Convolutional Neural Networks for Risso’s Dolphins Identification

Photo-identication is one of the best practices to estimate the abundance of cetaceans and, as such, it can help to obtain the biological information necessary to decision-making and actions to preserve the marine environment and its biodiversity. The Risso's dolphin is one of the least-known cetacean species on a global scale, and the distinctive scars on its dorsal n proved to be extremely useful to photo-identify single individuals. The main novelty of this paper is the development of a newmethod based on deep learning, called Neural Network Pool (NNPool), and specically devoted to the photo-identication of Risso's dolphins. This new method also includes the unique function of recognizing unknown vs known dolphins in large datasets with no interaction by the user. Moreover, the new version of DolFin catalogue, collecting Risso's dolphins data and photos acquired between 2013-2018 in the Northern Ionian Sea (Central-eastern Mediterranean Sea), is presented and used here to carry out the experiments. Results have been validated using a further data set, containing new images of Risso's dolphins from the Northern Ionian Sea and the Azores, acquired in 2019. The performance of the NNPool appears satisfying and increases proportionally to the number of images available, thus highlighting the importance of building large-scale data set for the application at hand

Ubiquitin Ligase Trapping Identifies an SCFSaf1 Pathway Targeting Unprocessed Vacuolar/Lysosomal Proteins

We have developed a technique, called Ubiquitin Ligase Substrate Trapping, for the isolation of ubiquitinated substrates in complex with their ubiquitin ligase (E3). By fusing a ubiquitin associated (UBA) domain to an E3 ligase, we were able to selectively purify the polyubiquitinated forms of E3 substrates. Using Ligase Traps of eight different F-box proteins (SCF specificity factors) coupled with mass spectrometry, we identified known, as well as previously uncharacterized substrates. Polyubiquitinated forms of candidate substrates associated with their cognate F-box partner, but not other Ligase Traps. Interestingly, the four most abundant candidate substrates identified for the F-box protein Saf1 were all vacuolar/lysosomal proteins. Analysis of one of these substrates, Prb1, showed that Saf1 selectively promotes ubiquitination of the unprocessed form of the zymogen. This suggests that Saf1 is part of a pathway that targets protein precursors for proteasomal degradation