The ontogeny of antipredator behavior: age differences in California ground squirrels (Otospermophilus beecheyi) at multiple stages of rattlesnake encounters

Newborn offspring of animals often exhibit fully functional innate antipredator behaviors, but they may also require learning or further development to acquire appropriate responses. Experience allows offspring to modify responses to specific threats and also leaves them vulnerable during the learning period. However, antipredator behaviors used at one stage of a predator encounter may compensate for deficiencies at another stage, a phenomenon that may reduce the overall risk of young that are vulnerable at one or more stages. Few studies have examined age differences in the effectiveness of antipredator behaviors across multiple stages of a predator encounter. In this study, we examined age differences in the antipredator behaviors of California ground squirrels (Otospermophilus beecheyi) during the detection, interaction, and attack stages of Pacific rattlesnake (Crotalus oreganus) encounters. Using free-ranging squirrels, we examined the ability to detect free-ranging rattlesnakes, snake-directed behaviors after discovery of a snake, and responses to simulated rattlesnake strikes. We found that age was the most important factor in snake detection, with adults being more likely to detect snakes than pups. We also found that adults performed more tail flagging (a predator-deterrent signal) toward snakes and were more likely to investigate a snake’s refuge when interacting with a hidden snake. In field experiments simulating snake strikes, adults exhibited faster reaction times than pups. Our results show that snake detection improves with age and that pups probably avoid rattlesnakes and minimize time spent in close proximity to them to compensate for their reduced reaction times to strikes

Detecting Suspected Pump Thrombosis in Left Ventricular Assist Devices via Acoustic Analysis

ObjectiveLeft ventricular assist devices (LVADs) fail in up to 10% of patients due to the development of pump thrombosis. Remote monitoring of patients with LVADs can enable early detection and, subsequently, treatment and prevention of pump thrombosis. We assessed whether acoustical signals measured on the chest of patients with LVADs, combined with machine learning algorithms, can be used for detecting pump thrombosis.Methods13 centrifugal pump (HVAD) recipients were enrolled in the study. When hospitalized for suspected pump thrombosis, clinical data and acoustical recordings were obtained at admission, prior to and after administration of thrombolytic therapy, and every 24 hours until laboratory and pump parameters normalized. First, we selected the most important features among our feature set using LDH-based correlation analysis. Then using these features, we trained a logistic regression model and determined our decision threshold to differentiate between thrombosis and non-thrombosis episodes.ResultsAccuracy, sensitivity and precision were calculated to be 88.9%, 90.9% and 83.3%, respectively. When tested on the post-thrombolysis data, our algorithm suggested possible pump abnormalities that were not identified by the reference pump power or biomarker abnormalities.SignificanceWe showed that the acoustical signatures of LVADs can be an index of mechanical deterioration and, when combined with machine learning algorithms, provide clinical decision support regarding the presence of pump thrombosis