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Register a new userDetection of foraging behavior from accelerometer data using U-Net type convolutional networks
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Narwhal is one of the most mysterious marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of this species, where behavioral information can be collected from instrumented individuals. This includes accelerometer data, diving and acoustic data as well as GPS positioning. An essential element in understanding the ecological role of toothed whales is to characterize their feeding behavior and estimate the amount of food consumption. Buzzes are sounds emitted by toothed whales that are related directly to the foraging behaviors. It is therefore of interest to measure or estimate the rate of buzzing to estimate prey intake. The main goal of this paper is to find a way to detect prey capture attempts directly from accelerometer data, and thus be able to estimate food consumption without the need for the more demanding acoustic data. We develop 3 automated buzz detection methods based on accelerometer and depth data solely. We use a dataset from 5 narwhals instrumented in East Greenland in 2018 to train, validate and test a logistic regression model and the machine learning algorithms random forest and deep learning, using the buzzes detected from acoustic data as the ground truth. The deep learning algorithm performed best among the tested methods. We conclude that reliable buzz detectors can be derived from high-frequency-sampling, back-mounted accelerometer tags, thus providing an alternative tool for studies of foraging ecology of marine mammals in their natural environments. We also compare buzz detection with certain movement patterns, such as sudden changes in acceleration (jerks), found in other marine mammal species for estimating prey capture. We find that narwhals do not seem to make big jerks when foraging and conclude that their hunting patterns in that respect differ from other marine mammals.
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Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.
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