RNA contact prediction by data efficient deep learning

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction

A fundamental study revisited: Quantitative evidence for territory quality in oystercatchers (Haematopus ostralegus) using GPS data loggers

Abstract A fundamental study by Ens et al. (1992, Journal of Animal Ecology, 61, 703) developed the concept of two different nest‐territory qualities in Eurasian oystercatchers (Haematopus ostralegus, L.), resulting in different reproductive successes. “Resident” oystercatchers use breeding territories close to the high‐tide line and occupy adjacent foraging territories on mudflats. “Leapfrog” oystercatchers breed further away from their foraging territories. In accordance with this concept, we hypothesized that both foraging trip duration and trip distance from the high‐tide line to the foraging territory would be linearly related to distance between the nest site and the high tide line. We also expected tidal stage and time of day to affect this relationship. The former study used visual observations of marked oystercatchers, which could not be permanently tracked. This concept model can now be tested using miniaturized GPS devices able to record data at high temporal and spatial resolutions. Twenty‐nine oystercatchers from two study sites were equipped with GPS devices during the incubation periods (however, not during chick rearing) over 3 years, providing data for 548 foraging trips. Trip distances from the high‐tide line were related to distance between the nest and high‐tide line. Tidal stage and time of day were included in a mixing model. Foraging trip distance, but not duration (which was likely more impacted by intake rate), increased with increasing distance between the nest and high‐tide line. There was a site‐specific effect of tidal stage on both trip parameters. Foraging trip duration, but not distance, was significantly longer during the hours of darkness. Our findings support and additionally quantify the previously developed concept. Furthermore, rather than separating breeding territory quality into two discrete classes, this classification should be extended by the linear relationship between nest‐site and foraging location. Finally, oystercatcher′s foraging territories overlapped strongly in areas of high food abundance

Compilation and assessment of selected anthropogenic pressures in the context of the Marine Strategy Framework Directive Descriptor 10 - marine litter

In the last decades, marine litter has become ubiquitous and has adverse impacts on marine animals through entanglement of mammals, reptiles, sea birds, fish and other animals in discarded and lost fishing gear and other plastic litter items, as well as through ingestion, especially of micro- and mesoplastics, by vertebrates and invertebrates (Figure 1). As part of a project embedded in the implementation of the Marine Strategy Framework Directive (MSFD), we were commissioned to analyze data from monitoring of marine litter, including microplastics, on beaches and in other compartments of the marine environment. Spatial and temporal trends should be identified, and results should be used to classify European marine waters according to their level of pollution with marine litter. Prior to evaluation, indicators of the Good Environmental Status (GES) should be defined, such as the existing OSPAR-EcoQO on the amount of plastic in the stomachs of northern fulmars. Finally for all marine compartments, recommendations for future monitoring of marine litter have to be given

RNA contact prediction by data efficient deep learning

Abstract On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps”) as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction