The primary goal for developing the new statistical framework is to mimic the human perception for tumour delineation and marry it with all the advantages of an analytic method using modern-day computing environment. Our proposed framework will explicitly use statistical models which can accommodate the 2D and 3D spatial relationships in medical images along with their natural ability to fuse images from different modalities (e.g. CT/MRI) and provide complementary information for image segmentation tasks. This project has six main aims:Goal Combine Statistical Learing with AI to provide a new framework for probabilistic contouringResearchers Wenhui Zhang Working on probabilistic contouring of medical images More Info Yalei Yang Working on probabilistic contouring of MRI images <a href="https://www.gla.ac.uk/schools/mathematicsstatistics/staff/yaleiyang/" class="stretched-l...

We have led the statistical analyses of one of the earliest study of vaccine effectiveness against Omicron SARS-CoV-2 infection in the recent Nature Microbiology paper “SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway”, along with Dr. Oscar MacLean of the MRC-University of Glasgow Centre for Virus Research. The results showed that Omicron had a greater ability to evade the effects of vaccination, but that there were protective effects of previous infection and third (booster) vaccine doses. The findings of the paper were presented at Scotland- and UK-wide meetings by Prof. Emma Thomson, who is a member of the Standing Committee on Pandemic Preparedness (ScotGov) and Variant Technical Group members (UKHSA) which fed into NHSScotland and UK-wide government decision-making processes.Current Projects Vaccine effectiveness against Omicron in adult population across Greater Glasgow Health Board (NHSGGC). Vaccine effectiveness in pediatric population across Scotland. Severity across successive SARS-CoV-2 variants measured in terms of Death, ICU admission and Hospitalisation. Preprint Adeno-associated virus 2 infection in children with non-A-E hepatitis. Preprint Analysis data arising from next generation sequencing (e.g. PhIP-Seq characterization of serum antibodies) to detect new and emerging viruses.Researcher Craig Wilkie Working on Ramganga water quality and Vaccine efficacy More Info Glasgow Collaborators<div class="columns is-multil...

Functional data analysis is a growing field of research and has been employed in a wide range of applications ranging from genetics in biology to stock markets in economics. A crucial but challenging problem is clustering of functional data. In this thesis, we review the main contributions in this field and discuss the strengthens and weaknesses of the different clustering functional data approaches. We propose a new framework for clustering functional data and a new paradigm for model selection that is specifically designed for functional data, which are designed to address many of the weaknesses of existing techniques.Researchers Adhiraj Mandal Working on modal clustering of functional data objects More Info Salihah S. Alghamdi Post Doctoral Researcher at Department of Statistics, KAUST, Saudi Arabia. <button type="button" class="btn bt...

Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice.GoalWe have developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score.Researcher Craig Wilkie Working on Ramganga water quality and Vaccine efficacy More Info External Collaborators Louise Mackenzie...

Traditional water sampling is based on a small number of sites, and is very labour intensive and expensive, and our proposal brings together data from new in-situ sensors, delivering data at high temporal frequency, coupled with intensive coupling high-end in-situ above and below water characterisation of the biogeo-optical properties of the Ramganga with measurements from calibrated miniaturised hyperspectral imaging radiometers deployed from drones, and data from new satellite missions (Sentinel 2). Together, these provide an efficient and unprecedented means of collecting significant data across a range of environments and pollution discharge scenarios of optical water types in the Ramganga basin. Coupling these data with conventional measures of WQ will provide the much desired framework for extrapolating WQ data at hitherto unachievable spatial and temporal resolutions.Covering 26% of India’s total landmass, water quality and water resources in the Ganges basin are vital for the wellbeing of one of the largest and densest global populations (43% of India’s population). However, they are being compromised due to activities such as rapid industrialization and urbanization, and mitigation efforts are hampered by lack of historical and contemporary discharge and quality data. This project will develop and implement new statistical methodology specifically for the Ramganga sub-basin to integrate the new and existing water quality data with remote sensing satellite data (both historical data from multiple sensors and new retrievals from recent Sentinel missions).—Goals Study will deploy recently available high-resolution earth observation data for a quick assessment of water quality Drone based mapping of water quality using hyperspectral imaging Date integration and modelling for creating a framework for a decision support systemResearchers Craig Wilkie Working on Ramganga water quality and Vaccine efficacy <a href="https://...