Effects of ocean acidification on the swimming ability, development and biochemical responses of sand smelt larvae

Ocean acidification, recognized as a major threat to marine ecosystems, has developed into one of the fastest growing fields of research in marine sciences. Several studies on fish larval stages point to abnormal behaviours, malformations and increased mortality rates as a result of exposure to increased levels of CO2. However, other studies fail to recognize any consequence, suggesting species-specific sensitivity to increased levels of CO2, highlighting the need of further research. In this study we investigated the effects of exposure to elevated pCO2 on behaviour, development, oxidative stress and energy metabolism of sand smelt larvae, Atherina presbyter. Larvae were caught at Arrábida Marine Park (Portugal) and exposed to different pCO2 levels (control: ~600μatm, pH=8.03; medium: ~1000μatm, pH=7.85; high: ~1800μatm, pH=7.64) up to 15days, after which critical swimming speed (Ucrit), morphometric traits and biochemical biomarkers were determined. Measured biomarkers were related with: 1) oxidative stress - superoxide dismutase and catalase enzyme activities, levels of lipid peroxidation and DNA damage, and levels of superoxide anion production; 2) energy metabolism - total carbohydrate levels, electron transport system activity, lactate dehydrogenase and isocitrate dehydrogenase enzyme activities. Swimming speed was not affected by treatment, but exposure to increasing levels of pCO2 leads to higher energetic costs and morphometric changes, with larger larvae in high pCO2 treatment and smaller larvae in medium pCO2 treatment. The efficient antioxidant response capacity and increase in energetic metabolism only registered at the medium pCO2 treatment may indicate that at higher pCO2 levels the capacity of larvae to restore their internal balance can be impaired. Our findings illustrate the need of using multiple approaches to explore the consequences of future pCO2 levels on organisms.

An automatic classifier of emotions built from entropy of noise

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.