No abstract

The evolution of communicative signals involves a major hurdle; signals need to effectively stimulate the sensory systems of their targets. Therefore, sensory specializations of target animals are important sources of selection on signal structure. Here we report the discovery of an animal signal that uses a previously unknown communicative modality, infrared radiation or ''radiant heat,'' which capitalizes on the infrared sensory capabilities of the signal's target. California ground squirrels (Spermophilus beecheyi) add an infrared component to their snake-directed tail-flagging signals when confronting infrared-sensitive rattlesnakes (Crotalus oreganus), but tail flag without augmenting infrared emission when confronting infrared-insensitive gopher snakes (Pituophis melanoleucus). Experimental playbacks with a biorobotic squirrel model reveal this signal's communicative function. When the infrared component was added to the tail flagging display of the robotic models, rattlesnakes exhibited a greater shift from predatory to defensive behavior than during control trials in which tail flagging included no infrared component. These findings provide exceptionally strong support for the hypothesis that the sensory systems of signal targets should, in general, channel the evolution of signal structure. Furthermore, the discovery of previously undescribed signaling modalities such as infrared radiation should encourage us to overcome our own human-centered sensory biases and more fully examine the form and diversity of signals in the repertoires of many animal species.animal communication ͉ signal evolution ͉ multimodal communication

Congenital heart disease (CHD) is the most common birth defect. Fetal survey ultrasound is recommended worldwide, including five views of the heart that together could detect 90% of complex CHD. In practice, however, sensitivity is as low as 30%. We hypothesized poor detection results from challenges in acquiring and interpreting diagnostic-quality cardiac views, and that deep learning could improve complex CHD detection. Using 107,823 images from 1,326 retrospective echocardiograms and surveys from 18-24 week fetuses, we trained an ensemble of neural networks to (i) identify recommended cardiac views and (ii) distinguish between normal hearts and complex CHD. Finally, (iii) we used segmentation models to calculate standard fetal cardiothoracic measurements. In a test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images, about 400 times the size of the training dataset) the model achieved an AUC of 0.99, 95% sensitivity (95%CI, 84-99), 96% specificity (95%CI, 95-97), and 100% NPV in distinguishing normal from abnormal hearts. Sensitivity was comparable to clinicians' task-for-task and remained robust on external and lower-quality images. The model's decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guidelines-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD and expand telehealth options for prenatal care at a time when the COVID-19 pandemic has further limited patient access to trained providers. This is the first use of deep learning to approximately double standard clinical performance on a critical and global diagnostic challenge.