Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

This project aims to develop a new approach to assessing and predicting poor health and welfare in captive fish, with potential application in aquaculture, laboratories and aquariums to help inform management decisions. Although video can allow animals to be monitored continually and at low cost, extracting relevant information is labour intensive and hence costly, prone to human error and biases, and is insensitive to subtle changes that could provide early warning indicators of future poor health and welfare. This project will instead apply the power of computing to extract behaviour from video (using computer vision) and advanced statistical approaches (using machine and deep learning) to predict the health and welfare status of fish in captive conditions. By doing so, the long term aim is to develop software readily available for research and industry that analyses video footage from holding tanks of fish in close to real-time, particularly to act as an early warning monitoring system that can allow staff to intervene before problems occur. Standard protocols for measuring health and welfare of fish and behavioural data extracted from video will be used to train machine/deep learning methods to generate models that can accurately predict the health of fish from only the video. The project will suit a student with a background in biology as the project requires handling animals and analysing biological samples under laboratory conditions. The student will also have a strong interest in data analysis and programming, including the willingness to learn new computational methods. Hence the student will gain extensive experience in assessing animal health and welfare, automated methods for extracting data from video, and analysis of 'big data' using well-established machine/deep learning methods. The farming of fish, i.e. aquaculture, has the potential to meet the growing demand for animal protein across the globe, and can be more sustainable than traditional livestock farming on land. Additionally, fish are well established as model laboratory organisms in medical and biological research. However, monitoring captive fish to maintain their health and welfare is much more difficult than monitoring their terrestrial counterparts, and detection of adverse health and welfare in fish often occurs when it is too late, with individuals or whole groups often needing to be culled. This project seeks to provide a low cost and low maintenance early-warning system for captive fish.

The nanotechnology industry is promising to develop new nanometer-sized materials (a millionth of a millimetre) that can significantly enhance our quality of life. However, the impact of these products upon human health and the environment is currently unknown. An important area governing health risk assessment is genotoxicology, which involves the study of genetic damage following exposure to new compounds. Such information is vital as DNA damage can cause cancer and can have an impact upon fertility and health of subsequent generations. Consequently, it is imperative that a clear understanding of the impact of nanomaterials on biological function is established. In the proposed study, we will examine the cellular and genetic damage induced by ultra-fine superparamagnetic iron oxide nanoparticles (USPION), which are destined for numerous purposes in medical diagnostics and treatments. We therefore aim to answer two main questions: 1. Do these nanoparticles cause genetic damage; 2. If so, at what concentrations does this genotoxicity arise. To achieve this, we will quantify the level of damage that occurs over a broad USPION concentration range in cultured human cells. Our experiments will therefore provide important information that could lead to the determination of safe exposure levels, facilitating health risk assessment. A secondary impact of this study is a reduced requirement for toxicity testing in animals, as it will identify the specific USPION concentrations that may need further assessment. Furthermore, it is also possible that if the USPION induce no cellular damage in the cultured cells, or if they only induce damage at excessively higher concentrations than expected occupational or medical exposures, then there will be little need for further testing in animals.

Terrorists and violent extremists have historically used social media to share terrorist and violent extremist content (TVEC), which can be algorithmically amplified in certain contexts (Alvari et al., 2019). Therefore, it is suggested that algorithms should be utilised to amplify counter-speech to target audiences, as a countering violent extremism (CVE) strategy (Schmitt et al., 2018). This PhD will explore the effectiveness and ethics surrounding the algorithmic amplification of counter-speech. By exploring the research questions, this research aims to gain a better understanding of the effectiveness of counter-speech at dissuading individuals from extreme narratives, identify counter-speech measurement, monitoring and evaluation (MME) challenges, and determine the effectiveness and limitations of algorithms in amplifying counter-speech to target audiences. Additionally, this research will identify the key ethical principles that should underpin the algorithmic amplification of counter-speech, and determine what ethical principles currently underpin campaigns. This research is important to inform policymakers, practitioners, and tech companies of how to ethically and effectively use algorithms to amplify counter-speech, to dissuade individuals from extreme narratives. This is particularly timely given recent regulatory efforts as evidenced by the Online Safety Bill. Furthermore, this research will identify where resources and research can address existing challenges. This will aid in combatting online extremism, and mitigate against potential negative impacts associated with counter-speech including; reinforcing extremist views, and increased polarisation.

The outcomes of this project will be of interest to scholars interested in food systems, health/wellbeing, behaviour change, and sustainability. Key contributions will include 1. One of the first empirical documentations of the effects of foraging activities on human and ecological wellbeing. Establishing the evidence base for the practice as a form of social intervention, 2. Development of understandings concerning the optimal forms of nature engagement to promote human and ecological wellbeing. I.e., can foraging deliver larger benefits than other forms of engagement such as walking or being mindful in nature, 3. Development of psychological theory surrounding the pathways through which foraging comes to deliver its beneficial effects, 4. Documentation of the antecedents to the decision to engage in foraging activities. To be used to inform behaviour change theory and corresponding strategies.Practically, findings will be valuable to conservation organisations, businesses, healthcare professionals, and policymakers. The Well-being of Future Generations (Wales) Act 2015 emphasizes the need to create "cohesive communities," "healthy functioning ecosystems," and "a society in which people's physical and mental well-being is maximised." One outcome of this project will be an accessible toolkit containing recommendations for stakeholders surrounding how best to implement foraging activities and measure their impact.