Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk mat ....Artificial intelligence algorithms to predict risk of injury in racehorses. This project will address the urgent need for predicting and preventing catastrophic and career limiting limb injuries and cardiac arrhythmias in racehorses due to over (or under) training. Using data from GPS and movement sensors integrated into saddlecloths, artificial intelligence algorithms will convert cumulative data on speed, gait, and stride characteristics during training, along with injury data, into a risk matrix. Recorded heart rate and ECG data will also be analysed using artificial intelligence to detect early evidence of the development of cardiac arrhythmias. The system will improve racehorse welfare, providing a simple interface to warn trainers when risk of injury becomes high, in order to prevent catastrophic breakdown.Read moreRead less
Proteomic and genetic analysis of subfertile bull spermatozoa. This project aims to identify protein changes on spermatozoa that are highly correlated with the fertility status of bulls. Bull fertility has approached an all-time low as breeding practice has focused predominately on milk production and beef tenderness. This project aims to understand the genetic causes that underpin bull and cattle infertility, and investigate better methods to predict the fertility status of bulls. This project ....Proteomic and genetic analysis of subfertile bull spermatozoa. This project aims to identify protein changes on spermatozoa that are highly correlated with the fertility status of bulls. Bull fertility has approached an all-time low as breeding practice has focused predominately on milk production and beef tenderness. This project aims to understand the genetic causes that underpin bull and cattle infertility, and investigate better methods to predict the fertility status of bulls. This project expects to contribute to better clinical management of cattle. This information can then be used for the development of a better diagnostic assay for both the dairy and beef industry.Read moreRead less