Aerodynamics of nonslender delta wings

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Studies on the molecular and functional properties of exosomes in the metastatic prostate cancer bone microenvironment

Prostate cancer is the most common cancer type and the second leading cancer related cause of death worldwide in men. Prostate cancer patients initially respond to standard treatment (e.g., hormonal, surgery/irradiation) but about 30% of them will develop resistance and progress to metastatic Castration Resistant Prostate Cancer (mCRPC). The cornerstone therapy selection for these patients is chemotherapy (e.g., taxanes). Bone is the most common metastatic site in prostate cancer (mCRPC) and the most frequent cause of death in mCRPC. One of the main bone targeted therapies for patients with bone metastatic CRPC is Radium223, an alpha emitter that has been shown to prolong survival as a single agent. Monitoring the bone tumor microenvironment (bone-TME) is challenging and is based on invasive bone biopsies that cannot be readily performed longitudinally. Liquid biopsies are an attractive approach to monitor the bone-TME including circulating tumor cells, ctDNA and extracellular vesicles (EVs). EVs have a lipid bilayer, contain nucleic acids and proteins. They have been shown to play an important role in homotypic and heterotypic intercellular communication, as well as serve as a source of biomarkers for response and resistance to therapy. In this thesis, the molecular properties of EVs were studied, co-clinically (in vitro, in vivo and in patient samples). In paper I, we characterized the EV transcriptome changes in response to Radium-223 in vivo and patients’ samples. We identified changes in genes related to bone, DNA repair and immune in both the pre-clinical models and patient samples. Treatment with Radium-223 showed a downregulation of bone related transcripts and an upregulation of DNA repair pathways (pharmacodynamic measure). Furthermore, changes in the immune system that are associated with immunosuppression and immune checkpoint activation were identified in patients with unfavorable overall survival. The data obtained indicate that EVs can detect changes in the bone-TME that were functionalized by combining Radium-223 with immunotherapy that improved treatment efficacy. In paper II, we characterized the EV transcriptome for patients treated with Cabazitaxel. Pathway and gene enrichment analysis identified several pathways and associated genes that were enriched in patients that did not respond to Cabazitaxel. Furthermore, at baseline EVs derived from the plasma of Non-responders (NR) were enriched in transcripts encoding genes that are related to oncogenesis, cytoskeleton and immune regulation. Two genes identified to be enriched in NR are Stathmin-1 and ITSN-1 both of which have been previously associated with resistance to Cabazitaxel. Further studies are needed to determine whether longitudinal monitoring of these and other genes identified in the EVs correlates with treatment response and clinical outcome. Taken together, our studies demonstrate that plasma derived EVs could be a useful tool in monitoring the bone TME as well as treatment responses and acquisition of resistance that correlate with clinical outcome

Covid-19 pandemic and the technological impact on the work and life of women academics

During the pandemic, the Higher Education had to rapidly adapt to new ways of working with both staff and students having to swiftly work in a remote environment for a prolonged period. This research is focusing on how women in academia within the Higher Education environment in the UK have adapted to virtual working and online learning and in what way they were affected by the pandemic in their professional and home life. For the purposes of this study, the research approach is to investigate women in academia perspectives during the transitional period from face-to-face onto e-learning and the mechanisms that they have used and are currently still using, in order to endure the challenges of work and everyday life

Teaching and Learning through the pandemic:the effects of remote work on women academics

During the pandemic, the Higher Education had to rapidly adapt to new ways of working with both staff and students having to swiftly work in a remote environment for a prolonged period. This research is focusing on how women in academia within the Higher Education environment in the UK have adapted to virtual working, online teaching and learning and in what way they were affected by the pandemic in their various professional and home life. The research approach is to investigate women in academia perspectives during the transitional period from face-to-face onto e-learning and the mechanisms that they have used, in order to endure the challenges of work and everyday life

Remote Work and the impact on Women Academics in the Covid-19 era

The Covid-19 pandemic has not only changed our daily life and the way we think and interact with others, but caused rapid changes in all sectors, especially in Higher Education. This research is focusing on how women in academia within the Higher Education environment in the UK have adapted to resilience trends during the sudden and rapid shift from face to face to online learning and their resilience levels for the duration of the pandemic to date. For the purposes of this study, my research approach is to investigate women in academia perspectives during the transitional period from face-to-face onto e-learning and the mechanisms that they have used and are currently still using, in order to endure the challenges of work and everyday life

