Risk factors for equine fractures in Thoroughbred flat racing in North America

The aim of this paper is to identify risk factors associated with equine fractures in flat horse racing of Thoroughbreds in North America. Equine fractures were defined as any fracture sustained by a horse during a race. This was a cohort study that made use of all starts from the racecourses reporting injuries. The analysis was based on 2,201,152 racing starts that represent 91% of all official racing starts in the USA and Canada from 1 st January 2009–31 st December 2014. Approximately 3,990,000 workout starts made by the 171,523 Thoroughbreds that raced during that period were also included in the analysis. During this period the incidence of equine fractures was 2 per 1000 starts. The final multivariable logistic regression models identified risk factors significantly associated (p < 0.05) with equine fracture. For example, horses were found to have a 32% higher chance of sustaining a fracture when racing on a dirt surface compared to a synthetic surface; a 35% higher chance if they had sustained a previous injury during racing and a 47% higher chance was also found for stallions compared to mares and geldings. Furthermore, logistic regression models based on data available only from the period 2009–2013 were used to predict the probability of a Thoroughbred sustaining a fracture for 2014. The 5% of starts that had the highest score in our predictive models for 2014 were found to have 2.4 times (95% CI: 1.9–2.9) higher fracture prevalence than the mean fracture prevalence of 2014. The results of this study can be used to identify horses at higher risk on entering a race and could help inform the design and implementation of preventive measures aimed at minimising the number of Thoroughbreds sustaining fractures during racing in North America

Pose-informed deep learning method for SAR ATR

Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10

Quality assurance : applying methodologies for launcing new products, services, and customer satisfaction/ Stamatis

xxxv, p.616.: ill.; 24 c

Quality assurance : applying methodologies for launcing new products, services, and customer satisfaction/ Stamatis

xxxv, p.616.: ill.; 24 c

Risk factors associated with fatal injuries in Thoroughbred racehorses competing in flat racing in the United States and Canada

Objective: To identify risk factors associated with fatal injuries in Thoroughbred racehorses in the United States and Canada. Design: Retrospective study. Animals: 1,891,483 race starts by 154,527 Thoroughbred racehorses at 89 racetracks in the United States and Canada from 2009 to 2013. Procedures: Data were extracted from the Equine Injury Database, which contained information for 93.9% of all official flat racing events in the United States and Canada during the 5-year observation period. Forty-four possible risk factors were evaluated by univariate then multivariable logistic regression to identify those that were significantly associated with fatal injury (death or euthanasia of a horse within 3 days after sustaining an injury during a race). Results: 3,572 race starts ended with a fatal injury, resulting in a period incidence rate of 1.9 fatal injuries/1,000 race starts. Twenty-two risk factors were significantly associated with fatal injury. Risk of fatal injury was greater for stallions than for mares and geldings and increased as the number of previous nonfatal injuries and race withdrawals and level of competitiveness (eg, horse's winning percentage and race purse) of the horse or race increased. Conclusions and clinical relevance: Results identified several risk factors associated with fatal injuries in Thoroughbred racehorses. This information can be used as a guideline for the identification of racehorses at high risk of sustaining a fatal injury and in the design and implementation of preventative measures to minimize the number of fatal injuries sustained by horses competing in flat racing in the United States and Canada