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Climate change induced heat stress has increased coral bleaching events worldwide. Differentially regulated immune genes are one of the primary responses to heat stress suggesting that immune activation is critical. However, the cellular immune mechanisms of coral bleaching is currently unknown, and it is still not known if the immune response documented during heat stress is a consequence of bleaching or is directly caused by the heat stress itself. To address this question, we have used two model system sea anemones (Order: Actiniaria): Exaiptasia diaphana and Nematostella vectensis . E. diaphana is an established sea anemone model for algal symbiont interaction, while N. vectensis is an established sea anemone model that lacks the algal symbiont. Here, we examined the effect of increased temperature on phagocytic activity, as an indication of immune function. Our data shows that immune cell activity increases during heat stress, while small molecule pinocytosis remains unaffected. We observed an increase in cellular production of reactive oxygen species with increasing temperatures. We also found that the cellular immune activity was not affected by the presence of the Symbiodiniaceae. Our results suggest that the immune activity observed in heat-stress induced bleaching in corals is a fundamental and basic response independent of the bleaching effect. These results establish a foundation for improving our understanding of hexacorallian immune cell biology, and its potential role in coral bleaching. 

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model’s prediction accuracy on the Earable device’s classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.