Advanced Raman Techniques for Real Time Cancer Diagnostics

Cancer is one of the greatest causes of death in modern societies, affecting over 350,000 new cases every year in the UK. Although there are currently more than 100 different cancer types, breast and prostate cancer remain the most common types for women and men respectively. A number of different cancer types follow, with bladder cancer being the ninth most significant type, accounting for 3% of the total new cases. The currently employed techniques aim to diagnose the cancer at an early stage, where the symptoms are easier to be treated and the disease more likely to be cured. A further issue is that many cancers diagnosed will not affect a patient in their lifetime. The current gold standard for cancer diagnosis, biopsy followed by histopathology, is an invasive, restrictive technique and the screening tests suffer from low specificity, the need for a novel diagnostic concept is vital. Furthermore, the current clinical approach does not identify those patients most at risk of advancing disease. A promising approach consists of molecular vibrational spectroscopy techniques, which are based on the interactions of light with matter. One of these is Raman spectroscopy, a technique with wide applications in research and industry, which has the advantage of being non-invasive and chemically highly specific. In this thesis we explore the potential of a group of minimally invasive diagnostic techniques, based on Raman scattering, for prostate, breast and bladder cancer. In the case of the two most prevalent types of cancer, prostate and breast cancer, deep Raman spectroscopy has been employed to study the origin of Raman scattering (Chapters 5 and 6) in animal tissue and tissue phantoms, containing highly scattering materials resembling suspicious features found in tissues (calcifications). The spatial distribution of the Raman signal through the sample volume has been studied in relation to the optical properties and the composition of the sample, showing that a couple of transmission measurements would potentially cover the measuring volume of prostate of typical dimensions. Deep Raman measurements were also extended to animal and human tissue samples, in order to investigate the feasibility of collecting Raman scattering from human prostate tissue and its major tissue components (Chapter 6). Further improvements on these measurements were attempted by introducing the ‘’photon diode’’ element (Chapter 7) in order to achieve signal enhancement, which proved to be in the range of ×1-2.4, depending on the optical properties of the tissue and the depth of the probing element. The same ‘’photon diode’’ concept was utilised to attempt depth prediction of a calcification feature in sample volume (Chapter 8). Regarding bladder cancer, the minimally invasive approach adopted was Raman spectroscopy on urine samples, rather than deep Raman spectroscopy. Raman microscopy was employed in order to discriminate pathological features of bladder cancer between healthy and malignant urine samples. For that reason, the potential differences in urea’s distribution and interactions in urine from healthy and patients with bladder cancer were studied, resulting in promising diagnostic values (73% sensitivity, 80% specificity). The results presented in this thesis are expected to lead to a better understanding of the Raman scattering signals collection through biological tissues and help in this way the future design of Raman instruments aiming to target disease specific signals. This study shows promise for future application of Raman spectroscopy and paves the way towards the future integration of Raman spectroscopy in a non-invasive cancer diagnosiSTFC Biomedical Network, EPSR

Studying the distribution of deep Raman spectroscopy signals using liquid tissue phantoms with varying optical properties

This is the final version of the article. Available from the publisher via the DOI in this record.In this study we employed large volume liquid tissue phantoms, consisting of a scattering agent (Intralipid), an absorption agent (Indian ink) and a synthesized calcification powder (calcium hydroxyapatite (HAP)) similar to that found in cancerous tissues (e.g. breast and prostate), to simulate human tissues. We studied experimentally the magnitude and origin of Raman signals in a transmission Raman geometry as a function of optical properties of the medium and the location of calcifications within the phantom. The goal was to inform the development of future noninvasive cancer screening applications in vivo. The results provide insight into light propagation and Raman scattering distribution in deep Raman measurements, exploring also the effect of the variation of relative absorbance of laser and Raman photons within the phantoms. Most notably when modeling breast and prostate tissues it follows that maximum signals is obtained from the front and back faces of the tissue with the central region contributing less to the measured spectrum.We thank the STFC BioMedical Network (STFC, STMA00012) and the University of Exeter for their financial support. An EPSRC grant [EP/K020374/1] partly funded the work presented here

Κατασκευή μοντέλου πρόβλεψης της εγκληματικότητας για την περιοχή της Αττικής με την αξιοποίηση τεχνικών της Μηχανικής Μάθησης και των Μεγάλων Δεδομένων (Big Data)

Η εγκληματικότητα είναι ένα κοινωνικό φαινόμενο που υπήρχε ανέκαθεν και ήταν άρρηκτα συνδεδεμένο με τις κοινωνικοπολιτικές συνθήκες που επικρατούσαν στην εκάστοτε πόλη ή χώρα. Η ραγδαία εξέλιξη της τεχνολογίας και των Μέσων Μαζικής Επικοινωνίας (ΜΜΕ) οδηγεί στην υιοθέτηση νέων τρόπων και πρακτικών αντιμετώπισης του φαινομένου για την ταχύτερη και αποτελεσματικότερη καταπολέμησή του. Σε μια προσπάθεια που έχει ξεκινήσει τα τελευταία χρόνια από το εξωτερικό έχει διαπιστωθεί ότι η ενσωμάτωση στον τομέα της αστυνόμευσης Τεχνικών της Μηχανικής Μάθησης, βελτιώνει τις παρεχόμενες υπηρεσίες και οδηγεί στην εξεύρεση αποτελεσματικότερων πρακτικών όσον αφορά στην αντιμετώπιση του εγκλήματος. Μέσω της ευρείας χρήσης των αλγορίθμων, οι αρχές έχουν την δυνατότητα να προβλέψουν τον τόπο του εγκλήματος, τα χαρακτηριστικά των ατόμων που θα μπορούσαν να προβούν σε οποιαδήποτε μορφή εγκληματικής ενέργειας ή ακόμα και το είδος του εγκλήματος. Στην παρούσα εργασία θα γίνει προσπάθεια για την κατασκευή ενός μοντέλου πρόβλεψης της εγκληματικότητας για την Αττική με την συλλογή ανοιχτών αδόμητων δεδομένων. Το ενδιαφέρον μας επικεντρώνεται στα εγκλήματα κατά ιδιοκτησίας που αφορούν τις διαρρήξεις- κλοπές και τις ληστείες εξετάζοντας παράλληλα και το κομμάτι της απάτης.Criminality is a social phenomenon, which has always been existed and was inseparable linked with social- political conditions prevailing in each city or country. The rapid evolution of technology and Μass Media Communication leads to the adoption of new ways of dealing with this phenomenon for its better and more effective treatment. In an effort that has begun in recent years abroad it has been found that the integration in the field of policing of Machine Learning Techniques, improves the services provided and leads to finding more effective practices in tackling crime. By using algorithms, Police authorities have the opportunity to predict the crime scene, the characteristics of individuals who could commit a crime or even the type of crime. In the present work, we attempted to construct a model for predicting crime in the area of Attica by collecting open unstructured data. Our interest focuses on property crimes involving burglary- theft and robbery examining at the same time the part of fraud