Maximizing male fertility: the role of CRISP proteins. This project aims to investigate the function of cysteine rich secretory protein (CRISP) family members in fertility. It is expected to generate new knowledge on the role CRISP1 and 4 play in sperm competition in vivo, and thus, evolutionary processes; to define the role seminal plasma CRISPs play in fertility; and identify the mechanism underpinning their biological activities. This will be achieved using a range of innovative, state-of-the ....Maximizing male fertility: the role of CRISP proteins. This project aims to investigate the function of cysteine rich secretory protein (CRISP) family members in fertility. It is expected to generate new knowledge on the role CRISP1 and 4 play in sperm competition in vivo, and thus, evolutionary processes; to define the role seminal plasma CRISPs play in fertility; and identify the mechanism underpinning their biological activities. This will be achieved using a range of innovative, state-of-the-art approaches. Expected outcomes and benefits include an enhanced knowledge of the mechanisms underpinning fertility and infertility, enhanced collaboration and research knowhow, and an evidence base for future applied projects aimed enhancing fertility in agricultural species.Read moreRead less
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