumentation for time-resolved spectrofluorometry, utilising spectrally resolved time-correlated single photon counting (TCSPC) detection and white light reflectometry. Following characterisation and validation, two of these instruments were deployed in clinical settings and their potential to report structural and metabolic alterations in tissue associated with osteoarthritis and heart disease was investigated. Osteoarthritis is a chronic and progressive disease of the joint characterised by irreversible destruction of articular cartilage for which there is no effective treatment. Working with the Kennedy Institute of Rheumatology, we investigated the potential of time-resolved autofluorescence spectroscopy as a diagnostic tool for early detection and monitoring of the progression of osteoarthritis. Our studies in enzymatically degenerated porcine and murine cartilage, which serve as models for osteoarthritis, suggest that autofluorescence lifetime is sensitive to disruption of the two major extracellular matrix components, aggrecan and collagen. Preliminary autofluorescence lifetime data were also obtained from ex vivo human tissue presenting naturally occurring osteoarthritis. Overall, our studies indicate that autofluorescence lifetime may offer a non-invasive readout to monitor cartilage matrix integrity that could contribute to future diagnosis of early cartilage defects as well as monitoring the efficacy of therapeutic agents. This thesis also explored the potential of time-resolved autofluorescence spectroscopy and steady-state white-light reflectometry of tissue to report structural and metabolic changes associated with cardiac disease, both ex vivo and in vivo, in collaboration with clinical colleagues from the National Heart and Lung Institute. Using a Langendorff rat model, the autofluorescence signature of cardiac tissue was investigated following different insults to the heart. We were able to correlate and translate results obtained from ex vivo Langendorff data to an in vivo myocardial infarction model in rats, where we report structural and functional alterations in the infarcted and remote myocardium at different stages following infarction. This investigation stimulated the development of a clinically viable instrument to be used in open-chest surgical procedures in humans, of which progress to date is described. 4 The impact of time-resolved autofluorescence spectroscopy for label-free diagnosis of diseased would be significantly enhanced if the cost of the instrumentation could be reduced below what is achievable with commercial TCSPC-based technology. The last part of this thesis concerns the development of compact and portable instrumentation utilising low-cost FPGA-based circuitry that can be used with laser diodes and photon-counting photomultipliers. A comprehensive description of this instrument is presented together with data from its application to both fluorescence lifetime standards and biological tissue. The lower potential cost of this instrument could enhance the potential of autofluorescence lifetime metrology for commercial development and clinical deployment.Open Acces

Illumination Invariant Deep Learning for Hyperspectral Data

Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth

Innovative Nanomaterial Approaches For Solar Energy Applications

The fundamental limitation of the conversion efficiency achievable with solar energy solutions (which includes photovoltaic and photothermal technology), requires the adaptation and integration of a series of innovative material strategies to continue the process of sustainably decarbonizing the global economy. Through the passive integration of additional nanoscale features which exploit and modify the solar spectrum through its interactions with luminescent molecules, metal nanoparticles, and/or thin-film optical coatings – the solar spectrum can be modulated and accordingly the collection efficiency of each respective technology enhanced. However, irrespective of the type of spectral conversion integrated into the technology (luminescent down-shifting, nanofluids, plasmonic luminescent down-shifting, or spectral beam splitting), a series of additional loss mechanisms are introduced as a result of the architectural modifications. Through a proposed series of innovative & iterative advancements in each one of these material strategies, the objective of alleviating the additional loss mechanisms through a suitable combination of the individual approaches could potentially be realised

Mid-infrared emissivity of partially dehydrated asteroid (162173) Ryugu shows strong signs of aqueous alteration

The near-Earth asteroid (162173) Ryugu, the target of Hayabusa2 space mission, was observed via both orbiter and the lander instruments. The infrared radiometer on the MASCOT lander (MARA) is the only instrument providing spectrally resolved mid-infrared (MIR) data, which is crucial for establishing a link between the asteroid material and meteorites found on Earth. Earlier studies revealed that the single boulder investigated by the lander belongs to the most common type found on Ryugu. Here we show the spectral variation of Ryugu’s emissivity using the complete set of in-situ MIR data and compare it to those of various carbonaceous chondritic meteorites, revealing similarities to the most aqueously altered ones, as well as to asteroid (101955) Bennu. The results show that Ryugu experienced strong aqueous alteration prior to any dehydration