Without antimicrobial drugs, the risk of bacterial infection would render many common medical procedures too dangerous to contemplate because of the risk of infections caused by "opportunistic bacteria". They can live on the patient's skin, or in their intestines, and infection occurs when bacteria get into parts of the body that are normally sterile. A perfect example is urinary tract infection (UTI) caused by faecal bacteria. E. coli is particularly abundant in human faeces so is perfectly placed to cause opportunistic infections. It is one of the most common causes of healthcare pneumonia, surgical site infection, bloodstream infection and UTI in the UK. In order to prevent against and treat opportunistic infections, patients are given antimicrobials. Almost all antimicrobials are "antibiotics", which means they are derived from natural chemicals produced by microbes found in the environment. Natural antibiotics have been present in the environment for millions of years, and so bacteria living in their presence have had time to evolve mechanisms that can resist their actions, encoded by "resistance genes". Opportunistic bacteria like E. coli can randomly acquire these pre-evolved resistance genes and in a single step, they become insusceptible to a particular antimicrobial. If that insusceptible E. coli colonises a person and then causes an opportunistic infection, the infection will not be treatable with that particular antimicrobial. We refer to this as "antimicrobial resistance" (AMR); however AMR bacteria don't just resist clinical antimicrobial therapy, they beat it. Animals also carry an abundance of E. coli in their intestines and are frequently treated with antimicrobials. This can select for the acquisition of AMR E. coli which can then be passed on to another animals, directly, or via contamination of the environment with faeces. Theoretically, the AMR E. coli could also be passed on to people, and there is much debate about whether such "zoonotic transmission" happens to any significant degree. This is an important debate because it has led to calls from some to dramatically reduce the amount of antimicrobials that are given to animals with the view that it will reduce the level of AMR in animals, and so the possibility of zoonotic transmission to people. But the potential impact on welfare and food production means this should only be done if there is evidence that it will work. In this project we will identify what drives acquisition of AMR in animals using E. coli as the exemplar bacterium and dairy cows and dogs as exemplar farmed and companion animals. We will test whether AMR bacteria encountered by an animal as it interacts with the environment influence the AMR profile in its faeces, and/or whether early life antimicrobial use plays a part in selection of AMR bacteria in animals. We will also test whether reducing antimicrobial use in dairy cows actually does reduce AMR in the near-farm environment that is contaminated with their faeces. We will test whether exercising in these contaminated near-farm environments influences the abundance of AMR bacteria in dogs, and whether there is any evidence of direct acquisition of AMR E. coli by dogs from near-farm environments, which might be brought into the home. Finally, we will investigate whether AMR abundance in human UTI E. coli reduces as antimicrobial drug prescribing reduces in primary care; whether living close to a farm affects AMR abundance in UTI E. coli; whether there is direct evidence for E. coli carried by dogs or found in near-farm environments contaminated by cattle faeces also causing UTIs in humans. These interlaced studies will provide much needed data about the management changes that might reduce AMR in animals and in humans, and are designed to address the fundamental question of whether zoonotic transmission is particularly significant as a driver of AMR in people relative to antimicrobial drug use by doctors.

Johne's disease has been rated by dairy farmers in the UK as the number one endemic disease affecting productivity. It causes chronic illness, which progressively, worsens and can spread throughout the herd. To tackle the disease effectively, vet practices and farmers need to optimise the use of existing data, whilst also making evidence-based risk assessments about their herds. Our multi-disciplinary project aims to make use of existing data sources and trial environmental sampling for risk assessments with the aim of enhancing Johne's Disease control. Our specific questions are: 1. What factors explain the differences in the success of Johne's control between herds? (WP1) 2. What are the major bottlenecks to farmer and veterinarian engagement in using disease test data and what are the solutions? (WP1) 3. Why are some veterinary practices markedly more successful in controlling the disease in their client base than other practices? (WP1) 4. What measures undertaken by farmers are most likely to be associated with successful control in infection? (WP1) 5. What risk factors identified in on-farm risk assessments are associated with the presence of infection? (WP2) 6. What level of confidence would environmental sampling give as a means of estimating the probability of infection or freedom from infection? (WP2) This proposal brings together a uniquely multidisciplinary team from across the UK to tackle Johne's disease. It combines a farmer (Abi Reader, project partner) with veterinary expertise in Johne's disease control (Peter Orpin, sub-contractor), specialists in data management (James Hanks, subcontractor), a stakeholder engagement specialist (David Rose), a veterinary epidemiologist (Abel Ekiri) and a veterinary microbiologist (Nick Wheelhouse). Within Northern Ireland AHWNI leads on the control of Johne's Disease. The proposal will work in each country of the United Kingdom. Strain (subcontractor and project partner), CEO of AHWNI has a long-standing involvement with Johne's Disease control through managing the NI control programme and his involvement in the all-island (Ireland) Technical Working Group for the infection. Findings from this study will identify relevant herd risk factors and biomarkers to use for prediction of Johne's disease risk. Subsequently, in the next phase after the 12 months, these data will be used to develop prediction models and a practical and cost-effective surveillance tool for Johne's risk assessment at the herd level.

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production. To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised. A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease. Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases. Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.